B I O L O G I C A L C O N S E RVAT I O N
1 4 1 ( 2 0 0 8 ) 1 6 6 5 –1 6 7 5
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/biocon
Potential effects of climate change on plant communities in
three montane nature reserves in Scotland, UK
Mandar R. Trivedia,b,*, Michael D. Morecroftc, Pamela M. Berrya, Terence P. Dawsond
a
Environmental Change Institute, Oxford University Centre for the Environment, South Parks Road, Oxford OX1 3QY, United Kingdom
Centre for Ecology and Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster LA1 4AP, United Kingdom
c
Centre for Ecology and Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford OX10 8BB, United Kingdom
d
School of Geography, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
b
A R T I C L E I N F O
A B S T R A C T
Article history:
Mountain ecosystems are often identified as being particularly sensitive to climate change,
Received 21 June 2007
however this has rarely been investigated at the scale of individual mountain ranges using
Received in revised form
local relationships between plants and climate. This study uses fine resolution data to
27 March 2008
assess the potential changes to internationally important Arctic-alpine plant communities
Accepted 7 April 2008
in three national nature reserves in the Scottish Highlands, United Kingdom. Distribution
Available online 27 May 2008
models were created for 31 species, representing a range of community types. A relationship between distribution and temperature was found for all species. These models were
Keywords:
aggregated to explore potential future changes to each community under two Intergovern-
Arctic-alpine plants
mental Panel on Climate Change warming scenarios for the 2080s. The results indicate that
Classification tree
Arctic-alpine communities in these reserves could undergo substantial species turnover,
Grampian highlands
even under the lower climate change scenario. For example, Racomitrium-Carex moss-
Mountains
heath, a distinctive community type of the British uplands, could lose suitable climate
Natura 2000
space as other communities spread uphill. These findings highlight the need to maintain
Special area of conservation
these communities in an optimal condition in which they can be most resilient to such
Species distribution models
change, to monitor them for signals of change and to develop more flexible conservation
Topography
policies which account for future changes in mountain protected areas.
2008 Elsevier Ltd. All rights reserved.
1.
Introduction
Human activities during the Industrial period have warmed
the climate and could lead to an increase in global average
surface temperature of 1.1–6.4 C by 2100, compared with an
increase of around 0.76 C over the last century (IPCC, 2007).
The warming trend has affected ecosystems worldwide (Parmesan, 2006) and mountain ecosystems are considered to
be particularly sensitive due to the rapid change in climate
with altitude and species’ close adaptations to the cold envi-
ronment (Körner, 1999). For example, plants and animals have
shifted to higher altitudes across Europe’s mountains (Grabherr, 1994; Klanderud and Birks, 2003; Peñuelas and Boada,
2003; Walther et al., 2005; Wilson et al., 2005; Pauli et al., 2007).
Due to the limited knowledge of species’ dynamic responses to a changing environment, the potential effects of
climate change are commonly projected using distribution
models which are based on correlations between current distributions and climate (Guisan and Zimmermann, 2000). Most
studies use coarse resolution (e.g. 50 km · 50 km) data and are
* Corresponding author: Present address: Global Canopy Programme, John Krebs Field Station, University of Oxford, Wytham OX2 3QJ,
United Kingdom. Tel.: +44 1865 724555.
E-mail addresses: [email protected] (M.R. Trivedi), [email protected] (M.D. Morecroft), [email protected] (P.M. Berry),
[email protected] (T.P. Dawson).
0006-3207/$ - see front matter 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biocon.2008.04.008
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useful for highlighting vulnerable regions or taxa (e.g. Bakkenes et al., 2002; Berry et al., 2002; Thomas et al., 2004; Thuiller et al., 2005). However, coarse models may not be
appropriate for mountains, where local climates diverge from
that of the region. Furthermore, the prediction of biotic impacts at fine scales is seen as one of the key scientific challenges for the development of conservation plans in
response to climate change (Brooker et al., 2007). The pattern
of vegetation change with altitude, resulting from the compression of climatic zones in mountain terrain (Barry, 1992),
presents researchers with a valuable opportunity to study
the potential impacts of climate change using a ‘space-fortime’ approach (Körner, 1999). In Europe, a small number of
studies – mainly in the Alps – have projected climate change
impacts using fine resolution species, climate and topographic data (e.g. Zimmermann and Kienast, 1999; Guisan
and Theurillat, 2000; Dirnböck et al., 2003; Dullinger et al.,
2004). The results suggest that climate warming will have significant negative impacts on high-altitude species in the Alps.
This paper presents the first application of such fine scale
models to montane plants within the British Isles.
