Age, competition, disturbance and elevation effects on tree and stand
growth response of primary Picea abies forest to climate
, Jesús Julio Camarero
, Pavel Janda
, Vojtch C
, Robert C. Morrissey
, Radek Bac
, Marius Teodosiu
, Miroslav Svoboda
Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamy
´cká 129, Praha 6 – Suchdol, 16521 Prague, Czech Republic
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
Faculty of Forestry, University Sßtefan cel Mare Suceava, Universita
˘tßii 13, 720229 Suceava, Romania
Received 14 April 2015
Received in revised form 24 June 2015
Accepted 27 June 2015
Available online 11 July 2015
Linear mixed-effects models
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 coefﬁ-
cients between ring-width indices (RWIs) and climate variables. The inﬂuence of stand and tree character-
istics 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 inten-
sity 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
Ó2015 Elsevier B.V. All rights reserved.
In European forests tree growth is constrained by low tempera-
tures 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
can modify how individual trees respond to climatic variables at
different spatial scales (Galván et al., 2014). Classical dendroclima-
tological studies have focused on trees with similar response to cli-
mate 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
0378-1127/Ó2015 Elsevier B.V. All rights reserved.
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.
E-mail addresses: firstname.lastname@example.org (I. Primicia), email@example.com
(J.J. Camarero),jandap@ﬂd.czu.cz(P. Janda), cada@ﬂd.czu.cz(V. C
gmail.com (R.C. Morrissey),trotsiuk@ﬂd.czu.cz (V. Trotsiuk), bace@ﬂd.czu.cz (R. Bac
firstname.lastname@example.org (M. Teodosiu), svobodam@ﬂd.czu.cz (M. Svoboda).
Forest Ecology and Management 354 (2015) 77–86
Contents lists available at ScienceDirect
Forest Ecology and Management
journal homepage: www.elsevier.com/locate/foreco
(Galván et al., 2014). Studying the variability of the climate–
growth response at the individual tree scale provides valuable eco-
logical 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 wide-
spread 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 compe-
tition) 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 temperature-
sensitive because they represent the upper part of the spruce dis-
tribution in the Carpathians but do not reach the alpine tree line
ˇejková and Kolár
ˇ, 2009; Treml et al., 2012; Wilson and
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
˘limani and Giuma
˘lau Mountains of the Eastern Romanian
Carpathians. We sampled ﬁfty pure Norway spruce plots, 21 in
˘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
˘lau, respectively (Supplementary Material, Fig. A.1). The
bedrock is composed of andesites (Seghedi et al., 2005) and phyl-
lite 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 m
in size were estab-
lished at each grid intersect; however, in plots with a high tree
density (>500 trees ha
) and homogenous structure, plot size
was reduced to 500 m
(n= 20). Stands with evidence of past log-
ging, 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 P10 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
2.2.1. Dendrochronological methods
In 2011, we cored 25 (for 1000-m
sample plots) or 15 (for
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 determi-
nation. 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 stere-
omicroscope 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 fre-
quency 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
˘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 dendrochrono-
logical statistics (Fritts, 2001) were calculated either from the raw
tree-ring series (mean and standard deviation of ring width; AC,
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.
Sites C2 C3 C4 C5 G1
Mean (range) elevation (m a.s.l.) 1626 (1599–1653) 1484 (1415–1549) 1557 (1505–1601) 1558 (1512–1598) 1430 (1249–1571)
Mean (range) slope (°) 38 (33–43) 22 (16–28) 28 (25–32) 20 (15–23) 29 (17–38)
No. plots 4 6 6 5 29
Mean (±SD) tree density (stems ha
) 365 ± 88 803 ± 107 408 ± 168 432 ± 130 516 ± 257
Mean (±SD) diameter at breast height (cm) 37.1 ± 3.4 23.9 ± 4.3 38.3 ± 7.9 39.7 ± 5.6 31.8 ± 9.2
Mean (±SD) basal area (m
) 46.3 ± 7.1 41.5 ± 12 53.2 ± 7.4 61 ± 4.1 47.6 ± 14.6
No. sampled trees 63 122 99 103 421
No. trees with competition index 43 82 72 73 258
Mean (range) tree age at 1 m (yrs.) 188 (84–257) 68 (56–78) 171 (51–276) 146 (53–237) 133 (50–304)
78 I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86
ﬁrst-order autocorrelation) or using the residual chronologies (ms
mean sensitivity; r
, mean correlation between trees). The AC
assesses the similarity between ring widths in consecutive years.
