Content uploaded by Josep M Serra-Diaz
Author content
All content in this area was uploaded by Josep M Serra-Diaz
Content may be subject to copyright.
Relationship between projected changes in future climatic
suitability and demographic and functional traits of forest
tree species in Spain
F. Lloret &J. Martinez-Vilalta &J. M. Serra-Diaz &
M. Ninyerola
Received: 18 July 2012 /Accepted: 5 June 2013 / Published online: 9 July 2013
#Springer Science+Business Media Dordrecht 2013
Abstract The response of plant species to future climate conditions is probably dependent on
their ecological characteristics, including climatic niche, demographic rates and functional
traits. Using forest inventory data from 27 dominant woody species in Spanish forests, we
explore the relationships between species characteristics and projected changes in their average
climatic suitability (occurrence of suitable climatic conditions for a species in a given territory)
obtained by empirical niche-based models, under a business-as-usual climate change scenario
(A1, HadCM3, 2001–2100). We hypothesize that most species will suffer a decline in climatic
suitability, with a less severe for species (i) currently living in more arid climates or exhibiting a
broader current climatic niche; (ii) with higher current growth rates; (iii) with functional traits
related to resistance to water deficits. The analysis confirm our hypothesis since apart from a
few Mediterranean species, most species decrease their climatic suitability in the region under
future climate, characterized by increased aridity. Also, species living in warmer locations or
under a wider range of climatic conditions tend to experience less decrease in climatic
suitability. As hypothesized, a positive relationship was detected between current relative
growth rates and increase in future climatic suitability. Nevertheless, current tree mortality
did not correlate with changes in future climatic suitability. In contrast with our hypothesis,
functional traits did not show a clear relationship with changes in climate suitability; instead
species often presented idiosyncratic responses that, in some cases, could reflect past manage-
ment. These results suggest that the extrapolation of species performance to future climatic
Climatic Change (2013) 120:449–462
DOI 10.1007/s10584-013-0820-6
Electronic supplementary material The online version of this article (doi:10.1007/s10584-013-0820-6)
contains supplementary material, which is available to authorized users.
F. Lloret (*):J. Martinez-Vilalta
Center for Ecological Research and Forestry Applications (CREAF), Edifici C. Universitat Autònoma
Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
e-mail: Francisco.Lloret@uab.es
F. Lloret :J. Martinez-Vilalta
Ecology Unit, Department of Animal Biology, Plant Biology and Ecology, Universitat Autònoma de
Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
J. M. Serra-Diaz :M. Ninyerola
Botany Unit, Department of Animal Biology, Plant Biology and Ecology, Universitat Autònoma de
Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
scenarios based on current patterns of dominance is constrained by factors other than species
autoecology, particularly human activity.
1 Introduction
Forecasting the ecological features of vegetation under future climate conditions is a major
concern in the assessment of the impacts of global change (Bonan 2008). Any assessment
may benefit from analyzing the relationship between the species ecological characteristics
and the projected change in the occurrence in a given territory of suitable climatic conditions
for such species (climatic suitability, hereafter). Projections of future species distributions
may be obtained from empirical niche-based models. These models geographically extrap-
olate the current relationship between species distribution and environmental (mainly cli-
matic) variables to expected suitable climatic conditions in the future via statistical
modelling (Franklin 2010). These models may also be useful for analyzing how expected
future conditions and current demographic trends correlate, thereby providing insights into
the vulnerability of species to the new scenarios. In spite of its usefulness, the correspon-
dence between plant traits and climate change response has scarcely been studied at present
(f.i., Esther et al. 2010).
Empirical niche-based models have some methodological shortcomings, however. These
statistical models do not directly consider the ecological processes that determine species
distribution, they merely represent an estimate of future climatic suitability, based on the
current relationship between species distribution and climate. Many factors, including
dispersal constraints, biotic interactions, disturbance regime and human intervention, make
significant contributions to species distribution but are not usually considered (Meier et al.
2010; Svenning et al. 2010). In addition, the assumption of climate-distribution equilibrium
has been challenged by paleoecological studies suggesting shifts of species realized niche
under natural climate change (Mellick et al. 2012). Finally, the variety and reliability of
modelling procedures and the quality of data imply technical challenges (Elith and Graham
2009; Foody 2011).
Functional traits, including life-history traits, can help to characterize the ecological
response of vegetation to climate change (Keith et al. 2008; Esther et al. 2010) although
historical stochasticity, microsite conditions and demographic processes may mask this
relationship, as can be seen in animals (Angert et al. 2011). Plant functional types have
been extensively applied in the development of vegetation dynamics models at different
scales (f.i., Prentice et al. 1992), often using a broad description of them, without any
specific link to the species level. However, functional traits reflect specific environmental
conditions—due, for example, to phenotypic plasticity or adaptation. Therefore, the analysis
of the relationship between traits and environmental changes is expected to be context-
dependent and should gain from trait estimations obtained from the target populations.
