CAFF Technical Report No. 21
August 2010
PROCEEDINGS OF THE FIFTH INTERNATIONAL WORKSHOP - CAFF FLORA GROUP
CIRCUMBOREAL VEGETATION MAPPING WORKSHOP, NOVEMBER 2008
ARCTIC COUNCIL
Acknowledgments
The Conservation of Arctic Flora and Fauna (CAFF) is a Working Group of the Arctic Council.
CAFF Designated Agencies:
•
Directorate for Nature Management, Trondheim, Norway
•
Environment Canada, Ottawa, Canada
•
Faroese Museum of Natural History, Tórshavn, Faroe Islands (Kingdom of Denmark)
•
Finnish Ministry of the Environment, Helsinki, Finland
•
Icelandic Institute of Natural History, Reykjavik, Iceland
•
The Ministry of Domestic Afairs, Nature and Environment, Greenland
•
Russian Federation Ministry of Natural Resources, Moscow, Russia
•
Swedish Environmental Protection Agency, Stockholm, Sweden
•
United States Department of the Interior, Fish and Wildlife Service, Anchorage, Alaska
CAFF Permanent Participant Organisations:
•
Aleut International Association (AIA)
•
Arctic Athabaskan Council (AAC)
•
Gwich’in Council International (GCI)
•
Inuit Circumpolar Conference - (ICC) Greenland, Alaska and Canada
•
Russian Indigenous Peoples of the North (RAIPON)
•
Saami Council
This publication should be cited as: Talbot, S., Charron, T., Barry, T. (eds.). 2010. Proceedings of
the Fifth International Workshop: Conservation of Arctic Flora and Fauna (CAFF) Flora Group. Circumboreal Vegetation Mapping (CBVM) Workshop, Helsinki, Finland, November 3-6th, 2008. CAFF
International Secretariat, CAFF Flora Expert Group (CFG), CAFF Technical Report No. 21.
Cover photo by Ari-Pekka Auvinen. The Nuortti River in North Eastern Finnish Lapland.
For more information please contact:
CAFF International Secretariat
Borgir, Nordurslod
600 Akureyri, Iceland
Phone: +354 462-3350
Fax: +354 462-3390
Email: caf@caf.is
Internet: http://www.caf.is
___ CAFF Designated Area
Udo Bohn
18 January 1939 - 13 August 2010
This report is in honor of the late Dr. Udo Bohn for his major contributions in the ield
of vegetation science. To experience and learn from nature was his lifeblood. His work
was a true inspiration for the Circumpolar Arctic Vegetation Map (CAVM) and continues
to be as we move forward with the Circumboreal Vegetation Map (CBVM). Udo may
have been the only person who still had the full European vegetation mapping
heritage in his blood and could integrate the Braun-Blanquet approach with mapping at
big scales.
We will move forward in his absence, but we really needed him. His death is big loss for
the project both from a scientiic standpoint and on a personal level.
We enjoyed his warm presence at our workshops. He was a great person.
Table of Contents
Circumboreal Vegetation Mapping (CBVM) Project: Introduction and Objectives - Stephen S. Talbot & Donald A.
Walker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
A Vegetation Map of Arctic Tundra & Boreal Forest Regions: Integrating the CAVM with the Circumboreal Vegetation
Map - Donald A. Walker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Vegetation Mapping and Classiication for Canada - Kenneth A. Baldwin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Experiences in Mapping the Boreal Zone in Canada - Jean-Pierre Saucier & Del Meidinger . . . . . . . . . . . . . . . . . . 22
Map of the Natural Vegetation of Europe and Its Contribution to the CBVM - Udo Bohn . . . . . . . . . . . . . . . . . . . 31
Development of a Boreal Vegetation Map of the Asian Part of Russia as a Part of the CBVM - Nikolai Ermakov . . . . . 38
The Integrated Mapping of Actual Vegetation of Asia - Kazue Fujiwara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Datasets Useful for the Circumboreal Vegetation Mapping Project - Carl Markon . . . . . . . . . . . . . . . . . . . . . . . . . 41
Ecological-Geographical Base for Biodiversity of Boreal Forests in Russia - Galina N. Ogureeva . . . . . . . . . . . . . . . 42
Zones and Altitudinal Zonality Types of Vegetation of Russia and Adjacent Territories - Galina N. Ogureeva . . . . . . 52
Vegetation Mapping in Boreal Alaska - Stephen S. Talbot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
A Geobotanical Impression of South Greenland with Some Remarks on Its “Boreal Zone” - Fred J. A. Daniёls . . . . . . 85
Large-Scale Vegetation Mapping in Iceland - Gudmundur Gudjonsson, Hördur Kristinsson, & Eythor Einarsson . . . 93
The Vegetation of the Faroe Islands - Anna Maria Fosaa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
The Plant Cover of the Kamchatka Peninsula (North of the Russian Far East) & Its Geobotanical Subdivision - Valetina
Yu. Neshataeva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Ecological-Floristic Approach to Typology of Forests for European Russian - L.B. Zaugolnova & T. Yu Braslavskaya . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
The Finnish Concept of Vegetation and Zones of Natural Forests and Mires - Tapio Lindholm & Raimo Heikkilä . . . 106
The Importance of Mire Complexes for the Development of a Circumboreal Vegetation Map (CBVM) - Klaus Dierssen .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Forest and Mire Vegetation on the Maps of two Nature Reserves: Comparison of European and Western Siberian
Northern Taiga Regions - Vasily Neshatayev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
An Approach to Mapping the North American Boreal Zone - James Brandt . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Bioclimatic Framework for the CBVM Project - Daniel Sánchez-Mata & Salvador Rivas-Martínez . . . . . . . . . . . . . . 132
Bioclimates and Distribution of Zonal Types of Boreal Vegetation in Northeast Asia - Pavel V. Krestov & Alexander M.
Omelko . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
GIMMS–NDVI Based Mapping of the Growing Season North of 50°N - Stein Rune Karlsen, Kjell-Arild Høgda, Bernt
Johansen, Arve Elvebakk, Violetta Fedotova & Anne Tolvanen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Mapping of the Eurasian Circumboreal Forest–Tundra Transition Zone by Remote Sensing - Gareth Rees, Olga Tutubalina, Hans Tømmervik, Mikhail Zimin, Anna Mikheeva, Elena Golubeva, Kelly Dolan, Annika Hofgaard . . . . . . . 144
On the Importance of Accounting for Disturbance Regimes and Forest Succession Ecosystem Dynamics in Boreal Vegetation Mapping - Steven G. Cumming, Yves Bergeron, & Sylvie Gauthier . . . . . . . . . . . . . . . . . . . . . . 151
Role of Disturbed Vegetation in Mapping the Boreal Zone in Northern Eurasia - Annika Hofgaard, Gareth Rees, Hans
Tømmervik, Olga Tutubalina, Elena Golubeva, Ekaterina Shipigina, Kjell Arild Høgda; Stein Rune Karlsen, Mikhail
Zimin, Viacheslav Kharuk . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Mapping of Natural and Anthropogenic Disturbances on Vegetation in Kola Peninsula - Tatjana Chernenkova, Mihail Puzachenko, Elena Tikhonova, Elena Basova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Circumboreal Forest Cover Mapping and Monitoring Using MODIS Time Series Imagery - Peter V. Potapov, Matthew
C. Hansen & Stephen V. Stehman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Vegetation Mapping & Disturbances Assessment in the Boreal Zone Using Time-Series of Moderate-Resolution Remote Sensing Data - Sergey Bartalev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .173
Comparison of Finnish and Russian Approaches for Large-Scale Vegetation Mapping: a Case Study - Olga Galanina &
Raimo Heikkilä . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 174
Integrated Ecoforest Mapping of the Northern Portion of the Continuous Boreal Forest, Québec, Canada - Andre
Robitaille . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Analysis of Terrain Relationships to Improve Mapping of Boreal Ecosystems - Torre Jorgenson . . . . . . . . . . . . . . 184
Appendices
Appendix I. Proposal for an IAVS Circumboreal Vegetation Map (CBVM) Working Group . . . . . . . . . . . . . . . . . . . 185
Appendix II. Resolution from the CBVM Workshop, Helsinki, Finland, November 3–6, 2008 . . . . . . . . . . . . . . . . . 186
Appendix III. CBVM Organizational Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
Appendix IV. CBVM Thematic Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Appendix V. CBVM Regional Team Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Appendix VI. CBVM Timeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
Appendix VII. CBVM Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
Circumboreal Vegetation Mapping (CBVM) Project: Introduction and
Objectives
Stephen S. Talbot1 & Donald A. Walker2
1
U.S. Fish and Wildlife Service, Anchorage, Alaska, U.S.A., stephen_talbot@fws.gov, 2Institute of Arctic Biology,
University of Alaska Fairbanks, Alaska, U.S.A., ffdaw@uaf.edu
Introduction
Our aim is to produce the irst boreal vegetation map
of the entire global Arctic biome at a comparable
resolution for understanding the region. Like the
Circumpolar Arctic Vegetation Map (CAVM), the
Circumboreal Vegetation Map (CBVM) project will be
one of the irst detailed vegetation maps of an entire
global biome. A common legend and language for the
various ecosystems that make up the boreal region
are needed as well as a map that provides a broad
view and a consistent treatment of the vegetation
of the entire biome through legend descriptions,
photographs, lists of major vegetation types, and
supplementary maps. It is also important that the
map recognizes the boreal region as a single geoecosystem with a common set of cultural, political,
and economic issues. Currently, various maps
already exist of the boreal biome, but they do not rely
on a uniied international method for classifying and
mapping boreal vegetation.
Uniied Classiication
Boreal forests are particularly appropriate for uniied
classiication because of their high level of loristic,
physiognomic, and syntaxonomic similarity across
the entire biome. The map can also serve as a key
component of circumboreal geographical information
systems (GIS), and such a map is needed for resource
development, land-use planning, studies of boreal
biota and biodiversity, education, anticipated global
changes, and human interactions. Documenting the
current distribution of the boreal is a irst step toward
monitoring these long-term changes. Our workshop
brings together an international group of vegetation
scientists to present the latest information regarding
boreal syntaxonomy, geobotany, mapping, and
new computer programs for studying boreal plant
communities. The basic workshop rationale is that
global-scale boreal research programs, modelling
efforts, educational materials, and conservation
efforts require a common language for describing
boreal ecosystems.
Creating a Compatible Map with the Circumpolar
Arctic Vegetation Map
A secondary goal is to make the CBVM compatible
with the CAVM (scale 1:7,500,000) to the north.
Linking these two global-scale maps is necessary
because very few issues relevant to the Arctic or the
boreal regions stop at tree line. For example, most
rivers lowing into the Arctic Ocean have their origin far
to the south of the tree line. Climate and vegetationchange models, analysis of animal migrations, roads
and industrial developments, and arctic–human
interaction all require maps that include both the Arctic
tundra and boreal forest regions. B
Project History
The need for such a Circumboreal Vegetation Map
was discussed at the Second International Workshop
on Circumpolar Vegetation Classiication and Mapping
held in Tromsø (Sommarøy), Norway, in June
2004. This need was further discussed at the Third
Conservation of Arctic Flora and Fauna (CAFF) Flora
Group Workshop in Helsinki, Finland, in May 2005, and
a proposal for funding was initiated. An organizational
meeting was held in Fairbanks, Alaska, in March
2006, and a funding proposal was further developed;
attendees at this meeting were: Teresa Hollingsworth
(Boreal Ecology Cooperative Research Unit, Paciic
Northwest Research Station, U.S. Department of
Agriculture Forest Service, Fairbanks), Stephen Talbot
(U.S. Fish & Wildlife Service, Anchorage), and Donald
“Skip” Walker (University of Alaska Fairbanks).
At the CAFF XI Biennial Meeting in Yllas, Finland,
in March 2006, the CAFF National Representatives
endorsed a Circumboreal Vegetation Map (CBVM).
1
This approval was followed by an endorsement by
the Senior Arctic Oficials representing the eight
Arctic States. In the interim, the CAFF Flora Group
received support from Environment Canada, Faroe
Islands Homeland Government, and the U.S.
Department of State to fund the Fourth International
Conservation of Arctic Flora and Fauna (CAFF) Flora
Group Workshop, 15–18 May 2007, Tórshavn, Faroe
Islands. This workshop helped pave the way for
the present Circumboreal Vegetation Map (CBVM)
workshop in Helsinki (Talbot, 2008). Workshop
funding for the CBVM was obtained from the Nordic
Council of Ministers by Finland.
Project Goal
The goal of this international project is to produce a
map of the natural vegetation of the boreal region.
Currently, there are a number of useful remotesensing products that display vast areas of the
North (Bartalev et al., 2004; Hansen et al., 2003).
These provide important data and perspective, but
our intent is to develop a true vegetation map such
as the one produced for Europe (Bohn et al., 2005).
Accordingly, it is essential that phytosociologists or
vegetation ecologists—guided by sound vegetation
science principles—be directly involved in making
the map. Our map should display the character of
the vegetation using the philosophical approaches
to vegetation mapping that are understood, debated,
and developed in the great schools of vegetation
mapping. Our boreal vegetation mapping effort
should be accomplished based on a uniform concept
using the most current knowledge and by the means
of close international cooperation of geobotanists
from nearly all boreal countries (cf. Bohn et al., 2005).
Objectives
We convene this international workshop to develop
a strategy to map the vegetation of the circumboreal
zone. Our workshop objectives are to:
1. review the status of boreal vegetation mapping in
each of the boreal countries;
2. present examples of possible approaches for
making the map;
3. deine the region to be mapped;
4. establish the project goals;
5. form a mapping team with representatives from
each boreal country;
6. develop a plan for making the map (identify
international collaborators, establish a loristic
and hierarchical legend approach, set a schedule
for making the CBVM);
7. develop a plan for writing a proposal or series
of proposals that would result in the inal CBVM;
and
8. publish the results of the workshop.
We need to keep the scope of this initial workshop
limited to these objectives and not lose sight of them.
I stress the objectives above so that we focus on them
at the very beginning of our workshop as there may
be peripheral issues that arise. The Nordic Council
of Ministers funded this workshop to address these
objectives, and they are to be kept in mind throughout
the workshop. Toward the conclusion of the workshop,
we will address each objective individually to make
certain each is fulilled.
Organizational Structure
A solid organizational structure will be key in developing
the map. It will be important to have one strong senior
person as the designated organizer from each country;
this person will be primarily responsible for pulling the
section maps of that country together. The scientists
responsible for the actual mapping, however, might
be young people that are just starting their careers.
Canada might be divided into two to three sections,
possibly on the basis of loristic provinces, but one
person will be responsible for synthesizing maps of
the whole country. Russia, similarly, might be divided
into four to ive loristic regions, but one overall leader
will need to be identiied from the beginning who will
integrate the whole Russian map. At the biome scale,
someone needs to be identiied from the beginning
as the overall CBVM project leader, who will be
responsible for synthesizing all the maps from the
various countries into a single circumboreal map.
Map Units
The basic map units will be physiognomic and/or
a combination of physiognomic-loristic units. As
the mapping effort will involve scientists from many
countries, the internationally recognized Braun-
2
Blanquet plant-community nomenclatural system, or
the closest equivalent that can be provided, is a logical
choice as the preferred foundation for cataloging plant
communities.
Determining Products
We should determine from the outset if we want
a vegetation map or a much larger document that
includes a book with discussion of the regional loras,
species distribution maps, vegetation dynamics,
etc. Initially, these extras probably should not be
included, because this project will go on for decades
with a budget that no one will be able to support.
Our immediate need is a good circumpolar portrayal
of boreal vegetation with a consistent legend that is
acceptable to the majority of vegetation scientists.
Other products could follow, but we need to keep the
scope of this initial project limited to the map and not
lose sight of this.
Transboundary Perspective
Viewing the world from a global perspective is an
opportunity to explore the potential for regional
environmental cooperation with the dual purpose of
conserving biodiversity and fostering friendly relations
among neighboring countries. By opening to a
transboundary perspective one hopes to achieve an
increased level of understanding that puts aside local
approaches and schools of vegetation mapping for a
truly international transfrontier approach.
References
Bartalev, S. A., Ershov, D. V., Isaev, A. S., Potapov,
P. V., Turubanova, S. A., & Yaroshenko, A. Yu.
2004. Russia’s Forests: Dominating Forest
Types and Their Canopy Density; Scale 1:
14,000, 000; Space Research Institute of
the Russian Academy of Sciences (RAN),
Forest Ecology and Production Center of the
Russian Academy of Sciences (RAN), Global
Forest Watch, Greenpeace Russia, Moscow.
Bohn, U., Hettwer, C., & Gollub, G., eds. 2005.
Anwendung und Auswertung der Karte der
natürlichen Vegetation Europas / Application
and Analysis of the Map of the Natural
Vegetation of Europe. – Bonn (Bundesamt
für Naturschutz) – BfN-Skripten 156: 1–452.
Hansen, M. C., DeFries, R. S., Townshend, J. R.
G., Carroll, M., Dimiceli, C., & Sohlberg, R.
A. 2003. Global percent tree cover at a spatial
resolution of 500 meters: First Results of
the MODIS Vegetation Continuous Fields
Algorithm. Earth Interactions 7 (10): 1–15.
Talbot, S. S., ed. 2008. Proceedings of the Fourth
International Conservation of Arctic Flora and
Fauna (CAFF) Flora Group Workshop, 15–18
May 2007, Tórshavn, Faroe Islands. CAFF
Technical Report No. 15. Akureyri, Iceland.
rtment of Agriculture Forest Service, Fairbanks),
Stephen Talbot (U.S. Fish & Wildlife Service, Anchorage),
and
3
A Vegetation Map of Arctic Tundra and Boreal Forest Regions: Integrating
the CAVM with the Circumboreal Vegetation Map
Donald A. Walker
Institute of Arctic Biology, University of Alaska Fairbanks, Alaska, U.S.A. ffdaw@uaf.edu
Abstract
The Circumpolar Arctic Vegetation Map (CAVM) is a
map developed by Arctic vegetation specialists that
depicts the vegetation of the global tundra biome at
a scale that is useful for regional and global-change
analysis and modelling. The proposed Circumboreal
Vegetation Map (CBVM) has a similar goal. This paper
presents an overview of the processes and hurdles
involved in making the CAVM. Several suggestions
are presented that would allow combining the CBVM
and CAVM into one map that portrays the vegetation of
both the Arctic and boreal biomes. These suggestions
involve: (1) selecting the right team and leadership; (2)
funding; (3) agreement on a plan for making the map;
(4) deining the extent, scale, projection, and base for
making the map; (5) phytogeographic subdivisions; (6)
map content; (7) legend approach; and (8) summary
tables of dominant plant communities.
Keywords: Braun-Blanquet approach, loristic
approach, GIS, hierarchical mapping, physiognomic
units, tundra.
Introduction
The worldwide efforts to understand the effects of
global climate change have awakened vegetation
scientists to the need for new international vegetation
maps that cover entire global biomes. These maps
would allow more detailed analysis and modelling of
the global system and the interactions between the
land, oceans, and atmosphere. A map that would
portray both the arctic tundra and boreal forest regions
is especially needed because of the many linkages
that unite these into a single global system centered
on the Arctic Ocean and the watersheds that low into
it.
The Circumpolar Arctic Vegetation Map (CAVM) was
the irst attempt to make a map of an entire global
biome with enough detail for regional and global
ecosystem studies. The Circumboreal Vegetation
Map (CBVM) will be the second. In this paper, a brief
overview is provided of the process used in making
the CAVM, focusing on some of they key elements
that would also be useful for the CBVM. At the end, I
present suggestions for integrating the CAVM with the
CBVM to create one vegetation map of the northern
part of the Earth.
A Short History of the Circumpolar Arctic
Vegetation Map
The Circumboreal Vegetation Map is being initiated
in a spirit much like that of the Circumpolar Arctic
Vegetation Map (CAVM). In March 1992, a group
of arctic vegetation scientists at the International
Workshop on Classiication of Arctic Vegetation
at the University of Colorado saw an urgent need
for a more detailed map of the vegetation in the
circumpolar region and formed a resolution to make
the CAVM. At that time the only maps that portrayed
the vegetation of the whole Arctic were coarse-scale
maps (greater than 1:10 M scale) that showed only
broad zones—usually a treeless region north of
the forested areas subdivided into tundra and polar
desert. These maps were too coarse for circumpolar
tundra ecosystem studies such as modelling the
lux of trace-gases and global vegetation-change
models. Many more detailed maps existed for small
regions of the Arctic, but these used many different
scales, classiication schemes, and base maps that
prevented a straightforward synthesis. Often, political
boundaries deined the edges of the maps instead
of natural vegetation boundaries. More critically, the
maps that were in use did not portray the vegetation
of the Arctic with terms that were compatible with
the actual vegetation—most were developed by
modelers or remote-sensing specialists for their
4
speciic purposes and had inconsistent terminology to
portray the vegetation of this vast and heterogeneous
region. There was, thus, a big need for a map that
was acceptable to the international group of arctic
vegetation specialists who worked in the Arctic and
intimately knew the vegetation. Following the Boulder
workshop, a proposal was co-funded by the U.S.
National Science Foundation and the U.S. Fish and
Wildlife Service to hold the irst CAVM workshop
devoted entirely to arctic vegetation mapping.
Lakta Workshop 1994
In March 1994 the Komarov Botanical Institute hosted
the workshop in the small village of Lakta near St.
Petersburg, Russia (Walker & Markon, 1996). It
is useful to review this workshop in some detail
because, in many respects, it was similar in intent
to this Helsinki workshop—it laid the foundation for
making the eventual map. At the Lakta meeting, 51
participants reviewed the status of arctic vegetation
mapping in each of the circumpolar countries and
developed a strategy for making a new series of maps
to portray the current knowledge of arctic vegetation.
An executive committee headed by D. A. Walker
was established with representatives from each of
the circumpolar countries with territories north of
the arctic treeline. Separate teams were identiied
for mapping Canada, Greenland, Iceland, Russian,
Norway, and Alaska. The participants agreed to make
the following products before the next workshop: (1)
a review of arctic vegetation maps; (2) maps showing
zonal divisions and loristic sectors according to
the scheme of Yurtsev (Yurtsev, 1994, 1996) and
the arctic treeline; (3) an image of the maximum
Normalized Difference Vegetation Index (NDVI)
for the whole Arctic to portray relative biomass or
vegetation density; and (4) a satellite-derived false
color image of the circumpolar region in a snow-free
state that would be used as the base-map for image
interpretation of vegetation at 1:7.5 M scale.
The group also agreed that the spatial domain for the
map would follow that of the Panarctic Flora (PAF)
initiative (Elvebakk et al., 1999), which considered the
Arctic to be equivalent to the Arctic Bioclimate Zone.
The tree line, which is the southern boundary of the
CAVM, was based on a variety of sources.
The Lakta workshop led to three publications. The irst
paper (Walker, 1995), published in Arctic and Alpine
Research, gave an overview of the workshop and set
forth a plan for making the maps. The second paper
reviewed the current status of vegetation mapping
in each of the circumpolar countries (Walker et al.,
1995). The third publication was a compilation of
all the abstracts and papers presented at the Lakta
workshop (Walker & Markon, 1996); it summarized
the various approaches to vegetation mapping in the
Arctic and also contained the two above-mentioned
publications as appendices.
Following the Lakta workshop, the project received
the endorsement of the International Arctic Science
Committee (IASC) and the U.S. Polar Research
Board (PRB) and was recognized as a priority task
of the Conservation of Arctic Flora and Fauna (CAFF)
Project.
In summary, the essential events that occurred at
the Lakta workshop were: (1) the goals and products
of the mapping effort were deined; (2) the spatial
domain, map-scale, map projection, and boundaries
of the map were deined; (3) a thorough review of
the existing maps in the Arctic was presented; (4)
the Arctic was divided into manageable regions with
leaders assigned to each region; (5) a plan was
deined for making a circumpolar base image from
satellite data; and (6) three key publications were
outlined and eventually published.
Arendal Workshop 1996
The CAVM team members met at Arendal, Norway
19–24 May 1996 (Walker & Lillie, 1997). This was a
key meeting because a detailed method for making
the map was developed and agreed to by all the
members of the CAVM team (Walker, 1999) (Fig. 1).
Additional Meetings
Additional meetings were necessary to discuss
prototype approaches for making the map (Anchorage,
Alaska, 1997; cf. Walker & Lillie [1997]) and to review
the progress being made in each of the countries
(St. Petersburg, Russia, 1999, Moscow, 2001; cf.
Raynolds & Markon [2001]), and Tromsø, 2004; cf.
Daniëls et al. [2005]). Once the funding for the map
5
was secured in 1998, it took ive more years until the
inal map was published (CAVM Team, 2003), and two
more years for the inal journal publication where the
method was described and the map analyzed (Walker
et al., 2005)—in total 13 years passed from initiation
of the idea in Boulder until the inal publication.
Key Aspects of the CAVM Relevant to the CBVM
The CAVM content and analysis are well described
elsewhere (Walker et al., 2005). My primary intention
here is to summarize some of the key aspects that
could be applied to the CBVM.
Selecting the Right Team and Leadership
At the outset everyone agreed that the inal map
would be a true vegetation map in the tradition of the
vegetation mapping schools in Europe and Russia.
It was not, however, easy to ind participants with
the right combination of talents to make the maps.
It required vegetation experts with broad knowledge
of Arctic vegetation who were also experienced with
making maps, and who were also interested in the
project and who had the necessary time and resources
to commit to the project. Thirty-four regional experts
were involved in producing the map and another 17
helped in the review of speciic areas of the map. A
relatively small group (essentially one or two from
each country) took responsibility for the inal CAVM
synthesis.
The map required a strong group of leaders who were
willing to commit to the project until it was inished.
The larger countries were divided into subregions
with their own subleaders. When someone dropped
out because of sickness, death, lack of interest, or
other reasons, it slowed the process considerably
because it required inding a replacement. The overall
leader had to have good knowledge of the Arctic, its
Fig. 1. Six-step integrated mapping approach used for the Circumpolar Arctic Vegetation Map (CAVM).
6
vegetation and cartographic methods, and also had
to be committed to achieving unanimous consensus
among the countries regarding the mapping methods
and the legend. He also needed the ability to obtain
the necessary funding to complete the map.
Funding
Achieving the necessary funding was a major hurdle.
In the beginning progress was slow because of lack
of funds. Progress was achieved through a series
of small workshops and meetings that were funded
primarily through the U.S. Fish and Wildlife Service
with moral support from the Conservation of Arctic
Flora Fauna (CAFF) Group. In 1998, ive years after
the project was initiated, The U.S. National Science
Foundation provided $225,000 for the CAVM as part
of a grant to examine trace-gas luxes in the Arctic
(Arctic Transitions in the Land Atmosphere System
(ATLAS) project). The map would not have been
completed without this major grant. Additional funds
came from the U.S. Fish and Wildlife Service, primarily
for meetings. Individual researchers and their host
institutions contributed most of the work involved in
making the regional maps.
Agreement on a Plan for Making the Map
At the outset, the team agreed that a purely
remote-sensing classiication approach would not
be appropriate because of problems with spectral
similarity of key mapping units in the Arctic and the very
dificult process that would be involved with creating a
sophisticated automated approach to classifying such
a diverse region. Also, the team agreed that a more
traditional mapping approach, such as that used for
the Map of the Natural Vegetation of Europe (Bohn et
al., 2000), would yield a more satisfactory product.
A six-step integrated mapping approach (Fig. 1) was
adopted. This was similar in principal to a variety of
landscape-guided mapping approaches (Walker et
al., 1986; Zonneveld, 1988; Dangermond & Harnden,
1990; Melnikov & Minkin, 1998). The boundaries of
major terrain features such as areas of hills, large
wetlands, large loodplains, and mountainous areas
were used as the irst criteria to delineate map polygon
boundaries. Vegetation map boundaries were further
deined by using a combination of information from
several map sources all registered to the same scale
as the circumpolar false color-infrared (FCIR) base
Carsten Egevang/ARC-PIC.COM
map (derived from AVHRR satellite imagery). These
source maps included digital products (the AVHRR
FCIR base image, maximum NDVI, and topography/
hydrology) and hard copy maps registered to the
same scale as the base map (e.g., maps of bedrock
geology, suricial geology, soils, vegetation, percent
water, and the phytogeographic boundaries of
Yurtsev). The boundaries were derived in a series of
steps shown in Figure 1 and described in Walker et
al. (2002).
The integrated mapping method (Fig. 1) was an
essential element that allowed everyone to follow
a standard approach for interpreting information on
the AVHRR base map. It was particularly useful for
establishing probable vegetation on landscapes
where there was little mapped information (e.g.,
Arctic Canada, Alaska, Greenland). Not everyone
followed this method because vegetation maps
were already available at comparable scale for all
of Russia, Svalbard, and Iceland. In these areas,
existing map-polygon boundaries were adjusted to
the base image and the legends made compatible
with the CAVM legend. Much of Russia was mapped
using a Landschaft approach developed at the Earth
Cryosphere Institute (Melnikov & Minkin, 1998) and
based on existing vegetation maps. Svalbard and
Iceland were mapped based on existing maps.
The mapping effort was divided according to countries.
Russia was divided into ive sections (European
Russia, West Siberia, Taimyr, East Siberia, and
Chukotka). Eventually, the Earth Cryosphere Institute
in Moscow, under the leadership of N. G. Moskalenko,
was responsible for synthesizing the separate draft
maps into a single map for all of Russia. W. A.
Gould, with help from S. A. Edlund, L. C. Bliss, and
M. K. Raynolds, completed the mapping of Canada.
Alaska was divided into three regions (Arctic North
Slope, Seward Peninsula, and the Yukon-Kuskokwim
river delta and Alaska Peninsula); Raynolds was
responsible for synthesizing these into a single Arctic
Alaska map. Raynolds also synthesized all the regional
circumpolar maps together into the inal CAVM.
Deining the Map Extent, Scale, and Projection
Deining the boundaries of the map had to be done
early in the project, but was a surprisingly dificult
task because initially there was not a consensus
7
regarding the boundaries of the Arctic. In the end,
the participants logically agreed to use a climate/
vegetation/loristic concept of the Arctic as deined by
the Pan Arctic Flora group―the region of the earth
with an arctic climate, tundra vegetation, and an
Arctic lora (Elvebakk, 1999). The southern boundary
was deined as treeline, but this also was not an
easily deined line, particularly in eastern Russia
where Pinus pumila stlaniks occur and are variously
portrayed as forests or shrublands. In the end, the
tree line for the CAVM was based on a variety of
sources. In Alaska, we used the Ecoregions Map
of Alaska (Joint Federal State Land Use Planning
Commission for Alaska, 1973). In Canada, we used
maps of tree line (Timoney et al., 1992) and the
extensive personal experience of S. Zoltai, who
had studied the Canadian boreal forest for several
decades. In Russia, we relied on several vegetation
maps at 1:2.5 M and 1:4 M scales and the personal
communication of Natalia Moskalenko and extensive
geographic information system (GIS) information at
the Earth Cryosphere Institute (Melnikov & Minkin,
1998) and Alexei Polezhaev (Zonal Research Institute
of Northeast Agriculture, Magadan).
The photo-interpretive approach for making the map
required a base image that was consistent across
the full Arctic. We used a 1:4 M-scale FCIR image
derived from the Advanced Very High Resolution
Radiometer (AVHRR), a space-borne sensor onboard
National Oceanic and Atmospheric Administration
(NOAA) satellites. The satellite data were obtained
and processed by the U.S. Geological Survey, Alaska
Geographic Science Ofice, Anchorage, Alaska. The
image is composed of 1 x 1-km picture elements
(pixels). Each pixel portrays the vegetation at the
maximum greenness during two years of 10-day
composite data (USGS-NASA Distributed Active
Archive Center, 2004) between 11 Jul and 30 Aug in
1993 and 1995, which were two relatively warm years
when summer-snow and cloud cover was at a minimum
in the Arctic. This allowed delineation of areas that are
predominantly covered by green vegetation (reddish
areas in the false CIR image) as opposed to areas of
sparse vegetation and barrens (blue or gray areas),
Carsten Egevang/ARC-PIC.COM
wetlands and water (dark gray or black areas), or ice
(white areas). Shorelines for the CAVM were adapted
from the Digital Chart of the World (DCW), which is a
1:1-M-scale geographic database developed for the
U.S. Defense Mapping Agency (ESRI, 1993). Small
islands less than 49 km2 were deleted from the DCW
iles, and the coastlines were simpliied by removing
arc vertices that were closer together than 5000 m.
Glaciers, oceans, and sea ice were masked out of the
image using information from the DCW.
Phytogeographic Subdivisions
It was considered important to subdivide the map into
north–south bioclimate subzones, and east─west
loristic provinces, following the tradition of Russian
small-scale vegetation maps. This was not immediately
accepted for North America, but an expedition to the
Canadian High Arctic in 1999 convinced the key
members of the project that the Russian zonation
approach could be applied consistently in North
America and Russia (Gould et al., 2003). An approach
using ive bioclimate subzones as deined by the PanArctic Flora (PAF) was accepted (Elvebakk, 1999),
but initially there was no consensus on how to name
the subzones (Walker et al., 2008). In the end a
compromise was accepted whereby the zones were
labeled with alphabetic designations, subzones A to
E (coldest to warmest) (Fig. 2, top). We also agreed
to accept the boundaries of the loristic provinces
as deined by PAF (Yurtsev, 1994, Yurtsev, 1996;
Elvebakk, 1999) (Fig. 2, bottom).
Map Content
At the outset, many items were suggested for inclusion
in the map, including detailed regional descriptions,
information on disturbances, more detailed maps in
areas with more information, and areas of special
ecological importance. However, it became clear that
there was not Arctic-wide coverage for syntaxonomic
variables and that to include them would not allow
us to meet the deadline for the map. In the end, we
realized that we had to keep the project focused on a
single relatively simple map and its publication.
8
Fig. 2. Phytogeographic boundaries: bioclimate subzones (top) and loristic provinces (bottom).
9
Legend
The inal legend condensed over 400 known plant
communities into 15 physiognomic mapping units:
B – Barrens
B1: Cryptogam, herb barren
B2: Cryptogam barren complex (bedrock, shield
areas)
B3: Non-carbonate mountain complex
B4: Carbonate mountain complex
G – Graminoid-dominated tundras
G1: Rush/grass, forb, cryptogam tundra
G2: Graminoid, prostrate dwarf-shrub, forb tundra
G3: Non-tussock sedge, dwarf-shrub, moss tundra
G4: Tussock graminoid, dwarf-shrub, moss tundra
P – Prostrate dwarf-shrub dominated tundras
the Arctic, particularly Canada, Greenland, and
Chukotka, had not been mapped previously, so
vegetation information had to be inferred from known
relationships between plant communities and terrain
features that were visible on the small-scale satellitederived image. The dominant plant community type
was derived from a look-up table that listed the
expected plant communities for each combination of
loristic subprovince, bioclimate subzone, soil reaction
class, and topographic position. Tables of dominant
plant community types were made for most regions
of the map based on the vegetation literature from
each region. Studies based on the Braun-Blanquet
approach (Weber et al., 2000) were most valuable
for these tables, so a variety of nomenclature formats
for plant communities appear in the tables (see
Appendix).
P1: Prostrate dwarf-shrub, herb tundra
P2: Prostrate/ hemi-prostrate dwarf-shrub tundra
S – Erect dwarf-shrub dominated tundra
S1: Erect dwarf-shrub tundra
S2: Low-shrub tundra
W – Wetlands
W1: Sedge/grass, moss wetland
W2: Sedge, moss, dwarf-shrub wetland
W3: Sedge, moss, low-shrub wetland
The colors on the inal map corresponded to dominant
physiognomic categories (Fig. 3). This was a fairly
radical departure from earlier maps where the primary
colors corresponded to bioclimate subzones at the
highest level in the legend.
The alpine areas presented a special problem
because many of the alpine tundra areas in the
Arctic extend well into the boreal zone. A decision
was made to portray only alpine areas that have
arctic tundra at lower elevations, which meant that
the vegetation displayed for north–south mountain
ranges was truncated at the tree line. The details of
alpine zonation could not be shown at the scale of the
map, so alpine complexes were coded according to
the scheme shown in Figure 4.
Summary Tables of Dominant Plant Communities
Mapped polygons at scale 1: 7.5M contain many
plant-community types. Vegetation for most of
Fig. 3. Color scheme for non-mountainous areas.
Colors portray physiognomic units (gray, barrens
(B); tan, brown and yellows, graminoids dominant
(G); pinks, prostrate dwarf shrubs dominant (P);
greens, erect dwarf shrubs dominant (S); blue to
aqua, wetlands (W).
The map for the most part portrays the dominant zonal
vegetation within each mapped polygon. Zonal sites
are areas where the vegetation develops under the
prevailing climate, uninluenced by extremes of soil
moisture, snow, soil chemistry, or disturbance, and
are generally lat or gently sloping, moderately drained
sites, with ine-grained soils (Vysotsky, 1927). Large
areas with azonal vegetation dependent on speciic
soil or hydrological conditions, large sand regions,
mountain ranges, and large wetlands were also
mapped. Information regarding the suite of common
zonal plant communities is contained in tables that
were not published with the map. Using the tabular
information, maps of dominant plant communities can
10
Fig. 4. Color scheme for elevation belts in mountainous areas. Mountain complexes were mapped using a
diagonal hachure pattern. The background color indicates the nature of the bedrock (magenta for noncarbonate
rocks, blue-purple for carbonate bedrock), and the color of the hachures indicate the bioclimate subzone at the
base of the mountains (purple, Subzone A; blue, Subzone B; green, Subzone C; yellow, Subzone D; and red,
Subzone E). The code numbers in mountainous areas have an additional small alphabetic sufix that indicates
the subzone at the base of the mountains. Carbonate mountains (map code B4) in Subzone E have a small
e added (map code B4e). Mountains in subzone E could have up to six elevation belts (if the mountains are
high enough). The belts are 333-m intervals, which correspond to about a 2ºC decrease in the mean July
temperature or about -6ºC per 1000 m elevation as predicted by the ecological adiabatic lapse rate. Since only
one elevational belt can be represented on each polygon, the color of the lowest belt is used for the polygon
although higher elevational belts may exist in that polygon.
be constructed at larger scale for smaller regions by
adding a decimal sufix to the existing physiognomic
codes. Such a map has been constructed for Arctic
Alaska (Raynolds et al., 2005) and could be done for
most of the Arctic from the existing tables.
Suggestions for integrating the lessons learned from
the CAVM into the CBVM process:
1. The map will require a dedicated international team
of boreal forest mapping experts representing
all countries with boreal forest vegetation. This
will require strong leadership in each country
and agreement on how the mapping effort will
be divided within the larger countries. A plan for
synthesizing the map of each country and then
for the global map will need to be agreed on early
in the project. The overall leader will have to be
committed to achieving international consensus
for all decisions so that the map has the greatest
possible acceptance by the international
community.
2. The CBVM would be most useful if it could be
seamlessly joined to the CAVM using a northern
boundary that conforms to the tree line of the
CAVM with a Lambert azimuthal equal area polar
projection and scale 1: 7.5M. Since the CAVM
is a digital map, it would be possible to improve
the delineation of the treeline, modify the CAVM
color scheme and adjust the legend of the CAVM
so that it conforms to a map that combines the
CBVM and CAVM on a single map sheet.
3. A base map with the boundaries showing the
extent of the proposed map area should be one of
the irst products of the project. Global modelling
efforts could most readily use the map if it included
the watersheds of all the major river systems
emptying into the Arctic Ocean as well as all of
the boreal forest. The mapping delineation of nonboreal-forest areas within these watersheds could
be derived from existing maps to ill the gaps. The
base image itself should draw on the expertise of
the remote-sensing community. The USGS has
shown interest in producing a mosaic of spectrally
uniform LANDSAT images that covers the boreal
forest region, but other approaches using AVHRR
or MODIS imagery should also be considered.
4. Agreement on a subzonal boundaries and loristic
divisions within the boreal forest will also be an
early requirement.
11
5. An integrated landscape-guided mapping
approach similar to that used for the CAVM
should be considered, as it will be necessary to
have a consistent method that all team members
can follow for image interpretation.
6. A tabular method for cataloging the known
primary plant communities that occur along
typical toposequences in each bioclimate
subzone and loristic province could prove useful
for the CBVM as it was for the CAVM. At present
it is unknown if this is feasible for all areas of
the map. There should be early agreement on
the use of the internationally recognized BraunBlanquet plant–community nomenclature system
as the preferred foundation for cataloging plant
communities.
7. The inal legend should place vegetation
physiognomy at the highest level in the legend
hierarchy.
8. Because of problems of inconsistent deinitions
and application of terms used among vegetation
scientists in different countries with different
schools of vegetation science, it is imperative
that the terms used on the map be clearly
deined and agreed to by the group of vegetation
scientists. This may be among the hardest tasks
faced by the group.
9. It is important to keep the project focused and
relatively simple, with a single deined product,
the CBVM. It is suggested that the projectshould
consider combining the Arctic and Boreal biome
maps in the inal published map. This will require
some adjustments to the CAVM. Although
disturbances (ire, insects, agriculture) are
important factors that cover large areas of the
boreal zone, it is recommended that the map
show potential natural vegetation with the focus
on the zonal vegetation except in large areas of
nonzonal conditions (e.g., large wetlands, very
large river systems, extensive nonzonal soil
conditions such as sand plains, shield areas with
near-surface bedrock, and mountainous areas).
10. The biggest challenge for the CBVM is the same
as that faced by the CAVM—how to develop
a legend framework with terminology that is
acceptable to all the circumboreal countries.
Differences in language, different deinitions of
common terms, and different mapping traditions
will cause major barriers for this international
synthesis. These differences are not trivial and
must be addressed and compromises will be
necessary.
I will advocate strongly for adopting the BraunBlanquet method as the foundation for the map.
This method has a long and successful heritage of
application of vegetation mapping at all scales in
Europe and more recently in Russia, and it has the
greatest potential as an international approach. It
is the most comprehensive method for vegetation
sampling classiication and analysis in the world.
It addresses all phases of vegetation description,
including the ield methods, the analytical methods,
and perhaps most importantly, a formal international
hierarchical classiication framework that allows the
plant-community units to be compared with other
internationally published vegetation literature. The
B-B method can be likened to the Linnean approach
to describing and classifying plant species or the U.S.
soil taxonomy approach for describing and classifying
soils (Soil Survey Staff, 1999). It is the only method
that can be considered an international vegetation
classiication approach.
The U.S. National Classiication System (The Nature
Conservancy and Environmental Systems Research
Institute, 1994) is a recent attempt at standardization
for classiication in North America. The method is
still being developed and is not widely used outside
of North America. Currently at the plant-community
level there are no standards for ield sampling, table
analysis, naming, and publishing the plant community
data. Researchers have a hard time understanding,
accessing and standardizing the source data,
and building on existing work. However, both the
European and North American approaches recognize
the necessity for using physiognomic units at the
higher mapping levels for developing the biome-scale
mapping units.
Developing a consistent nomenclature system will
be a dificult task. I would like to promote a spirit of
international cooperation and compromise when
necessary in developing the legend we will adopt.
This will be necessary because many of the words
crucial to our science have different meanings in
different countries, but I think we all recognize the
underlying kernels of similarity in all the approaches.
12
With regard to collaboration on the assembly of the
synthesized regional maps into a single vegetation
map of the Arctic and boreal biomes, the task is much
more challenging for the boreal forest than it was for
the Arctic. I am hoping that we can learn from the
Russian and Canadian approaches to mapping the
boreal forest and also from the previous European
collaborative. The European Vegetation map already
faced many of our same challenges at the continental
scale. Now we are faced with the same challenge at
the global scale.
Gould, W. A., Walker, D. A., & Biesboer, D. 2003.
Combining research and education: bioclimatic
zonation along a Canadian Arctic Transect. Arctic
56: 45–54.
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Westfälischen Museum für Naturkunde 70: 387–
400.
Walker, D. A., Webber, P. J., Walker, M. D., Lederer,
N. D., Meehan, R. H., & Nordstrand, E. A. 1986.
Use of geobotanical maps and automated mapping
techniques to examine cumulative impacts in
the Prudhoe Bay Oilield, Alaska. Environmental
Conservation 13:149–160.
Webber, P. J. 1978. Spatial and temporal variation in
the vegetation and its productivity, Barrow, Alaska.
Pages 37–112 in L. L. Tieszen, ed. Vegetation and
Production Ecology of an Alaskan Arctic Tundra.
Springer-Verlag, New York.
Weber, H. E., Moravec, J., & Therurillat, J.-P.
2000. International Code of Phytosociological
Nomenclature. 3rd Edition. Journal of Vegetation
Science 11: 739–768.
Yurtsev, B. A. 1994. Floristic division of the Arctic.
Journal of Vegetation Science 5: 765–776.
Yurtsev, B. A. 1996. Latitudinal (zonal) and
longitudinal (sectoral) phytogeographic division of
the circumpolar arctic in relation to the structure of
the vegetation map legend. Pages 77-83 in Walker,
D. A. & Markon, C. J., eds. Circumpolar Arctic
Vegetation Mapping Workshop, Komarov Botanical
Institute, St. Petersburg, Russia, March 21–25,
1994.
Zonneveld, I. S. 1988. The ITC method of mapping
natural and semi-natural vegetation. Pages 401-426
in Küchler, A. W. & Zonneveld, I. S., eds. Vegetation
Mapping. Kluwer Academic Publishers, Boston.
Appendix
Table A.1 is an example table of dominant plant
communities for Subzone C, Northern Alaska loristic
subprovince. See Fig. A.1 for circumpolar maps of
bioclimate subzones, loristic provinces, substrate
pH, and the conceptual mesotopographic gradient.
Dominant plant functional types and species are
listed where data were available. The units contain
the dominant plant functional types from the top of the
plant canopy to the base of the canopy, followed by
the dominant plant species for each plant functional
type (in parentheses). Literature citations in the table
include unit names, habitat, citation and location.
Similar tables were constructed for each combination
of loristic subprovince and bioclimate subzone.
14
Table A.1. Subzone C (Northern part of the Arctic Coastal Plain).
Habitat along the
meso-topographic
gradient
Dry exposed sites
Moist sites
Acidic substrates
(community # 1--7)
Non-acidic substrates
(community # 8--12)
1. Prostrate dwarf-shrub (Salix rotundifolia), lichen
(Alectoria nigricans, Bryocaulon divergens, Dactylina
arctica), rush (Luzula confusa, L. arctica), grass
(Arctagrostis latifolia), forb (Potentilla nana, Pedicularis
kanei), bryophyte (Polytrichum strictum, Dicranum
elongatum, Gymnomitrion corallioides).
Nodum II; Sphaerophorus globosus-Luzula confusa
comm., subtype Salix rotundifolia (Elias et al., 1996)
(Barrow, dry beach and river terraces).
2. Sedge (Carex aquatilis, Eriophorum angustifolium),
grass (Poa arctica, Dupontia fisheri), rush (Luzula
arctica), prostrate dwarf-shrub (Salix rotundifolia), forb
(Saxifraga cernua, S. hieraciifolia, S. hirculus,
Cardamine pratensis, Petasites frigidus, Ranunculus
nivalis), moss (Oncophorus wahlenbergii,
Sarmenthypnum sarmentosum, Aulacomnium
turgidum).
Nodum IV (Webber, 1978); Type 6 and 7 (Walker,
1977); Saxifraga cernua-Carex aquatilis comm. (Elias
et al., 1996) (Barrow, moist, fine-grained soils).
8. Prostrate dwarf-shrub (Dryas
integrifolia), sedge (Carex rupestris),
lichen (Lecanora epibryon, Thamnolia
subuliformis).
Type B12, coastal dry nonacidic gravelly
sites (Walker, 1985) (North Slope,
Alaska).
9. Sedge (Carex aquatilis), prostrate
dwarf-shrub (Salix pulchra, S. reticulata,
Dryas integrifolia), moss (Tomentypnum
nitens, Oncophorus wahlenbergii,
Campylium stellatum, Distichium
capillaceum).
Type U12, moist calcareous coastal
meadows (Walker, 1985) (North Slope,
Alaska).
3. Rush (Luzula confusa, L. arctica), grass (Poa
arctica), forb (Potentilla nana, Pedicularis kanei), lichen
(Alectoria nigricans, Sphaerophorus globosus,
Dactylina arctica, Cladonia spp., Ochrolechia frigida),
moss (Polytrichastrum alpinum, Polytrichum strictum,
Sarmenthypnum sarmentosum).
Wet sites
Snow beds
Nodum I (Webber, 1978); Type 5 (Walker, 1977);
Sphaerophorus globosus-Luzula confusa comm.,
subtype Saxifraga foliolosa (Elias et al., 1996) (mesic
high-centered polygons, zonal vegetation in Barrow
area).
4. Sedge (Eriophorum angustifolium, Carex aquatilis),
grass (Dupontia fisheri, Arctophila fulva), moss
(Sarmenthypnum sarmentosum, Limprichtia revolvens).
Noda V and VI (Webber, 1978); Types 9, 10, 12 and 13
(Walker, 1977); Eriophorum angustifolium-Carex
aquatilis comm. (Elias et al., 1996) (Barrow, wet sites
without standing water).
5. Prostrate dwarf shrub (Salix rotundifolia), lichen
(Cetrariella delisei).
10. Sedge (Carex aquatilis, Eriophorum
angustifolium), grass (Dupontia fisheri),
moss (Drepanocladus brevifolius).
Type M10 , wet calcareous coastal
meadows (Walker, 1985) (North Slope,
Alaska).
11. No data. Probably similar to
snowbeds in Subzone D.
Salix rotundifolia-Cetraria delisei comm. (Elias et al.
1996) (Barrow, early-melting snow beds).
6. Grass (Phippsia algida, Alopecurus alpinus), forb
(Cochlearia groenlandica, Ranunculus pygmaeus,
Stellaria humifusa, Saxifraga rivularis).
Riparian areas
Nodum VIII; Type 15 (Barrow, late-melting snow beds).
7. Grass (Phippsia algida, Alopecurus alpinus), forb
(Cochlearia officinalis, Ranunculus pygmaeus, Stellaria
humifusa, Saxifraga rivularis).
Nodum VIII (Webber, 1978); Type 15 (Walker, 1977)
(Barrow, unstable stream margins).
12. Forb (Epilobium latifolium, Artemisia
arctica, A. campestris ssp. borealis,
Papaver lapponicum, Polemonium
boreale, Astragalus alpinus, Wilhelmsia
physodes, Parrya nudicaulis).
Epilobio latifolii-Salicetum alaxensis ass.
prov. (Schickhoff et al., 2002) (North
Slope, coastal active floodplains).
Fig. A.1
15
Vegetation Mapping and Classiication for Canada
Kenneth A. Baldwin
Natural Resources Canada–Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario,
Canada, kbaldwin@NRCan.gc.ca
Abstract
Currently, there is no existing Canadian ecological
or vegetation map that is suitable for use within the
context of the Circumboreal Vegetation Mapping
(CBVM) project. Available national maps are either
inappropriate or out of date. The most inclusive map
of vegetation zones being used for Canada is Forest
Regions of Canada, originally published by Rowe
in 1959. A similar product, Ecoclimatic Regions of
Key words: boreal, Canadian National Vegetation
Classiication (CNVC), Circumboreal Vegetation
Mapping (CBVM) project, mapping.
Introduction
This presentation provides a general overview of
the status of ecological classiication and mapping
for Canada as a whole, including an introduction
to the Canadian National Vegetation Classiication
Canada (Ecoregions Working Group, 1989), is less
well known but has the potential for possible updating.
The contemporary oficial federal standard for
ecological regionalization of Canada is the National
Ecological Framework for Canada, but this standard
does not feature vegetation zonation. The jurisdictional
political realities within Canada, with respect to the
identiication, inventory, and management of natural
resources, will strongly inluence the approaches that
are possible for creating a Canadian map of boreal
vegetation.
(CNVC) and how it might characterize broad
variation in Canadian boreal vegetation. In a separate
presentation, Del Meidinger, British Columbia Ministry
of Forests and Range, Victoria, Canada, and JeanPierre Saucier, Ministère des Ressources naturelles
et de la Faune, Québec, Canada, discuss some
speciic experiences with classifying and mapping
boreal vegetation in western and eastern Canada,
respectively.
The Canadian National Vegetation Classiication
(CNVC) is currently being developed as a nationally
standardized classiication of Canadian vegetation at
various levels of taxonomic generalization. To date,
the CNVC has been developed primarily for forest
associations. However, a provisional taxonomic
hierarchy has been adopted in conjunction with the
United States National Vegetation Classiication
Standard (USNVCS) (Federal Geographic Data
Committee, 2008). It is proposed that upland North
American boreal forests and woodlands be treated in
four regional Macro-Groups (level 5 of the CNVC/NVCS
hierarchy), representing the following subdivisions:
east, west, boreal cordillera, and subarctic. Level
6 (Group) recognizes broad site-related ecological
afinities across overstory dominance conditions. For
example, within the western North American MacroGroup, it is proposed to recognize three site-related
Groups.
Most of the contemporary ecological mapping at
a national scale in Canada is being conducted by
the remote sensing community. Although I am not
qualiied to discuss the range of these activities, and
none of our Canadian colleagues were available
to attend this meeting, I can say that considerable
work is underway along the lines of biomass/carbon
mapping, modelling, and monitoring; land-cover
change monitoring; and forest and wetland inventory.
If the Circumboreal Vegetation Mapping (CBVM)
project proceeds in Canada, we will seek to engage
members of the Canadian remote sensing community
in the actual mapping process.
Landcover Mapping
Ecological and Vegetation Mapping
Ecozones/Ecoregions of Canada
The oficial spatial ecological framework for Canada
(Ecological Stratiication Working Group, 1995;
16
Fig. 1) comprises 15 ecozones, each subdivided
into numerous ecoregions. This is a useful
ecoregionalization at the Canadian national scale
for general planning and reporting purposes, but the
map units tend to follow physiographic boundaries,
so they are not always reliable for vegetation and
phytogeographic applications.
Potential Vegetation Mapping for Canada
The most widely used map of vegetation zones for
Canada is Forest Regions of Canada (Rowe, 1959,
1972; Fig. 2). The primary vegetation regions were
originally published by Halliday (1937) as a Canadian
adaptation of a map of North American climax
formations developed by Weaver and Clements
(1929). The map was revised by Rowe in 1972 but
has not been updated since.
A more recent map of vegetation potential, the
Ecoclimatic Regions of Canada, published by
the Canadian Committee for Ecological Land
Classiication (Ecoregions Working Group, 1989; Fig.
3), uses zonal concepts. This map is obscure and little
Fig. 1. Ecozones and ecoregions of Canada (Ecological Stratiication Wroking Group, 1995). Legend
classes represent ecozones; ecoregions are the subunits within the ecozones.
Fig. 2. Forest regions of Canada (Rowe, 1959, 1972).
17
Fig. 3. Ecoclimatic provinces and regions of Canada (Ecoregions Working Group, 1989). Legend classes
represent ecoclimatic provinces; ecoclimatic regions are the subunits within the ecoclimatic provinces.
used by ecologists but has the potential to be updated
with new data and climatic modelling methods.
Provincial/Territorial
and Mapping
Ecological
Classiication
access to their data and expertise and (2) for buyin at the provincial/territorial level that will facilitate
acceptance of the new product and its use as a new
Canadian standard.
Canadian National Vegetation Classiication
Canada is a federation comprising 10 provinces
(mostly south of latitude 60º) and three territories in
the north. Constitutionally, most matters of resource
management and stewardship are under provincial/
territorial jurisdiction. Over the last 20–30 years,
the role of the federal government in ecological
classiication and inventory has been largely to
support the provinces and territories with their
jurisdictional programs. Over this time period, most
jurisdictions have developed classiications and
inventories, especially of their forests, but there has
been no effort to coordinate or crosswalk the systems
across provincial/territorial borders. The result is a
patchwork of mapped classiication systems across
the country, often broken at jurisdictional boundaries
(Fig. 4). Since the most recent ecological classiication,
provinces and territories have conducted inventory
and mapping activities, and this is where the current
expertise and data now reside across Canada. Any
effort to develop a nationally consistent vegetation
mapping product for Canada will require cooperation
from the provinces and territories for two reasons: (1)
The Canadian National Vegetation Classiication
(CNVC) is a project that is developing a national
standard for vegetation classiication in a cooperative
partnership involving all relevant jurisdictions in
Canada. The CNVC is coordinated by the federal
government (Natural Resources Canada), with
data and expertise provided by the provinces and
territories. The primary objectives of the CNVC are:
•
to classify the natural vegetation of Canada in an
ecologically meaningful manner on the basis of
growth-form, physiognomy, dominance, loristics,
and diagnostic indicator value;
•
to coordinate classiication standards and
correlate CNVC classiication units with Canadian
provincial, territorial, and regional classiications,
as well as with the U.S. National Vegetation
Classiication Standard (USNVCS) (Federal
Geographic Data Committee, 2008), to provide
a mechanism for the exchange of ecological
information across jurisdictional boundaries; and
18
Fig. 4. Current status of ecological regionalizations among provincial and territorial jurisdictions within Canada.
•
to engage partners with relevant expertise, data,
and jurisdictional authority in order to complete
the classiication with the greatest degree of
consensus.
The CNVC partnership currently includes over
20 international, federal, provincial, and territorial
governmental and nongovernmental agencies,
including provincial/territorial forest ecology programs,
provincial & territorial conservation data centres,
NatureServe and NatureServe Canada, Parks
Canada, Natural Resources Canada─Canadian
Forest Service, and Environment Canada. The project
has access to approximately 80,000 relevés from the
provincial/territorial classiication programs, mostly in
the southern portion of Canada. Diagnostic species
are selected on the basis of the ecological expertise of
provincial/territorial ecologists as relected in existing
provincial/territorial classiication units.
hierarchy. Four regional Macro-Groups are proposed,
representing the following subdivisions (Fig. 6): (1)
west and east biogeographic Macro-Groups that
are characterized by a shift in dominant tree species
from Abies lasiocarpa [(Hook.) Nutt.] (west) to A.
balsamea [(L.) Mill.] (east); from Pinus contorta var.
latifolia [Engelm. ex S. Wats.] (west) to P. banksiana
[Lamb.] (east); and the increasing prevalence of Picea
mariana [(Mill.) BSP] relative to P. glauca [(Moench)
Voss] on zonal sites towards the east; and on the
forest loor, Hylocomium splendens [(Hedw.) Schimp.
ex B.S.G.] is dominant in the west, while Pleurozium
schreberi [(Brid.) Mitt.] is dominant in eastern Canada;
(2) at higher elevations in the boreal zone of western
Canada, a Cordilleran Macro-Group is recognized
where scrub birch (Betula spp.) and willow (Salix spp.)
become signiicant on zonal sites; and (3) in the treed
portion of the boreal zone, a band of lichen woodlands
relects the northward climatic progression to the
treeline in the Subarctic Macro-Group.
The CNVC is a hierarchical vegetation–ecological
taxonomy (Fig. 5). The upper levels of the hierarchy
relect growth-form and physiognomic differences that
are driven by broad climatic factors; the middle levels
relect biogeographic and broad ecological variation at
the continental and regional scales; and the Alliance
and Association levels relect loristic and dominance
variability in relation to local site-level ecology. Boreal
vegetation is discriminated at the Formation: Subclass
level.
The CNVC proposes to treat North American upland
Fig. 5. Canadian National Vegetation Classiication
boreal forests and woodlands at levels 5 to 8 (Macro(CNVC) provisional hierarchy.
Group, Group, Alliance, and Association) of the
19
Using the western Macro-Group as an example (Fig.
7), it is proposed to be subdivided into three groups
that relect broad site-related ecological afinities
across overstory dominance conditions. These groups
can be mapped into coordinate envelopes of the
edatope to relect generalized local or regional sitelevel moisture and nutrient gradients. Groups comprise
multiple alliances and associations, which describe
phytosociological variability in terms of communityscale dominance and loristic composition.
inappropriate (ecozones) or out of date (forest regions
and ecoclimatic regions). The most current ecological
regionalizations exist only for individual provinces or
territories.
There are two general approaches for developing a
new Canadian vegetation map that can serve as a
component of the CBVM―an executive process or a
process that emphasizes federal/provincial/territorial
cooperation. In order to mobilize the greatest amount of
expertise, data, and endorsement, the latter approach is
recommended. Development of a Canadian component
of the CBVM by interjurisdictional cooperation within
Canada will take time but will result in the best product.
The Canadian National Vegetation Classiication
(CNVC) partnership is a good governance model for
the Canadian component of the CBVM. Its incipient
taxonomy, which is derived from recently collected
relevé data, and the CNVC partnership can inform the
development of a CBVM map for Canada.
Fig. 6. Proposed Canadian National Vegetation
Classiication (CNVC) Macro-Groups with diagnostic
dominant species for North American upland boreal
forests and woodlands.
References
Ecological Stratiication Working Group. 1995.
A National Ecological Framework for Canada.
Agriculture and Agri-Food Canada, Research Branch,
Centre for Land and Biological Resources Research
and Environment Canada, State of the Environment
Directorate, Ecozone Analysis Branch. Ottawa,
Ontario / Hull, Québec, Canada. 125 pp and map at
1:7 500 000 scale.
Fig. 7. Proposed Canadian National Vegetation
Classiication (CNVC) Groups with generalized
edatopic coordinates for the Western North American
upland boreal forests and woodlands Macro-Group. On
the edatope axes: p–poor; r–rich; d–dry; w–wet.
Conclusion
There is probably no existing Canadian ecological or
vegetation map that is suitable for use within the context
of the proposed Circumboreal Vegetation Mapping
(CBVM) project. The existing national maps are either
Ecoregions Working Group. 1989. Ecoclimatic Regions
of Canada, irst approximation. Ecoregions Working
Group of the Canada Committee on Ecological Land
Classiication. Ecological Land Classiication Series,
No. 23. Sustainable Development Branch, Canadian
Wildlife Service, Conservation and Protection,
Environment Canada, Ottawa, Ontario, Canada. 119
pp and map at 1:7 500 000 scale.
Federal Geographic Data Committee (FGDC) 2008.
National Vegetation Classiication Standard, FGDCSTD-005, Version 2. Washington, D.C., U.S.A. http://
www.fgdc.gov/standards/projects/FGDC-standardsprojects/vegetation/NVCS_V2_FINAL_2008-02.pdf.
Halliday, W. E. D. 1937. A Forest Classiication for
Canada. Department of Mines and Resources,
Lands, Parks and Forests Branch, Forest Service.
20
Bulletin 89. Ottawa, Ontario, Canada. 50 pp and
map at 1:6 336 000.
Rowe, J. S. 1959. Forest Regions of Canada.
Department of Northern Affairs and National
Resources, Forestry Branch. Bulletin 123. Ottawa,
Ontario, Canada. 71 pp and map at 1:6 600 000
scale.
Rowe, J. S. 1972. Forest Regions of Canada.
Department of the Environment, Canadian Forestry
Service. Publ. No. 1300. Ottawa, Ontario, Canada.
172 pp and map at 1:6 600 000 scale.
Weaver, J. E. and F. E. Clements. 1929. Plant Ecology.
McGraw-Hill Book Company, Inc. New York, U.S.A.
and London, United Kingdom. 601 pp.
21
Experiences in Mapping the Boreal Zone in Canada
Jean-Pierre Saucier1 & Del Meidinger2
1
2
Ministère des Ressources naturelles, Québec, Canada, jean-pierre.saucier@mrnf.gouv.qc.ca,
British Columbia Ministry of Forests and Range, Victoria, Canada
Abstract
Introduction
Canada has a strong tradition of ecosystem
classiication and mapping, one that combines
climate, vegetation, and soil conditions to classify
ecosystems rather than relying upon vegetation alone.
These classiications and maps are particularly useful
for forest and other resource management, as they
not only contain information on current vegetation,
but also the potential vegetation within an ecological
context. However, each province in Canada follows a
different conceptual mapping system.
Although the boreal zone occurs across Canada, a
map of boreal vegetation, using consistent criteria
is complicated by the political landscape where the
responsibility for resource management lies with the
provinces. As such, provincial agencies produce the
resource maps required for management within their
boundaries, and provincial agencies have the data
upon which a vegetation map could be produced.
For example, two different systems for mapping the
boreal region are demonstrated in the provinces of
Québec and British Columbia, which contain a vast
portion of the North American boreal region. The role
of vegetation within ecosystem classiication is critical
to developing the various hierarchical classiication
schemes of both the eastern and western regions.
In the east, coniferous forests of the boreal region
transition to mixed-wood forests and then to temperate
hardwood forests, so it is relatively straightforward
to delineate the boreal zone on the basis of coniferhardwood composition. In the west, however,
coniferous forests dominate both the boreal and
temperate forests, so a combination of tree species
composition and overall lora is needed to differentiate
the boreal from the temperate zone.
Ecological and forest cover maps are available at
various scales, but only a few products currently exist
that map the entire boreal region of both provinces.
Keywords: boreal zone, British Columbia, Canada,
ecological mapping, elevation gradient, forest
ecosystem classiication, Québec.
Canada has a strong tradition of ecosystem
classiication and mapping, one that combines
climate, vegetation, and soil conditions to classify
ecosystems rather than relying upon vegetation alone.
These classiications and maps are particularly useful
for forest and other resource management, as they
not only contain information on current vegetation,
but also the potential vegetation within an ecological
context. However, each province follows a different
conceptual system.
The approaches in two provinces―Quebec and
British Columbia―demonstrate two different mapping
systems and their strengths and weaknesses for
developing a map of the boreal zone.
British Columbia
Ecological Classiication System
British Columbia’s ecological classiication system is
called Biogeoclimatic Ecosystem Classiication (BEC)
(Pojar et al., 1987). The system is a hierarchical
classiication
scheme
that
combines
three
classiications: climatic (or zonal), vegetation, and
site (Fig. 1). At a regional level, vegetation and soil
relationships are used to infer the regional climate; this
climatic or zonal classiication deines biogeoclimatic
units. At the local level, ecosystems are classiied, by
22
vegetation and soil information, into vegetation and
site units. Ecosystems are organized according to sitespeciic chronosequences. To do this, the vegetation
units recognized for a particular site unit are arranged
according to site history and successional status. For
practical purposes, users need only be concerned
with the zonal and site classiications; the vegetation
classiication, however, is integral to developing both.
with the biogeoclimatic subzone being the basic unit
(Fig. 1). Subzones are grouped into zones, regions,
and formations and divided into variants or phases.
Site units represent groups of sites or ecosystems
that, regardless of present vegetation, have the same,
or equivalent, environmental properties and potential
vegetation. The potential vegetation of a group of
sites, as determined by climax or near-climax plant
associations and subassociations, provides the
initial delimitation of site associations, or the basic
unit of site classiication. The site association can be
considered as all ecosystems capable of producing
vegetation belonging to the same plant association at
climax. Thus, a site association is a group of related
ecosystems physically and biologically similar enough
that they have, or would have, similar vegetation at
climax.
A site association can contain ecosystems from
several different climates and so be variable in
actual site conditions. Dividing the association into
site series using subzones and variants produces
site units that are climatically, and therefore usually
edaphically, more uniform. As a result, site series are
more predictable in their response to management.
Boreal Region
Fig. 1. Biogeoclimatic Ecosystem Classiication
(BEC).
In keeping with the Braun-Blanquet approach
(Westhoff & van der Maarel, 1980), vegetation units are
loristically uniform classes of plant communities. They
are arranged in a hierarchy where the plant association
is the basic unit; alliances, orders, and classes are
groups of associations; and subassociations are
divisions of an association (Fig. 1). Vegetation units
are differentiated by using “diagnostic combinations
of species” (Pojar et al., 1987).
Biogeoclimatic units are the result of zonal (climatic)
classiication and represent classes of ecosystems
under the inluence of the same regional climate. As in
vegetation classiication, there is a hierarchy of units,
The British Columbia Ministry of Forests & Range
currently recognizes 16 biogeoclimatic zones in the
province. Within the boreal region, three biogeoclimatic
zones are present: Boreal White and Black Spruce
(BWBS), Spruce─Willow─Birch (SWB), and Boreal
Altai Fescue Alpine (BAFA).
Most of the BWBS zone in British Columbia occurs
on an extension of the Great Plains (the Alberta
Plateau) into the northeastern corner of the province.
The zone also occupies the lower elevations of the
main valleys west of the northern Rocky Mountains.
In northeastern British Columbia, this lowland to
montane zone occurs north of roughly 54ºN latitude
and at elevations ranging from 230 m to ca. 1,300 m.
In northwestern British Columbia, it occurs north of 56º
from the valley bottoms to 1,000–1,100 m elevation.
The vast majority of the zone occurs above 600 m; the
main exceptions are the Fort Nelson Lowland (+ 450
m) and some deeply incised valleys.
23
There are three primary subzones of the BWBS: the
lowlands of northeastern British Columbia; the eastern
foothills of the northern Rocky Mountains; and the
valleys of the western Cordillera.
The SWB zone is the most northerly subalpine zone
in British Columbia. It extends north from 56.5–
57ºN latitude, well into the Yukon Territory and the
Mackenzie District of the Northwest Territories, where
it reaches 60–70ºN latitude. In British Columbia, the
SWB occupies the middle elevations of the northern
mountains. Elevations range between 1,000 m and
1,700 m in the southern portion of the zone, and
between 900 m and 1,500 m in the north. The SWB
is usually the subalpine zone above the BWBS.
There are two subzones: forest and scrub, the latter
occurring as a transition to the BAFA zone. The BAFA
is an alpine tundra zone that is not presently separated
heterophylla), western redcedar (Thuja plicata), and
hybrid spruce (Picea engelmannii x glauca), and a
few species that are also found in the boreal, including
lodgepole pine (Pinus contorta) and subalpine ir
(Abies lasiocarpa). A combination of tree species
composition and overall lora helps differentiate the
boreal from the temperate coniferous forests.
The western North American cordillera has coniferous
forests at high elevations that extend all the way to
Mexico. How mountain forests will be treated in
the proposed circumboreal vegetation map needs
to be determined through evaluation of species
composition, climate characteristics, and the
elevational sequence of vegetation type. Western
Canadian ecologists maintain that the Engelmann
spruce (Picea engelmannii)–subalpine ir forests of
the Rocky Mountains (and adjacent ranges) should be
into subzones.
considered mountain forests of the temperate zone.
