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 mice 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 mice 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.

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 mice 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 mice 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.

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 mice 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 mice 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.

This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for the Biological Objectives for the Gulf Coast Project’s Beach Mice Bayesian network model. The data release was produced in compliance with 'open data' requirements as a way to make the scientific products associated with USGS research efforts and publications available to the public. The release consists of six items: 1. Bayesian network model that predicts the annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Tabular datasets) 2. Bayesian network model beach mice casefile (Tabular dataset) 3. Bayesian network model detection casefile (Tabular dataset) 4. Bayesian network model output of the 2009 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) 5. Bayesian network model output of the 2010 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) 6. Bayesian network model output of the 2011 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) 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. The model was created using a combination of expert elicitation, simplifying assumptions, literature-derived empirical values, and a beach mouse detection and nondetection survey. 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.

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.

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.

This USGS data release represents tabular and geospatial data for the Gulf Sturgeon Bayesian Network Model. The Gulf Sturgeon is a federally listed, anadromous species, inhabiting Gulf Coast rivers, estuaries, and coastal waters from Louisiana to Florida. The data release was produced in compliance with 'open data' requirements as way to make the scientific products associated with USGS research efforts and publications available to the public. The dataset consists of 2 separate items: 1. Bayesian network model that predicts the probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon at a 30-m pixel scale in Apalachicola Bay, FL (Tabular datasets) 2. Bayesian network model outputs of the probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon at a 30-m pixel scale in Apalachicola Bay, FL for 35 physiological and habitat scenarios (Raster datasets)

This U.S. Geological Survey (USGS) data release represents data for the creation of a spatially explicit Bayesian network model that predicts Brown Pelican nests on islands across the U.S. portion of the Gulf of Mexico. Well-targeted management plans are needed to achieve Brown Pelican population objectives (number of breeding pairs) because conservation and restoration opportunities are limited and costly. To aid the design of such plans, we estimated population objectives for 10 U.S. Fish and Wildlife Service Gulf Coast Biological Planning Units (BPUs). We then developed a Bayesian network model that uses an island’s characteristics to predict pelican nest count, a proxy measure for breeding pairs. We used the model and 2000-2015 bird survey data to estimate each BPU’s total nest count given the existing islands’ characteristics. We then used the model to hypothesize island-specific actions and simulate management scenarios that implemented these actions opportunistically (randomly selected islands) or strategically (target islands with the highest nest count) until the population objective was met. We then estimated each BPU’s (1) total number of islands; (2) total nesting, roosting, and loafing habitat needed to achieve its population objective (habitat objective); and (3) the effort required to achieve the habitat objective (management efficiency).

This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for the creation and application of a Bayesian network model that predicts Black Skimmer (Rynchops niger) and Gull-billed Tern (Gelochelidon nilotica) on bare ground sites across the U.S. portion of the Gulf of Mexico. Management plans with clear priorities can help to achieve Black Skimmer and Gull-billed Tern population targets (number of breeding pairs) because conservation and restoration opportunities can be limited and costly. These species form breeding colonies on bare ground sites, where the number of breeding pairs may be influenced by numerous site conditions such as site area, soil texture, and topography; island area, shrub area, and elevation; predators, and human disturbances. These data were used to develop a Bayesian network model that uses site conditions to predict Black Skimmer and Gull-billed Tern nest counts as a proxy for breeding pairs. We used the model and 2005-2015 bird survey data to estimate total nests and nest deficits for Gulf Coast Joint Venture Initiative Areas (IA) under existing conditions and simulated scenarios that presumed managers changed site conditions. We selected a best scenario for each IA based on its ability to simultaneously achieve both species targets for the least perceived effort. We used the best scenario to prioritize sites for management until the simulations suggested both species targets might be met. We then repeated the simulations while excluding sites that had attributes that limited their management application.

The Gulf Sturgeon is a federally listed, anadromous species, inhabiting Gulf Coast rivers, estuaries, and coastal waters from Louisiana to Florida. The U.S. Geological Survey partnered with the U.S. Fish and Wildlife Service (USFWS), U.S. Army Corps of Engineers, University of Georgia, and their conservation partners to support adaptive management of Gulf Sturgeon (Acipenser oxyrinchus desotoi) by developing a quantitative, spatial model. The model is a Bayesian network that predicts the probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon at a 30-m pixel scale in estuarine critical habitat. The model predicts habitat availability (days) for 75 alternative physiological and habitat scenarios, which were the unique combination of river discharge, winter month, and month of arrival to the estuary. The probability of habitat availability (days) is predicted from habitat characteristics that could be influenced by management actions. The model's structure was defined by empirical data, expert elicitation, and simplifying assumptions.