Stone Environmental, Inc, Montpelier, Vermont, USA.
Syngenta Crop Protection, LLC, Greensboro, North Carolina, USA.
Integr Environ Assess Manag. 2019 Nov;15(6):936-947. doi: 10.1002/ieam.4191.
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.
确定受联邦威胁和濒危（“列出”）物种分布与已登记农药使用模式之间潜在的空间重叠，对于准确评估受威胁和濒危物种的风险非常重要。由于此类稀有物种的准确范围信息通常有限，且农业农药使用模式具有动态性，因此简单的空间共现方法可能会高估或低估重叠程度，并导致既不利于列出的物种，也不利于监管过程的决策。在这里，我们展示了一种新的共现分析方法，该方法运用概率论来估计稀有物种种群的空间分布和农药使用区域，以确定潜在暴露的可能性。具体而言，我们：1）描述了一种基于作物生产模式来估算农药使用的概率方法；2）构建了两个先前未完全描述其范围的列入名单的昆虫物种的物种分布模型，锈斑熊蜂（Bombus affinis）和 Poweshiek 滑行者（Oarisma poweshiek）；3）开发了一种概率共现方法和评估框架。我们运用贝叶斯定理的原理，通过整合土地覆盖空间数据、农业统计数据和遥感数据的信息，构建了农药使用区域的概率空间模型。我们使用最大熵方法，根据物种收集和观察记录以及与物种生物地理学和自然历史相关的预测变量，为两种列入名单的昆虫构建了物种分布模型。我们进一步开发了新颖的方法，以便在与美国鱼类和野生动物管理局（FWS）监管重点（例如，关键栖息地区域）相关的空间尺度上对这些模型进行精细化。通过整合概率评估并专注于美国鱼类和野生动物管理局的优先管理区域，我们证明了空间重叠（即潜在暴露）不是确定性的，而是物种分布和土地利用模式的函数。我们的工作为利用物种共现的数据驱动可能性分析来提高受威胁和濒危物种评估的准确性和效率提供了一个框架。
