Journal of Biogeography (J. Biogeogr.) (2005) 32, 1117–1127
ORIGINAL
ARTICLE
Environmental determinants of vascular
plant species richness in the Austrian Alps
Dietmar Moser1,3*, Stefan Dullinger3, Thorsten Englisch2, Harald Niklfeld2,
Christoph Plutzar3, Norbert Sauberer1,3, Harald Gustav Zechmeister1 and
Georg Grabherr1
1
Institute of Ecology and Conservation Biology,
University of Vienna, 2Institute of Botany,
University of Vienna and 3Vienna Institute for
Nature Conservation and Analyses, Vienna,
Austria
ABSTRACT
Aim To test predictions of different large-scale biodiversity hypotheses by
analysing species richness patterns of vascular plants in the Austrian Alps.
Location The Austrian part of the Alps (c. 53,500 km2).
Methods Within the floristic inventory of Central Europe the Austrian part of
the Alps were systematically mapped for vascular plants. Data collection was based
on a rectangular grid of 5 · 3 arc minutes (34–35 km2). Emerging species richness
patterns were correlated with several environmental factors using generalized
linear models. Primary environmental variables like temperature, precipitation
and evapotranspiration were used to test climate-related hypotheses of species
richness. Additionally, spatial and temporal variations in climatic conditions were
considered. Bedrock geology, particularly the amount of calcareous substrates, the
proximity to rivers and lakes and secondary variables like topographic, edaphic
and land-use heterogeneity were used as additional predictors. Model results were
evaluated by correlating modelled and observed species numbers.
Results Our final multiple regression model explains c. 50% of the variance in
species richness patterns. Model evaluation results in a correlation coefficient of
0.64 between modelled and observed species numbers in an independent test data
set. Climatic variables like temperature and potential evapotranspiration (PET)
proved to be by far the most important predictors. In general, variables indicating
climatic favourableness like the maxima of temperature and PET performed better
than those indicating stress, like the respective minima. Bedrock mineralogy,
especially the amount of calcareous substrate, had some additional explanatory
power but was less influential than suggested by comparable studies. The amount
of precipitation does not have any effect on species richness regionally. Among the
descriptors of heterogeneity, edaphic and land-use heterogeneity are more closely
correlated with species numbers than topographic heterogeneity.
*Correspondence: Dietmar Moser, Department
of Conservation Biology, Vegetation and
Landscape-Ecology, Institute of Ecology and
Conservation Biology, Althanstr. 14, A-1090
Vienna, Austria.
E-mail: [email protected]
Main conclusions The results support energy-driven processes as primary
determinants of vascular plant species richness in temperate mountains. Stressful
conditions obviously decrease species numbers, but presence of favourable habitats
has higher predictive power in the context of species richness modelling. The
importance of precipitation for driving global species diversity patterns is not
necessarily reflected regionally. Annual range of temperature, an indicator of shortterm climatic stability, proved to be of minor importance for the determination of
regional species richness patterns. In general, our study suggests environmental
heterogeneity to be of rather low predictive value for species richness patterns
regionally. However, it may gain importance at more local scales.
Keywords
European Alps, generalized linear model, species–energy hypothesis, species
richness, vascular plants.
ª 2005 Blackwell Publishing Ltd www.blackwellpublishing.com/jbi
doi:10.1111/j.1365-2699.2005.01265.x
1117
D. Moser et al.
INTRODUCTION
When focusing on determinants of vascular plant species
diversity, an appropriate consideration of scaling issues is
imperative (Rosenzweig, 1995). Determinants of species
diversity patterns obviously vary on both spatial (Auerbach
& Shmida, 1987; Huston, 1994, 1999; Cornell & Karlson, 1997)
and temporal (e.g. Rosenzweig, 1995) scales. Concerning
space, biological interactions, e.g. competition, facilitation,
and conceptually linked disturbance effects (Connell, 1978),
certainly play an important role at local scales (Huston, 1999;
Whittaker et al., 2001) but are rather irrelevant for explaining
meso- or macro-scale patterns (Shmida & Wilson, 1985).
Concerning time, a fundamental distinction is that between
historical or evolutionary drivers like long-term climatic
fluctuations, dispersal or endemism, and contemporary
ecological processes (e.g. Brown & Lomolino, 1998; Ricklefs
et al., 1999), which may work in parallel, but do not necessarily
do so (Whittaker et al., 2001).
