Automated identification of fossil benthic foraminifera from the Peruvian margin using convolutional neural networks

Benthic foraminifera tests preserved in marine sediments are well-established proxies for bottom-water dynamics, yet their minute size and high diversity demand laborious manual identification of hundreds of individuals to reconstruct subtle faunal shifts and are prone to observer-dependent taxonomic inconsistencies. The recent advances in image acquisition hardware and image identification software have made it possible to acquire and identify large image datasets quickly. Here, we trained convolutional neural networks (CNNs) to identify benthic foraminifera morphospecies from 31 samples from two sedimentary cores from offshore Peru, spanning the past 18 000 years. Our best-performing model achieves 92 % overall classification accuracy, 93.4 % precision, and 92.4 % recall, enabling high-temporal-resolution reconstructions of benthic foraminifera assemblages along the Peruvian margin. Automated outputs closely matched manual results across 31 samples, from counts and relative abundances to diversity indices, multivariate assemblage patterns, and dissolved oxygen estimates, indicating the suitability of automated identification for paleo-ecological applications. The highest-performing CNN model (trained on a dataset of 5860 images) from this study can be adapted to analyse benthic foraminifera from equivalent depths of the Peruvian margin, providing high-resolution insights into the eastern tropical Pacific oxygen minimum zone (OMZ). In addition to offering a scalable, objective alternative for high-temporal-resolution analysis of benthic foraminifera, this study also highlights the current limitations of automated workflows.