Heintze, PatrikPatrikHeintzeHassani, AmirhosseinAmirhosseinHassaniPanagos, PanosPanosPanagosOrgiazzi, AlbertoAlbertoOrgiazziKöninger, JuliaJuliaKöningerLabouyrie, MaëvaMaëvaLabouyrieShokri, NimaNimaShokri2026-01-122026-01-122025-04Soil Health Now! 2025https://hdl.handle.net/11420/60716Diverse microbial communities are fundamental to healthy and productive soils, accommodating essential ecosystem services including nutrient cycling, organic matter decomposition, land-atmosphere carbon exchange, water and climate regulation, and contaminant control. The immense taxonomic and functional diversity of soil microorganisms makes deciphering the intricate interactions between soil, its inhabitants, and the far-extending effects for life on earth a complex challenge. Advances in the analysis of eDNA, like metabarcoding to determine community composition from soil samples, enable large-scale assessments across manifold habitat conditions. Based on the LUCAS 2018 soil biodiversity datasets, we aim to (i) identify key drivers shaping soil microbial community composition, and (ii) quantify marginal changes in soil microbial abundance, richness, and diversity forced by soil properties, climatic, and anthropogenic pressures. To improve the understanding of interactions between external drivers and soil microbial communities, we employ machine learning algorithms, in particular generalized additive models for increased interpretability (Hassani et al., 2024), to investigate and identify the parameters influencing the observed soil microbial diversity and richness in the LUCAS datasets. Our modeling efforts will enable us to predict changes in soil biodiversity under the influence of anthropogenic pressures and projected climate scenarios. Such an analysis can further support decision-making in land management with potential policy implications on a pan-European scale.enhttps://creativecommons.org/licenses/by/4.0/soil microbial communitiesSoil HealthNatural Sciences and Mathematics::550: Earth Sciences, GeologyNatural Sciences and Mathematics::579: Microorganisms, Fungi and AlgaeComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.31: Machine LearningConference Posterhttps://doi.org/10.15480/882.16440https://pretalx.earthmonitor.org/soil-health-now-2025/talk/L9REDV/10.15480/882.16440Conference Poster