GoKawiilcGENIE Earth System Model
We use the cGENIE Earth System Model of Intermediate Complexity (EMIC) to simulate the climate changes and plankton ecosystem dynamics across the K–Pg boundary. The cGENIE model comprises three-dimensional ocean physics (GOLDSTEIN) and marine biogeochemistry (BIOGEM) (for example, C, P, O, Fe, Si), a two-dimensional atmosphere (EMBM), a trait-based plankton ecosystem model (EcoGENIE) and a sediment component (SEDGEM). The application of this model to the modern climate has demonstrated its ability to capture realistic ocean physics including deep-water formation and large-scale ocean circulation (Supplementary Figs. 6 and 7).
Previous K–Pg studies have used the cGENIE model to constrain the post-impact carbon cycle (on million-year timescales) using proxy data12 and to assess the impact of volcanic sulfur deposition from flood basalt eruptions61. These studies provide an important foundation for us because the Late Cretaceous model has been tuned to reproduce realistic climate (Extended Data Fig. 1), ocean circulation (Supplementary Fig. 8) and ocean biogeochemistry (Supplementary Figs. 5 and 9). However, these studies were assessed in steady-state conditions and without an explicit ecosystem component. In this work, we ran the cGENIE model with transient solar radiation, pCO 2 and nutrient forcings and coupled with EcoGENIE at a century scale, expanding on these studies. The ecosystem model has a small timestep (7.3 h) to capture the trophic cascaded suggested by Alvarez and colleagues23. The plankton ecosystem, as the base of the marine food web, also provides an indication of the status of, and changes to, higher trophic levels.
Trait-based plankton ecosystem model
EcoGENIE is a trait-based mechanistic (forward) plankton ecosystem model integrated within the cGENIE framework. ‘Traits’ here refer to organism characteristics that influence ecophysiological processes and fitness. EcoGENIE receives environmental inputs, such as temperature, light availability and nutrient concentrations, from cGENIE’s physical (GOLDSTEIN) and biogeochemical (BIOGEM) modules. These inputs inform plankton food web dynamics within a variable mixed layer, driving the biomass evolution for each plankton functional type.
EcoGENIE captures ecosystem dynamics explicitly by simulating each plankton’s metabolic processes including photosynthesis, grazing, respiration and mortality (Supplementary Information). These processes are governed by abiotic factors (temperature, nutrient availability and light), plankton traits (for example, size, heterotrophy, calcification and Chl:C ratio based on the photo-acclimatization model of Geider and colleagues62) and biotic interactions (for example, competition and predator–prey relationships) (Supplementary Information). Through these interactions, the plankton community structure emerges naturally, reflecting resource competition and adaptation to specific local environmental conditions. Because the model aggregates species into functional types (defined by similar traits), the diversity represented in this study is functional rather than species-specific and arises directly from environmental selection rather than imposed a priori.
The emergent plankton ecosystem structure influences organic matter export. Specifically, larger plankton typically have a higher particulate organic carbon (POC) to dissolved organic carbon (DOC) export ratio relative to smaller plankton (Supplementary Fig. 1), thereby affecting the efficiency of the biological carbon pump and the overall biogeochemical cycling of the marine environment.
EcoGENIE simulates the plankton community as vertically integrated over the mixed layer. This simplified strategy improves the computational efficiency and reflects the nature that most plankton abundance63 and diversity (particularly phytoplankton diversity64) are found in the upper ocean. The MLD is calculated using a Kraus–Turner scheme and influences nutrient supply and light availability (with deeper MLD corresponding to higher nutrient supply and lower light availability). In the modern ocean, the model’s MLD compares well with the ECCOv.4 data product (Supplementary Fig. 6). A recent study35 further supports the use of this simplified strategy, showing similar model performance compared with higher-complexity plankton models in response to climate change. However, EcoGENIE does not explicitly resolve the direct impact of water structure change (for example, destratification) on plankton specific vertical habitats. This represents a potentially important limitation, as the physical loss of depth habitats with higher mixing may have contributed to the K–Pg extinction selectivity, particularly for deep-dwelling zooplankton65.
A key advantage of EcoGENIE is its trait-based feature, which uses allometric (size-based) relationships to parameterize plankton ecology (for example, growth rate; Supplementary Fig. 1), thereby avoiding subjective bias or over-reliance on modern taxa, making it particularly well-suited for deep-time studies66. The trait-based framework also allows us to account for the trait diversity of marine plankton in our model. For instance, the recent development of the calcification trait enables us to model calcareous zooplankton (that is, foraminifera) by modifying generic zooplankton’s ecological parameters given assumed trade-offs3. The model also shows realistic trait distributions in the modern3 and the Last Glacial Maximum35 oceans, constraining the simulated latitudinal distribution of functional diversity and extinction selectivity in taxon distributions (Supplementary Fig. 10). However, the model does not yet explicitly simulate shell mineralogy and sensitivity to changes in carbon chemistry. As a result, our interpretation of calcareous plankton’s extinction risk is probably conservative. Instead, calcite production in our model is parameterized using a constant CaCO 3 :POC rain ratio.
Extinction process
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