Symbiont virulence is a poor predictor of impacts on host population dynamics

Symbiont virulence is a poor predictor of impacts on host population dynamics | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Symbiont virulence is a poor predictor of impacts on host population dynamics View ORCID Profile Marcin K. Dziuba , View ORCID Profile Kristina M. McIntire , View ORCID Profile Elizabeth S. Davenport , View ORCID Profile Fiona E. Corcoran , View ORCID Profile Taleah Nelson , View ORCID Profile Paige McCreadie , View ORCID Profile Riley T. Manuel , Emma Baird , Natalia Ferreira dos Santos , Mia Robbins , View ORCID Profile Emma Dismondy , View ORCID Profile Kira J. Monell , Cristian Huerta , View ORCID Profile Lindsey C. Selter , Katya Deckelbaum , View ORCID Profile Michael H. Cortez , View ORCID Profile Meghan A. Duffy doi: https://doi.org/10.1101/2025.04.10.648206 Marcin K. Dziuba 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marcin K. Dziuba For correspondence: marcinkdziuba{at}gmail.com marcind{at}umich.edu Kristina M. McIntire 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kristina M. McIntire Elizabeth S. Davenport 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth S. Davenport Fiona E. Corcoran 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fiona E. Corcoran Taleah Nelson 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Taleah Nelson Paige McCreadie 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paige McCreadie Riley T. Manuel 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Riley T. Manuel Emma Baird 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natalia Ferreira dos Santos 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mia Robbins 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emma Dismondy 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emma Dismondy Kira J. Monell 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kira J. Monell Cristian Huerta 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lindsey C. Selter 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lindsey C. Selter Katya Deckelbaum 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael H. Cortez 2 Department of Biological Sciences, Florida State University ; Tallahassee, Florida, 32304, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael H. Cortez Meghan A. Duffy 1 Department of Ecology & Evolutionary Biology, University of Michigan ; Ann Arbor, Michigan, 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Meghan A. Duffy Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Symbionts are classified as parasites or mutualists based on their impacts on host individuals, but timescale, ecological context, and level of biological organization can all influence symbiosis outcomes, and hence whether they are deemed harmful or beneficial. We designed experiments exposing the key freshwater grazer Daphnia dentifera to two symbionts varying in virulence, to test how well individual-level effects translate into population-level patterns. We found that a seemingly mildly virulent microsporidian strongly reduced host population size, with consequences for primary production, while a yeast that was highly virulent to individuals had no detectable negative effects on host population size. Our experiments show that studies that fail to consider the impacts of symbionts at multiple scales risk focusing mitigation and control measures on the wrong symbionts. Main text Scientists have long recognized that symbionts can have major effects on host fitness ( 1 ), classifying symbionts with net positive impacts as mutualists and those with net negative impacts as parasites ( 2 ). This classification is typically based on individual-level measurements of changes in fitness-related traits of the host, like symbiont-induced changes in host survival ( 3 – 5 ) or reproductive success (e.g., ( 5 – 7 )). A common measure of the impact of symbionts on hosts is virulence, a term that has a variety of definitions, but in a broad sense is defined as damage inflicted on the host by the symbiont (e.g., ( 5 )), and in a narrow sense as symbiont-induced increases in mortality ( 3 – 5 , 8 ). Here, we use the broad sense definition, but emphasize that both definitions of virulence refer to an individual-level measure of symbiosis outcome. Measuring virulent effects of parasites on life-history traits helps assess the impact of a given symbiont on its host, and is commonly used in human and veterinary medicine, conservation, and organismal biology ( 4 , 9 , 10 ). However, besides the individual-level impacts, it is also important to identify how symbionts affect host population trajectories; after