Butterfly Landscape

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How does landscape context contribute toeffects of habitat fragmentation on diversity andpopulation density of butterflies?

Jochen Krauss*, Ingolf Steffan-Dewenter and Teja Tscharntke Agroecology, University of

Gottingen, Waldweg 26, Gottingen, Germany

Abstract

Aim Studies on habitat fragmentation of insect communities mostly ignore the impactof the surrounding landscape matrix and treat all species equally. In our study, onhabitat fragmentation and the importance of landscape context, we expected thathabitat specialists are more affected by area and isolation, and habitat generalists moreby landscape context.

Location and methods The study was conducted in the vicinity of the city of Go ttingenin Germany in the year 2000. We analysed butterfly communities by transect counts on

thirty-two calcareous grasslands differing in size (0.035.14 ha), isolation index (210086,000/edge-to-edge distance 551894 m), and landscape diversity (ShannonWiener:0.091.56), which is correlated to percentage grassland in the landscape.

Results A total of 15,185 butterfly specimens belonging to fifty-four species are re-corded. In multiple regression analysis, the number of habitat specialist (n 20) andhabitat generalist (n 34) butterfly species increased with habitat area, but z-values(slopes) of the speciesarea relationships for specialists (z 0.399) were significantlysteeper compared with generalists (z 0.096). Generalists, but not specialists,showed a marginally significant increase with landscape diversity. Effects of landscapediversity were scale-dependent and significant only at the smallest scale (landscapecontext within a 250 m radius around the habitat). Habitat isolation was not relatedto specialist and generalist species numbers. In multiple regression analysis the densityof specialists increased significantly with habitat area, whereas generalist density in-creased only marginally. Habitat isolation and landscape diversity did not show anyeffects.

Main conclusions Habitat area was the most important predictor of butterfly com-munity structure and influenced habitat specialists more than habitat generalists. Incontrast to our expectations, habitat isolation had no effect as most butterflies couldcope with the degree of isolation in our study region. Landscape diversity appeared to beimportant for generalist butterflies only.

Keywords

Burnets, butterflies, conservation, generalists, habitat area, habitat fragmentation,habitat isolation, habitat quality, specialists, speciesarea relationships.

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

Most species live in habitat islands of fragmented landscapes(Wiens, 1997; Hanski, 1999). Habitat area (Connor et al.,

2000; Zschokke et al., 2000), habitat isolation (Hanskiet al., 1994; Kruess & Tscharntke, 1994; Steffan-Dewenter& Tscharntke, 1999; Ricketts et al., 2001; Thomas et al.,2001) and habitat quality including effects of managementand age (Feber et al., 1996; Gonzalez, 2000; Swengel &Swengel, 2001; Thomas et al., 2001) are known to affectspecies richness and population densities. Unlike oceanicislands, where the marine environment is a clear isolation

*Correspondence: Jochen Krauss, Agroecology, University of Go ttingen,

Waldweg 26, D-37073 Go ttingen, Germany.

E-mail: [email protected]

Journ al of Biogeography, 30, 889900

2003 Blackwell Publishing Ltd


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barrier, the matrix of the surrounding landscape may con-tribute to the survival of populations in terrestrial habitatislands (Marino & Landis, 1996; Jonsen & Fahrig, 1997;Thies & Tscharntke, 1999; Weibull et al., 2000; Steffan-Dewenter et al., 2002).

Speciesarea relationships have been shown for manytaxonomic groups (Rosenzweig, 1995), including butterflies

(Wilcox et al., 1986; Baz & Garcia-Boyero, 1995; Robert-son et al., 1995; Wettstein & Schmid, 1999; Steffan-Dew-enter & Tscharntke, 2000; Zschokke et al., 2000).However, comparative studies on species groups differing inecological traits are rare (Harrison & Bruna, 1999). Popu-lation density studies for insects are also not common, andshow that densities can decrease or increase with increasinghabitat area (Connor et al., 2000; Matter, 2000; Steffan-Dewenter & Tscharntke, 2000).

Most studies on fragmentation of terrestrial habitatsignore the potential influence of the surrounding landscapeon species richness and population density, and focus onarea and isolation only (Wiens, 1997; Hanski, 1999;Vandermeer & Carvajal, 2001; Tscharntke et al., 2002).