Thefateofagivenspeciesisalsolikelytobeinfluencedbytheperformanceofits
current populations. Population decline (i.e. high mortality rates or low individual growth)
is an indicator of species vulnerability, particularly if the new climatic conditions are
expected to increase abiotic stress (Allen et al. 2010). This could be the case with
populations currently established at the southern edge of their distribution in the temperate
regions of the Northern Hemisphere, where climatic projections forecast an overall increase
in aridity (IPCC 2007). Alternatively, current low mortality and/or high growth rates could
indicate that populations are robust enough to cope with the upcoming conditions—at least
450 Climatic Change (2013) 120:449–462
in the short term -, or even that they are still responding positively to the new climatic
environment (Doak and Morris 2010).
In this study we explore the relationship between projected changes in future climatic
suitability due to climate change and demographic and plant functional traits of species. We
estimate species climatic suitability from the climatic conditions modelled from species
current pattern of distribution. We use 27 dominant woody species from forests in Spain, a
Mediterranean region that is predicted to experience an overall increase in aridity in the
coming decades (IPCC 2007; Giorgi et al. 2004; Felicisimo 2011). More specifically, we
address whether the projected future changes in climatic suitability are related to:
(1) The current climatic conditions where dominant species are currently found. We
hypothesize that due to an expected increase of aridity (i) climatic suitability will
remain higher for species currently living in more arid climates, and (ii) the broader
range of climatic conditions where species are currently found, the lower the projected
loss of future climatic suitability.
(2) The current trends in demographic variables (plant growth and mortality). We aim to
explore if future reduction in climatic suitability in a given territory could exacerbate
current demographic trends leading to decline that are observed in populations of such
territory. In the case of plant growth we hypothesize a positive relationship between
growth rate and change in climatic suitability. We base this hypothesis on the fact that
populations are already responding to climate trends that have the same direction as
expected in the future (Jump et al. 2008,2009).
(3) The functional traits of species (maximum tree height, wood density, specific leaf area,
nitrogen content of leaves, leaf area and seed mass). We hypothesize that traits related
to resistance to stress, particularly water deficit, will perform better in the future
scenario of increasing aridity, that is, they would correlate accordingly with changes
of climatic suitability.
2 Methods
2.1 Study area
The study area is continental Spain. The general climate is temperate, but there is a large
elevation gradient, and the climate shows a wide variation from semiarid in the Southeast
to humid and moderately cold in the Northwest, with different types of Mediterranean
climate in the remaining area, where mountain ranges are common. There is also a
continentality gradient, with hot summers and cold winters inland. Average annual
temperature ranges between less than 5 °C on mountain peaks (up to ~3,500 m)—mostly
in N Spain—to ca. 19 °C in the South. Total annual rainfall ranges between ca.
2,000 mm in the Northwest to less than 250 mm in the Southeast. The vegetation
consists mostly of managed sclerophyllous, evergreen shrublands and broadleaf, decidu-
ous, and coniferous forests.
2.2 Demographic, functional and climatic characterization of species
The final set of 27 species considered in this study (Supplementary material, Annex 1) was
the result of a compromise between the information available from estimates of climatic
Climatic Change (2013) 120:449–462 451
suitability and the information on demographic traits provided by forest inventories. We only
considered species for which at least 100 individual trees were re-measured in two succes-
sive inventories. Thus we focus on dominant species in these forests.
We used average tree growth and mortality as indicators of the species’current demo-
graphic trend in the territory, as described elsewhere (Martínez-Vilalta et al. 2010). Briefly,
these parameters were obtained by comparing the second and third Spanish National Forest
Inventories (IFN2, period 1986–96, and IFN3, period 1997–2007, respectively; Dirección
General de Conservación de la Naturaleza 2006), which sampled the whole forested area of
Spain (ca. 10.7·10
6
ha) at a density of approximately 1 plot km
−2
following a regular design,
(N=54,300). We excluded from the study those plots with evidence of forest management,
i.e. cutting or thinning, in the plot or the surrounding area as recorded during the IFN3
survey (remaining N=40,373 plots). Plantation non-native species that are primarily grown
for timber production in Spain were excluded from the analyses. Tree growth and mortality
was only calculated from the adult individuals, with DBH >12.5 cm for species with typical
tall trees (typical DBH >15 cm and typical height >10 m), and with DBH >7.5 cm for
species with typical short trees or shrubs (typical DBH <15 cm and typical height <10 m)
(Supplementary Material, Annex 1).
Species growth was estimated as the average of the relative growth rate (RGR) of the
trees belonging to each species. RGR was calculated for each individual tree as
(ln[DBH
IFN3
]−ln[DBH
IFN2
]) ⁄time, where DBH
IFN2
and DBH
IFN3
are the diameters at breast
height measured in the second and third Spanish Forest Inventories, respectively, and time is
the time interval between measurements.
Annual mortality rate (MR) was calculated as (ln[N
IFN2
]−ln[N
IFN3
]) ⁄time, where N
IFN2
is the number of trees of a given species that were recorded in the IFN2, N
IFN3
the number of
those that were still alive in the IFN3, and time is the interval between surveys. Although the
precise period may vary among plots, we considered these estimations to reflect the
demographical tendencies at a decadal scale during the whole surveyed period. The different
sample size used for each species did not imply a significant bias in MR estimations
(Martínez-Vilalta et al. 2010). Tree size distribution had no relevant overall effect on the
estimated demographic rates (Martínez-Vilalta et al. 2010).
