What is the Ecosystem Genomics Initiative?

In 2012 University of Arizona (UA) envisioned a grand opportunity for implementing a new science of ‘Ecosystem Genomics’. The Ecosystem Genomics Initiative seeks to “Discover the emergent properties and processes of ecosystems through ‘top down’ analysis of communities, populations and organisms, and ‘bottom up’ analysis of genomes, transcriptomes and metabolomes.”

What is Ecosytem Genomics?

The term ‘ecosystem genomics’ has been used in the past by both geneticists and ecosystem biologists as an aspiration for disciplinary integration; yet, the aim of placing genomes into a framework capable of predicting the rates and outcomes of ecosystem processes has remained elusive. The Ecosystem Genomics Initiative will provide a basis to investigate principles for integrating data and theory to synthesize a unified, genes-to-ecosystems framework for predicting ecosystem processes and managing them to sustainable ends. The discipline of genome biology is poised to drive broad, integrative research projects in ecosystem ecology. For example, the Earth Microbiome Project aims to ‘analyze microbial communities across the globe and within the environmental parameter space of different biomes’. The NIH launched the Human Microbiome Project to understand how the ‘ecosystem’ of the human body controls the expression of complex diseases. The Biology Directorate at NSF established a community Wiki to relieve bottlenecks that impede progress on the genome-to-phenome grand challenge. At the same time, ecosystem scientists have shown that some of the complex feedbacks in Earth Systems Models (ESMs) are more accurately represented when genome and trait data are included. Ecosystem biologists have asked: ‘how can we use genomic details about organisms and communities to accurately predict the roles of specific traits in controlling ecosystem processes and their relation to challenges such as climate change’?

The central factors that are missing, but required, to translate genomics into ecosystem processes are foundational theory, case studies and models. The absence of these elements prevents us from addressing questions such as: Why are some genes more important than others in determining the aggregate traits of ecosystems? To what degree do plant genomes control the community assembly of soil microbial genomes, and produce a microbiome that determines ecosystem aggregate traits? What are the scaling ‘conveniences’ that will enable us to represent the translation from genes to traits to processes in models of the entire earth system? To link genomics to processes, and thus create the predictive framework we seek, foundational theory must be developed to define the complete genes-to-ecosystems translation. Case studies should be used to align measurements of genes, traits and processes in complementary environments and systems. Through this alignment, we can be sure we have all the elements of the theory represented in specific ecosystems. With theory and case studies in hand, we can iteratively develop, test, and refine hierarchically-organized models. The discipline of ecosystem genomics that we envision will advance a broad framework for predictive ecosystem genomics. Our research will build this framework through an integrated portfolio of complementary questions, scales, and ecosystems. Through research and education activities we will create international impact by designing new types of models that inform global climate policy, identifying genes and genomic interactions that enhance crop yield, and training a new generation of students to join the national workforce in fields such as ecosystem management, medical genetics and food security

How does UA benefit from such an initiative?

  1. Funding opportunities are diverse and generous including government and private foundations.
  2. The science is inclusive and embraces strengths across campus (from Biosphere 2 to CyVerse ).
  3. A Graduate Interdisciplinary Program (GIDP) in this area would nucleate intellectual exchange and prepare UA graduates in an emerging field.
  4. “BIG DATA” needs interface with flagship NSF programs ($434M NEON), and UA-led iPlant Collaborative (uniting initiatives in Health Informatics, iPlant and microbiology via iMicrobe).