Stone Environmental, Inc., Montpelier, Vermont, USA.
Syngenta Crop Protection, Inc., Greensboro, North Carolina, USA.
Integr Environ Assess Manag. 2022 Jun;18(4):1088-1100. doi: 10.1002/ieam.4542. Epub 2021 Dec 2.
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.
第 7 节濒危物种法案要求美国环境保护署（US EPA）与服务机构（美国鱼类和野生动物管理局和国家海洋渔业局）协商，以评估潜在农药对联邦清单物种的影响。协商工作因农药产品和清单物种数量庞大，以及对于开展共存分析的最佳实践缺乏共识而变得复杂。之前的工作表明，对物种分布范围和农药使用模式的概率估计可以改善这些分析。在这里，我们证明可以为共生的清单物种套件做出此类估计。我们专注于两个流域，一个在爱荷华州，另一个在密西西比州，为其中存在的 13 种陆地和水生列名植物和动物获得了分布记录。我们使用最大熵建模和生物气候、地形、水文和土地覆盖变量来预测物种在高空间分辨率下的分布范围。我们根据美国农业部的耕地数据层构建了两种农药的使用区域概率空间模型，并通过使用基于对象的图像分析纳入了有关单个像素与其邻居之间关系的信息，从而减少了分类错误。然后，我们将物种分布和作物足迹模型相结合，得出列出的物种和农药使用的总体共存概率。对于水生物种，我们还整合了下游残留运输的估计。我们报告了每个单独的物种-使用区域共存估计，并在流域内将这些模型化的共存概率结合起来，以在流域层面产生这些列出的物种潜在农药暴露风险的总体指标。我们建议美国环保署与服务机构之间的协商过程基于这种在区域尺度上对多个列出物种的概率共存进行批量估计。综合环境评估与管理 2022；18:1088-1100。©2021 SETAC。