We used the following functional traits (cf. Westoby and Wright 2006): maximum tree
height (hereafter, height, Hmax) (m), wood density (WD) (g·cm
−3
), specific leaf area (SLA)
(mm
2
·mg
−1
), nitrogen content of leaves (Nmass) (% mass), leaf area (LA) (cm
2
) and seed
mass (SM) (mg) as traits characterizing, on average, the Spanish populations of the studied
species, as detailed in Martínez-Vilalta et al. (2010). Thus, we assume that these functional
traits are more variable between than within species, as supported by Martínez-Vilalta et al.
(2010). Trait information was mostly acquired from the Catalan Ecological and Forest
Inventory (IEFC, Burriel et al. 2000–2004; http://www.creaf.uab.es/iefc/), which was
performed between 1988 and 1998 and covers a range of environmental conditions compa-
rable to those of continental Spain in terms of altitudinal and climatic gradients. The
information on WD, SLA and Nmass was completed by published values following
comparable methodologies (see Martínez-Vilalta et al. 2010), LA information was complet-
ed by local flora (Bolós et al. 1990), and SM was taken entirely from published sources,
primarily from Spanish populations. These data sources provided reliable average informa-
tion of the considered traits for the conditions found in the geographical range of the study.
An average description of the climatic conditions where each species is currently
found in the considered region was estimated from the mean of some climatic parameters
(obtained from Digital Climatic Atlas of the Iberian Peninsula DCAIB, Ninyerola et al.
2005;http://opengis.uab.es/wms/iberia/english/en_model.htm) corresponding to the plots
452 Climatic Change (2013) 120:449–462
where the species was recorded: average mean annual temperature (MAT), mean annual
precipitation (MAP) and mean ratio of summer (June–August) precipitation to potential
evapotranspiration (P/PET). We used the standard deviations of these parameters (named
MAT SD, MAP SD and P/PET SD, respectively) to characterize the climatic variability of
each species’range.
2.3 Climatic suitability modelling
We estimated the projected change in climatic suitability for each species in the studied
territory by comparing the average climatic suitability from 1950 to 2000, as provided by
DCAIB and considered as the climate baseline, to the one projected for the period 2001–
2100, as generated by the HadCM3 global circulation model (GCM) using the A1 scenario
(IPCC 2007). We choose such a relatively long period for the climate change situation to
stress changes on the climatic suitability of long-lived tree species. The A1 scenario
considers an increase in atmospheric CO
2
to 810 ppm by 2080, with an associated increase
in average temperature of 3.1 °C by 2080 for the area included in this study (in comparison
to 1960–1990 period), and an average decrease in precipitation of 95 mm. We choose this
relatively non-conservative scenario in order to stress changes in species vulnerability. Also,
the considered combination of GCM and scenario represents a plausible situation for the
Iberian Peninsula according to the current climatic trends and the ability of GCM to
reproduce historical patterns.
Current climate variables were extracted from DCAIB at 200 m spatial resolution.
Future climates were obtained at the same resolution by statistical downscaling of GCM
grids (see Keenan et al. 2011 and Supplementary Material, Annex 2for further informa-
tion). Climatic suitability maps for each species were built from Generalized Linear
Models (GLMs) based on presence/absence data of IFN3, after geographically fitting
each IFN3 plot to the climatic regionalization. Given that IFN3 provided information on
presence/absence, this procedure was preferred to other approaches only considering
presence. Also, the GLM technique provides a balance between discrimination and
calibration, an appropriate feature in our case, since we seek for changes in the proba-
bility of occurrence, in front of other highly discriminatory techniques. Presence was
selected when the target species was dominant in the respective plot (the first or second
most abundant species according to basal area). The IFN3 does not record the entire
distribution of all species considered. This problem is minimized by the large extension
of the Iberian Peninsula, with its broad range of environmental conditions, and the fact
that relative suitability change was estimated rather than spatial outcomes. We built 250
replication datasets with different random selections of absence plots, keeping prevalence
constant (Number of absence plots = Number of presence plots).
Stepwise GLMs were run for each replication dataset considering minimum, maximum
and mean temperature and precipitation on a seasonal and yearly time scale, and water
availability, computed as precipitation minus evapotranspiration (see Supplementary Mate-
rial, Annex 2for further details about model calculations). Suitability models for each
species produced an output (suitability index, ranging from 0 to 1) that could be interpreted
as the probability of dominance given a set of climatic parameters. Accordingly, we use the
total area of the Iberian Peninsula as a surrogate of the respective species regional suitability.
Although the resulting suitability outputs can be influenced by the different number of plots
across species, we consider that the use of the ratio between projected future and current
suitability, as well as the introduction of the respective number of plots of each species in the
statistical analysis (see below) minimize this problem.
Climatic Change (2013) 120:449–462 453
2.4 Analyses
We assessed the weight of phylogeny on the relationship between climatic suitability
change and species characteristics by using phylogenetic generalized least squares
(PGLS, Freckelton et al. 2002) and comparing partial least square models, both with
phylogenetic effects included and without them. We assumed an Ornstein–Uhlenbeck
model of character evolution (Martins and Hansen 1997). We constructed a phylogenetic
tree for the study species using Phylomatic (Webb and Donogue 2005) based on the
maximally resolved tree, considering all branch lengths to be set equal to 1 due to the multiple
sources of information (see Martínez-Vilalta et al. 2010 for further details) (Supplementary
Material, Annex1).
