COS 55-1
Connecting the environment to a maximum entropy prediction of the species-abundance distribution across continents and taxa

Wednesday, August 7, 2013: 8:00 AM
L100C, Minneapolis Convention Center
Daniel J. McGlinn, Biology, Utah State University, Logan, UT
Ethan P. White, Department of Wildlife Ecology & Conservation and the Informatics Institute,, University of Florida, Gainesville, FL

The species-abundance distribution (SAD) is a fundamental pattern in community ecology yet until recently there was not an a priori model for the shape of the SAD.  The Maximum Entropy Theory of Ecology (METE) provides a statistical framework that predicts the shape of the SAD from two key empirical constraints: the total number of species (S) and the total number of individuals (N).  The goal of our project is to test how well S and N and subsequently the SAD can be predicted using remotely-sensed environmental data across 6 continental-scale datasets that encompass birds, trees, butterflies, and small mammals.


In general, the environmental variables explained approximately equal amounts of variance in S (average R2=  0.42) and N (average R2 = 0.38).  Additionally, we observed a positive correlation between the R2 value of S and N.  Predictions of S and N were most accurate for mammals and trees. Winter and summer bird communities were equally predictable.  We explained the least amount of variance in S and N for the butterfly dataset.  The predicted SADs based on predicted S and N were surprisingly accurate (average R2 = 0.62), indicating that the exact empirical values of S and N are not necessary to generate reasonable empirical predictions of the SAD using the METE approach.  Our findings suggest that remotely-sensed environmental data can provide a quick and relatively accurate method of predicting the pattern of dominance and rarity in a community that has yet to be sampled.  Additionally our results demonstrate how a constraint-based, maximum entropy approach can be combined with other modeling approaches to yield simple yet powerful predictions using relatively little information.