ORIGINAL RESEARCH
published: 26 January 2022
doi: 10.3389/fevo.2021.825751
Regional Patterns of Late Medieval
and Early Modern European Building
Activity Revealed by Felling Dates
Edited by:
Martin De Luis,
University of Zaragoza, Spain
Reviewed by:
Katarina Čufar,
University of Ljubljana, Slovenia
Chris J. Caseldine,
University of Exeter, United Kingdom
*Correspondence:
Fredrik Charpentier Ljungqvist
fredrik.c.l@historia.su.se
† These
authors have contributed
equally to this work
Specialty section:
This article was submitted to
Paleoecology,
a section of the journal
Frontiers in Ecology and Evolution
Received: 30 November 2021
Accepted: 31 December 2021
Published: 26 January 2022
Citation:
Ljungqvist FC, Seim A, Tegel W,
Krusic PJ, Baittinger C, Belingard C,
Bernabei M, Bonde N, Borghaerts P,
Couturier Y, Crone A, van Daalen S,
Daly A, Doeve P,
Domínguez-Delmás M, Edouard J-L,
Frank T, Ginzler C, Grabner M,
Gschwind FM, Haneca K, Hansson A,
Herzig F, Heussner K-U, Hofmann J,
Houbrechts D, Kaczka RJ, Kolář T,
Kontic R, Kyncl T, Labbas V,
Lagerås P, Le Digol Y, Le Roy M,
Leuschner HH, Linderson H,
Ludlow F, Marais A, Mills CM,
Neyses-Eiden M, Nicolussi K,
Perrault C, Pfeifer K, Rybníček M,
Rzepecki A, Schmidhalter M,
Seifert M, Shindo L, Spyt B,
Susperregi J, Svarva HL, Thun T,
Walder F, Ważny T, Werthe E,
Westphal T, Wilson R and Büntgen U
(2022) Regional Patterns of Late
Medieval and Early Modern European
Building Activity Revealed by Felling
Dates. Front. Ecol. Evol. 9:825751.
doi: 10.3389/fevo.2021.825751
Fredrik Charpentier Ljungqvist 1,2,3* † , Andrea Seim 4,5† , Willy Tegel 4,6 , Paul J. Krusic 7,8 ,
Claudia Baittinger 9 , Christelle Belingard 10 , Mauro Bernabei 11 , Niels Bonde 9 ,
Paul Borghaerts 12 , Yann Couturier 13 , Anne Crone 14 , Sjoerd van Daalen 15 , Aoife Daly 16 ,
Petra Doeve 17 , Marta Domínguez-Delmás 18 , Jean-Louis Edouard 19 , Thomas Frank 20 ,
Christian Ginzler 21 , Michael Grabner 22 , Friederike M. Gschwind 23 , Kristof Haneca 24 ,
Anton Hansson 25 , Franz Herzig 26 , Karl-Uwe Heussner 27 , Jutta Hofmann 28 ,
David Houbrechts 29 , Ryszard J. Kaczka 30 , Tomáš Kolář 31,32 , Raymond Kontic 33 ,
Tomáš Kyncl 34 , Vincent Labbas 35 , Per Lagerås 36 , Yannick Le Digol 13 , Melaine Le Roy 37 ,
Hanns Hubert Leuschner 38 , Hans Linderson 25 , Francis Ludlow 39,40 , Axel Marais 13 ,
Coralie M. Mills 41,42 , Mechthild Neyses-Eiden 43 , Kurt Nicolussi 44 , Christophe Perrault 45 ,
Klaus Pfeifer 46 , Michal Rybníček 31,32 , Andreas Rzepecki 43 , Martin Schmidhalter 47 ,
Mathias Seifert 48 , Lisa Shindo 49 , Barbara Spyt 50 , Josué Susperregi 51 ,
Helene Løvstrand Svarva 52 , Terje Thun 52 , Felix Walder 53 , Tomasz Ważny 54 ,
Elise Werthe 13 , Thorsten Westphal 20 , Rob Wilson 41 and Ulf Büntgen 7,21,32,55
1
Department of History, Stockholm University, Stockholm, Sweden, 2 Bolin Centre for Climate Research, Stockholm
University, Stockholm, Sweden, 3 Swedish Collegium for Advanced Study, Uppsala, Sweden, 4 Chair of Forest Growth
and Dendroecology, Institute of Forest Sciences, University of Freiburg, Freiburg, Germany, 5 Department of Botany,
University of Innsbruck, Innsbruck, Austria, 6 Amt für Archäologie, Kanton Thurgau, Frauenfeld, Switzerland, 7 Department
of Geography, University of Cambridge, Cambridge, United Kingdom, 8 Department of Physical Geography, Stockholm
University, Stockholm, Sweden, 9 National Museum of Denmark, Copenhagen, Denmark, 10 GEOLAB, University of Limoges,
Limoges, France, 11 Institute of BioEconomy, National Research Council, Trento, Italy, 12 Borghaerts Houtdatering, Easterein,
Netherlands, 13 DendroTech, Betton, France, 14 AOC Archaeology Group, Edinburgh, United Kingdom, 15 Van Daalen
Dendrochronologie, Deventer, Netherlands, 16 The Saxo Institute, University of Copenhagen, Copenhagen, Denmark,
17
BAAC Archaeology and Building History, Graaf van Solmsweg, Netherlands, 18 Amsterdam School for Heritage, Memory
and Material Culture, University of Amsterdam, Amsterdam, Netherlands, 19 CNRS, CCJ, Aix-Marseille Université,
Aix-en-Provence, France, 20 Laboratory of Dendroarchaeology, Department of Prehistoric Archaeology, University
of Cologne, Cologne, Germany, 21 Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf,
Switzerland, 22 Institute of Wood Technology and Renewable Materials, University of Natural Resources and Life Sciences,
Vienna, Austria, 23 Büro für Dendrochronologie und Baudenkmalpflege, Planegg, Germany, 24 Flanders Heritage Agency,
Brussels, Belgium, 25 The Laboratory for Wood Anatomy and Dendrochronology, Department of Geology, Lund University,
Lund, Sweden, 26 Bavarian State Office for Monument Protection, Thierhaupten, Germany, 27 German Archaeological
Institute, Berlin, Germany, 28 Jahrringlabor Hofmann, Nürtingen, Germany, 29 Association du Patrimoine Artistique, Brussels,
Belgium, 30 Department of Physical Geography and Geoecology, Faculty of Science, Charles University, Prague, Czechia,
31
Department of Wood Science and Technology, Mendel University in Brno, Brno, Czechia, 32 CzechGlobe Global Change
Research Institute CAS, Brno, Czechia, 33 Labor Dendron, Basel, Switzerland, 34 DendroLab Brno, Brno, Czechia,
35
Department of Historical Sciences, Art, Archaeology, Heritage, University of Liège, Liège, Belgium, and Royal Institute for
Cultural Heritage (KIK-IRPA), Brussels, Belgium, 36 The Archaeologists, National Historical Museums, Lund, Sweden,
37
Climate Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University
of Geneva, Geneva, Switzerland, 38 Department of Palynology and Climate Dynamics, Georg-August-University, Göttingen,
Germany, 39 Department of History and Trinity Centre for Environmental Humanities, School of Histories and Humanities,
Trinity College Dublin, Dublin, Ireland, 40 Department of History and Harvard University Center for the Environment, Harvard
University, Cambridge, MA, United States, 41 School of Earth and Environmental Sciences, University of St Andrews, Fife,
United Kingdom, 42 Dendrochronicle, Edinburgh, United Kingdom, 43 Generaldirektion Kulturelles Erbe Rheinland-Pfalz,
Direktion Rheinisches Landesmuseum Trier, Trier, Germany, 44 Department of Geography, Universität Innsbruck, Innsbruck,
Austria, 45 Centre d’Etudes en Dendrochronologie et de Recherche en Ecologie et Paléo-Ecologie, Besançon, France,
46
Labor für Dendrochronologie, Egg, Austria, 47 Dendrosuisse: Labor für Dendrochronologie, Brig-Glis, Switzerland,
48
Archäologischer Dienst Graubünden, Amt für Kultur, Chur, Switzerland, 49 Cluster of Excellence ROOTS, Kiel University,
Kiel, Germany, 50 Faculty of Natural Sciences, University of Silesia in Katowice, Katowice, Poland, 51 Arkeolan Foundation,
Irun, Spain, 52 The National Laboratory for Age Determination, NTNU University Museum, Norwegian University of Science
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Regional Patterns of Building Activity
and Technology, Trondheim, Norway, 53 Competence Center for Underwater Archaeology and Dendrochronology, Zurich,
Switzerland, 54 Centre for Research and Conservation of Cultural Heritage, Faculty of Fine Arts, Nicolaus Copernicus
University, Toruń, Poland, 55 Department of Geography, Masaryk University, Brno, Czechia
Although variations in building activity are a useful indicator of societal well-being and
demographic development, historical datasets for larger regions and longer periods
are still rare. Here, we present 54,045 annually precise dendrochronological felling
dates from historical construction timber from across most of Europe between 1250
and 1699 CE to infer variations in building activity. We use geostatistical techniques
to compare spatiotemporal dynamics in past European building activity against
independent demographic, economic, social and climatic data. We show that the
felling dates capture major geographical patterns of demographic trends, especially
in regions with dense data coverage. A particularly strong negative association is
found between grain prices and the number of felling dates. In addition, a significant
positive association is found between the number of felling dates and mining activity.
