Change-in-ratio density estimator for feral pigs is less biased than closed mark–recapture estimates
Laura B. Hanson A E , James B. Grand B , Michael S. Mitchell C , D. Buck Jolley A , Bill D. Sparklin D and Stephen S. Ditchkoff AA School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA.
B US Geological Survey, Alabama Cooperative Wildlife Research Unit, Auburn University, Auburn, AL 36849, USA.
C US Geological Survey, Montana Cooperative Wildlife Research Unit, University of Montana, Missoula, MT 59812, USA.
D Montana Cooperative Wildlife Research Unit, University of Montana, Missoula, MT 59812, USA.
E Corresponding author. Email: laurabhanson@hotmail.com
Wildlife Research 35(7) 695-699 https://doi.org/10.1071/WR08076
Submitted: 20 May 2008 Accepted: 16 August 2008 Published: 17 November 2008
Abstract
Closed-population capture–mark–recapture (CMR) methods can produce biased density estimates for species with low or heterogeneous detection probabilities. In an attempt to address such biases, we developed a density-estimation method based on the change in ratio (CIR) of survival between two populations where survival, calculated using an open-population CMR model, is known to differ. We used our method to estimate density for a feral pig (Sus scrofa) population on Fort Benning, Georgia, USA. To assess its validity, we compared it to an estimate of the minimum density of pigs known to be alive and two estimates based on closed-population CMR models. Comparison of the density estimates revealed that the CIR estimator produced a density estimate with low precision that was reasonable with respect to minimum known density. By contrast, density point estimates using the closed-population CMR models were less than the minimum known density, consistent with biases created by low and heterogeneous capture probabilities for species like feral pigs that may occur in low density or are difficult to capture. Our CIR density estimator may be useful for tracking broad-scale, long-term changes in species, such as large cats, for which closed CMR models are unlikely to work.
Acknowledgements
We give much thanks to the Mitchell ‘wet lab’ for their support, assistance with critical thinking, and aid in developing strong scientific ideas. We appreciate all pig people who helped with fieldwork, especially B. Williams, C. Newbolt and K. Hasapes. Thank you to P. Swiderek, R. M. Thornton and B. Miley at the Fort Benning Natural Resource Management Branch for ideas and support for this research. Thank you to two anonymous reviewers for useful comments. This research was funded by the US Department of the Defence, Fort Benning Military Reservation.
Alho, J. M. (1990). Logistic regression in capture–recapture models. Biometrics 46, 623–635.
| Crossref | GoogleScholarGoogle Scholar | CAS | PubMed |
Caley, P. (1993). Population dynamics of feral pigs (Sus scrofa) in a tropical riverine habitat complex. Australian Wildlife Research 20, 625–636.
| Crossref | GoogleScholarGoogle Scholar |
Dillon, A. , and Kelly, M. J. (2008). Ocelot home range, overlap and density: comparing radio telemetry with camera trapping. Journal of Zoology 275, 391–398.
| Crossref | GoogleScholarGoogle Scholar |
Hone, J. (2002). Feral pigs in Namadgi National Park, Australia: dynamics, impacts and management. Biological Conservation 105, 231–242.
| Crossref | GoogleScholarGoogle Scholar |
Huggins, R. M. (1989). On the statistical analysis of capture experiments. Biometrika 76, 133–140.
| Crossref | GoogleScholarGoogle Scholar |
Woodall, P. F. (1983). Distribution and population dynamics of dingoes (Canis familiaris) and feral pigs (Sus scrofa) in Queensland, 1945–1976. Journal of Applied Ecology 20, 85–95.
| Crossref | GoogleScholarGoogle Scholar |