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Mangroves are among the most threatened and rapidly vanishing natural environments worldwide. They provide a wide range of ecosystem services and have recently become known for their exceptional capacity to store carbon. Research shows that mangrove conservation may be a lowcost means of reducing CO2 emissions. Accordingly, there is growing interest in developing market mechanisms to credit mangrove conservation projects for associated CO2 emissions reductions. These efforts depend on robust and readily applicable, but currently unavailable, localized estimates of soil carbon. Here, we use over 900 soil carbon measurements, collected in 28 countries by 61 independent studies, to develop a global predictive model for mangrove soil carbon. Using climatological and locational data as predictors, we explore several predictive modeling alternatives, including machine-learning methods. With our predictive model, we construct a global dataset of estimated soil carbon concentrations and stocks on a high-resolution grid (5arc min). We estimate that the global mangrove soil carbon stock is 5.00±0.94Pg C (assuming a 1 meter soil depth) and find this stock is highly variable over space. The amount of carbon per hectare in the world’s most carbon-rich mangroves (approximately 703±38MgC ha 1 ) is roughly a 2.6±0.14 times the amount of carbon per hectare in the world’s most carbon-poor mangroves (approximately 272±49 Mg C ha 1 ). Considerable within country variation in mangrove soil carbon also exists. In Indonesia, the country with the largest mangrove soil carbon stock, we estimate that the most carbon-rich mangroves contain 1.5±0.12 times as much carbon per hectare as the most carbon-poor mangroves. Our results can aid in evaluating benefits from mangrove conservation and designing mangrove conservation policy. Additionally, the results can be used to project changes in mangrove soil carbon stocks based on changing climatological predictors, e.g. to assess the impacts of climate change on mangrove soil carbon stocks. S Online supplementary data available from stacks.iop.org/ERL/9/104013/mmedia

A global predictive model of carbon in mangrove soils