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.