Improving estimation of puma (Puma concolor) population density: clustered camera-trapping, telemetry data, and generalized spatial mark-resight models

Sean M Murphy,Mark A Peyton,David T Wilckens,Ben C Augustine,Glenn C Harper

doi:10.1038/s41598-019-40926-7

Abstract

Obtaining reliable population density estimates for pumas (Puma concolor) and other cryptic, wide-ranging large carnivores is challenging. Recent advancements in spatially explicit capture-recapture models have facilitated development of novel survey approaches, such as clustered sampling designs, which can provide reliable density estimation for expansive areas with reduced effort. We applied clustered sampling to camera-traps to detect marked (collared) and unmarked pumas, and used generalized spatial mark-resight (SMR) models to estimate puma population density across 15,314 km2 in the southwestern USA. Generalized SMR models outperformed conventional SMR models. Integrating telemetry data from collars on marked pumas with detection data from camera-traps substantially improved density estimates by informing cryptic activity (home range) center transiency and improving estimation of the SMR home range parameter. Modeling sex of unmarked pumas as a partially identifying categorical covariate further improved estimates. Our density estimates (0.84–1.65 puma/100 km2) were generally more precise (CV = 0.24–0.31) than spatially explicit estimates produced from other puma sampling methods, including biopsy darting, scat detection dogs, and regular camera-trapping. This study provides an illustrative example of the effectiveness and flexibility of our combined sampling and analytical approach for reliably estimating density of pumas and other wildlife across geographically expansive areas.

Highlights

Pumas are the most widely distributed large carnivore in the western hemisphere[1]
Recent studies have applied spatially explicit models to multiple types of detection data to estimate puma population density; for example, tissue samples collected by biopsy darting pumas that were treed using hounds[20,21,22], puma scat collected via area searches by scat detection dogs[23], and photographs of pumas collected from regular or contiguous arrays of remote camera-traps[24,25,26,27]
Using Global Positioning Systems (GPS) collars as marks can permit unambiguous individual identification for most camera-trap detections of marked individuals, assist with determining whether an animal is marked or unmarked, and provide telemetry location data that can be integrated in spatially explicit models to improve estimation of individual activity centers, the detection function spatial scale parameter (σ), and population density[26,36,37,39]

Summary

Introduction

Pumas (cougars or mountain lions; Puma concolor) are the most widely distributed large carnivore in the western hemisphere[1]. A critical assumption of most capture-recapture models is that all detected animals are individually identifiable[19] This can be difficult to achieve if camera-traps are used to detect pumas or other wildlife that lack visible, individually unique natural markings, such as the rosettes on jaguars (Panthera onca)[24,33]. For pumas and other species that lack unambiguous natural markings, physically capturing and applying artificial marks, such as radiocollars or ear tags, to a portion of animals in a population is likely necessary for accurate density estimation when using camera-traps for detection[26,32,34,35,36,37] Such mark-resight methods can be viable, cost-effective alternatives to capture-recapture methods, because only a single marking event of a portion of a population is required and camera-trapping to collect resighting data is efficient. Using Global Positioning Systems (GPS) collars as marks can permit unambiguous individual identification for most camera-trap detections of marked individuals, assist with determining whether an animal is marked or unmarked, and provide telemetry location data that can be integrated in spatially explicit models to improve estimation of individual activity centers, the detection function spatial scale (home range) parameter (σ), and population density[26,36,37,39]

Methods

Results

Conclusion