When mother knows best: A population genetic model of transgenerational versus intragenerational plasticity
Bibliographic record
Abstract
Many organisms exhibit phenotypic plasticity; producing alternate phenotypes depending on the environment. Individuals can be plastic (intragenerational or direct plasticity), wherein individuals of the same genotype produce different phenotypes in response to the environments they experience. Alternatively, an individual's phenotype may be under the control of its parents, usually the mother (transgenerational or indirect plasticity), so that mother's genotype determines the phenotype produced by a given genotype of her offspring. Under what conditions does plasticity evolve to have intragenerational as opposed to transgenerational genetic control? To explore this question, we present a population genetic model for the evolution of transgenerational and intragenerational plasticity. We hypothesize that the capacity for plasticity incurs a fitness cost, which is borne either by the individual developing the plastic phenotype or by its mother. We also hypothesize that individuals are imperfect predictors of future environments and their capacity for plasticity can lead them occasionally to make a low-fitness phenotype for a particular environment. When the cost, benefit and error parameters are equal, we show that there is no evolutionary advantage to intragenerational over transgenerational plasticity, although the rate of evolution of transgenerational plasticity is half the rate for intragenerational plasticity, as predicted by theory on indirect genetic effects. We find that transgenerational plasticity evolves when mothers are better predictors of future environments than offspring or when the fitness cost of the capacity for plasticity is more readily borne by a mother than by her developing offspring. We discuss different natural systems with either direct intragenerational plasticity or indirect transgenerational plasticity and find a pattern qualitatively in accord with the predictions of our model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".