Plant susceptibility to a shared herbivore is reduced by belowground competition with neighbors.

Spatial variation in plant community composition is an important driver of variation in susceptibility to herbivores. In close proximity, certain neighbors can attract or repel herbivores to a focal plant ("associational effects"). Neighboring plants may also compete for resources, modifying their phenotype in ways that affect susceptibility to herbivores. To test whether and how competition contributes to associational effects, we manipulated the sharing of belowground resources among plant neighbors (spotted Joe Pye weed and common boneset) that serve as alternate hosts for an herbivorous beetle. In the field, the beetle Ophraella notata laid more eggs and inflicted more damage on plants of both species that were released from belowground competition with neighbors. Competition also weakened the effects of neighbor identity during field trials, reducing associational susceptibility. When beetles were forced to choose between the two host species in cage trials, competition again reduced beetle use of Joe Pye weed as a secondary host. To test the role of plant traits related to herbivore defense and nutrition, we quantified leaf protein, specific leaf area, and trichomes, and conducted behavioral assays on leaf disks. Beetles did not distinguish between Joe Pye weed treatments at the leaf disk level, and competition did not impact specific leaf area and protein. Trichome density was higher in both species in the preferred treatment. Overall, our results suggest that belowground interactions between plants may mediate the strength of associational effects, as secondary hosts become more attractive when released from competition with primary host plants.

Coinfections by noninteracting pathogens are not independent and require new tests of interaction.

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

Viral infection can reduce the net nitrogen inputs of legume break crops and cover crops.

Legumes are used in crop rotations by both large-scale and smallholder farmers alike to increase soil fertility, especially before high-nitrogen-demanding crops such as corn (maize). Legume crop residues and green manures are rich in nitrogen due to mutualistic rhizobia, bacteria that live in their roots and convert atmospheric nitrogen into a biologically available form. Growers can obtain recommendations from local extension offices about how much less inorganic nitrogen fertilizer needs to be added to a subsequent crop following different legume break crops for the predominant soil type (the nitrogen fertilizer replacement value, or NFRV). Due to the intimate relationship between legumes and rhizobia, conditions that affect plant health can also affect the rhizobia and how much nitrogen they provide. We use a combination of empirical data and previously published values to estimate reductions in nitrogen inputs under outbreaks of plant viruses of varying severity. We also use historical fertilizer prices to examine the economic impacts of this lost fertilizer for farmers. We find that fertilizer losses are greatest for crops that fix large amounts of nitrogen, such as clover and alfalfa as opposed to common bean. The economic impact on farmers is controlled by the proportion of plants with viral infections and the price of synthetic fertilizer. In a year of high disease prevalence, attention is normally focused on the yield of the diseased crops. We suggest that farmers growing legumes as break crops should be concerned about yields of subsequent crops as well. Viral diseases can be difficult to diagnose in the field, so the easiest way for farmers to prevent unexpected yield losses in subsequent crops is to test their soil when it is feasible to do so.

Landscape-dependent effects of varietal mixtures on insect pest control and implications for farmer profits.

