Monday, November 23, 2009

Sex and death: a model of density-dependent virulence

Providing evidence that supports the role of parasites driving the maintenance of sex (i.e. the Red Queen hypothesis) has been a challenge ever since it was proposed. Both theoreticians and empiricists have tackled this hypothesis with vigor to mixed results. This week we read Lively (2009) which focuses on a singular effect to help build a theoretical argument for the Red Queen, density-dependent virulence. Here virulence is defined as the effect of the parasite on the host population growth rate. The density-dependent part comes into play in that the virulence increases with host population size.

The main argument of the paper is that as an asexual population invades a sexual population, the level of virulence changes and this can in turn change the outcome of the overall winner. Parasites with large density-dependent effects can change the balance and allow the maintenance of sexual populations. Presented in several graphs, virulence is a population measure of the effect of the parasites on the hosts. I'm still curious about the magnitude of selection on the individual genotypes in the model. When interpreting the results of this model, I was only able to see what happens when a group of asexual organisms invades a sexual one.

Lively provides an excellent ion description and understanding of the cost of sex. Of course the cost of sex has been detailed before, but the mathematical explanation helps with a basic intuition. The model described in the paper identifies two populations of hosts: asexual and sexually reproducing individuals. What he identifies is that in a sexual population, males provide little and females must produce at least two offspring to replace themselves. These males are using up resources. They are also decreasing the overall density of hosts that could be achieved in a complete female (or asexual) population.

One of the topics that came up during out discussion was how sex ratio may change or evolve during the evolution of sex. The simulation results presented in Lively (2009) assumes a sex ratio of 50/50 which makes sense in an evolutionary context. This has the effect of setting the advantage of the asexual population to be two fold over the sexual population. What happens when instead of two separate populations that do not interbreed, we have females choosing to produce offspring via sex or parthenogenesis? Will rare males in such a population change the early dynamics enough to produce different results?


Lively, C. M. 2009. The maintenance of sex: host-parasite coevolution with density-dependent virulence. J Evolution Biol 22:2086-2093.

LIVELY, C. (2009). The maintenance of sex: host-parasite coevolution with density-dependent virulence Journal of Evolutionary Biology, 22 (10), 2086-2093 DOI: 10.1111/j.1420-9101.2009.01824.x

Saturday, November 14, 2009

Is the Red Queen showing her face? Evidence of negative frequency dependent selection by parasites

Recently Wolinska and Spaak (2009) provide a survey of Daphnia infections by genotype across a number of lakes in Italy and Switzerland. They present their results as empirical evidence of Red Queen dynamics in which coevolution with virulent parasites generates continued evolution. Although Van Valen (1973) originally presented a macroevolutionary argument where by reciprocal selection of hosts and their parasites generates conditions for continuous change, Bell (1982) narrowed the focus as a mechanistic explanation for the evolution or maintenance of sexual reproduction through cyclical changes in genotype frequencies. Wolinska and Spaak (2009) are not addressing the evolution of sex, but looking for evidence that parasites in Daphnia populations are generating negative frequency dependent selection such that a rare genotype has an advantage. Evidence consistent with the Red Queen has been found in other systems using spatially distributed samples (e.g. Dybdahl and Lively 1995) to look non-random infection rates as well as more directly looking at changes in frequencies of common genotypes (e.g. Dybdahl and Lively 1998).

Wolinska and Spaak (2009) propose three hypotheses to test with their data. The first is that common genotypes should be either over or under infected compared to a random sample. This prediction is based on stereotyped cyclical dynamics of genotypes of hosts and parasites (image two out of sync sine waves). At some points, the common clones will be targeted by the parasites and become overly infected. As a genotype becomes common, parasites haven't started attacking this genotype yet (i.e. time lagged), so it is under infected. In their survey, the found that indeed, some of the populations showed over infection (n = 1) and other showed under infection (n = 11), although the majority of cases did show no significant difference from random infection probabilities which is predicted as being a rare event. Their second hypothesis was that common genotypes should over the course of time decline if they are being tracked by parasites. The previous sample included only different lakes; where as the data needed to test this hypothesis are temporal samples from the same location. Their additional data is consistent with common genotypes declining over time (9 out of 10 cases). However, it is unclear to me how the general trend in this data of common genotypes decreasing over time, leads to the evidence supporting the first hypothesis. Shouldn't they find many more over infected common clones? A third hypothesis that they tested regarded host-parasite interactions maintaining diversity and an evenness of genotype frequencies which their data supported.

