More than just a metaphor, Wright’s Adaptive Landscape provides inspiration

[Originally appeared on Nothing in Biology Makes Sense]

Review of The Adaptive Landscape in Evolutionary Biology edited by Erik Svensson and Ryan Calsbeek

Have you ever wished you could go back in time to be present at a particular historical event? The 1932 International Congress of Genetics sounds perfect, right? There R. A. Fisher, J. B. S. Haldane, and Sewall Wright all presented papers of their recent research. If you’re a student of population genetics, you probably recognize these names as some of the founders of the field. At this meeting, Wright was asked to condense some of his more technical mathematical framework into a form that was more widely accessible to the audience of biologists. The result was his conceptualization of the Adaptive Landscape where an analogy is made between the fitness of an individual or population and the varied topographic landscape (pictured on the cover of the book). Wright used this metaphor to describe aspects evolutionary dynamics of populations.

The editors of a recent book, The Adaptive Landscape in Evolutionary Biology, gathered together contributions from evolutionary biologists, ecologists, and philosophers to demonstrate the impact that the Adaptive Landscape has had on the field of biology. This book embraces an 80 year old metaphor created by one of the founders of the modern synthesis to explore the breadth and depth of research generated in evolutionary biology. Unlike a recent book addressing aspects of the modern synthesis, Evolution: The Extendend Synthesis (Pigliucci and Müller, 2010) which called for a revolution, Svensson and Calsbeek have assembled authors that explore the innovations and contributions that build upon the fundamental ideas of population genetics and seek to grow the field. Early in this book, Pigliucci asks about the utility of the Adaptive Landscape metaphors, even titling his chapter with the question, “what are they good for?” I think the rest of the book provides a more than sufficient answer to his question.

Living at the edge, range expansion is a losing battle with mutations

[I originally posted this at the blog Nothing in Biology Makes Sense]

Environments can vary substantially in habitat quality, local population abundance, or carrying capacity. Under some climate change scenarios, new, higher quality habitats become available along the margin of a species’ range (e.g. higher latitudes or altitudes) (Thomas et al 2001). These new habitats may be able to support larger population sizes. Factors of demography, evolution, and qualities of the abiotic and biotic communities all interact to determine where a species is found and may influence the ability of a species to expand its range. New research is building genetically explicit models in order to understand how the interplay of these different factors influence evolutionary changes,

The authors of a recent study focus on how the interaction of the demographic process of range expansion changes the way that natural selection favors beneficial and deleterious mutations (Peischl et al 2013). Using both computer simulations as well as mathematical approximations, the authors find that at the range margins, individuals carry a substantial load of deleterious mutations.

Experiments beyond evolution, a primer on coevolution in the laboratory

A review paper on coevolution published online ahead of the print version of Trends in Ecology and Evolution caught my eye a couple of weeks ago. Brockhurst and Koskella (2013) review that state of affairs of experimental coevolution research. Both of these researchers have a rich experience in this field and present a concise review of the field.

The major contributions of experimental coevolution thus far have been to provide direct evidence of the tempo and mode of antagonistic coevolutionary dynamics, the role of antagonistic coevolution in increasing diversity within and among populations, including the role of parasitism in maintaining sexual recombination, and the structure of specificity in coevolving antagonistic interactions.

Within the article, the authors summarize the expected outcomes or results from common experimental coevolution studies.

Approaches to quantifying reciprocal adaptation.
(Modified figure 2 from Brockhurst and Koskella, 2013). 

The process of rapid reciprocal adaptation inherent to antagonistic coevolution can be driven by at least two contrasting modes of reciprocal selection: ‘Fluctuating Selection Dynamics’ (FSD) where changing allele frequencies in host and parasite populations are driven by parasite-mediated selection against common host resistance alleles or ‘Arms Race Dynamics’ (ARD) where recurrent selective sweeps of novel host resistance and parasite infectivity alleles occur through time, leading to increases in the host range of the parasite and the subsequent host resistance traits. Experimental coevolution has revealed evidence for the operation of both of these modes of reciprocal selection.

The authors also do a good job of pointing to a path rich in research aims for understanding coevolutionary interactions. To date, most experimental coevolution studies have focused on single pair, antagonistic interactions. Beyond the common critique of laboratory experiments (a need to increase “reality”), they suggest that studying more complex communities as well as different forms of interactions.

