Landscape Ecology 18: 253–263, 2003.© 2003 Kluwer Academic Publishers. Printed in the Netherlands.

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Research article

Arthropod reaction to landscape and habitat features in agriculturallandscapes

Ph. Jeanneret1, B. Schüpbach1, L. Pfiffner2 & Th. Walter1

1Swiss Federal Research Station for Agroecology and Agriculture (FAL), Reckenholzstrasse 191, 8046 Zurich,Switzerland2Research Institute of organic agriculture, Ackerstrasse, Postfach, 5070 Frick, SwitzerlandE-mail to: philippe.jeanneret@fal.admin.ch

Key words: arthropods, biodiversity, canonical correspondence analysis, environmental control, landscape andhabitat influence, variation partitioning

Abstract

Determining explanatory environmental factors that lead to patterns of biodiversity in cultivated landscapes is animportant step for the assessment of the impact of landscape changes. In the context of an assessment of the effectof agricultural national extensification programme on biodiversity, field data of 2 regions were collected accordingto a stratified sampling method. A distribution model of 3 indicator species taxa (butterflies, spiders, and carabidbeetles) is related to influencing factors by means of multivariate statistics (CCA, partial CCA). Hypotheticalinfluencing factors are categorised as follows: (1) habitat (habitat type, management techniques) and (2) landscape(habitat heterogeneity, variability, diversity, proportion of natural and semi-natural areas). The correlation modelsdeveloped for spider, carabid beetle and butterfly assemblages revealed that there are no general rules relatingspecies diversity to habitat and landscape features. The relationship strongly depends on the organism and on theregion under study. Therefore, biodiversity response to landscape and habitat changes has to be identified by meansof a multi-indicator concept in different landscape situations.

Introduction

The structure of a given biotic community is gener-ally related to two classical models: the environmentalcontrol model (e.g., Whittaker 1956) and the bioticcontrol model (e.g., Southwood 1987). These twogroups of influencing factors are not mutually exclu-sive, but complementary together with other factorslike historical events and disturbances of various kinds(Quinn and Dunham 1983). On a broader scale, land-scape characteristics are relevant explanatory variablesfor plant and animal communities because they definethe ecosystem arrangement and interactions (Formanand Godron 1986; Forman 1995) and thus affect pop-ulations via complementation and supplementationprocesses (Dunning et al. 1992). The spatial arrange-ment of habitat elements and the spatio-temporal het-

erogeneity of the landscape are essential for speciesdiversity (Burel 1992; Huston 1995). In the agricul-tural landscapes in particular, undisturbed habitats,their proportion to cultivated fields and their positionin the landscape, play an important role as refugesand sources of individuals for recolonization (Den-nis and Fry 1992; Lys and Nentwig 1994; Pfiffnerand Luka 2000). However, it is difficult to gener-alise as to the significance of spatial structure becausespecies ecology and dispersal abilities are different forevery organism (Burel and Baudry 1995; Burel andBaudry 1999). For many arthropods, survival in agri-cultural landscapes depends on the suitability of thehabitats, which is largely influenced by field manage-ment, but also on the characteristics of the surroundinglandscape.

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Our study aims to analyse, discriminate and com-pare with correlative models two components of theenvironmental control, i.e., the habitat and the land-scape, acting on three arthropod taxa, i.e., spiders,carabid beetles and butterflies, collected and observedin two regions that are different as to their landscapeconfigurations and land uses. The results of both re-gions were discussed separately in Jeanneret et al.(2002, and 2002 submitted), and showed that the threearthropod taxa react differently to the habitat and land-scape features in both regions. In this paper, we willcompare the reaction of the three arthropod commu-nities between the two regions. We hypothesized thatone given taxa would react differently to the habitatand landscape features depending on the region.

Both control components are divided into poten-tial explanatory factors: (1) habitat descriptors: theplant species richness, the habitat type; and (2) land-scape descriptors: the surrounding habitat variabilityand heterogeneity, an index of landscape pattern, thesurrounding land use.

