Forest Ecology and Management 354 (2015) 77–86

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Age, competition, disturbance and elevation effects on tree and stand growth response of primary Picea abies forest to climate Irantzu Primicia a,⇑, Jesús Julio Camarero b, Pavel Janda a, Vojtch Cˇada a, Robert C. Morrissey a, Volodymyr Trotsiuk a, Radek Bacˇe a, Marius Teodosiu c,d, Miroslav Svoboda a a

´cká 129, Praha 6 – Suchdol, 16521 Prague, Czech Republic Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamy Instituto Pirenaico de Ecología (IPE, CSIC), Avda. Montañana 1005, Apdo. 202, 50192 Zaragoza, Spain National Institue for Research-Development in Forestry ‘‘Marin Dra˘cea’’, Eroilor 128, 077190 Voluntari, Romania d Faculty of Forestry, University Sßtefan cel Mare Suceava, Universita˘ßt ii 13, 720229 Suceava, Romania b c

a r t i c l e

i n f o

Article history: Received 14 April 2015 Received in revised form 24 June 2015 Accepted 27 June 2015 Available online 11 July 2015 Keywords: Climate warming Climate–growth responsiveness Climate sensitivity Dendroclimatology Linear mixed-effects models Norway spruce

a b s t r a c t Stands and trees may exhibit different climate–growth responses compared to neighbouring forests and individuals. The study of these differences is crucial to understanding the effects of climate change on the growth and vulnerability of forests and trees. In this research we analyse the growth responsiveness of primary Norway spruce forests to climate as a function of different stand (elevation, aspect, slope, crowding, historic disturbance regime) and tree (age, tree-to-tree competition) features in the Romanian Carpathians. Climate–growth relationships were analysed using Pearson correlation coefficients between ring-width indices (RWIs) and climate variables. The influence of stand and tree characteristics on the RWI responses to climate were investigated using linear mixed-effects models. Elevation greatly modulated the climate–growth associations and it frequently interacted with competition intensity or tree age to differentially influence growth responsiveness to climate. Old trees were more sensitive to climate than young trees, but while old tree’s response to climate highly depended on elevation (e.g. positive influence of summer temperature on old trees’ RWIs at high elevations, but negative effect at low elevations), differences of the young trees’ response across the elevation gradient were less evident. The severity of the past disturbance also modified the climate–growth associations because of contrasting canopy structures. Our results suggest that although an increase in temperature might enhance growth at high elevations, it may also induce growth declines due to drought stress at lower elevations, particularly for old trees or trees growing under high levels of competition, which may increase their vulnerability to disturbances. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction In European forests tree growth is constrained by low temperatures in northern regions and at high elevations, and by low water availability in warmer southern regions or in drought-prone, low-elevation sites (Babst et al., 2013). Although climate is acknowledged as a major driver of growth, site and tree features Abbreviations: RWI, ring-width index; DBH, diameter at breast height; AC, first-order autocorrelation; msx, mean sensitivity; rbt, mean correlation between trees; CRI, stand crowding index; CI, competition index; DI, disturbance index; PCA, Principal Components Analysis; PC1 and PC2, first and second principal components of the PCA. ⇑ Corresponding author. E-mail addresses: [email protected] (I. Primicia), [email protected] (J.J. Camarero), jandap@fld.czu.cz (P. Janda), cada@fld.czu.cz (V. Cˇada), robcmorrissey@ gmail.com (R.C. Morrissey), trotsiuk@fld.czu.cz (V. Trotsiuk), bace@fld.czu.cz (R. Bacˇe), [email protected] (M. Teodosiu), svobodam@fld.czu.cz (M. Svoboda). http://dx.doi.org/10.1016/j.foreco.2015.06.034 0378-1127/Ó 2015 Elsevier B.V. All rights reserved.

can modify how individual trees respond to climatic variables at different spatial scales (Galván et al., 2014). Classical dendroclimatological studies have focused on trees with similar response to climate and on the summarizing of those responses in a mean growth series or site chronology for the whole stand (Fritts, 2001). Typically, site and tree selection are intended to enhance the climate signal (Cook and Kairiukstis, 1990; Schweingruber, 1996). However, trees show divergent climate–growth associations from their neighbours within a stand, because growth responsiveness to climate depends on site and tree characteristics like forest composition (Pretzsch and Dieler, 2011), tree-to-tree competition intensity (Linares et al., 2010) or tree age and size (Carrer and Urbinati, 2004; Martín-Benito et al., 2008; Szeicz and MacDonald, 1994). The differential sensitivity of tree individuals to climate implies they are differentially adapted to varying levels of climatic stress being for example more or less drought-responsive individuals

