Trophic interactions influence thermal adaptation of phytoplankton size and stoichiometry
Bibliographic record
Abstract
Understanding how temperature affects adaptation of cell size is challenging because cell size mediates numerous physiological and ecological trade-offs. Previous work has identified physiological mechanisms that lead to decreases in cell size with warming (the temperature-size rule; TSR). However, it is unclear how ecological processes (e.g., competition, predation) combine to modify the TSR. Here, we evaluate how ecological interactions affect thermal adaptation of phytoplankton cell size. We perform an eco-evolutionary analysis of a nutrient-phytoplankton-zooplankton model. The model assumes phytoplankton experience size-dependent constraints on resource allocation that cause small cells to sacrifice investment in growth machinery, thereby reducing maximum growth rate but increasing competitive ability. We find that trophic interactions strongly impact the evolutionarily stable cell size across temperatures. Without zooplankton, cell size declines monotonically with temperature, consistent with the TSR. With zooplankton, cell size varies unimodally with temperature, due to temperature-dependent shifts in the grazer's capacity to ease nutrient competition by controlling phytoplankton biomass. Size-selective grazing does not qualitatively alter this result but can facilitate coexistence via a competition-predation trade-off. Trophic interactions therefore can produce temperature-size responses that differ qualitatively from the canonical TSR, and an understanding of how temperature affects cell size is incomplete without this ecological component.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".