Jaguar (Panthera onca) population dynamics in an “isolated” national park in the Cerrado biome, Brazil

Abstract

Abstract The jaguar (Panthera onca) is endangered along all its geographical distribution, including Brazil. Assessments of jaguar population dynamics are scarce despite their relevance to efficiently design conservation measures and acknowledge the demographic health of jaguar populations. This study uses camera trap data from 4 surveys spanning 8 years to gain knowledge on jaguar population dynamics in Emas National Park (ENP), one of the largest Conservation Units in the Brazilian Cerrado biome. ENP is located within a major grain cropland area and provides refuge for species that occur in its interior and surroundings. We used spatially explicit capture-recapture models (SCR) to estimate jaguar population density and population trends, and Cormack-Jolly Seber models (CJS) to estimate survival. We derived recruitment into the adult population from estimates of population trends and survival. Across all surveys, we identified 26 individuals, 9 female (F), 13 male (M), and 4 with unidentified sex (NI). The estimated sex ratio was not statistically different from even. Distance to river positively affected jaguar detection rates; baseline detection rates were higher for males than females and on-road than off-road. The movement parameter σ was higher for males than females. Survey specific density estimates ranged from 1.87 to 2.42 ind./100km², leading to an average annual population growth rate of 0.99, i.e., a stable population. Survival was high (0.87), and some individuals remained in the population for over 10 years, pointing towards a healthy population with low turn-over rates. But recruitment into the adult population was low (0.16), possibly suggesting a somewhat isolated and saturated population. Such population stability supports the role of ENP in local-scale jaguar conservation. Therefore, population management strategies should be adopted to prevent population decline from anthropogenic pressures and stochastic factors.