The evolution of phenotypic plasticity under global change
Bibliographic record
Abstract
Marine ecosystems are currently in a state of flux, with ocean warming and acidification occurring at unprecedented rates. Phenotypic plasticity underpins acclimatory responses by shifting the mean phenotype in a population, which may buffer the negative effects of global change. However, little is known about how phenotypic plasticity evolves across multiple generations. We tested this by reciprocally-transplanting the polychaete Ophryotrocha labronica between control and global change scenarios (ocean warming and acidification in isolation and combined) over five generations. By comparing the reaction norms of four life-history traits across generations, we show that juvenile developmental rate in the combined scenario was the only trait that changed its plastic response across generations when transplanted back to control conditions, and that adaptive plasticity was conserved in most traits, despite significant levels of selection and strong declines in individual fitness in the multi-generational exposure. We suggest the change in level of plasticity in the combined scenario is caused by differential allocation of energy between the mean and the plasticity of the trait along the multigenerational exposure. The ability to maintain within-generational levels of plasticity under global change scenarios has important eco-evolutionary and conservation implications, which are examined under the framework of assisted evolution programs.
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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 | 0.001 |
| 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".