Understanding How Emergent Community Trait-based Modeling (Genome-based EmergeNt Ocean Microbial Ecosystem Model) Influences Biogeochemical, Ecological, and Microbial System Responses

Victoria Coles, University of Maryland Center for Environmental Science Horn Point Laboratory, Cambridge, MD, United States, Jiaze Wang, University of Maryland Center for Environmental Science, Horn Point Laboratory, Cambridge, MD, United States, Olivia Mason, Florida State University, Earth, Ocean, and Atmospheric Department, Tallahassee, United States and Michael R Stukel, Florida State University, Tallahassee, FL, United States
Recent advances in trait-based modeling of microbial communities and their biogeochemical impacts are reducing the prescriptive structure of functional group modeling, and provide tantalizing evidence of the importance of model complexity. However, systematic analysis of how these emergent property models reveal patterns in ecology that escape functional group models are missing. Are there fingerprints in ocean metabolism, biogeochemistry and molecular measurements that reveal microbial community structure and interaction? What is the level of model complexity required to make accurate predictions of community response to disturbance? A new version of the Genome-based EmergeNt Ocean Microbial Ecosystem Model (GENOME Model) has been formulated to include representation of both energy and nutrient limitation as well as different levels of gene function regulation. The model is applied in a one-dimensional framework in the Gulf of Mexico to explore the community and biogeochemical responses to a large system perturbation. The GENOME model is compared with simplified functional group models of varying complexity to understand how model complexity influences the macroscale properties of the system and ecological resilience and diversity.