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The adaptive dynamics of function-valued traits.

Ulf Dieckmann1, Mikko Heino, Kalle Parvinen

  • 1Evolution and Ecology Program, International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria. dieckmann@iiasa.ac.at

Journal of Theoretical Biology
|February 8, 2006
PubMed
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This study generalizes adaptive dynamics to function-valued traits, crucial for understanding evolution in complex environments. It provides a mathematical framework for analyzing long-term evolutionary trajectories of such traits.

Area of Science:

  • Evolutionary Ecology
  • Theoretical Biology
  • Population Genetics

Background:

  • Adaptive dynamics theory models evolutionary change under ecological feedback.
  • Function-valued traits are common in diverse biological settings like environmental heterogeneity and phenotypic plasticity.
  • Existing models often simplify traits, limiting their applicability to complex adaptive systems.

Purpose of the Study:

  • To extend the adaptive dynamics framework to accommodate function-valued traits.
  • To derive general equations for the evolutionary dynamics of function-valued traits.
  • To illustrate the framework's utility with ecological examples.

Main Methods:

  • Derivation of generalized adaptive dynamics equations from individual-based principles.

Related Experiment Videos

  • Analysis of mutation-limited evolution for function-valued traits.
  • Incorporation of evolutionary constraints into the adaptive dynamics model.

    • Main Results:

    • A generalized canonical equation for the adaptive dynamics of function-valued traits.
    • Detailed methods for accounting for evolutionary constraints.
    • Demonstration of the framework's applicability to metabolic strategies and flowering schedules.

    Conclusions:

    • The extended adaptive dynamics framework provides a powerful tool for studying the evolution of complex traits.
    • This approach is broadly applicable to various ecological and evolutionary scenarios.
    • The study offers new insights into the evolution of adaptive strategies in dynamic environments.