A Comparison of Methods for Sampling Fish Diversity in Shallow Offshore Waters of Large Rivers
Bibliographic record
Abstract
Abstract Few studies of fish assemblages have been conducted in large rivers owing to the difficulties of sampling such complex systems. We evaluated the effectiveness of six different gear types (seine nets, boat electrofishers, hoop nets, Windermere traps, trap nets, and minnow traps) in sampling the fish assemblage at 30 sites in the shallow offshore waters of the middle Detroit River in July and August 2003. A total of 2,449 fish representing 38 species in 15 families were captured by seining (1,293 fish, 29 species), boat electrofishing (398 fish, 23 species), hoop nets (524 fish, 26 species), and Windermere traps (234 fish, 14 species). Trap nets and minnow traps were not effective in sampling offshore littoral sites. Significantly higher fish species richness and abundance were obtained and more unique species were captured by seine nets than by any other gear type. When effort is constant, the highest richness and abundance are obtained by seine nets. Windermere traps produced significantly lower abundance and richness than all other gear types, but proportionally more benthic species. Total species accumulation rates were not markedly reduced when Windermere trap data were excluded. Use of additional Windermere traps at each site could increase abundance, but samples taken by Windermere traps had the lowest rarefied richness among gear types at any level of abundance. Nonmetric multidimensional scaling showed that seine-net catches, which were dominated by midwater schooling species (brook silverside Labidesthes sicculus, emerald shiner Notropis atherinoides, and mimic shiner N. volucellus), were most dissimilar from Windermere trap catches, which were dominated by centrarchids. Seine nets were the most effective gear for sampling offshore waters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".