Science of the Total Environment 609 (2017) 506–516 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv Environmental factors exert strong control over the climate-growth relationships of Picea abies in Central Europe Jan Altman a,⁎, Pavel Fibich a,b, Hana Santruckova b, Jiri Dolezal a,b, Petr Stepanek c, Jiri Kopacek d, Iva Hunova e, Filip Oulehle f, Jan Tumajer g,h, Emil Cienciala g a Institute of Botany, Czech Academy of Science, Průhonice, Czech Republic Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic Global Change Research Institute CAS, Brno, Czech Republic d Biology Centre CAS, Institute of Hydrobiology, České Budějovice, Czech Republic e Czech Hydrometeorological Institute, Prague, Czech Republic f Czech Geological Survey, Klárov 3, 118 21 Prague, Czech Republic g IFER – Institute of Forest Ecosystem Research, Jílové u Prahy, Czech Republic h Charles University, Faculty of Science, Department of Physical Geography and Geoecology, Albertov 6, 12843 Prague, Czech Republic b c H I G H L I G H T S G R A P H I C A L A B S T R A C T • Understand how individual trees cope with changing climate in various environments. • A large tree-ring network covering the whole area of the Czech Republic was utilized. • Detailed individual-based, spatiotemporal and multivariate analyses • High acidic deposition and geographical variables affects climate-growth association. • Significant growth reduction under the predicted climate changes can be expected. a r t i c l e i n f o Article history: Received 16 March 2017 Received in revised form 12 July 2017 Accepted 14 July 2017 Available online xxxx Editor: Elena PAOLETTI Keywords: Tree rings Air pollution Climate change Individualistic approach Altitude Divergence problem a b s t r a c t The growth response of trees to changing climate is frequently discussed as increasing temperatures and more severe droughts become major risks for forest ecosystems. However, the ability of trees to cope with the changing climate and the effects of other environmental factors on climate-growth relationships are still poorly understood. There is thus an increasing need to understand the ability of individual trees to cope with changing climate in various environments. To improve the current understanding, a large tree-ring network covering the whole area of the Czech Republic (in 7 × 7 km grids) was utilized to investigate how the climate-growth relationships of Norway spruce are affected by 1) various geographical variables, 2) changing levels of acidic deposition, 3) soil characteristics and 4) age, tree diameter and neighbourhood competition. The period from 1930 to 2013 was divided into four, 21-year long intervals of differing levels of acidic deposition, which peaked in the 1972–1993 period. Our individual-based, spatiotemporal, multivariate analyses revealed that spruce growth was mostly affected by drought and warm summers. Drought plays the most important negative role at lower altitudes, while the positive effect of higher temperature was identified for trees at higher altitudes. Increased levels of acidic deposition, together with geographical variables, were identified as the most important factors affecting climate-growth association. Tree age, tree size and soil characteristics also significantly modulate climategrowth relationships. The importance of all environmental variables on climate-growth relationships was ⁎ Corresponding author. E-mail addresses: altman.jan@gmail.com, Jan.Altman@ibot.cas.cz (J. Altman). http://dx.doi.org/10.1016/j.scitotenv.2017.07.134 0048-9697/© 2017 Elsevier B.V. All rights reserved. J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 507 suppressed by acidic deposition during periods when this was at a high level; growth was significantly more enhanced by spring and summer temperatures during these periods. Our results suggest that spruce will undergo significant growth reduction under the predicted climate changes, especially at the lower altitudes which lie outside of its natural range. © 2017 Elsevier B.V. All rights reserved. 1. Introduction Global climate change is placing pressure on forest ecosystems (Bonan, 2008). The major constraints on tree growth are rising temperature and increasing drought (Hartmann et al., 2015), which affect ecosystem productivity (Vicente-Serrano et al., 2013), carbon balance (Frank et al., 2015) and make trees more vulnerable to forest insects and disease (Anderegg et al., 2013). Recent advances in the understanding of tree mortality suggest that these constraints induce forest mortality worldwide (Choat et al., 2012). If the anticipated increase of climate extremes occurs (Coumou and Rahmstorf, 2012; Fischer and Schär, 2009), it is expected that forests will face increasing mortality (Altman et al., 2013) and species distribution will change; this will play an important role in carbon sequestration (McDowell and Allen, 2015). These changing climatic conditions will challenge forest and nature conservation managers, as the need for alternative management strategies based on detailed knowledge of species-specific responses to altered climates will increasingly be required (Bolte et al., 2009; Elliott et al., 2015). One of the best opportunities for evaluating the future capacity of individual species is a retrospective analysis of past growth responses to climate (Zang et al., 2014). High-resolution long-term proxies, where annual changes can be delineated, are needed for describing the previous environmental variability (Black et al., 2016). Trees, as long-living organisms, form annual rings and these provide a retrospective information record of the response of tree growth to past environmental conditions (Altman et al., 2016; Sohar et al., 2017; Speer, 2010). Dendrochronologists typically build mean growth chronologies by averaging the individual tree-ring series to reveal the common growth signals of a population of trees (Buras et al., 2016; Zang et al., 2014). This population-based approach magnifies the mean climate signal, but at the same time reduces the tree-specific variability given by differences in microsites, age, competition and other factors affecting tree growth at the individual level (Ettl and Peterson, 1995; Grimm and Railsback, 2005; Rozas, 2015). Thus, an individual-based approach was recently introduced to achieve a more specific, in-depth assessment of variability in growth responses to climate (Carrer, 2011). This individual-based approach has, however, only rarely been used to retrospectively track the radial growth response of trees to climate conditions (Galván et al., 2014), despite the fact that it could improve the quality and reliability of the ecological inferences derived from the climate-growth relationships (Carrer, 2011). The understanding of climate-growth relationships at the individual level provides information with very high spatial resolution when compared with the commonly-used mean population or regional chronologies. This information is fundamental for the assessment of environmental factors that modulate climate-growth relationships at the individual scale, and thus it provides vital knowledge for understanding forest dynamics (Primicia et al., 2015; Rozas, 2015). Additionally, interactions between individuals and variation in tree size and spatial heterogeneity of environmental conditions could play a critical role in the stabilizing effect of diversity (Aussenac et al., 2017). In Europe, the Norway spruce (Picea abies) is the most economically important and widely-distributed tree species. The dominance of Norway spruce forests is the result of countermeasures designed to eliminate the severe wood shortage caused by the devastation of natural forests and soil degradation by exploitation, grazing and litter racking (Spiecker, 2003). Consequently, the managed Norway spruce forests have expanded far beyond the limits of their natural range, especially to lowland areas, at the expense of natural broadleaved species (Klimo et al., 2000). Such an unnatural and extremely broad ecological spectrum emphasizes the impact of climate change on these widespread forest plantations (Tumajer et al., 2017). Additionally, CentralEuropean forests