Macroecological patterns of spider species richness across Europe

Biodivers Conserv (2008) 17:2849–2868 DOI 10.1007/s10531-008-9400-x ORIGINAL PAPER

Macroecological patterns of spider species richness across Europe O.-D. Finch Æ T. Blick Æ A. Schuldt

Received: 26 June 2007 / Accepted: 8 April 2008 / Published online: 25 April 2008 Ó Springer Science+Business Media B.V. 2008

Abstract We analysed the pattern of covariation of European spider species richness with various environmental variables at different scales. Four layers of perception ranging from single investigation sites to the whole European continent were selected. Species richness was determined using published data from all four scales. Correlation analyses and stepwise multiple linear regression were used to relate richness to topographic, climatic and biotic variables. Up to nine environmental variables were included in the analyses (area, latitude, elevation range, mean annual temperature, local variation in mean annual temperature, mean annual precipitation, mean July temperature, local variation in mean July temperature, plant species richness). At the local and at the continental scale, no significant correlations with surface area were found, whereas at the landscape and regional scale, surface area had a significant positive effect on species richness. Factors that were positively correlated with species richness at both broader scales were plant species richness, elevation range, and specific temperature variables (regional scale: local variation in mean annual, and mean July temperature; continental scale: mean July temperature). Latitude was significantly negatively correlated with the species richness at the continental scale. Multiple models for spider species richness data accounted for up to 77% of the total variance in spider species richness data. Furthermore, multiple models explained variation in plant species richness up to 79% through the variables mean July temperature and elevation range. We conclude that these first continental wide analyses grasp the overall O.-D. Finch (&) Terrestrial Ecology Working Group, Department of Biology and Environmental Sciences, Faculty V, Carl-von-Ossietzky-University of Oldenburg, 26111 Oldenburg, Germany e-mail: [email protected] T. Blick Zoological research in Hessian strict forest reserves, Senckenberg Research Institute, Senckenberganlage 25, 60325 Frankfurt am Main, Germany e-mail: [email protected] A. Schuldt Department of Ecology and Environmental Chemistry, University of Lu¨neburg, Scharnhorststr. 1, 21314 Lu¨neburg, Germany e-mail: [email protected]

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pattern in spider species richness of Europe quite well, although some of the observed patterns are not directly causal. Climatic variables are expected to be among the most important direct factors, although other variables (e.g. elevation range, plant species richness) are important (surrogate) correlates of spider species richness. Keywords Araneae
Biodiversity
Diversity gradients
Environmental variables
Species richness determinants

Introduction When we look at broad scale diversity (i.e. at the landscape or regional scale), the ‘‘species–area relationship’’ and the ‘‘latitudinal diversity gradient’’ are some of ecology’s few general principles (Lawton 1999; Gaston 2000; Hawkins and Agrawal 2005). The positive linear species–area relationship is usually obvious when a log–log plot of surveyed area against species richness is plotted. The latitudinal diversity gradient—although not without exceptions—predicts an increase in species richness going e.g. from north to south in the Holarctic. This is one of the oldest known patterns of macroecology, for which the underlying causes are still intensively discussed (Rohde 1992; Blackburn and Gaston 2004; Hawkins and Diniz-Filho 2004; Hillebrand 2004). Apart from these principles, the species richness of terrestrial ecosystems is influenced by several other scale dependent factors (Willis and Whittaker 2002). Local diversity is related to the regional species richness and can be influenced by microclimate, site fertility, habitat structure, habitat heterogeneity and biotic interactions (Ricklefs 1987; Goldberg and Miller 1990; Uetz 1991; Cornell and Lawton 1992; Huston 1994; Lawton 1999; Mittelbach et al. 2001). At the landscape scale, climate, habitat area, isolation, and diversity of habitats have to be considered (MacArthur and Wilson 1967; Currie 1991; Rosenzweig 1997; Lomolino 2000). The regional species pool was formed during historical processes like speciation, migration and geological stability, and its size also depends on latitude and area, commonly being larger at low latitudes and in larger areas (Pianka 1966; Hillebrand and Blenckner 2002). Hillebrand and Blenckner (2002) suggested a model that assumes that the structure of a local community is the result of the species passing through a series of filters. These filters represent historical (e.g. dispersal, speciation) and ecological constraints (e.g. competition, predation, abiotic environmental factors) for each species to reach a certain site and to manage to survive there (Lawton 1999). The relative filtering importance of individual factors remains uncertain (Hillebrand and Blenckner 2002). One of the main problems in macroecological studies is the insufficient availability of species richness data for many (especially the invertebrate) taxa as well as of data about environmental variables at large scales (Blackburn 2004; Hawkins and Agrawal 2005). In a macroecological context, spiders (Araneae)—of which more than 40,000 species are presently known worldwide (Platnick 2008)—are among the less well-documented groups. They have seldom been studied in a macroecological framework, as compared to more popular groups such as birds (Lennon et al. 2000; Storch and Kotechy 1999; Storch et al. 2003). During the last decade, however, considerable knowledge was compiled about the spider faunas in different geographic regions throughout Europe. This lead to a currently well documented data-set of species richness for this particular taxonomic group. Though the data-set is certainly incomplete, it is already well suited for a test of hypotheses

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concerning the pattern of species richness at larger scales. Thus, a first continental wide analysis becomes attractive for Europe, especially as this continent is well suited for such an analysis as it comprises a large number of relatively small countries (Maraun et al. 2007). Furthermore, for many environmental variables sufficient data are available from free and reliable sources in the internet. This makes an analysis of macroecological patterns of European spiders possible, updating older studies (e.g. Koponen 1993) and expanding more recent attempts (e.g. Kuntner and Sereg 2002). Thus, this study aims at (1) testing general species–area relationships for European spiders at different scales to confirm general species–area patterns for spiders; (2) seeking for further correlations using different additional explanatory variables in order to determine possible explanations for geographical species richness patterns of spiders throughout Europe. Beside the surface area effect, we supposed that other environmental variables, i.e. elevation range, climatic conditions (temperature and precipitation), and biotic variables, may also correlate with the spider species richness at different scales (e.g. Hawkins et al. 2003). Furthermore, (3) in order to understand the interactions of variables, covariations of spider species richness with different environmental variables were investigated using multiple analyses. Unravelling these patterns may be very helpful to understand further local patterns in nature (Storch and Gaston 2004). Furthermore, since species richness is correlated with many other biodiversity measures (Gaston 1996) it may help better understanding biodiversity patterns in general.