However, many grassland butterflies exist as metapopula-tions in fragmented habitats and may benefit from adja-cent vegetation depending on the matrices of thesurrounding landscapes (Thomas & Hanski, 1997). Ha-bitat specialists have been predicted to be particularly af-fected by habitat fragmentation (Thomas et al., 1992;Warren et al., 2001), whereas generalists should be morestrongly affected by the surrounding landscape diversity(Jonsen & Fahrig, 1997). Landscape analyses have foundnegative effects for species richness with increasing habitatisolation (Wettstein & Schmid, 1999; Ricketts et al., 2001)and variable effects with landscape context, as differentspecies may respond at different spatial scales (e.g. Jonsen

& Fahrig, 1997; Weibull et al., 2000; Steffan-Dewenteret al., 2001, 2002).In the temperate zone, butterfly species richness is known

to be high for grasslands (Oates, 1995; Pfaff & Wolters,1999). Calcareous grasslands rank as the most species-richhabitat for butterflies in Europe (Van Swaay, 2002). In thisstudy, we tested whether habitat area, habitat isolation,habitat quality or landscape context affect species richnessand population density of specialist and generalist butterfliesand burnets on calcareous grasslands. The following pre-dictions were tested:

(1) Species richness increases with increasing habitat area,decreasing habitat isolation and increasing habitatquality.

(2) Species richness increases with increasing habitat diver-sity of the surrounding landscape.

(3) Total butterfly and burnet densities, and densities of sin-gle species, depend on habitat area, habitat isolation andhabitat quality, and are affected by landscape context.

(4) Habitat specialists are more sensitive to habitat area andisolation, whereas generalists are more sensitive tolandscape context.

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

Study region and study sites

A total of thirty-two calcareous grasslands in the vicinity ofthe city of Go ttingen in Lower Saxony (Germany) werestudied (Fig. 1). The study sites were chosen to cover the fullgradient of habitat area and isolation in the study region.

Calcareous grasslands cover only 0.26% of the area in thestudy region and are sharply delimited from the surroundinglandscape matrix. The natural fragmentation of the semi-natural calcareous grasslands increased greatly because ofintensification of agricultural development during the lastdecades in Germany (WallisDeVries et al., 2002). In LowerSaxony the total area of calcareous grasslands is much lowerand probably more fragmented than in southern Germany(WallisDeVries et al., 2002). Compared with northern Eur-ope, our study region might be less fragmented. The studysites were neither grazed nor mown during the samplingperiod in the year 2000 and belong to the plant associationGentiano-Koelerietum. Our study region includes a total of285 calcareous grasslands with an area of 5.01 km2 (un-

published data following official maps from the Nied-ersachsisches Landesamt fu r O kologie, UntereNaturschutzbeho rden Stadt und Landkreis Go ttingen,Landkreis Northeim und Landkreis Heiligenstadt, and

Regierungsprasidium Kassel).

Butterflies

Butterflies (Lepidoptera: Hesperioidea and Papilionoidea)and burnets (Lepidoptera: Zygaenidae) were sampled in theyear 2000 (26 April24 August) by visual counts along fiverandomized transect walks through each of the thirty-twostudy sites. In the following study, butterflies always included

burnets. The number of species and individuals were recor-ded within a 5 m corridor when weather conditions weresuitable for butterfly activity (see Pollard, 1977; Erhardt,1985). All study sites were sampled in a randomized sequenceevery 34 weeks within the total sampling time. During eachof the five sampling periods all thirty-two study sites weresampled within 611 days, to minimize phenology effects. Toachieve adequate sample sizes, transect time varied from 20to 60 min, depending on the size of the grassland. Transecttime for twelve small grasslands (3141326 m2) was 20 minper transect walk, for twelve intermediate grasslands (19147887 m2) 40 min, and for eight large grasslands (11,52851,395 m2) 60 min. Counts were conducted in 5 min inter-vals to calculate species accumulation curves (following

Colwell, 1997, see below). Transect distance was measuredduring each transect walk with an electronic step counterallowing calculations of butterfly densities per 100 m2 forall thirty-two study sites. Species numbers and populationdensities from the five transect walks were pooled for eachstudy site over the sampling period.

We defined butterfly species as habitat specialists (n 20)for calcareous grassland when they are found in Lower

2003 Blackwell Publishing Ltd, Journal of Biogeography, 30, 889900

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Saxony almost exclusively on calcareous grasslands (based onunpublished data from the known distribution of butterfliesof Lower Saxony, personal communication Hans Joger

Niedersachsisches Landesamt fu r O

kologie

, Hildesheim).Most of these species were also recorded in Zub (1996) andSettele et al. (1999) as calcareous grassland specialists or withstrong preferences for this habitat type. However, some ofthese specialists can inhabit additional habitats in other re-gions of Germany or Europe; although in Lower Saxony theyare restricted to calcareous grasslands because of larval foodplant limitation or climatic tolerances. We defined butterfliesas habitat generalists (n 34) when they are ubiquitousspecies or species with preferences for other habitat types,following Zub (1996) and Settele et al. (1999). SimilarlyWarren et al. (2001) classified British butterflies into twogroups as wider-countryside or habitat specialist species.

