Accidental Bait: Do Deceased Fish Increase Freshwater Turtle Bycatch in Commercial Fyke Nets?
Bibliographic record
Abstract
Abstract Bycatch of turtles in passive inland fyke net fisheries has been poorly studied, yet bycatch is an important conservation issue given the decline in many freshwater turtle populations. Delayed maturity and low natural adult mortality make turtles particularly susceptible to population declines when faced with additional anthro-pogenic adult mortality such as bycatch. When turtles are captured in fyke nets, the prolonged submergence can lead to stress and subsequent drowning. Fish die within infre-quently checked passive fishing nets and dead fish are a potential food source for many freshwater turtles. Dead fish could thus act as attractants and increase turtle captures in fishing nets. We investigated the attraction of turtles to decomposing fish within fyke nets in eastern Ontario. We set fyke nets with either 1 kg of one-day or five-day decomposed fish, or no decomposed fish in the cod-end of the net. Decomposing fish did not alter the capture rate of turtles or fish, nor did it alter the species composition of the catch. Thus, reducing fish mortality in nets using shorter soak times is unlikely to alter turtle bycatch rates since turtles were not attracted by the dead fish. Interestingly, turtle bycatch rates increased as water temperatures did. Water temperature also influences turtle mortality by affecting the duration turtles can remain submerged. We thus suggest that submerged nets to either not be set or have reduced soak times in warm water conditions (e.g., [20 C) as turtles tend to be captured more frequently and cannot withstand prolonged submergence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".