Journal of Animal Ecology

2001

70

, 426–441

© 2001 British Ecological Society

Blackwell Science, Ltd

Density–distribution relationships in British butterflies. II. An assessment of mechanisms

M. J. R. COWLEY*, C. D. THOMAS, R. J. WILSON, J. L. LEÓN-CORTÉS, D. GUTIÉRREZ and C. R. BULMAN

Centre for Biodiversity & Conservation, School of Biology, University of Leeds, Leeds, LS2 9JT, UK

Summary

1.

The interspecific density–distribution relationship is a general and robust pattern thathas been described as a rule in community ecology. Many theoretically plausible causes of therelationship have been described, but it is still disputed which factor(s) are most important.

2.

Using data on the densities and distributions of butterflies and their host plantscollected in a 35-km

2

area of north Wales, and data on butterfly mobility, niche breadth,habitat breadth and distance from range margins, we examined five of the principalexplanatory mechanisms.

3.

We found that several variables were significantly correlated with density or distribution.Habitat breadth, mobility and distance from range margin had significant positive effectson butterfly distribution. Host-plant density was significantly positively related to butterflydensity; mobility was significantly negatively related to density.

4.

Despite these results, we could not unambiguously demonstrate that one hypothesis(or several interacting hypotheses) generated density–distribution correlations. Themost conclusive evidence was that statistical patterns of distribution (aggregationmodels) underpinned the positive density–distribution relationship seen amongst themore mobile butterflies. The results provided evidence against the metapopulationdynamic explanation, and were equivocal with respect to the contributions of rangeposition, niche breadth and resource availability.

5.

An alternative approach was to explore deviations from the underlying relationshipbetween density and distribution, rather than concentrating on the correlation itself.This approach was

much

more successful: we demonstrated that species that occurred athigh densities relative to their distributions used aggregated resources and were relativelysedentary; whereas those that occurred at low densities relative to their distributionsused less aggregated resources, and were more mobile. Mobile species had less aggregateddistributions than did relatively sedentary species.

6.

Given that the interspecific density–distribution pattern appears to be almost ubiquitousand that the proposed explanations are not mutually exclusive, faster progress may be madeby examining deviations from the pattern than from further analysis of the pattern itself.

Key-words

: abundance, niche breadth, metapopulation dynamics, range-size, resourcedensity.

Journal of Animal Ecology

(2001)

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, 426–441

Introduction

The positive relationship between the local density andregional distribution of species in a taxonomic assem-blage is a general and robust pattern that has frequentlybeen described as a rule in community ecology (e.g.

Hanski, Kouki & Halkka 1993; Lawton 1993; Gaston1994, 1996). Although well documented, there is stillno consensus as to which of the proposed explanatorymechanisms are the most important. (e.g. see Hanski

et al

. 1993; Blackburn

et al

. 1997; Quinn

et al

. 1997).At present, nine principal mechanisms have beenproposed, of which three can be considered artefactualand six biological (for reviews see Lawton 1993; Gaston1994; Gaston

et al

. 1997; see also Hartley 1998; Gaston,Blackburn & Lawton 1998). These mechanisms are not

*Present address and correspondence: Dr M. J. R. Cowley, EMECEcology, The Old Ragged School, Nottingham NG1 1EA.E-mail: EMEC@dial.pipex.com

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mutually exclusive and may be complementary or inter-related (Collins & Glenn 1990; Hanski 1991; Gaston1994; Holt

et al

. 1997). On present evidence, no singlemechanism has unequivocal support (Gaston

et al

. 1997).A recent examination of density–distribution patterns

in British butterflies revealed a positive relationshipbetween density and distribution at local, regionaland national scales (Cowley

et al

. 2001). Using existingecological information and data on the local densityand regional distribution of all major butterfly hostplants, in this paper we attempt to explain the patternsof butterfly density and distribution that were docu-mented at a regional scale. Two of the artefactual causesof the density–distribution relationship (points 1 and 2,below) have already been discounted as unlikely explana-tions: the reported relationship was not due to speciesphylogenetic association and it was extremely unlikelyto be due to insufficient sampling (Cowley

et al

. 2001).Five of the remaining seven mechanisms are examined.

All of the principal mechanisms are summarizedbelow.

1.

Sampling artefact

: the positive relationship resultsfrom the systematic under-recording of the distributionsof species that occur at lower density as they are lesslikely to be detected on surveys (Brown 1984; McArdle1990; Wright 1991; Hanski

et al

. 1993).

2.

Phylogenetic non-independence

: positive relationshipresults from species being considered as independentdata points in analysis. Species can share traits due tocommon ancestry rather than through their independentevolution (Harvey & Pagel 1991; Harvey 1996). Likewise,the positive density–distribution relationship couldrepresent simple differences between taxonomic groups,rather than any general tendency for high-density speciesto have wide distributions.

Phylogenetic non-independence has been rejected asan explanation for the density–distribution relationshipin all previous studies that have controlled for its effects(Gaston

et al

. 1997), and density and distribution aregenerally observed to exhibit rather little phylogeneticconstraint (Gaston

et al

. 1998; Gaston & Chown 1999).In analyses of British butterfly distributions and dens-ities, phylogenetic analyses strengthened, rather thanweakened, the association between the two (Cowley

et al

. 2001). A phylogenetic approach is adopted in thecurrent paper.

3.

Patterns of aggregation

: a positive relationship canbe generated as a result of an underlying (theoretical)spatial distribution of individuals (Wright 1991; Hartley1998). For a given level of aggregation, a species withmore individuals in a given landscape is expected to occurin more locations and at a higher average density thana species with fewer individuals in the same landscape.

4.

Range position

: a decline in occupancy and densitymoving from the centre to the margins of a species’geographical range has been documented for a varietyof taxa (e.g. Hengeveld & Haeck 1982; Brown 1984).Assuming this pattern is general, then a positive density–distribution relationship in any particular region might

result because species are at different positions relativeto the centre of their ranges (Bock & Ricklefs 1983).

The occupancy of British butterfly species tends todecline as they approach their range margins in Britain,although there is less evidence of declining local densitytowards the range margins (Thomas

et al

. 1998). Distancefrom species’ northern range margin in Britain is includedin the analyses to test for any such effects.

5.

Niche breadth

: the range of resources a species canexploit might be expected to affect local populationdensity and regional distribution. Species utilizing awide range of resources will be locally common andwidespread whilst those with narrow requirementsfor resources and conditions will be locally rare andrestricted, thus producing a positive density–distributionrelationship (Brown 1984).

In the present analyses, we include niche-breadth,incorporating host-plant specialization and ant associ-ation, and habitat-breadth, measured as the proportionof habitat types occupied by each species. We do notconsider the effects of species microclimatic limits inthis analysis, although these may be important com-ponents of realized niche breadth, particularly at theedges of species ranges (Thomas

et al

. 1999). Such effectscould enter our analysis, as aspects of distance to rangemargin, which is included (see explanation 4, above).

6.

Resource availability

: if the local density and regionaldistribution of resources determine the density anddistribution of the species utilizing them, then a positivedensity–distribution relationship for the resource willgenerate the same relationship in the consumer (Hanski

et al

. 1993; Gaston 1994).Relationships between the spatial distribution of

consumers and their resources are most easily addressedfor species using readily definable resources such asherbivorous insects and their host plants. However, thereare a number of complications. For butterflies, resourcesinclude requirements for host plants of a particularquality as well as other habitat characteristics, e.g.microhabitat or thermal requirements and mutualisticrelations with ants (e.g. Thomas 1985; Thomas

et al

.1989). Several previous studies have demonstrated apositive interspecific density–distribution relationshipfor plant assemblages (e.g. Brown 1984; Rapoport

et al

.1986; Gotelli & Simberloff 1987; Collins & Glen 1990;Rees 1995). However, a recent study of the British florafound that the local densities of plant species withincentral England were not significantly correlated withtheir distributions at local, regional or national scales,although significant positive correlations were com-mon within habitats (Thompson, Hodgson & Gaston1998).

Several studies have attempted to assess the relation-ship between the density of insect species and thedensity of their resources, although many concern onlyindividual or limited groups of insects (e.g. Root 1973;Kareiva 1983; Strong, Lawton & Southwood 1984; Dixon& Kindlmann 1990) and the evidence is generallyrather weak.

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Nor is there a simple relationship between the rangesize of insects and the range size of their resources (e.g.Strong, Lawton & Southward 1984; Gaston & Lawton1988; Leather 1991; Whitcomb

et al

. 1994). The twoare generally positively correlated (Quinn, Gaston &Roy 1997; Quinn, Gaston & Roy 1998) but weakly.These studies have shown that individual phytophagousinsects do not occupy the entire geographical rangesof their hosts, whilst other insects may switch hostsand have a wider distribution than that of any singlehost-plant species.

7.

Density-dependent habitat selection

: if species tendto choose to inhabit more habitats when densities arehigh and fewer when they are low, then locally abun-dant species will tend to occupy more habitats and havewider distributions (O’Connor 1987).

There is no current evidence for density-dependenthabitat selection in butterflies (Gaston

et al

. 1997;although there is some evidence for the reverse; Kuus-saari, Nieminen & Hanski 1996) so this explanation isunlikely to be important in the present study systemand we do not consider it further.

8.

Metapopulation dynamics

: a positive density–distribu-tion relationship may be generated from metapopulationdynamics as a result of (i) species that occur at higherdensity being less likely to go extinct on a patch of agiven area than a species that occurs at lower density,and (ii) immigration is likely to increase with density inother patches, promoting the colonization of emptypatches and the rescue of small populations. Thus, highlocal densities and high patch occupancy (distribution)should be positively associated with one another (Hanski1991; Gyllenberg & Hanski 1992; Hanski

et al

. 1993).One of the predictions of the metapopulation dynamic

hypothesis is that more mobile species will show negat-ive deviations from the underlying density–distributionrelationship, i.e. they will occur at a lower density for agiven distribution (Hanski

et al

. 1993). This predictionhas been supported for butterflies in Britain (Hanski

et al

. 1993; Cowley

et al

. 2001). However, as the mostmobile British butterflies do not operate as metapopu-lations (Thomas 1995), it seems unlikely that this can bethe only explanation for the positive density–distributionrelationship. Negative deviation from the underlyingdensity–distribution pattern could also be the resultof mobile species being more likely to be recorded asvagrants, and therefore being recorded over a widerarea (Gaston 1994). By dispersing into adjacent habitatsand not remaining within the natal habitat patch, therecorded density of mobile species may also be genuinelyreduced (i.e. they are less aggregated; explanation 3).

Metapopulation hypotheses are based on patchdynamics. Therefore, to provide an additional test weconsidered distributions not simply as the number ofoccupied grid squares but also as the number of occu-pied grid squares that are likely to contain at least somesuitable habitat.

We also considered separately those species thatwere most likely to exhibit metapopulation dynamics at

this regional scale. British butterflies have been dividedinto those that are sedentary, with relatively ‘closed’populations, and those that are mobile with ‘open’ popu-lations (Thomas 1984). Species that have so-called‘closed’ populations are likely to remain in the natalhabitat patch, although some individuals do movebetween patches, whilst species with ‘open’ populationsare likely to sample several or many patches during thecourse of their adult lives. The categories are simplistic(Cowley

et al

. 2001), but provide a convenient way todivide the data so as to examine the potential contri-bution of metapopulation dynamics to the overallrelationship: metapopulation theory was relevant tothe distributions and population dynamics of the moresedentary species but not of the mobile species at thisregional scale.

9.

Vital rates

: if species density on a site is determinedby the population growth rate, and species distribution isthe number of sites with a positive population growthrate, any factor that increases the rate of population growthacross all sites will increase both the number of occupiedsites and density within occupied sites, thus generatinga density–distribution correlation (Holt

et al

. 1997).To test this explanation requires data on density-

dependent birth and death rates (Gaston

et al

. 1997;Holt

et al

. 1997). These are not currently available forbutterflies, so this explanation is not tested.

Using data on butterfly and host-plant density anddistribution, and information on niche breadths, mobil-ity and distance from range margins, we assess which ofthe proposed hypotheses are most closely related topatterns of density and distribution and to the density–distribution correlation in British butterflies at a regionalscale. We also explore variation around the density–distribution relationship. Understanding residual vari-ation from density–distribution relationships can helpto assess the relative roles of the explanatory mechanismslisted and also provide separate insights into the factorsthat determine variation in species densities and distri-butions (Blackburn, Gaston & Gregory 1997; Quinn

et al

. 1997). Given that several non-biological explana-tions can generate density–distributions relationshipsand the considerable scatter around many of the docu-mented examples, it would be useful to assess if differenttypes of species deviate from the underlying relation-ship in predictable ways.

Methods

Data on the density and distribution of butterflies andtheir host plants were collected in an approximately35 km

2

area, the Creuddyn Peninsula, located along thenorth coast of Wales, UK (53

°

18

′

N, 3

°

50

′

W) (OrdnanceSurvey, 10-km squares SH77–88). The methods usedto generate butterfly densities and distributions aredescribed in detail in Cowley

et al

. (2001). In summary,the study area was stratified into 16 major habitat types(Table 7). Samples of each habitat type were chosen anda butterfly transect was located in each. Most transects

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were 300 m in length. There were 147 separate transectroutes with a total length of 43·1 km. Transects werewalked every other week from April to October 1997and the results used to generate a standardized densityterm for each butterfly species at each transect location(number of butterflies per 300 m, per year) (Pollard1977; Pollard & Yates 1993). Species regional densitywas calculated as mean density at all occupied sites. Asite was considered occupied if one or more individualswere recorded during the sampling period.

In certain analyses, separate density values for themigratory butterflies

Colias

croceus

Geoffroy,

Cynthiacardui

L. and

Vanessa atalanta

L. were used for 1996and 1997. These density values were not derived fromthe habitat sampling described above. Instead, thedensities were determined from weekly transect walksbetween April–October 1996 and 1997 at three sites inthe north Wales study area. Each transect route wasapproximately 3 km in length and was divided intosections according to the habitats listed in Table 6. Allsection counts were standardized using the same methodas for the habitat sampling and were expressed as numberof butterflies counted per 300 m, per year. Speciesdensity values for a given year were calculated as meandensity on all occupied sections. Sections were con-sidered occupied if one or more individuals wererecorded, per 300 m, during the sampling period.

