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Trends Ecol Evol. 2017 Jul;32(7):477-487. doi: 10.1016/j.tree.2017.03.001. Epub 2017 Mar 27.

Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks.

Author information

1
Agroécologie, AgroSup Dijon, INRA, University of Bourgogne Franche-Comté, F-21000 Dijon, France. Electronic address: David.Bohan@inra.fr.
2
BIOGECO, INRA, University of Bordeaux, 33615 Pessac, France.
3
Computational Bioinformatics Laboratory, Department of Computing, Imperial College London, London, SW7 2AZ, UK.
4
Syngenta Crop Protection AG, PO Box 4002, Basel, Switzerland.
5
School of Biological Sciences, University of Essex, Colchester, Essex, CO4 3SQ, UK.
6
Department of Life Sciences, Imperial College London, Silwood Park Campus, Berkshire, SL5 7PY, UK.

Abstract

We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth's major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change.

PMID:
28359573
DOI:
10.1016/j.tree.2017.03.001
[Indexed for MEDLINE]
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