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Macroecological patterns of spider species richness across Europe

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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 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.
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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, cli-
matic 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: oliver.d.finch@uni-oldenburg.de
T. Blick
Zoological research in Hessian strict forest reserves, Senckenberg Research Institute,
Senckenberganlage 25, 60325 Frankfurt am Main, Germany
e-mail: theo.blick@senckenberg.de
A. Schuldt
Department of Ecology and Environmental Chemistry, University of Lu
¨
neburg, Scharnhorststr. 1,
21314 Lu
¨
neburg, Germany
e-mail: schuldt@uni.leuphana.de
123
Biodivers Conserv (2008) 17:2849–2868
DOI 10.1007/s10531-008-9400-x
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 ‘spe-
cies–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 his-
torical 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 pat-
terns 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 deter-
mine 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 per-
ception based on Willis and Whittaker (2002):
(1) 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.
(2) 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 north-
western 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.
(3) 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.
(4) 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) mean annual temperature (regional, continental)
e) mean annual precipitation (regional, continental)
f) mean July temperature (regional, continental)
g) 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 Diniz-
Filho 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). Sur-
face areas of federal states (regional scale) were obtained from (http://www.statistik-
portal.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 pre-
cipitation came from 4,748 climate stations. Mean annual temperature, mean July
temperature and their local variation was calculated for 672 stations. Climatic data sup-
plied in Mitchell et al. (2002) were used for analyses at the continental scale. The
geographic coordinates used (latitude and longitude) represent a single point, corre-
sponding to a point in or near the center of the (political) entity. These were found via
Google Earth
TM
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 (Kolmogorov-
Smirnov-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 log
10
transformed area and log
10
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
Biodivers Conserv (2008) 17:2849–2868 2853
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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 log
10
transformed surface area, the strongest connection with log
10
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 km
2
, whereas at the landscape scale a weaker
but also significantly positive relationship was noticed (area between 0.24 and 30.7 km
2
)
(Table 2). No relationship was apparent at the continental scale (European countries,
which range from 488 to 410,934 km
2
) and at the local scale. At this smallest scale the area
size varied between 0.021 and 2 km
2
(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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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.
Table 1 Summary of spider and plant species richness and environmental variables at the four scales of
perception
Minimum Maximum Mean SD
Local scale
Spider species richness 58 170 109.3 27.8
Area (km
2
) 0.02 2.00 0.62 0.69
Latitude (decimal degrees) 52.40 53.78 53.30 0.49
Landscape scale
Spider species richness 38 220 110.6 56.9
Vascular plant species richness 60 669 429.8 212.2
Area (km
2
) 0.2 30.7 12.9 10.8
Latitude (decimal degrees) 53.58 53.78 53.69 0.08
Regional scale
Spider species richness 487 842 637.2 92.3
Vascular plant species richness 1,318 2,533 1,769.5 314.5
Area (km
2
) 891.8 70,549.2 25,441.5 17,917.8
Latitude (decimal degrees) 43.38 54.23 50.9 2.7
Mean annual temperature (°C) 6.8 9.1 8.3 0.7
Variation of mean annual temperature (°C) 0.8 14.4 4.8 3.6
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
Continental scale
Spider species richness 84 1,569 736.1 319.7
Vascular plant species richness 377 5,598 2,463.5 1,337.6
Area (km
2
) 488 410,934 85,553.6 122,584.9
Latitude (decimal degrees) 39.00 65.00 51.1 7.1
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
Biodivers Conserv (2008) 17:2849–2868 2855
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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 (Spearman-
Rho 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.