The forests of this western Canadian boreal are
comprised of white spruce (Picea glauca), trembling
aspen (Populus tremuloides), lodgepole pine (Pinus
contorta), black spruce (Picea mariana), balsam poplar
(Populus balsamifera), tamarack (Larix laricina),
subalpine ir (Abies lasiocarpa), common paper birch
(Betula papyrifera), and Alaska paper birch (Betula
neoalaskana). There are considerable areas of
wetlands both mixed with forests and encompassing
larger areas of low-lying terrain.
Mapping
Sixty plant associations have been described and
classiied from the boreal region of British Columbia.
These are mostly forested but include some wetlands.
Alpine areas have been sampled, but a classiication
has not yet been inalized.
Transition to Temperate Zone
In western North America, the transition from boreal
to temperate forests cannot be determined by the
proportion of coniferous versus hardwood forest, as
the temperate forests are also coniferous. Lowland
temperate forests in British Columbia are dominated
by various conifers, including Ponderosa pine (Pinus
ponderosa), Douglas-ir (Pseudotsuga menziesii),
western larch (Larix laricina), western hemlock (Tsuga
The only ecological maps with complete coverage
for the boreal region map the biogeoclimatic zones,
subzones, and variants at a small scale―ca. scale
1:250,000 (see Fig. 2). The maps are available on a
1:20,000 map base but are not accurate at that scale
of presentation.
Site units of the BEC are mapped at scale 1:20,000
for some regions of the boreal in British Columbia.
Forest cover mapping, which maps tree cover at scale
1:20,000, is available for the entire region (Fig. 3).
Québec
Main Ecological Gradients in Québec
Québec’s territory is huge with an area of 1,514,100
km2 and a North–South extent from latitude 45º00’N to
62º30’N. Therefore, very strong ecological gradients
occur over the landscape. For example, mean annual
temperature declines from 7ºC at its southern border to
-11ºC at its northern extent (Fig. 4a). Other gradients,
affecting precipitation (Fig. 4b) or growth season
length, can be seen along a South-West–North-East
axis or with altitude variations.
24
Fig. 3. Example forest cover map. Polygon
map codes contain tree species, age class,
height class, and stocking density.
Fig. 2. Area of biogeoclimatic subzone mapping.
Fig. 4. Principal gradients in Québec: mean annual temperature and mean annual precipitation.
25
Ecological Land Classiication in Québec
In response
to climatic gradients, vegetation
formations are changing form South to North.
Québec’s Ministère des Ressources naturelles et
de la Faune (MRNFQ) designed an Ecological Land
Classiication, or ELC (Saucier et al., 1998; Robitaille
and Saucier, 1996) to express the relations between
climate or other abiotic factors and the vegetation.
This ELC is used to (1) summarize ecological
information on composition and natural dynamics of
vegetation; (2) analyze distribution of the ecosystems
in the landscape; (3) map the ecosystems at various
resolution according to a hierarchical system of
ecological land and ecosystem classiication; and
(4) deliver the results to the people responsible for
managing the forests and their resources (Saucier and
Robert, 1995) in both scientiic and more accessible
publications.
The hierarchy of the Québec ELC has 11 levels,
from a local to a continental scale (Table 1). Each
level is deined by key ecological factors and is
better expressed at a speciic scale of resolution.
The system was derived from bottom to top, using
detailed information from lower levels to deine the
levels above. The Québec ELC three upper levels are
shown on Figure 5.
The two classiication units at the lowest levels of the
hierarchy are expressed at local scales. These are the
forest type and the ecological type. The forest type, or
vegetation type, describes the actual composition and
physiognomy of vegetation on a particular site type.
It describes current vegetation in terms of dominant
tree species and understory indicator species. Those
indicator species relect local conditions, soil fertility,
or the dynamic status of the forest type. The ecological
type represents an area that exhibits a permanent
combination of the site’s potential vegetation and
physical features. It integrates both the dynamics of
the vegetation and the soil and site characteristics,
such as soil texture, moisture regime, or aspect. The
concept of this classiication unit is similar to the one
deined by Jurdant et al. (1977) and very close to Hill’s
“site type” (Hill, 1959), or to the British Columbian unit
called “site series” (Pojar et al., 1987). Ecological types
are deined by ecological regions and subregions,
by analyzing potential vegetation in relation with the
suricial deposits on which it grows, by taking into
account heir textural features and moisture regime,
or by the particular locations in the landscape where
vegetation types can be found. The arrangement of
ecological types in the landscape is expressed by
means of a toposequence characteristic of a given
subregion.
At a more general scale, there are three hierarchical
levels expressing the landscape structure: altitudinal
vegetation level, land district, and regional landscape
unit. A land district is characterized by its particular
relief pattern, as well as its geology, hydrography,
geomorphology, and regional vegetation. This
deinition is consistent with that given by Jurdant
et al. (1977). Land districts are delineated on the
basis of the spatial arrangement of relief forms,
suricial deposits, and bedrock geology. Vegetation is
conditioned by those site factors and by the climate,
which is considered homogeneous throughout a
land district given the relatively small size of those
units, 100 to 300 km2 in average. When the altitude
has such an impact on climate that it changes the
physiognomy and often the nature of the vegetation
in some land districts with high altitudinal gradients,
they are subdivided in altitudinal vegetation levels.
This latter hierarchical level is used to distinguish
the sites within a given land region, where signiicant
variations in altitude result in vegetation that differs
from the vegetation that is typically present in the
region, creating a montane, subalpine, or alpine level.
Adjacent land districts with a recurrent arrangement of
the main permanent factors of the environment and the
vegetation are grouped to form a regional landscape
unit. The main ecological factors considered at this
level are the type of relief, the average altitude, the
nature and proportion of the main suricial deposits,
and, inally, the hydrography. The effect of those
abiotic factors on the nature and distribution of the
ecological types (combining potential vegetation and
physical environment) and the distribution of certain
species indicative of the climate are used to validate
the regional landscape units. The method used to
delimit and map regional landscape units has been
described in previous publications by Robitaille and
Saucier (1996 and 1998). The regional landscape unit
is the level making the link with the regional scale.
At a regional scale, ecological regions, or land regions,
are characterized by the arrangement of ecological
26
Hierarchical level
Key ecological factors
Average size
Scale
≥106 km2
Continental
105 km2
National
104 km2
Regional
103 km2
Landscape
Local
Vegetation zone
Major plant formation
Vegetation subzone
Plant formation dominant in the landscape
Bioclimatic domain
Potential vegetation on zonal sites
Bioclimatic subdomain
Difference in climate inducing change in
disturbance regime, thus in succession
scheme
Ecological region
Distribution of potential vegetations along the
landscape, on zonal sites and azonal sites
Ecological subregion
Differences in distribution of potential
vegetations along the landscape expressing
transitional climatic characteristics
Regional landscape
Nature, abundance and recurrence of the
main permanent ecological features of the
physical environment and of the vegetation
Ecological district
Nature and distribution pattern of the physical
features of the environment
Altitudinal vegetation
level
Vegetation structure modified by altitude
variations
Ecological type
Permanent combination of potential
vegetation and environmental features
0,2 km2
Forest type
Present composition and structure of the
vegetation
0,1 km2
Table 1. Québec’s ecological land classiication hierarchy and key ecological factors.
types in the landscape, and particularly by forest
composition and vegetation dynamics on mesic sites.
The sites that are considered mesic are those sites
with no severe limitations or especially rich conditions
for plant growth, which are usually zonal sites. To
deine an ecological region both the late-successional
and the different transitional forest types are studied,
as are the links between the vegetation and the main
physical features of the environment, such as relief,
altitude, and suricial deposits, in order to identify
any changes in toposequences. Some land regions
expressing a transition between average bioclimatic
conditions and warmer or colder adjacent ecological
regions are divided into subregions. Subregions
are described as typical, southern, or northern.
The boundaries of the land regions regularly it with
signiicant changes in the abundance or presence of
some species or ecosystems, induced by a speciic
combination of climatic conditions, soil richness, and
relief or altitude.
The next higher level of the hierarchy is expressed
at the national scale. A bioclimatic domain is a large
area characterized by the nature of the potential
vegetation on the mesic sites, expressing the effects
of the climate. In fact, the expression of the climate
through the vegetation nature and structure is the
main general criterion used to separate the domains.
For instance, Québec is divided into ten bioclimatic
domains. There are six bioclimatic domains in
southern Québec: the Sugar maple–Bitternut hickory
(Acer saccharum─Carya cordiformis), the Sugar
maple─Basswood (Acer saccharum─Tilia americana),
the Sugar maple─Yellow birch (Acer saccharum–
Betula alleghaniensis), the Balsam ir–Yellow birch
(Abies balsamea─Betula alleghaniensis), the Balsam
ir–White birch (Abies balsamea–Betula papyrifera),
and the Black spruce–moss (Picea mariana–
Pleurozium schreberi) domains. Four more bioclimatic
domains are located in northern Québec: the Black
spruce–lichen (Picea mariana–Cladina rangiferina),
Forest tundra, Shrub tundra, and Herbaceous tundra
27
domains. Figure 5 shows the boundaries of the
bioclimatic domains in Québec and the main climatic
gradients. Following a continentality gradient from
dry in the South-West to wet in the North-East, the
bioclimatic domains in southern Québec are divided
into subdomains according to variations in vegetation
or in natural disturbance regime mainly caused by
differences in precipitation patterns.
The vegetation zone and subzone levels are at
the top of the hierarchy. A vegetation zone is a
huge, continental-scale area characterized by the
physiognomy of the dominant plant formations in
the landscape. There are three vegetation zones
in Québec (see Fig. 5): (1) the Temperate Northern
Zone dominated by hardwood or mixed-wood
formations; (2) the Boreal Zone, characterized
by evergreen coniferous formations; and (3) the
Arctic Zone, dominated by shrubs and herbaceous
formations. These three zones correspond to a
speciic lora and distinct plant formations and relect
the major bioclimatic subdivisions and place Québec
in the context of the world biomes classiication
system. When needed, vegetation zones are divided
into subzones according to the physiognomy of the
dominant late-succession vegetation in the landscape.
The limits of the various hierarchical levels have been
mapped at relevant scales for southern Québec,
under latitude 52ºN, using a bottom up approach from
the ecological district to the vegetation zone (Saucier
et al., 1998). Ecological types are also delineated
over very large areas for forest management units
and expressed in digital integrated eco-forest maps at
the scale of 1:20,000. For northern Québec, only the
bioclimatic domain level and above have been mapped
using work borrowed from other authors (Grondin et
al., 1996; Lavoie and Payette, 1994; Richard, 1987;
Morisset et al., 1983; Gérardin, 1980).
Data Available for
Mapping in Québec
Circumboreal
Vegetation
From the ecological inventory and the forest surveys
conducted in Québec by the MRNFQ, from work done
in universities, and from various sources in different
agencies, a fair amount of information can be gathered
to help map the vegetation of the boreal area. The
information readily available includes climatic data
and maps, species or ecosystem distribution maps,
and potential vegetation maps. Also, a classiication
of vegetation associations, made in the context of the
Canadian National Vegetation Classiication (CNVC),
Fig. 5. Bioclimatic domains, vegetation zones, and subzones in Québec.
28
will be available for the southern part of the boreal.
The associations deined by this project are close
matches to the Braun-Blanquet association concept.
Conclusion
Using examples from two Provinces of Canada, we
show that there is current data that can be used to
help complete the Circumboreal Vegetation Mapping
project (CBVM). Most of this information resides
in provincial agencies and universities, so the
collaboration of those agencies is key for a precise and
accurate circumboreal vegetation map in Canada.
To achieve this project for Canada and North America,
discussion should be carried out to clearly deine the
concepts that are to be mapped. Transition areas
between boreal and temperate zones can become
problematic. For example, in Québec the boreal
excludes the temperate mixed forest in the south,
which is not the case everywhere. The northern limit
of the boreal is more straightforward as it is the tree
limit.
A zonal approach to mapping has advantages in British
Columbia and Québec because site series or potential
vegetation units are available and mapped already for
large parts of the boreal region. Some high elevation
mountainous areas of British Columbia should not
be included as boreal, even if they are dominated
by coniferous species, because their climate is too
mild and they extend very far south in areas that are
clearly not boreal. This ambiguity emphasizes again
that deining concepts is an essential step toward
obtaining consistent results in North America.
We believe that the CBVM should map the “true”
boreal, excluding the areas transitional to the
temperate zone, for two reasons: (1) the concept of
these areas appears to vary more across jurisdictions,
and depending upon concept, could add considerable
area to the boreal map; and (2) these areas are very
sensitive to climate change, quickly changing to
temperate vegetation.
With climate change, the area considered boreal will
also change, and northern and southern boundaries
will effectively move northward. However, vegetation
response will lag behind climate change, leaving
remnants of boreal vegetation where they are presently
found. It is important to generate a boreal map soon
in order to characterize the present distribution of the
boreal, as portions of the boreal in British Columbia
seem very sensitive to climate changes.
In summary, due to the jurisdictional nature of
Canadian work on the boreal, clear deinition of
concepts is essential to obtaining consistent results
in North America. The hierarchical level of vegetation
classiication to map will inluence scale and timing
of the inal product, with a “higher-level” classiication
unit (e.g., formation) easier to map with presently
available information.
Acknowledgments
We would like to thank the organizers of the CBVM
workshop in Helsinki for providing a forum for
the initiation of this project and for supporting our
participation.
References
Gérardin, V. 1980. L’inventaire du Capital-Nature du
territoire de la baie James. Les régions écologiques
et la végétation des sols minéraux, tome 1.
Méthodologie et descriptions. Service des études
écologiques régionales, Min. Environ., Québec. 398
pp.
Grondin, P. et al. 1996. Écologie forestière. Pages
133–279 in Coté, M., Bérard, J.A., eds., Manuel
de foresterie, Ordre des ingénieurs forestiers du
Québec, Presses de l’Université Laval, Québec.
Hill, G. A. 1959. A Ready Reference to the Description
of the Land of Ontario and its Productivity. Division
of Research, Department of Lands and Forests,
Toronto, Ontario. 142 pp.
Jurdant, M., Bélair, J. -L., Gérardin, V., & Ducruc, J.
-P. 1977. L’inventaire du Capital-Nature. Pêches et
Environ. Can., Série de la classiication écologique
du territoire, no 2. 202 pp.
Lavoie, C. & Payette, S. 1994. Recent luctuations of
the lichen-spruce forest limit in Subarctic Quebec. J.
Ecol. 82 : 725–734.
Morisset, P., Payette S., & Deshaye, J. 1983. The
vascular lora of the northern Quebec-Labrador
peninsula: Phytogeographical structure with respect
to the tree-line. Nordicana 47: 141–151.
29
Pojar, J., Klinka, K., & Meidinger, D. V. 1987.
Biogeclimatic ecosystem classiication in British
Columbia. Forest Ecology and Management 22:
119–154.
Richard, P. J. H. 1987. Le couvert végétal du QuébecLabrador et son histoire postglaciaire. Dép. Géogr.,
Univ. Montréal, Notes Doc., 87-01. 74 pp. (with
color map of the vegetation of Québec-Labrador).
Robitaille, A. & Saucier, J. -P. 1996. Land district,
ecophysio-graphic units and areas: the landscape
mapping of the Ministère des Ressources
naturelles du Québec. Environmental Monitoring
and Assessment 39 (1–3): 127–148.
Robitaille, A. & Saucier, J. -P. 1998. Les paysages
régionaux du Québec méridional. Les publications
du Québec, Québec. 213 pp.
Saucier, J. -P., Bergeron, J. -F. Grondin, P., &
Robitaille, A. 1998. Les régions écologiques
du Québec méridional (3rd version): un des
éléments du système hiérarchique de classiication
écologique du territoire mis au point par le ministère
des Ressources naturelles du Québec. Supplément
de L’aubelle, no 124. 12 pp.
Saucier, J. -P. & Robert, D. 1995. Présentation du
programme de connaissance des écosystèmes
forestiers du Ministère des Ressources naturelles
du Québec. Revue forestière française XLVII(1):
71–74.
Westhoff, V. & van der Maarel, E. 1980. The BraunBlanquet approach. Pages 287–399 in Whittaker,
R. H., ed. Classiication of Plant Communities. Dr.
W. Junk, The Hague.
30
Map of the Natural Vegetation of Europe and Its Contribution to the
Circumboreal Vegetation Map
Udo Bohn
Formerly: Federal Agency for Nature Conservation, Bonn, Germany, u.bohn@arcor.de
Abstract
Introduction
The Map of the Natural Vegetation of Europe,
produced by close international co-operation, was
published in printed form and as an interactive CDROM. The printed work consists of three parts: maps
at the scales 1:2.5M and 1:10M, a legend volume
containing all mapping units in hierarchical order, and
an extensive explanatory textbook. All the map and
textual information is also available on an interactive
The Map of the Natural Vegetation of Europe was
published in printed form and as an interactive CDROM. The printed work (Bohn et al., 2000/2003)
consists of three parts: maps of the (potential) natural
vegetation of Europe on nine sheets, scale 1:2.5M,
along with a colored legend sheet and an overview
map, General Map of the Natural Vegetation of
Europe, scale 1:10M; a legend volume containing
CD-ROM in English and German versions. This allows
multiple use and analysis of the comprehensive
database.
all 700 mapping units in hierarchical order with
German and English texts; and an extensive German
explanatory textbook, inter alia with information on
the project history, the concept of the vegetation
map, description of the natural vegetation on different
levels of classiication, and detailed information for
each mapping unit on standardized data sheets (on
CD-ROM). The interactive CD-ROM (Bohn et al.,
2004) contains all map and text information related
to the complete printed work in digital form, both
in German and English. It allows multiple use and
analyses of the European vegetation data. Finally, the
contributions and results of an international workshop
on “Application and Analysis of the Map of the Natural
Vegetation of Europe” were published (Bohn et al.,
2005).
On the Map of the Natural Vegetation of Europe the
boreal zone comprises nine different main vegetation
formations. These consist of ive zonal (A, B, C, D,
E) and four azonal (P, S, T, U) formations. The boreal
core formations are birch forests (formation C) and
coniferous forests (formation D). All formations are
hierarchically classiied according to zonal, altitudinal,
geographical, and edaphic criteria. Within the boreal
zone of Europe, there are 90 mapping units, each
of them described in detail by standardized data
sheets.
The European classiication system is on the higher
levels to a great extent compatible with that of the
Circumpolar Arctic Vegetation Map (CAVM). Thus, it
will not be dificult to link up the Map of the Natural
Vegetation of Europe with the CAVM. Because of its
detailed information and digital database, the Map of
the Natural Vegetation of Europe provides an ideal
regional base map for the planned Circumboreal
Vegetation Map.
Keywords: digital vegetation map; Europe; hierarchic
classiication system, potential natural vegetation.
The mapping area covers all of Europe, including the
Urals and Caucasus in the east, as well as Iceland,
Svalbard, Franz Josef Land, and Novaya Zemlya in
the north, but excludes the Atlantic islands of Azores,
Madeira, and the Canary Islands. In this international
mapping project, which extended over two decades
(from 1979 to 2003), more than 100 vegetation
scientists from 31 European countries were involved,
including experts from all Nordic countries (Iceland,
Denmark, Norway, Sweden, Finland, and Russia)
that participate substantially in the arctic and boreal
zones.
31
The three main coordination centers for the project
implementation were located in Bonn (Federal Agency
for Nature Conservation, BfN), Průhonice near
Prague (Botanical Institute of the Czech Academy of
Sciences), and St. Petersburg (Komarov Botanical
Institute of the Russian Academy of Sciences, Dept.
of Vegetation Geography and Cartography).
Methods
A fundamental requirement for the production of a
uniied European vegetation map was the common
development of a classiication system, nomenclature,
and mapping concept that could be used by all
contributors from the different phytosociological
“schools” in Europe. Therefore, the titles of hierarchic
items and mapping units were named in a neutral,
generally understandable form.
from north to south, or indicating altitudinal belts in
mountains.
List of Main Vegetation Formations
(Classiication according
environmental conditions)
to
physiognomy
and
Zonal and extra zonal vegetation (depending
primarily on climate):
A. – Polar deserts and subnival-nival vegetation of
high mountains
B. – Arctic tundras and alpine vegetation
C. – Subarctic, boreal, and nemoral-montane
open woodlands, as well as subalpine and oroMediterranean vegetation
D. – Mesophytic and hygromesophytic coniferous
and mixed broad-leaved-coniferous forests
E. – Atlantic dwarf shrub heaths
The map legend as well as the vegetation map are
clearly hierarchically structured and take into account
that vegetation structure and species combination
are decisive criteria at any level of classiication.
The highest units of classiication are formed by
19 vegetation formations based on physiognomicecological features, of which 14 are zonal units,
representing the main macroclimatic zones in a
sequence from north to south, or altitudinal belts
in the mountains from higher to lower levels. Five
formations represent azonal vegetation, which is
shaped irstly by edaphic factors, that is, speciic
soil and hydrological conditions, and is modiied only
secondarily by macroclimatic factors. All formations
are characterized by their vegetation structure (life
forms etc.), species combination (mainly dominant
species), and ecology.
F. – Mesophytic deciduous broad-leaved and
mixed coniferous-broad-leaved forests
Corresponding vegetation types with similar structure
and species composition, such as polar deserts and
subnival vegetation of high mountains, or tundras
and alpine vegetation, are combined in one formation
group because of their close structural, loristic, and
climatic relations.
O. – Deserts
The main vegetation formations are coded by the
capital letters A-U, in alphabetical order, with their
further subdivision indicated by numbers. The main
formations are depicted in the vegetation map and
legend by basic colors, which are shaded according
to their subdivision along macroclimatic gradients
G. – Thermophilous mixed deciduous broadleaved forests
H. – Hygro-thermophilous mixed deciduous broadleaved forests
J. – Mediterranean sclerophyllous forests and
scrub
K. – Xerophytic coniferous forests, woodlands,
and scrub
L. – Forest steppes (meadow steppes alternating
with deciduous broad-leaved forests) and dry
grasslands alternating with xerophytic scrub
M. – Steppes
N. – Oroxerophytic vegetation (thorn-cushion
communities, tomillares, mountain steppes,
partly scrub)
Azonal vegetation (depending on speciic soil and
hydrological conditions)
P. – Coastal vegetation and inland halophytic
vegetation
R. – Tall reed vegetation and tall sedge swamps,
aquatic vegetation
S. – Mires
T. – Swamp and fen forests
U. – Vegetation of lood plains, estuaries and freshwater polders, and other moist or wet sites
32
The main vegetation formations are classiied
into subgroups according to dominant species
and species composition depending on spatial
macroclimatic and edaphic variation. The zonal
vegetation, such as birch, spruce, or pine forests in the
boreal zone, is irstly differentiated along temperature
(N–S, altitudes) and oceanity (W–E) gradients. The
hierarchic classiication system applied to the Map of
the Natural Vegetation of Europe is demonstrated in
detail by the European boreal vegetation.
The circumboreal phytogeographical zone is
principally characterized by dominating birch, spruce
and pine forests at low and middle altitudes. Its
northern boundary corresponds with the northern
natural tree line; the southern boundary follows the
southern natural distribution limit of coherent (more
or less closed) coniferous forests. The hemiboreal
subzone of mixed broad-leafed-coniferous forests
adjoining to the south is already assigned to the
temperate zone according to the numerous nemoral
plant species occurring there.
The phytogeographical map of Europe (Meusel
& Jäger 1992) provides a system of global zones
that are divided into loristic regions, subregions,
provinces, and subprovinces. Thus, the boreal zone
is subdivided from west to east into the boreo-Atlantic,
Scandinavian, and boreo-Russian provinces, and
within these provinces it is subdivided from north to
south into loristic subprovinces. This map provides
basic information for the geographical vegetation
classiication.
Results
In the Map of the Natural Vegetation of Europe, the
boreal zone comprises ive zonal (A─E) and four
azonal (P, S, T, U) main formations, with altogether
90 mapping units. Because of the high mountain
ranges in Iceland, Scandinavia, and the Urals,
besides the W─E and N─S oriented variation of the
birch and coniferous forests (formations C, D), there
is also a decisive altitudinal differentiation reaching
up into the treeless alpine (formation B) and subnivalnival (formation A) vegetation belts. Characteristic
azonal formations appearing in the boreal zone are:
coastal sand-dune, rock and halophytic vegetation
(formation P), mires (formation S), swamp and fen
forests (formation T), and vegetation of lood plains
(formation U). These formations are represented by
speciic boreal vegetation types and mapping units.
Due to the small scale of the European vegetation
map, the mapping units represent landscape-speciic
vegetation complexes consisting of various natural
plant communities and, as a rule, are named after the
dominating plant community(ies). The characteristic
features and contents of the individual mapping units
are described in detail in standardized data sheets.
Zonal Boreal Forest Vegetation
The boreal forest vegetation of Europe is composed of
two core formations: formation C represents subarctic
and boreal open woodlands dominated, especially in
the western part, by birch trees and shrubs (mostly
Betula pubescens s.l.), and formation D represents
coniferous forests with different dominating tree
species, such as spruce, ir, and pine.
Formation C – Subarctic, boreal, and nemoralmontane open woodlands – consists of two
geographically differentiated subgroups:
C.1 represents the eastern boreal open woodlands
with different combinations of the main tree species
Betula pubescens subsp. czerepanovii, Picea
obovata, and Pinus sylvestris.
C.2 represents the western boreal birch forests,
partly in complex with pine forests, in the more
oceanic inluenced regions, especially western
Scandinavia and Iceland.
Formation D – Mesophytic and hygromesophytic
coniferous forests—are mainly distributed in the
central and eastern parts of northern Europe, and in
the higher mountains. It is divided into six subgroups
according to the dominant conifer species. Three of
these subgroups are predominantly distributed in
the boreal zone and are characterized by a more
continental climate:
D.1 represents the western boreal spruce forests
dominated by Picea abies, P. obovata (in the
eastern part), and their hybrid.
D.2 represents the eastern boreal pine-spruce and
ir-spruce forests that are composed of Siberian
conifer species, such as Picea obovata, Pinus
sibirica, Abies sibirica, and Larix sibirica, spreading
33
from western Siberia over the Urals into the east
European lowland.
D.5 consists of pine forests, with Pinus sylvestris
being predominant, and changing admixture of birch
and spruce trees. These forest types naturally occur
only under extreme soil conditions: on nutrient-poor
and dry sandy or rocky, or wet and peaty sites.
Pine forests are not restricted to the boreal zone
and extend on suitable sites further south into the
temperate zone.
The subdivision of western boreal spruce forests
(subformation D.1) follows the climatic gradient from
north to south into northern, middle, and southern
boreal subzones and types. This subdivision is mainly
caused by different lengths of the growing season. But
the delimitation of the three subzones is also based on
loristic, structural, and ecological features; however,
the boundaries between these subzones are more or
less blurred. In the mountain regions of Scandinavia
the three subzones represent altitudinal belts.
The tree layer of the eastern boreal coniferous forests
(subformation D.2) consists of Siberian conifer
species that spread over the Urals to a varying extent
into its western foothills and lowland. These form dark
coniferous forests, the so-called “dark taiga.” Even in
their shrub and herb layers, many Uralian─Siberian
species occur in addition to widely distributed boreal
species. The group of eastern boreal coniferous forests
is on the irst level subdivided into lowland-colline
and (submontane-) montane (Ural) types, and on the
second level within the lowland-colline subgroup also
into northern, middle, and southern boreal types.
The third group of boreal coniferous forests
(subformation D.5) consists of pine forests with
dominating Pinus sylvestris. Because of the lack of
climatic indicator species, pine forests are divided
only into two subzones: the northern, and the middle
and southern boreal types. On the second level there
is an altitudinal differentiation into lowland-colline and
montane types. Montane types have been identiied
only in Scotland and the southern Urals.
Unwooded Zonal Vegetation Within the Boreal
Zone
Another typical but unwooded vegetation formation
within the boreal zone comprises mountain tundras
and boreo-alpine vegetation (formation B). The units
concerned are mainly distributed in mountain regions
of Iceland, the Faroe Islands, Scandinavia, and the
northern Urals and occur as altitudinal belts above the
belt of birch and open spruce woodlands in the boreal
zone. Because of gradual transitions between arctic
and boreal vegetation, it is dificult in many cases to
decide whether these units belong to arctic tundras or
to the boreal alpine vegetation.
Subformation B.1.5 – Mountain tundras and sparse
mountain vegetation—is distributed in the mountains
of Iceland, Kola Peninsola, and in the northernmost
part of the Urals. Mountain tundras are situated in the
transition zone between arctic and boreal vegetation
and, therefore, are closely related to tundra vegetation
in the southernmost part of the arctic zone.
Subnival-nival vegetation of high mountains
(subformation A.2) and alpine vegetation in the boreal
zone (subformation B.2) are mainly distributed in the
Scandinavian high mountains, in small outposts also
in the interior mountains of the Kola Peninsula, in the
Scottish Highlands, and on the Faroe Islands.
Azonal Vegetation Within the Boreal Zone
Within the boreal zone, besides zonal vegetation,
natural azonal vegetation occurs on large areas too
and plays an important role. It belongs to the following
main vegetation formations:
P – Coastal vegetation
S – Mires
T – Swamp and fen forests
U – Vegetation of lood plains
These formations, their zonal subgroups and mapping
units are also characteristic elements of the natural
vegetation of the boreal zone and therefore should be
mapped.
Formation P – Coastal vegetation—comprises the
vegetation of coastal sand dunes, of shingle beaches,
and of rocky seashores, as well as coastal halophytic
vegetation. Boreo–Atlantic sand dune vegetation is
mapped for example in a larger area on the lat coast
of south-western Iceland.
Formation S – Mire vegetation—in different types
and complexes represents the most widespread and
34
characteristic azonal vegetation in the boreal zone.
Its main level of classiication focuses on the nutrient
supply of ground water in three groups:
S – Mires in the boreal zone (16 Mapping Units)
1 – Ombrotrophic mires (bogs) (7 MU)
1.1 – Sphagnum fuscum-raised bog complexes in
the boreal zone (S1-S4)
1.2 – Sphagnum papillosum-blanket bog and raised
bog complexes with Erica tetralix and Narthecium
ossifragum in the oceanic regions (S6-S8)
2 – Ombro-minerotrophic mires (1MU)
2.2 – Palsa mires (S14)
3 – Minerotrophic mires (fens) (8 MU)
3.1 – Boreal aapa mire complexes (S15, S16)
The most characteristic and widespread mire types
of the boreal zone are palsa and aapa mires. Palsa
mires are bound to the subarctic and northern
boreal subzones, whereas aapa mire complexes
are widespread in the northern and middle boreal
subzones.
Correlation Between Maps
With regard to the planned Circumboreal Vegetation
Map (CBVM), questions arise how the Circumpolar
Arctic Vegetation Map (CAVM) and the Map of the
Natural Vegetation of Europe correlate in the North
European contact area. For this purpose I have
compared details of the CAVM with corresponding
sections of the Map of the Natural Vegetation of Europe
at different scales. The examined map sections lie in
the northeastern part of European Russia (Fig. 1).
3.2 – Transitional mires (incl. nutrient-poor fens)
(S17-S20)
3.3 – Small sedge-brown-moss fens (S22-S24)
In Figure 1 a section of the CAVM (scale 1:7.5M) is
compared with a detail of the General Map of the
Natural Vegetation of Europe at the scale of 1:10M.
Detail of CAVM, scale
1:7.5 million,
NE European Russia
S
G
B
W
Erect-shrub tundras
Graminoid tundras
Mountain complexes
Wetlands
Detail of European
General Map, scale Detail of CAVM, scale
1:10 million,
1:7.5 million,
NE European RussiaNE European Russia
B
C
D
P
S
U
Arctic tundras and
S
alpine vegetationG
Open woodlandsB
Coniferous forests
W
Coastal vegetation
Mires
Alluvial vegetation
Erect-shrub tundras
Graminoid tundras
Mountain complexes
Wetlands
Detail of European
Fig. 1. Comparison of corresponding map sections (northeast European Russia) from the Circumpolar
General Map, scale
Arctic Vegetation Map and the overview map, General Map of the Natural Vegetation of Europe.1:10 million,
NE European Russia
35
B
Arctic tundras and
alpine vegetation
It shows a rather good correlation between both
maps according to the small scale. In the CAVM the
southern arctic tundras and mountain complexes
are more differentiated, whereas in the European
overview map the wetlands are divided into three
formations: coastal halophytic vegetation, mires
(three types distinguished), and alluvial vegetation.
Adjacent to the south in the boreal zone occur
subarctic open woodlands (pink color) and northern
boreal coniferous forests (light violet, yellow-brown
and red-brown colors).
The comparison with the corresponding detail of
the basic Map of the Natural Vegetation of Europe
Relationship between map units of the CAVM and the European Vegetation Map
Map CAVM, scale 1:7.5
units million
Map
units
European Vegetation Map, scale 1:2.5 million
S
Erect-shrub tundras
B.1.31.5
Arctic tundras and alpine vegetation
S1
B7-B15,
B20Erect dwarf-shrub tundras,
B30,
Subzone D
B38,
B39
S2
Low-shrub tundra,
Subzone E
B20B29,
B31B33
B.1.4 Arctic shrub tundras
G
Graminoid tundras
B.1.31.5
Arctic tundras and alpine vegetation
G2
Graminoid, prostrate
dwarf-shrub, forb tundra
B7-B9
B.1.3 Southern arctic tundras
G3
Nontussock sedge, dwarfshrub, moss tundra
B8-B15,
B20B.1.3 Southern arctic tundras,
B27,
B.1.4 Arctic shrub tundras, B.1.5 Mountain tundras
B39
B
Mountain complexes
B.1.5
Mountain tundras and sparse mountain vegetation
B3e
Noncarbonate mountain
complex, Subzone E
B37,
B38
B.1.5 Mountain tundras and sparse mountain
vegetation
B4e
Carbonate mountain
complex, Subzone E
B37,
B38
B.1.5 Mountain tundras and sparse mountain
vegetation
W
Wetlands
P; S; U
Coastal vegetation; Mires; Alluvial vegetation
W2
Sedge, moss, dwarf-shrub
wetland, Subzone D
S13,
S21;
P17
S.2.1 Polygon mires, S.3 Minerotrophic mires
P.2.1 Coastal halophytic vegetation
Sedge, moss, low-shrub
wetland, Subzone E
S13,
S14,
S16,
S19,
S21;
P17;
U1
S.2, S.3 Ombro-minerotrophic and minerotrophic
mires;
P.2.1 Coastal halophytic vegetation;
U.1 Southern arctic alluvial scrub
W3
B.1.3 Southern arctic tundras,
B.1.4 Arctic shrub tundras,
B.1.5 Mountain tundras and sparse mountain
vegetation
Table 1. Relationship between the map units of the Circumpolar Arctic Vegetation Map and the Map of the Natural
Vegetation of Europe, within the northeast European part of Russia.
36
(EuroVegMap) at the scale of 1:2.5M shows a similar
but more detailed result. In Table 1 the relationship
between the mapping units of the CAVM and those of
the Map of the Natural Vegetation of Europe for the
same southern arctic map section is shown. The result
is that within tundra vegetation a close relationship
occurs only between the unit S2 (CAVM) and the
EuroVegMap units of B.1.4, as well as between
unit G2 (CAVM) and the EuroVegMap units of B.1.3
(marked by grey color). Whereas the units S1 and G3
of the CAVM have no clear corresponding group of
mapping units in the Map of the Natural Vegetation
of Europe.
and modiied without major dificulties according to
the mapping concept that will be developed by the
project team.
The mountain complexes B3e and B4e of the CAVM
are closely correlated to the map units of group B.1.5
in the EuroVegMap, but on the CAVM they are further
differentiated due to carbonate and noncarbonate
Bohn, U., Gollub, G., Hettwer, C, Neuhäuslová, Z.,
Raus, Th., Schlüter, H., & Weber, H., Bearb./eds.
2004. Karte der natürlichen Vegetation Europas /
Map of the Natural Vegetation of Europe, Maßstab/
bedrock. On the other hand, the wetland units W2
and W3 of the CAVM are divided and assigned in the
EuroVegMap to three different formations: coastal
halophytic vegetation, mires, and alluvial vegetation.
Scale 1:2.500.000, Interaktive/Interactive CDROM – Er-läuterungstext, Legende, Karten/
Explanatory Text, Legend, Maps, Münster,
Landwirtschaftsverlag.
http://www.loraweb.de/
vegetation/dnld_eurovegmap.html
Conclusion
The classiication system of the legend of the Map
of the Natural Vegetation of Europe is on the higher
levels widely corresponding to that of the Circumpolar
Arctic Vegetation Map, but the differentiation into
mapping units as well as their codes and colors
are different; however, it should not be dificult to
link the European vegetation map with the CAVM.
Moreover, the European classiication system is to
a great extent compatible with the Russian-Siberian
and the North American systems. Thus, the Map of
the Natural Vegetation of Europe at the scale 1:2.5
million provides an ideal regional base map for the
Circumboreal Vegetation Map that can be generalized
References
Bohn, U., Neuhäusl, R., unter Mitarbeit von Gollub, G.,
Hettwer, C, Neuhäuslová, Z., Schlüter, H., & Weber,
H. 2000/2003. Karte der natürlichen Vegetation
Europas/Map of the Natural Vegetation of Europe,
Maßstab/Scale 1:2.500.000, Teil 1/Part 1: Erläuterungstext/Explanatory Text, 655 S./pp., Teil 2/Part 2:
Legende/Legend, 153 S./pp., Teil 3/Part 3: Karten/
Maps, Münster, Landwirtschaftsverlag.
Bohn, U., Hettwer, G., & Gollub, G., bearb/eds.
2005. Anwendung und Auswertung der Karte der
natürlichen Vegetation Europas/Application and
Analysis of the Map of the Natural Vegetation of
Europe. Bonn (Bundesamt für Naturschutz), BfNSkripten 156, 452 S./pp. (Download: http://www.
bfn.de, BfN-Skripten 156, 2007).
CAVM Team. 2003. Circumpolar Arctic Vegetation
Map. Conservation of Arctic Flora and Fauna
(CAFF) Map No. 1, U.S. Fish and Wildlife Service,
Anchorage, Alaska.
Meusel, H. & Jäger, E. 1992. Vergleichende Chorologie
der zentraleuropäischen Flora, Karten, Bd. 3, Jena
u. a., G. Fischer, 689 S.
37
Development of a Boreal Vegetation Map of the Asian Part of Russia as a
Part of the Circumboreal Vegetation Map
Nikolai Ermakov
Central Siberian Botanical Garden SB RAS, Novosibirsk, Russia
Extended Abstract
Small-scale vegetation maps covering large
territories of whole continents may be produced only
on the basis of international cooperation and through
the synthesis of national traditions and resources.
Currently, the Map of the Natural Vegetation of Europe
is the foremost example of international efforts with a
uniied approach to mapping small-scale regularities
of vegetation across an entire continent. Ideology and
methodology of that project could be used as a starting
point for the proposed Circumboreal Vegetation Map
(CBVM). Since the Asian part of Russia is an adjacent
region to Europe, it would follow that some of the
same basic approaches in the creation of the Map
of the Natural Vegetation of Europe could be applied
to mapping the vegetation of Eurasia. For example,
some of the principles of vegetation classiication
applied in the Map of the Natural Vegetation of
Europe (Bohn et al., 2000–2004) could be taken into
account for the proposed Vegetation Map of the Asian
Part of Russia and the Circumboreal Vegetation
Map (CBVM). Speciically, the hierarchical system of
mapping units could be applied, such as the structure
and physiognomy of the plant cover at the highest
level and the characteristic species combinations
and further loristic differentiations on the basis of
geographical and site variability at lower levels.
The principle of “the dominant species at middle
level” used for the legend of the Map of the Natural
Vegetation of Europe cannot be applied for boreal
vegetation because dominants of boreal forests
are represented by very few tree species with wide
ecological amplitudes and geographical ranges.
Thus, loristic compositions of plant communities
indicate latitudinal, longitudinal, altitudinal, and
regional ecological subdivisions of boreal vegetation
better than dominant species. The second level of
hierarchical system may be based on the classiication
of boreal vegetation performed with the use of the
Braun-Blanquet approach. The system of higher units
(classes, orders and alliances) produced for Northern
Asia during last 20 years relects new essential
regularities of vegetation in relation to latitudinal and
altitudinal zonations, oceanity-continentality of climate
and site conditions.
Vast areas of the boreal zone in the Asian part of
Russia are covered by nonforest azonal vegetation,
including mires and high-mountain subalpine and
tundra landscapes. These areas comprise various
vegetation types of different ecology and loristic
composition. Accordingly, an important principle
of small-scale boreal vegetation mapping is the
acceptance of a system of vegetation complexes
and combinations. This approach is also used for
oro-boreal vegetation in mountain systems as well,
where the scale of 1:7.5M allows combinations of
ecologically different forest and non-forest plant
communities within altitudinal sub-belts at the lowest
hierarchical level.
Geographic Information Systems (GIS) - platforms and
techniques provide new approaches for vegetation
mapping and for cartographic modelling of vegetation
on the basis of thematic maps of relief and climate.
The use of satellite images (Landsat-7, TerraModis, etc) for distinguishing primary and secondary
vegetation types in the boreal zone is the most reliable
and uniied cartographic basis for developing smallscale maps of natural vegetation across vast areas.
References
Bohn, U., Neuhäusl, R., Hettwer, C., Gollub, G., &
Weber, H. (2000–2004). Karte der natürlichen
Vegetation Europas–Map of the Natural Vegetation
of Europe. Scale 1: 2.5M. Part 1: Explanatory Text
(in German) with CDROM. Part 2: Legend (German/
English), Part 3: Map. Bundesamt für Naturschutz,
Bonn.
38
The Integrated Mapping of Actual Vegetation of Asia
Kazue Fujiwara
Yokohama National University, Institute & Graduate School of Environment and Information Sciences,
Yokohama, Japan, kazue@ynu.ac.jp
Extended Abstract
When the European vegetation map appeared
in 1997 at the 40th International Association for
Vegetation Science (IAVS) symposium in the Czech
Republic, I was shocked to see it. The map was a
natural vegetation map that integrated the whole area
of Europe with one physiognomic legend. At the time,
I questioned whether scientists in Asia could make
an integrated map of the entire continent. One of the
obstacles to creating such a map is that there are not
enough vegetation scientists in each country in Asia.
Many scientists from all over Europe put efforts toward
creating the map. When the Institute of Biology & Soil
Science Vladivostok held an international symposium
in 2003, Dr. Irina Safronova, Komarov Botanical
Institute of the Russian Academy of Sciences, showed
a large map of Russia based on natural vegetation
and suggested an integrated vegetation map of all of
Asia be made. As it was a long time goal, I talked
with Japanese vegetation scientists about making
a vegetation map of Asia that integrated the whole
area of Asia from the Far East to northern Southeast
Asia and with an integrated physiognomic legend. I
received a grant for this project, “Integrated Vegetation
Mapping.” Prof. Yukito Nakamura, Tokyo University of
Agriculture, and Dr. Pavel Krestov, Institute of Biology
and Soil Science, Russia, provided ieldwork and
information for Far East Russia and alpine vegetation
in Asia. Dr. Irina Safronova provided information about
the western part of Asian Russia. Although Dr. Nikorai
Ermakov, Central Siberian Botanical Garden, Russian
Academy of Sciences could not attend ieldwork, he
gave us information about central Asian Russia.
Consequently, the project, “Integrated Vegetation
Mapping in Asia,” was funded by a Grant-in-Aid for
Scientiic Research Grant 16255003. For the project,
we had great Prof. Yong-Chan Song and young Prof.
Liangjun Da, Chinese Eastern Normal University
Shanghai, in China. They are excellent vegetation
scientists in China and also experts of evergreen
broad-leaved forests. They provided ieldwork and
information for the vegetation of middle and southern
China.
We also included in ieldwork, a discussion of making
an integrated vegetation map of Asia with Prof.
Elgene Box, University of Georgia, U.S.A., with his
global perspective and experiences working with Dr.
H. Walter (Walter & Box, 1983). For the vegetation
mapping, we included remote sensing scientists who
have veriied actual vegetation types in Asia based on
satellite data with ieldwork and vegetation scientists
who have veriied vegetation types by ground-truthing.
The initial draft of a legend for a vegetation map of
Asia was based on the global, pheno-physiognomic
vegetation classiication system developed at Tokyo
University for mapping both potential and actual
vegetation at the global scale (Box, 1995; Box &
Fujiwara, 2005). This classiication, with 50 basic
vegetation types, was designed to be useable for both
climate-based and satellite-based global vegetation
mapping because of its strict deinition of vegetation
types by pheno-physiognomy (i.e., physical structure
and its seasonal variations).
These types were irst adapted for the Asian vegetation
map at a team meeting at Chiba University, Japan,
in January 2005. They were updated and improved
each year thereafter at other Chiba meetings. The
inal version before actual mapping (from January
2007) was used to guide development of the irst map
drafts, keeping the pheno-physiognomic approach
while adding other landcover types, such as cropland.
Much discussion centered on considerations of scale,
dynamics, and vegetation mosaics. The study area
included a region from the northern part of Southeast
Asia to Asian Russia. The map was produced from
satellite data and thus represents actual vegetation.
The satellite data used for the vegetation mapping in
this project were the Moderate Resolution Imaging
39
Spectroradiometer (MODIS) data observed in 2003.
The MODIS sensor, installed on the Terra satellite,
was launched into space by NASA in December 1999.
The source of MODIS data is the “MODIS/TERRA
Nadir BRDF- Adjusted Relectance 16-DAY L3 Global
1 KM SIN Grid Product (MOD43B4 NBAR)” from the
U.S. Geological Survey. The source MODIS data were
processed further at the Center for Environmental
Remote Sensing (CEReS), Chiba University. This
processing included mosaicking, reprojection, and
clearing contamination by cloud pixels.
large areas. Many of the legend units were based on
zonal vegetation types, that is, physiognomically fairly
uniform types of potential natural vegetation of the
major bioclimatic zones.
It was not possible to show all the vegetation types
known from ieldwork or from the literature, sometimes
due to the small areas mapped and at other times
due to the lack of training sites guiding the satellite
recognition algorithm. Some original vegetation types
were at once very extensive but have been greatly
Box, E. O. & Fujiwara, K. 2001. Ecosystems of Asia.
Pages 261–291 in Levin, S. et al., eds. Encyclopedia
of Biodiversity, vol. 1. Academic Press, San Diego.
reduced, appearing now only as very small, widely
scattered areas on the map, even though they may
still represent the potential natural vegetation over
References
Box, E. O. 1995. Global and local climatic relations
of the forests of East and Southeast Asia. Pages
23–55 in Box, E.O. et al., eds. Vegetation Science
in Forestry, Handbook Vegetation Sci., vol. 12/1.
Dordrecht.
Walter, H. & Box, E. O. 1983. Chapters 2–9 (deserts
of Eurasia) in Temperate Deserts and Semi-Deserts
(West, N. E., ed.). Ecosystems of the World, vol.
5. Elsevier, Amsterdam. New York: Springer-Verlag.
40
Datasets Useful for the Circumboreal Vegetation Mapping Project
Carl Markon
Alaska Science Center, U.S. Geological Survey, Anchorage, Alaska, U.S.A., markon@usgs.gov
Extended Abstract
There are a number of available databases that could
be used to support the Circumboreal Vegetation
Mapping project. The databases are of three general
types: those that are provided as one contiguous data
set; those that are either provided on a continental
or regional basis; and those that are of landscape
levels. Contiguous global datasets include data
from the United Nations Environment Programme
(UNEP), Global Resource Information Database
(GRID) global datasets; the NASA Goddard Space
Flight Center, Global Change Master Directory; the
International Research Institute (IRI) for Climate and
Society, Data Library; and the U.S. Geological Survey,
Global Land Cover Characterization dataset and its
counterpart, the Global Land Cover Characteristics
Database. Associated global data sets of interest may
also include information on different climate variables,
such as that provided by the National Ocean and
Atmospheric Administration (NOAA), National Climate
and Data Center. Other databases provide information
on more continental or regional areas such as the
United Nations Economic Commission for Europe
(UNECE), Forest Resources Assessment (regional to
global assessments); Environment Canada, State of
the Environment INFOBASE, and Earth Observations
Data Service; Global Forest Watch (circumboreal
regional maps); Centre National De La Recherche
Scientiique (CNRS; phenology over European
forests); NATO International Scientiic Exchange
Programmes (Norwegian-Russian Border area), and
UNEP World Conservation Monitoring Centre (UNEPWCMC; circumboreal regional maps). In addition to
static datasets, there exists a large number of satellite
image databases that could be used, including
global data holdings of Landsat and Advanced Very
High Resolution Radiometer (AVHRR) imagery, both
accessible through the U.S. Geological Survey, Earth
Resources Observation Systems Data Center; and
Moderate Resolution Imaging Spectroradiometer
(MODIS) imagery, available through NASA.
Keywords: AVHRR, circumboreal, datasets, forests,
imagery, landcover, Landsat, MODIS.
41
Ecological-Geographical Base for Biodiversity of Boreal Forests in
Russia
Galina N. Ogureeva
M.V. Lomonosov Moscow State University, Moscow, the Russian Federation
Abstract
In Russia the boreal forests (taiga) occupy vast
continuous areas, thus relecting the zonal distribution
of vegetation. The share of boreal forests in this
country is estimated as more than 70% of the total
forest-covered territory in Eurasia. The boundary of
the boreal forest zone is determined by bioclimatic
indices. The most signiicant indices are the amounts
of heat (mean annual temperature, the sum of active
temperatures >10ºC, duration of the vegetation
period) and moisture (mean annual precipitation and
the regime of their seasonal distribution) as well as
their interrelation within geographical regions.
Dark-and light-coniferous forests mainly represented
by spruce, ir, and pine larch, respectively, dominate
the taiga zone. Boreal forest diversity is intrinsically tied
to landscape variety. General regularities in changing
vegetation cover are evidenced by gradual changes
in species abundance, productivity, and structure of
plant communities taking place from north to south
and from west to east. Due to these differences in
vegetation at the landscape level the taiga zone is
divided into ive subzones.
In these subzones, ecological regions (ecoregions)
are distinguished as comparable to the structure
of ecological units with geographical taiga variants
inherent to them. This is an averaged link of
biogeographic subdivisions of boreal forests relecting
regional peculiarities in the bioclimatic potential
(taking into complete account the experience gained
in elaborating global and regional models by means
of such indices as air temperature and precipitation)
and the landscape structure of the territory.
including forest tundra, northern, middle, southern
taiga, and the subzone of hemiboreal forests; (2)
according to gradient of the distance from the ocean
(continentality extent), there are three oceanic and
three continental sectors; and (3) according to
gradient of heat, moisture, and pressure in mountains,
altitudinal belts and climatic strips of taiga forests are
distinguished. Thus, the zone of boreal forests is
represented by 28 ecoregions, with 14 ecoregions in
plains and 14 ecoregions in mountains.
To characterize vegetation cover in ecoregions, it
seemed reasonable to use information about the main
geographic-genetic complexes of plant formations.
The coenotic composition of ediicators and different
plant species, as well as the available endemic relic
communities and species occurring due to particular
historical development of biotic complexes, are
indicated for dominant plant formations. The plant
communities accompanying the zonal taiga type, which
are predominant in some regions, for instance, the
mires in the taiga zone of West Siberia, characterize
the geographical variants of boreal forests.
The structure of altitudinal zonality indicating the
vegetation belts and their altitude limits is shown for
mountain ecoregions. In the mountains, the following
complexes of plant formations represent boreal
forests: Ural-Siberian, Eastern-Siberian, Okchotian,
and Beringian. The main formations of mountaintaiga forests with peculiar plant species of initial
communities are also given.
Keywords: biodiversity, boreal forests, ecoregion,
taiga, vegetation mapping, zone.
Introduction
In the map Zones and Altitudinal Zonality Types
of Vegetation in Russia (Ogureeva et al., 1999) at
1:8M scale, boreal forest divisions are based upon a
three-dimensional model: (1) according to gradient of
warmth, ive subzones are distinguished southwards,
The biome of boreal forests (taiga) is widespread
and occupies vast continuous areas, thus relecting
the zonal distribution of vegetation in Russia. Boreal
forests occupy more than 70% of the total forest-
42
covered territory in Eurasia. In Siberia alone, the
taiga covers nearly 90,000 km2.
The bioclimatic situation and various landscape
conditions determine the diversity of boreal forests
in Russia. An ecological-geographical approach to
mapping the boreal forest relects forest diversity
as dependent upon the natural conditions of the
mapped territory at different regional levels. Such a
subdivision of the biosphere as biomes, ecoregions,
biogeographical areas, and landscapes may be taken
as registration units in the biodiversity of the forestcovered territory.
the structure of plant communities taking place from
north to south, from west to east, and from plains to
mountains as well.
A regional biogeographic analysis of the boreal zone
shows the natural differentiation of the territory. It is
directed to identify vegetation macrostructures and
their interrelation with the environment including:
•
identifying the types of phytocoenotic structures
at different levels of their organization; special
attention is paid to distributing the macrostructures
of the vegetation cover in plains and altitudinal
zonality types in mountains;
Mapping of Vegetation Complexes
•
In Zones and Altitudinal Zonality Types of the Natural
Vegetation in Russia, at scale 1:8M, there were
classifying the types of phytocoenotic structures,
the estimation of their structural-morphological
peculiarities; and
•
mapping the phytocoenotic structures at local and
advantages to using a zonal approach to vegetation
mapping. The scientiic concept of this map is to
display general regularities in the distribution of
vegetation cover in plains and altitudinal belts and
their regional peculiar features. On this map the
spatial regularities of vegetation macrostructures
correspond to real climatic and landscape conditions.
The mountain vegetation is classiied according to
types of altitudinal zonality.
Within every subzone, ecological regions (ecoregions)
are distinguished as comparable in their structure of
ecological units with the geographical taiga variants
inherent to them. The ecoregion is an averaged link
of biogeographical subdivisions of boreal forests,
relecting regional peculiarities in the bioclimatic
potential and the landscape structure of the territory.
Geographical taiga variants are identiied within every
ecoregion. Regional maps of vegetation with detailed
characteristics of the forest coenotic diversity are
compiled for a great number of vast areas.
Biodiversity of Boreal Forests in Russia
The diversity of boreal forests is dependent on different
climatic conditions and the variety of landscapes for
all of the forest-covered territory. General regularities
in the changing vegetation are evidenced by gradual
changes in species abundance, productivity, and
regional levels.
Limits of Boreal Forests in Russia
The boundaries of the boreal forest zone are
determined by bioclimatic indices. The most signiicant
are amounts of heat (mean annual temperature,
average sums of active temperatures above 10ºC,
duration of the vegetation period) and moisture
(mean annual precipitation and the regime of their
seasonal distribution) as well as their relationships
within geographical regions.
There is a bioclimatic model of vegetation zones,
subzones, and sectors in climatic space (Nazimova
et al., 2004). This model is based on data obtained
in 650 climatic stations of Scandinavian countries
and Russia and relects the geographical position of
zones and subzones in accordance with temperature
gradient (the sum of active temperatures above 10ºC)
and continentality extent. The following bioclimatic
sectors are represented: Atlantic-European; CentralEuropean; East-European; West-Siberian; CentralSiberian; East-Siberian; Far East continental; Far
East oceanic. In each sector the position of the boreal
forest, which has distributional limits, is determined by
the coeficient of continental climate from the Atlantic
Ocean towards the Paciic Ocean. The common limits
of boreal forest distribution are derived from the sum
of active temperatures above 10ºC–1700ºC
43
Fig. 1. Zones and Altitudinal Zonality Types of Vegetation in Russia (Ogureeva et al., 1999). Five main zones are recognized for Russia (from north to south): A ─
Tundra; B ─ Taiga (boreal coniferous forests); C ─ Deciduous broad-leaved forests and forest-steppes; D─Steppes; and E ─ Deserts.
44
–1850ºC in the north and up to 1900ºC–2000ºC in
the south. The 10ºC isotherm closely coincides with
the northernmost limit of the tree growth. It serves as
a dividing line between the boreal forest region and
the treeless tundra. The mean annual precipitation
varies from 500 mm to 800 mm.
The zone and provincial position of boreal forests
determine the composition of plant communities.
Zonal Characteristics of the Boreal Vegetation
The map Zones and Altitudinal Zonality Types of
Vegetation in Russia, scale 1:8M was compiled as
a series of maps for higher education in Russia. It
relects macrostructures of the vegetation cover in
conformity with the environmental conditions. Five
main zones are recognized for Russia (from north
to south): A – Tundra; B – Taiga (boreal coniferous
Therefore, it is characterized by zonal vegetation
combined with azonal plant communities such as
mires, loodplains, and coastal or halophytic areas.
Due to differences in the climate and topography from
west to east every subzone reveals regional plant
community combinations of different forest formations.
These geographical variants are named according
to certain geographical locations or landscapes.
Characteristic plant species are selected for the main
subunits–subzonal and regional combinations of
plant formations.
For example, the subzone of southern taiga
(B.4)—dwarf-shrub and herb-moss coniferous
forests in combination with mires—is divided into ive
geographical variants:
— East European taiga (B.4a) consists of spruce
(Picea abies) and pine (Pinus sylvestris) forests
with Oxalis acetosella and nemoral herb species
(Galeobdolon luteum, Hepatica nobilis, Stellaria
holostea, Pulmonaria obscura);
forests); C – Deciduous broad-leaved forests and
forest-steppes; D – Steppes; and E – Deserts (Fig. 1).
On this map, boreal forest division is based upon
a three-dimensional model: (1) according to the
temperature gradient of latitudinal differentiation,
ive subzones are distinguished southwards; (2)
according to the gradient of the distance from the
ocean (continentality extent), there are two oceanic
and six continental sectors, including 32 geographical
variants of the boreal forest formations complex;
and (3) according to the gradient of temperature,
precipitation, and pressure in the mountains, the
altitudinal belts and climatic strips of taiga forests
are distinguished. Thus, 55 altitudinal zonality types
together with boreal forest belts are identiied for the
total territory of Russia.
Due to these differences the taiga zone is divided
into ive subzones: forest tundra (B.1), northern (B.2),
middle (B.3), southern (B.4) taiga and subtaiga, or
hemiboreal mixed broadleaved-coniferous forests
(B.5). In the map there are 32 mapping units of
boreal forests in the plains (geographical variants of
combinations of plant formations). Special attention is
given to characteristics of vegetation macrostructures
and description of their loristic and coenotic
peculiarities. The prevalence of one or several boreal
forest formations depends on climatic conditions in
the zone and provincial position. The forest zone is
a heterogenous structural unit of the forest cover.
— Pre-Ural taiga (B.4b) consists of spruce and irspruce (Picea obovata, Abies sibirica) forests
with nemoral herbs (Pulmonaria obscura,
Asarum europaeum, Aegopodium podagraria)
and Siberian tall forb species (Aconitum
septentrionale, Crepis sibirica, Cacalia hastata);
— West Siberian taiga (B.4c) ─ ir-spruce (Picea
obovata), pine sibirica - ir (Abies sibirica, Pinus
sibirica), and pine forests (Pinus sylvestris) with
the herb (Carex macroura, Circaea alpina)-moss
layer;
— Central Siberian taiga (B.4d) ─ larch (Larix
sibirica), pine (Pinus sylvestris) and ir-spruce
forests with the herb–dwarf-shrub (Carex
macroura, Vaccinium uliginosum) – moss layer;
and
— East Siberian–Far East taiga (B.4e) contains
larch (Larix gmelinii) and pine (Pinus sylvestris)
forests with the shrub layer (Rhododendron
dauricum, Ledum palustre).
As a result, subzones include the following
geographical variants of taiga complexes: forest–
tundra ─ 6, northern taiga ─ 6, middle taiga ─
8, southern taiga ─ 5, and subtaiga ─ 6. The
Eastern European subtaiga (mixed broadleaved-
45
dark-coniferous forests) has three variants of forest
formations. In Siberia there are three variants of the
subtaiga (small-leaved forests). This subzone is often
considered as an individual zone.
Characteristics of Mountain Boreal Vegetation
Integral manifestations of latitudinal and altitudinal
zonal peculiarities in the distribution of vegetation
is typical for mountains (Ogureeva, 2000). Each
altitudinal belt has a speciic vegetation type
(the combination of plant communities of several
formations). The main regional unit for differentiating
the mountain vegetation is an altitudinal zonality
type. The name of each type relects particular
features of altitudinal belts, indicating the speciic
composition of the main plant communities and their
detailed geographic position. In the map there are 55
units of mountain vegetation (55 altitudinal zonality
types, with 25 geographical variants and 43 subtypes).
For example, Central Altai type (N 27) consists
of several belts: Nival (Waldheimia tridactylites,
Lupinaster ezimium, Saussurea glacialis) ─ alpine
tundra (Ranunculus altaicus, Seseli monstrosa,
Schultzia crinita, Sibbaldia procumbens) ─
subalpine (tall and low herb meadows, pine, ir, larch
open woodlands, dwarf birch thickets with Betula
rotundifolia) ─ dark coniferous forests (taiga with
Pinus sibirica, Abies sibirica, Picea obovata) ─ light
coniferous taiga (forests with Larix sibirica, Pinus
sylvestris) ─ forest steppes (forests in combination
with bunch-grass steppes).
A higher correlation exists between the bioclimatic
parameters of altitudinal belts and the altitudinal
climatic gradients. For example, the temperature
factor determines the boundaries between belts such
as taiga and subalpine, although humidity acts more
locally and indicates the diversity of plant formations
and their changes within belts, for instance, light-and
dark-coniferous forests.
Every belt has a characteristic loristic-coenotic
complex. The mountain boreal forests are
represented by complexes of plant formations such
as Ural-Southern Siberian, Angaridian, Beringian,
Ochotian, and Mandshurian. The main formations
of mountain-taiga forests with peculiar plant species
of initial communities are also identiied.
Classiication of Altitudinal Zonality Types
The geographic-genetic principle is used for classifying
altitudinal zonality types. The types are divided
into groups, subclasses, and classes, relecting
structural-genetic peculiarities of the mountain
vegetation at the regional level and its connection
with zones and phytogeographic areas (Table 1).
Classes and groups of altitudinal zonality types bear
the main geographic-genetic information about the
mountain vegetation. The class integrates groups
of types with their uniied complex of genetically
associated plant formations in the main belt,
locating them by similar ecological conditions and by
similar structural-dynamic properties. A set of plant
communities in similar belts of different types can
be determined, relecting regional peculiarities of
mountain territories. Subclasses represent regional
speciic features of plant communities.
A group integrates altitudinal zonality types with the
same set of altitudinal belts within the geographically
uniform area. The analysis of vegetation
macrostructures in mountains of Russia distinguishes
44 altitudinal zonality types of mountain boreal forests;
23 types are included into four groups of Hypoarctic
subclass (taiga-open woodlands); and 32 types are
included in nine groups of Boreal class.
Characteristics of Ecoregions in the Boreal Zone
In each subzone the ecological units (ecological
regions) are distinguished as comparable in their
structure with the geographical taiga variants inherent
to them. These ecoregions are considered as an
averaged link of biogeographical subdivisions of
boreal forests, relecting regional peculiarities of the
bioclimatic potential and the landscape structure of
the territory. In mountains the ecoregions correspond
to groups of altitudinal zonal types. Thus, the zone of
boreal forests is divided into 28 ecoregions, among
them 14 in plains and 14 in mountains (Fig. 2).
Information about the main geographic-genetic
complexes of plant formations was used to characterize
the vegetation cover of ecoregions. Not only are the
coenotic composition of ediicators and different plant
species indicated for dominant plant formations, but
also the available endemic relic communities and
46
and species connected with the peculiar features of
the historical development of biotic complexes.
A database has been synthesized for the main
vegetation types of ecoregions in Russia. To
characterize every geographical variant of zonal
vegetation and altitudinal zonality type, the following
parameters were used for elaborating this database:
(a) characteristics of bioclimatic parameters in
local sites of any altitudinal belt; (b) phytocoenotic
characteristics of plant formations within the altitudinal
belt and geographical variant; and (c) the loristic
richness of each plant formation and its integral
estimation in all the macrostructures (Tables 2─3).
Classes of types
Subclasses of types
Groups of types
Hypoarctic types
Hypoarctic (taiga)
East European – Ural
Central Siberian
Verkhoyansky – Kolyma
Northern Okhotian
Ural - Southern Siberian Ural – Central Siberian
Boreal (Taiga)
Altai – Saiany
(coniferous forests)
Tuva – Southern Transbaikal
types
Baikal region
Eastern Siberian
(Angaridiаn)
Transbaikal
Aldan – Maya
Beringian
Islands of Northern part of the
Pacific ocean
Ochotian
Аmur – Zeya
Amur – Uda
№
legend
10–12
13
14–19
20-22
23–25
26–28
29–31
32–34
35–39
40–44
45–46
47–49
68–72
Table 1. Classiication of altitudinal zonality types of mountain vegetation of Russia. Twenty-three types
concern four groups of a Hypoarctic (taiga) subclass and 32 types—to nine groups of Boreal class.
Fig. 2. Ecoregions of Russia. Zone of boreal forests is divided into 28 ecoregions.
47
№ecoregion
Ecoregions and Geographic variants of
forests
Climatic
parameters *
Plant formations and corresponding vegetation types
1
2
3
4
5
6
* 3- mean annual temperature, 4- the sum of air temperatures >10ºC, duration of the vegetation period; 5- mean annual precipitation (mm)
Hypoarctic (taiga) (forest- tundra and northern taiga)
9
Kola -Karelia
- 0,5º
700
500-670
Open woodlands: spruce (Picea obovata), pine forest (Pinus
sylvestris), birch (Betula tortuosa, B. pubescens)
Open woodlands and light forests - spruce, pine forests, birch forests: herb-dwarf shrub, lichen, with Hylocomium, Dicranum,
with Vaccinim uliginosum, Ledum palustre, with V. myrtillus and Empetrum hermaphroditum, with Betula nana, with Equisetum
sylvaticum; haircap-sphagnum moss; sedge fen, cotton grass swamps, ridge-pool complexes, sphagnum-hypnum moss bogs,
herbaceous-moss fens.
13
17
Larch taiga (Larix gmelinii, L. cajanderi)
- 12º
1084
184-200
º
- 13
Open woodlands and light forests: larch with shrubs (Salix udensis, Betula exilis), dwarf-shrubs (Vaccinium uliginosum,
Empetrum subholarcticum)-lichen with Hylocomium, Rhytidiadelfus, Dicranum; moss maries.
Boreal taiga (northern middle and southern taiga)
Spruce (Picea obovata), pine forest (Pinus sylvestris), Siberian
Obi-Irtysh
- 0,8º
1570-1800
570-600
º
pine (Pinus sibirica), firn (Abies sibirica), larch (Larix sibirica)
- 2,7
forests
Low Kolyma R.
Middle-and southern taiga forests: spruce-Siberian pine, Siberian pine spruce-fir forests herbaceous with Hylocomium, pine,
birch and aspen forests; pine sphagnum, dwarf shrub forests, Siberian pine-spruce-birch forests with grass, Equisetum
sylvaticum and sages, Sphagnum; string bogs.
19
Central Jakutia
- 8,2º
-10,2º
1550-1600
Larch (Larix gmelinii), spruce (Picea obovata), pine
sylvestris)
250
(Pinus
Middle taiga forests: larch, spruce-larch, pine forests grass-dwarf shrub with Ledum palustre, Arctostaphylos uva-ursi,
Vaccinium vitis-idaea, V. uliginosum, with grass (Limnas stelleri); alas meadows (Calamagrostis langsdorffii, Carex juncella).
Table 2. The ecoregions of plain boreal forests in Russia (fragment).
1
Hypoarctic taiga types
42
Boreal taiga types
46
49
53
2
3
4
5
6
1000 130
Larch (Larix gmelinii, L. cajanderi) with Pinus
950 400
pumila
500 800
Goltsy - tundra (Cassiope tetragona, C. ericoides, Dryas punctata, Empetrum subholardticum, Kobresia
myosuroides) – stlanik (Pinus pumila, Alnus fruticosa) – open woodlands – taiga forests
Verkhoyansky Range-Kolyma
- 13
-16,6
Altai-Saiany
+ 2,0
Siberian pine (Pinus sibirica), fir (Abies
210018
400
+ 0,1
sibirica), spruce (Picea obovata), pine (P.
501200
700
- 0,5
sуlvestris), larch (Larix sibirica) forests
1500
2000
- 3,0
Nival–alpine (Sibbaldia procumbens, Schultzia crinita, Ranunculus altaicus) – subalpine (луга-Rhaponticum
carthamoides, Aquilegia glandulosa, A.sibirica, Geranium albiflorum; dwarf birch- Betula rotundifolia; larch, fir open
woodlands) – taiga – forest-steppes
Transbaikal
- 0,9
Larch (Larix gmelinii), pine (Pinus sуlvestris),
1800 250
- 1,5
spruce (Picea obovata)
800 400
-11,0
600
Goltsy-tundra (lichen, dwarf shrub-Dryas punctata, Cassiope ericoides, Empetrum nigrum)–stlanik (Pinus pumila,
Betula divaricata, B. exilis, Alnus fruticosa, Rhododendron aureum) – open woodland (larch, birch-Betula lanata)
– taiga – forest-steppes (larch, birch, pine forests, bunch-grass steppes).
Birch (Betula ermanii, B. kamtschatica), larch
400
(Larix gmelinii, L. cajanderi), spruce (Picea
600
ajanensis)
1000
Nival-alpine-tundra (Cassiope lycopodioides, Phyllodoce aleutica, Rhododendron kamtschaticum) – stlanik
(Pinus pumila, Alnus kamtschatica, Sorbus sambucifolia) – taiga forests
Kamchatka
- 0,2
- 2,4
120011
00700
Table 3. The ecoregions of mountain boreal forests in Russia (fragment).
48
Tuva-Southern Transbaikalian and Transibaikalian Regions
Fig. 3. The altitudinal zonality types (numbers of types in the legend of the map Zones and Altitudinal
Zonality Types of Vegetation in Russia (Ogureeva et al., 1999). Tuva-Southern Transbaikalian ecoregion:
32─Primorsky Range, 33─West Baikal, 33.1─Baikal region variant, 34─West Bargusin Range, Transbaikalian
ecoregion: 35─North Baikal, 35a─Vitim plateau, 36─Kadar-Kalarsky range, 37─East Bargusin, 37.2─Ulan
Bargusinsky variant, 38─Upper Amur River, 39─Patomsky, 39.1─West Patomsky variant; the altitudinal
belts: 1-goltsy, mountain tundras (2-upper strip, 3-down strip), subgoltsy (strips: 4-stlanik, 5-dwarf larch forests,
6-dwarf larch forests with dark coniferous trees), taiga dark coniferous forests with Pinus sibirica, Abies sibirica
(strips: 7-upper, 8-down), 9-larch forests, 10-pine, larch-pine forests (strips: 11-upper, 12-down), 13-inversion
of stlanik belt, 14-forest-steppes, 15-steppes.
