Sympatric wolf and coyote populations of the western Great Lakes region are reproductively isolated
Bibliographic record
Abstract
Interpretation of the genetic composition and taxonomic history of wolves in the western Great Lakes region (WGLR) of the United States has long been debated and has become more important to their conservation given the recent changes in their status under the Endangered Species Act. Currently, the two competing hypotheses on WGLR wolves are that they resulted from hybridization between (i) grey wolves (Canis lupus) and western coyotes (C. latrans) or (ii) between grey wolves and eastern wolves (C. lycaon). We performed a genetic analysis of sympatric wolves and coyotes from the region to assess the degree of reproductive isolation between them and to clarify the taxonomic status of WGLR wolves. Based on data from maternal, paternal and bi-parental genetic markers, we demonstrate a clear genetic distinction between sympatric wolves and coyotes and conclude that they are reproductively isolated and that wolf-coyote hybridization in the WGLR is uncommon. The data reject the hypothesis that wolves in the WGLR derive from hybridization between grey wolves and western coyotes, and we conclude that the extant WGLR wolf population is derived from hybridization between grey wolves and eastern wolves. Grey-eastern wolf hybrids (C. lupus × lycaon) comprise a substantial population that extends across Michigan, Wisconsin, Minnesota and western Ontario. These findings have important implications for the conservation and management of wolves in North America, specifically concerning the overestimation of grey wolf numbers in the United States and the need to address policies for hybrids.
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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 | 0.000 |
| 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 | 0.001 |
| 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".