New Predictive Models Developed For Bacterial Diversity Of Soils

By Joslyn Neiderer

A new set of quantitative models that incorporates pH into the metabolic theory of ecology (MTE) has been developed by an international team that includes Penn State assistant professor of plant science Francisco Dini-Andreote.

The work is included in a new paper published by the Proceedings of the National Academy of Sciences, titled, "Integrating pH into the metabolic theory of ecology to predict bacterial diversity in soil."

"Soils are the most complex and biodiverse ecosystems on Earth," said Dini-Andreote, a member of Penn State's Microbiome Center. "In soils, microbial diversity plays indispensable roles in the anabolic and catabolic cycles of carbon, nitrogen and sulfur, without which the diversity of life forms—including plants, animals and other microbes—that evolved on our planet would not have been possible. In addition, advancing our ability to predict patterns of soil biodiversity is critical to better understanding how climate change will affect soil functioning and how soil microbes will respond to shifts in temperature and precipitation regimes."

In general terms, the metabolic theory of ecology links rates of organism diversification (i.e., the metabolic rate of an organism) with the organisms' body size and body temperature, explained Dini-Andreote. Building upon the factors that are parametrized in the MTE, the researchers introduced variation in local pH as an additional variable that acts as a stringent selective filter of biodiversity in soils, impacting the species of microbes acting and surviving in the soil.

By considering all these factors—the metabolic rate, mass, and temperature as well as pH—the researchers were able to capture and account for previously unexplained variation in the relationship between soil edaphic properties (the physical, chemical, and biological properties of the soil), temperature, and biogeographical patterns of bacterial diversity. The team then continued to test and validate their models across multiple scales—such as single bacterial strain diversification rates, local and continental scale soil communities—yielding robust results.

"By layering these models, researchers can start to better understand patterns of microbial distribution in soils and start to answer long-standing questions in this field, such as: 'What determines variation in soil biodiversity?' and "How dynamic changes in soil biodiversity can be modeled and predicted?" said Dini-Andreote.

"With that, we will be able to better harness the genomic and functional potential of these soil microorganisms to effectively manipulate them for desirable outcomes. These outcomes vary from essential ecosystem functions, such as carbon storage in soil, to the manipulation of beneficial plant-associated microorganisms to enhance crop productivity in agriculture."