In the current study, we attempt to test some of the most
frequently cited general hypotheses on contemporary ecological determinants of species diversity patterns at broader
geographical scales (Table 1). The first and most important
theory considered is the ‘species–energy hypothesis’. Originally
formulated by Hutchinson (1959) and extended by Wright
(1983), results of various empirical studies seem to be
consistent with its predictions (e.g. Richerson & Lum, 1980;
Currie, 1991; Wylie & Currie, 1993; Austin et al., 1996; Qian,
1998). Recently, another important insight has been put
forward in the ‘water–energy dynamics’ theory of O’Brien
(1998), which suggests that broad-scale patterns of plant
species richness basically depend on the interaction of available
energy (heat/light) and water (rainfall). Although effective
primarily at large geographical scales (> 100 · 100 km), these
climatic determinants may be condensed along steep altitudinal gradients (Austin et al., 1996; O’Brien et al., 2000). The
environmental stress hypothesis (Fraser & Currie, 1996;
Whittaker et al., 2001) relies on the assumption that the pool
of species available for colonization declines with increasing
harshness of environmental conditions and is supposed to be
important particularly for predicting species richness of
extreme habitats (e.g. alpine or desert ecosystems). On the
other hand, the hypothesis of environmental favourableness
(Pianka, 1966; Richerson & Lum, 1980; Mourelle & Ezcurra,
1996) emphasizes the reverse: increase of species richness when
approximating some optimum of habitat conditions. The
environmental stability hypothesis originally stated that longterm stability of environmental, and particularly of climatic
conditions promotes niche differentiation, i.e. evolutionary
adaptation and speciation (Pianka, 1966; Currie, 1991).
According to a variant of the stability hypothesis, however,
fewer species should physiologically be equipped to tolerate
annual, i.e. short-term variations in climatic conditions (Fraser
& Currie, 1996). Finally, environmental heterogeneity is
commonly expected to be positively correlated with species
numbers (Richerson & Lum, 1980; Shmida & Wilson, 1985;
Huston, 1994; Rosenzweig, 1995). As the effect of heterogeneity is conceptually linked to resource partitioning and thus to
biotic interactions, heterogeneity variables are generally
assumed to gain in importance with decreasing spatial scale
(Linder, 1991; Austin et al., 1996; O’Brien et al., 2000;
Whittaker et al., 2001).
Concerning vascular plants, various mapping projects have
provided large, spatially explicit data sets of vascular plant
distributions at national to continental scales during the last
decades (e.g. Jalas & Suominen, 1972–94; Welten & Sutter,
1982; Haeupler & Schönfelder, 1989; Poldini, 1993; Farkas,
1999). For Austria, the floristic inventory (Niklfeld, 1971)
provides distribution data of vascular plants for analysing
broad-scale patterns of species richness in the alpine region,
which covers about two-thirds of the country. In this study, we
use these data together with data on possible environmental
Table 1 General hypotheses explaining species richness patterns. Modified from Fraser & Currie (1996, Table 1)
Hypothesis
Argument
Factor used to test
Available energy*
Partitioning of energy among species limits richness
Environmental stress Fewer species are physiologically equipped to tolerate
harsh environments
Better life conditions promote higher species numbers
Temperature, potential evapotranspiration
and precipitation
Minimum values of temperature and potential
evapotranspiration
Maximum values of temperature and potential
evapotranspiration
Annual variation in temperature
Environmental favourablenessà
Environmental stability§
Environmental heterogeneity–
Fewer species are physiologically equipped to tolerate
variable environments
Habitat differentiation and resource partitioning facilitate
coexistence and enhance species richness
Topographic, spatial climatic,
edaphic and land-use heterogeneity
*Hutchinson (1959), Wright (1983).
Fraser & Currie (1996), Whittaker et al. (2001).
àPianka (1966), Richerson & Lum (1980).
§Pianka (1966), Currie (1991), Fraser & Currie (1996).
–Richerson & Lum (1980), Shmida & Wilson (1985).
1118
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
Plant species richness in the Alps
determinants of species diversity in order to test whether the
number of vascular plants present in a certain area is consistent
with predictions of the diversity hypotheses in Table 1.
indicators of underlying drivers (Richerson & Lum, 1980;
Austin et al., 1996; Heikkinen & Neuvonen, 1997; O’Brien
et al., 2000). A summary of all variables used in the analysis is
given in Table 2. For ranges and units, see Table 3.
METHODS
Primary variables
Study area
About 63% of Austria (total area c. 83,850 km2) is covered by
the Alps. Within the alpine region, elevation ranges from below
300 m.s.l. in peripheral areas to 3797 m.s.l. at the highest peak
(Mt Growßglockner). Annual precipitation varies from c. 700
to 2500 mm, and even higher locally. Precipitation maxima
usually fall into the growth period. Because of predominantly
north-westerly winds and the accumulation of clouds, humidity
is highest in the northern Alps and decreases towards the more
continental interior mountain chains (Fink, 1993). Mean
annual temperature varies from c. 9 C in the warmest parts
to below 0 C on the highest peaks, with heavy frosts in winter.
Limestones build up the lower northern and southern fractions
of the Austrian Alps, whereas silicate bedrocks predominate in
the higher interior parts (Richter, 1974).
Plant distributional data
Data collection was based on a regular grid of cells (¼quadrants hereafter; Niklfeld, 1971, 1978), each spanning 5 · 3 arc
minutes of the geographical net (¼c. 34–35 km2, depending on
latitude). For Austria, systematic data collection started in
1969 and the inventory is more or less complete now, at least
for the alpine parts of the country (Niklfeld, 1999). Overall,
2922 species and subspecies of indigenous and neophytic
vascular plants have been recorded within these alpine regions.
