Developmental Plasticity and the Origin of Novel Forms: Unveiling Cryptic Genetic Variation Via“Use and Disuse”
Bibliographic record
Abstract
Natural selection eliminates phenotypic variation from populations, generation after generation-an observation that haunted Darwin. So, how does new phenotypic variation arise, and is it always random with respect to fitness? Repeated behavioral responses to a novel environment-particularly those that are learned-are typically advantageous. If those behaviors yield more extreme or novel morphological variants via developmental plasticity, then previously cryptic genetic variation may be exposed to natural selection. Significantly, because the mean phenotypic effect of "use and disuse" is also typically favorable, previously cryptic genetic variation can be transformed into phenotypic variation that is both visible to selection and biased in an adaptive direction. Therefore, use-induced developmental plasticity in a very real sense "creates" new phenotypic variation that is nonrandom with respect to fitness, in contrast to the random phenotypic effects of mutation, recombination, and "direct effects" of environment (stress, nutrition). I offer here (a) a brief review of the immense literature on the effects of "use and disuse" on morphology, (b) a simple yet general model illustrating how cryptic genetic variation may be exposed to selection by developmentally plastic responses that alter trait performance in response to "use and disuse," and (c) a more detailed model of a positive feedback loop between learning (handed behavior) and morphological plasticity (use-induced morphological asymmetry) that may rapidly generate novel phenotypic variation and facilitate the evolution of conspicuous morphological asymmetries. Evidence from several sources suggests that handed behaviors played an important role both in the origin of novel forms (asymmetries) and in their subsequent evolution.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".