Changes in capture availability due to infection can lead to detectable biases in population-level infectious disease parameters.

Correctly identifying the strength of selection that parasites impose on hosts is key to predicting epidemiological and evolutionary outcomes of host-parasite interactions. However, behavioral changes due to infection can alter the capture probability of infected hosts and thereby make selection difficult to estimate by standard sampling techniques. Mark-recapture approaches, which allow researchers to determine if some groups in a population are less likely to be captured than others, can be used to identify infection-driven capture biases. If a metric of interest directly compares infected and uninfected populations, calculated detection probabilities for both groups may be useful in identifying bias. Here, we use an individual-based simulation to test whether changes in capture rate due to infection can alter estimates of three key metrics: 1) reduction in the reproductive success of infected parents relative to uninfected parents, 2) the relative risk of infection for susceptible genotypes compared to resistant genotypes, and 3) changes in allele frequencies between generations. We explore the direction and underlying causes of the biases that emerge from these simulations. Finally, we argue that short series of mark-recapture sampling bouts, potentially implemented in under a week, can yield key data on detection bias due to infection while not adding a significantly higher burden to disease ecology studies.

Simulating the effects of long-distance dispersal and landscape heterogeneity on the eco-evolutionary outcomes of range expansion in an invasive riverine fish, Tench (Tinca tinca).

Predicting how quickly populations expand their range and whether they will retain genetic diversity when they are introduced to new regions or track environmental conditions suited to their survival is an important applied and theoretical challenge. The literature suggests that long-distance dispersal, landscape heterogeneity and the evolution of dispersal influence populations' expansion rates and genetic diversity. We used individual-based spatially explicit simulations to examine these relationships for Tench (Tinca tinca), an invasive fish expanding its geographical range in eastern North America since the 1990s. Simulated populations varied greatly in expansion rates (1.1-28.6 patches year-1 ) and genetic diversity metrics, including changes in observed heterozygosity (-19 to +0.8%) and effective number of alleles (-0.32 to -0.01). Populations with greater dispersal distances expanded faster than those with smaller dispersal distances but exhibited considerable variation in expansion rate among local populations, implying less predictable expansions. However, they tended to retain genetic diversity as they expanded, suggesting more predictable evolutionary trajectories. In contrast, populations with smaller dispersal distances spread predictably more slowly but exhibited more variability among local populations in genetic diversity losses. Consistent with empirical data, populations spreading in a longer, narrower dispersal corridor lost more neutral genetic variation to the stochastic fixation of alleles. Given the unprecedented pace of anthropogenic environmental change and the increasing need to manage range-expanding populations, our results have conservation ramifications as they imply that the evolutionary trajectories of populations characterised by shorter dispersal distances spreading in narrower landscapes are more variable and, therefore, less predictable.

Evolutionary adaptation of trees and modelled future larch forest extent in Siberia.

With changing climate, the boreal forest could potentially migrate north and become threatened by droughts in the south. However, whether larches, the dominant tree species in eastern Siberia, can adapt to novel situations is largely unknown but is crucial for predicting future population dynamics. Exploring variable traits and trait adaptation through inheritance in an individual-based model can improve our understanding and help future projections. We updated the individual-based spatially explicit vegetation model LAVESI (Larix Vegetation Simulator), used for forest predictions in Eastern Siberia, with trait value variation and incorporated inheritance of parental values to their offspring. Forcing the model with both past and future climate projections, we simulated two areas - the expanding northern treeline and a southerly area experiencing drought. While the specific trait of 'seed weight' regulates migration, the abstract 'drought resistance' protects stands. We show that trait variation with inheritance leads to an increase in migration rate (∼ 3% area increase until 2100). The drought resistance simulations show that, under increasing stress, including adaptive traits leads to larger surviving populations (17% of threatened under RCP 4.5 (Representative Concentration Pathway)). We show that with the increase expected under the RCP 8.5 scenario vast areas (80% of the extrapolated area) of larch forest are threatened and could disappear due to drought as adaptation plays only a minor role under strong warming. We conclude that variable traits facilitate the availability of variants under environmental changes. Inheritance allows populations to adapt to environments and promote successful traits, which leads to populations that can spread faster and be more resilient, provided the changes are not too drastic in both time and magnitude. We show that trait variation and inheritance contribute to more accurate models that can improve our understanding of responses of boreal forests to global change.