Upland climates of the British Isles are dominated by the
influence of the Atlantic Ocean, resulting in a distinctly oceanic character to the vegetation (Averis et al., 2004). Several
vegetation types, such as wet heaths and Racomitrium mossheaths, are more common in these uplands than elsewhere
in Europe (Averis et al., 2004). The mountains are small in a
European context (up to 1344 m), but there are large changes
in climatic conditions with moderate increases in altitude
(Manley, 1952; Pepin, 1995). Consequently, Arctic-alpine vegetation exists in a relatively narrow zone above about 750 m
a.s.l. (Nagy, 2003), with limited potential to track suitable climate space by migration to higher altitudes.
While there have been investigations of climate change
impacts on nature reserve networks (Dockerty et al., 2003;
Araújo et al., 2004), little attention has been paid to specific
conservation sites. Under the European Community’s Habitats Directive (92/43/EEC), each member state is obliged to
designate, monitor and maintain ‘Special Areas of Conservation’ (SAC) to contribute to the pan-European Natura 2000 reserve network. This study focused on three neighbouring
Scottish national nature reserves, which were designated as
SACs for their internationally important high-altitude plant
communities. For example, they have the most extensive
development of alpine and subalpine calcareous grasslands
in the United Kingdom, as well as siliceous alpine and boreal
grassland and sub-Arctic Salix spp. scrub (Jackson and McLeod, 2000). The study aimed to test the sensitivity of plant species and communities to two warming scenarios – ‘low’ and
‘high’ – corresponding to the B1 and A1FI greenhouse gas
emissions scenarios proposed by the Intergovernmental Panel on Climate Change (IPCC) (Nakicenovic and Swart, 2000).
The first stage of the SAC designation process involves
vegetation mapping surveys of all candidate SACs to allow
land managers to assess the extent and status of each target
plant community. Here we used these baseline survey data for
the common species found in each community type to model
plant species distributions in relation to topography and soils.
The projections of the individual species models were subsequently amalgamated into species assemblages, for current
1 4 1 ( 2 0 0 8 ) 1 6 6 5 –1 6 7 5
climate and the two potential future climates. This ‘‘predict
first, assemble later’’ approach (Ferrier and Guisan, 2006) allows individualistic species responses to climate change,
which are supported by evidence from both palaeoecological
(Huntley, 1991; Hewitt, 1999) and experimental (Bruelheide,
2003) research. There are few examples of such an approach
to projecting climate change impacts on community composition (e.g. Guisan and Theurillat, 2000; Peppler-Lisbach and
Schröder, 2004; Thomas et al., 2004), but it has the advantage
of providing information on both species of interest and species assemblages (Peppler-Lisbach and Schröder, 2004). This
strategy produces projections of future vegetation changes
at specific survey locations within the nature reserves, which
can be compared with future monitoring data or field
experiments.
2.
Methods
2.1.
Study site
The study area covers the Breadalbane range in the south of
the Grampian Highlands of Scotland, UK (lat: 5630 0 to
5634 0 and long: 413 0 to 419 0 , Fig. 1), including three nature
reserves (Ben Lawers, Ben Heasgarnich and Meall na Samhna)
covering a total area of 9690 ha. The main axis of the mountains is aligned in a roughly south-west/north-east direction.
Summits vary between 1000 and 1214 m a.s.l. Unusually for
the mainly acidic Scottish Highlands, the geology consists of
soft Dalradian calcareous mica schists which give rise to basic
soils supporting a rich Arctic-alpine flora (Lusby and Wright,
2001). Below the alpine zone there are more acidic glacial
drifts. The annual maximum temperature is 11.6 C, minimum temperature 4.7 C and precipitation 1252 mm per annum at 130 m a.s.l. at Ardtalnaig (Fig. 1). There is a
precipitation gradient across the site, increasing by about
20 mm per annum per kilometre westwards, favouring greater peat formation in the west (Poore, 1993). Due to the cool,
moist climate the soils are continually leached, giving rise
to podsolic soils even where the underlying bedrocks are
nutrient-rich schists. Large areas of podsols underlie much
of the Nardus stricta grasslands and heaths of the area. Floristically richer vegetation is found where the soil is enriched
through deposits of weathered rock (e.g. at cliff bases) or
flushing with water which has run through or over the bedrock (Poore, 1993).
2.2.
Botanical data
A field survey, following the UK National Vegetation Classification (NVC) protocol (Rodwell, 2006), was carried out between
2002 and 2004 (Smith et al., 2003; Cornish and Dayton, 2005;
Dayton and Cornish, 2005) to map out the three nature reserves’ vegetation communities in accordance with their SAC
designation. Cover estimates of all species (including bryophytes and lichens) were recorded in 282 2 m · 2 m quadrats,
stratified by community type and covering the full range of
topographical/environmental conditions. The survey design
was based on sampling representative vegetation rather than
random sampling, resulting in some quadrats being close together. To reduce the impact of spatial autocorrelation on the
B I O L O G I C A L C O N S E RVAT I O N
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Fig. 1 – The study area of the Breadalbane range, south Scottish Highlands. Circles represent the location of survey quadrats
across three Special Areas of Conservation: Ben Heasgarnich, Ben Lawers and Meall na Samhna.
subsequent models (Guisan and Zimmermann, 2000), quadrats were removed from the data set at random so that no
two were closer than 200 m, leaving 213 for the analysis. Distribution models were only constructed for species occurring in
more than ca. 10% of the plots to avoid problems associated
with modelling species with low prevalence (McPherson
et al., 2004; Liu et al., 2005; Jiménez-Valverde and Lobo, 2006).
