HotSpotter: Using a computer-driven photo-id application to identify sea turtles

Photo identification (PID) in animal studies has been a widely used method for identifying individuals of many species based on unique natural markings and patterns. The use of PID has facilitated investigations in which residency, home ranges, and growth rates have been assessed. However, many PID studies in the past have relied heavily on manual photo matching. More recently, computer-assisted PID programs have been used to identify individuals of different sea turtle species, and reduced time investment in identifying individuals within specific populations. Still, some computer-based PID programs require significant time investment in ensuring photos are captured at consistent angles and lighting conditions, pre-processing image manipulations, and post-processing manual matching confirmation of potential matches provided by the program. For PID to be an effective time and money saving mechanism for wildlife research and conservation, these common drawbacks need to be addressed with a computer-assisted PID program that reduces manipulation and time investment burden, and consistently provides accurate and reliable results. In this study, we evaluated the accuracy of matching individual face images using the HotSpotter (HS) PID program by building a database of 2136 images of hawksbill (Eretmochelys imbricata) turtles, then querying the database with 158 new images to find matches for individual turtles. Overall, we found that with almost no pre-processing manipulation, and with images from highly variable underwater conditions, qualities, and angles, HS correctly matched individuals in the first choice 80% of the time, increasing to 91% in the first six choices. When assessing in-water images only, accuracy for matching increased from 84% in the first choice, to 94% by the sixth choice. We suggest that the integration of HS technology into a global, web-based PID system will increase the ability to remotely identify individual marine organisms on a global scale, and improve usability for community scientists who may have little to no technical training.

Automated summary

PID is a widely used method in animal studies, but traditional manual photo matching methods have limitations. Computer-assisted PID programs have been developed to improve efficiency in identifying individuals within specific populations. Addressing common drawbacks in computer-assisted PID programs is crucial for PID to be an effective mechanism for wildlife research and conservation.