Are dispersal and dormancy alternative strategies for overcoming environmental variability?
Bibliographic record
Abstract
Dispersal and dormancy serve as strategies for persistence in varying and uncertain environments and are critical to ecological models of biodiversity maintenance. Theories of specific ecological scenarios that favor dispersal, dormancy, or their covariance are rarely tested empirically, particularly in response to realistically complex patterns of spatiotemporal environmental variation. To resolve these complexities, we collected 20 populations of Vulpia microstachys, an annual grass native to California, from the field and grew them in a greenhouse, and on the offspring generation measured seed dispersal ability and seed dormancy rates. We hypothesized that seed dormancy rates, but not dispersal abilities, would be highest in populations from more productive, temporally variable sites, causing dispersal and dormancy to evolve independently-in other words, we leveraged evolved differences among populations to identify what ecological strategy (i.e., dispersal, dormancy, or both) is most likely to evolve at different parts of a variability gradient. Our data suggest that both dispersal and dormancy evolve to combat different axes and scales of spatial heterogeneity and can evolve independently (thus, they are not forced to covary). Most surprisingly, seed dormancy appears to have evolved as a strategy for overcoming microgeographic heterogeneity, an outcome that to our knowledge has not been considered by theory; we confirm the plausibility of this conclusion with a simulation. In sum, we provide much needed empirical data on the evolution of ecological strategies for coping with environmental variance, 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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".