Material and methods Spider species data A list of total spider species richness was compiled from all existing references, which included literature, reliable internet sources, and our own studies (for detailed data and references see Appendices 1 and 2). These data were aggregated at four scales of perception based on Willis and Whittaker (2002): (1)

(2)

(3)

(4)

At the local scale 11 study areas were included at which spiders were caught intensively using various methods to guarantee a high representativeness of the results. For the landscape scale, spider species richness data from the 10 East Frisian Islands at the German North Sea coast were used as a landscape data set, as for these islands detailed faunistical knowledge was compiled during former studies including all recent (last 30 years) records of spider species. All investigations at the local and at the landscape scale were conducted in northwestern Germany. Thus, a similar species pool is supposed to occur in all sites—an important assumption for an analysis of species richness at these two smallest scales. At the regional scale, data from 14 of the 16 German federal states were consulted. The city state of Bremen was joined with Lower Saxony, and data for Hamburg were not available. For the continental scale, available data on spider species richness from 28 European countries were used.

Although the use of federal states or countries as the grain size for the regional and continental analysis may not be the most straightforward way to test biogeographical hypotheses, it has been shown to yield adequate results (e.g. Pandit and Laband 2007; Qian

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2007). Moreover, for many invertebrate animal taxa (i.e. spiders) it is the only approach that can actually be realized, because grid based data usually are not available for larger areas like whole countries (see also Maraun et al. 2007). Environmental variables We used the following variables to analyse species richness patterns of spiders across Europe (in brackets the scales, for which they were used): I) Topographic and spatial variables: a) surface area (local, landscape, regional, continental) b) latitude of middle point (regional, continental) c) elevation range (regional, continental) II)

Climatic variables d) e) f) g)

mean annual temperature (regional, continental) mean annual precipitation (regional, continental) mean July temperature (regional, continental) local variation of mean annual temperature between different climate stations (regional) h) local variation of mean July temperature between different climate stations (regional) III) Biotic variables i)

vascular plant species richness (landscape, regional, continental).

When we look at these environmental variables, we have to keep in mind that many first order descriptive variables (e.g. latitude) are often surrogates and can only be plausibly understood together with their covariates (latitude: predominantly components of climate). These covariates then deliver the empirically plausible relationship (Hawkins and DinizFilho 2004). One of the dominant explanatory variables in geographical analyses of species richness frequently was latitude. In general, it mainly represents a south to north climatic gradient, but many other explanations were also discussed (e.g. habitat diversity, primary production, and historical factors) (Turner 2004; Hawkins and Agrawal 2005; Maraun et al. 2007). As we used the latitude of the middle point, our approach has, however, the limitation that areas may span different ranges of latitudes and thus may be inhabited by different faunas. Furthermore, the species–area relationship has to be considered in the analyses (see Introduction). Besides latitude and area we introduced a third topographical variable using the elevation range as a rough measure of surface heterogeneity, assuming a correlation between surface heterogeneity and habitat diversity as well as heterogeneity of local climate. Countries with a strong topography (i.e. Norway, Switzerland) are expected to provide a greater habitat diversity that is not only related to altitudinal gradients but also to a higher variation in local climates (e.g. lee and weather sites) (Maraun et al. 2007). Since, at larger scales, diversity can be influenced by available energy, temperature may be a useful predictor (Currie 1991; Gaston 2000; Lennon et al. 2000; Whittaker et al. 2001; Turner 2004), especially for poikilothermal arthropods like spiders. Another important climate factor may be precipitation (Whittaker et al. 2001). Furthermore, since the number of plant species is widely hypothesised to influence the number of animal species (Hawkins

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and Porter 2003; Hawkins and Pausas 2004), this number was also included in regression models, this being the only biotic variable. Most values of environmental variables were taken from reliable internet sites or atlases. Geographic information for the continental scale was taken from the CIA world fact book (https://www.cia.gov/library/publications/the-world-factbook/index.html). Surface areas of federal states (regional scale) were obtained from (http://www.statistikportal.de/Statistik-Portal/de_jb09_jahrtabf1.asp). For the landscape and the local scale area data were obtained from the references (Appendix 2). Selected climatic data of the period between 1961 and 1990 were taken from (http://www.dwd.de) for the regional scale. Average values of whole-year means in precipitation came from 4,748 climate stations. Mean annual temperature, mean July temperature and their local variation was calculated for 672 stations. Climatic data supplied in Mitchell et al. (2002) were used for analyses at the continental scale. The geographic coordinates used (latitude and longitude) represent a single point, corresponding to a point in or near the center of the (political) entity. These were found via Google EarthTM for the landscape and the regional scale and in the CIA world fact book (see above) for the continental scale. Plant species richness data are from Groombridge (1992, 1994), Davis et al. (1994), Walter and Gillett (1998), Korneck et al. (1996), and Metzing et al. (2008). Statistical analyses All (log transformed) species richness data followed a normal distribution (KolmogorovSmirnov-Test; P [ 0.05) and thus were analysed further using correlation analyses and linear regression models. These are adequate techniques for statistical description and explanation of relationships between environmental variables and species richness (e.g. Hawkins and Pausas 2004). Except for area and plant species richness, the influence of most variables was only testable at the regional and continental scale, as data at the finer scale were missing or analyses were aberrant (e.g. effects of latitude at the finest scale). We corrected our data on species richness of spiders and plants for sample area effect before correlation analyses proceeded by regressing log10 transformed area and log10 transformed species richness first and then, second, using corrected residuals of species richness as dependent variable for correlations with the remaining environmental variables (Howard et al. 1998; Lamoreux et al. 2006; Qian et al. 2007). 1) First, environmental variables were related separately to residuals of area corrected species richness data by correlation analyses. This way statistical descriptions are obtained of how species richness is related to the specified environmental variable. This test of alternative relationships identifies the ‘‘best’’ models by mere statistical criteria, possible covariation between the environmental variables is not being considered. It is thus essential to interpret the results taking into account the empirical expectations. 2) Patterns of covariation between environmental variables at both broad scales were analysed using bivariate correlation analysis. The problem of spatial autocorrelation (e.g. Diniz-Filho et al. 2003) was accounted for by using the Dutilleul’s method (1993) for a correction of the P-values. This method is implemented in SAM (Rangel et al. 2006). SAM 2.0 was used for correlation analyses. 3) Next, stepwise multiple linear regression analyses were performed using SPSS 11.5 (SPSS Inc. 2002) for a simultaneous analysis of several variables and species richness

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at the large and continental scale (e.g. Zhao et al. 2006). To reduce the problem of multicollinearity in multiple regression, we used only previously selected variables that were correlated with a Spearman-Rho \ 0.7 (see step 2) as input variables to generate a statistical model of the overall pattern at the two largest scales (Quinn and Keough 2002). We also tested performance of Principal Components Analysis (PCA) to account for multicollinearity in environmental variables. However, this did not deliver better results than testing the influence of only previously selected variables in multiple linear models. Thus, we used the latter approach as it allows for better evaluation of single factors influencing spider species richness (Quinn and Keough 2002).