These strong associations, with well-known macro-economic indicators from preindustrial Europe, corroborate the use of felling dates as an independent source for
exploring large-scale fluctuations of societal well-being and demographic development.
Three prominent examples are the building boom in the Hanseatic League region of
northeastern Germany during the 13th century, the onset of the Late Medieval Crisis
in much of Europe c. 1300, and the cessation of building activity in large parts of
central Europe during armed conflicts such as the Thirty Years’ War (1618–1648 CE).
Despite new insights gained from our European-wide felling date inventory, further
studies are needed to investigate changes in construction activity of high versus low
status buildings, and of urban versus rural buildings, and to compare those results with
a variety of historical documentary sources and natural proxy archives.
Keywords: archeology, cultural heritage, dendrochronology, dendroarchaeology, felling dates, history, historical
demography
shipwrecks to estimate commerce (e.g., Wilson, 2011), pollen to
reconstruct agricultural production (e.g., Izdebski et al., 2016),
glacier microfossil records to estimate changes in land use
(Brugger et al., 2021), ancient DNA to reconstruct migration
patterns (e.g., Margaryan et al., 2020), and anthropometric
estimates of the biological standard of living from skeletonbased stature measurements (e.g., Kopke and Baten, 2005).
These disparate types of data have the potential to detect and
analyze patterns of prosperity and hardship in human history
(Fischer, 1996), whereas the heterogeneous character of most
documentary sources limits their suitability for such studies (see,
e.g., Turchin and Nefedov, 2009).
Inferring variations in building activity from annually resolved
and absolutely dated felling dates can reveal information about
large-scale societal changes. The very large amount of available
data in combination with their exact dating to a calendar year
makes felling dates unique among non-documentary sources of
human activity. Felling dates have hitherto mainly been used
to infer large-scale demographic and societal changes only for
those periods and regions lacking written sources from which
to reconstruct demographic trends, e.g., among the Ancestral
Puebloans in the southwestern United States (pioneered by
INTRODUCTION
Variations in building activity reflect changing demographic,
economic, and social conditions (e.g., Barras, 2009; Aksözen
et al., 2017a,b) and, potentially, provide a broad overview
of societal well-being over space and time. However, prior
to the modern period building activity rates are poorly
documented in Europe, precluding the use of written sources
for their assessment. An alternative source for estimating
building activity rates are large datasets of dendrochronologically
obtained felling dates of historical construction timbers. Such
data, derived from the efforts of many to date individual
constructions in archeological research and cultural heritage
work, when collated into an aggregate dataset, can be highly
useful for exploring large-scale changes in building activity
(e.g., Ljungqvist et al., 2018).
Recent historical scholarship has increasingly employed data
from the natural sciences and archeology to understand macro
scale changes not readily detectable or quantifiable using
documentary sources or traditional archeological materials. Such
studies include pollution data from ice-cores as a proxy for
mining activity (e.g., McConnell et al., 2019), numbers of
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last measured tree ring is the terminal ring (so-called waney edge)
were used. The determination of the exact year of tree felling
is termed waney edge dating (Bannister, 1962). The majority
of felling dates were derived from timbers in different parts of
buildings such as roof trusses, ceiling joints, basement pillars, etc.
A small number of felling dates were obtained from archeological
materials (Figure 2).
The tree species used for constructions can vary greatly from
region to region. For exterior/exposed applications like bridges
and hydraulic engineering, oak (Quercus spp.) and fir (Abies alba
Mill.) were used almost exclusively. Conifer species such as fir,
spruce (Picea abies L.), pine (Pinus sylvestris L.) and larch (Larix
decidua Mill.) were preferred for half-timbering and roof trusses
(Tegel et al., 2010). However, the species’ natural distribution also
played a decisive role in dictating the application. In the western
Euro-Atlantic region, where oak woodlands predominated, this
was the species used for construction purposes. In northern
Europe, pine was the preferred species for construction timber
(e.g., Mills et al., 2017), whereas fir and spruce were mostly used
in central Europe (Grabner et al., 2018; Kolář et al., 2021). The
natural distribution of larch is limited to the high altitudes of
the Alps and parts of the Tatra Mountains (Büntgen et al., 2009),
where this species was preferred as construction timber.
Douglass, 1921, 1929, 1941; more recently, see Bocinsky et al.,
2016; Robinson et al., 2021).
In Europe, there are limited examples in archeological and
historical research of using felling dates for reconstructing
building activity changes at large spatial scales, but several smallscale studies have provided valuable insights into demographic
declines or the timing of societal crises (Baillie, 1982, 1995, 1999;
Mallory and Baillie, 1988; Schweingruber, 1988; Wrobel and
Eckstein, 1993; Nicolussi, 2002; Eckstein, 2007), and also into
past settlement and demographic dynamics in the Swiss Alps
(Büntgen et al., 2006), the northwestern Carpathian arc (Büntgen
et al., 2013), the north-eastern France (Tegel et al., 2016), in
Sweden (Bartholin, 1989, 1990; Lagerås et al., 2016), in parts of
Norway (Thun and Svarva, 2018), in eastern Austria (Grabner
et al., 2018), and Ireland (Brown and Baillie, 2012; Campbell and
Ludlow, 2020).
Using 49,640 precisely dated felling dates, between 1250
and 1699 CE, Ljungqvist et al. (2018) identified variations
in European building activity, and attempted to quantify the
major drivers behind those variations. They found (a) building
activity decreased during periods with multiple or severe plague
outbreaks, (b) building activity was significantly lower when
grain prices were high, (c) first evidence of the Late Medieval
Crisis as early as c. 1300 CE and lasting until c. 1415 CE, and
(d) building activity decreased abruptly by about 36 percent of
prior levels during the Thirty Years’ War (1618–1648 CE). In
sum, Ljungqvist et al. (2018) demonstrated how sudden declines
in building activity are sensitive indicators of the onset of a
crisis. However, the spatial aspects of the timing, duration, and
amplitude of decreased building activity associated with the Late
Medieval Crisis and the Thirty Years’ War (1618–1648) were
not investigated.
To date, no study has attempted to use dendrochronological
felling dates from historical construction timbers to investigate
the spatial dynamics of past European-scale building activity.
This article aims to fill that gap by assessing regional patterns of
construction activity during the 1250–1699 CE period in relation
to regional demographic, economic, and social conditions. To do
so we apply geostatistical analysis to explore regional differences
in the number of felling dates to infer the timing, duration,
and intensity of periods of crisis and prosperity at different
spatial and temporal scales. This study thereby constitutes a
considerable extension in scope, and aim, and geographical
coverage, compared to Ljungqvist et al. (2018).
Other Datasets Used for Comparisons
Inferred building activity levels were compared with different
independent indicators of periods of “crisis” and “prosperity”
representative of large spatial scales. The indicators of past
economic, societal, and demographic conditions include: (a)
atmospheric lead emissions data, an indicator of mining activity
(in particular silver production); (b) church construction,
an indicator of societies’ available economic surplus as well
as demographic trends; (c) grain price data, reflecting the
availability of the most important food source and general
economic well-being; (d) wine prices, indicating economic
conditions and general purchasing power; and (e) consumer
price indices, also indicating general purchasing power and
thus economic well-being. In addition, we compare building
activity levels with reconstructed growing season temperature,
soil moisture levels and a reconstruction of southwestern German
groundwater levels.
Changes in the amount of atmospheric lead in high-resolution
Arctic and alpine ice-cores have been linked to the mining and
smelting of raw ores, especially silver, in Europe. Such records
have proved to be an indicator of prosperity and crisis (i.e., in
times of prosperity, pollution levels were high) over the past
two millennia (Hong et al., 1994; Rosman et al., 1997; More
et al., 2017; Loveluck et al., 2018; McConnell et al., 2018, 2019;
Carvalho and Schulte, 2021). We employ the lead pollution series
of McConnell et al. (2019) as proxies for mining activity in
western Europe and central Europe.
