Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting 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, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here we document our initial progress toward removing these confounding and/or rate-limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species-level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) 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~27,000 images of these identified planktonic foraminifera. The best-performing model provided the correct species name for an image in the validation set 87.4% of the time and included the correct name in its top three guesses 97.7% 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 for applications such as paleotemperature reconstruction.

Efficient searching is crucial for timely location of food and other resources. Recent studies show that diverse living animals use a theoretically optimal scale-free random search for sparse resources known as a Lévy walk, but little is known of the origins and evolution of foraging behavior and the search strategies of extinct organisms. Here, using simulations of self-avoiding trace fossil trails, we show that randomly introduced strophotaxis (U-turns)-initiated by obstructions such as self-trail avoidance or innate cueing-leads to random looping patterns with clustering across increasing scales that is consistent with the presence of Lévy walks. This predicts that optimal Lévy searches may emerge from simple behaviors observed in fossil trails. We then analyzed fossilized trails of benthic marine organisms by using a novel path analysis technique and find the first evidence, to our knowledge, of Lévy-like search strategies in extinct animals. Our results show that simple search behaviors of extinct animals in heterogeneous environments give rise to hierarchically nested Brownian walk clusters that converge to optimal Lévy patterns. Primary productivity collapse and large-scale food scarcity characterizing mass extinctions evident in the fossil record may have triggered adaptation of optimal Lévy-like searches. The findings suggest that Lévy-like behavior has been used by foragers since at least the Eocene but may have a more ancient origin, which might explain recent widespread observations of such patterns among modern taxa.Brownian motion | superdiffusion | scale invariance | climate change

Abstract. Late-glacial palaeoclimate reconstructions from deep-sea sediment archives provide valuable insight into past rapid changes in ocean chemistry. Unfortunately, only a small proportion of the ocean floor with sufficiently high sediment accumulation rate (SAR) is suitable for such reconstructions using the long-standing age–depth model approach. We employ ultra-small radiocarbon (14C) dating on single microscopic foraminifera to demonstrate that the long-standing age–depth model method conceals large age uncertainties caused by post-depositional sediment mixing, meaning that existing studies may underestimate total geochronological error. We find that the age–depth distribution of our 14C-dated single foraminifera is in good agreement with existing bioturbation models only after one takes the possibility of Zoophycos burrowing into account. To overcome the problems associated with the age–depth paradigm, we use the first ever dual 14C and stable isotope (δ18O and δ13C) analysis on single microscopic foraminifera to produce a palaeoclimate time series independent of the age–depth paradigm. This new state of the art essentially decouples single foraminifera from the age–depth paradigm to provide multiple floating, temporal snapshots of ocean chemistry, thus allowing for the successful extraction of temporally accurate palaeoclimate data from low-SAR deep-sea archives. This new method can address large geographical gaps in late-glacial benthic palaeoceanographic reconstructions by opening up vast areas of previously disregarded, low-SAR deep-sea archives to research, which will lead to an improved understanding of the global interaction between oceans and climate.