Machine-assisted image analysis facilitates conservation of iconic elasmobranchs

Photo-identification (photo-ID) is commonly used in wildlife ecology and management, but its effectiveness depends on reliable determination of whether an observed individual is already contained within the database. This is typically done by trained human operators but becomes an increasingly demanding task as photo-ID databases expand, potentially limiting the method's applicability. Artificial intelligence approaches have been suggested as potential solutions to this problem. We present a case study involving a photo-ID database of flapper skate (Dipturus intermedius) from Scotland, which was used to train a multistage, deep learning model using a method similar to high-performing facial recognition system, FaceNet, to enable automatic assessment of the similarity between flapper skate newly submitted to the database and those pre-existing in the database. Evaluation using a blind test set of 100 images taken from the database and also a second, smaller test set of tagged animals of known identity to confirm the model's photo-ID performance for end users. When assessed against the previously unseen test set, the model achieved a mean average precision (mAP) of 84.1% and a top-1 accuracy of 80%. The resulting photo-ID model was integrated into the photo-ID database, which was further tested with images of tagged individuals of known identity. This integration significantly reduced the time required to confirm whether new skates were already included in the database. With a top-1 accuracy of 80%, the matching skate, if contained in the database, will likely be returned as a match, removing the need to check by eye against 2500+ individuals already in the database. Solution. The integration of machine-assisted image analysis into a photo-ID database of skate improved our ability to track individuals and understand movement and residency at far larger scales, essential for the continued management of the species. This approach is suitable for a wide range of research projects reliant on photo-ID and should be considered for new and legacy data sets.