We performed several PGLS analyses in which the response variables were (i) the ratio
between the averages of the projected future and current values of climatic suitability for the
whole territory, calculated for each species (hereafter, climatic suitability change), and (ii)
the projected climatic suitability of the species, averaged over the whole territory (hereafter,
projected climatic suitability). First we performed analyses separately for each species
characteristic as explanatory variables. We also performed additional models including the
number of plots in which the species occurs as an additional explanatory variable. Data on
SLA for Juniperus thurifera L. were not available and this species was excluded in the
analysis of this functional trait.
Additionally, we built comprehensive PGLS models of the climatic suitability change and
projected climatic suitability of each species as a function of several descriptors of species
characteristics, including the number of plots in which each species was present. We built
models including all first-order interactions between species characteristics and we finally
selected the best model according to the AIC criterion. For demographic rates, we selected
RGR since it proved to be significantly correlated with the climatic suitability variables in
the previous variable-by-variable analyses. Given the large number of descriptors in com-
parison to the number of species, we obtained integrative estimates of the climatic conditions
where species are currently found and functional characteristics using the coordinates of
species on the first component of three PCA ordinations: average climatic characterization
resulting from a PCA ordination of MAT, MAP and P/PET; climatic amplitude characteri-
zation was obtained from a PCA ordination of MAT SD, MAP SD and P/PET SD and
functional traits characterization was undertaken from a PCA ordination of Hmax, WD,
SLA, Nmass, LA and SM. In this analysis, the value of SLA for J. thurifera was considered
to be the same as in the congeneric J. phoenicea L., given that both species develop very
similar scale-like leaves (do Amaral Franco 1986).
Whenever required, we applied logarithms or square roots to the variables to attain
normality (see Tables 1and 2). The R packages ‘ape’and ‘nlme’were used to perform
the PGLS analyses and JMP 5.0 (SAS Insitute Inc) was used for the other analyses.
3 Results
Overall, most species tended to decrease their regional climatic suitability under the
projected future climatic conditions, as observed when comparing projected and current
climatic suitability (HadCM3 A1 scenario, Fig. 1). Only a few tree species showed an
increase in climatic suitability. Most of these (Quercus ilex L., Q. suber L., Pinus halepensis
Mill., P. pinea L., P. pinaster Ait.) are typically distributed in the Mediterranean region, in
agreement with our first hypothesis. Nevertheless, the climatic suitability of Sorbus
454 Climatic Change (2013) 120:449–462
aucuparia L., a widespread European species that lives in mountain ranges of the Iberian
Peninsula, also increased. The species with lower projected future climatic suitability are
Gymnosperms of the Juniperus genus and trees that typically have northern distribution
centers (Fagus sylvatica L., Pinus sylvestris L., Ilex aquifolium L.).
In agreement with to our second hypothesis, climatic suitability change correlated
positively with current RGR, irrespective of phylogeny (Table 1, Fig. 2). Pinus sylvestris
and, to a lesser extent, P. nigra Arnold, evade this general trend to some degree by showing a
greater decrease in climatic suitability than expected from their current RGR. This corre-
spondence between suitability change and growth concurs with a positive relationship
between projected climatic suitability and species RGR (Table 2). In contrast with the
expectation of coincident patterns of current demographic rates and climate tendency,
mortality rate did not show any significant relationship with climatic suitability change or
projected future climatic suitability (Tables 1and 2). Similarly, functional traits failed to
show any significant relationship with climate suitability change and projected climatic
suitability (Tables 1and 2).
After accounting for the phylogenetic effect, as hypothesized, there was a strong
positive relationship between the range of climatic conditions where species are currently
found, estimated by P/PET SD, and the climatic suitability change (Table 1). This pattern
was not reflected in a significant relationship between this estimate of current species
climatic range and projected climatic suitability (Table 2). Moreover, species living in
localities with higher MAT are expected to experience more climatic suitability changes,
resulting in an increase in their projected climatic suitability, with and without considering
phylogenetic effects (Tables 1and 2). Accordingly, species currently living in localities
with higher P/PET showed lower projected average suitability (Table 2).
