Diatom communities preserved in sediment samples are valuable indicators for understanding the past and present dynamics of phytoplankton communities, and their response to environmental changes. These studies are traditionally achieved by counting methods using optical microscopy, a time-consuming process that requires taxonomic expertise. With the advent of automated image acquisition workflows, large image datasets can now be acquired, but require efficient preprocessing methods. Detecting diatom frustules on microscope images is a challenge due to their low relief, diverse shapes, and tendency to aggregate, which prevent the use of traditional thresholding techniques. Deep learning algorithms have the potential to resolve these challenges, more particularly for the task of object detection. Here we explore the use of a Faster R-CNN (Region-based Convolutional Neural Network) model to detect siliceous biominerals, including diatoms, in microscope images of a sediment trap series from the Mediterranean Sea. Our workflow demonstrates promising results, achieving a precision score of 0.72 and a recall score of 0.74 when applied to a test set of Mediterranean diatom images. Our model performance decreases when used to detect fragments of these microfossils; it also decreases when particles are aggregated or when images are out of focus. Microfossil detection remains high when the model is used on a microscope image set of sediments from a different oceanic basin, demonstrating its potential for application in a wide range of contemporary and paleoenvironmental studies. This automated method provides a valuable tool for analysing complex samples, particularly for rare species under-represented in training datasets.

Phytoplankton productivity and export sequester climatically significant quantities of atmospheric carbon dioxide as particulate organic carbon through a suite of processes termed the biological pump. How the biological pump operated in the past is therefore important for understanding past atmospheric carbon dioxide concentrations and Earth’s climate history. However, reconstructing the history of the biological pump requires proxies. Due to their intimate association with biological processes, several bioactive trace metals and their isotopes are potential proxies for past phytoplankton productivity, including: iron, zinc, copper, cadmium, molybdenum, barium, nickel, chromium, and silver. Here we review the oceanic distributions, driving processes, and depositional archives for these nine metals and their isotopes based on GEOTRACES-era datasets. We offer an assessment of the overall maturity of each isotope system to serve as a proxy for diagnosing aspects of past ocean productivity and identify priorities for future research. This assessment reveals that cadmium, barium, nickel, and chromium isotopes offer the most promise as tracers of paleoproductivity, whereas iron, zinc, copper, and molybdenum do not. Too little is known about silver to make a confident determination. Intriguingly, the elements that are least sensitive to productivity may be used to trace other aspects of ocean chemistry, such as nutrient sources, particle scavenging, organic complexation, and ocean redox state. These complementary sensitivities suggest new opportunities for combining perspectives from multiple proxies that will ultimately enable painting a more complete picture of marine paleoproductivity, biogeochemical cycles, and Earth’s climate history.