Changes in habitat specificity of species at their climatic range boundaries

L E T T E RChanges in habitat specificity of species at their

climatic range boundaries

Tom Oliver,1* Jane K. Hill,2 Chris

D. Thomas,2 Tom Brereton3 and

David B. Roy1

1NERC Centre for Ecology &

Hydrology, Maclean Building,

Benson Lane, Crowmarsh

Gifford, Oxfordshire OX19 8BB,

UK2Department of Biology (Area

18), University of York, PO Box

373, York YO10 5YW, UK3Butterfly Conservation, Manor

Yard, East Lulworth, Wareham,

Dorset BH20 5QP, UK

*Correspondence:

E-mail: [email protected]

Abstract

Species are thought to have more restricted niches towards their range boundaries,

although this has rarely been quantified systematically. We analysed transect data for 41

butterfly species along climatic gradients within Britain and show that 71% of species

have broader niches at sites with milder winters. Shifts in habitat associations are

considerable across most species� ranges; averaged across all 41 species, we estimate

that if 26% of individuals were associated with the favoured habitat on the species�warmest transect, then 70% of individuals would be confined to this habitat on the

species� coldest transect. Species with more southerly distributions in Britain showed

the greatest changes in their habitat associations. We conclude that geographic

variation in realized niche breadth is common and relatively large, especially near range

boundaries, and should be taken into account in conserving species under changing

climates.

Keywords

Bioclimatic envelope model, butterfly habitat management, habitat associations, niche

breadth, range margin.

Ecology Letters (2009) 12: 1091–1102

I N T R O D U C T I O N

Many animal and plant species have responded to recent

climate warming (Parmesan & Yohe 2003; Root et al. 2003).

Most studies of the responses of species to climate have

focused on changes in the phenology and distributions of

species (Walther et al. 2002; Thomas et al. 2006), and possible

climate-driven changes in the habitat associations of species

have largely been ignored. For example, most species

distribution models operate under the assumption of niche

conservatism (Guisan & Zimmermann 2000). However, this

may not be the case and such changes need to be incorporated

into conservation management plans and need to be

accounted for when projecting distribution changes.

Changes in species� associations with habitat will affect

the extent of suitable breeding habitat, and thus affect both

the ability of species to survive in existing parts of their

ranges, as well as the ability of species to occupy new,

climatically-suitable areas in future. Recent information for a

small number of species suggests that changes in species�habitat associations are taking place (Thomas et al. 2001;

Davies et al. 2006), although data on the generality, extent

and direction (i.e. wider or narrower habitat associations

with warmer climate) of such changes are lacking. System-

atic multi-species studies of patterns of geographic variation

in habitat associations are needed to evaluate the extent to

which these patterns may be influenced by climate.

The physical environment is important in determining

the association of species with particular habitats, partic-

ularly at the edges of their ranges (Brown 1984; Thomas

et al. 1999; Lennon et al. 2002). At the high-latitude leading

edge boundaries of species� ranges, populations may

become restricted to particular habitats. This may be due

to these habitats having especially warm microclimates

(thus increasing the chances of species completing their life

cycles, and ⁄ or increasing survival and fecundity), or

because these habitats support high population growth

for other reasons (e.g. habitats with high resource

availability), enabling populations to persist despite

increased climate-related mortality at range margins. By

contrast, in the centre of species� ranges, a relatively wide

range of locations and habitats are likely to meet the

species� requirements for growth and survival. It is also

possible that because local abundances tend to be greater

towards the centre of ranges (Brown 1984), increased

density dependence in the core habitats in these areas may

Ecology Letters, (2009) 12: 1091–1102 doi: 10.1111/j.1461-0248.2009.01367.x

� 2009 Blackwell Publishing Ltd/CNRS

lead to increased use of a wider range of subordinate

habitats.

Changes in species� habitat associations have been

recognized in certain cases (Lennon et al. 2002; Davies

et al. 2006; Schofield et al. 2009). However, information on

the generality and causes of such patterns are required

because spatial and temporal variation in climate-driven

species� habitat associations will have important implica-

tions for developing appropriate management plans under

changing climates. It is possible that the greatest immediate

need for conservation management responses to climate

change will relate to species� habitat changes. First,

prescriptions for habitat management to maximize num-

bers of individuals may need to recognize that spatial

variation in habitat use exists, such that management varies

from one location to another within a species� range.

Second, climate change may modify species� habitat

associations in a given location over time, such that

current static habitat management prescriptions could

become inappropriate, and possibly detrimental, in the

longer term. Third, predicting range shifts of species under

future climate change requires understanding of climate–

habitat interactions. This has special relevance at leading

edge expanding range margins, where habitat availability

may be a major determinant of range expansion (Hill et al.

2001; Wilson et al. 2009), and at trailing range edges where

species may become increasingly confined to core habitats.

However, there is currently insufficient information on the

generality of climate-driven shifts in habitat associations to

inform adaptation strategies.

Here we analyse data for 41 species of southerly-

distributed British butterflies, and examine the degree to

which species� habitat associations change spatially across

Britain, in relation to climate. For each species, we analyse

abundance data from line transects (Pollard & Yates 1993)

to estimate the proportions of individuals associated with

different habitat types in different parts of Britain, and we

relate these changes to spatial variation in climate. Our study

species have the cores of their British distributions in

relatively warm southern regions of Britain (and also occur

further south within Europe), and so we predict that habitat

associations will become increasingly constrained towards

species� leading edge (cool) range limits. We also carry out a

multispecies analysis to test whether the locations of species��cold� range limits in Britain affect the degree to which niche

breadth varies across climate space. If climate limits species�habitat use, then we may expect habitat associations to

become rapidly constrained towards range edges as climatic

suitability tails off to zero. Therefore, we predict that species

with more southerly distributions, and therefore occupying

more marginal areas of their range in Britain, will show the

greatest rate of change in habitat associations across climate

space. Finally, we illustrate the implications of our results for

projecting species� range expansions by mapping variation in

niche breadth of an expanding species across its geograph-

ical range in Britain.

M A T E R I A L A N D M E T H O D S

Data sources

Butterfly abundance data were derived from the United

Kingdom Butterfly Monitoring Scheme (UKBMS) transect

surveys. Full details of the UKBMS scheme can be found in

Pollard & Yates (1993), but we briefly outline them here.

Transects are 5 m wide, between 0.5 and 7 km in length and

each transect is subdivided into sections of 10–2000 m,

corresponding to 12 habitat types. For all British species, the

numbers of butterflies in each section of a transect are

recorded weekly from April to September, when prescribed

weather conditions are met. In this study, we analysed all

butterfly transects that were recorded between 1976 and

2007 [mean duration of records at each transect loca-

tion = 14.4 years ± 0.8 (SE)], for which habitat types of all

sections were known and for which more than one habitat

type was present on the transect. In total we analysed 117

transects which were distributed across Britain (Fig. 1).

