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hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBARROSO‐BERGADA, Didac
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorPAUVERT, Charlie
hal.structure.identifierSanté et agroécologie du vignoble [UMR SAVE]
dc.contributor.authorVALLANCE, Jessica
hal.structure.identifierSanté et agroécologie du vignoble [UMR SAVE]
dc.contributor.authorDELIERE, Laurent
hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBOHAN, David
hal.structure.identifierInteractions Arbres-Microorganismes [IAM]
dc.contributor.authorBUÉE, Marc
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVACHER, C.
dc.date.issued2021
dc.identifier.issn1755-098X
dc.description.abstractEnEnvironmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next‐Generation Biomonitoring proposes the use of this data to reconstruct ecological networks in real‐time and then compute network‐level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (1) the replicability of DNA‐based networks in the absence of ecosystem change; and, (2) the benefits and shortcomings of community‐ and network‐level properties for monitoring change. We selected crop‐associated microbial networks as a case study since they support disease regulation services in agroecosystems and analyzed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network‐level properties, especially β‐properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, that differed between agricultural systems. These findings complemented those obtained with community‐level data, that showed, in particular, a greater microbial diversity in the organic system. The limitations of network‐level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and, (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding datasets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.
dc.description.sponsorshipBiosurveillance Next-Gen des changements dans la structure et le fonctionnement des écosystèmes - ANR-17-CE32-0011
dc.description.sponsorshipInitiative d'excellence de l'Université de Bordeaux - ANR-10-IDEX-0003
dc.description.sponsorshipCEnter of the study of Biodiversity in Amazonia - ANR-10-LABX-0025
dc.language.isoen
dc.publisherWiley/Blackwell
dc.subjectEcologie des communautés
dc.subject.enEnvironmental DNA
dc.subject.enMetabarcoding
dc.subject.enCommunity ecology
dc.subject.enEcosystem services
dc.subject.enMicrobial networks
dc.subject.enNetwork inference
dc.subject.enNetwork comparison
dc.title.enMicrobial networks inferred from environmental DNA data for biomonitoring ecosystem change: strengths and pitfalls
dc.title.enMicrobial networks for biomonitoring
dc.typeArticle de revue
dc.identifier.doi10.1111/1755-0998.13302
dc.subject.halSciences de l'environnement/Biodiversité et Ecologie
bordeaux.journalMolecular Ecology Resources
bordeaux.page762-780
bordeaux.volume21
bordeaux.peerReviewedoui
hal.identifierhal-03049733
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03049733v1
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