faced another human-induced pressure from the 1970s to the early 1990s (Kopacek et al., 2016) caused by high and persistent levels of atmospheric acidification. This led directly to reduced growth, changes in growth dynamics, damage to foliage, and forest dieback, especially in mountains during the 1970s and 1980s (Elling et al., 2009; Kern et al., 2009; Kolar et al., 2015; Sensula et al., 2015; Schulze, 1989). Acidic deposition also accelerated soil acidification, which has been specifically magnified in Central Europe by intensive forestry production based on spruce monocultures and clear-cut systems. Since the 1990s, a significant decrease in sulphur deposition has been measured. Nitrogen deposition, though, still causes considerable stress to Czech forests (Hunova et al., 2017; Hunova et al., 2016; Hunova et al., 2014). The combined effects of climate change, variation in acidic deposition and other environmental factors (e.g. altitude, tree age, competition, soil conditions) on tree-growth are still poorly understood and longterm, high-resolution studies on a large-scale are needed to address them. Our objective was to determine the environmental drivers causing variability in the climate-growth relationships of Picea abies during periods with different climates and levels of acidic deposition over the last century. Specifically, we aimed to: (1) reveal the climatic factors controlling the radial growth of Norway spruce and their course in time and space, and (2) determine how various environmental factors and tree characteristics influenced the response of trees to climate. We hypothesized that: (1) the radial growth will be controlled by temperature during the growing season and by drought at high and low altitudes, respectively, and (2) temporally variable external stress, e.g. high levels of acidic deposition, would alter radial growth sensitivity to climate as the noise given by stress may be stronger than the signal given by climate. 2. Methods 2.1. Study area We used the network of the Czech landscape inventory (CzechTerra; hereafter “CZT”) based on a 7 × 7 km grid across the whole Czech Republic, Central Europe (Cienciala et al., 2016). Within each grid cell there was a randomly placed locality of size 450 × 450 m (n = 1599 localities). A circular inventory plot of size 500 m2 was established within the centre of each locality if forest or woody vegetation was present (n = 680 plots). In this study, only the forest plots containing Norway spruce with a stand age of at least 30 years (according to the records of the forest management plans) were included (n = 258). 2.2. Data collection 2.2.1. Tree-ring data During the 2014–2015 period, over 1300 core samples from Norway spruce trees (Picea abies) were collected. In each plot, 3 to 8 increment cores were taken from the trees next to the plot border (coring inside the inventory plot was prohibited). The trees were cored at a height of 1.3 m above the ground, using a steel borer. The cores were dried and a thin layer of wood was sliced from each using a core microtome 508 J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 (Gartner and Nievergelt, 2010) to highlight the tree-ring boundaries. Rings were counted from pith to bark and tree-ring widths were measured to the nearest 0.01 mm using the TimeTable measuring device and PAST4 software (http://www.sciem.com). 2.2.2. Climate data Given that our study covered a large area, we used climate data with a high spatial resolution as they were specific for every plot. For climategrowth relationships, we utilized (i) monthly precipitation, (ii) monthly average air temperature, and (iii) 12-month Standardized PrecipitationEvapotranspiration Index (SPEI). Precipitation and air temperature time series for the given locations were based on quality-controlled and homogenized (Stepanek et al., 2013) station measurements, distributed across the whole Czech Republic (data by the Czech Hydrometeorological Institute). The calculation of plot-specific climate variables for the CZT sites was based on geostatistical interpolation methods, improved by standardization of the neighbouring stations' values to the altitude of a given location by regional regression analysis (Stepanek et al., 2011). To calculate SPEI, the R package SPEI (Vicente-Serrano et al., 2010) and ProClimDB software (Stepanek, 2010) were used. The calculation of evapotranspiration was based on the method developed by Vicente-Serrano et al. (2010) and Begueria et al. (2014). The climate variables needed for the evapotranspiration calculation were derived in a similar way as that mentioned above, data on evapotranspiration were then used to estimate plot-specific SPEI. 2.2.3. Environmental variables We divided our environmental characteristics into four groups and these were used in further analyses: (1) Geographical variables. Latitude, longitude, altitude, aspect and slope were recorded for each plot. (2) Sulphur and nitrogen depositions. The time series (1930–2015) in throughfall depositions of S-SO4, N-NO3 and N-NH4 were derived for individual CZT sites, with respect to their latitude, longitude, and altitude in three steps. Firstly, we computed the bulk concentrations of S-SO4, N-NO3, and N-NH4 for the individual CZT sites using the model by Oulehle et al. (2016). This model is based on the long-term measured depositions of S and N compounds at 32 monitoring sites in the Czech Republic (and its close surroundings) and their correlations with the respective emission rates of SO2, NOx, and NH3 in the study region. The uncertainty associated with the concentration calculations was estimated as 20%, 18%, and 28% for S-SO4, N-NO3, and N-NH4, respectively (Oulehle et al., 2016). Secondly, the annual bulk depositions of S and N compounds were computed as products of their bulk concentrations and the annual precipitation at the individual CZT sites. Data on annual precipitation were prepared by interpolation methods as described by Stepanek et al. (2011). Thirdly, throughfall depositions of S and N compounds were taken as the products of their bulk deposition and the respective dry deposition factor (DDF). The DDF for S was, for each site and year, computed from the model by Oulehle et al. (2016). Throughfall depositions of N-NO3 and N-NH4 were calculated from their bulk depositions using the respective (time dependent) DDFs, which were reconstructed by Kopacek et al. (2012). Throughfall deposition of N-NH4 was further corrected for its retention (and transformation to organic N forms) in the canopies (Kopacek et al., 2009). The input data for the deposition models were from quality-controlled precipitation chemistry data from a nation-wide Ambient Air Quality database (ISKO) run by the Czech Hydrometeorological Institute (Hunova et al., 2016). (3) Soil characteristics. In 2008–2009, 12 soil subsamples were taken on two diagonals from each circular plot. Before sampling, the undecomposed litter was removed and soil was taken from 0 to 30 cm depth. Soil from each sample was homogenized, sieved (b 2 mm) and then subsamples from a plot were combined into one representative bulk sample for each plot and used for the determination of soil texture and chemistry. Soil texture was classified according to the ISSS classification (Gee and Bauder, 1986). The exchangeable pH was measured in 1 M KCl solution (1:2.5, w/v). The chemical analyses were carried out on finely ground soil. Total C and N concentrations were determined by dry combustion using an elemental analyser (ThermoQuest, Italy). Exchangeable base cations (BCex) and exchangeable acidity (Al3 +ex and H+ex) were determined at natural soil pH by extracting 2.5 g soil with 50 ml of 1 M NH4Cl and 1 M KCl, respectively (Kopacek et al., 2004). In the extracts, base cations were determined by atomic absorption spectrometry (Varian, Austria) and Al3 +ex and H+ex by titration (phenolphthalein, 0.1 M NaOH (Thomas, 1982)). Effective cation exchange capacity (CEC) was the sum of BCex, Al3+ex and H+ex; and all concentrations are given on an equivalent basis (meq kg−1; 1 equivalent is 1 mol of charge). Base saturation (BS) was calculated as the percentage of BCex in CEC. For more detail, see Cienciala et al. (2016). (4) Tree and competition characteristics. At each inventory plot, a full array of dendrometric data were measured and estimated using the Field-Map technology (www.field-map.cz). For each cored tree, the position, species and dimensions of all neighbouring trees up to 5 m distance were measured and/or recorded. The effect of the local tree-tree competition was characterized by (i) the number of neighbouring trees, (ii) the basal area of neighbouring trees, and (iii) the competition index (according to Hegyi, 1974). All competition characteristics were computed for all neighbouring trees up to a 5 m distance. In addition to the competition, we also used tree age and diameter at breast high (DBH). 