When discussing this paper, we were interested in what happens to predictions based on Red Queen dynamics when more than one parasite is involved. Previous empirical papers and theory seems to be generally focused on a host and a common parasite, but we know hosts are attacked by all kinds of parasites and pathogens. The system described by Wolinska and Spaak (2009) involves a host hybrid complex as well as four different parasites and questions about host specialization and hybrid maintenance were addressed in a previous paper (Wolinska et al. 2007). Where is the companion theoretical work to provide testable hypotheses?


Bell, G. 1982. The Masterpiece of Nature: The Evolution and Genetics of Sexuality. University of California Press, Berkeley.

Dybdahl, M. F., and C. M. Lively. 1995. Host-Parasite Interactions: Infection of Common Clones in Natural Populations of a Freshwater Snail (Potamopyrgus antipodarum). Proceedings of the Royal Society of London. Series B: Biological Sciences 260:99-103.

Dybdahl, M. F., and C. M. Lively. 1998. Host-parasite coevolution: Evidence for rare advantage and time-lagged selection in a natural population. Evolution 52:1057-1066.

Van Valen, L. 1973. A new evolutionary law. Evolutionary Theory 1:1-30.

Wolinska, J., B. Keller, M. Manca, and P. Spaak. 2007. Parasite survey of a Daphnia hybrid complex: host-specificity and environment determine infection. Journal of Animal Ecology 76:191-200.

Wolinska, J., and P. Spaak. 2009. The cost of being common: evidence from natural Daphnia populations. Evolution 63:1893-1901.

Wolinska, J., & Spaak, P. (2009). The cost of being common: evidence from natural Daphnia populations Evolution, 63 (7), 1893-1901 DOI: 10.1111/j.1558-5646.2009.00663.x

Wednesday, September 9, 2009

Can the Red Queen keep running? A case against recombination

In 2004, Otto and Nuismer published a theoretical paper on the evolution of sex where they examined a range of stereotyped models (e.g. gene-for-gene) of species interactions (both antagonistic and beneficial) that are often used by theoreticians. Their results indicated that sex and recombination were generally selected against regardless of the model of interaction given the assumptions of the quasi linkage equilibrium (QLE, in this case, weak selection and strong recombination). In their numerical simulations that explored parameter space potentially outside the assumptions of the QLE, they found that some cases of the matching-genotypes model (or a strict matching alleles model) of interactions would favor sex and recombination.

Kouyos et al (2007) looked at a wide range of matching alleles models (MAM) and found that when selection was strong, some models would favor sex and recombination. Salathé et al (2008b) also provide evidence of strong selection favoring recombination under the MAM. However, both did find that the closer these models were to a multiplicative form of the MAM, sex and recombination were selected against. These multiplicative matching alleles models (MMAM) were described by Otto and Nuismer (2004) as the negative control in their numerical simulations because they never favored recombination. Their QLE results also indicated that this model of interaction should not generate linkage disequilibrium and therefore neither favor nor select against recombination. Contrary to this, in a surprising result by Kouyos et al (2007), their simulations found that there was strong selection against recombination (rather than no selection at all) in the parameter space near a MMAM.

