The Maintenance of Genetic Variation Due to Asymmetric Gene Flow in Dendritic Metapopulations
Bibliographic record
Abstract
Dendritic landscapes can have ecological properties that differ importantly from simpler spatial arrangements of habitats. Most dendritic landscapes are structured by elevation, and therefore, migration is likely to be directionally biased. While the population-genetic consequences of both dendritic landscape arrangements and asymmetric migration have begun to be studied, these processes have not been considered together. Simple conceptual models predict that if migration into branch (headwater) populations is limited, such populations can act as reservoirs for potentially unique alleles. As a consequence of the fact that dendritic landscapes have, by definition, more branches than internal habitat patches, this process may lead to the maintenance of higher overall genetic diversities in metapopulations inhabiting dendritic networks where migration is directionally biased. Here we begin to address the generality of these simple predictions using genetic models and a review of empirical literature. We show, for a range of demographic parameters, that dendritic systems with asymmetric migration can maintain levels of genetic variation that are very different, sometimes very elevated, compared with more classical models of geographical population structure. Furthermore, predicted patterns of genetic variation within metapopulations--that is, stepwise increases in genetic diversity at nodes--do occur in some empirical data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".