To a large extent, church building in medieval and early
modern times reflected a society’s surplus, although it can be
argued the Catholic Church, as an institution and major financial
power, was much more resilient to crises that would otherwise
affect secular constructions. We employ an independent timeseries of urban church building in western Europe, measured in
MATERIALS AND METHODS
Felling Dates
We collected 54,045 georeferenced felling dates for the period
1250–1699 from western and central Europe, north of ∼42◦ N
(Figure 1A). This collection is the result of a European-wide
collaborative network that includes data from laboratories in
Austria, Belgium, Czechia, Denmark, France, Germany, Ireland,
Italy, Netherlands, Norway, Poland, Slovakia, Spain, Sweden,
Switzerland, and United Kingdom (Table 1). The same criteria as
in Ljungqvist et al. (2018) were applied and only data where the
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FIGURE 1 | (A) The geographical distribution of all 54,045 tree felling dates 1250–1699 CE from archeological and historical construction timber displayed using the
Lambert conformal conic projection in ArcGIS and displayed with contemporary political boundaries. (B) Number of felling dates per year for each regional dataset
[color-coded as in (A)] over the period 1250–1699 CE. The black line shows the significant regime shifts in the number of felling dates using the full dataset.
millions of cubic meters per 20-year period, compiled from data
of extant church structures (Buringh et al., 2020). The spatial
coverage of this time-series is restricted to present-day Italy,
France, Switzerland, Germany, the Low Countries, and Great
Britain, and it only extends to 1500 CE.
Grain-based foods were by far the most important food
source in medieval and early modern Europe (Rahlf, 1996;
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Bateman, 2015). During medieval and early modern times, the
price of grain had a determining effect on the entire economy and
the general standard of living (Allen, 2000, 2001). To compare
building activity history with the price of grain we used the 300year detrended European grain price average as published by
Ljungqvist et al. (2022). This grain price record consists of 56
series from cities across most of central and western Europe,
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TABLE 1 | List of data contributors of the felling dates, contribution size, the geographical origin of their samples, and key references to the contributed data.
Country or region
Data contributor(s)
Austria
Michael Grabner; Kurt Nicolussi; Klaus Pfeifer
Number
4654
3
Key references
Nicolussi, 2002; Buchinger and Grabner, 2017;
Karanitsch-Ackerl et al., 2017; Grabner et al., 2018
Basque Country, Spain
Josué Susperregi
Belgium
Kristof Haneca; David Houbrechts; Willy Tegel
455
Czechia
Tomáš Kyncl; Tomáš Kolář; Michal Rybníček
7695
Kolář et al., 2012, 2021, 2022
Denmark
Claudia Baittinger; Niels Bonde; Aoife Daly
298
Daly, 2007
France
Yann Couturier; Yannick Le Digol; Jean-Louis Edouard; Thomas
Frank; David Houbrechts; Vincent Labbas; Axel Marais; Elise
Werthe; Melaine Le Roy; Christophe Perrault; Lisa Shindo; Willy
Tegel
2346
Edouard, 2010; Tegel et al., 2010; Meirion-Jones and
Grandchamp, 2013; Davy and Foisneau, 2014; Durandière
et al., 2015; Biguet and Letellier-d’Espinose, 2016; Le Roy
et al., 2017; Shindo et al., 2018
Germany
Thomas Frank; Friederike M. Gschwind; Franz Herzig; Karl-Uwe
Heussner; Jutta Hofmann; Hanns H. Leuschner; Mechthild
Neyses-Eiden; Klaus Pfeifer; Andreas Rzepecki; Willy Tegel;
Thorsten Westphal
19894
Hollstein, 1980; Becker, 1991; Kelly et al., 2002; Westphal,
2002, 2003
Ireland
Francis Ludlow
Italy
Mauro Bernabei; Kurt Nicolussi
392
Bernabei et al., 2016, 2017, 2021; Nicolussi, 2002
Netherlands
Paul Borghaerts; Sjoerd van Daalen; Petra Doeve; Marta
Domínguez-Delmás; Esther Jansma
1485
houtdatering.nl; https://doi.org/10.34894/ZWBVSW
40
Susperregi, 2007; Susperregi et al., 2017
Haneca et al., 2009, 2020; Haneca and van Daalen, 2017; Van
Eenhooge et al., 2018
Baillie, 1982, 1995, 1999, 2006; Brown and Baillie, 2012;
Campbell and Ludlow, 2020
Norway
Thomas S. Bartholin; Helene Løvstrand Svarva; Terje Thun
1537
Thun, 2009; Thun and Svarva, 2018
Poland
Ryszard J. Kaczka; Tomasz Ważny
1496
Ważny, 1990, 2001; Konieczny, 2009, 2010, 2011, 2012;
Ruszczyk and Konieczny, 2012; Klajmon, 2013; Spyt et al.,
2016
Slovakia
Tomáš Kolář; Tomáš Kyncl; Michal Rybníček
811
Prokop et al., 2016
Sweden
Anton Hansson; Per Lagerås; Hans Linderson; Andrea Seim
1591
Meissner et al., 2012; Seim et al., 2015; Lagerås et al., 2016
Switzerland
Raymond Kontic; Martin Schmidhalter; Mathias Seifert; Willy
Tegel; Felix Walder
9008
Seifert, 2018
United Kingdom
Vernacular Architecture*; Anne Crone; Coralie Mills; Robert
Wilson
2340
Alcock, 1987, 1993, 1998; Miles, 1997; Meeson, 2012
*Digitized from data published in Vernacular Architecture originating from numerous investigators.
FIGURE 2 | Examples of type of buildings and material used to derive the felling dates from. Top: Half-timbered building in Sonterswil (Thurgau, Switzerland) from the
17th century. Tower of the town wall from Ribeauvillé (Alsace, France) from the 16th century. Log houses in the Lötschental Valley (Valais, Switzerland) from the 18th
century. Below: Roof truss of the Chapelle Saint-Denis in Marmoutier (Alsace, France) from the 16th century. Roof truss from the church in Forshem, southwestern
Sweden, with a nave from the 12th century and a chancel from the 13th century. Cross section of an oak beam from the Cathedral of Reims (France) from the 13th
century.
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in a way that made the most historical sense, acknowledging
that any such clustering is to a degree subjective. The seven
regions are: (1) the British Isles; (2) the Nordic countries,
including Schleswig-Holstein, present-day Germany; (3) France,
excluding Alsace, and Basque Country (Spain); (4) the Benelux
countries, Belgium, the Netherlands, and Luxembourg; (5)
central Europe (north), consisting of Germany, excluding BadenWürttemberg, Bavaria and Schleswig-Holstein, and Poland; (6)
central Europe (south), consisting of Alsace, Austria, BadenWürttemberg, Bavaria, Czechia, Slovakia, and South Tyrol
(Italy); and (7) Switzerland. The groups are based on natural,
geographical, cultural, historical, ecological, social and economic
features which, over the period of investigation or at least
over longer historical periods, shared close economic relations
and cultural unity.
Long-term trends toward fewer available dates in the felling
dataset are partly related to a general decrease in the preservation
of constructions back in time. This time-dependent constraint
must be removed to provide a meaningful construction history.
As in Ljungqvist et al. (2018), we thus removed the longterm trends by calculating the ratios between the raw values
and 300-year cubic smoothing splines (Cook and Peters, 1981).
After removing the long-term trend, the felling date data were
transformed to standard normal deviates with a mean of zero and
a standard deviation of one.
For correlation statistics all datasets are, when not using the
annual values, smoothed using 10-year splines as well as 10-year,
non-overlapping, box-car filters. Throughout this study, we use
the p < 0.05 significance level for both the Pearson correlation
(r) and Spearman rank correlation (rs ) coefficients. The fewer
available degrees of freedom were taken into consideration
when using 10-year smoothed series. For detecting the timing
of significant regime shifts, i.e., trends toward increasing or
decreasing numbers, we employ the Rodionov (2004) sequential
algorithm, as updated by Rodionov (2006) to accommodate
autocorrelation, and consider only years with regime shifts
significant at the p < 0.05 level. The software is a macrobased implementation for Microsoft Excel (called Regime test
shift v6-2). Shifts in the time-series were computed on the
mean felling date numbers using the default cut-off length of
20 years and omitting the last 10 years at each end of the
time-series.
Heat maps for consecutive 50-year periods between 1250 and
1699, and for periods of 31 years around the Late Medieval
Crisis and the Thirty Years’ War, were generated using the
software ArcGIS 10.6. Here, the kernel density function in the
Spatial Analyst toolbox was applied on the recorded felling
date location (i.e., point data) for the above-mentioned periods
(DiBiase et al., 2006). We used a search radius of 60 km to
estimate the spatial concentrations of the felling dates within the
individual periods. The Kernel density maps were reclassified
for final comparison. To evaluate the regional impact of the
Late Medieval Crisis and the Thirty Years’ War, we calculated
the absolute differences in the number of felling dates between
the 31-year periods by using the Raster Calculator in the Map
Algebra toolbox (Spatial Analyst Tools) in ArcGIS 10.6. Again,
the resulting maps were standardized (i.e., reclassified) to make
standardized to z-scores relative to the 1546–1650 period. These
data include 25 price series of wheat, 14 of rye, 10 of barley, and of
7 oats. This new price compilation is much more comprehensive
than the European grain price average of 19 series from Esper
et al. (2017) used in Ljungqvist et al. (2018), and represents
a much larger spatial domain. We also calculated an average
of multiple wine price series from across Europe (Allen and
Unger, 2019), as wine was an important market commodity in
much of Europe, and its price level is indicative of the economic
conditions on a broad scale (Chilosi and Federico, 2021).
Recognizing that other living costs besides grain and wine
affected the population’s purchasing power, and thus economic
well-being, we use annual consumer price indices for London
(1264–1699), Strasbourg (1386–1699), Krakow (1409–1699)
(Allen, 2001), and Stockholm (1290–1699, Edvinsson and
Söderberg, 2010) to estimate changes over time in consumer
price indices. These indices are highly correlated to changes in
real wages (Allen, 2000) and thereby provide an indication of
relative societal wealth among ordinary people (De Pleijt and van
Zanden, 2016). Despite considerable uncertainties in calculating
the annual consumer price indices, we maintain that these indices
still capture the economic feasibility to construct new buildings or
to undertake major repair of existing ones.
To represent growing season temperature, we used the June–
August temperature reconstruction by Luterbacher et al. (2016),
as updated by Ljungqvist et al. (2019), resolved on a 5◦ × 5◦
grid across Europe. From these data we extracted the gridcells covering 60◦ N–40◦ N by 10◦ W–30◦ E. For relative soil
moisture availability, we use data from the Old World Drought
Atlas (OWDA; Cook et al., 2015). The OWDA is a tree-ringbased reconstruction of annually resolved June–August (JJA)
self-calibrating Palmer Drought Severity Index (scPDSI) values
(Palmer, 1965; van der Schrier et al., 2011, 2013) resolved on a
0.5◦ × 0.5◦ grid across Europe. For obvious reasons, the spatial
and temporal extent of both reconstructions is limited by the
distribution and length of the tree-ring chronologies available for
reconstruction. Consequently, for portions of Europe as one goes
further back in time, the quality of the reconstructions decreases
(Cook et al., 2015).
For further comparison, we used an annually resolved
groundwater level (GWL) reconstruction for the Upper Rhine
Valley (Germany and France) derived from tree-ring width data
(Tegel et al., 2020). This reconstruction is entirely independent
from the tree-ring data used to produce the OWDA. The GWL
reconstruction is a proxy for long and short-term hydroclimate
variations over western and central Europe, which has been
linked to the multi-decadal North Atlantic climate variability
(Tegel et al., 2020).
Regional Grouping of the Felling Dates
and (Geo)statistical Analyses
Applying quantitative spatial and geostatistical analyses to the
felling date data (such as grouping analysis) in ArcMap 10.6
(program package of ArcGIS; ESRI, 2017), produced no distinct
regional groups or clusters. Hence, we manually grouped the
data, over the period 1250–1699 CE, into seven separate regions
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Regional Patterns of Building Activity
them comparable. Regarding the Thirty Years’ War, only the
regions with a population loss of at least 33% (Eickhoff et al., 2012,
p. 26) were georeferenced and digitized by us for this purpose.
RESULTS
Patterns of Spatio-Temporal Change in
Building Activity
The felling dates show a clear decline just prior to c. 1300 followed
by a second decline c. 1340 (Figure 1B). Step-wise increases
in building activity subsequently occurred during the late 14th
century and throughout the 15th century. This feature is not
merely a consequence of increasing data coverage over time, as
it is also clearly apparent in the detrended data. Only toward
the end of the 15th century did building activity levels become
comparable to those of the 13th century. A peak in building
activity can be observed in the mid-16th century, followed by
a rather pronounced decline. The sharpest, and most distinct
decline in building activity coincides with the Thirty Years’
War (1618–1648). By the end of the 17th century building
activity reached levels comparable to those of the 13th and
mid-16th centuries.
Considerable regional-scale variations in building activity
were found. These variations are expressed in the relatively low
correlation coefficients between the seven regions (see Figure 3
and Table 2). Exceptions are the strong correlation between
the regions central Europe (south) and the British Isles, and
between central Europe (south) and central Europe (north).
The biggest differences between the regional-scale datasets
relate to the timing and duration of the Late Medieval Crisis
and the effect of the Thirty Years’ War (1618–1648). On the
whole, times of high and low building activity are far from
synchronized across regions even though some general patterns
can be identified (Table 3). Moreover, the building activity
history for the Nordic countries looks rather different from
that of the regions on the continent. This may, partly, be
related to the lower number of felling dates available for the
Nordic countries.
Clear spatial patterns in the form of changing hot-spots of
building activity can be observed for the 50-year periods ranging
from 1250–1299 to 1650–1699 (Figure 4). However, as the maps
in Figure 4 contain absolute values, with their increasing trends
over time, the periods of relative increase and decrease activity
are arguably better captured in Figure 3. What is notable is
that the different sub-regions, to a large extent, show different
and even opposing trends for certain periods. For example, in
the British Isles, in central Europe (north) and Switzerland,
building activity increases around the 1530s. At the same time in
France building activity begins to decline. In general, the different
sub-regions show rather similar long-term trends, albeit with
different start and end years containing periods of high and low
building activity.
FIGURE 3 | The felling date data 300-year filtered and standardized to
z-score values over the entire 1250–1699 CE period for the regional subsets
as well as for the full dataset. The red lines show the significant regime shifts in
the number of felling dates, using a 30-year cut-off length.
the number of felling dates between 1250–1299 and 1300–1349
is 37%, whereas the declines between 1300–1349 and 1350–1399
are insignificant an 1%. Between 1350–1399 and 1400–1449
building activity recovers by 28% (Table 4). On a 31-year
The Late Medieval Crisis
The decreasing building activity during the Late Medieval Crisis
occurs well before the Black Death (1346–1353). The decline in
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Regional Patterns of Building Activity
TABLE 2 | Annual correlation between the 300-year filtered and standardized regional subsets of felling dates.
British Isles
Nordic
British Isles
France
Benelux
C. Europe (N.)
C. Europe (S.)
Switzerland
—
0.09 (0.20)
0.14 (0.20)
0.40 (0.66)
–0.09 (–0.13)
0.35 (0.44)
0.48 (0.73)
Nordic
0.09 (0.20)
—
0.01 (–0.02)
0.21 (0.38)
0.27 (0.41)
0.36 (0.52)
0.22 (0.36)
France
0.14 (0.20)
0.01 (–0.02)
—
0.23 (0.39)
0.16 (0.23)
0.19 (0.31)
0.19 (0.39)
Benelux
0.40 (0.66)
0.21 (0.38)
0.23 (0.39)
—
0.13 (0.23)
0.34 (0.31)
0.42 (0.39)
C. Europe (N)
–0.09 (–0.13)
0.27 (0.41)
0.16 (0.23)
0.13 (0.23)
—
0.32 (0.38)
0.05 (0.10)
C. Europe (S)
0.35 (0.44)
0.36 (0.52)
0.19 (0.31)
0.34 (0.31)
0.32 (0.38)
—
0.55 (0.68)
Switzerland
0.48 (0.73)
0.22 (0.36)
0.19 (0.39)
0.42 (0.39)
0.05 (0.10)
0.55 (0.68)
—
Values within parenthesis refer to correlations of 10-year low-pass filtered values. Values significant at the p < 0.05 level, using a two-tailed significance test, are
highlighted in bold.
TABLE 3 | List, in chronological order, of significant regime shifts in the unfiltered regional subsets of felling dates as well as in the full dataset.
