Mechanistic understanding of the relationship between microbial community structure and ecosystem function is the key to moving microbial ecology from an observation-based to a hypothesis-driven science. This project adapts a life history strategy framework developed in plants and animals to microbial ecology to enable mechanistic, hypothesis-driven examination of soil bacterial response to changes in environmental conditions. By examining individual species of soil bacteria and taking a whole-organism approach, this project investigates the role of tradeoffs between growth rate and efficiency on the nutrient requirements and biogeochemical consequences of growth across a diverse range of soil bacteria. This holistic approach allows generalizations to emerge from patterns in traits and genome allocation effectively defining bacterial life history strategies. Life history strategies allow generation of hypotheses at both the individual organism and bacterial community levels.
Soil bacteria are responsible for the processing and recycling of the majority of C fixed in terrestrial environments. By defining life history strategies this project deepens understanding of the effects of changes in environmental conditions on soil microbial community dynamics and feedbacks affecting other ecosystem processes. The generalizations discovered in this project promise to be applicable not only in the field of ecosystem ecology, but anywhere understanding the mechanistic basis of microbial community function is important including bioremediation, synthetic ecology, and human health microbiology.
Soil respiration accounts for as much carbon dioxide release into the atmosphere annually as all fossil fuel combustion combined. Soil bacteria account for a substantial fraction of this respiration, thus understanding what affects the growth efficiency of soil bacteria can potentially guide efforts to manage bacterial communities to achieve goals for reduction in greenhouse gas emissions. In this project, we asked whether there was a trade-off between growth rate and growth efficiency, and whether this could be influenced by differences in which carbon substrates were consumed by individual bacterial isolates. We found no indication that which carbon substrates could be consumed influenced growth rate or efficiency as faster growing bacteria that could grow on many substrates were growing on the same compounds that slower growing bacteria specialized on only a few substrates were consuming. However, we did find evidence for a trade-off between growth rate and growth efficiency as bacteria that had slower metabolic pathways with higher energy yield were unlikely to also have faster metabolic pathways with lower energy yield. This suggests that efficient bacteria have evolved under energy limitation. Thus, management of bacterial communities to reduce greenhouse gas emissions requires approaches to induce energy limitation. These findings can also be applied to improve models of growth of specific bacteria, which in turn can be used to improve the production of biofuels and other bacterial products. This project achieved broader impacts by supporting the research of a graduate student and facilitating her collaboration and training with Argonne National Laboratory, where gained training in computational methods, including metabolic network modeling and evolutionary analyses and continued to improve her computer programming skills, including graphical presentation of data.