Results Species richness The number of currently known spider species at the regional scale varied between 487 (Saarland) and 842 (Bavaria) per federal state in Germany (mean: 637 ± 92 (SD); Table 1 and Appendix 1). At the two smaller scales, species numbers ranged from 109 ± 28 (local) to 111 ± 57 (landscape), respectively. In Europe, species richness varies greatly and lies between 84 (Iceland) and 1,569 (France) per country (748 ± 325; Table 1). Within Europe, France, Italy and Germany are richest in spider species ([1,000 species), whereas Iceland, Ireland, Latvia, Lithuania, and Luxembourg have the lowest numbers of currently known species (\500 species). Regression analyses Individual environmental variables For log10 transformed surface area, the strongest connection with log10 transformed species richness was found in the log-log plots at the regional scale (German federal states) with area sizes varying between 892 and 70,550 km2, whereas at the landscape scale a weaker but also significantly positive relationship was noticed (area between 0.24 and 30.7 km2) (Table 2). No relationship was apparent at the continental scale (European countries, which range from 488 to 410,934 km2) and at the local scale. At this smallest scale the area size varied between 0.021 and 2 km2 (Table 1). All other variables were analysed using area-corrected spider diversity (area was regressed out also for local and continental scales to remove also weak effects on species richness). Elevation range varies between 75 and 2,855 m a.s.l. at the regional scale and between 180 and 4,809 m a.s.l at the continental scale. It was positively correlated with spider species richness at both broader scales. At the landscape scale, plant species richness ranged from 60 to 669 species and at the regional scale from 1,318 to 2,533 species. For European countries, species richness data ranged from 377 to 5,598 species per country. Area corrected plant species richness was the overall best single predictor variable at both broad scales. Indeed, spider species richness was found to co-vary highly positively with plant species richness (Spearman-Rho [ 0.8). At the landscape scale no such significant correlation was found.

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Table 1 Summary of spider and plant species richness and environmental variables at the four scales of perception Minimum

Maximum

Mean

SD

Spider species richness

58

170

109.3

27.8

Area (km2)

0.02

2.00

0.62

0.69

Latitude (decimal degrees)

52.40

53.78

53.30

0.49

Spider species richness

38

220

110.6

56.9

Vascular plant species richness

60

669

429.8

212.2

Area (km2)

0.2

30.7

12.9

10.8

Latitude (decimal degrees)

53.58

53.78

53.69

0.08

Spider species richness

487

842

637.2

92.3

Vascular plant species richness

1,318

2,533

1,769.5

314.5

Area (km2)

891.8

70,549.2

25,441.5

17,917.8

Local scale

Landscape scale

Regional scale

Latitude (decimal degrees)

43.38

54.23

50.9

2.7

Mean annual temperature (°C)

6.8

9.1

8.3

0.7 3.6

Variation of mean annual temperature (°C)

0.8

14.4

4.8

Mean July temperature (°C)

15.6

18.4

17.0

0.7

Variation of mean July temperature (°C)

0.9

16.5

5.5

4.1

Mean annual precipitation (mm)

551

983

753.4

151.1

Elevation range (m)

75

2855

854

718

Spider species richness

84

1,569

736.1

319.7

Vascular plant species richness

377

5,598

2,463.5

1,337.6

Area (km2)

488

410,934

85,553.6

122,584.9

Latitude (decimal degrees)

39.00

65.00

51.1

7.1

Continental scale

Mean annual temperature (°C)

1.5

15.4

7.9

3.6

Mean July temperature (°C)

8.5

24.4

17.1

3.3

Mean annual precipitation (mm)

536

1,537

840.8

246.7

Elevation range (m)

1.80

4,809

2,030

1,384

At the regional scale, for species richness throughout Germany (latitude between 43.38 and 54.23 decimal degrees north), no significant correlation with latitude was apparent, whereas at the continental scale (between 39.00 and 65.00 north), a conspicuous north to south gradient in spider species richness was found. Also, several covariations with climatic variables became obvious. At the regional scale, local variation of mean annual temperature as well as local variation of mean July temperature, respectively were positively related predictors of spider diversity, whereas absolute means of annual temperature, July temperature, and precipitation were unrelated variables (Table 2). At the continental scale, mean July temperature had a significant influence (Spearman-Rho = 0.49). Precipitation and mean annual temperature at this scale had a negligible effect on spider species richness. To summarize the results of the correlation analyses with regard to a potential scale dependency, at the landscape scale area seems to rule spider species richness pattern.

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Table 2 Correlation coefficients (Spearman-Rho) of environmental variables against the pattern of spider species richness at three spatial scales (significant values in bold) Variable

Spatial scale Landscape (10 islands)

Regional (14 federal states) 0.771**

Continental (28 countries)

Area (log–log plot)

0.624*

Latitude

–

Mean annual temperature

–

0.011

0.437

Variation of mean annual temperature

–

0.661*

–

-0.464

0.154 -0.709*

Mean July temperature

–

0.248

0.490*

Variation of mean July temperature

–

0.582*

–

Mean annual precipitation

–

0.270

Elevation range

–

0.499*

0.727*

Plant species richness (corrected for area)

0.454

0.908**

0.807***

-0.106

All P-values were corrected for spatial autocorrelation using the Dutilleul’s method (1993); with the exception of the variable ‘area’ species richness in all correlations was corrected for area; – = not calculated due to meaningless results or missing data; * P \ 0.05, ** P \ 0.01, *** P \ 0.001

