Parameterized maximum entropy models predict variability of metabolic scaling across tree communities and populations

The maximum entropy theory of ecology (METE) applies the concept of "entropy" from information theory to predict macroecological patterns. The energetic predictions of the METE rely on predetermined metabolic scaling from external theories, and this reliance diminishes the testability of the theory. In this work, I build parameterized METE models by treating the metabolic scaling exponent as a free parameter, and I use the maximum-likelihood method to obtain empirically plausible estimates of the exponent. I test the models using the individual tree data from an oak-dominated deciduous forest in the northeastern United States and from a tropical forest in central Panama. My analysis shows that the metabolic scaling exponents predicted from the parameterized METE models deviate from that of the metabolic theory of ecology and exhibit large variation, at both community and population levels. Assemblage and population abundance may act as ecological constraints that regulate the individual-level metabolic scaling behavior. This study provides a novel example of the use of the parameterized METE models to reveal the biological processes of individual organisms. The implication and possible extensions of the parameterized METE models are discussed.