Data on the density and distribution of all majorbutterfly host plants were also collected from the sameregional landscape, to test whether patterns of butterflydensity and distribution reflect those of their host plants.Lists of species’ main larval host plants were takenfrom Heath, Pollard & Thomas (1984) and Thomas &Lewington (1991) and are listed in Appendix 2. The147 habitat sample sites containing butterfly transectswere used to collect host-plant density information.Approximately 30 random quadrats (25 cm

×

25 cm or1 m

×

1 m) were placed along each transect (total

=

3305 quadrats) and were used to estimate percentagehost-plant cover.

Mean host-plant density in the study region (meandensity) was calculated as the mean cover at all siteswhere a host plant occurred in one or more quadrats.For butterfly species that used more than one hostplant, the mean density of all hosts added together wascalculated, including any site in which one or more ofthe hosts was recorded in one or more quadrats. A host-plant density value was not calculated for

Gonepteryxrhamni

L. The larvae of this butterfly feed on smalltrees, the estimated densities of which are not strictlycomparable with those calculated for the other host-plantspecies, most of which are low-growing herbs and grasses.Certain host-plant species that are likely to be usedonly occasionally by butterfly species, namely

Geraniummolle

L. and

Erodium cicutarium

L. for

Aricia agestis

Denis & Schiffermüller and

Trifolium

spp. for

Poly-ommatus icarus

Rottenburg, were included in correla-tions between host-plant and butterfly distributionsbut not host-plant and butterfly densities.

For the collection of regional host-plant distributiondata, the study area was divided into 140 cells using a500-m grid, based on Ordnance Survey maps (OrdnanceSurvey 1994). Host-plant records were collected at a100-m resolution and expressed as species presence orabsence in each of the 135 cells in the 500 m grid. Thisrecording technique was the same as that used to collectregional butterfly distribution data (Cowley

et al.

2001).All the distributions were collected over a 3-year period(1996–98) by a team of five researchers. During thissampling period more than 14 000 butterfly distribu-tion records and over 5000 host-plant records werecollected: an average of over 135 records per grid square.

The methods used to produce a continuous index ofbutterfly mobility are described by Cowley

et al

. (2001).In summary, questionnaires were sent to leading butterflyecologists in Britain and northern Europe, and respond-ents were asked to rank butterfly species according totheir experience of the relative mobility of each species.The results from all replies (

n

=

24) were used to gen-erate a ‘consensus’ mobility ranking for each species(Appendix 1).

To calculate species’ relative statistical aggregation,annual counts of each species across the 147 habitattransects were used. Aggregation was measured as

k

inthe negative binomial (using maximum likelihood andparametric estimates) (Krebs 1989) and as variance tomean ratio (index of dispersal

I

) (Krebs 1989).

Aphantopushyperantus

L. could not be included as it was notrecorded during the habitat transect sampling (adensity term for this species was determined in 1997using a 300-m transect through an area of semi-improvedgrassland that supported the only known local popu-lation in the study area).

To calculate the distance from species range marginsin Britain, the maps contained in Heath, Pollard &Thomas (1984) were used. For each species, the distancesouth to the Creuddyn peninsula from the northern-most distribution record in Britain was calculated(Appendix 1). All of the species in the study area are‘southern’ species so distance from the northern rangemargin was appropriate in each case.

Habitat breadth was calculated as the proportion ofthe 16 regional habitat types that a species was recordedin during the transect sampling. Species were defined aspresent in a habitat if one or more individuals wererecorded during the sampling period.

Sample-size effects (Gaston 1994) confound manyempirical studies of the relationship between habitatbreadth and range size. If habitat breadth is determinedfrom recording more individuals or at a greater numberof sites for high density and widespread species than forlow density and restricted species, then an artefactualpositive correlation is expected. In this case, these effectswere reduced: there was a fixed number of transect pointsthat were located independently of species distribution.However, distribution and habitat breadth still cannotbe regarded as independent: species are unlikely to havea wide distribution without occupying many habitats.

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Likewise, they are unlikely to occupy many habitatswithout being widely distributed. Higher density orwidespread species are also more likely to occur asvagrants in habitats that do not support breedingpopulations, thus inflating their recorded habitat breadth(Gaston 1994). Due to the almost inevitable correlationbetween habitat breadth and distribution, and the resultantdifficulty in determining causation, habitat breadth isexcluded from certain multiple regression analyses.

To provide a test of the metapopulation explanation,which is based on the occupancy of habitat patches,butterfly distributions were considered as the propor-tions of occupied grid squares that were likely tocontain at least some suitable habitat. Distributionswere expressed as the number of occupied grid squareswith at least one record of a species larval host plantor the number of occupied grid squares that containedeither limestone grassland, semi-improved grasslandor coastal dune habitat. These semi-natural habitatshad a high diversity of butterfly species and all extantspecies in the study area were recorded from them.

All regressions between density and distribution werecalculated using the ordinary least squares (model 1)method (McArdle 1990; Blackburn & Gaston 1998).Density was always used as the dependent variable.Before statistical analyses were conducted, we log

10

-transformed density values and arcsine-transformeddistributions (distributional data are proportions ofoccupied grid squares) to homogenize variance.

The relation between density, distribution and eachof the explanatory variables was analysed both acrossspecies and within taxa using a method designed tocontrol for phylogenetic association (Harvey & Pagel1991). One way to control for the effects of phylogeneticrelatedness is to examine relations within each pair oftaxa below a node in a bifurcating phylogeny. Therelationship between variables is then unaffected byphylogeny, since the taxa in each comparison are equallyrelated to each other. This method requires that thetrue phylogeny is known (Felenstein 1985).

Here, we use a model (comparative analysis by inde-pendent contrasts (CAIC); Purvis & Rambaut 1994,1995), which applies Felenstein’s approach to data setsfor which only approximate phylogenies are available.CAIC calculates the difference (or contrasts) in thetraits of interest between extant pairs of species: thiscontrast represents the amount of evolutionary diver-gence since they speciated from their common ances-tor. In addition, CAIC calculates contrasts at internalnodes of the phylogeny. Since we do not know what theancestral species at these nodes were like, values at nodesare averages of the species (or nodes) that evolved fromthem. It is possible to weight these averages by thebranch lengths but, in our analyses, all branch lengthswere assumed equal. The standardized linear contrastscalculated by the CAIC programme can be analysedusing standard regression techniques (Pagel & Harvey1989; Harvey & Pagel 1991) although regressions mustpass through the origin (Garland

et al

. 1992).

An approximate phylogeny for British butterflieswas produced using data from Geiger (1981); Martin &Pashley (1992); Weller, Pashley & Martin (1996); Aubert

et al

. (1996); De Jong, VaneWright & Ackery (1996);S. Nylin

et al.

(unpublished); A. Brower (unpublished)and N. Wahlberg (unpublished).

Results

There was no relationship between density and distri-bution if species relative mobility was not controlledfor statistically, either treating species as independentdata points (

n

=

26,

r

2

=

0·17 and

P

=

0·52) or control-ling for their phylogenetic association (

n

=

24,

r

2

=

0·52and

P

=

0·27). In general, the most mobile species werewidespread at low density and the less mobile specieswere localized at high density (Fig. 1). All three measuresof statistical aggregation were correlated with mobility(Table 1), showing that the mobile species were lessaggregated.

