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Palouse Coevolution Discussion Group We meet weekly to discuss papers and current research in coevolution. We are interested in both empirical research and theory work. The group of regular attendees includes faculty, post-docs, and graduate students from Washington State University and the University of Idaho. This is a blog based on the papers and discussions of this group. After each week's reading, I will be putting up a short post describing some of the topics that came up regarding the details of the paper.
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.A winged adult aphid being attackedby two wasp parasitoids. Photoby Christoph Vorburger.Biological system: The Vorburger group studies a crop pest aphid, Aphis fabae, and its common wasp parasitoid, Lysiphlebus fabarum. The adult parasitoids lay their eggs in unsuspecting aphid hosts. As the parasitoids develop they battle the hosts defenses. Some aphid hosts are also infected with a bacterium symbiont, Hamiltonella defensa, which can provide protection against the parasitoid by releasing bacteriophages that target the parasitoid invader (Vorburger et al 2009; Vorburger and Gouskov 2011). If the wasp parasitoid can evade all the host defenses then eventually it develops inside the still living aphid. Eventually, as the authors describe in grisly detail“metamorphosis takes place within a cocoon spun inside the host’s dried remains, forming a ‘mummy’ from which the adult wasp emerges” (Rouchet and Vorburger 2012).In order to test the ability of particular parasitoid genotypes to infect hosts, you need a clever trick to generate replicates. Just like Star Wars, the authors act as the benevolent New Republic and generate a clone army of parasitoids. They also generate a series of clonal lines for the hosts as well. They are able to accomplish this feat because both species sometimes use parthenogenesis to produce offspring without fertilization instead of the common sexual reproduction. With parthenogenesis, mothers produce offspring with only their genetic material.Experiment: The authors created 15 different host lines, most of which were naturally infected with the protective symbionts (more on this bit later). They infected each of these host lines with three different parasitoid clones in a fully crossed design (45 separate comparisons).Key result: Rouchet and Vorburger (2012) demonstrate that there is variation in the infectivity of the parasitoids clones and this was dependent on the host lines with a significant interaction. This interaction term in their statistical analysis is evidence of the specificity of the infection and defense.In building up this experiment, the authors include natural infections of the defensive symbiont. In doing so, as the authors point out themselves, the host-symbiont genotypes become confounded statistically. Therefor, the specificity of infection ability of the different parasitoids may be the result of interactions not with the host genome, but with the symbiont genome. However, Rouchet and Vorburger suggest that there is a strong amount of vertical transmission of the symbiont making its genome heritable as well as generating a shared fitness among host and symbiont. Previously, this same group looked for specificity in of the same host-parasitoid interaction using hosts that lacked the symbiont. That research (Sandrock et al 2010) did not find any evidence of the genotype-by-genotype specificity found in this experiment. This further supports their conclusion that the aphid defense is the result of the particular symbiont strain they carry.Future directionsIn order to develop a causal link that aphid (host) defense is mediated by the symbiont, the authors propose that the next logical experiment would be to cure the aphid lines of the symbiont, ridding the hosts of their protection. If the symbionts are the mechanism generating the specificity of protection from the parasitoids, then we would expect to see increased infection rates and a lack of genotype by genotype interaction, no specificity. The Vorburger research group has already done some of this research by looking at experimentally infected aphids in a pape... Read more »
Rouchet R, & Vorburger C. (2012) Strong Specificity in the Interaction between Parasitoids and Symbiont-Protected Hosts. Journal of Evolutionary Biology. DOI: 10.1111/j.1420-9101.2012.02608.x
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.The model: While I’ll leave it to you to check out the full supplemental information for the details of their model, it is firmly rooted in the quantitative genetics framework of Lande land (Lande 1979; Lande and Arnold, 1983). As such, evolutionary change is measured by how selection changes the variance/covariance structure (G-matrix) of the underlying traits. The analytical model assumes a fixed G-matrix, however their simulations relax this assumption. The primary goal of the analysis is to calculate the ability of the victim/host to escape the exploiter/parasite measured as the maximum evolutionary escape rate. This evolutionary escape rate is the difference in the rates of change of the host defense traits compared to the parasite attacking trait. When the host is winning host it is said to be outrunning the parasite.Key point: As the number of interacting traits increases, the chance of the host escaping increases. The reason this is true is that the host has to escape the pathogen in only one trait at a time. On the other hand, the pathogen has to track and overcome all of the host defenses.To