Restoration of genetic connectivity among Northern Rockies wolf populations
Bibliographic record
Abstract
Probably no conservation genetics issue is currently more controversial than the question of whether grey wolves (Canis lupus) in the Northern Rockies have recovered to genetically effective levels. Following the dispersal-based recolonization of Northwestern Montana from Canada, and reintroductions to Yellowstone and Central Idaho, wolves have vastly exceeded population recovery goals of 300 wolves distributed in at least 10 breeding pairs in each of Wyoming, Idaho and Montana. With >1700 wolves currently, efforts to delist wolves from endangered status have become mired in legal battles over the distinct population segment (DPS) clause of the Endangered Species Act (ESA), and whether subpopulations within the DPS were genetically isolated. An earlier study by vonHoldt et al. (2008) suggested Yellowstone National Park wolves were indeed isolated and was used against delisting in 2008. Since then, wolves were temporarily delisted, and a first controversial hunting season occurred in fall of 2009. Yet, concerns over the genetic recovery of wolves in the Northern Rockies remain, and upcoming District court rulings in the summer of 2010 will probably include consideration of gene flow between subpopulations. In this issue of Molecular Ecology, vonHoldt et al. (2010) conduct the largest analysis of gene flow and population structure of the Northern Rockies wolves to date. Using an impressive sampling design and novel analytic methods, vonHoldt et al. (2010) show substantial levels of gene flow between three identified subpopulations of wolves within the Northern Rockies, clarifying previous analyses and convincingly showing genetic recovery.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".