a regional impact assessment of climate and land-use change on alpine vegetation

A regional impact assessment of climateand land-use change on alpine vegetationThomas Dirnbock*, Stefan Dullinger and Georg Grabherr Institute of Ecology and

Conservation Biology, University of Vienna, Vienna, Austria

Abstract

Aim Assessing potential response of alpine plant species distribution to different futureclimatic and land-use scenarios.

Location Four mountain ranges totalling 150 km2 in the north-eastern Calcareous Alpsof Austria.

Methods Ordinal regression models of eighty-five alpine plant species based on envi-ronmental constraints and land use determining their abundance. Site conditions aresimulated spatially using a GIS, a Digital Terrain Model, meteorological station data andexisting maps. Additionally, historical records were investigated to derive data on timespans since pastures were abandoned. This was then used to assess land-use impacts onvegetation patterns in combination with climatic changes.

Results A regionalized GCM scenario for 2050 (þ 0.65 �C, )30 mm August precipi-tation) will only lead to local loss of potential habitat for alpine plant species. Moreprofound changes (þ 2 �C, )30 mm August precipitation; þ 2 �C, )60 mm Augustprecipitation) however, will bring about a severe contraction of the alpine, non-forestzone, because of range expansion of the treeline conifer Pinus mugo Turra and manyalpine species will loose major parts of their habitat. Precipitation change significantlyinfluences predicted future habitat patterns, mostly by enhancing the general trend.Maintenance of summer pastures facilitates the persistence of alpine plant species byproviding refuges, but existing pastures are too small in the area to effectively prevent theregional extinction risk of alpine plant species.

Main conclusions The results support earlier hypotheses that alpine plant species onmountain ranges with restricted habitat availability above the treeline will experiencesevere fragmentation and habitat loss, but only if the mean annual temperature increasesby 2 �C or more. Even in temperate alpine regions it is important to consider precipi-tation in addition to temperature when climate impacts are to be assessed. The main-tenance of large summer farms may contribute to preventing the expected loss ofnon-forest habitats for alpine plant species. Conceptual and technical shortcomings ofstatic equilibrium modelling limit the mechanistic understanding of the processesinvolved.

Keywords

Alpine plants, European Alps, habitat distribution model, ordinal regression, generalizedlinear model, vegetation modelling.

Abbreviations: DD, temperature degree days; DEM, digital elevation model; G,geological unit; NCA, north-eastern Calcareous Alps; RJ, solar radiation for July; RM,solar radiation for May; RS, solar radiation for September; S, slope inclination; SC, soilcover type; SCD, snow cover duration; SWB, site water balance; TPA, time since pastureabandonment; WET, wetness index.

*Correspondence: Federal Environment Agency, Spittelauer Lande 5, A-1090 Vienna, Austria. E-mail: [email protected]

Journal of Biogeography, 30, 401–417

� 2003 Blackwell Publishing Ltd

INTRODUCTION

High mountain ecosystems are considered particularly vul-nerable to climate change (Beniston, 1994; Grabherr et al.,1995; Beniston et al., 1996; Theurillat & Guisan, 2001).The European Alps experienced a 2 �C increase in annualminimum temperatures during the twentieth century, with amarked rise since the early 1980s (Beniston et al., 1997).Upward moving of alpine plants has occurred (Grabherret al., 1994; Pauli et al., 2001), community composition haschanged at high alpine sites (Keller et al., 2000), and treelinespecies have responded to climate warming by invasion ofthe alpine zone or increased growth rates during the lastdecades (Gindl, 1999; Paulsen et al., 2000; Motta & Nola,2001). Predictions suggest that Austria will experience afurther annual mean temperature increase of 0.8 �C and�7% annual precipitation anomalies by 2050 comparedwith the average of the period 1961–95 (Lexer et al., 2002).

We present here a study from the north-eastern Calcar-eous Alps (NCA), which are part of the catchments for highquality water of the Austrian capital, Vienna, supporting1.7 million inhabitants. The mountain ranges are karstic,exhibiting fast drainage. Considerations of plant cover andsoils are therefore crucial for water protection (Dirnbock &Grabherr, 2000). Apart from the potential of climate chan-ges for direct impact on the hydrological cycle (Loaicigaet al., 2000), they will very probably also change vegetationproperties. Nevertheless, in which way and to what extentsuch changes will occur remains unclear as yet. An assess-ment of impacts of climate and land-use changes on thevegetation was therefore of high priority as a prerequisite forfuture strategies of water management. From a managementperspective it is particularly important to evaluate changeswhich might occur at the local scale. In addition to climaticinfluences, other potential contributing factors also have tobe taken into account of which land-use may be mostimportant. Historical human impacts on the Alp’s vegetationhave limited most of the approaches attempting to detectthe signal solely attributable to recent climatic changes(Tappeiner et al., 1998; Bolliger et al., 2000; Carcaillet &Brun, 2000; Didier, 2001; Motta & Nola, 2001). In thestudy area, traditional summer pasturing reaches back to atleast the sixteenth century shaping subalpine and alpinevegetation (Dullinger et al., 2003). Shrublands and forestswere, like in many other parts of the Alps, clear-cut orburned and the resulting pastures were grazed in summer.Since pasture abandonment, a long-term trend during thelast 150 years, is omnipresent, secondary succession iscommonplace and interacts with climate-induced perturba-tion of plant communities (Dullinger et al., 2003).

To assess vegetation response to different climatic andland-use scenarios, distribution models of the eighty-fivemost frequent alpine plant species of the study area arederived based on environmental constraints controlling theirabundance. Site conditions are spatially simulated using GISand represent resource gradients [site water balance (SWB),solar radiation], gradients of direct physiological importance[temperature, snow cover duration (SCD)], and indirect

variables which reflect soil properties (topographical indices;geological unit) (see also Austin & Smith, 1989; Guisan &Zimmermann, 2000). Historical records were investigated toderive data on time spans since pastures became abandoned(Dullinger et al., 2003). This was then used to assess land-use impacts on vegetation patterns in combination with cli-matic changes. Static equilibrium models, as used here, wereapplied in different parts of the world and various biomes toassess potential impacts of a changing climate (Austin, 1992;Box et al., 1993, 1999; Brzeziecki et al., 1995; Huntleyet al., 1995; Gottfried et al., 1998, 1999; Guisan et al.,1998; Sætersdal et al., 1998; Bolliger et al., 2000; Duck-worth et al., 2000; Guisan & Theurillat, 2000; Richardsonet al., 2000; Aber et al., 2001; Hansen et al., 2001; Lexeret al., 2002). The present study provides several improve-ments over most of these models: (1) Precipitation decreaseis incorporated in addition to temperature increase. (2) Cli-mate scenarios are based on regionalized GCM scenarios.(3) Land-use impacts are investigated in combination withanticipated climatic changes. (4) Species abundance wasused instead of pure presence–absence data. (5) In contrastwith climate envelop approaches, we aimed to incorporate acomprehensive suite of abiotic environmental controls lim-iting plant species distributions.