Region
Unfiltered
Entire dataset
1297↓
1405↑
1444↑
1482↑
1537↑
Benelux
1291↓
1447↑
1524↑
1565↓
1670↓
1564↓
1618↓
British Isles
1397↑
1428↑
1533↑
1626↓
1675↓
C. Europe (N)
1297↓
1332↓
1489↑
1537↑
1621↓
C. Europe (S)
1405↑
1444↑
1544↑
1647↑
1677↑
1617↓
1648↑
1668↑
France
1293↓
1556↑
Nordic countries
1350↓
1546↑
1482↑
1537↓
1586↓
1583↑
1674↓
Switzerland
1354↑
1411↑
1484↑
1536↑
Region
1583↓
1617↓
1648↑
1664↑
1683↑
1658↑
Unfiltered and 10-year spline low-pass filtered
Entire dataset
1293↓
1340↓
1389↑
1420↑
1445↑
1470↑
1492↑
1536↑
1565↓
Benelux
1292↓
1444↑
1462↑
1483↓
1508↑
1524↑
1566↓
1587↑
1669↓
British Isles
1306↑
1385↑
1424↑
1443↑
1479↓
1533↑
1570↑
1594↓
C. Europe (N)
1267↓
1297↓
1307↓
1332↓
1488↑
1537↑
1619↓
1645↑
C. Europe (S)
1306↑
1387↑
1419↑
1444↑
1470↑
1521↓
1546↑
France
1271↓
1294↓
1396↑
1412↓
1456↑
1480↑
1537↓
Nordic countries
1297↓
1348↓
1545↑
1577↑
1634↑
1653↓
1663↑
Switzerland
1307↑
1354↑
1365↓
1401↑
1470↑
1489↑
1535↑
Region
1618↓
1647↑
1626↓
1638↓
1674↓
1659↑
1680↑
1569↓
1602↑
1617↓
1585↓
1661↑
1680↑
1575↓
1606↓
1633↑
1649↑
1663↑
1680↑
1666↑
1679↑
1659↑
300-year filtered and standardized
Entire dataset
1297↓
1444↑
1617↓
1659↑
1683↑
Benelux
1291↓
1413↓
1447↑
1483↓
1524↑
1568↑
British Isles
1305↑
1346↓
1444↑
C. Europe (N)
1304↓
1360↑
1555↑
1607↓
1648↑
1683↑
C. Europe (S)
1270↓
1306↑
1339↓
1420↑
1449↑
1523↓
France
1304↓
1459↑
1537↓
1676↑
Nordic countries
1350↓
1392↑
1462↓
1583↑
Switzerland
1443↓
1443↑
1617↓
Region
1617↓
1669↑
300-year filtered and standardized and 10-year spline low-pass filtered
Entire dataset
1295↓
1340↓
1383↑
1445↑
1487↑
1520↓
1536↑
1617↓
1647↑
1664↑
1683↑
Benelux
1275↑
1291↓
1343↑
1370↓
1403↓
1444↑
1482↓
1523↑
1567↓
1642↑
1669↓
British Isles
1268↓
1306↑
1339↓
1426↑
1456↓
1536↑
1636↓
C. Europe (N)
1278↑
1299↓
1332↓
1360↑
1381↑
1428↓
1489↑
1520↓
1537↑
1578↓
1608↓
1625↓
C. Europe (S)
1270↓
1294↑
1339↓
1372↑
1420↑
1445↑
1498↓
1569↓
1618↓
1664↑
France
1271↓
1294↓
1324↑
1396↑
1412↓
1456↑
1480↑
1537↓
1586↓
1614↑
1660↑
1681↑
Nordic countries
1348↓
1381↑
1462↓
1577↑
1653↓
Switzerland
1291↓
1308↑
1365↓
1404↑
1432↓
1484↑
1536↑
1575↓
1617↓
1634↑
1660↑
1646↑
1678↑
A downward arrow marks a year with a shift toward a decreasing number of felling dates and an upward arrow a year with a shift toward an increasing number
of felling dates.
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Regional Patterns of Building Activity
FIGURE 4 | Heat-maps of the number of felling dates for each 50-year period covered by our dataset. The maps are shown with contemporary political boundaries.
TABLE 4 | Summary statistics for the felling dates for the 31-year periods around the time of the Late Medieval Crisis including the number (n) mean (x̄), median (x̃), and
standard deviation (SD).
Region
Entire dataset
1286–1316
1317–1347
1348–1378
1379–1409
n
x̄
x̃
SD
n
x̄
x̃
SD
n
x̄
x̃
SD
n
x̄
x̃
SD
2174
70
73
20.22
1797
58
56
13.19
1572
51
51
13.22
2015
65
65
14.58
Benelux
61
2
1
2.34
51
2
1
1.74
51
2
1
1.36
59
2
2
1.62
British Isles
45
1
1
1.15
68
2
2
1.68
62
2
2
1.55
96
3
3
1.64
C. Europe (N)
1292
42
41
19
666
21
21
9.11
589
19
18
6.97
700
23
22
9.29
C. Europe (S)
278
9
7
5.00
419
14
11
8.16
333
11
9
6.37
540
17
18
6.52
France
77
2
1
3.37
60
2
2
1.73
55
2
1
2.03
101
3
2
3.00
Nordic countries
1796
6
5
4.96
158
5
4
4.96
44
1
1
1.43
122
4
2
4.94
Switzerland
242
8
6
6.57
375
12
10
5.92
438
14
14
9.71
407
13
12
6.25
of felling dates between 1286–1316 and 1348–1378 is 28%.
The 28% increase between 1348–1378 and 1379–1409, brings
building activity levels to nearly the same levels as between
1286–1316 (Table 4).
time-scale, the decrease between 1286–1316 and 1317–1347
is 18% whereas between 1317–1347 and 1348–1378, following
the establishment and spread of the plague, the decrease is
only 12% (Figure 5). In total, the decrease in the number
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Regional Patterns of Building Activity
FIGURE 5 | Changes in the number of felling dates for four 31-year periods around the time of the Late Medieval Crisis. (A) Between 1286–1316 and 1317–1347,
(B) Between 1317–1347 and 1348–1378, (C) Between 1348–1378 and 1379–1409. The maps are shown with contemporary political boundaries.
and 1649–1679 (Table 5). However, the impact of the Thirty
Years’ War is very unevenly distributed across Europe, and
largely limited to the parts of central Europe directly affected
by the conflict. The decrease in the number of felling dates was
largest in central Europe (north) (43%) compared to 24% in
central Europe (south). However, in central Europe (south) the
recovery of building activity first starts in the 1660s. Switzerland
also experienced a decrease in the number of felling dates by
16% during the Thirty Years’ War, but this decrease is mainly
limited to the c. 1618–1634 period (Table 5). Swiss building
activity levels were again close to the long-term average during
the latter portion of the Thirty Years’ War, with successive years
of unusually high construction levels. Considering the spatial
pattern in more detail, the strongest decrease during the Thirty
Years’ War occurred in central Germany and portions of eastern
Germany (Figure 6).
In regions affected by the Thirty Years’ War, and having
an estimated population loss of less than 33%, the decrease
in number of felling dates was 31.84% between 1587–1617
and 1618–1648. In contrast, the decrease in number of felling
dates was 41.4% in war-affected regions with an estimated
population loss between 33 and 66%. Finally, in regions with
a population loss exceeding 66%, the decrease in number of
felling dates was as high as 68.3% (Figure 6). These differences
are statistically significant at a p ≤ 0.01 level. Thus, it is
clearly evident that there is a strong correspondence between the
magnitude of population loss and the magnitude of decreased
building activity, demonstrating the ability of the felling dates
to capture not only the timing, but also the extent, of larger
demographic changes.
The magnitude and timing of the decrease in building activity
during the Late Medieval Crisis is, however, highly variable
between regions (Figure 5). The strongest decrease, pre-dating
the Black Death, is seen in northeastern Germany and in France.
In central Europe (south) this decrease is smaller and first occurs
following the arrival of the plague. Considering the timing of
significant regime shifts in the 300-year detrended and 10-year
low-pass filtered building activity data of the entire dataset, the
late medieval decrease in felling dates first appears in c. 1295 and
then intensifies c. 1340. The recovery occurs in three phases c.
1383, 1445, and 1487. In more detail, several regions either show
periods of declining building activity or an increasing building
activity prior to the Black Death (Figure 5 and Table 3). The
Nordic countries, dominated by data from Sweden, stand out
for their lack of any significant evidence of decreasing numbers
of felling dates prior to 1348 and the Black Death (Figure 3).
The recovery from the Late Medieval Crisis shows a similar
heterogeneous pattern between regions. Both central Europe
(south) and central Europe (north), France and Switzerland show
increases in the second half of the 14th century followed by new
decreases in the first half of the 15th century (Figure 3). Looking
at the heat-maps of building activity, the expansion of building
activities continues from the 13th century into and throughout
the 14th century in Austria, Czechia, and Switzerland. In these
southern regions the evidence, as derived from felling date
data, for any crisis is not clear. Other regions contain too few
felling dates for the period to arrive at any more definitive
conclusions (Figure 4).
The Thirty Years’ War (1618–1648)
A distinct decrease in building activity is recorded during the
Thirty Years’ War (1618–1648) (Figure 6). The number of felling
dates is reduced by 22% between 1587–1617 and 1618–1648,
followed by a subsequent increase of 41% between 1618–1648
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Periods of High Building Activity
Despite small regional variations, common periods of high
building activity are detected c. 1250–1295, c. 1445–1520, for
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Regional Patterns of Building Activity
FIGURE 6 | Changes in the number of felling dates for three 31-year periods of the time of the Thirty Years’ War (1618–1648) to the period 1587–1617 and to the
period 1649–1679 CE. The maps are shown with contemporary political boundaries. Regions with a population loss of 66% and above, of 33–66%, and below 33%
are shown on the map.
