Occupancy modeling is a common approach to assess spatial and temporal species distribution patterns, while explicitly accounting for measurement errors common in detection-nondetection data. Numerous extensions of the basic single species occupancy model exist to address dynamics, multiple species or states, interactions, false positive errors, autocorrelation, and to integrate multiple data sources. However, development of specialized and computationally efficient software to fit spatial models to large data sets is scarce or absent. We introduce the
R package designed to fit single species, multispecies, and integrated spatially-explicit occupancy models. Using a Bayesian framework, we leverage Póly-Gamma data augmentation and Nearest Neighbor Gaussian Processes to ensure models are computationally efficient for potentially massive data sets.
spOccupancy provides user-friendly functions for data simulation, model fitting, model validation (by posterior predictive checks), model comparison (using information criteria and k-fold cross-validation), and out-of-sample prediction. We illustrate the package’s functionality via a vignette, simulated data analysis, and two bird case studies, in which we estimate occurrence of the Black-throated Green Warbler (Setophaga virens) across the eastern USA and species richness of a foliage-gleaning bird community in the Hubbard Brook Experimental Forest in New Hampshire, USA. The
spOccupancy package provides a user-friendly approach to fit a variety of single and multispecies occupancy models, making it straightforward to address detection biases and spatial autocorrelation in species distribution models even for large data sets.