Don’t wait for the article to show up in print, check out the review online now. The Brockhurst lab website can be found here. You can also see what Dr. Koskella is up by reading her blog, Nature's Microcosm.


Reference

Brockhurst MA, Koskella B (2013) Experimental Coevolution of Species Interactions. Trends in Ecology & Evolution: DOI: http://dx.doi.org/10.1016/j.tree.2013.02.009

When mummies attack! Why specificity matters for coevolution

Evolutionary change by means of Natural Selection needs a couple of things in order to happen: heritability and variation in fitness. That is, offspring need to resemble their parents at least a little (heritability) and individuals need to differ in their survival and offspring production (fitness). We’ll worry about heritability in another post, but variation is something that seems like it might be hard to maintain. Some forms of Natural Selection will reduce variation as more fit individuals become frequent and all the different kinds of less fit individuals are eliminated from the population. However, there is a force, common in nature, which may maintain variation, parasites.

Interactions between hosts and parasites can generate strong selective pressures on each player, especially if your life depends on infecting a host. Often, biologists make an analogy to an arms race where players are developing bigger and better defenses or weapons. Antagonistic interactions may also generate negative frequency dependence where a rare host type is favored because the parasites are adapted to a common type. You can learn more by checking out two posts over at Nothing in Biology makes Sense (CJ’s post on the Red Queen Hypothesis or Jeremy’s post on a different coevolutionary puzzle). A key component for maintaining variation via negative frequency dependent selection is specificity. There must variation in the interaction among different host genotypes and parasite genotypes. This is sometimes referred to as a GxG interaction. If parasites can infect all the hosts, there is no specificity. Specificity allows different hosts to be favored over time depending on the composition of the parasite population.

Theoreticians love to use different models of interactions between hosts and parasites, but without empirical evidence, there seems little point. In a recent paper by Rouchet and Vorburger (2012), the authors looked for evidence of just the kind of genetic specificity would result in the maintenance of genetic variation.

Multidimensional coevolution, no oscillation overthruster required

Conventional wisdom suggests that pathogens and parasites are more rapidly evolving because of various reasons such as short generation time or stronger selection. Yet somehow, they have not completely won the battle against the host. Recently, a theoretical paper on coevolution in Nature caught my eye (Gilman et al., 2012). Here the authors address this paradox: “How do victim species survive and even thrive in the face of a continuous onslaught of more rapidly evolving enemies?”

Instead of treating a coevolutionary interaction between two species as the interaction of only two traits, the authors investigate the nature of an interaction among a suite of traits in each species. It’s not hard to think of a host having a fortress of defenses against attack from a parasite with an arsenal loaded with many weapons.

Bacterial Thunderdome: Decoding virulence, spiteful interactions, and diversity

What happens when two parasites infect the same host individual? Is the outcome similar to the Thunderdome: two parasites enter, one parasite leaves? Host-parasite interactions are rarely so simple. While a reductionist approach to understanding the interaction of a parasite or pathogen with its host may decompose the system to a single infection, nature is full of much more complex puzzles. Within the host, the battle itself raging between parasites (within-host competition) may have cascading effects on the host.

A recent paper on virulence caught my eye (Bashey et al., 2012) which provides an update to a very interesting result from the group a few years ago. The system includes bacterial parasites, along with parasitic nematodes, that infect insect larvae and eat/digest them from the inside out. Vigneux et al. (2008) found that when multiple parasite isolates are mixed in a host, the host mortality decreased. However, this only occurred when the isolates were not related. In the experiment, the researchers created low relatedness by mixing populations with migration. I reviewed the 2008 paper previously. The hypothesis was that chemical warfare among the parasites decreased the parasite load and reduced the negative effects on the host, virulence.

The bacteria produce chemical weapons, bateriocins, which can broadly harm other isolates, but relatives are left unharmed. These chemical weapons can be classified as spiteful: in the process of harming others they also harm the focal individual. This self-harm comes from the cost of making the chemical weapon. Others have labeled this antagonistic trait a greenbeard gene.

Greenbeards are genes that can identify the presence of copies of themselves in other individuals, and cause their bearer to behave nepotistically toward those individuals (Gardner and West, 2010).

Gardner and West explain that the origin of this term comes from Richard Dawkins illustrative example where individuals bearing this trait had green beards.

