'Small worlds' and the evolution of virulence: infection occurs locally and at a distance

Multiscale selection in spatially structured populations

The spatial structure of populations is key to many (eco-)evolutionary processes. In such cases, the strength and sign of selection on a trait may depend on the spatial scale considered. An example is the evolution of altruism: selection in local environments often favours cheaters over altruists, but this can be outweighed by selection at larger scales, favouring clusters of altruists over clusters of cheaters. For populations subdivided into distinct groups, this effect is described formally by multilevel selection theory. However, many populations do not consist of non-overlapping groups but rather (self-)organize into other ecological patterns. We therefore present a mathematical framework for multiscale selection. This framework decomposes natural selection into two parts: local selection, acting within environments of a certain size, and interlocal selection, acting among them. Varying the size of the local environments subsequently allows one to measure the contribution to selection of each spatial scale. To illustrate the use of this framework, we apply it to models of the evolution of altruism and pathogen transmissibility. The analysis identifies how and to what extent ecological processes at different spatial scales contribute to selection and compete, thus providing a rigorous underpinning to eco-evolutionary intuitions.

Associating with kin selects for disease resistance and against tolerance

Behavioural and physiological resistance are key to slowing epidemic spread. We explore the evolutionary and epidemic consequences of their different costs for the evolution of tolerance that trades off with resistance. Behavioural resistance affects social cohesion, with associated group-level costs, while the cost of physiological resistance accrues only to the individual. Further, resistance, and the associated reduction in transmission, benefit susceptible hosts directly, whereas infected hosts only benefit indirectly, by reducing transmission to kin. We therefore model the coevolution of transmission-reducing resistance expressed in susceptible hosts with resistance expressed in infected hosts, as a function of kin association, and analyse the effect on population-level outcomes. Using parameter values for guppies, Poecilia reticulata, and their gyrodactylid parasites, we find that: (1) either susceptible or infected hosts should invest heavily in resistance, but not both; (2) kin association drives investment in physiological resistance more strongly than in behavioural resistance; and (3) even weak levels of kin association can favour altruistic infected hosts that invest heavily in resistance (versus selfish tolerance), eliminating parasites. Overall, our finding that weak kin association affects the coevolution of infected and susceptible investment in both behavioural and physiological resistance suggests that kin selection may affect disease dynamics across systems.

Markets as drivers of selection for highly virulent poultry pathogens

Theoretical models have successfully predicted the evolution of poultry pathogen virulence in industrialized farm contexts of broiler chicken populations. Whether there are ecological factors specific to more traditional rural farming that affect virulence is an open question. Within non-industrialized farming networks, live bird markets are known to be hotspots of transmission, but whether they could shift selection pressures on the evolution of poultry pathogen virulence has not been addressed. Here, we revisit predictions for the evolution of virulence for viral poultry pathogens, such as Newcastle's disease virus, Marek's disease virus, and influenza virus, H5N1, using a compartmental model that represents transmission in rural markets. We show that both the higher turnover rate and higher environmental persistence in markets relative to farms could select for higher optimal virulence strategies. In contrast to theoretical results modeling industrialized poultry farms, we find that cleaning could also select for decreased virulence in the live poultry market setting. Additionally, we predict that more virulent strategies selected in markets could circulate solely within poultry located in markets. Thus, we recommend the close monitoring of markets not only as hotspots of transmission, but as potential sources of more virulent strains of poultry pathogens.

Host-Parasite Coevolution in Continuous Space Leads to Variation in Local Adaptation across Spatial Scales

AbstractPrevious host-parasite coevolutionary theory has focused on understanding the determinants of local adaptation using spatially discrete models. However, these studies fall short of describing patterns of host-parasite local adaptation across spatial scales. In contrast, empirical work demonstrates that patterns of adaptation depend on the scale at which they are measured. Here, we propose a mathematical model of host-parasite coevolution in continuous space that naturally leads to a scale-dependent definition of local adaptation. In agreement with empirical research, we find that patterns of adaptation vary across spatial scales. In some cases, not only the magnitude of local adaptation but also the identity of the locally adapted species will depend on the spatial scale at which measurements are taken. Building on our results, we suggest a way to consistently measure parasite local adaptation when continuous space is the driver of cross-scale variation. We also describe a way to test whether continuous space is driving cross-scale variation. Taken together, our results provide a new perspective that can be used to understand empirical observations previously unexplained by theoretical expectations and deepens our understanding of the mechanics of host-parasite local adaptation.

The evolution of parasite virulence under targeted culling and harvesting in wildlife and livestock

There is a clear need to understand the effect of human intervention on the evolution of infectious disease. In particular, culling and harvesting of both wildlife and managed livestock populations are carried out in a wide range of management practices, and they have the potential to impact the evolution of a broad range of disease characteristics. Applying eco-evolutionary theory we show that once culling/harvesting becomes targeted on specific disease classes, the established result that culling selects for higher virulence is only found when sufficient infected individuals are culled. If susceptible or recovered individuals are targeted, selection for lower virulence can occur. An important implication of this result is that when culling to eradicate an infectious disease from a population, while it is optimal to target infected individuals, the consequent evolution can increase the basic reproductive ratio of the infection, R 0 , and make parasite eradication more difficult. We show that increases in evolved virulence due to the culling of infected individuals can lead to excess population decline when sustainably harvesting a population. In contrast, culling susceptible or recovered individuals can select for decreased virulence and a reduction in population decline through culling. The implications to the evolution of virulence are typically the same in wildlife populations, that are regulated by the parasite, and livestock populations, that have a constant population size where restocking balances the losses due to mortality. However, the well-known result that vertical transmission selects for lower virulence and transmission in wildlife populations is less marked in livestock populations for parasites that convey long-term immunity since restocking can enhance the density of the immune class. Our work emphasizes the importance of understanding the evolutionary consequences of intervention strategies and the different ecological feedbacks that can occur in wildlife and livestock populations.

