植物病害流行模拟的渐近行为和阈值标准

本文由Vanderlpank微分差异方程推导出植病流行中阈值标准和渐近行为的两个数量性结果,通过分析表明这些结果依赖于初始病害量,并澄清文献中的几点谬误,同时指出植病流行学应当同医学流行学等同类学科联系起来,以更加丰富和发展植病流行理论。     

植物病害时空流行动态模拟模型的构建

一个描述在二维空间中单一种植或混合种植的植物群体内病害时、空流行动态的计算机随机模拟模型构建完成。模型由寄主、病原2个组分和病斑产孢、孢子传播、孢子着落、孢子侵染、病斑潜育、寄主生长、病害控制等一系列代表病害流行生物学过程的子模型构成。模型采用了面向对象的程序设计方法,用C++语言编写,能以病害流行曲线图、空间分布图、数据列表等方式显示模拟结果。测试结果表明,模型能反映植物病害流行过程的本质规律,既可作为植物病害流行学教学工具,帮助学生理解病害流行的时、空动态规律和不同因子对病害流行的影响,也可以作为研…     

梨黑星病流行动态模拟模型程序设计

梨黑星病流行动态模拟模型(PSSM)能以一日为一个步长模拟整个生长季节梨黑星病的流行动态。PSSM的计算机程序采用了面向对象的程序设计。模型中,梨园每一天的状态,如寄主、病原物的相对数量,果园环境的温度、湿度,果园管理措施等,由一个果园状态对象表示。果园状态对象由18个子对象和10个子模型构成。18个子对象分别表示寄主、病原物的相对数量、果园环境条件和管理措施等。10个子模型模拟孢子传播、侵染、病斑显症等病害流行动态过程。模型运行时,子模型根据果园前期状态和当日环境,计算获得数据,创建表示当日果园状态的对象,并依次类推。程序结构清晰自然,能较好地再现病害发生与流行的生物学过程。     

模拟植物病害流行时间动态的通用模型——Richards函数

 本文介绍了Richards生长函数及其在植物病害流行时间动态模拟中的应用。该函数的微分形式为(dx)/(dt)=rx((1/x)1-m-1)/(1-m),式中r为病害发展的速率,x表t日期的病情值率(0 < x < 1),m为流行曲线的形状参数。当m=0,m→1,m=2和m→∞时,从理论上证明Richards函数成为单分子、Compertz、Logistic和指数函数模型。以水稻纹枯病和马铃薯晚疫病的田间进展曲线进行摸拟分析发现,当m取值适当时还可获得较Gompertz或Logistic更逼真的Richards病害曲线拟合模型,而适当m取值的Richards模型比小分子模型对玉米粗缩病的拟合性也更好。因此认为,Richards函数是植病流行时间动态的通用模型。     

玉米灰斑病空间流行动态模拟模型组建及传播距离研究

 以玉米品种郑单958为试材接种灰斑病菌,在田间形成不同发病梯度,分析病害传播的空间流行动态,利用SAS9.13统计软件分别构建病害传播梯度的一维、二维和三维模型。结果表明:1.指数模型和GOMPERTZ模型是沈阳地区玉米灰斑病单向传播梯度的最佳模拟模型;2.高斯模型是模拟病害平行于垄向和垂直于垄向方向传播的最佳模型;3.含有(x²+y²)形式的圆形模型和含有a (x²+y²)+bx+cy+d形式的椭圆形模型是模拟病害在二维平面上传播过程的最佳模型。通过模型推导得到病害的传播距离约20~50m。     

稻瘟病流行的模拟模型SIMBLAST-2

 稻瘟病(Pyricularia oryzae Cav.)流行的模拟模型SIMBLAST-2主要由5个子模型组成:(1)水稻生长的子模型GROWTH,(2)稻瘟病日传染率的子模型INFECTION,(3)潜育期和显症率的子模型INCUBATION,(4)病斑扩展的子模型LESION,(5)空中孢子捕获的子模型SPORE。
SIMBLAST-2经两年的检验和模拟试验,认为其结构合理、实用性强。
最后讨论了该模型的不足之处和改进途径。     

