Mangrove restoration potential: A global map highlighting a critical opportunity

Kurzfassung

Rapid losses of mangroves over the past 50 years have had negative consequences on the environment, climate, and humanity, through diminished benefits such as carbon storage, coastal protection and fish production. Restoration of mangrove forests is possible, and has already been undertaken in many settings, but such efforts have been piecemeal, and many have failed. The current work describes the findings from a new effort to locate and map, on a global scale, the places where mangroves can be restored, and to calculate the potential benefits from such restoration. As a framework for the study, a new global typology of mangroves has been developed with Mangrove areas classified into deltaic, estuarine, lagoonal and fringing systems, and subsequent analyses based on the resulting 6000 individual typological units. In total, some 8,120 km2, or 6%, of former mangrove area are considered restorable. An expert-derived model for restorability has been developed based on the key environmental components, tidal range; recent sea Level rise; projected future sea level rise; recent change in sediments; time since loss; average size of loss patches; and the proximity of loss areas to remaining mangroves, that influence the ease of restoration. Using this model, some 6,665 km2 are considered highly restorable. The work overlays current and potential mangroves areas with models of ecosystem services to assess potential benefits from restoration, including carbon sequestration in aboveground biomass amounting to 69 million tonnes of carbon, the saving of 296 million tonnes of soil carbon stocks through a combination of avoided emissions and sequestration and the addition of commercial fisheries species in mangrove waters totalling 23 trillion young-of year finfish and 40 trillion crabs, shrimp and molluscs. The output of the work the Mangrove Restoration Potential Map, provides a critical tool for encouraging restoration and enabling robust, data-driven policy changes and investments.