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Biomonitoring for the 21st century: integrating next-generation sequencing into ecological network analysis

hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorDEROCLES, Stéphane A.P.
hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBOHAN, David
hal.structure.identifierSchool of Biological Sciences
dc.contributor.authorDUMBRELL, Alex J.
hal.structure.identifierSchool of Natural and Environmental Sciences
dc.contributor.authorKITSON, James J.N.
hal.structure.identifierLaboratoire de Génétique et Evolution des Populations Végétales [GEPV]
dc.contributor.authorMASSOL, François
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorPAUVERT, Charlie
hal.structure.identifierInstitut de Génétique, Environnement et Protection des Plantes [IGEPP]
dc.contributor.authorPLANTEGENEST, Manuel
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVACHER, Corinne
hal.structure.identifierSchool of Natural and Environmental Sciences
dc.contributor.authorEVANS, Darren M.
dc.date.issued2018
dc.identifier.isbn978-0-12-813949-3
dc.description.abstractEnEcological network analysis (ENA) provides a mechanistic framework for describing complex species interactions, quantifying ecosystem services, and examining the impacts of environmental change on ecosystems. In this chapter, we highlight the importance and potential of ENA in future biomonitoring programs, as current biomonitoring indicators (e.g. species richness, population abundances of targeted species) are mostly descriptive and unable to characterize the mechanisms that underpin ecosystem functioning. Measuring the robustness of multilayer networks in the long term is one way of integrating ecological metrics more generally into biomonitoring schemes to better measure biodiversity and ecosystem functioning. Ecological networks are nevertheless difficult and labour-intensive to construct using conventional approaches, especially when building multilayer networks in poorly studied ecosystems (i.e. many tropical regions). Next-generation sequencing (NGS) provides unprecedented opportunities to rapidly build highly resolved species interaction networks across multiple trophic levels, but are yet to be fully exploited. We highlight the impediments to ecologists wishing to build DNA-based ecological networks and discuss some possible solutions. Machine learning and better data sharing between ecologists represent very important areas for advances in NGS-based networks. The future of network ecology is very exciting as all the tools necessary to build highly resolved multilayer networks are now within ecologists reach.
dc.description.sponsorshipPredicting and enhancing the Resilience of European Agro-ecosystems to environmental change using crop Rotations - ANR-15-SUSF-0002
dc.language.isoen
dc.source.titleNext Generation Biomonitoring: Part 1
dc.subject.ennext-generation sequencing
dc.subject.enmolecular approach
dc.subject.enmetabarcoding
dc.subject.enmachine learning
dc.subject.enecosystem services
dc.subject.enenvironmental changes
dc.subject.enrobustness
dc.subject.enbiomonitoring
dc.subject.enecological network
dc.subject.enfood web
dc.title.enBiomonitoring for the 21st century: integrating next-generation sequencing into ecological network analysis
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1016/bs.aecr.2017.12.001
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halSciences de l'environnement
dc.subject.halSciences du Vivant [q-bio]/Biologie végétale
bordeaux.page293 p.
bordeaux.volume58
bordeaux.title.proceedingNext Generation Biomonitoring: Part 1
hal.identifierhal-02790509
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02790509v1
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