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(Galván et al., 2014). Studying the variability of the climate– growth response at the individual tree scale provides valuable ecological information on how trees respond to climate and how these responses determine forest dynamics (Carrer, 2011; Rozas, 2014). Identifying growth patterns and trends at the stand and tree scales is therefore crucial when forecasting how climate change will affect forest dynamics and tree adaptation to new climatic scenarios (Aitken et al., 2008), especially if drought and natural disturbances (e.g., beetle outbreaks) are thought to increase in the future (IPCC, 2007; Seidl et al., 2014). Norway spruce (Picea abies (L.) Karst.) is one of the most widespread conifers in the European temperate forests (Spiecker, 2003), usually occupying mesic and managed sites and showing reduced growth in response to cold temperatures or low water availability during the growing season (Aakala and Kuuluvainen, 2011; Büntgen et al., 2007; Mäkinen et al., 2003, 2002). Primary forests of Norway spruce are very rare in Europe because of a long history of anthropogenic influence. Natural disturbances (e.g., windstorms, bark beetle outbreaks) are the major drivers of primary Norway spruce forest dynamics (Lännenpää et al., 2008; Shorohova et al., 2008; Svoboda et al., 2014; Trotsiuk et al., 2014), and could also influence the climate–growth response of trees (Rozas, 2001). In this study we investigate how tree (age, tree-to-tree competition) and stand (elevation, aspect, slope, plot crowding, historic disturbance regime) features modulate climate growth relationships of primary Norway spruce forests in the Romanian Carpathians. The study forests are considered temperaturesensitive because they represent the upper part of the spruce distribution in the Carpathians but do not reach the alpine tree line ˇ ejková and Kolárˇ, 2009; Treml et al., 2012; Wilson and (C Hopfmueller, 2001). Our main objectives were to determine the main climatic variables influencing Norway spruce growth and to elucidate how stand and individual tree conditions influence the trees’ growth responses to climate. Our working hypotheses were that: (i) Norway spruce growth is mainly limited by temperature as elevation increases; and (ii) those trees under more stressful conditions (e.g. old trees or trees growing in dense, high-elevation stands or under high-severity disturbance regime) will exhibit higher sensitivity to climate variables.

2. Material and methods 2.1. Study area The study was conducted in five sites within two localities, the Ca˘limani and Giuma˘lau Mountains of the Eastern Romanian Carpathians. We sampled fifty pure Norway spruce plots, 21 in Ca˘limani and 29 in Giuma˘lau, between 1249 and 1653 m a.s.l. (Table 1). Mean annual temperature is 3.3 and 6.2 °C with a mean annual precipitation of 822.7 and 715.8 mm for Ca˘limani and

Giuma˘lau, respectively (Supplementary Material, Fig. A.1). The bedrock is composed of andesites (Seghedi et al., 2005) and phyllite in Ca˘limani, and of gneiss in Giuma˘lau (Balintoni, 1996), and podzols are the most common soils in both ranges (Valtera et al., 2013). For a more detailed description of the study area see Svoboda et al. (2014). 2.2. Data collection and processing A stratified random design based on a 2-ha grid cell size was used to sample each site. Circular plots 1000 m2 in size were established at each grid intersect; however, in plots with a high tree density (>500 trees ha1) and homogenous structure, plot size was reduced to 500 m2 (n = 20). Stands with evidence of past logging, grazing, and stands close to formerly grazed areas were not sampled. In each plot, spatial location, species, and diameter at breast height (DBH) of all living trees P 10 cm were recorded; crown area of five randomly selected canopy trees was estimated using the crown width of two orthogonal axes. Physiographic attributes such as slope, aspect, and elevation were recorded for each plot. 2.2.1. Dendrochronological methods In 2011, we cored 25 (for 1000-m2 sample plots) or 15 (for 500-m2 sample plots) randomly selected dominant or co-dominant trees per plot. One radial core per tree was extracted at 1.0 m above ground level for growth analysis and age determination. The cores were air-dried, mounted on wood boards, and shaved with a razor blade until annual growth rings were clearly visible. For cores that missed the pith, the number of missing rings was estimated using the method of Duncan (1989). Samples were visually cross-dated using pointer years (Yamaguchi, 1991), and verified using the COFECHA program (Holmes, 1983). Annual tree ring widths were measured to the nearest 0.01 mm using a stereomicroscope and a LintabTM sliding-stage measuring device in conjunction with TSAP-WinTM software (Rinntech, Heidelberg, Germany). Tree-ring width series were standardized and detrended by fitting a 50-year cubic spline with a 50% cut-off frequency to remove age- and size-related trends (Cook and Peters, 1981). Autoregressive modelling removed most of the temporal autocorrelation (usually of first order) to obtain residual series of dimensionless ring-width indices (RWI). Individual tree RWI were averaged at the locality (Ca˘limani, Giuma˘lau) and plot scales to develop master chronologies for each scale. Series detrending and chronologies building were done using the Dendrochronology Program Library (dplR) package (Bunn, 2010) in the R software (R Core Team, 2013). For each locality chronology, several descriptive dendrochronological statistics (Fritts, 2001) were calculated either from the raw tree-ring series (mean and standard deviation of ring width; AC,

Table 1 Physiographic parameters and stand structural characteristics of the study plots. Competition index was calculated only for those trees which zone of influence did not extend outside the plot boundary. Locality

Ca˘limani

Sites

C2

C3

C4

C5

Giuma˘lau G1

Mean (range) elevation (m a.s.l.) Mean (range) slope (°) No. plots Mean (±SD) tree density (stems ha1) Mean (±SD) diameter at breast height (cm) Mean (±SD) basal area (m2 ha1) No. sampled trees No. trees with competition index Mean (range) tree age at 1 m (yrs.)

