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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2019-294
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-294
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 09 Aug 2019

Submitted as: research article | 09 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Drivers and modelling of blue carbon stock variability

Carolyn J. Ewers Lewis1, Mary A. Young2, Daniel Ierodiaconou2, Jeffrey A. Baldock3, Bruce Hawke3, Jonathan Sanderman4, Paul E. Carnell1, and Peter I. Macreadie1 Carolyn J. Ewers Lewis et al.
  • 1School of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, 221 Burwood Highway, Burwood, Victoria 3125, Australia
  • 2School of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, Princes Highway, Warrnambool, Victoria 3280, Australia
  • 3Commonwealth Scientific and Industrial Organisation, Agriculture and Food, PMB 2, Glen Osmond, South Australia 5064, Australia
  • 4Woods Hole Research Center, 149 Woods Hole Road, Falmouth MA 02540, USA

Abstract. Tidal marshes, mangrove forests, and seagrass meadows are important global carbon (C) sinks, commonly referred to as coastal blue carbon. However, these ecosystems are rapidly declining with little understanding of what drives the magnitude and variability of C associated with them, making strategic and effective management of blue C stocks challenging. In this study, our aims were threefold: (1) identify ecological, geomorphological, and anthropogenic variables associated with C stock variability in blue C ecosystems; (2) create a predictive model of blue C stocks; and, (3) map regional blue C stock magnitude and variability. We had the unique opportunity of using a high-spatial-density C stock dataset from 96 blue C ecosystems across the state of Victoria, Australia, integrated with spatially explicit environmental data to reach these aims. We used an information theoretic approach to create, average, validate, and select the best general linear mixed effects model for predicting C stocks across the state. Ecological drivers (i.e. ecosystem type or dominant species/ecological vegetation class) best explained variability in C stocks, relative to geomorphological and anthropogenic drivers. Of the geomorphological variables, distance to coast, distance to freshwater, and slope best explained C stock variability. Anthropogenic variables were of least importance. We estimated over 2.31 million Mg C stored in the top 30 cm of sediment in coastal blue C ecosystems in Victoria, 88 % of which was contained within four major coastal areas due to the extent of blue C ecosystems (~ 87 % of total blue C ecosystem area). Regionally, these data can inform conservation management, paired with assessment of other ecosystem services, by enabling identification of hotspots for protection and key locations for restoration efforts. Globally, these methods can be applied to identify relationships between environmental drivers and C stocks to produce predictive C stock models at scales relevant for resource management.

Carolyn J. Ewers Lewis et al.
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Carolyn J. Ewers Lewis et al.
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Short summary
Blue carbon ecosystems – tidal marsh, mangrove, and seagrass – serve as important organic carbon sinks, mitigating impacts of climate change. We utilized a robust regional carbon stock dataset to identify ecological, geomorphological, and anthropogenic drivers of carbon stock variability and create high-spatial-resolution predictive carbon stock maps. This work facilitates strategic conservation and restoration, and applied to other regions can improve management of blue carbon ecosystems.
Blue carbon ecosystems – tidal marsh, mangrove, and seagrass – serve as important organic carbon...
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