A Novel Method to Simultaneously Estimate Bacterial Respiration and Growth from Oxygen Dynamics

Abstract Bacterial growth and respiration are fundamental metabolic processes that drive energy transformation and allocation within organisms and impact carbon sequestration at the ecosystem scale. However, these traits are usually measured independently, bacterial growth is quantified with endpoint biomass measurements while respiration is determined by monitoring oxygen or carbon dioxide. Because the two physiological traits are collected at different temporal and volumetric scales (hours-to-days for growth versus minutes-to-hours for respiration), reconciling them is challenging and often introduces scale-mismatch bias, obscuring causal links between metabolism and environmental drivers. In this study, we develop a novel method for quantifying the rates of bacterial growth and respiration from a single dissolved-oxygen time series. Our approach introduces a model that couples exponential biomass growth with biomass-specific respiration, enabling simultaneous inference of growth rate and respiration rate from each oxygen trajectory. We applied our high throughput method to 15 bacterial taxa isolated from natural environments. Our approach yielded growth estimates in close agreement with measurements based on popular methods using optical density or flow cytometry (R² 0. 9) with no evidence of taxon-specific bias. We also tested our approach in quantifying the effects of temperature on respiration, growth and carbon use-efficiency in Pseudomonas sp. Our method yielded typical unimodal thermal response curves for growth and respiration where rates were highest at moderate temperatures, while carbon-use efficiency increased with temperature, peaked around the growth thermal optimum (∼30–35 °C), and declined at the highest temperature. By quantifying respiration and growth within a single assay and in high throughput, our approach effectively enables measurement of microbial metabolic strategies and adaptations to stress. It offers a non-invasive and scalable tool for high throughput phenotyping and studies of environmental perturbations, enabling a new class of trait-based microbial ecology that links cellular physiology to broader ecosystem function.