Habitat area, isolation, quality and the landscape

context

Habitat areaThe area of the thirty-two calcareous grasslands was meas-ured in the year 2000 with a differential GPS GEOmeter 12L(GEOsat GmbH, Wuppertal, Germany) and ranged from31451,395 m2. The area of former grassland, which wascompletely covered by shrubs, was excluded in this meas-urement.

Habitat isolationTo calculate the isolation of grasslands we checked the 285officially mapped calcareous grasslands in the region. Of

these, 222 of them were still grasslands, whereas sixty-threewere covered by shrubs and trees because of lack of man-agement, and were excluded for isolation calculations in thisstudy. Estimation of habitat area from the official maps withexclusion of the area covered by shrubs was closely corre-lated with our GPS measurements (n 32, r 0.936,P < 0.0001) for the thirty-two study sites. Habitat isolation(I) of each study site (i) was measured from edge-to-edge andtook into account all known calcareous grasslands in aradius of 8 km around our study sites, using the followingformula:

IX

expa dij Ab

j

where Aj is the size (in m2) of neighbouring calcareous

grasslands and dij the distance (in km) from the neighbouringgrassland j to the study site i. The parameter a determines theeffect of distance on isolation and the parameter b, thescaling of immigration. The formula is based on Hanskiet al. (2000), and was used in a simpler form with a 1 andb 1 by Hanski et al. (1994) and Steffan-Dewenter &Tscharntke (2000). Larger values of the isolation index Iindicate lower isolation (and better connectivity) thansmaller values. We tested dij multiplied with the factorsa 1 and a 3, and Aj with the exponents of b 1, 0.2

Figure 1 Location of the thirty-two calcareous grasslands studied, and all further calcareous grasslands around the city of Go ttingen (the city is

in the centre of the map). Each grassland is marked by a circle (with 500 m radius), and the study sites are numbered from the largest (1) to the

smallest (32).


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and 0.5 (plus all combinations) to take into account thepossibility of different dispersal distances and immigrationrates of butterflies (see Moilanen & Nieminen, 2002). Theresulting habitat isolation indices were similar and closelycorrelated (r > 0.6). We also tested the distance to thenearest calcareous grassland, following Thomas et al. (1992,2001). Isolation index (a 1, b 1) and distance were

correlated (r )

0.44, P 0.012) and showed very similarresults with respect to the butterfly communities. Log10-transformed indices and distances were tested for specialist,generalist and total butterfly species richness and density andalways gave non-significant results. Therefore, we only showresults of the isolation index with a 1 and b 1.

Landscape contextLandscape context was analysed using modified digital the-matic maps (ATKIS-DLM 25/1 Landesvermessung &Geobasisinformationen Niedersachsen 19911996, Hann-over, Germany, and ATKIS-DLM 25/2 Hessisches Lan-desvermessungsamt 1996, Kassel, Germany). In total, thestudy region covered an area of c. 1944 km2. The mainly

semi-natural land-use types grassland (12.14%), garden land(0.31%), hedgerows (0.30%), calcareous grasslands(0.26%), orchard meadows (0.20%) and fen (0.05%) werepooled and defined as grasslands (13.26%), because thesehabitats were assumed to be the most suitable nectar habi-tats for adult butterflies. The remaining land-use types con-tained arable land (42.15%), forest (36.80%), built-up area(6.24%), other habitats (1.48%) and plantations (0.06%)(see Steffan-Dewenter et al., 2001, 2002). The proportion ofthe defined grassland habitats showed no significant rela-tionships and was correlated with landscape diversity (seeResults).

We used the ShannonWiener index to calculate land-

scape diversity following Thies & Tscharntke (1999) andSteffan-Dewenter et al. (2002) as

Hs X

pi lnpi

where pi is the proportion of each of the mentioned elevenland-use types (Krebs, 1989). Landscape context was quan-tified for each of the thirty-two grassland fragments using anested set of circles around the center of the study sites from0.25 to 3.00 km in 0.25 km steps. As a result of a fewmissing ATKIS values, we could test the landscape contextfor a radius of 0.25 and 0.50 km for all thirty-two studysites, whereas 0.752.00 km scales were tested for onlythirty-one sites, 2.25 km for thirty sites, and 2.503.00 kmfor twenty-nine sites. For each landscape analysis, habitat

area of the study site was excluded to reduce the habitat areaeffect.

