Ecological links between salmon, large carnivore predation, and scavenging birds
Bibliographic record
Abstract
We compared scavenging bird abundance and diversity across 17 estuaries on the Central Coast of British Columbia, Canada in relation to landscape characteristics and biomass of spawning salmon and senescent and depredated salmon carcasses. We discovered that all metrics for spawning salmon and carcass biomasses were strong predictors of scavenger abundance and diversity. Specifically, Shannon’s diversity, which emphasizes rare species richness, and total abundances of scavengers, corvids ( Corvus spp.), and small and large gulls ( Larus spp.) were most strongly predicted by total biomass of carcasses. In contrast, the abundance of bald eagles Haliaeetus leucocephalus was most strongly predicted by biomass of carcasses that had been killed or scavenged by other predators (mostly bears and wolves). Simpson’s diversity, which emphasizes evenness of common species, was best predicted by total spawning salmon biomass. Estuary area also featured prominently among top predictors of most scavenger metrics. Our results suggest a link between terrestrial salmon predators and bald eagles, and that available salmon biomass is important for maintaining the abundance and diversity of scavenging birds that congregate at estuaries throughout the spawning season.
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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 | 0.001 |
| 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 | 0.000 | 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".