Plant neighbours can make or break the disease transmission chain of a fungal root pathogen

Biodiversity can reduce or increase disease transmission. These divergent effects suggest that community composition rather than diversity per se determines disease transmission. In natural plant communities, little is known about the functional roles of neighbouring plant species in belowground disease transmission. Here, we experimentally investigated disease transmission of a fungal root pathogen (Rhizoctonia solani) in two focal plant species in combinations with four neighbour species of two ages. We developed stochastic models to test the relative importance of two transmission-modifying mechanisms: (1) infected hosts serve as nutrient supply to increase hyphal growth, so that successful disease transmission is self-reinforcing; and (2) plant resistance increases during plant development. Neighbouring plants either reduced or increased disease transmission in the focal plants. These effects depended on neighbour age, but could not be explained by a simple dichotomy between hosts and nonhost neighbours. Model selection revealed that both transmission-modifying mechanisms are relevant and that focal host-neighbour interactions changed which mechanisms steered disease transmission rate. Our work shows that neighbour-induced shifts in the importance of these mechanisms across root networks either make or break disease transmission chains. Understanding how diversity affects disease transmission thus requires integrating interactions between focal and neighbour species and their pathogens.

Modelling the key drivers of an aerial Phytophthora foliar disease epidemic, from the needles to the whole plant

Understanding the epidemiology of infectious diseases in a host population is a major challenge in forestry. Radiata pine plantations in New Zealand are impacted by a foliar disease, red needle cast (RNC), caused by Phytophthora pluvialis. This pathogen is dispersed by water splash with polycyclic infection affecting the lower part of the tree canopy. In this study, we extended an SI (Susceptible-Infectious) model presented for RNC to analyse the key epidemiological drivers. We conducted two experiments to empirically fit the extended model: a detached-needle assay and an in vivo inoculation. We used the detached-needle assay data to compare resistant and susceptible genotypes, and the in vivo inoculation data was used to inform sustained infection of the whole plant. We also compared isolations and real-time quantitative PCR (qPCR) to assess P. pluvialis infection. The primary infection rate and the incubation time were similar for susceptible and resistant genotypes. The pathogen death rate was 2.5 times higher for resistant than susceptible genotypes. Further, external proliferation of mycelium and sporangia were only observed on 28% of the resistant ramets compared to 90% of the susceptible ones. Detection methods were the single most important factor influencing parameter estimates of the model, giving qualitatively different epidemic outputs. In the early stages of infection, qPCR proved to be more efficient than isolations but the reverse was true at later points in time. Isolations were not influenced by the presence of lesions in the needles, while 19% of lesioned needle maximized qPCR detection. A primary infection peak identified via qPCR occurred at 4 days after inoculation (dai) with a secondary peak observed 22 dai. Our results have important implications to the management of RNC, by highlighting the main differences in the response of susceptible and resistant genotypes, and comparing the most common assessment methods to detect RNC epidemics.

Methods for estimating disease transmission rates: Evaluating the precision of Poisson regression and two novel methods

Precise estimates of disease transmission rates are critical for epidemiological simulation models. Most often these rates must be estimated from longitudinal field data, which are costly and time-consuming to conduct. Consequently, measures to reduce cost like increased sampling intervals or subsampling of the population are implemented. To assess the impact of such measures we implement two different SIS models to simulate disease transmission: A simple closed population model and a realistic dairy herd including population dynamics. We analyze the accuracy of different methods for estimating the transmission rate. We use data from the two simulation models and vary the sampling intervals and the size of the population sampled. We devise two new methods to determine transmission rate, and compare these to the frequently used Poisson regression method in both epidemic and endemic situations. For most tested scenarios these new methods perform similar or better than Poisson regression, especially in the case of long sampling intervals. We conclude that transmission rate estimates are easily biased, which is important to take into account when using these rates in simulation models.

