23 citations as recorded by crossref.

1. The microbiome of the Arctic planktonic foraminifera Neogloboquadrina pachyderma is composed of fermenting and carbohydrate-degrading bacteria and an intracellular diatom chloroplast store C. Bird et al. https://doi.org/10.5194/bg-22-4545-2025
2. Global biodiversity and biogeography of mangrove crabs: Temperature, the key driver of latitudinal gradients of species richness S. Sharifian et al. https://doi.org/10.1016/j.jtherbio.2020.102692
3. Planktic Foraminiferal and Pteropod Contributions to Carbon Dynamics in the Arctic Ocean (North Svalbard Margin) G. Anglada-Ortiz et al. https://doi.org/10.3389/fmars.2021.661158
4. ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model R. Ying et al. https://doi.org/10.5194/gmd-16-813-2023
5. Determinants of Planktonic Foraminifera Calcite Flux: Implications for the Prediction of Intra‐ and Inter‐Annual Pelagic Carbonate Budgets P. Kiss et al. https://doi.org/10.1029/2020GB006748
6. A combination of salinity and pH affects the recruitment of Gladioferens pectinatus (Brady) (Copepoda; Calanoida) D. Hemraj et al. https://doi.org/10.1002/lno.10534
7. A trait-based modelling approach to planktonic foraminifera ecology M. Grigoratou et al. https://doi.org/10.5194/bg-16-1469-2019
8. Migrating is not enough for modern planktonic foraminifera in a changing ocean S. Chaabane et al. https://doi.org/10.1038/s41586-024-08191-5
9. The Foraminiferal Response to Climate Stressors Project: Tracking the Community Response of Planktonic Foraminifera to Historical Climate Change T. de Garidel-Thoron et al. https://doi.org/10.3389/fmars.2022.827962
10. Planktonic foraminiferal assemblages reflect warming during two recent mid-latitude marine heatwaves M. Lane et al. https://doi.org/10.3389/fmars.2023.1155761
11. Past and future decline of tropical pelagic biodiversity M. Yasuhara et al. https://doi.org/10.1073/pnas.1916923117
12. Investigating the benefits and costs of spines and diet on planktonic foraminifera distribution with a trait-based ecosystem model M. Grigoratou et al. https://doi.org/10.1016/j.marmicro.2021.102004
13. Regional restructuring in planktic foraminifera communities through Pliocene-early Pleistocene climate variability E. Larina et al. https://doi.org/10.1038/s41467-025-60362-8
14. Modern planktic foraminifers in the high-latitude ocean R. Schiebel et al. https://doi.org/10.1016/j.marmicro.2017.08.004
15. Ocean acidification over the next three centuries using a simple global climate carbon-cycle model: projections and sensitivities C. Hartin et al. https://doi.org/10.5194/bg-13-4329-2016
16. Contrasting response in test size and abundance of planktic foraminifera Morozovella and Acarinina to the EECO in the subtropical Pacific Ocean G. Filippi et al. https://doi.org/10.1016/j.palaeo.2025.112840
17. A proxy modelling approach to assess the potential of extracting ENSO signal from tropical Pacific planktonic foraminifera B. Metcalfe et al. https://doi.org/10.5194/cp-16-885-2020
18. Determining climate change impacts on ecosystems: the role of palaeontology D. Schmidt & A. Smith https://doi.org/10.1111/pala.12335
19. Single-cell metabarcoding reveals biotic interactions of the Arctic calcifier Neogloboquadrina pachyderma with the eukaryotic pelagic community M. Greco et al. https://doi.org/10.1093/plankt/fbab015
20. Reassessment of the global distribution and diversity of modern planktonic foraminifera from the FORCIS database S. Chaabane et al. https://doi.org/10.5194/jm-45-195-2026
21. Cyanobacterial endobionts within a major marine planktonic calcifier (Globigerina bulloides, Foraminifera) revealed by 16S rRNA metabarcoding C. Bird et al. https://doi.org/10.5194/bg-14-901-2017
22. 16S rRNA gene metabarcoding and TEM reveals different ecological strategies within the genus Neogloboquadrina (planktonic foraminifer) C. Bird et al. https://doi.org/10.1371/journal.pone.0191653
23. Exploring the impact of climate change on the global distribution of non‐spinose planktonic foraminifera using a trait‐based ecosystem model M. Grigoratou et al. https://doi.org/10.1111/gcb.15964