At the regional scale, throughout Germany plant species richness, surface area, variation of mean annual temperature as well as variation of July temperature, and elevation range were significant variables with an Spearman-Rho C 0.5 when tested separately. For the spider species richness across Europe, plant species richness, elevation range, latitude, and mean July temperature were significant variables with an Spearman-Rho C 0.5 (Table 2). Covariation of variables At both broad scales, several variables significantly covariate (Table 3). At the regional scale, mean annual temperature showed a highly significant positive correlation with mean July temperature. Mean July temperature was slightly positively correlated with area. Within the German federal states, elevation range was positively correlated with plant species richness. Additionally, we analysed on this scale the factors local variation of mean annual temperature and local variation of July temperature, which both were correlated highly significantly (Spearman-Rho = 0.991***). Furthermore, both measures of variation in temperature were significantly correlated with elevation range (Spearman-Rho C 0.9***) and with richness in plant species (SpearmanRho C 0.57*). For the continental scale, the two variables (log) area and mean annual precipitation were not correlated with any other variables. Mean annual temperature and mean July temperature correlated naturally, as it was already found at the regional scale. There was a highly significant negative correlation between latitude and mean annual temperature and mean July temperature. Furthermore, plant species richness correlated in a highly positive manner with elevation range, mean annual temperature, and mean July temperature, respectively. In contrast, it was considerably negatively correlated with latitude. Thus, at the continental scale, the species numbers of plants and spiders both show a clinal pattern throughout Europe.

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Continental Regional Latitude

Latitude

Log area

–

0.277

Log area

-0.086

Elevation range

-0.486

0.499

0.059

-0.292

Mean ann. temperature

–

Mean ann. precipit.

-0.626

0.305

Mean July temperature

-0.235

-0.231*

Residual plant species r

-0.477

0.090

Elevation range -0.586 0.054 – -0.574 0.398 -0.363 0.578*

Mean ann. temperature

Mean ann. precipit.

Mean July temperature

Residual plant species r

-0.821***

-0.124

-0.807**

-0.808**

-0.250

-0.184

-0.118

-0.023

0.246

0.232

0.259

0.735**

–

0.048

0.833***

0.511*

-0.077 0.749** -0.174

– -0.169 0.191

-0.276

0.019

–

0.638**

0.125

–

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Table 3 Correlations between the environmental variables at both broad scales (upper right site = continental scale, lower left site = regional scale)

All P-values were corrected for spatial autocorrelation using the Dutilleul’s method (1993); with the exception of the variable log area, species richness of plants was corrected for area in correlations * P \ 0.05, ** P \ 0.01, *** P \ 0.001

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Multiple environmental models As many of the environmental variables were correlated at each scale (Table 3), only the most relevant variables and only variables correlated with a Spearman-Rho \ 0.7 were selected for the multiple environmental models. At the regional scale these most plausible variables were local variation in mean annual temperature (positively correlated with elevation range, and local variation in mean July temperature), mean annual temperature (positively correlated with mean July temperature), mean annual precipitation, and area corrected plant species richness. Other variables were excluded from analyses due to the correction for area (area), due to their pure function as ‘‘dummy’’ variables (latitude), or due their significant correlations (Spearman-Rho [ 0.7) with at least one of the variables included in the multiple models. Using such a set of variables area corrected plant species richness was the only variable included in the model and explained 77% of the total variability in area corrected spider species richness at the regional scale. At the same scale a reduced set of explanatory variables without plant species richness as a variable (justified through its relatively high correlation with variation in mean annual temperature, Spearman-Rho = 0.666*) explained 42% (P = 0.013) of the total variability in area-corrected spider diversity with variation in mean annual temperature being the only variable retained in the multiple model. At the continental scale latitude was also excluded from multiple analyses due to its ‘‘dummy’’ function (see above). Plant species richness was excluded due to its strong positive correlation with elevation range (Spearman-Rho [ 0.7; Table 3). We excluded mean annual temperature from analyses at the continental scale as it also correlates with mean July temperature. Using the variables elevation range, mean July temperature, and mean annual precipitation during the stepwise multiple regression analysis a model including elevation range and mean July temperature as the most relevant variables was build (R2 = 0.56; P \ 0.001). Similar to spider species richness, plant species richness itself was significantly related to variation in mean annual temperature at the regional scale. In the model with the input variables local variation in mean annual temperature, mean annual temperature and mean annual precipitation the only significant variable was local variation in mean annual temperature which explained 55% of the total variability (P = 0.002). At the continental scale, variation in plant species richness was explained to 79% through the variables mean July temperature and elevation range (P \ 0.001; a model with the input variables elevation range, mean July temperature, and mean annual precipitation).

Discussion Several relationships between European spider species richness and environmental parameters were uncovered during this study. When analysed individually, using correlation analyses with species richness data that were corrected for area effects, spider species richness showed a scale-dependent correlation with environmental variables that can be classified according to Lamoreux et al. (2006) to range from moderate (SpearmanRho[ 0.25–0.5; regional scale: elevation range; continental scale: mean July temperature) to large (Spearman-Rho [ 0.5; regional scale: local variation of mean annual temperature, local variation of mean July temperature, area corrected plant species richness; continental scale: latitude, elevation range, area corrected plant species richness). In the multiple models one or two variables account for at least 40% of the explained variability. However,