all, a common reason for measuring individual-level effects is to predict the population-level patterns. Accurate prediction of the large-scale, long-term consequences of symbiosis outcomes is crucial to identify risks and benefits of symbiont spread through host populations, and can be used in improving management practices in agriculture and conservation biology ( 11 , 12 ). Yet, there is growing evidence that effects at the individual-level can poorly predict effects at the population-level ( 12 ), and that symbionts that seem benign at the individual-level can have harmful population-level effects and vice versa (e.g., ( 13 , 14 )). These contradictory patterns stem from scale-dependent impacts like density-dependent effects, trans- and multigenerational effects or environmental context ( 13 , 15 – 18 ), and studies of symbionts at the individual level cannot detect these impacts. Hence the individual-level studies of virulence are burdened with a risk of incorrectly classifying a symbiont as harmful, neutral or beneficial for the population. Such incorrect labeling of a symbiont can lead us astray when we attempt to assess the food web- and ecosystem-level impacts of symbionts, or when we try to decide which symbionts should be combated or propagated. To improve our understanding of the role symbionts play in host populations, food webs and ecosystems, we need to recognize that the outcomes of symbiosis can vary depending on the time scale, level of biological organization, and ecological context in which the outcome is measured (e.g., ( 12 – 14 )). In this study, we explored how the net impact of symbiosis may change with scale and context, using the freshwater zooplankton Daphnia dentifera as a model organism. Daphnia , like all organisms, are subject to and dependent on symbiotic interactions; some of these interactions are vital for their survival (e.g., Daphnia without gut microbiomes die ( 19 )), while others are detrimental (e.g., highly virulent parasites, ( 20 )). A well-studied symbiont of Daphnia spp. is Metschnikowia bicuspidata , a yeast pathogen considered highly virulent mostly due to its large reduction in host lifespan ( 21 – 23 ). A recently discovered and seemingly much less virulent microsporidian symbiont of Daphnia species, Ordospora pajunii ( 24 , 25 ), shows potential to become a mutualist when M. bicuspidata is present in the environment ( 26 ). Ordospora pajunii infection reduces the penetrability of Daphnia guts to attacking M. bicuspidata spores ( 26 , 27 ). Additionally, an experimental investigation of potential drivers of context-dependent mutualism revealed that O. pajunii causes Daphnia to become a dead-end host for M. bicuspidata upon sequential co-infection due to increased host mortality ( 27 ). However, O. pajunii has also been found to greatly reduce offspring quality through transgenerational effects, which should decrease host population size over longer time scales ( 28 ). Therefore, our expectation was that the net impact of O. pajunii might change depending on whether the impacts were measured at the individual-level over short timescales versus at the population-level over longer time scales, as well as depending on whether the ecological context included the highly virulent M. bicuspidata . We first investigated the short-term, individual-scale effects of each symbiont on the host with a life-table experiment, then tested for potential shifts in outcomes based on ecological context and longer timescales (spanning generations) using a population-level experiment. Individual-level effects At the individual-level, infections affected host reproduction (parasite:clutch interaction F =43.52, p <0.001) and lifespan (parasite effect F =29.35, p 5.4 and p <0.001 for each comparison, Fig. 1A ) and lifespan by 63.5% ( t -ratio=6.22, p <0.001, Fig. 1B ), the latter resulting in the host producing no more than four clutches. Ordospora pajunii infection, on the other hand, was less harmful to host individuals. The infections of O. pajunii had no effect on the number of offspring in the first three clutches, but this microsporidian had a negative effect on later reproduction, with the size of clutches 4-8 reduced by 8-29% ( t -ratio>3.2 and p 11.2 and p <0.001 for each comparison). O. pajunii did not significantly reduce host lifespan ( t -ratio=1.74, p <0.27, Fig. 1B ). Parasite treatment had a significant effect on reproduction time of Daphnia in clutches 1-4 (parasite effect F =5.00, p =0.007) (Fig. S1). While neither M. bicuspidata nor O. pajunii exposed Daphnia differed from the control (C-MB t -ratio=-0.74 and p =1.000; C-OP t -ratio=1.43 and p =0.466), the O. pajunii