OOS 14-5 - On the outcomes of interactions: using relatedness and trait variation to understand patterns of plant productivity

Tuesday, August 4, 2009: 9:20 AM
Brazos, Albuquerque Convention Center
Marc W. Cadotte, Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada, Jeannine Cavender-Bares, Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN, David Tilman, Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN and Todd H. Oakley, Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA
Background/Question/Methods

Two decades of research showing that increasing plant diversity results in greater community productivity has been predicated on greater functional diversity allowing access to more of the total available resources. Thus, understanding phenotypic attributes that allow species to partition resources is fundamentally important to explaining diversity-productivity relationships. Here we use data from a long-term experiment (Cedar Creek, MN) and compare the extent to which productivity is explained by seven types of community metrics of trait variation: 1) species richness, 2) variation in 10 individual traits, 3) functional group richness, 4) a distance-based measure of functional diversity, 5) a hierarchical multivariate clustering method, 6) a nonmetric multidimensional scaling approach, and 7) a phylogenetic diversity, which may be a surrogate for ecological differences. 
Results/Conclusions

Although most of these diversity measures provided significant explanations of variation in productivity the presence of a nitrogen fixer and phylogenetic diversity were the two best explanatory variables. Further, a statistical model that included the presence of a nitrogen fixer, seed weight and phylogenetic diversity was a better explanation of community productivity than other models. These results reveal that functional differences among species involve a complex suite of traits and that using evolutionary history instead of individual or small groups of traits may provide a better measure of the functional differences that contribute to productivity.

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