Anieke Brombacher

Oxygen (O ₂ ) is essential for almost all life on Earth, but O ₂ concentrations are rapidly declining in the oceans under global warming, stressing marine ecosystems and biogeochemical cycles. Numerical model simulations suggest greatly expanded North Atlantic Oxygen Minimum Zones (OMZs) under warm Pliocene conditions, but sparse data limit validation and evaluation of mechanistic drivers. Here we use fossil tests of Globorotaloides hexagonus , a rare species of planktonic foraminifera associated with OMZs today, to reconstruct past variability in oxygenation in the Eastern Tropical North Atlantic Ocean (ETNA). Our astronomically resolved records from ODP Site 659 cover the warm Pliocene through intensification of North Hemisphere Glaciation (3.45–2.33 Ma) and the last glacial cycle (150–0 ka). We document deoxygenation of the ETNA during both the warm Late Pliocene and marine isotope stage (MIS) 5, followed by re‐oxygenation upon the introduction of colder conditions associated with intensified Northern Hemisphere Glaciation (∼3 Ma) and MIS4 (∼70 ka) respectively. Under deoxygenated conditions, OMZ‐strength fluctuated, paced by precession. We rule out “top‐down” control on deoxygenation by changes in ETNA sea surface temperature, upper ocean stratification, upwelling strength or ocean productivity. Instead, we infer “bottom up” control by changes in the ventilation of intermediate waters supplied to the ETNA. Mechanistic clues emerge from the sign of the relationship that we document: deoxygenation peaks occur during insolation maxima, when tropical rainbelt expansion freshened the Mediterranean Sea and altered its outflow properties to the Atlantic Ocean. We propose that Mediterranean Outflow variability modulated OMZ strength in the ETNA.

Paleontology provides insights into the history of the planet, from the origins of life billions of years ago to the biotic changes of the Recent. The scope of paleontological research is as vast as it is varied, and the field is constantly evolving. In an effort to identify “Big Questions” in paleontology, experts from around the world came together to build a list of priority questions the field can address in the years ahead. The 89 questions presented herein (grouped within 11 themes) represent contributions from nearly 200 international scientists. These questions touch on common themes including biodiversity drivers and patterns, integrating data types across spatiotemporal scales, applying paleontological data to contemporary biodiversity and climate issues, and effectively utilizing innovative methods and technology for new paleontological insights. In addition to these theoretical questions, discussions touch upon structural concerns within the field, advocating for an increased valuation of specimen-based research, protection of natural heritage sites, and the importance of collections infrastructure, along with a stronger emphasis on human diversity, equity, and inclusion. These questions offer a starting point—an initial nucleus of consensus that paleontologists can expand on—for engaging in discussions, securing funding, advocating for museums, and fostering continued growth in shared research directions.

The fossil record provides the most powerful evidence of large-scale biodiversity change on Earth, but it does so at coarse and often idiosyncratic temporal scales. One critical problem that arises concerns the evolutionary consequences of individual environmental experience. Individuals respond to their environment instantaneously, whereas the resolution of most fossil records aggregates multiple paleoenvironments over time scales beyond individual lifespans. Therefore, the presence of phenotypic plasticity in deep time and the extent of its influence on macroevolution remain poorly understood. Using coupled computed tomography and laser ablation inductively coupled plasma mass spectrometry protocols, we studied the environmental dependence of developmental trajectories across three sister species of macroperforate planktonic foraminifera. A foraminiferal shell preserves all stages of the individual’s ontogeny, as well as the environmental state experienced throughout its lifetime. Generalized additive mixed effect (GAMM) models show that somatic growth rates differ among the three Menardella species and that these are inversely correlated with calcification temperature, as reconstructed from Mg/Ca measurements through ontogeny. This environmental dependence varies among species: The thermal sensitivity of individual chamber-to-chamber growth rates of Menardella limbata and Menardella pertenuis is double that seen in Menardella exilis . In contrast, no such environmental signal was recovered for architectural shape traits. Our integrated approach is widely applicable and demonstrates that detecting developmental plasticity in the fossil record is feasible. Extrapolating these techniques in deep time promises to revolutionize our understanding of the ways in which environmentally associated trait variation drove the diversification of life on Earth.

Planktonic foraminifera are extremely well suited to studying evolutionary change in the fossil record due to their abundant deposits and global distribution. Species are typically conservative in their shell morphology, with the same geometric shapes appearing repeatedly through iterative evolution, but the mechanisms behind the architectural limits on foraminiferal shell shape are still not well understood. To determine how these developmental constraints arise, we study morphological change leading up to the origination of the unusually ornate species Globigerinoidesella fistulosa . We measured the size and circularity of more than 900 specimens of G. fistulosa , its ancestor the Trilobatus sacculifer plexus, and intermediate forms from a site in the western equatorial Pacific. Our results show that the origination of G. fistulosa from the T. sacculifer plexus involved a combination of two heterochronic expressions: earlier onset of protuberances (pre-displacement) and a steeper allometric slope (acceleration) as compared with its ancestor. Our work provides a case study of the complex morphological and developmental changes required to produce unusual shell shapes and highlights the importance of developmental deviations in evolutionary origination.

For at least the last 11 million years, the North African landscape has repeatedly oscillated on astronomical timescales between the dry dusty conditions of today and more humid, vegetated conditions such as those documented for the mid‐Holocene. These changes were primarily driven by expansion and contraction of the tropical rainbelt in response to changes in summer insolation. However, other mechanisms are needed to explain temporal variability in the sensitivity of African humidity to this rhythmic forcing. A main interval of observed change is the Pliocene‐Pleistocene transition (∼3.5–2.4 Ma) when Africa is widely (but not universally) suggested to have become drier and dustier. Here we present new suborbitally resolved records of surface ocean temperature, foraminiferal stable isotopes and export productivity from the Northwest African margin and the eastern equatorial Atlantic Ocean and compare them to published records. We find strong coupling at astronomical timescales between productivity and dust fluxes throughout our study interval, indicating the sustained influence of the northeast trade winds on dust transport, upwelling strength and perhaps dust‐driven ocean fertilization. We attribute observed increases in dust fluxes delivered to the NW African margin and eastern equatorial Atlantic to strengthening of the trade winds driven by the steepening latitudinal temperature gradients associated with the intensification of Northern Hemisphere glaciation. Taken together with published evidence of increased strength in the mid‐latitude westerlies at this time, our results point to invigoration of large‐scale atmospheric circulation globally during intensified glacial periods of the Pliocene‐Pleistocene transition.

The rapid and repeatable characterization of individual morphology has advanced automated taxonomic classification. The most direct study of evolutionary processes is, however, not from taxonomic description, but rather of the evolution of the traits that comprise individuals and define species. Repeatable signatures of individual morphology are crucial for analysing the response to selection at scale, and thus tracking evolutionary trajectories through time and across species boundaries. Here, we introduce our 3DKMI—an open‐source MATLAB package designed for the study of morphology using three‐dimensional (3D) Krawtchouk moment invariants. The volumetric features derived from the 3D images remain stable under translation, scaling and rotation and, for an image of size 128 × 128 × 128 can be computed in less than 0.1 s. We applied our package as a case study on a collection of 300 X‐ray computed tomography scans of planktonic foraminifera specimens across five species to (1) assess the invariance of the features under different transformations and (2) analyse morphological differences among species based on the extracted characteristics. We show that 3DKMI has the capacity to efficiently and repeatedly characterize the signatures of individual morphology. In the future, we hope that the 3D feature extraction technique 3DKMI will be widely applied to digital collections to advance research in ecology and evolution.

Deep learning has emerged as a robust tool for automating feature extraction from three-dimensional images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artficial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared with the external structure, owing to low contrast differences between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from three-dimensional images.

Deep learning has emerged as a robust tool for automating feature extraction from 3D images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artificial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images and that data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared to the external structure, due to low contrast between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from 3D images. Subject Category Life Sciences – Computer Science Subject Areas computational biology, Artificial Intelligence

Introduction: Lines of least resistance, or the direction of maximum phenotypic variation, are reliable predictive tools for directions of evolutionary divergence through time. However, the consistency of trait covariation patterns through space, i.e. in different populations of the same taxa inhabiting different environmental settings, remains poorly established.Methods: To test whether the predicted direction of evolutionary change is the same through time as it is across space, we compare within- and among-population trait covariation patterns across six Atlantic populations of two planktonic foraminifera species. Our study interval is 600,000 years long, spans the Neogene/Quaternary boundary and includes Earth’s most recent major natural shift in global climate state: the intensification of Northern Hemisphere Glaciation.Results: We show that, despite powerful global temporal changes in climate, there is a strong spatial signal in the evolutionary response. Population-specific trait covariation slopes vary among sites, climate phases and core/edge position within the species’ biogeographic range.Discussion: Our results imply that the direction of expected evolutionary change does not align across populations. This suggests that trait covariation patterns in the study species are driven by adaptation to local environmental settings rather than species-wide constraints. Single populations should therefore not be used to predict the response of other populations, even if they are faced with similar environmental conditions. We caution against using individual populations to project future response of other/global populations unless the underlying mechanism for trait covariation is shown to be universal.

Foraminifera are one of the few taxa that preserve their entire ontogeny in their fossilised remains. Revealing this ontogeny through micro-computed tomography (CT) of fossil planktonic foraminifera has greatly improved our understanding of their life history and allows accurate quantification of total shell volume, growth rates and developmental constraints throughout an individual's life. Studies using CT scans currently mainly focus on chamber size, but the wealth of three-dimensional data generated by CT scans has the potential to reconstruct complete growth trajectories. Here we present an open-source R package to analyse growth in three-dimensional space. Using only the centroid xyz coordinates of every chamber, the functions determine the growth sequence and check that chambers are in the correct order. Once the order of growth has been verified, the functions calculate distances and angles between subsequent chambers, determine the total number of whorls and the number of chambers in the final whorl at the time each chamber was built, and, for the first time, quantify trochospirality. The applications of this package will enable repeatable analysis of large data sets and quantification of key taxonomic traits and ultimately provide new insights into the effects of ontogeny on evolution.