Using machine learning to model nontraditional spatial dependence in occupancy data

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
modeling nontraditional spatial dependence such as
discontinuities and abrupt transitions. Machine learning approaches have
the potential to 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 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.