PREDATORS ALTER HOST‐PARASITE INTERACTIONS VIA TRAIT‐MEDIATED INDIRECT EFFECTS BY CHRISTOPHER ROBERT BERTRAM THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Ecology, Evolution, and Conservation Biology in the Graduate College of the University of Illinois at Urbana‐Champaign, 2011 Urbana, Illinois Master’s Committee: Associate Professor Carla Caceres, Chair Assistant Professor Rachel Whitaker Professor Jeff Brawn
Abstract Predators directly interact with their prey. These direct interactions can indirectly alter the interactions between prey and other community members. For example, predators can alter prey life history, behavior, or morphology resulting in an indirect change in the interaction between their prey and another species (Trait‐ mediated indirect effects). We used two dominant predators of the zooplankter Daphnia, Bluegill sunfish (Lepomis macrochirus) and larvae of the phantom midge fly Chaoborus punctipennis, to observe how predator presence altered Daphnia traits. These trait changes were predicted to influence the interaction between Daphnia and Metschnikowia bicuspidata, a virulent and common (>40% prevalence in some lakes) fungal endo‐parasitoid. In a series of susceptibility and life history assays, we determined that predators altered prey susceptibility in a genotype specific manner, they influenced parasite fitness, and affected prey life history. All of these effects have important implications in disease dynamics. This study highlights the importance of predator‐induced trait‐mediated indirect effects on host‐parasite interactions and more generally of examining species interactions in the context of the community in which they occur.
Table of Contents CHAPTER 1: INTRODUCTION. . . 1 CHAPTER 2: METHODS. . . 5 CHAPTER 3: RESULTS. . . 10 CHAPTER 4: DISCUSSION . . . .13 CHAPTER 5: REFERENCES. . . .17 CHAPTER 6: TABLES . . . 24 CHAPTER 7: FIGURES . . . 28 CHAPTER 8: SUPPLEMENTAL FIGURES . . . .31
CHAPTER 1 INTRODUCTION Ecological processes often determine when and where epidemics will erupt. Diversity clearly influences disease outbreaks; theoretical and empirical advances over the last two decades have documented the roles of species diversity and genetic diversity in determining disease spread (Elderd et al. 2008, Garrett et al. 2009). Direct interactions resulting from species diversity can influence disease dynamics by altering the density of competent hosts, or changing the rate at which parasites contact competent hosts (Keesing et al. 2010). However, indirect effects that result from species diversity can also influence disease spread via density‐ and trait‐mediated indirect effects (Keesing et al. 2010). In particular, genotype‐specific response of a host to its predators or competitors may indirectly influence the host‐ parasite encounter rate (Decaestecker et al. 2002, Keesing et al. 2006). However, an optimal response to one particular natural enemy may in fact lower prey fitness in the presence of a different natural enemy (Declerck and Weber 2003, Relyea 2003, Hoverman and Relyea 2007). In addition, genetic diversity may influence disease spread when hosts differ in their ability to avoid, tolerate or control their parasite (Boots and Haraguchi 1999, Carius et al. 2001, Agrawal and Lively 2002). Thus, this interaction of genetic diversity and species diversity may alter the eco‐evolutionary dynamics of disease. Predators, in particular can alter disease dynamics in prey populations by consuming infected animals or by lowering host densities (Packer et al. 2003, Ostfeld and Holt 2004, Duffy et al. 2005, Hall et al. 2005). However, predators do not
always “keep the herds healthy” and whether predators increase or inhibit disease is often system dependent (Cáceres et al. 2009, Choisy and Rohani 2006, Duffy et al. 2011, Roy and Holt 2008). Moreover, predators do not simply affect the density of individuals, they also influence behavioral, morphological or life history traits of their prey (Lass and Spaak 2003). Although in many systems these indirect effects are not as well quantified as direct effects, there is mounting evidence documenting how these so‐called “trait‐mediated indirect effects” influence interspecific interactions (Schmitz and Suttle 2001, Werner and Peacor 2003, Schmitz 2008). In many predator‐prey systems, these responses can arise simply through contact with predator kairomones—chemicals that are specific to a group of predators (Spitze 1992, Weider and Pijanowska 1993, Loose and Dawidowicz 1994, Tollrian 1995, Boersma et al. 1998). As a result, indirect effects can be quantified in isolation of direct effects. We used a well‐studied system to explore how trait‐mediated indirect effects of predators may influence disease spread. Daphnia (Crustacea: cladocera) are a dominant consumer of algae and an important food source for both fish and predatory insects in freshwater systems (Sarnelle 1993, Wellborn et al. 1996, Ives et al. 1999, Sarnelle 2005). In the presence of fish, Daphnia generally reproduce earlier and at a smaller size (Machacek 1995, Reede 1995, Weber and Declerck 1997, Boersma et al. 1998, Lass and Bittner 2002, Sakwinska 2002). In contrast, Daphnia tend to respond to gape‐limited insects such as Chaoborus by delaying reproduction and instead put resources towards rapid growth and production of external defenses (Tollrian 1993, Tollrian 1995, Brancelj et al. 1996, Weber and Declerck
