Environmental variability and the evolution of bet-hedging strategies
Bibliographic record
Abstract
Abstract Bet-hedging strategies, such as dispersal and dormancy, are predicted to evolve in varying and uncertain environments and are critical to ecological models of biodiversity maintenance. Theories of the specific ecological scenarios that favor the evolution of dispersal, dormancy, or their covariance are rarely tested, particularly for naturally-evolved populations that experience complex patterns of spatiotemporal environmental variation. We grew 23 populations of Vulpia microstachys , an annual grass native to California, in a greenhouse, and on the offspring generation measured seed dispersal ability and dormancy rates. We hypothesized that seed dormancy rates and dispersal abilities would be highest in populations from more productive, temporally variable sites, causing them to covary positively. Contrary to our hypothesis, our data suggest that both dispersal and dormancy evolve to combat different axes and scales of spatial heterogeneity, and are underlain by different seed traits, allowing them to evolve independently. Dormancy appears to have evolved as a strategy for overcoming microgeographic heterogeneity rather than temporal climate fluctuations, an outcome that to our knowledge has not been considered by theory. In sum, we provide much needed empirical data on the evolution of bet hedging, as well as a new perspective on the ecological function dormancy provides in heterogeneous landscapes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".