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Population dynamics and competitive outcome derive from resource allocation statistics: The governing influence of the distinguishability of individuals

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  • Zhang, Yu J.
  • Harte, John

Abstract

Model predictions for species competition outcomes highly depend on the assumed form of the population growth function. In this paper we apply an alternative inferential method based on statistical mechanics, maximizing Boltzmann entropy, to predict resource-constrained population dynamics and coexistence. Within this framework, population dynamics and competition outcome can be determined without assuming any particular form of the population growth function. The dynamics of each species is determined by two parameters: the mean resource requirement θ (related to the mean metabolic rate) and individual distinguishability Dr (related to intra- compared to interspecific functional variation). Our theory clarifies the condition for the energetic equivalence rule (EER) to hold, and provide a statistical explanation for the importance of species functional variation in determining population dynamics and coexistence patterns.

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  • Zhang, Yu J. & Harte, John, 2015. "Population dynamics and competitive outcome derive from resource allocation statistics: The governing influence of the distinguishability of individuals," Theoretical Population Biology, Elsevier, vol. 105(C), pages 53-63.
    • Handle: RePEc:eee:thpobi:v:105:y:2015:i:c:p:53-63
    DOI: 10.1016/j.tpb.2015.07.003
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    References listed on IDEAS

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    1. Colleen K. Kelly & Michael G. Bowler, 2002. "Coexistence and relative abundance in forest trees," Nature, Nature, vol. 417(6887), pages 437-440, May.
    2. Kang, Yun & Chesson, Peter, 2010. "Relative nonlinearity and permanence," Theoretical Population Biology, Elsevier, vol. 78(1), pages 26-35.
    3. Herwig Leirs & Nils Chr. Stenseth & James D. Nichols & James E. Hines & Ron Verhagen & Walter Verheyen, 1997. "Stochastic seasonality and nonlinear density-dependent factors regulate population size in an African rodent," Nature, Nature, vol. 389(6647), pages 176-180, September.
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    1. Samir Hammami & Omar Durrah & Syed Ahsan Jamil & Mohammedosman Eltigani, 2022. "Engaging Knowledge Capabilities to Sustain the Application of Information Technology Governance in Healthcare Institutions," SAGE Open, , vol. 12(4), pages 21582440221, October.

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