2.3. Data analysis 2.3.1. Tree-ring data Ring-sequences were visually cross-dated using the pattern of wide and narrow rings (Yamaguchi, 1991) and statistically verified by percentage of parallel variation (p b 0.05, Gleichläufigkeit; see Eckstein and Bauch, 1969) and the similarity of growth patterns between individual series (Baillie-Pilcher's t-value; see Baillie and Pilcher, 1973). Specifically, we firstly cross-dated trees within individual plots and subsequently cross-dated trees for the larger region (i.e. neighbouring plots, more than 40 cores were used for cross-dating within individual regions). Only well cross-dated series, i.e. series with Gleichläufigkeit N65% and t N 4.5 between series and mean chronology (excluding the given series) for the larger region, were used in further calculations. To remove the non-climatic age-related growth trend in the timeseries, residual chronologies of individual tree-ring width series were developed using packages dplR (Bunn, 2008) and detrendeR (Campelo et al., 2012) in R (R Core Team, 2016). A double detrending procedure was made by a modified negative exponential curve followed by a cubic smoothing spline (a wavelength equal to the 67% of the series length, 50% wavelength cutoff) (Cook and Briffa, 1990). In these indices, the remaining first-order temporal autocorrelation was removed by autoregressive modelling (Cook and Briffa, 1990). Individual indexed series were used in calculations to reveal the effects of climate on tree-ring growth and subsequently assess how environmental variables may affect the climate-growth relationship. 2.3.2. Periods definition To ascertain the variability of the climate-growth relationship and the changing effects of environmental variables on this relationship through the course of time, we performed all of the following analyses for four, 21-year intervals; 1930–1950, 1951–1971, 1972–1992 and J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 1993–2013. These intervals correspond with the periods preceding the start of acidic deposition (1930–1950), its increase (1951–1971), maximum level (1972–1992) and decrease (1993–2013) (Fig. 1). We did not investigate the period before 1930 due to the limited number of tree-ring series available. 2.3.3. Climate-growth relationship The relationships between individual standardized tree-ring indices and the climatic variables for individual plots were assessed by a bootstrapped Pearson's correlation estimate using the treeclim package (Zang and Biondi, 2015) in R (R Core Team, 2016). The bootstrapped confidence intervals were used to estimate the significance (p b 0.05) of the correlation coefficients. A 13-month time window of climate variables (temperature, precipitation and SPEI) beginning from the previous October to the October in the year of tree-ring formation was used. Since autocorrelation was removed from the chronologies, the effect of weather conditions prior to the growing season was minimized. 2.3.4. Variability in climate and climate-growth relationships Tukey's contrasts (from R package multiplecomp version 1.4-6) and a non-linear mixed effect model (R package nlme version 3.1-128), with plot identity as a random effect, were used to test if temperature, precipitation and SPEI for individual months differed between periods. Moreover, to test if correlations between tree growth and temperature, precipitation and SPEI differed among individual periods, we applied the same approach as above, but tree identity was additionally nested in the plot as a random effect. 2.3.5. Effect of environmental drivers on climate-growth relationship Firstly, principal component analysis (PCA) was used to evaluate the interrelationships between environmental variables (separately for 509 each period) and thus minimize the co-variation between them. Specifically, PCA was performed separately for individual groups of environmental variables, i.e. (i) geographical characteristics, (ii) soil properties, (iii) acidic deposition (the mean values for individual periods) and (iv) competition and tree characteristics (DBH, age). If covariation was found between variables within the individual groups, the relationship with the first two PCA axes at most was estimated to determine which variables defined the ordination axes; the scores of individual samples on ordination axes were used in the subsequent analyses. To assess the effect of environmental variables on climate-growth relationships, we performed a redundancy analysis (RDA). A forward selection permutation procedure (Monte Carlo Permutation test using 9999 permutations) was applied to identify environmental variables that explained a statistically significant (p b 0.05) portion of the variation in climate-growth correlations. Individual trees represented samples, climate-growth relationships (i.e. Pearson correlations) were used as response variables, environmental characteristics (or scores on the ordination axes representing correlated characteristics) as explanatory variables and affiliation of trees to plot (i.e. plot ID) as covariate. The results of the RDA forward selection model of best predictors were visualized by biplot ordination diagrams, and 15 best fitting response variables and statistically significant explanatory variables were shown. In addition to the forward selection model, we also performed RDA analyses concerning 1) marginal effects (no covariates except plot ID) and 2) unique effects (all other environmental factors as covariates) of each explanatory variable. In all analyses, the effect of competition, DBH and soil characteristics was investigated only for the last period (when these characteristics were measured) as they could change during the whole study period. All multivariate statistical analyses were performed in program CANOCO 5, version 5.04 (ter Braak and Smilauer, 2012). Fig. 1. Spatiotemporal variability in sulphur throughfall deposition among individual periods in the Czech Republic. Average value for each period is presented (kg/ha/year). All studied forest plots (position indicated by + in the maps) were used here in all four periods. Distribution of nitrogen deposition has basically the same spatiotemporal pattern. 510 J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 3. Results 3.1. General characteristics of tree-ring data In total, there were 1201 time-series (85,893 successfully crossdated tree-ring width measurements), which were used for further calculations. The number of series and plots naturally differ among individual periods and thus also covered a range of geographical gradients (Fig. 2). Specifically, there were 357 series from 100 plots in the 1930– 1950 period, 746 series from 181 plots in the 1951–1971 period, 1093 series from 239 plots in the 1972–1992 period and 1201 series from 258 plots in the last period (1993–2013) (for basic information about tree-ring chronologies see Table 1). 