LAUSR

Camera‐trapping is widespread in wildlife studies, especially for species with individually unique markings to which capture–recapture analytical techniques can be applied. The large volume of data such studies produce have encouraged researchers to increasingly look to computer‐assisted pattern‐ recognition software to expedite individual identifications, but little work has been done to formally assess such software for camera‐trap data. We used 2 sets of camera‐trap images—359 images of jaguars (Panthera onca) and 332 images of ocelots (Leopardus pardalis) collected from camera traps deployed in 4 study sites in Orange Walk District, Belize, in 2015 and 2016—to compare the accuracy of 2 such programs, HotSpotter and Wild‐ID, and assess the effect of image quality on matching success. Overall, HotSpotter selected a correct match as its top rank 71–82% of the time, whereas the rate for Wild‐ID was 58–73%. Positive matching rates for both programs were highest for high‐quality images (85–99%) and lowest for low‐quality images (28–52%). False match rates were very low for HotSpotter (0–2%) but these were greater in Wild‐ID (6–28%). When lower ranks were also considered, both programs performed similarly (overall 22–24% nonmatches for HotSpotter, 17–26% nonmatches for Wild‐ID). We found that in both programs, images more often matched to other images of the same quality; therefore, including multiple reference images of an individual, of different qualities, improves matching success. These programs do not provide fully automatic identification of individuals and human involvement is still required to confirm matches, but we found that they are effective tools to expedite processing of camera‐trap data. We also offer usage recommendations for researchers to maximize the benefits of these tools. © 2020 The Wildlife Society.