A MULTIMOLECULAR PERSPECTIVE ON THE SUCCESS OF WOLF RECOVERY AND RELOCATION
Bibliographic record
Abstract
With global biodiversity in decline and many species isolated in fragmented habitats, conservation efforts increasingly rely on natural recolonization and translocation to restore ecosystems. These strategies face challenges such as genetic erosion and habitat limitations, highlighting the need for effective monitoring practices. This research assesses the success of wolf recovery initiatives by examining long-term genetic and genomic health using both traditional and advanced molecular methods. First, we investigated the genetic health of 19 gray wolves (Canis lupus) relocated to Isle Royale National Park to restore ecosystem balance. Initial genetic assessments with microsatellites found that these wolves had genetic diversity metrics similar to other western Great Lakes (WGL) populations. However, wolves from a single translocation source, Michipicoten Island, Ontario, consisted of a family group, raising concerns about the retention of genetic variation. Agent-based models (ABMs) predicted a decline in genetic variation over the next 50 years, emphasizing the need for continued monitoring and possibly further relocations to prevent genetic erosion. Second, we developed a genotyping-in-thousands (GTseq) single nucleotide polymorphism (SNP) panel to monitor wolves on Isle Royale post-translocation. Traditional genetic methods for monitoring wildlife like microsatellites often suffer from allelic dropout and genotyping errors. Our findings indicate the GTseq panel contained low genotyping error (0.2%) and the ability to genotype samples with DNA concentrations as low as 0.05ng/μL, enabling reliable individual identification and pedigree reconstruction from noninvasive samples. Further, we found the panel could differentiate gray wolves, eastern wolves (C. lycaon), coyotes (C. latrans), domestic dogs (C. lupus familiaris), and red foxes (Vulpes vulpes). Given our findings, GTseq offers promise for efficient, long-term genetic monitoring. Third, we implemented the GTseq SNP panel for long-term monitoring of relocation success on Isle Royale. Our results showed increased inbreeding since relocation, though levels remained well below those associated with severe inbreeding depression. The increased inbreeding is mainly driven by a single breeding pair of full siblings translocated from Michipicoten Island, raising questions about the mechanisms that drive kin avoidance in gray wolves when mate selection is limited. Empirically informed ABMs projected that natural migration alone will not counteract inbreeding, emphasizing the importance of periodic translocations to maintain genetic viability. This highlights the value of noninvasive genetic monitoring to inform management decisions. Last, we assessed the success of natural recolonization for gray wolves in the WGL using genomic diversity as an index of adaptive potential. Our findings show that recent gray wolf expansions, particularly in
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".