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. 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 color. We were more likely to detect coyotes with mange
in images that were less blurry, in color, 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 neighborhoods, 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.