When all of the explanatory variables were includedin a multiple regression analysis, much of the variationin density was explained by mobility, habitat breadthand host-plant density. This result was unaffected bycontrolling for the effects of phylogenetic association(Table 2). Distribution did not have a significant effecton density if habitat breadth was also included in themultiple regression analysis. Given the almost inevitablestatistical association between habitat breadth and dis-tribution, at this spatial scale, the data were re-analysedwithout habitat breadth: distribution was significant(along with host-plant density and mobility) although

Fig. 1. Density–distribution for all species showing relativemobility, from 1 (least mobile) to 25 (most mobile). Ties inrank mobility are given the same number: 1997 densities.

Table 1. The Pearson product moment correlation betweenmobility and aggregation measured as (a) k in the negativebinomial using maximum likelihood estimate, (b) k in thenegative binomial using the parametric estimate, and (c) indexof dispersion I

n r P

(a) Mobility k 25 0·670 0·001(b) Mobility k 25 0·612 0·01(c) Mobility I 25 –0·645 0·001

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slightly less of the variation in density was explained(Table 3). We do not think that it is safe to conclude whichof the two variables, distribution or habitat breadth, isactually more closely associated with density for thefollowing reason. Habitat breadth may appear to be abetter predictor of density than distribution (havingcontrolled for mobility) simply because the habitatbreadth is closer to a log-normal distribution providingadditional power in parametric analysis: using untrans-formed data, the proportion of 500 m grid squares occu-pied was significantly different from normal (skewnessrelative to log-normal distribution

=

−

0·247,

n

=

26 and

P

< 0·05) whilst habitat breadth was not (skewnessrelative to log-normal distribution

=

−

0·088,

n

=

26 and

P

=

0·811).Individual regressions between density and distribu-

tion and each of the explanatory variables show thatdensity was significantly negatively related to mobility

and significantly positively related to host density(Table 4), and that distribution was significantly pos-itively related to habitat breadth and mobility (Table 5).Distribution was also positively correlated with distanceto range margin in the phylogenetic analysis only(Table 5b), indicating that species near their rangemargins were more localized.

The relationship between density and distance fromspecies-range margin was also assessed separately forspecies with ‘open’ and ‘closed’ population structures(Thomas 1984), but was significant for neither: open(Pearson correlation

r

=

0·26,

n

=

11 and

P

=

0·44); closed(Pearson correlation

r

=

−

0·21,

n

=

15 and

P

=

0·45).Analyses using distributions considered as the pro-

portions of 500 m squares occupied out of those thatcontained (i) at least one record of a species’ larval hostplant, or (ii) some areas of either limestone grassland,semi-improved grassland or coastal dune habitat did

Table 2. Summary of multiple regression of mean regional density (density) against proportion of occupied regional 500-m gridsquares (distribution), mobility, proportion of regional habitat types occupied (habitat breadth), larval host-plant density (hostdensity), larval host-plant distribution (host-plant distribution), distance from northern range margin in Britain (range margin)and niche breadth. Only significant variables (P < 0·05) are included(a) Treating species as independent data points

(b) Controlling for phylogenetic non-independence, N is the number of independent contrasts

Table 3. Summary of multiple regression of mean regional density (density) against proportion of occupied regional 500 m gridsquares (distribution), mobility, larval host-plant density (host density), larval host-plant distribution (host-plant distribution),distance from northern range margin in Britain (range margin) and niche breadth. Only significant variables (P < 0·05) areincluded (a) Treating species as independent data points

(b) Controlling for phylogenetic non-independence N is the number of independent contrasts

Dependent variable

Independent variable N Slope SE Intercept SE r 2 P

Density Mobility 25 –0·038 0·008 0·620 0·168 0·725 0·002Habitat breadth 0·548 0·154 0·002Host density 0·444 0·142 0·005

Dependent variable

Independent variable N Slope SE r 2 P

Density Mobility 23 –0·051 0·010 0·733 0·0001Habitat breadth 0·629 0·126 0·011Host density 0·488 0·173 0·0001

Dependent variable

Independent variable N Slope SE Intercept SE r 2 P

Density Mobility 25 –0·100 0·009 0·670 0·175 0·692 0·0003Distribution 0·400 0·132 0·006Host density 0·450 0·151 0·007

Dependent variable

Independent variable N Slope SE r 2 P

Density Mobility 23 –0·062 0·013 0·641 0·0001Distribution 0·457 0·125 0·002Host density 0·495 0·201 0·023

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not generate significant positive density–distributionrelationships (Table 6).

The same analyses conducted separately for specieswith ‘open’ and ‘closed’ population structures (Table 6)detected a significant positive relationship betweendensity and distribution only amongst the relative mobilespecies with ‘open’ population structures (Fig. 2). Thus,species with ‘closed’ population structures consideredin isolation and taking account of the likely distribu-tion of potential habitat showed no significant density–

distribution relationship. However, if these analyseswere repeated controlling for the effects of mobility (asa continuous variable), there was a significant positiverelationship (r 2 = 0·488, n = 15, P = 0·02).

The relationships between density and distributionwere also considered within each of the 16 habitat types(Table 7). In this case, distribution was the proportionof samples of each habitat that was occupied and dens-ity was the mean density where present (> 1 individualcounted per year) of each species in that habitat. Within

Table 4. Summary of results of regressions between mean regional density (density) and mobility, number of regional habitattypes occupied (habitat breadth), larval host-plant density (host density), larval host-plant distribution (host-plant distribution)and distance from northern range margin in Britain (range margin) (a) Treating species as independent data points

(b) Controlling for phylogenetic non-independence N is the number of independent contrasts

Table 5. Summary of results of individual regressions between proportion of occupied regional 500-m grid squares (distribution)and mobility, number of regional habitat types occupied (habitat breadth), larval host-plant density (host density), larval host-plant distribution (host-plant distribution) and distance from northern range margin in Britain (range margin) (a) Treating species as independent data points

(b) Controlling for phylogenetic non-independence N is the number of independent contrasts

Dependent variable

Independent variable N Slope SE Intercept SE r 2 P

Density Mobility 26 –0·040 0·010 1·223 0·145 0·408 0·0004Density Niche breadth 26 –0·016 0·069 0·872 0·689 0·002 0·814Density Habitat breadth 26 0·307 0·248 0·531 0·170 0·060 0·223Density Host-plant density 25 0·711 0·180 0·309 0·129 0·403 0·006Density Host-plant distrib. 26 0·273 0·247 0·361 0·327 0·049 0·279Density Range margin 26 –0·405 0·340 0·1769 0·894 0·056 0·245

Dependent variable

Independent variable N Slope SE r 2 P

Density Mobility 24 –0·048 0·016 0·271 0·008Density Niche breadth 24 –0·127 0·070 0·126 0·082Density Habitat breadth 24 0·424 0·215 0·144 0·061Density Host-plant density 23 0·850 0·328 0·234 0·017Density Host-plant distrib. 24 0·208 0·335 0·017 0·540Density Range margin 24 0·014 0·669 0·001 0·983

