A multi‐state occupancy model to non‐invasively monitor visible signs of wildlife health with camera traps that accounts for image quality

Camera traps are an increasingly popular tool to monitor wildlife distributions. However, traditional analytical approaches to camera trap data are difficult to apply to visible wildlife characteristics in single images, such as infection status. Several parasites produce visible signs of infection that could be sampled via camera traps. Sarcoptic mange Sarcoptes scabiei is an ideal disease to study using cameras because it results in visible hair loss and affects a broad host range. Here, we developed a multi‐state occupancy model to estimate the occurrence of mange in coyotes Canis latrans across an urban gradient. This model incorporates a secondary detection function for apparent by‐image infection status to provide detection‐corrected estimates of mange occurrence. We analysed a multi‐year camera trap dataset in Chicago, Illinois, United States, to test whether the apparent occurrence of sarcoptic mange in coyotes Canis latrans increases with urbanization or varies through time. We documented visible signs consistent with current or recovering mange infection and variables we hypothesized would improve mange detection: The proportion of the coyote in frame, image blur and whether the image was in colour. We were more likely to detect coyotes with mange in images that were less blurry, in colour, and if a greater proportion of the coyote was visible. Mangy coyote occupancy was significantly higher in urban developed areas with low housing density and higher canopy cover whereas coyote occupancy, mangy or otherwise, decreased with urbanization. By incorporating image quality into our by‐image detection function, we provide a robust method to non‐invasively survey visible aspects of wildlife health with camera traps. Apparently mangy coyotes were associated with low‐density forested neighbourhoods, which may offer vegetated areas while containing sources of anthropogenic resources. This association may contribute to human–wildlife conflict and reinforces posited relationships between infection risk and habitat use. More generally, our model could provide detection‐corrected occupancy estimates of visible characteristics that vary by image such as body condition or injuries. The authors developed a new class of multi‐state occupancy model to non‐invasively survey visible aspects of wildlife health. This model corrects for site‐specific issues in species detectability and apparent by‐image infection status (i.e., failure to detect disease signs due to poor