It may be exempliied by two close groups of highaltitude types: Altai-Sayan and Tuva-SouthernTransbaikalia ecoregions. The dark coniferous taiga
is especially typical for the Altai Mountains, and the
light larch taiga is predominant in the Sayan-Tuva
region. Thus, dark coniferous forests are striving to
be in the upper part of high-altitude spectrum (Fig. 3).
Forests occupy about 70% of the total Altai-Sayan
area. In the northern and western regions the forest
is distributed up to 1,700─1,800 m; landwards, the
tree line rises to 2,200─2,400 m and locally reaches
glaciers. Three mountain forest classes occur: subboreal dark, boreal dark, and light taiga. The subboreal dark taiga is dominated by Abies sibirica with
the nemoral group of plant species in the herb layer
located at the height of 400 to 800 m. The boreal dark
taiga consists of Abies sibirica, and Pinus sibirica
associations with tall herbaceous plants occurring
in the lower (400─800 m) and upper (800─1,200
m) parts of the taiga belt. It is conined to the more
humid climatic conditions in western and northeastern
provinces, where the annual precipitation can reach
800 mm–1,000 mm. The light taiga formed by Larix
sibirica, Pinus sylvestris, and Larix-Betula forests
develops in foothills and in central Altai, where it
extends to the altitude of 1,700 m. Larch forests are
dominant in the arid regions of Altai too. The high
mountain taiga occupies the upper part of the taiga belt
reaching the height of 1,600 m, 1,800 m, and 2,100 m
in different regions. It is formed by Larix sibirica and
Pinus sibirica forests, combined with subalpine shrub
and herbaceous species.
49
The highly variable topography of the Sayan
Mountains causes the very complicated altitudinal
zonality to change from west to east. In the
northeastern part with the more continental climate,
the forests are composed of Pinus sylvestris and Larix
sibirica with a rich herbaceous cover occupying the
lower mountains. Forests composed of Pinus sibirica
and Abies sibirica have the most prominent position,
extending across the middle mountains. In contrast to
the Altai and Western Sayan Range, the upper part of
the mountains is covered by Pinus pumila and heath
passing into mountain tundra.
of physiognomic and loristic plant community
for vegetation classiication. Zonal types of plant
communities can be supplemented by characteristics
of accompanying vegetation types, such as bog,
loodplain vegetation, etc., which should be shown as
independent units on the map. A variety of mountain
forests can be relected, proceeding from their
altitudinal-belt position in the structure of mountain
territory. The high-mountainous vegetation can relect
regional areas speciic to high-mountain belts and can
be shown according to regional features of altitudinal
zonality types and geographical position.
Experience was gained by mapping mountain boreal
forests at different scales and relected a variety of
forest formations in connection with the altitudinal belt
structure of mountain territories (Ogureeva, 2002;
Jurkovskaja at al., 2005).
Acknowledgments
The map Ecoregions of Russia should be considered
a good basis for compiling a circumboreal vegetation
map, with the latter being useful for phytogeographic
classiication of the territory at the upper level of the
legend. Ecoregions represent the landscape-speciic
vegetation complexes in various environmental
conditions. The map can be also used for the
estimation of natural loristic, plant community, and
ecosystem biodiversity.
Conclusion
The diversity of boreal forests in Russia is rather high
due to a great variety of environmental conditions
over vast areas. Experience gained in mapping
boreal forests at different scales makes it possible
to show regional speciics of natural forests. As a
mapping approach, it is feasible to use ecologicalgeographical principles to differentiate the territory,
proceeding from the zonal and altitudinal-zonal
structure of the vegetation cover at the highest
levels of the legend. The maps Zones and Altitudinal
Zonality Types of Vegetation of Russia (map scale
1:8M) and Ecoregions of Russia (map scale 1:8M)
contain information on the main bioregions of boreal
forests and regional variants of combinations of forest
communities. Flat and mountain forest regions should
be relected in a corresponding map legend rubric.
The basic mapping unit should be a combination
I am grateful to Dr. Udo Bohn for support in my work
related to vegetation mapping. I am also grateful to my
colleagues. The theme content of the map Zones and
Altitudinal Zonality Types of the Vegetation of Russia
(scale 1: 8M) (1999) was prepared by G. Ogureeva,
& I. Miklaeva from The M. V. Lomonosov Moscow
State University and I. Safronova & T. Jurkovskaya
from The Komarov Botanical Institute of Academy
Sciences of Russia. The cartographical editing was
done by T. Kotova from MSU.
References
Jurkovskaja, T. K., Iljina, I. S., Saphronova, I. N. 2005.
The vegetation of Russia (map scale 1: 15,000,000).
National Atlas of Russia. Т.1:.370–371.
Nazimova, D. I., Ermakov, N. B., Andreeva, N. M.,
& Stepanov, N. V. 2004. Concept model of the
structure biodiversity of the zonal classes of the
forest ecosystems in Northern Asia. Siberian
Ecological Journal 13-5: 745–756.
Ogureeva, G. N., Mikljaeva, I. M., Safronova I. N.,
& Jurkovskaja, T. K. 1999. Zones and altitudinal
zonality types of the vegetation of Russia (map
scale 1: 8,000,000). The map on two sheets and a
book of the legend with the text, 64 p. M.: Ecor.
Ogureeva, G. N. 2002. Diversiication of mountain
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B., & Rumanzev, V. J. 2004. Biome diversity
50
and ecoregions of Russia. Pages 392–398 in
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России и сопредельных территорий (масштаб
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Огуреева, Г.Н., Даниленко, А.К., Леонова, Н.Б.,
Румянцев, В.Ю. 2004. Биомное разнообразие
и экорегионы России. География, общество,
окружающая среда. Том III: Природные ресурсы,
их использование и охрана. М.: Издательский
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51
Zones and Altitudinal Zonality Types of Vegetation of Russia and Adjacent
Territories
Scale 1: 8,000,000
Authors: I.N. Safronova, T.K. Yurkovskaya, I.M. Miklyaeva (zonality vegetation), G.N. Ogureeva (altitudinal
zonality types of mountain vegetation)
Editor-in-chief: G.N. Ogureeva
Editor: T.V. Kotova
Legend to the Map
arctisibirica); sedge–grass–moss–mires.
b. Central Siberian (Taimyr–Lena) Salix polaris,
S. reptans, Dryas punctata, D. octopetala,
Cassiope tetragona, Saxifraga hieracifolia,
S. hirculus, S. serpyllifolia ssp. glutinosa,
Alopecurus alpinus, Deschampsia borealis,
D. brevifolia, Dupontia isheri, Luzula confusa,
Zones
A. TUNDRA ZONE
1. Subzone of high arctic tundras (Polar Deserts)
─ herb-lichen-moss spotted tundras, grass-moss
mires.
L. nivalis, Eriophorum angustifolium, E.
scheuchzeri, Carex arctisibirica); sedge–grass–
moss–mires.
a. East European (Franz–Joseph Land, New
Land) herb–lichen–moss spotted tundras,
moss, lichen tundras (Deschampsia alpina,
Cerastium arcticum, C. regelii ssp. caespitosa,
Papaver polare, Saxifraga nivalis, S. caespitosa,
S. cernua, S. oppositifolia, Alopecurus alpinus,
Phippsia algida); grass–moss mires.
c. East Siberian ─ dwarf shrub–herb–lichen–
moss, herb–lichen–moss polygonal, spotted
tundras; (prostrate dwarf shrubs: Cassiope
tetragona, Salix polaris, S. reptans, Dryas
punctata; Saxifraga hieracifolia, S. hirculus,
S. serpyllifolia ssp. glutinosa, Alopecurus
alpinus, Deschampsia borealis, D. brevifolia,
Dupontia isheri, Luzula confusa, L. nivalis,
Eriophorum angustifolium, E. scheuchzeri,
Carex arctisibirica, C. lugens); sedge–grass–
moss–mires.
b. Siberian (Severnaya Zemlya, De-Long Islands)
herb–lichen–moss spotted tundras, moss,
lichen tundras (Ranunculus sabinii, Cerastium
bialynickii, C. regelii s. str., Deschampsia
brevifolia, Papaver polare, Saxifraga caespitosa,
S. cernua, S. oppositifolia, Alopecurus alpinus,
Phippsia algida); grass–moss mires.
d. Chukotkan ─ prostrate dwarf shrub–herb–
lichen–moss, herb–lichen–moss polygonal,
spotted tundras; (prostrate dwarf shrubs: Salix
rotundifolia, Dryas integrifolia, D. punctata,
Salix polaris, S. pulchra, S. reptans; Saxifraga
hieracifolia, S. hirculus, S. serpyllifolia,
Alopecurus alpinus, Deschampsia borealis,
D. brevifolia, Dupontia isheri, Luzula confusa,
L. nivalis, Eriophorum triste, E. scheuchzeri,
Carex lugens); sedge–grass–moss–mires.
2. Subzone of arctic tundras ─ prostrate dwarf
shrub–herb-–lichen–mass,
herb–lichen–moss
polygonal spotted tundras; sedge–grass–mossmires.
a. East European─West Siberian (Novaya
Zemlya─Gydan Peninsula) prostrate dwarf
shrub–herb–lichen–moss, herb–lichen–moss
polygonal, spotted tundras; (prostrate dwarf
shrubs: Salix nummularia, Dryas octopetala,
Salix polaris, S. reptans; herbs: Saxifraga
hieracifolia, S. hirculus, Alopecurus alpinus,
Arctagrostis latifolia, Deschampsia borealis,
D. brevifolia, Dupontia isheri, Eriophorum
angustifolium,
E.
scheuchzeri,
Carex
3. Subzone of northern hypoarctic (typical)
tundras ─ hemiprostrate dwarf shrub–lichen–moss,
herb–lichen–moss hummock-spotted, tussock, low
shrub tundra; polygonal herb–dwarf shrub–lichen–
moss mires.
52
a. East European (Kolguev Island-Bolshaya
Zemlya) ─ hemiprostrate dwarf shrub–lichen–
moss, (Loiseleuria procumbens, Phyllodoce
caerulea, Arctous alpina, Dryas octopetala,
Empetrum
hermaphroditum,
Vaccinium
uliginosum ssp. microphyllum, V. vitis-idaea ssp.
minus), tussock (Eriophorum angustifolium),
herb-lichen-moss
hummock-spotted
(Arctagrostis latifolia, Dupontia psilosantha, D.
isheri, Deschampsia borealis, D. brevifolia),
low shrub tundras (Betula nana, Salix glauca,
S. lanata), polygonal herb–dwarf shrub–lichen–
moss mires.
b. Ural–West Siberian (Yugor–Gydan Peninsula) ─
prostrate dwarf shrub–lichen–moss (Cassiope
tetragona, Empetrum subholarcticum, Arctous
alpina, Dryas octopetala, Vaccinium uliginosum
ssp. microphyllum, V. vitis-idaea ssp. minus,
Ledum decumbens), tussock (Eriphorum
angustifolium)
herb–lichen–moss
(Carex
arctisibirica), Arctagrostis latifolia, Dupontia
isheri, D. psilosantha, Deschampsia borealis,
D. brevifolia), low shrub (Betula nana, Salix
arctica, S. glauca, S. lanata) tundras; polygonal
herb–dwarf shrub–lichen–moss mires.
exilis, Salix richardsonii, S. glauca, S. pulchra)
tundras; polygonal herb–dwarf shrub–lichen–
moss mires.
e. Chukotkan ─ hemiprostrate dwarf shrub-lichenmoss (Loiseleuria procumbens, Cassiope
tetragona, Rhododendron camtschaticum ─ on
Eastern part, Salix fuscescens, Arctous alpina,
Dryas punctata, D. integrifolia, Vaccinium
uliginosum ssp. microphyllum, V. vitis-idaea
ssp.
minus,
Empetrum
subholarcticum,
Ledum decumbens), tussock (Eriophorum
vaginatum, Carex lugens), herb–lichen–
moss (Carex lugens, Arctagrostis latifolia,
Dupontia psilosantha, Deschampsia borealis,
D. brevifolia), low shrub (Betula exilis, Salix
krylovii, S. richardsonii, S. glauca, S. pulchra)
tundras; polygonal herb–dwarf shrub–lichen–
moss mires.
4. Subzone of southern hypoarctic tundras ─ shrub
and tussock tundras; palsa (in Europe) and polygonal
herb–dwarf shrub–lichen–moss (in Asia) mires. (A 41
─ northern strip, A 42 ─ southern strip):
a. East Scandinavian (Kola Peninsula) ─ shrub
(Betula nana, Salix lanata, S. phylicifolia, S.
glauca) tundras, herb–dwarf shrub (Empetrum
hermaphroditum, Salix herbacea; Calluna
vulgaris, Carex bigelowii, Deschampsia
lexuosa, Festuca ovina) tundras; palsa dwarf
shrub–moss–lichen mires.
c. Central Siberian (Taimyr Peninsula) ─
hemiprostrate
dwarf
shrub–lichen–moss
(Cassiope tetragona, Dryas punctata, D.
octopetala,
Empetrum
subholarcticum,
Arctous alpina, Vaccinium uliginosum ssp.
microphyllum, V. vitis-idaea ssp. minus,
Ledum decumbens), tussock (Eriophorum
vaginatum, E. angustifolium), herb-lichenmoss (Arctagrostis latifolia, Carex arctisibirica,
Deschampsia borealis, D. brevifolia), low shrub
(Betula nana, B. exilis, Salix glauca, S. lanata,
S. pulchra) tundras; polygonal herb–dwarf
shrub–lichen–moss mires.
b. East European–West Siberian (Kanin Peninsula)
shrub (Betula nana, Salix lapponum, S.
phylicifolia, S. dasyclados, S. glauca, S. lanata),
herb–dwarf shrub (Empetrum hermaphroditum;
Vaccinium myrtillus, V. uliginosum ssp.
microphyllum, V. vitis-idaea ssp. minus
s.str., Deschampsia lexuosa, Festuca ovina,
Aconitum septentrionale, Cirsium helenioides,
Trolius europaeus, T. asiaticus) tundras; palsa
dwarf shrub–moss–lichen and polygonal herb–
dwarf shrub–lichen–moss mires.
d. East Siberian (Yana–Kolyma RR.) ─ prostrate
dwarf shrub–lichen–moss (Salix fuscescens,
Arctous alpina, Dryas punctata, Vaccinium
uliginosum ssp. microphyllum, V. vitis-idaea
ssp. minus, Ledum decumbens, Empetrum
subholarcticum),
tussock
(Eriophorum
vaginatum, E. angustifolium), herb–lichen–
moss (Carex arctisibirica, Arctagrostis latifolia,
Dupontia psilosantha, D. isheri, Deschampsia
borealis, D. brevifolia), low shrub (Betula
c. Central Siberian (Yeniseik) ─ shrub (Betula
exilis, B. nana, Salix alaxensis, S. boganidensis,
S. glauca, S. pulchra, S. lanata), alder (Alnus
fruticosa), tussock (Eriophorum vaginatum),
herb- dwarf shrub (Carex arctisibirica, Ledum
decumbens, Vaccinium uliginosum ssp.
microphyllum, Dryas punctata, D. octopetala,
53
udensis, S. schwerin) dwarf shrub–lichen–
moss; sparce
Cassiope tetragona) tundras; polygonal herb–
dwarf shrub–lichen–moss mires.
d. East Siberian (Lena–Kolyma RR.) ─ shrub
(Betula exilis, Salix alaxensis, S. boganidensis,
S. pulchra, S. glauca, S. reptans, S.
richardsonii), alder (Alnus fruticosa), tussock
(Eriophorum vaginatum), herb–dwarf shrub
(Ledum decumbens, Vaccinium vitis-idaea ssp.
minus) tundras; polygonal herb–dwarf shrub–
lichen–moss mires.
e. Chukotka–Koryakian ─ shrub (Betula exilis,
Salix pulchra, S. glauca, S. krylovii, S. alaxensis,
S. boganidensis, S. richardsonii), alder (Alnus
fruticosa), tussock (Eriophorum vaginatum,
Carex lugens) tundras; herb and palsa-shrub
mires.
f.
woodlands; open woodlands; lood plain forests
(Chosenia arbutifolia, Populus suaveolens).
g. East Kamchatkan ─ birch (Betula ermanii)
open woodlands; stlanik (Pinus pumila); herbSphagnum with Myrica tomentosa mires.
2. Subzone of northern taiga ─ coniferous dwarfshrub-lichen-moss open woodlands and sparse
forests; in Europe ─ with ridge-hollow herb-peatmosshypnum aapa and raised bogs, in West Siberia ─ with
palsa herb-peatmoss and sedge-hypnum mires; in
Kamchatka ─ with suboceanic mires. (B21 ─ northern
strip, B22 ─ southern strip)
a. East Scandinavian (Kola–Karelia) ─ pine (Pinus
sylvestris, P. friesiana), spruce (Picea obovata)
B. TAIGA ZONE
dwarf-shrub–lichen–moss (Calluna vulgaris,
Empetrum
hermaphroditum,
Vaccinium
myrtillus, V. vitis-idaea, V. uliginosum, Ledum
palustre) sparse forests; ridge-hollow herb–
peatmoss–Hypnum aapa and raised bogs.
1. Subzone of forest–tundra ─ pretundra open
woodlands in combination with southern hypoarctic
tundras; herb-lichen-moss palsa and herb–
Sphagnum–Hypnum aapa mires. B11 ─ northern
strip; B12 ─ southern strip):
b. East European (Onega R.–Ural Mts.) ─ spruce
(Picea obovata, P. abies x P. obovata), pine
(Pinus sylvestris) dwarf-shrub (Empetrum
hermaphroditum, Vaccinium myrtillus, V.
uliginosum, Ledum palustre) –moss, lichen–
moss sparse forests; ridge-hollow herb–
peatmoss–Hypnum aapa and raised bogs.
a. East Scandinavian (Kola Peninsula) ─ birch
(Betula czerepanovii) herb–dwarf shrub,
lichen and moss open woodlands; palsa dwarf
shrubmoss-lichen
and
herb–Sphagnum–
Hypnum aapa mires.
b. East European (Kanin Peninsula–Ural Mts.)
─ spruce (Picea obovata), birch (Betula
czerepanovii) shrub (Betula nana, Salix
lapponum) dwarf shrub-lichen-moss open
woodlands; palsa and dwarf shrub moss-lichen
and herb–Sphagnum–Hypnum aapa mires.
c. West Siberian ─ larch, larch–spruce, larch–pine,
larch–spruce–Siberian pine (Picea obovata,
Larix sibirica, Pinus sibirica, P. sylvestris)
forests; dwarf-shrub–lichen–moss (Ledum
palustre, Vaccinium vitis-idaea, V. uliginosum;
V. myrtillus, Empetrum subholarcticum, Betula
nana) sparse forests; palsa herb–peatmoss and
sedge–Hypnum mires.
c. West Siberian ─ larch, spruce-larch (Larix
sibirica, Picea obovata) shrub (Betula nana,
Salix phylicifolia, S. dasiclada, S. lapponum)
dwarf shrub-lichen-moss open woodlands;
palsa dwarf shrub–moss–lichen mires.
d. Central Siberian (Olenek–Lena RR.) ─
larch, spruce-–arch (Larix gmelinii, Picea
obovata) dwarf–lichen–moss (Ledum palustre,
Vaccinium vitis-idaea, V. uliginosum, Empetrum
subholarcticum, Betula exilis) forests; larch
peatmoss shrub bog.
d. Central Siberian (Kotuy–Lena RR.) ─ larch
(Larix gmelinii, spruce (Picea obovata) shrub
(Betula exilis) dwarf shrub–lichen, moss–lichen
open woodlands.
e. East Siberian (Indigirka–Kolyma RR.) ─ larch
(Larix gmelinii, L. cajanderi) moss, dwarfshrub (Vaccinium uliginosum, Empetrum
e. East Siberian (Indigirka–Kolyma RR.)─larch
(Larix gmelinii, L. cajanderi) shrub (Salix
54
subholarcticum, Betula
forests; moss mires.
f.
f.
exilis)–moss–lichen
West Kamchatkan ─ birch open forests (Betula
ermanii); suboceanic mires (Empetrum sibiricum,
Myrica tomentosa, Carex middendorii).
3. Subzone of middle taiga ─ coniferous short herb
(Maianthemum bifolium, Trientalis europaea, Orthilia
secunda, Pyrola spp., Luzula pilosa) – dwarf shrubmoss forests; in Central Siberia ─ with moss muskegs,
in East Siberia ─ with alas meadows, ridge-hollow
peatmoss raised bogs and herb–Hypnum–peatmoss
string fens. (B31 ─ northern strip, B32 ─ southern strip)
g. Far East ─ larch (Larix gmelinii), ir-spruce
with larch (Picea ajanensis, Abies nephrolepis,
Betula platyphylla, B. mandshurica) dwarfshrub-moss (Ledum palustre, Vaccinium
vitisidaea, V. uliginosum) forests; raised mires
with Larix gmelinii and shrubs (Betula exilis, B.
divaricata, B. fruticosa).
a. East Scandinavian (Karelia) pine (Pinus
sylvestris), spruce (Picea abies, P. abies x P.
obovata, Betula pendula, B. pubescens) short
herb–dwarf shrub–moss (Vaccinium myrtillus, V.
vitis-idaea, Calluna vulgaris, Linnaea borealis),
dwarf shrub-lichen, dwarf shrub–moss, moss
forests; ridge-hollow peatmoss raised bogs and
herb–Hypnum–peatmoss string fens.
b. East European (Ladoga Lake–Vychegda R.) ─
spruce (Picea abies, P. abies x P. obovata, P.
obovata, Betula pendula, B. pubescens), pine
(Pinus sylvestris) short herb–dwarf shrub–moss
(Vaccinium myrtillus, V. vitis-idaea, Linnaea
borealis) forests; palsa raised bogs and herb–
Hypnum–peatmoss string fens.
East Siberian (Lena–Aldan RR.) ─ larch,
spruce-larch, pine (Larix gmelinii, Picea
obovata, Pinus sylvestris, Betula cajanderi)
short herb–dwarf shrub–moss (Vaccinium vitisidaea, V. uliginosum, Arctostaphylos uva-ursi,
Ledum palustre) forests; larch forests with grass
(Limnas stelleri) and alas meadows (Carex
juncella, Calamagrostis langsdorfii).
h. Sakhalin ─ spruce-larch (Larix gmelinii,
Picea ajanensis) with stlanik (Pinus pumila)
dwarf-shrub-moss (Ledum palustre) forests;
suboceanic mires (Empetrum nigrum, Myrica
tomentosa).
4. Subzone of southern taiga ─ coniferous dwarf
shrub and herb–moss forests; in Far East ─ with
dwarf birch-larch muskegs, Sphagnum raised bogs.
a. East European (Baltic-Vetluga R.) ─ spruce
(Picea abies, Betula pendula, B. pubescens,
Populus tremula, Alnus glutinosa), pine (Pinus
sylvestris) nemoral-herb (Oxalis acetosella,
Galeobdolon luteum, Hepatica nobilis, Stellaria
holostea, Pulmonaria obscura, Convallaria
majalis), forests; Sphagnum raised bogs.
c. Ural ─ spruce, ir-spruce, pine with Pinus sibirica
(Picea obovata, Abies sibirica, Pinus sylvestris,
P. sibirica, Betula pendula, B. pubescens) short
herb–dwarf shrub–moss (Linnaea borealis,
Vaccinium myrtillus, V. vitis-idaea) forests;
palsa raised bogs and herb–Hypnum–peatmoss
string fens.
b. Ural ─ spruce, ir-spruce, with lime-trees (Picea
obovata, Abies sibirica, Pinus sylvestris, Betula
pendula, B. pubescens, Populus tremula, Tilia
cordata), nemoral-herb (Pulmonaria obscura,
Asarum europaeum, Aegopodium podagraria)
with Oxalis acetosella forests, dwarf-shrubherb forests with Siberian tall herbs (Aconitum
septentrionale, Crepis sibirica, Cacalia hastata)
and fen forests.
d. West Siberian ─ spruce-Pinus sibirica-ir with
larch, pine (Pinus sibirica, Picea obovata, Abies
sibirica, Larix sibirica, Pinus sylvestris, Betula
pendula, B. pubescens) short herb–dwarf
shrub–moss (Vaccinium vitis-idaea, V. myrtillus,
Linnaea borealis, Ledum palustre) forests;
raised bogs.
c. West Siberian ─ Pinus sibirica–spruce–ir,
spruce-ir, pine with lime-trees (Abies sibirica,
Picea obovata, Pinus sibirica, Pinus sylvestris,
Betula pendula, B. pubescens, Populus
tremula, Tilia cordata) short-herb–moss (Oxalis
acetosella, Carex macroura, Stellaria bungeana,
Circae alpina) forests; Sphagnum raised bogs.
e. Central Siberian (Tunguska R.) ─ larch (Larix
gmelinii) with Picea obovata, Abies sibirica,
Pinus sibirica), pine (Pinus sylvestris) short
herb–dwarf shrub–moss (Arctostaphylos uvaursi, Ledum palustre, Vaccinium vitis-idaea)
forests; raised mires with Betula fruticosa.
55
d. Central Siberian (Angara R.) ─ larch, pinelarch, spruce-ir (Larix sibirica, Pinus sylvestris,
Abies sibirica, Picea obovata, Betula pendula,
B. pubescens, Populus tremula) dwarf–shrub–
herb–moss (Carex macroura, Vaccinium
uliginosum) with Rhododendron dauricum
forests.
(Rhododendron dauricum, Alnus fruticosa) dwarf
shrub (Vaccinium vitis-idaea, Arctostaphulos
uva-ursi) forests; sedge fen-hypnum bogs with
Betula exilis.
f.
e. East Siberian–Far East ─ larch (Larix gmelinii,
Betula platyphylla), pine (Pinus sylvestris) shrub
(Rhododendron dauricum, Alnus manshurica)
dwarf-shrub-herb (Calamagrostis langsdorii,
Vaccinium vitis-idaea) forests; shrubs (Betula
fruticosa, B. ovalifolia), larch mires and herb
bogs.
5. Subzone of subtaiga ─ coniferous, coniferous broad-leaved (mixed), nemoral in European part of
Russia (with black alder swamps) and in Far East
(with dwarf birch─larch muskegs); in West Siberia
─ small-leaved grass-herb forests (with pine-dwarf
shrub-peat moss raised bogs and herb poor fens).
(B51 ─ northern strip, B52 ─ southern strip)
Far East (Amur R.) ─ oak-pine, oak-larch (Pinus
sylvestris, Larix gmelinii, Quercus mongolica,
Betula davurica, B. platyphylla) shrub
(Lespideza bicolor, Rhododendron dauricum)
forests.
g. Far East (Manchzhuria) ─ oak-larch, oak-pine,
spruce-ir with broad-leaved trees (Larix gmelinii,
Quercus mongolica, Abies nephrolepis, Picea
ajanensis, Tilia amurensis, Acer mono) forests;
birch–larch mires and shrubs (Betula ovalifolia,
B. fruticosa).
C. BROAD-LEAVED FOREST ZONE
1. Subzone of broad-leaved forests ─ broad-leaved
nemoral forests.
a. Central European (Carpathians–Dnieper R.) ─
beech, oak-hornbeam, oak (Fagus sylvatica,
Carpinus betulus, Quercus robur) shrub
(Cornus mas, Crataegus monogyna, Viburnum
lantana) forests.
a. East European (Baltic) ─ spruce-broad-leaved,
pine-broad-leaved (Quercus robur, Fraxinus
excelsior, Ulmus laevis, U. glabra, Picea abies,
Pinus sylvestris) forests (with Carpinus betulus,
Fagus sylvatica – in Western part); alder (Alnus
glutinosa) forests, subatlantic herb and peat
moss raised bogs with Myrica gale.
b. East European (Dnieper–Volga R.) ─ oak, ashoak, broadleaved, pine-broadleaved (Quercus
robur, Tilia cordata, Acer platanoides, Fraxinus
excelsior, Pinus sylvestris), shrub (Euonymus
europaea, E. verrucosa, Corylus avellana,
Acer campestre) forests; alder swamps (Alnus
glutinosa).
b. East European (Baltic–Vetluga R.) ─ sprucebroad-leaved, pine-broad-leaved, pine (Quercus
robur, Tilia cordata, Acer platanoides, Ulmus
laevis, U. glabra, Picea abies, Pinus sylvestris)
forests; alder swamps (Alnus glutinosa); peat
moss raised bogs and herbaceous swamps.
c. Trans Volga ─ oak-lime tree, lime tree,
broadleaved (Tilia cordata, Quercus robur, Acer
platanoides, Ulmus glabra) shrub (Euonymus
europaea, E. verrucosa, Corylus avellana,
Lonicera xylosteum) forests.
c. Ural ─ spruce-broad-leaved, lime tree-spruceir, broad-leaved-ir-spruce, broad-leaved-pine
(Quercus robur, Tilia cordata, Acer platanoides,
Ulmus glabra, Picea obovata, P. abies x P.
obovata, Abies sibirica, Pinus sylvestris) forests.
d. Far East (Manchzhuria) ─ lime tree-oak (Quercus
mon-golica, Tilia amurensis, Betula davurica)
shrub (Corylus manshu-rica, C. heterophylla,
Spiraea ussuriensis) fen-herbaceous forests.
d. West Siberian ─ birch, aspen (Betula pendula,
Populus tremula) herb─grass (Calamagrostis
arundinacea,
Brachypodium
pinnatum,
Aegopodium podagraria) forests, pine (Pinus
sylvestris) dwarf shrub-herb forests; herbaceous
swamps.
2. Subzone of forest-steppe ─ meadow steppes and
steppiicated meadows in combination with broadleaved and small-leaved forests. (C21 ─ northern
strip, C22 ─ southern strip)
e. Central Siberian (Angara R.) ─ pine, larchpine (Pinus sylvestris, Larix sibirica) shrub
a. Dniester–Dnieper RR. ─ oak forest-steppes
56
(Quercus robur; Stipa pulcherrima, S. tirsa, S.
dasyphylla, Bromopsis riparia, B. inermis, Poa
angustifolia, Phleum phleoides, Carex humilis,
Filipendula vulgaris, Salvia stepposa, Galium
verum, Trifolium montanum).
D. STEPPE ZONE
1. Subzone of northern (forb-bunchgrass) steppes
─ forb-bunchgrass steppes.
a. East European (West and East Black Sea
region) ─ forb-bunchgrass steppes (Stipa
ucrainica, S. tirsa, S. dasyphylla, S. anomala,
S. capillata, S. lessingiana, Helictotrichon
schellianum, Festuca valesiaca, Koeleria
cristata, Bromopsis riparia, Filipendula vulgaris,
Trifolium montanum, Medicago romanica,
Salvia nutans, Euphorbia stepposa, Carex
humilis), with shrubs (Cerasus fruticosa, Prunus
spinosa).
b. Crimea–Caucasus ─ oak forest-steppes
(Quercus robur, Q. petraea, Stipa pulcherrima,
S. pontica, S. tirsa, Bromopsis riparia, B.
inermis, Carex humilis, Filipendula vulgaris,
Salvia stepposa, Galium verum, Trifolium
montanum).
c. East European (Dnieper–Volga RR.) ─ lime
tree-oak forest-steppes (Quercus robur; Tilia
cordata, Acer platanoides; Stipa pulcherrima,
S. tirsa, S. dasyphylla, S. pennata, Bromopsis
riparia, B. inermis, Poa angustifolia, Phleum
phleoides, Helictotrichon schellianum, Carex
b. Trans Volga (Volga–Ural RR.) ─ forbbunchgrass steppes (Stipa zalesskii, S.
capillata, S. tirsa, S. lessingiana, S. pennata, S.
korshinskyi, Helictotrichon desertorum, Festuca
valesiaca, Koeleria cristata, Salvia stepposa,
Filipendula vulgaris, Medicago romanica,
Pulsatilla multiida, Carex humilis), with shrubs
(Rosa majalis, Cytisus ruthenicus, Amygdalus
nana, Spiraea crenata, S. hypericifolia).
humilis, Filipendula vulgaris, Salvia stepposa,
Galium verum, Trifolium montanum, T. alpestre).
d. Trans Volga ─ maple-lime tree-oak foreststeppes (Quercus robur; Tilia cordata,
Acer platanoides; Stipa pulcherrima, S.
dasyphylla, S. tirsa, S. zalesskii, S. pennata,
Bromopsis riparia, B. inermis, Poa angustifolia,
Calamagrostis epigeios, Phleum phleoides,
Helictotrichon schellianum, H. desertorum,
Salvia pratensis, Filipendula vulgaris, Trifolium
alpestre).
c. West Siberia–North Kazakhstan ─ forbbunchgrass steppes (Stipa zalesskii, S.
capillata, S. lessingiana, S. pennata, S.
korshinskyi, Helictotrichon desertorum, Festuca
valesiaca, Koeleria cristata, Peucedanum
morisonii, P. ruthenicum, Pulsatilla lavescens,
Salvia stepposa, Filipendula vulgaris, Seseli
krylovii, Artemisia frigida, Carex supina) with
shrubs (Spiraea hypericifolia, S. crenata,
Caragana frutex).
e. West Siberian ─ kolki small-leaved foreststeppes (Betula pendula, Populus tremula;
Stipa tirsa, S. zalesskii, Bromopsis inermis,
Poa angustifolia, Calamagrostis epigeios,
Phleum phleoides, Helictotrichon schellianum,
Carex pediformis, Filipendula vulgaris, Salvia
pratensis, Peucedanum morisonii, Vicia
unijuga, Trifolium lupinaster).
f.
Far East (Amur R.) ─ oak forest-steppes
(Quercus
mongolica,
Betula
davurica;
Arundinella anomala, Filifolium sibiricum,
Clematis hexapetala).
d. Dahuria–Mongolian (Onon R.) ─ forbbunchgrass steppes (Stipa baicalensis,
Leymus chinensis, Festuca lenensis, Clematis
hexapetala, Hemerocallis minor, Phlojodicarpus
sibiricus).
2. Subzone of middle (dry) steppes ─ bunchgrass
steppes.
g. Far East (Manchzhuria oak forest-steppes
(Quercus mongolica, Ulmus pumila, Pinus
funebris; Corylus heterophylla, Lespedeza
bicolor, L. juncea).
a. Black Sea region ─ bunchgrass steppes
(Stipa ucrainica, S. lessingiana, S. brauneri, S.
capillata, Festuca valesiaca, Leymus ramosus,
Tanacetum odessanum, Linaria biebersteinii,
Iris pumila, Crinitaria villosa, Artemisia
austriaca, Carex supina).
57
b. East European (Don–Volga RR.) ─ bunchgrass
steppes (Stipa ucrainica, S. lessingiana, S.
capillata, Festuca valesiaca, Leymus ramosus,
Tanacetum achilleifolium, Iris pumila, Crinitaria
villosa, C. tatarica, Artemisia austriaca, Carex
supina).
E. DESERT ZONE
1. Subzone of northern deserts ─ dwarf semishrub,
psammophytic shrub deserts.
c. Trans Volga–Kazakhstan ─ bunchgrass steppes
(Stipa lessingiana, S. capillata, S. pennata, S.
korshinskyi, S. kirghisorum, Festuca valesiaca,
Leymus ramosus, Tanacetum achilleifolium,
Crinitaria villosa, C. tatarica, Artemisia austriaca,
Carex supina), shrubs (Spiraea hypericifolia,
Caragana frutex, C. pumila).
a. Caspian Sea region ─ sagebrush deserts
(Artemisia lerchiana, A. paucilora, A. arenaria,
with bunchgrass – Poa bulbosa, Stipa sareptana,
S. lessingiana, Agropyron fragile), dwarf shrubs
deserts (Anabasis salsa, Atriplex cana), salt
bush deserts (Halocnemum strobilaceum,
Halimione verrucifera), psammophytic shrub
deserts
(Calligonum
aphyllum,
Tamarix
ramosissima, T. laxa).
d. Dahuria–Mongolian (Onon–Argun RR.) ─ low
bunchgrass (4-bunchgrass) steppes (Stipa
krylovii, Cleistogenes squarrosa, Koeleria
cristata, Festuca lenesis, Agropyron cristatum,
b. West-North Turan (North Aral Sea region)
─ sagebrush deserts (Artemisia semiarida,
A. paucilora, A. arenaria, A. lerchiana,
with bunchgrass – Poa bulbosa, Stipa
Poa botryoides, Leymus chinensis, Artemisia
frigida, Carex duriuscula).
sareptana, Agropyron fragile), dwarf shrubs
deserts (Anabasis salsa, Atriplex cana,
Krascheninnikovia ceratoides), salt bush
deserts (Halocnemum strobilaceum, Halimione
verrucifera), psammophytic shrub deserts
(Calligonum spp., Haloxylon aphyllum).
3. Subzone of southern (desertiied) steppes ─
dwarf semishrub–bunchgrass steppes.
a. Caspian Sea region ─ sagebrush semidesertbunchgrass steppes (Stipa sareptana, S.
capillata, S. lessingiana, S. pennata, Festuca
valesiaca, Agropyron desertorum, Leymus
ramosus,
Kochia
prostrata,
Tanacetum
achilleifolium,
Artemisia
lerchiana,
A.
marschalliana, A. nitrosa, A. paucilora, with A.
taurica as dominant in Sivash region).
b. Trans Volga–West Kazakhstan ─ sagebrush
semidesert-bunchgrass
steppes
(Stipa
sareptana, S. capillata, S. lessingiana, S.
pennata, Festuca valesiaca, Agropyron
desertorum, Leymus ramosus, Kochia prostrata,
Artemisia lerchiana, A. semiarida, A. nitrosa,
A. paucilora, A. marschalliana, Tanacetum
achilleifolium), shrub (Spiraea hypericifolia,
Caragana frutex, C. balchashensis).
c. East Kazakhstan ─ sagebrush semidesert–
bunchgrass
steppes (Stipa sareptana,
S. capillata, S. lessingiana, S. pennata,
S.
kirghisorum,
Festuca
valesiaca,
Agropyron desertorum, Kochia prostrata,
Artemisia sublessingiana, A. gracilescens,
A. marschalliana, A. nitrosa) with shrubs
(Spiraea hypericifolia, Caragana frutex, C.
balchaschensis).
c. Central–North Turan ─ sagebrush deserts
(Artemisia semiarida, A. terrae-albae, with
bunchgrass – Stipa sareptana, S. richteriana,
Agropyron fragile A. paucilora, A. schrenkiana),
dwarf shrubs deserts (Anabasis salsa, Atriplex
cana, Krascheninnikovia ceratoides), salt bush
deserts (Salsola arbusculiformis, Halimione
verrucifera, Halocnemum strobilaceum)
2. Subzone of middle deserts ─ dwarf semishrub,
petrophytic and psammophytic shrub deserts.
a. West–North Turan (Mangyshlak–Usturt) ─
sagebrush deserts (Artemisia terrae-albae,
A. gurganica, A. santolina, A. tschernieviana,
with bunchgrass – Stipa caspia, Agropyron
fragile, Poa bulbosa, Carex physodes), dwarf
shrubs deserts (Anabasis salsa, Salsola
orientalis, Nanophyton erinaceum), petrophytic
and psammophytic shrub deserts (Atraphaxis
replicata, Salsola arbuscula, Astragalus spp.,
Krascheninnikovia ceratoides, Calligonum
spp.), Haloxylon desert woodlands (Haloxylon
aphyllum, H. persicum), salt bush deserts
(Halocnemum
strobilaceum,
Kalidium
caspicum).
58
b. Central–North Turan ─ sagebrush deserts
(Artemisia terrae-albae, A. turanica, A. santolina,
with bunchgrass – Stipa hohenakeriana,
Agropyron fragile, Carex physodes), dwarf
shrubs deserts (Anabasis salsa, Salsola
orientalis, Nanophyron erinaceum, Salsola
arbusculiformis, Krascheninnikovia ceratoides),
psammophytic shrub deserts (Calligonum
spp.), Haloxylon desert woodlands (Haloxylon
aphyllum), астрагаловые (Astragalus spp.),
salt bush deserts, Haloxylon desert woodlands
(Halocnemum
strobilaceum,
Kalidium
schrenkianum).
c. East–North Turan ─ sagebrush deserts
(Artemisia terrae-albae, A. albicerata, A.
santolina, Carex physodes, Agropyron fragile),
dwarf shrubs deserts (Anabasis salsa, Salsola
orientalis,
Krascheninnikovia
ceratoides,
Salsola arbusculiformis), psammophytic shrub
deserts (Calligonum spp.), Haloxylon desert
woodlands (Haloxylon aphyllum, H. persicum),
salt bush deserts (Halocnemum strobilaceum,
Kalidium schrenkianum).
3. Subzone of southern deserts ─ psammophytic
shrub, dwarf semishrub deserts.
ARCTIC TYPES
1. Nival ─ high arctic tundra (Phippsia algida,
1.
Cerastium arcticum, Puccinellia vahliana, P.
angustata, Deschampsia borealis) ─ arctic
tundra (Hypnum-cottongrass, sedge-moss,
dryas tundras -Dryas octopetala, Salix polaris,
Carex arctisibirica) [Novaya Zemlya Isl.].
2. High arctic tundra ─ arctic tundra (Salix
2.
polaris, Cerastium bialynickii, Alopecurus
alpinus,
Luzula
confusa,
Eriophorum
brachyantherum, Dryas punctata, Salix reptans)
[New Siberian Isls.].
3. High arctic tundra ─ arctic tundra (Carex
3.
lugens, C. rupestris, Cerastium bialynickii,
Alopecurus alpinus, Deschampsia borealis,
Puccinellia colpodioides, Luzula confusa, L.
nivalis, Minuartia macrocarpa, Papaver spp.,
Salix polaris, S. phlebophylla, S. rotundifolia,
S. reptans, S. pulchra, Dryas punctata, D.
integrifolia) [Wrangel Isl.].
HYPOARCTIC TYPES (tundra)
Ural─North Siberian group
4. Nival ─ high arctic tundra (Cassiope
4.
hypnoides, Luzula confusa, L. nivalis, Salix
rotundifolia) ─ arctic tundra (Salix arctica, S.
nummularia, S. polaris) ─ northern tundra
(Carex arctisibirica, C. rupestris, Dryas
octopetala, Rhodiola quadriida) ─ southern
tundra (Betula nana, Salix glauca, S. lanata, S.
pulchra, Alnus fruticosa) [Polar Ural].
a. West–South Turan (Caspian Sea region-KaraKum) ─ sagebrush deserts (Artemisia kemrudica
A. santolina, A. kelleri, Carex physodes,
Poa bulbosa), dwarf shrubs deserts (Salsola
gemmascens, S. orientalis), Haloxylon desert
woodlands (Haloxylon persicum, H. aphyllum),
psammophytic shrub deserts (Calligonum
spp., Ephedra strobilacea, Salsola arbuscula,
S. richteri), salt bush deserts (Halocnemum
strobilaceum, Halostachys caspica, Salsola
dendroides).
4a. Arctic tundra ─ southern tundra subtype
[Pay Khoy Mts.].
5. Nival ─ high arctic tundra (Phippsia algida,
5.
Petasites frigidus, Equisetum variegatum) ─
arctic tundra (Dupontia ischeri, Salix polaris,
S. arctica) ─ northern tundra (herb- dwarf
willow-moss, sedge-dwarf willow, dryas spotted
tundras ─ Dryas punctata, D. octopetala,
Cassiope tetragona, Salix arctica, S. polaris, S.
pulchra, S. reptans, S. lanata ssp. richardsonii,
Acomastylis glacialis, Kobresia myosuroides) ─
southern tundra (Betula exilis, B. nana, Salix
lanata, S. pulchra, S. alaxensis) [East Byrranga
Mst.].
4. Subzone of southern foot-hill deserts ─
ephemeroid
sagebrush,
ephemeroid-perennial
saltwort deserts.
a. Trans Caucasus region ─ ephemeroid
sagebrush, ephemeroid-perennial saltwort
deserts (Artemisia fragrans, Salsola nodulosa,
S. ericoides, Poa bulbosa).
ALTITUDINAL ZONALITY TYPES OF MOUNTAIN
VEGETATION
5a. Arctic tundra [North Byrranga Mts.];
59
5b. Arctic tundra ─ northern tundra (sedgecottongrass,
spotted
moss,
grass,
dwarfshrub-sedge tundras ─ Eriophorum
polystachion,
Alopecurus
alpinus,
Deschampsia borealis, Carex bigelowii,
Dryas punctata, Cassiope tetragona) ─
southern tundra (Salix pulchra, S. lanata,
S. reptan, S. richardsonii, Betula nana)
[West Byrranga Mts.].
ssp. tschuktschorum, Cassiope ericoides,
Loiseleuria procumbens, Phyllodoce caerulea,
Dryas ajanensis ssp. beringensis, D. punctata
ssp. punctata, D. punctata ssp. alaxensis) ─
southern tundra (Betula exilis, Salix pulchra,
S. lanata, Ledum decumbens, Rhododendron
camtschaticum) [East Chukotka].
9. Nival ─ high arctic tundra (Dicentra peregrina,
9.
Phyllodoce caerulea) ─ tundra (Eriophorum
vaginatum, Carex lugens, C. soczavaeana,
Empetrum subholarcticum, Cassiope ericoides)
─ stlanik (stlanik – thickets of large creeping
shrubs ─ Pinus pumila, Alnus fruticosa, Betula
exilis, B. divaricata, Rhododendron aureum,
R. camtschaticum) with alluvial forests of
Chosenia macrolepis, Populus suaveolens and
fragments of open woodlands ─ Larix cajanderi,
6.
6. High arctic tundra (Alopecurus alpinus,
Poa abbreviata) ─ arctic tundra (Dryas
punctata, Cassiope tetragona, Salix arctica, S.
stenophylla, S. nummularia) ─ southern tundra
(Betula exilis, Salix glauca, S. pulchra) ─ open
woodland (Larix cajanderi) [Khara–Ulakh Mts.].
6а.
Arctic tundra ─ southern
[Chekanovsky Range].
tundra
Betula cajanderi, B. ermanii) [Penzhina R.-–
Koryakiya].
Chukotka─Koryakiya group
7. High arctic tundra ─ arctic tundra ─
7.
northern tundra (tussock tundras ─
Eriophorum
vaginatum,
Carex
lugens,
dwarf shrub tundras ─ Diapensia obovata,
Dryas punctata, Empetrum subholarcticum,
Huperzia arctica, Rhododendron parvifolium,
Lycopodium dubium, Vaccinium uliginosum
ssp. microphyllum, V. vitis-idaea ssp. minus,
Kobresia communities
─ Kobresieta) ─
southern tundra (shrub tundras ─ Alnus
fruticosa, Salix glauca, S. krylovii, S. pulchra, S.
tschuktschorum, S. phlebophylla, S. alaxensis,
S. boganidensis, Betula extremiorientalis,
Ledum decumbens with fragments of cryophytic
steppes – Festuca lenensis, Carex duriuscula,
C. pediformis, C. obtusata, C. spaniocarpa,
Poa arctostepporum, Helictotrichon krylovii,
Artemisia arctisibirica) [West Chukotka].
7.1. Anadir variant (southern dwarfshrub - moss
tundras ─ Cassiope ericoides in combination
with groves ─ Pinus pumila, Betula exilis, B.
divaricata, B. extremiorientalis).
9.1. Southern Kolymian variant (sedgecottongrass,
spotted
moss,
grass,
dwarfshrub-sedge tundras; stlanik with Pinus
pumila, Alnus fruticosa; tall herb meadows);
9.2. East Koryakiy variant (stlanik with Pinus
pumila, Alnus fruticosa; tall herb meadows).
HYPOARCTIC TYPES (taiga)
East-European group
10.
10. Subnival (Salix polaris, S. herbacea)
-–
montane tundra (Empetrum hermaphroditum,
Dryas octopetala, Diapensia lapponica,
Harrimanella hypnoides, Salix polaris, S.
nummularia, S. herbacea, Vaccinium myrtillus,
Carex bigelowii) ─ dwarf birch thickets
(Betula nana, Salix hastata, S. myrsinites, S.
pulchra, S. glauca, Luzula arcuata) ─ open
woodland (Betula tortuosa, B. kusmisschefii)
─ taiga forests (Picea abies, P. obovata, Pinus
sylvestris) [Khibiny Mts.].
10a. Montane tundra ─ dwarf birch thickets
─ open woodland ─ taiga forest
[Kandalaksha].
7a. Montane tundra.
8.
8. High arctic tundra (Saxifraga hyperborea,
Phippsia
algida)
─
northern
tundra
(Eriophorum vaginatum, E. triste, Carex lugens,
Hedysarum mackenzii, H. hedysaroides
11.
11. Nival ─ goltsy ─ tundra (Betula nana, Salix
phylicifolia, Carex globularis with mesophilic
meadows) ─ open woodland (Betula tortuosa,
60
Picea obovata) [West–North–Ural].
12.
12. Nival ─ goltsy ─ northern tundra (Empetrum
nigrum,
Arctostaphylos
uvaursi,
Dryas
octopetala, Pyllodoce caerulea) ─ southern
tundra (Betula nana, Salix glauca, S. lanata,
S. pulchra, Ledum palustre) ─ stlanik (Alnus
fruticosa) ─ open woodland (Betula tortuosa,
Larix sibirica) [East –North–Ural].
Verkhoyansky Range─Kolyma (North Angarida)
group
14. Goltsy (Rhodiola borealis, Senecio jacuticus)
─ tundra (Salix polaris, S. reticulata, Cassiope
tetragona, С. ericoides, Dryas punctata, D.
incisa, Kobresia myosuroides, K. sibirica,
Empetrum subholarcticum) ─ stlanik (Alnus
fruticosa, Betula exilis) ─ open woodland─
taiga forest (Larix cajanderi with fragments of
northern taiga forests; sedge cottongrass mires
Central Siberian group
─ Eriophorum vaginatum, Carex arctisibirica)
─ (sparse forests of Larix cajanderi) [Polousnyi
Range].
13. Goltsy (crustaceous lichens ─ Rhizocarpon
geographicum,
Umbillicaria)
─
tundra
(lichen, moss, herb, dwarf shrub tundras
- Dryas octopetala, D. punctata, Arctous
alpina, Vaccinium uliginosum, Cassiope
tetragona) ─ dwarf birch thickets (Betula
exilis, Rhododendron aureum, Salix pulchra,
S. myrtilloides, S. polaris) ─ open woodland
(Betula tortuosa, B. cajanderi, B. pubescens)─
taiga forests (Larix gmelinii) [Putorana Mts.].
14a. Stlanik ─ open woodland ─ taiga forest
[Kondakovskaya upland].
15. Goltsy (crustaceous lichens ─ Rhizocarpon
geographicum, Umbillicaria ssp., Haematomma
ventosum, tundras ─ Cassiope ericoides,
Luzula confusa, Artemisia lagocephala, Salix
berberifolia) ─ tundra (lichen, dwarf shrub
tundras, Kobresia communities – Dryas punctata,
Cassiope tetragona, Carex arctisibirica, C.
concolor, Kobresia myosuroides) ─ creeping
birch thickets (Betula exilis, B. divaricata, Alnus
fruticosa, Rhododendron parvifolium, Ledum
decumbens) ─ open woodland ─ taiga forest
(Larix cajanderi) [Verkhoyansky Range ].
13.1. West Putorana variant (inversion belt
of larch sparse forests around the lakes,
open woodlands with participation of
Siberian and East-European species -–
Larix sibirica, Picea obovata; alder groves
-– Alnus fruticosa; subgoltsy shrubs and
open woodlands – Betula nana, B. tortuosa,
B. cajanderi).
15.1. West Verkhoyansky variant (alder,
creeping pine thickets, larch dwarf birch,
alder open woodlands with participation
of siberian species; fragments of spruce
forests ─ Picea obovata);
13a. Goltsy ─ tundra (Kobresia myosuroides)
─ open woodland (nothern territories of
Putorana plateau);
13b. Montane tundra (Dryas crenulata,
Rhododendron adamsii) ─ open woodland
(with participation of cryophytic steppe
communities) (on limestone);
15.2. South Verkhoyansky variant (larch
forests, locally steppe communities).
16. Goltsy ─ tundra ─ stlanik (Pinus pumila,
Alnus fruticosa) ─ open woodland ─ taiga
forest ─ forest-steppe (forests ─ Larix
gmelinii, L. cajanderi; steppes ─ Helictotrichon
krylovii, Festuca lenensis, F. kolymensis, Stipa
krylovii, Carex duriuscula, C. pediformis, Poa
botryoides, Pulsatilla lavescens) [Yana Range].
13c. Montane tundra ─ stlanik (Alnus fruticosa)
─ open woodland (Larix cajanderi)
[Anabara plateau];
13d. Montane tundra ─ open woodland ─
taiga forest (forests with south Siberian
elements) (on ancient lava sheets);
13e. Open woodland ─ taiga forest (alder,
ledum moss larch forests – Larix gmelinii,
thickets of prostrate alder) [Vilyui Plateau,
on trapps].
16a. Goltsy ─ tundra ─ stlanik ─ open
woodland [northern part of Chersky Range];
16b. Mountain tundra ─ open woodland
[Elginskoye plateau].
61
17. Goltsy ─ tundra (cryophytic steppes ─ Festuca
auriculata, F. altaica, Koeleria cristata, Poa
botryoides; dwarf birch ─ Betula exilis; Kobresia
communities ─ Kobresia myosuroides) ─ stlanik
(Pinus pumila) ─ open woodland ─ taiga forest
(Larix cajanderi) ─ forest-steppe (steppe
larch forests ─ Larix gmelinii, L. cajanderi,
Poa botryoides, Betula extremiorientalis,
B. fruticosa, B. exilis; cryophytic steppes ─
Koeleria cristata, Agropyron cristatum, Poa
botryoides, Carex obtusata, C. pediformis,
C. rupestris, Helictotrichon krylovii, Artemisia
frigida, Pulsatilla multiida) [Indigirka Range].
19a. Stlanik ─ open woodland [Oimyakon
plateau].
North Okhotian group
20. Goltsy ─ tundra (Dryas punctata, Cassiope
tetragona,
Carex
arctisibirica,
Dicentra
peregrina, Artemisia arctica) ─ stlanik (Pinus
pumila, Alnus fruticosa, Salix krylovii, Betula
divaricata) ─ open woodland (Betula lanata,
Larix cajanderi, with fragments of montane
steppes ─ Agropyron jakutorum, Poa attenuata,
P. botryoides, Festuca kolymensis, Artemisia
gmelinii, A. lagocephala) [Upper Kolyma
Range].
17a. Stlanik ─ open woodland (creeping larch
with Betula exilis) [Nerskoye plateau].
20.1. Olsky variant (Dryas tundras with wet
swampy herb-moss and dwarf shrub-moss
tundras ─ Salix arctica, S. polaris; groves of
18. Goltsy ─ tundra (Arctous alpina, Dryas
punctata, Cassiope ericoides, C. tetragona,
Betula lanata, thickets of Caragana jubata)
[Olsky basalt plateau];
Salix tschuktschorum, Diapensia obovata,
Rhododendron redowskianum, Loiseleuria
procumbens, Empetrum nigrum s.l.) creeping
pine thicket (stlanik – Pinus pumila) ─ open
woodland (open woodlands of Larix cajanderi
with poplar and Chosenia forests along river
valleys [Omolon Range].
20.2. North Okhotian coastal variant (montane
tundras, stlaniks with Kamtchatkan elements
─ Salix richardsonii, S. tschuktschorum,
Rhododendron
camtschaticum,
R.
redowskianum, Cassiope ericoides, Sorbus
sambucifolia, Artemisia lagopus, Saxifraga
punctata; birch and birch larch open
woodlands).
18.1 North ─ Momsky Range variant
(predominance of sparse larch forests.
18a. Stlanik ─ open woodland [Alazeyskoye
plateau];
21.
18b. Stlanik ─ open woodland [Jukagirskoye
plateau];
18c. Creeping dwarf birch thicket (Betula exilis,
B. divaricata) ─ mire ─ open woodland
(with fragments of montane steppes ─
Artemisia frigida, Carex duriuscula, Festuca
kolymensis, Helictotrichon krylovii in Omolon
valley) [Omolon depression].
Goltsy─tundra
(Cassiope
ericoides,
Rhododendron
redowskianum,
Artemisia
lagocephala) ─ stlanik (Pinus pumila) ─ open
woodland (Betula lanata, Larix gmelinii, L.
cajanderi, Picea obovata) [Judoma].
21.1. Sette-Daban Range variant (montane
taiga forests in combination with dwarf birch
thickets).
22. Nival ─ goltsy ─ tundra (Empetrum nigrum,
Cassiope ericoides) ─ stlanik (Pinus
pumila, Betula divaricata, Alnus fruticosa, A.
kamtschaticus) ─ open woodland (Betula
lanata) ─ taiga forest (Larix cajanderi) [North
Okhotian].
19. Nival ─ goltsy (Minuartia ssp., Arenaria ssp.,
Androsace ssp., Saxifraga ssp.) ─ tundra
(Cassiope ericoides, Dryas punctata, Salix
tschuktschorum, S. pulchra, Betula exilis;
Kobresia myosuroides, Carex rupestris; alpine
meadows ─ Claytonia arctica, Valeriana
capitata, Anemone sibirica) ─ stlanik (Pinus
pumila, Rhododendron parvifolium, Betula
exilis, Ledum decumbens) ─ open woodland
(Larix cajanderi) [Oimyakon].
22a. Stlanik ─ open woodland ─ taiga forest
[Urakskoye plateau].
62
BOREAL (TAIGA) TYPES
with complex of nemoral species of Atlantic
group);
Ural─Central Siberian group
26.2. West–East Sayany variant (ir forests
poor of species with participation of nemoral
species of Paciic group).
23. Goltsy ─ tundra (Empetrum nigrum, Arctous
alpina, Dryas octopetala, Vaccinium myrtyllus,
V. uliginosum) ─ dwarf birch thicket (Betula
nana) ─ open woodland (Betula tortuosa)
─ taiga (pine spruce, ir spruce forests with
Picea obovata, Pinus sibirica, Abies sibirica)
[West─Middle─Ural].
26a. Montane taiga (subnemoral taiga forests)
[Salairsky Range and Kuznetsky Alatau];
26b. Montane taiga (subnemoral taiga forests)
─ forest-steppe (pine, birch forests and
meadow steppes) [Kuznetskaya depression].
23a. Montane tundra ─ dark coniferous
taiga (spruce ir, pine ir forests and open
woodlands) [western macroslope of Middle
Ural].
27. Nival (Waldheimia tridactylites, Lupinaster
ezimium, Saussurea glacialis) ─ alpine
(Ranunculus altaicus, Seseli monstrosa, Schulzia
crinita. Paraquilegia, Sibbaldia procumbens)
─
subalpine
(meadows─Rhaponticum
carthamoides, Aquilegia
glandulosa, A.
24. Goltsy ─ tundra ─ dwarf birch thicket (Betula
nana) ─ open woodland (Betula tortuosa)
─ taiga (Pinus sylvestris, Larix sibirica)
sibirica, Geranium albilorum; pine, larch
open woodlands; dwarf birch thickets─Betula
rotundifolia) ─ taiga (dark coniferous ir, pine
ir forests – Pinus sibirica, Abies sibirica,
Picea obovata) – forest ─ steppe (forests
– Larix sibirica; bunchgrass steppes – Stipa
capillata, Koeleria gracilis, Festuca pseudovina,
Helictotrichon
desertorum,
Cleistogenes
squarrosa) [Central Altai].
[East─Middle─Ural].
24a. Montane tundra ─ taiga (dark coniferous
with Pinus sibirica, Picea obovata, Abies
sibirica, pine larch forests) [eastern
macroslope of Middle Ural].
25. Montane tundra ─ dwarf birch thicket (Betula
nana, B.exilis, Juniperus communis) ─ open
woodland (Betula tortuosa, Larix sibirica) ─
taiga (dark coniferous forests - Abies sibirica,
Pinus sibirica, Picea obovata, Larix sibirica)
[Eniseysky kryazh].
27.1. Salair ─ Kuznetsk variant (pine larch
forests and larch birch forest steppe).
27a. Taiga ─ forest-steppe (forests ─ Betula
pendula, Populus tremula; meadow steppes)
[North Altai];
25a. Light coniferous taiga (light coniferous
forests – Pinus sylvestris, Larix sibirica)
[Severnye Uvaly].
27b. Taiga ─ forest-steppe (forests ─ Larix
sibirica, Pinus sibirica, birch, larch forest ─
steppe ─ meadowsteppes ─ Calamagrostis
epigeios, Poa angustifolia, Phleum phleoides;
bunchgrass steppes ─ Festuca pseudovina,
Koeleria cristata, Cleistogenes squarrosa,
Poa botryoides, Carex duriuscula, Artemisia
frigida) [Minusinskaya kotlovina].
Altai-Sayan group
26. Alpine (alpine tundra vegetation ─ Seseli
monstrosa, Doronicum altaicum, Sibbaldia
procumbens,
Anthoxanthum
odoratum,
Deschampsia
caespitosa)
─
subalpine
(meadows ─ Rhaponticum carthamoides,
Trollius asiaticus, Saussurea latifolia; dwarf
willow birch thickets ─ Betula rotundifolia, Salix
glauca, S. vestita) ─ taiga (subnemoral taiga
forests ─ Abies sibirica, Pinus sibirica, Populus
tremula, Sorbus sibirica) [North Altai].
28. Alpine ─ subalpine ─ taiga (dark coniferous
forests with Pinus sibirica, Abies sibirica) ─
shrub (Caragana arborescens, C. frutex,
Lonicera tatarica, Rosa pimpinellifolia, Rosa
ssp., Spiraea media, S. chamaedrifolia,
S. crenata, S. hypericifolia, Cotoneaster
melanocarpa; ─ forest-steppe (shrubs; forests
26.1. North-East Altai variant (various ir forests
63
ir, aspen, pine; forb ─ bunchgrass steppes ─
Stipa pennata, Poa attenuata, Carex pediformis,
Peucedanum morissonii, Iris ruthenica) ─
steppe (bunchgrass steppes) [West Altai].
commutata).
30а. Subgoltsy ─ taiga ─ forest-steppe
(steppiicated
larch,
pine
forests;
bunchgrass, meadow steppes) [Tunkinskaya
depression];
Tuva─Southern Transbaikal group
29.
Nival (Rhodiola quadriida, Saxifraga
oppositifolia, Oxygraphis glacialis) ─ goltsy ─
tundra (Dryas oxyodonta, Phyllodoce caerulea,
Rhododendron adamsii); shrub ─ (Betula
rotundifolia, Salix glauca, S. lanata, S. krylovii,
Alnus fruticosa) ─ taiga (dark coniferous forests
- Pinus sibirica, Abies sibirica, Picea obovata
and light ─ Larix sibirica, Pinus sylvestris) [East
Sayans].
30b. Montane taiga (larch, pine forests)
[Todzhinskaya depression].
31. Goltsy ─ tundra (Dryas oxyodonta) ─ taiga
(Larix sibirica, Pinus sibirica, Picea obovata) ─
forest steppe (larch, pine forests; steppes of
Filifolium sibiricum) ─ steppe (Koeleria gracilis,
Cleistogenes squarrosa, Festuca lenensis)
[Western Transbaikal region].
31.1. Dzhidinsky variant (larch, birch forest
steppe, reduced belt of pine forests).
29.1. Khamar─Daban variant (subnemoral
taiga forests with Abies sibirica, Pinus
31a. Forest-steppe ─ steppe (Caragana
sibirica, pine stlanik ─ Pinus pumila).
30. Nival ─ goltsy (nival meadows and heath)
─
tundra (Dryas oxyodonta, Betula
rotundifolia, Salix glauca, Caragana jubata,
Festuca kryloviana, Kobresia myosuroides,
K. simpliciuscula, K. ilifolia) ─ taiga (Larix
sibirica, Pinus sibirica, P. sylvestris) ─ forest
steppe (forests ─ Larix sibirica, Betula
pubescens, steppes ─ Festuca lenensis, Poa
botryoides, Carex pediformis, Koeleria cristata,
Iris ruthenica) ─ steppe (Stipa krylovii, S.
capillata, Helictotrichon desertorum, Agropyron
cristatum, Carex duriuscula) [Sayan─Tuva].
30.1. Central Tuva variant (grass ─ wormwood
steppes ─ Artemisia caespitosa, A.
obtusiloba, Stipa orientalis, Cleistogenes
squarrosa,
Poa
attenuata,
Festuca
valesiaca, Koeleria cristata, Nanophyton
erinaceum, Caragana pygmaea, C. spinosa,
C. bungei; larch forests with Iris ruthenica,
Carex pediformis in forest─steppe belt);
30.2. Northern Tannuola Range variant (in
high belt ─ shrubs with Betula rotundifolia,
Rhododendron
parvifolium,
Caragana
jubata);
microphylla, C. pygmaea, C. spinosa,
thickets
of Amygdalus
pedunculata)
[Kyakhta];
31b. Montane taiga ─ forest-steppe ─ steppe
(pine–larch forests with Rhododendron
dahuricum) [Selenga upland].
Baikal region group
32. Montane tundra ─ stlanik (Pinus pumila) ─
taiga (Abies sibirica, Pinus sibirica) ─ foreststeppe (pine, larch forests with Pinus sylvestris,
Larix sibirica, Rhododendron dahuricum;
bunchgrass steppes – Stipa krylovii, Agropyron
cristatum, Koeleria cristata, Poa attenuata,
Festuca lenensis, Cleistogenes squarrosa)
[Primorsky Range].
32а. Stlanik (Pinus pumila) ─ taiga ─ foreststeppe (pine-larch forests ─ Pinus sylvestris,
Larix
sibirica;
bunch-grass
steppes)
[Onotskaya upland].
33. Goltsy (nival meadows) ─ tundra (Cassiope
ericoides, Ledum decumbens, Salix saxatilis)
─ stlanik (Pinus pumila, Betula divaricata, B.
rotundifolia, Rhododendron aureum) ─ open
woodland (ir, birch sparse forests – Betula
lanata) ─ taiga (ir ─ pine, larch forests ─
Larix gmelinii, Abies sibirica, Pinus sibirica
with creeping pine with Bergenia crassifolia)
30.3. Okinsky variant (forests ─ Larix sibirica,
Pinus sylvestris, P. sibirica; steppes ─
Festuca lenensis, Stipa krylovii, Poa
botryoides, Сarex duriuscula, Artemisia
64
─ forest-steppe (pine, larch forests, grass ─
wormwood steppes ─ Agropyron cristatum,
Artemisia sericea, A. commutata) [West Baikal].
exilis, B. fruticosa, B. divaricata; sedge ─
Calamagrostis meadows and herb mires ─
Calamagrostis langsdorfii, Carex schmidtii)
[Vitim plateau];
33.1. Baikal region variant (predominance of
pine larch, larch forests with Rhododendron
and Ledum; pseudosubgoltsy belt of
creeping pine).
35b. Montane taiga (larch, pine forests with
Rhododendron dahuricum, Betula exilis,
Alnus fruticosa; dwarf birch, alder) ─
forest steppes (herb─dwarf shrub larch,
pine forests, steppes – Helictotrichon
schellianum, Poa angustifolia, P. attenuata,
Carex pediformis, C. argunensis, Pulsatilla
davurica; dwarf birch thickets ─ Betula
fruticosa; Alnus fruticosa; Calamagrostisequisetum-sedge mires) [Upper Angara
depression];
33а. Montane taiga (polydominant dark
coniferous taiga of Siberian trees) [LenaAngara plateau].
34. Alpine (meadows ─ Sibbaldia procumbens,
Pyrethrum pulchellum, Ranunculus altaicus,
Viola altaica, Doronicum altaicum) ─ tundra
(Dryas punctata, Kobresia myosuroides) ─
stlanik (Pinus pumila, Betula exilis) ─ open
woodland(sparse ir, birch ─ Betula lanata,
spruce forests with subalpine meadows
─ Aquilegia glandulosa, Trollius asiaticus,
Anemonastrum
narcissilorum,
Geranium
albilorum) ─ taiga (Abies sibirica, Pinus
sibirica, Picea obovata, Pinus sylvestris) ─
pseudosubgoltsy (creeping pine and sparse
larch forests ─ Larix sibirica with Arctous alpina
and Empetrun nigrum) [West Bargusin Range].
35c. Montane taiga (larch forests ─ Larix
gmelinii with Pinus sylvestris, dwarf birch
thickets, meadows, mires Carex wiluica, C.
caespitosa) [Muiskaya depression].
36. Goltsy ─ tundra (Diapensia obovata,
Cassiope tetragona) ─ stlanik (Pinus pumila,
Betula divaricata, Rhododendron aureum, R.
parvifolium) ─ open woodland (birch ─ Betula
lanata, spruce ─ Picea obovata, larch ─ Larix
gmelinii) ─ taiga (forests of Larix gmelinii,
with dwarf birch – Betula divaricata, B. exilis,
alder, Alnus fruticosa, pine larch forests with
Rhododendron dahuricum) [Kodar-Kalarsky
Range].
36а. Stlanik ─ taiga (forests of Larix gmelinii with
Betula exilis, Alnus fruticosa, Ledum palustre,
dwarf birch thickets ─ Betula fruticosa, B.
exilis, B. divaricata, Ledum palustre; mires)
[Olekminsky stanovik].
Transbaikal
35. Goltsy (lichens, fragments of lichen
tundras) ─ tundra (Empetrum nigrum, Cassiope
ericoides, Dryas punctata, D. crenulata, D.
octopetala, Salix sphenophylla, S. cuneata)
─ stlanik (Pinus pumila, Betula divaricata, B.
exilis, Alnus fruticosa, Rhododendron aureum)
─ open woodland (larch, spruce, birch ─ Betula
lanata ─ sparse forests) ─ taiga (larch forests
– Larix gmelinii with dwarf birch, creeping pine,
alder) [North Baikal].
