DOI: 10.5061/dryad.jk6rf
title: Data from: Robust inference on large-scale species habitat use with interview data: the status of jaguars outside protected areas in Central America

description: 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
 &lt;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.
description: Jaguar Model Main
 CodeR
 code to build models of jaguar habitat use, with and without false
 positives, using JAGSJaguarModel_MainCode_forDryad.RCross-validation Main CodeR code for two cross-validation methods used for our data: one novel method that seeks to replicate the observation process, leading to percent accuracy & probabilities of omission and commission, and the other method (based on Hooten & Hobbs 2015 and Miller & Grant 2015) that tests whether deviance is reduced via inclusion of hierarchical structure. Both methods implemented in JAGS.CrossVal_MainCode_forDryad.RCorridor ID.csv with Corridor IDCorridor_Max6_FP.csvEffort.csv with Effort data (% of year spent in sampling unit)Field Season ID.csv with Field Season IDFieldSeason.csvJaguar Detection HistoryData re: whether or not a jaguar or jaguar sign had been seen by the interviewee in the given sampling unit. Sampling units are rows, interview replicates are columns.Jaguar_Max6_FP.csvMore Than FourData corresponding to whether or not &gt;4 interviews were conducted in the unit (1/0)MoreThanFour_v3.csvSite CovariatesAll site covariates considered in the analysis. Summarized at sampling unit level.SiteCovariates.csv