Geology topics

E. Rivenbark

Publications and source records attributed to E. Rivenbark.

2 recordsLinked to original sources

USGS2024

Accounting for multiple uncertainties in a decision-support population viability assessment

Conservation and management decisions often must be made on strict timelines, based on the “best available information” regarding a species’ current and expected future status. Simulation models are valuable tools for predicting a species’ future status but must incorporate multiple types of uncertainty in order to provide a complete understanding of plausible outcomes. Here we present a population viability analysis for a data-deficient species proposed for protection under the U.S. Endangered Species Act, the alligator snapping turtle. We used a matrix population model to simulate population trajectories, incorporating both parametric uncertainty and temporal variation into demographic parameters. We used expert elicitation to generate modified survival rates in the presence of specific anthropogenic threats, for which empirical estimates were unavailable. Because uncertainty in the expert elicited values was of particular interest to decision makers, we constructed a set of simulation scenarios to evaluate the sensitivity of model conclusions to the accuracy of expert elicited parameters. Our model predicted steep population declines under all scenarios with anthropogenic threats, indicating that under- or overestimation by experts would not change the overall conclusion that populations would decline. An additional sensitivity analysis revealed that a parameter related to nest survival for which there was high disagreement among experts had a negligible effect on model outcome, while other parameters (e.g., the effect of poaching) had more influence. Our analyses demonstrate the use of an expert-parameterized decision-support population viability analysis that explicitly evaluates the effects of multiple sources of uncertainty on model predictions.

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗

USGS2020

Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment

The US Fish and Wildlife Service has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of a species’ current status and predicted future status. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, recovery planning, etc. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting species conservation decisions in current data and cutting edge analytical and modeling techniques. Here we describe a novel analysis of available data to assess current condition of eastern black rail across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future conditions under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With results of these data analyses and predictive modeling, the US Fish and Wildlife Service concluded that protections of the Endangered Species Act were warranted for this subspecies.

Endangered Species Research↗