TABLE 5 | Summary statistics for the felling dates for the 31-year periods around the Thirty Years’ War (1618–1648) including the number (n) mean (x̄), median (x̃) and
standard deviation (SD).
Region
1587–1617
1618–1648
1649–1679
n
x̄
x̃
SD
n
x̄
x̃
SD
n
x̄
x̃
SD
Entire dataset
5508
178
173
28.03
4293
138
138
26.50
6061
196
188
37.46
Benelux
222
7
8
3.09
218
7
8
3.83
176
6
5
3.66
British Isles
321
10
11
3.45
205
7
7
3.41
150
5
4
2.88
C. Europe (N)
1282
41
43
10.71
734
24
21
10.90
1561
50
48
14.11
C. Europe (S)
2099
68
69
18.84
1600
52
50
16.08
2531
82
78
25.04
France
109
4
3
2.91
123
4
4
1.97
147
5
4
2.87
Nordic countries
452
15
12
10.75
550
18
17
8.95
480
15
12
10.83
Switzerland
1033
32
31
11.41
864
28
26
12.26
1029
33
32
9.95
Switzerland. Finally, the building activity boom in the second
half of the 17th century, following the Thirty Years’ War, is
most evident in central Europe (both north and south) and
France (Table 3).
a few decades in the mid-16th century, and in the second
half of the 17th century (Figures 3, 4). Pre-Late Medieval
Crisis construction peaks are absent from Switzerland and the
Nordic countries, while occurring earlier in the British Isles,
France, central Europe (south), the Benelux countries, and
central Europe (north). Regarding the late-15th century period
of high building activity levels, this building boom is most
evident in the Benelux countries, central Europe (both north
and south), and in France where it occurs slightly later. It is
absent in the British Isles, Switzerland, and the Nordic countries.
The mid-16th century period of high building activity is only
distinctive in the Benelux countries, central Europe (north), and
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Building Activity, Grain Price Level,
Economic Well-Being, and Climatic
Change
A significant negative correlation, r = –0.29 (rs = –0.29) using
annual data and r = –0.55 (rs = –0.59) at decadal time-scales, is
found between European building activity levels and the average
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Regional Patterns of Building Activity
TABLE 6 | Correlation between 300-year filtered and standardized subsets for comparison with the entire felling date dataset.
Correlated variables
None
Pearson r value
Spearman’s rs value
Data series smoothing
Data series smoothing
10-year spline
10-year box-car
None
10-year spline
10-year box-car
Lead pollution (western Europe)
0.22
0.39
0.45
0.26
0.43
0.48
Lead pollution (eastern Europe)
0.12
0.21
0.27
0.18
0.28
0.24
Grain price average
–0.27
–0.52
–0.55
–0.29
–0.56
–0.59
Wine price average
–0.20
−0.30
–0.31
–0.22
−0.24
−0.23
CPI London
–0.11
−0.18
−0.20
–0.18
−0.28
–0.31
CPI Strasbourg
–0.22
−0.36
–0.41
–0.21
−0.31
−0.34
CPI Krakow
–0.30
–0.44
–0.50
–0.21
−0.31
–0.40
CPI Stockholm
−0.03
−0.01
0.08
0.08
−0.21
−0.11
Church construction
JJA temperature
Drought index
SW. Germany groundwater
–
–
−0.05
−0.14
–
–
−0.17
0.03
–0.14
0.38
–0.11
−0.17
0.34
−0.01
0.13
−0.01
−0.02
0.14
−0.24
–0.30
–0.16
−0.26
–0.31
−0.17
Values significant at the p < 0.05 level, using a two-tailed significance test, are highlighted in bold.
European grain price level (Table 6). This significant negative
grain price–building activity association strongly suggests less
construction activity occurred when grain prices were high and
vice versa. No other indicator of economic or social well-being
shows such a strong relationship with building activity as grain
prices. For example, the correlations between European building
activity levels and average European wine prices are much weaker
and only partly significant with r = –0.20 (rs = –0.22) at annual
and r = –0.29 (rs = –0.23) at decadal time-scales (Table 6).
Common regime shifts are also detected in the grain prices and
the felling date numbers, whereas such a similarity is lacking in
the wine price data.
A positive correlation is also found between European
building activity and lead pollution originating from mining in
western Europe at both annual time-scales (r = 0.22 and rs = 0.26)
and decadal times-scales (r = 0.45 and rs = 0.48) (Table 6).
The lead pollution record originating from eastern Europe
shows a much weaker positive relationship with building activity
levels and is only statistically significant at annual resolution.
Decreasing values of lead pollution from western Europe c. 1308
suggests the onset of the Late Medieval Crisis started about a
decade later than that indicated by the felling dates (Table 7).
A further decrease in lead pollution occurs in 1360, as opposed
to the 1340 downturn in the felling dates (Figure 7). The stepwise recovery from this crisis mostly coincides in timing with a
recovery in European building activity. Shifts toward decreased
lead pollution, originating from western Europe, are seen in
1569 and 1589, signaling the start of the so-called Crisis of the
Seventeenth Century (Hobsbawm, 1954; Parker, 2013). Unlike
the felling date data, the Thirty Years’ War (1618–1648) is not
defined by significant regime shifts in lead pollution. The lead
pollution record for eastern Europe shows only five regime shifts,
suggesting a later onset and longer duration of the Late Medieval
Crisis (Figure 7).
A significant negative relationship is found between annual
consumer price indices from Krakow and Strasbourg, and
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European building activity levels; r = –0.30 and r = –0.22,
respectively for annual data; and r = –0.50 and r = –0.44,
respectively, for 10-year box-car filtered data (Table 6).
Interestingly, the Pearson correlations are higher than the
Spearman’s implying a rather linear relationship between the
two indices (Table 6). The relationship with the London annual
consumer price index is considerably weaker and of a more nonlinear nature. Correlations with the Stockholm annual consumer
price index, despite being the second longest index considered,
are weak and insignificant. It should be noted that the Stockholm
index also does not show much agreement with the other three
consumer price indices. Finally, a positive correlation (r = 0.38,
rs = 0.34) is found between church construction rates and
European building activity level.
Weak, but in some cases significant, are the associations
between climate reconstructions and European building
activity levels. The strongest negative correlations are obtained
using the tree-ring-width-based southwestern Germany GWL
reconstruction (r = –0.14 for annual data, and r = –0.30 for 10year box-car filtered data). Thus, periods of low GWL s appear to
correspond to periods of higher building activity and vice versa.
On the other hand, no significant correlations can be found
between building activity levels and the tree-ring based drought
(scPDSI) reconstruction. The relationship between building
activity and reconstructed June–August temperature is negative
but does not reach statistical significance. We also analyzed the
presence of regime shifts in the climate reconstructions and
found little agreement with the regime shifts in building activity.
DISCUSSION
Felling Dates as a Historic Source
Material
The results of this study demonstrate that felling dates contain
genuine information about variations in past societal well-being
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Regional Patterns of Building Activity
TABLE 7 | List of significant regime shifts in the in 300-year filtered and standardized subsets for comparison with the regime shifts in the felling date dataset.
Variable
Entire felling date dataset
1295↓
1340↓
1383↑
1445↑
1487↑
1520↓
1536↑
1617↓
1647↑
1664↑
1683↑
Lead pollution (western Europe)
1293↑
1308↓
1360↓
1387↑
1415↑
1445↑
1484↑
1540↑
1569↓
1589↓
1651↓
Lead pollution (eastern Europe)
1328↓
1359↓
1495↑
1535↑
1559↓
Grain price average
1443↓
1526↑
1551↑
1569↑
1587↑
1621↑
1639↓
1654↓
Wine price average
1382↑
1400↑
1427↓
1499↓
1527↑
1551↑
1576↑
1588↑
1628↑
1658↓
CPI London
1307↑
1325↓
1377↓
1442↓
1464↓
1553↑
1572↑
1593↑
1611↑
1630↑
CPI Strasbourg
1448↓
1543↑
1569↑
1622↑
1644↓
1674↑
CPI Krakow
1506↓
1533↑
1551↑
1588↑
1636↑
1662↓
CPI Stockholm
1528↓
1580↓
1604↓
1627↑
1646↑
1679↓
JJA temperature
1304↑
1328↓
1354↑
1426↓
1450↓
1472↑
1567↓
1588↓
1610↑
1636↑
Drought index
1298↓
1336↑
1392↓
1484↑
1610↓
1639↑
SW. Germany groundwater
1313↑
1340↑
1370↓
1444↓
1491↓
1523↑
1558↑
1594↓
1644
1671↑
1677↓
1671↓
Regime shifts detected using the 10-year spline filtered data. A downward arrow marks a year with a shift toward a decreasing number of felling dates and an upward
arrow a year with a shift toward increasing values.
mining district experienced lesser stagnation than most other
regions subsequent to the Black Death. The high building activity
level in the 16th century in this region coincides with a phase
mining industry growth in central Europe as well as elsewhere
(Bohdálková et al., 2018).
