Integrating Multi-scale Data for Soil Biodiversity and Ecosystem Function Across Diverse Landscapes: From Omics to Artificial Intelligence

Global ecosystems are undergoing rapid transformation driven by intensified land use, climate change, and anthropogenic pressures. These shifts threaten soil biodiversity - the "biological engine" that underpins essential ecosystem functions, including nutrient cycling, carbon sequestration, water regulation, and contaminant remediation. While urban and peri-urban areas represent extreme examples of these transformations - projected to house two-thirds of the global population by 2050 - the need to understand and monitor soil health across all biomes, from managed agricultural lands to pristine natural reserves, has never been more urgent.Recent technological advances have revolutionized our capacity to characterize soil biodiversity and ecosystem functions. Omics technologies (genomics, metagenomics, proteomics, and metabolomics) now enable comprehensive profiling across biological scales, from microbial communities (bacteria, fungi) to soil fauna (nematodes, arthropods, and earthworms). Simultaneously, remote sensing platforms provide landscape-scale environmental context, while in-situ sensors capture real-time physicochemical dynamics. Artificial intelligence (AI) and machine learning (ML) offer powerful tools for pattern recognition and predictive modeling across these heterogeneous data types. However, integrating these multi-scale datasets into a cohesive understanding of ecosystem health presents significant methodological challenges.The complexity of soil ecosystems - characterized by high spatial heterogeneity and intricate biotic interactions - is further compounded by specific stressors such as habitat fragmentation, pollution gradients, altered hydrology, and the "heat island" effects typical of urbanized landscapes. This Research Topic addresses the critical need for methodological advances in multi-scale data integration. We aim to provide a platform for developing robust approaches to soil biodiversity assessment that can inform evidence-based land management, conservation, and sustainable urban planning across a wide range of global ecosystems.This Research Topic aims to address the challenge of selecting and integrating heterogeneous, multi-scale datasets to assess soil biodiversity and predict ecosystem functions in dynamic environments. We seek to bridge the gap between technological innovation and ecological application across all landscape types.Specifically, we welcome contributions that:- Develop frameworks for evaluating data quality, compatibility, and complementarity across omics technologies, remote sensing, and AI-driven analytical approaches.- Advance methodological approaches for multi-source data integration that link taxonomic and functional diversity to ecosystem processes across various biological and geographical scales.- Identify robust soil biodiversity indicators that predict ecosystem functions under diverse pressures, including (but not limited to) those found in urban, agricultural, and industrial environments.- Translate integrated biodiversity-function relationships into actionable metrics and decision-support tools for ecosystem service assessment and land-use management.We welcome contributions addressing methodological innovations in multi-scale data integration for soil biodiversity assessment in natural, managed, and anthropogenically stressed environments. Specific themes include:- Novel applications of omics technologies for characterizing soil biodiversity across biological scales in diverse biomes.- Machine learning and AI approaches for pattern recognition, predictive modeling, and integration of heterogeneous data sources (e.g., linking DNA sequences with satellite imagery).- Data integration frameworks evaluating compatibility across omics, remote sensing, and in-situ sensor platforms.- Methodological approaches linking diversity to function, specifically exploring how microbial and faunal communities drive ecosystem services.- Strategies for addressing spatial and temporal variability in soil monitoring, with a focus on high-heterogeneity environments like urban centers or fragmented landscapes.- Case studies demonstrating integrated data approaches for soil management, restoration, and conservation.- Standardization protocols and best practices for multi-source data harmonization and open-science initiatives.We encourage submissions of Original Research, Methods, Reviews, Systematic Reviews, Perspectives, and Opinion articles that advance theoretical frameworks or provide practical solutions for data-driven soil biodiversity assessment and ecosystem management. Topic editors: Dalma Emoke Kovacs, Sergi Maicas, Jaume Segura-Garcia, Melinda Haydee Kovacs Source: https://www.frontiersin.org/research-topics/80132/integrating-multi-scale-data-for-soil-biodiversity-and-ecosystem-function-across-diverse-landscapes-from-omics-to-artificial-intelligence