37. Goltsy ─ tundra (Dryas octopetala, Cassiope
tetragona,
Ledum
decumbens,
Salix
berberifolia; meadows ─ Viola bilora, Veronica
densilora, Doronicum altaicum, Campanula
dasyantha, Trollius asiaticus) ─ stlanik (Pinus
pumila, Betula divaricata, Rhododendron
aureum, Sorbus polaris, Alnus fruticosa) ─ open
woodland (larch, birch) ─ taiga (forests ─ Pinus
sibirica, Larix gmelinii, Pinus sylvestris) ─ forest
steppe (pine forests, larch steppe forests; ─
Larix sibirica; bunchgrass steppes ─ Stipa tirsa,
Koeleria gracilis, Artemisia subviscosa) [East
Bargusin].
35.1. Tsypinsky variant (larch, pine ─ larch
forests with Vaccinium vitis-idaea and
Rhododendron dahuricum with participation
of dark coniferous trees ─ Picea obovata,
Abies sibirica, Pinus sibirica; in hollows
─ Calamagrostis meadows and sedge,
cottongrass mires).
35а. Stlanik ─ open woodland ─ taiga
(larch forests with Betula exilis and Ledum
palustre; dwarf birch thickets ─ Betula
65
37.1. Ikatsky variant (larch pine forests, dwarf
shrub moss larch forests with Rhododendron
dahuricum and Bergenia crassifolia,
Vaccinium ssp.);
tetragona, Diapensia obovata, Phyllodoce
caerulea, Rhododendron redowskianum) ─
stlanik (Pinus pumila, Betula exilis, B. divaricata,
Rhododendron aureum, R. parvifolium)─open
woodland (Betula lanata) ─ taiga (Larix gmelinii,
L. sibirica, Pinus sibirica, Picea obovata) [Upper
Aldan].
37.2. Ulan–Barguzinsky variant (with subbelt of pine forests with Rhododendron
dahuricum, Alnus fruticosa, Vaccinium ssp.).
40.1. Amginsky variant (pine larch forests of
Limnas stelleri group with Picea obovata,
Pinus sibirica).
37а. Stlanik ─ open woodland (sparse forests
with Abies sibirica, Pinus sibirica, Larix
sibirica) ─ taiga ─ forest steppe (pine
steppe forests ─ forb ─ grass, steppes)
[Svyatoinos Peninsula];
40а. Montane taiga (larch forests with Alnus
fruticosa, Betula exilis) [Olekma─Chara
plateau];
37b. Stlanik ─ taiga ─ forest steppe (pine
steppe forests, forb ─ grass, forb ─ sedge
steppes ─ Carex duriuscula, Stipa tirsa,
Poa transbaikalica, Agropyron cristatum,
Koeleria gracilis, Potentilla acailis, P. bifurca)
[Barguzin depression].
40b. Stlanik ─ taiga (bogged larch forests and
open woodlands) [Nimnyrskoye plateau];
40c. Stlanik ─ open woodland ─ taiga (larch
forests with Ledum palustre and Betula
exilis) [Chulmanskoye plateau].
38. Open woodland (larch woodlands with
Pinus pumila and Betula exilis) ─ taiga (pinelarch and larch forests with Ledum palustre,
Rhododendron
dahuricum,
birch─Betula
platyphylla forests with elements of Amur
subtaiga ─ Betula davurica) [Upper Amur R.].
41. Goltsy ─ tundra (lichen, heath tundras) ─
stlanik (Pinus pumila) ─ open woodland (larch,
birch with Betula lanata) ─ taiga (Larix gmelinii,
L. sibirica, Pinus sylvestris, Picea ajanensis)
[Uchur-Sunnaginsky].
41.1. Gynym─Uchursky variant (bogged
tundras, in taiga belt pine forests with Limnas
stellerii, Rhododendron dahuricum; larch
dwarf shrub-moss forests with Dryas viscosa
on limestone).
38.1. Schilkinskya variant (pine, larch forests
with Rhododendron dauricum, Betula
platyphylla; steppes with Filifolium sibiricum,
Ulmus macrocarpa, Armeniaca sibirica;
steppe meadows ─ Pulsatilla multiida; dwarf
birch ─ Betula fusca).
42. Goltsy ─ tundra (Dryas ajanensis, Cassiope
ericoides, Diapensia obovata, Phyllodoce
caerulea, Loiseleuria procumbens) ─ stlanik
(Pinus pumila, Rhododendron aureum, Betula
divaricata, Alnus fruticosa) ─ open wood (Betula
lanata) ─ taiga (Larix gmelinii, Picea ajanensis)
[Tokinsky].
39. Goltsy ─ tundra ─ stlanik (Pinus pumila) ─
open woodland (Betula lanata) ─ taiga (larch
forests─Larix gmelinii with Pinus pumila, Betula
exilis, Ledum palustre) [Patomsky].
39.1. West Patomsky variant (forests of Larix
gmelinii with participation of Siberian dark
coniferous trees ─ Pinus sibirica, seldom
Abies sibirica);
42а. Stlanik ─ open woodland (Sphagnum,
lichen larch woodlands with Pinus pumila) ─
taiga [Depression of Toko Lake].
39.2. Momsky variant (forests of Larix gmelinii
with Pinus sibirica).
43. Goltsy ─ tundra (Cassiope ericoides, Sieversia
pusilla, Dryas ajanensis) ─ stlanik (Pinus
pumila, Rhododendron aureum, Alnus fruticosa)
─ open woodland (peatmoss, lichen, creeping
pine – larch woodlands, woodlands of Betula
lanata) ─ taiga (Larix gmelinii, Pinus sylvestris,
Aldan–Maya
40. Goltsy ─ tundra (lichens, dwarf shrubs ─ Dryas
punctata, Loiseleuria procumbens, Cassiope
66
46.1. Lower Gilyui variant (forests with
participation Quercus mongolica, Tilia
amurensis, Betula davurica);
Picea ajanensis) [West Dzhugdzur].
43а. Stlanik ─ taiga (larch and pine forests of
Limnas stelleri group) [Nizhne-Mayskoye
plateau /on limestone];
46.2. Selemdzha variant (forests with
participation Abies nephrolepis, Picea
ajanensis).
43b. Montane tudra ─ stlanik ─ open
woodland [Nelkanskoye plateau].
44. Goltsy ─ tundra (lichen–moss, creeping pine
larch woodlands) ─ stlanik (Pinus pumila,
Alnus fruticosa, Rhododendron aureum, R.
parvifolium) ─ open woodland (larch lichenmoss woodlads with Pinus pumila) ─ taiga
(larch ─ Larix gmelinii, dark coniferous ─ Abies
nephrolepis, Picea ajanensis forests with
forests of Chosenia macrolepis and Populus
suaveolens in valley rivers) [Pribrezhnyi
Range].
Amur─Uda group
47. Goltsy ─ tundra (Cassiope ericoides, Dryas
ajanensis, Arctous alpina, Rhododendron
redowskianum,
Salix
sphenophilla,
S.
phlebophylla, Carex rigidioides, Artemisia
lagocephala) ─ stlanik (Pinus pumila, Alnus
fruticosa, Betula divaricata, Rhododendron
aureum, R. parvifolium, Sorbaria pallasii;
meadows ─ Geranium albilorum, Festuca
altaica, Aquiledia amurensis) ─ open woodland
(larch, spruce birch woodlands with Betula
lanata) ─ taiga (Larix gmelinii, Picea ajanensis)
[Upper Zeya].
Amur─Zeya group
45. Goltsy ─ tundra (Arctostaphylos uvaursi,
Cassiope tetragona) ─ stlanik (Pinus
pumila) ─ open woodland (Larix gmelinii)
─ taiga (larch forests of Larix gmelinii with
Pinus pumila, Ledum palustre, Betula exilis)
[Upper Gilyusky].
47.1 Maisky variant (lower strip of montane
taiga belt – pine–larch forests with Betula
dahurica).
48. Goltsy ─ tundra (Cassiope ericoides,
Rhododendron
redowskianum,
R.
camtschaticum, Arctous alpina, Ledum
hypoleucum with eastern species -–
Selaginella, Claytonia acutifolia. Dicentra
peregrina, Artemisia lagocephala) ─ stlanik
(Pinus pumila, Alnus fruticosa, Weigela
middendorii, Sorbus sambucifolia) ─ open
woodland (Betula lanata, Larix gmelinii)
─ taiga (nothern ir-spruce, spruce forests
─ Picea ajanensis, Abies nephrolepis)
with poplar – Chosenia forests along river
valleys (Populus maximowiczii, Padus
asiatica, Chosenia arbutifolia) and with herb
– peatmoss mires (Carex meyeriana, C.
juncella) [South Okhotik].
45а. Stlanik ─ open woodland ─ taiga
(pines, larch forests with Ledum palustre,
Rhododendron dahuricum in combination
with mires and dwarf birch thiket) [GilyuskoNyukzhinskoye interluve];
45b. Open woodland ─ dwarf birch thicket
(Betula fruticosa) ─ taiga (larch forests with
Ledum palustre, Betula exilis and mires
[Tungir–Olekma interluve].
46. Goltsy ─ tundra (Dryas ajanensis, Diapensia
obovata, Loiseleuria procumbens, Phyllodoce
caerulea,
Rhododendron
redowskianum,
Empetrum nigrum, Arctous alpina, Salix
sphenophylla, Artemisia lagocephala) ─
stlanik (Pinus pumila, Alnus fruticosa, Betula
divaricata, B. exilis, Juniperus sibirica) ─ open
woodland (spruce, larch, birch forests ─ Picea
ajanensis, Larix gmelinii, Betula lanata) ─ taiga
(larch forests with Rhododendron dauricum,
spruce forests with Picea ajanensis) [YankanTukuringra Range].
48а. Stlanik ─ dark coniferous taiga
(spruce─ir forests) [Amgun river basin];
48b. Stlanik (Pinus pumila, Duschekia
kamtschatica,
Rhododendron
camtschaticum,
Sorbus
sambucifolia,
Vaccinium praestans, Salix kimurana, S.
udensis) ─ taiga (spruce, birch–larch–
67
Cotinus coggygria) ─ coniferous-broadleaved forests (Quecus pubescens, Arbutus
andrachne, Juniperus excelsa, J. oxycedrus,
Pistacia mutica with evergreen plants) [South
Crimea].
spruce forests ─ Picea ajanensis, Larix
gmelinii, Betula lanata) [Schantarskiye Isls.].
49.
Goltsy ─ tundra (Cassiope redowskii,
Rhododendron redowskianum) ─ stlanik (Pinus
pumila, Alnus fruticosa) ─ open woodland (irspruce, larch, birch – Betula lanata) ─ taiga
(dark coniferous forests – Picea ajanensis,
Abies nephrolepis) [Bureinsky].
52а. Broad-leaved forests ─ shibliak ─ foreststeppe (forests – Q. pubescens, Carpinus
orientalis, Pyrus elaeagrifolia, steppes ─
Stipa syreistschikowii, Festuca valesiaca,
Asphodelina taurica) [East Crimea].
49.1. Turansky variant (larch - spruce, larch
forests).
NEMORAL (BROAD - LEAVED FORESTS)
53. Meadow-steppe (jaila) ─ broad-leaved forest
(Quercus petraea, Q. pubescens, Fraxinus
excelsior, Fagus orientalis) ─ forest-steppes
with fragments of shibliak ─ Dactylis glomerata,
Poa pratensis, Phleum phleoides, Festuca
valesiaca, Bromopsis riparia, Filipendula
vulgaris,
Adonis
vernalis,
Bothriochloa
ischaemum, Paeonia tenuifolia) [North Crimea].
Central Carpatians group
50. Alpine (Carex curvula, C. sempervirens,
Juncus triidus, Festuca supina, Sesleria bielzii,
Hamogyne alpina) ─ meadow ─ (Nardus stricta,
Carex leporina) ─ stlanik (Pinus mughus, Alnus
viridis) ─ open woodland ─ dark coniferous
(forests with Abies alba, Picea abies) ─ broadleaved forests (Fagus sylvatica) [Carpatians].
North Caucasus group
54. Nival ─ alpine ─ subalpine (meadows – Festuca
airoides, Koeleria ledebourii, Carex pontica,
Geum speciosa; thickets – Rhododendron
caucasicum; elin wood ─ Betula litwinowii, B.
raddeana, Acer trautvetteri, Fagus orientalis)
─ forest (dark coniferous forests ─ Picea
orientalis, Abies nordmanniana; broad-leaved
forests ─ Quercus petraea, Q. robur, Fraxinus
excelsior, Fagus orientalis, Carpinus betulus) ─
forest-steppe [Kuban].
50.1. Chernogoria variant (ir–beech, spruce–
beech forests).
51. Alpine (Campanula polymorpha, Soldanella
montana, Festuca picta) ─ meadow (Nardus
stricta, Vaccinium myrtillus, Aconitum irmum,
Heracleum palmatum, Archangelica oficinalis,
Campanula latifolia, Telekia speciosa) ─
stlanik (Rhododendron kotschyi, Alnus viridis,
Juniperus sibirica) ─ open woodland (Fagus
sylvatica) ─ coniferous-broadleaved (Fagus
sylvatica, Abies alba) ─ broadleaved forests
(Quercus robur, Q. petraea, Fagus sylvatica,
Tilia cordata, T. tomentosa, Ulmus scabra,
Fraxinus excelsa) [Transcarpathian].
54а. Montane forest ─ shibliak (Quercus
pubescens, Q. petraea, Fagus orientalis,
Juniperus excelsa, J. oxycedrus, J.
foetidissima) [Novorossiysk].
55. Nival ─ alpine ─ subalpine (meadows –
Festuca varia, Bromus variegatus; thickets;
elin wood – Betula litwinowii, B. raddeana, Acer
trautvetteri) ─ forests (pine-birch forests ─ Pinus
hamata, Betula pendula, broad-leaved forests
─ Quercus petraea, Q. robur, Fagus orientalis,
Carpinus betulus) ─ forest-steppe [Elbrus].
Crimea group
52. Meadow-steppes (jaila) (Festuca taurica,
Koeleria cristata, K. brevis, Carex humilis,
Teucrium chamaedrys, Thymus tauricus) ─
broad-leaved forest (beech: hornbeam-beech
forests – Fagus orientalis, F. sylvatica, Carpinus
betulus, oak forests – Quercus petraea, pine
forests – Pinus pallasiana, P. sylvestris) ─
shibliak (Q. pubescens, Carpinus orientalis,
Paliurus spina-christi, Jasminum fruticans,
56. Nival ─ alpine ─ subalpine (meadows; high
mountaineus steppes; elin woods ─ Betula
litwinowii, B. raddeana, Acer trautvetteri, pine
forests ─ Pinus hamata) ─ forests ─ Quercus
68
57.
petraea, Q. robur, Carpinus betulus, Fagus
orientalis) ─ meadow - steppe [Terek].
forests ─ Quercus robur, Carpinus orientalis)
[South Osetiya].
56а. Forest-steppe [Sunzhinsky Ridge].
59а. Alpine ─ subalpine ─ forests (oak,
hornbeam, pine, beech forests) ─ foreststeppe [Trialetsky Range].
Alpine─subalpine (xerophilic meadows;
thickets of Rhododendron caucasicum; elin
woods ─ Betula litwinowii, B. raddeana, Acer
trautvetteri; pine forest ─ Pinus hamata) ─
broad-leaved forest (Quercus pedunculilora,
Q. pubescens, Q. iberica, Q. robur, Carpinus
betulus, Fagus orientalis) ─ arid open
woodland (Palliurus spina-christi, Rhamnus
pallasii with shibliak) ─ steppe (wormwood
-grass steppes ─ Stipa tirsa, S. dagestanica,
Bothriochloa ischaemum) [Daghestan].
Caucasus (subtropic) group
58. Nival ─ alpine ─ subalpine (meadows;
60. Montane steppe (steppes with tragacanth
thickets, phrygana, shibliak) ─ subtropic
forest (growths of Quercus macranthera with
meadows, broad-leaved forests ─ Quercus
iberica, Fagus orientalis, Carpinus betulus;
subtropic forests ─ Quercus castaneifolia,
Parrotia persica, Acer velutinum, Fraxinus
coriariifolia,
Albizia
julibrissin,
Zelkova
carpinifolia) [Talysh].
Southern Ural group
61. Goltsy ─ subalpine (tall herb meadows) ─ taiga
(Picea obovata, Abies sibirica)─broad-leaved
forest (Quercus robur, Tilia cordata, Acer
platanoides, Ulmus laevis) [Southern─West
Ural].
thickets of Rhododendron caucasicum;
elin woods and sparse forests ─ Betula
litwinowii, Acer trautvetteri, Fagus orientalis)
─ subtropic forest (dark coniferous
forests ─ Abies nordmanniana, Picea
orientalis; colchis broad-leaved forests
─ Castanea sativa, Fagus orientalis,
Carpinus betulus, Acer laetum, Quercus
imeretina, Q. hartwissiana, Tilia begoniifolia
with understorey of evergreen shrubs ─
Rhododendron ponticum, Laurocerasus
oficinalis, Vaccinium ovalifolium) [Colchis
lowland].
61а. Open woodland (sparse forests with
Quercus robur) ─ broad-leaved forest
(Syrts);
61b. Broad-leaved forests ─ forest-steppes
[Zilair plateau].
62. Montane tundra ─ taiga ─ forest-steppes
(forests – Pinus sylvestris, Larix sibirica;
meadows, forb-bunchgrass steppes) [South
East Ural].
58.1. Small Caucasus.
58а. Subtropic forests (beech, hornbeam, oak
forests) [Imeret upland];
58b. Boggy - broad-leaved forest (alder forests
─ Alnus barbata, Pterocarya fraxinifolia,
Rhododendron ponticum; broad-leaved
forests; mires ─ Carex remota, Osmunda
regalis) [Colchis lowland].
Amur─Sikhot-Alin group
63. Goltsy ─ tundra (tundras – Cassiope redowski,
C. ericoides, Empetrum nigrum, Weigela suavis,
Sorbaria pallasii with alpinotipic meadows ─
Aquilegia turczaninovii, Sieversia pentapetala,
Potentilla elegans, Mertensia rivularis, Viola
bilora) ─ stlanik (Pinus pumila, Rhododendron
aureum, Weigela middendoriana, Alnus
fruticosa) ─ open woodland (ir, spruce,
larch, birch open woodlands – Betula lanata)
─ taiga (Abies nephrolepis, Picea ajanensis)
─ coniferous - broad-leaved forest (northern
pine-broad-leaved forests) [Malyi Khingan
Range].
59. Nival ─ alpine ─ subalpine (meadows; elin
woods Betula litwinowii, Acer trautvetteri)
and sparse forests ─ Fagus orientalis, Pinus
kochiana) ─ forests (dark coniferous forests ─
Abies nordmanniana, Picea orientalis, broadleaved forests ─ Quercus iberica, Carpinus
betulus, Acer laetum, A. hyrcanum) ─ foreststeppes (arid open woodlands and shibliaksi
69
64. Montane tundra ─ stlanik (Pinus pumila,
Rhododendron
mucronulatum,
Bergenia
paciica) ─ open woodland (Betula lanata, Larix
gmelinii) ─ taiga (northern pine forests with Tilia
amurensis, Acer mono, Betula costata; amur
dark coniferous forests with Abies nephrolepis
Picea ajanensis, Larix gmelinii; nemoral spruce
forests) [Nothern Sikhote–Alin].
67.1. Yalu-Suchan variant (polydominant broadleaved forests ─ Abies holophylla, Pinus
koraiensis, Tilia taquetii, Betula schmidtii,
Carpinus cordata, Acer komarovii, Kalopanax
septemlobus, Actinidia arguta, Schisandra
chinensis).
67а.
Coniferous-broad-leaved
forest
(polydominant oak – maple – lime forests)
[Pogranichny Range];
67b.
Forest-steppe
(Quercus
dentata,
Rhododendron schlippenbachii, Weigela
praecox, Lonicera cyrtobotrya, Arundinella
anomala) [Khasan Lake].
64.1. Middle Amur variant (ir-spruce forests
with participation of nemoral species).
65. Montane tundra (Empetrum nigrum, Arctous
alpina, Cassiope ericoides, C. redowskii, Ledum
hypoleucum, Rhododendron redowskianum)
─ stlanik (Juniperus sibirica, Alnus fruticosa,
Microbiota decussata) ─ open woodland
(Betula lanata, Bergenia paciica) ─ taiga (Picea
ajanensis, Abies nephrolepis, Sorbus amurensis,
Acer ukurunduense) ─ coniferous and broadleaved forest (Pinus koraiensis, Quercus
mongolica, Acer mono, A. ukurunduense, A.
tegmentosum, Betula costata, Tilia amurensis,
Fraxinus rhynchophylla) [Western Sikhote–Alin].
66. Montane tundra (Arctous alpina, Cassiope
ericoides, Bergenia paciica) ─ stlanik ─
open woodland ─ taiga ─ coniferous and
broad-leaved
forest
(Pinus
koraiensis,
Abies nephrolepis, Tilia amurensis, Fraxinus
rhynchophylla with belt of oak forests ─ Quercus
mongolica along sea shore) [Eastern SikhoteAlin].
67.
Montane tundra (Diapensia obovata,
Ledum macrophyllum, Cassiope ericoides) ─
stlanik (Microbiota decussata, Syringa wolii,
Pinus pumila) ─ open woodland (Betula
lanata, Abies nephrolepis, Picea ajanensis,
Weigela middendorfiana) ─ taiga (Abies
nephrolepis, Picea koraiensis, Oplopanax
elatus, Clintonia udensis) ─ coniferousbroad-leaved forests (Pinus koraiensis, Tilia
amurensis, T. mandshurica, Acer mono, A.
pseudosieboldianum, Fraxinus rhynchophylla,
Actinidia kolomikta, Vitis amurensis) ─ broadleaved forest (Quercus mongolica, Betula
davurica) [Southern Sikhote–Alin].
North Paciic Islands Group
68. Nival (Saxifraga merkii, Poa malacantha,
Ranunculus escholtzii, Sibbaldia procumbens)
─ alpine (meadows ─ Trollius riederianus,
Primula
cuneifolia,
Dryopteris
expansa,
Geranium erianthum) ─ tundra (Cassiope
lycopodioides, Arctous alpina, Vaccinium minus,
Dryas punctata, Phyllodoce aleutica, P. caerulea,
Diapensia
obovata,
Bryanthus
gmelinii,
Harrimanella
stelleriana,
Rhododendron
redowskianum, R. aureum, R. camtschaticum)
─ stlanik (Pinus pumila, Alnus kamtschatica,
Sorbus sambucifolia) ─ open woodland
(Betula
ermanii,
Vaccinium
praestans,
Daphne kamtschatica, Ledum decumbens;
meadows “ushkha”─Calamagrostis langsdorfii,
Filipendula camtschatica, Heracleum lanatum,
Senecio palmatus, Mertensia pubescens,
Cirsium kamtschaticum, Angelica ursina) ─ taiga
(Betula ernanii, B. kamtschatica, Larix cajanderi,
Picea ajanensis,) with the belt of coastal heaths
(Empetrum sibirica, E. nigrum) [Kamtchatka].
68.1. West Kamtchatka variant (alpine
meadows ─ Artemisia arctica, Trisetum
sibiricum, Festuca rubra, Veronica stelleri,
V. grandilora, Aster consanquineus, Lagotis
gmelinii, Viola langsdorfii, Gentiana glauca).
68.а. Montane tundra (Empetrum nigrum,
Cassiope
lycopodioides,
Harrimanella
stelleriana,
Dryas
kamtschatica,
Rhododendron
camtschaticum)─stlanik
(Pinus pumila, Alnus kamtschatica) [Southern
Kamtchatka, Northern Kurile Islands.];
70
69.
68b. Montane tundra ─ stlanik (Juniperus
sibirica, Sorbus sambucifolia, with meadow
“ushkha” ─ Heracleum lanatum, Filipendula
camtschatica,
Senecio
palmatus)
[Commander Islands];
Acer mono, A. ukurunduense, Ulmus japonica,
U. laciniata, Aralia cordata, Ilex rugosa, Sasa
kurilensis; meadow “ushkha” ─ Polygonum
sachalinense, species of Filipendula, Senecio,
Cacalia, Angelica, Heracleum) [West Sakhalin].
68c. Montane taiga (Picea ajanensis, Abies
gracilis, Larix cajanderi) [Central Kamtchatka
depression].
70а. Stlanik ─ open woodland (birch elin
woods) ─ taiga (birch-spruce forests)
[Shmidt Peninsula].
Montane tundra (Phyllodoce aleutica,
Rhododendron camtschaticum, Bryanthus
gmelinii, Cassiope lycopodioides) ─ stlanik
(Pinus pumila, Alnus maximoviczii, Ledum
hypoleucum, Weigela middendorfiana, Ilex
rugosa, Skimmia repens) ─ open woodland
(Betula ermanii, Acer tschonoskii, Sasa
kurilensis) ─ coniferous forest (Abies
sachalinensis, Picea ajanensis, P. glehnii,
71.
Taxus
cuspidata,
Larix
kamtschatica,
Hydrangea petiolaris, Toxicodendron orientale,
T. trichocarpum) ─ broad-leaved forest
(Quercus dentata, Q. crispula, Acer mayrii, A.
ukurunduense, Phellodendron sachalinense,
Kalopanax septemlobus, Betula ulmifolia,
Magnolia hypoleuca) [Southern Kurily Isls.].
crenata) [East Sakhalin].
72. Montane tundra ─ stlanik (Pinus pumila,
Alnus maximowiczii, Hydrangea paniculata)
─ open woodland (Betula ermanii, Sasa
kurilensis, Hydrangea paniculata) ─ coniferous
- broad-leaved forest (ir forests ─ Abies
sachalinensis with Taxus cuspidata, Acer
pictum, A. ukurunduense, Kalopanax ricinifolia,
Phellodendron
sachalinense,
Quercus
crispula, Ilex crenata, Aralia elata, A. cordata,
Juglansailantifolia, Vitis coignetiaei, Actinidia
kolomicta, Skimmia repens, Sasa spiculosa,
S. senanensis; meadows “ushkha”) [South
Sakhalin].
69.1. Central Kurile Islands variant (heather
tundras; stlaniks ─ Pinus pumila, Alnus
kamtschatica; birch forests ─ Betula ermanii
with participation of broad-leaved trees;
thickets of Alnus maximoviczii; meadows
“ushkha”).
69а. Stlanik (Juniperus sargentii, Lonicera
sachalinensis, Eubotrioides grayana, Ilex
crenata, Sasa kurilensis) ─ coniferousbroad-leaved forest (Picea ajanensis,
P. glehnii, Abies sachalinensis, Larix
kamtschatica, Betula ermanii, B. platyphylla,
Sasa kurilensis; meadows with Polygonum
sachalinense) [Small Kurile Islands].
70. Montane
tundra
(Diapensia obovata,
Loiseleuria procumbens, Phyllodoce caerulea)
─ stlanik (Pinus pumila, Rhododendron
сamtchaticum, R. aureum, Betula divaricata) ─
open woodland (Betula ermanii, Sasa kurilensis,
S. spiculosa, Vaccinium ovalifolium, V. smallii)
─ taiga (ir-spruce forests ─ Picea ajanensis,
Abies sachalinensis, Larix kamtschatica with
Quercus mongolica, Fraxinus mandshurica,
Montane tundra (Diapensia obovata,
Louseleuria procumbens, Phyllodoce coerulea,
Dryas tshonoskii, Dicentra peregrina) ─
stlanik (Pinus pumila, Betula divaricata,
Rhododendron kamtschaticum) ─ open
woodland (Betula ermanii) ─ taiga (ir-spruce
forests ─ Picea ajanensis, Abies sachalinensis,
Taxus cuspidata, Acer ukurunduense, Ulmus
japonica, Quercus mongolica, Ilex rugosa, I.
72а. Open woodland (Betula ermanii) ─
coniferous - broad-leaved forest (spruceir forests with Abies sachalinensis, A.
mayriana, Picea glehni, Taxus cuspidata
and broad-leaved trees) [Tonino-Anivsky
Peninsula].
SUBARID
East Caucasus (dry subtropic) group
73.
71
Nival ─ alpine (steppe meadows) ─
subalpine (Festuca versicolor, Carex humilis,
Rhododendron caucasicum, groves Quercus
macranthera) ─ forests (broad-leaved forests
─ Castanea sativa, Fagus orientalis, Quercus
castaneifolia, Q. iberica) ─ arid open woodland
(with fragments of shibliak, phrygana;
bunchgrass, dwarf semishrub-bunchgrass
steppes ─ Juniperus polycarpos, J. foetidissima,
Pistacia mutica, Pinus eldarica, Quercus iberica,
Carpinus orientalis) [Lagodekhsko-Zakatal sky].
Mongolia - Altai group
77. Alpine (Saxifraga oppositifolia, Sibbaldia
tetrandra, S. procumbens) ─ meadows
(Ranunculus altaicus, Gentiana grandilora,
Trollius lilacinus) ─ tundra (tundras ─
Dryas oxyodonta, Betula rotundifolia, Salix
berberifolia, S. krylovii, S. reticulata, heaths
─ Kobresia myosuroides, K. humilis, Carex
rupestris, C. stenocarpa, stepiied tundras;
cryoitic tundras ─ Festuca kryloviana, F.
supina, Ptilagrostis mongolica) ─ steppe (dwarf
semishrub-bunchgrass steppes with separate
groves of Betula microphylla ─ Stipa glareosa,
Cleistogenes squarrosa, Artemisia frigida,
Caragana bungei, C. pygmaea; bunchgrass
steppes ─ Festuca lenensis, Poa attenuata,
Agropyron
cristatum,
Koeleria
cristata,
Cymbaria dahurica) ─ foreststeppes (forests of
Larix sibirica with groves of Betula microphylla)
[Tuva –South Western Altai].
73а. Subalpine ─ forests ─ arid open
woodland [Tsyvi-Gomborsky];
73b. Juniper arid open woodland ─ steppe
(with ephemeroid-saltworm communities)
[Yorskoye Plateau].
74. Alpine (Festuca versicolor, Bromus variegata,
Nardus stricta) ─ subalpine (meadows, elin
woods ─ Betula litwiniwii, Acer trautvetteri,
groves of Quercus macranthera) ─ broadleaved forests (Quercus iberica, Fagus
orientalis, Carpinus betulus, C. orientalis) ─
arid open wood (Juniperus polycarpos, J.
foetidissima, Rhamnus pallasi, Paliurus spinachristi, phrygana with Bothriochloa ischaemum)
[Centere Small─Caucasus].
Карта составлена на основе картографических
и литературных материалов, а также
материалов
многолетних
полевых
исследований авторов в разных регионах
России и сопредельных территориях.
Основными картографическими источниками
послужили:
• Географический Атлас России. М.: ПКО
Картография.1997
75. Alpine ─ subalpine (xerophitic meadows,
groves of Quercus macranthera) ─ forest (oak
forests – Quercus iberica, groves of Quercus
macranthera; broad-leaved forests ─ Carpinus
betulus, C. orientalis, Acer campestre, Tilia
begoniifolia) ─ arid open woodland (Juniperus
polycarpos, J. foetidissima, Pistacia mutica,
Paliurus spina-christi, Rhamnus pallasii,
Punica granatum, with steppes of Bothriochloa
ischaemum) [Karabakh–Zangezur].
76. Alpine (meadows – Festuca varia, Festuca
woronowii; tragacanth cushions) ─ subalpine
(high mountain steppes ─ Bothriochloa
ischaemum, Stipa pulcherrima, S. tirsa;
tragacanths frigana, shibliak ─ forests (oak
forests – Quercus macranthera, juniper forests
─ Juniperus polycarpos, J. foetidissima, J.
oblonga, Pinus kochiana, broad-leaved forests
─ Quercus iberica, Carpinus betulus, Fraxinus
oxycarpa, F. excelsior, Acer ibericum) ─ arid
open woodland (Pistacia mutica, Amygdalus
nairica, Celtis caucasica, Juniperus foetidissima,
J. polycarpos) ─ montane xerophytic steppe
(Stipa tirsa, S. pontica, Acantholimon, Cousinia,
Salvia) ─ desert (Artemisia armeniaca, A.
fragrans) [Armenia].
72
•
Карта растительности Юга Восточной
Сибири. М.1:1500 000 на 4-х листах.
М.:ГУГК, 1972
•
Карта растительности бассейна Амура.
М. 1:2 500 000. М.:ГУГК.1969
•
Карта восстановленной растительности
Центральной и Восточной Европы. М. 1:4
000 000 на 6-ти листах. СпБ. 1996
•
Карта растительности Европейской части
СССР. М. 1:2500 000 на 6-ти листах. М.:
ГУГК.1976
•
Карта растительности Казахстана и
Средней Азии (в пределах пустынной
области) М. 1:2 500 000. М.ГУГК. 1995
•
•
Корреляционная
экологофитоценотическая
карта
Азиатской
России. М.1:8 000000. СО АН СССР. Ин-т.
географии Сибири и ДВ.
Растительность
Западно-Сибирской
равнины. М. 1:1500 000 на листах
[карта].М.:ГУГК. 1976
•
Растительность Сахалинской области.
М.1:1500 000 [карта]// Атлас Сахалинской
области. М.:ГУГК. 1976
•
7. Лавренко Е.М. Типы вертикальной поясности
растительности в горах СССР// Современные
проблемы географии. М., 1964.
Растительность Алтайского края. М.:1600
000 [карта] //Атлас Алтайского края.Т.1.
М.-Барнаул: ГУГК.1978
•
•
6. Комаров В.Л. Растительность Сибири. Избр.
соч. Т.IX. М.: АН СССР, 1953.
8. Лавренко Е.М., Карамышева З.В., Никулина
Р.И. Степи Евразии. Л.1991.
9. Огуреева Г.Н. Ботаническая
Алтая. М.: Наука, 1980.
география
10. Павлов Н.В. Ботаническая география СССР.
Алма-Ата, 1948.
11. Растительность Европейской части СССР.
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Растительность СССР. М.: 1:4 000 000 на
4-х листах [карта]. Для высших учебных
заведений. М.: ГУГК. 1990.
12. Растительный покров СССР. Т.I-II. М., 1956.
13. Растительный покров Западно-Сибирской
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Растительность Якутской АСССР. М.1:5
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М.:ГУГК. 1981
14. Сочава
В.Б.
Классификация
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Земли
//Современные
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ОСНОВНАЯ ЛИТЕРАТУРА
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Географические
аспекты
сибирской тайги. Новосибирск, 1980.
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В.Д.
Геоботаническое
районирование Арктики и Антарктики//
Комаровские чтения. XXIX. Л.: Наука, 1977.
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в горах СССР// Изв. ВГО. Т.87, вып. 3, 1955.
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Юрковская Т.К.
(ред).
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18. Шумилова Л.В. Ботаническая география
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1998.
73
Vegetation Mapping in Boreal Alaska
Stephen S. Talbot
U.S. Fish and Wildlife Service, Anchorage, Alaska, 99503, U.S.A., stephen_talbot@fws.gov
Abstract
One of the irst tasks to be addressed within the
Circumboreal Vegetation Mapping (CBVM) project
was to review the status of boreal vegetation
mapping in each of the circumboreal countries. This
paper summarizes the vegetation maps presently
available for boreal Alaska. The large number of
maps of Alaska varies widely in detail and quality,
ranging from intermediate-scale maps of large areas
to large-scale maps of small areas, and from maps
based on photographic interpretation to those based
on computer classiied digital data. Collectively,
these maps could provide a valuable resource for
reinterpreting and synthesizing the boreal vegetation
of Alaska into a single map. Representative examples
are provided to indicate the spectrum of vegetation
units displayed in some of these maps.
Introduction
The goal of the Circumboreal Vegetation Mapping
(CBVM) project is to provide a common international
framework for understanding the boreal region.
Currently, various maps exist of the boreal biome,
but they do not rely on a uniied international method
for classifying and mapping boreal vegetation.
By recognizing the boreal region as a single geoecosystem with a common set of cultural, political, and
economic issues, the CBVM will be the irst detailed
vegetation map of the entire global biome. Information
on boreal vegetation has increased markedly in recent
years, and one of the irst tasks to be addressed was
reviewing the status of boreal vegetation mapping
in each of the circumboreal countries. This paper
compiles a bibliography of all relevant boreal Alaska
vegetation maps and assesses preliminarily if these
data are adequate for making a small-scale (1:4 M)
vegetation map of boreal Alaska.
Inventorying available vegetation maps of the boreal
zone is a logical irst step in mapping the vegetation of
boreal Alaska (Gribova & Isachenko, 1972). The term
“boreal” has been variously deined. As used here,
the term includes the northern, middle, and southern
boreal subzones of Tuhkanen (1984) south of the
southern limit of the Arctic as deined by Walker et al.
(2005) but excludes the hemiboreal subzone as the
southern limit of the zone. This report identiies the
major vegetation maps that are currently available.
From these maps, representative examples are
provided of the vegetation units that have been
portrayed. These examples were selected to indicate
the spectrum of mapping efforts and their products.
Mapping studies are organized irst from an historical
perspective—starting with the early period from 1901
to 1963—then by more recent, small-scale mapping
studies that cover all Alaska. These examples are
followed by large-scale mapping surveys of small
areas that in turn are followed by medium-scale
computer classiied mapping projects. In the latter
category the studies are sometimes arranged by the
agency that supported the mapping and sometimes
by the area of Alaska where the mapping took place.
The references that follow indicate there are a large
number of Alaska boreal vegetation maps, and they
vary widely in their detail and quality. They range
from intermediate-scale maps of large areas to largescale maps of small areas, and from maps based
on photographic interpretation to those based on
computer classiied digital data.
Status of Vegetation Mapping
Early Period
The irst catalog of vegetation maps of Alaska was
published by Küchler & McCormick (1965); it lists
27 maps from boreal Alaska published during the
period 1901–1963 and 48 small-scale maps of North
America, all of which were derived from photographic
interpretation. Their catalog provides a wealth of
information arranged in three groups: title of the
map, date of preparation and color and scale; map
74
legend; and author of the map with date and place
of publication. Smaller-scale maps of Alaska depict
broad types of vegetation, with map units ranging
from two or three types, such as “timbered” and
“timberless,” to one study delimiting nine types
(Spetzman, 1963): (1) very high evergreen hemlockspruce forest; (2) high evergreen spruce forest; (3)
moderately high mixed evergreen and deciduous
forest; (4) low mixed evergreen and deciduous forest;
(5) high brush; (6) low brush-muskeg; (7) moist tundra;
(8) wet tundra and coastal marsh; and (9) barren
and sparse dry tundra. Most of these maps appear
to have been prepared from an economic, forestry
perspective. Larger-scale maps vary in their degree
of generalization. Some maps indicate broad types,
such as those of Whittier, Alaska in the Southcentral
Region, which portray hemlock and muskeg, hemlock
and alder, timberline transition, alpine vegetation,
and snow alder (Thompson, 1954). Others are more
detailed, such as the map of Fort Greely, Alaska in
the Interior Region, which delineates 16 map units
(Benninghoff, 1957): Forest─evergreen forest, mixed
evergreen-deciduous forest, deciduous forest, and
black spruce muskeg; Scrub and/or shrub─evergreen
scrub, mixed evergreen-deciduous scrub, and
deciduous scrub; Tundra─shrub tundra, rock desert,
and bog; Meadow–meadow and marsh; Vegetation
of Local Signiicance─lichen barrens and aquatic
communities; and areas not mapped with respect
to vegetation─barren lood plain areas and areas
disturbed by culture.
Small-Scale Maps Covering Alaska
More recent efforts have produced several smallscale vegetation maps of Alaska (scale 1:2.5–10M)
that depict subclasses of the boreal such as those of
Knapp (1965), Küchler (1970), Viereck & Little (1972),
Joint Federal-State Planning Commission for Alaska
(1973), Tuhkanen (1984), Brown et al. (1998), and
Rivas-Martinez et al. (1999). As an example of the
kind of types delimited, Rivas-Martinez et al. (1999)
indicated 11 subsector types within Alaska within two
provinces: the Yukonian-Alaskan Province, including
the Continental Alaskan Sector (Boreal Subcontinental
Alaskan Subsector; Boreal Eucontinental Alaskan
Subsector; Boreal Alaskan Subsector) and the Boreal
Oceanic Alaskan Province, including the Boreal
Oceanic Western Alaskan Sector (Kenai Peninsula
Subsector, Kodiak Island Subsector); the Boreal
Oceanic Eastern Alaskan Sector (Glacier Coast
Subsector, Haines-Juneau Subsector); the Aleutian
and Alaska Peninsula Sector (Aleutian Islands
Subsector, Alaska Peninsula Subsector); and the
Canadian Coastal Mountains Sector (North Canadian
Coastal Mountains Subsector, Queen Charlotte
and Prince of Wales Islands Subsector). Ecoregion
maps have been published by Gallant et al. (1995),
Commission for Environmental Cooperation (1997),
and Nowacki et al. (2001).
Large-Scale Maps of Small Areas
Large-scale studies are focused on rather small areas
distributed throughout the boreal. For the Aleutian
Islands, vegetation maps exist only for the Naval
Complex on Adak Island (Soil Conservation Service,
1990), Bogoslof Island, in part (Byrd et al. 1980), Buldir
Island (Byrd, 1984), Amchitka Island (Amundsen,
1977), Atka Island, in part (Friedman, 1984), Kodiak
Island, in part (Tande & Boggs, 1995; Tande, 2003,
Tande & Michaelson, 2004), Choweit Island, Semidi
Islands (Hatch, 1978, unpub. rpt.), Elmendorf Air
Force Base (Tande, 1984), Hazen Bay, Yukon Delta
National Wildlife Refuge, in part (Tande & Jennings,
1987), Semidi Islands (Hatch, 1978, unpub. rpt.), and
Simeonof Island (Talbot et al., 1984, unpub. rpt.). For
south-central Alaska, Klein (1999) used conventional
aerial photographic interpretation to map Anchorage
parks and greenbelts, and similarly for central Alaska,
Mount Prindle (Juday, 1988). By example, the map
units depicted by Juday (1988) include birch (dry)
upland (dwarf) shrub, ericad (dry) upland (dwarf)
shrub, foliose lichen, rock and felsic lichen, snowbed
herb-graminoid meadow, tall shrub, upland tussock
meadow, white spruce, and lood plain willow tall
shrub.
Computer Classiied Landsat and Related Maps
In response to factors such as increasing resource
development, planning mandates, and wildlife-habitat
relationships, federal and state agencies sought
eficient vegetation mapping methods to inventory
regions within the boreal. Beginning in the 1980s
vast boreal landscapes were mapped using satellite
images at intermediate scales, 1:250,000, and maps
covering the greatest portions of boreal Alaska are
at this scale (Markon, 1995). It should be noted that
vegetation plot data are available for many of these
75
areas and could be used as ground reference data.
Two major approaches were used with Landsat data:
visual-interpretation and computer classiication.
Visually-interpreted Landsat maps and conventional
aerial photograph interpretation were prepared for
several national parks and wildlife refuges: Lake Clark
National Park (Racine & Young, 1978; Tande, 1996),
Katmai western extension (Young & Racine, 1978);
Kenai Lowlands (Gracz et al., 2008), Fort Wainwright
(Jorgenson et al., 1999), and Kodiak National
Wildlife Refuge (Northern Technical Services 1984;
Tande, 2003; Tande & Michaelson, 2004). Computer
classiication of digital data included several portions
of western Alaska–Alaska Peninsula and Bristol Bay
area (Wibbenmeyer et al., 1982) and Izembek Lagoon
(Ward & Stehn, 1989). An ecological subsection map
was produced for Aniakchak National Monument and
Preserve (Tande & Michaelson, 2001).
Most of these intermediate-scale maps, and many of
the large-scale maps, are based on physiognomicecological classiications. Map units relect the
structure of the vegetation and are sometimes
supplemented with ecological information. The most
frequently used descriptor is moisture with terms such
as wet, moist, and dry. Other map unit characteristics
include terms such as riparian, dune, and marsh.
Dominant species are occasionally included, but their
use is often inconsistent, even within a map legend.
Computer classiication using digital Landsat data
included a number of National Wildlife Refuges
such as Kenai (Talbot et al., 1985), Innoko (Talbot &
Markon, 1986, Talbot & Markon, 1988), Kanuti (Talbot
et al., 1986, unpub. rpt.), Kodiak (Fleming & Spencer,
2005; U.S. Fish & Wildlife Service, 2006), Nowitna
(Talbot & Markon, 1986), Tetlin (Talbot et al., 1984,
unpub. rpt.), and Yukon Delta (in part) (Talbot et al.,
1986, unpub. rpt.). A number of vegetation land cover
maps were produced by the U.S. Geological Survey
for large portions of Alaska: Nowitna National Wildlife
Refuge (Markon, 1988a), Upper Kuskokwim resource
management area (Markon, 1988b), Selawik National
Wildlife Refuge (Markon 1988c), Innoko National
Wildlife Refuge (Markon, 1987), Meade River
(U.S. Geological Survey, 1988c), Tetlin NWR (U.S.
Geological Survey, 1987), Valdez (U.S. Geological
Survey, 1987), Fairbanks (U.S. Geological Survey,
1988b), Dillingham (U.S. Geological Survey, 1988a),
and Copper River-Wrangell Mountains (University
of Alaska & U.S. Forest Service, 1977). Land cover
maps are also available for southeast Alaska–Glacier
Bay National Park and Preserve (Boggs et al., 2008);
Interior Alaska–Denali National Park and Preserve
(Dean & Heebner, 1982; Boggs et al., 2001), YukonCharley Rivers National Preserve (Boggs & Sturdy,
2005), Steese White Mountains (Markon, 1993,
unpub. rpt.); and for Southcentral Alaska─Kenai
Fiords National Park (Boggs et al., 2008), Kenai
Lowlands (Gracz et al., 2008), and Katmai National
Park and Preserve (Boggs et al. 2003). As an example,
Boggs et al. (2003) distinguished 25 land cover types:
dense-open spruce forest, open-woodland spruce
forest, open-woodland stunted spruce, broadleaf
forest, mixed spruce-broadleaf forest, alder shrub,
willow shrub, closed low birch shrub, low shrub
birch-ericaceous-willow, low shrub-sedge, peatland,
herbaceous/shrub, dwarf shrub, dwarf shrub-rock,
dry-mesic herbaceous, wet herbaceous, aquatic
herbaceous, sparse vegetation, bare ground, snow/
ice, shadow indeterminate, silty water, clear water,
burn, and cloud.
Ecological land surveys (ELS) view landscapes not
merely as aggregations of separate biological and
earth resources, but as ecological systems with
functionally related parts that can provide a consistent
conceptual framework for ecological applications.
Generally, these surveys map at three spatial scales:
ecotypes (1:50,000 scale) are delineated areas with
homogenous topography, terrain, soil, surface-form,
hydrology, and vegetation; ecosections (1:100,000
scale) are homogeneous with respect to geomorphic
features and water regime and, thus, have recurring
patterns of soils and vegetation; and ecodistricts
(1:500,000) are broader areas with similar geology,
geomorphology, and physiography. These surveys
were prepared for Pogo Mining area (Burgess et
al., 2000, unpub. rpt.), Fort Greely (Jorgenson et
al., 2001), Fort Wainwright (Jorgenson et al., 1999),
Poker-Caribou Creek Watershed (Jorgenson et
al., 1984, Jorgenson et al., 1986, unpub. rpt.), Fort
Richardson (Jorgenson et al., 2003), and Wrangell-St.
Elias National Park and Preserve (Jorgenson et al.,
2008). By example, the land cover map of Jorgenson
et al. (2008) was developed through classiication
of 11 Landsat scenes. Their rule-based modelling of
ecotypes integrated physiography, soil, and vegetation
76
composition into 69 classes, 47 of which were boreal
ecotypes. For forest ecotypes they distinguished six
types: boreal subalpine poplar forest, boreal subalpine
spruce woodland, boreal upland aspen forest, boreal
upland birch forest, boreal upland spruce-birch forest,
and boreal upland white spruce forest.
A soil survey of Denali National Park area, including
landtype association maps and ecological sites, was
produced by Clark & Duffy (2006) and is available
online (http://soildatamart.nrcs.usda.gov/Manuscripts/
AK651/0/DenaliPark.pdf). A number of soil surveys
are available online (http://soils.usda.gov/survey/
online_surveys/alaska/) and contain information on
plant communities for the following areas: Anchorage
(2001), Copper River (1999), Delta River (2005), Fort
Greely and Donnelly Training (2005), Fort Wainwright
(2006), Gerstle River (2001), Goldstream-Nenana
(1977), Greater Fairbanks (2004), Greater Nenana
(2007), Gulcana River (1999), Haines (1998),
Kantishna (2001), Kobuk Preserve (1995), Lower
Kenai Peninsula (2000), Matanuska-Susitna (1998),
Northeastern Kodiak Island (1960), Northstar (2000),
Salcha-Big Delta (1973), Stewart River (2005),
Totchaket (1980), Upper Tanana (1999), Western
Interior Rivers (2008), Western Kenai Peninsula
(2005), and Yetna (1998).
A number of maps were published by the U.S.
Department of the Interior, Bureau of Land
Management (BLM) as Technical Reports in
collaboration with Ducks Unlimited and various
government agencies, for example, BLM, U.S. Fish
and Wildlife Service, U.S. Air Force, and National
Park Service, and are available online (www.blm.gov/
ak/st/en/info/gen_pubs/tr.html). Soil and Vegetation
Survey of the Gulkana River Area, Alaska (Clark
& Kautz, 1999, Tech. Rpt. 20); Galena Military
Operations Area/Nowitna National Wildlife Refuge
Earth Cover Classiication (2002, Tech. Rpt. 23);
Haines Earth Cover Classiication (2002, Tech. Rpt.
26); Kanuti National Wildlife Refuge/Ray Mountains/
Hogatza River Earth Cover Classiication (2002, Tech.
Rpt. 28); Vegetation Survey of Campbell Tract Facility,
Anchorage, Alaska (Guyer, 2000, Tech. Rpt. 35);
Steese─White Mountains Earth Cover Classiication
(2002, Tech. Rpt. 42); Stony River Military Operations
Area Earth Cover Classiication (2002, Tech. Rpt.
43); Susitna Military Operations Area Earth Cover
Classiication (2002, Tech. Rpt. 44); Tiekel Watershed
Earth Cover Classiication (2002, Tech. Rpt. 46);
Innoko Earth Cover Classiication (2002,Tech. Rpt.
47); Yukon-Charley/Black River/Fortymile Earth
Cover Classiication (2002, Tech. Rpt. 48); and Soil
and Vegetation Survey of the Delta River (Clark, 2005,
Tech. Rpt. 55). There are a number of Earth Cover
Fig. 1. Alaska Earth Cover Initiative.
77
Classiication reports that are works in progress and
are pending: Bering Glacier, Copper River Delta,
Goodnews Bay, Gulkana River Watershed, Illiamna,
Kenai Peninsula, Koyukuk/Melozitna, Kvichak, Minto
Flats, and Naknek, and Northern and Southern Yukon.
The location of these projects is indicated in Fig. 1.
The technical reports published by the BLM (listed
above) vary greatly in their detail and quality. For
example, the Steese─White Mountains Earth Cover
Classiication (2002, Tech. Rpt. 42) is a physiognomic
classiication that uses a classiication scheme
developed at a BLM Earth Cover Workshop; it
includes the names of some tree species; this is
signiicant because it adds a diversity component
expanding the ecological value of the map: 1.0 Forest
(1.1 Closed Needleleaf, 1.2 Open Needleleaf, 1.21
Open Needleaf Lichen, 1.3 Woodland Needleleaf,
1.31 Woodland Needleaf Lichen, 1.4 Closed
Deciduous, 1.41 Closed Birch, 1.42 Closed Aspen,
1.43 Closed Cottonwood/Balsam Poplar, 1.44 Closed
Mixed Deciduous, 1.5 Open Deciduous, 1.51 Open
Birch, 1.52 Open Aspen, 1.53 Open Cottonwood/
Balsam Poplar, 1.54 Open Mixed Deciduous, 1.6
Closed Mixed Needleleaf/Deciduous, 1.7 Open Mixed
Needleleaf/Deciduous); 2.0 Shrub (2.1 Tall Shrub, 2.2
Low Shrub, 2.21 Willow/Alder Low Shrub, 2.22 Other
Low Shrub/Tussock Tundra, 2.23 Other Low Shrub/
Lichen, 2.24 Other Low Shrub, 2.3 Dwarf Shrub,
2.31 Dwarf Shrub/Lichen, 2.32 Other Dwarf Shrub);
3.0 Herbaceous (3.1 Bryoid, 3.11 Lichen, 3.12 Moss,
3.2 Wet Herbaceous, 3.21 Wet Graminoid, 3.22
Wet Forb, 3.3 Mesic/Dry Herbaceous, 3.31 Tussock
Tundra, 3.311 Tussock Tundra/Lichen, 3.312 Tussock
Tundra, 3.32 Mesic/Dry Sedge Meadow, 3.33 Mesic/
Dry Grass Meadow, 3.34 Mesic/Dry Graminoid; 4.0
Aquatic Vegetation (4.1 Aquatic Bed, 4.2 Emergent
Vegetation); 5.0 Water (5.1 Snow, 5.2 Ice, 5.3 Clear
Water, 5.4 Turbid Water); 6.0 Barren (6.1 Sparsely
Vegetated, 6.2 Rock/Gravel, 6.3 Mud/Silt/Sand); 7.0
Urban; 8.0 Agriculture; 9.0 Cloud/Shadow (9.1 Cloud,
9.2 Shadow); 10.0 Other (burn). While the Vegetation
Survey of Campbell Tract Facility, Anchorage, Alaska
(Guyer, 2000, Tech. Rpt. 35) mapped cover types
including balsam poplar closed forest, balsam poplar/
willow scrub, black spruce/greenleaf alder forest,
disturbed site, low shrub birch mixed scrub, paper
birch closed forest, paper birch open forest, paper
birch-white spruce open forest, paper birch woodland,
spruce/moss forest, sweetgale/bluejoint bog, white
spruce/paper birch forest, white spruce woodland,
and developed.
A geospatial dataset of forest biomass across
the United States is available on the U.S.
Department of Agriculture, Forest Service (USFS)
Geodata Clearinghouse (http://svinetfc4.fs.fed.us/
rastergateway/biomass/) (Blackard et al., 2008,
Ruefenacht et al., 2008). The predictor data were a
geospatial dataset with a spatial resolution of 250
m. Among the predictor layers used were digital
elevation models (DEM) and DEM derivatives;
Moderate Resolution Imaging Spectroradiometer
(MODIS) multi-date composites, vegetation indices,
and vegetation continuous ields; class summaries
from the 1992 National Land Cover Dataset (NLCD);
various ecologic zones; and summarized PRISM
climate data. Six forest type groups were mapped
for Alaska: (1) aspen/birch; (2) elm/ash/cottonwood;
(3) ir-spruce-mountain hemlock; (4) hemlock/Sitka
spruce; (5) lodgepole pine; and (6) spruce/ir.
The National Land Cover Database (NLCD) 2001
is a Landsat-derived, 30 m spatial resolution land
cover map that describes land cover of Alaska (http://
alaska.usgs.gov/science/geography/nlcd.html) and a
national page (http://www.mrlc.gov/). Eighteen land
cover types are depicted: open water; perennial ice/
snow; developed, low intensity; developed, medium
intensity; developed, high intensity; barren land;
deciduous forest; evergreen forest; mixed forest;
dwarf scrub; shrub/scrub; grassland/herbaceous;
sedge/herbaceous; moss; pasture/hay; cultivated
crops; woody wetlands; and emergent herbaceous
wetlands.
LANDFIRE is a ive-year, multipartner project
producing consistent and comprehensive maps
and data describing vegetation, wildland fuel, and
ire regimes across the United States (http://www.
landire.gov/). Their legend is based on ecological
systems; these are deined as a group of plant
communities that tend to co-occur within landscapes
with similar ecological processes, substrates, and/
or environmental gradients. Alaska LANDFIRE is a
work in progress with a projected completion date of
September 2010. NLCD and LANDFIRE both provide
a consistent legend and scale for the entire state.
78
National Wetlands Inventory Maps
Detailed National Wetlands Inventory maps are
published for south-central, east-central and southeast Alaska, and Kodiak Island (http://alaska.fws.
gov/fisheries/nwi/pdf/NWI_AK_Status_1-08.pdf);
Cowardin et al. (1979). Systems form the highest level
of the classiication hierarchy, and ive classiications
are deined: marine, estuarine, riverine, lacustrine, and
palustrine. Marine and estuarine systems each have
two subsystems, subtidal and intertidal; the riverine
system has four subsystems, tidal, lower perennial,
upper perennial, and intermittent; the lacustrine
has two subsystems, littoral and limnetic; and the
palustrine has no subsystems. Within the subsystems,
classes are based on substrate material and looding
regime, or life form. The same classes may appear
under one or more of the systems or subsystems. Six
classes are based on substrate and looding regime:
rock bottom with a substrate of bedrock, boulders,
or stones; unconsolidated bottom with a substrate
of cobbles, gravel, sand, mud, or organic material;
rocky shore with the same substrates as rock bottom;
unconsolidated shore with the same substrates as
unconsolidated bottom; streambed with any of the
substrates; and reef with a substrate composed of
the living and dead remains of invertebrates (corals,
mollusks, or worms). The bottom classes are looded
all or most of the time and the shore classes are
exposed most of the time. The class streambed is
restricted to channels of intermittent streams and tidal
channels that are dewatered at low tide. The life form
of the dominant vegetation deines the ive classes
based on structure: aquatic bed, dominated by plants
that grow principally on or below the surface of the
water; moss-lichen wetland, dominated by mosses or
lichens; emergent wetland, dominated by emergent
herbaceous angiosperms; scrub-shrub wetland,
dominated by shrubs or small trees; and forested
wetland, dominated by large trees. The dominance
type, named for the dominant plant or animal forms,
is the lowest level of the classiication hierarchy. Only
examples are provided for this level; dominance
types must be developed by individual users of the
classiication. Modifying terms applied to the classes or
subclasses are essential for use of the system. In tidal
areas, the type and duration of looding are described
by four water regime modiiers: subtidal, irregularly
exposed, regularly looded, and irregularly looded. In
nontidal areas, eight regimes are used: permanently
looded, intermittently exposed, semipermanently
looded, seasonally looded, saturated, temporarily
looded, intermittently looded, and artiicially looded.
A hierarchical system of water chemistry modiiers
is used to describe the salinity of the water. Fresh
waters are further divided on the basis of pH. Use of a
hierarchical system of soil modiiers taken directly from
U.S. soil taxonomy is also required. Special modiiers
are used where appropriate: excavated, impounded,
diked, partly drained, farmed, and artiicial.
Other Maps and Works of Interest
Soils maps could serve as a valuable asset in
mapping the vegetation, as each mapped soil unit
includes information on its associated vegetation.
For example, an exploratory soil survey of Alaska is
available at a scale of 1:1M and includes information
on the vegetation (Rieger et al., 1979), and more
detailed soils surveys are available for certain areas.
Natural Resources Conservation Service (NRCS)
is processing an update of the statewide soil map,
referred to as the Alaska STATSGO. Interim products
including a map at 1:250,000 scale and tabular
attribute dataset are planned for December 2009. The
map stratiies the state by physiographic area based
on the Natural Resources Conservation Service Major
Land Resource Areas (MLRA). Map units group soils
based on similar landform, lithology, and life zone.
The Alaska Vegetation Classiication of Viereck et al.
(1992) summarizes vegetation types in a hierarchical
arrangement and provides citations to published
works. These referenced papers provide a useful
supplement for speciic areas in interpreting vegetation
from conventional aerial photography and Landsat
images. A new literature review of plant community
studies in Alaska would be useful to update the work
of Viereck et al (1992); an update was initiated by
G. F. Tande and is iled at Alaska Natural Heritage
Program, Anchorage.
Toward a Small-Scale Vegetation Map of Boreal
Alaska
At present, there is no true vegetation map for the
Alaska boreal such as those produced in one of
the major vegetation mapping schools of Germany,
France, Japan, or Russia. Small-scale maps that
cover all of Alaska, such as the province and subsector
79
maps of Rivas-Martinez et al. (1999) and the climaticphytogeographical system of Tuhkanen (1984) help
provide insight into mapping upper-level map units. In
addition, there are several manually interpreted and
useful maps of the entire state, such as the vegetation
map of Spetzman (1963). However, Walker (1999b)
stated that the Spetzman (1963) map is based on
information assembled before the vegetation was
as well known as it is presently. Walker (1999b) also
noted that the map portrays very broad vegetation
units that may be dificult to resolve with modern
vegetation maps based on satellite imagery.
Although they vary widely in their detail and quality,
there is a wealth of information in the Alaska maps.
This information in combination with soil maps such
as Rieger et al. (1979) and The Alaska Vegetation
Classiication of Viereck et al. (1992) could be
beneicial. The integrated mapping approach of Walker
(1999a & 1999b) that proved successful in mapping
the circumpolar Arctic vegetation could provide a
useful approach to mapping the circumboreal region.
It may be possible to use intermediate-scale maps of
large areas and large-scale maps of small areas as
guides to interpret AVHRR digital data for production
of a true circumboreal vegetation map. On the positive
side, a number of computer-generated vegetation
maps are now available and cover a major portion
of the Alaska boreal and account for the majority of
boreal vegetation diversity along both an east-west
longitudinal gradient and a north-south latitudinal
gradient. However, there are areas within the boreal
such as the Aleutian Islands for which little mapping
has been accomplished.
Acknowledgments
I gratefully thank the following, listed alphabetically,
for discussions and making map sources available:
Keith Boggs (Alaska Natural Heritage Program),
Mark Clark (Natural Resources Conservation Service
[NRCS]), Dan Fehringer (Ducks Unlimited), Amy E.
Miller & Beth Koltun (National Park Service [NPS]),
Torre Jorgenson (ABR, Inc.), Carl Markon (U.S.
Geological Survey [USGS]), Gerry Tande (U.S. Fish
and Wildlife Service [USFWS]), and Ken Winterberger
(U.S. Forest Service [USFS]).
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84
A Geobotanical Impression of South Greenland with Some Remarks on
its “Boreal Zone”
Fred J. A. Daniёls
Institute of Plant Ecology, Münster, Germany
Abstract
This paper addresses the non-arctic area of Greenland,
which is conined to the inland area of southernmost
Greenland. Some key literature pertaining to this
area is presented, and some general information of
relevance for vegetation and landscape is given. The
vegetation of Narsarsuaq, a core boreal area of South
Greenland, is also briely described. Characterization
and extension of a boreal zone in Greenland should
be primarily based on the actual and potential natural
zonal woodland vegetation as an expression of the
climatological conditions. In Greenland where native
coniferous trees are completely lacking, Betula
pubescens and Sorbus groenlandica, both occurring
on mesic, zonal sites, are considered key indicators
of a boreal zone in southernmost Greenland. Betula
pubescens woodland is considered the zonal
vegetation in the lowlands; however, intensive land
use, especially farming in the past and present,
strongly reduced woodland extension. Alnus crispa
occurs up to 67ºN in Southwest Greenland and is
mainly conined to azonal, moist habitats. Vegetation
studies according to the Braun-Blanquet approach are
recommended for characterization and delimitation of
vegetation and vegetation zones.
Only the southernmost inland part of Greenland is
considered non-arctic (Daniёls et al., 2000) and does
not match the features of the arctic subzone E in the
CAVM (Walker et al., 2005). This non-arctic “boreal,”
“subarctic,” or “hemi-arctic” area is named here
“boreal.” Its extension is dificult to assess mainly
because of insuficient meteorological data from
the inland, variation in local climate due to diversity
of topography, inadequate vegetation studies, and
scantiness of actual natural tree growth (cf. also
Tuhkanen, 1984).
Just as in Iceland, nearly all of the natural forest
disappeared in South Greenland due to intensive
land use. Deforestation began with the introduction
of agriculture and tree cutting by the Norse settlers
living in the area from ca. 924–1500 A.D. and resulted
in devastation of the landscape. Further degradation
of the landscape occurred with the introduction
of modern sheep farming in the beginning of the
20th century with tree cutting, overgrazing, and soil
erosion counteracting forest regeneration (Fig. 1), (cf.
Fredskild & Ødum, 1991; Hansen, 1991; Hester et al.,
2005).
Keywords: Betula pubescens forest, climate, farming,
Narsarsuaq, syntaxa, tree-growth, tundra, zonal
vegetation.
Introduction
According to the Circumpolar Arctic Vegetation
Map (CAVM) (Walker et al., 2005) almost the entire
ice-free territory of Greenland is considered arctic
and subdivided into ive bioclimatic subzones. The
characterization and delimitation of these subzones
are based both on loristical-vegetational and
meteorological-climatological features pertaining to
the zonal vegetation of the lowlands (<300 m).
Fig. 1. Degradated, treeless landscape by overgrazing.
Qassiarsuk, July, 2008; photo, Fred Daniёls.
The present contribution aims to present some
general information on South Greenland and to
give a short loristical and vegetational impression
of the non-arctic area around Narsarsuaq (Fig. 2).
85
Moreover, its nomenclature, characterization, and
extension in Greenland in the scope of the proposed
Circumboreal Vegetation Map (CBVM) are addressed.
Nomenclature of taxa mainly follows Böcher et al.
(1978), and syntaxa follows Daniёls (1994, 2009) and
Bültmann (2005).
Previous Studies
In terms of phytogeography and vegetational history
South Greenland (59º46’ N–62º20’ N) is rather well
known. The publications by Warming (1888) and
Rosenvinge (1896) are among the irst on the plant
cover of South Greenland. The Icelandic colonization
of this climatologically rather mild area was described
by Bruun (1918) in the beginning of the last century.
Agriculture in South Greenland from the Norse
Landnam to Present (984–1985) and its impact on
vegetation change has been treated by Fredskild
(1988, 1992). Earlier, Fredskild (1973, 1978)
summarized the vegetational history of Greenland.
Böcher (1979) discussed birch woodlands and tree
growth in southern Greenland, whereas Fredskild
(1991) summarized the Holocene history, present
distribution, and synecology of the genus Betula
in Greenland. Fredskild & Ødum (1991) compiled
a couple of scientiic contributions addressing the
“Greenland Mountain birch zone.” In this work
Feilberg & Folving (1991) described and mapped
the still rather well-developed woodland and scrub
vegetation in Qinguadalen, whereas Ødum (1991)
reported on forestation attempts. The fundamental
publication by Böcher (1954) deals with relationships
between climate, plant, and vegetation distribution in
South-Southwest Greenland.
Knapp (1964) deals with the main plant communities
(as Braun-Blanquet associations in synoptic tables) as
a basis for the distinction of physiognomic vegetation
zones according to continentality and elevation.
Unpublished results of the many Greenland Botanical
Survey (GBS) expeditions conducted since 1962
have been partly used in later publications by Feilberg
(1984) and Fredskild (1996).
Feilberg (1984) presents a thorough phytogeographical
study of the vascular plant species of South Greenland,
including 345 species and their distribution on small
dot maps. Moreover, species are grouped into
several distribution types. Another map depicts six
“vegetational” zones of South Greenland. This map,
provided with a short characterization of the zones
(Fig. 3), was mainly used for delimitation of the nonarctic zone on the CAVM (Fig. 3). Stumböck (1993)
also described plant communities and habitat in the
low- and uplands near Narsarsuaq and mapped the
vegetation (Fig. 4). A fundamental phytogeographical
study of the vascular plants of West Greenland
(62º20’–74º00’ N) by Fredskild (1996) actualizes and
generalizes the present knowledge of vascular plant
distribution in Greenland.
Fig. 2. Salix glauca–Betula glandulosa shrubland with small stands of Betula pubescens woodland. Narsarsuaq,
July, 2007; photo, Marinus Werger.
86
Fig. 3. Vegetation zones of South Greenland (from Feilberg 1984, reprinted with permission, Meddelelser
om Grønland, Bioscience). For explanation see text.
Fig. 4. Map of South Greenland (from Stumböck, 1993, reprinted with permission, http://www.schweizerbart.de).
87
South Greenland
Most of South Greenland (59º46’ N–62º20’ N)
belongs to the municipality of Kujalleq with nearly
8,000 inhabitants spread over many small towns
and settlements. Human impact on the landscape in
the past and in the present is considerable. Sheep
farming is rather popular, and the region around
Tasiusaq, northeast of Nanortalik, counts nearly 80
sheep farms today (a.o. Feilberg & Høegh, 2008).
As a result, the percentage of non-native vascular
plant species in the lora is fairly high (cf. Böcher et
al., 1959; Pedersen, 1972). More pristine areas are
completely restricted to inaccessible places.
The bedrock in Southern Greenland is very old
and mainly of Archean origin and preponderantly
acidic with granites and gneisses. Soil types range
from syrosem, rankers, brown soils, and podsols
(Stumböck, 1993). Climate information is available
south of 62ºN from 15 weather stations, with most
of the stations situated along the coast. The inland
is distinctly warmer than the coast, particularly in
summer; however, the amount of annual precipitation
is rather similar. There is a distinct climatic gradient
from the coast to the inland resulting in the distinction
of different vegetation bioclimatic zones and subzones
with different lora and vegetation features (Knapp,
1964; Feilberg, 1984).
The inland weather station at Narsarsuaq shows
the highest Conrad-continentality index, 16, and the
lowest de Martonne-humidity index, 59 (Table 1 in
Feilberg, 1984). Mean annual T of Narsarsuaq (1961–
1967) is 1. 9ºC, and mean annual precipitation is 590
mm and in summer, 168 mm. In summer the sum of
mean monthly T is 28.9ºC and mean de Martonnehumidity index 5.8 (Feilberg, 1984). The mean annual
warmth sum is 42.3, and mean T of the warmest
month, July, is 10.5ºC (Daniëls et al., 2000).
Feilberg (1984) considers the stations Narsarsuaq
and Igaliko (and Ivittuut, in the southernmost part
of Southwest Greenland) situated in the subarctic
zone, since all have a mean July T above 10ºC.
All other stations are considered in the arctic zone.
The presence of birch copses and open woodlands
support this concept.
Knapp (1964) distinguishes two plant growth-zones in
southern Greenland, based on climate and vegetation
physiognomy: a coastal oceanic zone, with two
altitudinal zones, and an inland subcontinental zone
with three zones. The irst zone is mainly dominated
and characterized by Empetrum hermaphroditum
dwarf shrub heath (Phyllodoco–Vaccinion), especially
in the lower zone. In the upper zone this dwarf shrub
heath type is still present, but snowbed vegetation
(Salicetea herbaceae) is rather common, whereas
Salix glauca low shrub vegetation is lacking.
