Spatial mismatch between wild bee diversity hotspots and protected areas.

Abstract

Wild bees are critical for multiple ecosystem functions but are currently threatened. Understanding the determinants of the spatial distribution of wild bee diversity is a major research gap for their conservation. We modeled wild bee α and β taxonomic and functional diversity in Switzerland to uncover countrywide diversity patterns and determine the extent to which they provide complementary information, assess the importance of the different drivers structuring wild bee diversity, identify hotspots of wild bee diversity, and determine the overlap between diversity hotspots and the network of protected areas. We used site-level occurrence and trait data from 547 wild bee species across 3343 plots and calculated community attributes, including taxonomic diversity metrics, community mean trait values, and functional diversity metrics. We modeled their distribution with predictors describing gradients of climate, resource availability (vegetation), and anthropogenic influence (i.e., land-use types and beekeeping intensity). Wild bee diversity changed along gradients of climate and resource availability; high-elevation areas had lower functional and taxonomic α diversity, and xeric areas harbored more diverse bee communities. Functional and taxonomic β diversities diverged from this pattern, with high elevations hosting unique species and trait combinations. The proportion of diversity hotspots included in protected areas depended on the biodiversity facet, but most diversity hotspots occurred in unprotected land. Climate and resource availability gradients drove spatial patterns of wild bee diversity, resulting in lower overall diversity at higher elevations, but simultaneously greater taxonomic and functional uniqueness. This spatial mismatch among distinct biodiversity facets and the degree of overlap with protected areas is a challenge to wild bee conservation, especially in the face of global change, and calls for better integrating unprotected land. The application of spatial predictive models represents a valuable tool to aid the future development of protected areas and achieve wild bee conservation goals.