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J. Nicole DeSha

Publications and source records attributed to J. Nicole DeSha.

3 recordsLinked to original sources
USGS2024

Common-garden experiment reveals outbreeding depression and region-of-origin effects on reproductive success in a frequently translocated tortoise

Human-mediated animal movement can expose wildlife populations to novel environments. Phenotypic plasticity can buffer against the challenges presented by novel environments, while adaptation to local ecosystems may limit resilience in novel ecosystems. Outbreeding depression during the mixing of disparate gene pools can also reduce reproductive success after long-distance movement. Here, we use a ‘common-garden’ population of gopher tortoises ( Gopherus polyphemus ), translocated from numerous sites across the state of Florida, USA, to a mitigation site in the north-west (panhandle) region to assess whether geographic origin, outbreeding effects, and behavioral plasticity influence reproductive success in this threatened keystone species. We found that females from north-east Florida produced clutches with lower hatching success than females from other regions. We detected regional differentiation in nest site selection behavior in the common environment of the translocation site, though these differences did not mediate the regional effect on hatching success. We also found evidence for outbreeding depression: hatching success declined with increasing parental geographic and genetic distances, dropping from 93% to 67% across the range of observed parental genetic distances. Together, these results suggest that newly admixed populations may suffer reproductive costs due to historical population differentiation, and that undetected outbreeding depression could significantly hamper conservation efforts for this species and others undergoing a variety of human-mediated movements.

Florida↗
USGS2024

Simulation modeling to assess line transect distance sampling under a range of translocation scenarios

The accuracy of posttranslocation population monitoring methods is critical to assessing long-term success in translocation programs. Translocation can produce unique challenges to monitoring efforts; therefore, it is 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 strategies, habitat conditions, and population demographics. Line transect distance sampling is an effective method for monitoring natural tortoise populations, but it 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 by using line transect distance sampling 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 greater than or equal to 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 in our study, may improve the applicability of line transect distance sampling surveys in a variety of contexts and that simulation can be a powerful tool for assessing survey design in complex sampling situations.

Florida↗