Title:

Southern Missouri feral swine monitoring and removal data from 2020-2024

Author(s):

Feuka, Abigail B.; Guerrant, Travis L.; Angerer, Cassie; Farrar, Randy O.; Jump Sr., Brad; Dykstra, Adrienne M.; Pepin, Kimberly M.

Publication Year:

2025

How to Cite:

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Feuka, Abigail B.; Guerrant, Travis L.; Angerer, Cassie; Farrar, Randy O.; Jump Sr., Brad; Dykstra, Adrienne M.; Pepin, Kimberly M. 2025. Southern Missouri feral swine monitoring and removal data from 2020-2024. Research Dataset Series. USDA, APHIS, WS National Wildlife Research Center. Ft. Collins, Colorado. https://doi.org/10.2737/NWRC-RDS-2025-001

Abstract:

This package contains all necessary data to fit a model of feral swine occupancy and abundance in southern Missouri from 2020-2024. These data include feral swine presence/absence data collected via systematic baiting, numbers of feral swine removal via traps and aerial operations, and the amount of time spent conducting these activities. We also include data on the proportion of oak cover, agricultural cover, and development to investigate the impact of these factors on feral swine distribution and population growth. Data are aggregated to the HUC10 watershed spatial and to either 10-day subperiod of 90-day primary period scales. Feral swine data were collected via USDA Wildlife Services management operations and covariate data were collected via remote sensing by other projects.

Keywords:

biota; farming; environment; Wildlife (or Fauna); Mammals; feral swine; systematic baiting; aerial operations; trapping; oak cover; agricultural cover; development cover; Sus scrofa; Missouri

Related publications:

• Feuka, Abigail B.; Guerrant, Travis L.; Angerer, Cassie; Farrar, Randy O.; Jump Sr., Brad; Dykstra, Adrienne M.; Pepin, Kimberly M. Unknown. Estimating probability of invasive species elimination and abundance trends using multiple operational data streams. Ecological Applications. [In review].

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