Monday, April 20, 2009

Universal understanding of host-parasite adaptation

We recently read a theory paper by Gandon and Day (2009). In this paper they describe a valuable method for dissecting how interactions between a host and parasite alter mean fitness. Their method uses an understanding built from Fisher's fundamental theorem. They partition changes in mean fitness based on three different factors: natural selection, environmental change, and mutation. We know that the rate of adaptation is going to result from the amount of genetic variance in the focal organism (Fisher's theorem), but what about the impact of an interacting species that evolves as well (i.e. a coevolving parasite? Here is the real beauty of their analysis because the coevolving species becomes the environment. By separating the changes in a population mean fitness into changes driven by different forces, the authors provide not only a mathematically useful model, but also a useful intuition for understanding how hosts and parasites coevolve.

There are several ways that theoreticians often describe a host-parasite interaction (e.g. gene-for-gene, matching alleles) and these describe natural systems to some degree of accuracy. The authors use their method to analyze some recent empirical evidence (Buckling and Rainey 2002; Decaestecker et al 2007). They use the time series data on the interaction to test hypotheses of the nature of the interaction. These empirical studies compare the fitness of parasites against hosts from the past that they have coevolved with and those from the future (hosts that evolve later in the study). By making these comparisons, they have the ability to hold other factors constant (the genetic variance of the parasite population) and vary the environment (the hosts). Their model makes different predictions that should be evident from empirical evidence about how parasite mean fitness should change when the environment is varied.

The authors very elegant proposed method of looking at changes over time works well for systems where archives of past populations are possible as in experimental evolution systems (Buckling and Rainey 2002) or clever natural systems (Decaestecker et al 2007), but what about the rest of us? Addressed in at the very end, but only briefly, is a comparison of spatial patterns of coevolution when temporal data is missing. I think this issue of substituting space for time is potentially very powerful, but also somewhat more complicated. Temporal samples of a coevolutionary system could be predicted to have a certain amount of autocorrelation, but does this hold for spatially distributed systems. It certainly would nice to assume that there is a relationship between distance and time and this will of course depend on gene flow. How would selection mosaics (Gomulkiewicz et al 2007; Thompson 1999, 2005) impact this potential relationship? I look forward to future research as it provides some answers.


Buckling, A., and P. B. Rainey. 2002. Antagonistic coevolution between a bacterium and a bacteriophage. P Roy Soc Lond B Bio 269:931-936.

Decaestecker, E., S. Gaba, J. A. M. Raeymaekers, R. Stoks, L. Van Kerckhoven, D. Ebert, and L. De Meester. 2007. Host-parasite 'Red Queen' dynamics archived in pond sediment. Nature 450:870-873.

Gandon, S., and T. Day. 2009. Evolutionary epidemiology and the dynamics of adaptation. Evolution 63:826-838.

Gomulkiewicz, R., D. M. Drown, M. F. Dybdahl, W. Godsoe, S. L. Nuismer, K. M. Pepin, B. J. Ridenhour, C. I. Smith, and J. B. Yoder. 2007. Dos and don'ts of testing the geographic mosaic theory of coevolution. Heredity 98:249-258.

Thompson, J. N. 1999. Specific hypotheses on the geographic mosaic of coevolution. American Naturalist 153:S1-S14.

Thompson, J. N. 2005.
The Geographic Mosaic of Coevolution. University of Chicago Press, Chicago.

Paper read

Gandon, S., & Day, T. (2009). EVOLUTIONARY EPIDEMIOLOGY AND THE DYNAMICS OF ADAPTATION Evolution, 63 (4), 826-838 DOI: 10.1111/j.1558-5646.2009.00609.x

Tuesday, April 7, 2009

Where did this infection come from? Covert infections selected by demographic variability

This week we continued along our current path of pathogen models and looked at a recent paper (Sorrell et al 2009) investigating covert infections, a common and unexplained phenomenon of some pathogens exhibiting long periods of infection where they are silent (or covert in the language of the paper). During this silent/covert stage, the infections are mostly avirulent and non-infectious. These authors extend a previous SI type model that incorporated a covert state (Boots et al 2003) to understand what forces select for this kind of pathogen.

Extending a previous SI model (Boots et al 2003), the authors build a two strain model that includes susceptible hosts and multiple classes of infected hosts. With two strains, there are two broad types of infected hosts. Each of these is split again. The hosts can carry a covert infection or an overt infection. Covert infections are allowed to become overt but not the other way around. There are multiple trade-offs built into this model. A covert infection does not cause an increased host death rate (avirulent), but it does impose a cost to host fecundity where as an overt infection is virulent but does not decrease fecundity. In addition, covert infections are only transmitted vertically (from parent to offspring), while on the other hand overt infections are only transmitted horizontally (among individuals within the population).

Without additional forces, they find no selection for covert infections. However, given the abundance of such pathogens in nature, there must be some forces that are generating the proper conditions. The paper explores three different mechanisms that may be involved in selection for covert infections. The first examines the effect of superinfection (multiple pathogen strains in the same host). They conclude that selection will favor covert infections that are protective, that is they prevent superinfection. The other two mechanisms consider nonequilibrium host dynamics, temporal variation in host density and transmission. When variation is high and the potential to be lost from the population because of a lack of hosts or a lack of transmission events, then covert infections which again are vertically transmitted become more likely.

A question that was brought up during our discussion was: are these results different from a horizontal vertical transmission trade-off? When transmission opportunities are likely (high populations), then horizontally transmitting virulent pathogens are favored. In situations when there are fewer opportunities (e.g. during host population declines), then a pathogen that retains some vertical transmission and will be favored. Favoring a more covert pathogen is really just selecting for these two fixed trade-offs. I think what this paper contributes thought is a more thorough mechanistic explanation for how this trade-off works. They provide many biological examples of pathogens with complex covert behavior and this study certainly provides evidence of how they may have arisen.

This paper was quite interesting to me in that it was the first adaptive dynamics analysis that I've really understood. The authors walk through their methods and explain how to read the pairwise invisibility plots (PIPs) and provide some helpful but uncomplicated simulations too. Recently Dercole and Rinaldi (2008) published an introduction to this modeling/analysis technique that I'm looking forward to reading in the near future.


Boots, M., J. Greenman, D. Ross, R. Norman, R. Hails, and S. Sait. 2003. The population dynamical implications of covert infections in host-microparasite interactions. Journal of Animal Ecology 72:1064-1072.

Dercole, F., and S. Rinaldi. 2008. Analysis of Evolutionary Processes: The Adaptive Dynamics Approach and its Applications. Princeton University Press, Princeton.

Sorrell, I., A. White, A. B. Pedersen, R. S. Hails, and M. Boots. 2009. The evolution of covert, silent infection as a parasite strategy. Proceedings of the Royal Society B: Biological Sciences: online early.

Paper read

Sorrell, I., White, A., Pedersen, A., Hails, R., & Boots, M. (2009). The evolution of covert, silent infection as a parasite strategy Proceedings of the Royal Society B: Biological Sciences DOI: 10.1098/rspb.2008.1915