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

Edith Cowan University
Carlos Duarte (Aggregated by) Clare Duncan (Aggregated by) Daniel Ierodiaconou (Aggregated by) Emily Nicholson (Aggregated by) Glenn Shiell (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.5061/dryad.qj472r2&rft.title=Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems [dataset]&rft.identifier=10.5061/dryad.qj472r2&rft.publisher=Edith Cowan University&rft.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.&rft.creator=Carlos Duarte&rft.creator=Clare Duncan&rft.creator=Daniel Ierodiaconou&rft.creator=Emily Nicholson&rft.creator=Glenn Shiell&rft.creator=Jeff Baldock&rft.creator=Mary Young&rft.creator=Oscar Serrano&rft.creator=Paul Carnell&rft.creator=Peter Macreadie&rft.date=2021&rft.relation=https://doi.org/10.1098/rsbl.2018.0416&rft.relation=https://ro.ecu.edu.au/ecuworkspost2013/5149/&rft_rights= http://creativecommons.org/publicdomain/zero/1.0/&rft_subject=carbon stock&rft_subject=mangrove&rft_subject=power analysis&rft_subject=sampling design&rft_subject=seagrass&rft_subject=tidal marsh&rft_subject=Life Sciences&rft.type=dataset&rft.language=English Access the data

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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.

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This dataset was originally published at:

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

This dataset is part of a larger collection

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133.77514,-25.2744

133.775136,-25.274398

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