Interspecific host competition and parasite virulence evolution

Virulence, the harm to hosts caused by parasite infection, can be selected for by several ecological factors acting synergistically or antagonistically. Here, we focus on the potential for interspecific host competition to shape virulence through such a network of effects. We first summarize how host natural mortality, body mass changes, population density and community diversity affect virulence evolution. We then introduce an initial conceptual framework highlighting how these host factors, which change during host competition, may drive virulence evolution via impacts on life-history trade-offs. We argue that the multi-faceted nature of both interspecific host competition and virulence evolution still requires consideration and experimentation to disentangle contrasting mechanisms. It also necessitates a differential treatment for parasites with various transmission strategies. However, such a comprehensive approach focusing on the role of interspecific host competition is essential to understand the processes driving the evolution of virulence in a tangled bank.

How do fluctuating ecological dynamics impact the evolution of hosts and parasites?

Theoretical models of the evolution of parasites and their hosts have shaped our understanding of infectious disease dynamics for over 40 years. Many theoretical models assume that the underlying ecological dynamics are at equilibrium or constant, yet we know that in a great many systems there are fluctuations in the ecological dynamics owing to a variety of intrinsic or extrinsic factors. Here, we discuss the challenges presented when modelling evolution in systems with fluctuating ecological dynamics and summarize the main approaches that have been developed to study host-parasite evolution in such systems. We provide an in-depth guide to one of the methods by applying it to two worked examples of host evolution that have not previously been studied in the literature: when cycles occur owing to seasonal forcing in competition, and when the presence of a free-living parasite causes cycles, with accompanying interactive Python code provided. We review the findings of studies that have explored host-parasite evolution when ecological dynamics fluctuate, and point to areas of future research. Throughout we stress the importance of feedbacks between the ecological and evolutionary dynamics in driving the outcomes of infectious disease systems. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

Limited host availability disrupts the genetic correlation between virulence and transmission

Virulence is expected to be linked to parasite fitness via transmission. However, it is not clear whether this relationship is genetically determined, nor if it differs when transmission occurs continuously during, or only at the end of, the infection period. Here, we used inbred lines of the macroparasitic spider mite Tetranychus urticae to disentangle genetic vs. nongenetic correlations among traits, while varying parasite density and opportunities for transmission. A positive genetic correlation between virulence and the number of transmitting stages produced was found under continuous transmission. However, if transmission occurred only at the end of the infection period, this genetic correlation disappeared. Instead, we observed a negative relationship between virulence and the number of transmitting stages, driven by density dependence. Thus, within-host density dependence caused by reduced opportunities for transmission may hamper selection for higher virulence, providing a novel explanation as to why limited host availability leads to lower virulence.

Application of Graphs in a One Health Framework

The One Health framework, which advocates the crucial interconnection between environmental, animal, and human health and well-being, is becoming of increasing importance and acceptance in health sciences over the last years. The hottest public health topics of the latest years, like zoonotic diseases (e.g., the recent pandemic) or the increasing antibiotic resistance, characterized by many as "pandemic of the future," make the more holistic and combinatorial approach of One Health a necessity to combat such complex problems. Multiple graphs and graph theory have found applications in health sciences for many years, and they can now extend to usage across all levels of a One Health approach to health, ranging from genome, one disease level, to epidemiology and ecosystem graphs. For that last ecosystem layer, a proposed approach is the utilization of process graphs from the chemical engineering field, in order to understand a whole system and what constitute the most crucial aspects of a One Health issue in ecosystem level. Here P-graphs are focused alongside their combinatorial algorithms, implemented in R, and their application researched in an effort to extract information and plan interventions.

Spatially structured eco-evolutionary dynamics in a host-pathogen interaction render isolated populations vulnerable to disease

While the negative effects that pathogens have on their hosts are well-documented in humans and agricultural systems, direct evidence of pathogen-driven impacts in wild host populations is scarce and mixed. Here, to determine how the strength of pathogen-imposed selection depends on spatial structure, we analyze growth rates across approximately 4000 host populations of a perennial plant through time coupled with data on pathogen presence-absence. We find that infection decreases growth more in the isolated than well-connected host populations. Our inoculation study reveals isolated populations to be highly susceptible to disease while connected host populations support the highest levels of resistance diversity, regardless of their disease history. A spatial eco-evolutionary model predicts that non-linearity in the costs to resistance may be critical in determining this pattern. Overall, evolutionary feedbacks define the ecological impacts of disease in spatially structured systems with host gene flow being more important than disease history in determining the outcome.

Aggregated Distribution as an Explanation for the Paradox of Plankton and Collective Animal Behavior

Simple Summary

ECA is similar to a cellular automaton that mimics the evolutionary dynamics of species in metapopulations, simulating the underlying mechanisms of natural selection. In this work, we carry out an in-silico study of the effects of different dispersal strategies on the evolutionary balance of interactions between digital organisms. In ECA we see how a specific type of distribution can significantly influence the dynamics, persistence, distribution, and abundance of populations of different species within a particular habitat. In the first place, we show that an aggregate distribution is more inefficient than a uniform distribution. Still, we verify that this aggregate distribution is essential in predator–prey type interactions so that the species involved do not become extinct. We also show that the aggregate distribution does not comply with the competitive exclusion principle and, for this reason, it results in a general and straightforward explanation for the paradox of plankton and the grouping of animals. Many animals and especially some planktonic species group together in specific spaces, although some travel adrift, leaving other areas or patches free where competitors or their prey can prosper, preventing them from becoming extinct.

