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

doi:10.1016/j.tree.2017.03.001

Metadatos

Mostrar el registro completo del ítem

Licencia de uso del documento

http://creativecommons.org/licenses/by-sa/

BOHAN, David
Agroécologie [Dijon]

VACHER, Corinne
Biodiversité, Gènes & Communautés [BioGeCo]

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

Idioma

Article de revue

Este ítem está publicado en

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

Elsevier

Resumen en inglés

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.< Leer menos

Palabras clave

biosurveillance

séquençage adn

changement d'ecosystème

DOI

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

Proyecto ANR

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

Orígen

Importado de Hal

Centros de investigación

BioGeCo (Biodiversité Gènes & Communautés) - UMR 1202