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)
Continental
(28 countries)
Area (log–log plot) 0.624* 0.771** 0.154
Latitude -0.464 -0.709*
Mean annual temperature 0.011 0.437
Variation of mean annual temperature 0.661*–
Mean July temperature 0.248 0.490*
Variation of mean July temperature 0.582*–
Mean annual precipitation 0.270 -0.106
Elevation range 0.499* 0.727*
Plant species richness (corrected for area) 0.454 0.908** 0.807***
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
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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)
Continental
Regional Latitude Log area Elevation range Mean ann. temperature Mean ann. precipit. Mean July temperature Residual plant species r
Latitude 0.277 -0.586 -0.821*** -0.124 -0.807** -0.808**
Log area -0.086 0.054 -0.250 -0.184 -0.118 -0.023
Elevation range -0.486 0.499 0.246 0.232 0.259 0.735**
Mean ann. temperature 0.059 -0.292 -0.574 0.048 0.833*** 0.511*
Mean ann. precipit. -0.626 0.305 0.398 -0.077 -0.276 0.019
Mean July temperature -0.235 -0.231* -0.363 0.749** -0.169 0.638**
Residual plant species r -0.477 0.090 0.578* -0.174 0.191 0.125
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 temper-
ature 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 (R
2
= 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 ele-
vation 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 corre-
lation 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 (Spearman-
Rho[ 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 het-
erogeneity, 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 tem-
perature, 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 con-
tinental 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 evapo-
transpiration 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
Biodivers Conserv (2008) 17:2849–2868 2859
123
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. Fur-
thermore, 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-evo-
lutionary 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
2860 Biodivers Conserv (2008) 17:2849–2868
123
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 (Ro-
senzweig 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
´
nez-
Valverde 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.
Biodivers Conserv (2008) 17:2849–2868 2861
123
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) 170
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) 89
Field A Local Lemke (1999) 110
Field B Local Lemke (1999) 113
Borkum Landscape Finch (2008) 208
Lu
¨
ttje Ho
¨
rn 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) 67
Mellum Landscape Finch (2008) 97
Baden-Wu
¨
rttemberg Federal state Na
¨
hrig et al. (2003) 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 Blick et al. (2004) 984
2862 Biodivers Conserv (2008) 17:2849–2868
123
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) 636
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 Bosmans and Chatzaki (2005) 846
Hungary Country Samu and Szineta
´
r (1999) 725
Iceland Country Agnarsson (1996) 84
Ireland Country Cawley (2001) 392
Italy Country Trotta (2005) 1,537
Latvia Country Relys and Spungis (2003) 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 Cardoso (2007) 730
Romania Country Weiss and Ura
´
k (2000) 972
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
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... The commonly discussed and observed increase in the number of species as latitude decreases may anyway depend on the spatial scale. Indeed, this may be the case of spiders, which have been found to be affected by climate at both European and Iberian scales (Finch et al. 2008;Ysnel et al. 2008;Carvalho et al. 2012) but less so at small scales, where the relevance of climate may be substituted by factors such as the spatial distribution of microclimatic conditions or habitat structure (represented by forest type). This may be the case in our study, where the number of species can be better explained by forest type and climatic similarity between plots. ...
... Climate may be a key driver of the assembly of spider communities at continental and Iberian scales (Finch et al. 2008;Carvalho et al. 2011a, b) but, contrary to our expectations, it had a smaller effect on the structure of the communities that we studied. Mean annual temperature and rainfall may have a substantial influence on taxonomic community structure at the regional scale (Fig. 5b). ...
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Understanding the causes behind species richness and endemicity is fundamental to explain biodiversity and assist conservation management, especially in biodiversity hotspots like the Mediterranean Basin. Here we investigate the patterns in Iberian forest spider communities and the processes behind their assembly, by testing hypotheses about the effects of climate and habitat on species richness, endemicity and structure of communities at different spatial scales, and about how microhabitat and dispersal affect the level of endemicity of species. We studied 16 spider communities in Iberian Quercus forests from different climatic zones, applying a standardised sampling protocol. We examined the contribution of habitat, climate, and geography to the differences in the composition of spider communities across spatial scales using distance-based redundancy analysis models (dbRDA) and principal coordinates of neighbour matrices (PCNM). We assessed the effects of the same variables on the endemicity of communities (measured by a weighted index), and tested the correlation between the microhabitat and the ballooning frequency (obtained from bibliography), and the endemicity of species through generalised linear models. Spider communities formed two groups—one southern and one northern—based on similarity in species composition. Precipitation and temperature were inversely related with the number of species while geography and forest type explained the compositional similarities between communities at different spatial scales. Endemicity of communities increased with temperature and decreased with precipitation, whereas species endemicity decreased with ballooning frequency. Our findings illustrate how niche-related processes may drive spider diversity while dispersal determines species distribution and identity and, ultimately, community composition. From a conservation viewpoint, when maximising species richness is incompatible with prioritising endemicity, the criteria to follow may depend on the geographic scale at which decisions are made.