Per-quadrant species richness varies by one order of magnitude (minimum: 60, maximum: 980; cf. Fig. 1). For the
purpose of this study, only quadrants totally within the borders
of Austria were considered. This restricted data set contains
1390 quadrants covering an area of c. 50,500 km2. Moreover,
cultivated species were excluded from the analysis.
Temperature: Temperature variables (minimum, mean and
maximum annual temperature, count of frost-free days) were
derived from climatic surfaces that had been interpolated
based on a digital elevation model (DEM, spatial resolution:
250 m) and data from 87 meteorological stations (between
1981 and 1991) by Loibl & Züger (2001). Both, annual
variation (based on the average temperature of the coldest and
warmest month within the period of 1981–91) and spatial
variation (based on coldest and warmest parts within a
quadrant) were considered.
Evapotranspiration: Potential evapotranspiration (PET) is a
composite variable, which combines the effects of temperature
and solar radiation estimating the net atmospheric energy
balance independent of water availability (Currie, 1991). PET
was calculated using the formula of Turc (1961), with the
interpolated temperature envelopes and estimates of topographically modified solar radiation income based on the
program solarflux (Dubayah & Rich, 1996). PET was
calculated for the summer and winter solstice and for the
vernal equinox, to get annual extreme values. Analogous to
temperature, minimum and maximum PET within a quadrant
serve as indicators of spatial variability.
Precipitation: Average annual precipitation was based on
interpolation data from Loibl & Züger (2001) within the
period 1981–91.
Proximity to rivers and lakes: A proximity map to rivers and
lakes was calculated based on the topographical map of Austria
(1 : 500,000).
Bedrock geology: Based on the Austrian soil-cover map (Fink
et al., 1979) the area of calcareous substrate per quadrant was
used as an additional predictor.
Secondary variables
Environmental data
Environmental data consist of various spatially explicit data
layers containing information on climatic conditions, topography, soil cover, landscape structure and neighbourhoods
(see below). All data layers are stored in a geographical
information system (GIS, ARC/INFO 8.0 and ArcView 3.2)
and were intersected with the grid of the floristic inventory.
Environmental variables were calculated as average values,
extreme values, ranges or inter-cell variation within a single
quadrant. Both spatial variation (within a quadrant) and
temporal variation (during a year) were considered. The
variables were classified into primary and secondary variables.
Primary variables (like climate) were supposed to have direct
effects on plant growth and thus species numbers, whereas
secondary variables (heterogeneity variables) primarily serve as
Topographic heterogeneity: The elevational range – and, alternatively, the range of temperature and PET – within a
quadrant, were used to describe topographic heterogeneity.
PET and temperature were considered as indicators of terrain
relief here because both depend on topography (e.g. adiabatic
lapse rates of temperature or higher flux of solar radiation on
south-exposed slopes). Additionally, the number of altitudinal
vegetation belts per quadrant was taken as a topographic
descriptor. The respective data were derived from a map of
altitudinal zones (Kilian et al., 1994).
Edaphic heterogeneity: The number of different soil types per
quadrant was calculated based on the Austrian soil-cover map
(Fink et al., 1979). Similarly, geological heterogeneity was
derived from the geological map of Austria (Geologische
Bundesanstalt, 1979).
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
1119
D. Moser et al.
(a)
(b)
Figure 1 Distribution of vascular plant species richness in the Austrian alpine region: (a) cold spots; (b) hot spots. Each point represents a
sampling area of 5 · 3 arc minutes. Species numbers are indicated by point size (see legend). In total, the map shows 1390 data points based
on the sampling grid of the floristic inventory of Austria (Niklfeld, 1971). The underlying hillshading serves for topographic orientation
only.
Land-use heterogeneity: The number of different land-cover
types within a quadrant was calculated on the basis of Corine
land cover (Aubrecht, 1998), which describes 27 different landcover types (1 : 100,000). Alternatively to Corine land-cover
information, a map of landscape types (Wrbka et al., 1997)
was used to derive a descriptor for land-use heterogeneity. This
map provides a classification of 42 different landscape types
1120
based on visual interpretation of satellite images (scale:
1 : 200,000).
