Download - Biogeographic deconstruction of alpine plant communities along altitudinal and topographic gradients
Journal of Vegetation Science && (2013)
Biogeographic deconstruction of alpine plantcommunities along altitudinal and topographicgradients
Borja Jim�enez-Alfaro, CorradoMarcen�o, �Alvaro Bueno, Rosario Gavil�an & Jos�e Ram�on Obeso
Keywords
Biogeography; Cantabrian Range; Community
assembly; Environmental filtering; Species
diversity; Species pool; Topography
Nomenclature
Flora Iberica (www.floraiberica.es); Flora
Europaea (Tutin et al. 1964); Flora Alpina
(Aeschimann et al. 2004)
Received 22 June 2012
Accepted 29 January 2013
Co-ordinating Editor: Zaal Kikvidze
Jim�enez-Alfaro, B. (corresponding author,
[email protected]): Department of Botany
and Zoology, Masaryk University, Kotlarska 2,
CZ-61137, Brno, Czech Republic
Marcen�o, C. ([email protected]) &
Bueno, A. ([email protected]): Jard�ın
Bot�anico Atl�antico, University of Oviedo, Jard�ın
Bot�anico 2230, E-33394, Gij�on, Spain
Jim�enez-Alfaro, B. & Gavil�an, R.
([email protected]): Departamento de
Biolog�ıa Vegetal II, Facultad de Farmacia,
Universidad Complutense deMadrid, E-28040,
Madrid, Spain
Obeso, J.R. ([email protected]): Research
Unit of Biodiversity (UO-CSIC-PA), University of
Oviedo, E-33600, Mieres, Spain
Abstract
Questions: Are species from different biogeographic groups (mediterranean,
alpine and endemic) filtered in different ways by altitude and topography in
alpine plant communities? What is the relative performance of these environ-
mental factors at predicting the species diversity of the communities as a whole
and of the geographic species groups?
Location: Picos de Europa, Cantabrian Range (Spain).
Methods: We sampled the presence and cover of vascular plants in 5-m radius
plots on alpine grasslands between 1900 and 2500 m a.s.l. Five GIS-based ter-
rain variables at 15 m 9 15 m were used to model species richness and cover
per plot using generalized and linear models, and the variation in species compo-
sition with redundancy analysis. The same analyses were repeated for the whole
data set and for subsets of species from alpine, mediterranean and endemic dis-
tributions.
Results: The influence of altitude and topography on species richness, cover
and composition differed for the whole data set and for the geographic species
groups. Altitude was themain variable affecting floristic diversity in the commu-
nities as a whole, but the separate species groups were more influenced by slope,
topographic wetness index and solar radiation. Richness and cover of mediterra-
nean species showed the strongest relationships with topography. Alpine and
endemic species showed relationships with topography for species cover and
composition, but not for species richness.
Conclusions: In alpine landscapes, biogeographic deconstruction of the species
pool can provide a better understanding of the influence of altitude and topogra-
phy on local communities than analysis of the entire community alone. Further-
more, the strong influence of local topography on species groups improves our
understanding of how alpine species will respond to climate change.
Introduction
Understanding the patterns and processes that determine
the number and composition of co-occurring species is of
major interest in community ecology (Vellend 2010). It is
generally accepted that the regional species pool is influ-
enced by broad-scale bioregional and evolutionary factors
that interact with local factors and give rise to complex
ecological processes, such as niche selection, ecological
drift, dispersal or speciation (Freschet et al. 2011; Zobel
et al. 2011; Hardy et al. 2012). Niche-based approaches
stress that the response of plant assemblages to the
environment can be viewed as a filter of the regional spe-
cies pool according to biotic (species interactions) and abi-
otic (environmental) factors (Keddy 1992; Cingolani et al.
2007). In alpine communities, ′environmental filtering′
seems to be the dominant niche-based process relating cli-
mate with species (Kikvidze et al. 2005; de Bello et al.
2012a; Pottier et al. 2012). When alpine habitats are scar-
cely perturbed, competition is relaxed (Grime 2002; Bruun
et al. 2006; Vonlanthen et al. 2006) and the harsh envi-
ronmental conditions determine a major influence of abi-
otic filters (Callaway et al. 2002; Kammer & M€ohl 2002;
Chase & Myers 2011). Environmental filtering of the
Journal of Vegetation ScienceDoi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science 1
regional species pool can therefore be expected to exert a
strong influence over the diversity and abundance of
alpine communities along local ecological gradients.