3.2. Climate variability Significant differences in monthly climatic variables between individual periods were found. Specifically, drought, characterized by SPEI, was gradually increasing (positive values represent wetness, negative values dryness) for investigated periods in all months (Figs. S1, S2); the driest month was April and the wettest was August (means for 1930–2013). The biggest change in drought index for the investigated periods was recorded in April, however, in all months, the driest conditions were recorded in the last period or in the last two periods for August and September (Fig. S1). The month with the highest temperature (mean for 1930–2013) was July (16.3 °C) and the coldest month was January (− 3.2 °C). Overall, the 1993–2013 period was the warmest, and the lowest temperatures usually occurred from 1930 to 1971. The highest increase in temperature was recorded in May and August (by 1.4 °C between the 1st and 4th periods). Temperatures were continuously increasing, most obviously during spring and summer (from Table 1 Basic characteristics of tree-ring chronologies for individual period. Number of series, number of plots, average (AvgSL), minimum (MinSL) and maximum (MaxSL) series length, and average tree-ring width (AvgTRW) is shown. Series Plots AvgSL MinSL MaxSL AvgTRW 1930–1950 1951–1971 1972–1992 1993–2013 357 100 100.8 84 153 1.77 746 181 85.5 63 153 1.83 1093 239 74.5 42 153 1.87 1201 258 70.7 30 153 1.71 April to August), when temperatures were always highest in the last period (Figs. S1, S2). Overall, the rainiest month was July (96.4 mm) and the lowest level of precipitation was in February (47.8 mm). When compared to drought and temperature, no distinct trends in precipitation occurred between the individual periods (Fig. S1). 3.3. Climate-growth relationship The main (and strongest) growth responses to monthly climate variables were observed for SPEI and these were predominantly positive for all months in all four periods (Figs. 3, 4, S3). Temperature and precipitation signals varied more between individual periods (Fig. 3). Growth responded mainly positively to precipitation, especially in the spring (March–May) and summer (June–August) months (Figs. 3, 4, S3). However, negative growth relationships were identified for precipitation during spring months in 1951–1971 and May in 1993–2013 (Figs. 3, 4, S3). Growth response to temperature varied greatly between the periods (Figs. 3, S3). Overall, the growth response to summer temperature was mostly negative, with exceptions for July in 1930–1971 (Figs. 3, S3). Fig. 2. (a) Spatiotemporal distribution of used tree-ring network in individual periods (I. 1930–1950, II. 1951–1971, III. 1972–1992, and IV. 1993–2013) with indication of forested area and visualization of (b) altitudinal and (c) slope gradients across the Czech Republic. J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 511 Fig. 3. Variation in Pearson's correlation coefficients representing growth response to SPEI (first column), temperature (second column) and precipitation (last column) in four, 21-year long periods. Months abbreviated with lower case letters correspond to the year prior to ring formation and upper-case letters refer to the current growth year. Columns sharing the same letter are not significantly different at p b 0.05 by Tukey's contrasts. Boxes represent 25–75% of values, black strips medians, whiskers 1.5 interquartile ranges, and dots outliers. 512 J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 Fig. 4. Spatiotemporal variability of June climate-growth relationships, i.e. Pearson's correlation coefficients, among individual periods in the Czech Republic. Position of studied plots in individual periods is indicated by grey crosses. Spatiotemporal variability for other months is shown in Fig. S3. Growth response to spring temperature was less uniform, with positive responses for March in 1951–2013, April in 1972–1992, and May in 1951–1971 and 1993–2013, and negative relationships for March– May in 1930–1950, April in 1951–1971 and 1993–2013, and May in 1972–1992 (Figs. 3, 4, S3). 3.4. Importance of environmental drivers for climate-growth relationship We identified high co-variation within individual groups of environmental variables, except for geographical characteristics. In subsequent RDA analyses, we thus used (i) all original geographical characteristics, (ii) sulphur and nitrogen depositions substituted by scores on the first PCA axis, which thus represents acidic deposition, (iii) soil characteristics substituted by scores on the first two PCA axes, where one axis represents soil exchangeable cations and second axis represents potential soil fertility (CEC, C and N), and (iv) competition characteristics substituted by scores on the first PCA axis (tree characteristics, i.e. age and DBH, were used in their original form as they did not show a clear fit to a single ordination axis). The interrelationship of individual groups of environmental variables used for subsequent RDA analyses is shown on Fig. S4. The RDA forward selection model of best predictors explained 11.4% of the total variation in the climate-growth relationship in the period 1930–1950, 7.4% in the period 1951–1971, 6.9% in the period 1972– 1992, and 13% in the period 1993–2013. Selected significant explanatory variables were relatively uniform among individual periods (Table S1). Acidic deposition was selected as the strongest predictor of climate-growth relationship in all periods (with the exception of 1951–1971), followed by altitude, longitude, latitude and slope (Table S1). The importance of these dominant factors was also shown by analyses of the unique and marginal effects of individual explanatory variables (Table 2). In addition, trees of different age and size (DBH) showed mostly significantly different growth responses to climate (Tables 2, S1). The forward selection model, as well as analyses of unique and marginal effects, showed that both soil exchangeable cations and fertility had significant effects on the climate-growth relationship (Tables 2, S1). The weakest effects from the studied environmental variables on climate-growth relationships were recorded for competition Table 2 Results of RDA analyses concerning marginal effects and unique effects of individual environmental variables on climate-growth relationships represented by Pearson's correlations in individual periods. Explained variation (%) by individual variables together with the level of significance and F-test are shown, as well as overall explained variation. DBH, competition and soil characteristics were not considered in the first three periods (1930–1992). Significance levels: n.s. p N 0.05; *p b 0.05; **p b 0.01; ***p b 0.001. Explanatory variable Acidic deposition Altitude Longitude Latitude Slope Aspect Age DBH Competition Exchangeable cations Soil fertility Explained variation 1930–1950 1951–1971 1972–1992 1993–2013 Marginal F Unique F Marginal F Unique F Marginal F Unique F Marginal F Unique F 5.5*** 4.3*** 3*** 2.5*** 1.2** n.s. 1* – – – – 17.5 21 16 11 9.1 4.4 – 3.5 – – – – – 1.4*** 2.1*** 2.1*** 0.9* 0.9* n.s. n.s. – – – – 7.4 4.8 7.4 7.5 3 3.1 – – – – – – – 2.1*** 3.4*** 1*** 1.1*** 0.4* 0.4** 0.5** – – – – 8.9 16.1 25.8 7.2 8 2.8 3 3.6 – – – – – 0.6*** 1.8*** 0.7*** 0.6*** 0.3* 0.5* 0.6*** – – – – 5.1 4.3 13.6 5.1 4.4 2.5 3.4 4.4 – – – – – 3.7*** 2*** 0.9*** 2.5*** 0.5*** 0.4*** 1.3*** – – – – 11.3 42.4 22 10 28 5 4 14.8 – – – – – 0.7*** 1.1*** 0.5*** 0.7*** 0.2* n.s. 1.3*** – – – – 4.5 7.4 12 5.2 7.5 2.3 – 13.7 – – – – – 7.4*** 4.3*** 2.1*** 4.3*** 1.3*** 1.1*** 0.8*** 0.9*** 0.7*** 1.7*** 1*** 25.6 96.3 54.2 26 53.8 16.2 12.7 9.2 11.3 8.3 16.6 10.3 – 0.9*** 1.6*** 0.4** 0.3** 0.4** n.s 0.6*** 1.1*** n.s. 0.3* 0.3* 5.9 8.4 15.7 3.6 3.1 3.9 – 5.4 11 – 2.6 2.7 – J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 (not selected by forward selection and only weak, but significant marginal effects) and aspect (the unique effect was mostly insignificant, with the exception of 1951–1971, which was also the only period when aspect was selected by forward selection) (Tables 2, S1). 