The implications of nongenetic inheritance for evolution in changing environments
Bibliographic record
Abstract
Nongenetic inheritance is a potentially important but poorly understood factor in population responses to rapid environmental change. Accumulating evidence indicates that nongenetic inheritance influences a diverse array of traits in all organisms and can allow for the transmission of environmentally induced phenotypic changes ('acquired traits'), as well as spontaneously arising and highly mutable variants. We review models of adaptation to changing environments under the assumption of a broadened model of inheritance that incorporates nongenetic mechanisms of transmission, and survey relevant empirical examples. Theory suggests that nongenetic inheritance can increase the rate of both phenotypic and genetic change and, in some cases, alter the direction of change. Empirical evidence shows that a diversity of phenotypes - spanning a continuum from adaptive to pathological - can be transmitted nongenetically. The presence of nongenetic inheritance therefore complicates our understanding of evolutionary responses to environmental change. We outline a research program encompassing experimental studies that test for transgenerational effects of a range of environmental factors, followed by theoretical and empirical studies on the population-level consequences of such effects.
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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 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".