| Abstract |
Widespread declines in wild bee populations necessitate urgent action, but insufficient data exist to guide conservation efforts. Addressing this data deficit, we investigated the relative performance of environmental and/or taxon-based indicators to predict wild bee richness in the eastern and central U.S. Our methodology leveraged publicly available data on bees (SCAN and GBIF data repositories), birds (eBird participatory science project) and land cover data (USDA Cropland Data Layer). We used a Bayesian variable selection algorithm to select variables that best predicted species richness of bees using two datasets: a semi-structured dataset covering a wide geographical and temporal range and a structured dataset covering a focused extent with a standardized protocol. We demonstrate that birds add value to land cover data as indicators of wild bee species richness across broad geographies, particularly when using semi-structured data. These improvements likely stem from the demonstrated sensitivity of birds to conditions thought to impact bees but that are missed by remotely sensed environmental data. Importantly, this enables estimation of bee richness in places that don't have direct observations of bees. In the case of wild bees specifically, we suggest that bird and land cover data, when combined, serve as useful indicators to guide monitoring and conservation priorities until the quality and quantity of bee data improve.
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