OOS 76
Trait-Based Ecology at the Micro-Scale

Thursday, August 13, 2015: 1:30 PM-5:00 PM
328, Baltimore Convention Center
Organizer:
Ariane L. Peralta, East Carolina University
Co-organizers:
Jay T. Lennon, Indiana University; and Sara F. Paver, National Science Foundation
Moderator:
Sara F. Paver, National Science Foundation
Trait-based approaches at the microscale hold tremendous potential to contribute significant theoretical advances to the field of ecology over the next 100 years. Functional traits include physiological, morphological, and behavioral characteristics that influence the fitness of organisms under various environmental conditions. Accordingly, traits provide a mechanistic foundation for community ecology and can be useful for studying processes from evolutionary to ecosystem scales. A major challenge for trait-based ecological studies is that traits can be difficult and time consuming to measure. This is especially true at the microscale where it can be hard to identify important features and trait characterization is most straightforward in single-species cultures, which represent a small fraction of microbial diversity. Microbial trait characterization has been revolutionized by rapid advances in technology that have provided microbial ecologists with the unprecedented opportunity to make phenotypic inferences from uncultured organisms using single-cell and meta- ‘omics data. The combination of culture-based and ‘omics approaches enables trait-based ecology to be applied at scales ranging from local to global and to exceptionally diverse communities. This symposium will highlight the work of ecologists using trait-based approaches to improve our understanding of the microbial world. These insights lay the foundation for investigating how microbial diversity has evolved through time and the eco-evolutionary feedbacks that will continue to shape and maintain this diversity into the future. Applied at the microscale, trait-based approaches have the potential to transform our understanding of microbial communities and processes as well as contribute to the development of mechanistic, trait-based frameworks. Trait-based frameworks have great potential to advance ecology towards a more predictive science and may hold the key to linking biodiversity to ecosystem function.
1:30 PM
 Microbiomes in light of traits: A phylogenetic perspective
Jennifer B.H. Martiny, University of California, Irvine; Stuart E. Jones, University of Notre Dame; Jay T. Lennon, Indiana University; Adam C. Martiny, University of California, Irvine
1:50 PM
 Linking genomic traits and phenotypic traits in microbial ecology
Albert Barberan, University of Colorado; Stuart E. Jones, University of Notre Dame; Noah Fierer, University of Colorado
2:10 PM
 Trait based approaches to microbial dormancy
Jay T. Lennon, Indiana University; Stuart E. Jones, University of Notre Dame
2:30 PM
 Microbial community assembly on micro-scale marine particles
Otto X. Cordero, ETH Zurich; Manoshi Datta, MIT
3:10 PM
3:20 PM
 Fungal traits that drive ecosystem dynamics
Kathleen K. Treseder, University of California, Irvine; Jay T. Lennon, Indiana University
3:40 PM
 Enzyme production as a key mycorrhizal trait
Colin Averill, University of Texas at Austin; Christine V. Hawkes, University of Texas
4:00 PM
 Functional traits predict competitive outcomes in fungi
Daniel Maynard, Yale University; Thomas Crowther, Yale University; Daniel L. Lindner, USDA Forest Service, NRS, Center for Forest Mycology Research; Mark A. Bradford, Yale University
4:20 PM
 Trait-based ecological strategies explain microbial responses to environmental change
Kelly Gravuer, University of California; Anu Eskelinen, University of California; Susan Harrison, University of California
4:40 PM
 Trait-based models as the nexus between environmental genomics and ecosystem biogeochemistry
Eoin L. Brodie, University of California, Berkeley; Eric King, Lawrence Berkeley National Laboratory; Sergi Molins, Lawrence Berkeley National Laboratory; Ulas Karaoz, Lawrence Berkeley National Laboratory; Yiwei Cheng, Lawrence Berkeley National Laboratory; Marco Voltolini, Lawrence Berkeley National Laboratory; Jonathan B. Ajo-Franklin, Lawrence Berkeley National Laboratory; David P. Trebotich, Lawrence Berkeley National Laboratory; Nicholas J. Bouskill, Lawrence Berkeley National Laboratory; Carl I. Steefel, Lawrence Berkeley National Laboratory