1997, Sell 2000, Weber and van Noordwijk 2002, Weber and Vesela 2002, Lass and Spaak 2003). Numerous studies have not only demonstrated the among‐genotype variation in these responses, but also suggested that these changes are adaptive (Agrawal et al. 1999, Petrusek et al. 2009, Lass and Spaak 2003). The parasitic fungus Metschnikowia bicuspidata can infect large portions of Daphnia populations in north‐temperate lakes and causes decreased host lifespan and reproduction (Stirnadel and Ebert 1997, Ebert et al. 2000a, Ebert et al. 2000b, Cáceres et al. 2006, Hall et al. 2006, Duffy and Hall 2008). Metschnikowia infects Daphnia during grazing by piercing the gut wall after ingestion. It then undergoes replication in the host heamolymph, fills the body cavity with spores, and finally kills the host (Ebert 2005). Larger Daphnia generally have increased susceptibility because they encounter more Metschnikowia spores due to their higher clearance rate (hereafter “feeding rate,” Hall et al. 2010). Larger hosts also tend to result in the production of parasite spores (a metric of parasite fitness). Size is determined both by genotype (Hall et al. 2010) and in response to the presence of predators (Lass and Spaak 2003). All else equal, predators that induce in larger hosts, should produce more infections (increase susceptibility), more infective propagules (host fitness) and larger clutches (more hosts). This combination should result in larger epidemics. Field patterns indicate disease prevalence is correlated with predator type and abundance: lakes with higher density of Chaoborus but lower intensity of fish predation tend to have larger epidemics and vice versa (Cáceres et al. 2009, Hall et al. 2010). Previously, we have explored the direct effects of both fish (Duffy and
Sivars‐Becker 2007, Duffy 2009) and Chaoborus (Cáceres et al. 2009) on disease spread. There is also evidence that Chaoborus (Duffy et al. 2011) and fish (Yin et al. 2011) may indirectly increase disease transmission. Here, we first investigate if these two different predators (fish and Chaoborus) induce genotype‐specific behavioral (filtering rate) or morphological changes (body size) that subsequently influence an individual host’s susceptibility to a parasite. Second, we ask how these changes in the host may influence parasite fitness (number of spores produced). Finally, we document how these predator‐induced changes influence reproduction of uninfected hosts, and ultimately, population growth rate of susceptible hosts. Taken together, these predator‐induced changes in susceptibility, parasite fitness and host densities can alter disease dynamics.
CHAPTER 2 METHODS To determine the indirect effects of predators on disease transmission, we raised replicate cultures Daphnia in the presence of Bluegill sunfish (Lepomis macrochirus) and larvae of the phantom midge fly Chaoborus punctipennis. Daphnia were collected from Bristol, Bassett, and Warner lakes in southwest Michigan (Barry and Kalamazoo Counties), and are known to vary in their susceptibility to Metschnikowia (Duffy and Sivars‐Becker 2007, Hall et al. 2010). The parasite was collected from Baker Lake in 2003 and has been farmed in vivo in a single host clone since. Previous research has found no host‐genotype‐parasite‐genotype specificity when this parasite infects these clones (Duffy and Sivars‐Becker 2007). To create media, for each treatment, we filled two replicate 19L tanks with aged lake water. Each fish tank contained 4‐5 Bluegill (total mass, 19.6 ± 3.0 g [mean ± s.e.]), fed 2.5 g frozen Daphnia/tank/day (Bio‐pure Daphnia, Hikari Aquatic Diets, Hayward, CA). Chaoborus tanks contained ~8 Chaoborus/liter, fed laboratory cultured live Daphnia ad libitum. Control tanks contained aged lake water. Prior to use, culture media was sieved twice through a 30 µm mesh. To minimize maternal effects, all experimental Daphnia were collected from the third or later clutch, following at least three generations of acclimation to control conditions. During the acclimation and experimental period, cultures were maintained at low density in 20°C on a 14:10 light:dark cycle and fed 40,000 cells/ml/day of Ankistrodesmus falcatus (a green alga).
