BOHAN, David; VACHER, Corinne; TAMADDONI-NEZHAD, Alireza; RAYBOULD, Alan; DUMBRELL, Alex J; WOODWARD, Guy

doi:10.1016/j.tree.2017.03.001

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BOHAN, David
Agroécologie [Dijon]

VACHER, Corinne
Biodiversité, Gènes & Communautés [BioGeCo]

TAMADDONI-NEZHAD, Alireza
Computational Bioinformatics Laboratory, Department of Computing

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Article de revue

Ce document a été publié dans

Trends in Ecology & Evolution. 2017, vol. 32, n° 7, p. 477-487

Elsevier

Résumé en anglais

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.< Réduire

Mots clés

biosurveillance

séquençage adn

changement d'ecosystème

DOI

http://dx.doi.org/10.1016/j.tree.2017.03.001

Project ANR

Predicting and enhancing the Resilience of European Agro-ecosystems to environmental change using crop Rotations - ANR-15-SUSF-0002

Origine

Importé de hal

Unités de recherche

BioGeCo (Biodiversité Gènes & Communautés) - UMR 1202