14.5.4 Collaboration for shared aquatic ecosystems
Metric: Maintaining a local ecosystem
Collaborate with the local community in efforts to maintain shared aquatic ecosystems
Tripartite Collaboration for Seagrass Ecosystem Management in the Andaman Sea
The research project, "The Feasibility and Efficiency Enhancement of Seagrass Species for CO₂ Sequestration in Thailand," stands as an exemplary model of collaborative ecosystem management. Led by Assoc. Prof. Ponlachart Chotikarn of the Coastal Oceanography and Climate Change Research Center (COCC), Faculty of Environmental Management at Prince of Songkla University, this vital initiative is defined by its robust, active partnership with three key pillars: the Electricity Generating Authority of Thailand (EGAT), PSU’s academic research team, and community of Pu Island, Krabi.
This collaborative framework is central to securing the long-term health and sustainability of this shared aquatic ecosystem. The two-year project was strategically designed to move beyond conventional academic study, forging a genuine partnership with the local residents. This unique coalition—supported financially and strategically by EGAT—ensures that the project benefits from scientific rigor (PSU), essential resources (EGAT), and local ecological knowledge (Pu Island). Collaboration was paramount, with community participation prioritized in the planning and development of the conservation program at Koh Pu. This approach directly empowered local people and fishermen through community engagement for seagrass conservation and restoration are owned and managed by the community itself. This establishes the residents as the definitive primary stewards of their local marine environment. This spirit of collaboration extended to technology: low-cost seagrass nursery technology was developed and made fully accessible to the community. This joint intervention facilitates the residents’ direct involvement in improving seagrass growth and survival rates—a crucial step for accelerating ecosystem recovery with a scalable, locally-driven method.