Although the duration of recording varied among transects,

there was no significant pattern with spatial climate

(accumulated temperatures above 5 �C) for the first or last

Figure 1 Locations of the 117 United Kingdom Butterfly Moni-

toring Scheme transects used for the analysis.

1092 T. Oliver et al. Letter


year that a transect was recorded, nor the total duration of

recording (regression of first year of recording on mean

growing degree days above 5 �C at each site :

F1,115 = 0.339, P = 0.562; last year: F1,115 = 0.438,

P = 0.509; duration: F1,115 = 0.549, P = 0.460), and so

data were summed over all years to produce a single

measure of habitat specificity for each species for each

transect (see below). We were interested in spatial patterns

of habitat association and so we omitted from analysis

species that occurred on fewer than five transects, because

regressions with so few data points are likely to have very

large errors. This resulted in 41 southerly-distributed species

available for analysis (Table 1).

Climate data were obtained from CRU ts2.1 (Mitchell &

Jones 2005) and CRU 61–90 climate (New et al. 1999)

datasets and interpolated to the 10 km GB Ordnance

Survey grid.

We used three bioclimate variables that are known to

affect butterfly physiology and distribution: growing degree

days above 5 �C (GDD5), mean daily temperature of the

coldest month (MTCO) and the ratio of actual to potential

evapotranspiration (APET) (Warren et al. 2001; Hill et al.

2002). For each transect location, each climate variable was

computed as the mean of a 10-year period between 1989

and 1999 for the 10 km grid square containing the transect.

Measures of habitat specificity

To assess spatial variation in habitat associations we first

quantified the habitat specificity of each species (at each site)

in relation to its most favoured habitat (Pobs, see below). We

defined most �favoured habitat� as the habitat type which

most frequently had the highest density of butterflies of that

species across all 117 transects; and �subordinate� habitat as

all other habitats present on transects with lower densities of

that species (including zero density). �Subordinate� habitat

was treated as a single entity; summing section lengths and

butterfly counts for the different non-favoured habitat types

on a given transect. Any transect that did not contain both

favoured (for a given species) and subordinate habitat types

was subsequently omitted from analysis for that species,

giving the mean number of transects analysed per species as

51.2 ± 4.9 (SE). We first standardized our analyses to

account for different areas of habitat within transect routes

by dividing butterfly abundance by habitat area to give

butterfly densities per km2 for each species in favoured and

subordinate habitat types. For each species, the ratio of the

density of butterflies in the favoured habitat to the density

of butterflies in the subordinate habitat was then expressed

as a proportion, following eqn 1. This value (Pobs)

represents the expected proportion of individuals in the

favoured habitat if all habitat types were of equal area, as

follows:

Pobs ¼na

ðnaþ m

bÞ ð1Þ

Pobs = proportional relative density of butterflies in the

favoured habitat

a = area of transect route containing the favoured

habitat (km2)

b = area of transect route containing subordinate

habitat (km2)

n = total number of butterflies in the favoured habitat

m = total number of butterflies in the subordinate

habitat

For each species, we carried out logistic regression on the

observed proportional relative densities (Pobs) across all

transects occupied by the species, to estimate how the

expected proportion of individuals in the favoured habitat,

termed pi, varied with climate. Sample sizes for each transect

were the total number of butterflies observed on the

transect in both favoured and subordinate habitats. By

assessing habitat use in this way by using proportional values

(i.e. rather than using raw count data), we minimize

problems caused by sampling effects, such as differences

in total butterfly abundance among transects, and differ-

ences in the area of available habitats on different transects

(Garshelis 2000; Manly et al. 2003).

Statistical analysis

Spatial variation in habitat specificity

We analysed spatial variation in habitat specificity for each

species across the UK using logistic regression. The aim of

our analysis was to examine how expected habitat specificity

on transect i (pi) varied with each of the three bioclimate

variables (GDD5, MTCO and APET) for the 10 km grid

square containing that transect. Some of the bioclimate

variables were strongly correlated (e.g. Pearson�s correlation

coefficient of GDD5 and MTCO was 0.85); therefore we

included each bioclimate variable in a separate logistic

regression model. Although models containing multiple

correlated explanatory variables can still produce fairly

robust predictions (hence their use in much bioclimate

envelope modelling), extracting individual variable coeffi-

cients can give misleading results. In our case, we were

interested in the individual bioclimate variable coefficients

that indicate the effect of each bioclimate variable on

species� habitat associations, so we fitted each bioclimate

variable in a separate model. For example, the effect of the

GDD5 on habitat specificity is reflected in the sign and

magnitude of the coefficient b in eqn 2. A negative

coefficient indicates that the proportion of individuals in the

favoured habitat decreases with increased GDD5 at a site.

Similarly, the effect of the MTCO on habitat specificity is

Letter Changing habitat specificity at range boundaries 1093


Table 1 Butterfly species used in the analysis and their relationships between habitat specificity and climate

