Ecological stoichiometry and life history theory, not the identity of genomic variants, predict rapid adaptation
Bibliographic record
Abstract
Determining the scale and scope of the predictability of evolution is fundamental to understanding biological processes and to managing biodiversity. Ecological frameworks, including life history theory and ecological stoichiometry, offer testable predictions about the direction of adaptation and trade-offs between traits in response to environmental change. Similarly, well-characterized molecular pathways, genotype phenotype linkages, and prior evolutionary genomic studies could be predictive of the genomic architecture underlying adaptation. We tested whether ecological frameworks and evolutionary genomic data can be used to forecast rapid adaptation in replicated outdoor populations of Drosophila melanogaster evolving in response to natural seasonal fluctuations from summer to late fall. Life history theory predicted the observed pattern of adaptive tracking: in summer, reproductive output increased and stress tolerance decreased, while in fall, this direction reversed, with evolution of increased stress tolerance at the expense of reproduction. Stoichiometric phenotypic evolution was also predictable, with phosphorus and magnesium content, both linked to growth rate, and alkali metal, associated with maintaining homeostasis in response to thermal stress, showing rapid and parallel evolution indicative of adaptation. Temporal genomic data revealed a complex genomic architecture of temporal adaptation and the SNPs and genes involved in adaptation were largely unpredictable. These results demonstrate that ecological frameworks, more than genomic data, have utility in forecasting adaptation in complex and variable environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".