It was this surprising result that was explained in the paper that we read this past week for Coevolvers (Kouyos et al 2009). Here the authors investigated why this parameter space shows strong selection again recombination. In a MMAM, there are no epistatic interactions between the loci involved in the fitness of the interaction between host and parasite. Despite this, previous observations (Kouyos et al 2007) and the current simulations have shown that strong linkage disequilibrium is built up and maintained. It turns out that here that an interaction governed by the MMAM can equilibrate to a region of high complementarity. The importance of this is that this equilibrium is such that any recombination among the loci will generate genotypes that have a lower fitness and recombination should be selected against.

I think that this recent paper (Kouyos et al 2009) sheds more light on specific potential microevolutionary mechanisms that drive the maintenance of recombination. We still need empirical test of some more of these new predictions. The challenge for empiricists is to find the right kind of systems and a challenge for the theoreticians is to help design the right kinds of experiments.

While I have just touched on a couple of recent results testing aspects of the Red Queen Hypothesis, Salathé et al (2008a) produced a wonderful review of many of many recent theoretical results on the evolution of sex and recombination driven by host-parasite interactions. In addition, this group has another paper on this topic out recently in the American Naturalist (Salathé et al 2009) that I'm looking forward to reading.


Kouyos, R., M. Salathe, and S. Bonhoeffer. 2007. The Red Queen and the persistence of linkage-disequilibrium oscillations in finite and infinite populations. BMC Evolutionary Biology 7:211.

Kouyos, R. D., M. Salathé, S. P. Otto, and S. Bonhoeffer. 2009. The role of epistasis on the evolution of recombination in host-parasite coevolution. Theoretical Population Biology 75:1-13.

Otto, S. P., and S. L. Nuismer. 2004. Species interactions and the evolution of sex. Science 304:1018-1020.

Salathé, M., R. D. Kouyos, and S. Bonhoeffer. 2008a. The state of affairs in the kingdom of the Red Queen. Trends in Ecology & Evolution 23:439-445.

Salathé, M., R. D. Kouyos, and S. Bonhoeffer. 2009. On the Causes of Selection for Recombination Underlying the Red Queen Hypothesis. The American Naturalist 174:S31-S42.

Salathé, M., R. D. Kouyos, R. R. Regoes, and S. Bonhoeffer. 2008b. Rapid parasite adaptation drives selection for high recombination rates. Evolution 62:295-300.

KOUYOS, R., SALATHE, M., OTTO, S., & BONHOEFFER, S. (2009). The role of epistasis on the evolution of recombination in host–parasite coevolution Theoretical Population Biology, 75 (1), 1-13 DOI: 10.1016/j.tpb.2008.09.007

Monday, September 7, 2009

How to optimize host transmission in a complex parasite

Hammerschmidt and colleagues (2009) recently published an empirical investigation of optimal host switching. Parasites that must infect multiple hosts to complete their life cycle face a complex set of challenges. One of these is determining the timing of the switch. The authors of this paper look at the trade-off involved in staying in an intermediate host so as to become larger and more fecund in the next host and the increased chance of mortality in the current host. The authors conduct two different experiments with a tapeworm parasite, Schistocephalus solidus. In one experiment they examined the behavior of the first intermediate host, cyclopoid copepods (Macrocyclops albidus). In the second experiment they directly measured differences in fecundity among different host switch timing between the first and second intermediate hosts (in this case the three-spine stickleback, Gasterosteus aculeatus). The authors also build an optimality model and use the data from these experiments as well as some previously published data to confirm that the switch from the first to second host occurs at an optimal time for parasite fecundity.

What was most novel about this paper to me was the modification of the host behavior that had the effect of reducing parasite transmission, at least in the short run. Since the parasite was transmitted trophically, the next host eats the previous host, predation enhancement or avoidance directly influences the rate of transmission. The authors found some evidence of predation enhancement after the optimal switch time, but the stronger evidence was at least a shift in behavior of the current host. Before the parasite is mature in the first intermediate host, or before the optimal switching time to the second intermediate host, there was a reduction in movement which translates into predator avoidance behavior. Manipulating the host so as to allow the parasite a longer time to grow is a very clever strategy. In hosts that have a high potential mortality, this strategy may be found among a diversity of trophically transmitted parasites.


Hammerschmidt, K., K. Koch, M. Milinski, J. C. Chubb, and G. A. Parker. 2009. When to go: Optimization of host switching in parasites with complex life cycles. Evolution 63:1976-1986.

Hammerschmidt, K., Koch, K., Milinski, M., Chubb, J., & Parker, G. (2009). Whe to go: Optimzation of host switching in parasites with complex life cycles Evolution, 63 (8), 1976-1986 DOI: 10.1111/j.1558-5646.2009.00687.x

Tuesday, July 7, 2009

Selection Mosaics or environmental interactions

Vale and Little (2009) published recent work on parasite infection variation across a temperature gradient. Specific parasite infections are often the result of genetic interactions of both the host and parasite, sometimes referred to as genotype by genotype interactions (GxG). The authors of this paper used an ideal interaction between Daphnia magna and a bacterial parasite, Pasteuria ramose. The experiment was such that they could test multiple levels on interactions. They isolated multiple host clonal lines (n = 4) as well as parasite lines (n = 4) and compared infection rates as well as parasite growth rates across three different temperatures. The paper details the experiment very well, so I'll spare details here, but a good model for future studies.

The authors found significant GxG interactions for most of the traits measured in the infection process, including both early (probability of infection) and later (parasite growth rate). However differences in genotype by environment (GxE) interactions showed up for different places in the infection timeline. The probability of infection showed a host genotype by temperature interaction, but this was a weak affect and the authors make the important point that the relative rank order wasn't changed. The reason this is key is that it is often emphasized that GxE interactions are a mechanism of the maintenance of different genotypes. If each genotype has high fitness in only some environments, and the environment varies, then there can be some period of time where polymorphism is maintained. In terms of interactions of the parasite genotype and the environment, there were initially some interactions with transmission potential and growth rate, however rank differences were again absent. The paper makes one further step and examines the combined transmission potential (spore production and infectivity). This isn't quite a measure of R0 because of complications with the effect of dose on infection rate and the interaction between parasite genotype and temperature disappears.

The study failed to find evidence of a GxGxE interaction, but the authors were correct to point out, that this is only the case for the environmental variable measured (temperature). Given that temperature is an important component of the environment for this interaction, I was surprised by this result. Perhaps, it would have been different if the difference were not just in constant temperature, but in some sort of variable environment. In the very last paragraph, Vale and Little (2009) emphasize that the lack of GxGxE interactions mean that the specificity of the interactions are robust to environmental noise. However, it is just such noise that others have proposed as important in maintaining variation. These interactions are the selection mosaics in the Geographic Mosaic Theory of Coevolution (Thompson 1999, 2005).


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.

Vale, P. F., and T. J. Little. 2009. Measuring parasite fitness under genetic and thermal variation. Heredity online early.

Paper read

Vale, P., & Little, T. (2009). Measuring parasite fitness under genetic and thermal variation Heredity DOI: 10.1038/hdy.2009.54

Monday, May 11, 2009

Selection mosaics and the GMTC

This past week in Coevolvers, we dropped back into the empirical world and ready a paper from Piculell et al (2008) on evidence of selection mosaics. Selection mosaics describe a case where the fitness function of the interacting players varies across space (Gomulkiewicz et al 2007; Thompson 1999, 2005), sometimes described as GxGxE interactions (G: genetic; E: environment). What does this mean more generally? Simply put, the fitness of a plant may change from one population to the next because the nature of the interaction with a mutualist is affected by the environment. This can occur even if the genotypes that make up those populations are exactly the same.

