Learning ecological networks from next-generation sequencing data

Abstract : Species diversity, and the various interactions that occur between species, supports ecosystems functioning and benefit human societies. Monitoring the response of species interactions to human alterations of the environment is thus crucial for preserving ecosystems. Ecological networks are now the standard method for representing and simultaneously analyzing all the interactions between species. However, deciphering such networks requires considerable time and resources to observe and sample the organisms, to identify them at the species level and to characterize their interactions. Next-generation sequencing (NGS) techniques, combined with network learning and modelling, can help alleviate these constraints. They are essential for observing cryptic interactions involving microbial species, as well as short-term interactions such as those between predator and prey. Here, we present three case studies, in which species associations or interactions have been revealed with NGS. We then review several currently available statistical and machine-learning approaches that could be used for reconstructing networks of direct interactions between species, based on the NGS co-occurrence data. Future developments of these methods may allow us to discover and monitor species interactions cost-effectively, under various environmental conditions and within a replicated experimental design framework.
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Corinne Vacher, Alireza Tamaddoni-Nezhad, Stefaniya Kamenova, Nathalie Dubois Peyrard, Yann Moalic, et al.. Learning ecological networks from next-generation sequencing data. Ecosystem Services: From Biodiversity to Society, Part 2, 54, 2016, Advances In Ecological Research, ⟨10.1016/bs.aecr.2015.10.004⟩. ⟨hal-01533861⟩



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