Predicting the potential distribution of the Gila Monster and evaluating the extent of protected natural areas for conservation

Abstract

A common challenge facing wildlife conservation efforts is obtaining accurate species assessments for use in informing management strategies. Predictive techniques such as species distribution modeling are often used to determine the geographic distribution of rare and cryptic species and can facilitate conservation planning. In this study we developed a MaxEnt model using historical occurrence data and a suite of environmental variables to predict the geographic distribution of the Gila Monster (Heloderma suspectum), a large and enigmatic lizard native to the deserts of the American Southwest and northwestern Mexico that is state listed as protected, threatened, or endangered across its range. MaxEnt (maximum entropy) is a modeling technique using presence-only occurrence data to model habitat suitability. The predicted distribution was overlayed with currently recognized protected natural areas (PNAs) to evaluate the extent of habitat conservation efforts. About 36 % of the predicted species distribution is currently contained within recognized PNAs, but only ∼ 11 % are considered high-quality areas that do not permit extractive activities and employ active management plans. We identified potential Gila Monster habitat in regions notably lacking historic surveys or occurrence information, including southwestern California (U.S.) and northwestern Chihuahua (Mexico). This study identifies gaps in both survey effort and in habitat protection status, especially in Sonora (Mexico), which contains ∼ 59 % of the species predicted distribution, but only 4.8 % having PNA designation. These gaps should encourage increased survey efforts in remote regions and the development of specific conservation plans in lower classification PNAs to ensure the protection of crucial Gila Monster habitat in the face encroaching anthropogenic habitat alteration and shifting climates.