Hide and seek shark teeth in Random Forests: machine learning applied to Scyliorhinus canicula populations

Abstract

Shark populations that are distributed alongside a latitudinal gradient often display body size differences at sexual maturity and vicariance patterns related to their number of tooth files. Previous works have demonstrated that Scyliorhinus canicula populations differ between the northeastern Atlantic Ocean and the Mediterranean Sea based on biological features and genetic analysis. In this study, we sample more than 3,000 teeth from 56 S. canicula specimens caught incidentally off Roscoff and Banyuls-sur-Mer. We investigate population differences based on tooth shape and form by using two approaches. Classification results show that the classical geometric morphometric framework is outperformed by an original Random Forests-based framework. Visually, both S. canicula populations share similar ontogenetic trends and timing of gynandric heterodonty emergence but the Atlantic population has bigger, blunter teeth, and less numerous accessory cusps than the Mediterranean population. According to the models, the populations are best differentiated based on their lateral tooth edges, which bear accessory cusps, and the tooth centroid sizes significantly improve classification performances. The differences observed are discussed in light of dietary and behavioural habits of the populations considered. The method proposed in this study could be further adapted to complement DNA analyses to identify shark species or populations based on tooth morphologies. This process would be of particular interest for fisheries management and identification of shark fossils.

Keywords

• Machine learning
• Geometric morphometrics
• Tooth morphology
• Scyliorhinus canicula
• Random Forests
• Linear discriminant analysis