Numerical analysis
A multivariable generalized linear model (GLM; McCullagh &
Nelder, 1989) was fitted for analysing the relationship between
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
Plant species richness in the Alps
Table 2 List of environmental
predictors whose effect on species richness
patterns across the Austrian Alps were
explored
Abbreviation
Explanation
Primary environmental variable
Environmental stability
TAnVar
Within-year variation of temperature
Available energy, environmental stress and favourableness
PET3 min mean max*
Minimum, mean and maximum values of PET
within a quadrant for the vernal equinox on 21 March
PET6 min mean max*
Minimum, mean and maximum values of PET
within a quadrant for the summer solstice on 21 June
PET12 min mean max*
Minimum, mean and maximum values of PET
within a quadrant for the winter solstice on 21 December
TMin min mean max*
Minimum, mean and maximum values of the average
temperature of the coldest month within a quadrant
TMean min mean max*
Minimum, mean and maximum values of the average
annual mean temperature within a quadrant
TMax min mean max*
Minimum, mean and maximum values of the average
temperature of the warmest month within a quadrant
TFrost*
Average count of frost-free days
PRECMean
Average annual precipitation
Bedrock geology
CArea
Area of calcareous bedrock
Proximity to rivers and lakes
RIVProx
Proximity to rivers and lakes
Secondary environmental variables (variation within a quadrant)
Topographic heterogeneity
PET3Range
Variation of the PET at vernal equinox
PET6Range
Variation of the PET at the summer solstice
PET12Range
Variation of the PET at the winter solstice
TMeanRange
Range of mean annual temperature
ERange
Range of elevation
Edaphic heterogeneity
SOILVar
Number of soil types
GEOVar
Number of geological units
Landscape heterogeneity
AZCnt
Count of altitudinal zonations
LCVar
Number of different land-cover types
LTVar
Number of different landscape types
PET, potential evapotranspiration.
*Highly correlated variables (correlation coefficient: > 0.65).
vascular plant species richness and environmental factors. The
number of species as the response was assumed to be a
Poisson-distributed random variable and we accordingly used
a logarithmic link function to develop the GLMs (Crawley,
1993; Austin et al., 1996). A forward stepwise selection and
backward elimination procedure (Yee & Mitchell, 1991) was
performed to select the significant predictors. Inclusion or
elimination of variables was based on the residual statistic by
analysing the deviance table. An F-ratio test (McCullagh &
Nelder, 1989; Crawley, 1993) was performed on a conservative
level of significance of 0.001 to avoid chance effects due to the
large number of degrees of freedom (Austin et al., 1996). At
each step of the forward stepwise selection, the variable that
caused the largest change in deviance was included in the
model. Once a new predictor had been included into the
model, all other terms were checked once more using
backward elimination. Only predictors that caused a significant increase in residual deviance when dropped from the
model were retained. Nonlinear effects (Currie, 1991; Austin
et al., 1996) were accounted for by separately testing quadratic
and cubic functions of each predictor. Secondary variables
were not included into the model until all primary variables
had been explored. Additionally, we tested the significance of
all possible interactions. Only the most significant of a guild of
highly correlated variables (e.g. temperature and PET; correlation coefficient > 0.65; Table 2) was included into the model
and all others were excluded from further steps. Model
evaluation was done by correlating (Pearson correlation)
mapped species numbers and predicted species numbers in a
test set of 10% randomly chosen samples, which were not
considered while parameterizing the models (1253 training
samples, 137 test samples).
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
1121
D. Moser et al.
Table 3 Range and units of predictors
Table 4 Results of the first step of variable selection for the
alpine training data. Each variable was tested against the null model.
The change of deviance reflects the exclusive effect of the variables
Variable
Minimum
Average
Maximum
Unit
TAnVar
PET3 min
PET3 mean
PET3 max
PET6 min
PET6 mean
PET6 max
PET12 min
PET12 mean
PET12 max
TMin min
TMin mean
TMin max
TMean min
TMean mean
TMean max
TMax min
TMax mean
TMax max
TFrost
PRECMean
CArea
RIVProx
PET3Range
PET6Range
PET12Range
TMeanRange
ERange
SOILVar
GEOVar
AZCnt
LCVar
LTVar
15.4
)2.7
)1.3
)0.5
)1.0
0.4
1.9
)1.3
)0.5
)0.1
)12.9
)11.3
)9.8
)5.9
)3.8
)1.7
3.1
5.3
7.2
69.0
600.4
0
361.9
0.2
0.4
0.0
0.2
130.0
1.0
1.0
1.0
2.0
2.0
19.6
)0.3
0.3
0.8
2.8
4.2
5.3
)0.3
)0.1
0.0
)5.9
)4.8
)3.8
3.0
4.7
6.2
12.2
14.0
15.7
160.1
1213.6
958.5
958.2
1.1
2.4
0.4
3.3
1070.8
4.0
2.2
4.5
5.4
5.8
23.2
1.1
1.4
1.9
6.0
6.4
6.7
0.1
0.2
0.3
0.0
0.1
0.1
9.7
10.3
10.5
19.5
20.1
20.4
274.0
1873.5
3541.6
3691.1
2.5
5.0
1.3
8.2
2440.0
10.0
7.0
8.0
11.0
13.0
C
mm day)1
mm day)1
mm day)1
mm day)1
mm day)1
mm day)1
mm day)1
mm day)1
mm day)1
C
C
C
C
C
C
C
C
C
days
mm
ha
m
mm day)1
mm day)1
mm day)1
C
m
count
count
count
count
count
For abbreviations see Table 2.
RESULTS
Even though the area of quadrants differed between 34.3 km2
(in the northern regions) and 35.5 km2 (in the south), area per
se had no significant effect on residual deviance suggesting that
the differences in area were negligible with respect to species
numbers.