1626 (1599–1653) 38 (33–43) 4 365 ± 88 37.1 ± 3.4 46.3 ± 7.1 63 43 188 (84–257)

1484 (1415–1549) 22 (16–28) 6 803 ± 107 23.9 ± 4.3 41.5 ± 12 122 82 68 (56–78)

1557 (1505–1601) 28 (25–32) 6 408 ± 168 38.3 ± 7.9 53.2 ± 7.4 99 72 171 (51–276)

1558 (1512–1598) 20 (15–23) 5 432 ± 130 39.7 ± 5.6 61 ± 4.1 103 73 146 (53–237)

1430 (1249–1571) 29 (17–38) 29 516 ± 257 31.8 ± 9.2 47.6 ± 14.6 421 258 133 (50–304)

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first-order autocorrelation) or using the residual chronologies (msx, mean sensitivity; rbt, mean correlation between trees). The AC assesses the similarity between ring widths in consecutive years. The msx measures the width variability of consecutive tree rings, while the rbt is a measure of the similarity in growth among trees. 2.2.2. Climate data Locality, plot, and tree RWI series were correlated against monthly mean temperature and precipitation. Climate data was obtained from the homogenised, quality-controlled CARPATCLIM (Antolovic´ et al., 2013) dataset that encompasses the entire Carpathian Mountain range gridded at 0.1° spatial resolution for the period of 1961–2010. Climate data were standardized to give all climatic variables the same weight, and mean values for each locality were calculated based on the values of the grid cells where the plots were located. Climate-indexed growth relationships were analysed using a 17-month window from June of the year prior to tree growth until October of the year of tree-ring formation. 2.2.3. Crowding and competition index at plot and tree scales The effects of competition on the climate–RWI relationships were analysed at plot and tree scales using different crowding and competition indices. At the plot scale we used a crowding index (CRI) based on the calculation of the plot basal area divided by the highest one found among the study plots (Kunstler et al., 2011, mod.). Thus, CRI ranged from 0 (no trees) to 1 (maximum crowding). At the tree scale we calculated the competition index (CI) proposed by Hegyi (1974):

CI ¼

X 1 ðdj di Þ=distij

ð1Þ

where dj is the DBH of neighbouring tree, di, the DBH of focal tree, and distij, the distance between the neighbouring and focal trees. The determination of the neighbouring trees actively competing with the target tree was based on the influence-zone concept proposed by Staebler (1951), whereby competition is assumed to exist when the zones of influence of two trees overlap. The radius of the influence zone of a tree has been considered to be equal to the crown radius of an open-grown tree of the same diameter, which can be estimated by using quantile regression techniques (Russell and Weiskittel, 2011). We fitted the 99th quantile to data on DBH and crown width of trees in the plots to calculate the maximum crown width for open-grown trees of the same DBH using the quantile regression package quantreg (Koenker, 2013) in the R software (R Core Team, 2013). If the zone of influence of a tree extended outside the plot boundary, no CI value was calculated for that tree. 2.2.4. Disturbance history The disturbance history reconstruction was based on the analyses done in the study of Svoboda et al. (2014). The disturbance history was reconstructed from two patterns of radial growth: (i) abrupt and sustained increases in radial growth because of the mortality of a former canopy tree, classified as ‘‘releases’’, and (ii) rapid early growth rates related to recruitment in canopy gaps, classified as ‘‘gap recruitment’’ (Frelich and Lorimer, 1991). The proportion of plot disturbed in each decade (i.e. disturbance severity) was calculated following the methodology of Lorimer and Frelich (1989) by summing each release and gap recruitment in each decade and weighting them with the current crown areas. The disturbance history was finally summarized in a disturbance index (DI) based on the Shannon index calculated for each plot: N X DI ¼ pi lnðpi Þ i¼1