大棚黄瓜霜霉病流行模拟模型的组建

由寄主生长、侵染、显症、病斑扩展、产孢和孢囊着落六个部分组成的黄瓜霜霉病流行模拟系统(简称SIMCD)能输出四个反映该病发展状态及势态的参量(R_(1-4))。其中R_4具有较强的预测能力。模拟试验结果表明:SIMCD较好地再现了大棚条件下黄瓜霜霉病流行速率持续较高这一主要特征;模拟的最高流行速率高达0.7以上,这比已报道的真菌病害中最高流行速率还高1/6。因此,建议在防治策略决策中优先考虑控制流行速率。     

辽宁省水稻纹枯病时间流行动态模型

水稻纹枯病是我国水稻主要病害,产量损失一般为15％～20％,重病田可达60％～70％。历史上该病在南方稻区较重,近年来由于气候、品种及栽培等因素的变化,东北稻区纹枯病的发生危害加重。目前,有关水稻纹枯病病原学、抗性评价和综合防治等领域研究较多,而流行学研究仅集中于侵染速率、初侵染模型构建和空间传播结构心等方面,北方稻区纹枯病时间流行动态和模型构建研究则鲜见报道。本研究连续3年系统调查了辽宁盘锦地区水稻纹枯病病情,并构建了病害时间流行动态及病情指数与病株率之间的数学模型,以期为病害测报和适期防控提供参考。     

A simulation model for the development of brown rust epidemics in winter wheat

A model simulating the progress of Puccinia recondita severity, expressed as a percentage of rusted leaf area (both as average and its 95% confidence interval) on individual wheat leaves over the course of a growing season, with a time step of one day, was elaborated using laboratory and field data from literature. Data on the stages of each infection cycle (uredospore germination, penetration, latency, uredium eruption and infectiousness) were transformed into model parameters by curve fitting, Montecarlo stochastic procedures, corrections and empirical assumptions. Data on host growth, like the timing of all phenological stages, the dynamic of the green area of each leaf from appearance to complete senescence, and tillering were obtained from a specific sub-model. Model validation was performed on actual data not used in model building and representing a wide range of conditions (several winter wheat cultivars grown at eight locations in northern Italy between 1990 and 1994) by using subjective, non-parametric and parametric tests: it revealed a satisfactory agreement between the data simulated by the model and actual data.     

Development of a stochastic spore germination model

The concept of dose/response with logarithm of time being the dose and percent germination the response is introduced into a parallel box car model, where each spore in a population passes through its own box during its change from an ungerminated to a germinated state. Population behaviour is modelled by simulating the quantal responses of each of its members as stochastic random variables. Waiting times until germination were generated using the normal, lognormal, gamma, and exponential distributions in a computer simulation program. Good agreement with observed germination of five fungal pathogens was obtained. Methods are discussed pertaining to hypothesis testing concerning the role individuals contribute to the behaviour of the population as a whole. Two methods of handling changing temperatures are examined. Effects of inoculum density, infection efficiency, and variable temperature were incorporated into the simulation program. The stochastic model using parallel boxes offers an alternative to deterministic serial box car models which are used to mimic dispersion in time during development.Samenvatting Het concept van dosis/response met de logaritme van tijd als de dosis en het percentage gekiemde sporen als response is geïntroduceerd in een model met parallelle boxcars, waarbij iedere spore zijn eigen boxcar bezit bij de overgang van de ongekiemde naar de gekiemde toestand. Het populatiegedrag is gemodelleerd door de kwantale responsie van iedere spore als stochastische variabele te hanteren. Wachttijden tot kieming werd gegenereerd uit de normaal, lognormaal, gamma en exponentiële verdeling die in het computerprogramma aanwezig waren. Er werd goede overeenkomst gevonden tussen gesimuleerde en waargenomen kieming bij een reeks van vijf pathogene schimmels. Verschillende hypothesen over de rol van individuele sporen voor het gedrag van het totaal werden getoetst en het effect van variabele temperatuur, inoculumdichtheid en infectie-efficiëntie werd nagegaan. Het stochastische model met parallelle boxcars is een goed alternatief voor de boxcars die in serie zijn geplaatst teneinde dispersie in tijd of ruimte na te bootsen, wanneer er verschillen in reactie tussen individuele sporen bestaan.     