Understanding these patterns is of special interest because
these habitats are biodiversity hotspots that are especially
sensitive to the effects of climate change (Pauli et al.
2012).
The effect of environmental filters on species assem-
blages can be assessed with regard to different dimensions
of diversity (Keddy 1992). Recently, many studies have
focused on phylogenetic (Anderson et al. 2011) or func-
tional traits (de Bello et al. 2012a), providing additional
information to the more traditional studies based on taxo-
nomical species names (Swenson 2011). However, a major
drawback of these approaches is the difficulty of linking
small- and large-scale biogeographic processes, partially
since evolutionary history has resulted in different regional
species pools (Chase & Myers 2011). Despite the need for
more integrative analyses (Weiher et al. 2011) and the
general contingencies associated with the community
assembly theory (Lessard et al. 2012), it is still necessary to
explore the effect of environmental filtering at different
diversity levels (de Bello et al. 2012b). One example of an
easy-to-apply method based on species information is bi-
ogeographic deconstruction, which separately analyses the
geographical components of communities along environ-
mental gradients (Real et al. 2008). Deconstruction proce-
dures generally involve the separate analysis of species
groups from a given community according to the ecologi-
cal factors that best explain their diversity (Marquet et al.
2004), and have been used to assess community patterns
in species with different habitats (Blamires et al. 2008) or
ecological requirements (Azeria et al. 2011). In plant com-
munities, it has been suggested that species with similar
distributions respond similarly at large and local ecological
gradients (Ferrer-Cast�an & Vetaas 2003). Since the high
species diversity of mountain regions is strongly influenced
by environmental filters (de Bello et al. 2012a), they are
good models to assess whether species with different geo-
graphic ranges respond differently to local environmental
factors. Analysing these patterns may provide useful infor-
mation for understanding species assemblages in alpine
communities.
The main abiotic factors affecting the composition of
alpine plant communities are pH, altitude, topography and
processes relating to soil development and composition
(Vonlanthen et al. 2006; Sch€ob et al. 2008). Since the
influence of pH is spatially determined by the dominant
bedrock (Virtanen et al. 2002), local variation in species
richness is ultimately driven by temperature along narrow
altitudinal and topographic gradients (Kammer & M€ohl
2002; Bruun et al. 2006). Increasing altitude is generally
associated with a decrease in local species richness
(Nogu�es-Bravo et al. 2008), but this trend is weaker at the
highest altitudes due to the influence of topographic varia-
tion, which locally buffers temperature changes (Scherrer
& K€orner 2011). One of the most evident effects of topog-
raphy is found at different exposures on mountain sum-
mits, affecting species distribution (Pickering & Green
2009), spatial structure (Guti�errez-Gir�on & Gavil�an 2010)
and plant responses to climate change (Cannone et al.
2007; Pauli et al. 2007). Besides the summit effect, the
influence of topography on alpine communities has been
traditionally explained through the effect of surface heter-
ogeneity, wind exposure and eco-hydrological settings
along the so-called meso-topographic gradient (Billings
1974). In its simplest form, this gradient explains transi-
tions from wind-exposed ridges to small micro-valleys,
producing different ecological conditions related to tem-
perature, snow accumulation and soil development
(Walker et al. 2001; Choler 2005; Bruun et al. 2006).
However, the high complexity of geomorphology and the
combined effect of altitude and topography require more
sophisticated models to explain the interactions between
vegetation and topography in alpine land forms (Bruun
et al. 2006).
Using GIS-basedmodels to predict alpine vegetation pat-
terns, Gottfried et al. (1998) highlighted the importance of
curvature and roughness as complementary variables to
altitude and slope for understanding species richness
and the local distribution of species and plant communi-
ties. Furthermore, variables such as solar radiation or
topographic indices are commonly used to investigate veg-
etation patterns of species composition in alpine environ-
ments (Barrio et al. 1997; Dirnb€ock et al. 2003a; Pfeffer
et al. 2003). More recently, the importance of these vari-
ables has been empirically demonstrated by measuring the
relationships between temperature and topographic varia-
tions (Fridley 2009; Scherrer & K€orner 2011), suggesting
that fine-scale topographic variables (measured with a res-
olution of between 1 and 25 m) are robust surrogates of
ecological variability in alpine communities (Scherrer
et al. 2010). However, few studies compare their ecological
significance in diversity estimates, and examples dealing
with this topic may shed light on patterns of community
assembly along environmental filters.