measures the width variability of consecutive tree rings,
while the r
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
´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
At the tree scale we calculated the competition index (CI) pro-
posed by Hegyi (1974):
is the DBH of neighbouring tree, d
, the DBH of focal tree,
, 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 pro-
posed 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 max-
imum 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 distur-
bance 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 ‘‘re-
leases’’, 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. distur-
bance 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:
is the proportion of plot disturbed belonging to the ith dec-
ade and Nis the number of decades. For the zero disturbance prob-
ability 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 theoret-
ical value (reaches 0) indicate that 100% canopy area was disturbed
during one decade (see Svoboda et al., 2014 for more details and
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 com-
mon 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 vari-
ables as dependent variables. At the plot scale, the proposed mod-
els 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, competi-
tion 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 correla-
tion on the sample site or plot (Pinheiro and Bates, 2000). The per-
tinence 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 inter-
actions. The best-ﬁtted models were considered those showing
the lowest Akaike Information Criterion values, i.e. those most par-
simonious (Burnham and Anderson, 2002); they were identiﬁed
using the Multi-Model Inference (MuMIn) package (Barton, 2013).
We calculated a pseudo-R
of the selected models following
Nakagawa and Schielzeth (2013), which comprises marginal
m) and conditional (R
values. The R
maccounts for the
proportion of variance explained by the ﬁxed factors, and the R
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).
3.1. Growth characteristics and climatic drivers of Norway spruce
The mean tree-ring width was 1.47 mm at Ca
1.41 mm at Giuma
˘lau, and mean sensitivity (ms
) was similarly
low (0.19) in both localities. The mean correlations between tree
) were 0.30 in Ca
˘limani and 0.32 in Giuma
˘lau, and the
I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86 79
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 temper-
ature 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
˘limani) and December (both localities), and in
˘lau), March (both), June (Ca
˘lau) of the year of tree-ring formation (Fig. 1).
Precipitation was important in February (Ca
˘limani) and April
˘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 com-
ponents 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 R
= 0.47, P< 0.001,
Fig. 2b). Warm previous October and current June temperatures
and high precipitation in February enhanced plot RWIs at high ele-
vations; 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 temper-
ature 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 character-
ized 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 cli-
mate–RWI associations, while slope did not show any signiﬁcant
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 sig-
niﬁcantly 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 compe-
tition neighbourhoods in high-elevation stands, while at low eleva-
tions, only RWIs of trees subjected to high competition levels were
negatively affected by the same variable (Table 3,Fig 4d).
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
˘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.
80 I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86
to climate at both scales, although it frequently interacted with
stand crowding index, tree age, and tree-to-tree competition inten-
sity. Severity of the historic disturbance regime was also an impor-
tant variable inﬂuencing climate–growth associations at the plot
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 maxi-
mum rates from June to July and ends in August–September
(Treml et al., 2014). Warm temperatures during autumn and win-
ter 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
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:
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.
Month Elevation Crowding
Elevation CRI CRI DI Aspect DI Intercept Residuals R
Temperature October 0.00101 0.02532 0.042 0.071 0.59 0.70
December 0.00021 0.03754 0.10350 0.00258 0.149 0.061 0.12 0.88
January 0.05932 0.02678 0.093 0.072 0.08 0.65
March 0.00018 0.08997 0.05994 0.00241 0.092 0.074 0.14 0.66
June 0.00076 0.000 0.072 0.48 0.48
September 0.00030 0.07956 0.072 0.060 0.23 0.68
Precipitation February 0.00026 0.04309 0.01481 0.04472 0.020 0.062 0.25 0.32
April 0.00072 0.09214 0.01359 0.00354 0.44524 0.062 0.057 0.46 0.75
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.
