From elaborate to compact seasonal plant epidemic models and back: is competitive exclusion in the details?

An Agent-Based Model Shows How Mixed Infections Drive Multiyear Pathotype Dynamics in a Plant-Virus System

Mathematical models are widely used to understand the evolution and epidemiology of plant pathogens under a variety of scenarios. Here, we used this approach to analyze the effects of different traits intrinsic and extrinsic to plant-virus interactions on the dynamics of virus pathotypes in genetically heterogeneous plant-virus systems. For this, we propose an agent-based epidemiological model that includes epidemiologically significant pathogen life-history traits related to virulence, transmission, and survival in the environment and allows for integrating long- and short-distance transmission, primary and secondary infections, and within-host pathogen competition in mixed infections. The study focuses on the tobamovirus-pepper pathosystem. Model simulations allowed us to integrate pleiotropic effects of resistance-breaking mutations on different virus life-history traits into the net costs of resistance breaking, allowing for predictions on multiyear pathotype dynamics. We also explored the effects of two control measures, the use of host resistance and roguing of symptomatic plants, that modify epidemiological attributes of the pathogens to understand how their populations will respond to evolutionary pressures. One major conclusion points to the importance of pathogen competition within mixed-infected hosts as a component of the overall fitness of each pathogen that, thus, drives their multiyear dynamics.

A prey–predator model and control of a nematodes pest using control in banana: Mathematical modeling and qualitative analysis

This study develops a mathematical model for describing the dynamics of the banana-nematodes and its pest detection method to help banana farmers. Two criteria: the mathematical model and the type of nematodes pest control system are discussed. The sensitivity analysis, local stability, global stability, and the dynamic behavior of the mathematical model are performed. Further, we also develop and discuss the optimal control mathematical model. This mathematical model represents various modes of management, including the initial release of infected predators as well as the destroying of nematodes. The theoretical results are shown and verified by numerical simulations.

Multi-seasonal modelling of plant-nematode interactions reveals efficient plant resistance deployment strategies

Root-knot nematodes, Meloidogyne spp., are soil-borne polyphagous pests with major impact on crop yield worldwide. Resistant crops efficiently control avirulent root-knot nematodes, but favour the emergence of virulent forms. Since virulence is associated with fitness costs, susceptible crops counter-select virulent root-knot nematodes. In this study, we identify optimal rotation strategies between susceptible and resistant crops to control root-knot nematodes and maximize crop yield. We developed an epidemiological model describing the within-season dynamics of avirulent and virulent root-knot nematodes on susceptible or resistant plant root-systems, and their between-season survival. The model was fitted to experimental data and used to predict yield-maximizing rotation strategies, with special attention to the impact of epidemic severity and genetic parameters. Crop rotations were found to be efficient under realistic parameter ranges. They were characterized by low ratios of resistant plants and were robust to parameter uncertainty. Rotations provide significant gain over resistant-only strategies, especially under intermediate fitness costs and severe epidemic contexts. Switching from the current general deployment of resistant crops to custom rotation strategies could not only maintain or increase crop yield, but also preserve the few and valuable R-genes available.

Separate seasons of infection and reproduction can lead to multi-year population cycles

Many host-pathogen systems are characterized by a temporal order of disease transmission and host reproduction. For example, this can be due to pathogens infecting certain life cycle stages of insect hosts; transmission occurring during the aggregation of migratory birds; or plant diseases spreading between planting seasons. We develop a simple discrete-time epidemic model with density-dependent transmission and disease affecting host fecundity and survival. The model shows sustained multi-annual cycles in host population abundance and disease prevalence, both in the presence and absence of density dependence in host reproduction, for large horizontal transmissibility, imperfect vertical transmission, high virulence, and high reproductive capability. The multi-annual cycles emerge as invariant curves in a Neimark-Sacker bifurcation. They are caused by a carry-over effect, because the reproductive fitness of an individual can be reduced by virulent effects due to infection in an earlier season. As the infection process is density-dependent but shows an effect only in a later season, this produces delayed density dependence typical for second-order oscillations. The temporal separation between the infection and reproduction season is crucial in driving the cycles; if these processes occur simultaneously as in differential equation models, there are no sustained oscillations. Our model highlights the destabilizing effects of inter-seasonal feedbacks and is one of the simplest epidemic models that can generate population cycles.

Modelling and control of a banana soilborne pest in a multi-seasonal framework

We study the infestation dynamics of banana or plantain plants by Radopholus similis, a plant-parasitic nematode that causes severe damages. Two control strategies are implemented in our model: pesticides, which are widely used, and fallows, which are more environmentally friendly. To represent the host-parasite dynamics, two semi-discrete models are proposed. During each cropping season, free nematodes enter the plant roots, on which they feed and reproduce. At the end of the cropping season, fruits are harvested. In the first model, the parent plant is cut down to be replaced by one of its suckers and pesticides are applied. In the second model, the parent plant is uprooted and a fallow period is introduced, inducing the decay of the free pest populations; at the beginning of the next cropping season, a pest-free vitroplant is planted. For both models, the effective reproduction number of pests is computed, assuming that the infestation dynamics are fast compared to the other processes, which leads to the model order reduction. Conditions on the pesticide load or the fallow duration are then derived to ensure the stability of the periodic pest free solution. Finally, numerical simulations illustrate these theoretical results.