ð2Þ

79

where pi is the proportion of plot disturbed belonging to the ith decade and N is the number of decades. For the zero disturbance probability we excluded this sequence from the sum. The disturbance index characterizes the overall severity of the disturbance regime at plot level. Low value (minimum reaches ca. 3) indicate diverse and low severity disturbance regime, while the maximum theoretical value (reaches 0) indicate that 100% canopy area was disturbed during one decade (see Svoboda et al., 2014 for more details and justifications). 2.3. Statistical analyses The relationships between locality, plot, and tree RWI series with climate data during the 1961–2010 period were quantified using bootstrapped Pearson correlation coefficients. The statistical significance of the correlations was tested using the 95% percentile range method (Dixon, 2001). For further analyses, only those monthly climate variables showing a significant relationship with the RWI series at the locality scale were used. To investigate common climate–growth responses among plots and trees we used Principal Components Analysis (PCA) performed on the matrix of the bootstrapped Pearson correlations obtained between the plot and tree RWIs and the selected climate variables. To investigate the effects of different plot and tree features on the relationship between RWIs and the climate variables we fitted linear mixed-effects models using the nlme package (Pinheiro et al., 2009). We used the Pearson correlations obtained by relating the RWI series at the plot and tree scales and the selected climate variables as dependent variables. At the plot scale, the proposed models included site as a random effect and elevation, aspect, slope, crowding index, disturbance index, and their pairwise interactions, as fixed effects. At the tree scale, the proposed models included the plot nested in site as a random effect and age, elevation, competition index, and their pairwise interactions, as fixed effects. For the models at the tree scale, we only included trees with a calculated CI value (n = 528, Table 1). We used an exponential correlation structure at both plot and tree scales to account for spatial correlation on the sample site or plot (Pinheiro and Bates, 2000). The pertinence of the random and the spatial correlation structures was determined by comparing nested models with and without the random effects and correlation structure with the likelihood ratio test using the restricted maximum likelihood estimation procedure (Zuur et al., 2009). Models ranged from the null model (only with an intercept) to models with all variables and the proposed interactions. The best-fitted models were considered those showing the lowest Akaike Information Criterion values, i.e. those most parsimonious (Burnham and Anderson, 2002); they were identified using the Multi-Model Inference (MuMIn) package (Barton, 2013). We calculated a pseudo-R2 of the selected models following Nakagawa and Schielzeth (2013), which comprises marginal (R2m) and conditional (R2c) R2 values. The R2m accounts for the proportion of variance explained by the fixed factors, and the R2c accounts for the proportion of variance explained by the whole model, i.e. fixed plus random factors. The statistical analyses were conducted using the R statistical software (R Core Team, 2013). 3. Results 3.1. Growth characteristics and climatic drivers of Norway spruce growth The mean tree-ring width was 1.47 mm at Ca˘limani and 1.41 mm at Giuma˘lau, and mean sensitivity (msx) was similarly low (0.19) in both localities. The mean correlations between tree RWIs (rbt) were 0.30 in Ca˘limani and 0.32 in Giuma˘lau, and the

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first-order autocorrelations (AC) were 0.80 and 0.83, respectively, indicating strong growth persistence between consecutive years. The main RWI responses to climate were observed for temperature variables and they were stronger at the plot than at the tree scale in both study localities. At the regional scale, Norway spruce RWIs mainly responded positively to temperature in previous October (Ca˘limani) and December (both localities), and in January (Giuma˘lau), March (both), June (Ca˘limani) and September (Giuma˘lau) of the year of tree-ring formation (Fig. 1). Precipitation was important in February (Ca˘limani) and April (Giuma˘lau), before the onset of xylem growth (Fig. 1). At the plot scale, 24% of the chronologies, and 11% of the RWIs at the tree scale significantly responded to the abovementioned climatic variables. Additionally, around 10% of individual trees responded positively to previous and current summer (June–July) precipitation (Fig. 1).

3.2. Influence of stand characteristics on the climate–RWI associations at the plot scale At the plot scale, the first (PC1) and second (PC2) principal components of the PCA accounted for 44.6% and 22.2% of the climate– RWI variability, respectively (Fig. 2a). The PC1 separated the plot RWI series by elevation (PC1-elevation R2 = 0.47, P < 0.001, Fig. 2b). Warm previous October and current June temperatures and high precipitation in February enhanced plot RWIs at high elevations; at low elevations, RWIs increased in response to high April precipitation (Fig. 2a and b, Table 2). At high elevations, plot RWIs were enhanced by previous December and current March temperature and April precipitation especially in low-density stands, while at low elevations RWIs responded more to these climate variables in high-density stands (Table 2, Fig. 3a). Plots characterized by a history of high-severity disturbances showed RWI series less positively influenced by temperature in previous December and current January and September, but more by precipitation in February. Under high-severity disturbance regimes, April

precipitation particularly enhanced plot RWIs in low-density stands, whilst under low-severity disturbance regimes, RWIs responded more to the same variable in high-density stands (Fig. 3b). Aspect only had a marginally significant effect on the climate–RWI associations, while slope did not show any significant influence. 3.3. Influence of stand and tree characteristics on the climate–RWI responses at the tree scale At the tree scale, PC1 and PC2 accounted for 38.8% and 16.2% of the variability of the climate–RWI response, respectively (Fig. 2c). Trees RWIs were enhanced by warm temperature in previous October, particularly at high-elevation stands. However, elevation, tree age and tree-to-tree competition frequently interacted to significantly affect climate–RWI associations (Table 3). Old trees had generally stronger positive responses to climatic variables than young trees (Table 3, Fig. 4). Warm temperature in June generally enhanced trees RWIs at high-elevation stands, but they negatively influenced the RWIs of old trees at low elevations (Fig 4b). Additionally, old trees’ RWIs showed a stronger correlation with April precipitation than young trees at high-density stands (Table 3). April precipitation had a positive influence on trees RWIs only at low elevations, particularly on those trees under higher levels of competition (Table 3, Fig 4c). Trees’ RWIs increased with increasing June temperatures especially at high tree-to-tree competition neighbourhoods in high-elevation stands, while at low elevations, only RWIs of trees subjected to high competition levels were negatively affected by the same variable (Table 3, Fig 4d). 4. Discussion The multiscale approach revealed similar patterns of Norway spruce growth (RWI) responsiveness to climate at the plot and tree scales. Elevation played a major role influencing growth sensitivity