Abstract

This work analyzes the evolutionary consequences of different aggregation levels of species distribution with an Evolutionary Cellular Automaton (ECA). We have found that in habitats with the same carrying capacity, aggregated distributions preserve smaller populations than do uniform distributions, i.e., they are less efficient. Nonetheless, we have also found that aggregated distributions, among other factors, can help the evolutionary stability of some biological interactions, such as predator–prey interactions, despite their granting less individual fitness. Besides, the competitive exclusion principle does not usually stand in populations with aggregated distribution. We have applied ECA to study the effects of aggregated distribution in two notorious cases: in the so-called paradox of the plankton and in gregarious animals. In doing so, we intend to ratify long-established ecological knowledge explaining these phenomena from a new perspective. In the first case, due to aggregate distribution, large aggregations of digital organisms mimicking very abundant planktonic species, leave large patches or oceanic areas free for other less competitive organisms, which mimic rare species, to prosper. In this case, we can see how effects, such as ecological drift and the small portion, act simultaneously. In the second case of aggregation, the aggregate distribution of gregarious animals could be explained under specialized predator–prey interactions and interdemic competition. Thus, digital organisms that imitate predators reduce the competitive capacity of their prey, destabilizing their competitiveness against other species. The specialized predator also goes extinct if the prey goes extinct by natural selection. Predators that have an aggregate distribution compensate the prey and thus avoid exclusion. This way there are more predator-free patches in which the prey can prosper. However, by granting greater colonization capacity to its prey, the predator loses competitiveness. Therefore, it is a multilevel selection event in which group adaptation grows to the detriment of the predator as an individual.

The effect of migration and variation on populations of Escherichia coli adapting to complex fluctuating environments

Migration, a critical evolutionary force, can have contrasting effects on adaptation. It can aid as well as impede adaptation. The effects of migration on microbial adaptation have been studied primarily in simple constant environments. Very little is known about the effects of migration on adaptation to complex, fluctuating environments. In our study, we subjected replicate populations of Escherichia coli, adapting to complex and unpredictably fluctuating environments to different proportions of clonal ancestral immigrants. Contrary to the results from simple/constant environments, the presence of clonal immigrants reduced all measured proxies of fitness. However, migration from a source population with a greater variance in fitness resulted in no change in fitness with respect to the no-migration control, except at the highest level of migration. Thus, the presence of variation in the immigrants could counter the adverse effects of migration in complex and unpredictably fluctuating environments. Our study demonstrates that the effects of migration are strongly dependent on the nature of the destination environment and the genetic makeup of immigrants. These results enhance our understanding of the influences of migrating populations, which could help better predict the consequences of migration.

Contrasting parasite-mediated reductions in fitness within versus between patches of a nematode host

Host and parasites interact across spatial scales, but parasite-mediated fitness effects are typically measured only at local scales. Recent work suggests that parasites can reduce host fitness during dispersal between patches, highlighting the potential for both within- and between-patch effects to contribute to the net fitness consequences of parasitism. Building on this work, we measured the contribution of the dispersal phase to parasite-mediated reductions in host fitness. We used the nematode Caenorhabditis elegans and its natural microsporidian parasite Nematocida parisii to quantify the fitness consequences of parasitism at the individual, population, and metapopulation level. Nematocida parisii reduced individual fecundity and population growth but had its greatest fitness impact at the dispersal stage: parasitism reduced the fitness of dispersing larvae by 62%-100%. These results indicate that the cost of parasitism in this system is greatly underestimated if the metapopulation level is not taken into account. We also found that the effects of N. parisii vary with host genotype, and the relative advantage of the most resistant genotype increases with inclusion of the dispersal stage. Taken together, our findings demonstrate that host-parasite interactions at the dispersal stage can magnify selection for parasite resistance.

Host phenology can drive the evolution of intermediate virulence strategies in some obligate-killer parasites

Traditional mechanistic trade-offs between transmission and virulence are the foundation of nearly all theory on parasite virulence evolution. For obligate-host killer parasites, evolution toward intermediate virulence depends on a trade-off between virulence (time to death) and transmission (the number of progeny released upon death). Although several ecological factors impact optimal virulence strategies constrained by trade-offs, these factors have been insufficient to explain the intermediate virulence levels observed in nature. The timing of seasonal activity, or phenology, is a factor that commonly influences ecological interactions but is difficult to incorporate into virulence evolution studies. We present a mathematical model of a seasonal obligate-killer parasite to study the impact of host phenology on virulence evolution. The model demonstrates that host phenology can select for intermediate parasite virulence even when a traditional mechanistic trade-off between transmission and virulence is omitted. The optimal virulence strategy is impacted by both the host activity period duration and the host emergence timing variation. Parasites with lower virulence strategies are favored in environments with longer host activity periods and when hosts emerge synchronously. The results demonstrate that host phenology can be sufficient to select for intermediate virulence strategies, providing an alternative driver of virulence evolution in some natural systems.

Shoaling guppies evade predation but have deadlier parasites

Parasites exploit hosts to replicate and transmit, but overexploitation kills both host and parasite. Predators may shift this cost-benefit balance by consuming infected hosts or changing host behaviour, but the strength of these effects remains unclear. Here we use field and lab data on Trinidadian guppies and their Gyrodactylus spp. parasites to show how differential predation pressure influences parasite virulence and transmission. We use an experimentally demonstrated virulence-transmission trade-off to parametrize a mathematical model in which host shoaling (as a means of anti-predator defence), increases contact rates and selects for higher virulence. Then we validate model predictions by collecting parasites from wild, Trinidadian populations; parasites from high-predation populations were more virulent in common gardens than those from low-predation populations. Broadly, our results indicate that reduced social contact selects against parasite virulence.

Virulence of a virus: How it depends on growth rate, effectors, memory cells, and immune escape

A viral strain may infect a host, proliferate rapidly, become controlled by immune reactions, and eventually be eliminated from the body. The virulence, or the magnitude of harm to the host due to infection, depends on the abundance and duration of the viral strain in the body, and the importance of the damaged tissue of the host. In this study, we investigated how the cumulative viral load (time-integral of the number of infected cells) depends on various factors, such as the viral growth rate, the effectiveness of immune cells to kill infected cells, speed of immune activation, formation of memory cells, and longevity of immune cells. In addition, viruses may produce a mutant with different antigen types, escape the immune reaction targeting the original type, and inflate virulence. We succeeded in deriving simple and explicit formulas of the virulence in four different parameter regions. We found that, in some parameter region, enhancing memory cells is very effective in suppressing virulence, but direct activation of immune effector cells is not effective; while the opposite is the case in other regions. The result could be important in designing drug suppressing virus effectively. Additionally, we discussed the reported correlation between virulence and molecular evolution rate.

