High-throughput sequencing combined with machine learning and network analysis as a method for efficient biomonitoring of coastal and estuarine ecosystems

• Applicants: Professorin Dr. Sabine Filker; Professor Dr. Thorsten Stoeck
• Subject Area: Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions ; Bioinformatics and Theoretical Biology
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 432977076

Marine coastal and estuarine environments are dynamic and complex habitats that host a rich biodiversity and provide numerous ecosystem services. They are subjected to high anthropogenic pressure from industry, urban areas, agriculture and tourism. To maintain a mutually satisfying equilibrium between economic development and ecosystem health, frequent environmental monitoring to assess Ecological Quality Status (EQS) is necessary. Current monitoring strategies implemented in (inter)national directives rely on morphological identification of benthic macroinvertebrates. This is too expensive and time consuming for timely and accurate environmental management. Therefore, new, more efficient and less costly eDNA metabarcoding technologies are currently scrutinized for implementation in monitoring programs. We want to develop this technology further and exploit the power of metagenomics and metatranscriptomics for the identification of structural and functional genetic indicator signatures in benthic samples. Supervised machine learning and ecological network analyses will be used to exploit the obtained datasets and predict EQS from metagenomic and metatranscriptomic data.

• DFG Programme: Research Grants