Past foraminiferal acclimatization capacity is limited during future warming – Nature
Cooley, S. et al. in Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pörtner, H.-O. et al.) 379–550 (Cambridge Univ. Press, 2022).
Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. 117, 12891–12896 (2020).
Google Scholar
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).
Google Scholar
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).
Google Scholar
Padfield, D., Yvon-Durocher, G., Buckling, A., Jennings, S. & Yvon-Durocher, G. Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol. Lett. 19, 133–142 (2016).
Google Scholar
Irwin, A. J., Finkel, Z. V., Müller-Karger, F. E. & Troccoli Ghinaglia, L. Phytoplankton adapt to changing ocean environments. Proc. Natl Acad. Sci. USA 112, 5762–5766 (2015).
Google Scholar
Lee, Y. H. et al. Epigenetic plasticity enables copepods to cope with ocean acidification. Nat. Clim. Change 12, 918–927 (2022).
Google Scholar
Yasuhara, M., Hunt, G., Dowsett, H. J., Robinson, M. M. & Stoll, D. K. Latitudinal species diversity gradient of marine zooplankton for the last three million years. Ecol. Lett. 15, 1174–1179 (2012).
Google Scholar
Chivers, W. J., Walne, A. W. & Hays, G. C. Mismatch between marine plankton range movements and the velocity of climate change. Nat. Commun. 8, 14434 (2017).
Google Scholar
Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl Acad. Sci. USA 116, 12907–12912 (2019).
Google Scholar
Neukermans, G. et al. Quantitative and mechanistic understanding of the open ocean carbonate pump – perspectives for remote sensing and autonomous in situ observation. Earth Sci. Rev. 239, 104359 (2023).
Google Scholar
Antell, G. S., Fenton, I. S., Valdes, P. J. & Saupe, E. E. Thermal niches of planktonic foraminifera are static throughout glacial–interglacial climate change. Proc. Natl Acad. Sci. USA 118, e2017105118 (2021).
Google Scholar
Waterson, A. M., Edgar, K. M., Schmidt, D. N. & Valdes, P. J. Quantifying the stability of planktic foraminiferal physical niches between the Holocene and Last Glacial Maximum: niche stability of planktic foraminifera. Paleoceanography 32, 74–89 (2017).
Google Scholar
Davis, C. V., Wishner, K., Renema, W. & Hull, P. M. Vertical distribution of planktic foraminifera through an oxygen minimum zone: how assemblages and test morphology reflect oxygen concentrations. Biogeosciences 18, 977–992 (2021).
Google Scholar
Vanadzina, K. & Schmidt, D. N. Developmental change during a speciation event: evidence from planktic foraminifera. Paleobiology 48, 120–136 (2022).
Google Scholar
Ward, B. A. et al. EcoGEnIE 1.0: plankton ecology in the cGEnIE Earth system model. Geosci. Model Dev. 11, 4241–4267 (2018).
Google Scholar
Ying, R., Monteiro, F. M., Wilson, J. D. & Schmidt, D. N. ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model. Geosci. Model Dev. 16, 813–832 (2023).
Google Scholar
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).
Google Scholar
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures? Ecol. Lett. 19, 1372–1385 (2016).
Google Scholar
Lombard, F., Labeyrie, L., Michel, E., Spero, H. J. & Lea, D. W. Modelling the temperature dependent growth rates of planktic foraminifera. Mar. Micropaleontol. 70, 1–7 (2009).
Google Scholar
Schmidt, D. N., Renaud, S., Bollmann, J., Schiebel, R. & Thierstein, H. R. Size distribution of Holocene planktic foraminifer assemblages: biogeography, ecology and adaptation. Mar. Micropaleontol. 50, 319–338 (2004).
Google Scholar
Darling, K. F. et al. Genetic diversity and ecology of the planktonic foraminifers Globigerina bulloides, Turborotalita quinqueloba and Neogloboquadrina pachyderma off the Oman margin during the late SW monsoon. Mar. Micropaleontol. 137, 64–77 (2017).
Google Scholar
Steinke, S., Yu, P.-S., Kucera, M. & Chen, M.-T. No-analog planktonic foraminiferal faunas in the glacial southern South China Sea: implications for the magnitude of glacial cooling in the western Pacific warm pool. Mar. Micropaleontol. 66, 71–90 (2008).
Google Scholar
Möller, V. et al. in Climate Change 2022: Impacts, Adaptation And Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pörtner, H.-O. et al.) 2897–2930 (Cambridge Univ. Press, 2022).
Hattich, G. S. I. et al. Temperature optima of a natural diatom population increases as global warming proceeds. Nat. Clim. Change 14, 518–525 (2024).
Google Scholar
Sexton, P. F. & Norris, R. D. Dispersal and biogeography of marine plankton: long-distance dispersal of the foraminifer Truncorotalia truncatulinoides. Geology 36, 899–902 (2008).
Google Scholar
Kucera, M. & Schonfeld, J. in Deep-Time Perspectives on Climate Change: Marrying the Signal from Computer Models and Biological Proxies (eds Williams, M. et al.) 409–425 (Geological Society of London on behalf of The Micropalaeontological Society, 2007).
Vargas, C., de, Renaud, S., Hilbrecht, H. & Pawlowski, J. Pleistocene adaptive radiation in Globorotalia truncatulinoides: genetic, morphologic, and environmental evidence. Paleobiology 27, 104–125 (2001).
Google Scholar
Morard, R. et al. The global genetic diversity of planktonic foraminifera reveals the structure of cryptic speciation in plankton. Biol. Rev. 99, 1218–1241 (2024).
Google Scholar
Jonkers, L., Hillebrand, H. & Kucera, M. Global change drives modern plankton communities away from the pre-industrial state. Nature 570, 372–375 (2019).
Google Scholar
Chaabane, S. et al. Modern planktonic Foraminifera: migrating is not enough. Preprint at https://doi.org/10.21203/rs.3.rs-3485983/v1 (2023).
Grigoratou, M., Monteiro, F. M., Wilson, J. D., Ridgwell, A. & Schmidt, D. N. Exploring the impact of climate change on the global distribution of non‐spinose planktonic foraminifera using a trait‐based ecosystem model. Glob. Change Biol. 28, 1063–1076 (2022).
Google Scholar
Greco, M., Werner, K., Zamelczyk, K., Rasmussen, T. L. & Kucera, M. Decadal trend of plankton community change and habitat shoaling in the Arctic gateway recorded by planktonic foraminifera. Glob. Change Biol. 28, 1798–1808 (2022).
Google Scholar
Pinkerton, M. H. et al. Zooplankton in the Southern Ocean from the continuous plankton recorder: distributions and long-term change. Deep Sea Res. Part I 162, 103303 (2020).
Google Scholar
Edgar, K. M. et al. Symbiont ‘bleaching’ in planktic foraminifera during the Middle Eocene Climatic Optimum. Geology 41, 15–18 (2013).
Google Scholar
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
Google Scholar
Boyd, P. W. Physiology and iron modulate diverse responses of diatoms to a warming Southern Ocean. Nat. Clim. Change 9, 148–152 (2019).
Google Scholar
Grigoratou, M. et al. A trait-based modelling approach to planktonic foraminifera ecology. Biogeosciences 16, 1469–1492 (2019).
Google Scholar
Galbraith, E. D. & Martiny, A. C. A simple nutrient-dependence mechanism for predicting the stoichiometry of marine ecosystems. Proc. Natl Acad. Sci. USA 112, 8199–8204 (2015).
Google Scholar
LeKieffre, C. et al. Assimilation, translocation, and utilization of carbon between photosynthetic symbiotic dinoflagellates and their planktic foraminifera host. Mar. Biol. 165, 104 (2018).
Google Scholar
Meilland, J., Howa, H., Lo Monaco, C. & Schiebel, R. Individual planktic foraminifer protein-biomass affected by trophic conditions in the Southwest Indian Ocean, 30°S–60°S. Mar. Micropaleontol. 124, 63–74 (2016).
Google Scholar
Anderson, R. F. et al. Wind-driven upwelling in the Southern Ocean and the deglacial rise in atmospheric CO2. Science 323, 1443–1448 (2009).
Google Scholar
Gray, W. R. et al. Deglacial upwelling, productivity and CO2 outgassing in the North Pacific Ocean. Nature Geosci 11, 340–344 (2018).
Google Scholar
Kawahata, H. et al. Perspective on the response of marine calcifiers to global warming and ocean acidification—behavior of corals and foraminifera in a high CO2 world “hot house”. Prog. Earth Planet. Sci. 6, 5 (2019).
Google Scholar
Deutsch, C., Penn, J. L. & Seibel, B. Metabolic trait diversity shapes marine biogeography. Nature 585, 557–562 (2020).
Google Scholar
Sauterey, B. et al. Phytoplankton adaptive resilience to climate change collapses in case of extreme events—a modeling study. Ecol. Model. 483, 110437 (2023).
Google Scholar
Gunderson, A. R. & Stillman, J. H. Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming. Proc. R. Soc. B 282, 20150401 (2015).
Google Scholar
Lan, X., Tans, P. & Thoning, K. W. Trends in globally-averaged CO2 determined from NOAA Global Monitoring Laboratory measurements. https://doi.org/10.15138/9N0H-ZH07 (2023).
Pohl, A. et al. Continental configuration controls ocean oxygenation during the Phanerozoic. Nature 608, 523–527 (2022).
Google Scholar
Edwards, N. R. & Marsh, R. Uncertainties due to transport-parameter sensitivity in an efficient 3-D ocean-climate model. Clim. Dyn. 24, 415–433 (2005).
Google Scholar
Marsh, R., Müller, S. A., Yool, A. & Edwards, N. R. Incorporation of the C-GOLDSTEIN efficient climate model into the GENIE framework: ‘eb_go_gs’ configurations of GENIE. Geosci. Model Dev. 4, 957–992 (2011).
Google Scholar
Ridgwell, A. et al. Marine geochemical data assimilation in an efficient Earth System Model of global biogeochemical cycling. Biogeosciences 4, 87–104 (2007).
Google Scholar
Davies-Barnard, T., Ridgwell, A., Singarayer, J. & Valdes, P. Quantifying the influence of the terrestrial biosphere on glacial–interglacial climate dynamics. Clim. Past 13, 1381–1401 (2017).
Google Scholar
Albani, S. et al. Paleodust variability since the Last Glacial Maximum and implications for iron inputs to the ocean. Geophys. Res. Lett. 43, 3944–3954 (2016).
Google Scholar
Morée, A. L. & Schwinger, J. A Last Glacial Maximum forcing dataset for ocean modelling. Earth Syst. Sci. Data 12, 2971–2985 (2020).
Google Scholar
Kageyama, M. et al. The PMIP4 contribution to CMIP6 – Part 4: Scientific objectives and experimental design of the PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity experiments. Geosci. Model Dev. 10, 4035–4055 (2017).
Google Scholar
Ödalen, M. Model Analysis of Ocean Carbon Storage and Transport Across Climate States. PhD thesis, Stockholm Univ. (2019).
Bouttes, N., Paillard, D. & Roche, D. M. Impact of brine-induced stratification on the glacial carbon cycle. Clim. Past 6, 575–589 (2010).
Google Scholar
Peterson, C. D., Lisiecki, L. E. & Stern, J. V. Deglacial whole-ocean δ13C change estimated from 480 benthic foraminiferal records. Paleoceanography 29, 549–563 (2014).
Google Scholar
Muglia, J., Skinner, L. C. & Schmittner, A. Weak overturning circulation and high Southern Ocean nutrient utilization maximized glacial ocean carbon. Earth Planet. Sci. Lett. 496, 47–56 (2018).
Google Scholar
Kageyama, M. et al. The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations. Clim. Past 17, 1065–1089 (2021).
