Garrett Street | USGS Science

USGS2023

Geographic distribution of the Puerto Rican Harlequin Butterfly (Atlantea tulita): An ensemble modeling approach

Species distribution models have become increasingly important tools for species conservation. This modeling approach can help guide conservation practitioners and inform decisions. Distribution models are particularly relevant for rare species, whose habitat associations are often not well understood. Using species occurrence data, and a variety of predictor variables, we developed three individual distribution models and a weighted ensemble model for the Puerto Rican harlequin butterfly ( Atlantea tulita ). The ensemble model had the greatest accuracy (AUC = 0.92). Further, the ensemble model indicated 7.1% of the main island of Puerto Rico encompassed suitable habitat for the harlequin butterfly. However, only 0.5% was classified as including the greatest suitability. Using an ensemble modeling approach to delineate areas of the island with suitable environmental conditions may improve habitat conservation efforts for the species.

Caribbean Journal of Science↗

USGS2021

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

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.

Arkansas, Florida, Georgia, Missouri, North Caroli↗