Data from: Machine-assisted image analysis facilitates conservation of iconic elasmobranchs

Photo-identification is commonly used in wildlife ecology and management,
but its effectiveness depends on a 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 (AI) 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 a high-performing facial recognition system, FaceNet,
to enable automatic assessment of the similarity between flapper skate
newly submitted to the database and those preexisting 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-identification 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–identification 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 2,500+ individuals already in the database. 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.