Climate Change Impacts on a Tropical Fishery Ecosystem: Implications and Societal Responses
Bibliographic record
Abstract
The present study aimed to map out the current threats and anticipated impacts of climate change on the most important hilsa shad (Tenualosa ilisha) fishery and the associated fishing communities based on fieldwork in six coastal fishing communities. To collect empirical data, individual interviews, focus group discussions, oral history, and key informant interviews were conducted. To supplement the empirical findings, time-series data of cyclones and sea-borne depressions in the Bay of Bengal were also analyzed. Analysis of secondary data regarding climate change-induced events and regional studies suggested that the biophysical conditions of the Bay of Bengal are likely to be aggravated in the future, potentially causing more frequent extreme events and affecting the livelihoods of coastal fishing communities in Bangladesh. The fisher respondents revealed that the main target hilsa shad fishery is particularly vulnerable to climate change in terms of alterations to migration patterns and breeding and growth performance. The fishers reported constant climate-related risks because they live in seafront locations, exposed to extreme events, and their occupation entails risky sea fishing. Fishers claimed that they often need return to the coast due to unsuitable weather conditions related to cyclones and frequent tropical depressions, which can cause financial losses or even causalities. Such events negatively affect fishers’ livelihoods, and wellbeing. To cope with the impacts of climate change the fishers have adopted various strategies at both sea fishing and household levels. However, these strategies only support the fishers in terms of immediate survival; they are not enough for long-term resilience. To improve the resilience of the hilsa fishers, the study argues for the implementation the Small-Scale Fisheries Guidelines (SSF Guidelines), which call for longer-term development goals, including in the immediate relief phase, and rehabilitation, reconstruction, and recovery to reduce vulnerabilities to climate and anthropogenic risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".