exposed Daphnia reproduced faster than those exposed to M. bicuspidata (MB-OP t -ratio=3.15 and p =0.005) (Fig. S1). Overall, M. bicuspidata had larger negative effects on the reproduction and survival of individuals than O. pajunii . Download figure Open in new tab Fig. 1. The number of offspring per clutch (A) and the lifespan (B) of Daphnia were strongly reduced by M. bicuspidata (red), while O. pajunii (orange) reduced clutch size only in late life stages (after clutch 3) and had no effect on host lifespan. Panel A shows fitted values±SE of clutch size (number of offspring per reproduction event) in consecutive clutches. Thick lines on the bottom of the figure indicate clutches at which the difference in clutch size between control and the treatment of corresponding color is significant. Panel B shows mean±SE lifespan (black points and error bars) of Daphnia in each treatment, with individual data points color-coded by treatment. Scaling up Based on the life-table experiment results and previous studies of these symbionts, we predicted that the highly virulent yeast M. bicuspidata would strongly reduce the population density of Daphnia . The life-table experiment suggested that O. pajunii should have negligible effects on Daphnia populations. However, we know that O. pajunii induces negative transgenerational effects (i.e., transgenerational virulence, ( 28 )), and therefore, we expected that the microsporidian could negatively impact Daphnia population density over multiple generations. We also hypothesized that O. pajunii would prevent, or at least reduce, M. bicuspidata outbreaks, benefiting the host. Hence, we exposed Daphnia populations to O. pajunii, M. bicuspidata , both, or neither as a control. Because O. pajunii outbreaks in natural lakes occur before those of M. bicuspidata ( 27 ), and the hypothesized protection mechanism against M. bicuspidata requires established infection of O. pajunii ( 26 , 27 ), we added O. pajunii spores at the start of the experiment and waited until day 31 for its epidemics to be established at ∼15% prevalence to add M. bicuspidata . We analyzed the growth rates and densities of host populations, as well as prevalences of each parasite. We also investigated differences in host demographics, expecting that transgenerational virulence of O. pajunii would reduce the proportion of juveniles in the populations. Additionally, we quantified the standing stock of algae in the microcosms to determine how the symbionts altered host-resource interactions. For the population-level experiment, the effects of single- and co-exposure to the symbionts were assessed during two time intervals: the initial growth phase (until day 23) and the post-peak phase (days 30-93). The separation was determined by both population dynamics and the experimental design: M. bicuspidata spores were added on day 31, when O. pajunii epidemics were established (∼15% prevalence) and the host populations had peaked in abundance. Thus, we used the time before the M. bicuspidata exposure to quantify how O. pajunii affects initial host population growth rate, and we combined the two groups exposed to O. pajunii into one group (OP and OPMB, labeled ‘OP’ in Fig. 2A ), and the two unexposed groups into a separate group (C and MB, labeled ‘C’ in Fig. 2A ). The post-peak phase was used to analyze the impact of both symbionts on host population trajectories, with the four treatments analyzed separately. Download figure Open in new tab Fig. 2. Daphnia initial population growth rate (A) and population size (B) were reduced by O. pajunii whereas M. bicuspidata increased population size. Panel A shows population growth rate during the initial growth phase (through day 23) in groups unexposed (C and MB) and exposed to O. pajunii (OP and OPBM), grouped into parasite treatment C or OP, respectively, because the population growth rates were measured before M. bicuspidata dosing. The black points and error bars indicate mean±SE, the data points are plotted underneath with colors corresponding to treatments. Panel B shows GAM modeled population densities in each treatment, the vertical gray line with black stripe on day 30 separates the time before the populations peaked (dashed lines) and after they peaked (solid lines); M. bicuspidata was dosed to the system on day 31, around when densities peaked. Only the modeled population sizes after day 30 were analyzed with the GAM. The population size in the single infection (OP) treatment was reduced on days 30-61, while, in the co-infection (OPMB) treatment, it was reduced on days 30-64. M. bicuspidata (MB) had a positive effect on the host population on days 71-83. The solid lines and gray