Climate and land-use change scenarios

Climate change scenarios are derived from simulation out-puts of the global circulation model ECHAM4 (Roeckeret al., 1996) which were recently downscaled for Austria forthe purpose of risk assessment of forests prone to climaticchanges (Lexer et al., 2001, 2002). The simulation is basedon the greenhouse emission scenario IS92a (Houghton et al.,1990). Statistical downscaling techniques were applied tothe GCM scenario which, compared with the average of1961–95, resulted in 0.8 �C mean annual temperatureincrease and �7% annual precipitation anomalies forAustria in the year 2050 (average of 2035–65). The tem-perature increase was highest during the warm season and inthe northern and westernmost part of Austria, which is inagreement with twentieth century trends (Beniston et al.,1997; Weber et al., 1997). Most precipitation changes areexpected in the winter season and the northern front rangeof the Alps, where precipitation is predicted to decrease(Lexer et al., 2001). The NCA should therefore experiencemajor climatic changes during the next decades. For thestudy area, a 0.65 �C increase of the annual temperature anda 200 mm reduction in annual precipitation might be ex-pected by the year 2050 in comparison with the average of1961–95 (Lexer et al., 2001). In addition to the so-called2050 scenarios we assessed two further ones in order touncover potential impacts of more pronounced changes. The2K scenario is based on a 2 �C increase of the annual meantemperature, and the 2Kplus scenario assumes a furtherdoubling of the precipitation decrease (Table 1).

Long-term trends of SCD of higher altitudes did not showthe same decreasing values as was observed for lower andmiddle altitudes (Beniston, 1997). Obviously, redistribution

� 2003 Blackwell Publishing Ltd, Journal of Biogeography, 30, 401–417

402 T. Dirnbock et al.

patterns override simple altitudinal controls on snow fall anddepletion (Friedel, 1961; Korner, 1999). Therefore, no SCDchange was included in the scenarios.

Land-use, in particular logging and grazing in the highmountain areas, influences a plant’s distribution as climatedoes. Human modified landscapes may pose severe con-straints on the re-adjustment of vegetation patterns subjectto altered climatic conditions, in particular because of frag-mentation of native vegetation and altered disturbanceregimes (Huntley, 1990; Pitelka & Plant Migration WorkingGroup, 1997; Box et al., 1999; Duckworth et al., 2000;Hobbs, 2000; Hansen et al., 2001; McCarty, 2001). If cli-matic conditions change in the NCA, it can be anticipatedthat land-use regimes will interact with these changes, byeither facilitating species resistant to grazing, or by inhibitingthe same species during succession after abandonment(Dullinger et al., 2002). This process can be regarded asrelatively short term [a few decades to several hundreds ofyears (Wildi & Schutz, 2000)]. Our study, however,intrinsically applies equilibrium models, which are capableof assessing potential long-term changes, assuming stablehabitat conditions which lead to steady-state communities.We therefore restrict the combined scenarios exclusively totwo specific land-use situations: (1) where all abandonedsites have reached a stable state and all presently used,remain used (LU3), and (2) where the whole study area hasbecome abandoned and all sites have reached a stable state(LU4). Climate change scenarios are assessed for both land-use regimes (Table 1). Comparison is accomplished by usinga baseline scenario with present climate conditions and theLU3 land-use status.

METHODS

Study area

The study area covers the subalpine and alpine zone of fourneighbouring mountain ranges of the NCA in Austria com-prising c. 150 km2 area (Mt Hochschwab, Mt Schneealpe,Mt Rax and Mt Schneeberg, 15� to 16�E and 47�30¢ to47 �50¢ N, see Fig. 1). Summits vary between 1900 and2300 m a.s.l. The mountain system consists of mesozoiclimestone and dolomites and is characterized by displacedplateaus of different altitudes (Fig. 1c). Climatical condi-tions are temperate humid. Mean annual temperatureapproximates 6–8 �C in the valleys decreasing to c. 0–2 �Cin the summit region. Annual precipitation averages 700 mm

(valleys) and 1500–2500 mm (summits). Summer pasturing(June to September) in the area dates back at least to thesixteenth century. Except for rock faces, debris cones andvery steep slopes most of the study area has been historicallyinfluenced by livestock grazing. Since the middle of thenineteenth century grazing intensity has decreased and muchformer pastureland has become abandoned (Dullinger et al.,2003). Approximately 30% of the study area is still pasturedby free-ranging cattle at an intensity of c. 0.5 cattle perhectare (S. Dullinger, unpubl. data).

Norway spruce (Picea abies (L.) H. Karsten), sometimeswith European larch (Larix decidua Mill.), dominates fromaround 1400 m a.s.l. up to a belt of prostrate pine (Pinusmugo Turra). The upper limit of P. mugo is between 1800and 1900 m a.s.l. (Zukrigl, 1973; Fig. 1c). In fact, the sub-alpine belt is a mosaic of woody and non-woody vegetationtoday with spruce and spruce–larch forests rarely occurringabove 1600 m a.s.l. Non-woody vegetation below treelinemainly consists of different kinds of pastures and naturalgrasslands with the latter covering disturbed sites like aval-anche paths and exposed ridges. Less frequent, tall herbcommunities (Adenostyles alliariae (Gouan) Kern., Aconi-tum napellus L.) and fens (Carex nigra (L.) Reichard,C. rostrata Stokes) occur. Above treeline, natural grasslandsare dominating with a gradual switch from prevailing Carexsempervirens Vill. to Carex firma Host with increasing alti-tude. Additionally, rock faces, scree and snowbeds are con-siderably widespread.