To some extent, the choice of detrending and filter length
influences our results, especially in the detection of statistically
significant regime shifts (Table 3). We noted a difference in the
number and timing (year) of regime shifts depending on whether
the data have been 300-year spline detrended as well as 10year smoothed. For example, considerably fewer regime shifts
were obtained using data without 10-year smoothing, suggesting
that for investigation of decadal regime shifts, filters of short
lengths are useful.
As expected, a strong agreement (r = 0.83) in the annual felling
dates, and in the timing of regime shifts, is found between the
slightly different datasets used by Ljungqvist et al. (2018) and the
one presented here. The new felling date dataset is slightly larger
but only contains data that are georeferenced. In comparison to
Ljungqvist et al. (2018), this new dataset reveals lower levels of
building activity around c. 1400 CE, c. 1500 CE, and a somewhat
less pronounced decline in activity during the Thirty Years’ War.
The latter difference can be explained by the larger geographical
coverage that includes more regions unaffected by the war.
However, the lower rates in construction during the fourteenth
and 15th century presented here cannot be readily explained and
are unrelated to the inclusion of new data from any particular
region. The most important difference is arguably a more stepwise, and slightly later, recovery in building activity following the
Late Medieval Crisis. Another noteworthy feature is an earlier
onset of the “Crisis of the Seventeenth Century” (Hobsbawm,
1954; Parker, 2013), also evident in the recent felling-date-based
study for Czechia by Kolář et al. (2022).
at regional and macro-historical scales. The ability to capture
the Late Medieval Crisis (Figure 5), the mid-16th century
population boom (Clark, 1977; McEvedy and Jones, 1978;
Turchin and Nefedov, 2009), the Thirty Year’s War (Figure 5),
and the demographic increase in the latter 17th century (Biraben,
1979; Bardet and Dupâquier, 1997; Bulst and Pfister, 1997)
demonstrates the usefulness of the felling dates as a historical
source material. In particular, the ability of the felling dates
to capture relative population losses in regions affected by the
Thirty Years’ War confirms their skill as a demographic proxy
(Figure 6). No notable population decreases were found in
regions known to have been little impacted by the war such
as the Netherlands (Parker, 2006; De Pleijt and van Zanden,
2016). However, large numbers of felling dates are needed
for establishing statistically significant changes, particularly on
smaller spatial scales. Thus, using the felling dates on their
own appears to have its limitations, rendering them most
useful within a source pluralistic framework (e.g., Myrdal,
2012).
Admittedly, the regional trends in building activity observed
here depend heavily on the regional groupings used in
this study. The merits of the groupings employed in this
study can be debated, and alternatives offered, but datadriven approaches proved unable to deterministically identify
statistically defendable and historically consistent alternatives
(see Section “Other Datasets Used for Comparisons”). Future
studies are needed to possibly disentangle and quantify
construction phases at sub-regional to regional scales. We
note, for example, the central Europe (north) region is heavily
dependent on felling dates from cities along the German
Baltic Sea coast (Figure 1A). Thus, the high building activity
levels in medieval times derived from these data follow the
expansion phases of the Hanseatic League during the 13th
century (Jahnke, 2013). High levels of building activity, related
to mining activity, in the Ore Mountain Range in German
Saxony and Czech Bohemia is particularly evident in the periods
1250–1299, 1350–1399, and 1500–1599. While the first period
represents the peak of medieval mining activity, the high level
of building activity during the second period suggests this
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Biases in the Collected Material
Our felling date dataset contains inevitable geographical biases
(Figure 1A), and smaller temporal ones (Figure 1B). It is also
biased toward artisan and bourgeois houses in urban settings and
ecclesiastical buildings. This implies that construction activities
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Regional Patterns of Building Activity
of recycled building timbers distributed over time. The latter is
difficult to directly address without more detailed construction
and renovation histories for each building. However, we find
it reasonable to presume that the proportion of freshly cut
to recycled building timber remains approximately the same
during both low and high periods of building activity. As noted
in Bannister (1962), a clustering of dates, when sufficient in
number, helps to distinguish between renovation work and
original construction. Thus, large groups of felling dates clustered
within a few years of each other suggest new constructions.
For studies such as this, the amount of data available
from any particular region and sub-region is largely related
to the length of time during which tree-ring dating has been
implemented to study historic buildings and to the continuity of
those studies. In the southwestern part of Europe, for example,
dendrochronological studies on historic buildings are almost
absent in Portugal, and while they started in the 1980s for
Spain (Richter, 1986; Richter and Eckstein, 1986), little has been
published in spite of the abundance of built heritage (RodriguezTrobajo, 2008; Domínguez-Delmás et al., 2015, 2017, 2018). An
exception is the Basque Country region in the northeast, where
dendrochronological techniques are regularly applied in historic
buildings since the late 1990s (Susperregi, 2007; Susperregi
et al., 2017). Similarly, the southeastern part of central Europe
poses a particular problem. For example, Slovenia has little
historical tree-ring material prior to c. 1500 CE (Čufar et al.,
2008, 2014a,b). A large proportion of medieval structures in
Slovenia were destroyed during Ottoman attacks in the 15th and
16th centuries and during the subsequent Ottoman–Habsburg
Wars (1526–1791 CE) (Murphey, 1999; Dávid and Fodor, 2000)
as well as the huge earthquake in 1511 CE (Ribarič, 1979;
Čufar et al., 2014b). The situation in Slovenia is, in a certain
way, similar to that in Hungary. We have, thus, not been able
to include these regions of southeastern Europe. By contrast,
though many felling date data exist in Sweden (Bartholin,
1990), only a fraction are accessible in digital form, and only
for parts of the country (Meissner et al., 2012; Lagerås et al.,
2016).
Compared to most other parts of the British Isles, limited
data is available from Scotland (Crone and Mills, 2003; Mills
et al., 2017). The timber-framed building tradition, which was
so ubiquitous on the Continent and in England and Wales,
did not develop to the same extent in Scotland, especially in
the countryside (Stell, 2010). The medieval housing stock that
survives is primarily high status, the tower houses, mansions,
churches and castles of the ruling and ecclesiastical elite, and of
these very few retain their original timbers. Of the 45 Scottish
buildings with felling dates only seven pre-date 1500. By the
16th century Scottish builders were almost entirely reliant on
oak and pine imports from Scandinavia and the Baltic Countries
(Crone and Mills, 2012).
FIGURE 7 | Figure showing the new entire felling date dataset, and the one
Ljungqvist et al. (2018) for comparison, together with other datasets indicating
general societal well-being and prevailing climate conditions. All data is
standardized to z-score values over the entire period and 300-year filtered
values. The red lines show the significant regime shifts in the number of felling
dates, using a 30-year cut-off length.
Comparison With Other Indicators of
Societal Well-Being
in areas where forestry systems such as coppice or coppice-withstandards were in place are not well reflected in our dataset.
Other biases may exist as well, particularly the unknown quantity
Frontiers in Ecology and Evolution | www.frontiersin.org
The strongest association between European building activity
rates and metrics of societal well-being is found with the
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Regional Patterns of Building Activity
(Ljungqvist et al., 2022) while this study has revealed a
strong positive association between grain price and building
activity. However, significant (negative) correlations between
building activity and groundwater supply are found for the
Upper Rhine Valley watershed (Table 6). The groundwater
reconstruction is located in the approximate center of our felling
data coverage. The observed association with building activity
strongly suggests that drier conditions coincided with higher
building activity and vice versa. The likely causal mechanism
here is a groundwater connection with grain production (for
details, see Ljungqvist et al., 2022). In addition, periods of
low building activity correspond to the first two maximum
glacier advances during the Little Ice Age in the Alps. The
Great Aletsch and Gorner glaciers peaked at c. 1300–1370
CE and 1600–1670 CE (Holzhauser et al., 2005; Holzhauser,
2010). Le Roy et al. (2015) show that Mer de Glace glacier
was advancing during the late 13th century and reached very
high levels as early as 1280s–1290s, before peaking in the c.
1350s, which is paralleled by late medieval drop in the number
of felling dates.
grain price average for Europe (see section “Building Activity,
Grain Price Level, Economic Well-Being, and Climatic Change”).
Grain was the main (>70%) calorie source for most of the
population (Allen, 2000; Collet and Schuh, 2018), despite
some minor regional variation as per the notable focus on
dairying in Gaelic Ireland (Campbell and Ludlow, 2020).