Habitat qualityHabitat quality was quantified after each transect walk (i) byestimating the proportion of area covered by plants in flowerwithin the transect area to quantify nectar resources and (ii)by identifying all plant species in flower inside the transectarea as an indicator of larval food plant availability. The

number of plant species in flower per study site was highlycorrelated with the number of all vascular plant species perstudy site (n 31, r 0.922, P < 0.0001) (J. Krauss, un-published data).

Statistical analysis

The statistical analyses were performed using the softwareStatgraphics Plus for Windows 3.0 (Statgraphics, 1995). Alldata were tested to see if they satisfy the assumption ofnormality. We calculated simple and multiple regressions,Pearson and Spearman rank correlations, multiple logisticregressions and comparison of regression lines (Sokal &Rohlf, 1995). We chose stepwise backward elimination formultiple regressions. The independent variables habitat areaand isolation were always log10-transformed and cover ofplant species in flower was arcsine-square root transformed,whereas the proportion of grassland habitats in the sur-rounding landscape was normally distributed. Species num-bers in regressions were also log10-transformed to calculateslopes of z-values to compare them with other studies. Ar-

ithmetic means of non-transformed values one standarderror are given in the text.

We calculated the species richness estimator Abundance-based Coverage Estimator (ACE) of species richness, com-puted by EstimateS, Version 5 (Colwell, 1997) to indicatethe percentage of sampled species in relation to estimatedspecies richness per habitat, showing the saturation of spe-cies richness. To avoid effects of season-dependent speciesturnover, we pooled the first 5 min of all five transect walksper habitat to a first 25 min interval (first step), the next5 min of the transects were pooled to a second 25 min in-terval (second step) and so on. Small habitats with 20 mintransect walks have therefore four steps, intermediate habi-

tats eight steps and large habitats twelve steps to calculatethe estimated species richness.

R E S U L T S

In total, we recorded forty-eight butterfly species and sixspecies of burnets, comprising a total of 15,185 individualson all thirty-two calcareous grasslands. Only Pieris rapae(L.) and P. napi (L.) could be found on all calcareousgrassland fragments (Appendix). The most abundant specieswere the satyrid Maniola jurtina (L.) (22.9% of all individ-uals), and the lycaenids Polyommatus coridon (Poda)(18.5%) and P. icarus (Rottemburg) (8.1%).

The comparison of sampled with estimated species, which

were calculated with the species richness estimator ACE,showed that 90.24 1.18% (minimum 74.73%, maximum98.91%) of the species were found in each study site. Nocorrelation between this proportion and the independentfactors habitat area (Spearman rank correlation n 32:r 0.212, P 0.237), isolation (r 0.023, P 0.899),number (r 0.168, P 0.351) and cover (r 0.129,P 0.471) of plant species in flower, landscape diversity(r 0.076, P 0.671) and the percentage of grasslands in




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the surrounding landscape (r 0.132, P 0.463) werefound. Smoothed sample sizebutterfly species numberaccumulation curves reach asymptote (J. Krauss, unpub-lished data). These results justify the usage of sampled spe-cies numbers instead of estimated species numbers.

Habitat area (0.035.14 ha) was not correlated withhabitat isolation (index: 210086,000), and only marginally

with landscape diversity (at a 250 m scale; ShannonWiener:0.091.56) (Table 1). Distance to the nearest calcareousgrassland differed from 55 to 1894 m. The proportion ofgrassland habitats (0.4258.37%) was correlated to land-scape diversity and the two characteristics of habitat quality,the number (average: 11.224.8 species) and cover (2.1014.09%) of plant species in flower were highly correlatedwith habitat area (Table 1). For further analysis, we exclu-ded (i) the proportion of grassland habitats and (ii) the twohabitat quality characteristics.

Species richness of butterflies

The speciesarea relationship for all butterflies was highly

significant. Species richness of both the specialists and gen-eralists increased significantly with increasing habitat area(Fig. 2, Table 2). Comparison of these regressions showedno differences in slopes (F 0.88, P 0.353). In contrast,the z-value (slope of loglog regressions) for all butterflyspecies was z 0.164, and was significantly higher forhabitat specialists (z 0.399) than for generalists(z 0.096; comparison of slopes: F 31.53, P < 0.0001).The presence of fourteen specialist (70%) and ten generalist(29%) butterfly species was determined by habitat area(Appendix).

