Suchergebnisse

Mangroves are among the most threatened and rapidly disappearing natural environments worldwide. In addition to supporting a wide range of other ecological and economic functions, mangroves store considerable carbon. Here, we consider the global economic potential for protecting mangroves based exclusively on their carbon. We develop unique high-resolution global estimates (5′ grid, about 9 × 9 km) of the projected carbon emissions from mangrove loss and the cost of avoiding the emissions. Using these spatial estimates, we derive global and regional supply curves (marginal cost curves) for avoided emissions. Under a broad range of assumptions, we find that the majority of potential emissions from mangroves could be avoided at less than $10 per ton of CO2. Given the recent range of market price for carbon offsets and the cost of reducing emissions from other sources, this finding suggests that protecting mangroves for their carbon is an economically viable proposition. Political-economy considerations related to the ability of doing business in developing countries, however, can severely limit the supply of offsets and increases their price per ton. We also find that although a carbon-focused conservation strategy does not automatically target areas most valuable for biodiversity, implementing a biodiversity-focused strategy would only slightly increase the costs.

With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kgm*3). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86–729 Mg C ha*1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30–122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products from this work are intended to serve nations seeking to include mangrove habitats in payment-for- ecosystem services projects and in designing effective mangrove conservation strategies.

With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kgm*3). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86–729 Mg C ha*1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30–122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products from this work are intended to serve nations seeking to include mangrove habitats in payment-for- ecosystem services projects and in designing effective mangrove conservation strategies.