Modelling framework

Three major assumptions cohere with the applied modellingapproach: (1) The abiotic environment has to be the majoragent controlling species distribution and abundancetogether with post-grazing succession. (2) The models arecalibrated using field data, and thus comprise any compet-itive constraint a species may force upon or experience fromits neighbour. (3) The speed of plant migration is consistentwith that of climate changes so that plant communities are ina permanent equilibrium with their environment. Admit-tedly, the third assumption is unrealistic in the mid-term. Weknow from observations that migration of alpine plants lagbehind climate warming (Grabherr et al., 1994; Pauli et al.,1996) and that dispersal mechanisms play a crucial role(Theurillat & Guisan, 2001; Kullman, 2002). As a conse-quence of the modelling framework applied, i.e. equilibriummodels of the realized niche of plants, we solely depict the

Table 1 Scenarios of climate and land-use change

Climate change scenario

Land use change scenario As present þ 0.65 �C*; )30 mm� þ 2 �C*; )30 mm� þ 2 �C*, )60 mm�

Present land use maintained LU3–baseline LU3-2050 LU3-K2 LU3-K2plus

All pastures become abandoned LU4 LU4-2050 LU4-K2 LU4-K2plus

*Annual mean (compared with the average of 1961–95; according to Lexer et al., 2001).�Monthly precipitation sum in August (compared with the average of 1961–95; according to Lexer et al., 2001).


Alpine vegetation change 403

spatial distribution of potential habitats in the long termwithout severe community deterioration (see Guisan &Zimmermann, 2000).

Vegetation data

The calibration data set is based on a stratified samplingapproach, using all combinations of categorized environ-mental variables and time of pasture abandonment (seebelow) as strata. Each stratum had to be sampled by at leastone plot, depending on total area of the stratum. Weincluded a large set of existing vegetation records (totally780) and did supplementary records for strata not coveredby these (totally 179). For the latter the position within eachstratum was randomly selected and localized in the field bymeans of a GPS (Garmin E-Trex, Garmin International Ltd;mean root mean square error: 7.5 m). The existing recordshad been marked on an infrared or black and white ortho-photo (scale 1 : 5000–1 : 10,000) and were digitized there-after (estimated geographical accuracy is �10 m). Theexisting data was originally collected in the course of map-ping the vegetation of the area (Greimler & Dirnbock, 1996;Dirnbock & Greimler, 1997; Dirnbock et al., 1998, 1999;Dullinger et al., 2001). Additionally, fifty-seven records stemfrom several transects at the treeline which cover the lowerboundary of P. mugo. The final data comprises records

between 1200 m a.s.l. and the summits. All records werecollected between 1994 and 2001 by the authors. Data werecollected according to standard phytosociological tech-niques: All plant species present on the plot were recordedtogether with a cover-abundance value estimated on a7-level ordinal scale (Braun-Blanquet, 1964). Plot size wasc. 30 m2 for grasslands, rock, scree and snowbeds, 150 m2

for P. mugo-krummholz and 300 m2 for forests. From thesedata eighty-five most frequent (> 100 times present) andsome further species were selected for modelling. These aremostly typical alpine species occurring above treeline butalso subalpine species of pastures and avalanche tracks.

Abiotic habitat variables

Several environmental variables were spatially derived usingmap data, a digital elevation model (DEM, 20 m resolution,Austrian National Mapping Agency), and meteorologicalstation data. The DEM was converted into a grid using thetriangulated irregular network model, with addition ofbreaklines, and bivariate quintic interpolation with thestandard methods incorporated in Arc/Info 7.1.

Daily net radiation for 15th May (RM), 15th July (RJ) and15th September (RS) was derived applying SOLARFLUX(Rich et al., 1995) under clear sky conditions with the DEM.The program accounts for relief shading and topographic

Figure 1 (a) Location of the study area in the European Alps (topographical shading). A: Austria, CH: Switzerland, D: Germany, F: France, FL:

Liechtenstein, I: Italy, MC: Monaco, SLO: Slovenia; (b) the four mountain ranges considered; (c) view of the easternmost mountain range(Mount Schneeberg; 2075 m a.s.l.) exemplifying the specific relief. Alpine non-forest sites (snow covered by a light autumn snowfall event)

predominantly occur at the plateau, slopes are covered by Pinus mugo Turra (upper dark area) and Picea abies (L.) H. Karsten (lower dark area).



position. For the whole area atmospheric transmissivity wasset to 0.8. Radiation was calculated at hourly intervalswhich were then summed for the whole day.

Temperature degree days (DD; daily mean temperature> 0 �C) were approximated using three meteorological sta-tions from Mt Schneealpe (770, 820 and 1740 m a.s.l.;M. Steinkellner, unpubl. data). Half hourly measures wereaveraged for each day and summed up for the year from1995 to 1999 (other years were either not measured or werenot measured consistently enough for the whole year). DDshow a strictly linear relationship with altitude and decreaseswith 5.6 days per 100 m (R2 ¼ 0.9, P-value < 0.001). DDwere offset with an increasing base temperature from west toeast. The base temperature was linearly regressed on datafrom seventeen standard meteorological stations locatedwithin and close to the study area (R2 ¼ 0.6, P < 0.05).

Site water balance was calculated for August since weassumed that water deficiency does not occur before the latesummer. SWB has been shown to be significant for plantdistribution in the area (Dirnbock et al., 2001). The SWBwas approximated by the precipitation less the potentialevapotranspiration (Turc, 1961) and was calculated as anaverage of the 1st and 15th August, and the 1st September.Daily net radiation was calculated applying SOLARFLUX(Rich et al., 1995) under the same settings as describedabove. Monthly mean temperature was input in the equationfor potential evapotranspiration and calculated applyinglinear regression on altitude (0.8 �C per 100 m altitudinalrange; R2 ¼ 0.54; P < 0.001) with data from the abovethree meteorological stations. Mean temperature in Augustdoes not show a significant longitudinal change within thestudy area. Mean daily precipitation for August was calcu-lated using geographic position in addition to altitude be-cause eastward precipitation decrease is known for the area(Wakonigg, 1978). However, systematic measurement error,topographical effects, and localized storms probably over-ride general patterns and may be responsible for the con-siderably low fit of the model (R2 ¼ 0.1, P ¼ 0.06). Theregression slopes for monthly precipitation are 1.5 mm per100 m elevation, and 6 mm per 10 longitudinal kilometres.

