From Microbial Traits to Predictive Management of Multifunctional Soils

Abstract

Soil microbial ecology is data‐rich yet struggles to predict ecosystem functions under ongoing environmental change, including climate warming, altered precipitation patterns, and land‐use intensification. The core challenge lies in linking micron‐scale microbial metabolism to field‐scale outcomes. We argue that progress requires: (i) shifting from taxonomic inventories to functional traits as predictors; (ii) integrating machine learning with ecological theory, rigorous uncertainty quantification, and cross‐validation; (iii) developing hybrid models that merge mechanistic understanding with data‐driven approaches; and (iv) building new theoretical and mathematical frameworks to bridge spatiotemporal scales. We distinguish soil function (ecosystem‐level outcomes) from microbial function (organismal or community‐level activities), as the relationship between them remains poorly understood. We focus on agricultural soils as a primary application domain, given their global significance for food security, climate mitigation, and the urgent need for management‐relevant predictions. Realising predictive microbiology demands standardised data, harmonised metadata, and transdisciplinary collaboration among soil scientists, ecologists, microbiologists, bioinformaticians, and data scientists. This commentary outlines a pathway from descriptive characterisation toward predictive, microbially informed management of multifunctional agricultural soils.