PHENOTYPE-DEPENDENT NATIVE HABITAT PREFERENCE FACILITATES DIVERGENCE BETWEEN PARAPATRIC LAKE AND STREAM STICKLEBACK
Bibliographic record
Abstract
Adaptive divergence between adjoining populations reflects a balance between the diversifying effect of divergent selection and the potentially homogenizing effect of gene flow. In most models of migration-selection balance, gene flow is assumed to reflect individuals' inherent capacity to disperse, without regard to the match between individuals' phenotypes and the available habitats. However, habitat preferences can reduce dispersal between contrasting habitats, thereby alleviating migration load and facilitating adaptive divergence. We tested whether habitat preferences contribute to adaptive divergence in a classic example of migration-selection balance: parapatric lake and stream populations of three-spine stickleback (Gasterosteus aculeatus). Using a mark-transplant-recapture experiment on morphologically divergent parapatric populations, we showed that 90% of lake and stream stickleback returned to their native habitat, reducing migration between habitats by 76%. Furthermore, we found that dispersal into a nonnative habitat was phenotype dependent. Stream fish moving into the lake were morphologically more lake-like than those returning to the stream (and the converse for lake fish entering the stream). The strong native habitat preference documented here increases the extent of adaptive divergence between populations two- to fivefold relative to expectations with random movement. These results illustrate the potential importance of adaptive habitat choice in driving parapatric divergence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".