Thomas H. G. Ezard

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.

Risks and costs associated with reproduction may induce iteroparous species to employ intermittent breeding, whereby individuals forgo attempting reproduction in some breeding periods following recruitment to the breeding population. We explore this behavior through population-level analyses of Somateria mollissima (Common Eider), quantifying the survival cost of breeding and hence one benefit of skipping. Nonbreeding in a given year may be a short-term response to expected low fitness returns, avoiding either breeding-induced mortality or low offspring value. Alternatively, or additionally, intermittent breeding may be a long-term strategy maximizing lifetime fitness, with breeding trajectories that include nonbreeding years resulting in more recruited offspring over the whole life course than those with the same number of consecutive breeding attempts. Reanalysis of 3 studies reporting annual mortality schedules for S. mollissima allowed estimation of the proportion of mortality incurred during the peak breeding season (~50%) and hence the difference in survival rates between breeders and nonbreeders. These were incorporated into a life cycle and associated matrix population model with a “refreshed breeder” stage to which individuals transition for the time step following nonbreeding. We show that the transition to this stage strongly influences population growth rate—being more than twice as important as reproduction by continued breeders—mostly driven by the possibility of differential survival after skipping breeding. Our results emphasize the benefits of long-term individual-based studies that can identify refreshed breeders to further our understanding of intermittent breeding. Specifically, the accuracy of population projections could be improved by factoring in transitions following skipped breeding, and management enhanced by interventions facilitating return to the breeding pool, such as nest shelter provision.

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.

Like all species, the demography of humans has been shaped under the framework of natural selection. Our understanding of human demography can thus be enhanced by viewing it through a comparative, cross-species, lens and exploring the position of humans among other animal species. Here we use demographic data in the form of matrix population models (MPMs) from humans and 90 other animal species to contextualize patterns of human evolutionary demography. We conduct an additional analysis using human MPM data derived from raw census data from 96 countries over a period spanning 1780 to 2014. For each MPM we calculate a suite of demographic variables that describe multi-component life history strategy and use principal component analysis (PCA) to contextualize human populations among the other vertebrates. We show that, across species, life history strategy can be described by position across two dominant axes of variation and that human life history strategy is indeed set apart from that of other animals. We argue that life history architecture -- the set of relationships among life history traits including their correlations and trade-offs -- is fundamentally different within humans than across all animal species - perhaps because of fundamental distinction in the processes driving within-species and among-species differences. We illustrate strong general temporal trends in life history strategy in humans and highlight both striking commonalities and some differences among countries. For example, there is a general for traversal across life history space that reflects increased life expectancy and life span equality but there is also among-country variation in the trajectories that remains to be explained. Our approach of distilling complex demographic strategies into principal component axes offers a useful tool for the exploration of human demography.

The study of phenotypic evolution has been transformed by methods allowing three-dimensional quantification of anatomical form. The present state-of-the-art 3D geometric morphometrics, which relies heavily on manual landmarking, is time-consuming, prone to operator bias, and cannot effectively compare disparate shapes. Emerging automated approaches, notably landmark-free techniques, offer promise but have only been applied to closely related species. Here, we compare landmark-free approaches with high-density geometric morphometric methods on 322 mammals across 180 families. Leveraging the benefits of Poisson meshes, which combine open and closed projections, we show how landmark-free methods have greater power to differentiate major taxonomic groups. Although there is coarse correspondence in shape variation between the two methods, the finer features of the landmark-free approach likely reflect its broader sampling of the surface structure. Our study underscores the robustness of landmark-free methods for large-scale comparative analysis and helps propel morphometrics into a new era of bigger data.

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

Basing a remaining carbon budget on warming targets is subject to uncertainty due to uncertainty in the relationship between carbon emissions and warming. Framing emissions targets using a warming target therefore may not prevent dangerous change throughout the entire Earth system. Here, we use a climate emulator to constrain a remaining carbon budget that is more representative of the entire Earth system by using a combination of both warming and ocean acidification targets. The warming targets considered are the Paris Agreement targets of 1.5 and 2 °C; the acidification targets are −0.17 and −0.21 pH units, informed by aragonite saturation states where coral growth begins to be compromised. The aim of the dual targets is to prevent not only damage associated with warming, but damage to corals associated with atmospheric carbon and ocean acidification. We find that considering acidification targets in conjunction with warming targets narrows the uncertainty in the remaining carbon budget, especially in situations where the acidification target is more stringent than, or of similar stringency to, the warming target. Considering a strict combination of the two more stringent targets (both targets of 1.5 °C warming and −0.17 acidification must be met), the carbon budget ranges from −74.0 to 129.8PgC. This reduces uncertainty in the carbon budget from by 29% (from 286.2PgC to 203.8PgC). This reduction comes from reducing the high-end estimate of the remaining carbon budget derived from just a warming target. Assuming an emissions rate held constant since 2021 (which is a conservative assumption), the budget towards both targets was either spent by 2019 or will be spent by 2026. Plain language summary: The relationship between atmospheric CO₂ and warming is uncertain, which means that we do not know precisely how much carbon we have left to emit until we reach the Paris Agreement warming targets of 1.5 and 2 °C. However, the relationship between atmospheric CO₂ and ocean acidification is better understood, so by considering targets for acidification rather than warming alone, we can narrow down our estimate of how much emitted carbon is acceptable. Including acidification targets as well as warming targets means that we can directly address the issue of ocean acidification, which poses a threat to corals and the ecosystems reliant on them. By considering acidification and warming targets together, we can lower uncertainty in acceptable carbon emissions by 29%.

Cost‐effective use of limited conservation resources requires understanding which data most contribute to alleviating biodiversity declines. Interventions might reasonably prioritise life‐cycle transitions with the greatest influence on population dynamics, yet some contributing vital rates are particularly challenging to document. This risks managers making decisions without sufficient empirical coverage of the spatiotemporal variation experienced by the species. Here, we aimed to explore whether the number of studies contributing estimates for a given life‐stage transition aligns with that transition's demographic impact on population growth rate, λ. We parameterised a matrix population model using meta‐analysis of vital rates for the common eider (Somateria mollissima), an increasingly threatened yet comparatively data‐rich species of seaduck, for which some life stages are particularly problematic to study. Female common eiders exhibit intermittent breeding, with some established breeders skipping one or more years between breeding attempts. Our meta‐analysis yielded a breeding propensity of 0.72, which we incorporated into our model with a discrete and reversible ‘nonbreeder’ stage (to which surviving adults transition with a probability of 0.28). The transitions between breeding and nonbreeding states had twice the influence on λ than fertility (summed matrix‐element elasticities of 24% and 11%, respectively), whereas almost 15 times as many studies document components of fertility than breeding propensity (n = 103 and n = 7, respectively). The implications of such mismatches are complex because the motivations for feasible on‐the‐ground conservation actions may be different from what is needed to reduce uncertainty in population projections. Our workflow could form an early part of the toolkit informing future investment of finite resources, to avoid repeated disconnects between data needs and availability thwarting evidence‐led conservation.