Optimal Effort Intensity in Backpack Electrofishing Surveys
Bibliographic record
Abstract
Abstract We evaluated the effect of backpack electrofishing effort intensity on the precision of estimates of fish density in a southern Ontario stream. Single-pass electrofishing was conducted on 30 sites at three electrofishing effort intensities (5, 10 and 15 s/m2). We found that an effort of 5 s/m2 yielded an average catch that was 78% of the 15-s/m2 effort. An asymptotic model effectively described the catch–effort relationship for most fish taxa in the stream (i.e., Rainbow Trout Oncorhynchus mykiss, Brown Trout Salmo trutta, sculpin Cottus sp., dace Rhinichthys sp., and darter Etheostoma sp.). Using the catchability parameters of this model, we evaluated the trade-off between sampling many sites at low intensity or fewer sites at higher intensity. The survey design that maximizes precision of fish-density estimates depends on the average time spent traveling between sites and the average area of sites. For southern Ontario streams, where sample sites were approximately 300 m2 and travel time between sites was 75 min, the optimum electrofishing effort intensity was approximately 5 s/m2. The applicability of these results to other systems was demonstrated by showing how this optimum intensity was affected by differences in catchability rates of fish and travel distances. These findings will be used to standardize single pass electrofishing catches in nonshield areas of Ontario, and the approach may prove useful in other areas where this fishing technique is effective. Received January 5, 2012; accepted December 7, 2012
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".