ribbons indicate fitted values±SE, thick lines on top and bottom of the figure indicate the time span at which the treatment of the corresponding color had significantly larger or smaller host population size, respectively, in comparison to the control. Population-level effects Contrary to what the individual-level measurements of virulence predicted, we found strong negative effects of O. pajunii on the host populations and no negative effects of M. bicuspidata . In the initial growth phase of the experiment, O. pajunii reduced the population growth rate by 13% ( χ 2 = 17.37, p <0.001, Fig. 2A ). This negative impact of O. pajunii on hosts continued after the peak in densities. In populations with only O. pajunii , host population size on days 30-61 was reduced by, on average, 47% ( t -ratio>2.7 and p 2.8 and p <0.026 for each comparison; Fig. 2B ). Conversely, M. bicuspidata had no observable negative effects on the host populations; instead, later in the experiment (days 71-83), the Daphnia population size was larger in comparison to the control populations by, on average, 41% ( t -ratio<-2.84 and p <0.028 for comparisons on each day; Fig. 2B ). The most likely explanation for the decimation of Daphnia populations by O. pajunii is transgenerational virulence (i.e., negative effects of maternal exposure on offspring fitness, even when those offspring are not themselves exposed ( 28 )). A mathematical model estimating the population-level impacts of transgenerational virulence predicted an approximately 30% reduction in population size ( 28 ), which is similar to the effect we observed in the population experiment. Additionally, we found a lower proportion of juveniles in the O. pajunii- exposed populations (treatment effect F =9.66, p <0.001; Fig. 3 ), which is consistent with high juvenile mortality caused by transgenerational virulence. Adverse transgenerational effects of maternal exposure to stressors have been observed before, via, for example, altered embryo nutrition or epigenetic inheritance ( 29 , 30 ) or, for some parasites, vertical transmission ( 31 ). We do not know yet the process responsible for the transgenerational virulence of O. pajunii , but we do know that the microsporidian is not vertically transmitted and that the embryos produced by exposed mothers are more fragile ( 28 ). Download figure Open in new tab Fig. 3. Proportion of juveniles decreased over time in all treatments, but populations with O. pajunii outbreaks consistently had a lower proportion of juveniles than either control populations or M. bicuspidata exposed populations. Fitted values of linear regression±SE are presented. Resource availability We also hypothesized that some of the discrepancy between the individual-level effects on host fitness and population-level patterns could be explained by host-resource interactions. Some models indicate that intraspecific host competition for resources can amplify the virulence of seemingly benign parasites ( 13 ). Specifically, strong negative effects of O. pajunii could be the result of an infection-driven increase in resource demand that led to starvation and smaller population sizes. Contrary to this, O. pajunii exposed populations had leftover algae on days 29-56 in single exposure and on days 25-64 in co-exposure (C-OP t -ratio<-2.88 and p <0.025 ; C-OPMB t -ratio<-2.68 and p <0.045 for the given time spans) (Fig. S2), when the host populations were undergoing the strongest reductions in density. This indicates that the decrease in population size of Daphnia exposed to the microsporidian was not driven by resource depletion and starvation. Conversely, it is possible that the exposed Daphnia were undergoing illness-mediated anorexia ( 32 , 33 ), which would lower the overall food consumption. In a different study, we found that O. pajunii can depress host feeding rates ( 34 ). Hence, the unexpected harmfulness of O. pajunii is not likely to be a result of resource shortage. We acknowledge that O. pajunii infection might hamper host nutrition through processes independent of food density in the environment, although assimilation efficiency of hosts seems to be unaffected by the microsporidian ( 34 ). In contrast, M. bicuspidata- exposed Daphnia populations consumed all the available food (Fig. S2). Previous work has suggested that infection-driven decreases in foraging rates in M. bicuspidata -infected hosts ( 32 , 35 ) can allow for increased resource uptake by other (susceptible, uninfected) hosts, driving a phenomenon known as a disease-induced hydra effect ( 36 ). The lack of a standing stock of algae in the M. bicuspidata- exposed treatment suggests that the very high