Fig. 1. Box plots showing the Pearson correlation coefficients calculated between plot and tree ring-width indices and temperature and precipitation variables, respectively. Symbols in the upper line indicate bootstrapped significant coefficients (P < 0.05) with the climate variables for the Ca˘limani (x), Giuma˘lau (⁄), and both the Ca˘limani and Giuma˘lau chronologies (). The lower bars of each graph indicate the relative number of plots or trees with significant (dark grey) and non-significant (P > 0.05, light grey) coefficients. Months abbreviated by lowercase italics or uppercase letters correspond to months from the previous year and year of tree-ring formation, respectively.

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Fig. 2. Relationships between growth responsiveness to climate and elevation observed at the plot (a, b) and tree (c, d) scales. Biplot of the first (PC1) and second (PC2) components of a principal component analysis (PCA) calculated on the Pearson correlations obtained by relating the plot ring-width indices (RWIs) and the significant monthly climate variables detected at the locality scale (a) and relationship between plot-PC1 scores and elevation (b). Graphs (c) and (d) are the same as (a) and (b), respectively, but they were calculated at the tree scale. Climatic variables’ abbreviations: TOct, temperature of previous October; TDec, temperature of previous December; TJan, temperature of current January; TMar, temperature of current March; TJun, temperature of current June; TSept, temperature of current September; PFeb, precipitation of current February; PApr, precipitation of current April. Months written in italics correspond to the year prior to tree-ring formation. Significance levels: ⁄p < 0.05; ⁄⁄p < 0.01; ⁄⁄⁄ p < 0.001.

Table 2 Parameter estimates for the selected models at the plot scale, with the climate–RWIs (ring-width indices) relationships (Pearson correlation) as the dependent variables. Months written in italics correspond to the year prior to tree-ring formation. Only factors or interactions between them with a significant effect on the climate–RWI relationships are shown. Bold values indicate P < 0.05, whereas values in italics indicate P < 0.1. Climate variable

Month

Elevation

Temperature

October December January March June September

0.00101 0.00021

February April

0.00026 0.00072

Precipitation

0.00018 0.00076 0.00030

Crowding index (CRI)

Disturbance index (DI)

0.03754

0.10350 0.05932 0.05994

Aspecta

Elevation  CRI

CRI  DI

Aspect  DI

0.02532

0.08997

0.00258 0.02678 0.00241

0.07956 0.09214

0.04309 0.01359

0.01481

0.04472 0.00354

0.44524

Intercept

Residuals

R2mb

R2cc

0.042 0.149 0.093 0.092 0.000 0.072

0.071 0.061 0.072 0.074 0.072 0.060

0.59 0.12 0.08 0.14 0.48 0.23

0.70 0.88 0.65 0.66 0.48 0.68

0.020 0.062

0.062 0.057

0.25 0.46

0.32 0.75

a Aspect values were transformed using the following formula: aspect = cosine (45-azimuth degrees) + 1. This formula transforms values so as to be maximal on NE slopes and minimal on SW slopes. b,c Marginal (proportion of variance explained by the fixed factors, R2m) and conditional (proportion of variance explained by fixed plus random factors, R2c) R2 values were calculated following Nakagawa and Schielzeth (2013).

to climate at both scales, although it frequently interacted with stand crowding index, tree age, and tree-to-tree competition intensity. Severity of the historic disturbance regime was also an important variable influencing climate–growth associations at the plot scale. 4.1. Climatic drivers of Norway spruce growth Norway spruce RWIs were enhanced by warm temperatures from the previous autumn to current summer and by high

precipitation in winter and early spring. Norway spruce radial growth in the study area likely starts in early May, reaches maximum rates from June to July and ends in August–September (Treml et al., 2014). Warm temperatures during autumn and winter enhance photosynthesis and carbohydrates synthesis, promote root growth, and favour bud maturation, which controls primary growth during the following year, and the combination of these factors likely favours stemwood formation (Schaberg, 2000; von Felten et al., 2007). Enhanced growth after humid winter-early spring highlights the importance of the recharge of soil water

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Fig. 3. Predicted effects of the linear mixed-effects models of the interactions between crowding index and (a) elevation or (b) disturbance index on the ring-width indices (RWIs) responses to April precipitation (Pearson correlation) at the plot scale. Points represent sample plots. High crowding index means high competition status; high disturbance index means high severity of the historic disturbance regime.

