Mechanisms underlying the increase in young-of-the-year Atlantic salmon (Salmo salar) density with habitat complexity
Bibliographic record
Abstract
We tested three hypotheses used to explain the increase in young-of-the-year (YOY) Atlantic salmon ( Salmo salar ) density with habitat complexity: the territory-size, predator-refuge, and foraging-benefits hypotheses. We manipulated habitat complexity in three different treatments (boulder-removed, control, and boulder-added) at eight sites in Catamaran Brook and the Little Southwest Miramichi River, New Brunswick. The density of juvenile salmon was two times higher in the boulder-added treatment than in the other treatments. Our data were consistent with the territory-size hypothesis; visual isolation was highest and territory size was smallest in the boulder-added treatment, and salmon selected microhabitats to maximize their field of view. Our results showed partial support for the predator-refuge hypothesis; salmon in the boulder-added sites were closer to cover and showed a reduced reaction distance to a novel stimulus, but did not preferentially select microhabitats closer to cover. We found no direct support for the foraging-benefits hypothesis; however, there is indirect evidence that boulders may improve the growth potential of instream habitat. Our results suggest that YOY Atlantic salmon may be attracted to complex environments for the increased cover and that the decreased visibility of these sites causes a reduction in territory size, allowing a higher density of fish.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".