Search NASA⌕ Search

DOE OSTI · 1626091

Functional Molecular Ecological Networks

Abstract

Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on “species” richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO 2 (eCO 2 ) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO 2 enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO 2 and ambient CO 2 (aCO 2 ) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO 2 and aCO 2 , at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO 2 dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhou, Jizhong, Deng, Ye, Luo, Feng, He, Zhili, Tu, Qichao, Zhi, Xiaoyang. 2010-10-05. Functional Molecular Ecological Networks. https://doi.org/10.1128/mbio.00169-10

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