We used eleven SPOT scenes, available for a third of thearea, classified into snow cover classes (acquired 1998, 1999,2000 from February to June, each at c. 10:45 a.m.; 20 mresolution; Jansa et al., 2000), the above-mentioned radi-ation model (Rich et al., 1995), a wind field model(Bachmann, 1998; Ross et al., 1988), elevation (as a com-pound surrogate for temperature and precipitation; from theDEM), relief indices, and plant cover type (krummholz vs.grasslands, screes and rocks) from existing vegetation mapsto calculate SCD. The dates when the satellite images wereacquired were used to capture seasonal accumulation andablation processes. Radiation melting influence is approxi-mated by calculating the cumulative sum of incomingshortwave radiation (SOLARFLUX; Rich et al., 1995).Fallen snow is subject to redistribution by wind, avalanchesand sloughing (Liston & Sturm, 1998). These topographicaleffects were simulated with state variables. To account fordownhill snow transport we used the slope of the surface

and a topographic index (steady-state wetness index apply-ing a multiple flow algorithm using Tapes-G; Gallant &Wilson, 1996). This index was successfully used to depictaccumulation areas in relief depressions vs. depletion areasat ridges (Hartman et al., 1999). Wind redistribution wassupposed to be crucial for overall snow patterns (Jansa et al.,2000; Tappeiner et al., 2001). The study area is exposed tonorth-westerly winds and snow is blown from the plateauridges in nearby leeward slopes and gullies. We used thediagnostic wind field model NUATMOS (Version 5N,07/31/91; Ross et al., 1988, integrated in a GIS by Bach-mann, 1998) to derive near surface wind velocities. Snowdistribution is inherently discontinuous, exhibiting nonlinearrelationships with climatic gradients (Tappeiner et al.,2001). In order to best account for such a pattern we used aclassification tree (Breiman, 1993; Balk & Elder, 2000).A tree is grown by recursively partitioning a training sample,such as to minimize model deviance in the dependent vari-able. A binary classification tree was built using randomsamples (0.02% of all pixels of each snow cover class, totally9439 samples) of the eleven SPOT scenes (only full snow andno snow classes were used for the analysis) and its respectiveradiation, wind, and topographic values. The final tree hadtwenty terminal nodes and a misclassification error rate of8%. All variables show a significant influence on snowpatterns; julian day is the most important variable, followedby the topographic accumulation index, elevation andwindspeed. In order to facilitate the extrapolation of snowpatterns to the whole study area we calculated all spatialdescriptor variables for the entire region. Following the treefrom its origin to a particular terminal node all node prob-abilities determine if a cell is covered by snow or not.Therefore, we calculated mean node probability (all proba-bilities divided by node number) as the snow cover measure.

A detailed geological map (Geological Survey of Austria,unpublished data) was categorized into five geological units(limestone, dolomite, clayey weathering carbonates, relictloam, carbonate debris) and wetland (G).

Soils are important determinants for alpine plant growthas they control water supply and nutrient status (Korner,1999). The gross soil distribution was derived from inter-pretation of aerial orthophotographs. We delineated bare oralmost bare areas (rocks, screes, eroded sites), areas coveredby a closed non-forest vegetation with shallow mineral soils,and forested (krummholz and forests) areas where mineralsoils are covered by organic layers (SC). It has to be men-tioned that these three categories also reflect plant cover andmay therefore be interpreted as a gradient of competitivestrength. In particular the forested sites pose significantlimitation for alpine and other shade intolerant non-forestspecies. Topographically driven erosion and accumulation,mediated by water runoff, was supposed to be the mostimportant factor controlling soil properties at finer scales. Inthe study area relict (but also aeolian), siliceous sedimentsare widespread and distributed according to topography(Solar, 1963). Thus, we applied a surface runoff model toaccount for these processes (TAPES-G; Gallant & Wilson,1996) The wetness index (WET) describes the spatial



distribution of zones of saturation as well as runoff gen-eration and proved to have potential use in predicting soilproperties that control plant distribution (Moore et al.,1993; Gessler et al., 1995; McKenzie & Ryan, 1999). Ad-ditionally to WET the slope inclination (S) was used.

Pasture abandonment (TPA)

Spatial land-use information was derived from catastralmaps and a suite of documents referring to the land-usestatus of individual parcels at different times (Dullingeret al., 2003). As delineation of parcels in high mountainareas is usually very coarse we refined their boundaries byadjusting them to landscape topography in that we excludedall inaccessible rock faces and scree areas from actually orhistorically pastured areas. From these historical documentswe derived dates of abandonment as exactly as possible. Insome cases personal communication with land owners pro-vided additional information. Nevertheless, exact dates ofabandonment were not available for a considerable partof historical summer farms. To achieve uniform precision ofthe land-use data for the whole study area we thus categ-orized time since abandonment using the six main historicaldocuments as reference points and assigning each parcel thedate of its last documented use as a pasture. In statisticalanalyses we used the number of years between the dates ofdocumented pasture uses and the year 2000. Areas whichhave never been used were assigned a value of 1000 assu-ming this period to be sufficient to completely eliminate allremnants of former pasturing.

Statistical analysis

We derived ordinal logistic regression models (proportionalodds models) using cover-abundance values of plant speciesas the response and environmental variables and succes-sional time as the predictors. Cover-abundance values werecategorized into five classes (0: absent, 1: present–1%, 2:1–10%, 3: 10–50%, 4: 50–100%). Proportional oddsmodels are based on the cumulative probabilities of theclasses, stemming from successive logistic regression models(McCullagh & Nelder, 1991; Guisan & Harrell, 2000;Harrell, 2001). Linear functions as well as restricted cubicsplines (piecewise third order polynomials with linearrestrictions in the lower and upper tails) with not more than4 knots were fitted to the data using the Design Library ofSplus2000 (Azola & Harrell, 2001). To circumvent pre-dictor co-linearity we added the three radiation variablesseparately to the model and used only the most significantone in the final model. Model and predictor significancewere obtained from Wald test statistic assuming a chi-squaredistribution with 1 d.f. (Harrell, 2001). Full models werereduced by backward elimination, knot reduction wherecubic splines were used, and linearization respectively(P-value < 0.05). Double linear interactions of DD with allother continuous variables were taken into account.

