Ecoregional scale seagrass mapping: A tool to support resilient MPA network design in the Coral Triangle

Seagrass beds are of exceptional economic, ecological and social value in the Coral Triangle. The large number of people who live close to the coast and rely directly on marine resources for food and income paradoxically increases the value of, but also the threats to, these ecosystems. A key strategy of the Coral Triangle Initiative is to protect shallow coastal ecosystems through the design and implementation of resilient networks of marine protected areas (MPAs). This strategy requires accurate spatial data on the distribution and extent of coastal habitats (coral reef, seagrass and mangrove) at scales which match conservation planning decisions. In the Coral Triangle, seagrass distribution maps are not readily available at ecoregional scales. The Lesser Sunda ecoregion, extending from Bali, Indonesia to Timor-Leste, is one of 11 ecoregions of the Coral Triangle and a high priority for conservation and sustainable management of marine resources. To support the design of a resilient MPA network for the Lesser Sunda ecoregion, a seagrass distribution map was generated based on Landsat imagery, literature review and groundtruth data. Seagrass beds were estimated to cover an area of 273km² at an overall accuracy of 78%. Use of the seagrass distribution map in the MPA design improved the habitat representation and connectivity - key criteria for resilient MPA design. The final MPA design included 80km² of seagrass beds, with more than half the beds adjacent to coral reefs and mangroves. This study demonstrates the effective use of Landsat imagery and remote sensing techniques to derive ecoregional scale seagrass maps supporting MPA network design. •First consistent ecoregional scale seagrass map for the Lesser Sunda Ecoregion.•Capabilities of remote sensing for seagrass quality data maps at relevant scales.•Case-study: map was instrumental to identify potential conservation priority sites.•Methods can be replicated worldwide for large-scales and data deficient scenarios.