I urval från Stockholms universitets publikationsdatabas

• Physiological and morphological scaling enables gigantism in pelagic protists

  2025. Janet E. Burke (et al.). Limnology and Oceanography 70 (2), 461-476

  Artikel

  Planktonic foraminifera are pelagic protists frequently used to study paleoenvironmental change. Many planktonic foraminifera, like other taxa in Rhizaria, reach gigantic proportions relative to other pelagic protists (> 600 μm), placing them in a size class dominated by metazoans. Here, we combine new and existing respiration rate measurements, micro-CT scans, and test size measurements to investigate allometric scaling of metabolic rates, relative biomass density, and mixotrophy in contributing to the ability of planktonic foraminifera to reach large sizes. Respiration rate increases with foraminiferal biovolume with a slope of 0.51 ± 0.18. This allometric scaling slope is lower than those reported in other plankton. Further, the basal respiration rates for planktonic foraminifera exceed those of other organisms in their size class when probable biomass, rather than test volume, is considered. Using the allometric regression on a published database of modern planktonic foraminifera from the Atlantic Ocean, we estimate that gigantic individuals account for 15.3–26.1% of foraminiferal community respiration in temperate and tropical/subtropical latitudes, despite making up only 4.5–8.3% of individuals. We hypothesize that shallow scaling of test size with metabolism and of test size to actual biomass is the key factor allowing for gigantism in planktonic foraminifera. Having a large test and broadcasting rhizopodial networks increases the functional volume of the organism, allowing higher passive prey encounter rates to support the elevated metabolic rates in planktonic foraminifera. Mixotrophy may act as a mitigating factor for metabolic challenges at low latitudes, accounting for the presence of large populations of giant, predominately mixotrophic Rhizarians in these assemblages.

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• Revealing patterns of homoplasy in discrete phylogenetic datasets with a cross-comparable index

  2025. Elizabeth M. Steell, Allison Hsiang, Daniel J. Field. Zoological Journal of the Linnean Society 204 (1)

  Artikel

  Investigating patterns of homoplasy can improve our understanding of macroevolutionary processes by revealing evolutionary constraints on morphology and highlighting convergent form–function relationships. Here, we test the performance of several widely-used methods that provide measures of homoplasy, including the consistency (CI) and retention indices (RI), using simulated and empirical discrete morphological datasets. In addition, we describe and test a new method employing a novel randomization protocol, which we term the relative homoplasy index (RHI). RHI outperforms other methods in a range of situations for measuring relative homoplasy and allows comparisons between different datasets. In line with some previous work, we show that relative levels of homoplasy remain constant with the addition of characters and decrease with the addition of taxa. We also show that the extent of homoplasy strongly influences the distribution of taxa in morphospace. Low homoplasy results in highly partitioned morphospace, while high homoplasy leads to clades overlapping in morphospace. Our results help illuminate the properties of relative homoplasy in morphological phylogenetic matrices, opening new potential avenues for research on homoplasy quantification in macroevolutionary studies.

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• The distribution and abundance of planktonic foraminifera under summer sea ice in the Arctic Ocean

  2025. Flor Vermassen (et al.). Biogeosciences 22 (9), 2261-2286

  Artikel

  Planktonic foraminifera are calcifying protists that represent a minor but important part of the pelagic microzooplankton. They are found in all of Earth's ocean basins and are widely studied in sediment records to reconstruct climatic and environmental changes throughout geological time. The Arctic Ocean is currently being transformed in response to modern climate change; however, the effect on planktonic foraminiferal populations is virtually unknown. Here, we provide the first systematic sampling of planktonic foraminifera communities in the "high"Arctic Ocean - defined in this work as areas north of 80° N - specifically in the broad region located between northern Greenland (the Lincoln Sea with its adjoining fjords and the Morris Jesup Rise), the Yermak Plateau, and the North Pole. Stratified depth tows down to 1000 m using a multinet were performed to reveal the species composition and spatial variability in these communities below the summer sea ice. The average abundance in the top 200 m ranged between 15 and 65 individuals m-3 in the central Arctic Ocean and was 0.3 individuals m-3 in the shelf area of the Lincoln Sea. At all stations, except one site at the Yermak Plateau, assemblages consisted solely of the polar specialist Neogloboquadrina pachyderma. It predominated in the top 100 m, where it was likely feeding on phytoplankton below the ice. Near the Yermak Plateau, at the outer edge of the pack ice, rare specimens of Turborotalita quinqueloba occurred that appeared to be associated with the inflowing Atlantic Water layer. Our results would suggest that the anticipated turnover from polar to subpolar planktonic species in the perennially ice-covered part of the central Arctic Ocean has not yet occurred, in agreement with a recent meta-analysis from the Fram Strait which suggested that the increased export of sea ice is blocking the influx of Atlantic-sourced species. The presented data set will be a valuable reference for continued monitoring of the abundance and composition of planktonic foraminifera communities as they respond to the ongoing sea-ice decline and the "Atlantification"of the Arctic Ocean basin. Additionally, the results can be used to assist paleoceanographic interpretations, based on sedimented foraminifera assemblages.

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• Artificial intelligence in paleontology

  2024. Congyu Yu (et al.). Earth-Science Reviews 252

  Artikel

  The accumulation of large datasets and increasing data availability have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fast-growing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, artificial intelligence (AI) has become widely practiced across scientific disciplines, but not so much to date in paleontology where traditionally manual workflows have been more usual. In this study, we review >70 paleontological AI studies since the 1980s, covering major tasks including micro- and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge-Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies most to the lowering of the entry bar in training and deployment of AI models rather than innovations in fossil data compilation and methods. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) that may interface with paleontological research in the future. Even though AI has not yet been a significant part of the paleontologist's toolkit, successful implementation of AI is growing and shows promise for paradigm-transformative effects on paleontological research in the years to come.

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• Visual Microfossil Identification via Deep Metric Learning

  2022. Tayfun Karaderi (et al.). Pattern Recognition and Artificial Intelligence, 34-46

  Konferens

  We apply deep metric learning for the first time to the problem of classifying planktic foraminifer shells on microscopic images. This species recognition task is an important information source and scientific pillar for reconstructing past climates. All foraminifer CNN recognition pipelines in the literature produce black-box classifiers that lack visualisation options for human experts and cannot be applied to open set problems. Here, we benchmark metric learning against these pipelines, produce the first scientific visualisation of the phenotypic planktic foraminifer morphology space, and demonstrate that metric learning can be used to cluster species unseen during training. We show that metric learning outperforms all published CNN-based state-of-the-art benchmarks in this domain. We evaluate our approach on the 34,640 expert-annotated images of the Endless Forams public library of 35 modern planktic foramini-fera species. Our results on this data show leading 92%92% accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data, and 66.5%66.5% accuracy (at 0.70 F1-score) when clustering species never encountered in training. We conclude that metric learning is highly effective for this domain and serves as an important tool towards expert-in-the-loop automation of microfossil identification. Key code, network weights, and data splits are published with this paper for full reproducibility.

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