Intraspecific plant diversity can significantly impact insect herbivore populations in natural systems. Yet, its role as an insect pest control strategy in agriculture has received less attention, and little is known about which crop traits are important to herbivores in different landscape contexts. Moreover, empirical economic analyses on the cost-effectiveness of varietal mixtures are lacking. We used varietal mixtures of Brassica oleracea crops on working farms to examine how two metrics of intraspecific crop diversity, varietal richness and number of plant colors (color richness), affect crop damage and the incidence and abundance of two insect pest species: Pieris rapae and Phyllotreta spp. We evaluated the context-dependency of varietal mixtures by sampling early- and late-season plantings of B. oleracea crops in farms across a gradient of landscape composition. We developed crop budgets and used a net present value analysis to assess the impact of varietal mixtures on input and labor costs, crop revenues, and profit. We found context-dependent effects of varietal mixtures on both pests. In early-season plantings, color richness did not affect Phyllotreta spp. populations. However, increasing varietal richness reduced Phyllotreta spp. incidence in simple landscapes dominated by cropland, but this trend was reversed in complex landscapes dominated by natural habitats. In late-season plantings, color richness reduced the incidence and abundance of P. rapae larvae, but only in complex landscapes where their populations were highest. Varietal richness had the same effect on P. rapae larvae as color richness. Unexpectedly, we consistently found lower pest pressure and reduced crop damage in simple landscapes. Although varietal mixtures did not affect crop damage, increasing color richness corresponded with increased profits, due to increased revenue and a marginal reduction in labor and input costs. We demonstrate varietal mixtures can significantly impact pest populations, and this effect can be mediated by intraspecific variation in crop color. However, the strength and direction of these effects vary by season, landscape composition, and pest species. The association between varietal color richness and profitability indicates farmers could design mixtures to enhance economic returns. We recommend additional research on the benefits of intraspecific trait variation for farmers.

Modeling Approach Influences Dynamics of a Vector-Borne Pathogen System.

The choice of a modeling approach is a critical decision in the modeling process, as it determines the complexity of the model and the phenomena that the model captures. In this paper, we developed an individual-based model (IBM) and compared it to a previously published ordinary differential equation (ODE) model, both developed to describe the same biological system although with slightly different emphases given the underlying assumptions and processes of each modeling approach. We used both models to examine the effect of insect vector life history and behavior traits on the spread of a vector-borne plant virus, and determine how choice of approach affects the results and their biological interpretation. A non-random distribution of insect vectors across plant hosts emerged in the IBM version of the model and was not captured by the ODE. This distribution led simultaneously to a slower-growing vector population and a faster spread of the pathogen among hosts. The IBM model also enabled us to test the effect of potential control measures to slow down virus transmission. We found that removing virus-infected hosts was a more effective strategy for controlling infection than removing vector-infested hosts. Our findings highlight the need to carefully consider possible modeling approaches before constructing a model.

Coinfection Timing Drives Host Population Dynamics through Changes in Virulence.

Infections of one host by multiple parasites are common, and several studies have found that the order of parasite invasion can affect both within-host competition and disease severity. However, it is unclear to what extent coinfection timing might be important to consider when modeling parasite impacts on host populations. Using a model system of two viruses infecting barley, we found that simultaneous infections of the two viruses were significantly more damaging to hosts than sequential coinfections. While priority effects were evident in within-host concentrations of sequential coinfections, priority did not influence any parameters (such as virulence or transmission rate) that affect host population dynamics. We built a susceptible-infected model to examine whether the observed difference in coinfection virulence could impact host population dynamics under a range of scenarios. We found that coinfection timing can have an important but context-dependent effect on projected host population dynamics. Studies that examine only simultaneous coinfections could inflate disease impact predictions.

Context-dependent interactions between pathogens and a mutualist affect pathogen fitness and mutualist benefits to hosts.

Plants and animals host many microbial symbionts, including both pathogens and mutualists. However, most experimental studies include only one symbiont, and few examine interactions of more than two microbes with their host. Here, we examined whether coinfection with two pathogens causes a synergistic reduction in the benefits that hosts receive from a microbial mutualist. We also measured the effects of a microbial mutualist on the within- and between-host competition between coinfecting pathogens. We manipulated the presence of Clover yellow vein virus (ClYVV), Bean common mosaic virus (BCMV), rhizobia bacteria, and nitrogen fertilizer in common beans (Phaseolus vulgaris). We found asymmetric, context-dependent interactions among the three microbial symbionts and their host. Coinfection with both viruses led to greater than additive negative effects on the amount of nitrogen that plants received from rhizobia. Rhizobia colonization decreased immune signaling in singly infected plants, but not in coinfected plants. Compared to single ClYVV infection, ClYVV reached higher concentrations within hosts coinfected with BCMV, but only in the presence of rhizobia. Coinfection increased BCMV vertical transmission rates for plants without supplemental nitrogen, but overall vertical transmission opportunities were not affected due to reduced seed production. Examining interactions between multiple microbes sharing a host can reveal important insights about nutrient cycling, disease severity, and pathogen epidemiology.

