Searching for responsible and sustainable recreational fisheries in the Anthropocene
Bibliographic record
Abstract
Recreational fisheries that use rod and reel (i.e., angling) operate around the globe in diverse freshwater and marine habitats, targeting many different gamefish species and engaging at least 220 million participants. The motivations for fishing vary extensively; whether anglers engage in catch-and-release or are harvest-oriented, there is strong potential for recreational fisheries to be conducted in a manner that is both responsible and sustainable. There are many examples of recreational fisheries that are well-managed where anglers, the angling industry and managers engage in responsible behaviours that both contribute to long-term sustainability of fish populations and the sector. Yet, recreational fisheries do not operate in a vacuum; fish populations face threats and stressors including harvest from other sectors as well as environmental change, a defining characteristic of the Anthropocene. We argue that the future of recreational fisheries and indeed many wild fish populations and aquatic ecosystems depends on having responsible and sustainable (R&S) recreational fisheries whilst, where possible, addressing, or at least lobbying for increased awareness about the threats to recreational fisheries emanating from outside the sector (e.g., climate change). Here, we first consider how the concepts of R&S intersect in the recreational fishing sector in an increasingly complex socio-cultural context. Next, we explore the role of the angler, angling industry and decision-makers in achieving R&S fisheries. We extend this idea further by considering the consequences of a future without recreational fisheries (either because of failures related to R&S) and explore a pertinent case study situated in Uttarakahand, India. Unlike other fisheries sectors where the number of participants is relatively small, recreational angling participants are numerous and widespread, such that if their actions are responsible, they have the potential to be a key voice for conservation and serve as a major force for good in the Anthropocene. What remains to be seen is whether this will be achieved, or if failure will occur to the point that recreational fisheries face increasing pressure to cease, as a result of external environmental threats, the environmental effects of recreational fishing and emerging ethical concerns about the welfare of angled fish.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".