In this work, we use biogeographic deconstruction to
test the hypothesis that species groups from different geo-
graphic regions are filtered in different ways by altitude
and topography in alpine plant communities. We selected
the Cantabrian range (Spain) as the study area, an exam-
ple of a biogeographic refuge for high-mountain species in
southern Europe. This region is influenced by alpine con-
ditions and the mediterranean climate to the south, and
supports numerous cold-adapted species that form part of
the postglaciation expansion (Taberlet et al. 1998). We
Journal of Vegetation Science2 Doi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science
Deconstruction of alpine communities B. Jim�enez-Alfaro et al.
anticipated that the alpine species would respond posi-
tively to altitude or snow-related topographic conditions,
while mediterranean species would prefer different (war-
mer) conditions. The influence of local environmental gra-
dients on the diversity and composition of these groups
may differ from the overall patterns of the whole commu-
nity, providing a complementary approach for the assess-
ment of alpine community responses. A secondary aim of
this study is to explore the relative performance of altitude
and topography at predicting diversity patterns in the com-
munities as a whole and in their geographic species
groups.
Methods
Study area
The study was conducted on the central massif of the Picos
de Europa, an isolated alpine landscape in Western Europe
with a substantial component of circumboreal mountain
species. This calcareous massif comprises the highest
elevation in the Cantabrian range (maximum altitude =2654 m) and one of the main outposts of alpine flora and
vegetation in Europe (Nagy 2006). The study was con-
ducted on alpine grasslands that are relatively well devel-
oped at altitudes between 1900 and 2400 m, forming
inter-connected patches interrupted by steep limestone
and dolomite rocks (Fig. 1). Local geomorphology is
related to Pleistocene glaciations, the Little Ice Age and
current periglacial processes (Gonzalez Trueba et al. 2008;
Moreno et al. 2010). The communities studied are biogeo-
graphically connected with the European mountain sys-
tem (Alps, Pyrenees, Carpathians), showing a relatively
high similarity with Pyrenean-related vegetation (Nava
1988; Peyre & Font 2011). Besides the presence of general-
ist non-mountain species, the floristic composition of the
study vegetation is mainly represented by arctic–alpine
species (e.g. Kobresia myosuroides), species endemic to the
mountains of northern Spain (e.g. Armeria cantabrica) and
high-mountain mediterranean species (e.g. Senecio boissi-
eri), indicating historic biogeographical connections with
(a)
(c)
(b)
Fig. 1. Study area and topographic landscape. (a) Location of the Picos de Europa National Park in Spain (SP), and (b) the calcareous massif representing
the study area. Red points and green patches (c) represent the distribution of the sample plots and the approximate cover of alpine grasslands,
respectively.
Journal of Vegetation ScienceDoi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science 3
B. Jim�enez-Alfaro et al. Deconstruction of alpine communities
the mountains of the Iberian Peninsula (Peredo et al.
2009).
Field sampling and species data
We used a 1:10 000 scale vegetation map of the study area
(Jim�enez-Alfaro & Bueno 2008) to create a systematic
sampling design in the patches identified as alpine grass-
lands (Fig. 1). To encompass much of the floristic diversity
of the alpine grasslands, we designed nine latitudinal and
longitudinal field transects covering the range of the vege-
tation in the study area. Fixed area plots with a radius of
5 m (size = 78 m2) were established at an approximate dis-
tance of 250 m from one another to avoid spatial autocor-
relation. In the centre of each plot we recorded the
geographical co-ordinates using a field GPS (Garmin e-
trex, error = 4 m). Field sampling was conducted during
the growing season (July–August) in 2008 and 2009 until
we obtained a wide, normally distributed series of altitudi-
nal and topographic ranges covering the whole study area.
A total of 101 plots (Fig. 1) were sampled, for which we
recorded the total number of vascular plants and estimated
their cover using a seven point species cover–abundance
scale (Braun-Blanquet 1964). In order to minimize bias in
plant identification and cover estimates (Vittoz & Guisan
2007), at least two observers with a good knowledge of the
local flora conducted sampling in all the plots. Since there
is a predominance of long-lived perennials in the vegeta-
tion studied, significant floristic changes are not expected
from year to year.