The Surroundings of Narsarsuaq
Certainly, boreal core areas are the Narsarsuaq area
in the inland of Southwest Greenland (ca. 61º11’
N, 45º25’ W) and inland areas behind Nanortalik
(Qinguadalen, Tasermiut iord, ca. 60º18’ N, 44º30’
W), where stands of low forest of Betula pubescens
(approx. 3–4 m high, sometimes with trees up to
5–6 m high) occur on mesic sites in valley bottoms
and foot slopes (Fig. 2). The well-developed forest
stands in Qinguadalen are now protected (Feilberg &
Folving, 1991).
Narsarsuaq (ca. 61º09’ N, 45º26’ W) is a small airport
settlement (ca. 150 inhabitants) at the east side of
the Tunulliarik Fiord. The area has a long history of
grazing, with the Norsemen using the area for grazing
sheep and cattle, and later, Inuit reindeer hunters. The
Narsarsuaq airport was built in 1941 by the Americans
during World War II. After the war the military base was
mainly used as a big hospital and recovery center for
soldiers. It was closed at the end of the Korean War.
Now, only ruins are left from this period.
The settlement itself is situated at the southernmost
part of a luvio-glacial outwash plain of the Kuussuup
Sermia glacier, which divides the mountainous Johan
Dahl Land in the North (up to 1400 m high) from the
somewhat lower (max. 970 m high) Mellem Landet
in the South. In 2008 the author explored the area
northeast of the settlement along Blomsterdalen and
Qassiarsuk (Brattahlid) (ca. 61º09’ N, 45º31’ W) in the
northern part of the Narsaq peninsula. The latter is a
sheep farming village just opposite of Narsarsuaq at
the west side of the Tunulliarik Fiord where Eric the
Red settled in 986. Presently, 14 sheep farms exist on
the Narsaq peninsula.
88
The Narsarsuaq area is situated in the subcontinental
zone, and three altitudinal zones are distinguished
here (Knapp, 1964, see also Stumböck, 1993). The
lower Betula–Elyna-Zone is characterized by many
plant community types, including a.o. mire vegetation,
low Betula pubescens–Deschampsia lexuosa forest
(Fig. 2), Salix glauca shrub vegetation types (Salix
glauca–Deschampsia lexuosa type, Salix glauca
type), dwarf shrub vegetation (Rhododendro–
Vaccinietum), herb vegetation (Alchemilla alpina–
Carex scirpoidea-Ass.), and lichen-rich dry grassland
vegetation (Agrostis borealis─Carex scirpoidea-Ass.)
(Agrostio─Rumicion acetosellae).
The
middle
Vaccinium─Cetraria-Zone
is
characterized by some low shrub vegetation (Salix
glauca─Coptis groenlandica), lichen-rich dwarf
shrub communities (Sphaerophoro-Vaccinietum,
other
Loiseleurio-Diapension
vegetation
and
Dryadion integrifoliae communities), herb vegetation
(Alchemilla glomerulans–Oxyria digyna-Ass.), snow
patch vegetation (Anthoxanthum alpina vegetation),
snowbed
vegetation
(Salicetea
herbaceae),
and lichen vegetation (Cladonion arbusculae).
The upper zone Dryas─Rhacomitrium-Zone) is
mainly characterized by very open vegetation,
the prominence of Rhacomitrium lanuginosumdominance, wind-exposed dwarf shrub heaths
(Loiseleurio-Diapension), Juncus triidus─Carex
scirpoidea-Ass. (Juncion triidi), Carici-Dryadetum,
chionophobous lichen vegetation (Cetrarion nivalis),
and snowbed vegetation (Salicetea herbaceae).
Vegetation types observed in and near the
Narsarsuaq settlement in the lowland include a.o.
Honkenyo–Elymetum
mollis,
Chamaenerietum
latifolii,
Eriophoretum
scheuchzeri,
Juncetum
arctici, Caricetum microglochinis, Rhododendro–
Vaccinietum, and herb-rich Salix glauca─Betula
glandulosa scrub (Fig. 2) with Ranunculus acris
and Angelica archangelica. Hippuridetum vulgaris
and Sparganium hyperboreum occur in ponds. Mire
vegetation (with several Scheuchzerio–Caricetea
associations) is well developed in the valley bottom
of Blomsterdalen.
In the farming areas of Qassiarsuk, pastures with
a.o. Festuca rubra, Plantago maritima, Leontodon
autumnalis, Rhinanthus cf. borealis, Poa pratensis,
and Achillea millefolium, and hay meadows with
Alopecurus and Poa are common (“Molinio–
Arrhenateretea”). Rey ields contain the European
weeds Capsella bursa pastoris, Polygonum aviculare,
Stellaria media, Matricaria discoides, and Cardamine
pratense (“Stellarietea mediae”). Along rivulets Montia
rivularis and Agrostis stolonifera are common. As in
South Greenlandic settlements, several crops (e.g.,
potatoes, carrots) are raised in small ields mainly for
private consumption.
In Narsarsuaq boreal species such as Lathyrus
maritimus, Chamaenerion angustifolium, Elymus
arenarius, Thymus drucei, Anthoxanthum odoratum
ssp. alpinum, and Juniperus nana and the low
arctic Betula glandulosa and Angelica archangelia
are extremely common and very conspicuous. The
Arboretum Groenlandicae (inoficially established in
1976, oficially opened 2004) contains many NorthAmerican non-native coniferous species. Their height
is still less than 10 m. Older plantations of coniferous
trees are still in existence in South and Southwest
Greenland, and nowadays several forestry projects
on tree raising and cultivation are carried out.
Total number of vascular plant species in the
Narsarsuaq area is estimated as 309 (Feilberg,
1984).
Vegetation types of the following classes could
be identiied for the Narsarsuaq and Qassiarsuk
surroundings: Asplenietea trichomanis (fern and herb
vegetation of rock issures and ledges), Thlaspietea
rotundifolii (herb vegetation of screes and alluvial
plains), Caricetea curvulae (dry herb- and grass
heath on acidic, mineral soil), Loiseleurio–Vaccinietea
(acidic dwarf shrub heath), Carici–Kobresietea
(grass- and dwarf shrub heath on weakly acidic-basic
soil), Salicetea herbaceae (snowbed vegetation) (incl.
Ranunculo–Anthoxanthion alpinae herb and grass
vegetation, Knapp, 1964), Mulgedio─Aconitetea
(hygrophytic tall herb, low shrub and low Betula
pubescens forest vegetation), Montio–Cardaminetea
(spring vegetation), Scheuchzerio–Caricetea (mire
and rich fen vegetation), Honckenyo–Elymetea
(vegetation of sandy-stony beaches), Juncetea
maritimi (coastal salt-marsh vegetation), Littorelletea
(amphiphytic vegetation on poor substrate),
Phragmito–Magno–Caricetea (tall sedge-, grass-, and
89
herb vegetation swamp vegetation), Utricularietea
(oligo-dystrophic aquatic vegetation), and Potametea
(other aquatic vegetation) (cf. Daniёls, 1994, 2009;
Daniёls et al., 2000). Well-developed vegetation of the
classes Saxifrago tricuspidatae–Calamagrostietea
purpurascentis (boreal and low arctic North-American
steppe and associated vegetation (cf. Drees &
Daniёls, 2009) and Oxycocco–Sphagnetea (bog
vegetation) have not been observed yet.
Vegetation of hay meadows and pastures most
likely belongs to the class Molinio–Arrhenatheretea
and the weed vegetation of arable ields to the class
Stellarietea mediae, both typical for the European
culture landscapes.
Discussion
The characterization and delimitation of the non-arctic
inland mountain birch area in Greenland was already
discussed by Tukhanen (1984). On Tukhanen’s
global map of circumboreal climatic-geographical
regions (1984, App. 1, ig. 18) a small part of the
southwestern coastal fringe area of Greenland is
considered Northern Boreal, enclosed by a narrow
Hemi-Arctic belt up to ca. 63ºN. The non-arctic
inland area appears to be hemi-arctic in his scheme.
However, the small-scale map does not allow a
precise assessment of the extension of the regions.
Böcher (1979) rightly argues that south of 67ºN in
Southwest Greenland thermic conditions occur in the
inland of the iords, which principally allow tree growth;
however, Betula pubescens and Sorbus groenlandica
are only found to occur in the southernmost inland
part of this area. Human activities certainly prevented
and still prevent hardwood tree growth as can be
concluded from pollen diagrams (e.g., Fredskild,
1991), however the same is true for climate features,
such as low summer temperatures at the outer coast
and low precipitation (thus dryness) in very continental
areas like in the Søndre Strømfjord area (ca. 67º00’ N,
50º40’ W), which prevent tree growth as well. Böcher
considers these continental inland areas without
native trees climatologically subarctic, whereas the
CAVM vegetation map considers these areas as low
arctic (Subzone 5, Walker et al., 2005) with low shrub
vegetation; Calamagrostio lapponicae–Salicetum
glaucae as zonal vegetation [cf. Sieg et al., 2006]).
In Böcher (1954) the non-arctic core area in South
Greenland is situated in the region with his subarctic
oceanic-suboceanic climate regime, and the extension
of this climate regime largely coincides with the nonarctic area depicted on the CAVM map (Walker et al.,
2005).
Feilberg (1984) (Fig. 3) considers the coastal fringe
of the south and southwest coast belonging to his
hyper-oceanic, low arctic zone (1), characterized
by the total absence of copses. The adjacent
oceanic, low arctic zone (2) has willow copses and
the presence of Luzula parvilora. The adjacent
suboceanic, low or subarctic zone (3) has willow
and birch copses with Sorbus groenlandica. The
western inland part of South Greenland is considered
subcontinental, low arctic (4) with willow copses
and the coincident occurrence of Carex supina and
Kobresia myosuroides, whereas the innermost part
of South Greenland with the Narsarsuaq area is
considered subcontinental subarctic (5) with birch
and willow copses and the coincident occurrence
of the boreal species Eleocharis quinquelora and
Sagina nodosa.
The inland part of the Tugtutoq and Tasermiut districts
and the entire Narsarsuaq district in the inland of the
southwestern region of South Greenland (cf. Feilberg,
1984) can be considered boreal. This area coincides
with the eastern part of Feilberg’s suboceanic, low- or
subarctic zone (3) and the subcontinental, subarctic
zone (5). Thus, on the CAVM, Feilberg’s zone 5
and the eastern part of zone 3 are considered nonarctic, representing roughly the inland part of South
Greenland between approx. 44ºW and 47ºW.
In my opinion, the actual natural or potential natural
zonal woodland vegetation with trees and shrubs
(>2m) in the North should be a key criterion for
considering an area either arctic or boreal (syn.
subarctic, hemi-arctic) in a phytogeographicalbioclimatical context. In Greenland, where native
coniferous trees are completely lacking, Betula
pubescens and Sorbus groenlandica (Fig. 5) should
be considered key indicators; this is not true of the
native and azonal Alnus crispa, which forms thickets
on moist soil in middle iord or inland parts between
67ºN and 61ºN.
90
Fig. 5. Distribution map of Sorbus groenlandica in Greenland (from Feilberg, 1984, reprinted with permission,
Meddelelser om Grønland, Bioscience).
Thus, the non-arctic zone on the CAVM (Walker et al.,
2005) is considered boreal and is mainly based on
loristical-vegetational, climatological, and historical
arguments.
As was shown by Böcher (1979), the percentage of
boreal species in the distribution spectrum of local
loras does not seem an important feature in the
distinction between arctic and subarctic (boreal).
However, this percentage might be different when
only applied to the zonal vegetation with all its
species (vascular plants, bryophytes and lichens).
The zonal coastal Empetrum hermaphroditum–
Vaccinium microphyllum dwarf shrub heath (Knapp,
1964) from the coastal hyperoceanic low arctic fringe
(zone 1, Feilberg, 1984) with a.o. Carex bigelowii,
Salix herbacea, and Phyllodoce coerulea likely has
a stronger component of arctic species than the birch
wood association of the boreal inland.
Since human impacts on vegetation and land-use
history (agriculture, farming) in the climatologically
and geologically different boreal Northwest Atlantic
areas of South Greenland, Iceland, and the Far
Oer Islands were similar, a methodically uniform
assessment and evaluation of the present situation
of vegetation and landscape of these areas is very
interesting. The application of the Braun-Blanquet
approach is strongly recommended for this purpose.
References
Böcher, T. W. 1954. Oceanic and continental
vegetational complexes in Southwest Greenland.
Meddelelser om Grønland 148 (1): 1─336.
Böcher, T. W. 1979. Birch woodlands and tree
growth in southern Greenland. Holarctic Ecology 2:
218─221.
Böcher, T. W., Holmen, K., & Jakobsen, K. 1959.
A synoptical study of the Greenland Flora.
Meddelelser om Grønland 163 (1): 1─32.
Daniёls, F. J. A., Bültmann, H., Lünterbusch, C., &
Wilhelm, M. 2000. Vegetation zones and biodiversity
of the North American Arctic. Ber. Reinh. Tüxen
Ges. 12: 131─151.
Drees, B. & Daniёls, F. J. A. 2009. Mountain
vegetation of south-facing slopes in continental
West Greenland. Phytocoenologia 39 (1): 1─25.
Feilberg, J. 1984. A phytogeographical study of
South Greenland. Vascular Plants. Meddelelser om
Grønland, Bioscience 15: 1─72.
Feilberg, J. & Folving, S. 1991. Mapping and
monitoring of woodlands and scrub vegetation in
Qingua-dalen, South Greenland. Meddelelser om
Grønland, Bioscience 33: 9─29.
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Feilberg, J. & Høegh, K. 2008. Greenland. NTNU
Rapport Zoologisk Serie 2008─3: 44─53. Norwegian
University of Science and Technology (NTNU),
Science Museum, Section of Natural History, 7491,
Trondheim.
Fredskild, B. 1973. Studies in the vegetational history
of Greenland. Meddelelser om Grønland 198 (4):
1─245.
Fredskild, B. 1978. Paleobotanical investigations of
some peat deposits of Norge age at Qagssiarssuk,
South Greenland. Meddelelser om Grønland 204
(5): 1─41.
Fredskild, B. 1988. Agriculture in a Marginal AreaSouth Greenland from the Norse Landnam (985
A.D.) to the Present (1985 A.D.). Pages 381─393
in Birks, H.H., Birks, H.J.H., Kaland, P.E., & Moe,
D., eds. The Cultural Landscape-Past, Present and
Future. Cambridge University Press, Cambridge.
Fredskild, B. 1991. The genus Betula in Greenland—
Holocene history, present distribution and
synecology. Nord. J. Bot. 11: 393─412.
Fredskild, B. 1992. Erosion and vegetational changes
in South Greenland caused by agriculture. Geogr.
Tidsskr. 92: 14─21.
Fredskild, B. 1996. A phytogeographical study of
the vascular plants of West Greenland (62º20’74º00’N). Meddelelser om Grønland, Bioscience
45: 1─157.
Fredskild, B. & Ødum, S. 1991. The Greenland
Mountain birch zone, Southwest Greenland.
Meddelelser om Grønland, Bioscience 33: 1─80.
Hansen, B.U. 1991. Monitoring natural vegetation in
southern Greenland using NOAA AVHRR and ield
measurements. Arctic 44, supp 1: 94─101.
Birch sapling responses to severity and timing
of domestic herbivore browsing-implications for
management. Ecological Studies 180: 139─155.
Knapp, R. 1964. Über Eigenschaften arktischer
und subarktischer Vegetation am Beispiel der
Planzenwelt in einigen Gebieten des südlichen
Grönland. Ber. Oberhess. Ges. f Natur- und Heilk.
zu Gießen, N.F, Naturwiss. Abt. 33: 91─129.
Ødum, S. 1991. Afforestation experiments relecting
the treeline conditions in Southwest Greenland.
Meddelelser om Grønland, Bioscience 33: 43─61.
Pedersen, A. 1972. Adventitious plants and cultivated
plants in Greenland. Meddelelser om Grønland 178
(7): 1─99.
Rosenvinge, L. K. 1896. Det sydligste Grønlands
vegetation. Meddelelser om Grønland 15: 73─249.
Sieg, B., Drees, B., & Daniёls, F. J. A. 2006. Vegetation
and altitudinal zonation in continental West
Greenland. Meddelelser om Grønland, Bioscience
57: 1─93.
Stumböck, M. 1993. Vegetation und Ökologie von
Narsarsuaq, Südwestgrönland. Diss. Bot. 203.
Tuhkanen, S. 1984. A circumboreal system of climaticphytogeographical regions. Acta Bot. Fenn. 127:
1─50.
Walker, D. A., Raynolds, M. K., Daniëls, F. J. A.,
Einarsson, E., Elvebakk, A., Gould, W. A., Katenin,
A. E., Kholod, S. S., Markon, C. J., Melnikov, E. S.,
Moskalenko, N. G., Talbot, S. S., Yurtsev, B. A., &
the other members of the CAVM Team. 2005. The
Circumpolar Arctic Vegetation Map. J. Veg. Sci. 16
(3): 267─282.
Warming, E. 1888. Om Grønlands vegetation.
Meddelelser om Grønland 12: 1─245.
Hester, A. J., Lempa, K., Neuvonen, S., Hoegh, K.,
Feilberg, J., Arnthorsdottir, S., & Iason, G. 2005.
92
Large-Scale Vegetation Mapping in Iceland
Gudmundur Gudjonsson, Hördur Kristinsson, & Eythor Einarsson
Icelandic Institute of Natural History
Extended Abstract
Large-scale vegetation mapping in Iceland began in
1955 with the main purpose of managing grazing areas
in the highlands. Fieldwork has now been completed
of more than two-thirds of the country. Smallerscale mapping has also been conducted and can be
combined with large-scale mapping to provide a clearer
picture of vegetation in Iceland. The vegetation data
that has been collected for Iceland should be useful
in classifying circumboreal vegetation in the proposed
Circumboreal Vegetation Map (CBVM). The CBVM
should include two levels of classiication, a large and
a small scale.
Fieldwork in vegetation mapping has now been
completed of more than two thirds of the country.
Vegetation mapping of the central highlands of Iceland
is mostly completed, but more than half of the lowland
still remains (Fig. 1). About 120 maps at the scale of
1:20,000 to 1:40,000 have been published. For the last
decade 22% of the mapped area has been digitized
and updated with the aid of new orthophotographic
maps along with Landsat-7 and Spot-5 satellite
images.
Keywords: central highlands vegetation mapping,
vegetation units.
Vegetation mapping in Iceland began in 1955 at the
Agricultural Research Institute (ARI) with the main
purpose of determining the carrying capacity of the
vegetation of grazing areas in the central highlands
and thus provide a basis for their management. The
ieldwork for this mapping effort was carried out with
the aid of aerial photographs, and the irst map was
published in 1957 at a scale of 1:40,000 (Jóhannesson
& Thorsteinsson, 1957). In 1968 vegetation mapping
of the lowlands also began, with the same main
purpose of managing grazing areas (Einarsson, 1994;
Walker et al., 1995; Gudbergsson, 1981).
Vegetation was classiied into six main vegetation
complexes: dry land vegetation, wetland fringes,
sloping fens, level fens, aquatic vegetation, and
sparsely vegetated land. The main vegetation
complexes were divided into 16 orders, which were
again divided into 98 associations, the smallest units
used. These mapping units are based on growth forms
and dominant species of vascular plants in the upper
layers of the vegetation and were developed by the
botanist Steindór Steindórsson (Steindórsson, 1975,
1981).
Fig. 1. Present status of large-scale vegetation
mapping in Iceland. No ield work has been carried
out in the white areas.
In 1995 the Icelandic Institute of Natural History (IINH)
took over the task of vegetation mapping in Iceland
from ARI. In 1998 an overview Vegetation Map of
Iceland (map scale 1:500,000) was published by
IINH and compiled by Gudjonsson & Gislason (1998).
This map was compiled with the help of analog
Landsat-7 satellite images (1:250,000) and based on
all vegetation data available at the time. Recently, the
Icelandic Forestry Research Institute has produced
a map of all forest plantations and revised the map
of natural birch woodlands in Iceland (Snorrason and
Kjartansson, 2004).
The IINH has taken part in compiling international
maps of smaller scales covering the Nordic Countries
or all of Europe. The Vegetation Map of the Council
of Europe Member States at the scale 1:3,000,000
was published in 1979 and revised in 1987 (Påhlson,
93
1994). The Map of the Physical Geographic Regions
was mainly based on natural vegetation and was
published by the Nordic Council of Ministers (Nordiska
Ministerrådet, 1984). Maps of the Natural Vegetation
of Europe including Iceland were published in the year
2000 in two scales, 1:10,000,000 and 1:2,500,000
(Bohn et al., 2000). Finally, the IIHN took part in
compiling the Circumpolar Artic Vegetation Map in
scale 1: 7,500,000 (CAVM, 2003).
Now, when it has been decided to proceed and extend
the Circumpolar Arctic Vegetation Map to cover also
the boreal area, we suggest the following steps:
•
The CBVM project should be approached in a
similar way as the CAVM.
•
The status of boreal vegetation mapping in each
of the boreal countries should be reviewed.
•
Classiication should be conducted the same way,
with additions to cover the boreal vegetation.
•
A mapping team should be established that
ensures all participants work along the same lines
from the beginning.
•
The southern limit should be deined.
•
Present vegetation maps should be collected
from the respective countries, and a common
classiication key for the whole area should be
produced.
•
Mapping should include two levels of classiication,
a larger scale and a smaller scale.
•
The inal map scale should be about 1:7,500,000.
References
Bohn, U., Gollub, G., & Hettwer, C. (compilers).
2000. Karte der Naturlichen Vegetation Europas.
Buntersamt Fur Naturschutz, Bonn. 153 pp. + maps.
CAVM Team. 2003. Circumpolar Arctic Vegetation
Map. Conservation of Arctic Flora and Fauna
(CAFF) Map No. 1, U.S. Fish and Wildlife Service,
Anchorage, Alaska.
Einarsson, E. 1994. Vegetation Mapping in Iceland.
Pages 52─53 in Walker D. A. & Markon, C. J., eds.
Circumpolar Arctic Vegetation Mapping Workshop.
Open File Report 96–251. U.S. Department of the
Interior, U.S. Geological Survey, National Mapping
Division. http://mapping-ak.wr.usgs.gov/research/
cavm/pdf/CAVM.pdf.
Gudbergsson, G. 1981: Vegetation mapping in
Iceland. Journ. of Agricult. Research in Iceland. 12:
(2) 59–83.
Gudjonsson, G. & Gislason, E. 1998: Vegetation Map
of Iceland. 1:500000. General overview. Icelandic
Institute of Natural History, Reykjavik (1st edition).
Jóhannesson, B & Thorsteinsson, I. 1957. Gródurkort
og lýsing Gnúpverjaafréttar. Dept. of Agricult.
Reports. Ecol. Surveys No.1. University Research
Institute. 31 pp.
Nordiska
Minesterrådet,
Stockholm.
1984.
Naturgeograisk regionindeling av Norden. 289 pp +
map.
Påhlsson, L., ed. 1994. Vegetationtyper í Norden.
Nordisk Minesterråd, Köbenhavn. 627 pp.
Snorrason, A. & Kjartansson, B. 2004. Íslensk
skógarúttekt─Verkefni um landsúttekt á skóglendum
á Íslandi. Skógræktarritið 2004 (2): 101–108.
Steindórsson, S. 1975. Studies on the Mire-Vegetation
of Iceland. Societas Scientiarum Islandica XLI..
Reykjavik. 226 pp.
Steindórsson, S. 1981: Vegetation classiication in
Iceland. Journ. of Agricult. Research in Iceland. 12
(2): 11–57.
Walker, D. A., Bay, C., Daniёls, F. J. A., Einarsson, E.,
Elvebakk, A., Johansen, B. E., Kapitsa, A., Kholod,
S. S., Murray, D. F., Talbot, S. S., Yurtsev, B. A., &
Zoltai, S. C. 1995. Toward a new Arctic vegetation
map: a review of existing maps. J. Veget. Science 6
(3) 427–436.
94
The Vegetation of the Faroe Islands
Anna Maria Fosaa
Faroese Museum of Natural History, Tórshavn, Faroe Islands, anmarfos@ngs.fo
Extended Abstract
Due to the lack of natural trees, the vegetation in the
Faroe Islands has been dificult to classify. The lowland
part of the islands was classiied by Tuhkanen (1987)
as a highly oceanic part of the hemiboreal subzone.
The predominant vegetation is grassland, which is
found from sea level to the mountaintop, while the
lowland is characterized by heathland vegetation.
Moist-dwarf shrub and heathland vegetation is often
found in plant communities that are similar to those
of the lowland zone in coastal Norway, which would
indicate that the lower elevation areas of the Faroe
Island (up to 200 m a.s.l.) falls into the middle boreal
zone (Fosaa, 2004). A preliminary map was drawn
from topographic maps, scale 1:20,000, and digitized.
This map divides the islands into the main classes of
grassland, heaths, mires, lakes, and fell-ield. From
this map, an approximate areal cover of each of these
vegetation types is given. In this presentation, an
overview of these vegetation types is given as well as
their distribution and the changes that have formed
the vegetation that we have in the Faroe Islands
today.
described as arctic continental, based on the presence
of Betula nana (Jóhansen, 1972, 1985). This species
disappeared again as the climate changed towards
more oceanic conditions. In the Boreal period (9,000–
8,000 B.P.), species such as Juniperus communis
and Salix spp. took over, together with Poaceae
and Cyperaceae, which covered the lowland at
that time. The climate became wetter in the Atlantic
period (8,000–5,000 B.P.) with evidence of strong
leaching of the soil. Peat was formed and Calluna
vulgaris invaded. Juniperus communis and Salix spp.
decreased but did not disappear completely. When
the climate became drier again (Sub-boreal 5,000–
2,500 B.P.), the two species increased again.
In the period from Sub-boreal (5,000–2,500 B.P.)
to Subatlantic (2,500 B.C.–0 A.D.), the vegetation
changes are described as a decline in tall-herb
vegetation and shrub, with an increase in the
distribution of Calluna vulgaris (Jóhansen, 1985).
The Faroe Islands are a small (1400 km2), but relatively
steep (highest peak 882 m a.s.l.), archipelago (18
islands) situated in the Northeastern Atlantic (62ºN,
7ºW). The climate of the islands is humid and windy.
On daily timescales, it is highly variable, but the
strong inluence from the North Atlantic Current helps
maintain a small seasonal temperature variation.
It was previously believed that the decline in Juniperus
and Salix spp. was due to human settlement. Newer
results based on macrofossil studies show, however,
that woodland species were already retreating prior to
human settlement, before the occurrence of peatland
and heathland species, and that the settlement only
accelerated the degradation process initiated by
climate change (Hannon, 2000; Hannon et al., 2001).
It is therefore unclear whether the treeless grasslands
and heathlands that characterize the islands today
are a cultural product due to sheep grazing, as has
been demonstrated elsewhere in northern Europe, or
have developed as a result of recent climatic change.
Vegetation Change
Distribution of Vegetation Types
In the irst period after the last ice age, natural climate
luctuations had the greatest impact on the vegetation.
After human settlement, the inluence of humans has
become the main impact. In the Preboreal period
(10,000–9,000 B.P.), the Faroe Islands have been
The main vegetation type of the islands is grassland
that goes from lowland to the top of the mountains and
covers ca. 77% of the total area. The grassland can be
divided into two main types, which are wet grassland
and dry grassland. Wet grassland is mainly found
Climate
95
in the lowland, while dry grassland is more common
on the minerogenic soils in the highland. Common
species are Festuca vivipara, F. rubra, Agrostis canina,
A. capillaris, Nardus stricta, Deschampsia lexuosa,
and Anthoxanthum odoratum.
Heathland covers 2.9% of the total area, mainly in
the lowland vegetation belt up to 200 m a.s.l. Three
main heath communities in the Faroe Islands are
deined by increasing moisture from dry heath to
relatively wet heath. The driest of the heaths is the
Calluna vulgaris─Erica cinerea community. When
the humidity increases, the Calluna vulgaris–Erica
cinerea community is replaced by the Calluna vulgaris
community and again by the Empetrum–Vaccinium
community. In these wet heaths, Narthecium
ossifragum and Juncus sqarrosus are frequent
(Böcher, 1940). Common species are Calluna vulgaris,
Empetrum nigrum, Erica cinerea, Vaccinium myrtillus,
V. uliginosum, Potentilla erecta, Nardus stricta, Carex
pilulifera, Galium saxatilis, and Hypericum pulcrum.
Mires cover 1.4% of the total area. Three different kinds
of mires are deined: topogenic mires with their origin
in overgrown lakes or tarns, soligenic mires on hills
and slopes, and ombrogenic mires in valleys. Common
species that are growing in the Sphagnum moss
are Eriophorum angustifolium, Juncus squarrosus,
Scirpus cespitosus, Narthecium ossifragum, Carex
panicea, Carex nigra, and Pinguicula vulgaris.
Lakes cover 0.9% of the total area. Most lakes are
small, less than 100 m2, and poor in nutrients. In shallow
water on the shores of the lakes, Litorella unilora
often hides the bottom like a mat. On gravel bottoms,
the most common plants are Isoëtes echinospora,
I. lacustris, Littorella unilora, Ranunculus lammula,
Subularia aquatica, and Juncus bulbosus. On soft
bottoms, the common species are Sparganium
angustifolium and Myriophyllum alternilorum, and
species of Potamogeton, such as P. natans, P.
gramineus, and P. perfoliatus.
The alpine zone covers 8% of the total area and
includes the lat mountaintops forming “fell ields.”
These fell-ields are formed as a result of erosion and
are found from an elevation of a few hundred metres
up to the highest mountaintop (882 m). Such plateaulike areas are exposed to strong winds, and the
substrate freezes and thaws repeatedly. Most of these
areas consist of a mosaic of cliff surfaces with moss
and lichens, and a few higher plants. The vegetation
is very distinctive, though sparse. It is the instability
of the ground and its constant movement that causes
the scarcity of lora. Snow cover is very important in
alpine areas, protecting the soil surface from frost
penetration in winter. Due to mild winter temperatures
plants must be able to withstand a winter with only
occasional snow cover, which is liable to disturb the
winter period of inactivity. Only limited information
on the duration of the snow cover in the alpine areas
is available from the Faroe Islands. Meteorological
information on the duration of snow cover in Tórshavn
in the period 1961–1991 shows that January had the
maximum number of days of snow cover (10.8 days),
while the period from May to October had no snow
cover.
The most common plants in the alpine zone are
Koenigia islandica, Cardaminopsis petreae, Luzula
spicata, Alchemilla alpina, Bistorta vivipara, Saxifraga
rosacea,
Loiseleuria
procumbens,
Sibbaldia
procumbens, Salix herbacea, and Ranunculus
glacialis.
Due to their shape, some of the pointed mountaintops
are not as exposed to wind as are the lat mountains.
On these pointed peaks, wind passage past the
mountain leaves a relatively calm area on the top.
These mountaintops have an almost 100% cover of
the grayish moss Racomitrium lanuginosum. This
vegetation typically covers a region from the top and
around 100 m down where the mountain again is
exposed to strong wind and the vegetation becomes
sparse. The Racomitrium heath vegetation is found
on sloping terrain on relatively dry soil protected from
wind. Racomitrium lanuginosum is widely distributed
at all elevations in the islands and is also typical for
areas where the snow melts late. In these areas we
ind so called snow-bed communities with Sibbaldia
procumbens, Saxifraga oppositifolia, Silene acaulis,
and Alchemilla alpina.
As no details on speciic plant communities are given
on the topographic map used to map the vegetation
types, it is not possible to tell how much of the islands
are covered with Racomitrium heath. Most likely,
Racomitrium heath is included in the grassland type.
96
Three altitudinal vegetation zones can be deined:
heathland in the lowland and up to 200 m a.s.l.;
a transition zone up to 400 m a.s.l., which has wet
grassland as the characteristic vegetation; and from
400 m a.s.l. up to the mountain tops, Racomitrium
vegetation and dry-vegetation.
References
Böcher, T. W. 1940. Studies on the plant-geography
of the North-Atlantic heath-formation. I The heath
of the Faroes. Kgl. Dan. Vid. Selsk. Biol. Medd. 15:
1–64.
Fosaa, A. M. 2004. Altitudinal distribution of plant
communities in the Faroe Islands. Fróðskaparrit 51:
200–211.
Hannon, G. E., Wastegård, S., Bradshaw, E., &
Bradshaw, R. H. W. 2001. Humane impact and
landscape degradation on the Faroe Islands.
Biology and Environment: Procedings of the Royal
Irish Academy 101B (1–2): 129–139.
Jóhansen, J. 1972. A palaeobotanical study indicating
a preViking settlement in the village of Tørnuvík,
Faroe Islands. Fróðskaparrit 19: 45–157.
Jóhansen, J. 1985. Studies in the vegetational history
of the Faroe and Shetland Islands. Fróðskaparrit,
Tórshavn, 117 pp.
Tuhkanen, S. 1987. The phytogeographical
position of the Faroe Islands and their ecoclimatic
correspondence on the other continents: Problems
associated with highly oceanic areas. Ann. Bot.
Fennici. 24: 111–135.
Hannon, G. 2000. Impact of timing of the irst human
settlement on vegetation in the Faroe Islands.
Quarternary Research 54: 404–413.
97
The Plant Cover of the Kamchatka Peninsula (North of the Russian Far
East) and Its Geobotanical Subdivision
Valentina Yu. Neshataeva
Komarov Botanical Institute, Russian Academy of Sciences, Saint Petersburg, Russia, vneshataeva@yandex.ru
Abstract
Y.Y. Vasiljev (1947) carried out the irst geobotanical
subdivision of Kamchatka Peninsula and distinguished
the territory as the Kamchatka herb-rich and broadleaved-forest region. At present it is considered to
be a special Kamchatka deciduous forest subregion
of the Euro-Asiatic taiga region. The plant cover
of the subregion was initially characterized by
the predominance of stone-birch (Betula ermanii)
forests on the plains. The area was subdivided into
six geobotanical provinces and 20 districts. Three
provinces of plains were distinguished: I – Eastern
Kamchatka suboceanic province, characterized by
the predominance of herb- and shrub-rich stonebirch forests and dwarf-shrub heath communities
dominated by Empetrum nigrum; II – Western
Kamchatka maritime province, which differed from the
other provinces by the prevalence of grass- and shortherb-rich stone-birch forests, tall-herb meadows with
Angelica ursina, and blanket bogs; and III – Central
Kamchatka subcontinental province, characterized
by the predominance of larch (Larix cajanderi) and
spruce (Picea ajanensis) taiga forests and secondary
Japanese-birch (Betula platyphylla) forests. Three
mountain provinces were distinguished: IV – Middle
Kamchatka mountain province with the predominance
of Siberian dwarf-pine (Pinus pumila) elin woods and
mountain tundra; V – Eastern Kamchatka volcanic
mountain province, with the special volcanogenic
variant of altitudinal zonality and the prevalence of
serial plant communities and secondary permanent
associations at the volcanic plateaus; and VI –
Southern Kamchatka mountain province, with a
suboceanic subtype of altitudinal zonality and the
prevalence of dwarf-alder (Alnus fruticosa s.l.) shrubs
and suboceanic dwarf-shrub heaths. It was shown
that the differentiation of Kamchatka vegetation cover
was subordinated mainly to zonal and altitudinal
patterns. Permanent volcanogenic inluences caused
irreversible plant succession processes. As a result of
Holocene volcanic activities, serial plant communities
and secondary permanent associations are prevalent
in the vast areas of Central Kamchatka: Japanesebirch (Betula platyphylla) forests and larch (Larix
cajanderi) forests covering the central part of the
peninsula replaced the virgin primeval spruce (Picea
ajanensis) and stone-birch (Betula ermani) forests.
Keywords:
altitudinal
zonality,
geobotanical
subdivision, Kamchatka Peninsula, vegetation cover.
Introduction
The Kamchatka Peninsula is situated north of the
Russian Far-East between 50º 52’–60º 52’ N and 155º
34’–164º 00’ E. The area of the peninsula is 350,000
km2. The extent of the peninsula from the north to
the south is 1,200 km, with a maximal width of 480
km. The northern border of the Kamchatka Peninsula
passes through the line from the Rekinniki Bay to
the gulf of Anapka, separating the peninsula from
the continental part of the Kamchatka administrative
region.
Relief and Geology
Several high mountain ridges divide the territory of
the peninsula. The largest of them, the Sredinny and
Vostochny ranges, are meridional and stretch for a
thousand kilometers. The main plains—the Central
Kamchatka depression, the Western Kamchatka, and
the Eastern Kamchatka lowlands—are meridional too.
The tectonic structure of the peninsula, the location
of volcanoes, river valley patterns, and the character
of the coastline are determined by large fractures
stretched in northeast and northwest directions. The
active tectonic movements are still in progress causing
the high seismicity of the peninsula and the modern
volcanism. The Kamchatka Peninsula is notable for its
active volcanoes, geysers, and hot springs. At present,
there are 30 active and 120 dormant volcanoes on
the peninsula; most of them are concentrated in
98
the eastern, central, and southern volcanic regions.
Volcanic eruptions with the ejection of 1–10 million
m3 of igneous rock are registered almost every year.
Volcanic eruptions of high magnitude (ejection of
more than 1 km3 of igneous rock) occur almost every
400 years and cause regional catastrophes. Gigantic
catastrophic eruptions with a volume of 10–100 km3
damage the plant cover over an area of 10–100,000
km2. During the Holocene, 24 of such gigantic
eruptions occurred within Kamchatka.
Climate
The climate of the peninsula is cold and extremely
wet with a high snow cover. The annual sum of active
temperatures more than 10ºC doesn’t exceed 1200ºC.
The average temperature of the warmest month is not
more than 15ºC, and of the coldest month, no less
than -20ºC. The annual amount of precipitation is from
350 mm for the Central Valley of Kamchatka up to
1,000 mm at the coastline and from 1,200 mm to 1,400
mm at the oceanic peninsulas and in the mountains.
The average thickness of snow cover is 100 cm. The
most mild and wet climate is at the eastern coast.
The climate of the western coast is colder; the annual
average temperature is below zero and the annual
precipitation is lower than at the Eastern coast. The
Central Kamchatka depression is characterized by the
subcontinental climate. In summer the temperature is
usually up to 25─30ºC and in winter falls to -40ºC.
The regime of humidiication is contrasting: there
is a dry spring season and a rainy autumn period.
The coldest climate is at the northern parts of the
peninsula. The annual average temperature is -2.5ºC.
The climatic differences between the eastern and the
western coasts of the peninsula are determined by
the different heat regimes of the Sea of Okhotsk, the
Bering Sea, and the Paciic Ocean and by the patterns
of the seasonal atmospheric circulation.
Soils
The speciic soils of Kamchatka are formed on acid
volcanic rocks under the inluence of a cold and wet
climate. The volcanic eruptions cause the heavy ash-,
sand- and scoria falls. The thickness of the soil proile
increases with the ash-falls. The volcanic soils have
the stratiied structure and the polygenic soil proile
consisting of several elementary soil proiles and
several volcanic-ash layers. They are characterized by
the high concentration of copper (Cu), the deiciency
of chrome (Cr), nickel (Ni), tin (Sn), strontium (Sr),
molybdenum (Mo), and silver (Ag) and by the high
extent of water permeability.
Methods
The plant cover of Kamchatka was studied for a
period of 18 ield seasons using sample plots and
transects. Sample plots measuring 400 m2 for
forests and 100 m2 for other plant communities were
laid along the altitude transects. The location of
each sample plot was estimated with the help of a
GPS personal navigator. The species composition,
including vascular plants, bryophytes, and lichens,
and the percentage cover of each species, were
estimated. More than 4,000 relevés were analyzed
using a standard phytosociological table method, and
several vegetation maps of key areas were compiled
at the scales of 1:1,000 and 1:5,000. For the means of
a geobotanical subdivision of the vegetation cover of
Kamchatka forest type, topographic maps and aerial
photographs were used. The phytosociological plant
community classiication was described. The plant
communities were classiied, taking into account not
only the loristic composition, but also the presence
and abundance of species. The syntaxonomic range
of relevé groups obtained was ranked according to
the Russian phytosociological tradition using the
principles irst elaborated by Sukatchov (1928). The
plant communities of the Kamchatka Peninsula belong
to 145 associations representing 96 formations, 25
classes of formations, and seven vegetation types.
Results and Discussion
Zonal Patterns
The main zonal patterns of the vegetation cover
are developed at vast maritime lowlands and wide
intermontane plains that are meridional as well. The
territory of Kamchatka is related to the Taiga (conifer
forest) zone. The phytogeographic phenomenon of
the prevalence of primary stone-birch (Betula ermanii)
forests replacing the coniferous forests at the sea
shores is connected to the cold and moist maritime
climate of eastern and southern coasts of the
peninsula and particularly with the lack of the summer
heat. The same phenomenon was irst described by
Hämet-Ahti (1963, 1987) and Hämet-Ahti (1969) for
99
the Atlantic coast of northwestern Europe, where
conifer forests were replaced by Betula pubescens
subsp. cherepanovii communities. The largest parts
of maritime plains are attributed to the Northern
taiga subzone and the wide intermountain trough
of the Central Kamchatka depression to the Middle
taiga subzone. It is characterized by the prevalence
of conifer (larch and spruce) forests formed by Larix
cajanderi and Picea ajanensis (= P. yezoensis).
Altitudinal Zonality
The territory of the peninsula is characterized by the
special Kamchatka type of altitudinal zonality (Fig.
1). It is subdivided into three subtypes of vertical
differentiation of vegetation cover. The Central
Kamchatka subcontinental subtype is characterized
by four altitudinal vegetation belts: the mountain taiga
(larch and spruce) forest belt, the stone-birch forest
belt, dwarf-woodlands (Siberian dwarf-pine Pinus
pumila and dwarf-alder Alnus fruticosa s.l.) belt, and
the mountain tundra belt. The Volcanogenic variant of
Central Kamchatka subtype differs by the lack of conifer
forests in the mountain taiga belt; under the inluence
of repeating volcanic eruptions they were replaced
by secondary Japanese-birch (Betula platyphylla)
forests. The Eastern and Western Kamchatka
subtype is characterized by three vegetation belts
(stone-birch forests, dwarf-woodlands, and mountain
tundra) and differs by the lack of a conifer forests
belt. The Southern Kamchatka suboceanic subtype
is characterized by only two belts (dwarf-woodlands
and mountain tundra) and also differs by the lack of
the forest belts.
Geobotanical Subdivision
Previously published data on the vegetation cover
of Kamchatka were fragmentary and incomplete. An
erroneous opinion existed about the prevalence of
primary meadows at the vast areas of the peninsula.
According to the irst geobotanical subdivision of
Kamchatka (Vasiljev, 1947), the territory of the
peninsula was distinguished as a special Kamchatka
herb-rich and broad leaved-forest region (“oblast”).
It was subdivided into seven circles (“okrug”). Later,
Lavrenko (1950) included Kamchatka with the
adjacent Kurile Islands, Commader, and Aleutian
Islands in the Northern Paciic meadow region.
Kolesnikov (1963) considered Kamchatka to belong
to two different geobotanical regions: the plains and
the lowlands of the peninsula were included in the
Northern Paciic meadow-leaved-forest region, and
the mountain areas were included in the Bering forest–
Fig. 1. Altitudinal zonality of the Kamchatka vegetation cover. Subtypes: 1 – Southern Kamchatka suboceanic
subtype; 2 – Eastern and Western Kamchatka subtype; 3 – Central Kamchatka subcontinental subtype; and
4 – Volcanogenic variant of Central Kamchatka subtype.
100
tundra region. Contrarily, some authors emphasized
the role of forest–tundra vegetation in the vegetation
cover of Kamchatka. For example, Khomentovsky et
al. (1989) considered the territory of the peninsula to
be a special Kamchatka forest–tundra region.
The geobotanical subdivision of Kamchatka was
carried out using the principles irst elaborated by
Lavrenko (1947, 1948) and developed in the numerous
works of Russian geobotanists. The territory of the
peninsula was distinguished as a special Kamchatka
deciduous forest subregion of the Euro-Asiatic taiga
(conifer forest) region. The plant cover of the subregion
is characterized in general by the predominance of
stone-birch (Betula ermanii) forests on the plains. The
area was subdivided into six vegetation provinces and
20 vegetation districts (Fig. 2).
Provinces of Plains
Three vegetation provinces of plains were
distinguished according to vegetation cover zonal
patterns: I – Eastern Kamchatka suboceanic province,
II – Western Kamchatka maritime province, and III
– Central Kamchatka subcontinental province. I –
Eastern Kamchatka suboceanic province (from the
Avacha Bay up to the Gulf of Uala) is characterized
by the predominance of tall-herb-rich (dominated by
Filipendula camtschatica, Senecio cannabifolius) and
herb- and shrub-rich stone-birch forests and dwarfshrub heath communities (dominated by Empetrum
nigrum and Vaccinium uliginosum) on well-drained
sites. On the maritime lowlands aapa-mires with Myrica
tomentosa are common. The three vegetation districts
were distinguished: 1 – Eastern maritime district, 2 –
Northeastern maritime district, and 3 – Karaginsky
insular district. The II – Western Kamchatka maritime
province (from Yavina River up to Palana River)
differs by the prevalence of herb-grass-rich and shortherb-rich stone-birch forests, tall-herb meadows
with Angelica ursina and dwarf-shrub-, sedge- and
Sphagnum-rich blanket bogs. In the northern part
of the province Siberian dwarf-pine (Pinus pumila)
dwarf-woodlands are common. The habitats of the
dwarf-pine woodlands are dry with poor soils—they
occur on well-drained stony slopes and sometimes
on the sandy maritime seashore banks. Two districts
were designated: 4 – Southwestern mire- and stonebirch forest district and 5 – Northwestern miredwarf-woodland district. The III – Central Kamchatka
subcontinental province (distinguished in the borders
Fig. 2. The geobotanical subdivision of Kamchatka.
The area was subdivided into six vegetation provinces
and 20 vegetation districts.
of the Central Valley of Kamchatka) is characterized
by the predominance of larch (Larix cajanderi) and
spruce (Picea ajanensis) conifer taiga forests and
by the presence of secondary permanent Japanesebirch (Betula platyphylla) and aspen (Populus
tremula) forests. Primeval low-herb-rich (Linnaea
borealis, Lycopodium annotinum, Gymnocarpium
dryopteris, Oxalis acetosella, Maianthemum bifolium,
M. dilatatum, Trientalis europaea) and moss-rich
(Pleurozium shreberi, Polytrichum commune,
Dicranum majus, Hylocomium splendens) spruce
forests are the climax formation on the watersheds.
Secondary herb-shrub-rich (Lonicera caerulea,
Spiraea beauverdiana, Rosa amblyotis, Geranium
erianthum, Thalictrum minus) larch, Japanese-birch,
and aspen forests are widespread. At the elevated
southern part of the province, stone-birch forests on
the slopes and secondary permanent Japanese-birch
forests in the valleys replace coniferous forests. The
three vegetation districts were distinguished: 6 – Upper
Kamchatka birch-forest district, 7 – Middle Kamchatka
conifer-forest district, and 8 – Low Kamchatka birchconifer forest district.
101
Mountain Provinces
Three mountain vegetation provinces were
distinguished according to the plant cover vertical
differentiation patterns: IV – Middle Kamchatka
mountain province, V – Eastern Kamchatka volcanic
mountain province, and VI – Southern Kamchatka
suboceanic mountain province (from the Plotnikova
river valley up to the end of the Sredinny ridge).
The vegetation cover was characterized by the
predominance of Siberian dwarf-pine (Pinus pumila)
elin woods and mountain tundra communities
(formed by Rhododendron aureum, Phyllodoce
caerulea, Cassiope tetragona, Cladonia spp.). The
mountain tundra belt is widely spread at the mountain
plateaus and low-grade slopes of the mountains
at the altitudes from 1,100 m to 1,400 m above sea
level. Four vegetation districts were distinguished: 9 –
Khangar high-mountain district, 10 – Ichinsky volcanic
district, 11 – Khuvkoitunsky high-mountain district,
and 12 – Shamansky low-mountain district. The V
– Eastern Kamchatka volcanic mountain province
includes eastern mountain areas of the peninsula,
Kluchevskaya volcano group, and Shiveluch volcano.
Larch open-forests with the understory formed by
Siberian dwarf-pine shrubs are common at the upper
forest limit. Mountain tundra communities (dominated
by Vaccinium uliginosum, Phyllodoce caerulea,
Dryas punctata, Diapensia obovata, Cassiope
lycopodioides) are widespread at the mountain
plateaus. Subalpine meadows (Geranium erianthum,
Thalictrum minus, Saussurea pseudo-tilesii, Artemisia
arctica) are common in the moderately wet sites at
the dwarf-woodland belt. On the well-drained slopes
of scoria cones dry alpine meadows (with Kobresia
myosuroides, Carex rupestris, Pulsatilla nutalliana,
Leontopodium kamchaticum) are found. The
vegetation cover of this province was characterized by
the special volcanogenic variant of altitudinal zonality
and the prevalence of serial plant communities and
secondary permanent associations at the vast areas
of volcanic plateaus. Primary vegetation cover was
strongly damaged by Holocene volcanic eruptions. Six
vegetation districts were distinguished: 13 – Southern
volcanic district, 14 – Ganalsky low-mountain district,
15 – Eastern high-mountain district, 16 – Eastern
volcanic district, 17 – Kluchevskoy volcanic district,
18 – Shiveluch volcanic district. The VI – Southern
Kamchatka suboceanic mountain province (from the
Avacha Bay up to the Cape of Lopatka) includes the
southernmost area of the Kamchatka Peninsula and
the northern group of the Kurile Islands. The plant
cover was characterized by the suboceanic subtype
of altitudinal zonality and the prevalence of fern- and
grass-rich dwarf-alder thickets (Alnus fruticosa s.l.,
Calamagrostis langsdorfii, Glyceria alnasteretum,
Athyrium ilix-femina, Dryopteris expansa) and
suboceanic
dwarf-shrub
heaths.
Dwarf-alder
communities were predominant on the wet maritime
slopes of volcanic plateaus and the seashore slopes of
peninsulas, where they could be found at the sea level.
Dwarf-shrub heath communities (formed by Empetrum
nigrum, Vaccinium uliginosum, V. minus, Ledum
decumbens) were predominant on the well-drained
maritime terraces of the plains of the Western coast
and in the Lopatka Peninsula. They were considered
to be analogous to the mountain tundra dwarf-shrub
communities. Mountain tundra belt was widely
spread at the plateaus and low grade slopes at the
altitudes from 800 m to 1,200 m and was represented
by Ericaceae shrub and lichen communities. At the
surroundings of snow-patches, speciic nival meadow
communities (formed by Primula cuneifolia, Sibbaldia
procumbens, Sieversia pentapetala, Lagotis glauca,
Phyllodoce aleutica) are common. The two vegetation
districts were distinguished: 19 – South Kamchatka
dwarf-woodland district and 20 – Lopatka-Northern
Kurile dwarf-shrub-heath district.
It was shown that the differentiation of Kamchatka
vegetation cover was subordinated mainly to
zonal and altitudinal patterns (Neshataeva, 2006).
Permanent volcanogenic inluences cause irreversible
plant succession processes. As a result of Holocene
volcanic activities, serial plant communities and
secondary permanent associations at the vast areas
of Central Kamchatka are prevalent: Japanese-birch
(Betula platyphylla) forests and larch (Larix cajanderi)
forests covering the central part of the peninsula
replaced the virgin primeval spruce (Picea ajanensis)
and stone-birch (Betula ermani) forests.
Acknowledgments
This study was supported by the Russian Foundation
for Basic Research (grants № 05-04-48035 and №
08-04-01294).
102
References
Hämet-Ahti, L. 1963. Zonation of the mountain birch
forests in northernmost Fennoscandia. Ann. Bot.
Soc. Vanamo 34(4): 1–127.
Hämet-Ahti, L. 1987. Mountain birch and mountain
birch woodland in NW Europe. Phytocoenologia
15(4): 449–453.
Hämet-Ahti, L. & Ahti, T. 1969. The homologies of the
Fennoscandian mountain and coastal birch forests
in Eurasia and North America. Vegetatio 19(1-6):
208–219.
Khomentovsky, P. A., Kazakov, N. V., & Chernyagina,
O. A. 1989. Tundroles’je Kamchatki: problemy
sokhranenija i ispol’zovanija. Pages 30─46 in
Problemy prirodopolzovanija v tajozhnoi zone.
(Хоментовский, П.А., Казаков, Н.В., Чернягина,
О.А. 1989. Тундролесье Камчатки: проблемы
сохранения и использования. Pages 30–46 in
Проблемы природопользования в таежной зоне.)
In Russian.
Kolesnikov, B. P. 1963. Geobotanicheskoje
raionirovanije Dal’nego Vostoka I zakonomernosti
razmeschenija
ego
rastitel’nych
resursov.
Voprosy geograii Dal’nego Vostoka 6:158–182.
(Колесников, Б.П. 1963. Геоботаническое
районирование
Дальнего
Востока
и
закономерности размещения его растительных
ресурсов. Вопр. геогр. Дальнего Востока 6: 158–
182). In Russian.
Lavrenko, E. M. 1947. Printsipy i edinitsy
geobotanicheskogo raionirovanija. Pages 9–13
in Geobotanicheskoye raionirovanije SSSR.
(Лавренко, Е.М. 1947. Принципы и единицы
геоботанического районирования. Pages 9–13
in Геоботаническое районирование СССР). In
Russian.
Lavrenko, E. M. 1948. O printsipach botanikogeograicheskogo
raschlenenija
Palearktiki.
Botanicheskiy Zhurnal 33(1):157.(Лавренко, Е.М.
1948. О принципах ботанико-географического
расчленения
Палеарктики.
Ботан.
Журн.33(1):157). In Russian.
Lavrenko, E. M. 1950. Osnovnyje cherty
botaniko-geograicheskogo razdelenija SSSR i
sopredel’nych stran. Problemy botaniki (1) 530548. (Лавренко, Е. М. 1950. Основные черты
ботанико-географического разделения СССР и
сопредельных стран. Проблемы ботаники. (1)
530-548). In Russian.
Neshataeva, V. Yu. 2006. Rastitel ‘nost’ poluostrova
Kamchatka. 62 pp. (Нешатаева, В.Ю. 2006.
Растительность
полуострова
Камчатка.
Автореферат докт. дисс. 62 pp). In Russian.
Sukatchov, V. N. 1928. Rastitel’nye soobschestva.
Vvedenije v itosociologiju. 232 pp. (Сукачев, В.Н.
1928. Pастительные сообщества. Введение в
фитосоциологию. 232 pp). In Russian.
Vasiljev, Y. Y. 1947. Kamchatskaya travyano-listvennolesnaya oblast. Pages 61─62 in Geobotanicheskoye
raionirovanije SSSR. (Васильев, Я.Я. 1947.
Камчатская травяно-лиственно-лесная область.
Pages 61–62 in Геоботаническое районирование
СССР). In Russian.
103
Ecological-Floristic Approach to Typology of Forests for European
Russia
L.B. Zaugolnova1 & T.Yu Braslavskaya2
1
Centre for Problems of Forest Ecology and Productivity of RAS, Moscow, Russia, ludmila@cepl.rssi.ru, 2Centre
for Problems of Forest Ecology and Productivity of RAS, Moscow, Russia, t_braslavskaya@yahoo.com
Extended Abstract
Recently proposed variants of forest typology for
European Russia (Zaugolnova & Morozova, 2006)
take into account tree dominance and cover ratio
of different ecological-coenotic groups of species in
the ground layer. Because of the signiicant zonal
particularities of woody and herbaceous lora, a
system of typological units was separately derived for
the following different botanical belts: boreal, hemiboreal, and nemoral. Information about this typology
is presented in the Website COENOFUND (http://mfd.
cepl.rssi.ru/lora/).
Keywords: forest classiication, loristic composition,
species groups, tree dominants.
Early typological schemes of Russian forests were
based primarily on the principles of forest type
dominance (Soukachev, 1938; Vorobiev, 1953).
But today, full loristic composition of communities
is in focus because of biodiversity problems. So, it
is necessary to renew forest typology for European
Russia by using methods of detailed loristic analysis
and its ecological interpretation and by also accounting
for the many geobotanical forest relevés conducted in
recent years.
Such work was recently executed by Zaugolnova &
Morozova (2006). New variants of forest typology
use two contemporary classifying features: (1) tree
dominance and (2) cover ratio of different ecologicalcoenotic groups of species in the ground layer. This
treatment arranges the typological scheme as a
matrix (two-way table). Columns of the typology
matrix correspond to the categories designated by
tree dominants (forest formations in traditional terms
of Russian phytocoenology), whereas rows illustrate
categories segregated by ecological composition of
ground layer (each row presents a subsection and
several neighboring rows constitute a section). A
subsection in the typology is usually determined to
be an analog of an association in the Braun-Blanquet
system.
According to the zonal particularities of woody and
herbaceous lora, different variants of the typology
matrix are designed for the following botanical belts
(zones) of European Russia: boreal (including northern
taiga & middle taiga), hemi-boreal (including southern
taiga & subtaiga zones), and nemoral (including
broad-leaved forests & forest-steppe zones). The
typology matrices for the northern & middle taiga
and the hemi-boreal belt are now presented in the
COENOFUND website (http://mfd.cepl.rssi.ru/lora/).
Materials from the nemoral belt are in preparation.
The ecological-coenotic species groups, differentiated
in the ground layer, are lichens, green mosses,
sphagnous mosses, dwarf shrubs, nemoral-forest
herbs, boreal-forest small and tall herbs, swampy
herbs, wet-meadow herbs, dry-meadow herbs, and
some others (full list of each group is presented in the
COENOFUND website). Each ecological-coenotic
group of species is considered as a collective
dominant in the ground layer: species belonging to
it are ecologically interchangeable. (For example,
communities dominated by boreal small herb
Oxalis acetosella L. are considered analogous to
other communities dominated by boreal small herb
Maianthemum bifolium (L.) F. Schmidt.). So, cover
differences between such species are supposed as
circumstantial and not principal for classiication,
and cover ratio between different ecological-coenotic
groups is the main feature used in classiication.
In parallel, the segregation of formations is also based
on collective dominants, or groups of tree species
that have closely related ecological properties (for
example, small-leaved formation is formed by both
104
birch or popular, dark-conifer formation by spruce or
ir, and light-conifer by pine or larch).
One cell of the matrix corresponds to a base unit
of the designed typology and is nominated as the
Group of Forest Types (GFT). A GFT includes forest
communities of several types (or associations,
segregated by dominant principles) that are similar in
being dominated by ecologically close species from
the same ecological-coenotic groups and by the same
tree formation. In contrast, when comparing a GFT
with a relative Braun-Blanquet association, it can be
shown that the GFT is usually a narrower unit and its
to one of its variants.
In such typology where matrices are formally
constructed from a number of species groups, some
cells can be empty because respective combinations
between tree and herb species are ecologically
unrealizable. On the other hand, some gaps in the
matrix help to reveal and to highlight forest types that
are poorly studied.
References
Soukachev, V. N. 1938. Dendrology with Bases of
Forest Geobotany. Moscow. 572 pp. (In Russian).
Vorobiev, D. V. 1953. Forest Types Within European
Part of the USSR. Moscow. 450 pp. (In Russian).
Zaugolnova, L. B. & Morozova, O. V. 2006. Typology
and classiication of forests in European Russia:
methodical approaches and their realization.
Lesovedenie 1: 34–48. (In Russian; English
abstract).
105
The Finnish Concept of Vegetation and Zones of Natural Forests and
Mires
Tapio Lindholm1 & Raimo Heikkilä2
Finnish Environment Institute, Helsinki, Finland, tapio.lindholm@ymparisto.i, 2Finnish Environment Institute,
Joensuu, Finland, raimo.heikkila@ymparisto.i
1
Abstract
The Finnish forest site type system is based on the
assumption that the presence of different plant species
is determined by the ecology of the habitat. The forest
habitat is constant, but the vegetation can change,
for instance, due to forest ire, or by tree cutting. The
primary vegetation can vary to some extent, but in
succession, primary habitat factors determine the
inal structure of vegetation. These factors do not
change. The secondary factors are the state of tree
stand, tree species, amount of light, etc., and they can
affect the vegetation, but they do not change the site
itself. The habitat characterized by certain vegetation
relects also the potential productivity of the site.
In practice, the identiication of a forest site type is
easier in mature forest, where the effect of secondary
factors is least.
approximately 80 mire site types have been
designated in Finland, and the vegetation depends
mainly on the moisture and acidity of the habitat.
From south to north there are seven climatic mire
massif zones in Finland: plateau bogs, concentric
bogs, eccentric bogs, sedge aapamires, lark
aapamires, pounikko aapamires, and palsamires.
Pounikko aapamires contain spatially and temporally
varying ground frost, and in palsamires there are up
to 7 m high peat mounds with permafrost cores. In
northernmost Finland, there are also snowmelt water
inluenced orohemiarctic mires on fells above the tree
limit.
Keywords: Finland; forest; mire; site type; vegetation
zone.
Introduction
The forest site types were originally described in
southern Finland, but soon it became evident that
forest vegetation and productivity of forests are
different in north Finland. Forests are divided in
hemiboreal, southern boreal, middle boreal, northern
boreal, and orohemiarctic zones, and in each zone,
there is a series of site types from dry heath forests to
herb-rich sites.
The Finnish mire site type system was developed
parallel to forest site types. The idea was that the
plant community is determined by the ecology of the
habitat.
In the Finnish approach, mires and forests have
been understood to form a continuum. In a botanical
sense, the marginal habitats are classiied as mires
if the vegetation is dominated by mire species (e.g.,
Sphagna) and there is a peat layer, even though in
forestry they are classiied as forests on the basis of
the density, size, and growth of tree layer. Botanically,
A.K. Cajander started the school of forest site types in
Finland more than 100 years ago, about the same time
as G.F. Morozov started studies in Russia. Originating
from botany, Cajander used the understory vegetation
as an indicator of forest productivity. Morozov, as a
forester, based his forest type classiication on soil
condition. Cajander’s principles for the classiication
of forests, and a little later of mires, became well
known all over the world (Sukatšev, 1960).
Although situated between the Scandinavian and
Russian schools of geobotany, the present tradition
of geobotany in Finland has been different from
both neighbouring traditions. It also differs from the
central European and British traditions. Johan Petter
Norrlin (1842–1917) put forth the founding ideas for
Finnish geobotany (e.g., Norrlin, 1871), and his ideas
can be seen in Aimo Kaarlo Cajander’s (1879–1943)
Finnish forest site type system (Cajander, 1909)
and mire site type system (Cajander, 1913). Many
106
following generations of geobotanists continued his
work. Rauno Ruuhijärvi (1960) and Seppo Eurola
(1962) have worked with the geobotany of mires.
The general synthesis of geobotanical zones has
also been stretched outside the Finnish boundary
in northwestern Europe (Ahti et al., 1968). Finally,
the geobotanical studies in Finland were widened
for the entire circumboreal zone (Tuhkanen, 1984).
In the Finnish approach, mires and forests have
been understood to form a continuum (Ruuhijärvi
& Lindholm, 2006). In a botanical sense, marginal
habitats are classiied as mires if the vegetation is
dominated by mire species and there is a peat layer,
even though in forestry these habitats are classiied
as forests on the basis of the density, size, and growth
of the tree layer.
Forest Vegetation and Vegetation Zones
In Finnish botany, geobotany, and forest ecology, as
well as in mire ecology, almost all classiication is
based on the Norrlin–Cajander schools of site types.
The basic deinition of forest site type by Cajander
(1949a) is: “All those stands are to be included in
the same forest site type, which, when the stand is
normally dense and ready to be cut or close to that,
are more or less similar in plant species composition.
Also, similarly all those forest stands, whose
vegetation differs temporarily, not permanently, from
the basic type deined above only due to reasons,
which e.g. can be derived from the age of the stand,
thinning or selective cuttings or the change of the
dominant tree species etc. are included in the site
types. Permanent differences result in a new forest
site type, if the differences are remarkable, and if not
so essential, in a subtype of forest site.” This deinition
has also been published in English (Cajander 1949b:
31), but unfortunately the translation is very unclear.
The Finnish forest site type system is based on
the assumption that the presence of different plant
species is determined by the ecology of the habitat.
The forest habitat is constant, but the vegetation can
change, for instance, due to forest ire or by cuttings.
The primary vegetation can vary to some extent, but
in succession, primary habitat factors determine the
inal structure of the vegetation. These factors do not
change. The secondary factors are the state of tree
stand, tree species, amount of light, etc., and they can
affect the vegetation, but they do not change the site
itself. The habitat characterized by certain vegetation
relects also the potential productivity of the site. In
practice the identiication of a forest site type is easier
in a mature forest, where the effect of secondary
factors is most stable.
Thus, all those plant communities, which relect the
same productivity and are in or are developing to a
more or less stable state (so called “normal stage”),
can be counted in the same forest site type.
The forest site types were, in the beginning, described
in southern Finland, but soon it became evident
that the forest vegetation and the productivity of
forests were different in northern Finland. The son
of A.K. Cajander, Aarno Kalela (1961), developed
the regionality of forest vegetation zones, which
was further developed by Ahti et al. (1968) (Fig. 1).
Presently, the regional forest site types published by
Kalliola (1973) are accepted (Table 1).
Fig. 1. Forest vegetation zones in Finland and
adjacent areas: Hemiboreal, Southern boreal, Middle
boreal, Northern boreal, and Orohemiarctic; O ─
oceanity, C ─ continentality, O3 is most oceanic and
C3 most continental (modiied after Ahti et al., 1968).
107
Table 1. The basic series of forest site types in different vegetation zones (modiied after Kalliola 1973).
Table 1. The basic series of forest site types in different vegetation zones (modified after Kalliola 1973).
Zone
Forest site
type group
Poor dry
Hemiboreal
and south
boreal
Cladina type
Dry
Calluna type
Semi dry
Vaccinium
vitis-idaea
type
Mesic
Vaccinium
myrtillus type
Semi herb
rich
OxalisVaccinium
myrtillus type
Herb rich
OxalisMaianthemum
type and others
Middle boreal
North boreal,
southern
subzone
North boreal,
middle
subzone
Cladina type
Cladina type
Cladina type
EmpetrumCalluna type
Vaccinium
myrtillusCallunaCladina type
EmpetrumVaccinium
vitis-idaea
type
Vaccinium
vitis-idaea-V.
myrtillus type
Geranium
sylvaticumOxalisVaccinium
myrtillus type
Geranium
sylvaticumOxallisMaianthemum
type and others
EmpetrumVaccinium
myrtillus type
HylocomiumVaccinium
myrtillus type
Vaccinium
uliginosum-V.
vitis-idaeaEmpetrum
type
Vaccinium
uliginosumEmpetrum-V.
myrtillus type
LedumVaccinium
myrtillus type
North boreal,
subalpine
subzone
Subalpine
Empetrum
Lichenes type
Subalpine
EmpetrumLichenesPleurozium
type
Subalpine
EmpetrumVaccinium
myrtillus type
Not typified
Geranium
sylvaticumVaccinium
myrtillus type
Not typified
Not typified
Geranium
sylvaticumDryopteris
type and others
Not typified
Not typified
108
Mire Vegetation
The classiication of mire vegetation in the Finnish
school was created parallel to forest site types. It is
based on ecological gradients, mainly moisture and
base content. The original aim for mire vegetation
classiication was to create a system suitable for
practical purposes, for the evaluation of productivity
potential of mires for agriculture and forestry (Cajander,
1913). Mire vegetation was divided into four main
groups: spruce mires, pine bogs, open mires, and rich
fens. This system was further developed by Ruuhijärvi
(1960) and Eurola (1962). Later, two more main
groups were added, spring fens and looded swamps
(Ruuhijärvi, 1983; Eurola et al., 1984). The reasoning
behind the creation of these ecological gradients was
recently clariied by Ruuhijärvi & Lindholm (2006). In
many cases one habitat gradually changes into another,
and, thus, habitats can be seen as continuums (Fig.
2). Topography and, for example, bedrock conditions
can cause more abrupt boundaries. At present, in
botanical classiication there are approximately 80
different mire site types. For practical purposes, like
forestry, some 30 site types are separated (Laine &
Vasander, 2005).
In the Finnish approach, mires and forests have
been understood to form a continuum. In a botanical
sense, marginal habitats are classiied as mires if
the vegetation is dominated by mire species (e.g.,
Sphagna) and there is a peat layer, even though in
forestry they are classiied as forests on the basis of
the density, size, and growth of tree layer.
Mire Zones
Mire massif types have basically been deined by
Cajander (1913), and the system was developed
further by Ruuhijärvi (1960) and Eurola (1962). When
good aerial photographs became available of the
entire country in the late 1970s, the zonation was
re-evaluated (Ruuhijärvi, 1983; Lindholm & Heikkilä,
2006) (Fig. 3).
Bog Massifs
Bog massifs are divided into three main climatic zones.
In addition, there are also azonal bog massifs, which
occur across the entire country in places suitable for
bogs (Sphagnum fuscum bogs and pine forest bogs).
Plateau bogs are typical in southwestern Finland. The
weakly developed low hummock ridges and hollows
are not clearly oriented in the plain centre of the
massif. There is no concentric structure. In the largest
plateau bogs there are bog pools. In the margins of
the central plateau there is more clear orientation of
the hummock ridges and hollows.
Fig. 2. Finnish mire site type system arranged along the main ecological gradients (Ruuhijärvi & Lindholm,
2006).
109
Concentric bogs are concave mire massifs, developed
on plain or very gently sloping terrain. Their hummock
ridges and hollows form circles around the highest
point of the mire. In the most even parts of concentric
bogs the hollows are wide, but in more sloping
parts the hollows are narrow and elongated along
contours.
Eccentric bogs are gently sloping bogs, which follow
the forms of mineral soil. Their centres do not rise
above the margins. Hummock ridges and hollows are
perpendicular to the slope of the mire.
Aapamires
Aapamires are mire massifs that are minerotropic
in the central parts and mainly have a thick peat
layer. The water in aapamires is to a great extent
lowing to the mire from the surrounding mineral soil
areas. Aapamires can preserve their minerotrophic
state despite peat accumulation because abundant
snowmelt waters wash humic acids from the mires
in springtime. Quickly rising spring loods low into
watercourses through mires and partly remain in the
mires. When the water table sinks in the summertime
due to evapotranspiration, the mire surface
consolidates and follows the water table, preserving
moisture, which hinders the growth of hummock
Sphagna (see Tahvanainen, 2005).