... Elevation and latitude are among the most prominent predictors of species richness globally (Gillooly & Allen, 2007), generally showing a decrease of richness towards harsh environments in which only specialists can survive (Hodkinson, 2005;Pellissier et al., 2012;Peters et al., 2016). We found support for this pattern along the studied elevation gradient, but previous studies focusing on larger latitudinal gradients on national scales did not observe such a pattern (Germany [Finch et al., 2008], Sweden [Arvidsson et al., 2016]). Other predictors in our analyses were more important for explaining local richness, particularly canopy cover. ...
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Understanding species richness variation among local communities is one of the central topics in ecology, but the complex interplay of regional processes, environmental filtering and local processes hampers generalization on the importance of different processes. Here, we aim to unravel drivers of spider community assembly in temperate forests by analyzing two independent data sets covering gradients in elevation and forest succession. We test the following four hypotheses: (H1) Spider assemblages within a region are limited by dispersal; (H2) Local environment has a dominant influence on species composition; and (H3) resources and (H4) biotic interactions both affect species richness patterns. In a comprehensive approach, we studied species richness, abundance, taxonomic composition and trait‐phylogenetic dissimilarity of assemblages. The decrease in taxonomic similarity with increasing spatial distance was very weak, failing to support H1. Functional clustering of species in general and with canopy openness strongly supported H2. Moreover, this hypothesis was supported by a positive correlation between environmental and taxonomic similarity and by an increase in abundance with canopy openness. Resource determination of species richness (H3) could be confirmed only by the decrease of species richness with canopy cover. Finally, decreasing species richness with functional clustering indicating effects of biotic interactions (H4) could only be found in one analysis and only in one dataset. In conclusion, our findings indicate that spider assemblages within a region are mainly determined by local environmental conditions, while resource availability, biotic interactions and dispersal play a minor role. Our approach shows that both the analysis of different aspects of species diversity and replication of community studies are necessary to identify the complex interplay of processes forming local assemblages.
... The spider communities from power pole islands and grassland fallows showed a higher proportion of species preferring the herb layer, which may be once again related to the more complex vegetation structure. In general, higher vegetation complexity is associated with higher species richness of spiders (Finch et al. 2008). ...
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Agricultural intensification and the concomitant landscape homogenization is leading to a worldwide decline in farmland biodiversity. Non-crop habitats in agroecosystems may counteract the loss of arthropods such as spiders and thus contribute to sustainable agriculture. However, the effectiveness of field margins and set-aside wildflower-sown patches in maintaining spider diversity is not well understood. Here, we investigated the effects of three different non-crop habitats, namely field margins, set-aside wildflower-sown patches under power poles (‘power pole islands’), and grassland fallows on spider diversity as compared to wheat fields in an agricultural landscape in western Germany. Using pitfall trapping and suction sampling, we show that species richness and overall conservation value were higher in non-crop habitats than in wheat fields. Interestingly, field margins and power pole islands differed from long-term grassland fallows only in conservation value, which was significantly higher in grassland fallows. Species assemblages differed considerably between grassland fallows, field margins and power pole islands, and wheat fields, documenting the added value of using different conservation strategies. Implications for insect conservation Small-scale non-crop habitats adjacent to wheat fields were surprisingly effective in promoting spider diversity in an agricultural landscape, with field margins and power pole islands being equally effective. To maximize overall diversity in agricultural landscapes, we propose a combination of larger long-term fallows and smaller non-crop habitats such as field margins or set-aside wildflower-sown patches.
... While most existing global macroecological studies focus on plants (Massante et al., 2019), birds (Jetz et al., 2014), mammals (Safi et al., 2011), and herpetofauna (Fritz & Rahbek, 2012), arthropods have been largely ignored (Beck & McCain, 2020). Among arthropods, predatory soil-dwelling communities have been particularly neglected in macroecological studies (Finch et al., 2008), as they typically consist of many cryptic species occurring in low abundance, making them difficult to detect. This is coupled with a lack of taxonomic expertise in identifying them to the species-level, as well as the presence of many undescribed species/lineages. ...