As a basic habitat characteristic, plant speciesrichness is supposed to have a major influence onthe spider, carabid beetle and butterfly assemblages.For spiders and carabid beetles, higher plant speciesrichness offers more diverse habitat structure (archi-tecture) and more niches for prey (Strong et al. 1984).Higher plant species richness represents more hostand feeding plants in time and space that should in-fluence butterfly assemblages (Erhardt 1985; Sparksand Parish 1995). In our context, the habitat typedistinguishes crops (cereal fields and high intensitymeadows) from ecological compensation areas (ECA)and forest edges. ECA are fields set aside encom-passing traditional landscape elements as well as newtypes of biotopes which are designed to enrich theagricultural landscape (Herzog et al. 2001). The habi-tat type should play an important role in determiningthe species composition of the arthropod taxa understudy as it represents the sum of the abiotic factorscharacterising the sites. On the landscape scale, vari-ability and heterogeneity of the surrounding habitatsmay influence the biodiversity measured at a givenpoint within the landscape (Duelli 1997). To comple-ment these simple measurements of spatial pattern, theD1 index of dominance based on information theorywas used (O’Neil et al. 1988).

Nevertheless, the causes explaining the species dis-tribution at landscape scale are usually very diverseand habitat and landscape descriptors as proposed inthis paper are not supposed to be able to explain all

aspects of this distribution. Therefore, we also intro-duced the spatial position of the sampling sites (rep-resented by geographical coordinates) since it can beconsidered as evidence for the various processes thathave generated the species distribution. In this studythe detection of spatial variation was not analysed perse but the spatial position of the sites was integratedas explanatory factor for playing the role of a syn-thetic indirect descriptor of the unmeasured factorsas defined by Borcard et al. (1992) and Borcard andLegendre (1994).

Method

Regions, sampling methods

The study was carried out in 2 regions of the cen-tral Swiss Plateau: region 1 (Ruswil, 20 km NW ofLucerne) and region 2 (Rafz, 20 km NW of Zurich).Region 1 has undulating hills situated between an alti-tude of 650 and 800 m. It comprises a total surface of885 hectares, mainly consisting of arable land (15%),grassland (59%), and forests (17%). Four ECA habi-tat types, usually small areas of approx. 400 m2, canbe found in the perimeter, namely extensively usedmeadows (no fertilisation, late mowing), low inten-sity meadows (restricted fertilisation, late mowing),hedgerows and standard fruit trees in traditional or-chards. Region 2 has a flat relief and is situated at amean altitude of 450 m. It comprises a total surface of1016 hectares, mainly consisting of arable land (47%),grassland (5%), forests (20%), gravel pits (11%), andspecial cultures (6%). In region 2, the same ECA typesoccur except that wild flower strips replace standardfruit trees. The difference between both land uses isthe proportion of arable land and grassland, and thepresence of gravel pits and special cultures in region 2.

Spiders, carabid beetles and butterflies wererecorded according to a stratified sampling method.ECA, cultivated areas and forest edges were definedas strata. The number of samples per ECA type wasdetermined in proportion to the number of elementsin each type occurring in the study areas (Table 1).This attribution of ECA samples was possible becausethe size of the elements in the ECA types was verysimilar. The minimum number of samples was givenby the ECA type having the smallest number of el-ements, i.e., hedgerows, and the number of samplesof the other ECA types was calculated proportionnallyto it. Seven highly intensive meadows and 20 winter

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wheat fields in region 1 and region 2, respectively,were chosen to serve as references for the cultivatedareas because they are predominant in the landscape.Seventeen observation sites were set up along the for-est edge belonging to 3 forest plots in region 1 and 6sites along the forest of 2 plots in region 2.