The experimental design was certainly setting up the case for a maximum chance of detection of interaction effects. With only levels of each factor, (e.g. two genotypes of the host) the authors had less power to detect any main effects, but that clearly wasn't the objective. They wanted to find evidence of significant GxGxE. Essentially this experiment had 4 environmental treatments, so they maximized the chance of an interaction. The authors of this paper were very upfront that they were not intending to measure a selection mosaic in the natural setting. Their objective was to demonstrate the possibility and they certainly obtained that goal. With that limitation in mind, how general are these results? Measuring the potential for a selection mosaic is one thing, but for this to really have an impact in generating or maintaining diversity as imagined in the Geographic Mosaic Theory of Coevolution (Thompson 1999, 2005) then it must hold for a broad sample of the populations under investigation. The authors are on a good track though to discovering more about this system. Perhaps they plan on taking the methodology outlined in Nuismer and Gandon (2008) on reciprocal-transplant designs. Picking a larger sample of the genetic variation found in nature for at least one of the players would extend their results from the possible into the probable.


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.

Nuismer, S. L., and S. Gandon. 2008. Moving beyond Common-Garden and Transplant Designs: Insight into the Causes of Local Adaptation in Species Interactions. American Naturalist 171:658-668.

Piculell, B., J. Hoeksema, and J. Thompson. 2008. Interactions of biotic and abiotic environmental factors in an ectomycorrhizal symbiosis, and the potential for selection mosaics. Bmc Biol 6:23.

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

Piculell, B., Hoeksema, J., & Thompson, J. (2008). Interactions of biotic and abiotic environmental factors on an ectomycorrhizal symbiosis, and the potential for selection mosaics BMC Biology, 6 (1) DOI: 10.1186/1741-7007-6-23

Monday, May 4, 2009

Does nestedness lead to more nestedness?

This past week in Coevolvers, we read a brand new paper in Nature from Bastolla et al (2009). The authors create a simple model to understand how network structure can lead to an increase in predicted biodiversity in a community. In this case, the authors were looking at how a network of mutualistic interactions will generally be nested. This network structure can reduce interspecific competition and allow a greater biodiversity. The nestedness of interactions in this kind of community refers to how many pollinators a pair of plants share compared to their total number of pollinators. The more they share, and the more this is the case across the entire network, then the higher the network nestedness. The authors use a set of previously published real networks to test predictions from their model.

I thought I would have a quick look at some of these "real" networks. The appendix of the paper directed me to Bascompte et al (2003). This paper summarized pollinator, seed dispersal, and food web networks of plant-animal interactions.

While there I noticed a reference to a review paper in Annals of Botany (Vazquez et al 2009) with an exciting title (Uniting pattern and process in plant-animal mutualistic networks). This looks like a great review and perhaps a future post. In the section outlining "patterns", they provide two contrasting topics, "Mutualistic networks tend to nested" but also "Mutualistic networks tend to be compartmentalized". This struck me as contradictory to the paper we read (Bastolla et al 2009) which predicted nested networks to emerge.

Vazquez et al (2009) had several citations for compartmentalized networks (Dicks et al 2002; Guimaraes et al 2007; Olesen et al 2007). I looked up the Dicks et al paper. I see they find compartmentalization. "The compartments reflected classic pollination syndromes to some extent, dividing the insect fauna into a group of butterflies and bees, and a group of flies, at both sites. The compartmentalization was also affected by phenology" (Dicks et al 2002). There are certainly more examples out in nature that are compartmentalized. Pollinator syndromes could create these compartments. There are other examples of real networks of mutualisms that show compartmentalization. Vazquez et al (2009) finally point to a paper from Lewinsohn et al (2006) where they propose how both patterns can co-occur (compartments with nestedness) and I think this is really what Dicks et al (2002) is finding. Olesen et al (2007) have a paper where they are essentially calling this modularity. You have compartments (modules) and then nested networks present within those. While the original paper we read (Bastolla et al 2009) contained a potential process for how mutualistic networks can form, it seems as though natural networks are probably the result of a complex set of processes.


Bascompte, J., P. Jordano, C. J. Melian, and J. M. Olesen. 2003. The nested assembly of plant-animal mutualistic networks. Proceedings of the National Academy of Sciences of the United States of America 100:9383-9387.

