North American Wildlife Agency CWD Testing and Ancillary Data (2000 – 2022)

The North American Wildlife Agency CWD Testing and Ancillary Data (2000-2022) dataset (“Dataset”) represents epidemiological, population, ecological, and anthropogenic data related to chronic wasting disease (CWD) surveillance in white-tailed deer (Odocoileus virginianus) in 16 US states (“Administrative Areas”) in North America. The data are summarized at the “Sub-Administrative Area” level. For most state wildlife agencies, counties or equivalent units within Administrative Areas serve as sub-administrative area. While the overall time-period represented by the Dataset spans the years 2000 through 2022, data availability varies by wildlife agency. 

Administrative Areas and associated wildlife agencies represented in the Dataset include Arkansas (Arkansas Game and Fish Commission), Florida (Florida Fish and Wildlife Conservation Commission), Georgia (Georgia Department of Natural Resources), Indiana (Indiana Department of Natural Resources), Iowa (Iowa Department of Natural Resources), Kentucky (Kentucky Department of Fish and Wildlife Resources), Maryland (Maryland Department of Natural Resources), Michigan (Michigan Department of Natural Resources), Minnesota (Minnesota Department of Natural Resources), Mississippi (Mississippi Department of Wildlife, Fisheries, and Parks), New York (New York State Department of Environmental Conservation), North Carolina (North Carolina Wildlife Resources Commission), Ohio (Ohio Department of Natural Resources), Tennessee (Tennessee Wildlife Resources Agency), Virginia (Virginia Department of Wildlife Resources), and Wisconsin (Wisconsin Department of Natural Resources). 

This dataset is intended for use in regional models to determine risk factors that can be used to predict locations of CWD incidence in white-tailed deer at the sub-administrative unit level. Version 2 (the current version) of the Dataset corrects errors present in Version 1 of the Dataset (Them et al. 2023). For Version 2, the original data sources were re-examined and re-processed to ensure the highest level of fidelity and accuracy possible. Data processing scripts were revised for improved precision and data quality and are available in a related repository (Hollingshead et al. 2024). All data and scripts were assessed for accuracy and consistency using a thorough QAQC process completed by internal and external collaborators.