Spiders and carabid beetles were collected in 1997and butterflies observed in 1998 in the 58 (region 1)and 51 (region 2) sites. Spiders and carabid beetleswere collected with 3 pitfall traps per site, during5 weeks (during the first 3 weeks of May and last2 weeks of June), as proposed by Duelli (1997) tooptimise the number of species caught compared tothe sampling effort (see also Section ‘Methodologicalaspects’). This relatively short sampling period is nec-essary to ensure uniformity of the habitat conditionsin winter wheat fields and high intensity meadows. Alonger sampling period would include a habitat change(i.e., winter wheat - harvest mid July - new crop, e.g.,winter barley). This change would strongly affect thecomparison between habitat types. The pitfall trapsused in this study consisted of funnel traps of 10.5 cmin diameter containing 2 cm of 90% alcohol/water so-lution. The 3 pitfall traps, and 4 (region 1) and 5 weeksof sampling per site are pooled for the analysis. Due tobad weather conditions in region 1, spiders and cara-bid beetles were identified and analysed for 4 weeksonly. Butterflies were observed across a 0.25 ha area,5 times for 10 min each from the beginning of May tothe end of August. Butterflies were monitored between10.00 a.m and 5.30 p.m in sunny weather conditions,with no or light wind and a minimum temperature of18◦ C. At forest edges, butterflies were recorded alongthe edge. The 5 observation runs per site are pooled forthe analysis. At each of the observation sites, the veg-etation was assessed over an area of 100 m2 accordingto the Braun-Blanquet method.

Measurement of environmental control

The explanatory environmental variables are dividedin three sets of descriptors (Table 2): (1) the habitat;(2) the landscape; and (3) the space.

Habitat descriptors. 1. Plant species richness: num-ber of plant species on 100 m2. 2. Habitat types:habitats were assigned to the 8 types listed in Table 2.

Landscape descriptors. To calculate the values ofthe landscape descriptors, each agricultural field in thecase study areas was visited, categorised according

to its use and digitised by means of a geographicalinformation system (GIS).

Four landscape descriptors were calculated in a200 m radius circle around the observation points(Table 2).

First, patches (a patch = a relatively homogeneousnon-linear area that differs from its surroundings) wereassigned to 22 land use types. Three landscape patternindices were calculated on the basis of the percentagecover of the different land use types within the circle:

1. Surrounding habitat variability = number of sur-rounding land use types (Duelli 1997)

2. Surrounding habitat heterogeneity = number ofpatches of different land use types (Duelli 1997)

3. D1 index of landscape pattern (O’Neil et al. 1988);D1 is a measure of dominance:D1 = ln n + � Pi ln Pi, where n is the total numberof land use types and Pi the proportion of patchesin land use type iSecond, a qualitative measurement of landscapediversity was carried out. The 22 land use typeswere aggregated in 4 land use classes to recordinformation about the landscape quality:

4. Surrounding land use classes: cultivated land, eco-logical compensation area, forest and built uparea.

Spatial descriptors. Geographical coordinates of thesites were used as spatial descriptors to detect theeffects of other not measured environmental factors.Eventual biogeographic or altitude effect (z coordi-nate) were not supposed to occur at the scale of thesecase studies.

Statistical analysis

To collect and retain all the information on the indi-cators observed, we defined species diversity as bothspecies variety and relative abundance of the species.Species-environment relationship was then analysedwith the help of ordinations and multivariate statis-tics. Multidimensional analysis was first performedthrough the correspondence analysis (CA) by meansof the CANOCO programme (Ter Braak and Smilauer1998) to obtain ordination diagrams. The result of acomplete linkage clustering was superimposed ontothe CA diagram to separate clusters of objects whichare distinct in dimensions that cannot be represented ina 2-dimension CA diagram, as proposed by Legendreand Legendre (1998).

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Table 1. Strata, habitat types, and the number of sampling sites in the area of region 1and region 2.

Number of sites

Strata Habitat type Region 1 Region 2

Cultivated areas High intensity meadows 7 –

Winter wheat fields – 20

Extensively meadows 16 3

Ecological Low intensity meadows 7 9

Compensation Hedgerows 3 2

Areas Standard fruit trees in traditional 8 –

orchards

Wild flower strips – 11

Forest edges Forest edges 17 6

To identify the main environmental variables hav-ing an effect on the species assemblages, a canonicalcorrespondence analysis (CCA) and a partial CCA,were carried out (Ter Braak 1996). In CCA, the sig-nificance of a particular environmental variable canbe assessed by Monte Carlo testing (bootstrapping) ofthe axis associated with that variable, using the axiseigenvalue as the test statistic.

Habitat, landscape and spatial descriptors were in-troduced in the CCA and partial CCA. Landscapedescriptors were calculated with GIS. To establish ahierarchy between explanatory variables and to elim-inate those which do not significantly explain anyvariation, we used CCA with each separate variableprior to a forward selection, to be followed by CCAinvolving all the variables (Jeanneret et al. 1999).Partitioning of variation was then performed throughpartial CCA (e.g., Borcard et al. 1992; Anderson andGribble 1998; Pozzi and Borcard 2001). The fractionof the variation explained (and its significance, ob-tained by means of a Monte Carlo permutation test)by each of the environmental descriptors is given sep-arately, after eliminating the variation due to the other(partialed) variables, which are used as covariables.

Results

Faunistic description of the sites

Altogether 16,057 (4 weeks of pitfall-captured) and15,500 (5 weeks of pitfall-captured) spiders belongingto 135 and 127 species were collected from the 58 and51 sites in region 1 and 2, respectively. Altogether,

9,325 (4 weeks) and 32,638 (5 weeks) carabid beetlesbelonging to 79 and 96 species were collected fromthe 58 and 51 sites in region 1 and 2, respectively.Due to the unequal sampling effort, the number ofspider and carabid individuals and species of the tworegions should be compared with caution. In both re-gions, forest edges are well characterised by the spiderand carabid beetle communities and represent a partic-ular habitat where typical forest species were foundtogether with species of adjacent meadows (resultspublished in detail in Jeanneret et al. 2000 and Pfiffneret al. 2000).

Altogether, 892 (region 1) and 966 (region 2) but-terflies belonging to 17 and 22 species were observedon the 58 and 51 sites, respectively. Butterfly speciesrichness was significantly higher in the extensivelyused and low intensity meadows and in the wild flowerstrips than in the high intensity meadows and winterwheat fields (results published in detail in Jeanneretet al. 2000).

Faunistic comparison of the regions and the habitats

Because of the unequal sampling effort when moni-toring spiders and carabid beetles in the two regions(region 1: 4 weeks sampling, region 2: 5 weeks sam-pling), we tested the time effect (week 1 to week5) on the species composition of region 2. As thesampling week missing in region 1 is situated in themiddle of the sampling period (week 4, the last weekof June in 1997) we tested the assumption that onesampling week situated in the middle of the samplingperiod in region 2 would not significantly change thespecies composition. First, we performed CCA with

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Table 2. Characterisation of the habitats and landscape acting as explanatory variables onbiodiversity.

Scale Environmental variables Land use types

1. Plant species richness

Habitat 2. Habitat type 8 types: High intensity, extensively

descriptors used and low intensity meadow,

hedgerow, standard fruit trees in

traditional orcharda, wild flower stripb,

winter wheat, forest edge

1. Surrounding habitat variability 22 types: Habitat type + cereal fields,

root crops, corn, rapeb, vegetableb ,

pasture, artificial meadow, grove, rape,

nursery, slope, brook, built up area,

natural area (forest)

Landscape 2. Surrounding habitat heterogeneity idem

descriptors 3. D1 index of landscape pattern idem

4. Surrounding land use 4 classes: Cultivated land, ecological

compensation area, built up area,

natural area (forest)

