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Population dynamics of mutualism and intraspecific density dependence: How θ-logistic density dependence affects mutualistic positive feedback

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  • Moore, Christopher M.
  • Catella, Samantha A.
  • Abbott, Karen C.

Abstract

Mutualism describes the biological phenomenon where two or more species are reciprocally beneficial, regardless of their ecological intimacy or evolutionary history. Classic theory shows that mutualistic benefit must be relatively weak, or else it overpowers the stabilizing influence of intraspecific competition and leads to unrealistic, unbounded population growth. Interestingly, the conclusion that strong positive interactions lead to runaway population growth is strongly grounded in the behavior of a single model. This model—the Lotka–Volterra competition model with a sign change to generate mutualism rather than competition between species—assumes logistic growth of each species plus a linear interaction term to represent the mutualism. While it is commonly held that the linear interaction term is to blame for the model's unrealistic behavior, we show here that a linear mutualism added to a θ-logistic model of population growth can prevent unbounded growth. We find that when density dependence is decelerating, the benefit of mutualism at equilibrium is greater than when density dependence is accelerating. Although there is a greater benefit, however, decelerating density dependence tends to destabilize populations whereas accelerating density dependence is always stable. We interpret these findings tentatively, but with promise for the understanding of the population ecology of mutualism by generating several predictions relating growth rates of mutualist populations and the strength of mutualistic interaction.

Suggested Citation

  • Moore, Christopher M. & Catella, Samantha A. & Abbott, Karen C., 2018. "Population dynamics of mutualism and intraspecific density dependence: How θ-logistic density dependence affects mutualistic positive feedback," Ecological Modelling, Elsevier, vol. 368(C), pages 191-197.
  • Handle: RePEc:eee:ecomod:v:368:y:2018:i:c:p:191-197
    DOI: 10.1016/j.ecolmodel.2017.11.016
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    References listed on IDEAS

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    1. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
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    Cited by:

    1. Czuppon, Peter & Gokhale, Chaitanya S., 2018. "Disentangling eco-evolutionary effects on trait fixation," Theoretical Population Biology, Elsevier, vol. 124(C), pages 93-107.
    2. Rao, Ruofeng & Yang, Xinsong & Tang, Rongqiang & Zhang, Yulin & Li, Xinggui & Shi, Lei, 2021. "Impulsive stabilization and stability analysis for Gilpin–Ayala competition model involved in harmful species via LMI approach and variational methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 571-590.
    3. Pastor, Juan Manuel & Stucchi, Luciano & Galeano, Javier, 2021. "Study of a factored general logistic model of population dynamics with inter- and intraspecific interactions," Ecological Modelling, Elsevier, vol. 444(C).

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