Geology topics

Virginia L. Robenski

Publications and source records attributed to Virginia L. Robenski.

2 recordsLinked to original sources

USGS2025

Decision support tools for brown pelican management in the northern Gulf of America (Gulf of Mexico)

Management plans with clear priorities can help to achieve brown pelican Pelecanus occidentalis conservation objectives in the northern Gulf of America (Gulf of Mexico). Efforts to establish clear priorities can be hindered by information gaps, especially those related to the uncertainty associated with changing conditions that influence brown pelican populations. We addressed these gaps by creating a model that uses island-specific conditions (e.g., geomorphology; predator-related conditions; brown pelican terrestrial nesting, roosting, and loafing habitats) to predict the nest count as a proxy for breeding pairs on the island. We used the model and 2000–2015 brown pelican nest count data to estimate if breeding pair targets that we identified or estimated for 10 U.S Fish and Wildlife Service Gulf Coast Biological Planning Units were met while accounting for uncertainty. Our results indicate that breeding pair targets were met in 7 of the 10 units by existing conditions. Our confidence in judging nest deficits tended to decrease from west to east because the model over-predicted total nests in the east Gulf Coast. Using an island from our data, we show how the model could be used to quantify the uncertainty of nest count outcomes under simulated changes in island conditions. The model indicated that the island's existing conditions most probably result in nests (probability = 0.51) and that increasing the area of nesting habitat (shrubs) could increase the probability of nests from 49% to 70%. Increasing shrub habitat in the model also increased nest count uncertainty by 60%, but this was due to a greater probability of larger nest counts. Our model suggests that nest count uncertainty could be reduced by improving data on island size, shrub area, and predator presence, depending on the unit and how isolated the island is from the mainland. These tools could help managers understand and incorporate the uncertainty associated with creating island conditions that are intended to help achieve brown pelican conservation objectives.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2020

Strategic habitat conservation for beach mice: Estimating management scenario efficiencies

The Perdido Key beach mouse ( Peromyscus polionotus trissyllepsis ), Choctawhatchee beach mouse ( P. p. allophrys ), and St. Andrew beach mouse ( P. p. peninsularis ) are 3 federally endangered subspecies that inhabit coastal dunes of Alabama and Florida, USA. Conservation opportunities for these subspecies are limited and costly. Consequently, well‐targeted efforts are required to achieve their downlisting criteria. To aid the development of targeted management scenarios that are designed to achieve downlisting criteria, we developed a Bayesian network model that uses habitat characteristics to predict the probability of beach mouse presence at a 30‐m resolution across a portion of the Florida Panhandle. We then designed alternative management scenarios for a variety of habitat conditions for coastal dunes. Finally, we estimated how much area is needed to achieve the established downlisting criterion (i.e., habitat objective) and the amount of effort needed to achieve the habitat objective (i.e., management efficiency). The results suggest that after 7 years of post‐storm recolonization, habitat objectives were met for Perdido Key (within its Florida critical habitat) and Choctawhatchee beach mice. The St. Andrew beach mouse required 5.14 km 2 of additional critical habitat to be protected and occupied. The St. Andrew beach mouse habitat objective might be achieved by first restoring protected critical habitat to good dune conditions and then protecting or restoring the unprotected critical habitat with the highest predicted probability of beach mouse presence. This scenario provided a 28% increase in management efficiency compared to a scenario that randomly protected or restored undeveloped unprotected critical habitat. In total, when coupled with established downlisting criteria, these quantitative and spatial decision support tools could provide insight into how much habitat is available, how much more is needed, and targeted conservation or restoration efforts that might efficiently achieve habitat objectives.

Alabama, Florida↗