Salmon consumption by Kodiak brown bears (<i>Ursus arctos middendorffi</i>) with ecosystem management implications
Bibliographic record
Abstract
The ecological role of large predators in North America continues to spark heated public debate. Although brown bears (Ursus arctos L., 1758) and the salmon (genus Oncorhynchus Suckley, 1861) they feed on have declined in many areas, the Kodiak archipelago is famous for large brown bears and abundant salmon. Salmon have generally been managed for maximum sustained yield in a fisheries sense, but those levels may be well below what is necessary for maximum ecosystem productivity. Consequently, we used stable isotopes and mercury accumulated in hair to estimate intake of salmon by Kodiak brown bears (Ursus arctos middendorffi Merriam, 1896). Salmon intake increased from subadult males (592 ± 325 kg·bear −1 ·year −1 ) to adult males (2788 ± 1929 kg·bear −1 ·year −1 ) and from subadult females (566 ± 360 kg·bear −1 ·year −1 ) to adult females (1364 ± 1261 kg·bear −1 ·year −1 ). Intake within each group increased 62% ± 23% as salmon escapement increased from ∼1 500 to ∼14 000 kg·bear −1 ·year −1 . The estimated population of 2300 subadult and adult bears consumed 3.77 ± 0.16 million kg of salmon annually, a mass equal to ∼6% of the combined escapement and commercial harvest (57.6 million kg). Although bears consume a small portion of the total mass of adult salmon, perpetuation of dense populations of large bears requires ecosystem-based management of the meat resources and environments that produce such bears.
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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 | 0.000 |
| 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 | 0.000 | 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".