Dependent variable

Independent variable N Slope SE Intercept SE r 2 P

Distribution Mobility 26 0·026 0·012 0·385 0·177 0·162 0·042Distribution Niche breadth 26 –0·055 0·070 1·255 0·695 0·025 0·442Distribution Habitat breadth 26 1·167 0·107 0·040 0·073 0·083 < 0·0001Distribution Host-plant density 25 0·105 0·240 0·797 0·172 0·008 0·667Distribution Host-plant distrib. 26 0·171 0·256 0·500 0·339 0·018 0·511Distribution Range margin 26 0·422 0·347 –0·389 0·911 0·058 0·234

Dependent variable

Independent variable N Slope SE r 2 P

Distribution Mobility 24 0·048 0·020 0·203 0·024Distribution Niche breadth 24 –0·122 0·088 0·078 0·177Distribution Habitat breadth 24 1·110 0·137 0·860 < 0·0001Distribution Host-plant density 23 –0·006 0·390 0·001 0·987Distribution Host-plant distrib. 24 0·532 0·363 0·085 0·157Distribution Range margin 24 1·370 0·497 0·249 0·011

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habitats, the relationships between density and distri-bution were positive and mostly significant. A similarset of analyses was conducted for the resources (i.e. oneor more larval host plants) used by each species. In thiscase, there were significant positive relationships in allbut three habitat types (Table 8).

The density–distribution relationship in the studyarea as a whole was also examined for all of the indi-vidual larval host plants for which data were available(n = 32) and for the resources (i.e. one or more larvalhost plants) used by each butterfly species (n = 25). Inboth cases, distribution was the proportion of 500-msquares occupied by the host plant (or plants) and dens-ity was their mean cover where present. There was asignificant positive density–distribution relationship

for the resources used by each butterfly species but notfor the individual host-plant species (Table 9).

There was a significant positive relationship betweenthe residuals from the butterfly density–distributionrelationship (having controlled for mobility) and theresiduals from the butterfly resources density–distribu-tion relationship (Table 9).

Discussion

Analysis of patterns of aggregation and residuals fromthe density–distribution relationship showed that but-terfly mobility (as a continuous variable) was a majordeterminant of patterns of density and distribution inthis landscape. The more mobile the species, the morelikely individuals were to be spread out ( less aggregated)relative to the underlying distribution of resources.In this study, the most mobile species occurred to thebottom right of the density–distribution scatter plot(spread out), and the less mobile at the top-left (aggre-gated) (Fig. 1). This is exactly as predicted by aggregationmodels of species distributions: variation in aggrega-tion resulted in deviation from the underlying density–distribution correlation, whereas species with similarpatterns of aggregation showed a positive density–distribution correlation (Hartley 1998).

This was confirmed by the observation that mobilespecies (Thomas 1984), which are likely to be quite similarto one another in mobility at the scale of our 35 km2

study area, showed a strong positive relationship betweenpopulation density and distribution, which was not foundamong the less mobile species (Table 6) or among the

Table 6. The relationship between density and distribution with: (i) distribution as the proportion of occupied grid squares thatcontain at least one a record of species larval host plant (ii) with distribution as the proportion of occupied squares with limestonegrassland, semi-improved grassland or coastal dune habitat (iii) only considering sedentary species (iv) only considering mobilespecies and (v) considering only sedentary species with distribution as the proportion of occupied grid squares that contain at leastone record of species larval host plants (a) Treating species as independent data points

(b) Controlling for phylogenetic non-independence N is the number of independent contrasts

Dependent variable

Independent variable N Slope SE Intercept SE r 2 P

(i) Density Distribution 26 –0·01 0·192 0·709 0·180 0·001 0·999(ii) Density Distribution 26 0·912 0·196 0·550 0·187 0·038 0·338(iii) Density Distribution 15 0·261 0·294 0·761 0·209 0·057 0·390(iv) Density Distribution 11 0·427 0·141 0·046 0·143 0·506 0·014(v) Density Distribution 15 0·207 0·244 0·781 0·202 0·049 0·411

Dependent variable

Independent variable N Slope SE r 2 P

(i) Density Distribution 24 0·133 0·164 0·028 0·428(ii) Density Distribution 24 0·150 0·240 0·017 0·536(iii) Density Distribution 13 0·046 0·260 0·003 0·864(iv) Density Distribution 10 0·460 0·124 0·605 0·005(v) Density Distribution 13 0·045 0·260 0·051 0·863

Fig. 2. Density–distribution relationship for the species withan ‘open’ population structure (Thomas 1984): migrantspecies plotted separately for 1996 and 1997 (Spearman rankcorrelation rs = 0·77, n = 14, P = < 0·01). Black and whitepoints indicate migratory species. For analysis with truemigrants counted only once, see Table 6.

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species as a whole. This can most easily be explained asa statistical consequence of differing numbers of indi-viduals in the entire landscape, than as a biologicallymeaningful correlation between density and distribu-tion. The pattern is exemplified by the true migrants.True migrants had different densities in different years,depending mainly on the number of individuals arrivingfrom continental Europe (C. croceus was only recordedin 1996; C. cardui and V. atalanta were recorded in 1996and 1997). These species did not differ in dispersal abilitybetween years (at the scale of the study area) and theyhad the same fundamental niche breadth. The onlydifference was that there were different numbers ofindividuals in different years. When abundant, thesespecies were relatively widespread and dense; when rare,they were relatively local and occurred at low density(Fig. 2). 1 of the density–distribution plot for mobilespecies showed that all localized species occurred at

very low densities, but that more widespread speciesoccurred at a range of densities (Fig. 2). This isexactly what one would expect when density and dis-tribution are simply two statistical facets of one setof individuals with a random or slightly aggregateddistribution (Wright 1991; Hartley 1998). In thesespecies, at this spatial scale, distribution and density areeffectively interchangeable, i.e. they are two measuresof the same thing.

Moving from the centre to the margin of a distribution,a species is expected to inhabit progressively fewerlocalities and perhaps exist at lower local densities wherepresent (e.g. Hengeveld & Haeck 1982; Brown 1984;Svensson 1992; Thomas et al. 1999). This pattern is arguedto result from the same processes as the resource-breadth

Table 7. The relationship between butterfly density and distribution within individual habitat types (a) Treating species as independent data points

(b) Controlling for phylogenetic non-independence N is the number of independent contrasts

Habitat type*

Dependent variable

Independent variable N Slope SE Intercept SE r 2 P

Amenity grassland Distribution Density 10 0·348 0·130 0·053 0·059 0·471 0·028Bracken Distribution Density 18 0·801 0·132 0·057 0·087 0·696 < 0·0001Ditch Distribution Density 17 0·979 0·161 0·040 0·128 0·844 < 0·0001Coastal dune Distribution Density 12 0·838 0·101 0·027 0·093 0·934 < 0·0001Hedgerow Distribution Density 13 0·769 0·125 –0·193 0·200 0·769 0·001Heathland Distribution Density 8 0·385 0·273 0·400 0·400 0·243 0·207Improved grassland Distribution Density 8 1·126 0·114 –0·095 0·044 0·966 < 0·0001Lane Distribution Density 12 0·446 0·139 0·122 0·131 0·711 0·010Limestone grassland Distribution Density 19 0·653 0·219 0·318 0·171 0·008 0·008Scrub Distribution Density 18 0·530 0·339 0·448 0·234 0·142 0·123Semi-improved

grasslandDistribution Density 18 0·896 0·189 0·320 0·111 0·584 0·0002

Urban Distribution Density 10 0·373 0·187 0·160 0·102 0·333 0·081Road verge Distribution Density 14 0·352 0·256 0·298 0·169 0·136 0·195Woodland Distribution Density 7 0·789 0·100 0·041 0·062 0·925 0·0005Woodland edge Distribution Density 19 0·258 0·252 0·599 0·170 0·058 0·321