Interplay between parasitism and host ontogenic resistance in the epidemiology of the soil-borne plant pathogen Rhizoctonia solani

Spread of soil-borne fungal plant pathogens is mainly driven by the amount of resources the pathogen is able to capture and exploit should it behave either as a saprotroph or a parasite. Despite their importance in understanding the fungal spread in agricultural ecosystems, experimental data related to exploitation of infected host plants by the pathogen remain scarce. Using Rhizoctonia solani / Raphanus sativus as a model pathosystem, we have obtained evidence on the link between ontogenic resistance of a tuberizing host and (i) its susceptibility to the pathogen and (ii) after infection, the ability of the fungus to spread in soil. Based on a highly replicable experimental system, we first show that infection success strongly depends on the host phenological stage. The nature of the disease symptoms abruptly changes depending on whether infection occurred before or after host tuberization, switching from damping-off to necrosis respectively. Our investigations also demonstrate that fungal spread in soil still depends on the host phenological stage at the moment of infection. High, medium, or low spread occurred when infection was respectively before, during, or after the tuberization process. Implications for crop protection are discussed.

Estimating the delay between host infection and disease (incubation period) and assessing its significance to the epidemiology of plant diseases

Knowledge of the incubation period of infectious diseases (time between host infection and expression of disease symptoms) is crucial to our epidemiological understanding and the design of appropriate prevention and control policies. Plant diseases cause substantial damage to agricultural and arboricultural systems, but there is still very little information about how the incubation period varies within host populations. In this paper, we focus on the incubation period of soilborne plant pathogens, which are difficult to detect as they spread and infect the hosts underground and above-ground symptoms occur considerably later. We conducted experiments on Rhizoctonia solani in sugar beet, as an example patho-system, and used modelling approaches to estimate the incubation period distribution and demonstrate the impact of differing estimations on our epidemiological understanding of plant diseases. We present measurements of the incubation period obtained in field conditions, fit alternative probability models to the data, and show that the incubation period distribution changes with host age. By simulating spatially-explicit epidemiological models with different incubation-period distributions, we study the conditions for a significant time lag between epidemics of cryptic infection and the associated epidemics of symptomatic disease. We examine the sensitivity of this lag to differing distributional assumptions about the incubation period (i.e. exponential versus Gamma). We demonstrate that accurate information about the incubation period distribution of a pathosystem can be critical in assessing the true scale of pathogen invasion behind early disease symptoms in the field; likewise, it can be central to model-based prediction of epidemic risk and evaluation of disease management strategies. Our results highlight that reliance on observation of disease symptoms can cause significant delay in detection of soil-borne pathogen epidemics and mislead practitioners and epidemiologists about the timing, extent, and viability of disease control measures for limiting economic loss.

Prediction of invasion from the early stage of an epidemic

Predictability of undesired events is a question of great interest in many scientific disciplines including seismology, economy and epidemiology. Here, we focus on the predictability of invasion of a broad class of epidemics caused by diseases that lead to permanent immunity of infected hosts after recovery or death. We approach the problem from the perspective of the science of complexity by proposing and testing several strategies for the estimation of important characteristics of epidemics, such as the probability of invasion. Our results suggest that parsimonious approximate methodologies may lead to the most reliable and robust predictions. The proposed methodologies are first applied to analysis of experimentally observed epidemics: invasion of the fungal plant pathogen Rhizoctonia solani in replicated host microcosms. We then consider numerical experiments of the susceptible-infected-removed model to investigate the performance of the proposed methods in further detail. The suggested framework can be used as a valuable tool for quick assessment of epidemic threat at the stage when epidemics only start developing. Moreover, our work amplifies the significance of the small-scale and finite-time microcosm realizations of epidemics revealing their predictive power.

Highly variable spread rates in replicated biological invasions: fundamental limits to predictability

Although mean rates of spread for invasive species have been intensively studied, variance in spread rates has been neglected. Variance in spread rates can be driven exogenously by environmental variability or endogenously by demographic or genetic stochasticity in reproduction, survival, and dispersal. Endogenous variability is likely to be important in spread but has not been studied empirically. We show that endogenously generated variance in spread rates is remarkably high between replicated invasions of the flour beetle Tribolium castaneum in laboratory microcosms. The observed variation between replicate invasions cannot be explained by demographic stochasticity alone, which indicates inherent limitations to predictability in even the simplest ecological settings.