Susceptibility and parasite fitness To determine if plastic responses to predators altered the susceptibility of Daphnia to Metschnikowia, four to eight replicates of each of 11 Daphnia genotypes were raised in four treatment medias: control, Chaoborus kairomone, fish kairomone, and a 1:1 mix of Chaoborus and fish medias. Treatment medias were changed every other day. Initially, eight newborn Daphnia were placed in 150 ml beakers containing 110 ml of treatment media. After six days, beakers were culled to six Daphnia, inoculated with 100 Metschnikowia spores/ml, and the Daphnia were measured to determine size at inoculation. The following day, all Daphnia were moved to fresh media. Infection was visually assessed using a dissecting microscope 10 days following inoculation. To determine the effects of the predators on parasite fitness (spore production), up to eight infected animals per genotype per treatment were retained and placed individually in 24 well tissue culture plates until they succumbed to infection. These infected animals were measured to determine size at death, and then gently homogenized in 1‐1.5 ml of water to release all spores from the body cavity. Parasite fitness (total spores) was then determined using a haemocytometer. We conducted a second susceptibility assay to determine if the treatment effects on susceptibility seen in the first transmission assay resulted only from predator‐induced changes in body size. This assay was identical to the first except that the beakers were inoculated with the parasite on day 8 instead of day 6 (allowing the host additional time to grow), six of the eleven genotypes were used in order to increase replication, and the mixed treatment removed. If changes in susceptibility result solely from a change in body size, then larger (older) Daphnia
should be more susceptible than younger (smaller) individuals. Alternatively, if predators change other aspects of Daphnia biology (e.g. feeding rate, immune response, etc.), then body size alone will not be a strong predictor of susceptibility. Feeding rate Prior research has demonstrated that when the Daphnia genotypes used in this study are fed high‐quality resources in control water, fast feeding clones become more susceptible to fungal infection, due to higher contact rate with spores (Hall et al. 2010). However, exposure to kairomones is also known to alter the feeding rate in invertebrates (Ramcharan et al. 1992, Gliwicz 1994). To determine if and how predators affect D. dentifera feeding rate, we conducted an assay using control, Chaoborus, and fish treatments. Experimental animals were prepared as in the previous assays. When six days old, Daphnia from each of the 11 genotypes were placed individually in 50 ml centrifuge tubes containing 45 ml of treatment media. After a two‐hour acclimation period, they were inoculated with 40,000 cells Ankistrodesmus/ml and allowed to feed for 24 hours. Ten identical tubes without animals were also inoculated, with half being preserved using Lugol’s iodine solution immediately and the other half being preserved at the end of the experiment. This provided the initial cell concentration with adjustment for reproduction/degradation of algae over the 24‐hour period. After 24 hours, Daphnia were removed from the tubes, preserved in 95% ethanol, and subsequently measured (top of the head to base of the tail spine). Algal cells were concentrated and density was determined using a haemocytometer. It was not necessary to adjust the initial cell concentration for algal reproduction/degradation. Clearance rate was calculated, with modification of
Sarnelle and Wilson (2008), by , where € C0 is the initial cell concentration (cells/ml), € C1 is the final cell concentration (cells/ml), is the volume of the suspension (ml), is the time from € C0 to € C1 (hours), and is the length of the animal (mm). All negative values were excluded. Life history Exposure to predators may change how Daphnia allocate resources to growth vs. reproduction. This change in allocation can then influence disease by altering the dynamics of susceptible hosts. To assess how predators change life history, we used a life‐table protocol modified from Lynch et al. (1989). Newborn Daphnia from six clonal lines were placed individually in 150 ml beakers containing 110 ml of treatment media (control, Chaoborus or fish). Each animal was moved to fresh treatment water with food (40,000 cells/ml Ankistrodesmus) every other day. Cultures were maintained at 20° C on a 14:10 light:dark cycle and Daphnia were checked daily for maturation and reproduction for 40 days. Age and size were recorded at the production of the first clutch. We used Euler‐Lotka equation and the first four clutches were to calculate the intrinsic clonal growth rate (r) (Desmarais and Tessier 1999, Hall et al. 2009, Hall et al. 2010):
where represents the age specific survivorship, the number of newborns on day , and the age in days.