Habitat type*

Dependent variable

Independent variable N Slope SE r 2 P

Amenity grassland Density Distribution 9 0·432 0·116 0·632 0·006Bracken Density Distribution 16 0·681 0·112 0·713 < 0·0001Ditch Density Distribution 16 0·884 0·136 0·737 < 0·0001Coastal dune Density Distribution 11 0·804 0·177 0·674 0·001Hedgerow Density Distribution 13 0·972 0·148 0·783 < 0·0001Heathland Density Distribution 7 0·118 0·325 0·147 0·739Improved grassland Density Distribution 8 1·06 0·098 0·944 < 0·0001Lane Density Distribution 8 1·064 0·098 0·944 < 0·0001Limestone grassland Density Distribution 18 –0·051 0·237 0·003 0·832Scrub Density Distribution 16 0·588 0·394 0·129 0·157Semi-improved

grasslandDensity Distribution 17 0·825 0·207 0·498 0·001

Urban Density Distribution 9 0·438 0·194 0·389 0·054Road verge Density Distribution 13 0·224 0·267 0·055 0·419Woodland Density Distribution 6 0·756 0·010 0·920 0·006Woodland edge Density Distribution 18 0·271 0·217 0·084 0·084

*Woodland ride is not included, as there were only two samples of this habitat type.

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hypothesis (Brown 1984; explanation 5) and is based onthe assumption that species distributions and densitiesare determined by environmental gradients at bothsmall and large scales.

For butterfly distributions, there is evidence tosupport this assumption (Thomas et al. 1998; Thomaset al. 1999). The range margins of many Europeanbutterflies appear to be determined by climatic vari-ables (Dennis & Shreeve 1991; Dennis 1993) and, at thenorthern limits of their ranges, populations of manyspecies are associated with warm microclimates such assouth-facing hillsides, sheltered woodland clearings(Thomas 1993; Thomas et al. 1999 ) or frost-free areas(Jordano, Retamosa & Fernández Haeger 1991).However, once a minimum set of resource and/orenvironmental requirements has been met there seemsno obvious reason why density should be lower at therange margins. There could even be reasons to suggestthe reverse. If patches are more isolated towards rangemargins, metapopulation theory would suggest thatonly the largest or best-quality patches would bepopulated (i.e. those able to support the largest popu-lations): the only populations to exist near rangemargins would be those with low extinction rates, giventhat re-colonization rates would be low.

In general, butterflies that were closest to theirnorthern range margins in Britain were those that werelocally distributed in our study area (Table 5b). However,these species did not occur at lower densities (Table 4).For example, the species closest to its northern rangemargin in our study area, Plebejus argus L., was largelyrestricted to south-facing slopes on limestone bedrock,but within these localized areas it occurred at extremelyhigh density. A previous analysis of British butterfliesthat considered distributions at a coarser spatial reso-lution (presence–absence in 10-km squares) reportedsimilar results. Species that reached their northern rangemargins within Britain occupied a declining fraction of10-km squares moving north, 200–500 km south of theabsolute margin (Thomas et al. 1998). In those analyses,the only species found to decline in density towards therange margins were mobile species (i.e. those with ‘open’population structures). It was suggested that becauseof their population structure, these species ‘averaged’the environment over relatively large areas. Con-sequently, a decline in the number of suitable breedingpatches towards range margins resulted in a paralleldecline in species average density. In any given regionwhere species differ in the distance to their range mar-gins, this could generate a positive density–distributionrelationship for the mobile species (as observed in thepresent study). In contrast, less mobile species (i.e.those with a closed population structure) could remainwithin suitable localized environments and attain highlocal densities, despite an inhospitable surroundinglandscape. Within any region where species differ in thedistance to their range margins, we would only expect apositive density–distribution relationship among themobile species (as observed). However, the reason themobile species are expected to show this relationship isthe aggregation hypothesis. Species near their peripheryoccur as relatively few individuals in the landscape.Given similar levels of aggregation, the species occur atfew locations relative to the core (abundant) species.

Our tentative conclusion is that proximity to a species’range margin has some effect on its regional distribution,but that other factors are then required to generatedensity–distribution correlations within the landscape.

The measure of niche breadth included in the analysisdid not explain significant variation in density or

Table 8. The Spearman rank correlations between the densityand distribution of the resources used by butterfly specieswithin habitat types

Habitat type* n r P

Amenity grassland 10 0·82 0·003Bracken 23 0·39 0·063Ditch 20 0·73 < 0·0001Coastal dune 22 0·76 < 0·0001Hedgerow 22 0·93 < 0·0001Heathland 18 0·88 < 0·0001Improved grassland 9 0·32 0·41Lane 18 0·68 0·002Limestone grassland 19 0·92 < 0·0001Scrub 23 0·54 0·008Semi-improved

grassland 20 0·73 < 0·0001Road verge 16 0·78 < 0·0001Woodland 12 0·99 < 0·0001Woodland edge 23 0·26 0·23

*‘Urban’ is not included as no quadrat information was collected from suburban gardens. ‘Woodland ride’ is not included as there were only two samples of this habitat type.

Table 9. The Pearson product moment correlation between (a) mean host-plant density (host density) and the proportion ofregional 500-m grid squares occupied by each host plant (host distribution), (b) mean density of one or more host plants used by eachbutterfly species (resource density) and the proportion of regional 500-m grid squares occupied by one or more host plants used byeach butterfly species (resource distribution), and (c) between the residuals of the density–distribution relationship for butterflies(controlling for mobility) and the residuals of the density–distribution relationship of the resources used by butterfly species

n r P

(a) Host density Host distribution 32 0·221 0·224(b) Resource density Resource distribution 25 0·458 0·021(c) Butterfly residuals Resource residuals 25 0·48 0·014

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distribution (Tables 4 and 5), or variation in thedensity–distribution relationship (Tables 1 and 2). Atthe level of the whole landscape, habitat breadth wassignificantly related to species distribution but not todensity, lending no support for the predictions madeby Brown’s (1984) niche explanation. Habitat breadthwas, however, found to explain significant variation indensity if the species’ relative mobility was controlledfor. However, the non-independence of distributionand habitat breadth made their relative roles difficultto quantify.