Vector population growth and condition-dependent movement drive the spread of plant pathogens.

Plant viruses, often spread by arthropod vectors, impact natural and agricultural ecosystems worldwide. Intuitively, the movement behavior and life history of vectors influence pathogen spread, but the relative contribution of each factor has not been examined. Recent research has highlighted the influence of host infection status on vector behavior and life history. Here, we developed a model to explore how vector traits influence the spread of vector-borne plant viruses. We allowed vector life history (growth rate, carrying capacity) and movement behavior (departure and settlement rates) parameters to be conditional on whether the plant host is infected or healthy and whether the vector is viruliferous (carrying the virus) or not. We ran simulations under a wide range of parameter combinations and quantified the fraction of hosts infected over time. We also ran case studies of the model for Barley yellow dwarf virus, a persistently transmitted virus, and for Potato virus Y, a non-persistently transmitted virus. We quantified the relative importance of each parameter on pathogen spread using Latin hypercube sampling with the statistical partial rank correlation coefficient technique. We found two general types of mechanisms in our model that increased the rate of pathogen spread. First, increasing factors such as vector intrinsic growth rate, carrying capacity, and departure rate from hosts (independent of whether these factors were condition-dependent) led to more vectors moving between hosts, which increased pathogen spread. Second, changing condition-dependent factors such as a vector's preference for settling on a host with a different infection status than itself, and vector tendency to leave a host of the same infection status, led to increased contact between hosts and vectors with different infection statuses, which also increased pathogen spread. Overall, our findings suggest that vector population growth rates had the greatest influence on rates of virus spread, but rates of vector dispersal from infected hosts and from hosts of the same infection status were also very important. Our model highlights the importance of simultaneously considering vector life history and behavior to better understand pathogen spread. Although developed for plant viruses, our model could readily be utilized with other vector-borne pathogen systems.

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya.

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

The community ecology of pathogens: coinfection, coexistence and community composition.

Disease and community ecology share conceptual and theoretical lineages, and there has been a resurgence of interest in strengthening links between these fields. Building on recent syntheses focused on the effects of host community composition on single pathogen systems, we examine pathogen (microparasite) communities using a stochastic metacommunity model as a starting point to bridge community and disease ecology perspectives. Such models incorporate the effects of core community processes, such as ecological drift, selection and dispersal, but have not been extended to incorporate host-pathogen interactions, such as immunosuppression or synergistic mortality, that are central to disease ecology. We use a two-pathogen susceptible-infected (SI) model to fill these gaps in the metacommunity approach; however, SI models can be intractable for examining species-diverse, spatially structured systems. By placing disease into a framework developed for community ecology, our synthesis highlights areas ripe for progress, including a theoretical framework that incorporates host dynamics, spatial structuring and evolutionary processes, as well as the data needed to test the predictions of such a model. Our synthesis points the way for this framework and demonstrates that a deeper understanding of pathogen community dynamics will emerge from approaches working at the interface of disease and community ecology.

Wild and weedy Hesperis matronalis hosts turnip mosaic virus across heterogeneous landscapes in upstate New York.