Statistical Analysis The susceptibility assays produced binomial data points (infected or not infected) so logistic ANOVA was carried out in PROC GENMOD (SAS systems, v9.2, SAS Institute, Cary, North Carolina, USA). In both assays, predator
was treated as a fixed effect and genotype was included as a random effect. The random effects of genotype and the genotype * predator interaction were evaluated as described by Littell et al. (2002). In brief, this technique uses differences in the ‐ 2 restricted log likelihood, which are χ2 distributed with 1 degree of freedom, between models with and without the particular random effect included (Littell et al. 2002). Body size and feeding rate were analyzed with a mixed model ANOVA in PROC MIXED. We also estimated variance components in PROC MIXED. Parasite fitness was estimated by counting the number of spores produced by a dead host of a given size. However, not all genotypes produced infection in all treatments (i.e., some treatment * genotype combinations did not have values) so genotype was removed from the statistical design and only the treatment main effects were analyzed. Differences in size at inoculation, size at death, and parasite fitness were analyzed using mixed‐model ANOVA, in PROC MIXED (SAS systems, v9.2). Linear regression was also used to determine the effect of body size or feeding rate at inoculation on susceptibility and the effect of size at death on parasite fitness using PROC REG (SAS systems, v9.2). Differences in life history traits (size at maturity, time to maturity) and clonal growth rate (r), as a function of treatment (fixed), genotype (random), and their interaction were estimated separately in PROC MIXED (SAS systems, v9.2).
CHAPTER 3 RESULTS Although predators influenced susceptibility of Daphnia to Metschnikowia, the particular effect that a predator had depended on the genotype of the host (Fig. 1A, Table 1). All genotypes suffered increased infection when exposed to Chaoborus relative to the control (average proportion infected 0.42 ± 0.04 S.E. in Chaoborus water vs. 0.24 ± 0.03 in Control water), but the response to fish was more variable (0.26 ± 0.08). Some genotypes experienced their highest infection when exposed to fish, whereas others remained completely uninfected in the fish treatment. Similarly, how hosts responded to the mixed predator treatment depended on the genotype (0.22 ± 0.07). Predator treatment explained only 5.5% of the variance, whereas genotype and the genotype*predator interaction explained 27% of the variance (Table 2). In the second susceptibility assay in which six genotypes were inoculated after eight days, there was again a significant genotype * treatment interaction as well as significant effects of both main factors (Fig. 1B, Table 1). Here, predators explained < 2% of the variance, whereas genotype and the genotype * predator interaction explained 56% of the variance (Table 2). For the subset of six clones used, there were no differences in susceptibility between the two assays (Fig. S1, = 0.92, p = 0.338). When Daphnia were inoculated on day 6, average size predicted average susceptibility (r2 = 0.394, p < 0.0001, Supplemental Fig. 2A). Control animals were smaller than all other treatments, Chaoborus exposed Daphnia were larger than all other treatments, and there was no difference between the fish and mixed
treatments (Fig. 1C, Table 1). When inoculated on day 8 there was a pronounced difference in size with Daphnia in the control and Chaoborus treatments being significantly larger than those in the fish treatment (Fig. 1D, Table 1), however these differences in size did not translate to differences in susceptibility (r2 = 0.001, p = 0.900, Supplemental Fig. 2B). In addition to morphological changes, predators also elicited behavioral changes. In a short‐term feeding assay, feeding rate was lower in both feeding treatments relative to the controls (Supplemental Fig. 3) and these values did not predict average susceptibility (Day 6, r2 = 0.0000, p = 0.8360; Day 8, r2 = 0.0428, p = 0.2049). In both assays, size after succumbing to infection was also influenced by kairomone treatment (Table 1). In the first assay, Chaoborus caused Daphnia to succumb at the largest size, control animals died at an intermediate size, and fish and mixed treatments caused the smallest size at death (Fig. 2A). The treatment effect on parasite fitness was manifest by larger hosts resulting in higher parasite fitness (r2 = 0.292, p < 0.0001, Fig. 2A). In the second assay, Chaoborus and control‐ treated Daphnia also succumbed at a larger size than fish (Fig 2B). Predators again had the indirect effect of higher parasite fitness, which was driven by larger body size at death (r2 = 0.609, p < 0.0001, Fig. 2B). Both predators and host genotype influenced size at maturity and intrinsic clonal growth rate (r); time to maturity was also influenced by the genotype * treatment interaction (Fig. 3, Table 3). In time to maturity, two genotypes did not respond to predators, three genotypes had delayed maturity in response to fish, and one genotype expressed delayed maturity in response to both predators (Fig. 3A).
