Microbial communities play a crucial role in ecosystem processes, yet their composition is often overlooked in ecosystem models. This review examines how microbial communities respond to disturbances and whether their composition is resistant, resilient, or functionally redundant. The study finds that most microbial groups are sensitive to disturbance and do not quickly recover, suggesting that changes in microbial communities can directly affect ecosystem processes. The authors propose a framework to incorporate microbial community composition into ecosystem models, emphasizing the need for more empirical data linking microbial phylogeny, physiological traits, and disturbance responses. Microbial diversity is vast, with estimates ranging from thousands to millions of species in a few grams of soil. This diversity makes it challenging to model microbial communities, as it is impractical to track each taxon. Ecosystem models often "black box" microbiology, simplifying microbial processes into kinetic constants and response functions. However, this approach may lead to inaccurate predictions of ecosystem processes, as microbial composition can influence these processes. The study highlights that microbial communities are often sensitive to disturbances such as elevated CO₂, temperature changes, and nutrient additions. While some microbial groups may be resilient and recover quickly, the overall composition is generally not resilient. Functional redundancy, where different taxa perform similar functions, may help maintain ecosystem processes even if microbial composition changes. However, recent studies suggest that many microbial communities are not functionally redundant, and changes in composition can affect ecosystem processes. The authors propose a simple model to incorporate microbial composition into ecosystem models, considering physiological traits and disturbance responses. This model helps predict how microbial processes respond to disturbances, even in diverse communities. The study emphasizes the need for more empirical data on microbial phylogeny, physiological traits, and disturbance responses to improve predictive models of ecosystem processes under global change.Microbial communities play a crucial role in ecosystem processes, yet their composition is often overlooked in ecosystem models. This review examines how microbial communities respond to disturbances and whether their composition is resistant, resilient, or functionally redundant. The study finds that most microbial groups are sensitive to disturbance and do not quickly recover, suggesting that changes in microbial communities can directly affect ecosystem processes. The authors propose a framework to incorporate microbial community composition into ecosystem models, emphasizing the need for more empirical data linking microbial phylogeny, physiological traits, and disturbance responses. Microbial diversity is vast, with estimates ranging from thousands to millions of species in a few grams of soil. This diversity makes it challenging to model microbial communities, as it is impractical to track each taxon. Ecosystem models often "black box" microbiology, simplifying microbial processes into kinetic constants and response functions. However, this approach may lead to inaccurate predictions of ecosystem processes, as microbial composition can influence these processes. The study highlights that microbial communities are often sensitive to disturbances such as elevated CO₂, temperature changes, and nutrient additions. While some microbial groups may be resilient and recover quickly, the overall composition is generally not resilient. Functional redundancy, where different taxa perform similar functions, may help maintain ecosystem processes even if microbial composition changes. However, recent studies suggest that many microbial communities are not functionally redundant, and changes in composition can affect ecosystem processes. The authors propose a simple model to incorporate microbial composition into ecosystem models, considering physiological traits and disturbance responses. This model helps predict how microbial processes respond to disturbances, even in diverse communities. The study emphasizes the need for more empirical data on microbial phylogeny, physiological traits, and disturbance responses to improve predictive models of ecosystem processes under global change.