A spatially explicit model of landscape pesticide exposure to bees: Development, exploration, and evaluation

Abstract

Pesticides represent one of the greatest threats to bees and other beneficial insects in agricultural landscapes. Potential exposure is generated through compound- and crop-specific patterns of pesticide use over space and time and unique degradation behavior among compounds. Realized exposure develops through bees foraging from their nests across the spatiotemporal mosaic of floral resources and associated pesticides throughout the landscape. Despite the recognized importance of a landscape-wide approach to assessing exposure, we lack a sufficiently-evaluated predictive framework to inform mitigation decisions and environmental risk assessment for bees. We address this gap by developing a bee pesticide exposure model that incorporates spatiotemporal pesticide use patterns, estimated rates of pesticide degradation, floral resource dynamics across habitats, and bee foraging movements. We parameterized the model with pesticide use data from a public database containing crop-field- and date-specific records of uses throughout our study region over an entire year. We evaluate the model performance in predicting bee pesticide exposure using a dataset of pesticide residues in pollens gathered by bumble bees (Bombus vosnesenskii) returning to colonies across 14 spatially independent landscapes in Northern California. We applied alternative model formulations of pesticide accumulation and degradation, floral resource seasonality, and bee foraging behavior to evaluate different levels of detail for predicting observed pesticide exposure. Our best model explained 73 % of observed variation in pesticide exposure of bumble bee colonies, with generally positive correlations for the dominant compounds. Timing and location of pesticide use were integral, but more detailed parameterizations of pesticide degradation, floral resources, and bee foraging improved the predictions little if at all. Our results suggest that this approach to predict bees' pesticide exposure has value in extending from the local field scale to the landscape in environmental risk assessment and for exploring mitigation options to support bees in agricultural landscapes.