The Chalan <i>beel</i> in Bangladesh: Habitat and biodiversity degradation, and implications for future management
Bibliographic record
Abstract
Abstract This study provides an overview of the current resources and fisheries status of the Chalan beel , and identifies the scope of opportunities to evolve existing fisheries management strategies, focusing on fish biodiversity conservation. Chalan beel is the largest, most important watershed in the North Central Bangladesh, and covers an area of about 375 km 2 during the monsoon season. The watershed serves about 5 million people, predominantly through fisheries and agricultural activities. Although considerably degraded over time, Chalan beel still supports a diverse ichthyofauna of major importance to the local economy and people's livelihoods. The Chalan beel area incorporates 21 rivers and 93 smaller seasonal beels of varying size. Most of the rivers and beels are at risk of partial or total degradation, as a result of agricultural encroachment, siltation and other anthropogenic activities. During the study, ≈ 83% of the rivers, and 68% of the beels in the lean season, shrunk to 0–5% of their maximum (monsoon) water‐spread area during the dry season (i.e. critical levels for survival of many fish species). A fish catch survey was carried out in the main fishing season (July to December) over two successive years in the three most important catchment areas; namely, the Gumani, Baral and Katagang Rivers. A total of 72 sampling episodes yielded 9818 individuals, representing 114 fish species. The most abundant fish species during the 2‐year study period were punti ( Puntius sophore and Puntius ticto ), followed by chanda ( Chanda nama and Parambassis ranga ), chapila ( Gudusia chapra ) and tengra ( Mystus vittatus ). The study results indicate at least 19 fish species once considered abundant can now be classified as threatened, with a possibility of local extinction. The mean fish catch was 2.08 ± 0.49 kg, and 1.29 ± 0.32 kg per hour per person, by suti jal (set bag net – the most common gear used in the beel ) in 2005 and 2006, respectively, with a significant ( P < 0.05) variation among catches in months and years at all three sample sites. In 2005–2006, the annual fish production in Chalan beel was 12 217 tonnes, being less than half of the production observed in 1982. The stakeholders identified several major causes they thought were directly responsible for the continued decrease in fish production and biodiversity of the beel area. Several management strategies were suggested by the same stakeholders, and the findings are discussed in the context of overall developments in the Chalan beel fisheries related to habitat and biodiversity management.
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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 | 0.002 |
| 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) | 0.004 | 0.001 |
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".