supplement their analytical findings, the authors also run individual based simulations which are consistent with their analytical results. One of the nice things about these simulations is that they remove the assumption that the G-matrix doesn’t evolve. The authors note that the G-matrix does evolve during the interaction between host and pathogen. When the interaction is based on the difference of the traits between host and pathogen, coevolution generates negative correlations. The logic here is similar to the Hill-Robertson effect. Correlations can affect the ability of the host to escape the pathogen. Generally, negative correlations among host traits would constrain evolution, but as the dimensionality of the traits increases the negative correlations decrease.This paper was eloquent in its intuitive simplicity. The authors address an interesting question and provide enough of the model details to understand the results. It is definitely worth your time to read.ReferencesGilman RT, Nuismer SL, Jhwueng D-C (2012) Coevolution in Multidimensional Trait Space Favours Escape from Parasites and Pathogens. Nature 483: 328-330. DOI: 10.1038/nature10853Lande, R (1979) Quantitative genetic analysis of multivariate evolution, applied to brain-body size allometry. Evolution 33:402-416. http://www.jstor.org/stable/2407630Lande R, Arnold SJ (1983) The measurement of selection on correlated characters. Evolution 37:1210-1226. http://www.jstor.org/stable/2408842Research BloggingGilman, R., Nuismer, S., & Jhwueng, D. (2012). Coevolution in multidimensional trait space favours escape from parasites and pathogens Nature, 483 (7389), 328-330 DOI: 10.1038/nature10853... Read more »
Gilman, R., Nuismer, S., & Jhwueng, D. (2012) Coevolution in multidimensional trait space favours escape from parasites and pathogens. Nature, 483(7389), 328-330. DOI: 10.1038/nature10853
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.ReferencesBashey 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.xGardner A, West SA (2010) Greenbeards. Evolution 64: 25-38. DOI: 10.1111/j.1558-5646.2009.00842.xHawlena 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.xHawlena 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: 10.1086/650375Vigneux 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.xMain PaperBASHEY, 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... Read more »
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
What are the evolutionary consequences of parasitesuperinfection (i.e. simultaneous infection by multiple parasites)? Whenparasites are genetically distinct, coexistence within a host generatesconflict because of limited resources. How this conflict is resolved is thesource of evolutionary research on the evolution of parasite life historytraits such as virulence, the negative effects on the host caused by infection,and transmission mode, how parasites infect a new host. The transmission modeof a parasite is often characterized as occurring in one of two differentmodes: vertical or horizontal. With vertical transmission, an offspring obtainsits parasites directly from its parents. In contrast, with horizontaltransmission, infections occur either directly from the environment orcontagiously by infection from other individuals.My interestin the evolution of transmission mode in parasites and symbionts led me to arecent paper (Ben-Amiet al. 2011), which addresses the consequences of superinfection by twodifferent parasites with different transmission modes of the waterflea, Daphnia magna, on virulence and parasitefecundity. Pasteuria ramosa is a castrating,horizontally transmitted, blood-infecting bacterium where spores are producedfrom the cadaver of the host Daphnia.Octosporea bayeri, a microsporidium, utilizesboth vertical transmission to eggs and horizontal transmission via waterbornespores.Photo by Paul Herbert in Gewin (2005)Conflict resolution: The difference in the transmissionstrategies among the parasites generates an extreme conflict. O. bayeri needs the host to produceoffspring for vertical transmission, that is the host and parasite have analigned interest in producing offspring. On the other hand, P. ramosa is using host resources, includingthe reproductive tissues, to produce spores for infecting other hosts. Because ofthe 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, virulentparasites are expected to be more competitive by exploiting host resources morequickly than less virulent parasites. Here, P.ramosa may reduce infection by O. bayeriby competitive exclusion but at the cost of additionally reducing hostsurvival.To test these hypotheses, Ben-Ami et al. (2011) used two differentinfection experiments. The first tested the impact of horizontally occurring superinfectionon host and parasite life history. These infections occurred simultaneously orsequentially (separated by 7 days). The second experiment used verticallyinfected hosts with O. bayeriparasites which were then exposed to P.ramosa for secondary infection. P.ramosa competitively excluded O. bayeriin double infections. Additionally, host fecundity was lower with superinfections than with P. ramosa infectionalone indicating an increase in virulence due to the interaction. The authorsalso found that vertical infection by O.bayeri provided no significant protection from future horizontal infectionby P. ramosa. In fact, they foundthat P. ramosa was able to clear O. bayeri vertical infections and wasclearly the superior competitor.The part of the paper that I found most interesting was howthe authors related their results to previous theoretical