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Nat Ecol Evol. 2017 Jun 22;1(7):176. doi: 10.1038/s41559-017-0176.

Connecting Earth observation to high-throughput biodiversity data.

Author information

1
State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650203 China.
2
Environment Canada, Canadian Rivers Institute, Department of Biology, University of New Brunswick, PO Box 4400, Fredericton, New Brunswick E3B 5A3, Canada.
3
CSIRO Land and Water, Canberra, Australian Capital Territory 2601, Australia.
4
Department of Wildlife, Fish, &Conservation Biology, 1088 Academic Surge, One Shields Avenue, University of California Davis, Davis, California 95616, USA.
5
Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Str. 17, 10315 Berlin, Germany.
6
EvoGenomics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen K, Denmark.
7
School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk NR4 7TJ, UK.
8
Centre for Landscape and Climate Research and Leicester Institute for Space and Earth Observation (LISEO), University of Leicester, University Road, Leicester LE1 7RH, UK.
9
NERC National Centre for Earth Observation (NCEO) at University of Leicester, University Road, Leicester LE1 7RH, UK.
10
Center for International Forestry Research (CIFOR), PO Box 0113 BOCBD, Bogor 16000, Indonesia.
11
Balaton Limnological Institute, Centre for Ecological Research, Hungarian Academy of Sciences, Tihany 8237, Hungary.
12
Robert Koch Institut, Berlin 13353, Germany.
13
Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow G12 8QQ, UK.
14
Department of Geography, King's College London, Strand Campus, London WC2R 2LS, UK.
15
IPNA-CSIC, La Laguna, Tenerife, Canary Islands 38206, Spain.
16
NTNU University Museum, Norwegian University of Science and Technology, Trondheim 7491, Norway.
17
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University &Research, Wageningen, The Netherlands.
18
Centre for Ecology and Hydrology, Environment Centre Wales, Deiniol Road, Bangor LL57 2UW, UK.
19
Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, Nevada 89119, USA.
20
Department of Biosciences, University of Helsinki, Helsinki FI-00014, Finland.
21
Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim 7491, Norway.
22
Environment Department, University of York, York YO10 5NG, UK.
23
Museum für Naturkunde - Leibniz Institute for Evolution and Biodiversity Science, Berlin 10115, Germany.
24
Department of Wildland Resources, Utah State University, Logan, Utah 84322, USA.
25
Forestry Commission, Edinburgh EH12 7AT, UK.
26
Department of Environmental Science and Policy, George Mason University, Fairfax, Virginia 22030, USA.
27
Department of Life Sciences, Natural History Museum, London SW7 5BD, UK.
28
Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot SL5 7PY, UK.
29
NERC National Centre for Earth Observation (NCEO) at King's College London, Strand, London WC2R 2LS, UK.

Abstract

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services.

PMID:
28812589
DOI:
10.1038/s41559-017-0176
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