A bifurcation theorem for evolutionary matrix models with multiple traits

J. M. Cushing, F. Martins, A. A. Pinto, Amy Veprauskas

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


One fundamental question in biology is population extinction and persistence, i.e., stability/instability of the extinction equilibrium and of non-extinction equilibria. In the case of nonlinear matrix models for structured populations, a bifurcation theorem answers this question when the projection matrix is primitive by showing the existence of a continuum of positive equilibria that bifurcates from the extinction equilibrium as the inherent population growth rate passes through 1. This theorem also characterizes the stability properties of the bifurcating equilibria by relating them to the direction of bifurcation, which is forward (backward) if, near the bifurcation point, the positive equilibria exist for inherent growth rates greater (less) than 1. In this paper we consider an evolutionary game theoretic version of a general nonlinear matrix model that includes the dynamics of a vector of mean phenotypic traits subject to natural selection. We extend the fundamental bifurcation theorem to this evolutionary model. We apply the results to an evolutionary version of a Ricker model with an added Allee component. This application illustrates the theoretical results and, in addition, several other interesting dynamic phenomena, such as backward bifurcation induced strong Allee effects.

Original languageEnglish (US)
Pages (from-to)491-520
Number of pages30
JournalJournal of mathematical biology
Issue number2
StatePublished - Aug 1 2017


  • Bifurcation
  • Equilibria
  • Evolutionary game theory
  • Nonlinear matrix models
  • Stability
  • Structured population dynamics

ASJC Scopus subject areas

  • Modeling and Simulation
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics


Dive into the research topics of 'A bifurcation theorem for evolutionary matrix models with multiple traits'. Together they form a unique fingerprint.

Cite this