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Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure.

ABSTRACT: Accurately modeling nitrification and understanding the role specific ammonia- or nitrite-oxidizing taxa play in it are of great interest and importance to microbial ecologists. In this study, we applied machine learning to 16S rRNA sequence and nitrification potential data from an experiment examining interactions between cropping systems and rhizosphere on microbial community assembly and nitrogen cycling processes. Given the high dimensionality of microbiome datasets, we only included nitrifers since only a few taxa are capable of ammonia and nitrite oxidation. We compared the performance of linear and nonlinear algorithms with and without qPCR measures of bacterial and archaea ammonia monooxygenase subunit A (amoA) gene abundance. Our feature selection process facilitated the identification of taxons that are most predictive of nitrification and to compare habitats. We found that Nitrosomonas and Nitrospirae were more frequently identified as important predictors of nitrification in conventional systems, whereas Thaumarchaeota were more important predictors in diversified systems. Our results suggest that model performance was not substantively improved by incorporating additional time-consuming and expensive qPCR data on amoA gene abundance. We also identified several clades of nitrifiers important for nitrification in different cropping systems, though we were unable to detect system- or rhizosphere-specific patterns in OTU-level biomarkers for nitrification. Finally, our results highlight the inherent risk of combining data from disparate habitats with the goal of increasing sample size to avoid overfitting models. This study represents a step toward developing machine learning approaches for microbiome research to identify nitrifier ecotypes that may be important for distinguishing ecotypes with defining roles in different habitats.

SUBMITTER: Lee C

PROVIDER: S-EPMC9309558 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Nitrogen availability limits the net primary productivity in alpine meadows on the Qinghai-Tibetan Plateau, which is regulated by ammonia-oxidizing microorganisms. However, little is known about the elevational patterns of soil ammonia oxidizers in alpine meadows. Here, we investigated the potential nitrification rate (PNR), abundance, and community diversity of soil ammonia-oxidizing microorganisms along the altitudinal gradient between 3,200 and 4,200 m in Qinghai-Tibetan alpine meadows. We found that both PNR and amoA gene abundance declined from 3,400 to 4,200 m but lowered at 3,200 m, possibly due to intense substrate competition and biological nitrification inhibition from grasses. The primary contributors to soil nitrification were ammonia-oxidizing archaea (AOA), and their proportionate share of soil nitrification increased with altitude in comparison to ammonia-oxidizing bacteria (AOB). The alpha diversity of AOA increased by higher temperature and plant richness at low elevations, while decreased by higher moisture and low legume biomass at middle elevations. In contrast, the alpha diversity of AOB increased along elevation. The elevational patterns of AOA and AOB communities were primarily driven by temperature, soil moisture, and vegetation. These findings suggest that elevation-induced climate changes, such as shifts in temperature and water conditions, could potentially alter the soil nitrification process in alpine meadows through changes in vegetation and soil properties, which provide new insights into how soil ammonia oxidizers respond to climate change in alpine meadows.IMPORTANCEThe importance of this study is revealing that elevational patterns and nitrification contributions of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) communities were primarily driven by temperature, soil moisture, and vegetation. Compared to AOB, the relative contribution of AOA to soil nitrification increased at higher elevations. The research highlights the potential impact of elevation-induced climate change on nitrification processes in alpine meadows, mediated by alterations in vegetation and soil properties. By providing new insights into how ammonia oxidizers respond to climate change, this study contributes valuable knowledge to the field of microbial ecology and helps predict ecological responses to environmental changes in alpine meadows.

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Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure.

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