Experimental results: Recently, natural system specific isolates of the parasite have been cultured in the lab allowing more specific tests of the within-host competition (Hawlena et al., 2010a; Hawlena et al., 2010b). In the most recent paper, Bashey et al. (2012) found that the bacterial isolates that produce the toxin have a higher growth rate in coinfections (where within-host competition might be important). However, in the absence of coninfections, there was no relative growth rate advantage of the toxin producing, inhibitory, isolates. In coninfections, even though the toxin producing isolate was winning the internal host battle, lower host mortality rate emerged as an outcome.

A beautiful world: As an evolutionary biologist, I’m interested not only in the diversity of the natural world, but also the mechanisms that keep that diversity around. We often think of natural selection as favoring the fittest. If a single type is better than the rest, then over time, diversity will decrease. Often the analogy of hill climbing is used. On the other hand, if the fittest depends on the context of the other players involved, than diversity might be maintained. That is, what if the shape of the mountain range is constantly changing. In relation to this research,

bacteriocins might promote local diversity when producer, sensitive, and resistant strains are engaged in a version of the rock‐paper‐scissors game (i.e., the producer can kill the sensitive strain, the resistant strain outcompetes the producer, and the sensitive strain outcompetes the resistant strain) in a spatially structured environment (Hawlena et al., 2010b).

If bacteriocins are costly to produce, than they must provide some benefit in some contexts. Bashey et al (2012) suggest that this mechanism, where the fitness of a particular parasite isolate is context dependent, may explain the high frequency of bacteriocin production found in the natural populations surveyed in their earlier work (Hawlena et al., 2010a).

Stay tuned for future research by Dr. Farrar Bashey as she assures me more pieces to this puzzle will be revealed.

References

• Bashey F, Young SK, Hawlena H, Lively CM (2012) Spiteful Interactions between Sympatric Natural Isolates of Xenorhabdus Bovienii Benefit Kin and Reduce Virulence. Journal of Evolutionary Biology 25: 431-437. DOI: 10.1111/j.1420-9101.2011.02441.x
• Gardner A, West SA (2010) Greenbeards. Evolution 64: 25-38. DOI: 10.1111/j.1558-5646.2009.00842.x
• Hawlena H, Bashey F, Lively CM (2010a) The Evolution of Spite: Population Structure and Bacteriocin-Mediate Antagonism in Two Natural Populations of Xenorhabdus Bacteria. Evolution 64: 3198-3204. DOI: 10.1111/j.1558-5646.2010.01070.x
• Hawlena H, Bashey F, Mendes Soares H, Lively CM (2010b) Spiteful Interactions in a Natural Population of the Bacterium Xenorhabdus Bovienii. The American Naturalist 175: 374-381. DOI: 10.1086/650375
• Vigneux F, Bashey F, Sicard M, Lively CM (2008) Low Migration Decreases Interference Competition among Parasites and Increases Virulence. Journal of Evolutionary Biology 21: 1245-1251. DOI: 10.1111/j.1420-9101.2008.01576.x

Main Paper
BASHEY, F., YOUNG, S., HAWLENA, H., & LIVELY, C. (2012). Spiteful interactions between sympatric natural isolates of Xenorhabdus bovienii benefit kin and reduce virulence Journal of Evolutionary Biology, 25 (3), 431-437 DOI: 10.1111/j.1420-9101.2011.02441.x

Double, double toil and trouble: a tale of two infections

What are the evolutionary consequences of parasite superinfection (i.e. simultaneous infection by multiple parasites)? When parasites are genetically distinct, coexistence within a host generates conflict because of limited resources. How this conflict is resolved is the source of evolutionary research on the evolution of parasite life history traits such as virulence, the negative effects on the host caused by infection, and transmission mode, how parasites infect a new host. The transmission mode of a parasite is often characterized as occurring in one of two different modes: vertical or horizontal. With vertical transmission, an offspring obtains its parasites directly from its parents. In contrast, with horizontal transmission, infections occur either directly from the environment or contagiously by infection from other individuals.

My interest in the evolution of transmission mode in parasites and symbionts led me to a recent paper (Ben-Ami et al. 2011), which addresses the consequences of superinfection by two different parasites with different transmission modes of the waterflea, Daphnia magna, on virulence and parasite fecundity. Pasteuria ramosa is a castrating, horizontally transmitted, blood-infecting bacterium where spores are produced from the cadaver of the host Daphnia. Octosporea bayeri, a microsporidium, utilizes both vertical transmission to eggs and horizontal transmission via waterborne spores.