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The Western Ghats (WG) mountain chain in peninsular India is a global biodiversity hotspot, one in which patterns of phylogenetic diversity and endemism remain to be documented across taxa. We used a well‐characterized community of ancient soil predatory arthropods from the WG to understand diversity gradients, identify hotspots of endemism and conservation importance, and highlight poorly studied areas with unique biodiversity. We compiled an occurrence dataset for 19 species of scolopendrid centipedes, which was used to predict areas of habitat suitability using bioclimatic and geomorphological variables in Maxent. We used predicted distributions and a time‐calibrated species phylogeny to calculate taxonomic and phylogenetic indices of diversity, endemism, and turnover. We observed a decreasing latitudinal gradient in taxonomic and phylogenetic diversity in the WG, which supports expectations from the latitudinal diversity gradient. The southern WG had the highest phylogenetic diversity and endemism, and was represented by lineages with long branch lengths as observed from relative phylogenetic diversity/endemism. These results indicate the persistence of lineages over evolutionary time in the southern WG and are consistent with predictions from the southern WG refuge hypothesis. The northern WG, despite having low phylogenetic diversity, had high values of phylogenetic endemism represented by distinct lineages as inferred from relative phylogenetic endemism. The distinct endemic lineages in this subregion might be adapted to life in lateritic plateaus characterized by poor soil conditions and high seasonality. Sites across an important biogeographic break, the Palghat Gap, broadly grouped separately in comparisons of species turnover along the WG. The southern WG and Nilgiris, adjoining the Palghat Gap, harbor unique centipede communities, where the causal role of climate or dispersal barriers in shaping diversity remains to be investigated. Our results highlight the need to use phylogeny and distribution data while assessing diversity and endemism patterns in the WG. Soil arthropod communities play an important role in the ecosystem, but have been poorly studied in terms of large‐scale patterns of diversity and distribution from a phylogenetic perspective. We studied centipedes, a group of ancient predatory soil arthropods, across the climatic gradient of the Western Ghats, India, a global biodiversity hotspot with a complex geological history, using extensive primary distribution data and a robust phylogeny. Our results reveal a latitudinal gradient in phylogenetic diversity of centipedes along the mountain range, identify unique hotspots of phylogenetic endemism, provide support to the existence of a past rainforest refuge, and indicate structuring of centipede communities across various subregions.
... The old fallows in the current study had higher vegetation cover and plant height than the cereal fields and new fallows during the study period . According to Finch et al. (2008), species richness of spiders is generally positively correlated to vegetation complexity. The proximity of semi-natural habitats with more complex vegetation could therefore enhance the number of spider species in adjacent agricultural fields (Öberg et al., 2008). ...
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• The European Union reformed the Common Agricultural Policy (CAP 2013) to include greening measures with the aim to decrease negative impacts of farming on the environment and biodiversity. The degree to which greening measures such as permanent grassland or fallows of different ages enhance biodiversity is still debated. • We investigate the effect of fallows in two different age classes and permanent grassland in the surrounding landscape on the taxonomic and functional diversity of two numerically dominant groups of natural enemies in cereal fields: soil-emerging carabid beetles (Family Carabidae, Order Coleoptera) and ground-active linyphiid spiders (Family Linyphiidae, Order Araneae). • The species richness, abundance and Hill–Shannon diversity of carabids and linyphiids did not differ significantly between fallows and cereal fields and was not significantly related to the proportion of permanent grassland in the surrounding landscape. The species composition of both communities differed significantly between cereal fields and fallows. The functional distinctness, as an index reflecting the similarity among species in terms of functional traits, of linyphiids was significantly higher in fallows than in cereal fields. The trait composition of carabids was significantly related to the proportion of permanent grassland depending on field type (cereal or fallow). Our results document considerable species turnover in natural enemy communities of adjacent cereal fields and fallows, and support the assumption that older fallows (>8 years) produce functionally more diverse natural enemy communities. Maintaining fallows for a long period is an important measure to promote the functional diversity in predaceous arthropod communities.
... This also corresponds with the patterns observed at the location-level; as the magnitude of difference in spider richness between TNC-Z and the other two locations corresponds well to the magnitude of difference in elevation among locations. Other studies have also found that spider species richness is positively correlated with elevation because higher elevation sites tend to have lower summer temperatures and higher plant richness [57,58]. These factors most likely result in an increase of moisture that may benefit many spider families, especially wolf spiders, which prefer areas with higher moisture [59,60]. ...