Epidemiological analysis of take-all decline in winter wheat

Take-all dynamics within crops differing in cropping history (the number of previous consecutive wheat crops) were analyzed using an epidemiological model to determine the processes affected during take-all decline. The model includes terms for primary infection, secondary infection, inoculum decay, and root growth. The average rates of root production did not vary with cropping history. The force of primary infection increased from a low level in 1st wheat crops, to a maximum in 2nd to 4th wheat crops, and then to intermediate levels thereafter. The force of secondary infection was low but increased steadily during the season in first wheat crops, was delayed but rose and fell sharply in 2nd to 4th wheat crops, and for 5th and 7th wheat crops returned to similar dynamics as that for 1st wheat crops. Chemical seed treatment with silthiofam had no consistent effect on the take-all decline process. We conjecture that these results are consistent with (i) low levels of particulate inoculum prior to the first wheat crop leading to low levels of primary infection, low levels of secondary infection, and little disease suppression; (ii) net amplification of inoculum during the first wheat crop and intercrop period; (iii) increased levels of primary and secondary infection in subsequent crops, but higher levels of disease suppression; and (iv) an equilibrium between the pathogen and antagonist populations by the 5th wheat, reflected by lower overall rates of primary infection, secondary infection, disease suppression and hence, disease severity.

Epidemiological models for invasion and persistence of pathogens

Motivated by questions such as "Why do some diseases take off, while others die out?" and "How can we optimize the deployment of control methods," we introduce simple epidemiological concepts for the invasion and persistence of plant pathogens. An overarching modeling framework is then presented that can be used to analyze disease invasion and persistence at a range of scales from the microscopic to the regional. Criteria for invasion and persistence are introduced, initially for simple models of epidemics, and then for models with greater biological realism. Some ways in which epidemiological models are used to identify optimal strategies for the control of disease are discussed. Particular attention is given to the spatial structure of host populations and to the role of chance events in determining invasion and persistence of plant pathogens. Finally, three brief case studies are used to illustrate the practical applications of epidemiological theory to understand invasion and persistence of plant pathogens. These comprise long-term predictions for the persistence and control of Dutch elm disease; identification of methods to manage the spread of rhizomania on sugar beet in the U.K. by matching the scale of control with the spatial and temporal scales of the disease; and analysis of evolutionary change in virus control to identify risks of inadvertent selection for damaging virus strains.

Estimation of multiple transmission rates for epidemics in heterogeneous populations

One of the principal challenges in epidemiological modeling is to parameterize models with realistic estimates for transmission rates in order to analyze strategies for control and to predict disease outcomes. Using a combination of replicated experiments, Bayesian statistical inference, and stochastic modeling, we introduce and illustrate a strategy to estimate transmission parameters for the spread of infection through a two-phase mosaic, comprising favorable and unfavorable hosts. We focus on epidemics with local dispersal and formulate a spatially explicit, stochastic set of transition probabilities using a percolation paradigm for a susceptible-infected (S-I) epidemiological model. The S-I percolation model is further generalized to allow for multiple sources of infection including external inoculum and host-to-host infection. We fit the model using Bayesian inference and Markov chain Monte Carlo simulation to successive snapshots of damping-off disease spreading through replicated plant populations that differ in relative proportions of favorable and unfavorable hosts and with time-varying rates of transmission. Epidemiologically plausible parametric forms for these transmission rates are compared by using the deviance information criterion. Our results show that there are four transmission rates for a two-phase system, corresponding to each combination of infected donor and susceptible recipient. Knowing the number and magnitudes of the transmission rates allows the dominant pathways for transmission in a heterogeneous population to be identified. Finally, we show how failure to allow for multiple transmission rates can overestimate or underestimate the rate of spread of epidemics in heterogeneous environments, which could lead to marked failure or inefficiency of control strategies.

Cost-effective control of a quarantine disease: a quantitative exploration using "design of experiments" methodology and bio-economic modeling

ABSTRACT An integrated approach to control of quarantine diseases at the level of the plant production chain is complicated. The involved actors have different interests and the system is complex. Consequently, control policies may not be cost effective. By means of a bio-economic model for brown rot in the Dutch potato production chain, the efficacy of different control options was quantitatively analyzed. An impact analysis was performed using the methodology of "design of experiments" to quantify the effect of factors in interaction on incidence and costs of brown rot. Factors can be grouped as policy, sector, economic, and exogenous factors. Results show that brown rot incidence and economic consequences are determined predominantly by policy and sector factors and, to a lesser extent, by economic and exogenous factors. Scenario studies were performed to elucidate how the government and sector can optimize the cost-effectiveness of brown rot control. Optimal cost-effectiveness of control requires cooperation of the sector and government, in which case brown rot incidence can be reduced by 75% and the costs of control can be reduced by at least 2 million euros per year. This study demonstrates quantitatively the potential contribution of an integrated approach to cost-effective disease control at chain level.

