Next Generation Biomonitoring of change in ecosystem structure and function – NGB

In a just published Opinion paper in Trends in Ecology & Evolution, we advocate that a next-generation, global-scale, ecological approach to biomonitoring will emerge in the coming decade, which can detect ecosystem change accurately, cheaply and generically. Next-generation sequencing (NGS) 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 in order to detect and predict ecosystem change.
In this Next Generation Biomonitoring (NGB) project, we will examine whether NGS samples from five distinct ecosystems undergoing global change can be used to reconstruct hypothetical networks of interaction using machine learning. We will then compare these reconstructed networks with the current state of knowledge for these systems to test whether NGS and machine learning approaches can be used to reconstruct valid ecological networks. These tests will include examining the NGS networks for specific, established interactions through to detailed comparisons against already-known ecological networks, built using classic network construction approaches. The five systems we will work on represent a cross-section of the organisational scales, drivers of change and data quality we would expect that a NGB approach could be applied to. From microbial interaction networks to macro-biome networks of interacting invertebrates, and across drivers of change such as invasion, disease, conservation, management and climate, the project will determine whether ecosystem change can be detected using an NGB approach. We will troubleshoot many of the technical, methodological and ecological problems facing the development of an NGB approach, such as the variable quality of NGS databases, taxa biases, identification errors, zero-rich data and asymmetric abundance distributions, and develop statistical approaches for detecting change and determining the size and power of biomonitoring programs.
Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction, using methods that we will develop in the project. 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.