The first step of the variable selection procedure was to
test each of the environmental variables on its own against a
null model (Table 4). Neglecting any intercorrelations,
descriptors of climatic conditions (temperature and evapotranspiration, e.g. PET6-max, TMax-max) accounted for the
most pronounced decrease in deviance, indicating reduced
species numbers at higher elevations. Among climatic
descriptors, PET6-max performed best. Moreover, its predictive power increased significantly when supplementing the
linear term by a quadratic or cubic one. The third
polynomial of PET6-max was thus the first variable included
into the model. The CArea was the only other significant
primary predictor. It also had a nonlinear effect. PRECMean
1122
Model
Change in
Deviance deviance d.f.
Null
44,303
Primary environmental variable
TAnVar
37,197
+TAnVar2
36,621
+TAnVar3
36,147
PET3min
37,563
35,616
+PET3min2
PET3mean
33,651
31,072
+PET3-mean2
PET3-max
32,623
+PET3-max2
31,256
+PET3-max3 30,963
PET6-min
39,022
+PET6-min2
36,712
+PET6-min3 34,822
PET6-mean
33,003
30,610
+PET6-mean2
+PET6-mean3 29,694
PET6-max
28,655
+PET6-max2
27,839
+PET6-max3 27,591
PET12-min
39,850
39,072
+PET12-min2
PET12-mean
36,865
+PET12-mean2 36,313
PET12-max
42,660
TMin-min
39,503
+TMin-min2
37,689
TMin-mean
37,653
35,569
+TMin-mean2
TMin-max
37,620
+TMin-max2
36,123
TMean-min
37,531
+TMean-min2
34,871
+TMean-min3 34,503
TMean-mean
33,982
+TMean-mean2 31,373
TMean-max
32,221
30,830
+TMean-max2
TMax-min
37,038
+TMax-min2
34,528
+TMax-min3 33,861
TMax-mean
33,299
+TMax-mean2
30,862
+TMax-mean3 30,475
TMax-max
31,296
+TMax-max2
30,073
TFrost
35,729
32,411
+TFrost2
PRECMean
44,010
+PRECMean2
43,641
CArea
41,416
37,259
+ CArea2
F
% Explained
variance
251.9
20.4
16.8
239.9
69.3
431.6
104.5
475.1
55.6
11.9
192.1
84.0
68.7
478.6
101.4
38.8
714.0
37.2
11.3
145.3
25.4
260.9
19.3
51.0
160.8
60.8
235.2
73.7
233.8
52.4
246.6
96.8
13.4
414.8
104.9
492.7
56.8
269.9
93.2
24.8
453.6
100.5
15.9
546.5
51.4
333.2
129.0
8.8
11.2
101.1
145.5
0.16
0.17
0.18
0.15
0.20
0.24
0.30
0.26
0.29
0.30
0.12
0.17
0.21
0.26
0.31
0.33
0.35
0.37
0.38
0.10
0.12
0.17
0.18
0.04
0.11
0.15
0.15
0.20
0.15
0.19
0.15
0.21
0.22
0.23
0.29
0.27
0.30
0.16
0.22
0.24
0.25
0.30
0.31
0.29
0.32
0.19
0.27
0.01
0.02
0.07
0.16
1252
7106
7682
8156
6741
8688
10,653
13,232
11,680
13,047
13,340
5282
7592
9481
11,301
13,694
14,610
15,648
16,464
16,713
4454
5231
7439
7990
1644
4800
6615
6651
8735
6683
8180
6772
9432
9800
10,321
12,930
12,082
13,474
7266
9775
10,442
11,004
13,442
13,829
13,007
14,230
8574
11,893
293
662
2887
4157
1251
1250
1249
1251
1250
1251
1250
1251
1250
1249
1251
1250
1249
1251
1250
1249
1251
1250
1249
1251
1250
1251
1250
1251
1251
1250
1251
1250
1251
1250
1251
1250
1249
1251
1250
1251
1250
1251
1250
1249
1251
1250
1249
1251
1250
1251
1250
1251
1250
1251
1250
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
Plant species richness in the Alps
Table 4 continued
Model
Change in
Deviance deviance
d.f.
RIVProx
+RIVProx2
42,225
41,816
2078
2487
1251
1250
Secondary environmental variables
PET3Range
43,605
699
PET6Range
44,278
25
+PET6Range2 43,194
1109
PET12Range
40,204
4099
5008
+PET12Range2 39,295
ERange
43,623
681
AZCnt
41,708
2595
+AZCnt2
41,279
3024
SOILVar
38,665
5639
GEOVar
39,089
5214
38,737
5566
+GEOVar2
LCVar
40,912
3391
+LCVar2
39,983
4320
LTVar
37,009
7294
36,609
7695
+LTVar2
1251
1251
1250
1251
1250
1251
1251
1250
1251
1251
1250
1251
1250
1251
1250
F
% Explained
variance
65.8 0.05
12.9 0.06
20.9
0.8
33.0
133.0
29.5
20.5
82.0
13.6
192.6
177.6
12.0
112.0
30.7
254.5
14.0
0.02
0.00
0.03
0.09
0.11
0.02
0.06
0.07
0.13
0.12
0.13
0.08
0.10
0.17
0.17
Polynomial terms are only shown in case of significant additional
change in deviance. All variables were significant at P £ 0.001, except
the linear terms of PRECMean (P ¼ 0.003) and PET6Range
(P ¼ 0.38). TMeanRange had neither a linear nor a nonlinear effect.