Considering this, and the fact that grain price levels reflected
the interplay of supply and demand (Persson, 1999), it is
reasonable to use grain price levels as an indicator for both
the availability of food, and the average standard of living
(Campbell, 2016). It is known that grain price levels considerably
affected real wage levels (Allen, 2001) as well as the longterm demographic development in the medieval and early
modern periods (Turchin and Nefedov, 2009; Alfani and Ó
Gráda, 2017). Thus, when grain price levels were low the
general standard of living was higher, demographic growth was
stronger, and there was both a need for new buildings and
the resources to fund their construction. Wine price level data
show a much weaker, but apparently more linear, association
with European building activity than grain prices. This can
be explained in two ways. First, there are fewer wine price
series than grain price series available for comparison, and their
spatial distribution is much more limited (Allen and Unger,
2019). Second, wine was a far less essential commodity than
grain, and its price thus reflected general societal well-being to
a lesser extent.
The strong positive association between European building
activity and lead pollution originating in western Europe,
compared to pollution originating from eastern Europe, can
presumably be explained by the dominance of felling dates
from western and central Europe in our dataset. The many
synchronized regime shifts, shared by both European building
activity and western European lead pollution levels, suggest
that the two datasets, when combined in this manner, may
be a defensible measure of large-scale development and
relative prosperity.
Some of the reconstructed annual consumer price indices
also show strong (negative) associations with reconstructed
European building activity levels. However, it is important to
emphasize that the reconstructed annual consumer price indices
are not independent from the grain price data (see, e.g., Allen,
2001). The Krakow annual consumer price index shows the
strongest correlation, followed by the Strasbourg index. This
is hardly surprising considering both cities are located in the
central European region, the region with the largest number
of felling date data. A positive, but insignificant, relationship is
also found between the independent church construction series
and reconstructed European building activity levels. However,
the short period of overlap (1250–1500 CE), and the fact that
the church construction series is only available at 20-year timesteps, means that the degrees of freedom are too few to compute
statistical significance.
The correlations between European building activity and past
temperature and drought are mostly negative and insignificant.
The weak relationship between temperature and building
activity is surprising considering that temperature has been
shown to have a strong negative association with grain prices
Frontiers in Ecology and Evolution | www.frontiersin.org
Prospects for Future Research
This study has made evident that the sparse coverage of
felling dates in many regions limits robust identification and
comparison of spatial patterns to certain portions of Europe.
Thus, efforts to collect felling dates from currently sparsely
covered regions should be encouraged when it is possible; we
acknowledge that not every region has old buildings preserved.
This problem could, in theory, be partly mitigated by including
all available felling dates, meaning even those without waney
edge felling dates, but for which estimated felling dates are
available (e.g., when samples contain sapwood rings). We
have refrained from doing so for mainly two reasons: (1)
The spatial coverage would not be dramatically increased.
(2) A degree of annual precision would be lost due to the
“smoothing” effect created by introducing dating uncertainties
(Bocinsky et al., 2016).
Future studies could address changes in the relative building
activity rates of different types of constructions e.g., high status
and low status buildings, to investigate social stratification, and
their (financial) resilience to sudden social or demographic
disruptions. For example, Haneca et al. (2020) demonstrated
that the Catholic Church as an institution and financial power
was much more resilient to social crises (e.g., recurrent plague
outbreaks) compared to the general population. Likewise, it
would be interesting to assess urban and rural building activity
separately to study the inter-linkage between urban and rural
economic and demographic development over time and space.
Furthermore, it would be of interest to investigate the changes
in the composition of tree species used for construction over
time. The use of less preferred tree species would indicate
changes in forest species composition, advancing deforestation
and resulting in a lack local construction timber. Similarly,
changes in the age of the trees used for construction activities
could be informative with regard to resource availability (e.g.,
Baillie, 1982, 1995; Eckstein, 2007). Answering these types
of questions, however, would require the collection of more
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Regional Patterns of Building Activity
extensive contextual data from each construction, a serious
challenge on its own.
Finally, the strikingly strong agreement found in this study
between reconstructed building activity history from felling
dates and grain prices merits further investigations. Felling
dates could be compared with grain prices on regional scales
where the grain prices are identified through hierarchical cluster
analysis to strongly co-vary. Future studies could strive to
compare the association between the number of felling dates
and plague outbreaks on local to regional scales [as done
for Ireland by Mallory and Baillie (1988) and Baillie (2006),
and for Czechia by Kolář et al. (2022)]. Ljungqvist et al.
(2018) found that the number of plague outbreaks, at an
aggregated scale, showed an even stronger association with
the number of felling dates than grain prices. However, this
required improved plague data at finer spatial scales. Extant
plague datasets contain too many geographical biases, for most
parts of Europe, to allow for meaningful regional studies of
plague–building activity associations (e.g., Roosen and Curtis,
2018). Furthermore, comparing the number of felling dates with
the number of major armed conflicts, or their intensity, on
local to regional scales would be of interest, for example for
Czechia (Kolář et al., 2022). This would be challenging as the
quantification of the number of armed conflicts is prone to bias
(see van Bavel et al., 2019; Ljungqvist et al., 2021) and available
war datasets do not properly capture the societal costs of the
conflicts of interest.
DATA AVAILABILITY STATEMENT
The data analyzed in this study is subject to the following
licenses/restrictions: Private data. Requests to access these
datasets should be directed to FCL, fredrik.c.l@historia.su.se.
AUTHOR CONTRIBUTIONS
FCL and AS designed the study, set up the experiments, and
wrote the manuscript together with WT, PJK, and UB. All
remaining authors contributed with felling date data and with
additions to, and edits of, the text. All the authors reviewed the
final manuscript.
FUNDING
FCL and AS were supported by the Swedish Research Council
(Vetenskapsrådet, grant no. 2018-01272). FCL conducted the
work with this article as a Pro Futura Scientia XIII Fellow
funded by the Swedish Collegium for Advanced Study through
Riksbankens Jubileumsfond. WT was supported by the German
Research Foundation (DFG, TE 613/3-1). AD received funding
from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme
(grant agreement no. 677152). AH was supported by Riksbankens
Jubileumsfond (grant no. IN20-0026). MD-D was funded by
the Dutch Research Council (Nederlandse organisatie voor
Wetenschappelijk Onderzoek, grant no. 016.Veni.195.502).
TK, MR, and UB were supported the SustES project –
“Adaptation strategies for sustainable ecosystem services
and food security under adverse environmental conditions”
(CZ.02.1.01/0.0/0.0/16_019/0000797). LS was supported by
Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation) under Germany’s Excellence Strategy – EXC 2150
ROOTS – 390870439. FL was supported by a European Research
Council (ERC) Synergy Grant (4-OCEANS; grant agreement no.
951649) under the European Union’s Horizon 2020 research and
innovation programme.
CONCLUSION
This article has demonstrated that large datasets of
dendrochronologically dated felling dates from historical
construction timbers can serve as useful source material
for exploring the spatial dimensions of construction rates.
The broader spatio-temporal patterns of building activity
rates in western and central Europe were investigated
employing 54,045 georeferenced waney edge felling dates
spanning the 1250–1699 CE period. Using geostatistical and
statistical techniques, we investigated the spatial-temporal
dynamics of reconstructed building activity, as inferred
from the felling dates, in relation to various measures of
demographic, economic, climate, and social conditions.
We compared regional similarities and differences in the
timing, duration, and magnitude of periods of “crisis” and
“prosperity” at different spatial and temporal scales as
reflected by changes in building activity. Our conclusions
are: (a) in regions with adequate data coverage, felling dates
capture major demographic trends, (b) there is a strong
negative association between the number of felling dates
and grain prices, (c) there is a moderately strong positive
association between the number of felling dates and mining
activity, and (d) there are more regime shifts detected in
regions with dense felling date coverage. Finally, we can
demonstrate that quantitative research based on felling
dates reflecting building activity offers an important tool to
test hypotheses, and better explain causal relationships in
demographic developments.
Frontiers in Ecology and Evolution | www.frontiersin.org
ACKNOWLEDGMENTS
We thank Barbara Leuschner, Dendrochronologisches Labor
Göttingen, for making felling date data from northern and
central Germany available for the study. We thank Esther
Jansma for constructive comments on an earlier version of
this article, for contributing with felling dates data from the
Netherlands, and for making them publicly available at https:
//doi.org/10.34894/ZWBVSW. We thank Mike Baillie and David
Brown for access to Irish felling dates data, and David Brown,
Brendan Maione-Downing, and Brianán Nolan for assisting their
compilation, as kindly supported by the Initiative for the Science
of the Human Past at Harvard University. Finally, we would
like to thank the British scholars that have published their
felling dates and associated metadata in the journal Vernacular
Architecture, including in particular Nat W. Alcock, Bob Meeson,
and Daniel Miles.
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