DOI: 10.5061/dryad.jq46143
title: Data from: Effect of detection heterogeneity in occupancy-detection models: an experimental test of time-to-first-detection methods

description: Imperfect detection can bias estimates of site occupancy in ecological
 surveys but can be corrected by estimating detection probability.
 Time-to-first-detection (TTD) occupancy models have been proposed as a
 cost-effective survey method that allows detection probability to be
 estimated from single site visits. Nevertheless, few studies have
 validated the performance of occupancy-detection models by creating a
 situation where occupancy is known, and model outputs can be compared with
 the truth. We tested the performance of TTD occupancy models in the face
 of detection heterogeneity using an experiment based on standard survey
 methods to monitor koala (Phascolarctos cinereus) populations in
 Australia. Known numbers of koala faecal pellets were placed under trees,
 and observers, uninformed as to which trees had pellets under them,
 carried out a TTD survey. We fitted five TTD occupancy models to the
 survey data, each making different assumptions about detectability, to
 evaluate how well each estimated the true occupancy status. Relative to
 the truth, all five models produced strongly biased estimates,
 overestimating detection probability and underestimating the number of
 occupied trees. Despite this, goodness-of-fit tests indicated that some
 models fitted the data well, with no evidence of model misfit. Hence, TTD
 occupancy models that appear to perform well with respect to the available
 data may be performing poorly. The reason for poor model performance was
 unaccounted for heterogeneity in detection probability, which is known to
 bias occupancy-detection models. This poses a problem because unaccounted
 for heterogeneity could not be detected using goodness-of-fit tests and
 was only revealed because we knew the experimentally determined outcome. A
 challenge for occupancy-detection models is to find ways to identify and
 mitigate the impacts of unobserved heterogeneity, which could unknowingly
 bias many models.
description: hide_seek_scatTime-to-first-detection data for 319 trees. The columns are: tree = a unique identifier for each tree; ttd = time-to-detection (in minutes) with NA indicating no detection within 10 minutes; pellets = the number of koala faecal pellets dropped around the tree; difficulty = the perceived level of search difficulty (e = easy, m = medium, d = difficult); tool = the search method (glove = gloved hand, rake = gloved hand + rake); observer = an identifier for each of the nine observers.Model specificationR code to specify the 10 models in JAGS code and write them to external filesFit models to koala data and draw figuresR code to fit the JAGS models (specified in ‘Model specification.R’) to the data (in ‘hide_seek_scat.csv’) and produce Table 1, Figures 1-3, Supplementary Table 1 and Figure S1.Fit models to koala data subsetsR code to fit the JAGS models (specified in ‘Model specification.R’) to subsets of the data defined by the number of pellets dropped around each tree (1,2,4 or 8). A summary of the model outputs are written to a csv file.Analyse subset resultsProduces Figure S2 from the csv file generated by ‘Fit models to koala data subsets.R’Simulate heterogeneityRuns the data simulation and model fitting to generate the data used to produce Figures 4 and S3.Analyse heterogeneity simulation resultsSummarises the results generated by ‘Simulate heterogeneity.R’ and draws Figures 4 and S3.