From the total species list, we identified the species
whose distribution is restricted to high-mountain habi-
tats (alpine sensu K€orner et al. 2011), and classified them
into the three predominant geographic groups repre-
sented in the study area: (1) alpine species (e.g. Saxifraga
oppositifolia) mainly distributed (>95% of their known
distribution) throughout the Alpine Biogeographic
Region (Roekaerts 2002), and including arctic–alpine
species present in the European mountain system and
northern (arctic) latitudes; (2) mediterranean species (e.
g. Festuca hystrix) mainly distributed throughout the
Mediterranean mountains of the Iberian Peninsula, or in
some cases with a wider distribution throughout the
Mediterranean mountains (Italy, northern Africa, south-
east France); (3) species endemic to the Cantabrian
range (e.g. Saxifraga conifera) or the Cantabro-Pyrenaean
mountains (e.g. Euphorbia pyrenaica) for which the study
area represents an important refuge. Species with other
distributions or not consistently associated with high-
mountain vegetation were not analysed separately, but
were included in the analyses of the whole community.
Nomenclature and species distribution were reviewed
according to Flora Iberica (www.floraiberica.es), Flora
Europaea (Tutin et al. 1964) and Flora Alpina (Aeschi-
mann et al. 2004).
Environmental variables
We used a geographic information system (ArcGIS 9.2,
ESRI Inc., Redlands CA, US) to generate topographic
variables commonly used for vegetation modelling (van
Niel et al. 2004). A digital elevation model (DEM) at
15 m 9 15 m (a scale fitted to the plot size and the GPS
error) was derived from 1:10 000 digital cartography of
the study area. Altitude (min. 1927 m; max. 2496 m;
mean = 2178 m) and slope (min. 0°; max. 45°; mean =21°) were derived from the DEM using the Spatial Ana-
lyst extension in ArcGIS. Solar radiation (WM2, min.
86.5; max. 179.0; mean = 145.0) was calculated from
altitude, exposure and solar trajectory using mean annual
global radiation and intermediate (0.5) values of light
transmittance according to the Solar Analyst utility (Fu &
Rich 2000). The topographic position index (TPI) was
generated following ArcView (Topographic Position Index
extension for ArcView 3.x, v. 1.3a), such that valleys <0,flats ~ 0 and ridge tops >1 (min. �5.3; max. 17.7; mean =2.0). To match this index to the scale of sampling, we
used a circular neighbourhood with a four-cell radius,
meaning that the TPI value reflects the difference
between the elevation of the target cell and the average
elevation of all cells within 60 m from that cell. Finally, a
topographic wetness index (TWI) was calculated using
the algorithm of Beven & Kirkby (1979), TWI = ln(a/
tanb), which is based on the upslope area per unit con-
tour length (a) and the local slope (tanb). This equation
assumes that topography controls the movement of water
and thus the spatial pattern of soil moisture (Schmidt &
Persson 2003), and provides an estimate of local water
flows and snow accumulation (Essery & Pomeroy 2004).
TWI (min. 0.9; max 5.3; mean = 2.5) was calculated with
the TOPOCROP Arcview Extension (Schmidt & Persson
2003) using the neighbourhood statistics (mean filter) to
even out major contrasts in values and to minimize
small-scale heterogeneity. Although we assessed other
topographic predictors, such as curvature and exposure
(northernness, southernness), a preliminary pair-wise
test (Pearson’s r) revealed high (>0.8) correlations for
these variables, and only those predictors with expected
higher relevance and lower correlations were selected.
Data analysis
We used generalized linear models (GLM) and linear mod-
els (LM) to analyse the influence of environmental vari-
ables on species richness and plant cover in the whole data
set and the subsets of mediterranean, alpine and endemic
Journal of Vegetation Science4 Doi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science
Deconstruction of alpine communities B. Jim�enez-Alfaro et al.
species. The most appropriate function for modelling the
data was selected according to the lowest residual deviance
and different data transformations. The number of species
per plot was analysed as a Poisson-distributed random vari-
able fitted with a logarithmic link function and GLM
(McCullagh & Nelder 1989). The total percentage cover
per plot was estimated by transformation of the ordinal
cover of all species to a continuous scale from 0 to 100,
assuming random overlap of species (Chytr�y et al. 2010),
as implemented in the JUICE software. Cover estimates
per plot (ranging from 0.2 to 0.8) were logit-transformed
to obtain normality (Warton & Hui 2011) and modelled
using LM. For both species richness and cover measures,
we used a forward step-wise model procedure to select the
best predictors according to the Akaike information crite-
rion (AIC), considering all variables and their interactions.
