Embracing ensemble species distribution models to inform at-risk species status assessments

Abstract

Conservation planning depends on reliable information regarding the geographic distribution of species. However, our knowledge of species' distributions is often incomplete, especially when species are cryptic, difficult to survey, or rare. The use of species distribution models has increased in recent years and proven a valuable tool to evaluate habitat suitability for species. However, practitioners have yet to fully adopt the potential of species distribution models to inform conservation efforts for information-limited species. Here, we describe a species distribution modeling approach for at-risk species that could better inform U.S. Fish and Wildlife Service's species status assessments and help facilitate conservation decisions. We applied four modeling techniques (generalized additive, maximum entropy, generalized boosted, and weighted ensemble) to occurrence data for four at-risk species proposed for listing under the U.S. Endangered Species Act (Papaipema eryngii, Macbridea caroliniana, Scutellaria ocmulgee, and Balduina atropurpurea) in the Southeastern United States. The use of ensemble models reduced uncertainty caused by differences among modeling techniques, with a consequent improvement of predictive accuracy of fitted models. Incorporating an ensemble modeling approach into species status assessments and similar frameworks is likely to benefit survey efforts, inform recovery activities, and provide more robust status assessments for at-risk species. We emphasize that co-producing species distribution models in close collaboration with species experts has the potential to provide better calibration data and model refinements, which could ultimately improve reliance and use of model outputs. We want to thank our collaborators at the USFWS (M. Harris, K. Lundh, M. Lombardi, A. Punsalan, M. Elmore, D. Caldwell), State Fish & Wildlife Agencies (A. Fowler, T. Patrick), NatureServe (H. Hamilton, R. Smith), species experts (J. Wiker, J. Bess, K. Bradley, Cecelia N. Dailey), and all the other partners that provided guidance in this project. Two anonymous reviewers and the Associate Editor provided comments that improved an earlier version of this manuscript. Two of the authors are U.S. Government Employees. This work was prepared as part of their official duties. Funding for this project was available thanks to cooperative agreement # F17AC00899 with the U.S. Fish and Wildlife Service. This project was also supported by McIntire-Stennis Funds # 1019539 and the Forest and Wildlife Research Center at Mississippi State University.