Neither number of species of all butterflies nor that of onlythe specialists or generalists showed a significant relationship

with the habitat isolation index (Fig. 3, Table 2). Addition-ally, all other isolation calculations did not show any signi-ficant effect of habitat isolation (results not shown). Only thepresence of the two specialist species Polyommatus coridonand Zygaena carniolica (Scopoli) appeared to increase withincreasing habitat connectivity (Appendix).

Species number of all butterflies, specialists and general-ists, increased significantly with increasing diversity of thesurrounding landscape, using a 250 m radius of the land-scape sector for the calculation (Fig. 4, Table 2). Compar-ison of regressions between specialists and generalists

showed no differences in slopes (F 0.15, P 0.702). Thepresence of two specialist and five generalist butterfly speciescould be explained by increasing landscape diversity(Appendix).

In multiple regression models, habitat area was the onlyexplanatory factor for the species number of all butterflies(70.0%), the specialists (69.8%) and generalists (49.5%; seethe simple regressions; Table 2). When we also includedmarginally significant factors (P < 0.1) in the multipleregression model, landscape diversity explained a further

3.1% for all butterfly species and 5.8% for generalists. Noneof the isolation measurements had a negative effect forspecies richness in multiple regressions (results not shown).

We analysed the effects of landscape context at twelvenested spatial scales. Landscape diversity and proportion ofgrassland habitats correlated best with the number of but-terfly species at the smallest scale of 250 m for both thespecialists and generalists (Fig. 5). Generalist butterfliesshowed higher correlations with landscape diversity thanspecialist butterflies (Fig. 5). In contrast, the proportion ofgrassland habitats showed no significant relationships,

Table 1 Pearson correlation coefficients (r) are shown for the relationships between the independent variables of the thirty-two calcareousgrasslands. Habitat area and habitat isolation are log10-transformed, cover of plant species in flower is arcsine-square root transformed

Habitat

isolation

Landscape diversity

(radius: 250 m)

Grassland habitats

(radius: 250 m)

Number of blooming

plant species

Cover of blooming

plant species

Habitat area )0.2 n.s. 0.32(*) 0.40* 0.69*** 0.46**

Habitat isolation )0.01 n.s. 0.15 n.s. )0.30(*) )0.28 n.s.

Landscape diversity (radius: 250 m) 0.64*** 0.17 n.s. 0.09 n.s.

Grassland habitats (radius: 250 m) 0.20 n.s. 0.01 n.s.

Number of plant species in flower 0.39*

Significance levels are: ***P < 0.001, **P < 0.01, *P < 0.05, (*)P < 0.1, n.s. not significant.

0

5

10

15

20

25

30Specialists

Generalists

100 1000 10,000 100,000

Habitat area (m2)

Butterflyspecies

Figure 2 Relationship between the number of specialist and gen-

eralist butterfly species and grassland area (n 32 fragments).Specialist butterflies (twenty species): y )12.18 5.33 log10x,

generalist butterflies (thirty-four species): y 5.39 4.37 log10x.Statistics see Table 2.




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neither for specialists (250 m: r 0.22, P 0.129) nor forgeneralists (250 m: r 0.28, P 0.117).

Density of butterflies

The density of all butterflies and of specialists increasedsignificantly with increasing habitat area, whereas gener-alist density increased only with marginal significance(Fig. 6, Table 2). Ten of twenty specialist species (50%)

showed significantly increasing population densities withincreasing habitat area, while only seven of thirty-fourgeneralists (21%) showed this trend. Density of P. napiwas significantly negatively correlated with increasinghabitat area (Appendix). Butterfly densities on calcareousgrasslands did not correlate with the habitat isolationindex, neither for all butterflies nor specialists or gener-alists (Table 2). All other isolation calculations alsoshowed no significant effects of habitat isolation (resultsnot shown). Except for Plebeius argus (L.), which showed

decreasing densities with decreasing isolation, none of theother fifty-three butterfly species showed any significantrelation to habitat isolation (Appendix). No correlationsbetween densities of all butterflies, specialists or general-ists, with landscape diversity were found (Table 2). Onlythe density of Spialia sertorius (Hoffmannsegg)increased significantly with increasing landscape diversity

(Appendix).In multiple regression models, habitat area was the only

factor related to the density of all butterflies (explaining20.7% of the variation) and specialists (22.4%), whereasgeneralists showed only a marginally significant relationship(9.1%) (see simple regressions Table 2). Correlations withhabitat isolation and landscape diversity were not evenmarginally significant. All other isolation calculations alsohad no significant effects on species density (results notshown).

Habitat area Habitat isolation

Landscape diversity

(radius: 250 m)

F r P-value F r P-value F r P-value

Species numbers

All species 69.96 0.836


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With an inclusion of cover of plant species in flower in themultiple model, this factor remains as the only explanatoryfactor (16.5%, P 0.021) for generalist density, whereasspecialist and total density were not correlated (results notshown).

D I S C U S S I O N

Species richness of butterflies

Habitat area

Species numbers increased significantly with increasinghabitat area for both habitat specialist and habitat generalistbutterflies, confirming the general validity of speciesarearelationships, as previously shown in similar studies of but-terfly communities (Wilcox et al., 1986; Baz & Garcia-Boyero, 1995; Robertson et al., 1995; Wettstein & Schmid,1999; Steffan-Dewenter & Tscharntke, 2000; Zschokkeet al., 2000). Range of habitat area in our study was 0.035.14 ha, but speciesarea relationships for butterflies can befound from very small scales (0.2520.25 m2; Zschokkeet al., 2000) to large scales (from 1003000 km2; Wilcoxet al., 1986). Not all species depend on habitat area equallyas shown by Thomas et al. (1992, 2001). Habitat areainfluenced species richness of specialists more than that of

generalists, indicating that generalists additionally use otherhabitat types in the surrounding landscape matrix, whereasspecialists completely depend on calcareous grasslands. Thez-value for specialists was 0.40 and was therefore higherthan expected from published values for oceanic islands orisolated mainland habitats (z 0.250.33), while the z-va-lue for generalists was only 0.09 and even lower than thoseknown for non-isolated mainland habitats (z 0.130.18)(Rosenzweig, 1995). This result is in support of the findingsof Steffan-Dewenter & Tscharntke (2000) that

monophagous habitat specialists have the highest z-values ina butterfly community.

Habitat isolationIn contrast to our predictions, habitat isolation did not havea consistent negative effect on butterfly species richness. Thisresult is in support of published studies, which also could notshow effects of habitat isolation on butterfly species richness(Wilcox et al., 1986; Baz & Garcia-Boyero, 1995; Steffan-

Dewenter & Tscharntke, 2000), but Ricketts et al. (2001)found fewer moth species in habitats with distances of> 3.5 km from a tropical forest reserve compared withdistances< 1.0 km. Wettstein & Schmid (1999) found morewetland indicator butterfly species when large additionalwetland areas occurred nearby. In our study, only two spe-cialist butterfly species responded negatively to habitat iso-lation, while Thomas et al. (1992, 2001) found that isolationaffected the occurrence of all six studied butterfly species.This difference may be the result of the greater range ofhabitat isolation in their study and the relatively low isola-tion in our study (< 2 km). In addition, single species withlow dispersal abilities may suffer from even comparativelylow isolation distances. Habitat isolation of calcareous

grasslands as found in our study appeared to be typical formany regions in central Europe, but the regions of northernEurope might be more fragmented thereby causing clearisolation effects. Further, patterns found in one region maynot hold for other regions because of a wealth of geo-graphical factors that change between regions (Tscharntkeet al., 2001). Evidence of habitat isolation (or connectivity)may also depend on the indices used (Moilanen & Niemi-nen, 2002). We tested two isolation indices, the distance tothe nearest grassland and an index (including a comparison

0

1

2

3

4

100 1000 10,000 100,000

Specialists

Generalists

Habitat area (m2)

Butterflyindividuals/100m2

Figure 6 Relationship between the total population density of

specialist and generalist butterflies and grassland area (n 32fragments). Specialist butterflies (twenty species): y )1.09 0.51log10x, generalist butterflies (thirty-four species):y 0.55 0.31 log10x. Statistics see Table 2.

0.2

0.1

0

0.1

0.2

0.3

0.4

0.5

0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00

****

Specialists

Generalists

Landscape scales (km)

Correlation

coefficient

Figure 5 Pearson correlation coefficients between number of spe-

cialist (20) and generalist (34) butterfly species and landscape

diversity (ShannonWiener Index). Landscape diversity is calculated

for different spatial scales ranging from a radius of 0.253.00 km.

**P < 0.01, *P < 0.05.




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of different values for the parameters a and b) based on theproportion of calcareous grassland area around the habitat.None of these calculations gave evidence for any negativeinfluence of isolation on species numbers of specialist orgeneralist butterflies. Summarizing, habitat connectivity ofcalcareous grasslands appeared to be sufficient for the but-terfly communities of our German study region, although

isolation has been reported to greatly affect some butterflyspecies in the UK.