Table 1 Relationship between climatic suitability change and species characteristics modelled using partial
least squares. Number of plots (log transformed) was also included in the models as an explanatory variable,
but it was never significant (P<0.05) and was removed when the fit of the model improved according to the
AIC criterion. Significant relationships are highlighted in bold. (Coef, coefficient; Mort, mortality rate); see
text for other abbreviations
Without phylogenetic effects Including phylogenetic effects
Model AIC Coef PAIC Coef P
Demographic RGR 75.51 2.003 0.010 77.14 1.864 0.023
MR 85.40 0.142 0.553 82.90 0.218 0.287
Functional Hmax 88.98 0.028 0.359 87.78 −0.004 0.893
WD 81.68 −0.021 0.991 79.35 −0.421 0.837
SLA 74.42 0.146 0.619 74.84 0.122 0.745
Nmass 81.23 1.132 0.159 80.34 0.754 0.452
LA 85.14 0.127 0.098 84.28 0.109 0.235
SM 87.77 0.046 0.489 85.35 0.061 0.449
Climatic MAT 84.26 0.188 0.108 80.69 0.219 0.035
MAT SD 80.79 1.152 0.104 80.21 0.792 0.221
MAP 95.29 0.001 0.255 92.33 <0.001 0.864
MAP SD 93.13 0.003 0.232 92.58 0.001 0.619
P/PET 84.21 −0.179 0.989 82.10 −0.226 0.640
P/PET SD 77.26 8.59 0.179 75.55 15.331 0.003
Climatic Change (2013) 120:449–462 455
The current species abundance in the territory, estimated by the number of plots on which
the species is found, did not correlate with climatic suitability change, but it did correlate
positively with projected climatic suitability. This effect was clear for most demographic,
functional and climate characteristics when the phylogenetic effect was not considered.
When including phylogenetic effects, the effect of the number of plots remained significant
only in the models of some functional traits (SLA, LA) and estimators of the climatic
amplitude (MAT SD, P/PET SD) (Table 2).
In the average climate PCA ordination, species living in more arid conditions correspond
to more negative values in the first ordination axis (PCA
clim
); in the climatic amplitude PCA,
species found across a wider range of climatic conditions tend to have more positive values
in the first axis (PCA
amp
), and in the functional trait PCA, species with higher LA, SLA and
Nmass had more positive values in the first ordination axis (PCA
trait
) (Supplementary
Material, Annex 3,4,5).
When analyzing the PGLS models that considered the several descriptors of species
characteristics (RGR, PCA
clim
, PCA
amp
, PCA
trait
, and number of plots), the best-fitting
model for species suitability change and projected suitability included RGR, PCA
clim
and PCA
amp
(Table 3). RGR was positively related to both suitability change and
projected suitability. The relationship of the suitability variables with PCA
clim
was
negative, indicating better performance by species currently living in more arid climates,
while the relationship with PCA
amp
was positive, pointing to a better response from
species living under a wider range of climatic conditions. Interactions between species
characteristics were not significant and were removed from the model, along with the
Table 2 Relationship between projected climatic suitability and species characteristics modelled using partial
least squares. Number of plots (log transformed) was also included in the models as an explanatory variable
but was removed when the fit of the model improved according to the AIC criterion. Significant relationships
are highlighted in bold. See Table 1and text for a description of abbreviations
Without phylogenetic effects Including phylogenetic effects
Species
characteristics
Number of
plots
Species
characteristics
Number of
plots
Model AIC Coef PCoef PAIC Coef PCoef P
Demographic RGR 91.68 2.565 0.015 89.51 2.388 0.028
MR 97.81 0.177 0.535 0.459 0.016 96.43 0.234 0.382 0.400 0.052
Functional Hmax 101.84 −0.023 0.543 0.463 0.016 99.00 −0.062 0.126
WD 93.18 2.103 0.332 0.439 0.017 91.60 2.200 0.404
SLA 89.82 0.212 0.615 0.485 0.011 88.83 0.078 0.988 0.412 0.023
Nmass 94.60 1.034 0.307 0.415 0.012 94.09 0.438 0.713 0.369 0.660
LA 98.66 0.124 0.199 0.482 0.010 98.37 0.088 0.425 0.396 0.046
SM 100.15 0.141 0.107 97.32 0.124 0.300
Climatic MAT 83.77 0.490 <0.001 80.37 0.456 <0.001
MAT SD 93.93 1.222 0.160 0.455 0.012 93.67 0.936 0.260 0.404 0.039
MAP 109.25 −<0.001 0.993 0.433 0.029 106.5 −<0.001 0.201
MAP SD 106.22 0.003 0.374 0.456 0.014 105.87 0.001 0.665 0.379 0.056
P/PET 89.95 −1.725 0.003 83.83 −1.885 <0.001
P/PET SD 90.56 7.680 0.366 0.499 0.013 89.92 5.475 0.514 0.427 0.048
456 Climatic Change (2013) 120:449–462
number of plots and the PCA ordination of functional traits. The same result was obtained when
including phylogenetic effects, but the fitting of this model was slightly worse than that of
models without phylogenetic effects.
