An integrated spatial capture–recapture approach reveals the distribution of a cryptic carnivore in a protected area

Abstract

AbstractQuantifying animal abundance, density, and distributions affords the opportunity to understand the effects of landscape structure and change on species of conservation interest, but estimating these parameters can be difficult for rare and cryptic species. Noninvasive sampling methods, such as remote cameras or scat DNA, can mitigate the challenges of studying rare and cryptic species while also minimizing effects on species of conservation interest or concern. Data derived from these methods can be integrated into robust, contemporary quantitative methods, including spatial capture–recapture (SCR) models, which provide a hierarchical framework for jointly estimating animal abundance, distribution, and, consequently, density. Herein, we developed an integrated SCR model to estimate the abundance, density, and distribution of a large carnivore of conservation interest, the cougar (Puma concolor), in a rugged and remote protected area, Yosemite National Park, California, USA. We combined spatial encounter data from DNA‐based individual identification of scats with detection count data derived from remote cameras to estimate cougar density and detection probability in Yosemite. We further estimated how cougar density and detection probability varied as a result of vegetation, topography, anthropogenic and natural linear features, and survey effort. Using data collected in 2019 and 2020, we estimated a median of 31 (SD = 3.96, 95% credible intervals = 24–39) cougars in Yosemite across the two years, with higher densities associated with productive, vegetated areas. We found detection probability by scent detection teams was higher for females than males and positively correlated with survey effort, proximity to trails, and distance farther from roads and streams. Our study illustrates the utility of noninvasive survey methods that yield individual identities in rugged and remote environments, where capture and handling of cryptic, low‐density animals is logistically challenging and cost prohibitive. Integrated modeling approaches, as used here, allow ecologists to leverage empirical data using a robust quantitative framework that can effectively address conservation objectives. Through this work, we demonstrate the importance of large, contiguous, and heterogeneous ecosystems to the ecology of wide‐ranging species that occur in dynamic landscapes.