Space Coordinate X

descriptors Coordinate Y

aonly in region 1bonly in region 2

the week as an explanatory variable. On the whole,the week significantly affected the species composi-tion (p ≤ 0.005, Monte Carlo permutation test), i.e.,there is a shift in the species composition and theirrelative abundance over the 5 sampling weeks in re-gion 2. Second, pairwise comparisons were madeand showed that there was no significant difference(p = 0.94, Monte Carlo permutation test) between thespecies composition of weeks 4 and 5. Therefore, the5 weeks of sampling in region 2 were maintained infurther analysis.

CA ordination diagrams of the sites based on spi-der, carabid beetle and butterfly assemblages differ-entiate region 1 from 2 (Figures 1, 2 and 3). Never-theless, superimposition of the results of a completelinkage clustering shows that spider assemblages offorest edges and hedgerows of both regions (Figure 1:cluster 1) show a closer similarity to each other than tothe other habitats of the same region (Figure 1: clus-ter 2 and 3). Three sites, 1 extensively used, 1 lowintensity and 1 high intensity meadow of region 1 areexceptions and grouped in cluster 2 with sites of re-gion 2 (Figure 1). Cluster 3 is exclusively composed ofsites of region 1. For carabid beetles assemblages, likefor spider assemblages, forest edges and hedgerows

of both regions were grouped together, but not theother habitats of the same region (Figure 2: cluster 1).Three forest edges and one hedgerow of region 1 weregrouped with meadows of this region (Figure 2: clus-ter 3). Clusters 2 and 3 are exclusively composed ofsites of region 2 and 1, respectively. For butterflyassemblages, sites of the same region were groupedtogether at first (Figure 3). One cereal field of region 2is an exception and belongs to cluster 1 together withsites of region 1.

Comparison of the habitat, landscape and spaceeffects on the arthropod groups between the regions

Within the scope of separate CCA and forward selec-tion procedures, environmental variables and classes- which explain a significant part of variation - arerecognised and then introduced in partial CCA. Thehabitat descriptors act differently according to arthro-pod groups and regions (Table 3). Plant speciesrichness explains a significant part of the variationfor every arthropod assemblage in region 1 (spiders:2.5%; carabid beetles: 2.6%; butterflies: 3.8%). Inboth regions the habitat type is a significant explana-tory variable for epigeal arthropod assemblages. Forbutterflies in region 2, significance is only achieved by

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Figure 1. CA ordination of the 58 sites (region 1) and 51 sites(region 2) based on spider assemblages. The ellipses represent theresult of a complete linkage clustering. r1, r2 = sites belonging tothe region 1 and region 2, respectively. For visual clarity, member-ship of well gathered sites is indicated by ‘Region 1’ or ‘Region 2’.The arrow indicates that the site belongs to region 2 and cluster 2.

adding the variation explained by plant species rich-ness and habitat type. On the one hand, landscapedescriptors have no influence on the arthropod as-semblages, if calculated as an index summarising theinformation like surrounding habitat variability andheterogeneity, and D1. On the other hand, if the landuse classes are taken into account, spider assemblagesof region 2 are significantly influenced by the presenceof both natural areas and ECA in the surroundings ofthe habitat where they were caught, carabid beetlesreact significantly to both cultivated land and naturalarea in region 1, and butterfly assemblages are sig-nificantly influenced by both natural area and ECAin region 1. Spatial position of the sites is a signif-icant explanatory variable for the epigeal arthropodsin both regions, but only in region 1 for the butterflyassemblages. The habitat type is the most influencingfactor for epigeal arthropods in both regions, whilesurrounding land use is more important for butterfliesin region 1. In region 2, when tested alone, no environ-mental variable measured explains any part of butterflyassemblages.