The surveyors classified each quadrat by NVC community type
and this information was used in interpreting the results.
Nomenclature follows Stace (1997) for vascular plants and
Smith (2004) for bryophytes.
2.3.
Environmental data
The Geographical Positioning System (GPS) location, altitude,
slope and aspect of each quadrat were recorded. Missing terrain data were filled in from a 10 m digital elevation model
(DEM, Ordnance Survey/EDINA). Further topographic variables – solar radiation, planform and profile curvature and a
wetness index – were derived from the DEM using ArcGIS
and ArcInfo (Environmental Science Research Institute, Redlands, California). Potential solar radiation on the equinoxes
and summer solstice was estimated using the ‘shortwave’
routine (Kumar et al., 1997; Zimmermann, 2001). The three
estimates were aggregated into one ‘srad’ variable using principal components analysis (PCA) in SPSS 14.0. Site wetness
was estimated using a compound topographic index, which
combines the catchment area draining into a grid cell with
the ability of water to drain away from that cell, based on
the FD8 multiple flow dispersion algorithm (Kirkby, 1975;
Moore et al., 1993).
Recent investigations of the potential impacts of climate
change on United Kingdom’s wildlife have used the climate
of the 1961–1990 period as a baseline against which to compare
future scenarios (e.g. Berry et al., 2005). Hence, monthly temperature averages for 1961–1990 were obtained for the Ardtalnaig Met Office station, at 130 m a.s.l. approximately 10 km
east of the study site. Since there are few high-altitude weather
stations in Britain, obtaining altitudinal lapse rates of temperature is difficult. We derived monthly lapse rates from highquality climate grids (Perry and Hollis, 2005) using the method
of Zimmermann and Kienast (1999). The annual mean of
6.3 C km1 was similar to previous published rates (Harding,
1978). The monthly lapse rates were applied to the Ardtalnaig
monthly averages to calculate the expected annual mean temperature, Tmean, at each survey location.
Temperature changes were added to the baseline Tmean to
project climate change impacts for the 2080s. Two future scenarios were investigated in which global greenhouse gas
emissions over the coming decades are either ‘low’ or ‘high’
(B1 and A1FI scenarios, respectively, of the IPCC, Nakicenovic
and Swart, 2000). The scenarios data were from a high-resolution (ca. 50 km) regional model of the European atmosphere
(HadRM3, Hulme et al., 2002), which has a 20-year history of
development and has been used in a number of climate impact studies in the UK (e.g. Berry et al., 2005; McEvoy et al.,
2006). The study site is predominantly located within two of
HadRM3’s grid cells. So the projected average temperature increases of these two cells were used, averaging: low = 1.7 C,
high = 3.3 C.
Soil type was assigned to each quadrat from soil maps at
1:63,360 and 1:50,000 scale (Macaulay Institute for Soil Research, 1982, 1985). Soils were categorised according to major
soil sub-group: (i) gleys, (ii) peats, (iii) leached soils, or (iv) rock
and scree.
2.4.
Data analysis
2.4.1.
Species distribution modelling
All analyses were carried out using the R statistical environment (version 2.4.1, R Development Core Team, 2005). Logistic
regression (Hosmer and Lemeshow, 1989) was used to model
the presence/absence of each species in response to the
environmental variables. The regressions were fitted using
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generalized additive models (GAM, Hastie and Tibshirani, 1990)
with a binomial probability distribution and a logistic link
function. GAMs are the non-parametric counterparts of generalized linear models (GLM) and are commonly used to model
species distributions (e.g. Araújo et al., 2005; Wintle et al.,
2005). Initially, univariate GAMs with smoothing splines with
approximately four degrees of freedom were used to investigate the importance of each continuous independent variable.
Variables showing significant relationships with the presence/
absence of the species were then entered into a multivariate
model selection process, using appropriate degrees of freedom
based on examination of the univariate plots. Variable selection and model simplification were based upon Akaike’s Information Criterion (AIC, Akaike, 1973; Wintle et al., 2005).