An Epidemiological Analysis of the Role of Disease-Induced Root Growth in the Differential Response of Two Cultivars of Winter Wheat to Infection by Gaeumannomyces graminis var. tritici

ABSTRACT Epidemiological modeling combined with parameter estimation of experimental data was used to examine differences in the contribution of disease-induced root production to the spread of take-all on plants of two representative yet contrasting cultivars of winter wheat, Ghengis and Savannah. A mechanistic model, including terms for primary infection, secondary infection, inoculum decay, and intrinsic and disease-induced root growth, was fitted to data describing changes in the numbers of infected and susceptible roots over time at a low or high density of inoculum. Disease progress curves were characterized by consecutive phases of primary and secondary infection. No differences in root growth were detected between cultivars in the absence of disease and root production continued for the duration of the experiment. However, significant differences in disease-induced root production were detected between Savannah and Genghis. In the presence of disease, root production for both cultivars was characterized by stimulation when few roots were infected and inhibition when many roots were infected. At low inoculum density, the transition from stimulation to inhibition occurred when an average of 5.0 and 9.0 roots were infected for Genghis and Savannah, respectively. At high inoculum density, the transition from stimulation to inhibition occurred when an average of 4.5 and 6.7 roots were infected for Genghis and Savannah, respectively. Differences in the rates of primary and secondary infection between Savannah and Genghis also were detected. At a low inoculum density, Genghis was marginally more resistant to secondary infection whereas, at a high density of inoculum, Savannah was marginally more resistant to primary infection. The combined effects of differences in disease-induced root growth and differences in the rates of primary and secondary infection meant that the period of stimulated root production was extended by 7 and 15 days for Savannah at a low and high inoculum density, respectively. The contribution of this form of epidemiological modeling to the better management of take-all is discussed.

Epidemiological dynamics and the efficiency of biological control of soil-borne disease during consecutive epidemics in a controlled environment

•  A combination of experimentation and modelling is used to examine the role of epidemiological dynamics on the production and infectivity of inoculum and the efficiency of biocontrol by Trichoderma viride during consecutive epidemics of damping-off disease caused by the pathogen Rhizoctonia solani in crops of radish. •  Changes in the net infectivity of inoculum at the beginning of first and second crops caused a switch in epidemiological dynamics. Epidemics of first crops were dominated by secondary infection leading to amplification of inoculum so that epidemics of second crops were overwhelmingly determined by primary infection. •  The biocontrol agent reduced primary infection and hence parasitic amplification of inoculum in both first and second crops but the efficiency of control dropped from 91.7% in first crops to 64.8% in second crops, with sudden outbreaks of disease in second crops which had previously been disease-free. •  We conclude that parasitic amplification can cause a rapid build-up of disease and inoculum over consecutive crops, leading to loss in the efficiency of biocontrol. This form of inoculum production is supplemented by saprotrophic infestation which can result in sudden outbreaks of disease in protected crops where control of disease had previously been fully successful.

Effects of dispersal mechanisms on spatio-temporal development of epidemics

The nature of pathogen transport mechanisms strongly determines the spatial pattern of disease and, through this, the dynamics and persistence of epidemics in plant populations. Up to recently, the range of possible mechanisms or interactions assumed by epidemic models has been limited: either independent of the location of individuals (mean-field models) or restricted to local contacts (between nearest neighbours or decaying exponentially with distance). Real dispersal processes are likely to lie between these two extremes, and many are well described by long-tailed contact kernels such as power laws. We investigate the effect of different spatial dispersal mechanisms on the spatio-temporal spread of disease epidemics by simulating a stochastic Susceptible-infective model motivated by previous data analyses. Both long-term stationary behaviour (in the presence of a control or recovery process) and transient behaviour (which varies widely within and between epidemics) are examined. We demonstrate the relationship between epidemic size and disease pattern (characterized by spatial autocorrelation), and its dependence on dispersal and infectivity parameters. Special attention is given to boundary effects, which can decrease disease levels significantly relative to standard, periodic geometries in cases of long-distance dispersal. We propose and test a definition of transient duration which captures the dependence of transients on dispersal mechanisms. We outline an analytical approach that represents the behaviour of the spatially-explicit model, and use it to prove that the epidemic size is predicted exactly by the mean-field model (in the limit of an infinite system) when dispersal is sufficiently long ranged (i.e. when the power-law exponent a</=2).