Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks. (July 2017) - Bohan, David A.; Vacher, Corinne; Tamaddoni-Nezhad, Alireza; Raybould, Alan; Dumbrell, Alex J.; Woodward, Guy
Abstract : We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth's major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change. Trends: Next-generation sequencing (NGS) can used to sample nucleic acids in the environment for the presence of species and ecological functions. Machine-learning software can search for 'the ghosts of interactions past' in the raw NGS data to reconstruct the networks of ecological interactions. NGS data and machine-learning in the cloud could be combined in the next generation of global biomonitoring. Autonomous NGS samplers would sequence and upload data for ecological network reconstruction, to detect ecosystem change accurately, cheaply and generically. Reconstruction of highly replicated networks of ecological interaction, using this next generation of biomonitoring, would provide general ecological information for ecosystem comparison and a revolution in the breadth of our understanding of the ecology of ecosystem change.