Models were evaluated using the area under the curve
(AUC) of the Receiver Operating Characteristic (ROC, Swets,
1988) and Miller’s calibration statistics, both estimated using
a resampling method (Ferrier and Watson, 1997; Wintle et al.,
2005). The predictions from the GAMs were converted from
decimal fraction suitability to binomial presence/absence
using a threshold value which minimised the difference between the sensitivity and specificity of the predictions (Bonn
1 4 1 ( 2 0 0 8 ) 1 6 6 5 –1 6 7 5
and Schröder, 2001). Models were constructed for 60 species,
but due to low prevalence among many montane species, adequately fitting models (AUC P 0.6) were found for only 31,
which represented a range of biome associations (Table 1).
These models were used to project the potentially suitable
quadrats under a future climate by changing Tmean according
to the two scenarios. The potential change in quadrat occupancy was then calculated for each species.
2.4.2.
Assessing change in species composition
The results from the individual species models were aggregated to predict the species assemblage at each quadrat. The
observed and predicted species assemblages were compared
using ordination techniques described in detail by Peppler-Lisbach and Schröder (2004). Detrended correspondence analysis
(DCA, Hill, 1979; Jongman et al., 1995) was applied to both predicted and observed species · site matrices to evaluate the
effect of species modelling on the predicted community structure and species-environment relationships. Observed and
predicted ordination axes were compared using Spearman’s
correlation, as in Guisan and Theurillat (2000). The observed
and predicted ordination patterns were compared using
Table 1 – Results of generalized additive models based on occurrence data for 31 species, ordered according to magnitude
of projected change in quadrat occupancy under ‘low’ and ‘high’ climate change scenarios
Species
Code
Distributiona
AUCb
Change in occupancy (%)c
Low
Narthecium ossifragum (L.) Huds.
Dicranum scoparium
Tricophorum cespitosum (L.) Hartm.
Erica tetralix L.
Molinia caerulea (L.) Moench
Calluna vulgaris (L.) Hull
Sphagnum capillifolium
Eriophorum vaginatum L.
Potentilla erecta (L.) Raeusch.
Festuca rubra L.
Viola riviniana Rchb.
Thymus polytrichus A. Kern. Ex Borbas
Carex pulicaris L.
Juncus squarrosus L.
Eriophorum angustifolium Honck.
Carex paniculata L.
Anthoxanthum odoratum L.
Empetrum nigrum L.
Nardus stricta
Vaccinium vitis-idaea L.
Carex nigra (L.) Reichard
Festuca vivipara (L.) Sm.
Alchemilla alpina L.
Deschampsia cespitosa (L.) P. Beauv.
Salix herbacea L.
Racomitrium lanuginosum
Carex bigelowii Torr.
Silene acaulis (L.) Jacq.
Cladonia arbuscula
Cladonia uncialis
Polytrichum alpinum
naross
dicsco
trices
eritet
molcae
calvul
sphcap
erivag
potere
fesrub
vioriv
thypol
carpul
junsqu
eriang
carpan
antodo
empnig
narstr
vacvit
carnig
fesviv
alcalp
desces
salher
raclan
carbig
silaca
claarb
claunc
polalp
Oceanic Boreo-temperate
Circumpolar Boreal-montane
Suboceanic temperate
Eurosiberian Boreo-temperate
European Boreo-temperate
Circumpolar Boreo-Arctic montane
Eurosiberian Boreo-temperate
Circumpolar wide-boreal
European temperate
European Boreo-temperate
Suboceanic temperate
Suboceanic temperate
Circumpolar wide-boreal
European Boreo-temperate
Circumpolar wide-temperate
Circumpolar Boreo-Arctic montane
European Boreo-temperate
Circumpolar Boreo-Arctic Montane
Eurosiberian Boreo-temperate
Circumpolar Boreo-Arctic montane
European Arctic-montane
Circumpolar Wide-boreal
European Arctic-montane
Circumpolar Arctic-montane
European Arctic-montane
0.69
0.63
0.74
0.67
0.69
0.78
0.64
0.84
0.74
0.67
0.62
0.70
0.66
0.66
0.68
0.67
0.69
0.70
0.59
0.61
0.61
0.64
0.81
0.71
0.82
0.75
0.90
0.69
0.61
0.69
0.86
120
112
108
93
78
58
51
41
31
26
22
18
17
6
0
0
1
8
11
20
26
40
44
57
83
85
88
89
96
97
100
a Hill et al. (1992), Preston et al. (2002).
b AUC is the Area Under the Curve of the Receiver Operating Characteristic (ROC), which is a measure of model accuracy.
c Projected change in quadrat occupancy (n = 213) is the modelled change in ‘climatic suitability’ of the quadrats.