For abbreviations see Table 2.
and RIVProx were not significant when analysed in a
multivariate context, although PRECMean was marginally
significantly correlated with species numbers when analysed
univariatly (see Table 4). No interaction terms of primary
predictors were significant.
Among heterogeneity variables, GEOVar, LCVar, LTVar and
PET12Range were found to be significant, but their effect was
rather weak. Variables which accounted for edaphic or landuse heterogeneity (SOILVar, GEOVar, LCVar, LTVar) were
more closely correlated with species richness than those
describing topographic heterogeneity (PETRange, ERange;
Table 4).
Table 5 presents the result of the final backward elimination.
It shows the drop of explained deviance caused by the
elimination of a model term. The amount of this decrease
equals the relative importance of a variable. Accordingly,
PET6-max is by far the most important variable for explaining
vascular plant species richness in the alpine region of Austria.
Among the other significant terms, the CArea and GEOVar are
most important.
The residual deviance of the final model was 22,017 on 1234
degrees of freedom. Considering a null deviance of 44,303
(1252 degrees of freedom), the model explained 50% of the
variance in the species richness data. Model evaluation based
on the comparison between observed and predicted species
richness within the 10% test data showed a correlation of 0.64
(Pearson correlation coefficient).
Table 5 Results of the final backward elimination procedure
for the alpine training data. The change in deviance after
elimination of one term from the model indicates the relative
importance of the variable for explaining species richness patterns
(% explained variance). In any case, the decrease of deviance
was significant (P £ 0.001)
Model
Change in
deviance d.f.
Null
Full
6656
)(PET6-max
+ PET6-max2
+ PET6-max3)
)(CArea + CArea2) 1594
)GEOVar
947
)LCVar
360
)PET12Range
424
)LTVar
213
Deviance F
1252 44,303
1243 22,017
1246 28,673
1245
1244
1244
1244
1244
23,611
22,964
22,377
22,440
22,229
% Explained
variance
0.503
126.0 0.353
45.5
54.0
20.5
24.2
12.1
0.467
0.482
0.495
0.493
0.498
For abbreviations see Table 2.
DISCUSSION
The focus on a mountainous area contrasts this study with
many others that test diversity hypotheses in less topographically varied regions (e.g. Richerson & Lum, 1980; Margules
et al., 1987; Currie, 1991; Linder, 1991; O’Brien, 1998; Qian,
1998; O’Brien et al., 2000). Although it is often argued that a
decline of species richness along altitudinal gradients is a
special case of the well-known latitudinal gradient and may be
explained by similar theories (e.g. Rohde, 1992; O’Brien et al.,
2000), the ecological literature gives little evidence for this
generalization until now (e.g. Heikkinen & Neuvonen, 1997;
Wohlgemuth, 1998).
The significant decline of species richness at higher altitudes
(see also Fig. 1), indicated by the pronounced positive
response to PET and temperature variables (Tables 4 and 5),
has also been demonstrated by other studies (White & Miller,
1988; Grabherr et al., 1995; Heikkinen, 1996; Heikkinen &
Neuvonen, 1997) and has usually been explained by the
species–energy hypothesis (Margules et al., 1987; Currie, 1991;
Austin et al., 1996; Qian, 1998). The species decline is also
consistent with the environmental stress hypothesis, as fewer
species are physiologically adapted to persist in the harsh
alpine environment (Billings & Mooney, 1968; Grabherr, 1997;
Körner, 1999). However, Table 4 shows that potential evapotranspiration (PET6-max) explained more than any temperature variable. As photosynthesis requires both heat and light,
PET is a better measure of the climatic energy regime for plants
than temperature or solar radiation taken as single variables
(Austin et al., 1996; O’Brien, 1998; Whittaker & Field, 2000;
Whittaker et al., 2001). However, it is remarkable in the
context of the environmental stress hypothesis that it is not the
minima of temperature or PET (indicating limitation and
stress) but the maxima (indicating favourable environment)
that had highest predictive power in the model, no matter
whether considering temporal (TMax, PET6) or spatial
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
1123
D. Moser et al.
(TMax-max, TMean-max, PET6-max) variation of climate.
When talking about temperate mountain ecosystems, we can
thus state that even though low temperatures handicap plant
life, higher temperatures support species diversity. Put it
another way: it is the favourable end of the within-quadrant
climatic gradient that mainly determines the quadrant-scale
diversity patterns in the Austrian Alps. Two complementary
hypotheses may explain this somewhat surprising result. First,
minimum temperatures occur during the winter months when
vegetation is in winter dormancy, thus predictions based on
minima are likely to be weak. Secondly, low temperatures most
likely pose absolute limits to the distribution of lowland plants,
whereas the growth of alpine species in lowland areas is mainly
controlled by biotic interactions, i.e. by competitive exclusion
(Brown et al., 1996; Guisan et al., 1998; Körner, 1999; Guisan
& Zimmermann, 2000). However, a multitude of lowcompetitive habitats provide gaps in the landscape (e.g.
gravel-banks on riversides) with opportunities for alpine
species to establish local (sink) populations at lowland-fringes
and in valley bottoms of high mountain systems. Thus, the
effective species pool of these lowland areas includes, in fact,
both lowland and alpine species.