Turnip mosaic virus (TuMV) is a widespread and economically important pathogen in agricultural crops and has the widest known host range in the virus family Potyviridae. While management of the virus and its aphid vectors in agricultural fields decreases virus incidence, many alternative wild hosts for TuMV may serve as source populations for crop infection through spillover. Over thirty years ago, research demonstrated that the introduced brassica, Dame's Rocket (Hesperis matronalis) hosts several viruses, including TuMV. Here, we use both enzyme-linked immunosorbent assays (ELISA) and next generation sequencing to document the frequent infection by TuMV of Dame's Rocket, which is common and widespread in disturbed areas around crop fields in upstate New York. Deep sequencing of multiple tissue types of symptomatic hosts indicate that the infection is systemic and causes diagnostic, visible symptoms. In a common garden experiment using host populations from across upstate New York, we found evidence for genetic tolerance to TuMV infection in H. matronalis. Field surveys show that TuMV prevalence varies across populations, but is generally higher in agricultural areas. Examining disease dynamics in this and other common alternative hosts will enhance our understanding of TuMV epidemiology and, more broadly, virus distribution in wild plants.

Consumers indirectly increase infection risk in grassland food webs.

Most pathogens exist within complicated food webs of interacting hosts, vectors, competitors, and predators. Although theory has demonstrated a variety of mechanisms by which predation and competition in food webs can indirectly control infection risk in hosts, there have until now been no experimental tests of this theory. We sampled the effect of long-term exclusion of large vertebrate herbivores on the prevalence of infection by a group of aphid-vectored viruses that infect grasses (barley and cereal yellow dwarf viruses) in an oak savannah in central California. We found that pathogen prevalence was approximately 4-fold higher in the presence of consumers than in areas where they were excluded. Vertebrate consumers did not directly alter infection rates by this aphid-vectored pathogen group, but rather increased infection risk by increasing the relative abundance of highly-competent hosts in the grassland community. This large-scale experiment, measuring changes in host abundance and infection risk in response to altered consumption rates, confirms theoretical predictions that consumers can indirectly increase infection risk by altering the composition of whole communities. Most importantly, these results demonstrate that, even in complex natural communities, alterations to food web composition such as consumer invasion or extinction can lead to significant impacts that cascade throughout entire communities, including changes in infection risk.

Ecology of Yellow Dwarf Viruses in Crops and Grasslands: Interactions in the Context of Climate Change.

Our understanding of the ecological interactions between plant viruses, their insect vectors, and their host plants has increased rapidly over the past decade. The suite of viruses known collectively as the yellow dwarf viruses infect an extensive range of cultivated and noncultivated grasses worldwide and is one of the best-studied plant virus systems. The yellow dwarf viruses are ubiquitous in cereal crops, where they can significantly limit yields, and there is growing recognition that they are also ubiquitous in grassland ecosystems, where they can influence community dynamics. Here, we discuss recent research that has explored (a) the extent and impact of yellow dwarf viruses in a diversity of plant communities, (b) the role of vector behavior in virus transmission, and (c) the prospects for impacts of climate change-including rising temperatures, drought, and elevated CO2-on the epidemiology of yellow dwarf viruses.

Farm size affects the use of agroecological practices on organic farms in the United States.

Organic agriculture outperforms conventional agriculture across several sustainability metrics due, in part, to more widespread use of agroecological practices. However, increased entry of large-scale farms into the organic sector has prompted concerns about 'conventionalization' through input substitution, agroecosystem simplification and other changes. We examined this shift in organic agriculture by estimating the use of agroecological practices across farm size and comparing indicators of conventionalization. Results from our national survey of 542 organic fruit and vegetable farmers show that fewer agroecological practices were used on large farms, which also exhibited the greatest degree of conventionalization. Intercropping, insectary plantings and border plantings were at least 1.4 times more likely to be used on small (0.4-39 cropland ha) compared with large (≥405 cropland ha) farms, whereas reduced tillage was less likely and riparian buffers were more likely on small compared with medium (40-404 cropland ha) farms. Because decisions about management practices can drive environmental sustainability outcomes, policy should support small and medium farms that already use agroecological practices while encouraging increased use of agroecological practices on larger farms.

Both consumptive and non-consumptive effects of predators impact mosquito populations and have implications for disease transmission.

