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dc.contributor.authorYoung, Mary A.
dc.contributor.authorMacreadie, Peter I.
dc.contributor.authorDuncan, Clare
dc.contributor.authorCarnell, Paul E.
dc.contributor.authorNicholson, Emily
dc.contributor.authorSerrano, Oscar
dc.contributor.authorDuarte, Carlos M.
dc.contributor.authorShiell, Glenn
dc.contributor.authorBaldock, Jeff
dc.contributor.authorIerodiaconou, Daniel
dc.date.accessioned2020-07-19T15:36:37Z
dc.date.available2020-07-19T15:36:37Z
dc.date.issued2018
dc.identifier.citationYoung, M. A., Macreadie, P. I., Duncan, C., Carnell, P. E., Nicholson, E., Serrano, O., Duarte, C. M., Shiell, G., Baldock, J., & Ierodiaconou, D. (2018). Supplementary material from "Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems". Figshare. https://doi.org/10.6084/M9.FIGSHARE.C.4227992.V2
dc.identifier.doi10.6084/m9.figshare.c.4227992.v2
dc.identifier.urihttp://hdl.handle.net/10754/664275
dc.description.abstractResearchers 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.
dc.publisherfigshare
dc.subjectEnvironmental Science
dc.titleSupplementary material from "Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems"
dc.typeDataset
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentMarine Science Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.institutionSchool of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, Geelong, Victoria, Australia mary.young@deakin.edu.au.
dc.contributor.institutionSchool of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, Geelong, Victoria, Australia.
dc.contributor.institutionSchool of Science, Centre for Marine Ecosystems Research, Edith Cowan University, Joondalup, Western Australia, Australia.
dc.contributor.institutionBMT Pty Ltd, Perth, Western Australia, Australia.
dc.contributor.institutionCSIRO Agriculture and Food, Glen Osmond, South Australia, Australia.
kaust.personDuarte, Carlos M.
dc.relation.issupplementtoDOI:10.1098/rsbl.2018.0416
display.relations<b> Is Supplement To:</b><br/> <ul> <li><i>[Article]</i> <br/> Young MA, Macreadie PI, Duncan C, Carnell PE, Nicholson E, et al. (2018) Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems. Biology Letters 14: 20180416. Available: http://dx.doi.org/10.1098/rsbl.2018.0416.. DOI: <a href="https://doi.org/10.1098/rsbl.2018.0416" >10.1098/rsbl.2018.0416</a> HANDLE: <a href="http://hdl.handle.net/10754/630571">10754/630571</a></li></ul>


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