FPU2025 - Solving the link between diversity and ecosystem functioning: from single cells to global scale rates

The relationship between taxonomic and functional diversity is essential to understand how communities drive ecosystem functioning. However, rates of ecosystem-scale functions remain difficult to predict from traditional taxonomic and functional diversity studies. Integrating cytomic, genomic and transcriptomic data, image analysis and artificial intelligence to identify and classify the microbes, and to link image-based information with spectral signatures and potential or realized metabolism (-omics data) will provide a new framework to address this yet unresolved question. Microbial cultures and natural communities sampled from contrasting environments will be analysed with a state-of-the-art spectral flow cytometer sorter to deliver a strong dataset to design, train, validate, test and evaluate convolutional neural network performance for image and spectral-based microbial and physiological status recognition. Sorted cells will be used for -omics sequencing and microscopic taxonomic verification. Network-based integrative multi-omics will be used to identify functional traits, molecular markers of physiology and metabolism, and taxa key to ecosystem functioning.