A Strong Test of the Maximum Entropy Theory of Ecology

@article{Xiao2015AST,
  title={A Strong Test of the Maximum Entropy Theory of Ecology},
  author={Xiao Xiao and Daniel J. McGlinn and Ethan P. White},
  journal={The American Naturalist},
  year={2015},
  volume={185},
  pages={E70 - E80}
}
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… 
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58 Citations

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Exploring the spatially explicit predictions of the Maximum Entropy Theory of Ecology
Aim The maximum entropy theory of ecology (METE) is a unified theory of biodiversity that attempts to simultaneously predict patterns of species abundance, size and spatial structure. The spatial…
Derivations of the Core Functions of the Maximum Entropy Theory of Ecology
TLDR
This work considers the discrete distributions predicted by the Maximum Entropy Theory of Ecology and explores the parameter space defined by the METE’s state variables and Lagrange multipliers.
Exploring the spatially explicit predictions of the Maximum Entropy Theory of Ecology
TLDR
A semi-recursive version of METE’s spatially explicit predictions for the distance decay relationship of community similarity are developed and tested and suggest that tests of spatial theories based only on the species-area relationship may appear to support the underlying theory despite significant deviations in important aspects of spatial structure.
Title: Exploring the Spatially Explicit Predictions of the Maximum Entropy Theory of Ecology 1
17 Aim: 18 The Maximum Entropy Theory of Ecology (METE) is a unified theory of biodiversity that 19 attempts to simultaneously predict patterns of species abundance, size, and spatial structure. The…
meteR: an r package for testing the maximum entropy theory of ecology
Summary Macroecological patterns appear to follow consistent forms across a range of natural systems; however, the origin of their regularity remains obscured. The maximum entropy theory of ecology…
Maximum information entropy: a foundation for ecological theory.
Land use, macroecology, and the accuracy of the Maximum Entropy Theory of Ecology: A case study of Azorean arthropods
TLDR
This work uses METE to simultaneously predict the species abundance distribution, the metabolic rate distribution of individuals, and the species–area relationship and compares these predictions to arthropod data from 96 sites at Terceira Island in the Azores archipelago and finds that the forest habitats are the best fit by METE predictions, while the semi-natural pasture consistently provided the worst fit.
Parametrized maximum entropy models predict variability of metabolic scaling across tree communities and populations.
  • Meng Xu
  • Environmental Science
    Ecology
  • 2020
TLDR
This study builds parametrized METE models by treating the metabolic scaling exponent as a free parameter, and uses the maximum likelihood method to obtain empirically plausible estimates of the exponent, and analysis show that the metabolic scaled exponents predicted from the parametrization models deviate from that of the metabolic theory of ecology and exhibit large variation.
Do modern theories of biodiversity fail to predict commonness and rarity among microbes
8 Ecological theories of biodiversity seek to predict and unify patterns of commonness and rarity across taxa. The maximum entropy theory of ecology (METE) is among the most unifying 10 theories of…
Metabolic partitioning across individuals in ecological communities
TLDR
The success of METE's predicted form across systems, including those that are clearly not light limited, enriches the capacity to predict patterns in macroecology without making explicit mechanistic assumptions and provides a unified framework that can capture ubiquitous features of those patterns across diverse ecosystems governed by a variety of mechanisms.
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References

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