Eco-evolutionary agriculture: Host-pathogen dynamics in crop rotations

Since its origins, thousands of years ago, agriculture has been challenged by the presence of evolving plant pathogens. Temporal rotations of host and non-host crops have helped farmers to control epidemics among other utilities, but further efforts for strategy assessment are needed. Here, we present a methodology for developing crop rotation strategies optimal for control of pathogens informed by numerical simulations of eco-evolutionary dynamics in one field. This approach can integrate agronomic criteria used in crop rotations—soil quality and cash yield—and the analysis of pathogen evolution in systems where hosts are artificially selected. Our analysis shows which rotation patterns perform better in maximising crop yield when an unspecified infection occurs, with yield being dependent on both soil quality and the strength of the epidemic. Importantly, the use of non-host crops, which both improve soil quality and control the epidemic results in similar rational rotation strategies for diverse agronomic and infection conditions. We test the repeatability of the best rotation patterns over multiple decades, an essential end-user goal. Our results provide sustainable strategies for optimal resource investment for increased food production and lead to further insights into the minimisation of pesticide use in a society demanding ever more efficient agriculture.

The invention of agriculture is a major evolutionary transition in the social evolution of the human race. Transforming the lifestyle from nomadic to sedentary, agriculture provided humankind with the stability necessary to make rapid advancements. However, agriculture, as we know it, is now in danger. While agriculture is a grand artificial selection experiment, it is in a constant battle with the brute force of natural selection, generating highly infectious plant pathogens. Traditional techniques such as slash-burn techniques are not sustainable for feeding the ever-increasing population. Crop rotation, on the other hand, has been developed over thousands of years as a sustainable method. We provide a computational model of how crop rotations can be used to tackle pathogen infection and what properties of rotation patterns make them sustainable in the long run. We hope that this study, together with other sustainable methods such as minimal pesticide use and biocontrol, can make agriculture more efficient.

Co-occurrence among three divergent plant-castrating fungi in the same Silene host species

The competitive exclusion principle postulates that different species can only coexist in sympatry if they occupy distinct ecological niches. The goal of this study was to understand the geographical distribution of three species of Microbotryum anther-smut fungi that are distantly related but infect the same host plants, the sister species Silene vulgaris and S. uniflora, in Western Europe. We used microsatellite markers to investigate pathogen distribution in relation to host specialization and ecological factors. Microbotryum violaceo-irregulare was only found on S. vulgaris at high elevations in the Alps. Microbotryum lagerheimii could be subdivided into two genetically differentiated clusters, one on S. uniflora in the UK and the second on S. vulgaris in the Alps and Pyrenees. The most abundant pathogen species, M. silenes-inflatae, could be subdivided into four genetic clusters, co-occurring in the Alps, the UK and the Pyrenees, and was found on both S. vulgaris and S. uniflora. All three fungal species had high levels of homozygosity, in agreement with the selfing mating system generally observed in anther-smut fungi. The three pathogen species and genetic clusters had large range overlaps, but occurred at sites with different elevations, temperatures and precipitation levels. The three Microbotryum species thus do not appear to be maintained by host specialization or geographic allopatry, but instead may occupy different ecological niches in terms of environmental conditions.

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 .

Can Oak Powdery Mildew Severity be Explained by Indirect Effects of Climate on the Composition of the Erysiphe Pathogenic Complex?

Coinfection by several pathogens is increasingly recognized as an important feature in the epidemiology and evolution of plant fungal pathogens. Oak mildew is induced by two closely related Erysiphe invasive species (Erysiphe alphitoides and E. quercicola) which differ in their mode of overwintering. We investigated how climate influences the co-occurrence of the two species in oak young stands and whether this is important for the disease epidemiology. We studied the frequency of flag-shoots (i.e., shoots developing from infected buds, usually associated with E. quercicola) in 95 oak regenerations over a 6-year period. Additionally, in 2012 and 2013, the oak mildew severity and the two Erysiphe spp. relative frequencies were determined in both spring and autumn in 51 regenerations and 43 1-year-old plantations of oaks. Both the frequency of flag-shoots and the proportion of Erysiphe lesions with E. quercicola presence were related to climate. We showed that survival of E. quercicola was improved after mild winters, with increase of both the flag-shoot frequency and the proportion of Erysiphe lesions with E. quercicola presence in spring. However, disease severity was not related to any complementarity effect between the two Erysiphe spp. causing oak powdery mildew. By contrast, increased E. alphitoides prevalence in spring was associated with higher oak mildew severity in autumn. Our results point out the critical role of between-season transmission and primary inoculum to explain disease dynamics which could be significant in a climate-warming context.

Seasonality and the evolutionary divergence of plant parasites

The coexistence of closely related plant parasites is widespread. Yet, understanding the ecological determinants of evolutionary divergence in plant parasites remains an issue. Niche differentiation through resource specialization has been widely researched, but it hardly explains the coexistence of parasites exploiting the same host plant. Time-partitioning has so far received less attention, although in temperate climates, parasites may specialize on either the early or the late season. Accordingly, we investigated whether seasonality can also promote phenotypic divergence. For plant parasites, seasonality generally engenders periodic host absence. To account for abrupt seasonal events, we made use of an epidemic model that combines continuous and discrete dynamics. Based on the assumption of a trade-off between in-season transmission and inter-season survival, we found through an "evolutionary invasion analysis" that evolutionary divergence of the parasite phenotype can occur. Since such a trade-off has been reported, this study provides further ecological bases for the coexistence of closely related plant parasites. Moreover, this study provides original insights into the coexistence of sibling plant pathogens which perform either a single or several infection cycles within a season (mono- and polycyclic diseases, or uni- and multivoltine life cycles).