3.5. Effect of environmental drivers on climate-growth relationship The drought-growth relationship showed the strongest variation along environmental gradients and dominated the best-fit response variables in all four periods (Fig. 5). Generally, growth was enhanced by increased wetness in areas with lower acidic deposition, lower altitude and in southeastern areas (Fig. 5). However, the strength of the effects of acidic deposition, altitude, latitude and longitude on growth response to drought slightly varied among the investigated periods. The effects of altitude and latitude on the drought-growth relationships were less important in the first period, when the impact of acidic deposition prevailed (Fig. 5). In the second period, on the other hand, the effect of altitude dominated: specifically, growth was enhanced by higher wetness in March to September at lower altitudes and the effects of acidic deposition, longitude and latitude were lower (Fig. 5). However, in the last two periods, the effects of all of these main variables aligned 513 with the aforementioned general trend (Fig. 5). In addition to the main geographical characteristics forming the drought-growth relationship, we found that the growth of younger trees was more negatively affected by drought than the growth of older trees during last three periods (Fig. 5). The positive correlation between SPEI and growth increased with the increase of exchangeable cations in the soil, but decreased with the increasing potential of soil fertility. The temperature-growth relationship was also substantially modulated by environmental variables. Spruce growth was enhanced by spring and summer temperatures at (i) higher altitudes, (ii) in areas with higher level of acidic deposition, (iii) on steeper slopes, and (iv) in the northern Czech Republic (Fig. 5). These relationships were strongest in periods with the highest level of acidic deposition, i.e. 1972– 1992 (Fig. 5). In addition, older trees had a more positive growth response to spring and summer temperatures when compared with younger trees (Fig. 5). The precipitation-growth relationship was the scarcest one between the best-fit climate-growth variables. Higher levels of precipitation during spring and summer generally enhanced the growth of younger trees at lower altitudes and at places with lower acidic deposition in the southeastern areas (Fig. 5). Fig. 5. Ordination biplot (forward selection RDA) of the climate-growth relationship (response variables), i.e. Pearson correlation coefficients, between tree-ring width and monthly climate variables, and environmental variables (explanatory variables) for individual periods. The angles between explanatory and response variables indicate correlations between variables (arrows pointing in the same direction means positive, opposite direction negative correlation). Months abbreviated with lower case letters correspond to the year prior to ring formation and upper-case letters refer to the current growth year; P = precipitation, T = temperature, SPEI = standardized precipitation-evapotranspiration index, e.g. P_MAY = correlation between growth and current year May precipitation or SPEI_JUN-OCT reflects relationship for all months between June and October. 514 J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 4. Discussion 4.1. Climate variation and climate-growth association Our high-resolution climate data confirmed the documented changes in increasing temperature and drought stress in Europe (Dai, 2013; Seneviratne et al., 2006). On the other hand, no clear long-term trends were revealed in the level of precipitation. Such findings are in accordance with previous studies, which identified an increasing trend in extreme rainfall events rather than changes in annual precipitation levels (Van den Besselaar et al., 2013; Westra et al., 2013). This leads to an uneven distribution of precipitation and thus contributes to increasing droughts or floods (Bouwer, 2011). However, precipitation is spatially dependent and thus it is difficult to find uniform trends across large heterogeneous areas, such as across the Czech Republic as a whole (Kysely, 2009). We identified three main reasons why the variation in growth response to climate did not correspond simply to the climate variation during individual periods. Firstly, climatic conditions varied among periods and these changes were more pronounced in some areas than in others, which necessarily resulted in different overall climate-growth relationships. Secondly, different numbers of trees from various environments were included in each period, and the characteristics of both localities and the trees themselves changed with time (this problem was investigated in the next step by our multivariate analyses). Lastly, growth sensitivity to various environmental factors, including climate, changes throughout a tree's lifespan and can also be sex-specific (Molina et al., 2016; Rozas et al., 2009). Hence, it is basically impossible to explain overall climate-growth variability between periods and across large areas by considering climate only, unless some climatic variable is the dominant limiting factor or climate characteristics change significantly during the time periods. Having noted this, however, some common effects of climate on Norway spruce growth can indeed be found. In all four periods, drought was the main factor related to radial growth (positive SPEI-growth relationship), which supports previous studies describing Norway spruce as being particularly vulnerable to drought (Boden et al., 2014; Zang et al., 2014). This is also reflected in the generally positive precipitationgrowth relationship during spring and summer and the negative temperature-growth association in summer. Nevertheless, our results show that spring precipitation could also negatively affect growth if there were low temperatures, as documented during 1951–1971. We suggest that clouds associated with the increased level of precipitation lower the amount of sunlight. This will cause lower temperatures and thus limit tree growth at the start of the growing season. It does indeed seem that higher temperatures in spring enhance tree growth by prolonging the growth season (Treml et al., 2015). 4.2. Effect of environmental drivers on climate-growth relationship Our results indicated a general temporal stability of climate-growth associations along environmental gradients. It should be noted that unified responses of environmental variables (even the constant ones as e.g. altitude) could not be expected as 1) different numbers of trees from various environments were included in each period, 2) consequently both response and explanatory variables differ between periods and 3) interaction between environmental characteristics prevent identification of unified response between periods. The geographical and acidic deposition patterns in climate-growth associations were the most noticeable in the study area. Specifically, altitude, longitude, latitude and slope were revealed to be the most important factors affecting climate-growth association, while aspect seems to be of minimal importance. Altitude, latitude and longitude were previously identified as important characteristics responsible for biogeographical patterns affecting climate-growth association (Galván et al., 2014; Mäkinen et al., 2002; Primicia et al., 2015). On the other hand, aspect seems to generally be of minor importance (Primicia et al., 2015; Tardif et al., 2003). Our results indicate that temperature is the more important climatic factor for spruce growth in the west of the Czech Republic, while drought dominates in the east. Hence, western areas of the Czech Republic are more affected by Atlantic conditions while the eastern parts are drier and subject to continental conditions, as previously shown by Kysely (2009). Although it is documented that acidic deposition causes tree-ring width reduction (Muzika et al., 2004; Rydval and Wilson, 2012), there is only limited knowledge on the effect of acidic deposition on climate-growth associations. We found the effect of acidic deposition on the individual climate-growth relationships to be rather uniform. However, overall explained variability, and thus the importance of all environmental variables on climate-growth association, was significantly lower during periods with increased levels of acidic deposition (1951–1992). It was previously documented that the climate-growth relationship is reduced due to a high level of acidic deposition (Kolar et al., 2015). Based on this, we assumed that these reduced climategrowth relationships would affect the distribution of Pearson's correlations along the studied environmental gradients and would result in a lower proportion of explained variability when compared to periods with low levels