Camera traps (CTs), used in conjunction with capture-mark-recapture analyses (CMR; photo-CMR), are a valuable tool for estimating abundances of rare and elusive wildlife. However, a critical requirement of photo-CMR is that individuals are identifiable in CT images (photo-ID). Thus, photo-CMR is generally limited to species with conspicuous pelage patterns (e.g., stripes or spots) using lateral-view images from CTs stationed along travel paths. Pumas (Puma concolor) are an elusive species for which CTs are highly effective at collecting image data, but their suitability to photo-ID is controversial due to their lack of pelage markings. For a wide range of taxa, facial features are useful for photo-ID, but this method has generally been limited to images collected with traditional handheld cameras. Here we evaluate the feasibility of using puma facial features for photo-ID in a CT framework. We consider two issues: 1) the ability to capture puma facial images using CTs, and 2) whether facial images improve human ability to photo-ID pumas. We tested a novel CT accessory that used light and sound to attract the attention of pumas, thereby collecting face images for use in photo-ID. Face captures rates increased at CTs that included the accessory (n = 208, χ2 = 43.23, P ≤ 0.001). To evaluate if puma faces improve photo-ID, we measured the inter-rater agreement of 5 independent assessments of photo-ID for 16 of our puma face capture events. Agreement was moderate to good (Fleiss’ kappa = 0.54, 95% CI = 0.48–0.60), and was 92.90% greater than a previously published kappa using conventional CT methods. This study is the first time such a technique has been used for photo-ID, and we believe a promising demonstration of how photo-ID may be feasible for an elusive but unmarked species.

Raw detections of pumas were collected using camera traps with and without an 'attention caller device' (ACD) to capture puma face images. Data include the time and camera site ID of detection, and whether the detection was at an ACD site. Detection data was further processed to include if the puma's face was captured, as well as behavioral reactions of pumas.

To generate the daily detection data, raw image data was processed in R to create a sitewise record of capture counts for each day of deployment. Columns also include whether the site used an ACD, whether the site was a community scrape site, and the age in days of the scent lure placed at sites.

The interrater agreement data was collected with an online application that was distributed to 5 independent users who rated 120 puma face image comparisons, giving each comparison a same (1) or different (0) rating.