OOS 3-8
Evaluating a general theory of macroecology using big data

Monday, August 5, 2013: 4:00 PM
101C, Minneapolis Convention Center
Ethan P. White, Department of Wildlife Ecology & Conservation and the Informatics Institute,, University of Florida, Gainesville, FL
Xiao Xiao, Biology & the Ecology Center, Utah State University, Logan, UT
Katherine M. Thibault, National Ecological Observatory Network (NEON)
Daniel J. McGlinn, Biology, Utah State University, Logan, UT
Justin A. Kitzes, Energy and Resources Group, University of California, Berkeley, CA

General theories for macroecological patterns have become increasingly prevalent in the last decade.  These theories potentially allow predictions to be made in the absence of detailed understanding of the processes structuring an ecosystem. We discuss research testing one of these general theories, the Maximum Entropy Theory of Ecology, which posits that many macroecological patterns are emergent statistical phenomena. If this theory is correct, it would mean that the form of many common patterns in ecology could be unlocked simply by knowing the total number of individuals and species in a system, and the total metabolic energy use of all of the individuals. To provide the most general test of the theory possible we compare it to large ecological datasets containing thousands of sites and species, and millions of individuals.


Three major macroecological patterns (the species-abundance distribution, the species-area relationship, and the individual size distribution) are well predicted by the theory (R2 > 0.8). Three other patterns (the distance decay of similarity, the relationship  between the size of a species and the number of individuals, and the distribution of individual body sizes within a species) are not well predicted by the theory. We discuss what the failures of the current theory suggest for future iterations of this approach.