4 Discussion
We did not find a significant relationship between current mortality rates and projected
values of change in climatic suitability in the studied territory. Tree mortality is associated to
a variety of interacting factors, which may result in a weak relationship between mortality
and climatic tendency at the temporal scale considered. In fact, management and land use
history may play an even more significant role on demographic rates than climate (Martínez-
Vilalta et al. 2012). Thus, the set of conditions where the species can live that are described
by species distribution models may not be fully recorded by presence locations, hence
overestimating climatic risk (Schwartz 2012). Nevertheless, our result suggests that
increasing climatic vulnerability would not exacerbate current trends of species mortality,
independently of their causes. There are increasing reports of climate-related tree mortal-
ity (van Mantgem et al. 2009) specifically associated with extreme climatic episodes
(Allen et al. 2010; Galiano et al. 2010). In many cases, these events may not result in
Fig. 1 Average for the whole territory of the projected future and the current climatic suitability for the 27
studied species. Aa, Abies alba; Am, Acer monspessulanus; Ac, Acer campestris; Ao, Acer opalus; Bp, Betula
pendula; Bu, Betula pubescens; Ca, Corylus avellana; Cs, Castanea sativa; Fs, Fagus sylvatica; Ia, Ilex
aquifolium; Jc, Juniperus communis; Jp, Juniperus phoenicea; Jt, Juniperus thurifera; Ph, Pinus halepensis;
Pi, Pinus pinea; Pn, Pinus nigra; Pp, Pinus pinaster; Ps, Pinus sylvestris; Pu, Pinus uncinata; Qf, Quercus
faginea; Qh, Quercus humilis; Qi, Quercus ilex; Qp, Quercus petraea; Qs, Quercus suber;Qy,Quercus
pyrenaica; Sa, Sorbus aria; Su, Sorbus aucuparia
Climatic Change (2013) 120:449–462 457
long-term changes in vegetation, suggesting that forests show important inertia to shift
(Lloret et al. 2012). Although this study does not aim to discriminate the effect of such climatic
anomalies, our results support the existence of drivers other than climate contributing to such
inertia at a decadal scale.
Fig. 2 Relationship between climatic suitability change and relative growth rate (RGR) for the 27 studied
species. Note that axes are logarithmic. Species abbreviations as in Fig. 1
Table 3 Summary of the PGLS models (best fitted model according to the AIC criterion) analyzing the
climate suitability change and projected climatic suitability in relation to species characteristics. Variables
describing climatic species characteristics correspond to the species values in the first axis of PCA ordinations
(see text). RGR was log-transformed
Climatic suitability change Projected climatic suitability
Without
phylogenetic
effects
Including
phylogenetic
effects
Without
phylogenetic
effects
Including
phylogenetic
effects
AIC=78.79 AIC=80.79 AIC=76.15 AIC=77.45
Fixed effect Coef. P Coef. P Coef. P Coef. P
Intercept 5.167 0.042 5.175 0.042 5.094 0.034 5.277 0.041
Relative growth rate (RGR) 1.725 0.017 1.727 0.017 2.195 0.002 2.236 0.003
Climate average (PCA first axis) −0.237 0.085 −0.234 0.090 −0.763 <0.001 −0.720 <0.001
Climate amplitude (PCA first axis) 0.393 0.025 0.389 0.020 0.560 0.001 0.462 0.004
458 Climatic Change (2013) 120:449–462
Nevertheless, our results indicate some degree of demographic vulnerability, since those
species that currently present lower growth rates are more prone to experience a loss of
average climatic suitability. This is the case with F. sylvatica, which has one of its southern
edges of distribution in humid areas in the Iberian peninsula (Jump et al. 2008). Juniperus
thurifera also exhibits a large reduction of its climatic suitability but this result may be due
to an underestimation in the distribution model resulting from its limited competitive
capacity (Gómez-Aparicio et al. 2011) and its history of strong control by livestock
browsing (De Soto et al. 2010). Contrarily, species with current higher growth rates, such
as Mediterranean pines (P. halepensis, which is a common colonist of secondary succes-
sions, and P. pinaster and P. pinea, which are frequently managed) experience an increase in
climatic suitability in the territory.
There are, however, species that diverge from the overall positive relationship between
growth and suitability change. Typical evergreen Mediterranean oaks (Q. ilex,Q. suber) with
relatively low growth rates associated with water-deficit conditions are projected to increase
their climatic suitability, in agreement with their expansion during the warmer conditions of
the Holocene (Ramil-Rego et al. 1998; Carrion 2000). In contrast, the climatic suitability of
P. sylvestris is expected to decrease dramatically (reflecting the location of the Iberian
populations in the southwestern limit of this species’distribution) even though it exhibits
relatively high growth rates. This is probably because its populations have been extensively
managed for forest exploitation and they mostly consist of young stands (Vilà-Cabrera et al.
2011). These results agree with climate-driven die-off episodes that are being observed in
this species (Bigler et al. 2006; Galiano et al. 2010).
Functional traits are known to correlate to some extent with current climatic conditions
(Thuiller et al. 2010; Martínez-Vilalta et al. 2010) but, contrary to our expectations, they failed
to correlate with the change and projection of climatic suitability in our models, probably
because average values of functional traits do not account for the variability over large
territories and thus for the ability of a species to adjust to spatial and temporal variability in
environmental conditions (Thuiller et al. 2010), particularly in the studied region (Sabate et al.
2002). Accordingly, some recent work has called for the explicit inclusion of intraspecific
variability in functional traits in studies of community ecology (Albert et al. 2011). Also,
increasing CO
2
emissions is an important source on uncertainty on species response to climate
change due to its direct effects on physiological performance (Keenan et al. 2011). Furthermore,
most current models cannot satisfactorily evaluate the potential role of acute climatic fluctua-
tions in species distribution (but see Zimmerman et al. 2009). However, the combination of
niche-based and process-based models can provide insights about the relevance of extreme
climatic variability on future patterns of species distribution (Morin and Thuiller 2009;Keenan
et al. 2011). Finally, our models do not account for other factors determining species dominance
and distribution, such as landscape structure and human impact, including forest management
and plantation. The consequence should be a dilution of the role of autoecological features in
interpreting their correspondence with climate.