densities of hosts consumed all the available food; if infected hosts consumed less food, that would leave additional food for the uninfected hosts, fueling a hydra effect, which is consistent with the periodically higher population size of the yeast-exposed population in comparison to the control ( Fig. 2B ). Additionally, during M. bicuspidata epidemics, the older and larger Daphnia experience the greatest infection-driven mortality. Old and large adults are effective grazers ( 37 ), but contribute very little to the population growth rate ( 38 ). Therefore, their increased mortality would release a fraction of resources from their control, and nourish the juveniles, increasing their survival, and stabilizing the whole population. Hence, we suspect that a joint effect of increased resource availability due to foraging depression, and age-dependent mortality eliminating large grazers, could be responsible for the lack of negative effects of M. bicuspidata at the population-level despite its high individual-level virulence. Antagonistic relationship of the symbionts This study was motivated by the hypothesis that O. pajunii is a context-dependent mutualist whose impact switches from mildly negative to positive in the presence of the more harmful yeast symbiont. Consistent with prior work on O. pajunii ( 26 , 27 ) and on its close relative ( 39 ), M. bicuspidata prevalence was reduced in our experiment in the presence of O. pajunii , and O. pajunii prevalence was lower during M. bicuspidata outbreaks ( Fig. 4A ). Specifically, the prevalence of M. bicuspidata was lower in co-exposure from day 56 of the experiment onward, and reached a 66% reduction by the end of the experiment (treatment:day interaction z -value=-2.33, p =0.020; Fig. 4A ). Ordospora pajunii was also less prevalent in the co-exposure treatment from day 44 onward, reaching a 57% reduction at the end of the experiment (treatment:day interaction F =15.39, p <0.001; Fig. 4B ). Despite the latter, co-exposed populations had similar host population dynamics as the populations exposed just to O. pajunii ( Fig. 2B ). All in all, our assumption that O. pajunii could be beneficial for Daphnia is not supported because O. pajunii had a strong negative population-level effect whereas M. bicuspidata did not. Citing Francis Bacon “ the remedy is worse than the disease ”. Download figure Open in new tab Fig. 4. Prevalence of M. bicuspidata (A) and O. pajunii (B) were lower in the co-infection treatment in comparison to the single infection treatments. The plots show fitted curves of beta regression for M. bicuspidata (A) and GAM for O. pajunii (B), with data points plotted underneath; the thick lines above the figures show the time span when the prevalence of the respective parasite was higher in the single infection treatment than in the co-infection treatment. Beyond individual-level virulence measurements Awareness of how ecological context affects the outcome of symbiosis is crucial for epidemiology, environmental management and conservation, medicine, and agriculture ( 14 ). While there is growing consideration of context-dependent changes in symbiosis outcomes, few studies consider the role that time span and level of biological organization play in the functioning of symbiosis. Switching from individual-level analysis to population-level studies done over multiple generations can dramatically change the perspective on the outcome or even nature of the focal symbiosis. For example, anthelmintic treatment that was predicted to help the buffalo population fight off tuberculosis infection actually increased the spread of bovine tuberculosis ( 12 ). Furthermore, Wolbachia is known to have complex transgenerational effects ( 40 ); for example, in Drosophila melanogaster , grandmother age determines the strength of the cytoplasmic incompatibility impact on fitness of the offspring of Wolbachia -infected males ( 41 ). These examples, and many other studies (e.g., ( 23 , 42 – 44 )), show that individual-level, single generation studies are burdened with a risk of overlooking important patterns and interactions, such as transgenerational inheritance, selection of host and symbiont genotypes, or shifts in symbiosis outcomes stemming from population/community level interactions. These effects have the potential to change our classification of a focal symbiosis from mutualistic to parasitic or vice versa. Here, we present how transgenerational virulence inflicted by a microsporidian that does minimal harm to the infected host can decimate the host population, and how a highly virulent yeast parasite that was expected to be extremely harmful to the host population turned out