Nutrient and Rainfall Additions Shift Phylogenetically Estimated Traits of Soil Microbial Communities.

Microbial traits related to ecological responses and functions could provide a common currency facilitating synthesis and prediction; however, such traits are difficult to measure directly for all taxa in environmental samples. Past efforts to estimate trait values based on phylogenetic relationships have not always distinguished between traits with high and low phylogenetic conservatism, limiting reliability, especially in poorly known environments, such as soil. Using updated reference trees and phylogenetic relationships, we estimated two phylogenetically conserved traits hypothesized to be ecologically important from DNA sequences of the 16S rRNA gene from soil bacterial and archaeal communities. We sampled these communities from an environmental change experiment in California grassland applying factorial addition of late-season precipitation and soil nutrients to multiple soil types for 3 years prior to sampling. Estimated traits were rRNA gene copy number, which contributes to how rapidly a microbe can respond to an increase in resources and may be related to its maximum growth rate, and genome size, which suggests the breadth of environmental and substrate conditions in which a microbe can thrive. Nutrient addition increased community-weighted mean estimated rRNA gene copy number and marginally increased estimated genome size, whereas precipitation addition decreased these community means for both estimated traits. The effects of both treatments on both traits were associated with soil properties, such as ammonium, available phosphorus, and pH. Estimated trait responses within several phyla were opposite to the community mean response, indicating that microbial responses, although largely consistent among soil types, were not uniform across the tree of life. Our results show that phylogenetic estimation of microbial traits can provide insight into how microbial ecological strategies interact with environmental changes. The method could easily be applied to any of the thousands of existing 16S rRNA sequence data sets and offers potential to improve our understanding of how microbial communities mediate ecosystem function responses to global changes.