Moreover, the significance of within-quadrant climatic
heterogeneity is strongly dependent on scale. A quadrant with
an average area of 35 km2 is much larger than single habitats of
plants, thus a small section with more favourable habitat
conditions is sufficient to enable the occurrence of more
thermophilous plants.
For broader scale raster data, variables indicating better
conditions seem to be more suitable for predicting species
richness than variables indicating limitations. Additional
evidence for this suggestion is given by other studies. In the
subarctic environment of Finland the lowest point within a
quadrant had higher predictive value than the highest point
(Heikkinen & Neuvonen, 1997). Linder (1991) showed that
maximum rainfall is the superior variable to explain species
richness in the south-western Cape Province of South Africa,
whereas the response to minimum rainfall remains unclear.
The hypothesis of environmental stability, as stated for
latitudinal trends in species richness, was not supported by our
data. The relationship between species richness and the annual
variation of temperature (TAnVar) was comparably weak and
indicated even more species in more variable climates. The
TAnVar variable shows that lower terrain has a higher withinyear temperature variation than high alpine regions, but
simultaneously supports more species because of more
hospitable habitat conditions. Classical measures of climatic
variability like the absolute range in temperature or PET
(annual and diurnal variation) fail to describe the real
influence of climate variability in the case of temperate
mountain environments. The crucial point is not the absolute
range of thermal variability, but its relation to the freezing
point. Variation near or even below freezing temporarily
affects or inhibits growth processes and is certainly more
significant to plant life than variation far above this temperature threshold (Körner, 1999).
1124
The weak correlation of species richness and precipitation is
not in line with the results of other studies that have
demonstrated precipitation as one of the most important
predictors (Richerson & Lum, 1980; Margules et al., 1987;
Linder, 1991; Hoffman et al., 1994; Austin et al., 1996;
O’Brien, 1998; O’Brien et al., 2000). This is most likely due
to the overall humidity of the Austrian Alps. In contrast to arid
regions like southern Africa, California or Australia, water
availability is rarely limiting for plant growth regionally.
Furthermore, as orographically driven rainfall patterns result
in maximum humidity in the lower northern and southern
parts of the Alps, the correlation of precipitation and elevation
(and therefore of precipitation with temperature as well as
PET) is weak and vague and does not parallel the predominating trend of decreasing species numbers with increasing
elevation.
While higher precipitation reduces (drought) stress in arid
ecosystems, with positive effects on plant species richness,
increasing (winter) precipitation in temperate mountains can
even be anticipated to have negative effects on plant growth
due to prolonged snow cover duration and therefore shortening of the vegetation period (Körner, 1999; Seastedt & Vaccaro,
2001; Virtanen et al., 2003). A high portion of precipitated
water is in an unavailable solid state (snow and ice) during a
comparatively long winter and cannot be used, until it melts.
Moreover, most of the water gets lost by runoff during
snowmelt. Accordingly, Wohlgemuth (1998, 2002) showed a
positive correlation between relative dryness (threshold of
minimum annual precipitation) and species richness in the
comparable environment of Switzerland. However, the correlation to precipitation in our data is too weak to verify these
assumptions and it remains unclear at which climatic thresholds the correlation to precipitation converts to a positive one
as assumed by O’Brien (1998).
Bedrock substrate, particularly calcareous substrate, which is
argued to be one of the most important primary factors for
explaining plant species richness in European mountains (e.g.
Wohlgemuth, 1998, 2002; Virtanen et al., 2003), seems to be of
minor importance compared to climatic variables. The humpshaped response of species richness to the amount of
calcareous substrate within a quadrant supports the heterogeneity theory, as pure calcareous as well as pure silicate
regions have fewer species than regions with both substrates
equally represented. This is obviously due to the co-occurrence
of silicate and calcareous plants, i.e. the mixture of two
different species pools.
Among the secondary variables, edaphic and land-use
heterogeneity turned out to be better predictors of species
richness than topographic heterogeneity (see also Nichols
et al., 1998). This might be due to the fact that variables based
on soil-cover, geology or land use are more directly linked to
habitat heterogeneity, whereas topographic heterogeneity is an
indirect factor in the sense of Austin et al. (1996). However,
heterogeneity variables were generally weaker predictors than
evapotranspiration or temperature, which support the assumption of a more local scale effect of environmental heterogeneity
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
Plant species richness in the Alps
(Linder, 1991; Austin et al., 1996; O’Brien et al., 2000;
Whittaker et al., 2001).
