Modelling cassava production and pest management under biotic and abiotic constraints.

KEY MESSAGE: We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its' pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

What makes or breaks a campaign to stop an invading plant pathogen?

Diseases in humans, animals and plants remain an important challenge in our society. Effective control of invasive pathogens often requires coordinated concerted action of a large group of stakeholders. Both epidemiological and human behavioural factors influence the outcome of a disease control campaign. In mathematical models that are frequently used to guide such campaigns, human behaviour is often ill-represented, if at all. Existing models of human, animal and plant disease that do incorporate participation or compliance are often driven by pay-offs or direct observations of the disease state. It is however very well known that opinion is an important driving factor of human decision making. Here we consider the case study of Citrus Huanglongbing disease (HLB), which is an acute bacterial disease that threatens the sustainability of citrus production across the world. We show how by coupling an epidemiological model of this invasive disease with an opinion dynamics model we are able to answer the question: What makes or breaks the effectiveness of a disease control campaign? Frequent contact between stakeholders and advisors is shown to increase the probability of successful control. More surprisingly, we show that informing stakeholders about the effectiveness of control methods is of much greater importance than prematurely increasing their perceptions of the risk of infection. We discuss the overarching consequences of this finding and the effect on human as well as plant disease epidemics.

Variability in commercial demand for tree saplings affects the probability of introducing exotic forest diseases.

Several devastating forest pathogens are suspected or known to have entered the UK through imported planting material. The nursery industry is a key business of the tree trade network. Variability in demand for trees makes it difficult for nursery owners to predict how many trees to produce in their nursery. When in any given year, the demand for trees is larger than the production, nursery owners buy trees from foreign sources to match market demand. These imports may introduce exotic diseases.We have developed a model of the dynamics of plant production linked to an economic model. We have used this to quantify the effect of demand variability on the risk of introducing an exotic disease.We find that: (a) When the cost of producing a tree in a UK nursery is considerably smaller than the cost of importing a tree (in the example presented, less than half the importing cost), the risk of introducing an exotic disease is hardly affected by an increase in demand variability. (b) When the cost of producing a tree in the nursery is smaller than, but not very different from the cost of importing a tree, the risk of importing exotic diseases increases with increasing demand variability. Synthesis and applications. Our model and results demonstrate how a balanced management of demand variability and costs can reduce the risk of importing an exotic forest disease according to the management strategy adopted. For example, a management strategy that can reduce the demand variability, the ratio of production to import cost or both, optimizes the nursery gross margin when mainly own-produced trees are commercialized. This can also translate into a reduction of the risk of introducing exotic forest diseases due to the small number of imported trees for sale.

Se conoce o sospecha que algunos patógenos forestales encontrados en el Reino Unido han sido introducidos a través de material de siembra importado. La industria de viveros es un negocio clave dentro de la red de comercialización forestal. Sin embargo, la demanda comercial de árboles varía frecuentemente. Esto resulta problemático para los viveros quienes deben calcular cuántos árboles necesitan plantar para su comercialización. Cuando la demanda al punto de venta es mayor que la producción del vivero, la demanda es satisfecha con importaciones de fuentes extranjeras. Estas importaciones pueden introducir plagas y enfermedades forestales exóticas.Desarrollamos un modelo de la dinámica de producción forestal vinculado a un modelo económico del vivero para cuantificar el efecto de la variabilidad en la demanda comercial sobre el riesgo de introducir una enfermedad exótica.Nuestro modelo muestra lo siguiente: i. Cuando el costo de producir un árbol en un vivero del Reino Unido es considerablemente menor que el costo de importar un árbol (en el ejemplo presentado es más de dos veces menor al costo de importación), el riesgo de introducir un patógeno forestal exótico apenas se ve afectado por incrementos en la variabilidad de la demanda. ii. Cuando el costo de producción de un árbol es menor, pero no muy diferente del costo de importación, el riesgo de introducir un patógeno forestal exótico incrementa a medida que la variabilidad en la demanda aumenta. Síntesis y aplicaciones. Nuestro modelo y resultados demuestran que un manejo equilibrado de los costos y la variabilidad en la demanda comercial de árboles en viveros puede reducir el riesgo de importación de enfermedades forestales exóticas de acuerdo con la estrategia de manejo adoptada. Por ejemplo, una estrategia de manejo que reduzca la variabilidad en la demanda, la relación entre producción y costo de importación, o ambas cantidades, optimiza el margen bruto del vivero cuando se comercializan principalmente árboles de producción propia. Esto también se puede traducir en una reducción del riesgo de introducción de enfermedades forestales exóticas debido a la baja comercialización de árboles importados.

Stress and sexual reproduction affect the dynamics of the wheat pathogen effector AvrStb6 and strobilurin resistance.

