Comparison of Electrofishing and Snorkeling Mark–Recapture Estimation of Detection Probability and Abundance of Juvenile Steelhead in a Medium-Sized River
Bibliographic record
Abstract
Abstract We compared nighttime electrofishing- and snorkeling-based mark–recapture methods for estimating the detection probability and abundance of juvenile steelhead Oncorhynchus mykiss in the Cheakamus River, British Columbia. The reliability of abundance estimates largely depends on the precision and accuracy of detection probability (the fraction of marked individuals detected) as well as a few key assumptions of closed population models that we evaluated in this study. There was minimal bias (−2.5%) in diver estimates of the fork lengths of juvenile steelhead, and the relationship between measured and estimated fork lengths was very precise (r2 = 95%). With a hierarchical Bayesian model, estimates of the detection probability for smaller juveniles (40–60 mm) ranged from 0.4 to 0.6 with electrofishing and were near zero with snorkeling. In contrast, snorkeling-based detection probability was 0.6 and independent of size for larger juvenile steelhead (>60 mm) and much greater than that with electrofishing. These results provide strong evidence that there is considerable individual heterogeneity in detection probability driven by fish size for both methods. Owing to these differences, the abundance of age-0 steelhead based on snorkeling was underestimated by 50%, but that of larger, age-1 fish was unbiased and more precise (10-fold) than that based on electrofishing. The use of electrofishing during marking resulted in a substantive reduction in snorkeling-based detection probability during recapture, but the converse was not true. Thus, there is strong evidence of behavioral heterogeneity in detection probability induced by electrofishing, but only when snorkeling is used to detect fish during recapture. The differences in detection probabilities among size-classes and sampling methods were probably driven by differences in concealment behavior, spatial distribution, and fright responses to sampling. Our results indicate that snorkeling is the better way to estimate abundance for larger juvenile steelhead, whereas electrofishing is preferred for smaller 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".