Despite these genotype‐specific responses in time to maturity, we did not find a corresponding significant genotype * treatment interaction on size at maturity. Rather when grown in the control or Chaoborus treatments, Daphnia matured at a 10% larger size than in fish media (Fig. 3B). Using the first four clutches to calculate the intrinsic clonal growth rate (r), there were differences between treatments and genotypes (Table 3). Fish caused a 63% reduction in growth rate as compared to the control and Chaoborus treatments (Fig. 3C).
CHAPTER 4 DISCUSSION We found that predator‐induced trait‐mediated indirect effects alter a prey’s susceptibility to infection and that the direction and magnitude that this effect is dependent on the species of predator and the genotype of the prey. These indirect effects occurred at multiple levels; change in prey size caused changes in parasite fitness, predators changed host feeding behavior which can alter encounter rate with the parasite, and predators affected a suite of prey life history traits. Changes in these life history traits altered clonal growth rate, which is likely to influence disease dynamics at the population level (Anderson and May 1986). Clearly, the rate at which a parasite spreads through a host population rests on a combination of factors including host genotype and predator specific direct and indirect effects. Chaoborus promote epidemics by selecting for large body size (Tollrian 1995, Hall et al. 2007, Hall et al. 2010, Duffy et al. 2011) and by releasing spores from infected Daphnia into the water column during predation (Cáceres et al. 2009). Fish limit epidemics by selecting for reduced Daphnia body size (Castro et al. 2007, Hall et al. 2007, Hall et al. 2010) and by selectively consuming infected Daphnia and, at least in part, digesting parasite spores (Duffy et al. 2005, Duffy and Hall 2008, Duffy 2009). In general, we observed indirect effects such that Chaoborus caused increased susceptibility and fish caused reduced susceptibility relative to the control treatment. Fish also caused reduced body size at death, which in turn reduced parasite fitness. Furthermore, Chaoborus exposure caused increased intrinsic clonal growth rate relative to fish. Assuming Metschnikowia outbreaks are
density dependant, populations with higher intrinsic clonal growth rate should have more severe epidemics because more susceptible hosts are available to infect (Anderson and May 1986). Together, these results demonstrate that Chaoborus may promote infection by spreading parasite spores, increasing host susceptibility, increasing host number (r), and increasing parasite fitness. Fish, generally, do the opposite and should reduce prevalence of Metschnikowia in natural populations (Duffy et al. 2005). It is important to note, however, that the genetic composition of a population may affect these patterns because different Daphnia genotypes respond differently to predators (Boersma et al. 2000). In general, intraspecific genetic diversity can exert strong influences on infectious diseases (Dwyer et al. 1997, Thrall and Burdon 2000, Elderd et al. 2008) and can sometimes exceed interspecific variation for particular traits (Bangert et al. 2005, Hughes et al. 2008). Plant pathologists in particular have emphasized the role of cultivar heterogeneity in reducing disease; specifically, planting genetically diverse cultivars can dramatically reduce disease prevalence, even among highly susceptible genotypes (Wolfe 1985, Zhu et al. 2000). In animal‐disease systems, genetic diversity of hosts also often correlates negatively with disease prevalence (e.g., Meagher 1999, Pearman and Garner 2005) in part because higher genetic diversity can speed up evolution of increased resistance to disease (Duffy and Sivars‐Becker 2007). Interestingly, intraspecific variation in disease traits can causally depend on traits that initially seem unrelated to disease. For example, among‐genotype differences in feeding rate can drive variation in susceptibility to a parasite that is eaten (Hall et al. 2010).
