Observations of Grizzly Bear (<i>Ursus arctos</i>) associated with abundance of spawning Kokanee (<i>Oncorhynchus nerka</i>) at an inland river, British Columbia, Canada
Bibliographic record
Abstract
Salmon (Oncorhynchus spp.) are an important food source for Grizzly Bear (Ursus arctos), but many salmon populations are declining. While most research on Grizzly Bear–salmon interactions occurs in coastal ecosystems, declining salmon may also affect Grizzly Bears in inland ecosystems where salmon are also an important part of their diet. We document changes in the number and distribution of observations of Grizzly Bears and changing Kokanee (i.e., landlocked Sockeye Salmon, Oncorhynchus nerka) abundance at an inland river. We hypothesized that reduced abundance of Kokanee would limit the number of Grizzly Bear observations at the river. We compared Kokanee abundance and Grizzly Bear observations (n = 535) between 2012 and 2019 at the Lardeau River, British Columbia, Canada. We used a generalized linear mixed model to test if the number of bear observations changed as a function of Kokanee abundance among four river reaches during eight consecutive years of study. Kokanee abundance was a strong statistical predictor of Grizzly Bear observations (β = 0.52, P = 0.001, CI = 0.12–0.87), and Kokanee abundance and reach explained 73% of the variance. Our results suggest that reduced Kokanee abundance also reduces Grizzly Bear presence, likely because bears seek out other, more available food sources, away from Kokanee spawning habitat. This pattern could limit ecosystem services provided by Grizzly Bears adjacent to spawning areas and it could have implications for bear management and conservation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".