Effects of Fish Size, Habitat, Flow, and Density on Capture Probabilities of Age‐0 Rainbow Trout Estimated from Electrofishing at Discrete Sites in a Large River
Bibliographic record
Abstract
Abstract We estimated size‐specific capture probabilities of age‐0 rainbow troutOncorhynchus mykissin the Lee's Ferry Reach of the Colorado River, Arizona, by backpack and boat electrofishing at discrete shoreline sites using both depletion and mark‐recapture experiments. Our objectives were to evaluate the feasibility of estimating capture probability for juvenile fish in larger rivers; to determine how it is influenced by fish size, habitat, flow, density, and recovery period; and to test population closure assumptions. There was no mortality among the 351 rainbow trout that were captured by electrofishing, marked, and held for 24 h. Of a total of 2,966 fish that were marked and released, only 0.61% were captured outside of mark‐recapture sites, and total emigration from mark‐recapture sites was 2.2‐2.6%. These data strongly suggest that populations within discrete sites can be treated as effectively closed for the 24‐h period between marking and recapture. Eighty percent of capture probability estimates from 66 depletion experiments and 42 mark‐recapture experiments ranged from 0.28 to 0.75 and from 0.17 to 0.45, respectively, and the average coefficient of variation of estimates was 0.26. There was strong support for a fish size‐capture probability relationship that accounted for the differences in vulnerability across habitat types. Smaller fish were less vulnerable in high‐angle shorelines that were sampled by boat electrofishing. There was little support for capture probability models that accounted for within‐day and across‐month variation in flow. The effects of fish density on capture probability were challenging to discern, variable among habitat types and estimation methodologies, and confounded with the effect of fish size. As capture probability estimates were generally precise and the closure assumption was met, our results demonstrate that electrofishing‐based mark‐recapture experiments at discrete sites can be used to estimate the abundance of juvenile fish in large rivers.
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 | 0.002 | 0.003 |
| 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) | 0.000 | 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 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".