To model or not to model: false positive detection error in camera surveys

Abstract

AbstractOccupancy models are commonly used with motion‐sensitive camera data to estimate patterns of species occurrence while accounting for false negative detection error (i.e., the species is present but not detected). False positive detection error (i.e., the species is not present but is detected) is present in camera data sets, especially when morphologically similar species co‐occur. Researchers use different approaches to address this problem: ignore the potential for false positive detections, remove all ambiguous detections and treat them as non‐detections, or model false positive detection error by dividing detections into ambiguous detections (could be true or false positives) and unambiguous detections (true positives). We performed a simulation study to compare these 3 strategies. To implement these modeling strategies, detections must be classified as ambiguous or unambiguous, or all ambiguous detections must be re‐classified as non‐detections. We also performed a simulation study to assess the impact of researcher confidence in the designation of ambiguous and unambiguous detections. Ignoring false positive detection error resulted in biased parameter estimates, whereas removing ambiguous detections and modeling false positive detections resulted in similar estimates of occupancy probability (ψ) in most situations. Researcher over‐confidence (i.e., the tendency for observers to overestimate their own ability) positively biased estimates of ψ. Moderate under‐confidence did not increase bias or decrease precision in estimates of ψ. Consistent with the patterns observed in simulations, analysis of example data from a chipmunk (Neotamias minimus atristriatus) population in the Sacramento Mountains of south‐central New Mexico during 2019 indicated that removing ambiguous detections and modeling false positives resulted in similar estimates of ψ and that over‐confidence biased estimates of ψ. Our results expand on previous literature, suggesting that removing ambiguous detections provides similar estimates of occupancy compared to modeling false positives in many scenarios, and emphasizing the importance of the designation of ambiguous and unambiguous detections. We provide guidance on simple methods to define ambiguous and unambiguous detections, thus mitigating the chances for erroneous inferences.