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

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journal contribution
posted on 2018-09-01, 00:00 authored by Mary YoungMary Young, Peter MacreadiePeter Macreadie, Clare Duncan, Paul CarnellPaul Carnell, Emily Nicholson, Oscar Serrano, Carlos M Duarte, Glenn Shiell, Jeff Baldock, Daniel IerodiaconouDaniel Ierodiaconou
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.

History

Journal

Biology letters

Volume

14

Issue

9

Publisher

The Royal Society Publishing

Location

London, Eng.

eISSN

1744-957X

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, The Author(s)