of acidic deposition. We suggest that tree-growth response to climate is maintained during periods with low levels of acidic deposition and thus growth sensitivity vary between sites (i.e. along environmental gradients). On the contrary, high acidic deposition reduce climate-growth signal and thus there is only low variation in growth response to climate along environmental gradients as climate signal is diminished by high acidic deposition on many sites. Next to the generally reduced variation in climate-growth association along an environmental gradient, we recognized a stronger, positive growth response to spring and summer temperatures during the period with the highest level of acidic deposition (1972–1992). Specifically, the pronounced importance of temperatures was identified in areas with higher levels of acidic deposition, higher altitude and in northeastern sites. We propose that under the stress conditions caused by acidic deposition, other stress factors such as low temperatures during the growth season will have a crucial impact on tree growth and future performance. The described variability in the climate-growth response along environmental gradients and the increased importance of spring and summer temperatures during acidic deposition should be considered in future dendroclimatological studies. The reduced sensitivity of tree growth to temperature, the so-called divergence problem (D'Arrigo et al., 2008), was previously described and tested as problematic for dendroclimatological reconstructions (Briffa et al., 1998; Büntgen et al., 2008; Loehle, 2009). Dendroclimatological reconstruction based on samples coming from forests which have been exposed to acidic deposition (or other factors responsible for changes in climate-growth associations) can thus significantly contribute to this problem (Kolar et al., 2015). Consequently, researchers should pay special attention to the use of such samples in dendroclimatological reconstructions, or more specifically, to the utilization of period(s) with high acidic deposition, which can reduce the quality of such reconstructions. Since the 1980s, acidic deposition has decreased dramatically over Europe (Smith et al., 2011) and semi-natural terrestrial and aquatic ecosystems have started to recover from air pollution (Monteith et al., 2014; Rose et al., 2016; Vrba et al., 2016). In mountain areas, dendrochronological analyses have revealed a decoupling of climate-growth relationships in last two decades (Cada et al., 2016; Kolar et al., 2015; Santruckova et al., 2007; Treml et al., 2012). Our analysis suggests that higher potential soil fertility might strengthen the positive temperature-growth relationships, while a higher amount of exchangeable soil cations might have enhanced growth during wetter conditions from 1993 to 2013. We suggest that the warmer climate in this period may have had a positive impact on soil nutrient mineralization and thus enhance nutrient (most likely nitrogen) availability for plant production. Organic matter, the main factor forming CEC, accumulates in J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 spruce forest soils. Its accumulation is enhanced in the acidified soils affected by N and S depositions but base saturation decreases at the same time, bringing nutrient deficiency (Kopacek et al., 2013). Furthermore, cessation of acidic deposition can trigger soil organic matter transformation (Oulehle et al., 2011) leading again to enhanced nutrient availability. Both processes would have the largest impacts in soils with high %N and cation exchange capacity, which is in line with our analysis. Tree age and diameter were also factors responsible for the variability in growth response to climate. This was expected, as climate-growth relationships had been previously shown to change during a tree's life (Carrer and Urbinati, 2004) and also differ between different crown classes (Martín-Benito et al., 2008). Surprisingly, our results showed that competition had a negligible impact on the climate-growth relationship and this contrasts with previous studies (Carnwath and Nelson, 2016; Linares et al., 2010; Primicia et al., 2015). However, this probably resulted from the structure of the managed forests used in our study. While dendrochronologists mainly study trees from natural or semi-natural forests with a large variation in competition caused by complex horizontal and vertical structures, managed forests have a rather uniform structure. 4.3. Future prospect In the majority of dendroclimatological studies, data were collected from a limited number of sites as sample replication on individual sites was favoured (Bosela et al., 2016). However, such data do not sufficiently represent the total variability of environmental conditions and differences between individual trees (Sánchez-Salguero et al., 2015). Hence, growth sensitivity to climate, aggregated from individual trees, provides estimates of species vulnerability to various environmental variables across diverse habitats (Clark et al., 2012). In agreement with Galván et al. (2014), we do not suggest that the individual-based approach is the only correct methodology and that it should be applied in every dendroecological study, however, we suggest that it is more appropriate for studies with high variations in climate-growth relationships. Consequently, an individual-based approach, combined with multivariate analysis, seems to be especially suitable for studies with a high number of sampling sites, aiming to be spatially representative for the forest cover across large scale, as e.g. national forest inventories, when high-sampling replication per site is basically impossible (Bosela et al., 2016). 5. Conclusions We found that an individual-based approach, combined with multivariate analysis, seems to be a useful tool when compared to standard approaches, especially for large-scale studies aiming to be spatially representative. Drought seems to be the strongest limiting factor of spruce growth, while tree growth reacts positively to wetter conditions. This agrees with the finding that summer temperatures negatively affect spruce growth. The growth sensitivity to drought showed the strongest variation along investigated environmental gradients. Growth was enhanced by increased wetness in areas with lower acidic deposition, lower altitude and in southeastern areas. On the contrary, spring and summer temperatures enhanced spruce growth at higher altitudes, in areas with higher levels of acidic deposition, on steeper slopes, and in the northern Czech Republic. During periods with increased levels of acidic deposition, we found a significantly lower importance of all environmental variables on climate-growth association. We propose that in future, dendroclimatological studies should pay special attention to the utilization of tree-ring records from periods with high environmental stress (e.g. acidic deposition), which can reduce the quality of the desired information. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2017.07.134. 515 Acknowledgements The study was funded by research grants 14-12262S of the Grant Agency of the Czech Republic and Long-term Research Development Project no. RVO 67985939. The work on final version of manuscript was partially supported by grant 17-07378S. We thank our technicians, Eva Navratova and Vit Pejcha, for tree-ring width measurement. Dr. Brian George McMillan kindly improved our English. References Altman, J., Doležal, J., Černý, T., Song, J.S., 2013. Forest response to increasing typhoon activity on the Korean peninsula: evidence from oak tree-rings. Glob. Chang. Biol. 19, 498–504. Altman, J., Fibich, P., Leps, J., Uemura, S., Hara, T., Dolezal, J., 2016. Linking spatiotemporal disturbance history with tree regeneration and diversity in an old-growth forest in northern Japan. Perspect. Plant Ecol. 21, 1–13. Anderegg, W.R., Kane, J.M., Anderegg, L.D., 2013. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Chang. 