Review of puma density estimates reveals sources of bias and variation, and the need for standardization

• DOI: 10.1016/j.gecco.2022.e02109
• Publication Date: 2022-03-30T06:02:42Z
• Authors: Sean M. Murphy; Richard A. Beauso...; Haley Stewart; John J. Cox
• Keywords: Puma; Carnivore; Population density

Range-wide status assessments of wildlife are critical to effective species conservation and management. Reliability these is contingent on having accurate precise demographic estimates for local populations, but large carnivores, such often biased, imprecise, or unavailable. Despite being the most widely distributed carnivore in Americas, little known about range-wide population puma (Puma concolor). Population density frequently primary metric used management decision-making policy; therefore, we conducted a comprehensive, range-wide, systematic review capture-recapture mark-resight model-based published through 2021 Bayesian multilevel models investigate potential sources bias variation. Model-based have been produced just 8 countries (42% with populations) study areas that cumulatively represent < 1% extant range. Most applied small (median = 265 km2), protected (70%), represented high quality habitats, as forests mixed savannas (89%). Nonspatial likely overestimated by an average 63%, inclusion dependent individuals (e.g., kittens) detection histories resulted were, average, ~33% higher than independent only, highlighting need standardization. After correcting those biases, mean median densities were 1.81 1.63 pumas/100 km2 (95% CI 1.62, 2.02), respectively, 95th percentile 3.64 km2. Although did not differ between North South America, unprotected areas, among human disturbance severities, support existed varying at landscape-scale function multiple geographical, environmental, climatic characteristics biome, precipitation, vegetation quality, elevation). However, imprecise (90% had CV > 0.20) positively primarily because area sizes issues associated some sampling analytical methods; example, observed 31–33% overestimation when spatially unstructured genetic was used. Consequently, many existing may be inadequate reliable decision-making, current number geographical extent insufficient inform useful continental species.