Species FH SH1 SH2 RL b c d

Aglais urticae G (0.49) HeMo DW 1124 0.0004 0.109 )1.246

Anthocaris cardamines HeMo (0.39) G DW 929 0.0002 0.105 2.681

Aphantopus hyperantus G (0.42) HeMo DW 859 )0.0016* )0.055 6.860*

Argynnis aglaja G (0.62) HeMo DW 976 )0.0024 )0.881* 19.431*

Argynnis paphia G (0.39) DW HeMo 567 )0.0032 )0.452 15.647*

Aricia agestis G (0.57) DW HeMo 483 0.0009 0.749* 3.441

Boloria euphrosyne DW (0.44) G HeMo 895 0.0001 0.286 8.182

Boloria selene G (0.54) DW Br 939 0.0026 0.611 )12.009

Callophrys rubi G (0.53) HeMo DW 905 0.0010 0.319 1.658

Carterocephalus palaemon G (0.67) HeMo DW 860 )0.0016 )0.498 8.378

Celastrina argiolus HeMo (0.42) G DW 592 )0.0009 )0.383* )1.304

Coenonympha pamphilus G (0.62) HeMo H 956 )0.0008 )0.307 4.867

Colias croceus G (0.62) HeMo DW 894 )0.0010 )0.300 5.504

Cupido minimus G (0.72) HeMo DW 915 )0.0017 )0.025 11.810

Euphydryas aurinia G (0.57) DW H 744 0.0048 )0.177 )36.789*

Gonepteryx rhamni HeMo (0.43) G DW 579 )0.0013 0.044 4.318*

Hamearis lucina G (0.66) HeMo DW 465 )0.0026 )0.271 )1.997

Hesperia comma G (0.88) H HeMo 192 )0.0284* )11.395* 77.967

Hipparchia semele G (0.44) H DW 911 )0.0183 )4.597 57.986*

Inachis io G (0.38) HeMo DW 1166 0.0010* 0.188 )3.646*

Leptidea sinapis G (0.56) DW A 567 0.0118 )1.018 )17.446

Limenitis camilla G (0.35) DW HeMo 385 )0.0023 )0.217 1.300

Lycaena phlaeas G (0.54) HeMo DW 977 )0.0014* )0.384* 6.201*

Maniola jurtina G (0.55) HeMo DW 1018 )0.0007 )0.294* 1.905

Melanargia galathea G (0.60) HeMo DW 503 0.0002 )0.113 1.266

Neozephyrus quercus DW (0.44) G HeMo 742 0.0015 0.655 0.578

Ochlodes venata G (0.46) HeMo DW 637 )0.0010 )0.432* 2.837

Pararge aegeria DW (0.50) HeMo G 938 0.0011 )0.001 )3.487

Pieris brassicae HeMo (0.38) G DW 1194 )0.0001 )0.056 )1.836

Pieris napi HeMo (0.40) G DW 1029 0.0004 0.080 )2.402

Pieris rapae G (0.40) HeMo DW 974 0.0000 )0.013 0.476

Plebeius argus H (0.69) G C 378 )0.0662 )2.163 )143.79

Polygonum c-album HeMo (0.42) DW G 708 )0.0002 )0.034 )1.868

Polyommatus coridon G (0.77) HeMo DW 277 0.0016 0.407 1.127

Polyommatus icarus G (0.58) HeMo DW 1035 0.0001 )0.078 )0.640

Pyrgus malvae G (0.58) HeMo DW 400 )0.0031 )0.019 11.448

Pyronia tithonus G (0.45) HeMo DW 564 )0.0015 )0.452* 2.127

Lasiommata megara G (0.45) HeMo DW 656 )0.0010 )0.261 4.632

Thymelicus sylvestris G (0.53) HeMo DW 607 )0.0015 )0.521* 5.950*

Vanessa atalanta HeMo (0.39) G DW 1197 0.0004 0.057 )0.968

Vanessa cardui G (0.49) HeMo UG 1198 )0.0003 )0.128 3.646

Habitat category codes for favoured habitats (FH) and the two most common subordinate habitats (SH1 and SH2) are as follows:

Br, bracken, DW, deciduous woodland, G, grassland, H, heathland, HeMo, hedgerow and mosaic habitats. The number in brackets after the

favoured habitat type refers to the proportional relative density of butterflies found in this habitat averaged across the UK, i.e. it is a measure

of habitat specificity that ranges from zero to one, with one indicating all individuals are found in the favoured habitat. Range limits of species

in Britain (RL) are the mean northing (in km) of the 10 most northerly records of a species from the UKBMS database. The relationship

between habitat specificity and GDD5 is reflected in the species� b coefficient. Negative values indicate species become less specific in

warmer locations. The relationship between habitat specificity and MTCO is reflected in species� c coefficients. Negative values indicate

species become less specific in locations with warmer winters. The relationship between habitat specificity and APET is reflected in the

species� d coefficient. Positive values indicate species become more specific in wetter sites. Values of b, c or d coefficients in bold indicate

which climate variable best explains habitat specificity for the species. Authorities for scientific names and vernacular names and are detailed

in Asher et al. (2001).

*Species with significant correlations of each bioclimate variable with habitat specificity (P < 0.05).



reflected in the sign and magnitude of the coefficient c in

eqn 3, and the effect of the APET on habitat specificity is

reflected in the sign and magnitude of the coefficient d in

eqn 4. In each model, we also included each species� mean

yearly total abundance at a site (Ni), the number of habitat

types on transects (Hi) and the proportion of each species�favoured habitat (Fi) as control variables, because we were

interested in the effects of climate on habitat specificity

independent of these factors. There was no significant

relationship between the number of habitat types on

transects and any of the three bioclimate variables. Similarly,

there was no significant relationship between the proportion

of each habitat type on transects and any of the three

bioclimate variables (Table S1). Thus, any trends of

increased habitat specificity in marginal climates were

unlikely to be caused by underlying spatial bias in the

numbers, or proportions, of habitat types on transects.

Including these factors as control explanatory variables,

however, allowed more residual spatial variation in butterfly

habitat specificity to be explained, thereby increasing the

sensitivity of the model to detect the importance of climate

on butterfly habitat use. We tested the residuals of all three

models (eqns 2–4) for spatial autocorrelation by calculating

Moran�s I and assessing the significance of the standard

deviate (Table S2). In no cases did residuals show significant

spatial autocorrelation and thus we are confident that non-

independence of local response values is not a problem.

Finally, for each species we examined deviance of each of

the logistic regression models (eqns 2–4), to ascertain which

climate variable best explained spatial variation in habitat

associations. All three models had been fitted to the same

variable and there were no missing values in the explanatory

variables, so in all three cases the full (saturated) model was

the same, and the null model was the same. Therefore, we

sought the bioclimatic variable that provided the greatest

change in deviance relative to the null model, an approach

equivalent to a standard likelihood ratio test.

logitð piÞ ¼ aþ bGDD5i þ h1Ni þ h2Hi þ h3Fi ð2Þ

logitð piÞ ¼ aþ cMTCOi þ h4Ni þ h5Hi þ h6Fi ð3Þ

logitð piÞ ¼ aþ dAPETi þ h7Ni þ h8Hi þ h9Fi ð4Þ

Statistical analyses were carried out using the program R (R

Development Core Team 2007) version 2.7.1, using

generalized linear models (Faraway 2006). In many cases,

models were overdispersed, as indicated by the residual

standard error exceeding the degrees of freedom, therefore

�quasibinomial� errors were specified (Faraway 2006; Craw-

ley 2007). To test whether variation in each climate variable

affected butterfly habitat specificity (pi) across species, we

carried out a binomial test on the number of positive versus

negative slopes (b, c or d coefficients) which were

significantly different from zero. We also carried out a

Wilcoxon signed rank test on all slope values to see if they

differed significantly from zero.

Location of species� range limits

We tested whether the latitude of a species� leading edge

�cold� range limit within the UK affected the relationship

between habitat specificity (pi) and climate (GDD5, MTCO

or APET). Range limit was defined as the mean northing (in

km) of the ten most northerly British records of a species

from the BNM (Butterflies for the New Millenium) database

which contains species� presences at 10 km resolution from

1995 to 2004 (Asher et al. 2001; Hickling et al. 2006).

Phylogenetic autocorrelation in the response and explana-

tory variables was tested using the Moran�s I-test with Geary

randomizations (1000 iterations) using the �ape� package in

the program R (Paradis 2006; Dray & Dufour 2007). This

autocorrelative approach to test for the importance of

phylogenetic non-independence introduced by Gittleman &

Kot (1990) is preferable to automatically correcting for

phylogeny. If there is no evidence for phylogenetic

autocorrelation in either the response variable, explanatory

variables or in the model residuals, then phylogenetic

corrections (which can increase type II (false-negative) error

rates), are unnecessary (Kunin 2008). We used a butterfly

phylogeny from Cowley et al. (2001) with branch lengths

assigned using Grafen�s (1989) method.

