  Spatial models for occupancy data are used to estimate and map the true
presence of a species, which may depend on biotic and abiotic factors as well
as spatial autocorrelation. Traditionally researchers have accounted for
spatial autocorrelation in occupancy data by using a correlated normally
distributed site-level random effect, which might be incapable of identifying
nontraditional spatial dependence such as discontinuities and abrupt
transitions. Machine learning approaches have the potential to identify and
model nontraditional spatial dependence, but these approaches do not account
for observer errors such as false absences. By combining the flexibility of
Bayesian hierarchal modeling and machine learning approaches, we present a
general framework to model occupancy data that accounts for both traditional
and nontraditional spatial dependence as well as false absences. We demonstrate
our framework using six synthetic occupancy data sets and two real data sets.
Our results demonstrate how to identify and model both traditional and
nontraditional spatial dependence in occupancy data which enables a broader
class of spatial occupancy models that can be used to improve predictive
accuracy and model adequacy.
