4.5 Article

Sedimentary Factors are Key Predictors of Carbon Storage in SE Australian Saltmarshes

Journal

ECOSYSTEMS
Volume 19, Issue 5, Pages 865-880

Publisher

SPRINGER
DOI: 10.1007/s10021-016-9972-3

Keywords

carbon sequestration; blue carbon; saltmarsh; salt marsh; spatial variability; geomorphic setting; ecosystem services; tidal marshes

Categories

Funding

  1. CSIRO Coastal Carbon Cluster and South East Local Land Services
  2. Australian Research Council DECRA Fellowship [DE13010 1084]

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Although coastal vegetated ecosystems are widely recognised as important sites of long-term carbon (C) storage, substantial spatial variability exists in quantifications of these 'blue C' stocks. To better understand the factors behind this variability we investigate the relative importance of geomorphic and vegetation attributes to variability in the belowground C stocks of saltmarshes in New South Wales (NSW), southeast Australia. Based on the analysis of over 140 sediment cores, we report mean C stocks in the surface metre of sediments (mean +/- SE = 164.45 +/- 8.74 Mg C ha(-1)) comparable to global datasets. Depth-integrated stocks (0-100 cm) were more than two times higher in fluvial (226.09 +/- 12.37 Mg C ha(-1)) relative to marine (104.54 +/- 7.11) geomorphic sites, but did not vary overall between rush and non-rush vegetation structures. More specifically, sediment grain size was a key predictor of C density, which we attribute to the enhanced C preservation capacity of fine sediments and/or the input of stable allochthonous C to predominantly fine-grained, fluvial sites. Although C density decreased significantly with sediment depth in both geomorphic settings, the importance of deep C varied substantially between study sites. Despite modest spatial coverage, NSW saltmarshes currently hold approximately 1.2 million tonnes of C in the surface metre of sediment, although more C may have been returned to the atmosphere through habitat loss over the past approximately 200 years. Our findings highlight the suitability of using sedimentary classification to predict blue C hotspots for targeted conservation and management activities to reverse this trend.

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