Probabilistic co‐occurrence assessment for suites of listed species. (2nd December 2021)

• Record Type: Journal Article
• Authors: Richardson, Leif L.; Dunne, Jonnie; Feken, Max; Brain, Richard; Ghebremichael, Lula; Winchell, Michael
• Is Part Of: Integrated environmental assessment and management. Volume 18:Number 4(2022)
• Page Start: 1088
• Page End: 1100
• Publication Date: 2021-12-02
• Subjects: Bayes' theorem -- Co‐occurrence analysis -- Endangered Species Act consultation -- Pesticide risk assessment -- Species distribution modeling; Environmental management -- Periodicals; Pollution -- Periodicals; Environmental toxicology -- Periodicals; Environmental risk assessment -- Periodicals; Environmental impact analysis -- Periodicals; 628
• DOI: 10.1002/ieam.4542
• Languages: English
• ISSNs: 1551-3777

Abstract

Abstract: Section 7 of the Endangered Species Act requires the US Environmental Protection Agency (US EPA) to consult with the Services (US Fish and Wildlife Service and National Marine Fisheries Service) over potential pesticide impacts on federally listed species. Consultation is complicated by the large number of pesticide products and listed species, as well as by lack of consensus on best practices for conducting co‐occurrence analyses. Previous work demonstrates that probabilistic estimates of species' ranges and pesticide use patterns improve these analyses. Here we demonstrate that such estimates can be made for suites of sympatric listed species. Focusing on two watersheds, one in Iowa and the other in Mississippi, we obtained distribution records for 13 species of terrestrial and aquatic listed plants and animals occurring therein. We used maximum entropy modeling and bioclimatic, topographic, hydrographic, and land cover variables to predict species' ranges at high spatial resolution. We constructed probabilistic spatial models of use areas for two pesticides based on the US Department of Agriculture Cropland Data Layer and reduced classification errors by incorporating information on the relationships between individual pixels and their neighbors using object‐based images analysis. We then combined species distribution and crop footprint models to derive overall probability of co‐occurrence of listed species and pesticide use. For aquatic species, we also integrated an estimate of downstream residue transport. We report each separate species‐by‐use‐area co‐occurrence estimate and also combine these modeled co‐occurrence probabilities across species within watersheds to produce an overall metric of potential pesticide exposure risk for these listed species at the watershed level. We propose that the consultation process between US EPA and the Services be based on such batched estimation of probabilistic co‐occurrence for multiple listed species at a regional scale. Integr Environ Assess Manag 2022;18:1088–1100. © 2021 SETAC Key Points: Co‐occurrence assessments of pesticide use areas and listed species ranges mandated by the Endangered Species Act are impaired by data gaps and statistical issues. These co‐occurrence assessments are also inefficient, and a substantial backlog exists. We show that incorporating probabilistic estimates of both pesticide use and species distributions improves these assessments. We also demonstrate that efficiencies may be realized by batch‐processing these assessments for groups of listed species.