Modes of response to environmental change and the elusive empirical evidence for bet hedging
Bibliographic record
Abstract
Uncertainty is a problem not only in human decision-making, but is a prevalent quality of natural environments and thus requires evolutionary response. Unpredictable natural selection is expected to result in the evolution of bet-hedging strategies, which are adaptations to long-term fluctuating selection. Despite a recent surge of interest in bet hedging, its study remains mired in conceptual and practical difficulties, compounded by confusion over what constitutes evidence for its existence. Here, I attempt to resolve misunderstandings about bet hedging and its relationship with other modes of response to environmental change, identify the challenges inherent to its study and assess the state of existing empirical evidence. The variety and distribution of plausible bet-hedging traits found across 16 phyla in over 100 studies suggest their ubiquity. Thus, bet hedging should be considered a specific mode of response to environmental change. However, the distribution of bet-hedging studies across evidence categories-defined according to potential strength-is heavily skewed towards weaker categories, underscoring the need for direct appraisals of the adaptive significance of putative bet-hedging traits in nature.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".