Transgenerational dispersal plasticity and its fitness consequences are under genetic control
Bibliographic record
Abstract
Abstract Phenotypic plasticity, the ability of one genotype to produce different phenotypes in different environments, plays a central role in species’ response to environmental changes. Transgenerational plasticity (TGP) allows the transmission of this environmentally-induced phenotypic variation across generations, and can influence adaptation. To date, the genetic control of TGP, its long-term stability, and its potential costs remain largely unknown, mostly because empirical demonstrations of TGP across many generations in several genetic backgrounds are scarce. Here, we examined how genotype determines the TGP of dispersal, a fundamental process in ecology and evolution. We used an experimental approach involving ~200 clonal generations in a model-species of ciliate to determine if and how TGP influences the expression of dispersal-related traits in several genotypes. Our results show that morphological and movement traits associated with dispersal are plastic, and that these modifications are inherited over at least 35 generations. We also highlight that genotype modulates the fitness costs and benefits associated with plastic dispersal strategies. Our study suggests that genotype-dependent TGP could play a critical role in eco-evolutionary dynamics as dispersal determines gene flow and the long-term persistence of natural populations. More generally, it outlines the tremendous importance that genotype-dependent TGP could have in the ability of organisms to cope with current and future environmental changes. Significance The genetic control of the transgenerational plasticity is still poorly understood despite its critical role in species responses to environmental changes. We examined how genotype determines transgenerational plasticity of a complex trait ( i.e. , dispersal) in a model-species of ciliate across ~200 clonal generations. Our results provide evidence that plastic phenotypic variation linked to dispersal is stably inherited over tens of generations and that cell genotype modulates the expression and fitness cost of transgenerational plasticity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".