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
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it