Predator-induced transgenerational plasticity in animals: a meta-analysis
Bibliographic record
Abstract
There is growing evidence that the environment experienced by one generation can influence phenotypes in the next generation via transgenerational plasticity (TGP). One of the best-studied examples of TGP in animals is predator-induced transgenerational plasticity, whereby exposing parents to predation risk triggers changes in offspring phenotypes. Yet, there is a lack of general consensus synthesizing the predator-prey literature with existing theory pertaining to ecology and evolution of TGP. Here, we apply a meta-analysis to the sizable literature on predator-induced TGP (441 effect sizes from 29 species and 49 studies) to explore five hypotheses about the magnitude, form and direction of predator-induced TGP. Hypothesis #1: the strength of predator-induced TGP should vary with the number of predator cues. Hypothesis #2: the strength of predator-induced TGP should vary with reproductive mode. Hypothesis #3: the strength and direction of predator-induced TGP should vary among offspring phenotypic traits because some traits are more plastic than others. Hypothesis #4: the strength of predator-induced TGP should wane over ontogeny. Hypothesis #5: predator-induced TGP should generate adaptive phenotypes that should be more evident when offspring are themselves exposed to risk. We found strong evidence for predator-induced TGP overall, but no evidence that parental predator exposure causes offspring traits to change in a particular direction. Additionally, we found little evidence in support of any of the specific hypotheses. We infer that the failure to find consistent evidence reflects the heterogeneous nature of the phenomena, and the highly diverse experimental designs used to study it. Together, these findings set an agenda for future work in this area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".