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Evolutionary graph theory derived from eco-evolutionary dynamics.

Karan Pattni1, Christopher E Overton2, Kieran J Sharkey1

  • 1Department of Mathematical Sciences, University of Liverpool, United Kingdom.

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Summary
This summary is machine-generated.

We developed a new framework for studying evolution in structured populations, integrating ecological and evolutionary dynamics. This model reveals how natural death can hinder the spread of beneficial mutations in certain network structures.

Keywords:
Ecological dynamicsEvolutionary dynamicsIndividual-based modelMarkov processNetworks

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Area of Science:

  • Evolutionary Biology
  • Theoretical Ecology
  • Network Science

Background:

  • Evolutionary graph theory models populations with fixed structures.
  • Existing models often lack ecological feedback, limiting biological realism.
  • Understanding eco-evolutionary dynamics in structured populations is crucial.

Purpose of the Study:

  • To develop a biologically motivated, individual-based framework for eco-evolutionary dynamics in network-structured populations.
  • To investigate the interplay between ecological factors and evolutionary processes.
  • To analyze the spread of adaptive mutations under realistic conditions.

Main Methods:

  • Construction of a network birth and death model.
  • Derivation of the evolutionary graph theory model as a special case.
  • Introduction of a negative ecological feedback loop coupling births and deaths.
  • Analysis of fitness in relation to individual life-history traits (birth/death rates).

Main Results:

  • The framework accommodates both ecological and evolutionary dynamics.
  • Coupled birth and death rates, driven by negative feedback, alter evolutionary outcomes.
  • The star network, an evolutionary graph theory amplifier, can inhibit adaptive mutation spread when natural death occurs.
  • Fitness is directly linked to individual birth and death rates.

Conclusions:

  • The developed framework offers a more biologically realistic approach to studying evolution in structured populations.
  • Ecological feedback loops significantly influence the spread of adaptive mutations.
  • Natural death can counteract selection in specific network architectures, challenging previous assumptions.