R E S U L T S

Spatial variation in habitat specificity

Species varied considerably in their habitat specificity. For

example, the proportion of butterflies in the favoured

habitat, averaged across all the sites occupied by a species,

ranged from 0.35 to 0.88 (Table 1). In addition, within any

species there was considerable variation in habitat specificity

across sites, and the degree of habitat specificity was often

associated with the site�s climate. The majority of species

showed negative relationships between MTCO and habitat

specificity (29 of 41 species; Wilcoxon signed-rank test on

slope values: V = 247, n = 41, P = 0.0166). Of the

significant regression slopes between MTCO and habitat

specificity, eight were negative and one was positive

(Fig. 2b; two-tailed binomial test, 8 ⁄ 9 significant slopes

negative, P = 0.039). This suggests that species tend to

become more constrained to their favoured habitats in

locations with colder winters. Nevertheless, some species

showed the opposite trend and became less constrained in

cooler locations.

With regards to APET, the majority of species showed

positive relationships between APET and habitat specificity



(27 of 41 species; Wilcoxon signed rank test on slope values:

V = 595, n = 41, P = 0.033). Of the significant regression

slopes between APET and habitat specificity, seven were

positive and two were negative (Fig. 2c; two-tailed binomial

test, 7 ⁄ 9 significant slopes positive, P = 0.18).

Finally, the majority of species showed negative relation-

ships between GDD5 and habitat specificity (24 of 41

species). However, these values were not significantly

different from zero, although values were approaching

significance (Wilcoxon signed rank test on slope values:

V = 282, n = 41, P = 0.054). Of the significant regression

slopes between GDD5 and habitat specificity, three were

negative and one was positive (Fig. 2a; two-tailed binomial

test, 3 ⁄ 4 significant slopes negative, P = 0.63).

To ascertain which of the three bioclimate variables

best explained spatial variation in habitat associations, we

compared the deviance of each of the logistic regression

models (eqns 2–4). The most important bioclimate

variable was APET which best explained spatial variation

in habitat associations for 20 species (Table 1). The next

best was MTCO (13 species), followed by GDD5 (eight

species).

Across all 41 species, the mean difference in habitat

specificity between sites with the coldest and warmest

winters (MTCO) in the proportion of butterflies in their

favoured habitat was 0.44 ± 0.04 (n = 41; range = 0–1).

For example, we calculate that a species with 70% of

individuals in the favoured habitat on the species� coldest

transect would have only 26% of individuals in the favoured

habitat on the species� warmest transect. Thus, there are

considerable shifts in habitat associations across most

species� ranges.

Species� abundance was also included in our logistic

regression models because we were interested in effects of

climate that were independent of changes in abundance.

There were significant effects of abundance on species�habitat associations (11 species showed significant effects of

abundance on habitat specificity in GDD5 models, eight

species in MTCO models and 11 species in APET models).

Comparing across species, however, revealed that the effects

of abundance were often in opposite directions. Thus, in

some species increasing abundance led to increased spec-

ificity, whilst in other species increasing abundance led to

decreased specificity [two-tailed binomial test to determine

if there was a consistent trend in the sign of significant

abundance slopes: GDD5 model: 4 ⁄ 11 abundance slopes

negative, P = 0.55; MTCO model: 2 ⁄ 6 abundance

slopes negative, P = 0.29; APET model: 4 ⁄ 11abundance

slopes negative, P = 0.55; Wilcoxon signed rank test on all

abundance slope values (n = 41): GDD5 model: V = 496

P = 0.40, MTCO model: V = 518 P = 0.26, APET model:

V = 530 P = 0.20]. Thus, abundance was unable to explain

the consistent spatial trend in habitat specificity shown

across species (i.e. that habitat specificity tended to be lower

in warmer and drier places with warmer winters).

Mapping habitat specificity

To illustrate the implications of our results we mapped the

changes in niche breadth of an expanding species, the

gatekeeper butterfly Pyronia tithonus L., across its geographi-

cal range in Britain. Abundance and habitat availability data

for the butterfly across Britain were unavailable at 10 km

resolution, and so we used a logistic regression model

Increased specificity in cooler sites

Increasedspecificity inwarmer sites

Increased specificity with colder winters

Increased specificity with warmer winters

Increased specificity in drier sites

Increasedspecificity inwetter sites

(b) (c)

Species' GDD5 β coefficient

Fre

qu

ency

–0.06 –0.04 –0.02 0.00 0.02

05

1015

2025 (a)

Species' APET δ coefficient

Fre

quen

cy

–150 –100 –50 0 50

05

1015

2025

30

Species' MTCO γ coefficient

Fre

quen

cy

–12 –10 –8 –6 –4 –2 0 2

05

1015

2025

30

Figure 2 Frequency distribution of b, c and d coefficients for relationships between (a) GDD5, (b) MTCO and (c) APET, and butterfly

habitat specificity (n = 41 species). The dashed vertical line at zero represents no change in specificity. The negative distribution in panel (a)

indicates that specificity of association with favoured habitats declines in warmer places. The negative distribution in panel (b) indicates that

specificity of association with favoured habitats declines in places with warmer winters. The positive distribution in panel (c) indicates that

specificity of association with favoured habitats increases in wetter locations.



including the three bioclimate variables (GDD5, MTCO and

APET) to project changes in the habitat specificity of this

species across Britain (Fig. 3). In the example shown, the

majority of individuals of P. tithonus are restricted to

grasslands in colder and wetter parts of the range, but

butterflies are much more evenly distributed across a range

of other habitat types, such as hedgerows, deciduous

woodland and heathland, in milder coastal regions and in

the south of Britain. As this species expands its range

northwards, colonists in cool northern locations are likely to

be concentrated in grassland habitats which will reduce

expansion rates compared with warmer locations where a

wider range of habitats are utilized.

Species� range limit effects

We tested whether the location of the leading edge northern

range limit in Britain of each of the 41 southerly-distributed

butterfly species was a significant predictor of the gradient

of change in habitat specificity with climate. There was no

evidence of phylogenetic autocorrelation in the response

variable, explanatory variables or in model residuals (1000

iterations of Geary randomization of Moran�s I test for

phylogenetic autocorrelation: range limit P = 0.95, GDD5

P = 46, MTCO P = 0.84, APET P = 0.73, range limit-

GDD5 residuals P = 0.88, range limit-MTCO residuals

P = 0.90, range limit-APET residuals P = 0.95), so we used

conventional regression including data for each species as an

independent data point (Kunin 2008). We found a signif-

icant effect of species� range limit on the relationship

between GDD5 and habitat specificity, such that species

with more southerly leading edge range limits in Britain

tended to have steeper negative relationships between

GDD5 and habitat specificity (i.e. more specific to favoured

habitats in colder places) (Fig. 4a; regression of range limit

location on GDD5 b coefficient, for all 41 species:

F1,39 = 4.81, P = 0.034). We repeated the analysis for only

those 50% of species that had the most reliable slope

estimates (i.e. lowest standard errors of slope estimates) and

the trend between GDD5 and habitat specificity was even

stronger (Fig. 4d; regression of range limit location on

GDD5 b coefficient for 21 species: F1,19 = 14.07,

P = 0.001). In this case, it was apparent that species with

more northerly range limits tended to have less steep, more

positive relationships (i.e. more specific to favoured habitats

in warmer places).