Vazquez, D. P., N. Bluthgen, L. Cagnolo, and N. P. Chacoff. 2009. Uniting pattern and process in plant-animal mutualistic networks: a review. Ann Bot.

Dicks, L. V., S. A. Corbet, and R. F. Pywell. 2002. Compartmentalization in plant-insect flower visitor webs. Journal of Animal Ecology 71:32-43.

Olesen, J. M., J. Bascompte, Y. L. Dupont, and P. Jordano. 2007. The modularity of pollination networks. Proceedings of the National Academy of Sciences 104:19891-19896.

Lewinsohn, T., P. Prado, P. Jordano, J. Bascompte, and J. Olesen. 2006. Structure in plant-animal interaction assemblages. Oikos 113:174-184.

Paper Read

Bastolla, U., Fortuna, M., Pascual-García, A., Ferrera, A., Luque, B., & Bascompte, J. (2009). The architecture of mutualistic networks minimizes competition and increases biodiversity Nature, 458 (7241), 1018-1020 DOI: 10.1038/nature07950

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

Thursday, March 26, 2009

Why doesn’t this pathogen kill me and why is it taking so long to clear?

This week the Coevolvers read a brand new paper by King et al (2009). The authors present a pathogen model that incorporates within host dynamics of pathogen growth as well as multiple forms of transmission among hosts which depend on pathogen load. The authors do motivate the study by telling us about two human disease pathogens, Bordetella pertussis and Bordetella parapertussis (which can cause whooping cough), but model is not meant to be a predictive model of future outbreaks. The main message of the paper is that including within host dynamics in conjunction with SIR models of populations leads to a better understand of disease evolution. Mideo et al (2008) wrote a recent review on including within host dynamics in evolutionary epidemiological models for more general information on this approach.

While the outline of the model was well written, how they combined the multiple different parts was unclear. The model consisted of three components: 1) within host pathogen replication 2) dose dependent transmission and 3) between host/SIR type model. What we found hard to understand was how the model incorporated the variation in pathogen loads among the hosts into the overall transmission rate. It appeared as if the model integrates over a number of classes of hosts (depending on their age of infection), but we felt that this then removed quite a bit of the variation that was being captured by including within host dynamics. A simplifying assumption that the authors made also was that each host was always infected with the same dose of pathogens and that their immune system had to be restarted each time. The authors do state that they have already worked on a stochastic model of this system which hasn't yet been published. We are very interested on the quantitative results from that analysis since some of these problems could be addressed there.

Why not make a population genetics model to address the questions posed by the authors at the beginning of the paper. This was question stimulated by our previous reading of Boots et al (2009) and Day and Gandon (2007) that provide detailed reviews of different modeling approaches as well as addressing specific problems in evolutionary epidemiology. King et al (2009) present their results of how intermediate within host pathogen growth rates can maximize R0 under some transmission models, but what they don't do is present an analysis where they look at how different pathogens might evolve. Is the intermediate growth rate a stable strategy? Given the model framework, there might be complex interactions between different pathogens mediated through hosts. Higher growth rates of an aggressive pathogen could lead to a tragedy of the commons.


Boots, M., A. Best, M. R. Miller, and A. White. 2009. The role of ecological feedbacks in the evolution of host defence: what does theory tell us? Philos. Trans. R. Soc. B-Biol. Sci. 364:27-36.

Day, T and S Gandon. 2007. Applying population-genetic models in theoretical evolutionary epidemiology. Ecology Letters 10 (10), 876–888.

King, A. A., S. Shrestha, E. T. Harvill, and O. N. Bjørnstad. 2009. Evolution of Acute Infections and the Invasion-Persistence Trade-Off. The American Naturalist 173:446-455.

Mideo, N., S. Alizon, and T. Day. 2008. Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases. Trends in Ecology and Evolution 23(9): 511-517.

