Geographic isolation reduces genetic diversity of a wide‐ranging terrestrial vertebrate, <i>Canis lupus</i>
Bibliographic record
Abstract
Abstract Genetic diversity is theorized to decrease in populations closer to a species' range edge, where habitat may be suboptimal. Generalist species capable of long‐range dispersal may maintain sufficient gene flow to counteract this, though the presence of significant barriers to dispersal (e.g., large water bodies, human‐dominated landscapes) may still lead to, and exacerbate, the edge effect. We used microsatellite data for 2421 gray wolves (Canis lupus) from 24 subpopulations (groups) to model how allelic richness and expected heterozygosity varied with mainland–island position and two measures of range edge (latitude and distance from range center) across >7.3 million km2 of northern North America. We expected low genetic diversity both at high latitudes, due to harsh environmental conditions, and on islands, but no change in diversity with distance to the range center due to the species' exceptional dispersal ability and favorable conditions in far eastern and western habitats. We found that allelic richness and expected heterozygosity of island groups were measurably less than that of mainland groups, and that these differences increased with the island's distance to the species' range center in the study area. Our results demonstrate how multiple axes of geographic isolation (distance from range center and island habitation) can act synergistically to erode the genetic diversity of wide‐ranging terrestrial vertebrate populations despite the counteracting influence of long‐range dispersal ability. These findings emphasize how geographic isolation is a potential threat to the genetic diversity and viability of terrestrial vertebrate populations even among species capable of long‐range dispersal.
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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 | 0.000 | 0.001 |
| 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".