Passage and the evolution of virulence in invertebrate pathogens: Fundamental and applied perspectives

Understanding the ecological and genetic factors that determine the evolution of virulence has broad value for invertebrate pathology. In addition to helping us understand the fundamental biology of our study organisms this body of theory has important applications in microbial biocontrol. Experimental tests of virulence theory are often carried out in invertebrate models and yet theory rarely informs applied passage experiments that aim to increase or maintain virulence. This review summarizes recent progress in this field with a focus on work most relevant to biological control: the virulence of invertebrate pathogens that are 'obligate killers' and which require cadavers for the production of infectious propagules. We discuss recent theory and fundamental and applied experimental evolution with bacteria, fungi, baculoviruses and nematodes. While passage experiments using baculoviruses have a long history of producing isolates with increased virulence, studies with other pathogens have not been so successful. Recent passage experiments that have applied evolution of virulence frameworks based on cooperation (kin selection) have produced novel methods and promising mutants with increased killing power. Evolution of virulence theory can provide plausible explanations for the varied results of passage experiments as well as a predictive framework for improving artificial selection.

How Local Interactions Impact the Dynamics of an Epidemic

Susceptible-Infected-Recovered (SIR) models have long formed the basis for exploring epidemiological dynamics in a range of contexts, including infectious disease spread in human populations. Classic SIR models take a mean-field assumption, such that a susceptible individual has an equal chance of catching the disease from any infected individual in the population. In reality, spatial and social structure will drive most instances of disease transmission. Here we explore the impacts of including spatial structure in a simple SIR model. We combine an approximate mathematical model (using a pair approximation) and stochastic simulations to consider the impact of increasingly local interactions on the epidemic. Our key development is to allow not just extremes of 'local' (neighbour-to-neighbour) or 'global' (random) transmission, but all points in between. We find that even medium degrees of local interactions produce epidemics highly similar to those with entirely global interactions, and only once interactions are predominantly local do epidemics become substantially lower and later. We also show how intervention strategies to impose local interactions on a population must be introduced early if significant impacts are to be seen.

The three Ts of virulence evolution during zoonotic emergence

There is increasing interest in the role that evolution may play in current and future pandemics, but there is often also considerable confusion about the actual evolutionary predictions. This may be, in part, due to a historical separation of evolutionary and medical fields, but there is a large, somewhat nuanced body of evidence-supported theory on the evolution of infectious disease. In this review, we synthesize this evolutionary theory in order to provide a framework for clearer understanding of the key principles. Specifically, we discuss the selection acting on zoonotic pathogens' transmission rates and virulence at spillover and during emergence. We explain how the direction and strength of selection during epidemics of emerging zoonotic disease can be understood by a three Ts framework: trade-offs, transmission, and time scales. Virulence and transmission rate may trade-off, but transmission rate is likely to be favoured by selection early in emergence, particularly if maladapted zoonotic pathogens have 'no-cost' transmission rate improving mutations available to them. Additionally, the optimal virulence and transmission rates can shift with the time scale of the epidemic. Predicting pathogen evolution, therefore, depends on understanding both the trade-offs of transmission-improving mutations and the time scales of selection.

Overcoming the growth-infectivity trade-off in a bacteriophage slows bacterial resistance evolution

The use of lytic bacteriophages for treating harmful bacteria (phage therapy) is faced with the challenge of bacterial resistance evolution. Phage strains with certain traits, for example, rapid growth and relatively broad infectivity ranges, may enjoy an advantage in slowing bacterial resistance evolution. Here, we show the possibility for laboratory selection programs ("evolutionary training") to yield phage genotypes with both high growth rate and broad infectivity, traits between which a trade-off has been assumed. We worked with a lytic phage that infects the bacterium Pseudomonas fluorescens and adopted three types of training strategies: evolution on susceptible bacteria, coevolution with bacteria, and rotation between evolution and coevolution phases. Overall, there was a trade-off between growth rate and infectivity range in the evolved phage isolates, including those from the rotation training programs. A small number of phages had both high growth rate and broad infectivity, and those trade-off-overcoming phages could slow or even completely prevent resistance evolution in initially susceptible bacterial populations. Our findings show the promise of well-designed evolutionary training programs, in particular an evolution/coevolution rotation selection regime, for obtaining therapeutically useful phage materials.

Evolution of an asymptomatic first stage of infection in a heterogeneous population

Pathogens evolve different life-history strategies, which depend in part on differences in their host populations. A central feature of hosts is their population structure (e.g. spatial). Additionally, hosts themselves can exhibit different degrees of symptoms when newly infected; this latency is a key life-history property of pathogens. With an evolutionary-epidemiological model, we examine the role of population structure on the evolutionary dynamics of latency. We focus on specific power-law-like formulations for transmission and progression from the first infectious stage as a function of latency, assuming that the across-group to within-group transmission ratio increases if hosts are less symptomatic. We find that simple population heterogeneity can lead to local evolutionarily stable strategies (ESSs) at zero and infinite latency in situations where a unique ESS exists in the corresponding homogeneous case. Furthermore, there can exist more than one interior evolutionarily singular strategy. We find that this diversity of outcomes is due to the (possibly slight) advantage of across-group transmission for pathogens that produce fewer symptoms in a first infectious stage. Thus, our work reveals that allowing individuals without symptoms to travel can have important unintended evolutionary effects and is thus fundamentally problematic in view of the evolutionary dynamics of latency.

The central role of host reproduction in determining the evolution of virulence in spatially structured populations

A substantial body of work has shown that local transmission selects for less acute, 'prudent' parasites that have lower virulence and transmission rates. This is because parasite strains with higher transmission rates 'self-shade' due to a combination of genetic correlations (self: clustered related parasite strains compete for susceptible individuals) and ecological correlations (shade: infected individuals clustering and blocking transmission). However, the interaction of ecological and genetic correlations alongside higher order ecological effects such as patch extinctions means that spatial evolutionary effects can be nuanced; theory has predicted that a relatively small proportion of local infection can select for highest virulence, such that there is a humped relationship between the degree of local infection and the harm that parasites are selected to cause. Here, we examine the separate roles of the interaction scales of reproduction and infection in the context of different degrees of pathogenic castration in determining virulence evolution outcomes. Our key result is that, as long as there is significant reproduction from infected individuals, local infection always selects for lower virulence, and that the prediction that a small proportion of local infection can select for higher virulence only occurs for highly castrating pathogens. The results emphasize the importance of demography for evolutionary outcomes in spatially structured populations, but also show that the core prediction that parasites are prudent in space is reasonable for the vast majority of host-parasite interactions and mixing patterns in nature.