3, 30–36. Aussenac, R., Bergeron, Y., Ghotsa Mekontchou, C., Gravel, D., Pilch, K., Drobyshev, I., 2017. Intraspecific variability in growth response to environmental fluctuations modulates the stabilizing effect of species diversity on forest growth. J. Ecol. 105, 1010–1020. Baillie, M.G.L., Pilcher, J.R., 1973. A simple crossdating program for tree-ring research. Tree-Ring Bull. 1973, 7–14. Beguería, S., Vicente-Serrano, S.M., Reig, F., Latorre, B., 2014. Standardized precipitationevapotranspiration index (SPEI) revisited: parameter fitting, evapotranspirationmodels, tools, datasets and drought monitoring. Int. J. Climatol 34, 3001–3023. Black, B.A., Griffin, D., van der Sleen, P., Wanamaker Jr., A.D., Speer, J.H., Frank, D.C., et al., 2016. The value of crossdating to retain high-frequency variability, climate signals, and extreme events in environmental proxies. Glob. Chang. Biol. 22, 2582–2595. Boden, S., Kahle, H.-P., von Wilpert, K., Spiecker, H., 2014. Resilience of Norway spruce (Picea abies (L.) Karst) growth to changing climatic conditions in Southwest Germany. For. Ecol. Manag. 315, 12–21. Bolte, A., Ammer, C., Löf, M., Madsen, P., Nabuurs, G.-J., Schall, P., et al., 2009. Adaptive forest management in central Europe: climate change impacts, strategies and integrative concept. Scand. J. For. Res. 24, 473–482. Bonan, G.B., 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449. Bosela, M., Gasparini, P., Di Cosmo, L., Parisse, B., De Natale, F., Esposito, S., et al., 2016. Evaluating the potential of an individual-tree sampling strategy for dendroecological investigations using the Italian National Forest Inventory data. Dendrochronologia 38, 90–97. Bouwer, L.M., 2011. Have disaster losses increased due to anthropogenic climate change? B. Am. Meteorol. Soc. 92, 39–46. ter Braak, C.J., Smilauer, P., 2012. Canoco Reference Manual and User's Guide: Software for Ordination, Version 5.0. Microcomputer Power. Briffa, K., Schweingruber, F., Jones, P., Osborn, T., Shiyatov, S., Vaganov, E., 1998. Reduced sensitivity of recent tree-growth to temperature at high northern latitudes. Nature 391, 678–682. Bunn, A.G., 2008. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124. Büntgen, U., Frank, D., Wilson, R., Carrer, M., Urbinati, C., Esper, J., 2008. Testing for treering divergence in the European Alps. Glob. Chang. Biol. 14, 2443–2453. Buras, A., van der Maaten-Theunissen, M., van der Maaten, E., Ahlgrimm, S., Hermann, P., Simard, S., et al., 2016. Tuning the voices of a choir: detecting ecological gradients in time-series populations. PLoS One 11, e0158346. Cada, V., Santruckova, H., Santrucek, J., Kubistova, L., Seedre, M., Svoboda, M., 2016. Complex physiological response of Norway spruce to atmospheric pollution–decreased carbon isotope discrimination and unchanged tree biomass increment. Front. Plant Sci. 7, 805. Campelo, F., Garcia-Gonzalez, I., Nabais, C., 2012. detrendeR - a graphical user interface to process and visualize tree-ring data using R. Dendrochronologia 30, 57–60. Carnwath, G.C., Nelson, C.R., 2016. The effect of competition on responses to drought and interannual climate variability of a dominant conifer tree of western North America. J. Ecol. 104, 1421–1431. Carrer, M., 2011. Individualistic and time-varying tree-ring growth to climate sensitivity. PLoS One 6, e22813. Carrer, M., Urbinati, C., 2004. Age-dependent tree-ring growth responses to climate in Larix decidua and Pinus cembra. Ecology 85, 730–740. Choat, B., Jansen, S., Brodribb, T.J., Cochard, H., Delzon, S., Bhaskar, R., et al., 2012. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755. Cienciala, E., Russ, R., Santruckova, H., Altman, J., Kopacek, J., Hunova, I., et al., 2016. Discerning environmental factors affecting current tree growth in Central Europe. Sci. Total Environ. 573, 541–554. Clark, J.S., Bell, D.M., Kwit, M., Stine, A., Vierra, B., Zhu, K., 2012. Individual-scale inference to anticipate climate-change vulnerability of biodiversity. Philos. T. Roy. Soc. B. 367, 236–246. Cook, E., Briffa, K., 1990. A comparison of some tree-ring standardization methods. In: Cook, E.R., Kairiukstis, L.A. (Eds.), Methods of Dendrochronology: Applications in the Environmental Sciences, pp. 153–162. Coumou, D., Rahmstorf, S., 2012. A decade of weather extremes. Nat. Clim. Chang. 2, 491–496. Dai, A., 2013. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 3, 52–58. 516 J. Altman et al. / Science of the Total Environment 609 (2017) 506–516 D'Arrigo, R., Wilson, R., Liepert, B., Cherubini, P., 2008. On the ‘divergence problem’ in northern forests: a review of the tree-ring evidence and possible causes. Glob. Planet. Chang. 60, 289–305. Eckstein, D., Bauch, J., 1969. Beitrag zur Rationalisierung eines dendrochronologischen Verfahrens und zur Analyse seiner Aussagesicherheit. Forstwiss. Centralbl. 88, 230–250. Elling, W., Dittmar, C., Pfaffelmoser, K., Rotzer, T., 2009. Dendroecological assessment of the complex causes of decline and recovery of the growth of silver fir (Abies alba Mill.) in Southern Germany. For. Ecol. Manag. 257, 1175–1187. Elliott, K.J., Miniat, C.F., Pederson, N., Laseter, S.H., 2015. Forest tree growth response to hydroclimate variability in the southern Appalachians. Glob. Chang. Biol. 21, 4627–4641. Ettl, G.J., Peterson, D.L., 1995. Extreme climate and variation in tree growth: individualistic response in subalpine fir (Abies lasiocarpa). Glob. Chang. Biol. 1, 231–241. Fischer, E.M., Schär, C., 2009. Future changes in daily summer temperature variability: driving processes and role for temperature extremes. Clim. Dyn. 33, 917–935. Frank, D., Reichstein, M., Bahn, M., Thonicke, K., Frank, D., Mahecha, M.D., et al., 2015. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Chang. Biol. 21, 2861–2880. Galván, J.D., Camarero, J.J., Gutiérrez, E., 2014. Seeing the trees for the forest: drivers of individual growth responses to climate in Pinus uncinata mountain forests. J. Ecol. 102, 1244–1257. Gartner, H., Nievergelt, D., 2010. The core-microtome: a new tool for surface preparation on cores and time series analysis of varying cell parameters. Dendrochronologia 28, 85–92. Gee, G.W., Bauder, J.W., 1986. Particle-size analysis. In: Klute, A. (Ed.), Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods. Soil Science Society of America, American Society of Agronomy, Madison, WI, pp. 383–411. Grimm, V., Railsback, S.F., 2005. Individual-based Modeling and Ecology. Princeton University Press. Hartmann, H., Adams, H.D., Anderegg, W.R., Jansen, S., Zeppel, M.J., 2015. Research frontiers in drought-induced tree mortality: crossing scales and disciplines. New Phytol. 205, 965–969. Hegyi, F., 1974. A simulation model for managing jack-pine stands. In: Fries, J. (Ed.), Growth Models for Tree and Stand Simulation, pp. 74–90. Hunova, I., Maznova, J., Kurfurst, P., 2014. Trends in atmospheric deposition fluxes of sulphur and nitrogen in Czech forests. Environ. Pollut. 184, 668–675. Hunova, I., Kurfurst, P., Vlcek, O., Stranik, V., Stoklasova, P., Schovankova, J., et al., 2016. Towards a better spatial quantification of nitrogen deposition: a case study for Czech forests. Environ. Pollut. 213, 1028–1041. Hunova, I., Kurfurst, P., Stranik, V., Modlik, M., 2017. Nitrogen deposition to forest ecosystems with focus on its different forms. Sci. Total Environ. 575, 791–798. Kern, Z., Popa, I., Varga, Z., Szeles, E., 2009. Degraded temperature sensitivity of a stone pine chronology explained by dendrochemical evidences. Dendrochronologia 27, 121–128. Klimo, E., Hager, H., Kulhavý, J., 2000. Spruce Monocultures in Central Europe: Problems and Prospects. Vol 33. European Forest Institute. Kolar, T., Cermak, P., Oulehle, F., Trnka, M., Stepanek, P., Cudlin, P., et al., 2015. Pollution control enhanced spruce growth in the “Black Triangle” near the Czech–Polish border. Sci. Total Environ. 538, 703–711. Kopacek, J., Kana, J., Santruckova, H., Picek, T., Stuchlik, E., 2004. Chemical and biochemical characteristics of alpine soils in the Tatra Mountains and their correlation with lake water quality. Water Air Soil Pollut. 153, 307–328. Kopacek, J., Turek, J., Hejzlar, J., Santruckova, H., 2009. Canopy leaching of nutrients and metals in a mountain spruce forest. Atmos. Environ. 43, 5443–5453. Kopacek, J., Posch, M., Hejzlar, J., Oulehle, F., Volkova, A., 2012. An elevation-based regional model for interpolating sulphur and nitrogen deposition. Atmos. Environ. 