Author ORCID Identifier

https://orcid.org/0000-0002-7908-2963

Semester

Fall

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Davis College of Agriculture, Natural Resources and Design

Department

Division of Plant and Soil Sciences

Committee Chair

Ember Morrissey

Committee Member

Edward Brzostek

Committee Member

Louis McDonald

Committee Member

James Kotcon

Committee Member

Craig Barrett

Abstract

Microbial functional diversity is the product of community structure and intraspecific trait variation. Due to microbial diversity and limited availability of microbial trait measurements, it has been challenging to quantitatively link community structure to microbial function. Although community-level activity rates vary with community composition, issues in the scale of their measurement inhibit our understanding of their relationship. Quantitative stable isotope probing (qSIP) is a method for quantitatively measuring taxon-specific microbial traits, providing new opportunities to apply trait-based approaches for studying microbial ecology. Quantitative microbial trait data from these experiments enables the functional characterization of microbial communities, potentially enabling the extrapolation of ecosystem processes, the inference of community function from phylogeny, and parameterization of microbially explicit ecosystem models. Many trait-based methods and theories were developed for the study of macroorganisms, so their suitability for the study of microbial ecology must be evaluated. In this doctoral dissertation, my aim was the evaluation of innovative experimental and analysis methods to quantitatively link microbial community structure to ecosystem functions.

First, I determined the potential for scaling taxon-specific traits to community-level microbial activity rates. I found that scaling taxon-specific nitrogen (N) assimilation by microbial biomass predicted community-level measurements of N transformation rates and microbial activity. Then, I evaluated the potential for predicting bacterial growth rates from phylogeny. I found that evolutionary history influenced bacterial growth rates sufficiently to allow prediction of growth rates for bacterial taxa based on their phylogenetic relationship to taxa with available trait data. Lastly, I developed and validated a soil organic matter (SOM) decomposition model that represents microbial functional groups derived from the carbon acquisition ecological strategies (CAES) framework. This model depicted microbial functional diversity and accurately simulated shifts in the structure and function of the microbial community in response to variation in the chemical composition and the rate of litter carbon (C) input, as well as capturing microbial response to reduced belowground C allocation under enhanced N fertilization. Attributing functional traits to microbial communities helps elucidate the relationship between community structure and microbial function. A more complete understanding of this relationship enables more accurate predictions of microbially-mediated ecosystem processes.

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