Genetic evidence to inform management of rare genetic variants and gene flow: Balancing the conservation of the rare “Spirit bear” allele and population genetic diversity across a complex landscape
Bibliographic record
Abstract
Abstract Incorporating genetic considerations into wildlife management can require balancing the conservation of rare genetic variants with the maintenance of gene flow. One system illustrating such trade‐offs is coastal British Columbia, Canada, where black bears ( Ursus americanus ) can carry a genetic variant responsible for white‐coated “Spirit bears.” We examined population genetic structure, diversity, and gene flow using 22 microsatellite loci for 357 individuals collected over a 23,500 km 2 area from a long‐term noninvasive bear monitoring collaboration among the Gitga'at, Kitasoo/Xai'xais, Nuxalk, Haíɫzaqv, and Wuikinuxv First Nations and partnering scientists. We found broad‐ (two groups) and fine‐scale (eight groups) population structures. At the finer scale, three islands formed unique genetic groups and four genetic groups showed heterozygote deficiency, including two populations containing Spirit bear alleles. We additionally created effective estimation of migration surfaces and found that breaks among genetic groups and areas of lower than average migration aligned with wide waterways (>2 km). Given the apparent isolation of island groups, heterozygote deficiencies, and the distribution of the rare Spirit bear allele, we provide recommendations to prevent the loss of Spirit bear allele carriers and individuals contributing genetic diversity to isolated, genetically depauperate groups. More broadly, we highlight the value of locally led, fine‐scale genetic monitoring for wildlife management.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".