Simulation Modeling to Assess Line Transect Distance Sampling Under a Range of Translocation Scenarios

Abstract

The accuracy of post-translocation monitoring methods is critical to assessing long-term success in translocation programs. Translocation can produce unique challenges to monitoring efforts, and it is therefore important to understand the flexibility and robustness of commonly used monitoring methods. In Florida, USA, thousands of gopher tortoises Gopherus polyphemus have been (and continue to be) translocated from development sites to permitted recipient sites. These recipient sites create a broad range of potential monitoring scenarios due to variability in soft-release strategy, habitat conditions, and population demographics. Line transect distance sampling (LTDS) is an effective method for monitoring natural tortoise populations, but is currently untested for translocated populations. We therefore produced 3,024 individual-based, spatially explicit scenarios of translocated tortoise populations that differed in recipient site and tortoise population properties (based on real-world examples, literature review, and expert opinion). We virtually sampled simulated tortoise populations using LTDS methods, and built a Bayesian hierarchical model to estimate the population density for each simulation, which incorporated individual-level covariates (i.e., burrow width and burrow occupancy). Line transect distance sampling was largely appropriate for the conditions that typify gopher tortoise recipient sites; particularly when detection probability on the transect lines was ≥ 0.85. Designing the layout of transects relative to the orientation of soft-release pens, to avoid possible sampling biases that lead to extreme outliers in estimates of tortoise densities, resulted in more accurate population estimates. We also suggest that use of individual-level covariates, applied using a Bayesian framework as demonstrated here, may improve the applicability of LTDS surveys in a variety of contexts, and that simulation can be a powerful tool for assessing survey design in complex sampling situations.