The conditions where species are currently found were related to the projected change in
suitability and, as hypothesized, the loss of habitat suitability would be lower for species
growing in warmer habitats. Nevertheless, as for functional traits, the scarcity of significant
relationships may be explained by the existence of factors other than climate that explain the
current distribution of species. Climatic amplitude, particularly P/PET SD, is the best
predictor of suitability change. As hypothesized, species able to successfully grow in a wide
range of water deficit conditions would be less vulnerable to the new climatic scenarios.
Current regional abundance is not consistently associated with changes in climatic
suitability, indicating that there is no strong bias in our estimates due to sample size. Overall,
Climatic Change (2013) 120:449–462 459
this result supports the role of refugia in a changing climate scenario, but the climatic
suitability of some rare species would diminish in the territory, as is the case of A. alba Mill.
or I. aquifolium, which are more likely to be restricted by summer water deficits. Further-
more, the inclusion of phylogenetic affinity between species improves our univariate
models. This trend is explained by the constraints imposed by phylogeny on species traits,
and it suggests that sets of species belonging to closely related lineages might be particularly
vulnerable or resilient to climate change.
This study confirms that the extrapolation to future climatic scenarios of species perfor-
mance based on current patterns of dominance distribution is also constrained by factors
other than species autoecology. In addition to biotic interactions and dispersal, land use and
forest management should be particularly relevant for population trends (Gómez-Aparicio et
al. 2011; Martínez-Vilalta et al. 2012). Also, species distribution models assume a set of
biological and methodological simplifications than accumulate uncertainty to the resulting
predictions (Thuiller 2004; Schwartz 2012). The relationship between the characteristics of
tree species and the response of those species to future climate change may be described
better by considering both the biological and historical factors.
Acknowledgments This study was supported by the Spanish Ministry of Education and Sciences (projects
CGL2006-01293, CSD2008-00041, CGL2009-08101, CGL2010-16373, CGL2012-32965) and by the Gov-
ernment of Catalonia (AGAUR grants 2009-SGR-247 and 2009-SGR-1511). JMSD acknowledges support
from the PIF-UAB PhD Grant program.
References
Albert CH, Grassein F, Schurr FM et al (2011) When and how should intraspecific variability be considered in
trait-based plant ecology? Persp Plant Ecol Evol Syst 13:217–235
Allen CD, Macalady AK, Chenchouni H et al (2010) A global overview of drought and heat-induced tree
mortality reveals emerging climate change risks for forests. For Ecol Manag 259:660–684
Angert AL, Crozier LG, Rissler LJ et al (2011) Do species traits predict recent shifts at expanding range
edges? Ecol Lett 14:677–689
Bigler C, Bräker OU, Bugmann H et al (2006) Drought as an inciting mortality factor in Scots pine stands of
the Valais, Switzerland. Ecosystems 9:330–343
Bolós O, Vigo J, Masalles RM, Ninot JM (1990) Flora Manual dels Països Catalans. Pòrtic, Barcelona
Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science
320:1444–1449
Burriel JA, Gracia C, Ibàñez JJ, et al. (2000–2004) Inventari Ecològic i Forestal de Catalunya.CREAF,
Bellaterra, Spain
Carrion JS (2000) Past distribution and ecology of the cork oak (Quercus suber) in the Iberian Peninsula: a
pollen-analytical approach. Divers Distrib 6:29–44
De Soto L, Olano JM, Rozas V et al (2010) Release of Juniperus thurifera woodlands from herbivore-mediated
arrested succession in Spain. Appl Veg Sci 13:15–25
Dirección General de Conservación de la Naturaleza (2006) Tercer Inventario Forestal Nacional 1997–2006.
Ministerio de Medio Ambiente, Madrid
Do Amaral Franco J (1986) Juniperus. In: Castroviejo S, Laínz M, Montserrat P et al (eds) Flora Iberica, vol 1,
Lycopodiaceae-Papaveraceae. Real Jardin Botanico, CSIC, Madrid, pp 181–188
Doak DF, Morris WF (2010) Demographic compensation and tipping points in climate-induced range shifts.