Photo by Paul Herbert in Gewin (2005)

Conflict resolution: The difference in the transmission strategies among the parasites generates an extreme conflict. O. bayeri needs the host to produce offspring for vertical transmission, that is the host and parasite have an aligned interest in producing offspring. On the other hand, P. ramosa is using host resources, including the reproductive tissues, to produce spores for infecting other hosts. Because of the alignment of interests between host and the vertically transmitting parasite, the question becomes: does infection by O. bayeri provide host protection from future infection by P. ramosa? In contrast, virulent parasites are expected to be more competitive by exploiting host resources more quickly than less virulent parasites. Here, P. ramosa may reduce infection by O. bayeri by competitive exclusion but at the cost of additionally reducing host survival.

To test these hypotheses, Ben-Ami et al. (2011) used two different infection experiments. The first tested the impact of horizontally occurring superinfection on host and parasite life history. These infections occurred simultaneously or sequentially (separated by 7 days). The second experiment used vertically infected hosts with O. bayeri parasites which were then exposed to P. ramosa for secondary infection. P. ramosa competitively excluded O. bayeri in double infections. Additionally, host fecundity was lower with super infections than with P. ramosa infection alone indicating an increase in virulence due to the interaction. The authors also found that vertical infection by O. bayeri provided no significant protection from future horizontal infection by P. ramosa. In fact, they found that P. ramosa was able to clear O. bayeri vertical infections and was clearly the superior competitor.

The part of the paper that I found most interesting was how the authors related their results to previous theoretical predictions. Many authors have addressed the interaction of parasites with different transmission modes (Altizer and Augustine 1997; Faeth et al. 2007; Haine et al. 2005; Jones et al. 2007, 2010; Lipsitch et al. 1996; Lively et al. 2005). Most of these previous models make assumptions about the lack of superinfection, suggesting one infection protects against a second. The authors of this paper point out that no one has specifically modeled the combination of a vertically transmitted parasite with one that can use both strategies and allows for superinfection.

In summary, I found that this paper and the results contained are clearly presented. While the authors did not find the support for the protective hypothesis, they did find evidence of increased virulence with coinfections as predicted. The authors do point out that these two parasites have a very narrow range of coexistence, in southwestern Finland, and suggest that coexistence may be a difficult or transient dynamic for this system. I would agree.

Interested in more? In addition to this interesting paper, Dieter Ebert’s group has recently published exciting research on the specificity and mechanism of infection by one of the parasites discussed the above paper, P. ramosa.: (Luickx et al. 2011; Duneau et al. 2011).

References

• Altizer SM, Augustine DJ (1997) Interactions between frequency-dependent and vertical transmission in host-parasite systems. Proceedings of the Royal Society of London Series B-Biological Sciences 264: 807-814. http://dx.doi.org/10.1098/rspb.1997.0113
• Ben-Ami F, Rigaud T, Ebert D (2011) The expression of virulence during double infections by different parasites with conflicting host exploitation and transmission strategies. Journal of Evolutionary Biology 24: 1307-1316. http://dx.doi.org/10.1111/j.1420-9101.2011.02264.x
• Duneau, D, Luijckx P, Ben-Ami F, Laforsch C, Ebert D (2011) Resolving the infection process reveals striking differences in the contribution of environment, genetics and phylogeny to host-parasite interactions. BMC Biology, 9:11. http://dx.doi.org/10.1186/1741-7007-9-11
• Faeth SH, Hadeler KP, Thieme HR (2007) An apparent paradox of horizontal and vertical disease transmission. Journal of Biological Dynamics 1: 45-62. http://dx.doi.org/10.1080/17513750601040367
• Haine ER, Boucansaud K, Rigaud T (2005) Conflict between parasites with different transmission strategies infecting an amphipod host. Proceedings of the Royal Society B-Biological Sciences 272: 2505-2510. http://dx.doi.org/10.1098/rspb.2005.3244
• Jones EO, White A, Boots M (2007) Interference and the persistence of vertically transmitted parasites. Journal of Theoretical Biology 246: 10-17. http://dx.doi.org/10.1016/j.jtbi.2006.12.007
• Jones EO, White A, Boots M (2010) The evolutionary implications of conflict between parasites with different transmission modes. Evolution 64: 2408-2416. http://dx.doi.org/10.1111/j.1558-5646.2010.00992.x
• Lipsitch M, Siller S, Nowak MA (1996) The evolution of virulence in pathogens with vertical and horizontal transmission. Evolution 50: 1729-1741. http://dx.doi.org/10.2307/2410731
• Lively CM, Clay K, Wade MJ, Fuqua C (2005) Competitive co-existence of vertically and horizontally transmitted parasites. Evolutionary Ecology Research 7: 1183-1190. http://www.evolutionary-ecology.com/issues/v07n08/iiar1894.pdf
• Luijckx P, Ben-Ami F, Mouton L, Pasquier L, Ebert D (2011) Cloning of the unculturable parasite Pasteuria ramosa and its Daphnia host reveals extreme genotype-genotype interactions. Ecology Letters 14:125-131. http://dx.doi.org/10.1111/j.1461-0248.2010.01561.x