The significance of predictors was calculated by means of a
deviance test (Nicholls et al. 1991) and only those with a
significant (a = 0.05) increase in residual deviance (or var-
iance) were retained, determined by v2 (in GLM) and F-
tests (in LM). Lastly, we estimated the cumulative percent-
age deviance explained as a measure of the lack of fit of the
data to the model. Analyses were conducted with R 2.14.1
(2003; R Foundation for Statistical Computing, Vienna,
AT). The contribution of the significant variables was
assessed using effect-plot displays in R (Fox 2003). Spatial
autocorrelation of the response variables and the model
residuals was tested using Moran’s (I) statistic (Fortin &
Dale 2005) and a Z score to test the null hypothesis that
there is no spatial clustering, as implemented in ArcGIS
9.2.
We used ordination methods to assess the variation in
species composition along altitudinal and topographic gra-
dients. An exploratory analysis of the data using detrended
correspondence analysis (DCA; length of first axis = 2.58
SD units) suggested a linear response by the species (Lep�s
& �Smilauer 2005). We therefore used redundancy analysis
(RDA) to test the null hypothesis that species composition
and their distribution are independent of environmental
variables (Legendre & Anderson 1999). The effect of each
variable was first assessed in a separate RDA using aMonte
Carlo permutation test with 999 permutations. The most
significant predictors were selected using a forward selec-
tion procedure, adding new variables in order of their
decreasing eigenvalues until the variables were non-signif-
icant (P > 0.05). Biplots of significant predictors and spe-
cies were generated. When only one predictor was
considered, species fit was calculated solely for the first
axis. The analyses were computed with CANOCO 4.5
(Biometris, Wageningen, NL) using square-root trans-
formed cover values as abundances. Different RDAs were
computed for the whole data set and for the different
geographic species groups.
Results
We recorded a total number of 164 vascular plant species,
of which 44 were alpine, 23 mediterranean and 31 ende-
mic. The remaining 66 species were identified as widely
distributed or not restricted to mountain habitats (see full
list in Appendix S1). The number of species per plot varied
between 11 and 39 (22.3 � 6.7; mean � SD). Of the three
geographic species groups, the alpine group contributed
the most species on average (7.9 � 2.6), followed by the
endemic group (5.5 � 2.4) and the mediterranean group
(3.2 � 1.7). The vascular plant cover estimated per plot
varied between 35% and 81% (59.6 � 10.6), with higher
cover values for alpine (40.8 � 14.3) compared to endemic
(27.9 � 12.4) andmediterranean (20.2 � 11.3) species.
As expected by the sampling procedure, neither the
response variables nor the model residuals were spatially
autocorrelated (Moran’s I; P > 0.05). GLM analysis
revealed that total species richness is negatively associated
with altitude and TWI (Fig. 2), explaining 14% and 11%
of the deviance, respectively (Table 1). From the three spe-
cies subsets, only the mediterranean group showed signifi-
cant relationships between species richness and the
predictor variables, with a negative effect of slope (explain-
ing 8% of variance) and TWI (an additional 8% of
explained variance) (Fig. 2). According to the LMs, the
species cover of the whole species data set was negatively
associated with altitude (Table 2), which explained 17.5%
of variance. In contrast with the patterns observed for spe-
cies richness, species cover was significantly associated
with predictors in all the geographic groups. The lowest
values of total explained variance were found in the alpine
subset (9.9% explained, with a negative effect of altitude
and a positive effect of slope) and the endemic subset
(10.9%, negative effect of TPI and positive effect of slope).
The cover of mediterranean species was better explained
(23.2%) than in the other groups, with negative relation-
ships for slope and TWI.
Redundancy analysis (RDA) showed a significant
response of species composition to environmental factors
(Table 3). The total explained variance was higher for the
whole data set than for the separate species groups, due in
part to the higher number of explanatory variables that are
included in the model. The species–environment correla-
tion was 0.75 for the first axis and 0.68 for the second axis
(76% of the total cumulative variance), and altitude was
the most important of the four selected variables. How-
ever, the results of species–environment relationships dif-
fered for the three species groups. Slope was the only
significant variable for the alpine subset (correlation = 0.52
for axis 1); altitude and solar radiation partially explained
variation in the mediterranean subset (correlation = 0.58
for axis 1 and 0.36 for axis 2); and solar radiation, slope
Journal of Vegetation ScienceDoi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science 5
B. Jim�enez-Alfaro et al. Deconstruction of alpine communities
and altitude were the factors influencing the endemic sub-
set (correlation = 0.47 for axis 1 and 0.58 for axis 2).
