Elsevier

Theoretical Population Biology

Volume 130, December 2019, Pages 170-181
Theoretical Population Biology

Strategies of host resistance to pathogens in spatially structured populations: An agent-based evaluation

https://doi.org/10.1016/j.tpb.2019.07.014Get rights and content

Abstract

There is growing theoretical evidence that spatial structure can affect the ecological and evolutionary outcomes of host-parasite interactions. Locally restricted interactions have been shown in particular to affect host resistance and tolerance. In this study we investigate the evolution of several types of host disease resistance strategies, alone or in combination, in spatially structured populations. We construct a spatially explicit, individual-based stochastic model where hosts and parasites interact with each other in a spatial lattice, and interactions are restricted to a given neighbourhood of varying size. We investigate several host resistance strategies, including constitutive (expressed in all resistant hosts), induced (expressed only upon infection), and combinations thereof. We show that a costly constitutive resistance cannot reach fixation, whereas an inducible resistance strategy may become fixed in the population if the cost remains low, particularly if it impacts host recovery. We also demonstrate that mixed strategies can be maintained in the host population, and that a higher investment in a recovery-boosting inducible resistance allows for a higher investment in a constitutive response. Our simulations reveal that the spatial structure of the population impacts the selection for resistance in a complex fashion. While single strategies of resistance are generally favoured in less structured populations, mixed strategies can sometimes prevail only in highly structured environments, e.g. when combining constitutive and transmission-blocking induced responses Overall these results shed new light on the dynamics of disease resistance in a spatially-structured host-pathogen system, and advance our theoretical understanding of the evolutionary dynamics of disease resistance, a necessary step to elaborate more efficient and sustainable strategies for disease management.

Section snippets

Background

Host resistance to pathogens presents an obvious evolutionary advantage and, because of the ubiquity of parasitism in the living world, such traits are typically and pervasively under strong, positive selection. Resistance, however, is usually associated with a fitness cost for the host, and therefore also subject to negative selective pressures. Such costs of resistance have been demonstrated in many organisms ranging from bacteria (Gomez and Buckling, 2011) to insects (Koella and Boëte, 2002,

General model characteristics

We introduce in this study a probabilistic cellular automaton, a stochastic, spatially-explicit individual-based model. The population is described as a square, homogeneous 100 × 100 lattice of individual sites, each of which may be empty or occupied by a single (infected or uninfected) host. To eliminate edge effects, we assume that the lattice has a toric topology, i.e. sites on a given edge are connected to sites on the opposite edge. The source code for the simulation is available on the

Results

In the first two scenarios, we only explored the effect of space on the spread of one type of resistance alone, first the constitutive one, then the inducible one. To do so the r1 parameter was respectively set to 1 then 0. We varied the cost associated with each type of resistance, as well as the size of the neighbourhood, in order to measure their combined impact on the spread of resistance in the host population as well as on the epidemiology of the disease.

Discussion

Host resistance to pathogens can be based on a variety of mechanisms and investment strategies, and the respective selective pressures associated with these different types of resistance can be correspondingly diverse. In this study, we elucidate how these mechanisms of resistance can be differentially selected, alone or in combination, not only based on the way the response to a pathogen is mounted (inducible or constitutive resistance), but also based on the specific trait of the

Conclusion

To conclude, our approach in this study has been to consider the evolution of various defence strategies, alone or in combination, in a spatial setting. As this model was designed to simulate the dynamics of generic host-parasite interactions, the results presented here should not be seen as practical lessons for any particular host-pathogen system. Nevertheless, this study highlights several essential points for a better understanding of the evolutionary dynamics of such systems. First, it

Declaration of Competing Interest

None.

Acknowledgements

The authors are grateful to Roland Regoes and to Samuel Alizon for helpful comments on an earlier version of this manuscript and to King Li for technical assistance. C.B. warmly thanks Karthik Ram for his Wes Anderson color palette for R allowing us to embark with Steve Zissou.

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