Research for Sustainable Bioenergy: Linking Genomic and Ecosystem Sciences

The design of sustainable biofuel systems requires knowledge about key plant-microbe- environment interactions that provide a range of ecosystem services. Most critical is a mechanistic understanding of how candidate biofuel plants interact with biotic and abiotic factors to affect the ecosystem outcomes that define sustainability. Recent advances in the genomic sciences can contribute immensely to the knowledge needed to design such systems. For example, progress in plant genomics will enable the inclusion of sustainability traits in future feedstocks, and advances in microbial genomics will allow insights into plant- microbe-soil interactions that might be used to manage and support plant productivity and vigor. Linking these advances to breakthroughs in ecosystem science enables the use of systems biology in the fundamental design of sustainable biofuel systems. To identify research opportunities in developing such systems, the Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) held the Research for Sustainable Bioenergy Work - shop on Oct. 2–4, 2013, in Germantown, Maryland. The workshop convened more than 30 researchers with a broad and diverse range of expertise, includ - ing ecology, microbiology, plant genetics, genomics, computational biology, and modeling. Participants discussed and identified research gaps, challenges, and opportunities for enhancing the understanding of influences that biotic, abiotic, and genetic factors have on long-term plant feedstock performance and the delivery of ecosystem services at multiple scales. This report identifies the key topics and questions that could be addressed effectively to achieve this understanding. Research opportunities are organized into four categories: (1) plant systems, encompassing plant productivity, resource use efficiency, genotype/ phenotype breeding, and crop diversity; (2) the plant microbiome, which includes microbes living in close association within or adjacent to plants; (3) ecosystem processes, such as carbon capture, greenhouse gas mitigation, and hydrologic processes; and (4) multi - scale modeling, which integrates and extends results acros spatiotemporal scales.