Alison G. Power

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.

If pathogen species, strains or clones do not interact, intuition suggests the proportion of co-infected 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 co-infected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of co-infected hosts is expected to be higher than multiplication would suggest. By modeling the dynamics of multiple non-interacting pathogens, we develop a pair of novel tests of interaction that properly account for non-independence. 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.

Many important endemic and emerging diseases are vector-borne. The functional traits of vectors affect not just pathogen transmission rates, but also the fitness and population dynamics of these animals themselves. Increasing empirical evidence suggests that vector traits vary significantly at time scales relevant to transmission dynamics. Currently, an understanding of how this variation in key traits impacts transmission is hindered by a lack of both empirical data and theoretical methods for mechanistically incorporating traits into transmission models. Here, we present a framework for incorporating both intrinsic and environment-driven variation in vector traits into empirical and theoretical vector-borne disease research. This framework mechanistically captures the effect of trait variation on vector fitness, the correlation between vector traits, and how these together determine transmission dynamics. We illustrate how trait-based vector-borne disease modelling can make novel predictions, and identify key steps and challenges in the construction, empirical parameterisation and validation of such models, as well as the organization and prioritization of data collection efforts.

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. [ ] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Agricultural ecosystems provide humans with food, forage, bioenergy and pharmaceuticals and are essential to human wellbeing. These systems rely on ecosystem services provided by natural ecosystems, including pollination, biological pest control, maintenance of soil structure and fertility, nutrient cycling and hydrological services. Preliminary assessments indicate that the value of these ecosystem services to agriculture is enormous and often underappreciated. Agroecosystems also produce a variety of ecosystem services, such as regulation of soil and water quality, carbon sequestration, support for biodiversity and cultural services. Depending on management practices, agriculture can also be the source of numerous disservices, including loss of wildlife habitat, nutrient runoff, sedimentation of waterways, greenhouse gas emissions, and pesticide poisoning of humans and non-target species. The tradeoffs that may occur between provisioning services and other ecosystem services and disservices should be evaluated in terms of spatial scale, temporal scale and reversibility. As more effective methods for valuing ecosystem services become available, the potential for ‘win–win’ scenarios increases. Under all scenarios, appropriate agricultural management practices are critical to realizing the benefits of ecosystem services and reducing disservices from agricultural activities.

In field experiments manipulating generalist pathogens and host community composition, the presence of a highly susceptible reservoir species drove disease dynamics in multiple nonreservoir species, sometimes decreasing their abundance through apparent competition. The dynamics of generalist pathogens in multispecies host communities remain a major frontier for disease ecology. Of particular interest are how host community structure controls pathogen transmission and how disease spread feeds back to influence the host community. Pathogen spillover occurs when epidemics in a host population are driven not by transmission within that population but by transmission from a reservoir population. Here we review examples of spillover in pathogens infecting humans, domesticated animals, and crops, noting that most empirical evidence for spillover results from nonmanipulative, observational studies. We then present results from two field experiments utilizing an experimentally tractable model system of annual wild grasses and a generalist virus, the barley yellow dwarf virus. In these experiments, the presence of a highly susceptible reservoir species, Avena fatua (wild oats), greatly increased pathogen prevalence in several other species. This result demonstrates pathogen spillover and illustrates the crucial role of host community structure in controlling the dynamics of generalist pathogens. Further, pathogen spillover from A. fatua decreased the abundance of two other host species through pathogen-mediated apparent competition. Thus, our results provide experimental support for theoretical predictions of strong feedbacks between host community structure and generalist disease dynamics.