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Generalized stacked contact process with variable host fitness

Part of: Special processes

Published online by Cambridge University Press:  04 May 2020

Eric Foxall  and
Nicolas Lanchier
Eric Foxall*
Affiliation:
University of British Columbia
Nicolas Lanchier*
Affiliation:
Arizona State University
*
*Postal address: University of British Columbia, Okanagan Campus, Kelowna, BC, Canada
*Postal address: University of British Columbia, Okanagan Campus, Kelowna, BC, Canada
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Abstract

The stacked contact process is a three-state spin system that describes the co-evolution of a population of hosts together with their symbionts. In a nutshell, the hosts evolve according to a contact process while the symbionts evolve according to a contact process on the dynamic subset of the lattice occupied by the host population, indicating that the symbiont can only live within a host. This paper is concerned with a generalization of this system in which the symbionts may affect the fitness of the hosts by either decreasing (pathogen) or increasing (mutualist) their birth rate. Standard coupling arguments are first used to compare the process with other interacting particle systems and deduce the long-term behavior of the host–symbiont system in several parameter regions. The spatial model is also compared with its mean-field approximation as studied in detail by Foxall (2019). Our main result focuses on the case where unassociated hosts have a supercritical birth rate whereas hosts associated to a pathogen have a subcritical birth rate. In this case, the mean-field model predicts coexistence of the hosts and their pathogens provided the infection rate is large enough. For the spatial model, however, only the hosts survive on the one-dimensional integer lattice.

Keywords

Multitype contact processforest fire modelhostpathogenmutualist

MSC classification

Primary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory
Type
Research Papers
Information
Journal of Applied Probability , Volume 57 , Issue 1 , March 2020 , pp. 97 - 121
Copyright
© Applied Probability Trust 2020

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