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