Host resistance and fungicide treatments are cornerstones of plant-disease control. Here, we show that these treatments allow sex and modulate parenthood in the fungal wheat pathogen Zymoseptoria tritici. We demonstrate that the Z. tritici-wheat interaction complies with the gene-for-gene model by identifying the effector AvrStb6, which is recognized by the wheat resistance protein Stb6. Recognition triggers host resistance, thus implying removal of avirulent strains from pathogen populations. However, Z. tritici crosses on wheat show that sex occurs even with an avirulent parent, and avirulence alleles are thereby retained in subsequent populations. Crossing fungicide-sensitive and fungicide-resistant isolates under fungicide pressure results in a rapid increase in resistance-allele frequency. Isolates under selection always act as male donors, and thus disease control modulates parenthood. Modeling these observations for agricultural and natural environments reveals extended durability of host resistance and rapid emergence of fungicide resistance. Therefore, fungal sex has major implications for disease control.

A method of determining where to target surveillance efforts in heterogeneous epidemiological systems.

The spread of pathogens into new environments poses a considerable threat to human, animal, and plant health, and by extension, human and animal wellbeing, ecosystem function, and agricultural productivity, worldwide. Early detection through effective surveillance is a key strategy to reduce the risk of their establishment. Whilst it is well established that statistical and economic considerations are of vital importance when planning surveillance efforts, it is also important to consider epidemiological characteristics of the pathogen in question-including heterogeneities within the epidemiological system itself. One of the most pronounced realisations of this heterogeneity is seen in the case of vector-borne pathogens, which spread between 'hosts' and 'vectors'-with each group possessing distinct epidemiological characteristics. As a result, an important question when planning surveillance for emerging vector-borne pathogens is where to place sampling resources in order to detect the pathogen as early as possible. We answer this question by developing a statistical function which describes the probability distributions of the prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximise the probability of early detection of an emerging pathogen within imposed sample size and/or cost constraints, and demonstrate its application using two simple models of vector-borne citrus pathogens. Under the assumption of a linear cost function, we find that sampling costs are generally minimised when either hosts or vectors, but not both, are sampled.

Early warning of critical transitions in biodiversity from compositional disorder.

Global environmental change presents a clear need for improved leading indicators of critical transitions, especially those that can be generated from compositional data and that work in empirical cases. Ecological theory of community dynamics under environmental forcing predicts an early replacement of slowly replicating and weakly competitive "canary" species by slowly replicating but strongly competitive "keystone" species. Further forcing leads to the eventual collapse of the keystone species as they are replaced by weakly competitive but fast-replicating "weedy" species in a critical transition to a significantly different state. We identify a diagnostic signal of these changes in the coefficients of a correlation between compositional disorder and biodiversity. Compositional disorder measures unpredictability in the composition of a community, while biodiversity measures the amount of species in the community. In a stochastic simulation, sequential correlations over time switch from positive to negative as keystones prevail over canaries, and back to positive with domination of weedy species. The model finds support in empirical tests on multi-decadal time series of fossil diatom and chironomid communities from lakes in China. The characteristic switch from positive to negative correlation coefficients occurs for both communities up to three decades preceding a critical transition to a sustained alternate state. This signal is robust to unequal time increments that beset the identification of early-warning signals from other metrics.

Monitoring invasive pathogens in plant nurseries for early-detection and to minimise the probability of escape.

The global increase in the movement of plant products in recent years has triggered an increase in the number of introduced plant pathogens. Plant nurseries importing material from abroad may play an important role in the introduction and spread of diseases such as ash dieback and sudden oak death which are thought to have been introduced through trade. The economic, environmental and social costs associated with the spread of invasive pathogens become considerably larger as the incidence of the pathogen increases. To control the movement of pathogens across the plant trade network it is crucial to develop monitoring programmes at key points of the network such as plant nurseries. By detecting the introduction of invasive pathogens at low incidence, the control and eradication of an epidemic is more likely to be successful. Equally, knowing the likelihood of having sold infected plants once a disease has been detected in a nursery can help designing tracing plans to control the onward spread of the disease. Here, we develop an epidemiological model to detect and track the movement of an invasive plant pathogen into and from a plant nursery. Using statistical methods, we predict the epidemic incidence given that a detection of the pathogen has occurred for the first time, considering that the epidemic has an asymptomatic period between infection and symptom development. Equally, we calculate the probability of having sold at least one infected plant during the period previous to the first disease detection. This analysis can aid stakeholder decisions to determine, when the pathogen is first discovered in a nursery, the need of tracking the disease to other points in the plant trade network in order to control the epidemic. We apply our method to high profile recent introductions including ash dieback and sudden oak death in the UK and citrus canker and Huanglongbing disease in Florida. These results provide new insight for the design of monitoring strategies at key points of the trade network.