Ecological traits affect the sensitivity of bees to land‐use pressures in European agricultural landscapes

Abstract

Summary: Bees are a functionally important and economically valuable group, but are threatened by land‐use conversion and intensification. Such pressures are not expected to affect all species identically; rather, they are likely to be mediated by the species' ecological traits. Understanding which types of species are most vulnerable under which land uses is an important step towards effective conservation planning. We collated occurrence and abundance data for 257 bee species at 1584 European sites from surveys reported in 30 published papers (70 056 records) and combined them with species‐level ecological trait data. We used mixed‐effects models to assess the importance of land use (land‐use class, agricultural use‐intensity and a remotely‐sensed measure of vegetation), traits and trait × land‐use interactions, in explaining species occurrence and abundance. Species' sensitivity to land use was most strongly influenced by flight season duration and foraging range, but also by niche breadth, reproductive strategy and phenology, with effects that differed among cropland, pastoral and urban habitats. Synthesis and applications. Rather than targeting particular species or settings, conservation actions may be more effective if focused on mitigating situations where species' traits strongly and negatively interact with land‐use pressures. We find evidence that low‐intensity agriculture can maintain relatively diverse bee communities; in more intensive settings, added floral resources may be beneficial, but will require careful placement with respect to foraging ranges of smaller bee species. Protection of semi‐natural habitats is essential, however; in particular, conversion to urban environments could have severe effects on bee diversity and pollination services. Our results highlight the importance of exploring how ecological traits mediate species responses to human impacts, but further research is needed to enhance the predictive ability of such analyses.