Table 3 Parameter estimates for the selected models at the tree scale, with the climate–RWIs (ring-width indices) relationships (Pearson correlation) as the dependent variables. Months written in italics correspond to the year prior to tree-ring formation. Only factors or interactions between them with a significant effect on the climate-indexed growth relationships are shown. Bold values indicate P < 0.05, whereas values in italics indicate P < 0.1. Climatic variable and months Temperature October December January March June September Precipitation February April a,b

Age

Elevation

Competition index (CI)

0.0004 0.0003 0.0004 0.0003 0.0007 0.0003

0.0087

0.0002 4.89E05 0.0002 0.0001

0.0009

0.0003

0.0004 0.0008

0.0023 0.0127

Age  elevation

Age  CI

4.00E06 2.79E06 5.61E06 4.10E06

5.85E06

Elevation  CI

Intercept

Residuals

R2ma

R2cb

0.0001

0.037 0.034 0.048 0.036 0.036 0.037

0.123 0.116 0.123 0.134 0.127 0.122

0.07 0.09 0.08 0.09 0.18 0.03

0.15 0.17 0.20 0.15 0.25 0.12

0.043 0.045

0.123 0.153

0.05 0.03

0.15 0.11

0.0001

0.0004

0.0002

Marginal (R2m) and conditional (R2c) R2 values were calculated following Nakagawa and Schielzeth (2013).

reserves prior to the onset of the cambial activity, agreeing with previous results in the Alps (Lévesque et al., 2013). Humid winters could be also associated with an increase in photosynthesis, which is apparently limited by low water availability during winter (Schaberg, 2000), or with the protection of snow cover. In high-elevation forests, trees are likely to suffer from frost-induced desiccation in mild late winters with shallow snow cover (Sperry and Robson, 2001). The enhancement of tree growth by warm summer conditions has frequently been observed in other high-elevation woodlands (e.g. Büntgen et al., 2007). Although xylem cell differentiation probably ended in August (Treml et al., 2014), the positive correlation between RWIs and September temperature could be explained by a lengthening of xylogenesis in years with warm early autumns, since the ending of wood formation at high-altitude forests is largely determined by temperature (Deslauriers et al., 2008). 4.2. The effect of elevation on the Norway spruce growth response to climate is modulated by competition intensity and tree age Our results revealed diverging climate–RWI relationships as a function of elevation at the plot and tree scales, even though our study area covers a relatively narrow elevation range (ca. 400 m, i.e. a lapse rate of ca. 2.4 °C), thus, highlighting the major role of the elevation-induced thermal gradient in growth responsiveness to climate. Previous autumn and current summer temperatures at high elevation enhanced tree RWI series, but they increased with

increasing winter temperature and spring precipitation at low elevations. Norway spruce growth generally increases with summer temperature towards higher elevation and it is mainly constrained by low water availability at lower elevations (e.g. Büntgen et al., 2007; Cˇejková and Kolárˇ, 2009; Mäkinen et al., 2002; Treml et al., 2012; Wilson and Hopfmueller, 2001). Nevertheless, the sensitivity of Norway spruce RWIs to climate at different elevations was frequently modulated by other stand and tree features, such as stand crowding, tree-to-tree competition, or tree age. In classical dendroclimatological studies, dominant and/or isolated trees are selected in order to maximize the climate signal (Cook and Kairiukstis, 1990; Schweingruber, 1996), though increased sensitivity of tree growth to climatic stress such as water deficit has been observed in dense compared to open areas (Linares et al., 2010). We observed that the influence of elevation on the climate–growth associations was modulated by the competition intensity. Warm winter temperatures and high spring precipitation enhanced RWIs in low-density stands at high elevations, but RWIs responded more to those variables in high-density stands at low elevations. During winter, lower air and soil temperature can occur more frequently and be more extreme in open areas than under forest canopy (Aussenac, 2000), which may constrain tree photosynthesis (Wu et al., 2012), lead to delayed onsets of xylogenesis (Lupi et al., 2012), and also result in freeze-thaw cycles causing xylem embolism and damaging the cambium in the most extreme cases such as those related to frost drought (Lens et al., 2013; Mayr et al., 2006). At high elevations, trees in open stands might thus

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Fig. 4. Predicted effects of the linear mixed-effects models of the interactions between tree age and elevation on the ring-width indices (RWIs) responses (Pearson correlation) to March (a) and June (b) temperatures; and effects of the interactions between competition index and elevation on RWIs responses to April precipitation (c) and June temperature (d) at the tree scale. Points represent sampled trees. High competition index means high tree-to-tree competition status.