Similarly for MTCO, there was also a positive relationship

between species� range limit location and the habitat

specificity-climate coefficient, such that more southerly

distributed butterflies were more specific to favoured

habitats in places with colder winters (Fig. 4b; regression

of range limit location on MTCO c coefficient for all 41

species: F1,39 = 5.237, P = 0.028; Fig. 4e; regression of

range limit location on MTCO c coefficient for 21 species:

F1,19 = 8.22, P = 0.010). Once again, slopes for more

southerly distributed species were steeper, indicating greater

rates of change in habitat specificity across climate space.

For the bioclimate variable APET, there was no

significant effect of location of species� range limit on the

relationship between APET and habitat specificity across all

41 study species (Fig. 4c; APET d coefficient-range limit

location regression; F1,40 = 0.145, P = 0.705). When only

the 50% of species with the most reliable slope estimates

were considered, however, there was a significant negative

trend between APET d coefficient and species� range limit

location, such that more southerly species were more

specific in wetter places and more northerly species were

more specific in drier places (Fig. 4f; APET d coefficient-

range limit regression: F1,19 = 4.65, P = 0.044).

D I S C U S S I O N

We found an overall trend for southerly distributed British

butterflies to be more constrained to their favoured habitats

in cooler and wetter locations. On average, there was a 44%

change in habitat specificity between the cold and warm

Proportion of population in grassland habitat

0.07 – 0.290.30 – 0.410.42 – 0.510.52 – 0.66

Figure 3 Spatial variation in niche breadth modelled for a range-

expanding butterfly species Pyronia tithonus L. (Gatekeeper).

Individuals are more constrained to their overall favoured habitat

type (grassland) in climatically unsuitable locations (black squares).

Butterfly distribution is from Fox et al. (2006) for the period 1995–

2004.



range limits of species within Britain (as measured by winter

temperatures, MTCO). Given that the habitat categories we

considered are very broad, we might expect even greater

differences to be apparent were finer-resolution habitat

categories to be considered. All three bioclimate variables

were important in determining spatial changes in habitat

specificity across species. Mean temperature of the coldest

month (MTCO) produced the most significant relationships,

with 71% of species (nine of which had significant slopes)

showing a pattern of more constrained habitat associations

in locations with colder winters. The moisture index APET

was also very important, and across species this bioclimate

variable most frequently best explained spatial patterns in

habitat specificity.

The final bioclimate variable, GDD5, also supported the

trend for higher habitat specificity at species� high-latitude

leading range edges. In addition, species with more southerly

distributed British ranges, and which are presumably most

constrained by climate within Britain, showed the greatest

change in their habitat associations across climate space.

For thermophilous insects such as butterflies, it is likely

that shifts in habitat associations are caused directly by

climate. This has been shown to be the case in a detailed

study of one butterfly species (Davies et al. 2006), which

suggests that climate may limit habitat use through the

existence of microclimatic thresholds (e.g. Thomas 1993;

Thomas et al. 1999; Roy & Thomas 2003; Davies et al.

2006). Our study revealed the most important bioclimate

variables affecting habitat specificity to be MTCO and the

APET moisture index. Thus, a tentative conclusion that we

might draw is that habitat associations appear to be

predominantly constrained by bioclimate variables that

relate to potential extremes; temperature of the coldest

month which may affect physiological survival, perhaps of

200 400 600 800 1000 1200

–0.0

2 –0

.04

–0.0

6 0.

00

Distribution limit (km northings)

Spe

cies

' GD

D5

β co

effic

ient

(a)

200 400 600 800 1000 1200

Distribution limit (km northings) S

peci

es' M

TC

O γ

coe

ffici

ent

–2

–4

–6

–8

–10

0

(b)

200 400 600 800 1000 1200


Spe

cies

' AP

ET

δ c

oeffi

cien

t 0

–50

–100

–1

50

50

(c)

Spe

cies

' GD

D5

β co

effic

ient

600 700 800 900 1000 1100 1200

–0.0

015

–0.0

005

0.00

05


(d)

–0.2

Spe

cies

' MT

CO

γ c

oeffi

cien

t

Distribution limit (km northings) 600 700 800 900 1000 1100 1200

–0.4

0.

0 0.

2

(e)

Spe

cies

' AP

ET

δ c

oeffi

cien

t Distribution limit (km northings) 600 700 800 900 1000 1100 1200

–4

–2

0 2

4 6

(f)

Figure 4 Effect of species� British range limit on the relationship between (a) habitat specificity and GDD5, (b) habitat specificity and MTCO

and (c) habitat specificity and APET. Panels (a–c) show all 41 species analysed, Panels (d–f) show the 21 species with the lowest standard

error of slope estimates. In panels (a and d), negative b values indicate species that show greater habitat specificity in sites with lower

accumulated annual temperatures. In panels (b and e), negative c values indicate species that show greater habitat specificity in sites with

colder winters. In panels (c and f), positive d values indicate species that show greater habitat specificity in wetter sites. The dotted line on

each panel indicates a slope coefficient of zero (no change in habitat specificity across climatic space). The bold dashed lines indicate a

significant trend from a linear regression.



the insects themselves, and drought that might predomi-

nantly act through changes in larval host plant quality.

Butterflies and their host plants may be relatively �protected�from such conditions within certain habitats. In contrast, the

total development time available (related to GDD5) appears

to have a weaker effect on spatial variation in habitat

associations, although this may allow increased abundance

through extended flight periods and more generations per

year for multivoltine species (Roy & Sparks 2000). These

conclusions are concordant with observations on the

responses of individual species to climatic extremes. Range

expansion of the sachem skipper, Atalopedes campestris

Boisduval, in southern USA has been linked to improved

over-winter survivorship with warmer winters (Crozier

2004). Also in Euphydryas editha Boisduval, aseasonal cold

caused the extinction of E. editha subpopulations associated

with one type of habitat ⁄ host plant, resulting in an

evolutionary contraction of host preference within the

metapopulation (Singer & Thomas 1996; Thomas et al.

1996). This suggests that extreme climatic conditions

commonly reduce populations to a subset of previously

occupied habitat types, followed by either gradual or limited

recovery of the original habitat range, the extent and speed

of recovery depending on whether evolutionary as well as

ecological changes have taken place. Hence, changes to the

frequency and severity of extremes associated with climate

change (in this paper represented by spatial variation in

climatic conditions) are expected to result in shifts in habitat

associations.

