Genetic variation across species’ geographical ranges: the central–marginal hypothesis and beyond
Bibliographic record
Abstract
There is growing interest in quantifying genetic population structure across the geographical ranges of species to understand why species might exhibit stable range limits and to assess the conservation value of peripheral populations. However, many assertions regarding peripheral populations rest on the long-standing but poorly tested supposition that peripheral populations exhibit low genetic diversity and greater genetic differentiation as a consequence of smaller effective population size and greater geographical isolation relative to geographically central populations. We reviewed 134 studies representing 115 species that tested for declines in within-population genetic diversity and/or increases in among-population differentiation towards range margins using nuclear molecular genetic markers. On average, 64.2% of studies detected the expected decline in diversity, 70.2% of those that tested for it showed increased differentiation and there was a positive association between these trends. In most cases, however, the difference in genetic diversity between central and peripheral population was not large. Although these results were consistent across plants and animals, strong taxonomic and biogeographical biases in the available studies call for a cautious generalization of these results. Despite the large number of studies testing these simple predictions, very few attempted to test possible mechanisms causing reduced peripheral diversity or increased differentiation. Almost no study incorporated a phylogeographical framework to evaluate historical influences on contemporary genetic patterns. Finally, there has been little effort to test whether these geographical trends in putatively neutral variation at marker loci are reflected by quantitative genetic trait variation, which is likely to influence the adaptive potential of populations across the geographical range.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".