The scale-dependent role of submerged macrophytes as drift-feeding lotic fish habitat
Bibliographic record
Abstract
Although submerged macrophyte (hereafter, “macrophyte”) communities are globally prevalent in low-gradient rivers, the net reach-scale effect of macrophytes on drift-feeding fish microhabitat preference is poorly understood. We used snorkeling and bioenergetics to study fish habitat selection for rainbow trout ( Oncorhynchus mykiss) in the Henrys Fork, ID, USA, investigating microhabitat preference across a reach-scale gradient of macrophyte growth. Fish preferred microhabitats with deep water, low velocity, and low macrophyte coverage. Preferences for microhabitats with higher net rate of energy intake (NREI) were modulated by reach-scale macrophyte coverage, higher coverage increasing preferences for higher NREI. Macrophyte coverage was a weak positive predictor for depth and NREI, and a weak negative predictor for water velocity and median substrate. Our results suggest trade-offs between fish predation risk and bioenergetic food intake, with macrophytes modulating these trade-offs across scales by affecting reach-scale geomorphology, bioenergetics, and predation risk. As such, this study highlights the important and dynamic role that macrophytes can play in fish population dynamics in rivers, with important implications for management decisions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".