Marginal (proportion of variance explained by the ﬁxed factors, R
m) and conditional (proportion of variance explained by ﬁxed plus random factors, R
calculated following Nakagawa and Schielzeth (2013).
I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86 81
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 tem-
perature could be explained by a lengthening of xylogenesis in
years with warm early autumns, since the ending of wood forma-
tion 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 ele-
vations. 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.,
ˇejková and Kolár
ˇ, 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 fre-
quently 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 cli-
mate–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 photo-
synthesis (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
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.
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 Age Elevation Competition index (CI) Age elevation Age CI Elevation CI Intercept Residuals R
October 0.0004 0.0087 0.0001 0.037 0.123 0.07 0.15
December 0.0002 0.0003 4.00E06 0.034 0.116 0.09 0.17
January 4.89E05 0.0004 2.79E06 0.048 0.123 0.08 0.20
March 0.0002 0.0003 5.61E06 0.036 0.134 0.09 0.15
June 0.0001 0.0007 0.0023 4.10E06 0.0001 0.036 0.127 0.18 0.25
September 0.0003 0.0127 0.037 0.122 0.03 0.12
February 0.0004 0.043 0.123 0.05 0.15
April 0.0008 0.0009 0.0003 5.85E06 0.0004 0.0002 0.045 0.153 0.03 0.11
m) and conditional (R
values were calculated following Nakagawa and Schielzeth (2013).
82 I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86
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 ele-
vations may be linked to competition among trees for soil water
availability even in these temperate areas. The competition inten-
sity also modiﬁed the inﬂuence of elevation on the response of tree
growth to summer temperatures. Thus, even though warm sum-
mer 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 sum-
mer temperature on tree RWIs is probably due to an indirect inﬂu-
ence 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 dif-
ferent 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 neigh-
bourhood 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 ele-
vations, 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 phys-
iological 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, sap-
wood 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 respon-
siveness to winter precipitation. Few researchers have previously
investigated changes in tree sensitivity to climate related to natu-
ral disturbances. Rozas (2001) observed an intensiﬁed climate sig-
nal 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 charac-
terized by short homogenous crowns organized in one vertical
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.
I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86 83
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 win-
ter 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
The ﬁnding that Norway spruce growth in the study area was
mainly temperature-driven has been observed in other studies
ˇejková and Kolár
ˇ, 2009; Treml et al., 2012; Wilson and
Hopfmueller, 2001), although we found that growth was also inﬂu-
enced by spring precipitation. Even though we found similar pat-
terns 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 particu-
larly 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 rela-
tively high precipitation (Vejpustková et al., 2004).
4.5. Methodological considerations
We have investigated how different tree and stand features mod-
ify 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, sim-
ilar 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 distur-
bances during the 1961–2010 period affected just around 2.3%
˘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 dis-
turbances (Rozas, 2001). However, given the magnitude of the dis-
turbances during the study period, we consider that only a small
part of the sampled trees might have been inﬂuenced by those dis-
turbances 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 fac-
tors (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 evi-
dent (Popa and Kern, 2009), while the frequency of drought events
have increased in recent decades in SW Romania (Levanic
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 frame-
work, 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 availabil-
ity 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 typogra-
phus L. outbreaks) through increased stress due to water shortage.
Our results suggest that both old trees and trees under high com-
petition pressure growing at the lower elevations are most vulner-
able to the predicted increase in temperature.
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 fre-
quently 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 particu-
larly noticeable at low elevations, especially for old trees or trees
growing under high neighbourhood competition. Additionally,
the individualistic approach revealed the existence of trees partic-
ularly 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.
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.
84 I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86
Aakala, T., Kuuluvainen, T., 2011. Summer droughts depress radial growth of Picea
abies in pristine taiga of the Arkhangelsk province, northwestern Russia.
Dendrochronologia 29, 67–75. http://dx.doi.org/10.1016/j.dendro.2010.07.001.
