Effects of Salmon-Derived Nutrients and Habitat Characteristics on Population Densities of Stream-Resident Sculpins
Bibliographic record
Abstract
Movement of nutrients across ecosystem boundaries can have important effects on food webs and population dynamics. An example from the North Pacific Rim is the connection between productive marine ecosystems and freshwaters driven by annual spawning migrations of Pacific salmon (Oncorhynchus spp). While a growing body of research has highlighted the importance of both pulsed nutrient subsidies and disturbance by spawning salmon, their effects on population densities of vertebrate consumers have rarely been tested, especially across streams spanning a wide range of natural variation in salmon densities and habitat characteristics. We studied resident freshwater prickly (Cottus asper), and coastrange sculpins (C. aleuticus) in coastal salmon spawning streams to test whether their population densities are affected by spawning densities of pink and chum salmon (O. gorbuscha and O. keta), as well as habitat characteristics. Coastrange sculpins occurred in the highest densities in streams with high densities of spawning pink and chum salmon. They also were more dense in streams with high pH, large watersheds, less area covered by pools, and lower gradients. In contrast, prickly sculpin densities were higher in streams with more large wood and pools, and less canopy cover, but their densities were not correlated with salmon. These results for coastrange sculpins provide evidence of a numerical population response by freshwater fish to increased availability of salmon subsidies in streams. These results demonstrate complex and context-dependent relationships between spawning Pacific salmon and coastal ecosystems and can inform an ecosystem-based approach to their management and conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".