RT Journal Article
SR Electronic
T1 Deep learning for species identification of modern and fossil rodent molars
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.20.259176
DO 10.1101/2020.08.20.259176
A1 Vincent Miele
A1 Gaspard Dussert
A1 Thomas Cucchi
A1 Sabrina Renaud
YR 2020
UL http://biorxiv.org/content/early/2020/10/16/2020.08.20.259176.abstract
AB Reliable identification of species is a key step to assess biodiversity. In fossil and archaeological contexts, genetic identifications remain often difficult or even impossible and morphological criteria are the only window on past biodiversity. Methods of numerical taxonomy based on geometric morphometric provide reliable identifications at the specific and even intraspecific levels, but they remain relatively time consuming and require expertise on the group under study. Here, we explore an alternative based on computer vision and machine learning. The identification of three rodent species based on pictures of their molar tooth row constituted the case study. We focused on the first upper molar in order to transfer the model elaborated on modern, genetically identified specimens to isolated fossil teeth. A pipeline based on deep neural network automatically cropped the first molar from the pictures, and returned a prediction regarding species identification. The deep-learning approach performed equally good as geometric morphometrics and, provided an extensive reference dataset including fossil teeth, it was able to successfully identify teeth from an archaeological deposit that was not included in the training dataset. This is a proof-of-concept that such methods could allow fast and reliable identification of extensive amounts of fossil remains, often left unstudied in archaeological deposits for lack of time and expertise. Deep-learning methods may thus allow new insights on the biodiversity dynamics across the last 10.000 years, including the role of humans in extinction or recent evolution.Competing Interest StatementThe authors have declared no competing interest.