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Record W2740312946 · doi:10.1093/biolinnean/blx079

Niche differentiation between coat colour morphs in the Kermode bear (Ursidae) of coastal British Columbia

2017· article· en· W2740312946 on OpenAlexafffundabout
T. E. Reimchen, DAN R. KLINKA

Bibliographic record

VenueBiological Journal of the Linnean Society · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaDavid Suzuki FoundationUniversity of Saskatchewan
KeywordsBiologyUrsusNicheEcologyWhite (mutation)Ecological nicheMammalNiche differentiationZoologyHabitat

Abstract

fetched live from OpenAlex

One of the most distinctive colour polymorphisms of any mammal occurs in black bears (Ursus americanus kermodei Hornaday) of the Great Bear Rainforest of coastal British Columbia. We use carbon and nitrogen stable isotope values and C/N ratios along segments of hair shafts obtained from 14 white and 12 black individual bears to quantify dietary niche of the morphs as a test of the multi-niche model for a polymorphism. On Gribbell Island, where the white bear reaches 30%, 15N is significantly (GLM repeated measures) more enriched (more marine-derived nitrogen) in the white morph than in the black morph in each season (spring, summer, autumn). On the adjacent Princess Royal Island, where the white morph is less common, both morphs are highly enriched during autumn (~δ15N = +11‰), but there are no isotopic differences between morphs in any season. On both islands, C/N ratios (~3.1) of the black morph decrease from spring to autumn, converging on the lower average values for the white morph. Our data suggest that niche of the white morph involves increased use of a marine-associated diet and that ecological segregation between the morphs has facilitated the historical persistence of the polymorphism.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.247
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2017
Admission routes3
Has abstractyes

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