Bayesian network model that predicts the annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat

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.

Data and Resources

Field Value
accessLevel public
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datagov_dedupe_retained 20220722114234
identifier USGS:5dc22f7ae4b069579750e456
metadata_type geospatial
modified 20210106
old-spatial {"type": "Polygon", "coordinates": [[[-87.521631775, 29.64109959], [-87.521631775, 30.424720137], [ -85.301664385, 30.424720137], [ -85.301664385, 29.64109959], [-87.521631775, 29.64109959]]]}
publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
source_datajson_identifier true
source_hash f3a1c4d63d2a72e1d02595bc9b16eeb203ef2d0a
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spatial {"type": "Polygon", "coordinates": [[[-87.521631775, 29.64109959], [-87.521631775, 30.424720137], [ -85.301664385, 30.424720137], [ -85.301664385, 29.64109959], [-87.521631775, 29.64109959]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • adaptive-management
  • amerigeo
  • amerigeoss
  • bayesian-network
  • beach-mouse
  • ckan
  • coastal-dunes
  • downlisting-criteria
  • florida
  • geo
  • geoss
  • gulf-of-mexico
  • landscape-conservation
  • national
  • north-america
  • strategic-habitat-conservation
  • united-states
  • usgs-5dc22f7ae4b069579750e456
isopen False
license_id notspecified
license_title License not specified
maintainer James Patrick Cronin
maintainer_email jcronin@usgs.gov
metadata_created 2025-11-22T08:33:58.076054
metadata_modified 2025-11-22T08:33:58.076058
notes 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.
num_resources 2
num_tags 18
title Bayesian network model that predicts the annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat