Community-based Fisheries Management Approach Adopted in Bangladesh

Aims: To promote the sustainable use of inland fisheries resources by empowering communities to manage their own resources.

Study Design: An investigation in the impact of the nationwide Community Based Fisheries Management (CBFM) approach to determine whether or not the approach was successful with respect to the management of floodplain-river fishery.

Place and Duration of Study: The study comprised community managed fisheries (sites) located in five different inland water habitat types in Bangladesh for the period 1997-2005.

Methodology: The assessment employed species-wise catch and gear-wise effort data sampled bi-monthly under catch assessment survey (CAS). Using quantitative indicators of fish production, abundance and biodiversity, the performance of community managed fisheries at up to 86 sites across the country was compared with that of fisheries managed under the existing government-driven regime using contingency table analysis and ANOVA.

Results: Production was found to have increased significantly through time at CBFM sites but not significantly more than at the control sites. However, annual changes in fish abundance were significantly higher at CBFM compared to control sites. In contrast, fish abundance at control sites was found to have decreased significantly through time. Changes in biodiversity were also found to be both positive at CBFM sites and significantly greater than control sites. Changes in fish abundance and fishing intensity explained much (60%) of the variation in fish production. Less (up to a maximum of 24%) of the total variation in the fish abundance and biodiversity indicators could be explained by the type of management although the presence or absence of closed seasons was significant in both cases. Fish sanctuaries had no detectable effects on management performance.

Conclusion: Community-based fisheries management appears to perform significantly better than the existing management regime in Bangladesh. Existing information sharing networks could support experimentation and learning under future initiatives.