| dc.description.abstract | 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. | es_ES |