Experimental evidence that local interactions select against selfish behaviour

How social behaviours evolve remains one of the most debated questions in evolutionary biology. An important theoretical prediction is that when organisms interact locally due to limited dispersal or strong social ties, the population structure that emerges may favour cooperation over antagonism. We carry out an experimental test of this theory by directly manipulating population spatial structure in an insect laboratory model system and measuring the impact on the evolution of the extreme selfish behaviour of cannibalism. We show that, as predicted by the theory, Indian meal moth larvae that evolved in environments with more limited dispersal are selected for lower rates of cannibalism. This is important because it demonstrates that local interactions select against selfish behaviour. Therefore, the ubiquitous variation in population structure that we see in nature is a simple mechanism that can help to explain the variation in selfish and cooperative behaviours that we see in nature.

Bidirectional interactions between host social behaviour and parasites arise through ecological and evolutionary processes

An animal's social behaviour both influences and changes in response to its parasites. Here we consider these bidirectional links between host social behaviours and parasite infection, both those that occur from ecological vs evolutionary processes. First, we review how social behaviours of individuals and groups influence ecological patterns of parasite transmission. We then discuss how parasite infection, in turn, can alter host social interactions by changing the behaviour of both infected and uninfected individuals. Together, these ecological feedbacks between social behaviour and parasite infection can result in important epidemiological consequences. Next, we consider the ways in which host social behaviours evolve in response to parasites, highlighting constraints that arise from the need for hosts to maintain benefits of sociality while minimizing fitness costs of parasites. Finally, we consider how host social behaviours shape the population genetic structure of parasites and the evolution of key parasite traits, such as virulence. Overall, these bidirectional relationships between host social behaviours and parasites are an important yet often underappreciated component of population-level disease dynamics and host-parasite coevolution.

Superinfection and the evolution of an initial asymptomatic stage

Pathogens have evolved a variety of life-history strategies. An important strategy consists of successful transmission by an infected host before the appearance of symptoms, that is, while the host is still partially or fully asymptomatic. During this initial stage of infection, it is possible for another pathogen to superinfect an already infected host and replace the previously infecting pathogen. Here, we study the effect of superinfection during the first stage of an infection on the evolutionary dynamics of the degree to which the host is asymptomatic (host latency) in that same stage. We find that superinfection can lead to major differences in evolutionary behaviour. Most strikingly, the duration of immunity following infection can significantly influence pathogen evolutionary dynamics, whereas without superinfection the outcomes are independent of host immunity. For example, changes in host immunity can drive evolutionary transitions from a fully symptomatic to a fully asymptomatic first infection stage. Additionally, if superinfection relative to susceptible infection is strong enough, evolution can lead to a unique strategy of latency that corresponds to a local fitness minimum, and is therefore invasible by nearby mutants. Thus, this strategy is a branching point, and can lead to coexistence of pathogens with different latencies. Furthermore, in this new framework with superinfection, we also find that there can exist two interior singular strategies. Overall, new evolutionary outcomes can cascade from superinfection.

Persistent effects of management history on honeybee colony virus abundances

Infectious diseases are a major threat to both managed and wild pollinators. One key question is how the movement or transplantation of honeybee colonies under different management regimes affects honeybee disease epidemiology. We opportunistically examined any persistent effect of colony management history following relocation by characterising the virus abundances of honeybee colonies from three management histories, representing different management histories: feral, low-intensity management, and high-intensity "industrial" management. The colonies had been maintained for one year under the same approximate 'common garden' condition. Colonies in this observational study differed in their virus abundances according to management history, with the feral population history showing qualitatively different viral abundance patterns compared to colonies from the two managed population management histories; for example, higher abundance of sacbrood virus but lower abundances of various paralysis viruses. Colonies from the high-intensity management history exhibited higher viral abundances for all viruses than colonies from the low-intensity management history. Our results provide evidence that management history has persistent impacts on honeybee disease epidemiology, suggesting that apicultural intensification could be majorly impacting on pollinator health, justifying much more substantial investigation.

Using lattice SIS epidemiological model with clustered treatment to investigate epidemic control

In epidemic control, how the size of treatment blocks influences the disease is a very interesting problem. Here, we construct an SIS model with spatially clustered treatment blocks using cellular automata and pair approximation to investigate the disease control and optimal arrangement of treatment blocks. On a same treatment intensity, the treatment blocks are built randomly and the individuals in the treatment blocks are cured in the next interval. The migration and neighboring structures are also considered in this model. We find that the size of treatment blocks will influence the control of disease. The bigger size of treatment blocks is more effective in epidemic control. The results obtained by lattice simulations and pair approximation respectively are consistent. On the same treatment arrange, higher migration rate has completely different influence on the epidemic depending on different neighboring structures. Besides, in different neighboring structures, both infected and susceptible populations will get clustered under the treatment blocks. Meanwhile, the neighboring structures can influence the degree of aggregation and the bigger treatment blocks can promote the aggregation compared to the smaller ones. The results here are significant for the control of epidemic diseases, especially when available medical resources are limited.

How long do Red Queen dynamics survive under genetic drift? A comparative analysis of evolutionary and eco-evolutionary models

Background

Red Queen dynamics are defined as long term co-evolutionary dynamics, often with oscillations of genotype abundances driven by fluctuating selection in host-parasite systems. Much of our current understanding of these dynamics is based on theoretical concepts explored in mathematical models that are mostly (i) deterministic, inferring an infinite population size and (ii) evolutionary, thus ecological interactions that change population sizes are excluded. Here, we recall the different mathematical approaches used in the current literature on Red Queen dynamics. We then compare models from game theory (evo) and classical theoretical ecology models (eco-evo), that are all derived from individual interactions and are thus intrinsically stochastic. We assess the influence of this stochasticity through the time to the first loss of a genotype within a host or parasite population.

Results

The time until the first genotype is lost (“extinction time”), is shorter when ecological dynamics, in the form of a changing population size, is considered. Furthermore, when individuals compete only locally with other individuals extinction is even faster. On the other hand, evolutionary models with a fixed population size and competition on the scale of the whole population prolong extinction and therefore stabilise the oscillations. The stabilising properties of intra-specific competitions become stronger when population size is increased and the deterministic part of the dynamics gain influence. In general, the loss of genotype diversity can be counteracted with mutations (or recombination), which then allow the populations to recurrently undergo negative frequency-dependent selection dynamics and selective sweeps.

Conclusion

Although the models we investigated are equal in their biological motivation and interpretation, they have diverging mathematical properties both in the derived deterministic dynamics and the derived stochastic dynamics. We find that models that do not consider intraspecific competition and that include ecological dynamics by letting the population size vary, lose genotypes – and thus Red Queen oscillations – faster than models with competition and a fixed population size.

Assessing virulence of Varroa destructor mites from different honey bee management regimes

The mite Varroa destructor is an important honey bee parasite that causes substantial losses of honey bee colonies worldwide. Evolutionary theory suggests that the high densities at which honey bees are managed in large-scale beekeeping settings will likely select for mites with greater growth and virulence, thereby potentially explaining the major damage done by these mites. We tested this hypothesis by collecting mites from feral bee colonies, "lightly" managed colonies (those from small-scale sedentary operations), and "heavily" managed colonies (those from large-scale operations that move thousands of colonies across the US on a yearly basis). We established 8 apiaries, each consisting of 11 colonies from a standardized lightly managed bee background that were cleared of mites, and artificially infested each apiary with controlled numbers of mites from feral, lightly managed, or heavily managed bees or left uninoculated as negative control. We monitored the colonies for more than 2 years for mite levels, colony strength (adult bee population, brood coverage, and honey storage), and survival. As predicted by evolutionary theory, we found that colonies inoculated with mites from managed backgrounds had increased V. destructor mite levels relative to those with mites from feral colonies or negative controls. However, we did not see a difference between heavily and lightly managed colonies, and these higher mite burdens did not translate into greater virulence, as measured by reductions in colony strength and survival. Our results suggest that human management of honey bee colonies may favor the increased population growth rate of V. destructor, but that a range of potential confounders (including viral infections and genotype-by-genotype interactions) likely contribute to the relationship between mite reproduction and virulence.

Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal-human interface

Historically, efforts to assess 'zoonotic risk' have focused mainly on quantifying the potential for cross-species emergence of viruses from animal hosts. However, viruses clearly differ in relative burden, both in terms of morbidity and mortality (virulence) incurred and the capacity for sustained human-to-human transmission. Extending previously published databases, we delineated host and viral traits predictive of human mortality associated with viral spillover, viral capacity to transmit between humans following spillover and the probability of a given virus being zoonotic. We demonstrate that increasing host phylogenetic distance from humans positively correlates with human mortality but negatively correlates with human transmissibility, suggesting that the virulence induced by viruses emerging from hosts at high phylogenetic distance may limit capacity for human transmission. Our key result is that hosts most closely related to humans harbour zoonoses of lower impact in terms of morbidity and mortality, while the most distantly related hosts-in particular, order Chiroptera (bats)-harbour highly virulent zoonoses with a lower capacity for endemic establishment in human hosts. As a whole, our results emphasize the importance of understanding how zoonoses manifest in the human population and also highlight potential risks associated with multi-host transmission chains in spillover. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Strategies of host resistance to pathogens in spatially structured populations: An agent-based evaluation

There is growing theoretical evidence that spatial structure can affect the ecological and evolutionary outcomes of host-parasite interactions. Locally restricted interactions have been shown in particular to affect host resistance and tolerance. In this study we investigate the evolution of several types of host disease resistance strategies, alone or in combination, in spatially structured populations. We construct a spatially explicit, individual-based stochastic model where hosts and parasites interact with each other in a spatial lattice, and interactions are restricted to a given neighbourhood of varying size. We investigate several host resistance strategies, including constitutive (expressed in all resistant hosts), induced (expressed only upon infection), and combinations thereof. We show that a costly constitutive resistance cannot reach fixation, whereas an inducible resistance strategy may become fixed in the population if the cost remains low, particularly if it impacts host recovery. We also demonstrate that mixed strategies can be maintained in the host population, and that a higher investment in a recovery-boosting inducible resistance allows for a higher investment in a constitutive response. Our simulations reveal that the spatial structure of the population impacts the selection for resistance in a complex fashion. While single strategies of resistance are generally favoured in less structured populations, mixed strategies can sometimes prevail only in highly structured environments, e.g. when combining constitutive and transmission-blocking induced responses Overall these results shed new light on the dynamics of disease resistance in a spatially-structured host-pathogen system, and advance our theoretical understanding of the evolutionary dynamics of disease resistance, a necessary step to elaborate more efficient and sustainable strategies for disease management.

Role of host genetic diversity for susceptibility-to-infection in the evolution of virulence of a plant virus

Predicting viral emergence is difficult due to the stochastic nature of the underlying processes and the many factors that govern pathogen evolution. Environmental factors affecting the host, the pathogen and the interaction between both are key in emergence. In particular, infectious disease dynamics are affected by spatiotemporal heterogeneity in their environments. A broad knowledge of these factors will allow better estimating where and when viral emergence is more likely to occur. Here, we investigate how the population structure for susceptibility-to-infection genes of the plant Arabidopsis thaliana shapes the evolution of Turnip mosaic virus (TuMV). For doing so we have evolved TuMV lineages in two radically different host population structures: (1) a metapopulation subdivided into six demes (subpopulations); each one being composed of individuals from only one of six possible A. thaliana ecotypes and (2) a well-mixed population constituted by equal number of plants from the same six A. thaliana ecotypes. These two populations were evolved for twelve serial passages. At the end of the experimental evolution, we found faster adaptation of TuMV to each ecotype in the metapopulation than in the well-mixed heterogeneous host populations. However, viruses evolved in well-mixed populations were more pathogenic and infectious than viruses evolved in the metapopulation. Furthermore, the viruses evolved in the demes showed stronger signatures of local specialization than viruses evolved in the well-mixed populations. These results illustrate how the genetic diversity of hosts in an experimental ecosystem favors the evolution of virulence of a pathogen.

The influenza of 1918: Evolutionary perspectives in a historical context

The 1918 influenza pandemic was the deadliest in known human history. It spread globally to the most isolated of human communities, causing clinical disease in a third of the world's population, and infecting nearly every human alive at the time. Determination of mortality numbers is complicated by weak contemporary surveillance in the developing world, but recent estimates put the death toll at 50 million or even higher. This outbreak is of great interest to modern day epidemiologists, virologists, global health researchers and evolutionary biologists. They ask: Where did it come from? And if it happened once, could it happen again? Understanding how such a virulent epidemic emerged and spread offers hope for prevention and strategies of response. This review uses historical methodology and evolutionary perspectives to revisit the 1918 outbreak. Using the American military experience as a case study, it investigates the emergence of virulence in 1918 by focusing on key susceptibility factors that favored both the influenza virus and the subsequent pneumococcal invasion that took so many lives. This article explores the history of the epidemic and contemporary measures against it, surveys modern research on the virus, and considers what aspects of 1918 human and animal ecology most contributed to the emergence of this pandemic.

Relational concurrency, stages of infection, and the evolution of HIV set point viral load

HIV viral load (VL) predicts both transmission potential and rate of disease progression. For reasons that are still not fully understood, the set point viral load (SPVL) established after acute infection varies across individuals and populations. Previous studies have suggested that population mean SPVL (MSPVL) has evolved near an optimum that reflects a trade-off between transmissibility and host survival. Sexual network structures affect rates of potential exposure during different within-host phases of infection marked by different transmission probabilities, and thus affect the number and timing of transmission events. These structures include relational concurrency, which has been argued to explain key differences in HIV burden across populations. We hypothesize that concurrency will alter the fitness landscape for SPVL in ways that differ from other network features whose impacts accrue at other times during infection. To quantitatively test this hypothesis, we developed a dynamic, stochastic, data-driven network model of HIV transmission, and evolution to assess the impact of key sexual network phenomena on MSPVL evolution. Experiments were repeated in sensitivity runs that made different assumptions about transmissibility during acute infection, SPVL heritability, and the functional form of the relationship between VL and transmissibility. For our main transmission model, scenarios yielded MSPVLs ranging from 4.4 to 4.75 log10 copies/ml, covering much of the observed empirical range. MSPVL evolved to be higher in populations with high concurrency and shorter relational durations, with values varying over a clinically significant range. In linear regression analyses on these and other predictors, main effects were significant (P < 0.05), as were interaction terms, indicating that effects are interdependent. We also noted a strong correlation between two key emergent properties measured at the end of the simulations-MSPVL and HIV prevalence-most clearly for phenomena that affect transmission networks early in infection. Controlling for prevalence, high concurrency yielded higher MSPVL than other network phenomena. Interestingly, we observed lower prevalence in runs in which SPVL heritability was zero, indicating the potential for viral evolution to exacerbate disease burden over time. Future efforts to understand empirical variation in MSPVL should consider local HIV burden and basic sexual behavioral and network structure.

Can Ebola virus evolve to be less virulent in humans?

Understanding Ebola virus (EBOV) virulence evolution not only is timely but also raises specific questions because it causes one of the most virulent human infections and it is capable of transmission after the death of its host. Using a compartmental epidemiological model that captures three transmission routes (by regular contact, via dead bodies and by sexual contact), we infer the evolutionary dynamics of case fatality ratio on the scale of an outbreak and in the long term. Our major finding is that the virus's specific life cycle imposes selection for high levels of virulence and that this pattern is robust to parameter variations in biological ranges. In addition to shedding a new light on the ultimate causes of EBOV's high virulence, these results generate testable predictions and contribute to informing public health policies. In particular, burial management stands out as the most appropriate intervention since it decreases the R0 of the epidemics, while imposing selection for less virulent strains.

The role of spatial heterogeneity in the evolution of local and global infections of viruses

Viruses have two modes spread in a host body, one is to release infectious particles from infected cells (global infection) and the other is to infect directly from an infected cell to an adjacent cell (local infection). Since the mode of spread affects the evolution of life history traits, such as virulence, it is important to reveal what level of global and local infection is selected. Previous studies of the evolution of global and local infection have paid little attention to its dependency on the measures of spatial configuration. Here we show the evolutionarily stable proportion of global and local infection, and how it depends on the distribution of target cells. Using an epidemic model on a regular lattice, we consider the infection dynamics by pair approximation and check the evolutionarily stable strategy. We also conduct the Monte-Carlo simulation to observe evolutionary dynamics. We show that a higher local infection is selected as target cells become clustered. Surprisingly, the selected strategy depends not only on the degree of clustering but also the abundance of target cells per se.

Viruses such as human immunodeficiency virus and measles virus can spread through physical contact between infected and susceptible cells (cell-to-cell infection), as well as normal cell-free infection through virions. Some experimental evidences support the possibility that high ability of cell-to-cell infection is selected in the host. Since the mode of spread affects the evolution of life history traits, it is important to reveal what condition favors high ability of cell-to-cell infection. Here we address what level of cell-to-cell infection is selected in different target cell distributions. Analysis of ordinary differential equations that keep track of dynamics for spatial configuration of infected cells and the Monte-Carlo simulations show that higher proportion of local infection is selected as target cells become clustered. The selected strategy depends not only on the degree of clustering but also the abundance of target cells per se. Our results suggest viruses have more chances to evolve the ability of local infection in a host body than previously thought. In particular, this may explain the emergence of measles virus strains that gained the ability to infect the central nervous system.

