A Modified Drop Net for Sampling Fish Communities in Complex Habitats: A Description and Comparison with Other Techniques
Bibliographic record
Abstract
Abstract A modified drop net (area = 19.3 m2) was constructed to enhance the collection of fish from within complex freshwater habitats. The net was evaluated in three pools located within the Pilbara region of north Western Australia. The net's efficiency was determined by comparison with gillnetting and beach seining; accuracy of the net was investigated using the toxicant rotenone. In terms of efficiency, the modified drop net and beach seine generated similar descriptions of the fish community (relative abundance, species richness, ordination of a species–abundance matrix); panel gill nets collected a diminished subset of the community. Efficiency of the drop net remained relatively constant among pools, whereas the seine became increasingly easy to use as habitat complexity decreased. In terms of accuracy, the drop net produced estimates of total fish abundance similar to those obtained by use of rotenone and adequately depicted site-related differences in fish community structure (multivariate ordination in space). The drop net and rotenone collected similar numbers of species, but the drop net missed some species that were present in very low abundance. The drop net also underestimated the abundance of one benthic species. We recommend use of the drop net when studying ephemeral pools where habitat complexity changes through time or when precise estimates of density are required. Beach seining, which has minimal gear requirements, is recommended for situations in which only a general description of the community (species–abundance matrix) or species richness information is required. When the drop net is used, gill nets should also be used to collect large size-classes that are in low abundance.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".