Aitken, S.N., Yeaman, S., Holliday, J.A., Wang, T., Curtis-McLane, S., 2008. Adaptation,
migration or extirpation: climate change outcomes for tree populations. Evol.
Appl. 1, 95–111. http://dx.doi.org/10.1111/j.1752-4571.2007.00013.x.
´, I., Mihajlovic
´, V., Ranc
´, D., Mihic
´, D., Djurdjevic
´, V., 2013. Digital climate
atlas of the carpathian region. Adv. Sci. Res. 10, 107–111. http://dx.doi.org/
Aussenac, G., 2000. Interactions between forest stands and microclimate:
ecophysiological aspects and consequences for silviculture. Ann. Forest Sci.
57, 287–301. http://dx.doi.org/10.1051/forest:2000119.
Babst, F., Poulter, B., Trouet, V., Tan, K., Neuwirth, B., Wilson, R., Carrer, M., Grabner,
M., Tegel, W., Levanic
ˇ, T., Panayotov, M., Urbinati, C., Bouriaud, O., Ciais, P.,
Frank, D., 2013. Site- and species-speciﬁc responses of forest growth to climate
across the European continent. Global Ecol. Biogeogr. 22, 706–717. http://
Balintoni, I., 1996. Geotectonica terenurilor metamorﬁce din Romania. The Babes-
Bolyai University, Cluj Napoca, Romania.
Barton, K., 2013. MuMIn: Multi-model inference. R package version 1.9.13.
Bunn, A.G., 2010. Statistical and visual crossdating in R using the dplR library.
Dendrochronologia 28, 251–258. http://dx.doi.org/10.1016/j.dendro.2009.
Büntgen, U., Frank, D.C., Kaczka, R.J., Verstege, A., Zwijacz-Kozica, T., Esper, J., 2007.
Growth responses to climate in a multi-species tree-ring network in the
Western Carpathian Tatra Mountains, Poland and Slovakia. Tree Physiol. 27,
Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A
Practical Information-theoretic Approach, second ed. Springer-Verlag,
Carrer, M., 2011. Individualistic and time-varying tree-ring growth to climate
sensitivity. PLoS One 6, e22813. http://dx.doi.org/10.1371/journal.
Carrer, M., Urbinati, C., 2004. Age-dependent tree-ring growth responses to climate
in Larix decidua and Pinus cembra. Ecology 85, 730–740. http://dx.doi.org/
ˇejková, A., Kolár
ˇ, T., 2009. Extreme radial growth reaction of Norway spruce along
an altitudinal gradient in the Šumava Mountains. Geochronometria 33, 41–47.
Cook, E., Kairiukstis, L., 1990. Methods of Dendrochronology – Applications in the
Environmental Sciences. Kluwer-IIASA, Dordrecht, The Netherlands.
Cook, E.R., Peters, K., 1981. The smoothing spline: a new approach to standardizing
forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull.
Deslauriers, A., Rossi, S., Anfodillo, T., Saracino, A., 2008. Cambial phenology, wood
formation and temperature thresholds in two contrasting years at high altitude
in southern Italy. Tree Physiol. 28, 863–871. http://dx.doi.org/10.1093/
Dixon, P., 2001. Bootstrap resampling. In: El-Shaarawi, A.H., Piegorsch, W. (Eds.),
The Encyclopedia of Environmetrics. Wiley, New York.
Duncan, R., 1989. An evaluation of errors in tree age estimates based on increment
cores in kahikatea (Dacrycarpus dacrydioides). New Zeal. Nat. Sci. 16, 31–37.
Frelich, L., Lorimer, C., 1991. Natural disturbance regimes in hemlock hardwood
forests of the upper Great-Lakes region. Ecol. Monogr. 61, 145–164. http://
Fritts, H.C., 2001. Tree-Rings and Climate. Blackburn Press, Caldwell, NJ, USA.
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. http://dx.doi.org/10.1111/1365-2745.12268.