Species–environment plots from the RDA of the whole
data set (Fig. 3) indicate that 13 species are associated with
increasing altitude (e.g. Galium pyrenaicum) or TWI (e.g.
Koeleria vallesiana) and decreasing solar radiation (e.g. Sesle-
ria albicans). In the geographic species groups, the number
of species fitting with the explained variance was lower.
Main species indicators were negatively associated to slope
in the alpine subset (e.g. Pritzelago alpina) and to altitude in
the mediterranean subset (e.g. Festuca hystrix). The species
composition of endemic species showed a more complex
relationship with environmental variables, with solar radi-
ation and slope partially explaining the distribution of up
to five species (e.g. Armeria cantabrica).
Discussion
Environmental filtering of geographic species groups
This study demonstrates that biogeographic deconstruction
produces species groups with predictable responses to
environmental filters in alpine communities. In particular,
Table 1. Generalized linear models fitted for species richness. Only the
parameters computed over the whole data set and the species geographic
groups with significant relationships (mediterranean) are shown. Variables
are ranked according to their importance as predictors. Exp Dev indicates
the cumulative percentage of deviance explained by the variables.
Parameter Res. dev v2 P Exp dev AIC
All species richness
Null 199.03
1. Altitude 171.45 48.1 <0.001 14% 671
2. TWI 149.84 21.2 <0.001 25% 651
Mediterranean species richness
Null 100.40
1. Slope 92.26 14.8 0.004 8% 387
2. TWI 84.30 7.8 0.005 16% 381
(a)
(b)
Fig. 2. Effect plots of the models computed for species richness (a) and logit-transformed cover (b). Plots represent the effect of each factor in the
generalized linear (a) and linear (b) models computed over the whole data set (All species) and the subsets of alpine, mediterranean and endemic species.
A 95% confidence interval (grey lines) is drawn around the estimated effect. Altitude in meters, Slope in degrees, Topographic wetness index (TWI) and
position index (TPI) are explained in the text.
Journal of Vegetation Science6 Doi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science
Deconstruction of alpine communities B. Jim�enez-Alfaro et al.
our separation of geographic species groups provides infor-
mation about the species’ relationships with topographic
gradients that may be neglected when using data for the
whole community. Although we cannot say whether this
pattern could be repeated in other habitats, biogeographic
deconstruction as performed here seems to be an informa-
tivemethod for analysing the diversity of alpine communi-
ties with mixed floristic composition. This procedure is
generally used on large scales (Real et al. 2008; Fløjgaard
et al. 2011) but we show that it is also feasible to link local
ecological patterns with the biogeographical context of the
species pool, following the expectations of the community
assembly theory (Ricklefs 2004). Moreover, our results
support the general expectation of the species pool hypoth-
esis regarding links between the abundance of species in
one habitat and their habitat preferences throughout evo-
lutionary history (Zobel et al. 2011). Biogeographic decon-
struction also agrees with similar attempts to reconcile
geographic and local ecological factors in order to interpret
community assembly across multiple levels of diversity
(Hardy et al. 2012).
Since our study system is rarely grazed by domestic
stock or impacted by other human activities, we assume
that niche-based patterns are mainly driven by abiotic fac-
tors. Although the data are restricted to one region and do
not consider other assembly processes (Spasojevic & Sud-
ing 2012), the observed pattern suggests a deterministic
selection from the regional species pool (phylogeographic
assembly) in local communities (ecological assembly). For
example, the combined influence of non-slope surfaces
and TWI on mediterranean species richness may be related
to the inhibitory effect of cryoturbation (repetition of
freezing and thawing processes in periglacial zones) on the
development of alpine soils (Amico & Previtali 2012), indi-
cating micro-habitat refuges for these species in the con-
text of a non-Mediterranean mountain. Moreover, the fact
that alpine species cover was negatively influenced by alti-
tude but positively by slopemay be explained by their pref-
erence for snow patches, which are more commonly
found at low or medium altitudes in the study area. Thus,
mediterranean species seem to be restricted to xeric and
sunnier micro-habitats, similar to those described in the
calcareous grasslands of the nearby Pyrenees (Sebasti�a
2004); while alpine species are more closely related to
steep topographies with high snow accumulation, more
commonly found in temperate mountains (Nagy & Grabh-
err 2009). Similarly, the influence of TPI and slope on the
cover of endemic species suggests a preference for micro-
valleys with high snow cover. Nevertheless, endemic
species showed different environmental relationships to
the other geographic groups, in agreement with studies
indicating that mountain endemics from calcareous bed-
rock have distinct ecological patterns (Essl et al. 2009).