A comparison of our results to those of Wohlgemuth (1998,
2002) in Switzerland reveals some differences. Although the
study areas were comparable (both are parts of the Alps) and
the variables tested were similar, the Austrian model was more
responsive to primary environmental (climatic) variables,
whereas in Switzerland heterogeneity variables predominately
explained species richness. We suppose the different shape of
sampling areas for collecting species data to be partly
responsible for these contradictory results. The Austrian
survey used approximately rectangular and equal-sized quadrants, whereas the Swiss inventory was based on irregular,
topographically defined mapping areas (Welten & Sutter,
1982). Equally shaped and sized quadrants are independent of
the topographic situation, whereas topographically defined
mapping units, unequal in area, may maximize the environmental heterogeneity between the mapping units (Whittaker
et al., 2001), which could enhance the predictive power of
heterogeneity variables and mask the effect of primary
variables. Additionally, the Swiss study was restricted to areas
above the timberline, hence the climatic gradient is less
pronounced and thus likely to be of lower predictive power.
Considering the overall goodness-of-fit of our final model,
c. 50% of the variance in among-quadrant species richness
remain unexplained. This fairly high amount of residual
variance may accrue from several sources. First, sampling error
may be considerable both for the response and the predictors:
species data have been collected by many different researchers
over three decades and predictor variables are spatial extrapolations based on models with their own residual variances.
Secondly, some well-known biogeographical patterns have not
been accounted for explicitly. Some tentative trend analyses
indicated increasing species number towards the eastern and
southern margins of the Austrian Alps independent of
environmental predictors. These geographical trends are most
likely due to the amalgamation of central European, eastern
European and sub-Mediterranean species pools in the biogeographical contact zones at the utmost fringes of the Alps.
Moreover, the north-eastern and south-eastern margins of the
Austrian Alps are known to be hot-spots of post-glacial relict
endemism. However, the distribution of endemics may hardly
affect the environmental correlations of overall diversity
patterns substantially as their contribution to per-quadrant
species numbers is low (0.86% on average, 8.33% for one
outlier, below 5.74% for all other quadrants). Their impact, if
any, is to somewhat attenuate the altitudinal decline of species
richness as endemic plants of the Austrian Alps tend to
accumulate in subalpine to subnival zones (Tribsch &
Schönswetter, 2003).
In contrast to endemic plants, alien invaders likely
accentuate the environmental correlations detected. At larger
scales, invasive plants tend to follow patterns of native species
richness (Lonsdale, 1999; Stohlgren et al., 1999), even if they
may displace native species locally. Accordingly, neophytic
species are nearly exclusively limited to the lowest parts of the
Austrian Alps and are lacking at higher altitudes (Essl &
Rabitsch, 2002).
To sum up, our results suggest energy-driven processes as
primary determinants of vascular plant species richness in
temperate mountains like the European Alps. The spatial
distribution of favourable thermal conditions more severely
affects diversity patterns than that of potentially stressful low
temperatures. Concerning annual climatic variability, we
assume that frequent oscillations around the freezing point
likely constrain species richness more severely than more
pronounced variation around any higher mean temperature or
overall annual temperature ranges. Biogeographical patterns
established at evolutionary time-scales may introduce noise in
environmental species richness correlations and should best be
accounted for statistically if appropriate data were available.
ACKNOWLEDGEMENTS
We thank Luise Schratt-Ehrendorfer who stands representatively for the hundreds of botanists who recorded the plant
distribution data in Austria. For their comments on the
manuscript we thank Barbara Holzinger, Johannes Peterseil
and Thomas Dirnböck and two anonymous referees. We thank
the Austrian Research Centers in Seibersdorf for providing
climatic data. Our study was part of an extensive research
network initiated and funded by the Federal Ministry of
Education, Science and Culture, which aims to provide
scientific advice for sustainable development of cultural
landscapes in Austria (http://www.klf.at). We received additional funding from the Federal Ministry of the Environment
and the Bristol-Stiftung.
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BIOSKETCHES
Dietmar Moser is an ecologist at the Institute of Ecology,
University of Vienna. His research is focused on landscape
ecology, GIS modelling and biodiversity issues.
Stefan Dullinger is an ecologist with his main interests in
vegetation dynamics and modelling.
Thorsten Englisch is an ecologist and is currently managing
the data base of the mapping project of the flora of Austria.
Harald Niklfeld is Professor of Botany at the Institute of
Botany, University of Vienna. He has worked for more than
35 years as coordinator of the mapping project of the flora of
Austria.
Christoph Plutzar is a zoologist and is currently working on
habitat modelling of breeding birds.
Norbert Sauberer is an ecologist with interests mainly in
biodiversity and conservation issues.
Harald Gustav Zechmeister is a botanist focusing on
bioindication with bryophytes and biodiversity issues.
Georg Grabherr is Professor of Vegetation Ecology at the
Institute of Ecology, University of Vienna. He is currently
focusing on climate change impacts on alpine plant diversity.
Editor: Philip Stott and Robert Whittaker
Journal of Biogeography 32, 1117–1127, ª 2005 Blackwell Publishing Ltd
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