Hegyi, F., 1974. A simulation model for managing jack-pine stands. In: Fries, J. (Ed.),
Growth Models for Tree and Stand Simulation. Royal College of Forestry,
Stockholm, Sweden, pp. 74–90.
Holmes, R.L., 1983. Computer-assisted quality control in tree-ring dating and
measurement. Tree-Ring Bull., 43
Hubbard, R.M., Bond, B.J., Ryan, M.G., 1999. Evidence that hydraulic conductance
limits photosynthesis in old Pinus ponderosa trees. Tree Physiol. 19, 165–172.
IPCC, 2007. Fourth Assessment Report: Climate Change 2007. Working Group I
Report. The Physical Science Basis, Geneva, Switzerland.
King, G.M., Gugerli, F., Fonti, P., Frank, D.C., 2013. Tree growth response along an
elevational gradient: climate or genetics? Oecologia 173, 1587–1600. http://
Koenker, R., 2013. Quantreg: Quantile Regression. R package version 4.01.
Kunstler, G., Albert, C.H., Courbaud, B., Lavergne, S., Thuiller, W., Vieilledent, G.,
Zimmermann, N.E., Coomes, D.A., 2011. Effects of competition on tree radial-
growth vary in importance but not in intensity along climatic gradients. J. Ecol.
99, 300–312. http://dx.doi.org/10.1111/j.1365-2745.2010.01751.x.
Lännenpää, A., Aakala, T., Kauhanen, H., Kuuluvainen, T., 2008. Tree mortality agents
in pristine Norway spruce forests in northern Fennoscandia. Silva Fenn. 42,
Lens, F., Tixier, A., Cochard, H., Sperry, J.S., Jansen, S., Herbette, S., 2013. Embolism
resistance as a key mechanism to understand adaptive plant strategies. Curr.
Opin. Plant Biol. http://dx.doi.org/10.1016/j.pbi.2013.02.005.
ˇ, T., Popa, I., Poljanšek, S., Nechita, C., 2013. A 323-year long reconstruction
of drought for SW Romania based on black pine (Pinus nigra) tree-ring widths.
Int. J. Biometeorol. 57, 703–714. http://dx.doi.org/10.1007/s00484-012-0596-9.
Lévesque, M., Saurer, M., Siegwolf, R., Eilmann, B., Brang, P., Bugmann, H., Rigling, A.,
2013. Drought response of ﬁve conifer species under contrasting water
availability suggests high vulnerability of Norway spruce and European larch.
Global Change Biol. 19, 3184–3199. http://dx.doi.org/10.1111/gcb.12268.
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. http://dx.doi.org/10.1111/j.1365-2745.2010.01645.x.
Llorens, P., Gallart, F., 2000. A simpliﬁed method for forest water storage capacity
measurement. J. Hydrol. 240, 131–144. http://dx.doi.org/10.1016/S0022-
Lorimer, C., Frelich, L., 1989. A methodology for estimating canopy disturbance
frequency and intensity in dense temperate forests. Can. J. Forest Res. 19, 651–
Lundberg, A., Lundberg, S., 2001. Snow interception evaporation. Review of
measurement techniques, processes and models. Theor. Appl. Climatol. 70,
Lupi, C., Morin, H., Deslauriers, A., Rossi, S., Houle, D., 2012. Increasing nitrogen
availability and soil temperature: effects on xylem phenology and anatomy of
mature black spruce. Can. J. Forest Res. 42, 1277–1288. http://dx.doi.org/
Mäkinen, H., Nöjd, P., Kahle, H.P., Neumann, U., Tveite, B., Mielikäinen, K., Röhle, H.,
Spiecker, H., 2003. Large-scale climatic variability and radial increment
variation of Picea abies (L.) Karst. in central and northern Europe. Trees 17,
Mäkinen, H., Nöjd, P., Kahle, H.-P., Neumann, U., Tveite, B., Mielikäinen, K., Röhle, H.,
Spiecker, H., 2002. Radial growth variation of Norway spruce (Picea abies (L.)
