IUCN / SSC Cat Specialist Group - Digital Cat Library |
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| Mrozinski, R.B. | |
| Estimating Mountain Lion Density Using Unmarked Bayesian Spatial Capture-recapture for the Davis Mountains, Texas | |
| 2018 Full Book | |
Arguably the most important ecosystem component in the Davis Mountains of Texas is the area's sole apex predator, the mountain lion (_Puma concolor_). Given its ecosystem-wide importance, effective mountain lion management is crucial and requires accurate estimates of mountain lion abundance, density, and distribution. These parameters are challenging to estimate as typical mountain lion studies result in low sample sizes due to the species' scarcity, elusiveness, and wide-ranging nature. Camera traps have emerged as a popular and resourceefficient method to sample such species. However, since mountain lions generally lack distinguishable features, reliable identification of individuals from photos is often difficult or impossible. Recently, spatial capture-recapture methods have been shown to work for unmarked (and unidentifiable) populations. The goal of this research was to evaluate the effectiveness of unmarked spatial capture-recapture methods for estimating mountain lion abundance, density, and distribution of mountain lions in the Davis Mountains of Texas, given camera trap encounters for unidentified individuals. Due to a small sample size, Bayesian inference methods were needed which enabled incorporation of a priori home range estimates as additional information. Results include an estimated distribution of mountain lion density, the maximum likelihood estimate and associated 95% credible interval for density, estimated detection function and home range size, and two-dimensional spatial distribution of density. The maximum likelihood (mode) of population density was 0.99 mountain lions per 100 km2 (95% credible interval = 0.31-10.90). The key lesson learned in employing unmarked spatial capture-recapture methods is that all available information must be collected and incorporated into the methods to obtain reasonable precision in the estimates. |
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