High
178
265
176
166
230
111
95
114
58
21
0
8
59
7
2
47
24
4
41
40
64
74
74
81
100
100
98
100
100
100
100
B I O L O G I C A L C O N S E RVAT I O N
Mantel tests (Manly, 1997; McCune and Grace, 2002) and Procrustes rotation (Peres-Neto and Jackson, 2001). Next, the
quadrats were classified into community types based upon
their similarity. The aim was to assess the potential impact
of climate change on species composition by seeing if all communities would still occur in the future. The method of Guisan
and Theurillat (2000) was followed:
1. The quadrats were assigned to groups sharing similar subsets of observed species using k-means non-hierarchical
cluster analysis, which partitioned the data around k medoids, selected on the basis of average silhouette width, an
index of cluster separation. A dissimilarity matrix of the
standardized binary occurrence data was input to the clustering algorithm (Legendre and Gallagher, 2001).
2. A classification tree was fitted to the clusters by using the
observed species occurrences as explanatory variables.
The tree was pruned to improve parsimony using 10-fold
cross-validation (De’ath and Fabricius, 2000).
3. The tree was used to predict cluster membership of each
quadrat based on the predicted species distributions from
the binomial models.
4. Step three was repeated using the projected species occurrences under the two climate change scenarios.
ful’ accuracy, with a mean area under the curve (AUC) of 0.7
(range: 0.6–0.9; Table 1). The best models (AUC > 0.8) tended
to be for Arctic-montane species such as Alchemilla alpina, Carex bigelowii and Salix herbacea. Models for some temperate and
boreo-temperate species, such as Juncus squarrosus, Viola riviniana, Carex nigra and Nardus stricta had much lower AUC values. On average, the models explained 28% (range: 14–55%)
of the deviance in the data. Temperature was a significant
predictor for all species (100%), followed by slope (81% of species models), wetness (77%), curvature (52%), solar radiation
(42%) and soil type (29%). Thus, considering species individually, all variables appear to play a role in structuring the community, with temperature (as determined by altitude) the
most important.
Higher average temperatures under climate change were
projected to result in a decline in climatically suitable habitat
for montane and Arctic-alpine species, such as C. bigelowii,
Racomitrium lanuginosum and A. alpina (Table 1). The magnitude of the projected change was large with some species projected to be absent from all quadrats under the ‘high’
scenario. Low-altitude species, such as Calluna vulgaris, Erica
tetralix, Molinia caerulea and Potentilla erecta were projected to
gain suitable climate space (Table 1).
3.2.
The classification tree was evaluated by using a confusion
matrix of observed and predicted cluster memberships to calculate the misclassification rate and kappa (Cohen, 1960), the
latter measure being less sensitive to the prevalence of the
community type (Manel et al., 2001). The effect on cluster
membership of substituting the observed species occurrences
with those predicted by the binomial models was also tested
in this way. Similarly, the potential effect of climate change
on species composition was examined by means of confusion
matrices.
3.
Results
3.1.
Species models
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Community models
The ordination plots show that the observed pattern of community composition and its environmental correlates (Fig. 2a)
Table 2 – Mantel test correlation statistics for the
relationship between the similarity structures of
observed and predicted species assemblages projected in
ordination space
r
DCA
DCA
DCA
DCA
sample
sample
species
species
scores
scores
scores
scores
axes
axes
axes
axes
1–2
1–4
1–2
1–4
0.56
0.52
0.79
0.73
All correlations significant at the 99% level, based on 1000
permutations.
3
According to the classification of Swets (1988), the generalized
additive models for the 31 species ranged from ‘low’ to ‘use-
3
empnig
2
1
1
claunc
srad
0
0
calvul
claunc
molcae
desces
silaca
slope
carpan
-2
-1
0
Axis 1
1
prof
naross
narstr
alcalp
desces
dicsco
molcae
vioriv
fesrub
-3
wet
silaca
-2
-1
wet
alt
claarb
eritet
alcalp
naross
plan fesviv
srad
sphcap
alt
eritet
-2
Axis 2
eriang
claarb
-1
2
calvul
sphcap
vacvit
empnig
erivag
vacvit
erivag
slope
2
3
-2
-1
0
1
2
3
Axis 1
Fig. 2 – Distribution of species on the first two DCA axes, with significant environmental variables fitted. (a) Observed species
and (b) model predicted species distributions.
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B I O L O G I CA L C O N S E RVAT I O N
was recreated by the aggregated species distribution models
(Fig. 2b). Procrustes rotation of the observed and predicted
species assemblages in ordination space showed that they
were strongly correlated (r = 0.69, P < 0.001, based upon
1000 permutations). Mantel tests confirmed the correlation
between the similarity structures of the observed and predicted DCA scores (Table 2). The correlation was stronger
Table 3 – Spearman’s correlation between observed and
predicted DCA sample scores
Predicted
Observed
Axis
Axis
Axis
Axis
1
2
3
4
Axis 1
Axis 2
Axis 3
Axis 4
0.79*
0.07
0.27*
0.03
0.07
0.56*
0.25*
0.12
0.02
0.00
0.27*
0.33*
0.25*
0.18*
0.27*
0.21*
Bold = strongest correlations, *denotes significance at 99% level.
