COS 41-4
Phytoplankton traits predict ecosystem function in a global set of lakes

Tuesday, August 11, 2015: 2:30 PM
318, Baltimore Convention Center
Jacob A. Zwart, Biological Sciences, University of Notre Dame, Notre Dame, IN
Stuart E. Jones, Biological Sciences, University of Notre Dame, Notre Dame, IN

Predicting ecosystem function from environmental conditions is a central goal of ecosystem ecology. However, many traditional ecosystem models are tailored for specific regions or ecosystem types, requiring several regional models to predict the same function. Alternatively, trait-based approaches have been effectively used to predict community structure in both terrestrial and aquatic environments and ecosystem function in a limited number of terrestrial examples. To generate trait-based predictions of lake gross primary production, we used the U.S. Environmental Protection Agency’s National Lake Assessment (NLA) data set to generate niche models for major phytoplankton groups. Niche models were combined with group-specific, lab-derived light use efficiencies to predict pelagic gross primary productivity (GPP). Performance of our trait-based model was compared to that of traditional lake models of GPP when compared to a global data set of GPP estimates from the Global Lake Ecological Observatory Network (GLEON).


Our trait-based model outperformed two ecosystem models of lake GPP both in a spatial and temporal context, demonstrating the efficacy of trait-based models for predicting ecosystem function over a range of environmental conditions. Both group-specific response and effect traits were important for predicting rates of GPP. Our results highlight the utility of a trait-based approach for extrapolating lake ecosystem functions spatially, as well as predicting lake GPP under future climate conditions.