Author Identifiers

Oscar Serrano

https://orcid.org/0000-0002-5973-0046

Collection Type

Dataset

Upload Date

2018

School or Research Centre

School of Science / Centre for Marine Ecosystems Research

Publisher

Dryad

Description

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 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 standardised methods for sampling programs to quantify soil C stocks at national scales.

Additional Information

This dataset was originally published at:

https://doi.org/10.5061/dryad.qj472r2

DOI

10.5061/dryad.qj472r2

Language

Eng

File Format(s)

.xls

File Size

683 KB

Viewing Instructions

AUS_BlueCarbon_SoilCore_Data

This dataset contains the soil core data from the Australian-wide dataset used for the within core sampling design methods.

VIC_BlueCarbon_SoilCore_Data

This file contains the soil core data from the Victorian blue carbon sampling. This dataset was used to conduct all the spatial sampling analyses.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Public Domain Dedication 1.0 License.

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Life Sciences Commons

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