Nature 467:959–962
Elith J, Graham CH (2009) Do they? How do they? Why do they differ? On finding reasons for differing
performances of species distribution models. Ecography 32:66–77
Esther A, Groeneveld J, Enright NJ et al (2010) Sensitivity of plant functional types to climate change:
classification tree analysis of a simulation model. J Veg Sci 21:447–461
460 Climatic Change (2013) 120:449–462
Felicisimo AM (2011) Impactos, vulnerabilidad y adaptación al cambio climático de la biodiversidad
española. 2. Flora y vegetación. Oficina Española de Cambio Climático, Ministerio de Medio
Ambiente y Medio Rural y Marino, Madrid
Foody GM (2011) Impacts of imperfect reference data on the apparent accuracy of species presence/absence
models and their predictions. Glob Ecol Biogeogr 20:498–508
Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University
Press, Cambridge
Freckelton RP, Harvey PH, Pagel M (2002) Phylogenetic analysis and comparative data. Am Nat
125:712–726
Galiano L, Martínez-Vilalta J, Lloret F (2010) Drought-Induced multifactor decline of Scots Pine in the
Pyrenees and potential vegetation change by the Expansion of co-occurring oak species. Ecosystems
13:978–991
Gómez-Aparicio L, García-Valdés R, Ruíz-Benito P et al (2011) Disentangling the relative importance of
climate, size and competition on tree growth in Iberian forests: implications for forest management under
global change. Glob Chang Biol 17:2400–2414
Giorgi F, Bi X, Pal J (2004) Mean, interannual variability and trends in a regional climate change experiment
over Europe. II: climate change scenarios (2071–2100). Clim Dynam 23:839–858
IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z,
Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change 2007. Cambridge University
Press, Cambridge
Jump A, Hunt JM, Peñuelas J (2008) Rapid climate change-related growth decline at the southern range edge
of Fagus sylvatica. Glob Chang Biol 12:2163–2174
Jump AS, Mátyás C, Peñuelas J (2009) The altitude-for-latitude disparity in the range retractions of woody
species. Trends Ecol Evol 24:694–701
Keenan T, Serra JM, Lloret F et al (2011) Predicting the future of forests in the Mediterranean under climate
change, with niche- and process-based models: CO2 matters! Glob Chang Biol 17:565–579
Keith DA, Akcakaya HR, Thuiller W et al (2008) Predicting extinction risks under climate change: coupling
stochastic population models with dynamic bioclimatic habitat models. Biol Lett 4:560–563
Lloret F, Escudero A, Iriondo JM et al (2012) Extreme climatic events and vegetation: the role of stabilizing
processes. Glob Chang Biol 18:797–805
Martínez-Vilalta J, Mencuccini M, Vayreda J et al (2010) Interspecific variation in functional traits, not
climatic differences among species ranges, determines demographic rates across 44 temperate and
Mediterranean tree species. J Ecol 98:1462–1475
Martínez-Vilalta J, Lloret F, Breshears DD (2012) Drought-induced forest decline: causes, scope and
implications. Biol Lett 12:689–691
Martins EP, Hansen TF (1997) Phylogenies and the comparative method: a general approach to incorporating
phylogenetic information into the analysis of interspecific data. Am Nat 149:646–667
Meier ES, Kienast F, Pearman PB et al (2010) Biotic and abiotic variables show little redundancy in
explaining tree species distribution. Ecography 33:1038–1048
Mellick R, Lowe A, Allen C et al (2012) Palaeodistribution modelling and genetic evidence highlight
differential post-glacial range shifts of a rain forest conifer distributed across a latitudinal gradient. J
Biogeogr 39:2292–2302
Morin X, Thuiller W (2009) Comparing niche- and process-based models to reduce prediction uncertainty in
species range shifts under climate change. Ecology 90:1301–1313
Ninyerola M, Pons X, Roure JM (2005) Atlas Climático Digital de la Península Ibérica. Metodología y
aplicaciones en bioclimatología y geobotánica. Universidad Autónoma de Barcelona, Bellaterra
Prentice IC, Cramer W, Harrison SP et al (1992) A global biome model based on plant physiology and
dominance, soil properties and climate. J Biogeogr 19:117–134
Ramil-Rego P, Rodriguez-Gutian M, Muñoz-Sobrino C (1998) Sclerophyllous vegetation dynamics in the
north of the Iberian peninsula during the last 16,000 years. Glob Ecol Biogeogr 7:335–351
Sabate S, Gracia CA, Sanchez A (2002) Likely effects of climate change on growth of Quercus ilex, Pinus
halepensis, Pinus pinaster, Pinus sylvestris and Fagus sylvatica forests in the Mediterranean region. For
Ecol Manag 162:23–37
Schwartz MW (2012) Using niche models with climate to inform conservation management decisions. Biol
Conserv 155:149–156
Svenning J, Fitzpatrick MC, Normand S et al (2010) Geography, topography, and history affect realized-to-
potential tree species richness patterns in Europe. Ecography 33:1070–1080
Thuiller W (2004) Patterns and uncertainties of species’range shifts under climate change. Glob Chang Biol
10:2020–2027
Climatic Change (2013) 120:449–462 461
Thuiller W, Albert CH, Dubuis A et al (2010) Variation in habitat suitability does not always relate to variation
in specie’s plant functional traits. Biol Lett 6:120–123
van Mantgem PJ, Stephenson NL, Byrne JC et al (2009) Widespread increase of tree mortality rates in the
Western United States. Science 323:521–524
Vilà-Cabrera A, Martínez-Vilalta J, Vayreda J et al (2011) Structural and climatic determinants of demograph-
ic rates of Scots pine forests across the Iberian Peninsula. Ecol Appl 21:1162–1172
Webb CO, Donogue MJ (2005) Phylomatic: tree assembly for applied phylogenetics. Mol Ecol Notes
5:181–183
Westoby M, Wright IJ (2006) Land-plant ecology on the basis of functional traits. Trends Ecol Evol
21:261–268
Zimmerman NE, Yoccoz NG, Edwards TC (2009) Climatic extremes improve predictions of spatial patterns
of tree species. Proc Natl Acad Sci 106:19723–19728
462 Climatic Change (2013) 120:449–462
