Multiple biotic interactions establish phytoplankton community structure across environmental gradients
Bibliographic record
Abstract
Abstract The combination of taxa and size classes of phytoplankton that coexist at any location affects the structure of the marine food web and the magnitude of carbon fluxes to the deep ocean. But what controls the patterns of this community structure across environmental gradients remains unclear. Here, we focus on the North East Pacific Transition Zone, a ~ 10° region of latitude straddling warm, nutrient‐poor subtropical and cold, nutrient‐rich subpolar gyres. Data from three cruises to the region revealed intricate patterns of phytoplankton community structure: poleward increases in the number of cell size classes; increasing biomass of picoeukaryotes and diatoms; decreases in diazotrophs and Prochlorococcus; and both increases and decreases in Synechococcus. These patterns can only be partially explained by existing theories. Using data, theory, and numerical simulations, we show that the patterns of plankton distributions across the transition zone are the result of gradients in nutrient supply rates, which control a range of complex biotic interactions. We examine how interactions such as size‐specific grazing, multiple trophic strategies, shared grazing between several phytoplankton size classes and heterotrophic bacteria, and competition for multiple resources can individually explain aspects of the observed community structure. However, it is the combination of all these interactions together that is needed to explain the bulk compositional patterns in phytoplankton across the North East Pacific Transition Zone. The synthesis of multiple mechanisms is essential for us to begin to understand the shaping of community structure over large environmental gradients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".