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overall a high collinearity between different variables makes it difficult to select the potentially ‘‘correct’’ predictor variables for spider species richness. Covariance between explanatory variables is one of the main problems when modelling species richness data (Margules et al. 1987; Gaston 1996; Lobo et al. 2002; Hawkins and Pausas 2004). A significant positive linear relationship (Spearman-Rho[ 0.62) between spider species richness and surveyed surface area exists at two different scales: at the landscape scale of small islands, and at the regional scale throughout the German federal states. At the continental scale only a weak positive relationship was observed. Also, at the local scale the relationship to area was small. There, e.g. habitat type, habitat structure, habitat heterogeneity, and microclimate seem to be more important for spider species richness than area. Generally, the importance of the area tends to be lower at local scales (Gaston 1996). As the species–area relationship is based on the assumption that the climate is consistent (Hawkins et al. 2003) and that habitat diversity also plays an important role (Williams 1964; Rosenzweig 1997; Storch et al. 2003), the weak relationship at the European level may be explained by an interference of the climate as well as by an overlapping of the latitudinal-climatic and topographical gradients. At the continental scale, latitude was found to have a clear negative effect on spider species richness. Throughout Europe (and not significantly: throughout Germany) species richness decreases from south to north forming a typical cline. Thus, spiders are in good association with other taxa showing similar types of clines like e.g. bats, reptiles, oribatid mites, termites and ants (Rosenzweig 1997; Gaston and Blackburn 2000; Willig 2001; Maraun et al. 2007), although a direct causality between species richness and degrees of latitude certainly cannot be constructed (Hawkins and Diniz-Filho 2004). Generally, climate is one of the main factors influencing species richness (Hawkins et al. 2003; Field et al. 2005). At the regional scale, we identified spider species richness to be significantly correlated with the local variation in mean annual and mean July temperature, respectively. Both variables themselves were correlated highly significant with elevation range. Thus, we suppose to regard elevation range as a surrogate variable for variability of local temperature. At the continental scale, mean value of July temperature showed a correlation with species richness data. Unfortunately, on this scale we were not able to test for influence of variation in temperature, but we found species richness of spiders to be strongly correlated with elevation range. This implies, that also at the continental scale variability in the temperature regime of a given spatial entity influences spider species richness strongly. For invertebrates in other studies, direct correlations of species numbers with mean temperature values have been found at country level: For Great Britain, Turner et al. (1987) claimed a dependency of butterfly richness from sunshine and temperature. Similar results were not detected for the spider species richness of Germany during our study. On the regional scale we found that orographic effects influencing the local variations in temperature to be more important than pure mean values. Our analyses concerning the influence of precipitation are flawed by the problem of neglecting the mean actual evapotranspiration. Unfortunately, extensive data on evapotranspiration were not accessible for our predefined areas. Consequently, estimating the true effect of the moisture variable is at least problematic, because it is impossible to compare the available water in e.g. northern with that in southern Germany. Overall, the climatic variables used in our study seem to influence species richness to a lesser extent than previously expected. This may be caused by various effects. First, in different parts of Europe different ecological constraints might be present: in the northern, cold climates, temperature may be the more important factor, whereas in the southern (mediterranean), warm climates water is likely to be the primary factor (Hawkins et al. 2003; Hawkins and

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Pausas 2004). Thus, within our analyses, one factor may be ruled out by another, at least at the broadest scale. Generally, the climatically based energy hypotheses for broad-scaled geographic patterns of species richness is one of the most plausible and best supported hypotheses there is (Lennon et al. 2000; Hawkins et al. 2003; Turner 2004). Besides other variables elevation range turned out to be a good individual predictor variable for spider species richness and also, as we could show as well (Table 3), for the species richness of plants in Europe and in Germany. This may be caused due to a correlated variation in the local temperature regime as it was already shown above. Furthermore, environmental heterogeneity, habitat diversity, and other unknown variables were suggested to be reflected by elevation range (Rosenzweig 1997; Gaston 1996; Hawkins and Porter 2003; Turner 2004). Elevation range influences the topographic relief. Landscapes with a higher heterogeneity in their topographic relief may harbour a higher species number due to a greater variety of available habitats (e.g. Kerr and Packer 1997; Kerr et al. 1998; Rahbek and Graves 2001), although this hypothesis has not been proved for all taxa, especially not for taxa including species with a restricted range size (Belbin 1993; Faith and Walker 1996; Gaston 1996; Arau´jo and Humphries 2001). More or less strong associations between plant and animal species richness have been found in several studies (e.g. Gaston 1992; Siemann et al. 1998; Hawkins and Pausas 2004). In our study, at both broad scales spider species richness was found to be strongly positively correlated with number of plant species. Similar results were obtained by e.g. Zhao et al. (2006) and Qian (2007) for terrestrial vertebrates and plants in China. In contrast, for example Hawkins and Pausas (2004) found that mammal and plant species richness match only weakly. In their study, mammals showed stronger associations with climatic variables. Strong covariation of plant and spider species richness as found in our study may be driven by similar responses of both species groups to specific environmental variables (Gaston 2000). We found spiders to depend on plant species richness and also on various other variables on both larger scales, and we found plants themselves depending on variation in mean annual temperature and elevation range (regional scale), or on latitude, elevation range, mean annual temperature, and mean July temperature, respectively (continental scale). A direct, causal dependency of spider species richness from number of plant species seems unlikely. Spiders do not depend on single plant species but rather on plant architecture, vegetation complexity, and habitat heterogeneity so that close co-evolutionary relationships cannot be expected between both taxa (e.g. Uetz 1991). In conclusion, despite the possibly similar responses of both species groups to specific environmental variables, a greater habitat heterogeneity caused by a larger number of plants may be also responsible for the correlations between numbers of plant and spider species (Tews et al. 2004). Overall, a direct connection between plant and animal richness patterns is hard to detect, even for herbivorous invertebrates like butterflies (Quinn et al. 1998; Hawkins et al. 2003; Hawkins and Porter 2003). Problems and limitations The quality of correlation analyses depends on the availability of qualitatively similar species richness data (Field et al. 2005). Although sampling intensity of spiders has improved during the last decades, still a varying degree of incompleteness of species lists has to be assumed for the considered European regions. As for other invertebrate groups, knowledge is not uniform across different areas (Prendergast et al. 1993; Gaston 1996; Lobo et al. 2002). Especially, it seems likely that some of the Mediterranean areas are