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Grassland restoration in North America has intensified but its impact on major invertebrate groups, including spiders, is unclear. We studied three grassland locations in the Pacific Northwest, USA, to (1) describe variability in spider communities, (2) identify environmental variables that may underlie patterns in spider communities, and (3) determine whether spiders and environmental variables differ between actively (removal of disturbances, then plant with natives) vs. passively restored sites (removal of disturbance only). We found spider richness, diversity, and composition differed among the three locations but abundance did not. Sites with more litter and invasive grass cover had more spiders while sites at higher elevation and with more forb and biological soil crust cover had increased spider richness and diversity. Spider community composition was associated with elevation and litter cover. Surprisingly, no spider community or environmental variables differed between actively and passively restored sites, except that litter cover was higher in passively restored sites. This study demonstrates that even in superficially similar locations, invertebrate communities may differ greatly and these differences may prevent consistent responses to active vs. passive restoration. If increasing biodiversity or the abundance of invertebrate prey are goals, then environmental factors influencing spider communities should be taken into account in restoration planning.
... Several species, particularly among the Linyphiidae and Gnaphosidae, were also associated with colder locations, which contrasts with the nearuniform preference of beetles for warmer sites. Previous studies showed that spider richness was generally positively correlated with temperature (Jiménez-Valverde and Lobo 2007;Finch et al. 2008), but negative relationships between species richness and temperature were also observed in Linyphiidae along latitudinal (Loboda and Buddle 2018) and elevational (Koponen 1993) gradients. The species that are mostly restricted to colder locations might have lower tolerances for warmer conditions or droughts, but to our knowledge these aspects were not documented in the scientific literature on forest spiders. ...
Article
Separating the influence of climate and habitat characteristics on forest communities could help better understand their potential sensitivity to environmental change. In this study, we sampled spiders and beetles in similar forest types, located along a ca. 4°C mean annual temperature spatial gradient in the boreal forest zone in Quebec, Canada. Specifically, we aimed to separate the effect on arthropod communities of two habitat-related factors that can be influenced by forest management (stand composition and stand age), and another one that cannot (climate). Overall, spider assemblages tended to be more abundant and species-rich in younger forest stands, while beetle assemblages were more abundant and species-rich in deciduous forest stands. Eight beetle and six spider species were significantly influenced by climate, independently from forest type, whereas 11 beetle and seven spider species were significantly influenced by both forest type and climate. While most of the beetle species affected by climate were associated with warmer locations, several spider species were more abundant in colder locations. By helping to ensure the retention of key forest types along potential dispersal pathways at the landscape level, forest management activities could help the conservation of species belonging to relatively cryptic taxa such as arthropods in a climate change context.
... Few studies have examined the latitudinal gradient of predatory arthropod diversity, while other macro-ecological patterns were investigated in these taxa (e.g., for spiders: Arvidsson et al., 2016;Finch et al., 2008;Kozlov et al., 2015;Pitta et al., 2019;Ysnel et al., 2008). ...
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Throughout the Neotropics, temperature and precipitation change with elevation and these changes affect the assemblage of species at any particular elevation. We documented the diversity of litter‐inhabiting spiders, (Arachnida: Araneae) along a Costa Rican elevational gradient as it relates to covarying abiotic factors such as temperature and precipitation. The spiders we collected were principally unidentifiable juveniles, and so we used Barcode Index Numbers (BINs) derived from DNA barcodes as proxies for species‐level interim names. We contrasted these taxon‐based estimates with phylogenetic measures of alpha‐ and beta‐diversity derived from both the mitochondrial DNA barcode region and a multi‐gene phylogeny of spiders and found that neither the abundance nor the species richness of spiders was significantly correlated with elevation, temperature, or precipitation. However, we did find that spider assemblages in the upper elevation cloud forests were phylogenetically clustered, (and this pattern was unrelated to whether the phylogenetic patterns were derived mitochondrially or from a multi‐gene analysis). One standard explanation for such a pattern is that harsh abiotic conditions in higher elevation forests have selected for particular spider lineages; however, this remains to be tested fully. The diversity of leaf‐litter spider species we uncovered was high and further sampling of spider abundance and diversity across the ACG is likely to yield many new species.