Model validation was obtained by resampling from thecalibration data (1000 resamples with replacement) using a

bootstrap method (validate function of Azola & Harrell,2001) to get bias corrected values of the generalized R2

(Nagelkerke, 1991; quoted as RN2 thereafter), judging thepredictive strength of the model, and Somer’s concordanceindex Dxy (Harrell, 2001) which is based on the Wilcoxon–Mann–Whitney two-sample rank test. Dxy takes valuesbetween 0 and 1 depicting totally random vs. perfectly dis-criminating models, respectively. As an additional validationmeasure, the maximum absolute error in prediction prob-ability (Emax) was calculated according to Efron (1983) (seealso Harrell, 2001). In order to represent all ordinal responsevalues in each bootstrap resample the data were stratified bythe species abundance. Maps of abundance of each specieswere drawn within the GIS summing up probabilities of theinverse logistic link function of the linear predictors of eachordinal class (Guisan & Harrell, 2000).

P. mugo distribution which served also as an indicator offorest soils and therefore as a predictor for all other species,was treated differently. The land-use maps do not reflect theabundance of the Pinus, which is often caused by selectivelogging or scattered re-invasion in former pastures, leadingto a patchy distribution pattern on a much finer scale(Dullinger et al., 2003). Consequently, for P. mugo presenceor absence is more meaningful for habitat preferences thanabundance. Probability of occurrence was modelled using abinary logistic model (McCullagh & Nelder, 1991; Harrell,2001). The calibration data used was restricted to thosesamples which were never used for cattle grazing butP. mugo is absent and those where P. mugo is present(n ¼ 477). This procedure guaranteed an appropriate fit ofenvironmental determinants. The distribution on presentlyused farmland was kept constant in the baseline and all LU3scenarios. In order to circumvent the likely case that P. mugowill be out-competed by subalpine trees (particularly Piceaabies) at its lower limit, all comparisons were restricted toabove 1600 m a.s.l.. The model was validated using thesame concordance and discrimination values as for all otherspecies models but by predicting on independent data(totally 809 samples), randomly derived from distributionmaps of P. mugo comprising the entire area. A thresholdprobability, where presence and absence separate was cal-ibrated using the highest chi-square test result applied onbinary contingency tables from successive predictionthresholds of 0.1, 0.2, 0.3, . . . , 0.9, resulting to 0.4.

Climate and land-use scenarios

An increasing annual temperature of 0.65 �C (Lexer et al.,2001) corresponds to 8 DD. This is particularly conservativeas minimum temperatures increased above-average duringthe last decades in the Alps (Beniston et al., 1997; Weberet al., 1997), and mean temperature may therefore under-estimate DD. According to Lexer et al. (2001) monthlysummer temperature will increase by c. 0.9 �C, and monthlysummer precipitation will decrease by 50–30 mm. From the1980s onward precipitation amounts in the Alps showedonly a modest decrease (Beniston et al., 1997). Therefore,the lower estimate (30 mm) was used to re-calculate SWB in



August for the 2050 scenarios. This amounts to 15% (higherelevations and western part) to 30% (lower elevation andeastern part) less precipitation in August.

The soil cover map was re-adjusted using the predictedP. mugo distribution according to the scenarios. All sitesonce occupied by either P. mugo or subalpine forests (Piceaabies, Larix decidua), from the present distribution to allsubsequent scenarios, fell in the forest soil class in therespective scenario. The two other soil categories were keptconstant, simply because no consistent procedure was athand. All calculations were restricted to above 1600 m a.s.l.,i.e. the subalpine and alpine area.

RESULTS

P. mugo distribution

The environmental factors included in the logistic regressionmodel for the distribution of the subalpine coniferous shrubP. mugo explain 55% of the deviance (Table 2). The modelachieves high predictive accuracy on an independent data set(RN2 ¼ 0:68; Dxy ¼ 0.86). Probability of occurrence isunder- and overestimated, but mostly in the lower as well asthe upper ranges of probability estimates, and thus ensuresgood discrimination between presence and absence (Fig. 2).DD is most important in explaining occurrence followed byS, G, RS and SCD (Table 2). Pinus mugo shows an unimodaldistribution along the DD gradient with a sharp decline atc. 220 DD, and it prefers intermediate SCD.

Models of alpine plant species

Predictive discrimination (Dxy), as obtained by bootstrapresampling from the calibration data set, is 0.57 on averageand RN2 is 0.25. Poa alpina L. shows the lowest Dxy (0.23)and goodness of fit (RN2 ¼ 0.09), Helictotrichon parlatorei(Woods) Pilg. the best (Dxy ¼ 0.87; RN2 ¼ 0.4). Maximalprediction error of probabilities lies between 0.003 and 0.09with an average of 0.03 (Fig. 3). All environmental variablesare significant predictors for plant species distribution and

abundance, nevertheless, different species comprise differentsuites of variables. DD is significant (P < 0.05) in almost allmodels, mostly with highest predictive power. S is the nextmost consistent and significant contributor, but shows rela-tively low chi-square values. In contrast, RS is only sig-nificant in 45% of the models but its contribution tovariance explanation is comparatively high. SWB comesclosely after SC, followed by G. The latter shows relativelylow chi-square values. Significant in less than 30% of themodels is the TPA, SCD, WET and RJ and RM (Fig. 4).

Potential habitat changes

Pinus mugo expansion because of climate and land-usechange leads to a 25% loss of non-forest habitat assumingthe 2050 scenarios and 48% assuming the K2 and K2plusscenario, if actual pastures remain. If pastures are elimin-ated, 42% and 64% will potentially be lost. Those popula-tions of alpine species of mountains with particularly lowsummits may experience severe fragmentation (Figs 5 & 6).Additionally, land-use may significantly reduce the decreas-ing trend stemming from climate change if only non-forestarea is considered. For lower mountain ranges most of thealpine habitat has already been lost assuming the 2050 cli-mate scenario, whereas at the higher M. Hochschwab afurther decline is elicited by the model (Fig. 5).

Figure 7 shows the distribution of relative habitat change(increase and decrease) of all investigated plant species(except for Pinus mugo). Decrease and increase of suitablehabitats occur for all climate scenarios and with and with-out maintenance of the summer pastures. Overall, habitatloss predominates. Under moderate climate change

Table 2 Significant predictor variables of the logistic regressionmodel for Pinus mugo Turra distribution

Variable Deviance d.f. P (Wald v2)

Degree days* 250.9 2 < 0.001

Slope inclination* 26.3 2 < 0.001

Snow cover duration* 8.8 2 0.02Geological unit� 22.6 5 0.001

September radiation* 21.3 2 < 0.001

Total 330 13 < 0.001

Null model 595.5 476

Deviance ¼ change in deviance when dropping a term from the full

model. d.f. ¼ change in degrees of freedom. Significance tested using

Wald v2 test.*Restricted cubic spline with 3 knots.