Paper read:

King, A., Shrestha, S., Harvill, E., & Bjørnstad, O. (2009). Evolution of Acute Infections and the Invasion‐Persistence Trade‐Off The American Naturalist, 173 (4), 446-455 DOI: 10.1086/597217

Tuesday, March 17, 2009

Parasites can maintain host diversity

In their recent paper, Morgan et al (2009) look at the role of an antagonistic interaction in promoting coexistence among different hosts. Using a bacteria and phage system (bacterium: Pseudomonas fluorescens and bacteriophage: SBW25Φ2), they determined that in the presence of a coevolving phage, a slower growing, but phage resistant host would persist with a susceptible faster growing host. Without the phage, the better competitor became fixed in experimental lines. This paper did not explicitly demonstrate coevolution between the phage and the bacterial host, however there is previous evidence in this system for reciprocal selection (Buckling and Rainey 2002). The point of this experiment was to demonstrate the cost of parasite resistance.

The authors also presented a second hypothesis that was a little less explicit: "the probability of coexistence would alter through time." This general hypothesis was supported and the authors provided several explanations for the fitness of the resistant mutant changing over time with respect to wild type. They narrow down the field to changes in the cost of resistance and compensatory mutations. Their evidence comparing growth rates from the beginning to the end of the experiment support a change in the cost, but I wasn't completely convinced that this ruled out compensatory mutations.

A disappointing portion of this system is a lack of understanding of the mechanism of phage resistance. This is no fault of the authors, as the paper is just the beginning of an investigation. They have some details about a general reaction (production of "cellulose-like polymer"). It would be very interesting to take this system to the next step and start targeting some genes


Morgan, A. D., R. C. Maclean, and A. Buckling. 2009. Effects of antagonistic coevolution on parasite-mediated host coexistence. J Evolution Biol 22:287-292.

Buckling, A. and Rainey, P.B. 2002. Antagonistic coevolution between a bacterium and a bacteriophage. Proc. R. Soc. Lond. B Biol. Sci. 269: 931–936.

MORGAN, A., CRAIG MACLEAN, R., & BUCKLING, A. (2009). Effects of antagonistic coevolution on parasite-mediated host coexistence Journal of Evolutionary Biology, 22 (2), 287-292 DOI: 10.1111/j.1420-9101.2008.01642.x

Friday, March 13, 2009

Why do I have so many parasites?

This week's paper (Bordes et al 2009) looked for forces that influence the parasite diversity or parasite species richness (PSR) among mammals. While it may seem almost impossible to think that there might be a single factor, there have been many different proposed influences (e.g. body size, geographic range, population density). The host home range, "area used in daily and seasonal movements" (Bordes et al 2009), could be related to the parasite diversity in two distinct ways. Their first prediction is that as home range increases so will PSR because it will result in an increased contact with diverse habitats (and therefore parasites). Their "spatial dispersion model of parasite acquisition" uses parasite transmission and host density to get this relationship to predict the opposite relationship. The results of their analyses supported the second prediction.

The group found the methods and results of this paper relatively straight forward. The use of independent contrasts to control for the effect of phylogeny was very appropriate in this paper. We did find one area of the analysis confusing with respect to the host sampling number. It is well known that sampling intensity may bias the number of parasites found on a host . The more hosts one searches the more parasites will be found up to some saturation point. The authors controlled for the bias by using the residuals of parasite richness and host number. However, we were then confused by the inclusion of "Host sample size" in the regression analyses. While other variables in the regression analyses were significant, it was hard to determine the impact of this highly significant variable on the total fit of the model. We were left wondering how much of the variation in PSR did the home range explain?

The main conclusion of this paper is to confirm a roll for epidemiological factors (density and transmission) on the relationship between home range and PSR. The results show a negative relationship between home range and PSR which is consistent with their second prediction. The strong negative relationship between home range and host density relates their effect to how this can influence the number of parasites. It seems that home range not only describes a complicated trait of a host species, but is perhaps influenced by a complicated set of other factors.