In middle boreal sedge aapamires, relatively dry
minerotrophic lawn surfaces dominate, especially in
the southern part of the zone. Typically, the strings are
very low or they are lacking. In the largest mires and
especially in Northern Ostrobothnia, there are wide
lark fens with clear string pattern. Further north, in
lark aapamires the structures are more pronounced.
It is characteristic for pounikko aapamires that the
larks are large and wet, and that the strings form a
net-like pattern that is not uniform. In the margins of
mires there are also Sphagnum fuscum bogs with
pounu hummocks formed by ground frost, which does
not melt completely during every summer.
Sloping fens occur typically as parts of aapamire
massifs and sometimes as independent entities in hill
and fell areas, where local climate is moist and cool,
and precipitation is abundant.
Fig. 3. The zonation of mire massifs in Finland
(modiied after Ruuhijärvi, 1983). 1 – concentric and
plateau bogs, 2 – eccentric bogs, 3 – sedge aapamires
4 – lark aapamires, 5 – pounikko aapamires, 6–7 –
palsamires and orohemiarctic mires.
Palsamires and Orohemiarctic Mires
Palsamires are a hemiarctic mire massif type (Seppälä,
2006). Palsas are formed in a continental climate with
cold winters and mean annual temperature ≤ -1º and
short growing season. Precipitation is low and due to
strong winds the snow cover is unevenly distributed.
Palsamires are named after the giant hummocks,
palsas, with a permafrost core.
The concept of orohemiarctic mires covers all mires
above the forest limit in fells. They are characterized
by a thin peat layer and constant inluence of
groundwater and meltwaters.
References
Ahti, T., Hämet-Ahti, L., & Jalas, J. 1968. Vegetation
zones and their sections in northwestern Europe.
Annales Botanici Fennici 5: 169–211.
Cajander, A. K. 1909. Über Waldtypen. Acta Forestalia
Fennica 28(2): 1–175.
Cajander, A. K. 1913. Studien über die Moore
Finnlands. Acta Forestalia Fennica 2(3): 1–208.
Cajander, A.K. 1949a. Metsätyypit ja niiden merkitys
(Forest Types and Their Signiicance). Acta
Forestalia Fennica 56. 69 pp.
Cajander, A. K. 1949b. Forest Types and Their
Signiicance. Acta Forestalia Fennica 56. 71 pp.
110
Eurola, S. 1962. Über die regionale Einteilung der
südinnischen Moore. Annales Botanici Societatis
Zoologicae Botanicae Fennicae Vanamo 33(2): 1–243.
Ruuhijärvi, R. 1960. Über die regionale Einteilung der
nordinnischen Moore. Annales Botanici Societatis
Zoologicae Botanicae Fennicae Vanamo 31(1): 1─360.
Eurola, S., Hicks, S,. & Kaakinen, E. 1984. Key to
Finnish mire types. Pages 11–117 in Moore, P.D., ed.
European Mires. Academic Press, London.
Ruuhijärvi, R. 1983. The Finnish mire types and their
regional distribution. Pages 47─67 in Gore, A.J.P., ed.
Mires: Swamp, Bog, Fen and Moor. Ecosystems of the
World 4B: Elsevier, Amsterdam.
Kalela, A. 1961. Waldvegetationszonen und ihre
klimatischen Paralleltypen. Archivum Societatis
Zoologicae Botanicae Fennicae Vanamo 16
Supplementum: 65–83.
Kalliola, R. 1973. Suomen kasvimaantiede. (The
Geobotany of Finland)–WSOY. Porvoo–Helsinki. 308
pp.
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in Lindholm, T. & Heikkilä, R., eds. Finland—Land of
Mires. The Finnish Environment 23/2006. http://www.
ymparisto.i/default.asp?contentid=194173&lan=en.
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Förhandlingar 11: 1─132.
Ruuhijärvi, R. & Lindholm, T. 2006. Ecological
gradients as the basis of Finnish mire site type
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Environment 23/2006. http://www.ymparisto.i/default.
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of Mires. The Finnish Environment 23/2006. http://www.
ymparisto.i/default.asp?contentid=194173&lan=en.
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(Forest Site Type Guide, Finnish translation from
Russian). Silva Fennica 99. 182 pp.
Tahvanainen, T. 2005. Diversity of water chemistry and
vegetation of mires in the Kainuu region, middle boreal
Finland. University of Joensuu, Ph.D. Dissertations in
Biology 33: 1─26.
Tuhkanen, S. 1984. A circumboreal system of climatic
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127: 1─50.
111
The Importance of Mire Complexes for the Development of a Circumboreal
Vegetation Map (CBVM)
Klaus Dierssen
kdierssen@ecology.uni-kiel.de, Ecology Centre, University of Kiel, Germany
Extended Abstract
A circumboreal vegetation map will be primarily
based on the distribution patterns of the various
taiga systems that make up the boreal biome.
These patterns will relect the loristic differences of
site conditions and regionally different climate and
vegetation developments since the last glaciation.
Besides the predominating forest systems, particularly
in Canada and Russia, vast areas of nonforested
wetlands and peatland systems comprise the boreal.
Mire systems may occur over broad areas, especially
in oceanic parts of the northern boreal zone, and
cover more than 50% of the land surface. On a global
scale, these mire systems contain by far the largest
peat deposits.
Mires were commonly seen as azonal vegetation
complexes because their species composition
strongly depends on the hydrology of the catchment
area features; these hydrological features may be
more important than climatic conditions. This is
especially true for the vegetation types of microsites,
for instance ombrotrophic bogs show similar largescale species composition, although clear regional
(zonal) differences are illustrated when mapping their
community complexes and structural composition.
Currently, the structural pattern of palsa and mixed
string fens, for example, can be distinguished quite
easily from the different surface structures of raised
bogs by using satellite imagery. One key task for
broad-scale phytogeographical mapping in boreal
zones will be to characterize potential regional
coincidences between mire and forest systems.
Following the approach used in the Circumpolar Arctic
Vegetation Map (CAVM), the internationally recognized
Braun-Blanquet plant community nomenclatural
system may be applied for the development of the
mapping units and legend hierarchy.
112
Forest and Mire Vegetation on the Maps of Two Nature Reserves:
Comparison of European and Western Siberian Northern Taiga Regions
Vasily Neshatayev
Saint-Petersburg State Forest-Technical Academy, Saint-Petersburg, Russia
Abstract
Vegetation was studied in the Russian northern taiga
state nature reserves of Lapland (Lapland State
Reserve [LSR], Murmansk region) and VerhneTazovsky (Verhne-Tazovsky Reserve [VTSR] western
Siberia. Six-hundred eighty-two relevés containing
data on loristic composition; tree-stand age, height,
diameter, basal area, soils and environmental
conditions and disturbances (especially ire); and 336
relevés with tree layer, soil proile, and forest type
were recorded. Forest vegetation was classiied by
the following using a two-dimensional approach: (1)
association series taking into account the similarity of
ground layer vegetation, position in the succession
after disturbances, environmental conditions, and
soil properties (especially wetness and fertility); and
(2) associations, formations, and types of vegetation
(taking into account dominant species and life forms).
Vegetation dynamics after forest ires was studied.
Mire vegetation was classiied taking into account
dominant species, life forms, and loristic composition
indicating wetness and fertility of peat. Vegetation
maps at scale 1:50,000 (LSR, 3,200 km2) and
1:100,000 (VTSR, 6,000 km2) were compiled. The
diversity of the plant cover of the reserves appeared
similar, differing mostly in the presence of Pinus
sibirica, Larix sibirica, and Abies sibirica in VTSR
and Siberian herb species herb-rich forests on wet
soils with running water. Satellite imagery aided in
the mapping of the vegetation at scale 1:50,000 and
smaller.
Keywords: boreal vegetation, Europe, forest ires,
northern taiga, plant succession, satellite imagery,
vegetation mapping, west-Siberia.
Introduction
Vegetation was studied in the Russian northern taiga
regions of Murmansk and Western Siberia. Before
the beginning of our investigations, the Murmansk
region was studied intensively; however, the VerhneTazovsky Reserve was one of the least studied areas
of northwestern Siberia. The targets of our study were:
(1) to determine the diversity of the plant and soil
cover of the two regions; (2) to determine correlations
between vegetation, soils, and climate; (3) to elucidate
the inluence of forest ires on boreal vegetation; and
(4) to interpret the vegetation from satellite imagery
for the purpose of mapping the vegetation at scale
1:50,000 and smaller.
Materials and Methods
Study Areas
The study was carried out in Lapland State Reserve –
LSR (area - 3,200 km2), located in the central part of
Murmansk Region, and in Verhne-Tazovsky Reserve
– VTSR area - 6,300 km2), located in western Siberia
on the watershed between Ob, Taz, and Enisei River
Basins. In LSR altitude varies from 143 m to 980 m
above sea level and in VTSR from 48 m to 285 m
above sea level. In both reserves well-drained sandyloams and sandy soils are predominant, and in LSR
granites and gneiss rocks are predominant. According
to geobotanical subdivisions, LSR and VTSR are
located in the northern taiga subzone.
Vegetation Survey and Classiication
The study of LSR vegetation was based on data
obtained at 240 sample plots in the forest belt of LSR
in 1986 and 1987, and in 132 sample plots located in
mires in 2000 and 2008. Sample plots were located
at transects crossing the main relief elements and
the key-areas representing the different parts of the
Reserves. In LSR 90 permanent sample plots were
surveyed in 1987 and again in 2006 and 2007. The
study of VTSR vegetation was based on data obtained
at 718 sample plots in 1991, 1993, 1997, and 1999.
113
In both reserves the dimension of sample plots
located in forests was 50 х 50 m and 20 x 20 m and in
shrub, grassy, and moss communities, 10 х 10 m, and
in natural boundaries of communities, 5 x 5 m. The
total density of the tree layer, average and maximal
height and diameter of stems, and the basal areas of
tree cohorts were measured at sample plots. At each
sample plot the association was determined. The age
of trees was determined by the scoring of tree rings
on the cores. The coverage of layers and species
was estimated at 372 sample plots in LSR and at 310
sample plots in VTSR.
For each sample plot the time of succession of
vegetation without the inluence of ires (TF) was
determined by counting tree rings from trees with
ire damage or by using iles of the reserve. For the
stands that appeared later, TF was accepted to be
equal to the age of the oldest tree generation without
ire damage. For spruce (Picea obovata) and Siberian
pine (Pinus sibirica) forests with no trace of ire, TF
was considered to be equal to the time that passed
since appearance of these tree species in the study
area (3,000 in LSR and 4,000 years in VTSR).
Probability of ires for 10 years (PF) was determined
for each sample plot in VTSR. For vegetation with
TF < 10 years it was considered to be equal to 1.0.
For vegetation with TF ≥ 10 years it was calculated
as the quotient from division of the number of ires
registered on the sample plot by the age of the oldest
tree generation (the maximal age - Т) expressed in
tens of years.
In LSR the probability of ires was determined for each
forest type for the period from 1895 to 1987 (92 years)
using a vegetation map, forest plans, and a ire history
map compiled by Pushkina (1960) and updated
in 1987 on the basis of LSR iles (see Neshatayev,
1991). The probability of ire was determined as a
proportion of burnt areas for each forest type. Fire
rotation period (R) was estimated according to the
following equations: R = 10/PF (for VTSR); R = 93/
PF (for LSR).
Classiication included preliminary sorting of relevés
and their ordination based on ecological scales of
plants after L.G. Ramensky (Aleksandrova, 1973)
and ordination of sample plots and species using
the method of reciprocal averaging by Hill (1973).
Interpretation of the rank of the preliminary relevés
groups was in accordance with the traditions of the
Soviet-Russian phytosociological school of V.N.
Sukachev (Aleksandrova, 1973). The classiication
of the forest vegetation included two subdivisional
pathways. One of them was based on the loristic
composition of the subordinate layers and the similarity
of the land type. The main syntaxa based on these
features was the series (cycle) of associations similar
to the forest type by Cajander (1909). The cycle of
associations steadily progressed toward a climax
or subclimax community and those that could pass
through it during the life of one generation of the main
tree dominant did so under the conditions of existing
geomorphological and climatic features.
The other successional path took into consideration
dominant tree species and the density of the tree
canopy. Using dominant associations, groups of
associations, formations, and classes of formations,
vegetation types were established. The associations
were distinguished by dominants and characteristic
combinations of ecological-phytosociological groups
of species. Associations were established by taking
into consideration the results of the study of vegetation
dynamics on different site types.
The formation united communities with identical or
ecologically and biomorphologically similar dominants
of the main layer (the layer with maximal phytomass
production). In the case of polydominant communities,
formations were distinguished by the dominant life
forms and the structure of the communities. The
formations with similar life form dominants and those
having similar ecological optimums of site wetness
and temperature factors were united into vegetation
types.
Vegetation Mapping
The LSR vegetation map at scale 1:50,000 was
compiled in 1987 by the Laboratory of Geography and
Cartography of Vegetation of the Komarov Botanic
Institute, Russian Academy of Sciences. The project
was led by the head of the laboratory, T.K. Yurkovskaya.
Forest vegetation was mapped by V.Y. Neshataev
and B.B. Kovalenko, tundra belt vegetation by S.S.
Holod, and mire vegetation by T.K. Yurkovskaya. In
the course of forest vegetation mapping, the aerial
114
photos at scale 1:40,000 were interpreted. As training
data for aerial photo interpretation, data was collected
at sample plots. Three key-areas (20-30 km2 each)
were mapped by visiting each contour. Observations
along routes 150 km length, forest inventory materials
implemented in 1983 by the North-Western Forest
Inventory Enterprise, and a ire history map compiled
by N.M. Pushkina were used.
In 2007–2008 on the basis of post-ire dynamics,
analysis was carried out on the sample plots
(including 90 permanent plots), N.M. Pushkina’s
successional scheme (1960) was reconsidered,
and new quantitative evidence that characterized
the rate of post ire succession was received. Using
this evidence, the information on community type
and forest stand age in 1986–1987, and information
from a ire history map, the type of community of
every unit was actualized (for 2007). As a result
an actual vegetation map of LSR at scale 1:50,000
was compiled. Supervised classiication of Landsat
images (2005) was used to estimate the possibilities
of computer-assisted vegetation mapping of LSR.
The vegetation cover of ive key areas (3–10 km2) in
VTSR in 1991, 1993, 1997, and 1999 was mapped
at the scale 1:25,000 by visiting each map contour
previously distinguished and by comparing aerial
photography with topographic maps. Vegetation was
mapped based on ive transects (length 5–10 km) that
crossed the reserve.
For each sample plot, the successional period without
the inluence of ires (TF) was determined by counting
tree rings from years covering ire damage or by using
iles of the reserve. For the stands that appeared later,
TF was equal to the age of the oldest tree generation
that had no ire damage. TF for ir and cedar forests,
with no trace of ire, was considered to be 4,000
years. That is the time that would have passed since
the moment of the appearance of the cedar on the
terrain. The probability of ires for 10 years (PF) was
determined for each sample plot. For TF < 10 years
it was assumed to be equal to 1.0. For TF ≥ 10 years
it was calculated as the quotient from division of the
number of ires registered on the sample plot (NF) by
the age of the oldest tree generation (the maximal
age - Т) expressed in tens of years.
Supervised classiication of Landsat for LSR and
KATE-200 satellite images were used for VTSR to
compile vegetation maps at scale 1:50,000 (LSR) and
1:100,000 (VTSR). As a teaching sample only half of
the relevé data were used. The other half of the relevé
data was used for testing the precision or accuracy
of the map. For testing the map’s precision (P) we
applied the original equation:
P=100*(N- Σ(Ei,j / Em))/N i=1,…,N, j=1,…,N,
where Ei,j – Euclidean distance between centroids
of i-th observed and j-th mapped syntaxon, Em –
Euclidean distance between centroids of herb-rich
spruce forest and lichen-rich pine forest (maximal
possible distance in our case, species cover used), N
– the number of sample plots used in the sample test.
Results and Discussion
Relationship Between Forest Vegetation and Soil
and Climate
In LSR spruce (with Picea obovata) is predominant,
and pine (Pinus sylvestris) and birch (Betula
pubescens subsp. subarctica) forest occur in the
lowland forest belt. Populus tremula is rare in LSR.
The mountain birch open forests (Betula pubescens
subsp. czerepanovii) form a speciic belt in mountains
at altitude 400-500 m a.s.l. Pinus sibirica and Pinus
sylvestris forests are predominant in VTSR, and
larch (Larix sibirica), birch (Betula pubescens), aspen
(Populus tremula), and willow (Salix viminalis) loodplain forests are common in VTSR. Fir forests (Abies
sibirica) are very rare in VTSR.
Diagnostic
species of vegetation series were
established (D – dominants, * - only in LSR, ** dominants at the irst stages of post ire succession,
*** - only in VTSR). Petrophyto-Cladinosa* (on
primitive soils on rocks): Calluna vulgaris* (D),
Vaccinium vitis-idaea (D), Vaccinium myrtillus (D),
Empetrum hermaphroditum (D), Arctostaphylos
uva-ursi (D), Diphasiastrum complanatum, Carex
ericetorum,
Cladonia
cariosa,
Rhacomitrium
microcarpum, Parmelia centrifuga, Cornicularia
odontella, Stereocaulon grande, Cladina stellaris
(D), C. arbuscula (D), C. rangiferina (D), Cladonia
borealis, C. cornuta (D), C. amaurocraea, C. uncialis
(D), C. gracilis, Cetraria nivalis, Stereocaulon
alpinum, S. paschale (D), Umbilicaria sp., Polytrichum
115
juniperinum (D)**, P. piliferum (D)**, Ceratodon
purpureus (D)**, Pohlia nutans (D)**; Cladinosa (on
well drained sands): Calluna vulgaris* (D), Vaccinium
vitis-idaea (D), Vaccinium myrtillus (D), Empetrum
hermaphroditum (D), Arctostaphylos uva-ursi (D),
Diphasiastrum complanatum, Carex ericetorum,
Cladina stellaris (D), C. arbuscula incl. C. arbuscula
subsp. mitis, (D), C. rangiferina (D), Cladonia
borealis, C. cornuta (D), C. amaurocraea, C. uncialis,
C. gracilis, C. crispata, C. furcata, Cetraria nivalis,
Stereocaulon alpinum, S. paschale (D), Polytrichum
juniperinum (D)**, P. piliferum (D)**, Ceratodon
purpureus (D)**, Pohlia nutans (D)**; HylocomiosoCladinosa (on well-drained sands): – intermediate
type between Cladinosa and Empetroso-Vacciniosa;
Empetroso-Vacciniosa (on well-drained sands):
Calluna vulgaris* (D), Vaccinium vitis-idaea (D),
Vaccinium myrtillus (D*), Empetrum hermaphroditum
pilosa, Trientalis europaea, Filipendula ulmaria, Viola
epipsila, Equisetum pratense, Trollius europaeus,
Dryopteris linneana, D. expansa, Athyrium ilix-femina,
Pleurozium schreberi (D), Hylocomium splendens (D),
Rhytidiadelpus triquetrus (D); Fruticoso-Herbosa***:
Veratrum lobelianum***, Atragene sibirica***, Cacalia
hastata***, Aconitum septentrionale***, Thalictrum
lavum***, Stellaria bungeana***, Calamagrostis
langsdorfii (D), Filipendula ulmaria, Senecio
nemorensis***, Delphinium elatum***, Aconitum
volubile***, Heracleum dissectum***, Trientalis
europaea, Pyrola rotundifolia (D), Linnaea borealis (D),
Maianthemum bifolium, Orthilia secunda, Geranium
krylovii***, Galium boreale, Hylocomium splendens
(D), Ptilium crista-castrensis, Rhytidiadelphus
triquetrus; Herbosa: Carex cespitosa (D), Filipendula
ulmaria (D), Calamagrostis canescens* (D), C.
langsdorfii (D), Comarum palustre, Linnaea borealis,
(D), Pleurozium schreberi (D), Dicranum sp. (D);
Fruticuloso-Hylocomiosa (on poorly drained sands,
normally drained loam sands and loams): Empetrum
hermaphroditum (D), Vaccinium myrtillus (D),
Vaccinium vitis-idaea (D), Vaccinium uliginosum (D),
Equisetum sylvaticum (D), Lerchenfeldia lexuosa*
(D**) Linnaea borealis, Solidago virgaurea, Luzula
pilosa, Trientalis europaea, Pleurozium schreberi
(D), Hylocomium splendens (D), Dicranum sp. (D),
Peltigera aphtosa, Nephroma arcticum, Polytrichum
commune; Fruticuloso-Polytrichosa (poorly drained
sands, loam sands and loams): ─ intermediate type
between Fruticuloso-Hylocomiosa and Sphagnosa
girgensohnii; Sphagnosa girgensohnii (peatland or
gley soils): Vaccinium vitis-idaea (D), V. myrtillus (D),
V. uliginosum (D), Empetrum hermaphroditum (D),
Equisetum sylvaticum (D), Ledum palustre (D), Rubus
chamaemorus (D), Carex globularis (D), Betula nana
(D), Sphagnum girgensohnii (D), S. angustifolium
(D), Polytrichum commune (D) **, S. nemoreum, S.
russowii, S. warnstorii; Sphagnosa angustifolii
(peatland): Vaccinium uliginosum (D), Ledum
palustre (D), Oxycoccus palustris, Carex vaginata,
Eriophorum vaginatum, Andromeda polifolia, Rubus
chamaemorus, Carex globularis, Betula nana (D),
Sphagnum angustifolium (D), S. nemoreum, S. fuscum
(D); Sphagnoso-Herbosa (peatland): ─ intermediate
type between Sphagnosa girgensohnii and Herbosa;
Geraniosa*: Geranium sylvaticum (D), Geum rivale
(D), Calamagrostis canescens (D), C. langsdorfii
(D), Linnaea borealis, Solidago virgaurea, Luzula
Solidago virgaurea, Luzula pilosa, Trientalis europaea,
Geranium sylvaticum*, G. krylovii***, Geum rivale,
Comarum palustre, Viola epipsila, Equisetum pratense,
Rubus arcticus, Trollius europaeus*, T. asiaticus***,
Hylocomium splendens (D), Rhytidiadelpus triquetrus
(D), Sphagnum squarrosum (D), Drepanocladus
uncinatus (D), Rhizomnium pseudopunctatum (D),
Rhizomnium punctatum (D).
Forest diversity in the reserves appeared similar,
differing mostly in the presence of Pinus sibirica, Larix
sibirica, Abies sibirica, aspen stands, and Siberian
herb species in herb-rich forests on wet soils with
running water in VTSR and with the absence of
forests on rocks in VTSR.
The comparison of northern and southern taiga
vegetation described in literature (Neshatayev &
Neshatayeva, 1993; Neshatayeva & Neshatayev,
1993, 1995; Dierßen, 1996; Fedorchuk et al., 2005)
showed that northern taiga forests on well drained
sites and southern taiga at mires were rich with dwarf
shrubs (Ledum palustre, Vaccinium uliginosum,
Empetrum nigrum s.l., Betula nana) and poor with
herbs (Maianthemum bifolium, Oxalis acetosella, etc.),
grasses (Calamagrostis arundinacea), and ferns.
Some forest types such as Piceetum cladinosum,
Pinetum sibirici cladinosum, Betuletum cladinosum,
Piceetum angustifolii sphagnosum occurred only in
northern taiga.
116
Vegetation Dynamics Under the Inluence of Fires
Stages of forest succession after ires and time of
forest change were established for different soil
conditions in Lapland (LSR) and Verhne-Tazovsky
(VTSR) State Reserves. It was established that all
pine forests of the studied reserves were induced by
ires. Cladinosa forests exist because of repeated
ires and could be replaced by dark coniferous
forests (Spruce or Siberian pine) rich with mosses
(Empetroso-Vacciniosa). The time needed for this
change (200–600 years) exceeded the ire rotation
period for Cladinosa series (154 years at LSR and 85
at VTSR, Table 1).
Fire rotation periods (Table 1) and stages of forest
succession after ire and time of forest change were
established for the different soil conditions present in
LSR and VTSR.
These results concerning the ire rotation period
conirm the hypothesis of Gordyagin (1900), who
was the irst to elucidate the role of forest ires in
the maintenance of lichen-dominated pine forests
and suggested that Pinetum cladinosum could be
replaced by spruce or Siberian pine forest with moss
cover. Similar opinions on the dynamic position of
pine, and especially lichen rich pine forests, were
expressed by Maikawa & Kershaw (1976), Foster
(1983), Faltinowicz (1986), Engelmark (1987), and
Ahti & Oksanen (1990). So, lichen-dominated pine
forest is not a climax community as some scientists
believed.
It appeared that changes after ire depended on the
intensity of ire. Intensive ires caused the change
from Empetroso-Vacciniosa to Cladinosa. Fires
of moderate intensity caused the change from
Empetroso-Vacciniosa to Hylocomioso-Cladinosa.
Low intensity ires did not change the series type.
Stages of recovery and the rate of succession after
intensive ires in the conditions of Cladinosa were
similar in VTSR and LSR. Characteristic species that
occurred in each successional stage are given next
with the average time that has passed since the last
ire given in parentheses: 1. Ceratodon purpureus +
Pohlia nutans + Polytrichum piliferum + P. juniperinum
(<15); 2. Cladonia cornuta + C. gracilis + C. sulphurina
+ C. borealis + C. deformis + C. uncialis (15–45); 3.
Cladina arbuscula (incl. C. mitis) + C. rangiferina
(45–105); 4. Cladina stellaris (105─230); 5. Cladina
stellaris + Pleurozium schreberi + Dicranum sp. (230–
270); 6. Pleurozium schreberi + Dicranum sp. (>270).
Comparison of our results with those of other studies
showed that the post-ire recovery of forest litter in
Scots pine forests in different regions of the boreal
zone has a similar sequence of stages (Maikawa &
Kershaw, 1976; Ahti & Oksanen, 1990; Gorshkov et
al., 1996).
The post ire succession on FruticulosoHylocomiosa sites differed from succession on
drained sands because they were wetter and had
more water capability and their loamy soil acted as
a molecular sieve preventing leaching of humus. In
Fruticuloso-Hylocomiosa, Chamerion angustifolium,
Calamagrostis epigeios, Avenella lexuosa (in LSR),
Polytricum commune, and P. juniperinum are common
on the irst stages of succession after ire. Birch and
aspen (only in VTSR) are more frequent than pine on
burnt areas of Fruticuloso-Hylocomiosa. The change
by spruce or Siberian pine forest lasted approximately
150 years.
Series of Associations
M
Cladinosa*
Hylocomioso-Cladinosa, Empetroso-Vacciniosa
Fruticuloso-Hylocomiosa*
Polytrichosa, Sphagnosa girgensohnii, Sphagnosa
Herboso-Filipendulosa, Herboso-Sphagnosa
154
LSR
SD
43
N
4147
M
85
VTSR
SD
37
196
53 5170 228
56
266 1082 24544 604
94
429 1075
301 1317 1825
529 1100
45 250 1901
N
153
195
252
105
54
* ─ the difference is signiicant (p < 0.1), M ─ mean value, SD ─ standard deviation, N ─
number of observations.
Table 1. Fire rotation periods (years) in Lapland State Reserve (LSR) and VerhneTazovsky Reserve (VTSR).
117
Fires on wet habitats occurred only in very dry years
(Table 1). Fires on these sites as a rule led to the total
destruction of tree layer consisting of spruce, Siberian
pine, or birch. Three stages of post ire succession were
found on wet sites with stagnant water (FruticulosoPolytrichosa, Sphagnosa girgensohnii, Sphagnosa
angustifolii): (1) Marchantia polymorfa + Funaria
hygrometrica + Leptobryum pyriforme + Polytrichum
commune + P. strictum (lasted approximately 10
years); (2) Polytrichum commune, P. strictum (10–50
years); and (3) Sphagnum girgensohnii (>50 years).
On burnt areas on wet habitats with running water
(Geraniosa, Fruticoso-Herbosa, and Herbosa) birch
usually regenerated. The full prevalence of spruce
or Siberian pine on these habitats occurred rarely,
even after a long period of succession. Time of
stabilization of species composition of the tree stand
was approximately equal to 250 years. In the grass
layer during the irst years after ire, the same species
were predominant as in the forests with no traces of
ire (Filipendula ulmaria, Calamagrostis canescens,
C. langsdorfii, Carex aquatilis, C. cespitosa). As a
whole the loristic structure of communities under the
inluence of ires on wet habitats with running water
sites varied insigniicantly.
Mire Vegetation
For mire communities we distinguished six main
formations including their dominants (D) and
diagnostic species (DS).
1. Sphagneta angustifolii – oligotrophic dwarfshrub bog moss communities. DS: Empetrum
hermaphroditum, Ledum palustre, Betula
nana, Vaccinium uliginosum, Chamaedaphne
calyculata (rare in LSR), Andromeda polifolia,
Oxycoccus palustris, O. microcarpus, Drosera
rotundifolia, Sphagnum fuscum (D), S.
angustifolium (D). Includes the association
Sphagnetum pinoso sibirici-cladinosum*** ─
slightly moist oligotrophic permafrost mire.
DS: Pinus sibirica***, Vaccinium vitis-idaea,
Pleurozium schreberi, Cladina stellaris, C.
rangiferina, C. arbuscula s.l.; association
Sphagnetum magnopinosum (slightly moist
oligotrophic mire, DS: Pinus sylvestris f.
litvinovii ); Sphagnetum nanopinosum (slightly
moist oligotrophic mire, DS: Pinus sylvestris
f. wilkomii); Sphagnetum pinosum sibirici***
(slightly moist oligotrophic mire); association
Sphagnetum fruticuloso-cladinosum - slightly
moist oligotrophic mire with lichens, DS: Cladina
rangiferina, C. arbuscula s.l.; Sphagnetum
fruticulosum – moderately moist oligotrophic
mire.
2. Sphagneta cuspidti – extremely moist
oligotrophic mire with Sphagnum sect.
Cuspidata as dominants, DS: Carex limosa
(D), Scheuchzeria palustris (D), Eriophorum
vaginatum (D), Drosera rotundifolia, D. anglica,
Sphagnum majus (D), S. balticum (D).
3. Herbosphagneta ─ mesotrophic sedge and herb
rich bog moss communities. DS: Andromeda
polifolia, Oxycoccus palustris, Carex limosa,
C. chordorrhiza, C. lasiocarpa, C. rostrata,
C. rotundata, Eriophorum polystachyon,
Menyanthes trifoliata, Comarum palustre,
Equisetum palustre, E. luviatile, Drosera
rotundifolia, D. anglica, Sphagnum angustifolium
(D), S. lexuosum, S. majus (D), S. fallax (D), S.
centrale, S. warnstorii, S. riparium, Aulacomnium
palustre, C. stramineum, Calliergon cordifolium,
Warnstoria exannulata. Includes moderately
moist mesotrophic mires: association with
open stands of trees─Herbosphagnetum
betuloso-pinosum (DS: Betula pubescens,
Pinus sylvestris); dwarf shrubs as dominants
association Herbosphagnetum fruticulosocaricosum (DS: Ledum palustre, Betula
nana, Vaccinium uliginosum, Chamaedaphne
calyculata); associations of extremely moist
mesotrophic mires with herbs: Herbosphagnetum
menyanthosum and sedges Herbosphagnetum
caricosum
limosae;
Herbosphagnetum
caricosum
rostratae,
Herbosphagnetum
caricosum lasiocarpae, Herbosphagnetum
caricosum
rotundatae,
Herbosphagnetum
caricosum chordorrhizae, Herbosphagnetum
eriophoretum polystachionis.
4.
118
Magnocariceta ─ sedge eutrophic mire:
Carex acuta (D), C. aquatilis (D), Warnstoria
exannulata,
Sphagnum
squarrosum,
Plagiomnium
ellipticum,
Pseudobryum
cinclidioides.
5. Limnoherbeta ─ herb vegetation of secondary
mire lakes: Menyanthes trifoliata (D), Comarum
palustre (D), Equisetum luviatile.
Types of mires based on predominant combinations of
plant communities were established:
1. Palsa mire*** – Sphagnetum pinoso sibiricicladinosum + Herbosphagneta;
2. Ridges-hollow bog – Sphagnetum nanopinosum +
Sphagnetum fruticuloso-cladinosum + Sphagnetum
fruticulosum, Sphagneta cuspidti;
and structure of herb–dwarf shrub and moss–lichen
layers. Further subdivision was made according to the
prevailing tree species (or lack of tree layer on burnt
area), tree age class, and dominant species groups
characterizing post-ire stages dynamics. The LSR map
also showed communities located in the atmospheric
pollution zone with a signiicantly damaged mosslichen layer and forest stands.
4. Dwarf shrub bog with open stands of coniferous
trees Sphagnetum magnopinosum, Sphagnetum
pinosum sibirici*** ;
The structure of the legend for forest vegetation and
mires with open tree stands is shown below. The
abbreviations for tree dominants occurring in each
series are given (*** ─ only in VTSR): S ─ Picea
obovata, C ─ Pinus sibirica***, L ─ Larix sibirica***, P
─ Pinus sylvestris, F ─ Abies sibirica ***, A ─ Populus
tremula ***, B ─ Betula pubescens.
5. Ridges-hollow aapa mire – Herbosphagnetum
fruticuloso-caricosum + Limnoherbeta;
A. Forests and potential forest vegetation (secondary
communities on burnt and cut areas) on the
6. Sedge swamp – Herbosphagneta (excluding
Herbosphagnetum betuloso-pinosum);
extremely and normally drained sands and loamy
sands:
3. Dwarf shrub bog – Sphagnetum fruticulosum;
7. Sedge bog with open tree stand Herbosphagnetum
betuloso-pinosum; and
— Cladinosa (S, C, P, L, B)
— Hylocomioso-Cladinosa, (S, C, P,L, B)
8. Sedge fen – Magnocariceta.
— Empetroso-Vacciniosa (S, C, P, L, B, A)
Vegetation Maps
The spectral differences of the vegetation units made
it possible to produce an algorithm for the computerassisted identiication of forest associations or groups.
Using image texture, analyses of satellite imagery
helped identify bog and aapa mire complexes.
Additional information on mire vegetation was obtained
from topographic maps where dwarf shrub, sedge
cover, and mire woodlands were shown. On the basis
of classiication and satellite imagery interpretation,
vegetation maps (scale 1: 50,000–1:1M) were compiled
for LSR and VTSR.
An ecological-dynamic approach was the basis for the
legend. In the legend created for the LSR vegetation
map, forest vegetation is represented as vegetation of
two belts: taiga─forest and mountain birch. For VTSR,
there is only one belt, the taiga─forest. The largest
subdivisions of forest vegetation and secondary
vegetation within the taiga-forest belt were habitat
types distinguished by wetness and soil texture
characteristics. Next, in every habitat type, series of
associations were distinguished that united forests
and nonforested areas similar in their composition
B. Forests and potential forest vegetation on the
poorly drained sands, normally drained loam sands
and loams (thickness of soil organic horizon less
then 10 cm):
— Fruticuloso-Hylocomiosa (S, C, P, L, B, A)
C. Forests on thin, initial soils of hard-rock outcrops
and secondary communities (only in LSR)
— Petrophyto-Cladinosa (P)
D. Forests and potential forest vegetation on the
poorly drained sites and secondary communities
or combination of A and D:
— Fruticuloso-Polytrichosa (S, C, P, L, B)
E. Forests and potential forest vegetation, sometimes
in a complex with bogs covered by open tree
stands on poorly drained habitats with stagnant
water (thickness of peat more then 15 cm):
— Sphagnosa girgensohnii (S, C, P, L, B)
— Sphagnoso-Herbosa (S, C, P, B)
— Sphagnosa angustifolii (S, C, P, L)
F. Forests and potential forest vegetation and open
woodlands on wet habitats with running water:
119
— Geraniosa (S, B)
References
— Fruticoso-Herbosa (S, C, P, F, B, A)
— Herbosa (S, C, P, B).
Types of mires were also shown on the maps.
Analyses of mapping precision showed that the best
results (P 86%-96%) were obtained when we used
supervised classiication of the satellite imagery of
near-infrared and red bands, normalized vegetation
index (NVI–combination of red and green bands), and
visual interpretation of mire types from a topographic
map and satellite imagery. Spectral brightness of
near-infrared bands were correlated with wetness
of the soil surface; spectral brightness of red bands
depended on the proportion of coniferous trees in
the stand, and the NVI was correlated with main
vegetation types, such as heath and mire, bush, open
forest, and closed forest. These correlations are in
agreement with basic observations of vegetation
relectance that were established earlier by Kuchler
& Zonneveld, 1990.
Acknowledgments
The author is highly grateful to T.K. Yurkovskaia
who was a supervisor of the LSR vegetation
mapping in 1987, I.A. Balonenko, A.A. Degtyarev,
S.S. Degtyareva; A.V. Fridman, S.S. Kholod, B.B.
Kovalenko, T.V. Arsenieva, E. Yatskevich, and V.V.
Yatskevich who took part in the ield works in LSR
in 1987; A.A. Potokin, I.F. Tomaeva, A.A. Egorov and
I.V. Cherniadieva who took part in the ield works in
VTSR, A.A. Dobrysh (Komarov Botanical Institute of
Russian Academy of Sciences), who participated in
the ield works in 1987, 2006, and 2007 in LSR and
in 1997 and 1999 in VTSR; M.V. Neshataev and A.O.
Pesterov (Saint-Petersburg State Forestry Academy)
,who took part in the ield works in LSR in 2006
and 2007; N.Yu. Nazvaladze, one of my partners in
studying mires of LSR in 2000, 2007; V. Sh. Barkan
(LSR) for the assistance in organizing ield works in
2006 and 2007; and my partner in many expeditions
and coauthor V. Yu. Neshatayeva. The ield works
in 2007 were a part of the Interreg-Tacis project
Northern boreal forests. Special thanks to ACONIT
Ltd. for inancial and technical support.
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communities of taiga and tundra regions. Vegetatio
86: 3–70.
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Fennica 1(1): 1–175.
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Regions of Russia: Typology, Dynamics, Forest
Management Features. Saint-Petersburg. 382 pp.
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Hill, M. O. 1973. Reciprocal averaging: an eigenvector
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E. & Kershaw, K.A. 1976. Studies on lichendominated systems. XIX. The postire recovery
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Neshatayev, V. Yu. 1991. Vegetation mapping and
forest succession after ires in the Lapland State
Reserve at the Kola Peninsula. Pages 361–364
in J. B. Falinski, ed. Phytocoenosis, Vol. 3 (N.S.).
Supplementum Cartographiae geobotanicae 2.
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121
An Approach to Mapping the North American Boreal Zone
James Brandt
Canadian Forest Service, Natural Resources Canada, Ontario, Canada, jbrandt@nrcan.gc.ca
Abstract
The circumpolar boreal zone is one of the world’s
largest and most important biogeoclimatic zones,
covering much of North America and Eurasia with
forests, woodlands, wetlands, and lakes. Because
the zone provides important biological and ecological
services and covers an extensive area, it is vitally
important to the economies of Canada, Finland,
Norway, Russia, and Sweden. The boreal zone is
also an important source of raw materials in China,
Kazakhstan, and Mongolia, even though boreal
forests cover a relatively small proportion of these
countries. Poor agreement exists amongst scientists
regarding this zone’s delimitation and the areal extent
of boreal forests, even though the zone has been well
studied. This paper discusses previous literature on
the phytogeography of the boreal zone and the use
of a geographic information system (GIS) to create
maps delineating the North American boreal zone and
the hemiboreal subzone, which is the transitional area
lying immediately to the south of the boreal zone that
is usually included in the deinition of the boreal zone
by Europeans but excluded by North Americans.
Keywords: boreal forest, hemiboreal, map, North
America, phytogeography.
Introduction
The circumpolar boreal biome, or zone, is one of the
world’s largest and most important biogeoclimatic
areas. It covers a large portion of North America and
Eurasia with coniferous forests, woodlands, wetlands,
and lakes. The circumpolar boreal zone is vitally
important to the economies of the principal boreal
countries of Canada, Finland, Norway, Russia, and
Sweden. The boreal zone is also important to China,
Kazakhstan, and Mongolia, even though forests cover
a relatively small proportion of the areas of these
countries (Finch 1999; United Nations Economic
Commission for Europe Committee on Environmental
Policy 2000; Wang et al. 2001; Kushlin et al. 2003).
There are large discrepancies in the literature as to the
extent of forests within the circumpolar boreal zone.
Similar problems affect the deinition of other forests
worldwide. Most maps of the boreal zone are of such
low resolution that they are of little value for estimating
areal extent. Furthermore, the methodologies
and terminologies of various phytoclimatologists,
phytogeographers, and phytosociologists have varied
substantially, resulting in differing opinions as to the
exact limits of the boreal zone in various regions
(Hämet-Ahti, 1981; Tuhkanen, 1984). Also, papers on
forests in the boreal zone (or global forests in general),
on forest ires, and on the global carbon budget, as well
as reports from various environmental organizations,
have provided widely different estimates of various
parameters used to describe the boreal zone and
its forests. These reports, often conlicting, have
caused confusion amongst scientists, governmental
and nongovernmental organizations, and the public
as to the exact nature and extent of the boreal zone.
Language barriers also impede an understanding of
the boreal zone as the relevant literature has been
written in several different languages (e.g., Chinese,
English, Finnish, French, Japanese, Korean,
Mongolian, Norwegian, Swedish, and Russian).
Two maps of the boreal zone in North America are
considered to provide adequate baseline information.
When J.S. Rowe published his Forest Regions
of Canada in 1972 it was viewed as an accurate
depiction and description of Canadian forests at a
national scale (Krajina, 1973), and it continues to be
cited in current scientiic literature. The map in Alaska
Trees and Shrubs by Viereck & Little (1972) is the
most recent map of the boreal zone in Alaska.
Geographic information systems (GIS) have improved
our ability to produce spatially accurate maps. This
technology, however, is limited by the accuracy and
resolution of the accessed spatial data. Several recent
vegetation studies within the boreal zone and other
recently developed spatial data sets provide new data
122
that can be used in combination with GIS to develop
an improved map of the North American boreal zone.
This presentation focuses on a previous study by the
author where existing maps of the North American
boreal zone were examined and a new map of the
zone was developed, including summaries of relevant
areal statistics (see Brandt, 2009). A new map and the
corresponding statistics of the North American boreal
zone can be viewed as a signiicant step toward
providing scientists, regulators, and environmental
groups with a common baseline or standard with which
to measure changes in attributes and conservation
efforts for a substantial portion of the circumpolar
boreal zone. A baseline map is especially important
because of the anticipated changes expected within
the zone resulting from climate warming as well as
the negative effects of various industrial sectors or
other anthropogenic activities that are likely to have
an additive or synergistic impact.
Creating a New Map of the Boreal Zone
Deining Vegetation Zones
Five major bioclimatic zones have generally been
recognized in the northern hemisphere: arctic, boreal,
temperate, subtropical, and tropical. The arctic
zone is deined primarily by the absence of trees,
the occurrence of continuous permafrost, and the
presence of tundra vegetation dominated by lowgrowing shrubs, herbaceous plants, mosses, and
lichens (Walker et al., 2002). The tree limit was used
by Walker et al. (2002) as the southern boundary of
the arctic zone in the Circumpolar Arctic Vegetation
Map (CAVM Team, 2003).
The boreal zone is deined as the broad, circumpolar
vegetation zone of high northern latitudes covered
principally with forests and other wooded land
consisting of cold-tolerant trees species primarily
within the genera Abies, Larix, Picea, or Pinus but
also Populus and Betula; the zone also includes lakes,
rivers, and wetlands, and naturally treeless areas such
as alpine areas on mountains, heathlands in areas
inluenced by oceanic climatic conditions, and some
grasslands in drier areas. Within the boreal zone,
three broad subzones are generally recognized: the
forest─tundra interface at the north (subarctic zone
of Löve, 1970, or hemiarctic zone of Ahti et al., 1968,
and Tuhkanen, 1984); the closed forest generally
occupying the mid portion of the boreal zone; and the
hemiboreal (or sub-boreal) at the south. Like Walker
et al. (2002), the tree limit was used to deine the
northern boundary of the forest─tundra in the study
described here. The closed forest of the boreal zone
is deined by the dominance of closed-crown forests
of tree species that tolerate extreme cold (i.e., tolerant
of temperatures of –80ºC or lower) within the genera
Abies, Larix, Picea, and Pinus but also Populus and
Betula. Although other features such as podzolic
soils, surplus moisture leading to runoff in rivers and
creeks, stable lake levels, the formation of wetlands or
peatlands, and permafrost are also considered to be
of importance in deining the boreal zone (Sjörs, 1963;
Ahti et al., 1968; Walter, 1973; Hogg, 1994; Hogg
& Bernier, 2005), these were not considered. The
hemiboreal subzone is deined by the co-occurrence
of cold-intolerant species, cold-tolerant species, and
species with intermediate cold-tolerance, with the
cold-tolerant species contributing substantially to the
forest cover. The temperate zone is deined by the
dominance on most sites of tree species intolerant of
extremely cold winter temperatures (i.e., they require
temperatures above –45ºC to survive). The interior
of both continents is arid, and no temperate forests
exist in the latitudinal belt south of the boreal zone
where they would exist if temperature was the only
critical factor governing their presence. Instead,
grasslands occupy the continental temperate zone,
and the occurrence of grasslands is dictated more
by drought rather than temperature. The hemiboreal
subzone in the continental interior of North America
consists of grasslands interspersed with droughttolerant hardwoods, mainly Populus tremuloides but
also Quercus macrocarpa.
Source Maps and Approach
Most maps of North America’s boreal zone and its
forests were scanned, digitized, and converted to GIS
shapeiles. Several maps were available as GIS iles.
Other maps were consulted but not digitized because
these maps (1) had insuficient or inadequate control
points to properly geo-reference them for GIS input;
(2) lacked suficient detail to warrant digitization; (3)
were of the same pedigree as an acceptable existing
map; (4) were superceded by more recent maps; or
(5) were adjacent to but outside the boreal zone. All
coverages were converted to the same projection and
datum.
123
A GIS was used to view and compare the boundaries
of 32 maps of the boreal zone. Scale, methods and
materials used to create the maps, and the criteria
used by the authors to delineate map boundaries
were analyzed (Brandt, 2009). To develop a revised
map of the North American boreal zone and the
hemiboreal subzone using consistent criteria and the
most current information, the maps of Rowe (1972) for
Canada and Viereck & Little (1972) for Alaska were
used as starting points because they are generally
perceived as being accurate for the scale at which
they are depicted and are still widely used in the
scientiic literature even though they are 36 years old.
One of the limitations of both maps that is not widely
recognized or acknowledged is that the authors
describe only briely the materials and methods they
used to compile their maps.
ocean (i.e., eastern Labrador, parts of Newfoundland,
the Aleutians), and lying adjacent to the boreal zone
were also placed within the boreal zone (Meades,
1983; Yurtsev, 1994; Talbot et al., 2006). Belts of
vegetation in mountain areas have traditionally been
classiied as montane, subalpine, or alpine.
Thus, the ecotone boundary depicted on the older
maps was replaced with a boundary from a more
recent study when (1) the more recent study used a
criterion the same as or similar to a criterion described
above, and (2) the more recent study provided a
thorough description of materials and methods that
would allow repeatable results. Two examples of
studies that meet both of these conditions are those
of Timoney (1988) and Payette (1983). In some
situations the scale or resolution of data also became
a factor, and preference was given to maps at larger
scales and data of higher resolution. For boundaries
for which a more recent study was not found and
for which other data were not readily available, the
default boundary was that of either Rowe (1972) or
Viereck & Little (1972). The hemiboreal subzone
was not mapped by Rowe (1972) or Viereck & Little
(1972). For this subzone, boundaries were selected
from studies meeting the two conditions listed above.
See Brandt (2009) for a complete list of maps that
were viewed in the GIS for each boundary and region
considered.
tolerance). Areas above the tree limit were treated as
alpine; these alpine areas were included in the boreal
zone when forests immediately below these areas
were classiied as boreal, or were included in the
hemiboreal subzone when the forests immediately
below these areas were classiied as hemiboreal;
(3) if the lowest elevations of the valley fall within the
temperate zone, then forests at higher elevation were
classiied as being in the hemiboreal subzone if they
consisted of species of intermediate cold-tolerance
and were contiguous with the hemiboreal subzone.
If they are not contiguous with forests classiied
as being in the hemiboreal zone, then they were
considered montane or subalpine subzones or belts
of the temperate zone.
There are problematic areas that defy classiication
on the basis of the deinitions and criteria described
above. These include treeless coastal areas and
islands, mountain areas that result in belts of
vegetation, and outliers of distinct vegetation found
at different elevations than the surrounding areas.
Coastal areas and islands covered with heaths (i.e.,
treeless), having cool climates moderated by the
In the study, mountain areas were assigned by
adhering to the following rules: (1) if the lowest
elevations of the valley fall within the boreal zone, then
forests at higher elevation are also in the boreal zone;
therefore, areas above the tree limit were classiied
as alpine but within the boreal zone; and (2) if the
lowest elevations of a valley fall within the hemiboreal
subzone, then forests at higher elevation consisting of
cold-tolerant species were placed in the boreal zone;
otherwise, they were classiied as hemiboreal (i.e.,
forests consisting of species of intermediate cold-
Areas above the tree limit were treated as alpine; these
alpine areas were included in the hemiboreal subzone
when the forests immediately below these areas
were classiied as hemiboreal. In British Columbia,
there are several outlying areas of vegetation usually
lying in isolated valleys that are distinct from the
surrounding vegetation; these outliers were included
in the same zone or subzone as the surrounding
vegetation. For example, there are valleys covering
a relatively small area with temperate species (i.e.,
Thuja plicata and Tsuga heterophylla) in northwestern
British Columbia east of Skagway, Alaska, that were
included in the boreal zone. Other outliers in nonmountainous terrain found at different elevations and
with vegetation distinct from surrounding areas were
124
treated similarly; these outliers were also included
in the zone or subzone of the surrounding areas.
Examples include the Cypress Hills in southern
Alberta and Saskatchewan, which was placed in the
temperate zone, and the Spruce Woods in Manitoba,
which was placed in the hemiboreal subzone.
methods of Payette (1983) and Saucier et al. (1998),
Timoney’s (1988) methods are well described and
repeatable. He used aerial photography and groundtruthing, as well as deinitions similar to those of
Payette (1983), to map the northern tree limit west of
Hudson Bay.
Locating Boreal Forest Ecotones
The regional and local topography of the Yukon
Territory and Mackenzie Mountains section of the
boreal zone is complex because several mountain
ranges dissect this vast area. In mountainous terrain
the climate, as affected by altitude, is one of the most
important factors dictating the tree limit, but factors
such as aspect, slope, cold air drainage, precipitation,
and soil also have an inluence. To improve on the
accuracy of previous maps of the region, digital
elevation data (Natural Resources Canada, 2001),
the ecoregion map of Oswald & Senyk (1977), and
Forest─Tundra Ecotone
Working from east to west across the northern forest–
tundra of North America, the boundary of Feilberg’s
(1984) subarctic zone was used as the boundary
of the forest–tundra ecotone in Greenland for three
reasons. First, Feilberg’s deinition of the subarctic
zone meets the criterion for the forest–tundra ecotone
(i.e., the boundary of his zone was deined by the tree
line). Second, Feilberg’s boundary provides greater
resolution than the only other map of the region (i.e.,
that of Tuhkanen, 1984). Finally, Fielberg provides a
thorough description of his methods.
The northern tree limit mapped by Payette (1983) was
used as the northern boundary of the boreal zone in
northern Labrador and that of Saucier et al. (1998),
which was adapted from Gerardin (1980), Richard
(1987), Lavoie & Payette (1994), and Grondin et
al. (1996), was used in northern Quebec. These
boundaries were chosen because they also used
the tree line as their boundary. Payette’s methods
are also well described and repeatable. He used
transects along meridians at 30-minute intervals and
along parallels near Hudson Bay, aerial photography,
and aerial surveys to map the tree limit in this region.
To the southeast, islands along the Atlantic coast off
Labrador and Newfoundland and extensive areas
on the island of Newfoundland proper are treeless
and covered primarily by heathlands (Rowe, 1972;
Meades, 1983); these areas were also included in the
boreal zone.
Moving west, James Bay and Hudson Bay deine the
northern limits of the boreal zone in Ontario. There
is a narrow coastal belt along the shores of both of
these bays a few kilometres wide that is treeless
(Sjörs, 1961). The northern tree limit from Churchill,
Manitoba, on the west coast of Hudson Bay to the
border between the Yukon Territory and the Northwest
Territories is provided by Timoney (1988). Like the
tree-limit elevation data from various regional studies
(Porsild, 1945; Drew & Shanks, 1965; Douglas, 1974;
Oswald & Senyk, 1977; Ritchie, 1982; MacDonald,
1983; Cwynar & Spear, 1991; Szeicz & MacDonald,
1995; Szeicz et al., 1995) were used to delineate
the northern tree limit as well as alpine areas above
the elevational tree limit in each of the ecoregions
of Oswald & Senyk, (1977), and the Mackenzie
Mountains of the Northwest Territories. The results of
this process are the boundaries of the northern tree
limit and alpine areas depicted in Brandt (2009).
In Alaska the boundaries of the boreal zone were
adapted from the scheme of Viereck & Little (1972)
and Yurtsev (1994). The boreal zone includes the
following vegetation types as described by Viereck
& Little (1972): closed spruce-hardwoods; open,
low-growing spruce; and treeless bogs. However,
because of the various mountain ranges distributed
across Alaska and the small scale of the map of
Viereck & Little (1972), digital elevation data and treelimit elevation data from several published studies
(Thompson, 1969; Hettinger & Janz, 1974; Anderson,
1975; Denton & Karlén, 1977; Viereck et al., 1983;
Goldstein et al., 1985; Cooper, 1986; Short et al.,
1986; Sveinbjörnsson et al., 1995; Suarez et al.,
1999; Lloyd & Fastie, 2002; Epting & Verbyla, 2005)
were used to delineate the northern tree limit as well
as alpine areas above the elevational tree limit. There
were also several small outliers mapped by Viereck &
Little (1972) that were excluded from the boreal zone
125
(see Brandt, 2009). The boreal zone in this region
also includes the Alaska Peninsula and the Aleutian
Islands because the vegetation in these areas has
many boreal taxa but lacks trees (i.e., treeless heaths)
(Yurtsev, 1994; Talbot et al., 2006).
Boreal–Hemiboreal Ecotone
Again, working from east to west across North
America, the southern boundary of the boreal zone
used in eastern North America is that of Saucier et al.
(1998) in Quebec and Halliday (1937) in Ontario as
modiied by Rowe (1972). The boundaries of Saucier
et al. (1998) were based on ecological plots, forest
inventory plots, data analysis, elevation data, and
other existing maps.
In the western interior of North America, Zoltai (1975)
mapped the southern limits of the distribution of
Larix laricina, Picea mariana, Picea glauca, Pinus
banksiana, and Pinus contorta var. latifolia in the
southern prairie provinces by using a series of
transects. A new boundary line was generated on the
basis of the presence of at least two of these ive boreal
conifers using Zoltai’s data. This line is the southern
boundary of the boreal zone in this region, except in
southwestern Alberta and southeastern Manitoba,
which fell outside Zoltai’s study area. Thus, the new
line generated from Zoltai’s data was edge-matched
with the boundary of the foothills natural region of
the Natural Regions Committee (NRC, 2006) in
southwestern Alberta and with Rowe’s (1972) boreal
forest region boundary in southeastern Manitoba.
Although the Spruce Woods in Manitoba has several
boreal conifers it is excluded from this paper’s boreal
zone because it is an outlier surrounded by aspen
parkland.
In the western cordillera, the mountainous terrain of
British Columbia and western Alberta complicates the
task of delineating the boundaries of the boreal zone
and the hemiboreal subzone. In many valleys several
distinct vegetation types may occur as relatively narrow
horizontal belts from valley bottom to mountain peak.
The boreal zone in this region has been adapted from
NRC (2006) and British Columbia Ministry of Forests
and Range (BCMFR, 2006). Basically, the upper and
lower foothills subregions and the subalpine natural
subregion north of the upper Red Deer River near
the continental divide (at about 51º38’ N) from NCR
(2006) are included in the boreal zone. In British
Columbia, the boreal white and black spruce and the
spruce–willow–birch biogeoclimatic zones of BCMFR
(2006) are included in boreal zone as both their lora
and climate show strong afinities to the boreal zone.
Also included are forests of the Engelmann spruce–
subalpine ir biogeoclimatic zone that are contiguous
with forests of the boreal white and black spruce
biogeoclimatic zone in northern British Columbia and
east of the Rocky Mountains in British Columbia.
The map of NRC (2006) was produced at a scale
of 1:250,000. Contour lines based on data from a
provincial digital elevation model were used in many
cases to delineate ecological boundaries. The map
of BCMFR (2006) is a result of detailed surveys and
mapping. About 60% of the province was mapped at
a scale of 1:20,000, about 30% at 1:250,000, and the
remainder at 1:600,000.
In Alaska, the boreal zone includes closed sprucehardwoods; open, low-growing spruce; and treeless
bogs as described by Viereck & Little (1972) and the
Alaska Peninsula and the Aleutian Islands (Yurtsev,
1994). The coastal spruce–hemlock forests of Viereck
& Little (1972) were considered part of the temperate
zone of western North America. There is no transitional
hemiboreal subzone in Alaska because alpine areas
and iceields along the coastal mountains separate
the boreal and temperate zones.
Hemiboreal–Temperate Ecotone
Crossing North America from east to west, the
hemiboreal subzone in the Atlantic Maritimes,
northeastern United States, and Great Lakes region
includes Rowe’s (1972) Acadian forest region and
most of the so-called “spruce–ir forest vegetation
type” of Shantz and Zon (1936) that is contiguous with
forests of the former forest region. The patches of
higher elevation spruce–ir forests in the Adirondack
Mountains of New York, the Green Mountains
of Vermont and Massachusetts, and the White
Mountains of New Hampshire mapped by Hawley
and Hawes (1912), Bray (1915), Dana and Greeley
(1930), Westveld (1930, 1956), and Hotchkiss (1932)
are not considered part of the hemiboreal subzone.
In Quebec, the hemiboreal subzone includes the
mixed forest vegetation subzone of Saucier et al.
126
(1998). It also includes land subregion 3d-S (Collines
du Mont-Mégantic) of the deciduous forest vegetation
subzone considered by Saucier et al. (1998) to be
transitional between the sugar maple–yellow birch
and the balsam ir–yellow birch bioclimatic domains.
Edge-matching was required at the Quebec–Maine
border. Thus, contour lines were followed from
Quebec into Maine to match the various polygons
taken from Shantz and Zon (1936) and from Saucier
et al. (1998).
ir biogeoclimatic zone lying at higher elevations.
Isolated patches of sub-boreal spruce and sub-boreal
pine–spruce biogeoclimatic zones are not included
in the hemiboreal subzone if they are surrounded
by forests consisting of cold-intolerant tree species
characteristic of the temperate zone. The bunchgrass,
coastal Douglas-ir, coastal western hemlock, interior
cedar–hemlock, interior Douglas-ir, and ponderosa
pine biogeoclimatic zones are considered temperate.
Conclusion
In the Great Lakes region of Ontario, Rowe’s (1972)
sections L.9–L.12 are included in the hemiboreal
subzone of the study. These areas can be considered
transitional because of the abundance of boreal
conifers here (Maycock & Curtis, 1960; Rowe, 1972;
Jackson et al., 2000). In Michigan, Minnesota, and
Wisconsin, pre-settlement forests of spruce, pine,
and mixedwoods mapped by Marschner (1974),
Finley (1976), and Comer et al. (1995a, 1995b) are
included in the hemiboreal subzone. These forests
are included because they are similar to Canadian
hemiboreal forests on the opposite side of the Great
Lakes on the basis of data presented by Daubenmire
(1936), Cunningham & White (1941), Braun (1950),
Buell & Cantlon (1951), Buell & Niering (1957),
Maycock & Curtis (1960), Maycock, (1961), Janssen
(1967), Janke et al. (1978), Frelich & Reich (1995),
Schmidt et al. (1996), and Friedman et al. (2001).
The southern boundary of the hemiboreal subzone
in the western interior parkland of North America is
that of the southern boundary of the aspen parkland
as mapped by Bird (1961) in Manitoba, Archibold &
Wilson (1980) in Saskatchewan, and NRC (2006) in
Alberta as the central parkland and foothills parkland
natural subregions. The boundary of the parkland in
Minnesota is based on the boundaries depicted by
Marschner (1974).
In the western cordillera of Alberta, NRC’s (2006)
montane natural subregion north of the height of
land separating the Livingstone River and Highwood
River watersheds (Wilkinson Summit at about
50º11’ N) to the Athabasca River valley near Jasper
is included in the hemiboreal subzone. In British
Columbia, areas within BCMFR’s (2006) sub-boreal
spruce and sub-boreal pine–spruce biogeoclimatic
zones are included in the hemiboreal subzone as
are forests within the Engelmann spruce–subalpine
The map of the North American boreal zone and
hemiboreal subzone should be considered a
reinement of the maps of Rowe (1972) and Viereck &
Little (1972). Boundaries on these maps were altered
based on more recent studies that have provided
well-described methods. These changes relect an
improvement in technology and data sets since the
development of the earlier maps.
Given the circumpolar boreal zone’s importance
to the economies of many nations and its key role
in regulating global and regional climates and
biogeochemical cycles, it is vital that a common
deinition of the boreal zone be agreed upon.
Scientists, regulators, and environmental groups can
use the map and the corresponding statistics of the
North American boreal zone in Brandt (2009) as a
common baseline against which future changes in the
zone and the success of conservation efforts can be
measured. The approach briely outlined here could
be applied to developing a map of, and comparable
statistics for, the Eurasian portion of the boreal zone.
Acknowledgments
J. Weber, R. Brett, M. Gartrell, and M. Newman
assisted with the GIS work. J.-P. Saucier of Québec’s
Ministère des Ressources naturelles et Faune
provided access to GIS coverages of Quebec’s
Ecological Land Classiication hierarchy. T. Scupien
of the United States Department of Agriculture, Forest
Service provided GIS coverages of original vegetation
in Michigan, Minnesota, and Wisconsin, U.S.A. T.
Hogg, B. Meades, J. Volney, and the anonymous
reviewers of Environmental Reviews are thanked
for their contribution in improving the manuscript. B.
Laishley and J. Thomas edited earlier drafts.
127
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131
Bioclimatic Framework for the Circumboreal Vegetation Mapping Project
Daniel Sánchez-Mata1* & Salvador Rivas-Martínez2
1
Department of Plant Biology II, Complutensian University, Madrid, Spain, *author for correspondence:
dsmata@farm.ucm.es, 2Phytosociological Research Center, Collado Villalba (Madrid), Spain
Extended Abstract
The Circumboreal Vegetation Mapping (CBVM)
workshop held in Helsinki, Finland, on November
3–6, 2008, concluded with resolutions regarding
the organization and implementation of mapping
the circumboreal biome. According to the schedule
established at the Helsinki workshop, teams and
leaders were assigned in a subsequent workshop
held in Uppsala, Sweden on March 31–April 3, 2009.
As one of the teams assigned at the Helsinki workshop
to assist in deining the delimitation of the boreal
bioclimate, including its boundaries and peculiarities,
particular emphasis will be on the following goals:
1. to form an international team of experts on
bioclimate and vegetation relationships;
2. to reach a broad consensus on scale, map
projection, base imagery, integrated mapping
methodology, ecological terminology, inal legend,
and boundaries of the proposed circumboreal
map;
3. to successfully use the internationally recognized
phytosociological plant-community nomenclature
system (Braun-Blanquet vegetation school)
and follow the current International Code of
Phytosociological Nomenclature (Weber et al.,
2000) as the preferred basis for cataloging plant
communities throughout the territories covered by
the map;
4. to obtain synthesized maps at different scales:
continental and circumpolar scales;
5. to obtain a circumboreal vegetation map with a
comprehensive legend that places agreed upon
vegetation units (high phytosociological units of
potential vegetation) at the highest level of the
legend hierarchy, as opposed to other “subjective”
considerations by different authors (zones,
subzones, ecological regions and subregions,
forest regions, ecotones, etc.); and
6. to accurately join the proposed circumboreal
vegetation map (CBVM) to the existing
Circumpolar Arctic Vegetation Map (CAVM), using
an agreed upon legend and scale.
We attach to this extended abstract some of our
more recent bioclimatic computerized maps covering
the North American continent in four preliminary
prints: bioclimates, thermotypes, ombrotypes, and
continentality of all of North America (Figs. 1–4). These
maps follow our bioclimatical proposals, including all
the bioclimatical indexes (Rivas-Martínez et al., 1999;
see http://www.globalbioclimatics.org).
132
Fig. 1. Bioclimate map of North America.
Fig. 2. Thermotype map of North America.
133
Fig. 3. Ombrotype map of North America.
Fig. 4. Continentality map of North America.
134
The following publications and contributions should
be considered relevant to the proposed goals above:
Baldwin, K. A. & Meades, W. J. 2008. Canadian
national vegetation classiication. Pages 66–69
in Talbot, S.S., ed. Proceedings of the Fourth
International Conservation of Arctic Flora and Fauna
(CAFF) Flora Group Workshop, 15–18 May 2007,
Tórshavn, Faroe Islands. CAFF Technical Report
n. 15. CAFF International Secretariat. Akureyri,
Iceland.
Bohn, U., Neuhäusl, R., unter Mitarbeit von Gollub,
G., Hettwer, C., Neuhäuslová, Z., Schlüter, H.,
& Weber, H. 2000/2003. Karte der natürlichen
Vegetation Europas / Map of the Natural Vegetation
of Europe, scale 1:2.5 M. Teil 1–3 / Part 1–3.
Landwirtschaftsverlag. Münster, Germany.
Bohn, U. 2008. Classiication and distribution of boreal
vegetation in Europe (in the Map of the Natural
Vegetation of Europe). Pages 70–76 in: Talbot,
S.S., ed. Proceedings of the Fourth International
Conservation of Arctic Flora and Fauna (CAFF)
Flora Group Workshop, 15–18 May 2007, Tórshavn,
Faroe Islands. CAFF Technical Report n. 15. CAFF
International Secretariat. Akureyri, Iceland.
CAVM Team. 2003. Circumpolar Arctic Vegetation
Map. Scale 1:7,500,000. Conservation of Arctic
Flora and Fauna (CAFF) Map No. 1. U.S. Fish and
Wildlife Service, Anchorage, Alaska.
Daniëls, F. J. A., Bültmann, H., Lünterbusch, Ch.,
& Wilhelm, M. 2000. Vegetation zonation and
biodiversity of North American Arctic. Ber. d. Reinh.Tüxen-Ges. 12: 131–151.
Ermakov, N. 2008. Boreal vegetation map of
Russian regions. Pages 77–83 in Talbot, S.
S., ed. Proceedings of the Fourth International
Conservation of Arctic Flora and Fauna (CAFF)
Flora Group Workshop, 15–18 May 2007, Tórshavn,
Faroe Islands. CAFF Technical Report n. 15. CAFF
International Secretariat. Akureyri, Iceland.
communities in the Faroe Islands. Fródskaparrit 51:
217─236.
Gudjonsson, G., Kristinsson, H., & Einarsson, E.
2008. Large-scale vegetation mapping in Iceland.
Pages 84–88 in Talbot, S.S., ed. Proceedings of the
Fourth International Conservation of Arctic Flora and
Fauna (CAFF) Flora Group Workshop, 15–18 May
2007, Tórshavn, Faroe Islands. CAFF Technical
Report n. 15. CAFF International Secretariat.
Akureyri, Iceland.
Jennings, M., Faber-Langendoen, D., Peet, R.,
Loucks, O., Glenn-Lewin, D., Damman, A., Barbour,
M., Pister, R., Grossman, D., Roberts, D., Tart, D.,
Walker, M., Talbot, S., Walker, J., Hartshorn, G.,
Waggoner, G., Abrams, M., Hill, A., & Rejmanek,
M. 2004. Guidelines for describing associations
and alliances of the U.S. National Vegetation
Classiication. Version 4.0. Ecological Society of
America Vegetation Classiication Panel (ESA),
Washington, D.C., U.S.A. (http://www.esa.org/
vegweb/docFiles/NVC_Guidelines-v40.pdf).
Krestov, P. V., Song, J. -S., Nakamura, Y., & Verkholat,
V. P. 2006. A phytosociological-survey of the
deciduous temperate forests of mainland Northeast
Asia. Phytocoenologia 36(1): 77–150.
Krestov, P. V. & Nakamura, Y. 2007. Climatic control
of forest vegetation distribution in Northeast Asia.
Ber. d. Reinh.-Tüxen-Ges. 19: 132–146.
Krestov, P.V., Omelko, A.M., & Nakamura, Y. 2008.
Vegetation and natural habitats of Kamchatka. Ber.
d. Reinh.-Tüxen-Ges. 20: 195–218.
Meades, W. J. 2008. Vegetation of Newfoundland.
Pages 16–20 in Talbot, S.S., ed. Proceedings of the
Fourth International Conservation of Arctic Flora and
Fauna (CAFF) Flora Group Workshop, 15–18 May
2007, Tórshavn, Faroe Islands. CAFF Technical
Report n. 15. CAFF International Secretariat.
Akureyri, Iceland.
Ermakov, N. & Alsynbayev, K. 2004. Modeling of
spatial organization of woodlands in southern part
of the Western Sayan. Siberian J. Ecol. 5: 687–702.
Ponomarenko, S. & Alvo, R. 2001. Perspectives on
Developing a Canadian Classiication of Ecological
Communities. Natural Resources Canada, Canadian
Forest Service, Science Branch. Information Report
ST-X-18E. 50 pp.
Faber-Langendoen, D., Tart, D. L., & Crawford, R.
H. 2009. Contours of the revised U.S. National
Vegetation Classiication Standards. Bull. Ecol.
Soc. America (ESA) 90(1): 87–93.
Rivas-Martínez, S., Sánchez-Mata, D., & Costa, M.
1999. North American boreal forests and western
temperate forest vegetation. Itinera Geobot. 12:
5–316.
Fosaa, A. M. 2004. Altitudinal distribution of plant
Saucier, J. -P. 2008. Deining the boreal in the
135
ecological land classiication for Québec (Canada).
Pages 53–57 in Talbot, S.S., ed. Proceedings of the
Fourth International Conservation of Arctic Flora and
Fauna (CAFF) Flora Group Workshop, 15–18 May
2007, Tórshavn, Faroe Islands. CAFF Technical
Report n. 15. CAFF International Secretariat.