Nutrients and flow shape the cyclic dominance games between Escherichia coli strains.

Evolutionary game theory has provided various models to explain the coexistence of competing strategies, one of which is the rock-paper-scissors (RPS) game. A system of three Escherichia coli strains-a toxin-producer, a resistant and a sensitive-has become a classic experimental model for studying RPS games. Previous experimental and theoretical studies, however, often ignored the influence of ecological factors such as nutrients and toxin dynamics on the evolutionary game dynamics. In this work, we combine experiments and modelling to study how these factors affect competition dynamics. Using three-dimensional printed mini-bioreactors, we tracked the frequency of the three strains in different culturing media and under different flow regimes. Although our experimental system fulfilled the requirements of cyclic dominance, we did not observe clear cycles or long-term coexistence between strains. We found that both nutrients and flow rates strongly impacted population dynamics. In our simulations, we explicitly modelled the release, removal and diffusion of toxin. We showed that the amount of toxin that is retained in the system is a simple indicator that can predict competition outcomes across broad parameter space. Moreover, our simulation results suggest that high rates of toxin diffusion might have prevented cyclic patterns from emerging in our experimental system. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'.

Mechanistic support for increased primary production around artificial reefs.

Understanding factors controlling primary production is fundamental for the protection, management, and restoration of ecosystems. Tropical seagrass ecosystems are among the most productive ecosystems worldwide, yielding tremendous services for society. Yet they are also among the most impaired from anthropogenic stressors, prompting calls for ecosystem-based restoration approaches. Artificial reefs (ARs) are commonly applied in coastal marine ecosystems to rebuild failing fisheries and have recently gained attention for their potential to promote carbon sequestration. Nutrient hotspots formed via excretion from aggregating fishes have been empirically shown to enhance local primary production around ARs in seagrass systems. Yet, if and how increased local production affects primary production at ecosystem scale remains unclear, and empirical tests are challenging. We used a spatially explicit individual-based simulation model that combined a data-rich single-nutrient primary production model for seagrass and bioenergetics models for fish to test how aggregating fish on ARs affect seagrass primary production at patch and ecosystem scales. Specifically, we tested how the aggregation of fish alters (i) ecosystem seagrass primary production at varying fish densities and levels of ambient nutrient availability and (ii) the spatial distribution of seagrass primary production. Comparing model ecosystems with equivalent nutrient levels, we found that when fish aggregate around ARs, ecosystem-scale primary production is enhanced synergistically. This synergistic increase in production was caused by nonlinear dynamics associated with nutrient uptake and biomass allocation that enhances aboveground primary production more than belowground production. Seagrass production increased near the AR and decreased in areas away from the AR, despite marginal reductions in seagrass biomass at the ecosystem level. Our simulation's findings that ARs can increase ecosystem production provide novel support for ARs in seagrass ecosystems as an effective means to promote (i) fishery restoration (increased primary production can increase energy input to the food web) and (ii) carbon sequestration, via higher rates of primary production. Although our model represents a simplified, closed seagrass system without complex trophic interactions, it nonetheless provides an important first step in quantifying ecosystem-level implications of ARs as a tool for ecological restoration.

Evaluation of Areawide Forecasts of Wind-borne Crop Pests: Sugarcane Aphid (Hemiptera: Aphididae) Infestations of Sorghum in the Great Plains of North America.

Airborne pests pose a major challenge in agriculture. Integrated pest management programs have been considered a viable response to this challenge, and pest forecasting can aid in strategic management decisions. Annually recurrent areawide sugarcane aphid [Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae)] infestations of sorghum [Sorghum bicolor (L.) Moench (Poales: Poaceae)] in the Great Plains of North America is one of such challenges. As part of the response, a spatially-explicit individual-based model was developed that simulates sugarcane aphid infestations over the southern-to-central part of the region. In this work, we evaluated model forecasts using 2015-2018 field data. The ranges of forecasted days of first infestation significantly overlapped with those observed in the field. The average days of first infestation observed in the field were approximated by the model with differences of less than 28 days in Texas and southern Oklahoma (2015-2018), and in northern Oklahoma (2016-2017). In half of these cases the difference was less than 14 days. In general, the modeled average day of first infestation was earlier than the observed one. As conceptual modeling decisions may impact model forecasts and as various socio-environmental factors may impact spatio-temporal patterns of field data collection, agreement between the forecasts and the observed estimates may vary between locations and seasons. Predictive modeling has the potential to occupy a central position within areawide integrated pest management programs. More detailed consideration of local agricultural practices and local environmental conditions could improve forecasting accuracy, as could broader participation of producers in field monitoring efforts.

The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions.

The dynamics of a population expanding into unoccupied habitat has been primarily studied for situations in which growth and dispersal parameters are uniform in space or vary in one dimension. Here, we study the influence of finite-sized individual inhomogeneities and their collective effect on front speed if randomly placed in a two-dimensional habitat. We use an individual-based model to investigate the front dynamics for a region in which dispersal or growth of individuals is reduced to zero (obstacles) or increased above the background (hotspots), respectively. In a regime where front dynamics is determined by a local front speed only, a principle of least time can be employed to predict front speed and shape. The resulting analytical solutions motivate an event-based algorithm illustrating the effects of several obstacles or hotspots. We finally apply the principle of least time to large heterogeneous environments by solving the Eikonal equation numerically. Obstacles lead to a slow-down that is dominated by the number density and width of obstacles, but not by their precise shape. Hotspots result in a speed-up, which we characterize as function of hotspot strength and density. Our findings emphasize the importance of taking the dimensionality of the environment into account.

Productivity, niche availability, species richness, and extinction risk: Untangling relationships using individual-based simulations.

It has often been suggested that the productivity of an ecosystem affects the number of species that it can support. Despite decades of study, the nature, extent, and underlying mechanisms of this relationship are unclear. One suggested mechanism is the "more individuals" hypothesis (MIH). This proposes that productivity controls the number of individuals in the ecosystem, and that more individuals can be divided into a greater number of species before their population size is sufficiently small for each to be at substantial risk of extinction. Here, we test this hypothesis using REvoSim: an individual-based eco-evolutionary system that simulates the evolution and speciation of populations over geological time, allowing phenomena occurring over timescales that cannot be easily observed in the real world to be evaluated. The individual-based nature of this system allows us to remove assumptions about the nature of speciation and extinction that previous models have had to make. Many of the predictions of the MIH are supported in our simulations: Rare species are more likely to undergo extinction than common species, and species richness scales with productivity. However, we also find support for relationships that contradict the predictions of the strict MIH: species population size scales with productivity, and species extinction risk is better predicted by relative than absolute species population size, apparently due to increased competition when total community abundance is higher. Furthermore, we show that the scaling of species richness with productivity depends upon the ability of species to partition niche space. Consequently, we suggest that the MIH is applicable only to ecosystems in which niche partitioning has not been halted by species saturation. Some hypotheses regarding patterns of biodiversity implicitly or explicitly overlook niche theory in favor of neutral explanations, as has historically been the case with the MIH. Our simulations demonstrate that niche theory exerts a control on the applicability of the MIH and thus needs to be accounted for in macroecology.

Evolutionary simulations clarify and reconcile biodiversity-disturbance models.

There is significant geographic variation in species richness. However, the nature of the underlying relationships, such as that between species richness and environmental stability, remains unclear. The stability-time hypothesis suggests that environmental instability reduces species richness by suppressing speciation and increasing extinction risk. By contrast, the patch-mosaic hypothesis suggests that small-scale environmental instability can increase species richness by providing a steady supply of non-equilibrium environments. Although these hypotheses are often applied to different time scales, their core mechanisms are in conflict. Reconciling these apparently competing hypotheses is key to understanding how environmental conditions shape the distribution of biodiversity. Here, we use REvoSim, an individual-based, eco-evolutionary system, to model the evolution of sessile organisms in environments with varying magnitudes and scales of environmental instability. We demonstrate that when environments have substantial permanent heterogeneity, a high level of localized environmental instability reduces biodiversity, whereas in environments lacking permanent heterogeneity, high levels of localized instability increase biodiversity. By contrast, broad-scale environmental instability, acting on the same time scale, invariably reduces biodiversity. Our results provide a new view of the biodiversity-disturbance relationship that reconciles contrasting hypotheses within a single model and implies constraints on the environmental conditions under which those hypotheses apply. These constraints can inform attempts to conserve adaptive potential in different environments during the current biodiversity crisis.

Modelling transmission and control of Mycobacterium avium subspecies paratuberculosis within Irish dairy herds with compact spring calving.

Paratuberculosis is a chronic bacterial infection of the intestine in cattle caused by Mycobacterium avium subspecies paratuberculosis (Map). To better understand Map transmission in Irish dairy herds, we adapted the French stochastic individual-based epidemiological simulation model to account for seasonal herd demographics. We investigated the probability of Map persistence over time, the within-herd prevalence over time, and the relative importance of transmission pathways, and assessed the relative effectiveness of test-and-cull control strategies. We investigated the impact on model outputs of calf separation from cows (calves grazed on pasture adjacent to cows vs. were completely separated from cows) and test-and-cull. Test-and-cull scenarios consisted of highly test-positive cows culled within 13 or 4 weeks after detection, and calf born to highly test-positive cows kept vs removed. We simulated a typical Irish dairy herd with on average 82 lactating cows, 112 animals in total. Each scenario was iterated 1000 times to adjust variation caused by stochasticity. Map was introduced in the fully naive herd through the purchase of a moderately infectious primiparous cow. Infection was considered to persist when at least one infected animal remained in the herd or when Map was present in the environment. The probability of Map persistence 15 years after introduction ranged between 32.2-42.7 % when calves and cows had contact on pasture, and between 18.9-29.4 % when calves and cows were separated on pasture. The most effective control strategy was to cull highly test-positive cows within four weeks of detection (absolute 10 % lower persistence compared to scenarios without control). Removing the offspring of highly test-positive dams did not affect either Map persistence or within-herd prevalence of Map. Mean prevalence 15 years after Map introduction was highest (63.5 %) when calves and cows had contact on pasture. Mean prevalence was 15 % lower (absolute decrease) when cows were culled within 13 weeks of a high test-positive result, and 28 % lower when culled within 4 weeks. Around calving, the infection rate was high, with calves being infected in utero or via the general indoor environment (most important transmission routes). For the remainder of the year, the incidence rate was relatively low with most calves being infected on pasture when in contact with cows. Testing and culling was an effective control strategy when it was used prior to the calving period to minimize the number of highly infectious cows present when calves were born.

Unpacking Conditional Neutrality: Genomic Signatures of Selection on Conditionally Beneficial and Conditionally Deleterious Mutations.

It is common to look for signatures of local adaptation in genomes by identifying loci with extreme levels of allele frequency divergence among populations. This approach to finding genes associated with local adaptation often assumes antagonistic pleiotropy, wherein alternative alleles are strongly favored in alternative environments. Conditional neutrality has been proposed as an alternative to antagonistic pleiotropy, but conditionally neutral polymorphisms are transient, and it is unclear how much outlier signal would be maintained under different forms of conditional neutrality. Here, we use individual-based simulations and a simple analytical heuristic to show that a pattern that mimics local adaptation at the phenotypic level, where each genotype has the highest fitness in its home environment, can be produced by the accumulation of mutations that are neutral in their home environment and deleterious in nonlocal environments. Because conditionally deleterious mutations likely arise at a rate many times higher than conditionally beneficial mutations, they can have a significant cumulative effect on fitness even when individual effect sizes are small. We show that conditionally deleterious mutations driving nonlocal maladaptation may be undetectable by even the most powerful genome scans, as differences in allele frequency between populations are typically small. We also explore the evolutionary effects of conditionally beneficial mutations and find that they can maintain significant signals of local adaptation, and they would be more readily detectable than conditionally deleterious mutations using conventional genome scan approaches. We discuss implications for interpreting outcomes of transplant experiments and genome scans that are used to study the genetic basis of local adaptation.

Evolutionary Hysteresis and Ratchets in the Evolution of Periodical Cicadas.

It has been previously hypothesized that the perfectly synchronized mass emergence of periodical cicadas (Magicicada spp.) evolved as a result of a switch from size-based to age-based emergence. In the former case, cicada nymphs emerge immediately (at the first opportunity) on reaching maturity, whereas in the latter case, nymphs wait in order to emerge at a specific age. Here we use an individual-based model to simulate the cicada life cycle and to study the evolution of periodicity. We find that if age-based emergence evolves in a constant abiotic environment, it typically results in a population that is protoperiodic, and synchronous emergence of the whole population is not achieved. However, perfect periodicity and synchronous emergence can be attained, if the abiotic environment changes back and forth between favorable and unfavorable conditions (hysteresis). Furthermore, once age-based emergence evolves, generally it can only be invaded by other age-based emergence strategies with longer cycle lengths (evolutionary ratchet). Together, these mechanisms promote the evolution of long periodic life cycles and synchronous emergence in the Magicicada. We discuss how our results connect to previous theories and recent phylogenetic studies on Magicicada evolution.

Understanding policing as a mechanism of cheater control in cooperating bacteria.

Policing occurs in insect, animal and human societies, where it evolved as a mechanism maintaining cooperation. Recently, it has been suggested that policing might even be relevant in enforcing cooperation in much simpler organisms such as bacteria. Here, we used individual-based modelling to develop an evolutionary concept for policing in bacteria and identify the conditions under which it can be adaptive. We modelled interactions between cooperators, producing a beneficial public good, cheaters, exploiting the public good without contributing to it, and public good-producing policers that secrete a toxin to selectively target cheaters. We found that toxin-mediated policing is favoured when (a) toxins are potent and durable, (b) toxins are cheap to produce, (c) cell and public good diffusion is intermediate, and (d) toxins diffuse farther than the public good. Although our simulations identify the parameter space where toxin-mediated policing can evolve, we further found that policing decays when the genetic linkage between public good and toxin production breaks. This is because policing is itself a public good, offering protection to toxin-resistant mutants that still produce public goods, yet no longer invest in toxins. Our work thus highlights that not only specific environmental conditions are required for toxin-mediated policing to evolve, but also strong genetic linkage between the expression of public goods, toxins and toxin resistance is essential for this mechanism to remain evolutionarily stable in the long run.

neogen: A tool to predict genetic effective population size (Ne ) for species with generational overlap and to assist empirical Ne study design.

Molecular genetic estimates of population effective size (Ne ) lose accuracy and precision when insufficient numbers of samples or loci are used. Ideally, researchers would like to forecast the necessary power when planning their project. neogen (genetic Ne for Overlapping Generations) enables estimates of precision and accuracy in advance of empirical investigation and allows exploration of the power available in different user-specified age-structured sampling schemes. neogen provides a population simulation and genetic power analysis framework that simulates the demographics, genetic composition, and Ne , from species-specific life history, mortality, population size, and genetic priors. neogen guides the user to establish a tractable sampling regime and to determine the numbers of samples and microsatellite or SNP loci required for accurate and precise genetic Ne estimates when sampling a natural population. neogen is useful at multiple stages of a study's life cycle: when budgeting, as sampling and locus development progresses, and for corroboration when empirical Ne estimates are available. The underlying model is applicable to a wide variety of iteroparous species with overlapping generations (e.g., mammals, birds, reptiles, long-lived fishes). In this paper, we describe the neogen model, detail the workflow for the point-and-click software, and explain the graphical results. We demonstrate the use of neogen with empirical Australian east coast zebra shark (Stegostoma fasciatum) data. For researchers wishing to make accurate and precise genetic Ne estimates for overlapping generations species, neogen facilitates planning for sample and locus acquisition, and with existing empirical genetic Ne estimates neogen can corroborate population demographic and life history properties.

Comparing the consequences of natural selection, adaptive phenotypic plasticity, and matching habitat choice for phenotype-environment matching, population genetic structure, and reproductive isolation in meta-populations.

Organisms commonly experience significant spatiotemporal variation in their environments. In response to such heterogeneity, different mechanisms may act that enhance ecological performance locally. However, depending on the nature of the mechanism involved, the consequences for populations may differ greatly. Building on a previous model that investigated the conditions under which different adaptive mechanisms (co)evolve, this study compares the ecological and evolutionary population consequences of three very different responses to environmental heterogeneity: matching habitat choice (directed gene flow), adaptive plasticity (associated with random gene flow), and divergent natural selection. Using individual-based simulations, we show that matching habitat choice can have a greater adaptive potential than plasticity or natural selection: it allows for local adaptation while protecting genetic polymorphism despite global mating or strong environmental changes. Our simulations further reveal that increasing environmental fluctuations and unpredictability generally favor the emergence of specialist genotypes but that matching habitat choice is better at preventing local maladaptation by individuals. This confirms that matching habitat choice can speed up the genetic divergence among populations, cause indirect assortative mating via spatial clustering, and hence even facilitate sympatric speciation. This study highlights the potential importance of directed dispersal in local adaptation and speciation, stresses the difficulty of deriving its operation from nonexperimental observational data alone, and helps define a set of ecological conditions which should favor its emergence and subsequent detection in nature.

Should I Change or Should I Go? Phenotypic Plasticity and Matching Habitat Choice in the Adaptation to Environmental Heterogeneity.

It can be challenging for organisms to achieve a good match between their phenotypic characteristics and environmental requirements that vary in space and time. The evolution of adaptive phenotypes can result from genetic differentiation at the population level. Individuals, however, could also change their phenotype (adaptive plasticity) or select an environment because it matches with their phenotype (matching habitat choice). It is poorly known under which conditions these different solutions to environmental heterogeneity evolve and whether they operate together. Using an individual-based simulation model, we assessed which solutions evolved depending on degree of temporal variation, costs of multiple underlying traits, and order of dispersal and development. Population genetic divergence was superseded by plasticity or matching habitat choice as temporal variation increased. Plasticity and matching habitat choice were limited by their trait costs, even when this involved only a part of the underlying traits. Independent of the order of dispersal and development, plasticity evolved more commonly than matching habitat choice, in part because the match a phenotype can achieve by matching habitat choice is limited by the types of environments available. Our results explain the apparent relative rarity of matching habitat choice in nature. At the same time, our results can be used to look for matching habitat choice in those biological systems where the conditions for other solutions seem unfavorable.

Conjugative plasmids enable the maintenance of low cost non-transmissible plasmids.

Some plasmids can be transferred by conjugation to other bacterial hosts. But almost half of the plasmids are non-transmissible. These plasmid types can only be transmitted to the daughter cells of their host after bacterial fission. Previous studies suggest that non-transmissible plasmids become extinct in the absence of selection of their encoded traits, as plasmid-free bacteria are more competitive. Here, we aim to identify mechanisms that enable non-transmissible plasmids to persist, even if they are not beneficial. For this purpose, an individual-based model for plasmid population dynamics was set up and carefully tested for structural consistency and plausibility. Our results demonstrate that non-transmissible plasmids can be stably maintained in a population, even if they impose a substantial burden on their host cells growth. A prerequisite is the co-occurrence of an incompatible and costly conjugative plasmid type, which indirectly facilitates the preservation of the non-transmissible type. We suggest that this constellation might be considered as a potential mechanism maintaining plasmids and associated antibiotic resistances. It should be investigated in upcoming laboratory experiments.

Alternative Reproductive Tactics in Context: How Demography, Ecology, and Behavior Affect Male Mating Success.