Changes in habitat associations could potentially also be

mediated through biotic factors (e.g. climate-related niche

shifts in host plants or natural enemies which then affect a

species� habitat associations). Biotic factors are more likely

to be important for specialist species. Many British

butterflies, however, use a number of larval host plants

and butterflies are usually attacked by generalist natural

enemies, so interactions tend to be diffuse. Indeed, seven of

the nine species that showed significant relationships

between habitat specificity and climatic variable MTCO in

our study are known as �wider countryside� rather than

�specialist� species (Asher et al. 2001), suggesting that habitat

shifts mediated solely through close interspecific interac-

tions are unlikely.

Another effect of climate on species� habitat associations

is that climate may affect population abundance and

through density dependent effects (e.g. host plant limitation,

pathogen and parasitoid aggregation) thereby alter the

relative quality of different habitat types. Thus, as popula-

tions in the favoured habitats increase, subordinate habitats

may yield increasing fitness returns relative to the main

habitats. In contrast to this density dependence hypothesis,

however, our results showed that climate was associated

with spatial patterns in habitat association independent of

butterfly abundance. The majority of species tended to be

more constrained to their favoured habitats in colder

climates even after accounting for the local abundance at

these sites. Abundance alone did not explain increasing

habitat specificity in colder places because there was no

consistent significant spatial trend across species associated

with changes in abundance.

Although we observed a significant trend among species

of more constrained habitat associations in colder places,

not all study species showed changes in habitat associations

in the direction expected. Some of these contrary results

may be simply be due to noise within the data; perhaps, for

example, because our measures of habitat type take no

account of habitat quality. Some of the variation among

species in relationships between habitat specificity and

climate, however, was explained by where species� leading

edge limits occurred in Britain. Our multispecies analysis

showed that species with more southerly British ranges were

more specific to favoured habitats in colder and wetter

places compared with species with more northern leading

edge boundaries in Britain. Furthermore, these more

southerly species tended to show steeper slopes between

habitat specificity and all three bioclimate variables (Fig. 4),

indicating that they showed the greatest change in their

habitat associations across space. This marked effect of a

local range limit suggests that the mechanisms that cause

niche shifts operate over relatively short climatic distances at

species� range edges. This would imply that niche breadth

may be relatively stable within core parts of a species� range

because abundances are at carrying capacity and ⁄ or micro-

climatic thresholds that limit habitat suitability have been

surpassed. Towards climatic range boundaries, however,

abundance may decline and ⁄ or certain habitat types become

climatically unsuitable quite abruptly; consequently there are

rapid changes in niche breadth over space close to range

boundaries.

Note that this study only investigated changes in habitat

associations at high-latitude leading boundaries of species�ranges, and we remain cautious about whether similar trends

will occur at species� trailing, low-latitude boundaries.

However, it is intriguing to observe that for species with

more reliable slope estimates (Fig. 4d–f), those with high

latitude ranges in Britain apparently show opposite trends to

southern species i.e. they appear to become more specific to

favoured habitats in warmer, drier places. Thus, we could

tentatively suggest that the bioclimate envelope of these

species may have moved sufficiently far north that they are

starting to become limited by hotter and drier, rather than

colder and wetter climatic extremes, at least in southern

England. Past evidence suggests that such dry extremes do

affect butterfly populations. In Britain, a number of

butterfly species exhibited population crashes following a

severe drought in 1976, in some cases associated with severe



host plant desiccation in particular habitats (Thomas 1983;

Pollard & Yates 1993). Of the species affected by this

drought, analysis of the dynamics of the ringlet butterfly,

Aphantopus hyperantus L., showed that it became constrained

to a subset of its previous habitat range (Sutcliffe et al.

1997). Likewise, exceptional droughts caused a species of

bush cricket, Metrioptera bicolour Phillipi, to disappear

disproportionately from habitat patches with uniformly

short grass in Sweden (Kindvall 1995, 1996), and for

populations of the butterfly E. editha associated with

particular habitats and host plants to become extinct in

California (Ehrlich et al. 1980).

This study holds potentially important messages for

species conservation management and for making reliable

predictions of species� future ranges. First, we have shown

that many British butterflies show significant spatial

variation in habitat associations, so a �one size fits all�national habitat management plan for each species may be

less suitable than multiple regional plans, each tailored to a

species� local needs. We suggest that conservationists should

aim to assess the habitat requirements of local populations

of their protected species and adopt relevant local habitat

management plans. Second, if habitat associations are driven

by climate, then we may expect temporal changes in habitat

association with climate change (Roy et al. 2001). Thus, we

recommend that flexibility is built into management policies,

with regular re-assessment of species� local habitat require-

ments to account for any temporal change in habitat use.

For example, a species such as the Grizzled Skipper Pyrgus

malvae L. which is currently mostly restricted to acid and

chalk grassland (6.7 and 0.2% of the UK land surface

respectively, Countryside Survey 2008) and generally in the

lowlands, could potentially expand its range by occupying

upland heath and bog sites containing one of its host plants,

Potentilla erecta L. (potentially allowing an extra 15.7% of the

UK land surface to be occupied, Countryside Survey 2008).

If niche expansion occurs as the climate warms, such that

habitat availability increases, rates of range expansion may

accelerate in future (Hill et al. 2001; Thomas et al. 2001;

Wilson et al. 2009). Conversely, contractions of habitat

availability at species� trailing, �warm� range margins might

accelerate declines over time. Models predicting range shifts

of species under future climate warming may need to

account for changes in habitat associations across spatial

and temporal climatic gradients in order to predict

probabilities of occurrence more reliably.

Currently, niche models, which are increasingly used to

inform conservation policy, often assume constancy in

habitat associations across a species� ranges (i.e. assume

spatial niche conservatism: Guisan & Zimmermann 2000;

Broennimann et al. 2007) as well as over evolutionary time

(temporal niche conservatism: Peterson et al. 1999; Marti-

nez-Meyer et al. 2004; Thuiller et al. 2005). More specifically,

the estimated coefficients which represent a species�association with different habitat types do not vary with

climate in most current formulations of generalized linear or

additive models, which are commonly used niche modelling

methods (Elith et al. 2006). We suggest that incorporating

interaction terms between habitat associations and climate

may improve the reliability of species distribution

projections.

We found that populations of species that were close to

the edge of the species� climatic range showed the most

spatial variation in habitat specificity. Therefore, to assume

constant climate–habitat associations may be most inappro-

priate when considering range boundaries. Given that many

distribution models have particular interest in the potential

expansion or contraction of species (Peterson & Vieglais

2001; Berry et al. 2002; Martinez-Meyer et al. 2004), our

results suggest that particular attention should be paid to the

constrained habitat associations evident at range boundaries.

Furthermore, recently developing models that aim to predict

niche expansion by combining demography and niche into a

single framework (e.g. Keith et al. 2008; Anderson et al.

2009) may find that population dynamics at leading

boundaries differ from dynamics towards a species� core,

due to a more limited range of climatically suitable habitat

types.

