A Comparison of Electrofishing and Visual Surveying Methods for Estimating Fish Community Structure in Temperate Rivers
Bibliographic record
Abstract
Abstract Studies attempting to describe fish community structure in shallow riverine environments typically rely on electrofishing and/or visual (snorkelling) surveys, but few have addressed the relative efficiencies of these two methods at estimating fish density and biomass across wide ranges of geography, taxonomy and life history stages. Multiple paired electrofishing and visual surveys were conducted in 18 temperate Canadian rivers in order to obtain community‐wide density and biomass estimates from both methods. Partial canonical multivariate analyses were applied to the paired fish community matrices comparing the results of both surveying methods at the taxonomic levels of family, genus and species, as well as size classes within families and species, to assess the particular effectiveness of each sampling method. Although electrofishing estimates of family and species richness were generally greater, snorkelling surveys tended to generate higher density and biomass estimates for different size classes of many salmonid and cyprinid species. Moreover, mean river biomass estimates derived from visual surveying matched those obtained from our best mean river biomass estimates arising from the two methods combined. This study provides empirical evidence that electrofishing and visual survey methods generate different types of information when assessing fish community structure at the family level or by size classes. Our results provide ample background information for determining the most accurate sampling method for a particular fish community assemblage, which is fundamental to fisheries management and research. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".