<i>Hesperia illinois</i>Dodge (Hesperiidae): An Invalid Neotype and the Designation of a Lectotype

Calhoun, John V.

doi:10.18473/lepi.v67i4.a4

Cited by 3 publications

(1 citation statement)

References 18 publications

(25 reference statements)

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“…We downloaded Poweshiek skipperling occurrence records from GBIF (n = 726), iDigBio (n = 212), Symbiota Collection of Arthropods Network (Gries et al 2014) (n = 574), and Biodiversity Information Serving Our Nation (BISON 2018) (n = 633). We also digitized and georeferenced records from the literature (n = 242) (Borkin 1995;Schlicht 1997;Swengel and Swengel 1997, 1999Selby 2008;Schlicht et al 2009;Semmler 2010;Swengel et al 2010;Dupont 2011;Calhoun 2013;Slaughter et al 2013;Pointon 2015).…”

Section: Dependent Variablesmentioning

confidence: 99%

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

Richardson¹,

Bang

Budreski³

et al. 2019

Integr Envir Assess & Manag

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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

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