The fact that there were significant density–distributionrelationships within individual habitats suggested thatsome elements of niche could be important. The posit-ive correlation within habitats may be a result of thestrong environmental similarity between and withinthe samples of any one narrowly defined habitat type.In other words, a species may have a broad niche withinthe habitat, allowing it to occur widely and at highdensity, whereas few species have broad niches withrespect to the wider level of variation experienced acrossall 16 vegetation types. Coenonympha pamphilus L., forexample, occurred at high densities and was widespreadon patches of unimproved limestone and coastal dunegrasslands where the grass species consumed by its larvawere abundant. However, in other, often adjacent areas,due to a combination of structure and/or vegetationcomposition, many habitats (e.g. woodland, urbangardens and agriculturally improved grasslands) weretotally unsuitable. Despite having a broad niche onunimproved grassland, this species did not have abroad niche in relation to the considerable habitatvariation in found in the study area. However, the sameresult could be obtained if each habitat type receivesvagrant individuals from other habitat types, with thevagrant species recorded only rarely, and typically atboth low density and with a limited distribution.

The niche breadth explanation is difficult to distin-guish from the habitat availability hypothesis. A speciesmay have large quantities of resources available notbecause it has broad niche within the habitat, butbecause it uses a common and widespread resource inthat habitat allowing it to occur widely and at highdensities (Hanski et al. 1993; Gaston 1994). This secondexplanation is likely to be more appropriate: in eachhabitat type there was a positive density–distributioncorrelation for the resources (i.e. host plants) used bybutterfly species, and in most cases these were signific-ant (Table 8).

Our results suggest that species’ regional distributionswere generated by factors other than merely foodplantavailability. Many species occupied only a small pro-portion of the range of their host plants, and speciesthat shared the same host plant had contrasting distri-butions. For example, Lotus corniculatus L. occurred in137 of the 500-m grid squares and was the main host

plant of Polyommatus icarus (130 squares) and Erynnistages L. (14 squares).

However, host-plant density was significantly pos-itively related to butterfly density (Table 4), and theresiduals from the host-plant density–distribution rela-tionship were significantly related to the residuals fromthe butterfly density–distribution relationship (havingcontrolled for mobility) (Table 9). Thus, those speciesthat showed positive deviation from the underlyingbutterfly density–distribution relationship (i.e. occurredat higher density than might be expected from theirdistribution) were those that had host plants that alsoshowed positive deviation from the host plant density–distribution relationship.

Both of these results provided evidence in supportof the hypothesis that resource availability affectedherbivore density–distribution relationships. However,it explained deviation from the underlying density–distribution relationship rather than explaining thedensity–distribution relationship itself, i.e. if resourceswere unusually aggregated for a particular butterfly,the butterfly was also unusually aggregated.

Some metapopulation models predict a positive rela-tionship between species density within patches and thenumber of occupied patches (e.g. Hanski 1991). However,this prediction can only be applicable to those speciesin an assemblage that exhibit metapopulation dynamicsat the spatial scale of analysis. This requires that speciesexist as a series of discrete local populations withinwhich many or most individuals are born and die, withthe populations linked by some dispersal of individuals,and local populations exhibiting extinction and recol-onization (Hanski 1991). Crudely, British butterflyspecies that form ‘closed’ populations (Thomas 1984) arethose most likely to exhibit metapopulation dynamics(Thomas 1995); whereas species with ‘open’ populationscertainly do not, at the landscape scale.

To test the metapopulation predictions, we specific-ally considered those species with closed populationstructures and we examined distributions as the pro-portions of occupied grid squares, out of those squareslikely to contain at least some suitable habitat. Thesecells can be considered, albeit loosely, as patches(Table 6). If metapopulation dynamics was the keydeterminant of the density–distribution relationship,then it should be stronger for species with ‘closed’ than‘open’ populations. We found the reverse. Therefore,metapopulation dynamics do not inevitably generatedensity–distribution correlations. However, meta-population dynamics may still make important con-tributions to the distributions of our study species.

In a metapopulation context, differences in dispersalhave frequently been discussed in terms of deviationsfrom density–distribution relationships (Gyllenberg &Hanski 1992; Hanski & Gyllenberg 1993; Hanski et al.1993; Gaston et al. 1997). Specifically, species that are

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more mobile are predicted to have larger range sizes fora given abundance (Hanski et al. 1993). Distributionwas significantly positively related to density amongstsedentary species in cells with suitable habitat if wecontrolled for species mobility (n = 15, r 2 = 0·49, P =0·03). This result is consistent with metapopulationdynamics contributing to deviations from the underlyingrelationship. However, statistical aggregation was alsocorrelated with mobility (Table 1), making the sameprediction. The metapopulation hypothesis additionallypredicts a stronger relationship for relatively sedentary(metapopulation-like) species. This is the opposite ofwhat was observed.

A recent analysis of British birds (Blackburn, Gaston& Gregory 1997) found that variation in mobility(migratory vs. non-migratory species) was not relatedto deviation from the underlying density–distributionrelationship. Because the dispersal abilities of all butthe most sedentary bird species are so great, and becausetrue migrants sometimes return to their natal habitatsto breed, this is not surprising. Dispersal ability was notincluded in similar analyses for British moths (Quinnet al. 1997). However, wing span did explain a significant,albeit small, proportion of residual variation in thedensity–distribution relationship: taxa with a largerwing span occurred at lower density for a given rangesize than smaller taxa. Given the importance of mobilitydemonstrated here and the relevance of mobility toaggregation and metapopulation mechanisms, a measureof dispersal ability should if possible be included in alldensity–distribution analyses.

A factor that may obscure the density–distributionrelationship in a particular taxonomic assemblage isextinction (Blackburn & Gaston 1998). The species thatare most likely to go extinct from the study area may bethose that are originally local and occur at low density:thereby skewing the list of remaining species. Fromhistorical sources, we know that seven species (32%)have become extinct in our Welsh study area in the last150 years (Table 2) and all have ‘closed’ populationstructures (Thomas 1984). The loss of these speciesmight be expected to have a marked effect on the formof the density–distribution relationship.

Mean butterfly densities at regional and national scalesare highly correlated (Cowley et al. 2001). Elsewhere inBritain, densities of the seven regionally extinct species(Cowley et al. 2001) were not significantly differentfrom densities of the remaining regional species ( log10-transformed data, t = 1·56, d.f. = 31, P = 0·13) or fromdensities of the other national species ( log10-transformeddata t = 0·95, d.f. = 47, P = 0·35). This suggests that thespecies that became extinct in the study area probablydid so primarily because of their limited distributionrather than their limited density. It seems unlikely there-fore that their inclusion would have generated a density–distribution pattern independent of mobility.

Conclusion

Faced with so many possible explanations for the pos-itive correlations between density and distribution, it isvery difficult to obtain unequivocal evidence to identifythe relative importance of each. We found that severalvariables were significantly correlated with density anddistribution, but could not unambiguously demonstratethat one hypothesis (or several interacting hypotheses)was the source of density–distribution correlations. Giventhe huge body of evidence supporting the generalphenomenon of density–distribution relationships, andthe plethora of non-exclusive explanations (e.g. Hanskiet al. 1993; Lawton 1993; Gaston 1994, 1996; Gastonet al. 1997; Hartley 1998), we believe faster progressmay be made by attempting to explain deviations fromthe patterns than by examining the positive relationshipitself (Hanski et al. 1993; Blackburn, Gaston & Gregory1997; Quinn et al. 1997).