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insufficiently investigated due to low sampling efforts in these areas (e.g. Sardinia, parts of the Balkan Peninsula; Deltshev 2004). In contrast, in Western, Northern and Central Europe knowledge on spider species richness seems to be quite good, although species lists are still being extended as new species are encountered in certain areas, or due to nomenclatural changes. For example, in Germany the species number increased by 48 species (5%) from 956 to 1,004 species between 1995 and 2004 (Platen et al. 1995; Blick et al. 2004). For Norway, between 1989 (Hauge 1989) and 2003 (Aakra and Hauge 2003) 26 species (4.8%) were added. However, although the data are undoubtedly incomplete, they seem to reflect the overall richness pattern quite well and a similar proportion of the overall state and country species richness can reasonably be assumed to be found in the species lists of the German states or European countries. Thus, we assumed our data not to reflect primarily the sampling effort. This legitimates our analyses. If possible, in forthcoming studies further variables should be included at the different scales (e.g. land use pattern; proportion of natural, semi-natural and forest habitats; human population densities; Evans et al. 2007; Hendrickx et al. 2007). Such further explanatory variables could not be analysed during our study but probably play an important role in the distribution of spider species richness across Europe, especially as climatic variables left up to 60% of variation unexplained in multiple regression analyses of spider species richness. Furthermore, deviations may be scale-dependent. Correlations may be weaker or inexistent at certain scales. In many studies where diversity patterns are investigated, grid cells of a certain size are used (e.g. Hawkins and Pausas 2004; Field et al. 2005). We were not able to use such data since there are no mapped spider data available for all of Europe. At present, such grid cell based data sets for spiders are scarce and often still incomplete (e.g. Harvey et al. 2002; Staudt 2007). However, we are convinced that our results grasp the main species richness pattern quite well and will be generally confirmed in more detailed studies which may become possible in the future. Therefore, mapping projects might help to generate such more precise species richness models at various scales. Future aspects Concluding, especially plant species richness, elevation range and elements of climate and local climate variability, respectively, were identified as good predictor variables for spider species richness at different scales throughout Europe. These associations are not thought to be strictly or directly causal in every case. Instead, especially topographic and spatial variables seem to be predominantly surrogates of primary factors like e.g. heterogeneity in habitat structure and microclimate or general climatic conditions and productivity (Rosenzweig 1997). Indeed, potentially the climate is the strongest underlying driving factor when effects on a broad scale are analysed (Hawkins et al. 2003). On finer scales, other factors like e.g. habitat complexity become more important as was shown by Jime´nezValverde and Lobo (2007) and in our study. Consequently, future studies concerning the macroecological patterns of spider species richness in Europe should focus on climatic factors and habitat complexity in order to understand the underlying gradients better. Such studies may also become useful for predicting species richness in regions for which environmental data were gathered but faunistic sampling data are either not available or insufficient (e.g. Margules et al. 1987; Prendergast et al. 1993; Gaston 1996; Hortal et al. 2001; Field et al. 2005). Deficiencies in investigation intensity as mentioned above for certain European areas, or peculiarities within individual areas may be highlighted due to the deviation from the regression lines.

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Acknowledgements We thank R. Biedermann (Oldenburg) and two anonymous reviewers for valuable comments on an earlier draft of this manuscript and for statistical advice.

Appendices Appendix 1 Number of spider species at the local, landscape (East Frisian Island chain), regional (German federal states) and at the continental scale (European countries) Scale

References

Number of spider species

Inland dune area

Local

Finch (1997)

Heathland

Local

Lisken-Kleinmans (1998)

137

Beech forest

Local

Finch (2005a)

113

Spruce forest

Local

Finch (2005a)

105

Pine forest

Local

Finch (2005a)

98

Deciduous forest

Local

Finch (2001)

106

Borkum salt marsh A

Local

Finch et al. (2007)

103

Borkum salt marsh B

Local

Finch et al. (2007)

58

Wangerooge salt marsh C

Local

Finch et al. (2007)

Field A

Local

Lemke (1999)

170

89 110

Field B

Local

Lemke (1999)

113

Borkum Lu¨ttje Ho¨rn

Landscape

Finch (2008)

208

Landscape

Finch (2008)

38

Memmert

Landscape

Finch (2008)

65

Juist

Landscape

Finch (2008)

101

Norderney

Landscape

Finch (2008)

222

Wangerooge

Landscape

Finch (2008)

143

Langeoog

Landscape

Finch (2008)

110

Spiekeroog

Landscape

Finch (2008)

87

Baltrum

Landscape

Finch (2008)

Mellum

Landscape

67

Baden-Wu¨rttemberg

Finch (2008) Federal state Na¨hrig et al. (2003)

97 738

Bavaria

Federal state Blick and Scheidler (2004)

842

Berlin

Federal state Platen and v. Broen (2002)

537

Brandenburg

Federal state Platen et al. (1999)

641

Hesse

Federal state Malten and Blick (2007)

695

Lower Saxony and Bremen Federal state Finch (2004)

675

Mecklenburg-Vorpommern Federal state Martin (1993)

533

North Rhine-Westphalia

Federal state Kreuels and Buchholz (2006)

677

Rhineland Palatinate

Federal state Staudt (2007)

657

Saarland

Federal state Staudt (2000)

487

Saxony

Federal state Hiebsch and Tolke (1996)

615

Saxony-Anhalt

Federal state Sacher and Platen (2004)

649

Schleswig-Holstein

Federal state Finch (2005b)

549

Thuringia

Federal state Sander et al. (2001)

626

Austria

Country

984

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Appendix 1 continued Scale

References

Number of spider species

Belgium

Country

Blick et al. (2004)

701

Bulgaria

Country

Blagoev et al. (2008)

989

Croatia

Country

Milosevic (2002)

643

Czech Republic

Country

Blick et al. (2004)

841

Denmark

Country

Scharff and Gudik-Sørensen (2008)

531

Estonia

Country

Vilcas (2003)

511

Finland

Country

Koponen (2007)

France

Country

Le Peru (2007)

1,569

Germany

Country

Blick et al. (2004)

1,004

Great Britain

Country

Merrett and Murphy (2000)

645

Greece

Country

846

Hungary

Country

Bosmans and Chatzaki (2005) Samu and Szineta´r (1999)

Iceland

Country

Agnarsson (1996)

Ireland

Country

Cawley (2001)

636

725 84 392

Italy

Country

Trotta (2005)

Latvia

Country

Relys and Spungis (2003)

1,537 446

Lithuania

Country

Vilkas (2003)

439

Luxembourg

Country

Finch et al., unpubl. data

353

Norway

Country

Aakra and Hauge (2003)

562

Poland

Country

Blick et al. (2004)

792

Portugal

Country

730 972

Romania

Country

Cardoso (2007) Weiss and Ura´k (2000)

Serbia

Country

Deltshev et al. (2003)

614

Slovakia

Country

Blick et al. (2004)

906

Slovenia

Country

Kuntner and Sereg (2002)

529

Sweden

Country

Almquist (2007)

715

Switzerland

Country

Blick et al. (2004)

945

The Netherlands

Country

Blick et al. (2004)

621

Appendix 2 Reference list of the publications used for the species richness data on European spiders. Aakra K, Hauge E (2003) Checklist of Norwegian spiders (Arachnida: Araneae), including Svalbard and Jan Mayen. Norw J Entomol 50:109–129 Almquist S (2007) Swedish Araneae, part 2, families Dictynidae to Salticidae. Insect Sys Evol Supplements 63:285–603 Agnarsson I. (1996) I´slenskar Ko¨ngulaer. Fjo¨lrit Na´ttu´rufraedistofnunar 31:1–175 Blagoev G, Deltshev C, Lazarov S (2008) The Spiders (Araneae) of Bulgaria. Institute of Zoology, Bulgarian Academy of Sciences. Internet: http://www.cl.bas.bg/bulgarian spiders/