Figure 2. CA ordination of the 58 sites (region 1) and 51 sites (re-gion 2) based on carabid beetles assemblages. The ellipses representthe result of a complete linkage clustering. r1, r2 = sites belongingto the region 1 and region 2, respectively. For visual clarity, member-ship of well gathered sites is indicated by ‘Region 1’ or ‘Region 2’The arrows indicate that the sites are grouped with the meadow sitesof region 1.

The large amount of unexplained variation is dueto factors overlooked in this study or to stochasticvariation.

Discussion

Methodological aspects

To estimate the total number of spider and carabidbeetle species occurring in our regions, pitfall trapsshould be operated for a longer sampling period thanin this study. Nevertheless, Duelli (1990) showed thatin comparison with a full season sample of 28 weeks,more than 70% of the number of species is obtainedin similar habitats in Switzerland within 4 samplingweeks from the beginning of May to the beginning ofJuly for both spiders and carabids. Depending on thehabitat, the percentage of species caught ranged from

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Table 3. Summary of the percentage variation explained by environmental variables for spiders, carabid beetles, andbutterflies in two regions after partitioning with partial CCA. 1 = region 1 and 2 = region 2. When given, percent ofvariation is significant at p < 0.05 (Monte Carlo permutation test). n.s.: not significant.

Spiders Carabid Butterflies

beetles

Region 1 2 1 2 1 2

Scale Environmental variables Explained variation (%)

Habitat

descriptors

Plant species richness

Habitat type

2.5

16.6

n.s.

14.2

2.6

9.8

n.s.

21.9

3.8

n.s.11.1

Surrounding habitat variability n.s. n.s. n.s. n.s. n.s. n.s.

Surrounding habitat heterogeneity n.s. n.s. n.s. n.s. n.s. n.s.

D1 index of landscape pattern n.s. n.s. n.s. n.s. n.s. n.s.

Landscape Surrounding land use classes:

descriptors

Cultivated land

Natural area (forest)

Ecological compensation area

n.s.

n.s.

n.s.

n.s.

4.2

4.4

n.s.

n.s.

n.s.

n.s.

n.s.

6.8

n.s.

n.s.

n.s.

Built up area n.s. n.s. n.s. n.s. n.s. n.s.

Space 4.9 4.0 4.4 3.5 6.4 n.s.

Total of the variation explaineda 26.3 28.6 25.3 33.2 17.4 11.1

aThe total takes into account the fraction of the variation explained in common between the environmental variables.

84.6% (winter wheat) to 86.0% (meadows) for cara-bids and from 73.5% (winter wheat) to 77.8% (mead-ows) for spiders (Duelli 1990). Within the frameworkof our study, these differences are acceptable and al-low the comparison between the habitat types. Duelli(1990) also showed that an additional sampling weekduring the ‘optimum period’ (May–July) results inless than 10% of the species. As mentioned in Sec-tion ‘Regions, sampling methods’, sampling in cerealfields for a longer period of time would cause dras-tic habitat changes by harvesting and sowing of thenext crop. These changes would strongly affect thecomparison between habitat types. Nevertheless, forcarabid beetles a short sampling period in May-Junecauses a bias when comparing forest with open habi-tats because species active in spring are more frequentin open habitats than in forests which are characterisedby species active in autumn (anonymous reviewer,personal communication). However, hedgerows andforest edges are a part of the open cultivated land-scape in our regions. The comparison with ‘pure’forests would be more problematic. Furthermore, inour study spider and carabid beetle assemblages dif-fered considerably in the open habitats and the forestedges as well as hedgerows (CA ordinations) despitethe probable underestimation of the species richness in

the latter. Sampling all over the year would show thedifferentiation of both types of habitats more acutely.

Examining species assemblages allows a compre-hensive appreciation of the impact of habitat andlandscape on biodiversity. We have used this approachinstead of summarising the biotic information in onesingle value such as species richness or a diversityindex where interpretation would be difficult and theloss of information too substantial.

