Recovery and genetics of Mexican wolves: a reply to Hedrick et al.
Bibliographic record
Abstract
Mexican wolves (Canis lupus baileyi) are a gray wolf subspecies found in northern Mexico and part of the southwestern United States.The subspecies was once extirpated in the wild and the current population is highly inbred, having descended from just 7 founders (U.S. Fish and Wildlife Service [USFWS] 2017).Accordingly, the subspecies is federally listed as endangered, and genetic monitoring and management are key components of the current recovery strategy and downlisting recovery criteria (USFWS 2017).In support of this recovery strategy, we recently evaluated inbreeding depression in wild Mexican wolves in Arizona and New Mexico, USA, from 1998 to 2022 (Clement et al. 2024).Our analysis did not detect any statistical association between inbreeding coefficients in a pack, as estimated from the Mexican wolf pedigree, and the number of pups surviving to 9 months (hereafter, recruitment).Hedrick et al. (2025) provided comments on our work, concluding that Mexican wolves "have shown inbreeding depression" and recommending that Mexican wolves hybridize with northern gray wolves (C.l. occidentalis) to induce genetic rescue.Here, we provide our perspective on inbreeding depression, genetic rescue, and other issues raised by Hedrick et al. (2025).
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.016 | 0.041 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".