This study addressed the challenge in soil microbiology of predicting an essential process, such as soil carbon sequestration, based on the type of microorganisms present. One of the often-used methods for grouping soil bacteria into types is whether they are adapted to be competitive and fast-growing, or slow-growing and resistant to starvation.
We found that this grouping isn’t always a good predictor of bacterial behavior in the soil. Rather, the potential for quick growth was found to be localized within a small number of species within the bacterial community in the complex and competitive soil environment. Only three of 1,793 species measured were consistently fast growers.
Soils are an important global reservoir of carbon. Understanding how soil microorganisms break down and use carbon is crucial to predicting how well soils will take up carbon in response to climate change. Many more microorganisms have been found to be in soils than scientists can grow and study. Thus, to estimate how unknown species contribute to a given process, such as soil carbon cycling and sequestration, scientists must often compare behavior with known species. This study is different because it measures the growth patterns of many bacteria directly instead of making indirect inferences. This work reveals which evolutionary strategies are most common and positions us to explore these strategies in relation to soil carbon sequestration more fully.
Life-history strategies help predict how microorganisms behave in nature, but commonly used frameworks fall short. We tested the effectiveness of one, the copiotroph-oligotroph framework, which distinguishes fast-growing bacteria with an affinity for elevated nutrients from slow-growing bacteria adapted to low nutrient conditions. We measured the changes of bacterial growth rates in response to added nutrients using quantitative stable isotope probing or qSIP.
We found that there was not a balance in numbers of fast growers and slow growers. Instead, most bacteria grew slowly, and a few were consistent fast growers. When predicting behavior based on relatedness, we found the greatest accuracy when grouping closely related bacteria. Prior approaches that grouped distantly related bacteria according to life history strategy were not as useful in predicting behavior. Using direct measurements of bacteria in the soil environment demonstrates new ways of organizing these communities that are more useful for building predictive ecosystem models.
Bram Stone, Pacific Northwest National Laboratory, firstname.lastname@example.org
This research was supported by grants from the Department of Energy’s (DOE) Biological Systems Science Division Program in Genomic Science, and the Lawrence Livermore National Laboratory (LLNL) ‘Microbes Persist’ Soil Microbiome Scientific Focus Area and by the National Science Foundation. Research conducted at LLNL was supported by the DOE Office of Science, via awards SCW1679 and SCW1590. Research conducted at Pacific Northwest National Laboratory (PNNL) was supported by the Department of Energy Office of Science. Bram Stone is grateful for support from the Linus Pauling Distinguished Postdoctoral Fellowship program through PNNL.
Published: May 24, 2023
Stone B., P. Dijkstra, B.K. Finley, R. Fitzpatrick, M. Foley, M. Hayer, and K.S. Hofmockel, et al. 2023. "Life history strategies among soil bacteria—dichotomy for few, continuum for many." The ISME Journal. doi:10.1038/s41396-022-01354-0