Akureyri, Iceland.
Sieg, B., Drees, B., & Daniëls, F. J. A. 2006.
Vegetation and altitudinal zonation in continental
West Greenland. Bioscience 57: 5–93.
Conservation of Arctic Flora and Fauna (CAFF)
Flora Group Workshop, 15–18 May 2007, Tórshavn,
Faroe Islands. CAFF Technical Report n. 15. CAFF
International Secretariat. Akureyri, Iceland.
Walker, D. A., Raynolds, M. K., Daniëls, F. J. A.,
Einarsson, E., Elvebakk, A., Gould, W. A., Katenin,
A. E., Kholod, S. S., Markon, C. J., Melnikov, E.
S., Moskalenko, M. N. G., Talbot, S. S., Yurtsev, B.
A., and CAVM Team. 2005. The Circumpolar Arctic
Vegetation Map. J. Veg. Sci.16: 267–282.
Talbot, S. S., Leblanc, M. C., & Aiken, S. G. 2008.
Walker, D. A. 2008. Circumpolar Arctic Vegetation
Map, the Alaska Arctic Tundra Vegetation Map, and
the Arctic Geobotanical Atlas. Pages 60–62 in Talbot,
S.S., ed. Proceedings of the Fourth International
Conservation of Arctic Flora and Fauna (CAFF)
Flora Group Workshop, 15–18 May 2007, Tórshavn,
Faroe Islands. CAFF Technical Report n. 15. CAFF
International Secretariat. Akureyri, Iceland.
Floristic subregions of the Canadian Arctic
Archipelago. Pages 42–45 in Talbot, S.S.,
ed., Proceedings of the Fourth International
Weber, H. E., Moravec, J., & Theurillat, J. -P.
2000. International Code of Phytosociological
Nomenclature. 3rd ed. J. Veg. Sci. 11: 739─768.
Svoboda, M. 2008. The Circumpolar Biodiversity
Monitoring Program (CBMP). Pages 58–59 in Talbot,
S.S., ed. Proceedings of the Fourth International
Conservation of Arctic Flora and Fauna (CAFF)
Flora Group Workshop, 15–18 May 2007, Tórshavn,
Faroe Islands. CAFF Technical Report n. 15. CAFF
International Secretariat. Akureyri, Iceland.
136
Bioclimates and Distribution of Zonal Types of Boreal Vegetation in
Northeast Asia
Pavel V. Krestov & Alexander M. Omelko
Institute of Biology and Soil Science, Vladivostok, Russia
Abstract
Bioclimatic ranges of phytosociological units
conirmed the biotemperature thresholds of major
biogeographical zones found by Hämet-Ahti et al.
(1974), Kira (1977), and Fang & Yoda (1990). The
critical warmth index values of 15ºC, 45ºC, 55°C,
and 85ºC correspond respectively to the southern
borders of subarctic, boreal and northern temperate,
and middle temperate subzones of the temperate
zone. The formation of vegetation cover of North
Asia is controlled by polar, boreal, and temperate
macroclimates. Basic intrazonal variations of
vegetation along the gradient of continentality depend
on the variations of the yearly heat and precipitation
distribution and can be classiied into seven climatic
types: hyperoceanic, oceanic, suboceanic, maritime,
continental, subcontinental, and ultracontinental.
Climatic factors, especially the amount of heat,
longevity of growing season, and amount of
precipitation in a growing season appear to be major
contributors into the zonal and sectoral differentiation
of vegetation complexes. The use of global
bioclimatic classiication of Rivas-Martínez (1999), in
combination with phytosociological knowledge and a
concept of zonal sites (Pojar et al., 1987), provides
powerful tools for three dimensional arrangement
of vegetation along the latitudinal, longitudinal,
and altitudinal gradients and for distinguishing the
particular climatic factors limiting the development of
each zonal vegetation type.
Keywords: classiication, mapping,
phytosociology zone, sector.
modelling,
Introduction
Large-scale vegetation studies are one of the
fundamental aspects of ecology and biogeography
and a key to the clariication of modern as well as past
processes in vegetation cover. The understanding of
vegetation changes that follow the climatic luctuation
in different time scales has became the one of
the most important questions for simulating and
predicting biota development. This study focuses on
the problem of indicating climatic gradients by boreal
vegetation complexes at local and regional scales and
aims to quantify the local and regional scale relations
of vegetation units, their complexes, and climatic
parameters within subarctic and boreal vegetation
zones in Northeast Asia.
Study Area
The Russian Far East, northeast China, Japanese
archipelago, and Korean peninsula represent the
northeast edge of the Asian continent. The whole
area covers approximately 5,000,000 km2, ranging
from 35ºN to 73ºN latitude and from 100ºE to 169ºW
longitude. Elevations range between the sea level and
4,885 m a.s.l. (Klyuchevskaya Sopka, Kamchatka).
Material and Methods
The problem is approached by analysis of extensive
phytosociological (over 5,000 relevés) and climatic
(2,200 climatic stations) databases. Identiication of
bioclimates was made in accordance with S. RivasMartínez et al. (1999) approach using different
climatic parameters that include Kira’s warmth (WK)
and coldness (CK) indices, continentality index
(CI), ombro-evapotranspirational (OEI) index, and
winter precipitation (WP). General multiple slope
linear regression models were developed to predict
bioclimatic indices on the basis of geographical
variables: latitude, longitude, and elevation. Because
of numerous compensational effects of edaphic or
local climatic factors on community development
(overmoisture, over-droughts, high insolation,
temperature inversions) inding relationships between
community types and a regional climate is possible
by comparing communities on the habitats, which are
equal in environmental characters. A link between
regional climate (Major, 1963) and vegetation units
137
can be found with the aid of a concept of zonal sites
formulated by Pojar et al. (1987).
To create a map with bioclimatic conditions favorable
for particular vegetation order or class we assumed
that a combination of mean values of each calculated
bioclimatic index would represent optimal conditions
for a given vegetation unit. Obviously, when moving
away from optimal values, the probability of vegetation
unit occurrence is reduced and can be described by
the law of normal distribution. Then, we can assume
that the most suitable for a given vegetation unit would
be areas with optimal bioclimatic indices. If the law
of optimum is expressed through normal distribution,
then such sites will be those areas where the sum
of the probabilities calculated for individual indices
is close to the maximum. Therefore, for constructing
predictive maps, we used the following equation:
P = 1/ ∑
1
e
σ i 2π
−
A preliminary prodromus of boreal forest vegetation
of Northeast Asia comprises 48 syntaxa of the
association, 12 of alliance, 7 of order, and 3 of class
ranks (Krestov, 2006). Of 48 associations of forest
communities known in the boreal zone of the region,
only 22 that occur on zonal sites were examined
(Table 1).
Analysis of indices calculated with the aid of developed
models showed signiicant differences in vegetation
units along the order of rank in bioclimatic ranges.
This information, in combination with the analysis
of community distribution along elevation gradient,
allowed the inding of zonal associations characteristic
to bioclimatic regions of northeast Asia (Table 2).
Kira’s warmth index (WK) decreases from values over
1 ( xi − µi ) 2
2 σi
where P ─ the probability of occurrence of a community
of particular order or class, μi ─ the average value of
i-th bioclimatic index, σi ─ standard deviation for the
i-th index, xi ─ the value of the i-th index, derived from
the model of index distribution. The probabilities of
order occurrence over 50% were mapped.
Class
Betuletea
glandulosodivaricatae
prov.
VaccinioPiceetea
Results and Discussion
75ºC in the middle temperate zone (Saso-Fagetalia,
Aceri-Quercetalia) to values less than 20ºC in the
subarctic zone. Among boreal vegetation units, the
orders of Betulo-Ranunculetea have the lowest
warmth index, with the index lower because of cool
summers in the conditions of oceanic climate. The
coldness index (CK) varies between values of -25ºC
and -150ºC within boreal and temperate zones with
the prevalence of deciduous broadleaved, mixed and
evergreen broadleaved forests (Fig. 1)
Order
Alliance
Larici gmelinii-Betulion
Larici cajanderiBetuletalia divaricatae divaricatae prov.
prov.
Association
Flavocetrario cuculatae-Betuletum
divaricatae
Salici krylovii-Laricetum gmelinii prov.
Vaccinio-Pinetalia
pumilae
Lathyro humilisLaricetalia cajanderi
Ledo palustrisLaricetalia cajanderi
Vaccinio-Pinetum pumilae
Ledo decumbentis-Pinetum pumilae
Lathyro-Laricetum cajanderi
Abieti-Piceetalia
jesoensis
Vaccinio-Pinion pumilae
Lathyro humilis-Laricion
cajanderi
Ledo palustris-Laricion
cajanderi
Rhododendro aureiLaricion cajanderi
Pino pumilae-Piceion
jezoensis
Abieti nephrolepidisPiceion jezoensis
Piceion jezoensis
Abietion mariesii
Betulo ermanii- Betuletalia ermanii
Ranunculetea
acris
Streptopo-Alnetalia
maximowiczii
Pino pumilae-Betulion
ermanii
Artemisio opulentaeBetulion ermanii
Athyrio brevifrontisWeigelion
middendorffianae
Ledo-Laricetum cajanderi
Sanguisorbo-Laricetum cajanderi
Vaccinio-Piceetum jezoensis
Oplopanaco elati-Piceetum jezoensis
Philadelpho-Piceetum jezoensis
Thujo koraiensis-Abietetum nephrolepidis
Piceo jezoensis-Abietetum sachalinensis
Asaro heterotropoidis-Abietetum
sachalinensis
Maiantho-Tsugetum diversifoliae
Abietetum veitchio-mariesii
Abietetum mariesii
Salici arcticae-Betuletum ermanii
Geranio erianthi-Betuletum ermanii
Artemisio opulentae-Betuletum ermanii
Weigelo middendorffii-Betuletum ermanii
Dryopterido-Alnetum fruticosae
Glycerio alnastereti-Alnetum fruticosae
Table 1. Vegetation units of boreal zone of Northeast Asia involved in analysis.
138
Table 2. Zonal associations/community types (Ermakov et al., 2002; Ermakov & Alsynbayev, 2004; Ermakov,
2003; Krestov & Nakamura, 2002; Krestov et al., 2006, 2009) characteristic to bioclimatic regions and vertical
belts of Northeast Asia (after Krestov & Nakamura, 2007).
Continentality sectors
Southern boreal
Northern boreal
Polar
Macrobioclimate /
Thermotype
Ultrabontinental
Continental
Maritime
Suboceanic
Oceanic
Suprapolar
--
--
Cryptogam comm.
Carex comm.
Mesopolar
--
Betula exilis comm.
Eriophorum
vaginatum comm.
Vaccinio-Empetretum Vaccinio-Empetretum
nigrae
nigrae
Thermopolar
FlavocetrarioBetuletum divaricatae
Salici kryloviiLaricetum gmelinii
Ledo-Pinetum
pumilae
Dryopterido-Alnetum
fruticosae
Artemisio-Arnicetum
unalascensis
Crioroboreal
Kobresia spp. comm.
Dryas comm.
Dryas comm.
Carex comm.
Cassiope comm.
Oroboreal
FlavocetrarioBetuletum divaricatae
Ledo-Pinetum
pumilae
Vaccinio-Pinetum
pumilae
Dryopterido-Alnetum
fruticosae
Dryopterido-Alnetum
fruticosae
Supraboreal
FlavocetrarioBetuletum divaricatae
FlavocetrarioBetuletum
divaricatae
Salici kryloviiLaricetum gmelinii
Salici arcticaeBetuletum ermanii
Dryopterido-Alnetum
fruticosae
Mesoboreal
Ledo-Laricetum
cajanderi
Ledo-Laricetum
cajanderi
Saussureo-Laricetum Geranio erianthigmelinii
Betuletum ermanii
Dryopterido-Alnetum
fruticosae
Thermoboreal
Lathyro-Laricetum
cajanderi
Ledo-Laricetum
cajanderi
Moneco-Piceetum
jezoensis
Artemisio opulentaeBetuletum ermanii
Glycerio-Alnetum
fruticosae
Crioroboreal
Kobresia spp. comm.
Dryas comm.
Dryas comm.
comm.of Loiseleurio-Vaccinetea
Oroboreal
Betula rotundifolia
comm.
Vaccinio-Pinetum
pumilae
Vaccinio-Pinetum
pumilae
Vaccinio-Pinetum
pumilae
--
Supraboreal
Larici-Pinetum pumilae
Larici-Pinetum
pumilae
SanguisorboLaricetum gmelinii
Weigelo-Betuletum
ermanii
--
Mesoboreal
Ledo-Laricetum
cajanderi
Ledo-Laricetum
cajanderi
PhiladelphoPiceetum jezoensis
Asaro-Abietetum
sachalinensis
--
Thermoboreal
Lathyro-Laricetum
cajanderi
VaccinioPiceetum
jezoensis
OplopanacoPiceetum jezoensis
Piceo-Abietetum
sachalinensis
--
The boreal orders Lathyro-Laricetalia and LedoLaricetalia, representing boreal deciduous coniferous
forests, are characterized by very low values of CK
that, in this case, are comparable to that of subarctic
orders. The ranges of orders along the continentality
gradient are relected by changes of vegetation types
within a zone with proximity to the ocean. In the boreal
zone the lowest values of continentality index (CI) are
characteristic to the class Betulo-Ranunculetea, and
deciduous coniferous orders Lathyro-Laricetalia and
Ledo-Laricetalia developed in conditions of maximum
values of continentality for the whole of North Asia.
Vegetation of the subarctic zone is characterized by
moderate values of CI due to the inluence of Arctic
Ocean.
Cassiope comm.
The distribution of ombro-evapotranspirational (OEI)
index among vegetation orders shows that boreal
Lathyro-Laricetalia and Ledo-Laricetalia, and partly
subarctic order Larici-Betuletalia, are developed in
critical conditions of signiicant moisture deicit that
normally do not support forest vegetation. Edaphic
moisture accumulating on the north-face of mountain
slopes supports forests in dry areas of the temperate
zone. The most important source of water in dry areas
of the boreal zone is melting permafrost. A signiicant
peak of OEI for the subalpine order WeigeloBetuletalia is caused by the monsoonal character of
precipitation in the mountainous areas of Hokkaido
and Sikhote-Alin, where the high summer precipitation
is enforced by low evapotranspiration due to the low
temperatures in the subalpine belt.
139
Fig. 1. Distribution of selected bioclimatic indices in Northeast Asia. A – Kira’s warmth index (ºС) (Kira, 1977):
1: 0–10; 2: 10–15; 3: 15–20; 4: 20–25; 5: 25–30; 6: 30–35; 7: 35–40; 8: 40–45; 9: 45–55; 10: 55–65; 11: 65–85;
12: 85–100; 13: >100. B - Kira’s coldness index (°С) (Kira, 1977): 1: >-10; 2: -10 – -20; 3: -20 – -50; 4: -50 –
-100; 5: -100 – -150; 6: -150 – -200; 7: -200 – -250; 8: <-250. C – Continentality index (°С) (Rivas-Martínez et
al., 1999): 1: >60; 2: 55–60; 3: 50–55; 4: 45–50; 5: 40–45; 6: 35–40; 7: 30–35; 8: 25–30; 9: <25. Precipitation
(mm) in months with mean temperature below 0.C: 1: <20; 2: 20–70; 3: 70–120; 4: 120–170; 5: 170–300; 6:
300–400; 7: >400 (after Krestov et al., 2008).
Fig. 2. Graphical representation of
probabilities of occurrence of zonal
communities of selected orders
in combination of ive bioclimatic
indices: WK, CK, CI, OEI and WP
calculated from models (Nakamura
et al., 2007). The orders
abbreviated as in Table 1. White
– probability of occurrence < 50%,
grey – probability 50–70%, black
– probability > 70%. Vegetation
orders: LAR-BET – Larici cajanderiBetuletalia divaricatae; VAC-PIN
– Vaccinio-Pinetalia pumilae; LATLAR – Lathyro humilis-Laricetalia
cajanderi; LED-LAR – Ledo
palustris-Laricetalia cajanderi; ABIPIC – Abieti-Piceetalia jesoensis;
BET – Betuletalia ermanii and
Streptopo-Alnetalia maximowiczii.
140
The relationships among vegetation units with snow
cover become signiicant in the conditions of the
oceanic sector of the boreal zone, where the strong
accumulation of snow causes a 2─3 week delay of its
melting and a considerable shortening of the growing
season. The communities of Betula ermanii, Alnus
fruticosa, and tall-forb meadows are characteristic to
the regions with slower-melting heavy snow deposits.
Figure 2 shows probabilities of occurrence (over
50%) of certain vegetation orders on the zonal sites
in conditions calculated from the distributional models
for ive bioclimatic indices: CI, WK, CK, OEI, and
WP. Simulated ranges in general correspond to the
actual vegetation distribution on the maps (Lavrenko
& Sochava, 1954; Anonymous, 1982; Miyawaki,
1981–1982); however, some simulated ranges show a
high probability of suitable climatic conditions in these
areas where the corresponding vegetation unit lacks
at present. Most remarkable discrepancies are the
following: (1) climatic conditions of the area currently
occupied by tundra are suitable for larch forests and
woodlands belonging to the orders Ledo-Laricetalia
cajanderi and Larici-Betuletalia divaricatae; (2) models
of bioclimatic indices showed suitable conditions for
the order Vaccinio-Pinetalia pumilae on the mountains
of Chukotka peninsula, where Siberian dwarf pine
does not form any continuous thickets as it does in
Kamchatka and Koryakia; and (3) simulated range of
Abieti-Piceetalia is very close to its actual geographical
range along the Paciic coast, but combinations of
bioclimatic indices of the inland part of the present
order distribution better correspond to the LedoLaricetalia cajanderi. These discrepancies suggest
that the current vegetation pattern of North Asia is not
in equilibrium with present climatic conditions.
Comparative loristic analysis showed that southern
and coastal loras as well as loras of mountainous
regions include the most taxa of northern and
continental loras. Zonal subarctic vegetation is
represented by communities of the provisional class
Betuletea glanduloso-divaricatae and composed of
species complexes of Paciic coastal mountainous
regions. In the boreal zone, vegetation of Asian
ultracontinental to maritime sectors is represented
by communities of Vaccinio-Piceetea, and in Asian
suboceanic to hyperoceanic sectors, by communities
of Betulo-Ranunculetea. The boreal classes were
likely differentiated in Pre-Pleistocene time due to the
well-developed loristic centres under the different
climatic situations.
Acknowledgments
This study was supported by the Russian foundation
for basic research (06-04-91451, 07-04-00654) and
the Russian Academy of Sciences (09-III-А06-172,
09-I-Π16-01).
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and Ordination. Izdatelstvo SO RAN, Novosibirsk.
232 pp. (in Russian).
Ermakov, N. & Alsynbayev, K. 2004. Modeling of
spatial organization of woodlands in southern part
of the Western Sayan. Siberian J. Ecol. 5: 687–702.
Ermakov, N., Cherosov, M., & Gogoleva, P. 2002.
Classiication of ultracontinental boreal forests in
central Yakutia. Folia Geobot. 37: 419–440.
Fang, J.Y. & Yoda, K. 1990. Climate and vegetation
in China. IV. Distribution of tree species along the
thermal gradient. Ecol. Res. 5: 291–302.
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of vegetation zones for Japan and adjacent regions.
Ann. Bot. Fennici 11: 59–88.
Kira, T.A 1977. Climatological interpretation of
Japanese vegetation zones. Pages 21–30 in
Miyawaki, A. & Tüxen, R., eds. Vegetation Science
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Krestov, P.V. 2006. Vegetation cover and
phytogeographical lines on northern Paciica. D.Sc.
Thesis, Institute of Biology and Soil Sci., Vladivostok,
Russia. 424 pp.
Krestov, P.V., Ermakov, N.B., Osipov, S.V., & Nakamura,
Y. 2009. Classiication and phytogeography of larch
forests of Northeast Asia. Folia Geobotanica 44. (In
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study of the Picea jezoensis forests of the Far East.
Folia Geobotanica 37(4): 441–473.
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Krestov, P.V. & Nakamura, Y. 2007. Climatic controls
of forest vegetation distribution in Northeast Asia.
Berichte der Reinhold-Tuxen-Gesellschaft 19: 132–
146.
Krestov, P.V., Nakamura, Y., & Omelko, A.M. 2008.
Vegetation and natural habitats of Kamchatka
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Krestov, P.V., Song, J.-S., Nakamura, Y., & Verkholat,
V.P. 2006. A phytosociological survey of the
deciduous temperate forests of mainland Northeast
Asia. Phytocoenologia 36(1): 77–150.
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Sciences, Leningrad. (in Russian).
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142
GIMMS–NDVI Based Mapping of the Growing Season North of 50°N
Stein Rune Karlsen1, Kjell-Arild Høgda1, Bernt Johansen1, Arve Elvebakk2, Violetta Fedotova3, Anne Tolvanen4
Northern Research Institute Tromsø (Norut), Tromsø, Norway, 2Tromsø University Museum, University of
Tromsø, Tromsø, Norway, 3Komarov Botanical Institute, Petersburg, Russia, 4Finnish Forest Research Institute,
Muhos Research Unit, Muhos, Finland
1
Extended Abstract
The aim of this study is to map the onset, end,
and length of the growing season north of 50ºN.
Phenology data on Betula spp. and Populus spp.
from Norway, Finland, Canada, and central Siberia,
and the half-month Global Inventory Modeling and
Mapping Studies–Normalized Difference Vegetation
Index (GIMMS–NDVI) satellite dataset with 8 km
resolution from the period 1982 to 2006 were used
to measure the growing season. For each pixel a
25-year mean NDVI value was computed. Threshold
values related to this mean NDVI value, which shows
the best correlations with the phenological ield
data, were chosen to produce maps showing the
mean date for onset, end, and length of the growing
season. The preliminary results show high correlation
values between NDVI data and spring phenophases,
whereas the end of the growing season shows a
lower correlation.
dataset for the 1982–2002 period to map the growing
season and bioclimatic zones in Fennoscandia. They
related the time integrated NDVI (TI NDVI) values
during the growing season to growing degree days
obtained from meteorological stations and found that
a TI NDVI map could be presented as a bioclimatic
map relecting growing degree days in Fennoscandia.
The presented study, with mapping of the growing
season, is the irst step in a bioclimatic mapping of
the whole area north of 50ºN.
Keywords: bioclimatic zone, circumboreal, GIMMSNDVI, growing season, phenology.
References
Karlsen, S. R., Elvebakk, A., Høgda, K. A., & Johansen,
B.. 2006. Satellite-based mapping of the growing
season and bioclimatic zones in Fennoscandia.
Global Ecology and Biogeography 15: 416─430.
Recently, Karlsen et al. (2006) used the GIMMS–NDVI
Figure 1. Time of onset of the growing season, based on mean values from the GIMMS–NDVI dataset for the
period 1982–2006.
143
Mapping of the Eurasian Circumboreal Forest–Tundra Transition Zone
by Remote Sensing
Gareth Rees1, Olga Tutubalina2, Hans Tømmervik3, Mikhail Zimin4, Anna Mikheeva2, Elena Golubeva2, Kelly
Dolan1, Annika Hofgaard4
1
Scott Polar Research Institute, University of Cambridge, United Kingdom, 2Faculty of Geography, M.V.
Lomonosov Moscow State University, Moscow, Russian Federation, 3Norwegian Institute for Nature Research
(NINA), Tromsø, Norway, 4Norwegian Institute for Nature Research (NINA), Trondheim, Norway
Extended Abstract
The interface between the boreal forest and the arctic
tundra is the Earth’s largest vegetation transition
(Callaghan et al., 2002). This interface region is over
13,000 km long and occupies approximately 5% of
the vegetated surface of the Northern Hemisphere. It
plays a key role in the global climate system (Harding
et al., 2002). While modelling predicts northward
shifts in boreal vegetation distributions in response
to global warming, systematic monitoring data are
scarce and provide scant evidence for these shifts.
Indeed, counterintuitive observations have been
noted, including southward shifts of species and
expansion of tundra, despite strong Arctic warming
trends. In addition, we still lack consistent data on
the location, nature, and dynamics of the tundra-taiga
interface (TTI) at all scales from global to landscape
levels (Rees, 2007), and this has major implications
for circumboreal vegetation mapping. In the context of
the proposed Circumboreal Vegetation Map (CBVM),
the interface region stretches from the southern limit of
the arctic tundra, as shown on the Circumpolar Arctic
Vegetation Map (CAVM, 2003) to the northernmost
limit of boreal forests (whose canopy cover starts at
about 30%). We note that the southern limit of the
tundra region in Eurasia still needs to be clariied in
some areas (for example, signiicant tundra areas
on the Kola Peninsula are not shown in the CAVM)
on the basis of remote sensing and ield data (Fig.
1). Dificulties are caused by scarcity of data at the
circumboreal scale, and by problems in adapting
ield-based deinitions of treeline, forest line, etc.,
to encompass a wide range of spatial scales. In
addition, we have the problem with change in contrast
due to different dominant tree species (conifers and
broadleaved species) forming the forest- and tree
lines along the interface region.
All these dificulties can be addressed through
the application of remote sensing data and new
quantitative methods of landscape analysis. Another
Fig. 1. Red, green, and blue lines: birch, evergreen conifer, and larch treelines, respectively, from Hustich
(1983). White line: treeline adopted by the Cimcumpolar Arctic Vegetation Map (CAVM) (CAVM Team, 2003).
Black line: boundary of the tundra biome (Olson & Dinerstein, 1997). The background map is a simpliied
representation of the Joint Research Centre’s Global Landcover 2000 (JRC, 2003).
144
issue is adequate representation of altitudinal forest–
tundra transition zones that have speciic elevational
belts depending on their longitudinal regions, such as
transition zones in Fennoscandia, the northern part of
European Russia, the Polar Urals, Western Siberia,
Central Siberia, Eastern Siberia, and the Russian
Far East. At the CBVM scale (1:7.5M) these regional
types may have to be shown by special symbols (not
to scale). Useful data are provided by the map Zones
and Types of Altitudinal Zonality in Russia, scale 1:8M,
by Ogureeva et al. (1999)
An international, coordinated programme of research
into the TTI has been developed under the auspices of
the International Polar Year (IPY). This programme has
the title “Present-day Processes, Past Changes and
Spatiotemporal Variability of Biotic, Abiotic and SocioEnvironmental Conditions and Resource Components
Along and Across the Arctic Delimitation Zone”
(PPS Arctic, see http://www.ipy.org/development/
eoi/proposal-details.php?id=151). The PPS Arctic
combines approximately 120 researchers from 13
countries and has some 20 ieldwork sites across the
circumarctic TTI region that were chosen to provide
adequate sampling of both latitudinal and altitudinal
treelines, the oceanicity gradient, and the broad range
of tree species composing the TTI. The major areas
of activity within the PPS Arctic Programme, which is
coordinated from Norway and the United Kingdom,
are currently within Canada and northwestern Europe.
Keywords: boreal forest, International Polar Year,
mapping, remote sensing, tundra.
Deinitions of Tree- and Forest Lines
In order to deine forest and tree lines it is essential
to determine the minimum level of crown density and
the minimum size of a tree. The basic questions,
according to Veijola (1998), are: What is a tree? and
What is a forest? In order to distinguish and assess
crown closure, the minimum stand area must be
stated, and Kullman (1983) considers 10 trees at least
2 m in height to be the minimum stand size. Sirois
(1992) considers the stand limit to be the smallest
stand distinguishable on an air photograph at a scale
of 1:50,000. On the other hand, Tsvetkov (1995)
suggests that for extensive surveys of forest and
tundra areas using satellite images at a scale of 1:1M,
areas of 30% forest or more can be denoted as forest.
The forest line is deined by Hustich (1966) as the
elevational limit at which forest canopy closure
ceases and the maximum distance between single
trees (height >3 m) is less than 30 m (Mork, 1970).
The latitudinal limit is equally signiicant. The treeline
is deined by solitary trees with heights more than 2
m (Mork, 1970; Juntunen et al., 2002). On the other
hand, Hustich (1979) deines the treeline to be the
limit of the arborescent growth form, usually at least
2 m high.
It is essential to select appropriate scales for detection
and monitoring of tree- and forest lines when using
remote sensing based tools. For example, the coarse
spatial resolution that Tsvetkov (1995) suggests could
be suitable for detecting and monitoring tasks of
the broader forest─tundra transition zones by using
coarse-spatial resolution (around 1 km) satellite
imagery such as Advanced Very High Resolution
Radiometer (AVHRR), Moderate Resolution Imaging
Spectroradiometer (MODIS), and Medium Resolution
Imaging Spectrometer (MERIS) imagery, while Sirois’
(1992) suggestion its well with medium to high
resolution imagery such as that provided by Landsat
ETM, IRS, ASTER, and SPOT sensors. For more
detailed studies of tree lines, only high-resolution
imagery based on aerial or spaceborne sensors (like
Ikonos or Quickbird) is suitable.
Methods
Several methods have been tested for detecting treeand forest lines (e.g., Leblanc et al., 2005a). At the ield
scale, many methods are possible for characterizing
forest structural parameters, and these can be adapted
to deine the treeline and forest line. Leblanc et al.
(2005a, b) used ield measurements, including digital
hemispherical photography to quantitatively estimate
vegetation characteristics of leaf area index (LAI) and
canopy crown closure in the Yukon and Northwest
Territories of Canada. (An example of this approach
is shown in Figures 2 & 3a, b). Crown closure was
used as a quantitative measure to assess forest
presence/absence based on a given forest deinition.
Other methods include visual estimation of stand
density along a transect. In the Leblanc et al. (2005a,
b) study, empirical relationships were derived using
145
LANDSAT ETM+ images that were normalized with
coarse resolution SPOT-VGT data. They achieved
the best results when linear combination of bands
for the broadleaf species and linear combination of
exponential relationships for the coniferous species
were used. The crown closure maps from LANDSAT
images were then used to calibrate low-resolution
forest cover maps from NOAA-AVHRR and SPOTVGT data.
growing season. The phenological signal can be
characteristic of the landcover type (e.g., DeFries and
Townshend, 1994; Heiskanen and Kivinen, 2008).
Fig. 3a. Spatial variation of gap fraction along a
50-m transect, birch forest, northern Russia.
Fig. 2. Hemispherical photograph of pine forest,
Senja, northern Norway, and gap fraction (fraction
of unobstructed sky) as a function of zenith angle.
The leaf area index calculated for this scene is 0.91.
Photograph courtesy of Ingrid Mathisen.
At coarser spatial resolutions, of the order of 1 km,
detection of tree- and forest lines is more complicated
because of the mismatch in scale between the
imagery and the processes that control the location
of individual trees. Image classiication depends
on the availability of a multidimensional parameter
space within which decision rules can be applied. In
the most familiar type of classiication, the parameter
space is multispectral, and the decision rule is applied
on a pixel-by-pixel basis, with each pixel assigned
to the most likely landcover type on the basis of its
spectral properties. A variant on this approach is the
use of multiangular data (e.g., Barnsley et al., 1997;
Asner et al., 1998; and the MISR [Multi-angle Imaging
SpectroRadiometer] instrument carried on board the
EOS Terra satellite mission, which has provided a
signiicant opportunity to test this technique [see
Heiskanen, 2006]). A third possibility is provided by
the use of multitemporal data, in which the usually high
temporal resolution available from satellite imaging
systems with low spatial resolution is exploited to
provide repeat coverage throughout the summer
Fig. 3b. Spatial variation of gap fraction along a 50-m
transect, birch forest, northern Russia.
Subpixel classiication is of particular interest in the
case of kilometer-scale imagery because of the
mismatch of scales mentioned above, which can
often cause under-representation of the less common
landcover classes (e.g., Virtanen et al., 2004). A useful
approach is through the calculation of “vegetation
continuous ields” (VCF), in which percentages of
different landcover types are assigned to each pixel,
rather than by simply assigning each pixel to a single
type according to some decision rule. For example,
146
VCFs can be calculated through linear unmixing of
multispectral data, linear regression, or the use of
neural networks (Foody & Boyd, 1999; Boyd et al.,
2002; Braswell et al., 2003; Price, 2003; Ranson et
al., 2004a; Joshi et al., 2006; Heiskanen, 2006).
The use of spaceborne LiDAR data offers
another method for detecting the forest line at the
circumboreal scale. Data from the Geoscience Laser
Altimeter System (GLAS) aboard the Ice Cloud and
land Elevation Satellite (ICESat-1, launched January
2003) have been shown to exhibit considerable scope
for canopy height retrieval at regional to global scales
(Ranson et al., 2004b; Rosette et al., 2008). The
height accuracy is around 5 m with a spatial resolution
of 70 m, although the proposed ICESat-2 should give
an improved height accuracy of around 1 m. The data
could be merged/analyzed together with imagery from
Landsat or MODIS.
Circumboreal Mapping Within PPS Arctic
As part of the PPS Arctic Programme, a methodology
is being developed for consistent and repeatable
mapping of the circumboreal forest line and its
attributes. Mapping will be based primarily on the
analysis of coarse-resolution (around 1 km) satellite
imagery—MODIS and MERIS imagery for the present
and future, and AVHRR imagery to potentially allow
for change detection over the past 30 years or so.
This coarse-resolution imagery offers the advantages
of large swath width, and, hence, high temporal
resolution (Rees et al., 2002). The MODIS and MERIS
instruments (operational since 1998 and 2002,
respectively) with similarly high temporal resolutions,
together with a number of high-level data products
with the potential to discriminate the tundra–taiga
interface, offer greater spectral and spatial resolution
than AVHRR. These products are being investigated
within the context of PPS Arctic. However, to date,
none of these instruments has proved optimal for
circumboreal vegetation mapping, so a programme of
new algorithm development is also being undertaken.
There are several promising lines of development.
These include approaches based on the seasonal
dynamics of vegetation indices or estimates of leafarea index and the use of VCFs. Such data products
are being developed from MODIS data and also from
AVHRR data.
However, it is clear that any candidate algorithms must
be validated against ield-based assessments and
other sources of high-resolution data, such as aerial
imagery and higher-resolution satellite data from
the QuickBird, SPOT, or Landsat satellites. It will be
important to consider scale relations since ield-scale
measurements used for training and for validating
algorithms are acquired at a resolution of typically
1:10M, whereas MODIS and similar imagery has a
resolution of a few hundred to around a thousand
metres. It will also be important to explicitly take into
account the spatial structure of the TTI itself, which
has been characterized as a pattern of tundra islands
within the forest, shading into a pattern of forest
islands within the tundra. Three-dimensional structure
of forest edge regions through the analysis of airborne
LiDAR data, recent research on forest edge structure
and new spatiotemporal models, and the application of
fractal concepts, such as scale-dependent lacunarity
(to characterize the “holes” of tundra in the forest
matrix and vice-versa) and edge density, all have
signiicant potential as new methods of upscaling
data. One possible approach to the task of upscaling
is provided by the “scaling ladder” (Wu, 1999), which
integrates hierarchy theory and patch dynamics and
can help simplify the complexity of systems under
study, enhance ecological understanding, and, at the
same time, minimize the danger of unacceptable error
propagation in translating information across multiple
scales.
Case Studies
Here, we present case studies from the forest–tundra
transition zone in two locations: Porsangmoen in
Finnmark County, Norway, and the Tuliok Valley in
the Khibiny Mountains, Murmansk Oblast, Russia.
In situ ield data were collected from both sites; highresolution imagery was acquired using airborne LiDAR
and ATM imagery for the Porsangmoen site (Rees,
2007) and Quickbird imagery for the Tuliok site.
Treelines and forest lines were identiied from ield
observations, and their interface was extrapolated by
using high-resolution imagery and Landsat 7 ETM+
or ASTER imagery and their spatial characteristics
were investigated. We then compared the location
and spatial coniguration of the transition with coarseresolution imagery from MODIS. As an example,
Figures 4 & 5 illustrate comparisons between high-
147
resolution data and tree cover estimated from MODIS
data at a resolution of 500 m, using tiled VCFs
for 2005 generated by the University of Maryland
(Hansen et al., 2003, 2007). Figure 5 includes an
automated classiication of the QuickBird image into
forest/nonforest areas, based on texture classiication
of the panchromatic image and an unsupervised
classiication of the multispectral image to provide
a vegetation/non-vegetation mask. In addition, we
assessed the scope for determining historical changes
in the position and coniguration of the transition region
in the Tuliok test site by using aerial photography from
1958 (Fig. 6).
Fig. 5. (Top left QuickBird false-colour infrared imagery
of Tuliok Valley, Russia; (top right) automated binary
classiication into forest/nonforest areas; (bottom
left) MODIS false-colour infrared composite (July) of
the same area; (bottom right) VCF representation of
percentage tree cover derived from MODIS imagery
(same grayscale palette as in Figure 4).
Fig. 4. (Top) map layers from Norwegian state
mapping agency showing water and forest in the
Porsangmoen area of Norway; (middle) LiDARderived Euclidean Distance Map of trees (green,
0–10 m; blue, 10–30 m; yellow, 30–100 m; red, 100–
300 m); (bottom) MODIS VCF image of percentage
tree cover.
Fig. 6. Manually delineated forest cover in the
Tuliok Valley, Kola Peninsula, Russia. (Top)
derived from an aerial photograph, 1958; (bottom)
derived from Quickbird satellite image extract,
2006. The extracts cover an area approximately
300 m square. Advance of the birch forest is
clearly demonstrated in the bottom image.
148
Acknowledgments
The authors are grateful to the Research Council
of Norway for inancial support through grants to A.
Hofgaard from the programme OST, project 185023/
S50, and the programme IPY, project 176065/S30. O.
Tutubalina is supported by a Young Scientist Project
Grant of the Faculty of Geography, Moscow State
University.
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150
On the Importance of Accounting for Disturbance Regimes and Forest
Succession Ecosystem Dynamics in Boreal Vegetation Mapping
Steven G. Cumming1, Yves Bergeron2, & Sylvie Gauthier3
Départment des sciences du bois et de la forêt, Université Laval, Québec, Canada, 2Université du Québec en
Abitibi-Témiscamingue, Canada, 3Service Canadien des Forêts, Canada
1
Extended Abstract
As with the Arctic biome, the circumpolar boreal “is
increasingly recognized as a single geoecosystem
with a common set of cultural, political, economic,
and ecological issues” (Walker et al., 2005:268).
Vegetation maps for the circumpolar boreal would
indeed have many applications to problems in
resource management, conservation planning,
and for studies of interactions between terrestrial
and climate systems. However, unlike the Arctic,
the forested portion of the boreal is known to be
structured by large disturbance events, notably ire
and insect defoliators. The purpose of this paper is
to consider the implications of such disturbances
for the construction of vegetation maps and for their
intended applications; for concreteness, we focus on
wildire. We irst present an overview of recent studies
quantifying spatial variation in ire regimes across
the Canadian boreal (Cumming et al., in prep.) and
regionally within Québec.
We discuss how these studies could provide the
empirical foundation for incorporation of ire regime
in vegetation classiications. We then review the
relationships between vegetation cover and ire
regime, and emphasize that at least in some regions,
ire regime is partially regulated by vegetation,
independent of climate (Krawchuk et al., 2006). The
implication of this is that future ire regimes will not
be determined entirely by future climate. Variation
in ire regime implies that the proportion of recently
burned area will vary markedly in space across the
boreal, and in some regions will account for large
proportions of classiied areas. This raises the
challenge of how such natural disturbances can be
separated from anthropogenic changes and how the
two types should be treated in a classiication: clearly
disturbed patches are by deinition not permanent
features of the landscape. Recent studies from Alaska
suggest that post-ire development of burnt areas is
largely controlled by topographic and climatic factors
(Duffy et al., 2005), which implies that the concept
of “successional trajectory” needs to be included in
the vegetation classiication; a further consequence
is that similar spectral signatures may represent
similar vegetation types on different successional
trajectories, depending on local climate, topography,
and ire regime. Finally, we illustrate an integrated
conservation planning and habitat modelling exercise,
in which remote sensed landcover data were used
both to establish ecological representation criteria and
as an input to a spatial simulation model (Leroux et
al., 2007). We emphasise that most planning-oriented
applications of boreal vegetation maps require that
the maps be projectable in this way. However, to our
knowledge, no systematic method exists to develop
deterministic or stochastic succession rules for
existing boreal vegetation maps, although a number
of local studies are underway, for example, in Alaska
and Québec. We recommend that the next generation
of boreal vegetation mapping be developed with these
applications in mind.
Keywords: biotic regulation, climate change,
conservation planning, ire regime, forest management
planning, habitat modelling, landscape modelling,
vegetation dynamics.
References
Cumming, S. G., Mackey, B., & Schmeiegelow, F. K.
A. (in prep.) A multivariate regionalisation of boreal
ire regimes.
Duffy, P. A., Walsh, J. E., Graham, J. M., Mann,
D. H., & Rupp T. S. 2005. Impacts of large-scale
atmospheric-ocean variability on Alaskan ire
season severity. Ecological Applications 15: 1317–
1330.
151
Krawchuk, M. A., Cumming, S. G., Flannigan, M. D.,
& Wein, R. W. 2006. Biotic and abiotic regulation
of lightning ire initiation in the mixedwood boreal
forest. Ecology 87:458–468.
Leroux, S. J., Schmiegelow, F. K. A., Cumming, S.
G., Lessard, R. B., & Nagy, J. 2007 Accounting
for system dynamics in reserve design. Ecological
Applications 17: 1954–1966.
Walker, D. A., Raynolds, M. K., Daniëls, F. J. A.,
Einarsson, E., Elvebakk, A., Gould, W. A., Katenin,
A. E., Kholod, S. S., Markon, C. J., Melnikov, E.
E., Moskalenko, N. G., Talbot, S. S., Yurtsev, B. A.,
& the CAVM Team. 2005. The Circumpolar Arctic
Vegetation Map. Journal of Vegetation Science 16:
267─282.
152
Role of Disturbed Vegetation in Mapping the Boreal Zone in Northern
Eurasia
Annika Hofgaard1, Gareth Rees2, Hans Tømmervik3, Olga Tutubalina4, Elena Golubeva4, Ekaterina Shipigina4,
Kjell Arild Høgda5, Stein Rune Karlsen5, Mikhail Zimin1, Viacheslav Kharuk6
Norwegian Institute for Nature Research (NINA), Trondheim, Norway, 2Scott Polar Research Institute,
University of Cambridge, United Kingdom, 3Norwegian Institute for Nature Research (NINA), Tromsø, Norway,
4
Faculty of Geography, M.V. Lomonosov Moscow State University, Moscow, Russian Federation, 5Northern
Research Institute (NORUT), Tromsø, Norway, 6Sukachev Forest Institute, Akademgorodok Krasnoyarsk,
Russian Federation
1
Extended Abstract
Arctic, subarctic, and northern boreal habitats are
notable for the signiicance of their “disturbance”
(Shugart et al., 1992; Crawford, 1997). A commonly
used deinition of disturbance is the one by Pickett
and White (1985): “any relatively discrete event
in time that disrupts ecosystem, community, or
population structure and changes resources,
substrate availability, or the physical environment.”
This description may also be considered as inluences
of limited spatial or temporal extent superimposed on
the broader-scale background conditions. However,
when considering changes in the boreal zone
and its transition to the arctic region in a long-term
perspective, this deinition of disturbance with focus
on limited spatial or temporal extent is not satisfactory
or complete. Disturbance is a highly scale-dependent
concept, and consideration has to be given to both
the spatial and short- and long-term extent of any
disturbance. Disturbances occur at widely different
spatial and temporal scales, and their evident
signiicance depends on the time scale at which we
are observing the system. In addition, many different
kinds of disturbances may interact over time.
Disturbances can be both naturally occurring
and anthropogenic. Examples of the former are
provided by climate and herbivory, and of the latter,
by locally generated air pollution, forest logging,
changed grazing regime, mineral exploitation, and
disturbances due to oil and gas development, etc.
Another example, ire, can be both naturally occurring
and man-caused. In some parts of the circumboreal
zone, these and other disturbances are dominant in
controlling the characteristics of the boreal forests
and the forest–tundra transition, and this fact needs
to be represented in any map of the zone.
Boreal disturbance regimes may have an episodic
character, a chronic character, or a character
interweaving the two (cf. Hofgaard, 1997).
Boreal communities are shaped through episodic
disturbances (e.g., ire and insect outbreaks) and
longer term, chronic disturbances (e.g., climate
stress, pollution, and grazing) occurring at all spatial
scales, from the landscape or region to individual
forest stands or single trees. In addition, a disturbance
event such as ire or logging may trigger development
of a chronic disturbance state through an intensiied
grazing regime or increased climate vulnerability.
A relevant question that should be asked and kept
in mind both when studying and mapping the
system is: “What is natural vegetation in a changing
environment?” When examined more closely, regions
and areas dominated by supposed natural vegetation
in balance with the environment are in most cases
products of current and/or previous natural or manaltered disturbance regimes (Sprugel, 1991). The
current vegetation structure is somewhere along the
road towards recovery from previous disturbances.
However, equilibrium with the environment will never
be reached in regions where environmental variability
and change is a natural and dominating part of the
reality, as in arctic, subarctic and boreal regions.
Recovery after disturbance is not a straightforward
matter either in the time needed for recovery or
involved succession steps towards pre-disturbance
conditions. Although disturbance driven dynamics are
challenging when constructing vegetation maps for
the zone, there is a need for the inclusion of disturbed
153
areas in maps showing vegetation types of the boreal
zone. Three dimensions of disturbance have to be
considered when mapping circumboreal vegetation:
spatial extent, time involved from disturbance to
recovery, and likelihood of interacting disturbance
types. These dimensions are crucial for how current
vegetation is interpreted and subsequently included
as map characteristics.
make successional pathways and forest recovery
predictions highly tentative. Consequently, there will
be a need for inclusion of information on dominating
defoliators, likely combinations of defoliators, and
other key herbivores maintaining post-disturbance
conditions or delaying succession towards predisturbance conditions (see grazing section below)
when mapping the boreal zone.
In this paper, we consider several examples from
northern Eurasia where disturbance dominates the
state and distribution of vegetation in the boreal zone,
and the possibilities for assessing the nature and
extent of the disturbed regions using remotely sensed
data.
The potential for using data from Landsat and the
Moderate Resolution Imaging Spectroradiometer
(MODIS–Terra satellite) to detect and monitor insect
outbreaks in the circumboreal tundra–forest transition
zone has been evaluated in study areas situated in
Varanger and Dividal in northern Norway. Cloudfree Landsat and MODIS images of the study areas
acquired in the periods 1992–1994 and 2000–2006,
respectively, taken before, during the peak, and after
the outbreaks, were compared in order to determine
the geographical extent and severity of the outbreaks.
Two methods were applied to identify the defoliated
areas, one visually based on color composites and
one quantitatively based on the Normalized Difference
Vegetation Index (NDVI). Visual comparison of the
colour composites taken before, during, and after the
outbreaks showed that areas affected by caterpillar
outbreaks were identiiable as transitions from green
to heavily red/brown areas based on Landsat images
(Fig. 1).
Keywords: boreal vegetation; disturbance; mapping;
remote sensing.
Natural Disturbances
Disturbance by Insects
In the mountain birch forests of Fennoscandia and
the Kola Peninsula, the caterpillars of the geometrids
Epirrita (Oporinia) autumnata and Operophtera spp.
have for a long period of time been the most important
leaf-eating and defoliation event causing insects.
These insect outbreaks are the principal cause
of succession in northern birch forests. However,
in recent decades these species have shown
range expansions, producing new combinations of
regionally dominating outbreak species that cause
severe stand defoliations (Jepsen et al., 2008). New
species and new combinations of defoliating species
The NDVI method produced results similar to the visual
analysis. Areas affected by caterpillar outbreaks, as
classiied from the NDVI images, resembled those
identiied from the color composites and in-situ data.
Fig. 1. Area of Dividalen, Norway, in July 1990 and 1994 surveyed by Landsat-5 TM. On the 1994 scene, the
Epirrita attacked area is highlighted in red colors due to total defoliation of birch, dwarf birch, and dwarf shrubs.
154
The NDVI values were calculated pixel by pixel, and
changes at the sites were compared. Ground survey
data were used to evaluate damage indicated by NDVI
derived from the Landsat and the MODIS data. The
outbreak areas extracted from the NDVI-change map
were superimposed on an existing vegetation map in
order to analyze to what extent the different vegetation
types within the area were attacked (Table 1).
For the MODIS-imagery, the period from 2000–2003
was used to compute reference values for detecting
affected areas. For each of four 16-day periods and
for each pixel, the 75% quartile NDVI values for the
four years were chosen as the reference NDVI value.
Areas where NDVI decreased, signiicantly, could
be clearly mapped and classiied into moderate and
heavy damage categories according to the decrease
in NDVI value (Fig. 2).
Comparison of the two sensors Landsat TM/ETM+
and MODIS showed that Landsat TM/ETM+ sensors
were more detailed in their detection of the outbreak
areas. However, while only a few cloud-free scenes
Vegetation cover types
were available from the Landsat sensors during the
whole study period, the MODIS sensor delivered
complete coverage of the growing season each year.
The Siberian forests are the habitat of many insect
species, and periodic outbreaks cause decrease in
growth, forest decline, and mortality over vast areas.
The most inluential species is the Siberian silk moth
(Dendrolimus superans sibiricus Tschetw.), which is
one of the principal causes of succession in Siberian
conifer forests. The outbreaks of this species are
especially destructive in the “dark needle conifer”
taiga (with ir, Abies sibirica Ledeb., and Siberian pine,
Pinus sibirica Du Tour dominance). For example, a
single catastrophic outbreak between 1954 and 1957
killed about 1.5M ha of forest, and between 1930 and
1957, insects damaged or killed about 7M ha of forests
in south-central Siberia. These outbreaks occur with a
periodicity of 15 to 25 years (Kharuk et al., 2003). From
1878 to 2004, 10 large-scale outbreak events were
known to have occurred in the southern Yenisey River
region. The latest catastrophic outbreak happened in
1994–1996, when 0.7M ha of forest were affected, but
Total area
km
%
Bilberry-crowberry woodland (birch forest)
63.3
7.8
Low-herb woodland/grassland (meadow birch forest)
25.8
3.2
2
Bilberry woodland (birch forest)
Tall-fern-herb woodland/grassland (meadow birch forest).
Cowberry-bilberry woodland (open pine forest)
Lichen woodland (pine forest)
14.4
1.8
0.6
8.5
Poor fen/mire
30.6
Poor carpet/mud-bottom fen/marsh
18.8
Fen
Mid-alpine poor grass ridge (Luzula type)
Trailing Azalea -Diapensia ridge/Mountain avens ridge (“Dryas”
type)
Poor sheep´s fescue heath/grassland
Mountain Crowberry/Dwarf birch heath
Dwarf birch heath/scrub
12.4
66.2
50.6
136.2
93.5
49.6
1.8
0.2
0.1
1.0
3.8
1.5
2.3
8.2
Attacked area
km
km2
%
11.6
26.1
51.7
6.7
3.5
7.9
22.3
2.9
1.8
0.0
0.0
0.3
0.3
0.3
0.2
0.0
4.1
0.0
0.0
0.7
5.4
12.2
45.7
14.2
32.0
Poor grass snow patch
52.5
6.5
0.0
0.0
Rich mountain meadow and snow patch meadow
Dwarf willow snow patch
Total
52.8
7.3
811.4
6.5
0.9
100.0
0.0
0.1
0.0
44.4
50.4
8.8
13.4
2.2
66.2
133.6
108.9
17.6
12.1
5.9
Bilberry-blue heath heath/dry grassland
Mid-alpine grass snow patch
8.1
18.7
0.0
11.5
3.9
0.6
0.5
0.7
0.5
6.1
1.8
30.3
0.2
2.6
12.5
0.7
6.2
16.8
Undisturbed area
%
2
0.0
0.2
0.0
100.
0
88.0
1.6
0.2
0.1
1.1
4.0
1.6
2.4
8.6
6.6
17.4
11.5
6.0
94.7
12.4
52.5
6.8
17.6
52.7
7.3
766.8
2.3
6.9
1.0
100.0
Table 1. Forest cover and mountain heath types in Dividalen, Norway related to attacks caused by Epirrita
autumnata.
155
the last outbreak in the south Siberian mountain taiga
occurred in 2002–2003. Landsat, Terra/MODIS, and
Spot Vegetation satellite data were applied for mapping
insect-affected territories from the 1994–1996 event
(Kharuk et al., 2003). The dynamics of the outbreaks
were analyzed based on NDVI temporal proiles,
which showed good correlation between satellite
and in-situ data. The relationship between forest
stand mortality from insects and topographic features
(azimuth, elevation, slope steepness) was analyzed
using a high-resolution digital elevation model. The
outbreak began between the elevations of ~430–480
m and on southwest slopes with a steepness <5º, and
these conditions appear to be the most favourable
Siberian silk moth habitat. During the mid-phase of
the outbreak, the insects moved both up- and downslope, resulting in an elevation distribution split within
a range of ~390–540 m and slope steepness up to
15º. In the inal phase, the azimuth distribution of
damaged stands became uniform, showing that the
insect at this phase settled in non-optimal habitat.
Data obtained show that satellite data sensors are
applicable for monitoring not only extent of, but also
the dynamic nature of Siberian silk moth outbreaks in
taiga landscapes vulnerable to outbreaks. Both are
essential when mapping the zone.
Disturbance by Fire
In large parts of the boreal region, the dominating
natural disturbance agent is ire. The frequency, areal
extent, and intensity of these ires varies between
geographical regions and time periods and are
principally set by the dominating climate regime of
the regions and changes of these climate regimes.
However, ire regimes are also to some extent
affected by insect outbreak regimes. Outbreaks
of insects promote wildires because killed stands
accumulate combustible material in the form of dead
wood, and because more ire receptive post-outbreak
communities, such as grass and shrub communities,
are a result of the outbreaks.
For the zone of larch dominance (Larix sibirica
Ledeb.; Larix gmelinii (Rupr.) Kuzen) in Central
Siberia a decrease of the ire return intervals from
about 100 years in the 19th century to 65 years in
the 20th century has been found, which can mostly
be attributed to changes in climate. However, similar
changes in the “larch-mixed taiga” ecotone with
a change in ire return interval from 97 years to 50
years has been interpreted as mainly caused by
anthropogenic impacts (Kharuk et al., 2008; Ranson
& Dvinskaya, 2008).
Disturbance by Grazing
Fig. 2. Decrease in NDVI value in 2004 caused
by insect (Epirrita autumnata) defoliation of birch
forest (shown in red), Tana-Varanger area in northeasternmost Norway. Birch forests are shown in
green. As indicated on the map, both moderate (5–
20%) and severe damage categories (> 20%) could
be mapped.
Northern boreal systems are regionally important
grazing areas to ungulate populations. The impact by
these grazers is signiicant in regions where dominant
stand-forming tree species are favourable food to these
herbivores at some stage in their life cycle. Further,
both post-insect outbreak and post-ire vegetation
communities attract ungulate grazers through the
assembly of palatable sprouts and the grass and herb
dominated communities. Consequently, grazing by
ungulates can act as an agent preserving conditions
after severe disturbances, such as ire and an insect
outbreak, for a prolonged period. Thus, grazing may
cause substantial lag in successional development
after disturbance and act as a chronic disturbance
to both previously disturbed as well as to previously
undisturbed communities.
For example, high densities of reindeer can lead
to removal of birch seedlings and saplings with
subsequent decrease in mountain birch forest extent.
The effect will be pronounced in the transition zone
156
between boreal and arctic and/or alpine regions. This
is the case in the summer grazing areas for reindeer
in northern Norway as well as in other places of
Fennoscandia (Dalen & Hofgaard, 2005; Stark et al.,
2007; den Herder et al., 2008; Moen et al., 2008).
However, grazing might also promote tree recruitment
and growth through altering edaphic conditions and
enhance substrate availability for tree recruits.
In an integrated study using remote sensing (mainly
Landsat data) and ield investigations, Tømmervik
et al. (2004, 2008) showed that birch forest biomass
in reindeer winter grazing areas in Finnmarksvidda,
Northern Norway, during the period 1957–2006,
increased signiicantly as a consequence of change
in grazing pressure by reindeer in the region. The
increase was signiicant for trees, shrubs, vascular
plants in the ield layer, and mosses. For birch the
increase was ca. 100% in the period from 1957 to
2000, with a subsequent decline of 5% in the period
from 2000 to 2006 (Fig. 3). Lichen biomass showed
a signiicant reduction (77%) in the period from 1957
to 2000 with high grazing pressure and a signiicant
increase during low grazing pressure (2000 to 2006).
It is hypothesized that the grazing removed “the
barrier effect” provided by the thick lichen cover, which
led to increased success for birch regeneration and
a subsequent densiication of sparse forest stands
and therefore increased the zone with scattered trees
between forest line and treeline.
Consequently, this promotion of the growth of
mountain birch in reindeer winter grazing areas
originally dominated by lichens increased the extent
of the mountain birch forests and caused an elevation
shift in the forest line location (Tømmervik et al.,
2008).
To a large extent grazing regimes are altered by
human activities. Locally and/or regionally, the grazing
exerted by domestic herbivores may override grazing
pressures caused by wild grazers/browsers, with a
signiicant effect on plant community structure and
distribution (Hofgaard, 1997; Mysterud, 2006). These
regions can be considered as being under chronic
disturbance and should be mapped accordingly.
Anthropogenic Disturbances
Disturbance by Industrial Pollution
In several places in the Russian part of the
circumboreal zone, major disturbances in the
vegetation have been caused by large-scale
industrial emissions into the atmosphere, particularly
from smelting of nickel and copper ores. Damage to
and transformation of vegetation is caused by the
emission of noxious gases (mainly SO2) and heavy
metals, as well as mining waste deposits, and loss
of landscape diversity and habitat diversity through
urbanization and industrialization (Rees & Rigina,
2003, Tømmervik et al., 2003).
Fig. 3. Forest biomass change for Finnmarksvidda,
Northern Norway, 1957–2006.
Approaches to the characterization of this industrial
impact can be exempliied by the use of remotely
sensed data from four industrial towns in the Russian
part of the circumboreal zone: Nikel, Zapolyarny,
Monchegorsk, and Norilsk. These towns provide
contrasting biogeographical conditions and allow
some general conclusions about the optimum
means of measuring large vegetation disturbances.
In Nikel and Zapolyarny, the terrestrial habitat was
monitored in the period from 1973 to 1999 by the
use of Landsat Multispectral Scanner (MSS) and
Thematic Mapper (TM) data (Tømmervik et al., 2003).
In order to classify the satellite images, unsupervised
and supervised classiication methods (hybrid
classiication) were combined, as a single supervised
classiication is incapable of detecting the earliest
signs of air pollution damage (Tikkanen & Niemela,
1995). The classiication procedure consisted of (1)
an unsupervised spectral clustering of the satellite
image and (2) a supervised rule-based classiication/
interpretation based on ground truth control points,
auxiliary data, and geobotanical knowledge. The total
157
overall accuracy of the vegetation maps produced in
the period varied between 75%─83%. It was concluded
that the main effect of air pollution was a reduction
in areas of lichen-dominated forests and mountain
heaths (Cladonia spp.): there was a decrease from
37% in 1973 to 10% in 1994, followed by a slight
increase to 12% in 1999. The lichen-dominated
vegetation types were changed into barrens, partly
damaged vegetation entities, and dwarf shrub (e.g.,
Vaccinium myrtillus)-dominated vegetation (Fig. 4).
Rees & Williams (1997) also successfully used this
mapping method in the Monchegorsk area.
Disturbance by Forest Logging
According to the Atlas of Russia’s Intact Forest
Landscapes (Aksenov et al., 2002), only 26% (289M
hectares) of Russia’s forests remain in areas that
have no signs of infrastructure or modern land use
and are at least 50,000 ha in size. Most of these
forests are in the boreal zone. Eastern Siberia is the
most pristine region, with 39% of the forest zone in
intact forest landscapes, followed by the Russian Far
East (30%) and Western Siberia (25%). European
Russia is by far the least pristine region, with only
9% of the forest intact. In Fennoscandia, less than
5% of the boreal forest zone was considered pristine
in the late 1990’s (Niemi et al., 1998). However, it is
also concluded that over 99% of the boreal forests in
Fennoscandia have been altered by forestry practices
(Griesser et al., 2007). Consequently, the waste
majority of Scandinavian boreal forests have manmade disturbance patterns and stand characteristics.
The Arkhangelsk region presents a typical example
of intensive logging in Russian boreal forests, with
large logging areas and associated deciduous mixed
coniferous successions easily detectable in mediumresolution remote sensing data, such as those from
Landsat satellites.
Disturbance by Oil and Gas Development
Parts of the boreal zone in Russia are signiicantly
developed for oil and gas extraction and transport. Oil
is piped westwards from the Nenets Okrug, and gas
from the West Siberian basin. Construction of pipelines
and associated roads requires forest logging and can
lead to erosion, damming of water courses, wetland
formation, and other disturbances. Disturbance is
also caused by oil spills (Vilchek & Tishkov, 1997). All
of these types of disturbances are easily detectable in
medium-resolution remote sensing data.
Disturbance by Fire
Disturbance by anthropogenic caused ires has varied
through time and space due to a region speciic history
of change in land use patterns. Regionally, ire has
been used as a tool to increase grazing conditions in
forested areas. In populated parts of Scandinavia, a
general change from high-frequency human-caused
ires during centuries pre-dating industrial times to
current low-frequency, spatially limited ire events is
evident (Zackrisson, 1979).
Predominantly, anthropogenic ire regimes affect
large areas in Kola Peninsula, Siberia, and the Far
East. Burnt areas in northern Eurasia in 2002─2006
varied from 6M to 40M hectares (TerraNorte, 2008); of
these, the majority are in the boreal forests with some
proportion of steppe ires, mostly in south Siberia.
Thus, up to a few percent of the total area of the boreal
forests in northern Eurasia were affected by ire each
year during this period. Forest ires can be connected
with intensiied logging activity, with some of the
ire-affected forests licensed for logging (Radford,
2005). Forest ire activity is monitored by national and
international bodies using coarse-resolution MODIS
and Advanced Very High Resolution Radiometer
(AVHRR) data, and forest ire disturbance maps can
be readily produced.
Disturbance and Mapping of Border Zones
It is generally considered that transition zones or
border zones between plant communities/regions,
such as the forest–tundra transition zone, represent
a delicate balance between opposing forces of nature
and should therefore be modiied by slight changes in
the environment (cf. Hofgaard, 1997). Disturbance as
the central factor in boreal vegetation dynamics and
in translating environmental change in vegetational
response will thus be of prominent importance at
transition zones. Consequently, knowledge of the
disturbance regime is essential for understanding,
mapping, and modelling system responses,
particularly in this region.
There is a lack of consistent data on the location, nature,
and dynamics of the forest─tundra transition zone at
all scales from global to landscape (Rees, 2007), and
158
Fig.4. Landcover map over the Pasvik–Nikel area from 1999. Lichen-dominated heathland and woodland
vegetation types (light yellow and brown colors); dwarf shrub dominated forests and heaths (green colors);
and barrens and damaged environments (air pollution) around the smelters in Nikel and Zapolyarny, Russia
(violet colors).
this has major implications for circumboreal vegetation
mapping. An international, coordinated program of
research into this topic has been developed under
the auspices of the International Polar Year (IPY).
This program has the title “Present-Day Processes,
Past Changes and Spatiotemporal Variability of
Biotic, Abiotic and Socio-Environmental Conditions
and Resource Components Along and Across the
Arctic Delimitation Zone” (PPS Arctic) (http://www.ipy.
org/development/eoi/proposal-details.php?id=151).
The PPS Arctic has around 110 researchers from
13 countries active at ca. 30 ield sites across the
circumarctic region, chosen to provide adequate
sampling of both latitudinal and altitudinal treeline
ecotones, the oceanicity gradient, and the broad
range of tree species composing the circumpolar
forest–tundra transition. The major areas of activity
within the PPS Arctic program, which is coordinated
from Norway and the United Kingdom, are currently
within Canada and northwestern Europe.
At a large scale, mapping of the forest─tundra
transition zone is based primarily on the analysis
of coarse-resolution (around 1 km) satellite
imagery—MODIS and Medium Resolution Imaging
Spectrometer (MERIS) imagery—for the present and
future, and AVHRR imagery to potentially allow for
change detection over the past 30 years or so. This
coarse-resolution imagery offers the advantages
of large swath width, and, hence, high temporal
resolution (Rees et al., 2002). However, by the use of
high-resolution data and selected test areas, mapping
precision of the forest–tundra transition is further
developed within the PPS Arctic program (cf. Rees et
al., this volume).
Conclusion
Disturbed vegetation occupies signiicant areas in
the boreal zone, and related vegetation successions
should be adequately represented when mapping this
159
region. If the proposed Circumboreal Vegetation Map
will focus on showing the potential vegetation types, it
would still be useful to show the types of disturbances
and successions through a system of color hatchings,
symbols and lines, overlaid on the vegetation type
contours, and to present this information as separate
layers in a corresponding geographic information
system (GIS) database.
Acknowledgments
The authors are grateful to the Research Council
of Norway for inancial support through grants to A.
Hofgaard from the program OST, project 185023/S50,
and program IPY, project 176065/S30.
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161
Mapping of Natural and Anthropogenic Disturbances on Vegetation in the
Kola Penninsula
Tatjana Chernenkova1, Mihail Puzachenko2, Elena Tikhonova1, Elena Basova1
1
2
Centre for Problems of Ecology and Productivity of Forests RAS, Moscow, Russia, chernenkova50@mail.ru,
Institute of Geography RAS, Moscow, Russia, puzak@ork.ru
Abstract
The structure and composition of boreal forests in the
Kola Peninsula, northwestern Russia, was investigated
between 1981 and 2008. Our examination of northern
taiga communities conirmed a close relationship
between ecological parameters of forests and the level
of anthropogenic inluence (ires, air pollution, cutting,
recreation). In spite of a reduction in pollution levels
(more then ive times reduction in the last 10 years),
all measured biodiversity parameters were affected
by pollution. However, positive forest biodiversity
regeneration was also registered. The objective of
our research was to reveal the spatial distribution
of forest types due to natural dynamics and as the
result of industrial pollution, their digression levels
and rates of regeneration. Remote sensing data and
ield research allowed an estimation of species and
typological diversity in north-taiga ecosystems in the
Imandra Lake basin.
Keywords: biodiversity, forest community, Kola
Peninsula, natural and anthropogenic disturbances,
remote sensing data, vegetation mapping.
Introduction
There is a concentration of large smelters and other
industrial plants (Nikel, Monchegorsk, Zapolyarny
and Apatity) in the Kola Peninsula, northwestern
Russia. Peculiarity of the study area (Monchegorsk
district, Murmansk region) is the high dynamics
of natural and anthropogenic transformation of
vegetation cover. Environmental factors inluencing
the area are clear-cutting, ires, and recreation, but
it is the air pollution from the Severonikel smelter
that has determined the state and condition of
forest communities near Monchegorsk over the last
50 years. Damage to vegetation from Severonikel
smelter has been the subject of many investigations
in recent years (Tommervik et al., 1995; Mikkola &
Ritari, 1992; Doncheva, 1978; Kozlov et al., 1993;
Kryuchkov, 1984; Rigina, 1998; Chernenkova, 1995;
Lukina & Nikonov, 1996; Chernenkova & Kuperman,
1999; Kapitsy & Risa, 2003).
Since the 1990s the volume of air pollutants from
Severonickel has decreased due to processing
technological improvements and ore quality change;
particularly, the total discharge amount of SO2 has
decreased from 232 thousand tons to 45 thousand
tons, and metal oxide dust from 15.8 thousand tons
to 5.4 thousand tons (Miroevsky et al., 2001). But
still, there is little research concerning vegetation
regeneration successions in smelter vicinities (Lukina
et al., 2005; Kataev, 2005).
The objective of our research was to reveal the
features of spatial distribution of forest types due
to natural dynamics, and as the result of industrial
pollution, their digression levels and rates of
regeneration. The aims of our investigation were:
(1) to estimate changes in the structure of forest
ecosystems along the spatial gradient of air pollution
in determination zones with different vegetation cover
damage; (2) to characterize regeneration succession
in forest ecosystems in vicinities of the Seneronikel
smelter under decreasing air emissions, which have
decreased ive times according to survey data (1981–
1983 and 2005–2008); and (3) to map the current
state of vegetation cover of the central part of Kola
Peninsula.
Materials and Methods
The investigation area (67º55’N, 32º48’E, elevation
from 120 m to 1200 m above sea level) is located in
the north taiga zone in Kola Peninsula. North taiga
nonswampy spruce forests are restricted to the bestdrained and warmest habitats on slopes and crests
162
of different glacial formations. In addition to spruce
forests, there are pine forests on plains and valleys;
peat lands occupy depressions and lake and river
beds. At the elevation from 350 m to 450–500 m
above sea level, coniferous forests are replaced by
crook-stem birch forests. Alpine tundra covers the
uppermost parts and tops of hills.
Forest typological structure was deined on regional
and landscape levels by using remote sensing data,
digital elevation models (DEM), and ield data (Isaev,
2008). This work included the following phases:
preliminary transformation and classiication of remote
sensing imagery; deining an optimal set of units for
vegetation classiication and types of information for
autodetection of typological diversity of land cover;
conducting ield surveys; using training sample sets
for statistical modelling and estimation of reliability for
deined units; and interpreting thematic data, including
ield data analysis.
Vegetation condition was estimated in a pre- and
post-ield period through automated dichotomy
classiication of Landsat 5 (1986–1992) and Landsat
7 (1999–2002) mosaics of initial images for June
and July conditions, respectively, with full sets of
spectral bands and resolution 57 m. The number of
sample plots in different types of landscapes was
ca. 300. Environmental (type of soil, rates of natural
and anthropogenic disturbances, etc.) and vegetation
parameters (diversity and abundance of mosses,
lichens, vascular plants species, vertical distribution,
vegetation cover structure) were estimated within 25
x 25 m sample plots.
A geographic information system was developed
with existing and renovated topographic materials
(1:200,000; 1:25,000) converted to vector format
(relief, lakes, settlements, road and rail network),
forest inventory and vegetation maps, remote sensing
data, and ield data points with a connected database
of parameters. Vector layers of relief contours, points
of relief height, and shorelines were interpolated in
ErdasImagine to a raster of relief–digital elevation
model (DEM).
