Bet-hedging in parasitoids: when optimization is not the best strategy to cope with climatic extremes
Why this work is in the frame
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Bibliographic record
Abstract
Bet-hedging occurs when unreliable environments select for genotypes exhibiting a lower variance in fitness at the cost of a lower mean fitness for each batch of progeny. This means that at the level of the genotype, the production of mostly non-optimal phenotypes may be favored when at least some phenotypes are successful. As extreme unreliable climatic events are increasing because of climate change, it is pertinent to investigate the potential of bet-hedging strategies that allow insects to cope with climate change. Evidence for bet-hedging is scarce in most insects, including parasitoids, but the unique lifestyle and biology of parasitoids leads to the expectation that bet-hedging may occur frequently. Here, we evaluate a range of parasitoid traits for which a bet-hedging strategy could be envisioned even if bet-hedging has not been identified as such yet. Under-identification of bet-hedging in nature could have resulted from a major focus of studies on parasitoid life history evolution and foraging behavior on optimality models, predicting how mean fitness can be maximized. Most environmental factors, however, vary unpredictably. Life history and behavioral adaptations are thus expected to be affected by environmental stochasticity. In this paper, we review different aspects of parasitoid behavior, physiology, and life histories and ask the question whether parasitoid traits could have evolved under selection by environmental stochasticity.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it