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.


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



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



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.



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%



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.



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.



biogeographic deconstruction of alpine plant communities along altitudinal and topographic gradients

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