Integrating resource selection into spatial capture‐recapture models for large carnivores. Issue 11 (20th November 2015)

• Record Type: Journal Article
• Authors: Proffitt, K. M.; Goldberg, J. F.; Hebblewhite, M.; Russell, R.; Jimenez, B. S.; Robinson, H. S.; Pilgrim, K.; Schwartz, M. K.
• Is Part Of: Ecosphere. Volume 6:Issue 11(2015)
• Page Start: 1
• Page End: 15
• Publication Date: 2015-11-20
• Subjects: Bayesian -- carnivore -- mountain lion -- non-invasive -- population estimation -- Puma concolor -- SCR; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1890/ES15-00001.1
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract : Wildlife managers need reliable methods to estimate large carnivore densities and population trends; yet large carnivores are elusive, difficult to detect, and occur at low densities making traditional approaches intractable. Recent advances in spatial capture‐recapture (SCR) models have provided new approaches for monitoring trends in wildlife abundance and these methods are particularly applicable to large carnivores. We applied SCR models in a Bayesian framework to estimate mountain lion densities in the Bitterroot Mountains of west central Montana. We incorporate an existing resource selection function (RSF) as a density covariate to account for heterogeneity in habitat use across the study area and include data collected from harvested lions. We identify individuals through DNA samples collected by (1) biopsy darting mountain lions detected in systematic surveys of the study area, (2) opportunistically collecting hair and scat samples, and (3) sampling all harvested mountain lions. We included 80 DNA samples collected from 62 individuals in the analysis. Including information on predicted habitat use as a covariate on the distribution of activity centers reduced the median estimated density by 44%, the standard deviation by 7%, and the width of 95% credible intervals by 10% as compared to standard SCR models. Within the two management units of interest, we estimated a median mountain lion density of 4.5 mountain lions/100 km 2 (95% CI = 2.9, 7.7) and 5.2 mountain lions/100 km 2 (95% CI = 3.4, 9.1). Including harvested individuals (dead recovery) did not create a significant bias in the detection process by introducing individuals that could not be detected after removal. However, the dead recovery component of the model did have a substantial effect on results by increasing sample size. The ability to account for heterogeneity in habitat use provides a useful extension to SCR models, and will enhance the ability of wildlife managers to reliably and economically estimate density of wildlife populations, particularly large carnivores.