Social Immunity: Emergence and Evolution of Colony-Level Disease Protection

Social insect colonies have evolved many collectively performed adaptations that reduce the impact of infectious disease and that are expected to maximize their fitness. This colony-level protection is termed social immunity, and it enhances the health and survival of the colony. In this review, we address how social immunity emerges from its mechanistic components to produce colony-level disease avoidance, resistance, and tolerance. To understand the evolutionary causes and consequences of social immunity, we highlight the need for studies that evaluate the effects of social immunity on colony fitness. We discuss the roles that host life history and ecology have on predicted eco-evolutionary dynamics, which differ among the social insect lineages. Throughout the review, we highlight current gaps in our knowledge and promising avenues for future research, which we hope will bring us closer to an integrated understanding of socio-eco-evo-immunology.

Evolutionary ecology of Chagas disease; what do we know and what do we need?

The aetiological agent of Chagas disease, Trypanosoma cruzi, is a key human pathogen afflicting most populations of Latin America. This vectorborne parasite is transmitted by haematophageous triatomines, whose control by large‐scale insecticide spraying has been the main strategy to limit the impact of the disease for over 25 years. While those international initiatives have been successful in highly endemic areas, this systematic approach is now challenged by the emergence of insecticide resistance and by its low efficacy in controlling species that are only partially adapted to human habitat. In this contribution, we review evidences that Chagas disease control shall now be entering a second stage that will rely on a better understanding of triatomines adaptive potential, which requires promoting microevolutionary studies and –omic approaches. Concomitantly, we show that our knowledge of the determinants of the evolution of T. cruzi high diversity and low virulence remains too limiting to design evolution‐proof strategies, while such attributes may be part of the future of Chagas disease control after the 2020 WHO's target of regional elimination of intradomiciliary transmission has been reached. We should then aim at developing a theory of T. cruzi virulence evolution that we anticipate to provide an interesting enrichment of the general theory according to the specificities of transmission of this very generalist stercorarian trypanosome. We stress that many ecological data required to better understand selective pressures acting on vector and parasite populations are already available as they have been meticulously accumulated in the last century of field research. Although more specific information will surely be needed, an effective research strategy would be to integrate data into the conceptual and theoretical framework of evolutionary ecology and life‐history evolution that provide the quantitative backgrounds necessary to understand and possibly anticipate adaptive responses to public health interventions.

Spatial evolutionary epidemiology of spreading epidemics

Most spatial models of host-parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in previous studies of parasite evolution in spatially structured host populations. Our analysis can be used to understand and predict the transient evolutionary dynamics of pathogens and the emergence of spatial patterns of phenotypic variation.

Fast-killing parasites can be favoured in spatially structured populations

It is becoming increasingly clear that the evolution of infectious disease is influenced by host population structure. Theory predicts that parasites should be more ‘prudent’—less transmissible—in spatially structured host populations. However, here we (i) highlight how low transmission, the phenotype being selected for in this in context, may also be achieved by rapacious host exploitation, if fast host exploitation confers a local, within-host competitive advantage and (ii) test this novel concept in a bacteria–virus system. We found that limited host availability and, to a lesser extent, low relatedness favour faster-killing parasites with reduced transmission. By contrast, high host availability and high relatedness favour slower-killing, more transmissible parasites. Our results suggest high, rather than low, virulence may be selected in spatially structured host–parasite communities where local competition and hence selection for a within-host fitness advantage is high.

This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.

Transmission dynamics: critical questions and challenges

This article overviews the dynamics of disease transmission in one-host-one-parasite systems. Transmission is the result of interacting host and pathogen processes, encapsulated with the environment in a 'transmission triangle'. Multiple transmission modes and their epidemiological consequences are often not understood because the direct measurement of transmission is difficult. However, its different components can be analysed using nonlinear transmission functions, contact matrices and networks. A particular challenge is to develop such functions for spatially extended systems. This is illustrated for vector transmission where a 'perception kernel' approach is developed that incorporates vector behaviour in response to host spacing. A major challenge is understanding the relative merits of the large number of approaches to quantifying transmission. The evolution of transmission mode itself has been a rather neglected topic, but is important in the context of understanding disease emergence and genetic variation in pathogens. Disease impacts many biological processes such as community stability, the evolution of sex and speciation, yet the importance of different transmission modes in these processes is not understood. Broader approaches and ideas to disease transmission are important in the public health realm for combating newly emerging infections.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges

Individual differences in contact rate can arise from host, group and landscape heterogeneity and can result in different patterns of spatial spread for diseases in wildlife populations with concomitant implications for disease control in wildlife of conservation concern, livestock and humans. While dynamic disease models can provide a better understanding of the drivers of spatial spread, the effects of landscape heterogeneity have only been modelled in a few well-studied wildlife systems such as rabies and bovine tuberculosis. Such spatial models tend to be either purely theoretical with intrinsic limiting assumptions or individual-based models that are often highly species- and system-specific, limiting the breadth of their utility. Our goal was to review studies that have utilized dynamic, spatial models to answer questions about pathogen transmission in wildlife and identify key gaps in the literature. We begin by providing an overview of the main types of dynamic, spatial models (e.g., metapopulation, network, lattice, cellular automata, individual-based and continuous-space) and their relation to each other. We investigate different types of ecological questions that these models have been used to explore: pathogen invasion dynamics and range expansion, spatial heterogeneity and pathogen persistence, the implications of management and intervention strategies and the role of evolution in host-pathogen dynamics. We reviewed 168 studies that consider pathogen transmission in free-ranging wildlife and classify them by the model type employed, the focal host-pathogen system, and their overall research themes and motivation. We observed a significant focus on mammalian hosts, a few well-studied or purely theoretical pathogen systems, and a lack of studies occurring at the wildlife-public health or wildlife-livestock interfaces. Finally, we discuss challenges and future directions in the context of unprecedented human-mediated environmental change. Spatial models may provide new insights into understanding, for example, how global warming and habitat disturbance contribute to disease maintenance and emergence. Moving forward, better integration of dynamic, spatial disease models with approaches from movement ecology, landscape genetics/genomics and ecoimmunology may provide new avenues for investigation and aid in the control of zoonotic and emerging infectious diseases.