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. 2011 Sep 22;278(1719):2814-22.
doi: 10.1098/rspb.2010.2146. Epub 2011 Feb 2.

Ploidy and the evolution of parasitism

Leithen K M'Gonigle  1 , Sarah P Otto
Affiliations

Ploidy and the evolution of parasitism

Leithen K M'Gonigle et al. Proc Biol Sci. .

Abstract

Levels of parasitism are continuously distributed in nature. Models of host-parasite coevolution, however, typically assume that species can be easily characterized as either parasitic or non-parasitic. Consequently, it is poorly understood which factors influence the evolution of parasitism itself. We investigate how ploidy level and the genetic mechanisms underlying infection influence evolution along the continuum of parasitism levels. In order for parasitism to evolve, selective benefits to the successful invasion of hosts must outweigh the losses when encountering resistant hosts. However, we find that exactly where this threshold occurs depends not only on the strength of selection, but also on the genetic model of interaction, the ploidy level in each species, and the nature of the costs to virulence and resistance. With computer simulations, we are able to incorporate more realistic dynamics at the loci underlying species interactions and to extend our analyses in a number of directions, including finite population sizes, multiple alleles and different generation times.

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Figures

Figure 1.
Figure 1.
Sample trajectories from simulations (after a burn-in period) in the MAM model with complete parasitism (f = 1). Left column is with haploid hosts and diploid parasites, right column is with diploid hosts and haploid parasites, and row labels indicate mutation rates. The background is coloured grey to indicate when the parasite is ‘losing’ the arms race with the host, and white when it is ‘winning’. Other parameters were αP = 0.05, βP =0.05, αH = 0.05, ψH = ψP = 1 and r = 0.5, and population sizes were 106 in both species. Solid line, host; dashed line, parasite.
Figure 2.
Figure 2.
Invasion conditions in the (a) MAM and (b) IMAM model. Solid (dashed) lines correspond to haploid (diploid) parasites, and thick (thin) lines to haploid (diploid) hosts. For a given case, parasitism is expected to evolve when selection is such that the point (αP, βP) lies below the corresponding line. The slopes of these lines can be inferred from the invasion conditions in table 4. Note that there are two lines with the same slope in (b).
Figure 3.
Figure 3.
Invasion conditions in the GFG model with (a) conditional (solid line, cP,c = 0.01; dashed line, cP,c = 0.05) and (b) unconditional (solid lines, cP,u = 0.01; dashed lines, cP,u = 0.05) costs. For a given case, parasitism is expected to evolve for all ploidy combinations when αP lies to the right of the plotted line. The three curves for each unconditional cost in (b), from left to right, correspond to different initial parasitism levels of f = 0.9, 0.7 and 0.5. Other parameters were αH = 0.05 and cH = 0.01.
Figure 4.
Figure 4.
Evolutionarily convergent level of parasitism (f) as a function of the strength of selection in parasites under the (a,c,e,g) MAM and (b,d,f,h) IMAM. The mutation rate was μ = 10−5, and thus large cycles occurred in all cases except (f). Dashed lines denote the analytical invasion condition assuming small cycles (table 4). Cells are shaded based on the mean level of parasitism present in the population after 106 generations of evolution in a single simulation (darker = higher; see shading scale in (h)). Initial frequencies at the A-locus were randomly drawn for each cell. Different ploidy combinations are indicated on the right-hand side. Other parameters were as in figure 1.
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References

    1. Frank S. A. 2002. Immunology and the evolution of infectious diseases. Princeton, NJ: Princeton University Press - PubMed
    1. Galvani A. P. 2003. Epidemiology meets evolutionary ecology. Trends Ecol. Evol. 18, 132–13910.1016/S0169-5347(02)00050-2 (doi:10.1016/S0169-5347(02)00050-2) - DOI - DOI
    1. May R. M., Anderson R. M. 1983. Epidemiology and genetics in the coevolution of parasites and hosts. Proc. R. Soc. Lond. B 219, 281–31310.1098/rspb.1983.0075 (doi:10.1098/rspb.1983.0075) - DOI - DOI - PubMed
    1. Hamilton W. D., Axelrod R., Tanese R. 1990. Sexual reproduction as an adaptation to resist parasites (a review). Proc. Natl Acad. Sci. USA 87, 3566–357310.1073/pnas.87.9.3566 (doi:10.1073/pnas.87.9.3566) - DOI - DOI - PMC - PubMed
    1. Peters A. D., Lively C. M. 2007. Short- and long-term benefits and detriments to recombination under antagonistic coevolution. J. Evol. Biol. 20, 1206–121710.1111/j.1420-9101.2006.01283.x (doi:10.1111/j.1420-9101.2006.01283.x) - DOI - DOI - PubMed

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