Metabolic inequality in microbial communities
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How metabolic activity is partitioned among individuals determines the scaling of cellular physiology to higher levels of biological organization. Yet the mechanisms that generate this heterogeneity and shape its distribution remain largely unresolved. We quantified single-cell metabolism in microbial communities spanning aquatic, terrestrial, and host-associated ecosystems. Across more than one million cells, metabolic activity followed a long-tailed distribution best described by a lognormal model, with a small subset of individuals contributing disproportionately to community metabolism. In some cases, the most active 20% of cells accounted for over 90% of metabolic output, but this pattern became less pronounced in more productive environments. To assess the consequences of metabolic inequality, we developed a stochastic simulation model linking single-cell activity to community respiration via enzyme kinetics. Because respiration responds nonlinearly to enzyme activity, variation among cells does not translate proportionally into ecosystem-level fluxes. As a result, ignoring metabolic heterogeneity can bias estimates of community respiration by up to 60%. Our findings reveal a general pattern of metabolic inequality in microbial communities that holds across a wide range of habitats. Accounting for this structure is critical for understanding how microorganisms regulate ecosystem processes and for improving predictions of large-scale biogeochemical dynamics.
Significance
Inequality and disparity are common features of social, economic, and physical systems. Such patterns also arise in nature, where a small fraction of individuals accounts for an outsized share of biological output, including reproduction, immunity, and diversity. Here, we show that metabolic activity in microbial communities follows a characteristic right-skewed distribution across diverse ecosystems, including lakes, soils, ocean plankton, marine sediments, and mammalian guts. Instead of the rich getting richer, metabolism becomes more evenly distributed among cells in more productive environments. By explicitly representing metabolic inequality, we can reduce bias in estimates of microbial activity and improve the scaling of cellular processes to ecosystem-level fluxes.