General influence of altitude and topography
When analysing the communities as a whole, we found
that altitude is the main abiotic filter influencing species
richness, cover and composition. The decrease in species
richness with increasing altitude is in agreement with
the pattern expected in narrow elevational ranges
(Nogu�es-Bravo et al. 2008), and is widely attributed to the
Table 2. Linear models fitted for species cover (logit transformed).
Results show the parameters of different models computed over the
whole data set and species groups with only alpine, mediterranean or
endemic species. Variables are ranked according to their importance as
predictors. Exp Var indicates the cumulative percentage of variance
explained by the variables.
Parameter Res var F P Exp var AIC
All species cover
Null 79.73
1. Altitude 66.05 21.36 <0.001 17.5% 250
Alpine species cover
Null 123.87
1. Altitude 118.37 6.20 0.031 4.5% 309
2. Slope 111.61 5.92 0.017 9.9% 305
Mediterranean species cover
Null 89.91
1. Slope 74.65 21.49 <0.001 16.9% 262
2. TWI 69.55 7.18 0.008 23.2% 257
Endemic species cover
Null 97.93
1. TPI 90.64 8.20 0.005 7.4% 282
2. Slope 87.16 3.91 0.050 10.9% 280
Table 3. Redundancy analysis (RDA) for altitude and topographic vari-
ables. Results show the statistics of partial RDAs computed over the whole
data set (all species) and species groups with only alpine, mediterranean
or endemic species. Only the significant variables selected by the forward
selection procedure (a = 0.05) are shown. Lambda (k) indicates the vari-
ance explained by each variable individually. Exp Var indicates the cumula-
tive percentage of variance explained by the model.
Parameter k F-ratio P Exp var
All species
1. Altitude 0.080 8.59 0.002 8.0%
2. Solar radiation 0.002 4.27 0.002 11.8%
3. TWI 0.002 3.40 0.002 14.8%
4. Slope 0.030 3.28 0.002 17.6%
Alpine species
1. Slope 0.060 6.50 0.002 6.2%
Mediterranean species
1. Altitude 0.050 5.59 0.002 5.5%
2. Solar radiation 0.030 2.65 0.026 8.1%
Endemic species
1. Solar radiation 0.050 4.99 0.004 4.8%
2. Slope 0.030 2.92 0.006 7.6%
3. Altitude 0.020 2.22 0.030 9.7%
Journal of Vegetation ScienceDoi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science 7
B. Jim�enez-Alfaro et al. Deconstruction of alpine communities
temperature decrease in similar habitats (Bruun et al.
2006; Qiong et al. 2010). However, we found that the
effect of altitude is reduced and the effect of topography is
increased when mountain species are analysed separately.
This may be explained by the exclusion of generalist spe-
cies that are not restricted to high mountains (40%) and
that can be expected to be more influenced by the eleva-
tional gradient (Bruun et al. 2006). The relative abun-
dance of generalist species in the study area may also
reflect former shifts from low altitudes in response to past
or recent climate changes, as has been indicated in other
regions (Choler et al. 2001). In addition, our results sug-
gest that shifts of high-mountain species can be driven by
topographic rather than elevation gradients, and those fac-
tors should also be considered to assess changes in species
richness and composition in alpine communities.
Total species richness was also influenced by TWI,
which is consistent with findings in dry alpine (Dirnb€ock
et al. 2003b) and arctic (Ostendorf & Reynolds 1998) com-
munities. The fact that TWI had a negative effect on species
richness but not on total plant cover suggests that it may
determine special conditions that limit the presence of
some plant species. Topographic wetness index has been
used to explain variation in species diversity in highly con-
trasting habitats, as a surrogate for water flow accumula-
tion (Vittoz et al. 2009), soil saturation (Gessler et al.
1995) and periglacial processes such as solifluction sheets
(Hjort et al. 2007) or patterned ground features (Feuillet
2011). In our study, the absence of surface water flows and
the periglacial structure of the landscape suggest that the
decrease in species richness with high values of TWI may
be better explained by processes such as cryoturbation that
limit soil formation and nutrient availability in cold envi-
ronments (Celi et al. 2010).