As emphasised by Anderson and Gribble (1998),we cannot suggest that the methodology of variancepartitioning would allow the establishment of anycausal effects, which would require proper experimen-tal design and analyses. The method corresponds tothe univariate multiple regression, including its warn-ings concerning issues of causal relationships, choiceof variables and redundancy of parameters causing in-creases in explained variation due to chance alone.Nevertheless, the landscape and its components shouldbe included in the environmental control model as ex-planatory variables, as it has been demonstrated in thisstudy. Thus, the search and analysis of correlationsis certainly the best methodology to be used becausedeliberate experiments which would result in manip-ulating the landscape in an experimental design arepractically unfeasible.

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Figure 3. CA ordination of the 58 sites (region 1) and 51 sites (re-gion 2) based on butterfly assemblages. The ellipses represent theresult of a complete linkage clustering. For visual clarity, member-ship of well gathered sites is indicated by ‘Region 1’ or ‘Region 2’.The arrow indicates that the site is grouped with sites of region 1.

Landscape and habitat features as environmentalcontrol factors

CA ordination diagrams associated with clustering re-sults show that regions 1 and 2 have their own specificpool of spider, carabid beetle and butterfly species,but the difference between the forest edges and theother habitats is greater than the difference betweenthe regions for epigeal arthropods.

The correlative models obtained from partial CCAallow to discriminate between both components of theenvironmental control, i.e., habitat and landscape, andspace. On the one hand, spider and carabid beetle as-semblages are influenced by the habitat type. This isconsistent with other studies on the characterisationof habitats with spiders (e.g., Duffey 1974; Clausen1986; Alderweireldt 1989; Martin 1991) and cara-

bid beetles (e.g., Luff et al. 1989; Turin et al. 1991;Kramer 1996). In both regions, forest edges are char-acterised by specific assemblages. The relationship isparticularly direct between the carabid beetle assem-blage and the habitat type in region 2. In this regionof arable fields, the habitat type gradient, in otherwords the difference between the habitats, is greaterthan in the meadow landscape (region 1), and thisplays an important role for less mobile organisms likecarabid beetles in comparison with spiders since lessexchanges with neighbour fields occur. Contrary tothat, spiders assemblages characterise the habitat typeirrespective of the habitat type gradient.

On the other hand, it is surprising that the habi-tat type plays such a minor role for butterflies sinceit significantly explains the species composition onlyin conjunction with the floristic richness in region 2,although the obviousness of the relationship has beendemonstrated in the literature (e.g., Dennis 1992; Kre-men 1992; Debinski and Brussard 1994). Our explana-tion is that the very poor butterfly species assemblages,17 and 22 species in region 1 and 2, respectively,are mainly composed of generalist species as definedby Ouin and Burel (2002), considering their disper-sal ability (Warren 1992) and degree of polyphagy(Scriber 1973). Generalists species are less infeodedto a particular habitat and therefore, the habitat typehas less influence.

Plant species richness is a significant explanatoryfactor for epigeal arthropods in region 1 and in bothregions for butterflies. For epigeal arthropods, thismay be explained by the species pools which are dif-ferent in both regions. The spider and carabid beetleassemblages captured in region 1, which is domi-nated by meadow ecosystems, are sensible to thehabitat structure, which is represented by the plantspecies richness, while assemblages captured in re-gion 2, which is dominated by arable fields, are notsignificantly influenced by this factor.

As nectar feeding insects, butterflies stronglydepend on flowering plants. Distribution of nectarsources in space and time plays a crucial role in thelocation and dispersal of butterfly populations (Boggs1987). Habitats with higher plant species richness of-fer a nectar source which is better distributed in timethan habitats with fewer species. Therefore, habitatsrich in plant species are occupied and visited by alarger butterfly species set.