50, 287–296. Kopacek, J., Cosby, B.J., Evans, C.D., Hruska, J., Moldan, F., Oulehle, F., et al., 2013. Nitrogen, organic carbon and sulphur cycling in terrestrial ecosystems: linking nitrogen saturation to carbon limitation of soil microbial processes. Biogeochemistry 115, 33–51. Kopacek, J., Hejzlar, J., Kram, P., Oulehle, F., Posch, M., 2016. Effect of industrial dust on precipitation chemistry in the Czech Republic (Central Europe) from 1850 to 2013. Water Res. 103, 30–37. Kysely, J., 2009. Trends in heavy precipitation in the Czech Republic over 1961–2005. Int. J. Climatol. 29, 1745–1758. Linares, J.C., Camarero, J.J., Carreira, J.A., 2010. Competition modulates the adaptation capacity of forests to climatic stress: insights from recent growth decline and death in relict stands of the Mediterranean fir Abies pinsapo. J. Ecol. 98, 592–603. Loehle, C., 2009. A mathematical analysis of the divergence problem in dendroclimatology. Clim. Chang. 94, 233–245. Mäkinen, H., Nöjd, P., Kahle, H.-P., Neumann, U., Tveite, B., Mielikäinen, K., et al., 2002. Radial growth variation of Norway spruce (Picea abies (L.) Karst.) across latitudinal and altitudinal gradients in central and northern Europe. For. Ecol. Manag. 171, 243–259. Martín-Benito, D., Cherubini, P., Del Río, M., Cañellas, I., 2008. Growth response to climate and drought in Pinus nigra Arn. trees of different crown classes. Trees 22, 363–373. McDowell, N.G., Allen, C.D., 2015. Darcy's law predicts widespread forest mortality under climate warming. Nat. Clim. Chang. 5, 669–672. Molina, J.G.A., Hadad, M.A., Domínguez, D.P., Roig, F.A., 2016. Tree age and bark thickness as traits linked to frost ring probability on Araucaria araucana trees in northern Patagonia. Dendrochronologia 37, 116–125. Monteith, D., Evans, C., Henrys, P., Simpson, G., Malcolm, I., 2014. Trends in the hydrochemistry of acid-sensitive surface waters in the UK 1988–2008. Ecol. Indic. 37, 287–303. Muzika, R., Guyette, R., Zielonka, T., Liebhold, A., 2004. The influence of O3, NO2 and SO2 on growth of Picea abies and Fagus sylvatica in the Carpathian Mountains. Environ. Pollut. 130, 65–71. Oulehle, F., Evans, C.D., Hofmeister, J., Krejci, R., Tahovska, K., Persson, T., et al., 2011. Major changes in forest carbon and nitrogen cycling caused by declining sulphur deposition. Glob. Chang. Biol. 17, 3115–3129. Oulehle, F., Kopacek, J., Chuman, T., Cernohous, V., Hunova, I., Hruska, J., et al., 2016. Predicting sulphur and nitrogen deposition using a simple statistical method. Atmos. Environ. 140, 456–468. Primicia, I., Camarero, J.J., Janda, P., Cada, V., Morrissey, R.C., Trotsiuk, V., et al., 2015. Age, competition, disturbance and elevation effects on tree and stand growth response of primary Picea abies forest to climate. For. Ecol. Manag. 354, 77–86. R Core Team, 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria https://www.R-project.org/. Rose, R., Monteith, D.T., Henrys, P., Smart, S., Wood, C., Morecroft, M., et al., 2016. Evidence for increases in vegetation species richness across UK Environmental Change Network sites linked to changes in air pollution and weather patterns. Ecol. Indic. 68, 52–62. Rozas, V., 2015. Individual-based approach as a useful tool to disentangle the relative importance of tree age, size and inter-tree competition in dendroclimatic studies. IFOREST. 8, 187. Rozas, V., DeSoto, L., Olano, J.M., 2009. Sex-specific, age-dependent sensitivity of tree-ring growth to climate in the dioecious tree Juniperus thurifera. New Phytol. 182, 687–697. Rydval, M., Wilson, R., 2012. The impact of industrial SO2 pollution on north Bohemia conifers. Water Air Soil Pollut. 223, 5727–5744. Sánchez-Salguero, R., Linares, J.C., Camarero, J.J., Madrigal-González, J., Hevia, A., SánchezMiranda, Á., et al., 2015. Disentangling the effects of competition and climate on individual tree growth: a retrospective and dynamic approach in Scots pine. For. Ecol. Manag. 358, 12–25. Santruckova, H., Santrucek, J., Setlík, J., Svoboda, M., Kopacek, J., 2007. Carbon isotopes in tree rings of Norway spruce exposed to atmospheric pollution. Environ. Sci. Technol. 41, 5778–5782. Schulze, E., 1989. Air pollution and forest decline in a spruce (Picea abies) forest. Science 244, 776–783. Seneviratne, S.I., Lüthi, D., Litschi, M., Schär, C., 2006. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209. Sensula, B., Wilczynski, S., Opala, M., 2015. Tree growth and climate relationship: dynamics of scots pine (Pinus sylvestris L.) growing in the near-source region of the combined heat and power plant during the development of the pro-ecological strategy in Poland. Water Air Soil Pollut. 226, 220. Smith, S.J., Jv, Aardenne, Klimont, Z., Andres, R.J., Volke, A., Delgado Arias, S., 2011. Anthropogenic sulfur dioxide emissions: 1850–2005. Atmos. Chem. Phys. 11, 1101–1116. Sohar, K., Altman, J., Lehečková, E., Doležal, J., 2017. Growth–climate relationships of Himalayan conifers along elevational and latitudinal gradients. Int. J. Climatol. 37, 2593–2605. Speer, J.H., 2010. Fundamentals of Tree-ring Research. University of Arizona Press. Spiecker, H., 2003. Silvicultural management in maintaining biodiversity and resistance of forests in Europe—temperate zone. J. Environ. Manag. 67, 55–65. Stepanek, P., 2010. ProClimDB–Software for Processing Climatological Datasets. CHMI, Regional Office, Brno http://www.climahom.eu/ProcData.html. Stepanek, P., Zahradnicek, P., Huth, R., 2011. Interpolation techniques used for data quality control and calculation of technical series: an example of a Central European daily time series. Idojaras. 115, 87–98. Stepanek, P., Zahradnicek, P., Farda, A., 2013. Experiences with data quality control and homogenization of daily records of various meteorological elements in the Czech Republic in the period 1961–2010. Idojaras. 117, 123–141. Tardif, J., Camarero, J.J., Ribas, M., Gutiérrez, E., 2003. Spatiotemporal variability in tree growth in the Central Pyrenees: climatic and site influences. Ecol. Monogr. 73, 241–257. Thomas, G.W., 1982. Exchangeable cations. In: Page, A.L. (Ed.), Methods of Soil Analysis. Part Vol. 2. Chemical and Microbiological Properties. American Society of Agronomy, Soil Science Society of America, Madison, WI, pp. 159–165. Treml, V., Ponocná, T., Büntgen, U., 2012. Growth trends and temperature responses of treeline Norway spruce in the Czech-Polish Sudetes Mountains. Clim. Res. 55, 91–103. Treml, V., Kašpar, J., Kuželová, H., Gryc, V., 2015. Differences in intra-annual wood formation in Picea abies across the treeline ecotone, Giant Mountains, Czech Republic. Trees 29, 515–526. Tumajer, J., Altman, J., Stepanek, P., Treml, V., Dolezal, J., Cienciala, E., 2017. Increasing moisture limitation of Norway spruce in Central Europe revealed by forward modelling of tree growth in tree-ring network. Agric. For. Meteorol (in press). Van den Besselaar, E., Klein Tank, A., Buishand, T., 2013. Trends in European precipitation extremes over 1951–2010. Int. J. Climatol. 33, 2682–2689. Vicente-Serrano, S.M., Beguería, S., López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718. Vicente-Serrano, S.M., Gouveia, C., Camarero, J.J., Beguería, S., Trigo, R., López-Moreno, J.I., et al., 2013. Response of vegetation to drought time-scales across global land biomes. P. Natl. Acad. Sci. USA. 110, 52–57. Vrba, J., Bojková, J., Chvojka, P., Fott, J., Kopáček, J., Macek, M., et al., 2016. Constraints on the biological recovery of the Bohemian Forest lakes from acid stress. Freshw. Biol. 61, 376–395. Westra, S., Alexander, L.V., Zwiers, F.W., 2013. Global increasing trends in annual maximum daily precipitation. J. Clim. 26, 3904–3918. Yamaguchi, D.K., 1991. A simple method for cross-dating increment cores from living trees. Can. J. For. Res. 21, 414–416. Zang, C., Biondi, F., 2015. treeclim: an R package for the numerical calibration of proxyclimate relationships. Ecography 38, 431–436. Zang, C., Hartl-Meier, C., Dittmar, C., Rothe, A., Menzel, A., 2014. Patterns of drought tolerance in major European temperate forest trees: climatic drivers and levels of variability. Glob. Chang. Biol. 20, 3767–3779.