Adaptive coat colour polymorphism in the Kermode bear of coastal British Columbia
Bibliographic record
Abstract
We investigated potential ecological attributes of the striking coat colour polymorphism in the black bear (Ursus americanus kermodei Hornaday) occurring on several small islands off the mid-coast of British Columbia, where a white morph (Kermode), fully recessive to the black morph, reaches 10–25% of the population. During three autumn field seasons (2000–2002; 697 h of observation time), we monitored salmon capture behaviour of individual bears (black, N = 37; white, N = 4). Both colour morphs foraged on salmon throughout daylight and darkness but with twice the rate during darkness. Log-linear analysis of capture efficiency (success/attempts) and average capture success per bout of the white morph was marginally lower than the black morph during darkness (22.8%, N = 158 versus 25.8%, N = 279 respectively; P < 0.04), although it was significantly higher during daylight (34.1%, N = 132 versus 25.3%, N = 896, respectively, P < 0.02), with similar trends in three different pursuit modes. Replicated (N = 10–14) field experiments involving a colour dimorphic simulated predator (i.e. human in a black or white cloak) showed that salmon were one-half as evasive during darkness than during daylight, with no differences (P > 0.4) in response to either the white or black models; however, during daylight, salmon were twice as evasive to the black compared to the white model (P < 0.001). The persistence of this coat colour polymorphism may be facilitated by increased salmon accessibility to the Kermode bear and diel foraging differences between morphs. These results are consistent with multi-niche models of adaptive variability.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".