Our approach to vegetation mapping using DEM
variables and relected spectral bands of satellite
images (including indices—Normalized Difference
Vegetation Index [NDVI], Normalized Difference Snow
Index [NDSI], Tasseled Cap and etc.) was based on
canonical stepwise discriminant function analysis
(Discriminant Function Analysis, 1984–2008), taking
into account ecosystem hierarchy (Kozlov et al., 2008;
Puzachenko et al., 2006). In analysis, ield parameters
were used as a test sample, which corresponded with
“external” variables: Landsat bands and indices, and
relief, with its derivates for different hierarchical levels
of organization. Thus, ield-measured parameters
(biodiversity parameters, land-cover destruction and
vegetation typology) were interpolated for all areas of
interest, and factors (discriminant analysis functions)
controlling vegetation differentiation and distribution
were estimated.
The comparison of forest ecosystem conditions at
different times (1981–1983 and 2005–2008) was
made at permanent sample plots (2 km, 5 km, 10
km, 20 km, and 30 km from the emission source) in
the dominant spruce forest type (Piceeta fruticulosohylocomiosa) formed on humic-illuvial podzol. A
reference site with background levels of atmospheric
deposition was selected 80 km to the west from the
smelter. Changes in the overstory structure were
characterized by estimating the composition and vigor
of trees from different age classes. Measurements,
including average vitality score (1 to 5) of upperstory,
basal area, the diameter at breast height (DBH), and
tree height were made for all trees in study plots.
Results
High levels of air pollution during the 1980s and
1990s contributed to signiicant concentrations of
heavy metals in biotic and abiotic compounds in
ecosystems surrounding the Severonikel smelter. The
prolonged impact of the metallurgical industry has
caused negative consequences on extant territory.
These consequences are clearly diagnosed by using
both ground-based and remote methods (by spectral
relectance characteristics of land cover) (Tutubalina
& Schpigina, 2004; Chernenkova, 2006).
Remote sensing data allowed us to estimate the area
damaged near the pollution source (Fig. 1). The land
cover of the damaged zone is distinguished from
undamaged areas by analyzing the relective spectral
properties of low crown density and the projective
163
cover of plants, different life forms (i.e., coniferous
trees were replaced by small leaves trees, green
moss disappeared, boreal herbs were displaced by
dwarf shrubs), quantity and distribution of soil organic
layer, and the anthropogenic environment. The area
with the most forest ecosystem damage (the impact
zone) was within 1,200 km2 of the Severonikel smelter.
Visible signs of forest damage were detected over an
area ca. 39,000 km2 (Rigina, 1998).
In the impact zone on lat and gentle terraces at the
elevation of 150–200 m above sea level, industrial
barrens and mixed forests of birch and willow with
sparse cover of dwarf shrubs and forest hairgrass
(Deschampsia lexuosa) are widely distributed;
these mixed forests are replaced at 200–250 m by
elin wood formations and alpine tundra. In the buffer
zone of partial damage, pine, spruce, and birch
forests with dwarf shrubs and lichens in the ground
layer are most common on middle parts of slopes.
On the outer boundary of this zone mosses become
abundant in forest communities. The crook-stem birch
forests have a lower elevational limit (approximately
at 300 m above sea level) in the impact zone. The
vegetation of the background zone is distinguished
from forest communities near the pollution source
by lower typological diversity and by spruce forests
of the Piceeta fruticuloso-hylocomiosum type, as well
as post-ire serial mixed pine and spruce communities
with various combinations of mosses and lichens in the
ground cover. In undisturbed reference sites, Spruce
fruticuloso-hylocomiosum forests are distributed 350–
400 m above sea level. On the uppermost slopes and
hill-tops, crook-stem forests and alpine tundra are
present.
Vegetation cover was assessed on the basis
of discriminant analysis using ield data, forest
biodiversity parameters were interpolated from
remote sensing data and DEMS, and typological units
were assigned for the total model area of Imandra
lake watershed (1:200,000) and on the zone in the
vicinity (3,100 km2) of the smelter area (1:50 000).
When analyzing the typological diversity of land cover
from remote sensing data, it is important to deine an
optimal set of units that are comparable with the spatial
scale of the study area. Upper level classiication units
consist of types similar to the land cover types (nival,
stone barrens, alpine tundra, open forests, forests,
cutting area, burned area, swamps, lood plains,
water, industrial barrens), which were detected with a
relative quality of discrimination, 89.3% (Fig. 2). About
32% of the study area is covered by forests; industrial
barrens occupy 6.4% (200 km2).
Groups and associations of vegetation are the lowest
classiication units of vegetation mapping in the study
Fig. 1. Location of the study area and damaged zones near the pollution source.
164
area. For their detection, ecologo-physiognomic
classiication was used because it is the most
applicable for deining species composition and
structure of plant communities using remote sensing
data. Syntaxonomic units embody information on
post-ire and post-cutting successions and relect
digression stages of plant communities due to
industrial damage.
Fig. 2. Types of land cover.
Fig. 3. Species diversity changes from 1981 to 2008.
Comparing the data of the irst observation period
(1981–1983) with the second one (2005–2008), we
noticed a general trend in vegetation rehabilitation,
which was determined by the number of species
and biomass (Fig. 3). Forest stand condition was
determined by an average mark of tree vitality. For
example, living spruces and pines were not present
in the impact zone in both periods. At the same time,
abundance of deciduous tree species in the polluted
zone, especially young birches, increased two times
more in the second period when compared to the
irst period. Average vitality score of coniferous trees
also increased 15–20% (p<0.05) in the area with
intermediate levels of deposition (the buffer zone).
The spatial distribution of forest stands with a different
degree of transformation in the vicinity of the smelter
is given in Figure 4 (the impact zone is marked with
a dark color (the worst conditions) on the basis of
the minimum vitality of forest trees). The discriminant
model of tree vitality has a relative quality of 60.1%.
Forest stands with good vital condition corresponding
to 1-2.5 points are discriminate with the most relative
quality of 83.3%. The worst relative quality was for
samples of trees with a vitality of three points, which
corresponded to trees in the middle vital condition.
Species richness in spruce forest in impact and
buffer zones increased 30% in comparison to the
1980s. Early succession species (Calamagrostis
epigeios, Chamaenerion angustifolium, Deschampsia
lexuosa), as well as species of hydromorphic and
oligotrophic soils (Betula nana, Calluna vulgaris,
Andromeda polifolia, Arctous alpina, Arctostaphylos
uva-ursi) and boreal forest species (Orthilia secunda,
Pyrola rotundifolia, Trientalis europaea, Linnaea
borealis), which were not registered on sample plots
in the 1980s, appeared within the disturbed zones.
It is important that lichens and mosses have appeared
and increased their abundance in the ground layer in
comparison to the 1980s. Impact and buffer zones
were characterized by abundance changes due to
pioneer species. Changes in species composition
are typical for fruticose lichens. Subdominant species
composition and ratios between mosses and lichens
indicate dynamic processes in communities. Pohlia
nutans and Dicranum undullatum have appeared
during the last observation period in the impact zone
in abundances enough to designate moss layer (20%
to 30%). This indicates the beginning of regeneration
succession as well as a signiicant difference with
a green moss stage of native and quasi-native
communities. Stereocaulon condensatum dominated
165
among lichens in the impact zone. The role of Cladonia
cenotea is locally signiicant in spruce forests. These
species show high tolerance to toxic environmental
conditions and are pioneer species even during postire successions. Barbilophozia lycopodioides has a
signiicant phytomass in the buffer zone, along with
species from the impact zone. The species composition
of the lichen layer in the buffer zone differs from the
impact zone by the appearance of Cladonia maxima
and Cetraria islandica. Spruce background forests
are characterized by the presence of green mosses
Pleurozium schreberi and Hylocomium splendens.
The largest part of the phytomass in the reference
zone consisted of green mosses (Ptilidium ciliare is
also abundant in spruce forests) and lichens—Cladina
stellaris, Cladina rangiferina (pine forests), Cladina
stellaris, Сladonia gracilis, and Nephroma arcticum
(spruce forests).
The spatial distribution of community species diversity
in the vicinity of the metallurgical smelter is shown in
Figure 5. In the igure the areas of ecosystems with
low species diversity (up to 12 species) are well
distinguished, while the species diversity, estimated
on plots in the reference area is more than 40.
Discussion
Direct and indirect effects of air and soil toxicants acting
jointly from 1935 in the vicinity of the metallurgical
smelter has caused the following: loristic composition
became signiicantly poorer; the structure of forest
communities simpliied, phytomass of all vegetation
stratums decreased, and their general condition
became worse (Kozlov et al., 1993). Our examination
of northern taiga spruce forests conirmed a close
relationship between ecological parameters and
pollution levels. In spite of a reduction in pollution
levels (more then ive times reduction) all measured
biodiversity parameters, including species richness
and abundance, vertical stratiication and understory
structure, were affected by pollution.
However, positive forest biodiversity regeneration
has also been marked. Comparative analysis of
data from the years 1981–1993 and 2005–2008
showed recovery succession when pollution volume
decreased. This decrease is indicated by: (1) abundant
birch shoots on the area of former industrial barrens;
(2) appearance of pioneer lichens and moss species
on the same territory; (3) increase of species diversity
within disturbed zones; (4) general increase in the
percentage of moss and lichen layer cover in forest
communities; and (5) changes in ecological coenotic
spectrum of vascular plants (expansion of explerent
species closer to pollution source and the appearance
of signiicant numbers of forest species both in impact
and in more distant zones).
Fig. 4. Vital status of trees.
166
(Project 07-04-01743) with the research program
“Regeneration Succession of Boreal Forests Due to
the Reduction of Air Pollution at Kola Peninsula.” We
would like to thank I. P. Kotlov for his participation in
ieldwork. We are most grateful to L. G. Bjazorov for
identiication of lichen samples and E.A. Ignatova for
identiication of moss samples.
References
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Fig. 5. Community species diversity.
Forest ecosystem biodiversity was estimated at
different spatial levels (local and landscape) by using
remote sensing data, digital elevation models (DEM),
and ield data. Stepwise discriminant function analysis
of cartographical modelling allowed evaluation of the
spatial variation of vegetation cover characteristics
determined by natural (relief, succession) and
anthropogenic (air pollution, ires, cutting) factors.
The knowledge of possible diversity parameters
and their spatial distribution in a deined territory
allows the current deviation from initial conditions
to be calculated and demonstrates the role critical
environmental stressors play in changing the original,
natural environment.
With continuous levels of atmospheric deposition, the
further rehabilitation of biodiversity, including more
sensitive boreal species, can be expected in the
future in forest ecosystems surrounding this source of
pollution. The joint analysis of forests in Fennoskandia
and Russia, which are similar in climatic characteristics
but different in forest condition and quality, will give the
unique experience of developing uniform vegetation
mapping of the boreal forest biome.
Acknowledgments
The investigation was supported by the Program
of Fundamental Research of Presidium of the RAS
“Scientiic Principles of Biodiversity Conservation
in Russia,” Russian Foundation for Basic Research
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nickel smelter. Ecology 6: 460–465 (in Russian with
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& Puzachenko, J. G. 2008. Relection of spatial
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& Fedyeva, M. V. 2006. Mapping of organogenic
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168
Circumboreal Forest Cover Mapping and Monitoring Using MODIS TimeSeries Imagery
Peter V. Potapov1, Matthew C. Hansen1, & Stephen V. Stehman2
1
Geographic Information Science Center of Excellence, South Dakota State University, Peter.Potapov@
sdstate.edu, 2College of Environmental Science and Forestry, State University of New York
Abstract
Introduction
Mapping and monitoring of forest cover is important
for boreal forests, which represent the largest
forested biome, because of their unique role in global
timber stock, carbon sequestration and deposition,
and high vulnerability to the effects of global climate
change. We used time-series data from the Moderate
Resolution Imaging Spectroradiometer (MODIS) to
produce circumboreal annual maps of forest cover
loss hotspots and forest loss factors. To validate
the MODIS data and depict the boreal forest biome
with an estimation of change from 2000 to 2005, a
probability-based sampling approach was employed.
Circumboreal maps of forest cover, total forest loss,
and forest loss attributable to ire were achieved
through the use of MODIS data inputs calibrated by
Landsat sample data.
Forest cover extent is a basic variable in the
quantitative analysis of global carbon exchange
and timber stock, and for ecological modelling and
ecosystem integrity assessment. Timely forest cover
change monitoring is especially important for boreal
forests, which represent the largest terrestrial biome,
because of their unique role in carbon deposition and
high vulnerability to global climate change effects.
The boreal region is vast with large areas lacking
infrastructure, making annual ield- and aircraftbased assessment expensive and dificult. Synoptic
observations from satellite-based sensors offer
an alternative information source for forest cover
mapping and monitoring at a fraction of the cost of
obtaining extensive ground inventory data. However,
satellite-based forest cover monitoring at the global
biome scale remains a challenge due to limitations
concerning data quality, data access, and processing
capabilities.
Area of forest cover gross loss from 2000 to 2005
within the boreal biome is estimated to be 1.63%
(standard error 0.10%) of the total biome area and
represents a 4.02% reduction from forest cover in
2000. The proportion of identiied forest cover loss
relative to regional forest area is much higher in North
America than in Eurasia (5.6% to 3.0%). Our results
reveal signiicant increases in forest cover loss due to
wildires in 2002 and 2003, with 2003 being the peak
year of loss within the 5-year study period. Of the total
forest cover loss identiied, 58.9% is attributable to
wildires. Validation results show a high correlation
between the MODIS- and Landsat-derived forest
cover and forest cover loss values for sample blocks.
Overall, our results illustrate the robustness of the
integrated use of MODIS and Landsat data for forest
cover mapping and monitoring.
Keywords: boreal forests, forest cover, forest cover
loss, MODIS, monitoring.
Our method, presented here, employed an internally
consistent and eficient probability-based sampling
approach that integrates low and high spatial resolution
satellite data sets. We used time-series data from the
Moderate Resolution Imaging Spectroradiometer
(MODIS) to produce circumboreal annual maps of
forest cover loss hotspots and forest loss factors.
For the year 2000 a tree canopy cover fraction map
derived from MODIS 500m spatial resolution data (part
of the global Vegetation Continuous Fields product;
Hansen et al., 2002) was used as a baseline for our
monitoring. For MODIS product validation and inal
forest cover and change estimation, we employed a
probability-based sampling approach. The stratiied
random sample of 118 blocks (18.5 km per side) was
interpreted for forest cover and forest cover change
using high spatial resolution Landsat ETM+ imagery
from 2000 and 2005. Circumboreal maps of forest
169
cover, total forest loss, and forest loss attributable to
ire were achieved through the use of MODIS data
inputs calibrated by Landsat sample data.
for areas covered by forest in the year 2000. Forest
was deined as areas with tree canopy cover greater
than 25%. The tree canopy cover threshold limited
the presence of forest cover commission errors.
Data and Methods
Boreal Forest Biome Boundaries
The boreal forest biome boundary was based on
the world terrestrial ecoregion map of Olson et al.
(2001). The biome boundaries were modiied to add
temperate coniferous and mixed forests ecoregions
characterized by similar seasonality and presence of
seasonal snow cover. Also, forested areas of foreststeppe and forest–tundra transitional ecoregions
were included in the boreal biome.
MODIS Data Time-Series Imagery
Visible and infrared bands (1–7) surface relectance
8-day composites with 500-m spatial resolution from
the MODIS/Terra Satellite, along with an 8-day, 1-km
land surface temperature product for the years 2000–
2005, were used as the primary inputs to our analysis.
The initial 8-day composites were combined into 32day composites that were subsequently transformed
to multi-temporal annual metrics that captured the
salient points of phenological variation by calculating
means of spectral information from 3-, 6- and 9-month
composites with the highest Normalized Difference
Vegetation Index (NDVI) and surface temperature
values. Multi-temporal metrics have been shown
to perform as well or better than time-sequential
composites in mapping large areas (Hansen et al.,
2002).
Circumboreal
Monitoring
Forest
Cover
Mapping
and
The algorithm of tree canopy cover fractional
mapping using MODIS data is described in Hansen
et al. (2002). The method employs a regression
tree algorithm. The high spatial resolution training
data (classiied Landsat imagery) were used as the
dependent variable, and the annual MODIS metrics
for the year 2000 were used as independent variables
to create a regression tree model. The model, applied
globally, yielded percent tree canopy cover per 500-m
MODIS pixel.
The analysis of forest cover loss was performed only
Our method of forest cover loss annual monitoring
employs a bagged regression tree algorithm (Breiman,
1996) to create a wall-to-wall biome-wide forest cover
loss hotspot map. The algorithm is described in
detail in Potapov et al. (2008). Regression trees are
derived using a training data set that relates spectral
relectance change signals to percent cover loss.
To create training data, pairs of single date MODIS
250-m images for the years 2001 and 2005 were
manually classiied to no-change and forest loss
classes. To build the regression tree model, percent
forest cover loss from the training data set was
used as the dependent variable, and MODIS annual
metrics plus their inter-annual differences were used
as independent variables. The regression tree model
was then applied to the biome-wide pairs of annual
MODIS inputs. The forest cover loss results for the
2000–2005 interval were used to create the 5-year
forest cover loss hotspot map, while annual change
results were used for the analysis of temporal trends.
The analysis of forest cover loss factors was based
on a decision tree classiication. The objective was
to discern the cause of the cleared forest cover per
MODIS pixel: burned forest areas and forest cover
loss hotspots attributed to other disturbance factors
(logging, tree mortality due to insect outbreaks, and
windfalls). An extensive training set was manually
created using available ancillary information. The
training data related manually assigned forest cover
loss class information with MODIS spectral data
derived from the year when change was detected.
The forest cover loss factors analysis was performed
only within the 5-year forest cover loss hotspot mask.
The circumboreal MODIS-derived forest cover and
annual forest cover change datasets are available at
http://globalmonitoring.sdstate.edu/projects/gfm.
Validation and Calibration of MODIS-Derived
Products
The Landsat-based sample block analysis was
used to calibrate biome-wide MODIS-derived forest
cover and forest cover loss area estimates and to
170
validate the forest cover loss hotspots map. Stratiied
sampling was implemented to enhance precision by
allocating greater sampling effort to areas likely to
exhibit change. The strata were determined based on
the MODIS-derived forest cover loss hotspot map. A
stratiied random sample of 118 blocks, 18.5-km per
side, was interpreted for forest cover, forest cover
loss, and forest change type using Landsat imagery
from 2000 and 2005. All blocks used in this analysis
can be viewed at http://globalmonitoring.sdstate.edu/
projects/gfm.
in order to determine if they are anomalous or within
the historical range.
The relationship between Landsat-based forest cover
area and mean Vegetation Continuous Fields (VCF)
tree canopy density was used to derive forest extent
for the year 2000. A simple linear regression (no
intercept) model was employed with mean VCF tree
canopy density per block as the independent variable.
Forest Cover Loss Inter-Annual Trend
A separate regression estimator (i.e., separate
regression models and parameter estimates allowed
for each stratum) was employed to estimate Landsatderived forest cover loss area using percent MODISderived change hotspots as an auxiliary variable. The
same approach was used to estimate burned forest
area fraction of total change area.
Results and Discussion
Forest Cover and Forest Cover Loss Area
The extent of the boreal biome used in our analysis
was 2,150.9 million hectares (Mha). Using Landsatderived forest cover classiication results, forest extent
within the study area was estimated to be 872.3 Mha
for year 2000. The estimated forest cover fraction of
the biome area is almost the same for North America
and Eurasia (43% and 39%, respectively).
Area of forest cover gross loss from 2000 to 2005
within the boreal biome is estimated to be 1.63%
(standard error 0.10%) of the total biome area and
represents a 4.02% reduction in year 2000 forest
cover. This translates to an estimated forest loss area
of 35.1 Mha (s.e. +/-2.2 Mha). Gross forest cover loss
within the boreal forests is higher than in the humid
tropics biome, where forest clearing is estimated to
be 1.39% of the biome area, or 27.2 Mha (Hansen et
al., 2008). The overall high rates of forest cover loss
need to be put into context with a longer-term analysis
Forest cover loss is distributed unevenly within the
biome. The proportion of identiied forest cover loss
relative to regional forest area is much higher in North
America than in Eurasia (5.63% to 3.00%). The largest
forest loss areas occurred within regions of intensive
logging operations (southern parts of Ontario, Québec
and British Columbia) and of large-scale wildires
(Northern Canada, Alaska, and Eastern Siberia).
Inter-annual analysis of forest cover loss highlights
years with increased wildire activity and illustrates
differences in change dynamics in North America
and Eurasia. The MODIS-derived annual total forest
cover loss hotspot area shows signiicant increases
of forest loss in 2002 and 2003, with a subsequent
decrease in total forest loss. A signiicant part of the
total forest cover loss in 2002 was connected with ires
in Yakutia (East Siberia) and in 2003 with ires in East
Siberia and in the Russian Far East. Regions where
forest change is primarily related to logging (Europe,
Southern Canada) relected a consistent inter-annual
rate of change.
Forest Cover Loss Due to Wildires
Of the total forest cover loss area, 58.9% was attributed
to wildires and the rest, 41.1%, to other disturbances,
including logging, wind and snow damage, and insect
outbreaks. The proportional contribution of ire to forest
loss is higher in Russia compared to Canada. Fire is a
major contributor to forest loss in the interior of North
America and Asia. Our method of forest burned areas
assessment differs from most oficial and remotesensing based analyses, as it was made only within
forests with high (above 25%) tree canopy density and
quantiied only those forested areas affected by standreplacement ires. Surface ires have a signiicantly
lower impact on tree canopy cover, and the effect of
these ires could be negligible in total forest cover
loss. Therefore, our forest cover loss area attributed
to wildires is smaller in comparison with national
burned forest area estimates, which include surface
ire area in total burned area calculation (Achard et al.,
2008; Canadian Council of Forest Ministers, 2007).
Stand-replacement and surface ires in boreal forests
have a different impact on forest cover, direct carbon
171
emissions, albedo change, and post-ire successional
trajectories. Our burned forest estimates could more
readily be incorporated into carbon emission models
and vegetation change analysis than existing ireaffected area estimates because our analysis relects
a strict biophysical interpretation of the presence/
absence of tree cover assessed using a synoptic,
biome-wide approach.
cover loss over time, even if change occurs at the
subpixel scale depicted from MODIS data.
Acknowledgments
This research was supported by National Aeronautics
and Space Administration under grant NNG06GD95G
managed under the NASA Land-Cover and Land-Use
Change Program.
Calibration and Validation of MODIS Products
The boreal forest cover area and change estimates
are derived through the integrated use of MODIS
and Landsat data. Area estimates are attained via a
probability sample of interpreted Landsat block data.
A sampling eficiency is obtained using strata based
on MODIS change indicator maps. Stratiication
successfully improved precision of the overall estimate
of forest clearing and reduced the cost of obtaining
high-resolution sampling data. The complete coverage
(spatially explicit) maps of forest cover, total forest
loss, and forest loss attributable to ire were achieved
through the use of MODIS data inputs calibrated by
the Landsat sample data.
Validation results show a high correlation between the
MODIS- and Landsat-derived forest cover and forest
cover loss values for sample blocks. The MODISbased forest cover loss hotspot fraction has an
estimated root mean square error (RMSE) of 2.53%
and an R2 of 0.75.
Conclusion
Our results illustrate the robustness of the integrated
use of MODIS and Landsat data for forest cover
mapping and monitoring. The validated wall-towall MODIS-based product could then be used for
further analyses in combination with: (1) available
carbon stock data to improve carbon accounting;
(2) high conservation value forest data to facilitate
conservation planning; and (3) timber resources and
accessibility data for forest use sustainability analysis.
Subpixel percent forest cover and change maps offer
advantages over convenient classiication schemes.
Forest fraction maps could better represent land cover
heterogeneity and could avoid problems with mapping
spatially complex areas. The calibrated percent forest
cover change maps are adequate in measuring forest
References
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2008. The effect of climate anomalies and human
ignition factor on wildires in Russian boreal forests.
Philosophical Transactions of the Royal Society B
363 (1501): 2331–2339.
Breiman, L. 1996. Bagging predictors. Machine
Learning 24: 123–140.
Canadian Council of Forest Ministers. 2007. National
Forestry Database Program. Canadian Forest
Service, Natural Resources Canada. http://nfdp.
ccfm.org/.
Hansen, M. C., DeFries, R. S., Townshend, J. R.
G., Sohlberg, R., Carroll, M., & Dimiceli, C. 2002
Towards an operational MODIS continuous ield
of percent tree cover algorithm: examples using
AVHRR and MODIS data. Remote Sensing of
Environment 83: 303–319.
Hansen, M. C., Stehman, S. V., Potapov, P. V.,
Loveland, T. R., Townshend, J. R. G., DeFries,
R. S., Pittman, K. W., Stolle, F., Steininger, M. K.,
Carroll, M., & Dimiceli, C. 2008 Humid tropical forest
clearing from 2000 to 2005 quantiied using multitemporal and multi-resolution remotely sensed data.
PNAS 105(27): 9439–9444.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D.,
Burgess, N. D., Powell, G. V. N., Underwood, E. C.,
Amico, J. A. D., Itoua, I., Strand, H. E., Morrison, J.
C. 2001 Terrestrial ecoregions of the world: A new
map of life on Earth. BioScience 51(10): 1–6.
Potapov, P., Hansen, M. C., Stehman, S. V., Loveland,
T. R., & Pittman, K. 2008 Combining MODIS and
Landsat imagery to estimate and map boreal forest
cover loss. Remote Sensing of Environment 112(9):
3708–3791.
172
Vegetation Mapping and Disturbances Assessment in the Boreal Zone
Using Time-Series of Moderate-Resolution Remote Sensing Data
Sergey Bartalev
Space Research Institute, Russian Academy of Sciences, Moscow, Russia
Extended Abstract
In boreal ecosystems, vegetation changes in
phenological tempo, species composition, and
biophysical and structural characteristics are not only
driven by succession and anthropogenic processes
but also by variation in the climate regimes. Largescale vegetation dynamics in the boreal zone include
wildires, forest logging, conversion of grasslands
to agriculture, and land abandonment following
institutional changes, as well as the long-term trends
of climate change.
In order to facilitate needs of climate change science
and natural resources management, a number of
thematic products describing the status and dynamics
of vegetation cover for Northern Eurasia and the entire
boreal zone is currently under development with the
use of Satellite Pour l’Observation de la Terre (SPOT)
Vegetation data and Moderate Resolution Imaging
Spectroradiometer (MODIS) data.
New MODIS data derived from 250 m spatial
resolution landcover mapping is updated with
monthly and seasonal multi-spectral cloud-free image
composites. This mapping approach learned lessons
from the Global Land Cover 2000 project, which in
particular, produced a 1-km resolution Northern
Eurasia landcover map from SPOT-Vegetation data.
Better classiication accuracy and thematic detail
of vegetation cover (such as phenological dynamic
features that are elaborated by the combined use
of monthly multi-spectral image mosaics and multiannual temporal proiles of spectral vegetation
indexes) can be achieved from the higher spatial
resolution of MODIS data.
A new burned area product for the entire boreal zone
for the years 2000–2007 was derived by combining
SPOT-Vegetation time series data and TerraMODIS active ire products. The burnt severity of
the forest is an essential input into carbon emission
modelling as well as important in the assessment of
the environmental and economical impact of ires. A
multi-annual burnt severity product was derived from
Terra-MODIS data with use of a spectral-temporal
mixture analysis technique and is in the process of
validation.
An arable lands mask for Russia was derived from
time-series 250-m resolution daily MODIS data
acquired during 2002–2007 with the use of an
advanced automatic method of data processing and
analysis and provides essential input into various
applications of agricultural monitoring and landcover
mapping.
Forest change mapping, with the particular focus
on logging areas, is an important component of
actual research and development with use of 250-m
resolution MODIS data.
The TerraNorte website (http://terranorte.iki.rssi.
ru/) was developed to provide a wide community
of users with information about the current state of
and dynamics of the Earth’s boreal ecosystems,
and particularly for Northern Eurasia. The thematic
information products and databases presented at
the TerraNorte website were derived from Earth
Observation data in the framework of scientiic projects
carried out by the Russian Academy of Sciences’
Space Research Institute in close cooperation with a
number of partners. The web interface of the system
provides interactive users access to the data and
information products in the forms of tables, graphs
and digital maps.
173
Comparison of Finnish and Russian Approaches for Large-Scale
Vegetation Mapping: A Case Study
Olga Galanina1 & Raimo Heikkilä2
Komarov Botanical Institute, Russian Academy of Sciences, Saint Petersburg, Russia, 2Finnish Environment
Institute, Joensuu, Finland
1
Abstract
The purpose of this study was to compare Russian
and Finnish mire vegetation classiications and largescale vegetation mapping methods. Two maps were
prepared using aerial photo interpretation and ield
observations for Härkösuo mire in Kuhmo, eastern
Finland. Härkösuo mire covers about 20 ha and
includes a small area of aapamire, together with spring
fen and pine bog types. The maps were compared
quantitatively using geographic information system
(GIS) techniques. Despite the different vegetation
classiication approaches, the maps have a great
deal in common. The correspondence of vegetation
patterns according to the Russian dominant approach
and the Finnish site type approach is discussed. The
main differences in vegetation classiication occurred
in the marginal zones of the mire. In the Finnish
approach, marginal areas with dense cover of spruce
trees are regarded as mire, while in the Russian
approach they are classiied as forest.
tradition. It was established by Professor N. Kuznetsov
in 1922. The laboratory of geography and vegetation
cartography at the Komarov Botanical Institute of the
Russian Academy of Sciences became the Russian
centre of ideas and development for this discipline
(Юрковская, 2004).
Under the leadership of academician Evgeny
Lavrenko from 1967 to 1979, the laboratory worked
actively and successfully for vegetation mapping
and became an international leader. As a result,
remarkable maps were published, including for
example, “Геоботаническая карта Нечерноземной
зоны РСФСР” (Geobotanical Map of Non-Chernozem
Zone of The Russian Federation RSFSR), at map
scale 1: 1.5 M in 1976 and the Vegetation Map
of European Part of USSR, at map scale 1:2.5 M
(Исаченко & Лавренко, 1979).
At Härkösuo mire, situated in the borderland of Finland
and Russia, we made an attempt to apply Finnish and
Russian mapping methodologies. The applicability
of the two cartographical methods and the value of
the results were tested. This article is based on the
results presented by Galanina & Heikkilä (2007).
In Russia, vegetation mapping commonly applies to
a large area, and the mappings are usually made at
a small scale. As a rule, the maps show the general
features and regularities of regional vegetation, or
they are compiled according to vegetation zones.
There is no checklist of forest or mire site types to
be used as a starting point, in contrast to the Finnish
practice. The mapping starts from an independent
preliminary interpretation of morphological and
vegetation structures visible in aerial photographs or
satellite images. The patterns identiied are veriied in
the ield by making vegetation relevés along proiles.
The legend to the map usually represents the mapping
author’s point of view.
Russian School of Vegetation Mapping
Finnish School of Vegetation Mapping
Vegetation mapping in Russia is a well-developed
theoretically based discipline, which can be applied
and adapted for practical purposes. The Russian
school of geobotanical cartography has a long
In Finland, vegetation mapping has a tradition of a
few decades. Paasio (1933) was the irst to produce
a detailed vegetation map of a mire at scale 1:15,000
using aerial photos. Seppälä & Rastas (1980) made
Keywords: aapamire, Fennoscandia, mire site type,
vegetation classiication, vegetation mapping.
Introduction
174
the irst small-scale vegetation map in northernmost
Finnish Lapland on the basis of satellite images,
where classes were deined mainly on the basis of
tree cover and moisture. Ruuhijärvi (1988) prepared
a small-scale map (1:1M) of the mire complexes
(massifs) of Finland, using six main mire complex
types as mapping units.
Routine vegetation mapping of nature reserves
started at the beginning of the 1980s to reveal the
biotope diversity of national parks and some other
nature reserves. Site types (Ruuhijärvi, 1983; Eurola
et al., 1984) were used as mapping units. Site types
were identiied in the ield and delimited on the basis
of aerial photo interpretation and ield observations.
The minimum size of a mire site is generally 100 m2.
Sometimes, for example in springs and other key
habitats, smaller units are also taken into account.
The scale of the maps is usually 1:10,000 or 1:20,000.
In Russia, three vegetation classiication approaches
are commonly used: dominant (ecologicalphytocoenotic), loristic (Central-European) and
topology-ecological (Scandinavian). Regional reviews
of mire vegetation have been compiled, for example,
in Leningrad region (Боч & Смагин, 1993) and in
the Karelian Republic (Kuznetsov, 2003; Кузнецов,
2005). However, they cannot be considered as
mapping units. The dominant approach is the main
instrument for vegetation mapping.
Traditionally, three main mire types are commonly
distinguished in Russia: верховые (raised bogs),
переходные (transitional mires), and низинные
(fens). These terms have been applied, for example, in
the legend of the vegetation map of Moscow region at
scale 1:200,000 (Огуреева et al., 1996). Also, trophic
status can be used in map legends: oligotrophic,
mesotrophic, and eutrophic.
Very few scientiic mappings have been made
in Finland. The maps are required mainly for
practical applications in nature conservation or
environmental impact assessments. Usually the
site type determinations have not been veriied with
relevés, but loristic lists have been compiled either
for the vegetation patterns or square-kilometer
quadrats according to the map coordinates (e.g.,
Heikkilä, 1986; Heikkinen & Kalliola, 1989; Heikkilä
et al., 2001). Generally, vegetation mapping is not
a theoretical discipline in Finland, and there are no
vegetation maps covering the whole country.
Карта растительности Европейской части СССР
(Map of European Part of USSR) (1979) shows ive
main mire types: Sphagnum mires, herb-lichenmoss-mires, herb–Sphagnum–Hypnum mires, herb
and herb–Hypnum mires, and forest mires. They are
distinguished on the basis of predominant (prevailing)
vegetation units. The irst attempt to compare the
Finnish and Russian schools of mire vegetation
classiication for mapping was published by Antipin et
al. (1997).
Finnish and Russian Classiications of Mire
Vegetation
Study Area
In Finland, approximately 80 mire site types have been
deined according to botanical criteria (Ruuhijärvi,
1983; Eurola et al., 1984). For practical applications
such as forestry there are 30–35 site types (Laine &
Vasander, 2005). The site types are organized into six
main groups: spruce mire, pine mire, open bog and
fen, rich fen, spring mire, and looded swamp. They
can be also classiied according to a pH gradient:
bog, poor fen, and rich fen (Tahvanainen et al., 2002).
Spruce mire types often have a very dense and tall
tree cover, and in central Europe and Russia they are
usually regarded as forests.
Materials and Methods
Härkösuo mire, covering 20 ha, is located in Kuhmo,
eastern Finland, in Elimyssalo nature reserve
(64º12’N, 30º26’E, 235 m a.s.l.). The mean annual
rainfall is 600 mm and the mean annual temperature
1.2ºC (Alalammi, 1987). The study area is situated
in the middle boreal climatic-phytogeographical
zone (Ahti et al., 1968; Tuhkanen, 1984) and in the
Archaean Karelian province of the Fennoscandian
bedrock shield. The bedrock is formed of granite and
gneiss (Luukkonen, 1992; Gorkovets & Rayevskaya,
2003).
The length of Härkösuo mire is 1,000 m from east
to west, and its mean north–south width is 200 m.
The mire slopes gently and is mostly soligenous.
175
Groundwater inluence is clear at the southern margin
of the western part of the mire. In the southeastern
part of the mire the groundwater inluence is weaker
(Tahvanainen et al., 2002). As a result of the highly
variable ecological conditions the vegetation of the
mire is very diverse.
quantitatively, using ArcGIS9 (ESRI) software
with Spatial Analyst extension, to reveal the
correspondence of the vegetation units and their joint
percent coverage.
Härkösuo mire is situated in a tectonic depression,
and the peat layer is very thick. The maximum peat
depth is 805 cm. The calibrated radiocarbon age of the
bottom layer of the mire is 10,240 years (Kuznetsov
et al., 2008).
Vegetation Maps
According to the map of mire provinces of Europe
by Кац (1971) the study area lies within the KareloFinnish province of middle taiga and Karelian mires of
mixed type. According to the Finnish system of mire
regions it belongs to the southern aapamire zone
(Ruuhijärvi, 1988). Russian literature sources deine
the study area as belonging to the northern taiga
(Юрковская & Паянская-Гвоздева, 1993).
Methods
The vegetation of Härkösuo mire was mapped in
detail using a false-color infrared aerial photograph
at scale 1:5,000 (taken in 1995). The scale of the
aerial photo negative was 1:20,000. In addition, lowaltitude oblique aerial color photos from 1997 were
used to clarify the patterns. Vegetation patches were
delimited visually on the aerial photographs, and the
delimitations were veriied in the ield between 5th
and 8th August 2002. The mapping was carried out
separately and independently by the Russian author
and the Finnish author, each using their own national
approach. Every element of pattern identiied from the
aerial photograph was studied to reveal the vegetation
structure and species composition.
The vegetation patches were classiied in the ield
and named according to Ruuhijärvi (1983) and
Eurola et al. (1984) by the Finnish author. A more
detailed classiication using subtypes was also made.
Vegetation relevés were made using cover percentage
for ield and ground layer in one 1 m2 randomly
placed plot in the middle of each pattern. In cases
where there was a mosaic structure of hummocks,
lawns and/or larks, a separate relevé was made on
each surface. In total, 163 relevés were made.
The vegetation patterns in the maps were compared
Results
Two original vegetation maps and legends were
prepared. Pine bog communities are present along
the margins except in places where groundwater
comes to the surface. Trichophorum communities
occupy most of the mire area. Aapa-complexes are
distributed in the central deepest part of Härkösuo
mire. Further from springs, groundwater inluence
becomes weaker (Tahvanainen et al., 2002), and
poor Scheuchzeria–Sphagnum majus communities
replace aapa-complexes. A homogeneous community
has developed in the shallow eastern depression of
Härkösuo mire.
In the case of the Russian vegetation map, the structure
of the legend usually looks like a hierarchical text.
The legend shows the physiognomic features of mire
vegetation by subtitle. The vegetation of Härkösuo
mire is very diverse due to the complex hydrological
regime. This diversity is relected in the legend,
where 21 different patterns are distinguished. The
numbered categories in the legend characterize the
mire communities according to a dominant vegetation
classiication approach by listing the dominant
plants in the vegetation layers. For example, in
“Carex rostrata +Scheuchzeria palustris–Sphagnum
papillosum,” “+” means that the dominant species are
present in the same layer, and they have more or less
equal phytosociological value, and “-” means another
vegetation layer.
The map compiled by the Finnish author includes
26 different vegetation categories, which are
characterized by indices of the Finnish mire type
classiication. They are arranged in subclasses and
in the order of Eurola et al. (1984). Only very small
patches of spruce mire occur in the margins, mostly
as narrow strips that cannot be shown in the map.
Only in the northeast corner of the site is there a
typical spruce mire patch (MK, number 1). Poor pine
mire types are typical in the margins of the mire and
as larger patches in the eastern part (RaR, number
7, LkR, number 25). Open fen types dominate in the
176
center. In the west there is intermediate fen (RhRiN,
number 14) and rich fen (RiL, number 17), and in the
east there is poor fen (LkN, number 8, RaSphRiN,
number 13). Mosaic-like combination types occur as
narrow zones between the marginal pine mire and the
central fen. In the southern margin of the western part
of the mire there are numerous springs, and there is
also groundwater seepage. There are small patches
of spring vegetation and one of rich spring fen (MeLä,
number 2, LäL, number 16). Under the inluence of
spring water there are also patches of rich pine fen
(LR, number 18, RL, number 19).
Comparison of Vegetation Patterns
The vegetation patterns in the two maps correspond
well. The authors have interpreted the patterning
visible in the aerial photograph in more or less the
same way. The essential differences are in the
margins of the site, where the tree cover is most
dense. The area covered by the Finnish map is
Russian legend
Community
Pine mire communities
1. Pinus sylvestris-Rubus chamaemorus+Empetrum hermaphroditumSphagnum fuscum
2. Pinus sylvestris-Vaccinium uliginosum-Carex pauciflora-Sphagnum
angustifolium
3. Pinus sylvestris-Chamaedaphne calyculata+Ledum
palustre+Vaccinium uliginosum-Carex globularis+C. paucifloraSphagnum russowii+Pleurozium schreberi
Mire communities with sparse pine
4. Betula nana+Eriophorum vaginatum-Carex pauciflora-Sphagnum
angustifolium
5. Eriophorum vaginatum+Empetrum hermaphroditum-Sphagnum fuscum
6. Carex lasiocarpa-Sphagnum angustifolium
Open mire communities
7. Carex rostrata - Sphagnum fallax
8. Menyanthes trifoliata+Trichophorum alpinum +Carex lasiocarpaSphagnum angustifolium+S. warnstorfii
9. Scheuchzeria palustris-Sphagnum balticum
10. Carex rostrata+Scheuchzeria palustris -Sphagnum papillosum
11. Trichophorum cespitosum-Sphagnum papillosum
Complex vegetation
12. hummocks: Betula nana+Chamaedaphne calyculata-Sphagnum
fuscum
hollows: Scheuchzeria-Sphagnum balticum
13. hummocks: Empetrum nigrum+Rubus chamaemorus-Sphagnum
fuscum
carpet: Carex rostrata+Scheuchzeria palustris+Carex paucifloraSphagnum balticum+S. papillosum
14. Menyanthes trifoliata+Scheuchzeria palustris-Sphagnum majus
+Warnstorfia exannulata
15. hummocks: Carex lasiocarpa-Sphagnum angustifolium,
flarks: Scheuchzeria palustris-Sphagnum majus
15a flarks: Scheuchzeria palustris-Menyanthes trifoliata-Utricularia
intermedia
16. Aapa-complexes
strings: Betula nana-Trichophorum alpinum-Sphagnum angustifolium
flarks: C. rostrata+Rhynchospora alba-Sphagnum
platyphyllum+Scorpidium scorpioides
17. carpet: Trichophorum alpinum-Sphagnum subfulvum
small depressions Rhynchospora alba+Carex limosa
18. hummocks: Molinia caerulea–Sphagnum fuscum
flarks: Carex lasiocarpa+Trichophorum alpinum-Scorpidium revolvens+
Campylium stellatum
Sloping fen communities with spring influence
19. Pinus sylvestris+Picea abies-Salix ssp.- Betula nana+Empetrum
hermaphroditum-Equisetum fluviatile-Sphagnum angustifolium+S.
warnstorfii+Pleurozium schreberi
20. Angelica sylvestris+Molinia caerulea-Menyanthes trifoliata–
Sphagnum angustifolium+S. warnstorfii
21. Carex lasiocarpa+Trichophorum alpinum+Molinia caerulea –
Sphagnum angustifolium+S. warnstorfii (with sparse pine trees)
Finnish legend
Mire site type
%
7. Sphagnum fuscum – Empetrum pine bog (RaR)
64
3. Eriophorum vaginatum pine bog with Sphagnum fuscum hummocks
(RaTR)
25. Carex pauciflora – Sphagnum angustifolium pine fen (LkR)
61
25. Carex pauciflora – Sphagnum angustifolium pine fen (LkR)
8. Eriophorum vaginatum – Carex pauciflora – Sphagnum angustifolium
fen (LkN)
3. Eriophorum vaginatum pine bog with Sphagnum fuscum hummocks
(RaTR)
9. Carex rostrata – Sphagnum fallax fen (VSN)
22. Carex rostrata – Sphagnum fallax pine fen (VNR)
81
60
29
76
30
26
10. Carex rostrata – Comarum palustre – Sphagnum riparium fen
(LuRiSSN)
15. Sphagnum subfulvum – Loeskypnum badium – Trichophorum
alpinum fen (LN)
20. Sphagnum subfulvum – Trichophorum alpinum pine fen (LNR)
8. Eriophorum vaginatum – Carex pauciflora – Sphagnum angustifolium
fen (LkN)
12. Carex rostrata – Sphagnum papillosum fen (SSKaN)
11. Trichophorum cespitosum – Carex pauciflora – Sphagnum
papillosum fen (LkKaN)
92
26. Hummock-hollow complex with Eriophorum vaginatum, Sphagnum
fuscum and S. balticum (KeR)
91
13. Sphagnum majus – Scheuchzeria palustris fen with Sphagnum
fuscum hummocks (RaSphRiN)
78
13. Sphagnum majus – Scheuchzeria palustris fen with Sphagnum
fuscum hummocks (RaSphRiN)
9. Carex rostrata – Sphagnum fallax fen (VSN)
66
14. Trichophorum alpinum – Sphagnum platyphyllum flark fen (RhRiN)
17. Scorpidium scorpioides rich flark fen (RiL)
40
38
20. Sphagnum subfulvum – Trichophorum alpinum pine fen (LNR)
21. Sphagnum subsecundum – Juncus stygius flark pine fen with
Sphagnum fuscum hummocks (RiRLN)
19. Scorpidium revolvens – Campylium stellatum rich pine fen
with Sphagnum fuscum hummocks (RL)
44
44
23. Carex rostrata – Sphagnum fallax pine fen with Sphagnum fuscum
hummocks (RaNR)
67
16. Tomentypnum nitens – Sphagnum angustifolium rich fen (LäL)
66
18. Sphagnum warnstorfii – Eriophorum latifolium rich pine fen (LR)
19. Scorpidium revolvens – Campylium stellatum rich pine fen
with Sphagnum fuscum hummocks (RL)
54
40
58
36
80
85
92
65
82
Table 1. Correspondence of vegetation patterns in the Russian map and Finnish map. In the Finnish legend
the abbreviations correspond to Finnish names of the vegetation types, commonly used in Finnish vegetation
maps.
177
almost 10% greater than that covered by the Russian
map, due to the different delimitation between mire
and forest. Differences occur also in distinguishing
between open mire and mire with sparse tree cover. In
a few cases the Finnish author has taken into account
a very sparse tree cover in naming the site types.
The measurements of common coverage for different
vegetation classes show that most Russian plant
communities correspond to site types in the Finnish
classiication (Table 1). In some cases a Russian
community with tree cover contains two Finnish types,
an open one and a similar one with a sparse tree cover
(e.g., numbers 4, 6 and 8, Russian legend, Table 1).
On the other hand, sometimes a Russian class is
divided into two classes in the Finnish classiication
on the basis of mire surface patterning (number 21,
Russian legend, Table 1).
type classiication system was practical for forestry
and agricultural purposes: to evaluate the potential
productivity of mires (Laine & Vasander, 2005). This
approach is also well suited to routine mappings
of habitat diversity. It gives a possibility to obtain
comparable results from mappings made by different
persons, and to map quickly. However, this requires
suficient knowledge and extensive experience.
Our mappings show that there are some problems
caused by subjectivity. Mapping the boundaries of
vegetation patterns is usually problematic because
there is a vegetation continuum of the abstract classes
(Мазинг, 1962; Ruuhijärvi & Lindholm, 2006).
Acknowledgments
We wish to thank Dr. Elena Volkova from Tula
Discussion
To conclude, on the basis of our studies, plant
communities distinguished by the Russian author
correspond rather well with Finnish mire site types.
The biggest differences arise when deining the
boundary between forest and mire. Thus, spruce
mires according to the Finnish classiication fall into
forests in the Russian approach.
The principles for distinguishing between open and
sparsely wooded mire types are vague in the Finnish
classiication (Ruuhijärvi, 1983; Eurola et al., 1984).
The situation seems to be similar in the Russian
classiication. There are no concrete quantitative
criteria for tree stands either in crown cover or stand
volume. This causes differences between maps
prepared by different people but not necessarily
between Russian and Finnish classiications.
Open
homogeneous fens give the best
correspondence between Russian and Finnish
classes, as well as ombrotrophic hummock-hollow
complexes. In minerotrophic complex communities
(mixed mires sensu Sjörs et al., 1965) there are
differences in understanding the vegetation units,
depending on the proportions of fen carpets and
hummocks. These approaches do not completely it
together, but it is nevertheless possible to achieve a
mutual understanding of the classes (Antipin et al.,
1997).
Originally, the main goal of the Finnish mire site
Pedagogical University for assistance in the ieldwork
and discussions in the early stages of the study.
Ms. Pirjo Appelgrén helped with the maps and GIS
analysis. The preparing of the manuscript was partly
inanced by a grant to Olga Galanina from The
Academy of Finland.
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179
Integrated Ecoforest Mapping of the Northern Portion of the Continuous
Boreal Forest, Québec, Canada
André Robitaille, Antoine Leboeuf, Jean-Pierre Létourneau, Jean-Pierre Saucier, and Éric Vaillancourt
Ministère des Ressources naturelles et de la Faune du Québec, Canada, corresponding author: andre.
robitaille@mrnf.gouv.qc.ca
Abstract
Since the late 1960s, the Ministère des Ressources
naturelles et de la Faune du Québec (MRNFQ) has
periodically carried out forest mapping activities at a
scale of 1:20,000 in Québec’s forest areas south of
51º 30’ north latitude. In the early 2000s, new needs
emerged for information about forest resources in a
data-poor area lying between 51º 30’ and 53º north
each polygon, the software performs segmentation
and classiication of the vegetation, based on Landsat
imagery. Cover type, density, barrens, spruce-lichen,
and spruce-moss forests can be distinguished. The
third phase consists of integrating the contours and
years of major disturbances, such as forest ires
and insect epidemics, into the mapping. These three
phases of integrated ecoforest mapping include
prior ield checks by geomorphology and forestry
latitude. This huge area, covering 240,000 km2,
specialists.
marks the northern portion of the spruce-moss forest
and the southern extent of the spruce-lichen forest,
which is characterized by less dense forests and
barrens. To perform the work, the MRNFQ decided
to explore new automated mapping approaches. The
latter had to deliver high-quality products, be relatively
inexpensive, and be well adapted to remote or hardto-reach areas. This paper presents the mapping
method, the software used, and the results obtained
to date, covering over 150,000 km2.
The mapping program began in 2005 and will end
in 2009. Aerial photos have been used for mapping
physical environmental variables (suricial deposits,
drainage) and satellite imagery for vegetation.
Integrating major disturbances with all of these
variables completes the integrated ecoforest map,
produced at a scale of 1:100,000.
The interpretation of suricial deposits and drainage
is initially carried out by analyzing aerial photos
on a computer screen in 3D using DVP software
in conjunction with ArcMap software. This makes
it possible to simultaneously synchronize the
topographic map and the photos so that the contours
recorded on the screen by the geomorphologist
automatically generate a shape ile and a database.
The polygons generated during this irst phase are
then integrated into the eCognition software. For
The approach developed by the MRNFQ has turned
out to be rewarding in the sense that it provides an
excellent picture of the vegetation and physical
environment of this previously data-poor portion of
Québec.
Keywords: automatized image classiication, cover
type, digital 3D photo interpretation, map, moisture,
suricial deposit.
Introduction
Since the late 1960s, the Ministère des Ressources
naturelles et de la Faune du Québec (MRNFQ) has
periodically carried out forest mapping activities at
a scale of 1:20,000 in Québec’s forest areas south
of 51º 30’ north latitude, the territory under forest
management. This map covers a little less than
600,000 km2 and is produced every 10 to 15 years.
It integrates permanent environment and vegetation
variables including potential vegetation. It is produced
on a computer screen in 3D aerial photos and is
updated annually with regard to forest work and
disturbances such as forest ires.
In the early 2000s, new needs emerged for information
about forest resources in a data-poor area located
between 51º 30’ and 53º north latitude. This huge area,
180
covering 240,000 km2, marks the northern portion of
the continuous spruce-moss forest and the southern
extent of the spruce–lichen open forest, characterized
by less dense forests and barrens. To perform the
work, the MRNFQ decided to explore new automated
mapping approaches. Such approaches had to deliver
high-quality products, be relatively inexpensive and
well adapted to remote or hard-to-reach areas. This
paper presents the mapping method, software used,
and results obtained to date, covering over 150,000
km2. The mapping program began in 2005 and will
end in 2009. Aerial photos have been used to map
physical environmental variables (suricial deposits,
drainage) and satellite imagery to map vegetation
variables. Integrating major disturbances with all of
these variables completes the integrated ecoforest
map, produced at a scale of 1:100,000.
Mapped Area
The 240,000 km2 area marks the transition between
spruce-moss forest to the south and spruce-lichen
forest to the north. It covers a 1,500 km east─west
transect, which is characterized by multiple relief,
altitude and climate zones, vegetation patterns,
ire and insect disturbances, suricial deposits, and
drainage. It is a sparsely populated, undeveloped
area. There are only three majors roads across the
territory.
In the western portion, it progresses from sea level to
about 500 m. The terrain is gentle. In the west-central
portion, the relief is gentle, but altitude increases from
an average of 500 m to 700 m. There are also a few
high hills with rugged topography up to 1,000 m in
altitude. The east-central portion is very rugged, with
height differences that are frequently as great as 500
m. Several peaks reach 900 m. Finally, the relief in
the eastern portion consists of rounded, relatively
gentle hills whose altitude progressively drops from
500 m to sea level.
•
Average annual precipitation is greater in the
center, with 1,000 mm and more on the higher
peaks. It is less than 800 mm toward the west.
•
Growing degree-days drop from 1,100 degreedays in the west to nearly 500 in the center.
Classiication Structure
As previously mentioned, the classiication structure
of the mapping is similar to the one developed farther
south and integrates permanent environment and
vegetation variables (Létourneau et al., 2008). Here
are the major classes of this classiication.
Suricial Deposits
The composition of suricial deposits inluences soil
development, productivity of vegetation, and drainage
conditions and is a signiicant variable in land-use
planning. The classes selected for the mapping refer
to major genetic groups (glacial, luvial, marine, etc.)
and can be recognized by their morphology using
aerial photos. Most of these classes are split into
subcategories that are distinguished by compactness,
granulometry, and stoniness.
Drainage
The following ive classes of drainage indicate levels
of soil moisture: xeric, xeric-mesic, mesic, subhydric,
and hydric. The presence of seepage can be reported
especially on the long slopes of hills.
Vegetation
The classes of vegetation are adapted to the
MRNFQ’s needs at this stage in the project, and the
tools used allow them to be well recognized. Seven
major characteristics can be used to describe the
stands.
1. Cover type (deciduous, mixed or coniferous).
2. Understory vegetation (lichen, moss, shrubs).
The three following examples illustrate the effect
of altitude, continentality, and water bodies on the
territory’s climate:
•
Average annual temperatures range from 0ºC to
1ºC on the west coast but are about -4ºC in the
center of the territory.
3. Density classes (ive classes from 10% to 100%).
4. Disturbances (ire, insects).
5. Development phase (mature, pre-mature, or
regeneration).
6. Vegetation without forest potential (wetland,
barren, etc.).
7. No vegetation (water, rock, etc.).
181
Approach and Tools
The mapping approach includes three principal
elements: (1) suricial deposits, (2) drainage
boundaries, and (3) vegetation. The interpretation of
suricial deposits and drainage is initially carried out
by analyzing aerial photos on a computer screen in
3D using DVP software (Groupe Alta Inc., 2008) in
conjunction with ESRI software. This makes it possible
to simultaneously synchronize the topographic map
and the photos so that the contours recorded on the
screen by the geomorphologist automatically generate
a geographic information system (GIS) shapeile and
an associated database. The polygons generated are
then integrated into the Deiniens software (Deiniens
Inc, 2006). For each polygon, the software performs
segmentation and classiication of the vegetation,
based on satellite imagery—Landsat TM in this
case. Image segmentation consists of automatically
delineating polygons based on thematic maps and
image homogeneity patterns. The user controls the
size of polygons and smoothness of contours. Cover
type, density, barrens, spruce-lichen, and sprucemoss forests are then distinguished by analysts,
using spectral characteristics of images and ancillary
data such as topographic maps, ire history maps, etc.
Finally, contours and years of major disturbances, such
as forest ires and insect epidemics, are integrated
into the map. The integrated ecoforest mapping
includes prior ield checks by geomorphology and
forestry specialists to guide and validate the process.
Results and Discussion
The quality of the map was assessed in 2005 when
the approach was developed for a training project.
It was a qualitative assessment carried out using
randomly selected Global Position System (GPS)
polygons. Results obtained were very satisfactory
and helped to identify the following advantages and
points to improve.
Points to Improve in the Mapping Approach
•
A few attributes are not mapped, for example,
height and species.
•
Some elements were dificult to discriminate
(commercial deciduous vs. shrubs; regeneration
on lichen understory).
Conclusion
The approach developed by the MRNFQ provides
an excellent picture of the vegetation and physical
environment of these huge, poorly known areas.
In fact, the substantial amount of information
about suricial deposits, drainage, vegetation,
and disturbances will be essential to improve our
knowledge about this fragile ecosystem. This map will
be linked to an ecological classiication. So, there will
be information about the potential natural vegetation
for each polygon. This map, which was associated
with the analysis of ground sample plots acquired
within the information acquisition framework, will
elaborate the frontiers between spruce-moss forest
and spruce-lichen open forest.
In this mapping approach it is evident that the map
scale is too small for the needs of the proposed
Circumboreal Vegetation Map (CBVM). However, the
approach of automatic integration of several layers
such as vegetation from satellite images, soil, relief,
geology, and water could be applied at a scale of
1:7.5M.
Finally, in spite of the limitations observed, this
mapping approach achieves good precision rates
and requires low investments, since satellite images
cover large areas and archived aerial photos can be
used to map suricial deposits and drainage. These
conclusions pave the way to extrapolating this
approach to other remote areas of the boreal zone.
Acknowledgments
Advantages of the Mapping Approach
•
Can cover a huge area in a short period of time.
•
10% of cost compared with traditional mapping
approach of southern Québec.
•
Data can be used for further work by reining
current map.
We acknowledge several colleagues from MRNFQ
for their support, advice, and insightful discussions:
Geneviève Auclair, Jacques Brunelle, Christian
Cantin, Lyne Carrier, Benoît De Serres, Marie-Pierre
Drouin, Pierre Grondin, Sebastian Matajek, and Sonia
Watts.
182
References
Deiniens A. G. 2006. Deiniens professional version
5. einiens A.G., München, Germany.
Groupe Alta inc. 2008. DVP version 7. Québec,
Canada.
Létourneau, J. P., Matejek, S., Morneau, C.,
Robitaille, A., Roméo, T., Brunelle, J., & Leboeuf,
A. 2008. Norme de cartographie écoforestière du
Programme d’inventaire écoforestier nordique.
Ministère des Ressources naturelles et de la Faune
du Québec. 51 pp.
183
Analysis of Terrain Relationships to Improve Mapping of Boreal
Ecosystems
Torre Jorgenson
Alaska EcoScience, Fairbanks, Alaska, U.S.A., tjorgenson@alaska.net
Extended Abstract
The mapping of boreal and tundra ecosystems at the
plant community level is enhanced by incorporating the
results of the quantitative analysis of the relationships
among vegetation, soils, geomorphology, and
physiography. Mapping of vegetation or the broader
integration of terrain characteristics into ecosystems
by image processing or photo-interpretation is
constrained by the ability to differentiate mainly plant
canopy structures. This kind of mapping also has
limited ability to differentiate plant communities with
similar vegetation structures. For example, broadleaf
forests on alkaline loodplains have very different
composition from birch forests on acidic, upland
terrain. Differentiation of plant communities, however,
can be enhanced by analyzing the relationships and
co-occurrence of plant communities on terrain with
differing physiography (e.g., riverine vs. upland), soil
texture, geomorphology (e.g., meander overbank
deposit vs. hillside colluvium), soil moisture and
drainage, soil pH and electrical conductivity, and
canopy structure. Quantiication of the central
tendencies of the co-occurrence of these associations
can be used to develop rules that improve mapping of
plant communities for both satellite image processing
and photo-interpretation. During analysis, we classify
the characteristics of each terrain component
independently using standard classiication systems,
although for vegetation, we identify plant communities
through cluster analysis, ordination, and sorted
table techniques. We found that 75–85% of our ield
plots have consistent relationships among terrain
components, and the remaining portion are outliers or
have inconsistent relationships that are not useful for
mapping. We have used this integrated-terrain-unit
(ITU) mapping approach for mapping ecosystems in
northern, western, central, and south-central Alaska.
For image processing, we use the results of the terrain
analyses and thematic overlays of physiography,
bedrock geology, and digital elevation models to
differentiate single vegetation structures (e.g., dwarf
shrub) that can be mapped with satellite imagery into
plant communities with differing loristics on alpine,
riverine, and organic-rich lowland terrain. For photointerpretation, we use an ITU approach to code each
polygon with geomorphology, surface form, and
vegetation structure. We then aggregate the large
number of ITU combinations into plant communities
using rules of terrain relationships developed from
analysis of the plot data.
Keywords: Alaska, boreal, classiication, ecosystems,
mapping, terrain analysis.
184
Appendices
Appendix I. Proposal for an IAVS Circumboreal
Vegetation Map Working Group
The tasks listed above are directly related to activities
of the International Association for Vegetation Science:
The goal of the Circumboreal Vegetation Map (CBVM)
is to provide a common international framework for
understanding the boreal region. Currently, various
maps already exist of the boreal biome, but they do not
rely on a uniied international method for classifying
and mapping boreal vegetation. By recognizing
the boreal region as a single geo-ecosystem with
a common set of cultural, political, and economic
issues, the CBVM will be the irst detailed vegetation
map of the entire global biome. Such a map is needed
for a wide variety of purposes related to resource
development, land-use planning, studies of boreal
biota and biodiversity, education, anticipated global
changes, and human interaction. A common legend
and language for describing boreal ecosystems is
essential for answering questions at a global scale.
Boreal forests are particularly appropriate for uniied
classiication because of their high level of loristic,
physiognomic, and syntaxonomic similarity across
the entire biome. A circumboreal vegetation map
will have numerous other application uses for boreal
scientists and managers, such as impact studies
on wildlife and feedback mechanisms in models or
studies of increased emission of greenhouse gases.
The CBVM will also contribute to global efforts to
improve understanding and communication with
policy-makers.
1. The Circumboreal Vegetation Map will synthesize
comprehensive knowledge about diversity,
ecology, geography, and disturbance of boreal
biome;
A secondary goal is to make the map compatible with
the Circumpolar Arctic Vegetation Map (CAVM, scale
1:7,500,000) to the north. Linking these two globalscale maps is necessary because very few issues
relevant to the Arctic or the boreal regions stop at
tree line. For example, most rivers lowing into the
Arctic Ocean have their origin far to the south of the
tree line. Climate and vegetation-change models,
analysis of animal migrations, roads and industrial
developments, and arctic-human interaction all
require maps that include both the Arctic tundra and
boreal forest regions.
2. The CBVM is based on international scientiic
cooperation of phytosociologists from wide
spectrum of countries; and
3. The basic scientiic problems solved within the
frame of the CBVM correspond to the actual
thematic activities in the IAVS meetings. These
include:
— classifying vegetation of large regions
— developing the system
subdivisions of vegetation
of
bioclimatic
— studying plant-geographical and evolutional
regularities in boreal vegetation
— monographic study of entire (largest) biome
— studying dynamics and disturbances of
boreal vegetation
— mapping (small-scale geobotanical mapping)
— mapping boreal vegetation with satellite
images
Presentations and discussions of these diverse
theoretical scientiic problems of vegetation science
related to the CBVM will be of great interest for the
international society of phytosociologists, plant
ecologists, and specialists on vegetation mapping.
Toward these goals we held an international workshop
in Helsinki, Finland, November 3–6, 2008, to develop
a strategy to map the vegetation of the circumboreal
zone. Fifty vegetation scientists from 10 northern
countries attended. A workshop summary follows
with our resolution; outline of the project objectives;
organizational chart; composition of mapping teams
that includes thematic, remote sensing, biogeoclimatic,
funding, regional and subregional divisions; and
process and timeline.
185
Appendix II. Resolution from the Circumboreal
Vegetation Mapping Workshop – Helsinki, Finland,
November 3–6th, 2008
Whereas the Boreal Biome is one global geoecosystem with a common set of cultural, political,
nature conservation and economic issues, and
whereas it is under pressure from human inluence
and climate change, it is of primary importance:
(1) to better express the nature and diversity of the
ecosystems adapted to cold climate; (2) to depict their
distribution and their extent; (3) to better know and
protect this biome that is also the home of indigenous
people; and (4) to promote the sustainable use of its
natural resources; let it be resolved to:
a. Develop a Circumboreal Vegetation Map (CBVM)
at a scale of 1:7.5M, depicting the nature and the
boundaries of boreal vegetation south of the arctic
zone by using recent and traditional vegetation
classiication and maps, remote sensing and
GIS tools, and a legend that is accepted by the
international community of vegetation scientists.
b. Publish and present the Circumboreal Vegetation
Map with the ancillary information developed for
the project to the international scientiic community
and other potential users.
c. Publish the mapping methods and descriptions of
the mapping units, including vegetation structure,
composition, dynamics, and ecological context.
d. Achieve goals a) to c) by harmonizing concepts in
a spirit of international collaboration among and
between regional mapping teams.
e. That this CBVM group will reconvene in 2010
for a workshop in Russia to accept the mapping
legend, biogeoclimatic framework, mapping tools,
and prototype maps to be presented by thematic
teams.
Appendix III. CBVM Organizational Chart
CBVM Overall Project Leader
Stephen Talbot
Team 1: Vegetation map legend
Team 2: Remote sensing
Nikolai Ermakov
Carl Markon
Team 3: Biogeoclimatic or climatic
framework
Team 4: Funding development
Daniel Sanchez-Mata
Stephen Talbot
Team Leader North America
Team Leader Eurasia
Bill Meades
Nikolai Ermakov
Team Leader Alaska
Team Leader Greenland, Iceland &
Faroe Islands
Torre Jorgenson
Gudmundur Gudjonsson
Team Leader Canada
Team Leader Europe
Bill Meades
Udo Bohn
Team Leader Russia & Asia
Nikolai Ermakov
186
Appendix IV. CBVM Thematic Team Composition
Team 1: Vegetation Map Legend
— Objectives: uniform classiication units, potential
vegetation concept, legend prototypes
— Team leader: Stephen Talbot
— Participants: Bill Meades
— Deadline: December 2010
Appendix V. CBVM Regional Team Composition
— Team leader: Nikolai Ermakov
— Participants: Klaus Dierssen, Annika Hofgaard,
Udo Bohn, Jean-Pierre Saucier
— Subgroup on particular vegetation: alpine
vegetation, mires and wetlands, lood plains,
maritime heath, coastal vegetation
— Subgroup Leader: Klaus Dierssen
— Participants: Kazue Fujiwara, Del Meidinger,
Hans Tommervik, Fred J.A. Daniёls, Raimo
Heikkilä, Valentina Neshataeva, Stephen
Talbot, Gudmundur Gudjonsson, Anna-Maria
Fosaa, Elena Golubeva, Olga Galanina, Yukito
Nakamura, Bill Meades, Kharuk Viacheslav
Deadlines
— Deadline 1: Legend prototype for December
2009
— Deadline 2: Legend test for December 2010
Team 2: Remote Sensing
— Objectives: thematic maps, map projection, map
scale and resolution, remote sensing products
— Team leader: Carl Markon
— Participants: Sergei Bartlev, Peter Potapov,
Ekaterina Shipigina, Gareth Reeds, Olga
Tutubalina, Kelly Dolan
— Deadline: December 2010
Team 3: Biogeoclimatic or Climatic Framework
— Objectives: essential climatic indices, boreal
deinition, extent of the boreal
— Team leader: Daniel Sánchez-Mata
— Participants: Torre Jorgenson, Ken Baldwin,
Pavel Krestov, Steve Cumming, Galina
Ogureeva, Elgene Box, Teuvo Ahti
— Deadline: December 2010
Team 4: Funding Development
— Objectives: obtain funding for the next workshop
and the project
North America
— Objectives: mapping North America (Alaska,
Canada, Saint-Pierre et Miquelon)
— Team leader: Bill Meades
— Participants: to be determined
— Alaska Subregion
— Subregion leader: Torre Jorgenson
— Participants: to be determined
— Canada Subregion
— Subregion leader: Bill Meades
— Participants: Ken Baldwin and others to be
determined
Deadlines
— Deadline 1: international correlation excursion
in summer 2012
— Deadline 2: regional maps in May 2013
Eurasia
— Objectives: mapping Eurasia (Greenland,
Iceland, and Faroe Islands, Europe, Russia,
and Asia)
— Team leader: Nikolai Ermakov
— Participants: to be determined
— Greenland, Iceland & Faroe Islands Subregion
— Subregion leader: Gudmundur Gudjonsson
— Participants: to be determined
— Europe (Scandinavia and Finland) Subregion
— Subregion leader: Udo Bohn
— Participants: to be determined
— Russia & Asia Subregion
— Subregion leader: Nikolai Ermakov
— Participants: to be determined
Deadlines
— Deadline 1: international correlation excursion
in summer 2011 and 2012
— Deadline 2: regional maps in May 2013
187
Appendix VI. CBVM Process and Timeline
Deadline
Team
Deliverable
Timeline
December 2009
Thematic team 1
Preliminary legend
1 year
December 2010
Thematic team 2
Thematic team 4
Thematic team 1
Thematic team 3
Regional teams
Base map definition and base image product
Funding development for next workshop
Legend of the vegetation map
Biogeoclimatic or climatic framework
Map prototype with preliminary legend
2 years
May 2011
Russia (Vladivostok?)
2nd CBVM Workshop for adoption of the thematic
teams recommendations
International correlation excursions according
expressed needs
Summer 2012
Alaska ?
May 2013
2 ½ years
3 ½ years
Anchorage IAVS meeting 2012 ?
Regional team mapping along the principles from
the second workshop
4 ½ years
December 2014
Integration team
Integrating regional maps with validation by
regional teams
6 years
May 2015
Where? Hosts?
3rd CBVM Workshop for delivery of the CBVM
map
6 ½ years
May 2016
Integration team
Regional teams
CBVM Map publication
CBVM Map description
7 6 months
Appendix VII - CBVM Leadership
— Udo Bohn, Königswinter, Germany
— Nicolai Ermakov, Novosibirsk, Russia
— Gudmundur Gudjonsson, Reykjavik, Iceland
— Torre Jorgenson, Fairbanks, Alaska, U.S.A.
— Carl J. Markon, Anchorage, Alaska, U.S.A.
— Bill Meades, Sault Ste. Marie, Canada
— Daniel Sánchez-Mata, Madrid, Spain
— Stephen S. Talbot, Anchorage, Alaska, U.S.A.
— Donald “Skip” Walker, Fairbanks, Alaska, U.S.A.
192
188