Dataset Information

Warming enhances old organic carbon decomposition through altering functional microbial communities.

ABSTRACT: Soil organic matter (SOM) stocks contain nearly three times as much carbon (C) as the atmosphere and changes in soil C stocks may have a major impact on future atmospheric carbon dioxide concentrations and climate. Over the past two decades, much research has been devoted to examining the influence of warming on SOM decomposition in topsoil. Most SOM, however, is old and stored in subsoil. The fate of subsoil SOM under future warming remains highly uncertain. Here, by combining a long-term field warming experiment and a meta-analysis study, we showed that warming significantly increased SOM decomposition in subsoil. We also showed that a decade of warming promoted decomposition of subsoil SOM with turnover times of decades to millennia in a tall grass prairie and this effect was largely associated with shifts in the functional gene structure of microbial communities. By coupling stable isotope probing with metagenomics, we found that microbial communities in warmed soils possessed a higher relative abundance of key functional genes involved in the degradation of organic materials with varying recalcitrance than those in control soils. These findings suggest warming may considerably alter the stability of the vast pool of old SOM in subsoil, contributing to the long-term positive feedback between the C cycle and climate.

SUBMITTER: Cheng L

PROVIDER: S-EPMC5520036 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Warming enhances old organic carbon decomposition through altering functional microbial communities.

Cheng Lei L Zhang Naifang N Yuan Mengting M Xiao Jing J Qin Yujia Y Deng Ye Y Tu Qichao Q Xue Kai K Van Nostrand Joy D JD Wu Liyou L He Zhili Z Zhou Xuhui X Leigh Mary Beth MB Konstantinidis Konstantinos T KT Schuur Edward Ag EA Luo Yiqi Y Tiedje James M JM Zhou Jizhong J

The ISME journal 20170421 8

Soil organic matter (SOM) stocks contain nearly three times as much carbon (C) as the atmosphere and changes in soil C stocks may have a major impact on future atmospheric carbon dioxide concentrations and climate. Over the past two decades, much research has been devoted to examining the influence of warming on SOM decomposition in topsoil. Most SOM, however, is old and stored in subsoil. The fate of subsoil SOM under future warming remains highly uncertain. Here, by combining a long-term field ...[more]

PMID: 28430189

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Project description:Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearson's correlation coefficients of.636 and.676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.

Dataset Information

Warming enhances old organic carbon decomposition through altering functional microbial communities.

Publications

Warming enhances old organic carbon decomposition through altering functional microbial communities.

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