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A long-standing objective of ecology has been to explain the basis for diversity patterns. Empirical evidence suggests that regional variation in richness of both animals and plants depends strongly on energy availability. The generality of the richness-energy hypothesis is limited by the paucity of analyses of invertebrates, which are much more diverse than the more thoroughly investigated vertebrate taxa. In this study, we consider two groups of North American Lepidoptera for which large-scale distribution data are available: the Papilionidae (swallowtail butterflies) and forest lepidopterans (moths based on the Canadian Forest Insect Survey). Energy, as measured by potential evapotranspiration (PET), statistically explains between 61 and 72% of the variability in the richness patterns of the Lepidoptera we have examined. It is the single best predictor of the richness of these groups, and the relationships have a very similar form to richness-PET relationships observed earlier in vertebrate taxa. After PET, Papilionidae richness is related to topographical heterogeneity. These patterns are true both within and among biomes. These results suggest that the richness-energy hypothesis applies generally to both vertebrates and insects in cold and temperate regions.
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An analysis of published quantitative data on 133 breeding bird communities was performed, comprising on almost all bibliographic sources of bird community studies undertaken in the Czech Republic. We examined the relative effect of (1) census plot area, (2) census technique, and (3) habitat type on three community characteristics (species number, index of dominance and equitability) and on species composition (using Canonical Correspondence Analysis, CANOCO). Census plot area correlated positively with species number (r = 0.262, P = 0.002) and negatively with equitability (r = -0.185, P = 0.033) as well as having a relatively small but significant effect on species composition even when the other factors were controlled for. Census technique had a significant effect on the species number (ANOVA, P < 0.0001), and the index of dominance (ANOVA, P < 0.0001). Species number in communities censused by the line transect method was significantly higher than in the communities censused by both the mapping method and the point count method. The index of dominance was influenced by the census technique due to its negative correlation with the species number. The communities censused by mapping method differed significantly from the communities censused by the other two methods in community composition. Habitat type had a significant effect on all community characteristics and there were large between-habitat differences in species composition. The major difference was between urban and non-urban communities, but if the area of census plot and census technique were filtered out, this difference vanished and the difference between reed communities and the other bird communities appeared to be the most important, whereas that between woody and non-woody habitats appeared less important. Habitat appears as the factor influencing community composition much more strongly than area and census technique.
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Global Biodiversity is the most comprehensive compendium of conservation information ever published. It provides the first systematic report on the status, distribution, management, and utilisation of the planet's biological wealth.
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Because a diversity of resources should support a diversity of consumers, most models predict that increasing plant diversity increases animal diversity. We report results of a direct experimental test of the dependence of animal diversity on plant diversity. We sampled arthropods in a well‐replicated grassland experiment in which plant species richness and plant functional richness were directly manipulated. In simple regressions, both the number of species planted ( \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $\mathrm{log}\,_{2}$ \end{document} transformed) and the number of functional groups planted significantly increased arthropod species richness but not arthropod abundance. However, the number of species planted was the only significant predictor of arthropod species richness when both predictor variables were included in ANOVAs or a MANOVA. Although highly significant, arthropod species richness regressions had low \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $R^{2}$ \end{document} values, high intercepts (24 arthropod species in monocultures), and shallow slopes. Analyses of relations among plants and arthropod trophic groups indicated that herbivore diversity was influenced by plant, parasite, and predator diversity. Furthermore, herbivore diversity was more strongly correlated with parasite and predator diversity than with plant diversity. Together with regression results, this suggests that, although increasing plant diversity significantly increased arthropod diversity, local herbivore diversity is also maintained by, and in turn maintains, a diversity of parasites and predators.
Article
This book had its origin when, about five years ago, an ecologist (MacArthur) and a taxonomist and zoogeographer (Wilson) began a dialogue about common interests in biogeography. The ideas and the language of the two specialties seemed initially so different as to cast doubt on the usefulness of the endeavor. But we had faith in the ultimate unity of population biology, and this book is the result. Now we both call ourselves biogeographers and are unable to see any real distinction between biogeography and ecology.