Estimating species occupancy across multiple sampling seasons with autologistic occupancy models via the autoOcc...

Dynamic occupancy models, which estimate local colonization and extinction rates from detection/non-detection data collected across multiple sampling periods (e.g. years), are powerful but data hungry statistical tools. However, many ecological studies lack sufficient sample sizes to estimate these dynamic parameters. Autologistic occupancy models, which estimate occupancy patterns through time and account for temporal autocorrelation in a species occupancy status, offer a parsimonious alternative that is well suited for datasets with fewer sites or seasons of data. Here, I introduce the autoOcc R package, which can be used to fit autologistic occupancy models in a frequentist framework. This package also supports model comparison via the Akaike information criterion (AIC) and making predictions from fitted models, making it a flexible and accessible option for those with detection/non-detection data collected over time. Through simulations I show that autologistic occupancy models estimate parameters with less bias and more precision than dynamic occupancy models across a wide range of scenarios and sample sizes. These results suggest that autologistic occupancy models are a useful alternative when data are limited-a common constraint in ecological studies. To illustrate practical use of autoOcc I provide two worked examples: estimating habitat associations of Virginia opossum (Didelphis virginiana) throughout Chicago, Illinois, USA and quantifying spatiotemporal patterns in black-backed woodpecker (Picoides arcticus) distributions as a function of fire severity throughout California's montane forests. These examples demonstrate not only how to implement fitting autologistic occupancy models, but also how meaningful ecological inference can be drawn from them. By formally introducing this modelling framework and lowering the barrier for others to use, autoOcc increases the range of tools available for researchers that study species occupancy dynamics, especially when data are limited.