Derocles, SAP and Bohan, DA and Dumbrell, AJ and Kitson, JJN and Massol, F and Pauvert, C and Plantegenest, M and Vacher, C and Evans, DM (2018) Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis. In: Networks of Invasion: Empirical Evidence and Case Studies. Advances in Ecological Research, 57 . Academic Press, pp. 1-62. ISBN 978-0-12-813328-6. Official URL: https://doi.org/10.1016/bs.aecr.2017.12.001
Derocles, SAP and Bohan, DA and Dumbrell, AJ and Kitson, JJN and Massol, F and Pauvert, C and Plantegenest, M and Vacher, C and Evans, DM (2018) Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis. In: Networks of Invasion: Empirical Evidence and Case Studies. Advances in Ecological Research, 57 . Academic Press, pp. 1-62. ISBN 978-0-12-813328-6. Official URL: https://doi.org/10.1016/bs.aecr.2017.12.001
Derocles, SAP and Bohan, DA and Dumbrell, AJ and Kitson, JJN and Massol, F and Pauvert, C and Plantegenest, M and Vacher, C and Evans, DM (2018) Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis. In: Networks of Invasion: Empirical Evidence and Case Studies. Advances in Ecological Research, 57 . Academic Press, pp. 1-62. ISBN 978-0-12-813328-6. Official URL: https://doi.org/10.1016/bs.aecr.2017.12.001
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 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.
Item Type: | Book Section |
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Uncontrolled Keywords: | Ecological network; Food web; Next-generation sequencing; Molecular approach; Metabarcoding; Machine learning; Ecosystem services; Environmental changes; Robustness; Biomonitoring |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Life Sciences, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 22 Apr 2021 12:13 |
Last Modified: | 16 May 2024 19:18 |
URI: | http://repository.essex.ac.uk/id/eprint/21424 |