Nutrient storage links past thermal exposure to current performance in phytoplankton
Bibliographic record
Abstract
The growth of populations and organisms often depends on their previous history of environmental exposure: a phenomenon referred to as "phenotypic memory." The field of ecology presently lacks a mechanistic theory describing phenotypic memory and, as such, evaluating the ecological consequences of this phenomenon is a major challenge. Here, we show that internal nutrient storage connects past thermal experience to current growth in phytoplankton. We develop a new mechanistic model showing how time lags in the incorporation of stored nutrients into new biomass produces phenotypic memory. By testing this model against experimental data of phytoplankton population growth rates following temperature perturbations, we find general patterns in the population consequences of phenotypic memory: prior exposure to warm temperatures depletes nutrient stores, and, in doing so, slows growth during subsequent temperature exposure and restricts the range of acute temperature exposures yielding a positive growth rate. Our model reveals how phenotypic memory produces temporal variation in critical thermal minima and maxima and predicts that the thermal niche is constricted by long-term exposure to warm temperatures (e.g., during summer months), but that high frequency temperature fluctuations can expand a population's thermal niche. This work provides a general, mechanistic basis for considering the ecological implications of phenotypic memory.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".