NEON Science

Abstract: 

A fundamental challenge of microbial ecology is to discover unifying links between microbial communities and ecosystem processes that hold across biomes. This EAGER project will develop a procedure to analyze soil microbial data collected by the National Ecological Observatory Network (NEON), which has the advantage of generating high-quality, comparable data through standardized and quality-controlled collection and processing methods at field sites across the continent. The high-risk, high-payoff aspect of this project will involve development and testing of a 'DNA barcoding' technique for characterizing soil microbiomes in the field. Improved understanding of the links between microbial community structure and ecosystem processes will enable scientists to better predict how environmental changes impact biogeochemical cycles and stewardship of natural resources. This project will include training opportunities at the undergraduate and graduate levels.

This EAGER project integrates soil microbiology with ecosystem ecology to improve understanding of ecosystem processes, and will chart new ground using a new method, based on a MinION genomics platform, for characterizing soil microbes in the field. Specific research objectives are to (1) develop a data analysis pipeline for the NEON soil microbiota data; (2) identify unifying links between microbial structure (species richness, abundance, diversity, composition) and ecosystem processes (soil respiration and nitrogen mineralization) through analyses of publicly available NEON data products; and (3) collect and analyze soil microbial data in the field using MinION based DNA barcoding technique at NEON domain sites (D10 ? CPER, D10 ? RMNP, D10 ? STER, D13 ? NIWO) near Boulder, CO to develop and benchmark [using next generation sequencing and Phospholipid Fatty Acid (PLFA) profiling] soil analysis methods by coordinating sampling with NEON scientists. The results will reveal consistencies across biomes in microbial community structure and ecosystem processes. The methods and pipeline developed for these studies will facilitate improvements in ecosystem models that are key to reducing uncertainty in predictions of carbon fluxes and stocks, nitrification/denitrification, carbon decomposition, and overall land management. The studies will include training of graduate students, development of a course on using large data sets, and use of tutorials to engage undergraduates in using and analyzing NEON data.. The data collected at the NEON sites will be shared through GitHub and repositories like DRYAD & PANGAEA. Results from this work will be shared with scientific community via presentations and workshops at scientific meetings, an "R" library, and peer-reviewed publications.