The evolution of dispersal in spatially varying environments
Bibliographic record
Abstract
We consider the evolution of dispersal in an environment that varies spatially but that is con-stant in time. We allow an age structure with dispersal possible in all life-stages. We suppose that demes are large enough that kin effects can be ignored. It has previously been shown that cost-free dispersal can persist over evolutionary time. However, several studies have shown that costly dispersal must in general be selected against. Here, we establish a fundamental result about stage-structured populations with stage-specific dispersal rates – that is, at evolutionary equilibrium, over each time step, the total reproductive value of the emigrants leaving each deme must equal the total reproductive value of the immigrants entering that deme. A simple consequence of this principle is that, if migration is restricted to a single stage – the same stage for all demes – then costly dispersal cannot evolve. Another corollary is that, with a ‘sequential’ age structure, over a complete life-cycle, the proportionate flow of genes out of a deme must equal the flow in. Finally, we present an example to show that dispersal may be evolutionarily stable, even when costly, if individuals can disperse more than once during their life-cycle.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".