Pincelli M. Hull | Search USGS

USGS2026

Shifting phenology in planktonic foraminifera in the Gulf of America (Mexico) with 21st century warming

We investigate trends in foraminiferal phenology using a 12-year (2008–2020), well-resolved (7 to 28-day resolution) time series of planktonic foraminiferal fluxes from a U.S. Geological Survey sediment trap in the northern Gulf of America (Gulf of Mexico; 27.5°N, 90.3°W). The total foraminiferal flux declined over this time. We used Horn-Morisita dissimilarity analysis, principal coordinate analysis, and correlation matrices to show that fall assemblages became more similar in composition to summer assemblages (July–September) over the 12 years. As a result, winter (January–March) and fall (October–December) assemblages, which dominate the total annual foraminiferal flux, became more dissimilar in composition. This pattern is consistent with multidecadal, non-uniform trends of increasing seasonal sea surface temperatures, affecting the seasonality and composition of foraminiferal communities through changing species phenology. We speculate that similar phenological changes across plankton communities might have led to trophic mismatch, causing the decline in total foraminiferal flux.

Journal of Foraminiferal Research↗

USGS2019

Endless forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks

Accurate planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting specific species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, species identification varies among taxonomic schools, few resources exist to train students in the difficult task of discerning amongst closely related species, and the number of taxonomic experts is limited. Here, we take the first steps towards removing these rate-limiting steps by generating the first extensive image library of modern planktonic foraminifera, providing taxonomic training tools and resources, and automating species-level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Taxonomic experts identified 34,640 images of modern planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/endless-forams/). A supervised machine learning classifier was then trained with more than 24,000 images of planktonic foraminifera and tested using the remaining ~10,000 images (i.e., the validation set). The best classifier provided the correct species name for an image in the validation set 87.4% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies.

Paleoceanography and Paleoclimatology↗