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Blick T, Scheidler M (2004) Rote Liste gefa¨hrdeter Spinnen (Arachnida: Araneae) Bayerns. Schriftenr Bayer Landesamt Umweltschutz 166:308–321 Blick T, Bosmans R, Buchar J et al (2004) Checklist of the spiders of Central Europe. (Arachnida: Araneae). Version 1. December 2004. Internet: http://www.arages.de/ checklist.html#2004_Araneae Bosmans R, Chatzaki M (2005) A catalogue of spiders of Greece. A critical review of all spider species cited from Greece with their localities. Newsl Belgian Arachnol Soc (Brussels) 20, Supplement:1–124 Cardoso P. (2007) Portugal spider catalogue (v1.3). Internet: http://www.ennor.org/ catalogue.php Cawley M (2001) Distribution records for uncommon spiders (Araneae) including five species new to Ireland. Bull Irish Biogeogr Soc 25:135–143 Deltshev CD, Curcic BPM, Blagoev GA (2003) The spiders of Serbia. Belgrad and Sofia Finch O-D (1997) Die Spinnen (Araneae) der Trockenrasen eines nordwestdeutschen Binnendu¨nenkomplexes. Drosera 97:21–40 Finch O-D (2001) Zo¨nologische und parasitologische Untersuchungen an Spinnen (Arachnida, Araneae) niedersa¨chsischer Waldstandorte. Arch zool Publ 4:1– 199 + appendix Finch O-D (2004) Rote Liste der in Niedersachsen und Bremen gefa¨hrdeten Webspinnen (Araneae) mit Gesamtartenverzeichnis. 1. Fassung vom 1.7.2004. Inform.d Natursch Nieders, Supplement 24:1–20 Finch O-D (2005a) Evaluation of mature conifer plantations as secondary habitats for epigeic forest arthropods (Coleoptera: Carabidae; Araneae). For Ecol Manage 204:21– 34. http://dx.doi.org/10.1016/j.foreco.2004.07.071 Finch O-D (2005b) Erga¨nzungen und Berichtigungen zum ‘‘Verzeichnis der Spinnen (Araneae) des nordwestdeutschen Tieflandes und Schleswig-Holsteins’’ von Fru¨nd et al (1994). Arachnol Mitt 29:35–44 Finch O-D (2008) Webspinnen, Weberknechte und Pseudoskorpione der Ostfriesischen Inseln (Arachnida: Araneae, Opilionida, Pseudoscorpionida). Schr.R Nationalpark Nieders Wattenmeer 11:103–112 Finch O-D, Krummen H, Plaisier F et al (2007) Zonation of spiders (Araneae) and carabid beetles (Coleoptera: Carabidae) in island salt marshes at the North Sea coast. Wetl Ecol Manage 15:207–228. http://dx.doi.org/10.1007/s11273-006-9024-4 Hiebsch H, Tolke D (1996) Rote Liste Weberknechte und Webspinnen. Materialien zu Naturschutz und Landschaftspflege. Sa¨chsisches Landesamt fu¨r Umwelt und Geologie, Radebeul 1996:1–12 Koponen S (2007) Checklist of spiders in Finland (Araneae). Internet: http://users.utu.fi/ sepkopo/checklist_of_spiders_in_Finland.htm ¨ kologie, Verbreitung und Gefa¨hrdungsstatus der WebsKreuels M, Buchholz S (2006) O pinnen Nordrhein-Westfalens—Erste u¨berarbeitete Fassung der Roten Liste der Webspinnen (Arachnida: Araneae). Wolf & Kreuels, Havixbeck-Hohenholte Kuntner M, Sereg I (2002) Additions to the spider fauna of Slovenia, with a comparison of spider species richness among European countries. Bull British Arachnol Soc 12: 185–195 Lemke A (1999) Die Bedeutung von eingesa¨ten Krautstreifen in intensiv gefu¨hrten Winterweizenfeldern fu¨r die Populationsdynamik von Spinnen und Getreideblattla¨usen. PhD Thesis University of Hannover Le Peru, B (2007) Catalogue et re´partition des araigne´es de France. Revue Arachnologique 16:1–468

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Lisken-Kleinmans A (1998) The spider community of a northern German heathland: faunistic results. Proc 17th Europ Coll Arachnol, Edingburgh:277–284 Malten A, Blick T (2007) Araneae (Spinnen). In: Naturwaldreservate in Hessen 7/2.2. Hohestein. Zoologische Untersuchungen 1994–1996, Teil 2.—Mitteilungen der Hessischen Landesforstverwaltung 42:7–93 Martin D (1993) Rote Liste der gefa¨hrdeten Spinnen Mecklenburg-Vorpommerns. Umweltministerium Mecklenburg-Vorpommern, Schwerin Merrett P, Murphy JA (2000) A revised check list of British Spiders. Bull British Arachnol Soc 11:345–358 Milosevic B (2002) Aranea—Popis vrsta Checklist. Internet: http://www.agr.hr/hed/ hrv/ento/inventar/liste/aranea.htm Na¨hrig D, Kiechle J, Harms KH (2003) Rote Liste der Webspinnen (Araneae) BadenWu¨rttembergs. Naturschutz-Praxis Artenschutz 7:7–162 Platen R, v. Broen B, Herrmann A et al (1999) Gesamtartenliste und Rote Liste der Webspinnen, Weberknechte und Pseudoskorpione des Landes Brandenburg (Arachnida: Araneae, Opiliones, Pseudoscorpiones) mit Angaben zur Ha¨ufigkeit und ¨ kologie. Natursch Landschaftspfl Brandenburg, Beilage (Heft 2) 1999:1–79 O Platen R, v. Broen B (2002) Checkliste und Rote Liste der Webspinnen und Weberknechte ¨ kologie. (Arachnida: Araneae, Opiliones) des Landes Berlin mit Angaben zur O Ma¨rkische Entomol Nachr, Sonderheft 2:1–69 Relys V, Spungis V (2003) Check list of spiders (Arachnida, Araneae) of Latvia. Internet: http://leb.daba.lv/Aranea.htm Sacher P, Platen R (2004) Rote Liste der Webspinnen (Arachnida: Araneae) des Landes Sachsen-Anhalt. Ber Landesamt Umweltsch Sachsen-Anhalt 39:190–197 Samu F, Szineta´r C (1999) Bibliographic check list of the Hungarian spider fauna. Bull British Arachnol Society 11:161–184 Sander FW, Malt S, Sacher P (2001) Rote Liste der Webspinnen (Arachnida: Araneae) Thu¨ringens. Naturschutzreport 18:55–63 Scharff N, Gudik-Sørensen O (2008) Checklist of Danish Spiders (Araneae). Version 24-01-2008. Internet: http://www.zmuc.dk/entoweb/arachnology/dkchecklist.htm Staudt A (2000) Neue und bemerkenswerte Spinnenfunde im Saarland und angrenzenden Gebieten in den Jahren 1996–99. Abh Delattinia 26:5–22 Staudt A (2007) Nachweiskarten der Spinnentiere Deutschlands. Internet: http://www. spiderling.de/arages/ Trotta A (2005) Introduzione ai ragni italiani (Arachnida Araneae). Mem Soc Entomol Ital 83 (2004):3–178 Vilkas A (2003) Spiders of Lithuania. Internet: http://lietvorai.puslapiai.lt/check_list.htm Weiss I, Ura´k I (2000) Checklist of the Romanian spiders (Arachnida: Araneae). Internet: http://members.aol.com/Arachnologie/Faunenlisten.htm

