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Each region was sampled twice: the first one between May and July, and the second between August and September. All beetles were identified to species level with the help of experts see Acknowledgements. Species that could not be identified in the field were collected and identified to species level with the help of experts see Acknowledgements. Lepidoptera were surveyed following a line-transect method in each of the grassland EPs between May and August Three surveys per grassland EP were conducted in a randomized sequence per region. Surveys consisted of a min walk along each transect during which all individuals within 2.

Individuals were identified to species level 51 in the field unless the taxonomic identity was unclear, in which case, specimens were kept for later identification by dissection of genitalia in the laboratory. Trait information was compiled from the literature for all species identified in our study.

Adult feeding breadth, larval feeding breadth, relative body size and activity period were used as response variables.


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Feeding breadth was categorized as specialist feeding on species within one family or generalist feeding on species within more than one family. Activity period was recorded as the number of months for which adults are active. Body size was defined as relative body size, which was calculated as the log difference between actual body size and the community average within each order. By considering body size as a relative measure within each order the influence of taxonomic difference on the overall trends of the community response to intensification was reduced.

In-field management intensity was defined through the land-use-intensity index 16 , which incorporates fertilizer inputs, mowing intensity and grazing intensity. For each grassland EP k , the land-use-intensity index L k is defined as the square root of the sum of the three variables, each standardized by the regional mean of that variable;. We used the mean L k for the years — as this best reflects the ongoing management intensity and has been shown to have a stronger effect on arthropod activity than single year indices Land cover features were classified in eight generalized categories arable, forest, grasslands: managed grasslands and semi-natural vegetation, roads, trees: woodlots smaller than 1 ha, urban areas and water bodies; Supplementary Fig.

Landscape-scale simplification was represented by both compositional heterogeneity measured as Shannon diversity of land cover types 18 , 54 and configurational heterogeneity average patch size within the surrounding landscape 18 , We chose Shannon diversity as a measure of compositional heterogeneity, rather than, for example, the proportional area of semi-natural habitats or non-arable land 55 , as our study includes many taxa that use and exploit a wide variety of different habitat types, and while Shannon diversity is highly correlated with the proportional area of arable fields in the landscapes, it displayed a wider range and a complementary independent gradient to landscape configurational heterogeneity.

These two spatial scales were chosen to identify field-scale and landscape-scale effects. Data from all sampling dates were pooled for each taxon and Hellinger transformations, using vegan package in R 56 , were applied to standardize abundance across taxa and to account for the long environmental gradients represented by the three regions For RLQ analysis, the R- and Q-tables first underwent principle component analysis the Q-table using the Hill and Smith method 58 for mixing quantitative variables and factors and the L-table underwent correspondence analysis.

RLQ analysis was conducted using the ade4 package in R Clusters within RLQ component-space were identified following Kleyer et al. The degree of correlation between species-traits and response groups is expressed in correlation ratios. Analyses were conducted using R-codes adapted from those provided as Supplementary Material in Kleyer et al.

Spatial autocorrelation was not observed. Landscape simplification filters species traits and drives biotic homogenization. Tscharntke, T. Landscape perspectives on agricultural intensification and biodiversity - ecosystem service management.

Supporting Information

Olden, J. Ecological and evolutionary consequences of biotic homogenization. Trends Ecol. Clavel, J. Worldwide decline of specialist species: toward a global functional homogenization. McKinney, M. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Devictor, V. Functional biotic homogenization of bird communities in disturbed landscapes. Global Ecol. On defining and quantifying biotic homogenization. Yachi, S.

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