Earth Metabolome and Digital Botanical Gardens Initiatives: Chemodiversity Knowledge for Biodiversity Conservation

The Earth Metabolome Initiative (EMI), and its pilot the Digital Botanical Gardens Initiative (DBGI), are developing transformative strategies to map and preserve the chemodiversity of all known life on Earth. While genomes are increasingly catalogued through projects such as the Earth BioGenome Project, we estimate that less than 0.02% of natural metabolites are currently described, despite their fundamental role in ecosystem functioning, biodiversity resilience, food security, and drug discovery . As the world faces accelerating biodiversity loss, documenting this chemical dimension is critical to better understand the biosphere functions and orient biodiversity conservation projects. We explore how large-scale metabolome digitization, through robust sample collection workflows, establishment of knowledge graphs, and machine learning approaches can open new pathways for biodiversity research and policy. We will highlight the development of the Earth Metabolome Data Portal, a reliable software designed to track information "from the field to the graph" and discuss how the DBGI, piloted in Swiss botanical gardens, and now extended to Kew Royal Botanical Gardens and Botanical Gardens Prague, is pioneering scalable workflows for in situ sampling, high-throughput metabolomics, and open data integration. We will address the value of chemodiversity for understanding biodiversity and for its contributions to human health and livelihoods, the importance of open and CARE-ing science in enabling these initiatives, and ways that semantic web technologies and machine learning can support predictions of relationships, functions, and novel metabolites. 

We will address how to envision future biodiversity scenarios using chemical as well as genetic and ecological understanding; how open science, and semantic web technologies can be integrated to ensure reproducibility and transparency of chemo- and biodiversity-related knowledge sharing, and how chemodiversity-informed models can support biodiversity monitoring, conservation prioritization, sustainable agriculture, and drug discovery .