Abundance data of butterflies was analysed using principal component analyses (PCAs). We chose these ordination methods because the length of the gradient of the first DCA axis was >3 for plants and birds and <3 for butterflies (Ter Braak and Prentice 1988). CHIR98014 concentration Assessment of the impact of survey effort reductions For a given group of species, we were interested in comparing the data from a “full survey effort” with that of a “reduced survey effort”. Our full survey effort consisted of ten plots per site for plant surveys, four repeats per site for butterfly

surveys, and four repeats per site for bird surveys. For each group, we considered species richness, species turnover and species composition. We treated Adriamycin clinical trial the results of species richness

and species composition resulting from the full survey effort as “observed” richness and composition, respectively. We simulated subsets of the full survey effort by randomly dropping one to seven plots (for plants) or one to three repeats (for birds and butterflies) from the dataset. Random sampling of reduced datasets was repeated 100 times for each selection, and agreement of the reduced set was compared with the full dataset. Species richness and turnover of the reduced datasets was compared to the full dataset using Spearman Rank correlations. We then assessed how strongly species composition changes when reducing the survey effort. This was done why by using Procrustes analyses, which identifies differences of the locations of objects between two ordinations. Comparisons were performed between the ordination of the reduced dataset and the full dataset and differences were quantified by calculating a correlation based on the symmetric sum of squares between the two ordinations (Peres-Neto and Jackson 2001). Power analysis of the effect of different survey designs Study design and data quality fundamentally influence the statistical power in the analysis of survey data. We therefore investigated the effect of different designs on the power of linear models relating species richness with environmental variables. We used

a simulation approach that reflects the nature of the variability in the field data, but in which the sample size can be varied. It is then possible to test how Ku-0059436 chemical structure strong the actual effect of a specific variable needs to be, for a dataset with a certain sample size to detect such an effect. Specifically, we applied power analyses to detect effects of landscape heterogeneity on species richness. The loss of landscape heterogeneity is a key concern in Europe’s agricultural landscapes (Benton et al. 2003), and is particularly relevant to our study area where low-input, small scale farming is increasingly replaces by industrialized high-input agriculture. We limited this analysis to arable sites, because this is where heterogeneity is most likely to be lost in the future due to land use intensification.