Altitude and topography were also good predictors of
species composition in the whole data set. The relatively
low number of species with a good fit to the constrained
(RDA) axes suggests that the pattern of species composi-
tion is governed by multiple gradients, and therefore few
species can be considered indicators of only one predictor.
Among these species we found arctic–alpine species (e.g.
Saxifraga oppositifolia) and local endemics (e.g. Jasione caval-
linesi) to be good indicators of high altitudes, and charac-
teristic species of alpine European grasslands (e.g. Carex
sempervirens) to be indicators of low solar radiation. More-
over, one mediterranean species that is closely associated
with TWI (Koeleria vallesiana) has elsewhere been associ-
ated with extremely low productivity soils (Martinez-Duro
et al. 2011), supporting the idea that this variable indicates
poorly developed soils. These results agree with the general
patterns detected for species richness and cover, and pro-
vide examples of the correlation between abiotic factors
Fig. 3. Species–environment plots from the RDA. The plots include the significant variables selected by forward selection (a = 0.05) in the whole data set
(All) and the subsets of alpine, mediterranean and endemic species. Only the species with a fit range higher than the percentage variation explained are
plotted. Review alphabetic order.
Journal of Vegetation Science8 Doi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science
Deconstruction of alpine communities B. Jim�enez-Alfaro et al.
and the geographic species groups. Furthermore, the con-
sistent effect of topographic variables across different
groups supports the importance of these factors in the
study system. Surprisingly, we found minimal or no effect
of TPI, despite this variable being directly related to ridge
top transitions along topographic gradients (Billings 1974).
In contrast, our results suggest a strong influence of TWI,
although more detailed studies are needed to understand
the relationship between TWI values (Kopeck�y & �C�ı�zkov�a
2010) and related factors (e.g. temperature or soil develop-
ment) in alpine communities.
Conclusions
Overall, our results demonstrate that the diversity patterns
of alpine communities across local environmental gradi-
ents may be partially determined by the ecological similar-
ity of mountain species with shared biogeographic history.
Explicitly accounting for biogeographymay assist in assess-
ing the responses of plant communities to climate changes
along temperature-related gradients, and more studies are
required to test similar patterns in different regions. In our
study, high-mountain species seem to be more influenced
by the topographic factors that determine thermal differ-
ences in alpine landscapes (Scherrer & K€orner 2011) than
generalist species. Analysing similar patterns in other
regions could at least partially explain the decline of cold-
adapted species and the increase of warm-adapted species
detected in the European mountains (Gottfried et al.
2012), especially in those regions with a complex biogeo-
graphic history. For example, arctic–alpine species are
expected to find topographic refuges in southern regions
where they have become rare (Jim�enez-Alfaro et al.
2012), while mediterranean species are subjected to local
extinction in alpine environments (Pauli et al. 2012).
Given the recent interest in measuring the effects of cli-
mate change along topographic gradients (Fridley 2009;
Scherrer et al. 2010), we conclude that biogeographic
deconstruction may provide a useful tool for the separate
assessment of the performance of high-mountain species
in alpine communities.
Acknowledgements
This study was funded by the Fundaci�on Biodiversidad
and the Spanish Ministry of Science (BIOALPI, CGL2008-
00901). We would like to thank the Picos de Europa
National Park for technical support and INDUROT in Ovi-
edo University for the project management. BJA was par-
tially supported by a post-doctoral grant from the
REMEDINAL2 network project (Madrid Autonomous
region, S-0505-AMB1783). CM has benefited from a
research stay from the Universit�a degli Studi di Palermo.
We thank David Zelen�y for reviewing the manuscript and
two anonymous reviewers for their helpful comments.
Author contributions
The idea was conceived by BJA, who collected and analy-
sed the data; CM collected data and compiled the database;
AB collected data; RG and JRO participated in the experi-
mental design and commented on themanuscript.
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Supporting Information
Additional supporting information may be found in the
online version of this article:
Appendix S1. Full list of species according to their
biogeographic classification. “Alpine species” include arc-
tic-alpine taxa mainly distributed in the European moun-
tain system. “Mediterranean species” are distributed
throughout the mountains of the Iberian Peninsula or the
Mediterranean basin. “Endemic species” are restricted to
the Pyreneo-Cantabrian mountains. “Other species”
includes taxa not restricted to high-mountain habitats.
Journal of Vegetation Science12 Doi: 10.1111/jvs.12060© 2013 International Association for Vegetation Science
Deconstruction of alpine communities B. Jim�enez-Alfaro et al.