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 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m−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.
Highlights
Mangrove forests, occupying less than 14 million ha (Giri et al 2011), just 2.5% of the size of the Amazon rainforest, provide a broad array of ecosystem services (Barbier et al 2011)
Spatial modelling of soil organic carbon In order to maximize the utilization of available soil carbon data, we developed a machine learning-based model of organic carbon density (OCD) which models OCD as a function of depth (d), an initial estimate of the 0–200 cm organic carbon stock (OCS) from the global SoilGrids 250 m model (Hengl et al 2017), and a suite of spatially explicit covariate layers (Xp): OCD(xyd) = d + OCSSG + X1(xy) +X2(xy) + ...Xp(xy) where OCSSG is the aggregated organic carbon stock estimated for 0–200 cm depth using global SoilGrids 250 m approach down-sampled from 250 m–30 m resolution, and xyd are the 3D coordinates northing easting and soil depth
Model results The random forest model was successful in capturing the major variation in OCD across the mangrove database (figure 1(a)) with an R2 of 0.84 and a root mean square error (RMSE) of 6.9 kg m−3 compared to the mean OCD value of 29.6 kg m−3
Summary
Mangrove forests, occupying less than 14 million ha (Giri et al 2011), just 2.5% of the size of the Amazon rainforest, provide a broad array of ecosystem services (Barbier et al 2011). Mangroves are critical nursery habitats for fish, birds and marine mammals (Mumby et al 2004, Nagelkerken et al 2008), act as effective nutrient filters (Robertson and Phillips 1995), buffer coastal communities from storm surges (Gedan et al 2011) and support numerous rural economies (Spalding et al 2014, Temmerman et al 2013). These ecosystem service benefits have been valued at an average of 4200 US$ ha−1 yr−1 in Southeast Asia (Brander et al 2012). The major drivers of loss are conversion for aquaculture, especially shrimp farming, agriculture and urban development (Alongi 2002, Valiela et al 2001, Spalding et al 2010, Richards and Friess 2016) but loss due to extreme climatic events are becoming more common (Duke et al 2017)
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