Relationships between soil microbial physiology, community structure and carbon and nitrogen cycling in temperate forest ecosystems

Soil bacteria and fungi play a central role in the biogeochemical cycling of both carbon (C) and nitrogen (N) through terrestrial ecosystems. In the C cycle, soil microbial groups regulate the depolymerization of large stocks of soil organic matter and contribute 35-69 Pg C to the atmosphere annually through heterotrophic respiration. Soil microbial groups also mediate several important transformations of N, including making limiting nutrients available for uptake by plants through N-fixation, converting N between inorganic forms through nitrification, and returning N to the atmosphere through denitrification. While each of these functions is performed by soil microbes, scaling microbial physiology and community structure to biogeochemical cycling remains a significant research challenge. This dissertation integrates three distinct approaches to characterizing relationships between microbial physiology, microbial community structure and biogeochemical cycling. First, I explore the role of microbial physiology in C cycling by developing a novel method to predict bacterial carbon use efficiency (CUE) from genomes using metabolic modeling. I find that bacterial CUE is phylogenetically structured, with the class and order levels explaining the greatest proportion of variance in CUE, and I identify particular bacterial traits that most strongly predict CUE. These findings highlight the importance of accounting for microbial physiology when modeling soil C cycling. Second, I explore how differences in the abundance and activity of microbial functional groups and their interactions with mycorrhizal fungi impact temperate forest N cycling. I find that N availability and rates of N-fixation, nitrification and denitrification are structured in relation to mycorrhizal fungal types, but that the abundances of bacterial functional groups are not correlated with biogeochemical fluxes. Finally, I use a soil biogeochemical model to identify sources of uncertainty and data needs in advancing our understanding of microbially-mediated soil biogeochemical cycling. I isolate specific microbial physiological and enzyme kinetic parameters that have disproportionately large impacts on projections of coupled C and N cycling, and I quantify the potential for particular types of data to help reduce uncertainties. Overall, this dissertation advances our understanding of how microbial processes impact the biogeochemical cycling of C and N in terrestrial ecosystems

Microbial carbon use efficiency predicted from genome-scale metabolic models

Respiration by soil bacteria and fungi is one of the largest fluxes of carbon (C) from the land surface. Although this flux is a direct product of microbial metabolism, controls over metabolism and their responses to global change are a major uncertainty in the global C cycle. Here, we explore an in silico approach to predict bacterial C-use efficiency (CUE) for over 200 species using genome-specific constraint-based metabolic modeling. We find that potential CUE averages 0.62 ± 0.17 with a range of 0.22 to 0.98 across taxa and phylogenetic structuring at the subphylum levels. Potential CUE is negatively correlated with genome size, while taxa with larger genomes are able to access a wider variety of C substrates. Incorporating the range of CUE values reported here into a next-generation model of soil biogeochemistry suggests that these differences in physiology across microbial taxa can feed back on soil-C cycling.Published versio

Parametric studies on tramcar suspension system

Suspension system plays an important role in the performance of a vehicle, especially the handling and ride comfort. The role of suspension parameter, particularly spring stiffness, in relation to ride quality is being analysed in this paper. This study focused on the suspension system of a non-commercial transport for recreational purposes designed by the Mechanical Engineering Faculty of Universiti Teknologi Malaysia, which is commonly known as ‘tramcar’. For the purpose of the analysis, a full car model for the tramcar suspension system was developed. The simulation on the model was performed using MATLAB Simulink software. The spring stiffness value was varied in the simulation, and the suspension response was observed. From the suspension parameter analysis, it was concluded that the ride comfort of the tramcar can be improved to an optimum level by having the lowest practical spring stiffness value. Lower suspension spring stiffness was shown to provide better ride comfort in term of lower acceleration, pitch rate and roll rate responses. However, the spring stiffness should not produce response frequencies lower than 1Hz in avoiding sensations assimilated to motion sicknes

Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model

International audienceAdvancements in microbially explicit ecosystem models incorporate increasingly accurate representations of microbial physiology and enzyme-mediated depolymerization of soil organic matter in predicting biogeochemical responses to global change. However, a major challenge with model structural improvements is the requirement for additional parameters, which are often poorly constrained sources of uncertainty. Furthermore, it is often unclear how to best focus data collection efforts toward reducing model uncertainty. Here, we use Dual Arrhenius Michaelis-Menten Microbial Carbon and Nitrogen Physiology, a microbially mediated, coupled soil C and N cycling model, as a tool to explore the influence of microbial physiological and enzyme kinetic parameters on model estimates. We first quantify the potential for constraining model parameters using empirical measurements of soil respiration. We then use simulated data to identify which additional sources of data collection from the field would provide the greatest impact for constraining model estimates. We find that modeled soil C and N pools and fluxes are disproportionately sensitive to only a few parameters (e.g., activation energies and microbial CUE), while others exert less influence (e.g., Michaelis-Menten half-saturation constants). While some parameters can be constrained by the available data on heterotrophic respiration, the collection of additional data on dissolved organic C and N pools in the soil is identified as a high-priority data need. Improving our ability to model the interactions of soil microbial physiology, soil chemistry, enzyme activities, and environmental factors on C and N cycling will require closely considering model uncertainties and prioritizing future data collection opportunities based on their impact on model performance