Deep learning for species identification of modern and fossil rodent molars

Abstract

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.

Archaeophenomics of ancient domestic plants and animals using geometric morphometrics : a review

“…Bickler, 2021;Horn et al, 2021), but only few bioarchaeological studies use yet such approaches (e.g. Miele, Dussert, Cucchi, & Renaud, 2020). Machine learning in general, and deep learning using convolutional neural networks in particular, will certainly help in the future for automatic data acquisition such as landmark coordinates (e.g.…”

Archaeophenomics of ancient domestic plants and animals using geometric morphometrics : a review

“…studies use yet such approaches (e.g. Miele, Dussert, Cucchi, & Renaud, 2020). Machine learning in general, and deep learning using convolutional neural networks in particular, will certainly help in the future for automatic data acquisition such as landmark coordinates (e.g.…”

Deep learning as a tool for ecology and evolution

“…It is not surprising then that identification of individuals or species from image, video, and sound data is the most common use of deep learning in the field (Figure 3). These efforts already span many taxa, from bacteria (Kotwal et al, 2021), through protozoans (Hsiang et al, 2019), plants (Carranza‐Rojas et al, 2017; Schuettpelz et al, 2017; Unger et al, 2016; Younis et al, 2018) to insects (Boer & Vos, 2018; Hansen et al, 2020; Marques et al, 2018; Valan et al, 2019) and vertebrates (Norouzzadeh et al, 2018; Villon et al, 2018), both extant and fossil (de Lima et al, 2020; Liu & Song, 2020; Miele et al, 2020) and at scales ranging from local to global.…”