Prevalence and Implications of “Must‐Kill” Angling Regulations for the Management of Invasive Fishes
Bibliographic record
Abstract
ABSTRACT Freshwater biodiversity is increasingly threatened by invasive species, which can disrupt native fish populations and the fisheries they support. Must‐kill regulations, which prohibit the live release of invasive fish caught by recreational anglers, are a management strategy that can be implemented to limit the negative effects of invasive fish populations. Our review of angling regulations around the globe revealed that must‐kill regulations for numerous species were frequently enacted in countries including Canada, Japan, and the USA. Suggested benefits of must‐kill regulations included limiting invasive species population size and preventing their dispersal, among others. While these benefits were plausible, we found no rigorous assessments of their effectiveness. Moreover, must‐kill regulations can introduce concerns such as angler opposition and species misidentification. Here, we bolstered sparse evidence with anecdotes and perspectives to identify potential advantages and drawbacks of must‐kill regulations. We also provided guidelines for implementing must‐kill regulations that emphasize strategies to increase the likelihood of success while minimizing unintended consequences. Specific guidelines vary depending on management objectives, but generally include a preliminary feasibility and risk assessment followed by post‐implementation monitoring of efficacy and consequences.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".