Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding

  • Yinqiu Ji
    State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan 650223 China
  • Louise Ashton
    Environmental Futures Centre and Griffith School of Environment Griffith University Nathan Queensland 4111 Australia
  • Scott M. Pedley
    School of Environmental Sciences University of East Anglia Norwich Research Park Norwich Norfolk NR47TJ UK
  • David P. Edwards
    Woodrow Wilson School of Public and International Affairs Princeton University Princeton New Jersey 08544 USA
  • Yong Tang
    Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Meng La Yunnan 666303 China
  • Akihiro Nakamura
    Environmental Futures Centre and Griffith School of Environment Griffith University Nathan Queensland 4111 Australia
  • Roger Kitching
    Environmental Futures Centre and Griffith School of Environment Griffith University Nathan Queensland 4111 Australia
  • Paul M. Dolman
    School of Environmental Sciences University of East Anglia Norwich Research Park Norwich Norfolk NR47TJ UK
  • Paul Woodcock
    Institute of Integrative and Comparative Biology and Faculty of Biological Sciences University of Leeds Leeds West Yorkshire LS29JT UK
  • Felicity A. Edwards
    Institute of Integrative and Comparative Biology and Faculty of Biological Sciences University of Leeds Leeds West Yorkshire LS29JT UK
  • Trond H. Larsen
    The Betty and Gordon Moore Center for Ecosystem Science and Economics Conservation International Arlington Virginia 22202 USA
  • Wayne W. Hsu
    Department of Ecology Evolution, and Environmental Biology Columbia University New York City New York 10027 USA
  • Suzan Benedick
    School of Sustainable Agriculture Universiti Malaysia Sabah Sandakan Sabah 9000 Malaysia
  • Keith C. Hamer
    Institute of Integrative and Comparative Biology and Faculty of Biological Sciences University of Leeds Leeds West Yorkshire LS29JT UK
  • David S. Wilcove
    Woodrow Wilson School of Public and International Affairs Princeton University Princeton New Jersey 08544 USA
  • Catharine Bruce
    School of Biological Sciences University of East Anglia Norwich Research Park Norwich Norfolk NR47TJ UK
  • Xiaoyang Wang
    State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan 650223 China
  • Taal Levi
    Cary Institute of Ecosystem Studies Millbrook New York 12545 USA
  • Martin Lott
    School of Computing Sciences University of East Anglia Norwich Research Park Norwich Norfolk NR47TJ UK
  • Brent C. Emerson
    Island Ecology and Evolution Research Group IPNA‐CSIC Tenerife Canary Islands 38206 Spain
  • Douglas W. Yu
    State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan 650223 China

書誌事項

公開日
2013-08-04
権利情報
  • http://creativecommons.org/licenses/by/3.0/
DOI
  • 10.1111/ele.12162
公開者
Wiley

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説明

<jats:title>Abstract</jats:title><jats:p>To manage and conserve biodiversity, one must know what is being lost, where, and why, as well as which remedies are likely to be most effective. Metabarcoding technology can characterise the species compositions of mass samples of eukaryotes or of environmental <jats:styled-content style="fixed-case">DNA</jats:styled-content>. Here, we validate metabarcoding by testing it against three high‐quality standard data sets that were collected in Malaysia (tropical), China (subtropical) and the <jats:styled-content style="fixed-case">United Kingdom</jats:styled-content> (temperate) and that comprised 55,813 arthropod and bird specimens identified to species level with the expenditure of 2,505 person‐hours of taxonomic expertise. The metabarcode and standard data sets exhibit statistically correlated alpha‐ and beta‐diversities, and the two data sets produce similar policy conclusions for two conservation applications: restoration ecology and systematic conservation planning. Compared with standard biodiversity data sets, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, less reliant on taxonomic expertise and auditable by third parties, which is essential for dispute resolution.</jats:p>

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