Long-Term Evaluation of Cougar Density and Application of Risk Analysis for Harvest Management

Estimates of cougar (Puma concolor) density are among the least available of any big game species in North America because of monetary and logistical challenges. Thus, wildlife managers identify cougar density estimates as a high priority need for population estimation, developing harvest guidelines, and evaluating management objectives. Cougar densities range from <1 to almost 7 cougars/100 km(2); however, the magnitude of spatial and temporal variation associated with these estimates is difficult to assess because this range of densities could potentially be reported for any given population using different demographic, temporal, durational, and analytical approaches. We used long-term global positioning system (GPS) data from collared cougars across 5 diverse study areas in Washington, USA, as the basis for calculating multiple annual independent-aged (>= 18 months) cougar densities, using consistent methods, and conducted a meta-analysis to assist with statewide harvest guidelines. To generate specific harvest guidelines for unobserved populations at the management unit scale, we employed a Bayesian decision-theoretic approach that minimizes statistical risk of failing to achieve a defined harvest rate. For the 16-year field effort, we calculated 24 annual densities for independent-aged cougars. Average annual densities ranged from 1.55 +/- 0.44 (SD) cougars/100 km(2) (n = 5 years) to 2.79 +/- 0.35 cougars/100 km(2) (n = 5 years) among the 5 study areas. Explicit delineation of the cougar population demonstrated that contribution to density can vary considerably by sex and age class. Application of a 12-16% harvest rate within the risk analysis framework yielded a potential annual harvest of 249 cougars over 91,000 km(2) of cougar habitat in Washington. Given the importance of density for establishing harvest guidelines, and the degree of uncertainty in projecting derived densities to future years and unstudied management units, our approach may lessen the ambiguity of extrapolations and increase the longevity of research results. Our risk analysis can be used for a diverse array of species and management objectives and be incorporated into an adaptive management framework for minimizing management risk. Our recommendations can improve standardization in reporting and interpretation of cougar density comparisons and bring clarity to the sources of variability observed in cougar populations. (c) 2021 The Wildlife Society.