Influence of Lakeshore Riparian Vegetation on Diet, Feeding Rate, and Body Condition of Adfluvial Coastal Cutthroat Trout
Bibliographic record
Abstract
ABSTRACT Terrestrial invertebrates provide energy and nutrients to lacustrine systems. However, the extent to which lakeshore and riparian vegetation affects the diet of lake‐associated fish is not well known. We sampled six small lakes (< 1 km 2 surface area) on the West Coast of British Columbia, Canada, to determine if lakeshore riparian vegetation composition and extent affected the diet, feeding rate, and body condition of adfluvial cutthroat trout ( Oncorhynchus clarkii clarkii ). We found strong evidence that cutthroat sampled from a lake with an intact, old forest riparian had a different diet composition comprised largely of terrestrial invertebrates than cutthroat sampled from lakes with riparian forests representing a gradient of vegetation age and cover. We identified positive relationships between the intake of terrestrial invertebrates by cutthroat with the percentage of riparian vegetation overhanging and submerged along and decaying wood within the littoral zone. We also found positive relationships between the percentage of vegetation overhanging and submerged along the littoral zone and the percentage of overstory terrestrial vegetation. Our study contributes to a growing body of evidence recognizing the connections between upland terrestrial and lakeshore riparian and aquatic ecosystems.
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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 | 0.000 |
| 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".