Evidence Synthesis in Recreational Fisheries Science and Management
Bibliographic record
Abstract
Abstract Researchers have studied the behaviours of recreational fishers and conducted science to support recreational fisheries management for over 50 years, resulting in a large volume of literature. Yet, evidence users (e.g., practitioners and fishery managers) rarely have the time to sift through and reconcile the literature, leading to biased approaches from selective use of evidence. To help evidence users make sense of these studies in a manner that minimizes bias, various evidence synthesis methods can be used. However, these methods are not all robust, can themselves be subject to bias, and their uncritical use could lead to poor management decisions and outcomes. Evidence synthesis has been applied in recreational fisheries management in several contexts (e.g., to understand how social–ecological systems theory applies to recreational fisheries; to evaluate the effectiveness of angler gear choice on biological outcomes for fish or the impact of recreation angling on other taxa than fish), yet is often conducted using less rigorous, informal synthesis methodologies. By contrast, meta-analysis to support stock assessment is reasonably common, but these approaches are uncommon in recreational fisheries. Here, we briefly review the suite of evidence-synthesis methods available, consider their strengths and weaknesses, and outline key steps involved in their conduct. We explore synthesis methods including traditional narrative literature reviews, systematic maps, rapid evidence syntheses, (quantitative) meta-analysis, and systematic reviews (which often include meta-analysis). In doing so, we also briefly review past evidence syntheses on this topic and summarize ways in which evidence synthesis can be best used to support recreational fisheries management in the future.
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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 | 0.207 | 0.517 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".