P> Recent advances in occupancy estimation that adjust for imperfect
detection have provided substantial improvements over traditional
approaches and are receiving considerable use in applied ecology. To
estimate and adjust for detectability, occupancy modelling requires
multiple surveys at a site and requires the assumption of `closure'
between surveys, i.e. no changes in occupancy between surveys.
Violations of this assumption could bias parameter estimates; however,
little work has assessed model sensitivity to violations of this
assumption or how commonly such violations occur in nature.
We apply a modelling procedure that can test for closure to two avian
point-count data sets in Montana and New Hampshire, USA, that exemplify
time-scales at which closure is often assumed. These data sets
illustrate different sampling designs that allow testing for closure but
are currently rarely employed in field investigations. Using a
simulation study, we then evaluate the sensitivity of parameter
estimates to changes in site occupancy and evaluate a power analysis
developed for sampling designs that is aimed at limiting the likelihood
Application of our approach to point-count data indicates that habitats
may frequently be open to changes in site occupancy at time-scales
typical of many occupancy investigations, with 71% and 100% of species
investigated in Montana and New Hampshire respectively, showing
violation of closure across time periods of 3 weeks and 8 days
Simulations suggest that models assuming closure are sensitive to
changes in occupancy. Power analyses further suggest that the modelling
procedure we apply can effectively test for closure.
Synthesis and applications. Our demonstration that sites may be open to
changes in site occupancy over time-scales typical of many occupancy
investigations, combined with the sensitivity of models to violations of
the closure assumption, highlights the importance of properly addressing
the closure assumption in both sampling designs and analysis.
Furthermore, inappropriately applying closed models could have negative
consequences when monitoring rare or declining species for conservation
and management decisions, because violations of closure typically lead
to overestimates of the probability of occurrence.
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