Community Ecology and Conservation of Bear-Salmon Ecosystems
Bibliographic record
Abstract
Apex predators play keystone roles in ecosystems through top-down control, but the effects of apex omnivores on ecosystems could be more varied because changes in the resource base alter their densities and reverberate through ecosystems in complex ways. In coastal temperate ecosystems throughout much of the Northern Hemisphere, anadromous salmon once supported abundant bear populations, but both taxa have declined or been extirpated from large parts of their former ranges with limited research on the consequences of diminished or absent interactions among species. Here we review the biogeography of bear-salmon interactions and the role of salmon-subsidized bears in (1) resource provisioning to plants and scavengers through the distribution of salmon carcasses, (2) competition among bears and other large carnivores, (3) predation of ungulate neonates, (4) seed dispersal, and (5) resource subsidies to rodents with seed-filled scats. In addition to our review of the literature, we present original data to demonstrate two community-level patterns that are currently unexplained. First, deer densities appear to be consistently higher on islands with abundant brown bears than adjacent islands with black bears and wolves, and moose calf survival is higher at low bear densities (<∼25 bears per 100 km 2 ) but is constant across the vast majority of bear densities found in the wild (i.e., ∼>25 bears per 100 km 2 ). Our review and empirical data highlight key knowledge gaps and research opportunities to understand the complex ecosystem effects related to bear-salmon interactions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".