Trophic controls on thermal adaptation of phytoplankton size and stoichiometry
Bibliographic record
Abstract
Temperature impacts physiological function, driving evolutionary adaptation in physiological traits that influence ecosystem properties. Temperature also impacts ecological rates, altering the strength of trophic and competitive interactions. Yet, possible effects of temperature-driven shifts in ecological interactions on physiological adaptation are unclear. We investigate how ecological interactions shape thermal adaptation of phytoplankton cell size, a trait that affects macromolecular composition, Phosphorus:Carbon ratio, competitive ability, and grazing susceptibility. We identify evolutionarily stable strategies in a nutrient-phytoplankton-zooplankton system. We find that trophic interactions strongly impact the evolutionarily stable cell size across temperatures. Without zooplankton, cell size and P:C ratio declines monotonically with temperature. With zooplankton, cell size and P:C ratio varies unimodally with temperature, due to temperature-dependent shifts in the grazer’s capacity to ease nutrient competition by controlling phytoplankton. Size-selective grazing does not qualitatively alter this result but facilitates diversification. We conclude that ecological interactions play critical roles in physiological adaptation to warming.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".