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.

This USGS data release represents 35 geospatial datasets that were the Gulf Sturgeon Bayesian network model's outputs. 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.

This data set was compiled for the Suwannee River Water Management District for use in determining Minimum Flows and Levels. The data set contains detection summaries of acoustically tagged adult Gulf sturgeon using the Upper Suwannee River areas equipped with Innovasea Vemco acoustic receivers (2011-2019).

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 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.

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 data set contains sensitive biological resource data for marine, estuarine, anadromous, and freshwater fish species in Maryland. Vector polygons in this data set represent fish distribution, concentration areas, and spawning areas. Species specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer.This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Maryland. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources.

This data set contains sensitive biological resource data for marine, estuarine, and freshwater species in the Florida Panhandle. Vector polygons in this data set represent fish distribution and aggregation areas. Species specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the ESI data for Florida. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources.

Overview The Central Valley Project Improvement Act (CVPIA) funds habitat improvement work and associated monitoring in the Central Valley of California to increase salmonid populations in furtherance of meeting CVPIA fish doubling goals. This data package contains three datasets for adult White Sturgeon (Acipenser transmontanus) monitoring in the San Joaquin River (SJR) conducted by the US Fish and Wildlife Service, Lodi Fish and Wildlife Office. The primary purpose for this sampling was to capture White Sturgeon and implant acoustic telemetry tags for a tracking project. Therefore, the data are useful for determining when and where White Sturgeon were captured, but they should not be used to determine actual distribution or abundance. SJR_Adult_WST_Set contains data from a sampling program using various methods to catch adult White Sturgeon in the San Joaquin River. Sets were made at targeted locations primarily from March-May in 2012-2018 (other dates were occasionally sampled). SJR_Adult_WST_Catch contains data for individual fish caught via gillnets, trammel nets, setlines, or angling in the San Joaquin River. Species and fork length were recorded for all fish. For White Sturgeon, girth, maturation, tag, and surgery information are provided. SJR_Fish_Taxonomy contains data for fish codes used in the Catch datafile. For each species that was captured, the Species codes are listed with the corresponding Interagency Ecological Program code, common name, taxonomy (Phylum, Class, Order, Family, Genus, and Species), and whether or not the species is native to the region.

This data release includes field data for fishes sampled through mid-water trawls and otter trawl methods from Suisun Bay, California to the mouth of the Sacramento River. These data were collected in August through September, 2022; October of 2023; and in September, 2024 as a part of a multi-year study of Suisun Dredging and Fish Distribution (SDFD). This data release includes quantitative data on collected fish taxa and environmental conditions focused on habitat differences between shipping channels and shoals. Sampling was done using short duration (~15 minute) trawls alongside associated water quality sample and Acoustic Doppler Current Profiler (ADCP) data collected at variable locations, depths, times, and currents.