Intrapopulation foraging niche variation between phenotypes and genotypes of Spirit bear populations
Bibliographic record
Abstract
Abstract Foraging niche variation within a species can contribute to the maintenance of phenotypic diversity. The multiniche model posits that phenotypes occupying different niches can contribute to the maintenance of balanced polymorphisms. Using coastal populations of black bears (Ursus americanus kermodei)from British Columbia, Canada, we examined potential foraging niche divergence between phenotypes (black and white “Spirit” coat color) and between genotypes (black‐coated homozygote and heterozygous). We applied the Bayesian multivariate models, with biotracers of diet (δ13C and δ15N) together comprising the response variable, to draw inference about foraging niche variation. Variance–covariance matrices from multivariate linear mixed‐effect models were visualized as the Bayesian standard ellipses in δ13C and δ15N isotopic space to assess potential seasonal and annual niche variation between phenotypes and genotypes. We did not detect a difference in annual isotopic foraging niche area in comparisons between genotypes or phenotypes. Consistent with previous field experimental and isotopic analyses, however, we found that white phenotype Spirit bears were modestly more enriched in δ15N during the fall foraging season, though with our modest sample sizes these results were not significant. Although also not statistically significant, variation in isotopic niches between genotypes revealed that heterozygotes were moderately more enriched in δ13C along hair segments grown during fall foraging compared with black‐coated homozygotes. To the extent to which the pattern of elevated δ15N and δ13C may signal the consumption of salmon (Oncorhynchusspp.), as well as the influence of salmon consumption on reproductive fitness, these results suggest that black‐coated heterozygotes could have a minor selective advantage in the fall compared with black‐coated homozygotes. More broadly, our multivariate approach, coupled with knowledge of genetic variation underlying a polymorphic trait, provides new insight into the potential role of a multiniche mechanism in maintaining this rare morph of conservation priority in Canada's Great Bear Rainforest and could offer new understanding into polymorphisms in other systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".