Tolerance-conferring defensive symbionts and the evolution of parasite virulence

Cameron Smith, Ben Ashby

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Defensive symbionts in the host microbiome can confer protection from infection or reduce the harms of being infected by a parasite. Defensive symbionts are therefore promising agents of biocontrol that could be used to control or ameliorate the impact of infectious diseases. Previous theory has shown how symbionts can evolve along the parasitism–mutualism continuum to confer greater or lesser protection to their hosts and in turn how hosts may coevolve with their symbionts to potentially form a mutualistic relationship. However, the consequences of introducing a defensive symbiont for parasite evolution and how the symbiont may coevolve with the parasite have received relatively little theoretical attention. Here, we investigate the ecological and evolutionary implications of introducing a tolerance-conferring defensive symbiont into an established host–parasite system. We show that while the defensive symbiont may initially have a positive impact on the host population, parasite and symbiont evolution tend to have a net negative effect on the host population in the long term. This is because the introduction of the defensive symbiont always selects for an increase in parasite virulence and may cause diversification into high- and low-virulence strains. Even if the symbiont experiences selection for greater host protection, this simply increases selection for virulence in the parasite, resulting in a net negative effect on the host population. Our results therefore suggest that tolerance-conferring defensive symbionts may be poor biocontrol agents for population-level infectious disease control.
Original languageEnglish
Article numberqrad015
Pages (from-to)262-272
Number of pages11
JournalEvolution Letters
Issue number4
Publication statusPublished - 5 May 2023

Bibliographical note

B.A. is funded by Natural Environment Research Council grants NE/N014979/1 and NE/V003909/1. C.A.S. is funded by Natural Environment Research Council grant NE/V003909/1.

Data Availability Statement

The Python code for the implementation of this model can be found at


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