Key Questions on the Role of Phenotypic Plasticity in Eco-Evolutionary Dynamics
Bibliographic record
Abstract
Ecology and evolution have long been recognized as reciprocally influencing each other, with recent research emphasizing how such interactions can occur even on very short (contemporary) time scales. Given that these interactions are mediated by organismal phenotypes, they can be variously shaped by genetic variation, phenotypic plasticity, or both. I here address 8 key questions relevant to the role of plasticity in eco-evolutionary dynamics. Focusing on empirical evidence, especially from natural populations, I offer the following conclusions. 1) Plasticity is--not surprisingly--sometimes adaptive, sometimes maladaptive, and sometimes neutral. 2) Plasticity has costs and limits but these constraints are highly variable, often weak, and hard to detect. 3) Variable environments favor the evolution of increased trait plasticity, which can then buffer fitness/performance (i.e., tolerance). 4) Plasticity sometimes aids colonization of new environments (Baldwin Effect) and responses to in situ environmental change. However, plastic responses are not always necessary or sufficient in these contexts. 5) Plasticity will sometimes promote and sometimes constrain genetic evolution. 6) Plasticity will sometimes help and sometimes hinder ecological speciation but, at present, empirical tests are limited. 7) Plasticity can show considerable evolutionary change in contemporary time, although the rates of this reaction norm evolution are highly variable among taxa and traits. 8) Plasticity appears to have considerable influences on ecological dynamics at the community and ecosystem levels, although many more studies are needed. In summary, plasticity needs to be an integral part of any conceptual framework and empirical investigation of eco-evolutionary dynamics.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".