Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis
MASSOL, Francois
Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 [Evo-Eco-Paléo (EEP)]
Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 [Evo-Eco-Paléo (EEP)]
PLANTEGENEST, Manuel
Institut de Recherche pour le Développement [IRD [Nouvelle-Calédonie]]
Institut de Génétique, Environnement et Protection des Plantes [IGEPP]
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Institut de Recherche pour le Développement [IRD [Nouvelle-Calédonie]]
Institut de Génétique, Environnement et Protection des Plantes [IGEPP]
Language
en
Article de revue
This item was published in
Advances in Ecological Research. 2018, vol. 58, p. 1-62
Elsevier
English Abstract
Ecological 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 ...Read more >
Ecological 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.Read less <
English Keywords
Ecological network
food web
next-generation sequencing
molecular approach
metabarcoding
machine learning
ecosystem services
environmental changes
robustness
biomonitoring
Origin
Hal imported