Predator-prey interactions influence prey traits through both consumptive and non-consumptive effects, and variation in these traits can shape vector-borne disease dynamics. Meta-analysis methods were employed to generate predation effect sizes by different categories of predators and mosquito prey. This analysis showed that multiple families of aquatic predators are effective in consumptively reducing mosquito survival, and that the survival of Aedes, Anopheles, and Culex mosquitoes is negatively impacted by consumptive effects of predators. Mosquito larval size was found to play a more important role in explaining the heterogeneity of consumptive effects from predators than mosquito genus. Mosquito survival and body size were reduced by non-consumptive effects of predators, but development time was not significantly impacted. In addition, Culex vectors demonstrated predator avoidance behavior during oviposition. The results of this meta-analysis suggest that predators limit disease transmission by reducing both vector survival and vector size, and that associations between drought and human West Nile virus cases could be driven by the vector behavior of predator avoidance during oviposition. These findings are likely to be useful to infectious disease modelers who rely on vector traits as predictors of transmission.

Mosquitoes are often referred to as the deadliest animals on earth because some species spread malaria, West Nile virus or other dangerous diseases when they bite humans and other animals. Adult mosquitoes fly to streams, ponds and other freshwater environments to lay their eggs. When the eggs hatch, the young mosquitoes live in the water until they are ready to grow wings and transform into adults. In the water, the young mosquitoes are particularly vulnerable to being eaten by dragonfly larvae, fish and other predators. When adult females are choosing where to lay their eggs, they can use their sense of smell to detect these predators and attempt to avoid them. Along with eating the mosquitoes, the predators may also reduce mosquito populations in other ways. For example, predators can disrupt feeding among young mosquitoes, which may affect the time that it takes for them to grow into adults or the size of their bodies once they reach the adult stage. Although the impacts of different predators have been tested separately in multiple settings, the overall effects of predators on the ability of mosquitoes to spread diseases to humans remain unclear. To address this question, Russell, Herzog et al. used an approach called meta-analysis on data from previous studies. The analysis found that along with increasing the death rates of mosquitoes, the presence of predators also leads to a reduction in the body size of those mosquitoes that survive, causing them to have shorter lifespans and fewer offspring. Russell, Herzog et al. found that one type of mosquito known as Culex ­ which carries West Nile virus ­ avoided laying its eggs near predators. During droughts, increased predation in streams, ponds and other aquatic environments may lead adult female Culex mosquitoes to lay their eggs closer to residential areas with fewer predators. Russell, Herzog et al. propose that this may be one reason why outbreaks of West Nile virus in humans are more likely to occur during droughts. In the future, these findings may help researchers to predict outbreaks of West Nile virus, malaria and other diseases carried by mosquitoes more accurately. Furthermore, the work of Russell, Herzog et al. provides examples of mosquito predators that could be used as biocontrol agents to decrease numbers of mosquitoes in certain regions.

A multi-scale, landscape approach to predicting insect populations in agroecosystems.

Landscape composition affects ecosystems services, including agricultural pest management. However, relationships between land use and agricultural insects are not well understood, and many complexities remain to be explored. Here we examine whether nonagricultural landscapes can directly suppress agricultural pests, how multiple spatial scales of land use concurrently affect insect populations, and the relationships between regional land use and insect populations. We tracked densities of three specialist corn (Zea mays) pests (Ostrinia nubilalis, European corn borer; Diabrotica virgifera, western corn rootworm; Diabrotica barberi, northern corn rootworm), and two generalist predator lady beetles (Coleomegilla maculata and Propylea quatuordecimpunctata) in field corn and determined their relationships to agricultural land use at three spatial scales (field perimeter, 1-km, and 20-km radius areas). Pest densities were either higher (D. virgifera and D. barberi) or unchanged (O. nubilalis) in landscapes with more corn, while natural enemy densities were either lower (C. maculata) or unchanged (P. quatuordecimpunctata). Results for D. virgifera and D. barberi indicate that decreasing the area of preferred crop in the landscape can directly suppress specialist insect pests. Multiple scales of land use affected populations of D. virgifera and C. maculata, and D. virgifera populations showed strong relationships with regional, 20-km-scale land use. These results suggest that farm planning and government policies aimed at diversifying local and regional agricultural landscapes show promise for increasing biological control and directly suppressing agricultural pests.

