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- Derivations of the Core Functions of the Maximum Entropy Theory of Ecology
- Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics
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Mathematical Modelling of Biosystems pp Cite as. A central focus of ecological research is to understand the distribution and abundance of species DAS across a range of spatial and temporal scales. Such knowledge is critical to our ability to design sustainable ecological reserves, to predict extinction rates under habitat loss or climate change, to estimate species diversity from incomplete census data, and to decipher the fundamental forces shaping ecosystems [16, 25, 44].
Derivations of the Core Functions of the Maximum Entropy Theory of Ecology
A goal of every science is comprehensive theory that is predictive, realistic, and parsimonious. Is such theory possible in ecology?
The sheer complexity, historical contingency, and scale-dependence of organisms and their interactions with their surroundings suggest to many a negative answer. This book answers yes.
Rather than building and combining mechanistic models of ecosystems, the approach here is grounded in information theory and the logic of inference.
Paralleling the derivation of thermodynamics from the maximum entropy principle, the state variable theory of ecology developed in th Paralleling the derivation of thermodynamics from the maximum entropy principle, the state variable theory of ecology developed in the book predicts realistic forms for all metrics of ecology that describe patterns in the distribution, abundance, and energetics of species across multiple spatial scales.
Part I is foundational, discussing the nature of theory, the relationship of ecology to other sciences, and the concept of the logic of inference.
Parts II and III, respectively, present the fundamentals of macroecology and of maximum information entropy from the ground up. Part IV integrates the fundamentals, leading to the derivation and testing of the predictions of the maximum entropy theory of ecology METE. Part V widens the perspective by showing how METE can help clarify several major issues in conservation biology, placing METE in context with other theories, and pointing readers along avenues for future research.
Keywords: macroecology , maximum entropy , biodiversity , scaling , abundance distribution , body size distribution , information theory , spatial ecology , ecological theory. Forgot password? Don't have an account? All Rights Reserved. OSO version 0. University Press Scholarship Online. Sign in. Not registered? Sign up. Publications Pages Publications Pages. Recently viewed 0 Save Search.
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Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics
Don't have an account? This chapter examines MaxEnt in application. What do we conclude when it fails? What are common examples of its predictions? How has it actually been used?
Published here under license by The Resilience Alliance. Go to the pdf version of this article. The following is the established format for referencing this article: Mayer, A. Donovan, and C. Information and entropy theory for the sustainability of coupled human and natural systems. Ecology and Society 19 3 : Audrey L.
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A goal of every science is comprehensive theory that is predictive, realistic, and parsimonious. Is such theory possible in ecology? The sheer complexity, historical contingency, and scale-dependence of organisms and their interactions with their surroundings suggest to many a negative answer.
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The Maximum Entropy Theory of Ecology METE , is a theoretical framework of macroecology that makes a variety of realistic ecological predictions about how species richness, abundance of species, metabolic rate distributions, and spatial aggregation of species interrelate in a given region. METE incorporates these state variables into constraints on underlying probability distributions. The method of Lagrange multipliers and maximization of information entropy MaxEnt lead to predicted functional forms of distributions of interest. We demonstrate how information entropy is maximized for the general case of a distribution, which has empirical information that provides constraints on the overall predictions. Primarily, we consider the discrete distributions predicted by METE.
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