Data from: Effect of detection heterogeneity in occupancy-detection models: an experimental test of time-to-first-detection methods

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.