Soil biodiversity plays a central role in regulating ecosystem functioning and maintaining soil health through its influence on nutrient cycling, carbon dynamics, soil structure formation and water regulation. At the same time, environmental change and land-use intensification alter soil physicochemical conditions and vegetation dynamics, with cascading consequences for belowground communities and ecosystem functioning. Despite increasing recognition of the importance of soil biodiversity within European soil health initiatives, identifying scalable and operational indicators capable of capturing soil ecological responses across contrasting pedoclimatic regions remains a major challenge.
Using harmonized datasets generated within the SOB4ES project, we integrated soil biodiversity indicators, soil physicochemical properties, land-use information and Earth Observation-derived climatic, vegetation and topographic variables across European sites. The analyses employed machine-learning approaches and explainable artificial intelligence methods to investigate large-scale relationships among soil biodiversity, environmental gradients and ecosystem service-related functions under different climatic and land-use contexts.
Across multiple organism groups, diversity-based metrics generally showed stronger and more consistent relationships with environmental gradients than abundance- or density-based indicators, supporting their relevance for large-scale soil health assessment frameworks. Soil pH, soil organic carbon, moisture conditions and soil texture parameters emerged as dominant cross-taxa predictors, while climatic and vegetation-related variables derived from Earth Observation data explained a substantial proportion of spatial variation in belowground biodiversity patterns. However, the influence of environmental predictors frequently differed among land-use types, indicating that biodiversity responses are strongly context-dependent and shaped by interactions among soil conditions, vegetation structure and management intensity.

Overall, the integration of harmonized biodiversity observations, remote-sensing data and machine-learning approaches demonstrated strong potential for supporting spatially explicit soil health monitoring and the identification of robust biological indicators under global change and land-use pressures across Europe.

Acknowledgments: The work and all the authors were supported by the Horizon Europe project SOB4ES under Grant Agreement No. 101112831. We acknowledge all participating investigators from the SOB4ES consortium who contributed to the existing sample collection and the field sampling for the generation of the spatial database used in the current analysis. Partners from KNAW, UVIGO, NUID UCD, UNICT, KU Leuven, CU, ARO, IBB, UL, UoC, SLU, EFWSL, Airfield, MFO, and INRAe provided these contributions.

Extreme weather events, including droughts, heatwaves, intense rainfall and episodic non-seasonal frosts, are increasingly disrupting soil functionality and agricultural production stability. In Mediterranean agricultural landscapes, these pressures interact with long-standing soil degradation processes such as declining organic matter, erosion, salinisation, compaction and reduced biological activity, creating new risks for food security and land management. There is therefore a growing need for integrated, spatially explicit tools that can assess not only the current condition of soils, but also their capacity to buffer climatic stress, recover after disturbance and support stable crop production over time.

The Soil Resilience and Food Security Index (SRFSI) is proposed as a composite framework for evaluating the resilience of agricultural landscapes under climate-extreme conditions. The index integrates four complementary dimensions: Soil Resilience Capacity, representing the ability of soils to sustain carbon storage, water regulation, nutrient cycling, structural stability and biological functioning; Biological and Agroecological Management, capturing the contribution of practices such as compost and manure application, biofertilisers, microbial inoculants, cover crops, crop-residue retention, reduced tillage, diversified rotations and landscape elements that enhance biodiversity and water regulation; Climate-Extreme Resilience, assessing exposure and response to drought, heatwaves, heavy rainfall, erosion risk, salinity stress and crop-relevant non-seasonal frosts; and Food Security Stability, reflecting yield stability, stress-year yield retention, production reliability and post-event recovery.

Through SRFSI, a framework is proposed that can function both as a baseline diagnostic approach and as a scenario-based tool for assessing soil and production resilience. By comparing index scores under current conditions, climate-extreme scenarios and post-intervention conditions, resilience loss and adaptation gain can be quantified across different crops, soil types and management systems. Remote-sensing proxies, open soil and climate datasets, field measurements and farm-management information are combined to support implementation at field, farm, municipal and regional scales. This approach is intended to support the identification of vulnerable agricultural areas, the evaluation of biological and agroecological soil-management interventions, and the design of strategies that help maintain productive, resilient and food-secure agricultural systems under increasing climate uncertainty.