A probabilistic co-occurrence approach for estimating likelihood of spatial overlap between listed species distribution and pesticide use patterns.

Abstract

Characterizing potential spatial overlap between federally threatened and endangered ( listed ) species distributions and registered pesticide use patterns is important for accurate threatened and endangered species risk assessment. Because accurate range information for such rare species is often limited and agricultural pesticide use patterns are dynamic, simple spatial co-occurrence methods may over- or under-estimate overlap and result in decisions that benefit neither listed species nor the regulatory process. Here we demonstrate a new method of co-occurrence analysis that employs probability theory to estimate spatial distribution of rare species populations and areas of pesticide use to determine the likelihood of potential exposure. Specifically, we 1) describe a probabilistic method to estimate pesticide use based on crop production patterns; 2) construct species distribution models for two listed insect species whose ranges were previously incompletely described, the rusty-patched bumble bee (Bombus affinis) and the Poweshiek skipperling (Oarisma poweshiek); and 3) develop a probabilistic co-occurrence methodology and assessment framework. Using the principles of the Bayes Theorem, we constructed probabilistic spatial models of pesticide use areas by integrating information from land cover spatial data, agriculture statistics, and remote sensing data. We used maximum entropy methods to build species distribution models for two listed insects based on species collection/observation records and predictor variables relevant to the species biogeography and natural history. We further developed novel methods for refinement of these models at spatial scales relevant to US Fish and Wildlife Service (FWS) regulatory priorities (e.g., critical habitat areas). Integrating both probabilistic assessments and focusing on USFWS priority management areas, we demonstrate that spatial overlap (i.e. potential for exposure) is not deterministic, but instead a function of both species distribution and land use patterns. Our work serves as a framework to enhance the accuracy and efficiency of threatened and endangered species assessments using a data-driven likelihood analysis of species co-occurrence. This article is protected by copyright. All rights reserved.