RESUMO
Regular evaluation of jaguar (Panthera onca) population status is an important part of conservation decision-making. Currently, camera trapping has become the standard method used to estimate jaguar abundance and demographic parameters, though evidence has shown the potential for sex ratio biases and density overestimates. In this study, we used camera trap data combined with satellite telemetry data from one female jaguar to estimate jaguar population density in the dry forest of Santa Rosa National Park in the Guanacaste Conservation Area of northwestern Costa Rica. We analyzed camera trap data collected from June 2016 to June 2017 using spatial capture-recapture methods to estimate jaguar density. In total, 19 individual jaguars were detected (11 males; 8 females) with a resulting estimated population density of 2.6 females (95% [CI] 1.7-4.0) and 5.0 male (95% [CI] 3.4-7.4) per 100 km2. Based on telemetry and camera trap data, camera placement might bias individual detections by sex and thus overall density estimates. We recommend population assessments be made at several consecutive 3-month intervals, that larger areas be covered so as not to restrict surveys to one or two individual home ranges, as in our case, and to carry out long-term camera monitoring programs instead of short-term studies to better understand the local population, using auxiliary telemetry data to adjust field designs and density estimations to improve support for jaguar conservation strategies.
RESUMO
Open population capture-recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture-recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack-Jolly-Seber and Jolly-Seber models, with and without activity center movement. The method is applied to a 12-year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root-mean-square error in predicting population density compared to closed population models.