Effects of initial epidemic conditions, sporulation rate, and spore dispersal gradient on the spatio-temporal dynamics of plant disease epidemics

ABSTRACT A stochastic model that simulates the spread of disease over space and time was developed to study the effects of initial epidemic conditions (number of initial inocula and their spatial pattern), sporulation rate, and spore dispersal gradient on the spatio-temporal dynamics of plant disease epidemics. The spatial spread of disease was simulated using a half-Cauchy distribution with median dispersal distance mu (units of distance). The rate of temporal increase in disease incidence (beta(I), per day) was influenced jointly by mu and by the sporulation rate lambda (spores per lesion per day). The relationship between beta(I) and mu was nonlinear: the increase in beta(I) with increasing mu was greatest when mu was small (i.e., when the dispersal gradient was steep). The rate of temporal increase in disease severity of diseased plants (beta(S)) was affected mainly by lambda: beta(S) increased directly with increasing lambda. Intraclass correlation (kappa(t)), the correlation of disease status of plants within quadrats, increased initially with disease incidence, reached a peak, and then declined as disease incidence approached 1.0. This relationship was well described by a power-law model that is consistent with the binary form of the variance power law. The amplitude of the model relating kappa(t) to disease incidence was affected mainly by mu: kappa(t) decreased with increasing mu. The shape of the curve was affected mainly by initial conditions, especially the spatial pattern of the initial inocula. Generally, the relationship of spatial autocorrelation (rho(t,k)), the correlation of disease status of plants at various distances apart, to disease incidence and distance was well described by a four-parameter power-law model. rho(t,k) increased with disease incidence to a maximum and then declined at higher values of disease incidence, in agreement with a power-law relationship. The amplitude of rho(t,k) was determined mainly by initial conditions and by mu: rho(t,k) decreased with increasing mu and was lower for regular patterns of initial inocula. The shape of the rho(t,k) curve was affected mainly by initial conditions, especially the spatial pattern of the initial inocula. At any level of disease incidence, autocorrelation declined exponentially with spatial lag; the degree of this decline was determined mainly by mu: it was steeper with decreasing mu.     

河南小麦叶枯类病害春季流行的时间动态规律研究

1997～1998年的研究结果表明:小麦叶枯病害在河南麦田春季的流行曲线基本呈S型曲线。始发期一般在3月20日～4月10日,指数增长期大约10～20d,该期流行速率最高;4月10～20日以后该病进入逻辑斯蒂增长期,可持续30～40d,流行速率比指数增长期稍低,没有明显的衰退期;影响小麦叶枯类病害始发期的气象因子为3月份的日照、降水量和气温,4月份的气温、日照、降水日数、相对湿度与发生程度关系密切。河南主栽小麦品种中没有对叶枯类病害免疫的品种,70%以上的主栽小麦品种为感叶枯病品种。     

Use of SADIE statistics to study spatial dynamics of plant disease epidemics

Using a previously developed stochastic simulation model for plant disease epidemics, the relationship of the SADIE aggregation statistic I _a with initial epidemic conditions, spore dispersal distance, sampling quadrat size and other spatial statistics was investigated. Most variation in I _a was attributable to the initial spatial pattern of infected plants and sampling quadrat size. The importance of initial spatial pattern on SADIE clustering indices (for patches and gaps) was also demonstrated using a number of selected data sets. Correlation of I _a with clustering indices was close to 1·0. Epidemics arising from the regular and random initial patterns resulted in the smallest and greatest I _a values, respectively, at sampling times after disease spread had occurred. Furthermore, the variability in I _a between simulation runs also varied greatly with initial patterns, being lowest and greatest for the clumped and random initial patterns, respectively. I _a increased initially and then decreased with increasing incidence, especially for the clumped and random initial patterns. Overall, the effect of median spore dispersal distance on I _a was very small, especially for the random initial pattern. The correlation between I _a and intraclass correlation was generally small and varied greatly between initial patterns. However, there was a high positive correlation between I _a and a parameter describing the rate of decline of autocorrelation over spatial lags, indicating that I _a, clustering indices and autocorrelations measure some common properties of patterns.     

