Widespread declines in wild bee populations necessitate urgent action, but there remains insufficient data 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 repository), birds (eBird participatory science project) and land cover data (USGS Cropland Data Layer). We used a Bayesian variable selection algorithm to select variables that best predicted bee richness 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 an indicator based on the combination of bird and land cover data was better at predicting wild bee richness across broad geographies than indicators based on land cover or birds alone, particularly for the semi-structured dataset. In the case of wild bees specifically, we suggest that bird and land cover data serve as useful indicators to guide monitoring and conservation priorities until the quality and quantity of bee data improve.

Indicators of a data-deficient taxa: combining bird and environmental data enhances predictive accuracy of wild bee richness