Adaptive Co‐Management and Future Resilience of Coastal Winter Migratory Bag Net Fisheries in Indian Sundarbans: Ensuring Livelihood Sustainability Amidst Climate Change
Bibliographic record
Abstract
ABSTRACT Winter Migratory Bagnet fishery (WBNF) in Indian Sundarbans contributes significantly to local food security and livelihoods, yet they are increasingly threatened by rising sea levels, declining fish stocks, and recurring cyclones. The main objective of the study is assessing its vulnerabilities and focusing on the adaptive response strategies employed by stakeholders to mitigate the challenges to sustain fisheries and fisher livelihoods. Baseline data were collected from 2019 to 2024 to assess species diversity and fishery dynamics. The findings reveal that WBNF not only provides a steady source of fish protein and income for thousands of households but also serves as a governance model for similar ecosystems, showcasing how community‐based practices, traditional knowledge, and adaptive management can sustain fisheries under ecological and socio‐economic pressures. Research further evaluates the vulnerability of Bagnet fisheries in response to current cyclonic events and climate change, which impacts reduced catches and also the damage to fishing crafts and gears. The Livelihood Vulnerability Score for winter bagnet fisheries in the Sundarbans ranged from 0.253 at Kalisthan to 0.402 at Fraserganj, indicating an overall high‐risk situation faced by fishing communities. Several environmental and socio‐economic drivers such as sea level rise, dependence on middlemen, decrease in species richness, decrease in Catch Per Unit Effort, shift in fish spawning season, changes in rainfall patterns, different adaptation strategies, and so on. influencing the sustainability of these fisheries were also identified and documented for the first time. To address these challenges, the study highlights adaptive management approaches, including stronger community participation, livelihood diversification, and institutional support.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".