1. Autonomous Recording Units (ARUs) are now widely used to survey communities of species. These surveys generate spatially and temporally replicated counts of unmarked animals, but such data typically include false negatives and misclassified detections, both of which may vary across sites in proportion to abundance. These data challenges can bias estimates of occupancy, and the typical approach of verifying individual detections is expensive.

2. We developed a Bayesian implementation of a two-species, false-positive N-mixture model for estimating occupancy from ARU data or other counts of unmarked animals that does not require manual verification. The model accounts for species misclassification and abundance-induced detection heterogeneity, as well as false negatives. To evaluate this model, we simulated 200 data sets for each of 29 scenarios, including scenarios in which misclassifications outnumbered correct classifications for rare species. We also applied the model to acoustic surveys of bats conducted on Fort Carson Army Post and Piñon Canyon Maneuver Site, Colorado, USA.

3. In the simulation study, bias, coverage, and root mean square error for occupancy estimates obtained from the two-species false-positive N-mixture model were superior to metrics obtained from two competing two-species false-positive occupancy models. Across 29 scenarios, absolute bias was consistently low (range: -0.03–0.07), while coverage averaged 93% (range: 74%–98%). For alternative occupancy models, absolute bias was often high (range: -0.36–0.39), and coverage averaged from 47%–65%. Although our model included an abundance parameter, abundance estimates were not reliable. For two species of Myotis bats, we estimated that 1%–5% of field-recorded detections were misclassified. Estimated occupancy (0.91 and 0.76) was lower than naïve estimates (1.00 and 0.94). Competing occupancy models implausibly estimated local occupancy of 0.00 at sites with numerous detections.

4. Our two-species, false-positive N-mixture model is significant because it accounts for detection heterogeneity and improves occupancy estimates without expensive manual verification of detections. Our field application indicated that misclassifications were not common, yet affected occupancy inferences. Given that ARUs are increasingly used to survey a broad range of taxa, such an occupancy model could be widely useful.