Using gopher tortoise monitoring data to inform habitat restoration and translocations

Translocation has become an increasingly important tool for mitigating the loss of wildlife populations; however, determining appropriate stocking levels for recipient sites is challenging for species that occur across a wide array of land cover types. The gopher tortoise ( Gopherus polyphemus ) occurs in nearly all terrestrial ecosystems in the southeastern United States and is one of the most frequently translocated reptiles in North America. There is a need to determine which habitat characteristics are most suitable to support translocated tortoises and to provide restoration and management targets across different land cover types. To address these needs, we used existing line transect distance sampling data to predict tortoise densities in different land cover types using a Bayesian implementation of a hierarchical distance sampling model. Data were collected from 2014 through 2020 as part of Florida's gopher tortoise monitoring program for state conservation lands, and our models included 2 covariates describing habitat condition: soil depth to water table and canopy cover. The predicted gopher tortoise densities varied by land cover type but generally increased with decreasing canopy cover. Among land cover types and under average soil depth to water table and canopy conditions in each land cover type, coastal strand, sandhill, coastal grassland, and coniferous plantation had the highest predicted tortoise densities (>1 tortoise/ha). Under the same conditions, the lowest predicted densities occurred in upland pine, upland coniferous, xeric hammock, and mesic flatwoods (<0.40 tortoise/ha). This study demonstrates how extensive monitoring data and metrics of land cover condition can be used to inform future translocation protocols and habitat management and restoration.