Given the current uncertainty of different species� habitat

associations over space and time, it would seem wise for

conservation policy-makers to seek to conserve the range

and ecological variability of species� habitats (Benton et al.

2003; Hopkins et al. 2007). Increasing habitat heterogeneity

may provide insurance against uncertainty both in models of

species� habitat use and also in climate change predictions.

To summarize, we present the first multispecies analysis of

climate-related spatial shifts in habitat specificity. In Britain,

we found that habitat use tends to be more constrained close

to the edges of species� climatic range boundaries, especially

in wetter regions with �cold� winter temperatures. Across the

whole globe, in any specific country the habitats used by

species will depend on the range of natural and modified

landscapes available. As bioclimatic envelopes shift with

climate warming, however, and the frequency of climatic

extremes increases, we can expect many species� niches to

change as they undergo shifts in habitat use. Understanding

climate-driven changes in species� habitat associations will

therefore be important for developing appropriate conser-

vation plans under changing climates.

A C K N O W L E D G E M E N T S

The authors are indebted to all the UKBMS volunteer

recorders for collecting data. The analysis was funded

through a NERC Ecology and Hydrology Funding Initiative

grant (NE ⁄ E011942 ⁄ 1). We thank Stephen Freeman, Nick



Isaac and Barbara Anderson for helpful discussion. Climate

data were provided by provided by Barbara Anderson and

Yvonne Collingham at York and Durham University

respectively through a UKPopNet collaboration.

R E F E R E N C E S

Anderson, B.J., Akcakaya, H.R., Araujo, M.B., Fordham, D.A.,

Martinez-Meyer, E., Thuiller, W. et al. (2009). Dynamics of range

margins for metapopulations under climate change. Proc. Roy. Soc.

B, 276, 1415–1420.

Asher, J., Warren, M., Fox, R., Harding, P., Jeffcoate, G. &

Jeffcoate, S. (2001). The Millenium Atlas of Butterflies in Britain and

Ireland. Oxford University Press, Oxford.

Benton, T., Vickery, J.A. & Wilson, J.D. (2003). Farmland biodi-

versity: is habitat heterogeneity the key? Trends Ecol. Evol., 18,

182–188.

Berry, P.M., Dawson, T.P., Harrison, P.A. & Pearson, R.G. (2002).

Modelling the potential impacts of climate change on the bio-

climatic envelope of species in Britain and Ireland. Glob. Ecol.

Biogeogr., 11, 453–462.

Broennimann, O., Treier, U.A., Muller-Scharer, H., Thuiller, W.,

Peterson, A.T. & Guisan, A. (2007). Evidence of climatic shift

during biological invasion. Ecol. Lett., 10, 701–709.

Brown, J.H. (1984). On the relationship between abundance and

distribution of species. Am. Nat., 124, 255–279.

Countryside Survey (2008). Chapter 2: The national picture. In: UK

Results from 2007. Available at: http://www.countrysidesur-

vey.org.uk/reports2007.html.

Cowley, M.J.R., Thomas, C.D., Roy, D.B., Wilson, R.J., Le�on-

Cortes, J.L., Gutierrez, D. et al. (2001). Density-distribution

relationships in British butterflies. I. The effect of mobility and

spatial scale. J. Anim. Ecol., 70, 410–425.

Crawley, M.J. (2007). The R Book. John Wiley and Sons, Chichester.

Crozier, L. (2004). Warmer winters drive butterfly range expansion

by increasing survivorship. Ecology, 85, 231–241.

Davies, Z.G., Wilson, R.J., Coles, S. & Thomas, C.D. (2006).

Changing habitat associations of a thermally constrained species,

the silver-spotted skipper butterfly, in response to climate

warming. J. Anim. Ecol., 75, 247–256.

Dray, S. & Dufour, A.B. (2007). The ade4 package: implementing

the duality diagram for ecologists. J. Stat. Softw., 22, 1–20.

Ehrlich, P.R., Murphy, D.D., Singer, M.C., Sherwood, C.B., White,

R.R. & Brown, I.L. (1980). Extinction, reduction, stability and

increase: the responses of checkerspot butterfly (Euphydryas)

populations to the California drought. Oecologia, 46, 101–105.

Elith, J., Graham, C.H., Anderson, R.P., Dudık, M., Ferrier, S., Gui-

san, A. et al. (2006). Novel methods improve prediction of species�distributions from occurrence data. Ecography, 29, 129–151.

Faraway, J.J. (2006). Extending the Linear model with R: Generalized

Linear, Mixed Effects and Nonparametric Regression Models.

Chapman and Hall, London.

Fox, R., Asher, J., Brereton, T., Roy, D.B. & Warren, M. (2006).

The State of Butterflies in Britain and Ireland. Pisces Publications,

Oxford.

Garshelis, D.L. (2000). Delusions in habitat evaluation: measuring

use, selection and importance. In: Research Techniques in Animal

Ecology: Controversies and Consequences (eds Boitani, L. & Fuller,

T.K.). Columbia University Press, New York, pp. 111–164.

Gittleman, J.L. & Kot, M. (1990). Adaptation: statistics and a null

model for estimating phylogenetic effects. Syst. Zool., 39, 227–241.

Grafen, A. (1989). The phylogenetic regression. Phil. Trans. Roy. Soc.

Lon. B, 326, 119–157.

Guisan, A. & Zimmermann, N.E. (2000). Predictive habitat dis-

tribution models in ecology. Ecol. Mod., 135, 147–186.

Hickling, R., Roy, D.B., Hill, J.K., Fox, R. & Thomas, C.D. (2006).

The distributions of a wide range of taxonomic groups are

expanding polewards. Glob. Chang. Biol., 12, 450–455.

Hill, J.K., Collingham, Y.C., Thomas, C.D., Blakeley, D.S., Fox, R.,

Moss, D. et al. (2001). Impacts of landscape structure on but-

terfly range expansion. Ecol. Lett., 4, 313–321.

Hill, J.K., Thomas, C.D., Fox, R., Telfer, M.G., Willis, S.G., Asher,

J. et al. (2002). Responses of butterflies to twentieth century

climate warming: implications for future ranges. Proc. Roy. Soc.

Lond. B, 269, 2163–2171.

Hopkins, J.J., Allison, H.M., Walmsley, C.A., Gaywood, M. &

Thurgate, G. (2007). Conserving biodiversity in a changind cli-

mate: guidance on building capacity to adapt. Defra report on

behalf of the UK Biodiversity Partnership.

Keith, D.A., Akcakaya, H.R., Thuiller, W., Midgley, G.F., Pearson,

R.G., Phillips, S.J. et al. (2008). Predicting extinction risks under

climate change: coupling stochastic population models with

dynamic bioclimatic habitat models. Biol. Lett., 4, 560–563.

Kindvall, O. (1995). The impact of extreme weather on habitat

preference and survival in a metapopulation of the bush cricket

Metrioptera bicolor in Sweden. Biol. Conserv., 73, 51–58.