With this perspective, we were far more successful.We can conclude that species that occurred at high dens-ities relative to their distributions used aggregatedresources and were relatively sedentary (resulting indistributions nearly as aggregated as their resources),whereas species that occurred at low density relative totheir distributions used less aggregated resources andwere mobile (producing distributions less aggregatedthan their resources).

Acknowledgements

We were supported by NERC (LSPE) grant GST/04/1211. M. Cowley had a NERC CASE studentship jointwith ITE. J. León-Cortés was supported by a scholar-ship from the National Council of Science in Mexico(CoNaCyT, 92535), D. Gutiérrez was supported by aMarie Curie Training grant from the commission ofEuropean Communities (ERBFMICT 961523). Weare grateful to Stephen Hartley and Tim Blackburn forassistance with the analysis and to Kevin Gaston forcomments on the manuscript. We thank Rosa Menéndezand Roger Dennis for distribution records, all whocompleted the butterfly mobility questionnaire, theNorth Wales Wildlife Trust, NERC ARS 98/5 and themany landowners in the north Wales study area. LizHowe at CCW provided Phase-1 survey data.

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Appendix 1

Species density, distribution, host-plant distribution and density, habitat breadth, niche breadth, mobility and statistical aggregation

Butterfly speciesMean density of butterflies

% 500-m squares occupied

% 500-m squares occupied*

% 500-m squares occupied†

% 500-m squares occupied by host plant(s)

Mean density of one or more host plants

Numberof habitat types occupied

Distance from northern range margin (km)

Niche breadth

Mobility classes (Thomas1984)

Mobility ranking

k (maximum likelihood estimate)

k (parametric estimate)

I Variance to mean ratio

Aglais urticae 11·82 100·00 100·00 100·00 100·00 4·89 14 580 7·5 2 20 0·29 0·18 46·59Anthocharis

cardamines 2·60 67·14 78·15 63·29 85·00 1·19 8 550 8 2 15 0·10 0·14 3·42Aphantopus

hyperantus 9·50 6·43 6·43 10·13 100·00 5·68 1 460 11 1 4Argynnis aglaja 2·25 25·71 36·73 43·04 70·00 0·63 2 580 10 2 13 0·03 0·03 3·55Aricia agestis 9·72 53·57 61·82 77·22 78·57 13·30 9 300 10 1 3 0·08 0·08 27·52Celastrina argiolus 2·43 86·43 87·68 81·01 98·57 5·05 8 300 9 2 17 0·15 0·19 3·18Coenonympha

pamphilus 10·40 48·57 49·28 63·29 98·57 12·10 7 570 11 1 5 0·09 0·18 15·83Colias croceus 1996 1·00 7·14Cynthia cardui 1·00 5·00 5·00 6·33 100·00 3·00 1 580 11 2 25 1·00 1·00 1·00Cynthia cardui 1996 4·7 100·00Cynthia cardui 1997 1·00 5·00Erynnis tages 2·50 10·00 10·22 13·92 97·86 1·52 3 480 9·5 1 2 0·02 0·02 3·86Gonepteryx rhamni 1·40 27·86 100·00 31·65 10·00 3 340 9·5 2 19 0·04 0·05 1·64Hipparchia semele 21·85 41·43 54·21 67·09 76·43 14·00 6 570 11 1 8 0·03 0·02 129·72Inachis io 4·05 86·43 86·43 93·67 100·00 4·89 12 560 7·5 2 21 0·22 0·25 5·84Lasiommata megera 2·14 44·29 43·57 68·35 100·00 6·79 2 440 11 1 14 0·04 0·05 2·78Lycaena phlaeas 2·82 68·57 83·33 82·28 81·43 1·42 8 540 7 1 12 0·12 0·14 4·33Maniola jurtina 19·40 98·57 98·57 100·00 100·00 12·80 15 580 11 1 11 0·39 0·41 34·97Ochlodes venata 3·83 50·71 50·71 68·35 100·00 4·30 8 310 11 1 10 0·05 0·04 7·72Pararge aegeria 13·47 72·14 72·14 77·22 100·00 5·68 13 510 11 1 9 0·20 0·21 31·17Pieris brassicae 2·06 91·43 90·53 96·20 67·86 0·94 13 580 10 2 23 0·41 0·40 2·57Pieris napi 5·78 87·86 93·33 86·08 85·71 1·19 15 580 11 2 18 0·33 0·31 10·82Pieris rapae 2·65 100·00 100·00 100·00 91·43 1·31 14 580 11 2 22 0·55 0·44 3·84Plebejus argus 92·86 16·43 16·79 27·85 97·86 14·90 4 170 7·5 1 1 0·01 0·02 156·39Polygonia c-album 2·27 40·00 40·00 48·10 100·00 4·89 8 300 10·5 2 16 0·11 0·16 2·64Polyommatus icarus 10·52 92·86 92·86 98·73 97·86 2·17 11 580 9 1 7 0·14 0·16 24·93Pyronia tithonus 18·08 95·00 96·38 98·73 98·57 11·70 13 340 11 1 7 0·28 0·38 31·54Thymelicus sylvestris 3·92 23·57 24·09 32·91 97·86 4·70 4 310 11 1 6 0·01 0·01 9·33Vanessa atalanta 1·77 82·86 82·86 88·61 100·00 4·89 11 580 9·5 2 24 0·26 0·22 2·40Vanessa atalanta 1996 2·15 60·71Vanessa atalanta 1997 2·95 65·00

*Distribution measured as the percentage of occupied grid squares with one or more records of species larval host plant. †Distribution measured as the percentage of occupied grid squares with some Limestone grassland, semi-improved grassland or coastal dune grassland habitat.

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Appendix 2

Host plants used by butterfly species

Butterfly species Host plants used

Aglais urticae Urtica dioicaAnthocharis cardamines Alliaria petiolata, Cardamine pratensis, Sisymbrium officinaleAphantopus hyperantus Dactylis glomerata, Brachypodium sylvaticumArgynnis aglaja Viola hirta, V. rivinianaAricia agestis Geranium molle, Helianthemum chamaecistus, Erodium cicutariumCelastrina argiolus Hedera helix, Ilex aquifoliumCoenonympha pamphilus Festuca ovina, F. rubraCynthia cardui Carduus nutans, Cirsium arvense, C. vulgareErynnis tages Lotus corniculatusGonepteryx rhamni Frangula alnus, Rhamnus catharticusHipparchia semele Ammophila arenaria, F. ovinaInachis io U. dioicaLasiommata megera D. glomerata, Holcus lanatusLycaena phlaeas Rumex acetosa, R. acetosellaManiola jurtina F. ovina, F. rubra, D. glomerata, H. lanatusOchlodes venata D. glomerataPararge aegeria B. sylvaticum, D. glomerataPieris brassicae Brassica spp.Pieris napi S. officinale, A. petiolata, C. pratensis, Rorippa nasturtium-aquaticumPieris rapae Brassica spp., S. officinalePlebejus argus L. corniculatus, H. chamaecistusPolygonia c-album U. dioica, Humulus lupulusPolyommatus icarus L. corniculatus, Ononis repens, Medicago lupulina, Trifolium spp.Pyronia tithonus F. ovina, F. rubraThymelicus sylvestris H. lanatusVanessa atalanta U. dioica

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