Surprisingly, we did not find an increase in clonal growth rate in the presence of fish, as many other studies have shown (Machacek 1995, Reede 1995, Lass and Bittner 2002, Sakwinska 2002). We instead observed a 60% reduction in intrinsic clonal growth rate due to the presence of fish. This reduction was caused both by individuals in the fish treatment maturing smaller and later. Our findings are similar to Dawidowicz et al. (2010) where multiple Daphnia genotypes were also assayed and substantial variation in reproductive life history in response to fish was observed. This included reduced intrinsic clonal growth rate by some genotypes in response to fish. Previous work has shown that, in the Daphnia‐Metschnikowia system, feeding rate drives susceptibility (Hall et al. 2007). Although we found that predators altered feeding rates, we did not find a relationship between altered feeding rates and altered susceptibility. Moreover, increasing size did lead to increased susceptibility when Daphnia were inoculated at six days of age, however, size did not predict susceptibility when Daphnia were inoculated after eight days. These negative results imply size and behavior (feeding rate) are not the only mechanisms for the patterns observed in susceptibility at all ages and indicate that other, non‐ mutually exclusive, drivers may be important. First, size (age) at inoculation may interact with feeding rate nonlinearly such that neither size, nor feeding rate alone are good predictors of susceptibility across age groups. A second possibility is that predators cause a reduction in immune function and thereby make some animals exposed to certain predators more susceptible to infection. For instance, in Daphnia, a genotype‐specific reduction in immune function has been observed in
response to chronic fish exposure (Pauwels et al. 2010) and more general reductions have been described in other systems (Rigby and Jokela 2000, proposed by Ramirez and Snyder 2009, Yin et al. 2011). Future work to elucidate the mechanism behind susceptibility should focus on the role of host immune function in predator‐prey dynamics, on the interaction between clearance rate and size (age) to determine if there is a nonlinear relationship, and on the combination of these two mechanisms. Predator‐induced trait‐mediated indirect effects have been shown to alter disease dynamics in other systems through changes in prey behavior (Thiemann and Wassersug 2000, Decaestecker et al. 2002, Daly and Johnson 2011) and changes in prey immune function (Rigby and Jokela 2000, Coslovsky and Richner 2011). Here we describe another potentially common and important influence of predators on disease. In this study, predators altered morphological and life history traits in prey which thereby indirectly affected the interaction between prey and their parasite. Trait variation is ubiquitous among organisms and these differences can influence interspecific interactions (Bolnick et al. 2011) such as predator‐induced indirect effects (Werner and Peacor 2003). We propose that, due to the ubiquity of trait variation and the relative importance of TMIE’s, this pathway could represent a common way in which predators affect host‐parasite interactions. Finally, we emphasize the importance of examining species interactions from the context of the community in order to capture complex dynamics and patterns.
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CHAPTER 6 TABLES Table 1: The effect of predators and Daphnia genotypes on susceptibility when infected on day 6 (and day 8 in parenthesis).
Table 2: Variance partitioning for the effects included in the analysis of the susceptibility assays.
Table 3: The effect of predators and Daphnia genotypes on life history traits.
Table 4: The effect of predators and Daphnia genotypes on clearance rate using ANOVA.
CHAPTER 7 FIGURES Fig. 1. (A and B) Proportion of Daphnia inoculated on day six (A) or day 8 (B) that were susceptible to Metschnikowia across kairomone treatments. The lines represent the average response of each of 11 genotypes across the kairomone treatments. (C and D) Effect of Daphnia size at inoculation on susceptibility at either day 6 (C) or day 8 (D).
Fig. 2. The relationship between Daphnia size after succumbing to infection and parasite fitness across treatments (A) when inoculated on day 6 and (B) when inoculated on day 8.
Fig. 3. Daphnia life history changes in response to kairomone treatments. (A) Daphnia size at reproductive maturity (mean ± s.e.). (B) Days from birth do reproductive maturity (mean ± s.e.). Lines represent the response of each of 6 genotypes. (C) Intrinsic clonal growth rate (r) (mean ± s.e.).
CHAPTER 8 SUPPLEMENTAL FIGURES Fig. S1. Compares both susceptibility assays. (A) Differences across treatments in both susceptibility assays (mean ± s.e.). (B) The response of individual clones in both susceptibility assays (mean ± s.e.).
Fig. S2 Relationship between average body size at innoculation and avearge propotion infected for each host genotype in the different treatment waters. (A) infected on day 6, (B) infected on day 8.
Fig. S3 Effect of kairomone treatment on feeding rate.