Landscape contextEffects of landscape context on speciesarea relationshipsare mostly unknown (Wiens, 1997; Hanski, 1999; Vander-meer & Carvajal, 2001; Tscharntke et al., 2002). In ourstudy, landscape diversity marginally influenced speciesnumber of generalists, but not of specialists. Therefore,generalists appeared to be more affected than specialists. Theinfluence of landscape context was most important at a smallspatial scale in that landscape diversity within a 250 mradius turned out to best predict species richness. Our find-ings are in support of the results of Weibull et al. (2000) for

butterflies and Steffan-Dewenter et al. (2002) for bees,where small spatial scales best predicted species richness. Inmetapopulation models, based on habitat area and isolation,no improvement was achieved by adding landscape contextand habitat quality (Moilanen & Hanski, 1998), whereasthe models of Vandermeer & Carvajal (2001) showed thatmatrix quality can be of major importance for metapopu-lations. The weak landscape effects documented for calcar-eous grasslands in our study region might be explained bythe relatively complex landscapes that surrounded our cal-careous grasslands. Although landscape context played aminor role for butterfly species richness, we found evidencefor the expected tendency that generalists were more affected

than specialists.

Habitat qualityHabitat quality for requirements of larvae has been pre-dicted to be the main factor for presence or absence ofbutterfly species (Thomas et al., 2001). In our study, we didnot discriminate between the factor habitat area and thefactor habitat quality which we characterized by thenumber of plant species in flower and the cover of plants inflower because of correlations between these variables.Higher habitat heterogeneity, measured as richness of plantsin flower, increased with habitat area and butterfly speciesrichness, which supports the habitat heterogeneity hypo-thesis (Stevens, 1986; Rosenzweig, 1995).

Density of butterflies

Most population density studies have been conducted formammals and birds (Bowers & Matter, 1997; Connor et al.,2000; Matter, 2000; Gaston & Matter, 2002). Empiricalstudies for insects are rare, particularly forinsect communities(Connor et al., 2000; Steffan-Dewenter & Tscharntke, 2000).

In our study, population densities increased with increas-ing habitat area for all butterfly and specialist butterfly

species, but only marginally for generalists. Steffan-Dewenter & Tscharntke (2000) found a similar result in thatspecialists were more affected by habitat area than otherspecies. In our study, population densities of most specialistand some generalist species increased with increasing habitatarea including the three most abundant species Maniola

jurtina, Polyommatus coridon and Polyommatus icarus.

This is in support of the meta-analysis of Connor et al.(2000), who assume an average increase of populationdensities with increasing habitat area. However, Thomaset al. (2001) found no correlations between populationdensities and habitat area for three butterfly species, whereasThomas et al. (1992) found in a four-species study increas-ing densities for Plebeius argus and Hesperia comma (L.).Decreasing population densities with area have been recor-ded for Melitaea cinxia (L.) (Hanski et al., 1994). Theseinconsistent results may have been the result of numerousdifficulties in calculating relationships between populationdensity and habitat area, like the problems of including orexcluding empty habitat patches and the difference betweenpatch (PIARs) or generalized (GIARs) approaches of indi-

vidualarea relationships (Gaston & Matter, 2002). Therange of habitat area and the number of replicates are alsoimportant (Bowers & Matter, 1997). Matter (2000)reported for the red milkweed beetle Tetraopes tetra-ophthalmus (Forster) increasing, decreasing or constantdensities with habitat area in different years.

We did not find correlations between population densitiesfor butterfly communities or single species and habitat iso-lation, thereby confirming the analysis of Steffan-Dewenter& Tscharntke (2000) and Thomas et al. (2001). However,increasing densities with decreasing isolation were reportedfor Melitaea cinxia (Hanski et al., 1994).

Similarly, landscape diversity had no consistent effect, and

was related to only a few species. We are aware of only oneother study testing butterfly densities in a landscape context.Weibull et al. (2000) reported increasing or decreasingabundances for butterflies depending on the spatial scalewhere landscape diversity was measured.

Summarizing, butterfly population densities were mainlyaffected by habitat area and not by isolation or landscapecontext in our study region, but generalizations are difficultbecause of limited and inconsistent data in the literature.

C O N C L U S I O N S

In conclusion, our data emphasize the importance of habitatarea on the butterfly community, while only a small impact

of landscape diversity and no impact of habitat isolation wasobserved. Habitat specialist butterflies were more affected byhabitat area than habitat generalists, whereas generalistswere also affected by landscape diversity. Therefore, pro-tection of large calcareous grasslands would contributesubstantially to the conservation of endangered habitatspecialists. Contrary to expectations, landscape context andhabitat connectivity appeared to be sufficient to supportspecies-rich butterfly communities in calcareous grasslandsin our study region.