Google Scholar
Annan, J. D., Hargreaves, J. C. & Mauritsen, T. A new global surface temperature reconstruction for the Last Glacial Maximum. Clim. Past 18, 1883–1896 (2022).
Google Scholar
Jonkers, L. & Kučera, M. Quantifying the effect of seasonal and vertical habitat tracking on planktonic foraminifera proxies. Clim. Past 13, 573–586 (2017).
Google Scholar
Cao, L. et al. The role of ocean transport in the uptake of anthropogenic CO2. Biogeosciences 6, 375–390 (2009).
Google Scholar
Mahowald, N. M. et al. Change in atmospheric mineral aerosols in response to climate: last glacial period, preindustrial, modern, and doubled carbon dioxide climates. J. Geophys. Res. 111, D10202 (2006).
Google Scholar
Morice, C. P. et al. An updated assessment of near‐surface temperature change from 1850: the HadCRUT5 data set. J. Geophys. Res. Atmos. 126, e2019JD032361 (2021).
Google Scholar
Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).
Google Scholar
Michaels, A. F., Caron, D. A., Swanberg, N. R., Howse, F. A. & Michaels, C. M. Planktonic sarcodines (Acantharia, Radiolaria, Foraminifera) in surface waters near Bermuda: abundance, biomass and vertical flux. J. Plankton Res. 17, 131–163 (1995).
Google Scholar
Fraile, I. et al. Modeling the seasonal distribution of planktonic foraminifera during the Last Glacial Maximum. Paleoceanography 24, PA2216 (2009).
Google Scholar
Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Sci. Data 4, 170109 (2017).
Google Scholar
Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813–819 (2005).
Google Scholar
Brummer, G.-J. A. & Kučera, M. Taxonomic review of living planktonic foraminifera. J. Micropalaeontol. 41, 29–74 (2022).
Google Scholar
Takagi, H. et al. Characterizing photosymbiosis in modern planktonic foraminifera. Biogeosciences 16, 3377–3396 (2019).
Google Scholar
Schiebel, R. & Hemleben, C. Planktic Foraminifers in the Modern Ocean (Springer Berlin, 2017).
Huber, B. T. et al. Pforams@microtax: A new online taxonomic database for planktonic foraminifera. Micropaleontology 62, 429–438 (2016).
Google Scholar
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003).
Tierney, J. E. et al. Glacial cooling and climate sensitivity revisited. Nature 584, 569–573 (2020).
Google Scholar
Rebotim, A. et al. Factors controlling the depth habitat of planktonic foraminifera in the subtropical eastern North Atlantic. Biogeosciences 14, 827–859 (2017).
Google Scholar
Muggeo, V. M. R., Torretta, F., Eilers, P., Sciandra, M. & Attanasio, M. Multiple smoothing parameters selection in additive regression quantiles. Stat. Model. 21, 428–448 (2021).
Google Scholar
Kremer, C. T., Thomas, M. K. & Litchman, E. Temperature- and size-scaling of phytoplankton population growth rates: reconciling the Eppley curve and the metabolic theory of ecology. Limnol. Oceanogr. 62, 1658–1670 (2017).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2023).
Ying, R. ruiying-ocean/lgm_foram_census: Foraminifera abundance data in the LGM/PI. Zenodo https://zenodo.org/doi/10.5281/zenodo.8189768 (2024).
Ying, R., Monteiro, F. M., Wilson, J. D. & Schmidt, D. N. Modelled planktic formainifera from LGM to future (ForamEcoGENIE). Zenodo https://zenodo.org/doi/10.5281/zenodo.8189647 (2024).
Ying, R. ruiying-ocean/quanternary_foram_niche: Foraminifera optimal niche reanalysis based on Antell et al. (2021) data. Zenodo https://zenodo.org/doi/10.5281/zenodo.8189772 (2024).
Ying, R. cgeniepy: a Python package for analysing cGENIE Earth System Model output. Preprint at https://doi.org/10.21203/rs.3.rs-3967633/v1 (2024).