�Six categories.

Figure 2 Validation of the logistic model of occurrence of Pinusmugo against an independent data set derived randomly from map

data (n ¼ 809). Calibrated risk distribution (grouped proportions

vs. mean predicted probability in group) is shown by means of a

logit and a nonparametric calibration curve additionally to thehistogram of logistic-calibration probabilities. Triangles represent

grouped proportions. Somer’s Dxy rank correlation and

Nagelkerke’s RN2 between predicted probabilities and presence/

absence outcome of the independent data set.



(2050 scenarios) fifty-eight species loose habitat, twenty-seven gain habitat; taking pasture abandonment intoaccount sixty-three lose and only twenty-two gain habitat.Severe habitat loss (i.e. > 50% of its former LU3 area)experience thirty-one vs. forty-two species when all pasturesbecome abandoned. In both of these scenarios, losers andwinners are quite evenly distributed over the frequencyclasses (Fig. 7). This changes severely for the K2 and theK2plus scenario, where losers clearly predominate. The K2and K2plus scenario show an almost total habitat loss formore than 40% of the species when land-use systems remainconstant and c. 50% when all pastures become abandoned.Comparing K2 and K2plus reveals that further precipitationdecrease shifts those species which already lost habitat in K2to the extreme side and fewer species gain. However, somespecies show reverse trends when further precipitation isconsidered (see Fig. 8; Deschampsia cespitosa (L.) P. B. andSilene acaulis (L.) Jacq.). The most extreme deteriorationoccur when climate changes strengthen in combination withpasture abandonment. Then, typical pasture species (e.g.Crepis aurea (L.) Cass., Trifolium pratensis L.), as well asalpine species (e.g. Carex firma, Carex sempervirens, Silene

acaulis) decline. Consequently, the K2plus scenario withtotal abandonment cause sixty-five species to decrease, ofwhich almost all (sixty-one species) lose more than 50% oftheir actual habitat, and only twenty species will potentiallygain additional habitat. Climate change impacts on potentialplant distribution are the more important compared withpossible effects of pasture abandonment the more extremechanges are.

Figure 8 illustrates that change in abundance is consistentwith habitat change, i.e. for those species which win habitatalso abundance increases (Helictotrichon parlatorei (Woods)Pilg.) and vice versa (Carex firma, Silene acaulis (L.) Jacqu.).

Overall sensitivity

We calculated overall sensitivity of a species as the meanrelative area change of all abundance classes (Table 3).Winners and losers exist in all sensitivity classes. The bulk ofspecies are categorized as moderately sensitive, however, thespecies involved may lose almost all of their potential habitatin singly scenarios. Overall sensitivity of species to climaticand land-use changes have to be interpreted cautiously

Figure 3 Frequency distribution of RN2 ,

Somer’s Dxy, and Emax in proportional oddsmodels of eighty-five alpine and subalpine

plant species of the area. The bars show

corrected values derived from bootstrap

evaluation (n ¼ 1000 resamples withreplacement); at least one sample per resam-

ple from each ordinal cover-abundance clas-

ses of the species were conditioned. Because

of singularity and divergence respectively forthirteen species, n is below 1000 (smallest

n ¼ 868).

Figure 4 Relative frequency and importance

of environmental variables. Solid line: per-

centage of PO-models of alpine plant speciesdistribution (n ¼ 85) the different environ-

mental and land-use variables significantly

contribute to (Wald test statistic; P < 0.05);

bars: mean v2–d.f. value (¼explanatorypower) of the respective variables in these

models.



because sensitivity and model fit are correlated to someextent (e.g. Pearson moment product correlation for Somer’sDxy and sensitivity shows P ¼ 0.008). Plant species assem-bling present communities do not at all respond equally,some may be very sensible to climate and land-use change,others are not. Table 3 also shows that decreasing orincreasing trend is not consistent in all consecutive scenarios.Some exhibit increase in the 2050 scenarios and a declinethereafter (e.g. Crepis aurea, Carex capillaris L., Salix alpinaScop.), whereas others decrease under moderate climate

change and increase when pronounced changes are assumed(e.g. Carex ferruginea Scop., Scabiosa lucida Vill.) (see alsoFigs 7 & 8).

DISCUSSION

Although temperature-limited environments like alpineregions are thought to be particularly vulnerable to climatechange (Beniston, 1994; Grabherr et al., 1995; Benistonet al., 1996; Theurillat & Guisan, 2001) different results andconclusions were drawn from recent studies on climatechange impacts on alpine plant’s distributions. On one side ofthe extreme are re-visitations of historical sites, which clearlyshow that alpine and nival species have responded to climatewarming by migrating to higher positions (Grabherr et al.,1994; Pauli et al., 2001), as did treeline species (Meshinevet al., 2000; Motta & Nola, 2001; Kullman, 2002). Com-parable results, i.e. major re-adjustment of alpine vegetationpatterns, were derived from modelling approaches (Gottfriedet al., 1998, 1999; Guisan & Theurillat, 2000). However,several studies found stasis or insignificant responses toobserved temperature increases. These include some treelineobservations on the one hand (Lavoie & Payette, 1994;Paulsen et al., 2000; Cullen et al., 2001) and populationstudies, either experimental or long-term observations, on theother hand (Molau, 1997; Arft et al., 1999; Totland, 1999;Suzuki & Kudo, 2000; Diemer, 2002). At least for theEuropean Alps Theurillat & Guisan (2001) concluded that,although alpine vegetation may tolerate an increase of1–2 �C of mean air temperature, in the case of more pro-nounced increases profound changes may be expected.

