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Maximum entropy production and plant optimization theories
Author(s) -
Roderick C. Dewar
Publication year - 2010
Publication title -
philosophical transactions of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.753
H-Index - 272
eISSN - 1471-2970
pISSN - 0962-8436
DOI - 10.1098/rstb.2009.0293
Subject(s) - survival of the fittest , entropy (arrow of time) , natural selection , entropy production , mathematics , principle of maximum entropy , fitness function , perspective (graphical) , mathematical optimization , selection (genetic algorithm) , computer science , ecology , statistical physics , artificial intelligence , biology , evolutionary biology , statistics , genetic algorithm , quantum mechanics , physics
Plant ecologists have proposed a variety of optimization theories to explain the adaptive behaviour and evolution of plants from the perspective of natural selection ('survival of the fittest'). Optimization theories identify some objective function--such as shoot or canopy photosynthesis, or growth rate--which is maximized with respect to one or more plant functional traits. However, the link between these objective functions and individual plant fitness is seldom quantified and there remains some uncertainty about the most appropriate choice of objective function to use. Here, plants are viewed from an alternative thermodynamic perspective, as members of a wider class of non-equilibrium systems for which maximum entropy production (MEP) has been proposed as a common theoretical principle. I show how MEP unifies different plant optimization theories that have been proposed previously on the basis of ad hoc measures of individual fitness--the different objective functions of these theories emerge as examples of entropy production on different spatio-temporal scales. The proposed statistical explanation of MEP, that states of MEP are by far the most probable ones, suggests a new and extended paradigm for biological evolution--'survival of the likeliest'--which applies from biomacromolecules to ecosystems, not just to individuals.

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