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 analyzed a multi-year camera trap dataset in Chicago, Illinois, USA to test whether the apparent occurrence of sarcoptic mange in coyotes (Canis latrans) increases with urbanization or varies through time. W...