Two-species occupancy modeling accounting for species misidentification and nondetection

1.In occupancy studies, species misidentification can lead to false positive detections, which can cause severe estimator biases. Currently, all models that account for false positive errors only consider omnibus sources of false detections and are limited to single species occupancy.

2.However, false detections for a given species often occur because of the misidentification with another, closely-related species. To exploit this explicit source of false positive detection error, we develop a two-species occupancy model that accounts for misidentifications between two species of interest. As with other false positive models, identifiability is greatly improved by the availability of unambiguous detections at a subset of site-occasions. Here, we consider the case where some of the field observations can be confirmed using laboratory or other independent identification methods (“confirmatory data”).

3.We performed three simulation studies to (1) assess the model's performance under ...