Climate and land-use change scenarios

In our study, the severe habitat loss of many species is linkedto the peculiar plateau-shaped topography of the NCA. Theplateau slopes are covered mostly by forests and the treelineis positioned right where the plateau flattens. The alpinevegetation belt ranges over only a few hundred altitudinalmetres and the alpine flora of each of these mountains isisolated (Fig. 1c). Severe loss of alpine diversity and frag-mentation of plant populations because of climate warmingwere expected for comparable high mountain systems world-wide (Grabherr et al., 1995; Sætersdal et al., 1998;Theurillat & Guisan, 2001). The majority of habitat declineof alpine plants may be expected to be caused by theexpansion of P. mugo into the alpine zone (Figs 5 & 6).Particularly threatened are moderate carbonatic slopes withintermediate SCD, and these are the sites which comprise themajor habitats of alpine grasslands today. Table 2 and Fig. 2illustrate the strong environmental constraints on the dis-tribution of Pinus mugo. Further evidence exists that growthand fecundity of Pinus mugo is controlled mostly by tem-perature (S. Dullinger, unpublished data). Nevertheless,recruitment rates in grasslands strongly depend on the den-sity and canopy height of the community to be invaded. Inparticular some pasture grasslands inhibit seedling estab-lishment. Reconstructed postglacial tree species fluctuations

Figure 5 Total area (> 1600 m a.s.l.) of non-forest habitats of each

isolated mountain range considered which remain after Pinus mugoreaches its potential distribution which can be expected from the

2050 and the K2 and climate and land-use scenarios. K2plus scen-

ario is not shown as SWB is not a significant explanatory variable for

Pinus mugo distribution.



at a nearby mountain range (Kral, 1970) revealed that thetreeline was situated c. 400 m higher in the Subboreal(c. 2950 BP) than today, which is in the range of Holocenefluctuations found in other parts of the Alps (e.g. Tinneret al., 1996; Carcaillet & Brun, 2000). Pinus mugo was thenmuch less dominating in the upper subalpine zone, experi-encing harsh suppression by other conifers (e.g. Pinuscembra L.) (Kral, 1970). As a consequence of the uneveninvasion process and underpinned by the paleoecologicalevidence available, spatio-temporally diverse patterns willmore likely evolve which are not exclusively determined byre-adjusting to environmental changes. In the long run,however, and focusing on the gross trend which is elicitedby the environmental change model of P. mugo, alpine, non-forest areas of the NCA may shrink considerably if tem-perature changes up to 2 �C and traditional land-use declines(Figs 5 & 6).

When considering the overall impacts, climate changeand land-use decline may have on alpine plant species, wesee that effects of moderate temperature increases andprecipitation decreases are rather weak, with species gain-ing and others losing potential habitat (Fig. 7, Table 3).Climate changes according to the 2050 scenarios(þ 0.65 �C; )30 mm in August) will cause local loss ofpotential habitats for alpine species but will, according toour results, not deteriorate major vegetation patterns anddiversity of the NCA. The cessation of summer farming hasbeen shown to cause a significant long term decline of plantspecies diversity at the landscape scale (Dullinger et al.,2003). In combination with climate warming and drying,the maintenance of pasture systems is important insofar asthey provide refuges for alpine species (illustrated in Figs 5& 7, and Table 3; see also Theurillat & Guisan, 2001).If climate changes more drastically (K2 and K2plus

Figure 6 Present distribution of Pinus mugoabove 1600 m a.s.l. (presence with black

shade) and probability of occurrence (seelegend) of P. mugo prone to climate and land-

use change; exemplified by a part of the

mountain range of M. Hochschwab, the

westernmost of the study area (see Fig. 1b).Only LU4-2050 and LU4-K2 scenarios are

shown (Table 1). Pinus mugo is successively

covering alpine areas leaving only scatterednon-forest habitats. Disappearance at the

lower limit reflects competitive exclusion by

subalpine forest trees.



scenarios), actual pastured area is too small to significantlywithhold the severe expected habitat loss of alpine plantspecies.

Water availability can co-determine spatial distributionpatterns leading to species specific responses under anassumed precipitation decrease. Such constraints are sur-prising since water availability is generally thought to havelittle impact on the vegetation in the humid high elevationenvironments of the European Alps (Korner, 1999; but see

Guisan et al., 1998). However, water availability can besignificant for alpine plant growth (Walker et al., 1994) orsoil formation (Bryant et al., 1997). In the study area E-Wmoisture gradients were successfully applied to explain localscale species distribution pattern (Dirnbock et al., 2001).Thus, water may well matter, particularly in the drier partsof the Alps, like the easternmost NCA and expected futureprecipitation decreases may not be restricted to lowlandforests (see e.g. Lexer et al., 2002 for Austrian forests) but

Figure 7 Frequency distribution of expected potential habitat change of all eighty-five alpine plant species (in per cent area of the baseline

scenario LU3 ¼ 100%) due to climate and land-use change. Area increase > 210% was pooled. Total number of species increasing (þ) vs.

decreasing ()) due to the assumed climate and land-use scenario is shown.



also to alpine vegetation of temperate regions. Overall, aprecipitation decrease enhances warming impacts, but somespecies exhibit reverse trends (Fig. 8).

Unexplained distribution patterns

Considerable variation of present distribution patterns ofalpine plants remained unexplained using environmentalvariables as predictors (Fig. 3). Besides technical difficultiesto correctly simulate climate and other environmental fac-tors across landscapes, microtopographic heterogeneity isassumed to be responsible for such a weak predictability

(Guisan & Theurillat, 2000). The suitability of the P. mugo-model, the only taller shrub investigated (illustrated inTable 2 and Fig. 2), points to the appropriateness of theresolution to the scale where this species acquires resourcesand is exposed to environmental stressors. In contrast, thedeviation of an alpine plant’s climate from that measured bycommon meteorological stations is well known (Geiger,1965; Korner, 1999). Micro-climates significantly affectregeneration, species distribution and richness (Moir et al.,1999; Grytnes, 2000; Carrer & Carlo, 2001; Erschbameret al., 2001) and have been shown to differently influenceplant community response to regional climate change (Gavin& Brubaker, 1999). Consequently, micro-relief niches en-able plant species to occur where they potentially would bemissing from the viewpoint of mesoscale environment, and,in turn, some are missing at fine scales where they potentiallyoccur at broader scales. Micro niches are captured to such anextent in the models that environmental conditions at themesoscale are probably linked to some extent to conditionsat finer scales. Nevertheless, patchiness on a broad range ofscales is fundamental for population dynamics, communityorganization and function (e.g. Levin, 1992), thus, predic-tions about future species distributions remain uncertain sofar.

Interestingly, a study of Gottfried et al. (1998, 1999)showed, that a considerable refinement of the DEM to even1 m resolution, does not substantially enhance model accu-racy. Although they used different statistical techniques andindirect topographic gradients, it could be asked whether theunexplained variance can be attributed to reasons other thanthe spatial resolution of the input data? Composition anddiversity of alpine plant communities is strongly determinedby specific disturbance regimes (Chambers, 1995; Bohmer,1999). The consequence for environmental response modelsis that disturbance driven patterns are correlated solely withstatic environmental gradients in an assumed equilibriumsetting (Dirnbock et al., 2002). Part of these patterns will beexplained by the comprised environmental variables (e.g.slope inclination will partly explain frequency of ava-lanches), but more complex ones will remain unexplained(see e.g. White et al., 2001).

