Bayesian network model that predicts the annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat
Dates
Publication Date
2021-01-06
Time Period
2020
Citation
Cronin, J.P., Tirpak, B.E., Dale, L.L., Robenski, V.L., and Tirpak, J.M., 2021, Data for Beach Mice Bayesian Network Model: U.S. Geological Survey data release, https://doi.org/10.5066/P9OWRTW8.
Summary
This U.S. Geological Survey (USGS) data release represents tabular data that were used to develop the Biological Objectives for the Gulf Coast Project’s Beach Mice Bayesian network model. The USGS partnered with the U.S. Fish and Wildlife Service (USFWS), the Florida Fish and Wildlife Conservation Commission, and their conservation partners to develop a Bayesian Network model that predicts the annual probability of beach mouse presence at a local (30-m) scale. The model was used to predict the annual probability of presence across a portion of the USFWS's Central Gulf and Florida Panhandle Coast Biological Planning Unit. This spatial extent included critical habitat for three endangered subspecies of beach mice (Peromyscus polionotus [...]
Summary
This U.S. Geological Survey (USGS) data release represents tabular data that were used to develop the Biological Objectives for the Gulf Coast Project’s Beach Mice Bayesian network model. The USGS partnered with the U.S. Fish and Wildlife Service (USFWS), the Florida Fish and Wildlife Conservation Commission, and their conservation partners to develop a Bayesian Network model that predicts the annual probability of beach mouse presence at a local (30-m) scale. The model was used to predict the annual probability of presence across a portion of the USFWS's Central Gulf and Florida Panhandle Coast Biological Planning Unit. This spatial extent included critical habitat for three endangered subspecies of beach mice (Peromyscus polionotus ssp). The annual probability of beach mouse presence is predicted from both local and neighborhood habitat characteristics that could be influenced by management actions. When coupled with established population objectives, this study can provide insight into how much habitat is available, how much more is needed, and where conservation or restoration efforts can most efficiently achieve established objectives. The results could be used to help guide strategic habitat conservation and adaptive management of beach mice.
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Bayesian network model that predicts the annual probability of beach mice.xml Original FGDC Metadata
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Purpose
To predict the annual probability of beach mouse presence at a local (30-m) scale, data were collected to create a spatially explicit Bayesian network. Available data were used to create spatial datasets across a portion of the USFWS's Central Gulf and Florida Panhandle Coast Biological Planning Unit. The tabular data associated with this metadata were a combination of expert elicitation, simplifying assumptions, literature-derived empirical values, and a beach mouse detection and nondetection survey. These tabular data were used to populate conditional probability tables for predicted variables.