Paper read

BEN-AMI, F., RIGAUD, T., & EBERT, D. (2011). The expression of virulence during double infections by different parasites with conflicting host exploitation and transmission strategies Journal of Evolutionary Biology, 24 (6), 1307-1316 DOI: 10.1111/j.1420-9101.2011.02264.x

A not so modest proposal for pathogens: evolutionary diversification

Due to a ground swell of interest, we recently read Robert Ricklefs inaugural article (Ricklefs 2010) in to the National Academy of Sciences (of the United States of America) in which he proposes a new mechanistic role for parasites and pathogens to generate diversity within the tree of life. In this paper, Ricklefs (2010) distinguishes between two compartments of the ecological niche of a species: 1) the individual niche space and 2) the population niche space. He contrasts these two concepts of niche space by indicating which processes are most involved in defining the boundaries: 1) evolution and adaptation of an individual versus 2) demographic properties of a population in a point in space. Being a fan of processes not patterns, I thought that these definitions were particularly helpful when reading the rest of the article and understanding his proposed novel mechanism of diversification at the end.

Ricklefs asks how different clades occupy population niche space: do more taxon rich clades occupy larger niche space or simply pack the available space more tightly with narrower species niches or larger overlap between species? Among several lines of evidence, the most crucial to his proposal is that there is independence of the diversity of a clade and the total population niche space occupied. That is, larger clades must pack niche space more tightly. But apparently they are doing it not by lowering the species densities because species abundance does not decrease with increasing local diversity. What Ricklefs suggests is it that the tighter packing is achieved via uneven filling of particular population niche space. This uneven filling is due to interactions with pathogens and parasites. The outcome of these interactions being determined by spatially and temporally varying antagonistic interactions that may also vary not just in the composition of those interactions but the diversity of the players involved (conjuring the idea of geographic mosaic of coevolution [Thompson 2005]).

How is this proposal different from adaptive radiations or escape and radiate coevolution? The paper makes the first contrast from adaptive radiations by presenting his mechanism in context of seemingly saturated niches rather than a having diversification happen in a wide open landscape. What about escape and radiate coevolution (Ehrlich and Raven 1964) which also has a role for interacting species in diversification? Again, this is a case were new adaptive zones (Simpson 1953) are opened up and allow species to occupy these new empty niches. Ricklefs' idea is fundamentally different in that pathogen interactions are seen as a mechanism that reduces efficient packing and saturation of population niches. This is achieved by affecting the population demographics which can result in a feedback to evolutionary dynamics at the individual niche level. This last part highlights the importance of linking demographic and evolutionary factors into models of coevolutionary interactions when concerned with patterns of diversification. Others have already pointed out this need in models exploring other evolutionary important traits (Mideo et al 2008)

While I was expecting something different at the conclusion of this article, what Ricklefs does do is lay out a program of study and call for data to defend his proposal. This request does not only extend to the field parasitologist but also to theoreticians as well.

References

Ehrlich, P. R., and P. H. Raven. 1964. Butterflies and plants: a study in coevolution. Evolution 18:586-608.

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

Ricklefs, R. E. 2010. Evolutionary diversification, coevolution between populations and their antagonists, and the filling of niche space. Proceedings of the National Academy of Sciences of the United States of America 107:1265-1272.

Simpson, G. G. 1953. The major features of evolution. Columbia University Press, New York.

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

Paper Read

Ricklefs, R. (2010). Inaugural Article: Evolutionary diversification, coevolution between populations and their antagonists, and the filling of niche space Proceedings of the National Academy of Sciences, 107 (4), 1265-1272 DOI: 10.1073/pnas.0913626107

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?

References

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

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?

References

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

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.

References

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

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.

Reference

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

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).

References

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

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.

References

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

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.

References

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

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.

References

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

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.

References

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

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.

References

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