The role of developmental plasticity in evolutionary innovation
Bibliographic record
Abstract
Explaining the origins of novel traits is central to evolutionary biology. Longstanding theory suggests that developmental plasticity, the ability of an individual to modify its development in response to environmental conditions, might facilitate the evolution of novel traits. Yet whether and how such developmental flexibility promotes innovations that persist over evolutionary time remains unclear. Here, we examine three distinct ways by which developmental plasticity can promote evolutionary innovation. First, we show how the process of genetic accommodation provides a feasible and possibly common avenue by which environmentally induced phenotypes can become subject to heritable modification. Second, we posit that the developmental underpinnings of plasticity increase the degrees of freedom by which environmental and genetic factors influence ontogeny, thereby diversifying targets for evolutionary processes to act on and increasing opportunities for the construction of novel, functional and potentially adaptive phenotypes. Finally, we examine the developmental genetic architectures of environment-dependent trait expression, and highlight their specific implications for the evolutionary origin of novel traits. We critically review the empirical evidence supporting each of these processes, and propose future experiments and tests that would further illuminate the interplay between environmental factors, condition-dependent development, and the initiation and elaboration of novel phenotypes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".