A phenotype‐based framework to measure functional redundancy in microbial ecosystems

Abstract

Functional redundancy is a key biological concept that offers critical insights into microbial ecosystem stability and responses to environmental perturbations. Existing methods to quantify redundancy typically rely on gene annotations as functional units, limiting their ability to capture complex or partially characterised traits and making analyses from metagenomic data—often fragmented or incomplete—challenging. Here, we present miFRED (microbial Functional REDundancy), an accessible computational approach to quantify functional redundancy of microbial phenotypes and communities based on phenotypic rather than genetic annotations. Leveraging 86 machine‐learning‐predicted phenotypes as functional units, miFRED bridges the genotype–phenotype gap and enables interpretable and accurate assessment of redundancy at both community and single phenotype levels, as demonstrated on simulated communities. As case studies, miFRED was applied to over 300 metagenomic samples from anaerobic systems, to a longitudinal dataset of a gut microbiome subject to antibiotic treatment and 100 marine and soil metagenomes. Across these applications, miFRED elucidated relationships between redundancy, community structure and functional organisation. Comparison to gene annotation‐based analyses highlighted the improved interpretability of phenotype‐based redundancy estimates, which allowed clearer resolution of functional changes associated with microbiome perturbation and recovery in the human gut dataset. In anaerobic digestion communities, analyses considering environmental conditions uncovered changes in redundancy, complexity and functional profiles with rising temperatures, with more extreme conditions favouring simpler, more redundant communities and shifts in redundant functions. Using phenotype‐based functional units, miFRED provides a practical and general framework for consistent functional redundancy analyses across environments and metagenomic datasets. By interpreting redundancy patterns in the context of community organisation and environmental factors, it enables exploration of microbial functional landscapes across environmental conditions in both natural and engineered ecosystems.