Preparing for a changing future in recreational fisheries: 100 research questions for global consideration emerging from a horizon scan
Bibliographic record
Abstract
Recreational fisheries hold immense ecological, social, and economic value. The management of these fisheries is increasingly important as we move forward in the Anthropocene. Recreational fisheries managers face several challenges as fisheries often involve diverse social and ecological systems comprised of complex feedback and stakeholder motivations and needs. Here, we used a horizon scanning exercise to yield 100 research questions related to recreational fisheries science and management in the Anthropocene. Initial research questions ( n = 205) were collected from recreational fisheries experts (i.e., stakeholders, managers, researchers) from various sectors (i.e., industry, government, NGOs) and geographic locations (14 countries: Australia, Brazil, Canada, Czech Republic, Germany, Italy, New Zealand, Norway, South Africa, Spain, Sweden, Switzerland, United Kingdom, USA). These questions were subsequently categorized, thematized, and refined by our authorship team, eventually yielding what we considered to be the top 100 research questions of relevance to management of recreational fisheries. The key themes include: human dimensions; bioeconomics; resource monitoring and data acquisition; governance; management—regulatory actions; management—stock and habitat enhancement; catch-and-release; impacts of recreational fisheries on populations, communities and ecosystems; threats and sustainability; and angler outreach, education and engagement. It is our intention that this comprehensive and forward-looking list will create a framework to guide future research within this field, and contribute to evidence-based recreational fisheries management and policy.
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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 | 0.044 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".