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

We thank Konrad Fiedler, Steve Matter, Marko Nieminen,Josef Settele, Thomas Schmitt, Christian H. Schulze and oneanonymous referee for helpful discussions and comments onthe manuscript. We thank Hans Joger and Thomas Meinekefor their butterfly advice and the following regionalauthorities: Niedersachsisches Landesamt fu r O kologie

(Reinhard Altmu ller), Untere Naturschutzbeho rden StadtGo ttingen (Roland Dillenburger), Landkreis Go ttingen(Bertram Preuschoff), Landkreis Northeim, Landkreis Heil-igenstadt, and Regierungspra sidium Kassel. This work wasfinancially supported by the German Science Foundation(Deutsche Forschungsgemeinschaft).

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B I O S K E T C H E S

Jochen Krauss is a PhD student of agroecology at theUniversity of Go ttingen. He finished his master degree inbiology on Collembola and Protura. He worked on

chamois in a tourism and wildlife project in Switzerlandand now investigates effects of landscape context andhabitat fragmentation on butterflies and plants for hisPhD thesis. His research interests include community and

population ecology and conservation biology.

Ingolf Steffan-Dewenter is Assistant Professor of agro-ecology at the University of Go ttingen. His main research

interests are in the community ecology of plants andinsects and in the effects of habitat fragmentation andsuccession on plantpollinator and plantherbivore

interactions.

Teja Tscharntke is Professor of agroecology at theUniversity of Go ttingen. His research focuses is on plantinsect interactions including parasitism, predation andpollination, insect communities at a landscape scale, andtemperatetropical comparisons.




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Appendix Number of individuals (NoI) of the fifty-four butterfly species and the number of inhabited habitat fragments ( NoH) out of thirty-twocalcareousgrasslands(sampled in theyear 2000). Multiple logisticregression forpresenceabsencedata andSpearman rankcorrelations forspecies

density are shown. The three independent habitat factors were tested: habitat area ( A), habitat isolation (I), and landscape diversity (L) as theShannonWiener Index calculated at a 250-m radius level. M (e) is the maximum likelihood estimate, M (P) is the significance level, M (%) is thepercentage of deviance explaining the model, (e) stands for maximum likelihood estimate, and (r) for the Spearman rank correlation coefficient

Presence or absence data logistic regression models

Population densities

Spearman rank

correlations

Taxon NoI NoH A (e) I (e) L (e) M (e) M (p) M (%) A (r) I (r) L (r)

SpecialistsPolyommatus coridon (Poda, 1761) 2815 23 3.70*** 3.32* n.s. )25.05 0.0002 40.81 0.63*** n.s. n.s.Coenonympha arcania (Linnaeus, 1761) 364 17 n.s. n.s. 3.73* )4.04 0.014 13.69 n.s. n.s. 0.31(*)

Zygaena carniolica (Scopoli, 1763) 344 19 4.92*** 3.60* n.s. )31.33


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Appendix continued

Presence or absence data logistic regression models

Population densities

Spearman rank

correlations

Taxon NoI NoH A (e) I (e) L (e) M (e) M (p) M (%) A (r) I (r) L (r)

Lasiommata megera (Linnaeus, 1767) 15 7 n.s. n.s. n.s. )1.27 n.s. n.s. n.s. n.s.

Celastrina argiolus (Linnaeus, 1758) 13 8 2.39** n.s. n.s. )9.96 0.002 26.68 0.47** n.s. n.s.Papilio machaon Linnaeus, 1758 12 8 n.s. 2.35(*) n.s. )11.23 0.094 7.81 n.s. n.s. n.s.

Nymphalis c-album (Linnaeus, 1758) 11 8 1.49* n.s. n.s. )6.54 0.029 13.28 0.31(*) n.s. n.s.Issoria lathonia (Linnaeus, 1758) 5 3 n.s. )6.45* 17.37** 1.74 0.003 58.07 Thecla betulae (Linnaeus, 1758) 4 2 n.s. n.s. n.s. )2.71 n.s. Limenitis camilla (Linnaeus, 1764) 4 3 2.57* n.s. n.s. )12.24 0.027 24.61

Lycaena tityrus (Poda, 1761) 2 2 n.s. n.s. n.s. )2.71 n.s.

Significance levels are: ***P < 0.001, **P < 0.01, *P < 0.05, (*) P < 0.1, n.s. not significant.

Note: Positive values of A (e, r) mean that the species occurrence is positively related to larger habitats, of I (e, r) that they profit from lessisolation (better connectivity), and L (e, r) that they profit from higher landscape diversity. Only species with occurrence in >1 and

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