to be benign. We would not have been able to predict such strong population-level consequences if we had relied solely on individual-level measurements of virulence in a single generation. Understanding the large-scale and long-term consequences of symbiosis is crucial for ecology, evolutionary biology, and epidemiology. The strong population-level effects of O. pajunii detected in this study indicate that long-lasting epidemics of this widespread microsporidian (as have been seen in nature; ( 26 , 45 , 46 )) have the potential to decrease the abundance of Daphnia ; given their central role in lake food webs, this likely has consequences for primary producers and ecosystem functioning. Indeed, our finding that there was excess algae in populations exposed to O. pajunii but not in control populations or those exposed to only M. bicuspidata suggest cascading effects on the food web. More generally, we found that switching between individual- and population-level studies revealed patterns that change our understanding of how a focal symbiosis functions and how it should be classified. Our study emphasizes that individual-based methods of virulence estimation are not enough to make accurate predictions about the impact of a symbiont on a host population, and hence parametrizing models using only within-generation, individual-level measurements may not be sufficient to predict the population- and food web-level effects of epidemics. To combat the spread and harmful effects of infectious diseases in humans, wildlife and agriculture (e.g., ( 47 – 49 )), and to use symbiosis in disease biocontrol (e.g., ( 50 , 51 )), we require a deep understanding of the outcomes of symbioses at levels beyond individuals. Such understanding comes from determining the impacts of symbionts at the population scale over longer time scales, as well as the role of ecological context in shaping those interactions. Incorporating these factors into our investigations of infectious diseases and symbioses will be crucial not only for furthering our knowledge of the ecology and evolution of symbionts but also for decision making in agriculture, conservation, and management. Funding This work was supported by NSF DEB-1748729 to MAD and by the Gordon and Betty Moore Foundation ( GBMF9202 ) to MAD. Author contributions Conceptualization: MKD, KMM, MHC, MAD Methodology: MKD, KMM, MHC, MAD Investigation: MKD, KMM, ESD, FEC, TN, PM, RTM, EB, NFS, MR, ED, KJM, CH, LS, KD Visualization: MKD Funding acquisition: MAD Project administration: MKD, MAD Supervision: MKD, MAD Writing – original draft: MKD, MHC, MAD Writing – review & editing: MKD, ESD, RTM, KJM, MHC, MAD Competing Interests There are no competing interests. Data and Materials availability Data and code used in this manuscript is publicly accessible on GitHub ( https://github.com/marcinkdziuba/Pitcher-experiment/releases/tag/v1.1 ) and through Zenodo ( https://doi.org/10.5281/zenodo.15186214 ). Acknowledgements Funder Information Declared National Science Foundation, https://ror.org/021nxhr62 , DEB-1748729 Gordon and Betty Moore Foundation, https://ror.org/006wxqw41 , GBMF9202 Footnotes The main text was condensed to fit journal requirements, the algae concentration analysis was changed, an analysis of time at reproduction in individual-level experiment was added to the supplementary information https://doi.org/10.5281/zenodo.15186214 References 1. ↵ J. Sapp , The dynamics of symbiosis: an historical overview . Can. J. Bot . 82 , 1046 – 1056 ( 2004 ). OpenUrl CrossRef Web of Science 2. ↵ T. L. F. Leung , R. Poulin , Parasitism, commensalism, and mutualism: exploring the many shades of symbioses . 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Share Symbiont virulence is a poor predictor of impacts on host population dynamics Marcin K. Dziuba , Kristina M. McIntire , Elizabeth S. Davenport , Fiona E. Corcoran , Taleah Nelson , Paige McCreadie , Riley T. Manuel , Emma Baird , Natalia Ferreira dos Santos , Mia Robbins , Emma Dismondy , Kira J. Monell , Cristian Huerta , Lindsey C. Selter , Katya Deckelbaum , Michael H. Cortez , Meghan A. Duffy bioRxiv 2025.04.10.648206; doi: https://doi.org/10.1101/2025.04.10.648206 Share This Article: Copy Citation Tools Symbiont virulence is a poor predictor of impacts on host population dynamics Marcin K. Dziuba , Kristina M. McIntire , Elizabeth S. Davenport , Fiona E. Corcoran , Taleah Nelson , Paige McCreadie , Riley T. Manuel , Emma Baird , Natalia Ferreira dos Santos , Mia Robbins , Emma Dismondy , Kira J. Monell , Cristian Huerta , Lindsey C. Selter , Katya Deckelbaum , Michael H. Cortez , Meghan A. 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