Karst.) across latitudinal and altitudinal gradients in central and northern
Europe. Forest Ecol. Manage. http://dx.doi.org/10.1016/S0378-1127(01)
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. http://dx.doi.org/10.1007/s00468-007-0191-6.
Mayr, S., Hacke, U., Schmid, P., Schwienbacher, F., Gruber, A., 2006. Frost drought in
conifers at the alpine timberline: xylem dysfunction and adaptations. Ecology
Nakagawa, S., Schielzeth, H., 2013. A general and simple method for obtaining R
from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142.
Pinheiro, J.C., Bates, D., DebRoy, S., Sarkar, S., Team, R.D.C., 2009. nlme: linear and
nonlinear mixed effects models. R package version 3, 1–104.
Pinheiro, J.C., Bates, D.M., 2000. Mixed-effects Models in S and S-PLUS. Springer-
Verlag, New York.
Popa, I., Kern, Z., 2009. Long-term summer temperature reconstruction inferred
from tree-ring records from the Eastern Carpathians. Climate Dyn. 32, 1107–
Pretzsch, H., Dieler, J., 2011. The dependency of the size-growth relationship of
Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.])
in forest stands on long-term site conditions, drought events, and ozone stress.
Trees 25, 355–369. http://dx.doi.org/10.1007/s00468-010-0510-1.
R Core Team, 2013. R: a language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria.
Rozas, V., 2001. Detecting the impact of climate and disturbances on tree-rings of
Fagus sylvatica L. and Quercus robur L. in a lowland forest in Cantabria, Northern
Spain. Ann. Forest Sci. 58, 237–251. http://dx.doi.org/10.1051/forest:2001123.
Rozas, V., 2014. Individual-based approach as a useful tool to disentangle the
relative importance of tree age, size and inter-tree competition in
dendroclimatic studies. iForest-Biogeosci. Forestry, e1–e8. http://dx.doi.org/
Russell, M.B., Weiskittel, A.R., 2011. Maximum and largest crown width equations
for 15 tree species in Maine. Northern J. Appl. 28, 84–91.
Ryan, M.G., Phillips, N., Bond, B.J., 2006. The hydraulic limitation hypothesis
revisited. Plant, Cell Environ. 29, 367–381. http://dx.doi.org/10.1111/j.1365-
Schaberg, P.G., 2000. Winter photosynthesis in Red Spruce (Picea rubens Sarg.):
limitations, potential beneﬁts, and risks. Arctic, Antarct. Alp. Res. 32, 375–380.
Schuster, R., Oberhuber, W., 2013. Age-dependent climate–growth relationships
and regeneration of Picea abies in a drought-prone mixed-coniferous forest in
the Alps. Can. J. Forest Res. 43, 609–618. http://dx.doi.org/10.1139/cjfr-2012-
Schweingruber, F.H., 1996. Tree rings and environment. Dendroecology. Swiss
Federal Institute for Forest, Snow and Landscape Research, Paul Haupt Verlag,
Seghedi, I., Szakacs, A., Pecskay, Z., Mason, P., 2005. Eruptive history and age of
magmatic processes in the Ca
˘limani volcanic structure (Romania). Geol.
Carpathica 1, 67–75.
Seidl, R., Schelhaas, M., Rammer, W., Verkerk, P.J., 2014. Increasing forest
disturbances in Europe and their impact on carbon storage. Nat. Climate
Change 4, 1–6. http://dx.doi.org/10.1038/nclimate2318.
Shorohova, E., Fedorchuk, V., Kuznetsova, M., Shvedova, O., 2008. Wind-induced
successional changes in pristine boreal Picea abies forest stands: evidence from
I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86 85
long-term permanent plot records. Forestry 81, 335–359. http://dx.doi.org/
Sperry, J.S., Robson, D., 2001. Xylem cavitation and freezing in conifers. In: Bigras, F.,
Colombo, S. (Eds.), Conifer Cold Hardiness. Kluwer Academic Publishers,
Dordrecht, pp. 121–136.
Spiecker, H., 2003. Silvicultural management in maintaining biodiversity and
resistance of forests in Europe-temperate zone. J. Environ. Manage. 67, 55–65.