show higher limitation to carbohydrates synthesis during winter and to the reactivation of the cambial activity in spring due to low soil temperature, being more responsive to warmer winter– spring conditions. The importance of the soil water recharge prior to the onset of cambial activity in crowded stands at the lower elevations may be linked to competition among trees for soil water availability even in these temperate areas. The competition intensity also modified the influence of elevation on the response of tree growth to summer temperatures. Thus, even though warm summer temperatures generally enhanced tree growth, particularly at higher elevations, they caused a negative effect on tree growth in high-density low-elevation sites. This negative influence of summer temperature on tree RWIs is probably due to an indirect influence on water availability and growth, since higher temperatures may reduce available soil water through higher evapotranspiration rates, as has been previously suggested (Schuster and Oberhuber, 2013). Reduced RWIs in years with warm summer conditions in the high-crowded low-elevation sites could be therefore related to competition for soil water. Old trees were more sensitive to climate than young trees in terms of RWI responsiveness, agreeing with previous findings (Carrer and Urbinati, 2004; Martín-Benito et al., 2008; Schuster and Oberhuber, 2013). Nonetheless, our results suggest divergent climate–growth response of trees of different age growing at different elevations and neighbourhood competition status. At low elevations, only old trees’ RWIs were enhanced by warm winter-to-early spring temperatures, although Norway spruce growth response to winter temperature has been observed to decrease with aging (Schuster and Oberhuber, 2013). Old trees’ RWIs generally increased with high winter precipitation, and with

high spring precipitation at low elevations or under high neighbourhood competition. Even though warm summer temperatures generally enhanced trees’ RWIs at high elevations, warm summers were frequently related to a reduction in tree growth at lower elevations, being this negative influence stronger as tree age increased. Szeicz and MacDonald (1994) observed a differential site-specific growth response to climate in subarctic Picea glauca (Moench) Voss trees of different ages, which they related to physiological changes, such as a less efficient hydraulic system in taller, older trees (Hubbard et al., 1999; Ryan et al., 2006). The differences in water relations due to aging (e.g. hydraulic conductance, sapwood water storage) could explain the higher responsiveness of old trees to spring precipitation as compared with young trees in low-elevation stands and under high neighbourhood competition levels, and the negative effect of summer temperature on old trees’ RWIs at low-elevation sites as an indirect influence of temperature on water availability of individual trees. 4.3. Influence of disturbance history on Norway spruce growth responsiveness to climate Stands with high-severity disturbance history (DI) showed lower RWI sensitivity to winter temperatures, but higher responsiveness to winter precipitation. Few researchers have previously investigated changes in tree sensitivity to climate related to natural disturbances. Rozas (2001) observed an intensified climate signal on Fagus sylvatica L., but a constant sensitivity of Quercus robur L. growth to climate during periods with a high frequency of intense disturbances. In our study, stands with high DI are characterized by short homogenous crowns organized in one vertical

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canopy layer, while those with low DI are characterized by a heterogeneous vertical structure with a multi-stratified canopy. Most of the stands with high disturbance regime (DI > 1.5, n = 9 out of 12 stands) were occupied by young trees (age < 100 years, see Supplementary Material, Fig. A.2). The higher response to winter precipitation in those high-DI stands was therefore surprising and may be related to canopy structure, given that old trees were generally more responsive to winter precipitation than young trees. Additionally, in low-severity disturbance regimes, RWI was enhanced by spring precipitation especially in high-density stands. The multi-stratified canopy of the low-DI would result in a thicker canopy layer with high leaf area index, especially in crowded stands, which would lead to higher rainfall and snow interception. Both rainfall and snow intercepted and temporarily stored by the canopy are partly evaporated, meaning a net water loss for the site vegetation. As canopy water storage capacity depends on canopy structure characteristics such as leaf area index (Llorens and Gallart, 2000), while, for instance, canopy closure is an important factor for snow interception (Lundberg and Halldin, 2001), lower soil water status at the onset of the growing season could be expected in these low-DI high-density stands. 4.4. Comparisons of the responses of RWIs to climate at the plot and tree scales The finding that Norway spruce growth in the study area was mainly temperature-driven has been observed in other studies ˇ ejková and Kolárˇ, 2009; Treml et al., 2012; Wilson and (C Hopfmueller, 2001), although we found that growth was also influenced by spring precipitation. Even though we found similar patterns of the climate–growth relationships at the plot and tree scales, the individualistic approach highlighted that while most trees positively responded to spring precipitation, some of them also reacted to precipitation in previous and current summers. These results emphasize the importance of tree water status for tree growth in these temperature-sensitive forests. Those trees particularly sensitive to water availability did not follow any apparent trend by elevation, competition status, disturbance severity or age. The response of tree growth to summer precipitation of certain trees could be related to parameters not analysed in the present study (e.g. soil type, topography) combined with the shallow root system of Norway spruce, which likely experiences drought stress on sites with steep slopes or rocky soils, even in regions with relatively high precipitation (Vejpustková et al., 2004). 4.5. Methodological considerations We have investigated how different tree and stand features modify the climate–growth relationships of Norway spruce in primary forests at tree and plot scales, but we are aware of the limitations of our study. To assess the influence of competition on the growth response to climate, we calculated a static competition index, similar to other indices used in previous researches focused on growth-competition associations (e.g. Linares et al., 2010; Weber et al., 2008). We assume that the current competition status broadly represents the competition pressure for the last 50 years, given the typically shade-tolerant nature of P. abies and the fact that disturbances during the 1961–2010 period affected just around 2.3% (Ca˘limani) and 17.8% (Giuma˘lau) of the area. Those disturbances could have also influenced the RWI response to climate, although both intensified and constant climate signal of tree growth have been recorded during periods with a high frequency of intense disturbances (Rozas, 2001). However, given the magnitude of the disturbances during the study period, we consider that only a small part of the sampled trees might have been influenced by those disturbances events. Lastly, the results based on the predictions of the

