Co‐evolution of dormancy and dispersal in spatially autocorrelated landscapes
Bibliographic record
Abstract
The evolution of dispersal can be driven by spatial processes, such as landscape structure, and temporal processes, such as disturbance. Dormancy, or dispersal in time, is generally thought to evolve in response to temporal processes. In spite of broad empirical and theoretical evidence of trade-offs between dispersal and dormancy, we lack evidence that spatial structure can drive the evolution of dormancy. Here, we develop a simulation-based model of the joint evolution of dispersal and dormancy in spatially heterogeneous landscapes. We show that dormancy and dispersal are each favored under different landscape conditions, but not simultaneously under any of the conditions we tested. We further show that, when dispersal distances are short, dormancy can evolve directly in response to landscape structure. In this case, selection is primarily driven by benefits associated with avoiding kin competition. Our results are similar in both highly simplified and realistically complex landscapes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".