Data from: Robust inference on large-scale species habitat use with interview data: the status of jaguars outside protected areas in Central America
Cite this dataset
Petracca, Lisanne S. et al. (2020). Data from: Robust inference on large-scale species habitat use with interview data: the status of jaguars outside protected areas in Central America [Dataset]. Dryad. https://doi.org/10.5061/dryad.jk6rf
Evaluating range-wide habitat use by a target species requires information on species occurrence over broad geographic regions, a process made difficult by species rarity, large spatiotemporal sampling domains, and imperfect detection. We address these challenges in an assessment of habitat use for jaguars (Panthera onca) outside protected areas in Central America. Occurrence records were acquired within 12 putative corridors using interviews with knowledgeable corridor residents. We developed a Bayesian hierarchical occupancy model to gain robust inference, allowing for heterogeneity introduced in the sampling process over space and time, using records of jaguar occurrence prone to false positives and false negatives. Probability of false detection of jaguars increased with the number of interviews conducted per unit (from 5.42% to 7.74% given <4 and ≥4 observers per unit). True probability of detection (mean=0.58) increased with the number of days interviewees spent in a survey unit per year. Failing to account for false positives biased predicted habitat use high (˜1.8x), especially where occurrence records were sparse. Probability of site use by jaguars increased with greater forest cover, prey richness, and distance from human settlements, and decreased with greater agricultural cover, elevation, and distance from protected areas. Site use probabilities averaged 0.15-0.97 by corridor, providing relatively fine-scale resolution of predicted jaguar occurrence consistent with known patterns of jaguar gene flow across Central America. Model validation, accounting for both false positives and negatives in the observation process, indicated moderate correspondence between model-predicted observations and actual observations for withheld data (0.65, 95% CRI 0.59–0.71), with sensitivity and specificity rates of 0.69 (0.61 – 0.77) and 0.59 (0.50 – 0.68), respectively. These results demonstrate that reliable predictions can be achieved despite the complexity of large-scale, interview-based analyses of species occurrence. Synthesis and applications. Our Bayesian hierarchical occupancy model accommodated heterogeneity caused by typical sampling inequities and idiosyncrasies associated with interview data, yielding robust estimates of jaguar habitat use. Our approach is applicable to any wide-ranging and readily identifiable species and has particular utility for rare species in human-dominated landscapes where traditional survey techniques (e.g., camera traps) may be impractical.