The primary observed vegetation axis correlates with altitudedriven temperature and wetness. The second axis correlates with
slope and solar radiation.
1 4 1 ( 2 0 0 8 ) 1 6 6 5 –1 6 7 5
between the observed and predicted data for axes 1 and 2
than for all four DCA axes together. This is supported by
examination of Spearman’s rank correlations between the
observed and predicted DCA axes (Table 3). The primary
and secondary floristic axes were well recreated by the predicted assemblages. As illustrated in Fig. 2, these two axes
were correlated with temperature/wetness (driven by altitude) and slope/solar radiation, respectively. In the case of
the predicted data, the second axis was also correlated with
topographic curvature, reflecting the importance of these
variables in the individual species models. The predicted
third and fourth axes were less well correlated with the
observations (Table 3).
Non-hierarchical cluster analysis partitioned the observed
quadrats into six reasonably homogeneous vegetation clusters. A classification tree with 12 terminal nodes was fitted
to these clusters using the observed species distributions as
predictor variables (See Electronic Appendix for description
of the vegetation clusters). The tree explained nearly threequarters of the deviance in the clusters (D2 = 0.71). Based upon
a confusion matrix, the agreement between the fitted and observed clusters was good (kappa = 0.77), with a misclassification rate of 18% (Fig. 3a).
Fig. 3 – Confusion matrices from classification tree predictions of vegetation clusters. The size of each box represents the
agreement between: (a) observed (ordinate axis) and fitted (abscissa) vegetation clusters; (b) observed and predicted clusters
from individual species models; and observed versus projected clusters under low (c) and high (d) climate change scenarios,
respectively. Note that under the high-climate change scenario (d), vegetation clusters 3 and 6 are no longer predicted to
occur.
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Cluster memberships were then assigned using the predicted species assemblages as input to the classification tree,
as opposed to using the observed species data. The predictions had a lower level of agreement with observed clusters
than the original fitted tree (kappa = 0.3, misclassification
rate = 51%, Fig. 3b). The reduced predictive ability was mainly
due to the misclassification of quadrats belonging to both
clusters 1 and 6, which were confused for other vegetation
types, particularly cluster 5 (Fig. 3b).
These species-level projections were aggregated into
changes in the species composition of the quadrats. Under a
low climate change scenario, quadrats which currently support vegetation clusters 1, 3 and 4 become less suitable for
these communities (Fig. 3c). Locations currently supporting
clusters 1 and 4 become more suitable for cluster 5, while those
of cluster 3 become suitable for cluster 4. In contrast, cluster 5
maintains its currently suitable quadrats. The pattern is similar under a high-climate change scenario (Fig. 3d). In this
instance, none of the quadrats are suitable for the high-altitude
vegetation of cluster 3. Those which are currently occupied by
cluster 3 become fairly evenly divided between clusters 1, 2, 4
and 5.
4.
Discussion
4.1.
Species and community models
Models were fitted for 60 species, but only models for 31 species showed adequate discriminatory power (AUC P 0.6) and
were therefore considered further. Such variable model success has been found in previous studies of alpine plants (Guisan and Theurillat, 2000; Guisan and Zimmermann, 2000) and
is likely to be caused, in part, by the low prevalence of many
species. Unmeasured variables, such as disturbance, soil
properties and water resurgence, which were not part of the
field survey, may also have limited model power (Guisan
and Theurillat, 2000). While potentially explanatory variables
were missing, the models still allowed projection of climate
change impacts based upon the primary role of temperature
in governing species’ distributions. Model inaccuracies may
also have resulted from discrepancies between the resolution
of the species data (2 m · 2 m) and that of predictors calculated from the DEM (10 m · 10 m) or the soil maps. Furthermore, at smaller scales, biotic interactions and dispersal
may have a more prominent effect on species distributions,
tending to make models less reliable (Pearson and Dawson,
2003).
Temperature was a significant term in all 31 species distribution models and was correlated with the primary axis of
the ordination (Fig. 2b). Slope angle, wetness index, curvature
and solar radiation were all also found to be significant in between 42% and 81% of the models. Previous studies have
found these variables to be important predictors of montane
species distribution (e.g. Guisan et al., 1998; Dirnböck et al.,
2003). Plants, particularly alpine plants, experience surface
microclimates which integrate the effects of fine-grained
topographic variations. In this study, the topographic variables acted as proxies for this true microclimate and bettercalibrated models, leading to more accurate predictions,
might be achieved through an emphasis on the measurement
1 4 1 ( 2 0 0 8 ) 1 6 6 5 –1 6 7 5
1671
of such surface microclimates (e.g. Gottfried et al., 1999; Löffler, 2007).
