Food abundance and fish density alters habitat selection, growth, and habitat suitability curves for juvenile coho salmon (<i>Oncorhynchus kisutch</i>)
Bibliographic record
Abstract
To understand how fish density and food availability affect habitat selection and growth of juvenile coho salmon (Oncorhynchus kisutch), we manipulated fish density (212 fish·m2) and natural invertebrate drift (0.0470.99 mg·m3) in 12 experimental stream channels constructed in a side-channel of Chapman Creek, British Columbia. Increased food resulted in increased growth of both dominant and subdominant fish and a shift to higher average focal velocities (from 6.5 to 8.4 cm·s1) with maximum growth in the range of 1012 cm·s1. Increased food appears to permit juvenile coho to exploit higher velocity microhabitats that might otherwise be bioenergetically unsuitable at lower food levels. Increased fish density resulted in lower growth of subdominant but not of dominant fish and a general displacement of fish to both higher and lower focal velocities. The shapes of habitat suitability curves were sensitive to food abundance, implying that differences in food availability may affect transferability of habitat suitability curves between streams of different productivity. While habitat suitability curves captured the change in extent of available habitat following prey enrichment, actual increases in growth rate with enrichment (i.e., changes in habitat quality) were poorly represented by habitat suitability values and better represented by bioenergetic model predictions.
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".