Elucidating the relationships between biodiversity and ecosystem functioning is one of the grand challenges in ecology, particularly in microbial ecology. Microorganisms, as the most abundant and diverse group of life on earth, are involved in essential ecosystem functioning and services around the planet. Although high-throughput metagenomic technologies provide massive, rich data on studying microbial biodiversity, its importance in ecosystem processes is highly controversial. One of the main reasons for such heavy debate is the difficulty in defining microbial functional traits and their diversity. Here we developed a novel framework to characterize microbial functional diversity based on high throughput metagenomics technologies, mainly GeoChip-based functional gene arrays. We also GeoChip to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide range of pH, uranium, nitrate, and other contaminants. In addition, we applied the developed new framework to assess microbial functional diversity in the groundwater microbiomes.
Results/Conclusions
We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium, nitrate) increased, or at low or high pH while some specific populations capable of utilizing or resistant to those contaminants would increase, thus such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) significantly (p < 0.05) increased as uranium or nitrate increased and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential of using microbial functional diversity to predict environmental contamination and ecosystem functioning.