predictions. Manyauthors have addressed the interaction of parasites with different transmissionmodes (Altizer and Augustine1997; Faeth et al. 2007; Haine et al. 2005; Jones et al. 2007, 2010; Lipsitch et al. 1996; Lively et al. 2005). Most of these previousmodels make assumptions about the lack of superinfection, suggesting oneinfection protects against a second. The authors of this paper point out thatno one has specifically modeled the combination of a vertically transmittedparasite with one that can use both strategies and allows for superinfection.In summary, I found that this paper and the results contained areclearly presented. While the authors did not find the support for theprotective hypothesis, they did find evidence of increased virulence withcoinfections as predicted. The authors do point out that these two parasiteshave a very narrow range of coexistence, in southwestern Finland, and suggest thatcoexistence may be a difficult or transient dynamic for this system. I wouldagree.Interested in more? In addition to this interesting paper, Dieter Ebert’s group has recentlypublished exciting research on the specificity and mechanism of infection byone of the parasites discussed the above paper, P. ramosa.: (Luickx et al. 2011;Duneau et al. 2011).ReferencesAltizerSM, Augustine DJ (1997) Interactions between frequency-dependent andvertical transmission in host-parasite systems. Proceedings of the RoyalSociety of London Series B-Biological Sciences 264: 807-814. http://dx.doi.org/10.1098/rspb.1997.0113Ben-Ami F,Rigaud T, Ebert D (2011) The expression of virulence during doubleinfections by different parasites with conflicting host exploitation andtransmission strategies. Journal ofEvolutionary Biology 24: 1307-1316. http://dx.doi.org/10.1111/j.1420-9101.2011.02264.xDuneau, D, Luijckx P, Ben-Ami F, Laforsch C,Ebert D (2011) Resolving the infection process reveals striking differencesin the contribution of environment, genetics and phylogeny to host-parasiteinteractions. BMC Biology, 9:11. http://dx.doi.org/10.1186/1741-7007-9-11Faeth SH,Hadeler KP, Thieme HR (2007) An apparent paradox of horizontal and verticaldisease transmission. Journal of ... Read more »
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
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.ReferencesEhrlich, 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 ReadRicklefs, 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... Read more »
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
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?ReferencesLively, 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... Read more »
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
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?ReferencesBell, 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... Read more »
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
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.ReferencesKouyos, 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... Read more »
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
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.ReferenceHammerschmidt, 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... Read more »
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
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).ReferencesThompson, 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 readVale, P., & Little, T. (2009). Measuring parasite fitness under genetic and thermal variation Heredity DOI: 10.1038/hdy.2009.54... Read more »
Vale, P., & Little, T. (2009) Measuring parasite fitness under genetic and thermal variation. Heredity. DOI: 10.1038/hdy.2009.54
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.ReferencesGomulkiewicz, 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 ReadPiculell, 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... Read more »
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), 23. DOI: 10.1186/1741-7007-6-23
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.ReferencesBascompte, 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 ReadBastolla, 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... Read more »
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
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.ReferencesBuckling, 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 readGandon, 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... Read more »
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
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.ReferencesBoots, 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 readSorrell, 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... Read more »
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
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. ReferencesBoots, 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... Read more »
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
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 genesReferences 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... Read more »
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
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?References 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... Read more »
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.References 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.xBashey, 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.... Read more »
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
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