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Aim We test the prediction that beta diversity (species turnover) and the decay of community similarity with distance depend on spatial resolution (grain). We also study whether patterns of beta diversity are related to variability in climate, land cover or geographic distance and how the independent effects of these variables depend on the spatial grain of the data. Location Europe, Great Britain, Finland and Catalonia. Methods We used data on European birds, plants, butterflies, amphibians and reptiles, and data on British plants, Catalonian birds and Finnish butterflies. We fitted two or three nested grids of varying resolutions to each of these datasets. For each grid we calculated differences in climate, differences in land-cover composition (CORINE) and beta diversity (bsim, bJaccard) between all pairs of grid cells. In a separate analysis we looked specifically at pairs of adjacent grid cells (the first distance class). We then used variation partitioning to identify the magnitude of independent statistical associations (i.e. independent effects in the statistical sense) of climate, land cover and geographic distance with spatial patterns of beta

Aim To assess the relative importance of climate, biotope and soil variables as well as geographical location for the species richness of plants, butterflies, day-active macromoths and wild bees in boreal agricultural landscapes. Location A total of 68 agricultural landscapes located in southern Finland. Methods Generalized linear mixed models were used to analyse the effects of environmental (climate, biotope and soil) and spatial (latitude and longitude) variables on species richness of four taxa in 136 study squares of 0.25 km2. Using partial regression, the variation in species richness was decomposed into the purely environmental fraction; the spatially structured environmental fraction; and the purely spatial fraction, including variables retained in cubic trend surface regression. Results Species richness of all taxa was positively correlated with temperature. Species richness of plants and butterflies was also positively correlated with the heterogeneity of landscape. The extent of low-intensity agricultural land and forest had a positive effect, and the extent of cultivated field a negative effect on the species richness of these taxa. The effect of soil characteristics on the number of observed species was negligible for all taxa. The greatest part of the explained variation for all taxa was accounted for by the pure effect of geographical location. To a somewhat lesser extent, the species richness of plants, butterflies and bees was also related to the effects of spatially structured environmental variables, and the species richness of macromoths to the effects of environmental variables. Main conclusions Multi-species richness of boreal agricultural landscapes at the scale of 0.25 km2 was associated with the heterogeneity of the local landscape. However, large-scale geographical variation in species richness was also observed, which indicates the importance of climate and geographical location for the taxa studied. These results highlight the fact that, even on a landscape scale, geographical factors should be taken into account in biodiversity studies. Heterogeneous agricultural landscapes, including forest and non-crop biotopes, should be preserved or restored to maintain biodiversity.

Aim We tested the usefulness of satellite based remote sensing data and geographical information system (GIS) techniques (1) in explaining the observed distribution of the threatened clouded apollo butterfly (Parnassius mnemosyne) and (2) in predicting the occurrence of the butterfly in two independent test areas with different landscape structure. Location The three study areas are located along the rivers Rekijoki and Halikonjoki in a boreal agricultural landscape in south-western Finland (60°40′ N; 23°20′ E). Methods Landsat satellite images were used to generate habitat maps of the three study areas. Topographical variables were calculated from a digital elevation model. These data were used to construct a multiple logistic regression model fitted with the observed occurrence of clouded apollo in 126 grid squares of 0.25 km2 within an area of 31.5 km2 (model building area). The parameterized model was used to predict the occurrence of the butterfly in an adjacent test area with a similar landscape structure as in the model building area, as well as in a more distant test area with a more fragmented pattern of semi-natural grassland. Results In the model building area probability of clouded apollo occurrence increased with connectivity of semi-natural grassland, cover of deciduous forest, cover of semi-natural slope grassland and topographical heterogeneity. The model accuracy was high, the correct classification rate being 98.4% overall and 95.0% for the butterfly presence squares. In both test areas the overall correct classification of the model prediction was high (c. 92%). However, in predicting the actual butterfly presence squares the model succeeded substantially better in the adjacent than in the distant test area (correct classification rates 91.3% and 66.7%, respectively). Main conclusions The results showed that the distribution of a habitat specialized butterfly may be quite successfully explained and predicted based on pure satellite imagery and topographical data. Nevertheless, the decrease of accuracy of the model prediction when applied to a different landscape structure than the one in which the model was parameterized suggests that the useful application of such models is limited by the environmental variability of the original model building data.