For all of these considerations we may conclude, that thepredicted patterns will be characterized by significant with-in-cell and between-cell variation, and hence that they rep-resent a response trend rather than real expecteddistributions. Although static habitat distribution modellingdoes not allow much conclusions on mechanisms underlyinga plant’s adaptation to environmental changes, such ananalysis can elicit landscape scale patterns which we mightobserve in the farther future, taking certain assumptions onplant responses to climate and land-use change into account.

ACKNOWLEDGEMENTS

We are grateful to M. Steinkellner, G. Mandl, G. Bryda, J.,Jansa, K. Kraus, R. Tscheliesnig for meteorological, geolo-gical, land-use, and snow cover data, and J. Greimler,I. Schmidsberger, N. Sauberer and T. Englisch for field data

Figure 8 Some plant species examples (Carex firma Host, Sileneacaulis acaulis (L.) Jacq., Helictotrichon parlatorei(Woods) Pilg.,

Deschampsia cespitosa (L.) P. B.) of area change of abundanceclasses due to climate change and pasture abandonment (according

to Table 1) compared with the baseline scenario with actual

pasture use (LU3). The darkness gradient of the shades represents

increasing abundance.



Table 3 Overall sensitivity of eighty-five plant species as the mean relative area change of all existing abundance classes (compared with the

baseline scenario LU3). Relative change of area of potential habitats was compared with the baseline scenario with actual pasture use (LU3)

Plant species Sensitivity LU3-2050 LU3-K2 LU3-K2plus LU4-2050 LU4-K2 LU4-K2plus

Helictotrichon parlatorei, Homogyne alpina,

Vaccinium vitis-idaea

Low

þ þ þ þ þ þ

Galium anisophyllon, Sesleria albicans þ ) ) þ þ þRhododendron hirsutum, Scabiosa lucida ) þ ) ) þ )Viola biflora ) ) ) ) þ )Deschampsia cespitosa, Nardus stricta ) ) ) ) ) )

Adenostyles alliariae, Betonica alopecuros,Chaerophyllum hirsutum, Trisetum alpestre,Vaccinium myrtillus

Moder

ate

þ þ þ þ þ þ

Veratrum album þ þ ) þ þ þCampanula alpina, Loiseleuria procumbens,Pedicularis rostratocapitata, Salix alpina

þ ) ) þ ) )

Acinos alpinus, Carex ferruginea ) þ þ ) þ þLeucanthemum atratum ) þ ) ) þ )Carex capillaris þ ) ) ) ) )Agrostis alpina, Androsace chamaejasme,Anthoxanthum odoratum agg., Anthyllisvulnerari ssp. alpestris, Armeria alpina,Aster bellidiastrum, Campanula pulla,Carex firma, Carex sempervirens,Dryas octopetala, Festuca pumila,Festuca versicolor ssp. brachystachys,Helianthemum alpestre, Heilianthemumglabrum, Juncus monanthos,Luzula glabrata, Minuartia sedoides,Poa alpina, Persicaria vivipara,Potentilla aurea, Primula clusiana,Ranunculus alpestris,Ranunculus montanus agg.,Salix reticulata,Salix retusa, Silene acaulis

) ) ) ) ) )

Trollius europaeus

Hig

h

þ þ þ þ þ þLeontodon hispidus þ þ þ ) ) )Gentiana pumila þ ) ) þ ) )Dianthus alpinus þ ) ) ) ) )Achillea clavenae, Alchemilla anisiaca,Phyteuma orbiculare, Thymus praecox ssp.polytrichus

) ) ) ) ) )

Calamagrostis varia

Ver

yhig

h

þ þ þ þ þ þTrifolium pratense þ þ þ ) ) )Tofieldia calyculata ) ) þ ) ) )Bartsia alpina, Campanula scheuchzeri,Euphrasia salisburgensis, Galium noricum,Geum montanum, Ligusticum mutellina,Minuartia gerardii, Myosotis alpestris,Parnassia palustris

) ) ) ) ) )

Heracleum austriacum, Rumex alpestris,Silene alpestris

Extr

eme

þ þ þ þ þ þ

Anemone narcissiflora þ ) þ þ ) þCrepis aurea þ ) þ ) ) )Euphrasia picta, Festuca rupicaprina,Gentiana clusii, Homogyne discolor,Lotus corniculatus, Luzula multiflora,Pedicularis verticillata, Soldanella alpina

) ) ) ) ) )

(þ) General increasing trend, ()) decrease. Sensitivity categories (categories represent means of all scenarios; before calculation no change was

set to 0, 100% decrease or increase to 1, > 100% increase was set to 1): low ¼ 0–0.2; moderate ¼ 0.2–0.4; high ¼ 0.4–0.6; very high ¼ 0.6–

0.8; extreme ¼ 0.8–1. Nomenclature of species follows Adler et al. (1994).



collection. Further thanks go to N. Zimmermann, A.Bachmann, J. Gallant, J. Wilson for several GIS software.H. Pauli, W. Willner, M. Abensperg-Traun, and twoanonymous referees helped improving the manuscript. Thestudy was funded by the Austrian Federal Ministry forEducation, Science and Culture and the Viennese WaterWorks.

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BIOSKETCHES

Dr Thomas Dirnbock is botanist and received his PhD at the Institute of Ecology and Conservation Biology, Universitiy ofVienna. He started this study after a postdoc position at the Department of Wildlife and Ecology, CSIRO, Australia. His researchinterests focus on alpine plant ecology, vegetation modelling, GIS, and environmental change issues. Recently, he changed to theAustrian Federal Environment Agency as a researcher within Long-Term Ecological Research (LTER).

Stefan Dullinger is PhD candidate in Ecology at the University of Vienna with main interests in vegetation dynamics andmodelling. His current research is focused on dynamics of alpine vegetation prone to climate and land-use changes by integratinghistorical land-use information, dendroecology and spatially explicit spread models.

Prof. Georg Grabherr is Professor of Vegetation Ecology at the Institute of Ecology and Conservation Biology, University ofVienna. He has worked for more than 30 years on alpine plant ecology, vegetation surveys and conservation issues and iscurrently focusing on climate change impacts on high alpine plant diversity and function.



a regional impact assessment of climate and land-use change on alpine vegetation

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