The classification tree fitted the observed vegetation clusters well (kappa = 0.77, Fig. 3a). But there was lower agreement
between predicted and observed cluster membership when
the predictions were made using the species distribution models (kappa = 0.3, Fig. 3b). Thus caution is required in interpreting the projections of future change in community type of each
quadrat. In a study of potential climate change impacts on
grasslands in the Swiss Alps, Guisan and Theurillat (2000) classified 205 quadrats into eight clusters using data for approximately twice the number of species used in our analysis,
reflecting the higher species richness of their site. They
achieved similar agreement between fitted and observed cluster memberships (kappa = 0.77), but had more success when
using modelled species distribution to predict cluster membership (kappa = 0.45). Their use of a larger species set allowed
better discrimination of communities and counteracted the errors associated with individual species models.
Reflecting the variation in discriminatory power of the individual species models, predictive accuracy varied among clusters. The mire and heath communities of clusters 1 and 6 were
poorly predicted by the model, often being misclassified as
belonging to the wet heaths of cluster 5. This confusion may
have arisen due to the reliance of the classification tree on
the indicator species Sphagnum capillifolium to discriminate between wet and dry communities. This species’ model had relatively low discriminatory power (AUC = 0.64) even though it
had a moderate prevalence (26%). This indicates that the topographic wetness index may not have given accurate estimates
of soil moisture. Clusters 3 and 4 (high-altitude heaths and upland grasslands) were better predicted since the artic-montane species indicating these clusters tended to be modelled
with greater accuracy (Carex bigelowii and Racomitrium lanuginosum, AUC = 0.9 and 0.75, respectively). Thus, the models for
higher altitude communities were better calibrated.
4.2.
Climate change projections
Under both warming scenarios, low-altitude grass and
heather species characteristic of boreo-temperate biomes
were projected to gain suitable climate space, while high-altitude Arctic-montane species lost space (Table 1). Under the
low and high-scenarios, Arctic-montane species were projected to lose 78% and 93%, respectively, of currently suitable
quadrats (Table 1). The main consequence of this in terms of
NVC communities would be a transition from snow bed communities – U10 Carex bigelowii–Racomitrium lanuginosum mossheath and U7 Nardus stricta–Carex bigelowii grass-heath – towards species-rich calcareous grasslands – CG11 Festuca ovina–Agrostis capillaris–Alchemilla alpina grass-heath and CG12
Festuca ovina–Alchemilla alpina–Silene acaulis dwarf-herb. The
site’s Carex-Racomitrium moss-heath and species-rich upland
calcareous grasslands have an abundance of Arctic-alpine
species such as Bartsia alpina, Cerastium alpinum, Minuartia
sedoides, Persicaria vivipara, Silene acaulis and Thalictrum alpinum (Anon, 2006). But since the individual species models
suggest that the Arctic-alpine component may become
diminished (Table 1), these communities may be transitional
towards upland grasslands such as U5 Nardus stricta-Galium
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B I O L O G I CA L C O N S E RVAT I O N
saxatile and U6 Juncus squarrosus-Festuca ovina. While it is difficult to completely separate the effects of inter-related environmental variables on vegetation composition, the analysis
found soil type to be the least important explanatory variable.
However, a study of Atlantic calcareous grasslands has highlighted the important role of soil in structuring the plant community (Duckworth et al., 2000). Thus, the basic soils of the
Breadalbane range might provide some favourable locations
for calcicolous Arctic-alpines in the future, especially at
high-altitudes where newly arriving thermophilous species
will require the ability to establish on thin soils and scree
(Kazakis et al., 2007).
The current sites of U5 and U6 upland grasslands would
become suitable for moorland species from the lower altitudes. There would be a transition from U5 and U6 towards
moorland communities such as M15 Trichophorum cespitosum-Erica tetralix wet heath. These moorland communities,
which currently occupy large areas of the uplands (Averis
et al., 2004), are projected to expand their potentially suitable
area to higher altitudes. It is however possible that they will in
turn be invaded by other species from lower altitudes. Blanket
mires such as M19 Calluna vulgaris-Eriophorum vaginatum are
an internationally important habitat but were poorly predicted by the model, possibly due to the topographic wetness
index employed.
The communities projected to lose suitable climate space
(U10 Carex-Racomitrium moss-heath, U7 Nardus-Carex grassheath, CG11 Festuca-Agrostis-Alchemilla grass-heath and CG12
Festuca-Alchemilla-Silene dwarf-herb) are part of two habitats
listed under Annex 1 of the European Habitats Directive.
