Sea to sky: impacts of residual salmon-derived nutrients on estuarine breeding bird communities
Bibliographic record
Abstract
Pacific salmon (Oncorhynchus spp.) returning to streams around the North Pacific Rim provide a nutrient subsidy to these ecosystems. While many species of animals feed directly on salmon carcasses each autumn, salmon-derived nutrients can also be stored in coastal habitats throughout the year. The effects of this storage legacy on vertebrates in other seasons are not well understood, especially in estuaries, which can receive a large portion of post-spawning salmon nutrients. We examine the effects of residual salmon-derived nutrients, forest habitats and landscape features on summer breeding birds in estuary forests. We compared models containing environmental variables and combined chum (Oncorhynchus keta) and pink (Oncorhynchus gorbuscha) salmon biomass to test predictions concerning bird density and diversity. We discovered that total bird, insectivore, golden-crowned kinglet and Pacific wren densities and Shannon's diversity in the summer were strongly predicted by salmon biomass in the autumn. For most metrics, this relationship approaches an asymptote beyond 40 000 kg of salmon biomass. Foliage height diversity, watershed catchment area and estuary area were also important predictors of avian communities. Our study suggests that the legacy of salmon nutrients influences breeding bird density and diversity in estuaries that vary across a wide gradient of spawning salmon biomass.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".