Staebler, G.R., 1951. Growth an spacing in an even-aged stand of Douglas ﬁr.
University of Michigan.
Svoboda, M., Janda, P., Bac
ˇe, R., Fraver, S., Nagel, T.a., Rejzek, J., Mikoláš, M., Douda, J.,
Boublík, K., Šamonil, P., C
ˇada, V., Trotsiuk, V., Teodosiu, M., Bouriaud, O., Birisß,
´kora, O., Uzel, P., Zelenka, J., Sedlák, V., Lehejc
ˇek, J., 2014. Landscape-level
variability in historical disturbance in primary Picea abies mountain forests of
the Eastern Carpathians, Romania. J. Veg. Sci. 25, 386–401. http://dx.doi.org/
Szeicz, J.M., MacDonald, G.M., 1994. Age-dependent tree-ring growth responses of
subarctic white spruce to climate. Can. J. Forest Res. http://dx.doi.org/10.1139/
Treml, V., Kašpar, J., Kuz
ˇelová, H., Gryc, V., 2014. Differences in intra-annual wood
formation in Picea abies across the treeline ecotone, Giant Mountains, Czech
Republic. Trees. http://dx.doi.org/10.1007/s00468-014-1129-4 (Online
publication date: 21.11.14).
Treml, V., Ponocná, T., Büntgen, U., 2012. Growth trends and temperature responses
of treeline Norway spruce in the Czech-Polish Sudetes Mountains. Climate Res.
55, 91–103. http://dx.doi.org/10.3354/cr01122.
Trotsiuk, V., Svoboda, M., Janda, P., Mikolas, M., Bace, R., Rejzek, J., Samonil, P.,
Chaskovskyy, O., Korol, M., Myklush, S., 2014. A mixed severity disturbance
regime in the primary Picea abies (L.) Karst. forests of the Ukrainian Carpathians.
Forest Ecol. Manage. 334, 144–153. http://dx.doi.org/10.1016/
Vaganov, E.A., Hughes, M.K., Kirdyanov, A.V., Schweingruber, F.H., Silkin, P.P., 1999.
Inﬂuence of snowfall and melt timing on tree growth in subarctic Eurasia.
Nature 400, 149–151. http://dx.doi.org/10.1038/22087.
Valtera, M., Šamonil, P., Boublík, K., 2013. Soil variability in naturally disturbed
Norway spruce forests in the Carpathians: bridging spatial scales. Forest Ecol.
Manage. 310, 134–146. http://dx.doi.org/10.1016/j.foreco.2013.08.004.
Vejpustková, M., Zahradník, D., Šrámek, V., Fadrhonsová, V., 2004. Growth trends of
spruce in the Orlické hory Mts. J. For. Sci. 50, 67–77.
Von Felten, S., Hättenschwiler, S., Saurer, M., Siegwolf, R., 2007. Carbon allocation in
shoots of alpine treeline conifers in a CO
enriched environment. Trees 21, 283–
Weber, P., Bugmann, H., Fonti, P., Rigling, A., 2008. Using a retrospective dynamic
competition index to reconstruct forest succession. Forest Ecol. Manage. 254,
Wilson, R.J.S., Hopfmueller, M., 2001. Dendrochronological investigations of Norway
spruce along an elevational transect in the Bavarian Forest, Germany.
Dendrochronologia 19, 67–79.
Wu, S.H., Jansson, P.-E., Kolari, P., 2012. The role of air and soil temperature in the
seasonality of photosynthesis and transpiration in a boreal Scots pine
ecosystem. Agric. For. Meteorol. 156, 85–103. http://dx.doi.org/10.1016/
Yamaguchi, D.K., 1991. A simple method for cross-dating increment cores from
living trees. Can. J. For. Res. http://dx.doi.org/10.1139/x91-053.
Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M., Savelieve, A.A., 2009.
Mixed effects models and extensions in ecology with R. Springer-Verlag, New
86 I. Primicia et al. / Forest Ecology and Management 354 (2015) 77–86