A Rapid Population Assessment Method for Wild Pigs Using Baited Cameras at 3 Study Site

Authors

Peter E. Schlichting, Arizona State UniversityFollow
James C. Beasley, University of Georgia
Raoul K. Boughton, University of Florida
Amy J. Davis, Wildlife Services, National Wildlife Research Center
Kim M. Pepin, Wildlife Services, National Wildlife Research CenterFollow
Michael P. Glow, Wildlife Services, National Wildlife Research Center
Ryan S. Miller, Wildlife Services, National Wildlife Research CenterFollow
Kurt C. VerCautern, Wildlife Services, National Wildlife Research CenterFollow
Jesse S. Lewis, Arizona State UniversityFollow

ORCID IDs

http://orcid.org/0000-0002-5171-6493

Date of this Version

3-5-2020

Citation

Schlichting, P.E., J.C. Beasley, R.K. Boughton, A.J. Davis, K.M. Pepin, M.P. Glow, N.P. Snow, R.S. Miller, K.C. VerCausteren, and J.S. Lewis. 2020. A rapid population assessment method for wild pigs using baited cameras at 3 study sites. Wildlife Society Bulletin 44(2):372-382. doi: 10.1002/wsb.1075

Abstract

Reliable and efficient population estimates are a critical need for effective management of invasive wild pigs (Sus scrofa). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture-recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions.