Transgenerational plasticity of dispersal‐related traits in a ciliate: genotype‐dependency and fitness consequences
Bibliographic record
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 phenotypic traits related to dispersal, a fundamental process in ecology and evolution. We used an experimental approach in Tetrahymena thermophila , a ciliate model‐species, to determine if and how phenotypic changes expressed following a dispersal treatment are inherited over multiple generations. Our results show that morphological and movement traits associated with dispersal are plastic, and that these modifications are inherited over at least 35 generations. The fitness costs and benefits associated with these plastic changes are also transmitted to further generations. We highlight that the genotype modulates the expression and reversibility of transgenerational plasticity of dispersal‐related traits and its fitness outcomes. Our study thus suggests that genotype‐dependent TGP could play an important role in eco‐evolutionary dynamics as dispersal determines gene flow and the long‐term persistence of natural populations.
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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 | 0.000 |
| 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 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".