Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-813-2023
https://doi.org/10.5194/gmd-16-813-2023
Development and technical paper
 | 
02 Feb 2023
Development and technical paper |  | 02 Feb 2023

ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model

Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt

Related authors

Reviews and syntheses: A trait-based approach to constrain controls on planktic foraminiferal ecology – key trade-offs and current knowledge gaps
Kirsty M. Edgar, Maria Grigoratou, Fanny M. Monteiro, Ruby Barrett, Rui Ying, and Daniela N. Schmidt
Biogeosciences, 22, 3463–3483, https://doi.org/10.5194/bg-22-3463-2025,https://doi.org/10.5194/bg-22-3463-2025, 2025
Short summary

Related subject area

Biogeosciences
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Benjamin F. Meyer, João P. Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
Geosci. Model Dev., 18, 4643–4666, https://doi.org/10.5194/gmd-18-4643-2025,https://doi.org/10.5194/gmd-18-4643-2025, 2025
Short summary
pyVPRM: a next-generation vegetation photosynthesis and respiration model for the post-MODIS era
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
Geosci. Model Dev., 18, 4713–4742, https://doi.org/10.5194/gmd-18-4713-2025,https://doi.org/10.5194/gmd-18-4713-2025, 2025
Short summary
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025,https://doi.org/10.5194/gmd-18-4317-2025, 2025
Short summary
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025,https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025,https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary

Cited articles

Anderson, O. R. and Bé, A. W. H.: The ultrastructure of a planktonic foraminifer, Globigerinoides sacculifer (Brady), and its symbiotic dinoflagellates, J. Foramin. Res., 6, 1–21, https://doi.org/10.2113/gsjfr.6.1.1, 1976. 
Anderson, O. R., Spindler, M., Bé, A. W. H., and Hemleben, Ch.: Trophic activity of planktonic foraminifera, J. Mar. Biol. Ass., 59, 791–799, https://doi.org/10.1017/S002531540004577X, 1979. 
Anderson, R. P.: When and how should biotic interactions be considered in models of species niches and distributions?, J. Biogeogr., 44, 8–17, https://doi.org/10.1111/jbi.12825, 2017. 
Anderson, T. R.: Plankton functional type modelling: running before we can walk?, J. Plankton Res., 27, 1073–1081, https://doi.org/10.1093/plankt/fbi076, 2005. 
Download
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Share