Seasonal patterns in bycatch composition and mortality associated with a freshwater hoop net fishery
Bibliographic record
Abstract
Abstract Although bycatch is well known and well studied in marine fisheries, comparatively little is known about bycatch in freshwater fisheries. Even basic information on bycatch composition and mortality in freshwater is unavailable, given that few inland jurisdictions require reporting of bycatch. A small‐scale inland hoop net fishery that targets pan fish (e.g. sunfish, Lepomis spp.) and operates primarily in the spring and fall was simulated in two lakes in south‐eastern Ontario to characterize both bycatch composition and mortality. We fished one lake in both spring and fall to compare catch rates, while in the other lake we set nets for 2 or 6 days during the spring to assess fish mortality associated with different net tending frequencies. In both lakes, bycatch consisted of gamefish, turtles (including several species at risk), and mammals. For fish, there was no difference in spring and fall catches. Turtles, however, were captured more often in spring. Fish mortality of both target and non‐target species increased from 0.3–0.9% to 3.0–3.7% (4–10 times) when set net duration increased from 2 to 6 days. Despite the provision of an air breathing space in our nets, we documented severe turtle mortality (33% in one lake) and all mammals died, suggesting that provision of air spaces is not always effective. Although all bycatch mortality is a concern, turtles are prone to population declines even with low levels of non‐natural mortality. As such, regulators may consider limiting commercial fishing to the fall in this region to reduce turtle captures. Seasonal restrictions on fishing or use of frequent net tending (e.g. < 2 days) will not prevent all turtle bycatch and therefore gear modifications should be investigated to further reduce turtle captures and mortality associated with hoop nets.
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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 | 0.000 |
| 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".