As stressed by Wagner and Edwards (2001), habi-tat variability and heterogeneity are simple to quan-tify, but depend on the habitat classification and it

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is assumed that all habitat types are equally differ-ent from each other so that the specific compositionof a landscape does not matter. Our study shows thatthe specific composition of the landscape does mat-ter and that the loss of information resulting fromsimple index calculations is too substantial and as aconsequence no significant part of the variation inspider, carabid beetle and butterfly assemblages canbe explained. If its specific composition is taken intoaccount, the surrounding landscape becomes a signif-icant explanatory factor for spiders and butterflies ofregion 2 (natural area + ECA), and carabid beetles ofregion 1 (cultivated land + natural area).

In the region where the species assemblages areless influenced by the habitat type, the landscapecomposition is a stronger explanatory factor, i.e., re-gion 2 for spiders and region 1 for carabid beetles andbutterflies.

For spiders, these results do not confirm previ-ous studies carried out by Asselin and Baudry (1989),Burel and Baudry (1995) showing no effect of thelandscape structure. Our results show that particularhabitats like ECA and natural areas in the surround-ings may control the attainability of the habitat forspiders.

In our study landscape descriptors and the sur-rounding habitat type in particular have no majorinfluence on butterfly assemblages in one of the re-gions as postulated for some groups by Dover et al.(1992) and as seen from results for particular species(Thomas and Harrison 1992; Thomas and Hanski1997). Most butterfly species fly over the landscape,visiting small or large areas. They need structure tomove and often require several habitats to completetheir life-cycles. Therefore, butterflies should be in-fluenced by the habitat arrangement around a pointthat they visit. In region 2, however, the lack of ver-tical structures like hedgerows, forest edges, ditches,etc. leads to a uniform attainability of the habitatsfor butterflies. Once again, an analysis of the assem-blages focussed on functional groups should revealdifferences between generalist and specialist species.

The range of variation explained by spatial vari-ables indicates that other factors that were not testedcould play a role. In particular, the action of a mi-croclimatic shift is possible in area 1 where slopesexposed to the north and therefore wetter and colder,are mixed with drier and warmer places exposed to thesouth. Furthermore, other landscape features like thefractal dimension, the contagion index (O’Neil et al.1988) and structural features defined by Marino and

Landis (1996) as well as connectivity measurementsshould be added in the model. An additional explana-tory factor which was not included and which could besignificant is the history of the sites. Indeed, while atthe same time the habitats are currently managed in thesame manner, their history can have an importance inthe determination of the assemblages of species whichwe observe in the present.

Conclusions

Because of the differentiated response shown by thearthropod taxa to habitat and landscape features, it isimportant to approach the environmental control byexamining different taxa particularly when the goal isto evaluate restoration programs or to found manage-ment recommendations in agricultural landscapes.

As it has been demonstrated in this study, land-scape and its components should be included inthe environmental control model as explanatory vari-ables. Nevertheless, the complex relationship betweenarthropods and landscape in a multi-indicator ap-proach is fragmentary in spite of studies in recentyears (e.g., Burel and Baudry 1995; Paoletti 1999;McCracken et al. 2000; Atauri and de Lucio 2001;Tscharntke et al. 2002; Weibull 2002) and in com-parison with the links to habitat features. Especially,temporal fluctuations of arthropod abundance are wellknown and influence the analysis of the impact of en-vironmental factors. Medium- and long-term studiesare necessary to analyse the temporal trends and toseparate them from the other effects. Our study willcontain 4 sampling years between 1997 and 2004 andwill therefore allow comparisons in the medium term.Furthermore, as the response of the taxa to the habi-tat and landscape features depends on the region, datashould be collected in other regions across Europe tomaximize landscape gradient. Then results could becompared to allow generalization.

Acknowledgements

This study was partly financed by the Swiss FederalOffice for Agriculture. The authors wish to thank J.Steiger, G. Blandenier and H. Hänggi for spider iden-tification, H. Luka for carabid beetle identification, S.Bosshart, M. Waldburger for their assistance in fieldwork.

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