Landscape heterogeneity and marine subsidy generate extensive intrapopulation niche diversity in a large terrestrial vertebrate
Bibliographic record
Abstract
1. Inquiries into niche variation within populations typically focus on proximate ecological causes such as competition. Here we examine how landscape heterogeneity and allochthonous (marine) subsidy might ultimately generate intrapopulation niche diversity. 2. Using stable isotope analysis, we detected extensive terrestrial-marine isotopic niche variation among subpopulations, social groups, and individual grey wolves (Canis lupus) that occupy a spatially heterogeneous landscape in coastal British Columbia comprising a mainland area and adjacent archipelago. 3. The inner island subpopulation exhibited the widest isotopic niche in the population, consuming extensive terrestrial and marine resources. Mainland and outer island subpopulations occupied comparatively narrow and primarily terrestrial, and primarily marine, niches respectively. Within these biogeographical subpopulations, social groups also diverged in niche. 4. To support examination at the individual level, we used an isotopic approach to test Van Valen's (1965) niche variation hypothesis. Consistent with the hypothesis, we observed that among-individual variation increased with subpopulation niche width. 5. Patterns at all levels related to how a spatially heterogeneous coastal landscape structured the competitive environment, which in turn mediated the availability and use of terrestrial and marine resources. Broadly, our results suggest that spatial heterogeneity and allochthonous subsidy--both widespread but commonly subject to contemporary anthropogenic change--might provide novel opportunities for examination and conservation of ecological variation within populations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".