Constraints to climate change adaptation and livelihood challenges: perspectives from the Sundarbans fishers' community in Bangladesh
Bibliographic record
Abstract
Fishers' are first-line observers of changes in the Sundarbans region and are among the first to be affected by the changes that occurred. In the Sundarbans fishers' communities, transformations of society have always been a part of life. In contrast, environmental changes were always interim and reversible, allowing them to understand and identify with the Sundarbans ecosystems as food and life providers. In this study, fishers' observations on climate change impacts and their livelihoods were compiled and analysed using a structured questionnaire in accordance with the tenets of grounded theory. The observations of fishers from the region of the Sundarbans demonstrated a rise in the frequency of extreme weather events caused by rising temperatures and changes in the weather pattern. Physical components such as rainfall, coastal erosion, sea-level rise, siltation alterations in fish species distribution ranges, and migratory spawning behaviours were also affected by changes in the region's weather and climate. As salinity levels rose, the diversity and productivity of river ecosystems were affected, particularly in the most vulnerable areas. As a result, river freshwater flow decreased over time. A high rate of siltation in rivers and river mouths was considered another major concern that led to seasonal flooding due to its low freshwater upstream flow rate. The Sundarbans region provides a vast array of resources for diversifying fishers' livelihoods, but climate change is diminishing these alternatives due to more frequent catastrophic events. Specifically, climate change limits the resilience of fishers' communities, restricting opportunities for diversification or forcing them to leave their homes or villages. Climate change generated an environment that was generally unfavourable for all fishing communities. In order to survive in an unfavourable environment, the social well-being of fishers (mostly women and children) was negatively impacted by a variety of challenges, including disease, lack of potable water, malnutrition, sanitary difficulties, lack of electricity, lack of food and clothing, lack of proper medical care, and so on. To evaluate the effects of climate change on fisheries in the study area, the biodiversity, abundance, and production of most freshwater species are drastically reduced due to the destruction of spawning grounds, a transition in the spawning season, and the obstruction of fish migration. The findings of this study show that the climate and livelihood conditions of fishers in the Sundarbans region have changed significantly over the past few decades. Regardless of GOs and NGOs taking the required steps, proper implementation of interdisciplinary adaptive policy and regular monitoring in the Sundarbans fisher's community in Bangladesh could effectively reduce climate change impacts and improve livelihood conditions. Asian J. Med. Biol. Res. 2022, 8 (2), 103-114
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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 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".