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Blackburn TM (2004) Method in macroecology. Basic Appl Ecol 5:401–412. doi:10.1016/j.baae. 2004.08.002 Blackburn TM, Gaston KJ (2004) Macroecology. Basic Appl Ecol 5:385–387. doi:10.1016/j. baae.2004.08.005 Blick T, Bosmans R, Buchar J et al (2004) Checklist of the spiders of Central Europe (Arachnida: Araneae), Version 1. December 2004. Internet: http://www.arages.de/checklist.html#2004_Araneae Currie DJ (1991) Energy and large-scale patterns of animal- and plant-species richness. Am Nat 137:27–49. doi:10.1086/285144 Cornell HV, Lawton JH (1992) Species interactions, local and regional processes, and limits to the richness of ecological communities: a theoretical perspective. J Anim Ecol 61:1–12. doi:10.2307/5503 Davis, DD, Heywood VH, Hamilton AC (eds) (1994) Centres of plant diversity: a guide and strategy for their conservation. Vol. 1: Europe, Africa, South West Asia and the Middle East. IUCN Publication Unit, Cambridge Deltshev C (2004) A zoogeographical review of the spiders (Araneae) of the Balkan Peninsula. In: Griffiths HI, Krystufek B, Ree JM (eds) Balkan biodiversity—pattern and process in the European hotspot. Kluwer, Dordrecht, pp 193–200 Diniz-Filho JAF, Bini LM, Hawkins BS (2003) Spatial autocorrelation and red herrings in geographical ecology. Glob Ecol Biogeogr 12:53–64. http://dx.doi.org/10.1046/j.1466-822X.2003.00322.x Dutilleul P (1993) Modifying the t test for assessing the correlation between two spatial processes. Biometrics 49:305–314. doi:10.2307/2532625 Evans KL, Greenwood JD, Gaston KJ (2007) The positive correlation between avian species richness and human population density in Britain is not attributable to sampling bias. Glob Ecol Biogeogr 16:300– 304. doi:10.1111/j.1466-8238.2006.00288.x Faith DP, Walker PA (1996) Environmental diversity: on the best-possible use of surrogate data for assessing the relative biodiversity of sets of areas. Biodivers Conserv 5:399–415. doi: 10.1007/BF00056387 Field R, O’Brien EM, Whittaker RJ (2005) Global models for predicting woody plant richness from climate: development and evaluation. Ecology 86:2263–2277. doi:10.1890/04-1910 Gaston KJ (1992) Regional numbers of insect and plant species. Funct Ecol 6:243–247. doi: 10.2307/2389513 Gaston KJ (1996) Species richness: measure and measurement. In: Gaston KJ (ed) Biodiversity. A biology of numbers and difference. Blackwell Science, Oxford, pp 77–113 Gaston KJ (2000) Global patterns in biodiversity. Nature 405:220–227. doi:10.1038/35012228 Gaston KJ, Blackburn TM (2000) Pattern and process in macroecology. Blackwell Sciences, Oxford Goldberg DE, Miller TE (1990) Effects of different resource additions on species diversity in an annual plant community. Ecology 71:213–225. doi:10.2307/1940261 Groombridge B (ed) (1992) Global biodiversity: status of the earth’s living resources. Chapman & Hall, London Groombridge B (ed) (1994) Biodiversity data sourcebook. World Conservation Press, Cambridge Harvey PR, Nellist DR, Telfer MG (2002) Provisional atlas of British spiders (Arachnida, Araneae), vols 1 and 2. Biological Records Centre, Huntingdon Hauge E (1989) An annotated check-list of Norwegian spiders (Araneae). Insecta Norvegiae 4:1–40 Hawkins BA, Agrawal AA (2005) Latitudinal gradients. Ecology 86:2261–2262. doi:10.1890/05-0004 Hawkins BA, Diniz-Filho AAF (2004) ‘Latitude’ and geographic patterns in species richness. Ecography 27:268–272. doi:10.1111/j.0906-7590.2004.03883.x Hawkins BA, Pausas JG (2004) Does plant richness influence animal richness? The mammals of Catalonia (NE Spain). Divers Distrib 10:247–252. doi:10.1111/j.1366-9516.2004.00085.x Hawkins BA, Porter EE (2003) Does herbivore diversity depend on plant diversity? The case of California butterflies. Am Nat 161:40–49. doi:10.1086/345479 Hawkins BA, Field R, Cornell HV et al (2003) Energy, water, and broad-scale geographic patterns of species richness. Ecology 84:3105–3117. doi:10.1890/03-8006 Hendrickx F, Maelfait J-P, van Wingerden W et al (2007) How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J Appl Ecol 44:340–351. doi:10.1111/j.1365-2664.2006.01270.x Hillebrand H (2004) On the generality of the latitudinal diversity gradient. Am Nat 163:192–211. doi: 10.1086/381004 Hillebrand H, Blenckner T (2002) Regional and local impact on species diversity—from pattern to processes. Oecologia 132:479–491. doi:10.1007/s00442-002-0988-3

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