Spatial patterns and rarity of the white‐phased ‘Spirit bear’ allele reveal gaps in habitat protection
Bibliographic record
Abstract
Abstract 1. Preserving genetic and phenotypic diversity can help safeguard not only biodiversity but also cultural and economic values. 2. Here, we present data that emerged from Indigenous‐led research at the intersection of evolution and ecology to support conservation planning of a culturally salient, economically valuable, and rare phenotypic variant. We addressed three conservation objectives for the white‐phased ‘Spirit bear’ polymorphism, a rare and endemic white‐coated phenotype of black bear ( Ursus americanus ) in Kitasoo/Xai'xais and Gitga'at Territories and beyond in coastal British Columbia, Canada. First, we used non‐invasively collected hair samples ( n = 385 bears over ∼18,000 km 2 ) to assess the spatial variation in the frequency of the allele that controls the white‐coloured morph (mc1r). Second, we compared our observed allele frequencies at mc1r with those expected under Hardy–Weinberg equilibrium. Finally, we examined how well current protected areas in the region aligned with spatial hotspots of Spirit bear alleles. 3. We found that landscape‐level allele frequency was lower than previously reported. For example our systematic sampling estimated a frequency of 0.25 (95% CI [0.13, 0.41]) on Gribbell Island compared with the previously reported estimate of 0.56. Also, in contrast with previous reports, we failed to detect a statistically significant departure from Hardy–Weinberg equilibrium at mc1r, which calls into question the previously posited role of homozygote gene flow, heterozygote disadvantage, and positive assortative mating in the maintenance of this polymorphism. Finally, we found a discrepancy between the placement of protected areas and the 90th percentile hotspots (upper 10% of all estimated values) of Spirit bear alleles, with ∼50% of hotspots falling outside of protected areas. 4. These results provide new insight into hypotheses related to the maintenance of this rare polymorphism, and directly relevant information to support evidence‐based opportunities for Indigenous Nations of the area to attend to gaps in conservation planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".