The magnitude of local adaptation under genotype‐dependent dispersal
Bibliographic record
Abstract
Dispersal moves individuals from patches where their immediate ancestors were successful to sites where their genotypes are untested. As a result, dispersal generally reduces fitness, a phenomenon known as "migration load." The strength of migration load depends on the pattern of dispersal and can be dramatically lessened or reversed when individuals move preferentially toward patches conferring higher fitness. Evolutionary ecologists have long modeled nonrandom dispersal, focusing primarily on its effects on population density over space, the maintenance of genetic variation, and reproductive isolation. Here, we build upon previous work by calculating how the extent of local adaptation and the migration load are affected when individuals differ in their dispersal rate in a genotype-dependent manner that alters their match to their environment. Examining a one-locus, two-patch model, we show that local adaptation occurs through a combination of natural selection and adaptive dispersal. For a substantial portion of parameter space, adaptive dispersal can be the predominant force generating local adaptation. Furthermore, genetic load may be largely averted with adaptive dispersal whenever individuals move before selective deaths occur. Thus, to understand the mechanisms driving local adaptation, biologists must account for the extent and nature of nonrandom, genotype-dependent dispersal, and the potential for adaptation via spatial sorting of genotypes.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".