Can gene flow have negative demographic consequences? Mixed evidence from stream threespine stickleback
Bibliographic record
Abstract
Dispersal and gene flow can have both positive and negative effects on population size, but little empirical support from nature exists for the negative effects. We test for such effects in a stream population of threespine stickleback (Gasterosteus aculeatus L.) that is subject to high gene flow from a lake and is thus maladapted to stream conditions. In this system, maladaptation increases with distance along the stream, and this increase is associated with decreasing population densities until stickleback are no longer present (2.5 km from the lake). We conducted field experiments to inform whether this association might reflect a negative role for gene flow in constraining population size and therefore causing a local range limit. We specifically tested predictions deriving from theory: peripheral populations should show partial local adaptation, be under strong selection and not simply be maintained by dispersal. First, a transplant experiment suggested a weak home-site advantage in the peripheral population. Second, a mark-recapture study showed directional selection for a stream-adapted phenotype in 1 of 2 years. Third, another mark-recapture experiment showed that dispersal is limited to the point that positive demographic effects of dispersal are probably minimal. We conclude that, although gene flow does constrain morphological maladaptation in the outlet stream population, the evidence for its contribution to population size and range limits is mixed. We discuss the implications of our work for the study of factors influencing the evolution of species' ranges.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".