Optimal soil carbon sampling designs to achieve cost-effectiveness: A case study in blue carbon ecosystems

Mary A. Young*, Peter I. Macreadie, Clare Duncan, Paul E. Carnell, Emily Nicholson, Oscar Serrano, Carlos M. Duarte, Glenn Shiell, Jeff Baldock, Daniel Ierodiaconou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Researchers are increasingly studying carbon (C) storage by natural ecosystems for climate mitigation, including coastal 'blue carbon' ecosystems. Unfortunately, little guidance on how to achieve robust, cost-effective estimates of blue C stocks to inform inventories exists. We use existing data (492 cores) to develop recommendations on the sampling effort required to achieve robust estimates of blue C. Using a broad-scale, spatially explicit dataset from Victoria, Australia, we applied multiple spatial methods to provide guidelines for reducing variability in estimates of soil C stocks over large areas. With a separate dataset collected across Australia, we evaluated how many samples are needed to capture variability within soil cores and the best methods for extrapolating C to 1 m soil depth. We found that 40 core samples are optimal for capturing C variance across 1000's of kilometres but higher density sampling is required across finer scales (100-200 km). Accounting for environmental variation can further decrease required sampling. The within core analyses showed that nine samples within a core capture the majority of the variability and log-linear equations can accurately extrapolate C. These recommendations can help develop standardized methods for sampling programmes to quantify soil C stocks at national scales.

Original languageEnglish (US)
Article number0416
JournalBiology Letters
Volume14
Issue number9
DOIs
StatePublished - Sep 1 2018

Keywords

  • carbon stock
  • mangrove
  • sampling design
  • seagrass
  • tidal marsh

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)

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