best fitted linear mixed-effects models should be interpreted with caution, since the observations number of some combination of factors (e.g. high-density high-elevation stands) might be scarce, in accordance with their representation in the study area. 4.6. Future perspectives We did not observe strong trends in air temperature and total precipitation during the last fifty years in the study area (Supplementary Material, Fig. A.3). However, an increase in air temperature after the 1980s in the Eastern Carpathians was evident (Popa and Kern, 2009), while the frequency of drought events have increased in recent decades in SW Romania (Levanicˇ et al., 2013). Under the projected increase in temperature (IPCC, 2007), more research is needed to estimate possible effects of future changes in climate on the stand growth dynamics. In this framework, altitudinal gradients have been proved to provide extremely valuable information for understanding climatic-driven changes over time (King et al., 2013). In our study site, higher temperatures could enhance Norway spruce radial growth in high elevation sites, because of the positive effect of warm summer temperatures on Norway spruce RWI, or due to longer growing seasons if increasing temperature advances the timing of snow melt (Vaganov et al., 1999). However, at lower elevations, a decrease in water availability due to warmer conditions or an increase in drought severity or frequency could lead to growth declines and an increase in trees’ vulnerability to other disturbances (e.g. windstorms, Ips typographus L. outbreaks) through increased stress due to water shortage. Our results suggest that both old trees and trees under high competition pressure growing at the lower elevations are most vulnerable to the predicted increase in temperature. 5. Conclusions Norway spruce showed similar patterns of growth (RWI) responsiveness to climate at the plot and tree scales. Elevation and the severity of the historic disturbance regime played a major role in the climate–growth associations, although their effects frequently depended on the competition intensity and/or tree age. Norway spruce growth in this subalpine forest was mainly temperature-driven, but soil water recharge prior to the onset of the cambial activity also greatly influenced tree growth. The importance of soil water status on growth dynamics was particularly noticeable at low elevations, especially for old trees or trees growing under high neighbourhood competition. Additionally, the individualistic approach revealed the existence of trees particularly sensitive to summer precipitation. Under forecasted climate warming scenarios, while trees located at high-elevation sites might be favoured by warmer conditions, old trees or trees under high competition pressure located at low-elevation sites will be the most vulnerable ones to drought. Acknowledgements This study was supported by Czech Science Foundation GACR 15-14840S and by Czech University of Life Sciences, Prague, CIGA No. 20154316. We thank the Ca˘limani National Park authorities, especially E. Cenusa and local foresters, for administrative support and assistance in the field. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foreco.2015.06. 034.

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May 11, 2007 - and FS) morphologies allowed us to determine each mu- ..... Bohannan, B. J. M., B. Kerr, C. M. Jessup, J. B. Hughes, and G. Sandvik. 2002.

Land Disturbance Permit and Inspection Report forms-Tier 2.pdf ...
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Land Disturbance Permit and Inspection Report forms-Tier 2.pdf ...
For TIER II SWPPPs, NOIs must be sent to the Kentucky Division of Water (KDOW) at the following address: Section Supervisor. Permit Support Section. Surface Water Permit Branch, KDOW. Frankfort Office Park. 200 Fair Oaks Lane, 4th Floor. Frankfort, K

Islam - Elevation of Women's Status
idea - at least in the West - is that Islam does not elevate the status of women, but that Islam ... author of Japanese decent (Francis Fukuyama) called "The End of Time". ... these ideas were scientific in nature, that the earth goes around the sun,

Set-valued Observers And Optimal Disturbance Rejection
the NSF under Grant ECS–92258005, EPRI under Grant #8030–23, and Ford. Motor Co. The authors are with the Department of Aerospace Engineering and. Engineering Mechanics, The .... storage of all measurements. Reference [33] goes on to provide an a

pdf-1499\physical-security-and-military-role-in-civil-disturbance ...
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LANDIS-II Drought Generator and Disturbance v1.2 Combined User ...
LANDIS-II Drought Generator and Disturbance v1.2 Combined User Guide.pdf. LANDIS-II Drought Generator and Disturbance v1.2 Combined User Guide.pdf.

Limited evidence of interactive disturbance and nutrient effects on the ...
Numerous theories have been developed ... competitive and adaptive capacities of the existing ..... Primer Software Version 5.0) based on multi-species data.

Ground Disturbance Statement 120716.pdf
In order to determine which projects have ground disturbing activities,. the property owner or agent/representative shall complete the following: Project Address.

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Sep 6, 2012 - While there is a rigorously proven relationship about uncertainties intrinsic to any quantum system, ... ''measurement-disturbance relationship'', using weak measurements to characterize a quantum system before and after it ..... [24] J

Cursor positioning device operable over various degrees of elevation
Apr 16, 1993 - 59-186035 10/1984 Japan . .... device 10 can be seen to include a skeleton 20, sand .... portable or laptop computer, although the trackball of.