Exploitation of sexual signals by predators or parasites increases costs to signalers, creating opportunities for establishment of alternative reproductive tactics (ARTs). In field crickets, males calling may attract acoustically orienting parasitoid flies. Alternatively, males behaving as satellites forgo calling and attempt to intercept females attracted to callers. We modeled the contribution of calling versus satellite behavior to male reproductive success in the larger context of variation in ecology (parasitism rate, background mortality), demography (density, sex ratio), and female behavior (phonotaxis, mating choosiness). Male mating success was most influenced by number of females (standardized effect size 0.42), followed by female choosiness (0.33), background mortality (-0.31), number of males (-0.28), and parasitism rate (-0.21). The smallest effects were phonotaxis (0.10) and satellite behavior (-0.09). Although satellite behavior ameliorated negative effects of parasitism, its comparative effect was slight. ARTs seem most likely to evolve and persist when a single selection pressure on signaling is particularly strong.

Coevolutionary feedback elevates constitutive immune defence: a protein network model.

BACKGROUND: Organisms have evolved a variety of defence mechanisms against natural enemies, which are typically used at the expense of other life history components. Induced defence mechanisms impose minor costs when pathogens are absent, but mounting an induced response can be time-consuming. Therefore, to ensure timely protection, organisms may partly rely on constitutive defence despite its sustained cost that renders it less economical. Existing theoretical models addressing the optimal combination of constitutive versus induced defence focus solely on host adaptation and ignore the fact that the efficacy of protection depends on genotype-specific host-parasite interactions. Here, we develop a signal-transduction network model inspired by the invertebrate innate immune system, in order to address the effect of parasite coevolution on the optimal combination of constitutive and induced defence. RESULTS: Our analysis reveals that coevolution of parasites with specific immune components shifts the host's optimal allocation from induced towards constitutive immunity. This effect is dependent upon whether receptors (for detection) or effectors (for elimination) are subjected to parasite counter-evolution. A parasite population subjected to a specific immune receptor can evolve heightened genetic diversity, which makes parasite detection more difficult for the hosts. We show that this coevolutionary feedback renders the induced immune response less efficient, forcing the hosts to invest more heavily in constitutive immunity. Parasites diversify to escape elimination by a specific effector too. However, this diversification does not alter the optimal balance between constitutive and induced defence: the reliance on constitutive defence is promoted by the receptor's inability to detect, but not the effectors' inability to eliminate parasites. If effectors are useless, hosts simply adapt to tolerate, rather than to invest in any defence against parasites. These contrasting results indicate that evolutionary feedback between host and parasite populations is a key factor shaping the selection regime for immune networks facing antagonistic coevolution. CONCLUSION: Parasite coevolution against specific immune defence alters the prediction of the optimal use of defence, and the effect of parasite coevolution varies between different immune components.

An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous.

Recent studies suggest the existence of a stochasticity in gene expression (SGE) in many organisms, and its non-negligible effect on their phenotype and fitness. To date, however, how SGE affects the key parameters of population genetics are not well understood. SGE can increase the phenotypic variation and act as a load for individuals, if they are at the adaptive optimum in a stable environment. On the other hand, part of the phenotypic variation caused by SGE might become advantageous if individuals at the adaptive optimum become genetically less-adaptive, for example due to an environmental change. Furthermore, SGE of unimportant genes might have little or no fitness consequences. Thus, SGE can be advantageous, disadvantageous, or selectively neutral depending on its context. In addition, there might be a genetic basis that regulates magnitude of SGE, which is often referred to as "modifier genes," but little is known about the conditions under which such an SGE-modifier gene evolves. In the present study, we conducted individual-based computer simulations to examine these conditions in a diploid model. In the simulations, we considered a single locus that determines organismal fitness for simplicity, and that SGE on the locus creates fitness variation in a stochastic manner. We also considered another locus that modifies the magnitude of SGE. Our results suggested that SGE was always deleterious in stable environments and increased the fixation probability of deleterious mutations in this model. Even under frequently changing environmental conditions, only very strong natural selection made SGE adaptive. These results suggest that the evolution of SGE-modifier genes requires strict balance among the strength of natural selection, magnitude of SGE, and frequency of environmental changes. However, the degree of dominance affected the condition under which SGE becomes advantageous, indicating a better opportunity for the evolution of SGE in different genetic models.