These are ‘siliceous alpine and boreal grasslands’ and ‘alpine
and subalpine calcareous grasslands’, and they qualify the reserves for Natura 2000 designation. As the climate changes
and montane vegetation responds, it will become increasingly
difficult to comply with the Directive’s requirement to maintain these habitats at a ‘‘favourable conservation status’’
(European Communities, 1992). As suggested by previous
authors (Peters, 1992; Sætersdal et al., 1998; Price and Neville,
2003), in order to limit the potential for future deterioration of
these habitats, it is important that current management
activities continue to reduce the existing anthropogenic stresses. However, a more flexible approach to conservation designation may ultimately be required, which accepts the climate
change-induced loss of species and habitats from some sites
(Brooker et al., 2007; Normand et al., 2007).
4.3.
Future research needs
As with all climate change impact assessments there are a
number of uncertainties to this modelling exercise, not least
of which is the uncertainty in the future course of climate
change (Araújo and New, 2007). The assumptions and uncertainties inherent in species distribution modelling have been
discussed in detail by previous authors (Pearson and Dawson,
2003; Hampe, 2004; Pearson and Dawson, 2004; Araújo and
Guisan, 2006; Guisan et al., 2006; Botkin et al., 2007). In particular, such ‘equilibrium’ models normally assume that species
will disperse and track suitable climate space and that their
biotic interactions will not change. However, many marginal
upland plants reproduce vegetatively and grow slowly; conse-
1 4 1 ( 2 0 0 8 ) 1 6 6 5 –1 6 7 5
quently they are likely to take a long time to disperse into new
climatically suitable areas (Dullinger et al., 2004). Also, the
type of plant–plant interactions may change from facilitative
to more competitive as the climate warms (Brooker, 2006). Another important biotic interaction is herbivory, which acts as
an environmental filter, selecting specific groups of plant species (De Bello et al., 2005; Louault et al., 2005; Pierce et al.,
2007). Centuries of intensive sheep grazing in the Breadalbane
hills have led to the development of extensive grasslands
(Averis et al., 2004). Therefore, future responses of montane
plant communities to climate change will depend to some extent on the grazing regime adopted by land managers. Such
local biotic effects might have resulted in relatively poor model fit for some species. Given the importance of investigating
climate change impacts on mountain vegetation at scales relevant to the climates experienced by plants, the challenge
now is to integrate such processes into fine scale distribution
models (e.g. Dullinger et al., 2004).
It is also essential to monitor changes in communities as
they occur in order to determine actual changes and to test
and refine models. This will also require site-based monitoring of climate (especially in mountain areas) and other potential causes of change, such as grazing management and air
pollution. Existing monitoring of designated sites in the UK
(Common Standards Monitoring, JNCC, 2006) does not provide
for this, nor for detailed quadrat-based recording. A number
of reports have identified the need for this sort of additional
monitoring of UK conservation sites (e.g. Ferris, 2006; Mitchell
et al., 2007) and proposals have been developed (Morecroft
et al., 2006). A wider international approach to monitoring
and modelling change in Europe’s mountains would also be
valuable (e.g. Nagy et al., 2003).
5.
Conclusion
Predicting climate change-driven shifts in species distributions has been dominated by large-scale studies in recent
years, although landscape to regional scale applications
would be of particular utility to conservation. This is especially relevant for mountainous terrain where the microand meso-climates may strongly deviate from the macro-climate. Previous landscape-level fine resolution modelling
studies in Europe have mainly focused on southern European
mountains such as the Alps (e.g. Zimmermann and Kienast,
1999; Guisan and Theurillat, 2000; Dirnböck et al., 2003),
which are thought to be most sensitive to climate change
(Thuiller et al., 2005). However, our results indicate that significant impacts could also occur in the maritime uplands of
Scotland, even under a ‘low’ warming scenario (B1 of the
IPCC; Nakicenovic and Swart, 2000). While the models can
be refined in terms of including processes such as dispersal
and species interactions, the results highlight the need to
continue to maintain these important high-altitude plant
communities in a condition in which they can be most resilient to climate change. Furthermore, monitoring is needed to
detect and attribute future changes to the correct causal factors and to test model predictions (Guisan and Theurillat,
2005). Such information is critical for the development of
more flexible conservation policies in the face of climate
change.
B I O L O G I C A L C O N S E RVAT I O N
Acknowledgements
We thank a number of agencies for providing datasets: Scottish Natural Heritage for botanical databases; the British
Atmospheric Data Centre and the United Kingdom Climate
Impacts Programme for climate data; and Allan Lilley of the
Macaulay Institute for soil maps and information. David Mardon and the staff of the National Trust for Scotland, Killin,
provided advice and institutional support to MT, who was
funded by a NERC studentship. Antoine Guisan and two anonymous reviewers gave valuable comments on the manuscript.
Appendix A. Supplementary data
Supplementary data associated with this article can be found,
in the online version, at doi:10.1016/j.biocon.2008.04.008.
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