Occupancy Modeling for Rare Species Using Large Datasets: A Subsampling Approach

Citizen science monitoring programs, such as the Breeding Bird Survey, provide a wealth of data for understanding species abundance and distribution. However, traditional approaches for occupancy modeling of rare species can be difficult to apply to large, imbalanced datasets. We propose a new method for occupancy modeling where the original dataset is subsampled seasonally, keeping all sites with at least one detection along with a random sample of sites with no detections. Occupancy models cannot be fit directly to these subsampled data because the assumption of binomial sampling no longer holds. However, we show that the occupancy probability is adjusted by an offset, meaning inference on the effects of predictors is still valid. We propose a method for model fitting via direct maximum likelihood and demonstrate via simulation that this leads to computational gains. We illustrate our method using data on Canada Warblers (Cardellina canadensis) from the Breeding Bird Survey in Ontario, Canada from 1997 to 2018, where 95% of sites have zero detections annually, demonstrating that we can accurately estimate the occupancy and detection parameters, including estimating the effects of habitat covariates, using just 10% of the original dataset.