Data from: History matters more when explaining genetic diversity within the context of the core-periphery hypothesis
Bibliographic record
Abstract
The core–periphery hypothesis (CPH) predicts that populations located at the periphery of a species' range should have lower levels of genetic variation than those at the centre of the range. However, most of the research on the CPH focuses on geographic distance and not on ecological distance, or uses categorical definitions of core and periphery to explain the distribution of genetic diversity. We use current climate data and historical climate data from the last glacial maxima to develop quantitative estimates of contemporary and historical ecological suitability using ecological niche models. We analysed genetic diversity using 12 polymorphic microsatellites to estimate changes in heterozygosity, allelic richness and population differentiation in 31 populations of the wood frog (Lithobates sylvaticus) spanning the species’ entire eastern clade (33o to 45o latitude) from Alabama, USA, to Nova Scotia, Canada. Our data support predictions based on the CPH. Populations showed significant differences in genetic diversity across the range, with lower levels of genetic variation at the geographic range edge and in areas with lower levels of historical and contemporary ecological suitability. However, history and geography (not current ecological suitability) best explain the patterns. This study highlights the importance of examining more than just geography when assessing the CPH, and the importance of historical ecological suitability in the maintenance of genetic diversity and population differentiation.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".