Suchergebnisse

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.

Policies aiming to preserve vegetated coastal ecosystems (VCE; tidal marshes, mangroves and seagrasses) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here, we present organic carbon (C) storage in VCE across Australian climate regions and estimate potential annual CO2 emission benefits of VCE conservation and restoration. Australia contributes 5-11% of the C stored in VCE globally (70-185 Tg C in aboveground biomass, and 1,055-1,540 Tg C in the upper 1 m of soils). Potential CO2 emissions from current VCE losses are estimated at 2.1-3.1 Tg CO2-e yr-1, increasing annual CO2 emissions from land use change in Australia by 12-21%. This assessment, the most comprehensive for any nation to-date, demonstrates the potential of conservation and restoration of VCE to underpin national policy development for reducing greenhouse gas emissions. ; This project was supported by the CSIRO Marine and Coastal Carbon Biogeochemical Cluster, CSIRO Oceans and Atmosphere, the ECU Faculty Research Grant Scheme and Early Career Research Grant Schemes, UTS Plant Functional Biology and Climate Change Cluster, NSW Southeast Local Land Services, Department of Environment, Land, Water and Planning (DELWP), Parks Victoria, Victorian Coastal Catchment Management Authorities (GHCMA, CCMA, PPWCMA, WGCMA, EGCMA), University of Queensland Centennial Scholarship, Hodgkin Trust Scholarship, Australian Institute of Nuclear Science and Engineering, Northern Territory Government Innovation Grant, Australian Research Council (DE130101084, DE140101733, DE150100581, DE160100443, DE170101524, DP150103286, DP150102092, DP160100248, DP160100248, DP180101285, LE140100083, LE170100219, LP150100519, LP160100242 and LP110200975), the Generalitat de Catalunya (MERS 2014 SGR-1356), the ICTA 'Unit of Excellence' (MinECo, MDM2015-0552), Obra Social "LaCaixa", SUMILEN, CTM 2013-47728-R, Ministry of Economy and Competitiveness and UKM-DIP-2017-005. The authors are grateful to G. Bastyan, D. Kyrwood, G. Davis, J. Bongiovanni, A. Jesse, Q. Hua, A. Zawadzki, J. Gudiño, P. Bray, H. Markham, M. Lepore, K-le Gómez-Cabrera, and J. Pandolfi for their help in field and/or laboratory tasks.