Data from: A strong test of the Maximum Entropy Theory of Ecology

The maximum entropy theory of ecology (METE) is a unified theory of biodiversity that predicts a large number of macroecological patterns using information on only species richness, total abundance, and total metabolic rate of the community. We evaluated four major predictions of METE simultaneously at an unprecedented scale using data from 60 globally distributed forest communities including more than 300,000 individuals and nearly 2,000 species. METE successfully captured 96% and 89% of the variation in the rank distribution of species abundance and individual size but performed poorly when characterizing the size-density relationship and intraspecific distribution of individual size. Specifically, METE predicted a negative correlation between size and species abundance, which is weak in natural communities. By evaluating multiple predictions with large quantities of data, our study not only identifies a mismatch between abundance and body size in METE but also demonstrates the importance of conducting strong tests of ecological theories.Data from: A strong test of the Maximum Entropy Theory of EcologyThis data product include Python code to reproduce the analyses in A strong test of the Maximum Entropy Theory of Ecology (Xiao et al.) with a processed subset of datasets included in the paper.Xiao_etal_2014.zi