Practices and attitudes of Indian catch-and-release anglers: identifying opportunities for advancing the management of recreational fisheries
Bibliographic record
Abstract
Recreational fishing, a popular leisure activity, can potentially support conservation of species and provide socio-economic benefits to local economies. Nonetheless, there are ecological concerns regarding this pastime, especially in developing economies such as India where little information is available concerning impacts on fish populations. An online survey targeting recreational anglers practicing catch-and-release (C&R) in India (n=200) revealed 25 states/union territories as prime angling locations, with Mahseer (Tor spp.) constituting the main target species group (53%). Angling season (28%) and pristine river surroundings (14%) were stated as major factors important for angling, while overfishing (31%) and the use of illegal fishing techniques were highlighted as chief threats to fish species. Respondents were in favour of strictly adhering to C&R guidelines (23%), controlling illegal fishing techniques and pollution (18%) and spreading awareness and education among local stakeholders (18%) to protect fish. Over 75% of respondents were also willing to contribute both their time and money to conservation initiatives focusing on key sport fish. With anglers targeting multiple fishing locations throughout India, there is a need for consistent governance structures and policy instruments to support the development of sustainable recreational fisheries while minimising conflict with other stakeholder groups. The impacts of C&R fisheries interactions on individuals and populations (especially for threatened species) need to be studied in the Indian context. Direct participation by recreational anglers in C&R research could strengthen broader conservation and management initiatives throughout the country by increasing stewardship.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".