4.0 Article

Thermodynamic extremization principles and their relevance to ecology

Journal

AUSTRAL ECOLOGY
Volume 39, Issue 6, Pages 619-632

Publisher

WILEY
DOI: 10.1111/aec.12130

Keywords

biodiversity; ecological thermodynamics; ecosystem ecology; entropy; maximum power

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Funding

  1. Monash University Sir James McNeill Foundation
  2. Victorian Life Sciences Computation Initiative

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Theories based on simple principles have provided much insight into the common processes that underpin complex ecological systems. Although such theories (e.g. neutral theory, metabolic theories) often neglect specific ecological details, they compensate for this with their generality and broad applicability. We review several simple principles based on 'thermodynamic extremization' (the minimization or maximization of a thermodynamic quantity) and explore their application and relevance to ecology. Thermodynamic extremization principles predict that certain energetic quantities (e.g. entropy production) will tend towards maxima or minima within ecological systems, subject to local constraints (e.g. resource availability). These principles have a long history in ecology, but existing applications have had a theoretical focus and have made few quantitative predictions. We show that the majority of existing theories can be unified conceptually and mathematically, a result that should facilitate ecological applications of thermodynamic extremization principles. Recent developments in broader ecological research (e.g. metabolic theories) have allowed quantitative predictions of ecological patterns from thermodynamic extremization principles, and initial predictions have been supported by empirical data. We discuss how the application of extremization principles could be extended and demonstrate one possible extension, using an extremization principle to predict individual size distributions. A key focus in the application of thermodynamic extremization principles to mainstream ecological questions should be the generation of quantitative predictions and subsequent empirical validation.

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