Local context drives infection of grasses by vector-borne generalist viruses.

Host characteristics commonly determine infection risk, but infection can also be mediated by regional- or local-scale variation in the biotic and abiotic environment. Experiments can clarify the relative importance of these factors. We quantified drivers of infection by barley and cereal yellow dwarf viruses (B/CYDV), a group of generalist, vector-borne grass pathogens, at hierarchically nested spatial scales (10(5)-1 m) by planting individuals of six common grass species into five Pacific Coast grassland sites spanning 7 degrees of latitude (> 5000 total hosts) and applying a factorial combination of nitrogen and phosphorus fertilizer. Infection differed most among experimental blocks (10(2)-10(3) m scale), suggesting that local factors control infection risk; infection increased with cover of long-lived hosts and phosphorus, but not nitrogen, fertilization. For B/CYDV, local context more strongly predicts infection risk than host species traits or regional context; such spatially nested experiments can clarify the factors underlying variation in infection risk.

Viral diversity and prevalence gradients in North American Pacific Coast grasslands.

Host-pathogen interactions may be governed by the number of pathogens coexisting within an individual host (i.e., coinfection) and among different hosts, although most sampling in natural systems focuses on the prevalence of single pathogens and/or single hosts. We measured the prevalence of four barley and cereal yellow dwarf viruses (B/CYDVs) in three grass species at 26 natural grasslands along a 2000-km latitudinal gradient in the western United States and Canada. B/CYDVs are aphid-vectored RNA viruses that cause one of the most prevalent of all plant diseases worldwide. Pathogen prevalence and coinfection were uncorrelated, suggesting that different forces likely drive them. Coinfection, the number of viruses in a single infected host (alpha diversity), did not differ among host species but increased roughly twofold across our latitudinal transect. This increase in coinfection corresponded with a decline in among-host pathogen turnover (beta diversity), suggesting that B/CYDVs in northern populations experience less transmission limitation than in southern populations. In contrast to pathogen diversity, pathogen prevalence was a function of host identity as well as biotic and abiotic environmental conditions. Prevalence declined with precipitation and increased with soil nitrate concentration, an important limiting nutrient for hosts and vectors of B/CYDVs. This work demonstrates the need for further studies of processes governing coinfection, and the utility of applying theory developed to explain diversity in communities of free-living organisms to pathogen systems.

The Role of Vector Trait Variation in Vector-Borne Disease Dynamics.

Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.

Diversity and composition of viral communities: coinfection of barley and cereal yellow dwarf viruses in California grasslands.

Most species host multiple pathogens, yet field studies rarely examine the processes determining pathogen diversity within a single host or the effects of coinfection on pathogen dynamics in natural systems. Coinfection can affect pathogen transmission and virulence. In turn, coinfection can be regulated within hosts by interactions such as cross-protective immunity or at broader spatial scales via vector distributions. Using a general model, we demonstrate that coinfection by a group of vectored pathogens is highest with abundant generalist vectors and weak cross-protection and coinfection-induced mortality. Using these predictions, we investigate the distribution of five coexisting aphid-vectored, viral pathogens (barley and cereal yellow dwarf luteoviruses and poleroviruses) in a native perennial grass (Elymus glaucus) in both space (700 km) and time (4 years). Observed coinfection rates were much higher than expected at random, suggesting that within-host processes exerted weak effects on within-host pathogen diversity. Covariance among viruses in space and time was highest for viral species sharing a vector. Temporal correlation arose from the synchronous invasion of two viruses transmitted by a shared aphid species. On the basis of our modeling and empirical results, we expect that factors external to individual hosts may affect the coinfection dynamics in other communities hosting vectored pathogens.