Effects of Intraspecific Density and Environmental Variables on Electrofishing Catchability of Brown and Rainbow Trout in the Colorado River
Bibliographic record
Abstract
Abstract We investigated electrofishing catchability (q) for brown trout Salmo trutta and rainbow trout Oncorhynchus mykiss in the Colorado River, Grand Canyon National Park, Arizona, over a range of fish densities, water temperatures, turbidities, conductivities, shoreline types, and seasons. The covariance of q with rainbow trout density strongly resembled random distributions, thereby suggesting no relationship between q and rainbow trout density. The catchability of rainbow trout was greater in turbid water (≥480 nephelometric turbidity units (NTU)) than in clear water (≤10 NTU), although lower water temperature may have contributed to this effect. The catchability of rainbow trout was greatest over sand–silt shorelines. The catchability of brown trout increased sharply to levels above those predicted from random chance up to about 0.025 fish/m2 and then assumed an asymptotic or declining relationship with intraspecific fish density. In contrast to the situation with rainbow trout, the catchability of brown trout was higher over rocky shorelines (cobbles, boulders, and bedrock) than sand–silt shorelines, suggesting that the variability of q in relation to shoreline type is species specific. We hypothesize that the catchability of rainbow trout is influenced more by environmental variables than by density. We also hypothesize that brown trout catchability varies with density because a greater proportion of fish occur in shallow, nearshore areas (where electrofishing is most effective) when fish density is high. This effect is enhanced by high catchability over rocky substrates. Our findings emphasize the need to understand the biological and environmental factors affecting electrofishing catchability, especially in monitoring programs that rely on catch-per-unit-effort data to accurately represent fish population status and trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".