Coexistence of Photosynthetic Marine Microorganisms, Viruses, and Grazers: Toward Integration in Ocean Ecosystem Models
Bibliographic record
Abstract
Abstract Photosynthetic microorganisms are responsible for primary production at the base of the marine food web that impacts ocean biogeochemistry and ecological interactions. The growth of these microorganisms is balanced by mortality processes, including top-down losses from zooplankton grazing and infection and lysis by viruses. Multiple types of grazers and viruses often coexist despite apparent competition for the same (or similar) microorganisms. Here, we develop a community model of photosynthetic microorganisms, grazers, and viruses that accounts for molar cell and virion elemental quotas suitable for incorporation into ocean ecosystem models. Our aim is to investigate mechanisms that enable coexistence of a virus and a grazer with a single phytoplankton type. To do so, we evaluate the extent to which coexistence is facilitated by: (i) infected phytoplankton, potentially subject to non-preferential intraguild predation, where grazers feed on virally infected microorganisms; (ii) heterogeneity in susceptibility to infection, where microorganisms vary in their resistance to the virus either intracellularly or extracellularly; and (iii) the inclusion of higher-order mortality terms for the predators. We find evidence for a trade-off between the virus latent period and virulence in facilitating a coexistence regime. The inclusion of an explicit latent period can generate oscillations of all populations that facilitate coexistence by reducing the growth rate of the free virus, or lead to system collapse when oscillations become too large. Heterogeneity in phage susceptibility promotes coexistence through resource partitioning between the predators, while higher-order mortality terms widen the coexistence regime by stabilizing the system. We observe strong sensitivity of model outcomes to viral life history traits, including shifts in infected cell percentages and the balance between virally and zooplankton-induced mortality. Finally, taking advantage of algebraic calculation of model equilibria, we identify life history trait parameter combinations that yield realistic ecological properties in simplified oligotrophic and mesotrophic epipelagic environments. Importantly, our ecological models suggest that ongoing efforts to embed virus dynamics in large-scale ocean ecosystem models should likely include phytoplankton types that are moderately to strongly resistant to viral infection and lysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".