Gear-Type Influences on Fish Catch and a Wetland Fish Index in Georgian Bay Wetlands
Bibliographic record
Abstract
Abstract The Laurentian Great Lakes are managed by many jurisdictions that use a variety of survey methods and gear types to monitor fish assemblages in coastal marshes. Lack of standardization in these methods makes it difficult for organizations to compare data because of inherent biases in gear types. Of equal concern is the uncertainty of the effect of gear bias on fish-related index scores for ecosystem health. Our first objective was to investigate whether there were differences in catch data between two commonly used sampling gears: fyke nets (FN) and boat electrofishing (EF). Secondly, we investigated whether catch differences in data associated with gear biases can lead to significant differences when these data are used to generate scores for biotic indices such as the published Wetland Fish Index (WFI). We sampled 26 coastal wetlands in Georgian Bay (Lake Huron) in the summers of 2004 and 2005. A majority (73%) of the more than 10,000 fish were caught by FN; this gear also captured a greater number of species and functional taxa and selected for larger piscivores. By comparison, EF captured larger invertivores. Fyke nets were more selective for individuals from the Centrarchidae, Cyprinidae, and Ictaluridae families, while EF was more effective for darters (e.g., the Iowa darter Etheostoma exile and johnny darter E. nigrum) and white suckers Catostomus commersonii. Despite these biases in catch data, we obtained statistically similar WFI scores with both gear types. Therefore, although the fish abundance and species composition information collected from FN and EF are not directly comparable, when necessary they can be used interchangeably to generate a fish-based index of ecosystem health. Received May 16, 2011; accepted December 8, 2011
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".