Today's group speculated on broader potential relationships of host traits and parasite diversity. Could there be a more universal law that predicts the parasite species load? This study and many of the citations contained within focus on animals and their macroparasites. Maybe there is a rule that works across such taxonomic divisions? The paper cited previous work on the role of body mass (Arneberg 2002, Lindenfors et al 2007) and parasite diversity with larger hosts being home to a larger number of parasites. Can this relationship be scaled up to incorporate host density? What about the total mass of a host species?


Bordes, F., S. Morand, D. A. Kelt, and D. Van Vuren. 2009. Home Range and Parasite Diversity in Mammals. The American Naturalist 173:467-474.

Arneberg, P. 2002. Host population density and body mass as determinants of species richness in parasite communities: comparative analyses of directly transmitted nematodes of mammals. Ecography 25:88–94.

Lindenfors, P., C. L. Nunn, K. E. Jones, A. A. Cunningham, W. Sechrest, and J. L. Gittleman. 2007. Parasite species richness in carnivores: effects of host body mass, latitude, geographical range and population density. Global Ecology and Biogeography 1:1–14.

Bordes, F., Morand, S., Kelt, D., & Van Vuren, D. (2009). Home Range and Parasite Diversity in Mammals The American Naturalist, 173 (4), 467-474 DOI: 10.1086/597227

Evolution of virulence revisited

In their recent paper, Vigneux et al (2008) address a classic idea in the evolution of virulence. When multiple genotypes of a parasite infect a single host, competition can influence the overall virulence. The paper is examines the interaction of relatedness and virulence. One viewpoint is that with a low level of relatedness, virulence should increase as competition among genotypes overexploits the host. Another hypothesis that the authors test is that different genotypes may engage in a "chemical warfare" inside the host. This would lead to a decrease in virulence as relatedness decreases.

Their overall results are completely consistent with their second hypothesis, increases in virulence with increases in relatedness as mediated through limited migration. Their evidence is that the host shows a quicker mortality in the low migration treatment. More compelling at least in gaining evidence for the role of interference competition is their growth inhibition assay. Bacterial clones from the low migration treatment did not inhibit the growth of other clones from the same host. When the authors tested clones from different hosts did still possessed some ability to inhibit growth.

While the details on the infection protocol in this paper seemed to make the results a little harder to understand, they did gain evidence that clearly support the role of interference competition on virulence. The proposed mechanism seems sound, but obviously could use further investigation. I initially misunderstood the role of migration in this experiment. To my understanding the effect of their different treatments was to reduce the variation among genotypes and increase the relatedness. Previous arguments about the role of transmission and virulence are not completely appropriate in the context of this experiment. Some of the discussion among our group focused on the role of kin selection in the evolution of greater virulence.

Some extra details: This experiment uses a rather complex host-parasite interaction consisting of a nematode (Steinernema carpocapsae) that is a parasite in insect larvae. However, unlike a previous paper (Bashey et al 2007) focusing on the nematode, here the main focus is a symbiotic bacterium of the nematode (Xenorhabdus nematophila) that along with the nematode induces mortality in the insect host. X. nematophila is also known to produce bacteriocins which inhibit the growth of other genotypes. Over the course of 20 host passages, the authors construct two types of experimental treatments. In one treatment (high migration), parasites from several lines are mixed together creating an infection containing bacteria. In the second treatment (low migration), the majority of parasite were transferred from a single host line. These two treatment setup a contrast of the potential relatedness of the bacteria in the current host.


Bashey, F., Morran, L.T. & Lively, C.M. 2007. Coinfection, kin selection, and the rate of host exploitation by a parasitic nematode. Evol. Ecol. Res. 9: 947-958.

VIGNEUX, F., BASHEY, F., SICARD, M., & LIVELY, C. (2008). Low migration decreases interference competition among parasites and increases virulence Journal of Evolutionary Biology, 21 (5), 1245-1251 DOI: 10.1111/j.1420-9101.2008.01576.x