A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns. (29th October 2019)

• Record Type: Journal Article
• Authors: Richardson, Leif; Bang, JiSu; Budreski, Katherine; Dunne, Jonnie; Winchell, Michael; Brain, Richard A; Feken, Max
• Is Part Of: Integrated environmental assessment and management. Volume 15:Number 6(2019)
• Page Start: 936
• Page End: 947
• Publication Date: 2019-10-29
• Subjects: Probabilistic risk assessment -- Co‐occurrence analysis -- Endangered species risk assessment -- Pesticide risk assessment -- Bombus affinis; Environmental management -- Periodicals; Pollution -- Periodicals; Environmental toxicology -- Periodicals; Environmental risk assessment -- Periodicals; Environmental impact analysis -- Periodicals; 628
• DOI: 10.1002/ieam.4191
• Languages: English
• ISSNs: 1551-3777

Abstract

ABSTRACT: Characterizing potential spatial overlap between federally threatened and endangered ("listed") species distributions and registered pesticide use patterns is important for accurate risk assessment of threatened and endangered species. Because accurate range information for such rare species is often limited and agricultural pesticide use patterns are dynamic, simple spatial co‐occurrence methods may overestimate or underestimate 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 2 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 2 listed insects based on species collection and 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. Integr Environ Assess Manag 2019;00:1–12. © 2019 SETAC Key Points: US Fish and Wildlife Service consultation to EPA on pesticide risk to endangered species is often based on incomplete knowledge of species distribution and crop use patterns, resulting in deterministic presence or absence conclusion about exposure risk. Estimates of pesticide exposure risk for endangered species can be substantially improved by incorporating Bayesian probability theory to modeling species ranges and spatial extent of pesticide use. Bioclimatic and topographic data and occurrence records for 2 listed insect species were used to model their distributions and construct spatial models of crop production patterns based on multiple years of data reported in the USDA Cropland Data Layer. Combining predictions, endangered species pesticide risk is a probabilistic function of species distributions and grower decision‐making practices, and the consultation process could be improved by use of such probabilistic methods for other listed species.