Kindvall, O. (1996). Habitat heterogeneity and survival in a bush

cricket metapopulation. Ecology, 77, 207–214.

Kunin, W.E. (2008). On comparative analyses involving non-

heritable traits: why half a loaf is sometimes worse than none.

Evol. Ecol. Res., 10, 787–796.

Lennon, J.J., Kunin, W.E., Corne, S., Carver, S. & Van Hees,

W.W.S. (2002). Are Alaskan trees found in locally more

favourable sites in marginal areas? Glob. Ecol. Biogeogr., 11, 103–

114.

Manly, B.F.J., McDonald, L.L., Thomas, D.L., McDonald, T.L. &

Erickson, W.P. (2003). Resource Selection by Animals – Statistical

Design and Analysis for Field Studies, 2nd edn. Kluwer academic

publishers, London.

Martinez-Meyer, E., Peterson, A.T. & Hargrove, W.W. (2004).

Ecological niches as stable distributional contraints on mammal

species, with implications for Pleistocene extinctions and climate

change projections for biodiversity. Glob. Ecol. Biogeogr., 13, 305–

315.

Mitchell, T.D. & Jones, P.D. (2005). An improved method of

constructing a database of monthly climate observations and

associated high-resolution grids. Int. J. Clim., 25, 693–712.

New, M., Hulme, M. & Jones, P.D. (1999). Representing twentieth

century space-time climate variability. Part 1: development of a

1961–90 mean monthly terrestrial climatology. J. Clim., 12, 829–

856.

Paradis, E. (2006). Analysis of Phylogenetics and Evolution with R.

Springer, Baltimore.

Parmesan, C. & Yohe, G. (2003). A globally coherent fingerprint of

climate change impacts across natural systems. Nature, 421, 37–

42.

Peterson, A.T. & Vieglais, D.A. (2001). Predicting species invasions

using ecological niche modelling: new approaches from bioinfor-

matics attack a pressing problem. Bioscience, 51, 363–371.



Peterson, A.T., Soberon, J. & Sanchez-Cordero, V. (1999). Con-

servatism of ecological niches in evolutionary time. Science, 285,

1265–1267.

Pollard, E. & Yates, T.J. (1993). Monitoring Butterflies for Ecology and

Conservation. Chapman and Hall, London.

R Development Core Team (2007). R: A Language and Environ-

ment for Statistical Computing (ed. Computing RFfS) Vienna,

Austria. ISBN 3-900051-07-0, Available at: http://www.R-pro-

ject.org.

Root, T., Price, T., Hall, K.R., Rosenzweigk, C. & Pounds, A.

(2003). Fingerprints of global warming wild animals and plants.

Nature, 421, 57–60.

Roy, D.B. & Sparks, T.H. (2000). Phenology of British butterflies

and climate change. Glob. Chang. Biol., 6, 407–416.

Roy, D.B. & Thomas, J.A. (2003). Seasonal variation in the niche,

habitat availability and population fluctuations of a bivoltine

thermophilous insect near its range margin. Oecologia, 134, 439–444.

Roy, D.B., Rothery, P., Moss, D., Pollard, E. & Thomas, J.A.

(2001). Butterfly numbers and weather: predicting historical

trends in abundance and the future effects of climate change.

J. Anim. Ecol., 70, 201–217.

Schofield, G., Bishop, C.M., Katselidis, K.K.A., Dimopoulos, P.,

Pantis, J.D. & Hays, G.C. (2009). Microhabitat selection by sea

turtles in a dynamic thermal marine environment. J. Anim. Ecol.,

78, 14–21.

Singer, M.C. & Thomas, C.D. (1996). Evolutionary responses of a

butterfly metapopulation to human and climate-caused envi-

ronmental variation. Am. Nat., 148, 59–539.

Sutcliffe, O., Thomas, C.D., Yates, T.J. & Greatorex-Davies, J.N.

(1997). Correlated extinctions, colonisations and population

fluctuations in a highly connected ringlet butterfly metapopula-

tion. Oecologia, 109, 235–241.

Thomas, J.A. (1983). The ecology and conservation of Lysandra

bellargus (Lepidoptera, Lycaenidae) in Britain. J. Appl. Ecol., 20,

59–83.

Thomas, J.A. (1993). Holocene climate changes and warm man-

made refugia may explain why a sixth of British butterflies possess

unnatural early-successional habitats. Ecography, 16, 278–284.

Thomas, C.D., Singer, M.C. & Boughton, D.A. (1996). Cata-

strophic extinction of population sources in a butterfly meta-

population. Am. Nat., 148, 957–975.

Thomas, J.A., Rose, R.J., Clarke, R.T., Thomas, C.D. & Webb,

N.R. (1999). Intraspecific variation in habitat availability among

ectothermic animals near their climatic limits and their centres of

range. Func. Ecol., 13, 55–64.

Thomas, C.D., Bodsworth, E.J., Wilson, R.J., Simmons, A.D.,

Davies, Z.G., Musche, M. et al. (2001). Ecological and evolu-

tionary processes at expanding range margins. Nature, 411, 577–

581.

Thomas, C.D., Franco, A.M.A. & Hill, J.K. (2006). Range retrac-

tions and extinction in the face of climate warming. Trends Ecol.

Evol., 21, 415–416.

Thuiller, W., Richardson, D.M., Pysek, P., Midgley, G.F., Hughes,

G.O. & Rouget, M. (2005). Niche based modelling as a tool for

predicting the risk of alien plant invasions at a global scale. Glob.

Chang. Biol., 11, 2234–2250.

Walther, G.-R., Post, E., Menzel, A., Parmesank, C., Beebee, T.,

Fromentin, J.-M. et al. (2002). Ecological responses to recent

climate change. Nature, 416, 389–395.

Warren, M.S., Hill, J.K., Thomas, J.A., Asher, J., Fox, R., Huntley,

B. et al. (2001). Rapid responses of British butterflies to

opposing forces of climate and habitat change. Nature, 414, 65–

69.

Wilson, R.J., Davies, Z.G. & Thomas, C.D. (2009). Modelling the

effect of habitat fragmentation on range expansion in a butterfly.

Proc. Roy. Soc. B, 276, 1421–1427.

S U P P O R T I N G I N F O R M A T I O N

Additional Supporting Information may be found in the

online version of this article:

Table S1 Habitat availability–climate relationships.

Table S2 Tests for spatial autocorrelation in habitat specificity.

As a service to our authors and readers, this journal provides

supporting information supplied by the authors. Such

materials are peer-reviewed and may be re-organized for

online delivery, but are not copy-edited or typeset. Technical

support issues arising from supporting information (other

than missing files) should be addressed to the authors.

Editor, David Kleijn

Manuscript received 18 June 2009

First decision made 15 July 2009

Manuscript accepted 17 July 2009



Changes in habitat specificity of species at their climatic range boundaries

Documents

Transcript of Changes in habitat specificity of species at their climatic range boundaries