A model of plant virus disease epidemics in asynchronously-planted cropping systems

A mathematical model was developed of the dynamics of a plant virus disease within a spatially-referenced lattice of fields of a host crop. The model can be applied to crops in continuous, contiguous cultivation such as tropical irrigated rice. Disease progress in each field of the host crop was assumed to be logistic and determined by incidence within the field itself as well as incidence in neighbouring fields, depending on the gradient of disease spread. The frequency distribution of planting dates (represented by the proportion of the total number of fields planted in successive months) was assumed to follow a normal distribution and the variance of planting date was used as a measure of cropping asynchrony. Analysis of the model revealed that disease incidence within the lattice (i.e. mean incidence over all fields) depended upon the infection efficiency, the slope of the dispersal gradient, and the variance in planting date. Disease endemicity depended mainly on planting date variance and disease persisted in the lattice if this variance exceeded a certain threshold. Above the threshold for persistence, the response of mean disease incidence to planting date variance was non-linear and the region of greatest sensitivity was closest to the threshold. Thus, disease systems that show moderate rather than high cropping asynchrony are more likely to be influenced by changes in the variance of planting date. Implications for the area-wide management of rice tungro virus disease are discussed.     

Contribution of the life-history traits of a plant pathogen to the development of field epidemics

The pathogenicity-related traits of biotrophic plant pathogens are usually measured on the individual host plant, at the scale of a single pathogen life cycle, whereas epidemic development in the field encompasses a succession of cycles. It remains unclear which traits make the greatest contribution to pathogen fitness in the field and to epidemic severity. The objective of this study was to determine the contributions of elementary pathogenicity traits to epidemic development in field conditions. We challenged a set of wheat cultivars with three different leaf rust isolates, under both controlled and field conditions, in 3 consecutive years. Infection efficiency, latent period, lesion size, spore production per lesion and spore production capacity were measured in the greenhouse, whereas disease severity was measured in the field. Most, but not all, of the pathogenicity traits were related to each other. All traits contributed to epidemic development in the field, but to different extents. Surprisingly, lesion size and spore production per lesion were inversely correlated with epidemic severity. Conversely, there was a strong positive correlation between spore production capacity and pathogen fitness in the field, in accordance with the concept of propagule pressure as a strong determinant of invasion success. Severe epidemics were mostly associated with small lesions with a high spore production capacity, high infection efficiency and a short latent period.     

Effect of crop growth and canopy filtration on the dynamics of plant disease epidemics spread by aerially dispersed spores

Most mathematical models of plant disease epidemics ignore the growth and phenology of the host crop. Unfortunately, reports of disease development are often not accompanied by a simultaneous and commensurate evaluation of crop development. However, the time scale for increases in the leaf area of field crops is comparable to the time scale of epidemics. This simultaneous development of host and pathogen has many ramifications on the resulting plant disease epidemic. First, there is a simple dilution effect resulting from the introduction of new healthy leaf area with time. Often, measurements of disease levels are made pro rata (per unit of host leaf area or total root length or mass). Thus, host growth will reduce the apparent infection rate. A second, related effect, has to do with the so-called "correction factor," which accounts for inoculum falling on already infected tissue. This factor accounts for multiple infection and is given by the fraction of the host tissue that is susceptible to disease. As an epidemic develops, less and less tissue is open to infection and the initial exponential growth slows. Crop growth delays the impact of this limiting effect and, therefore, tends to increase the rate of disease progress. A third and often neglected effect arises when an increase in the density of susceptible host tissue results in a corresponding increase in the basic reproduction ratio, R(0), defined as the ratio of the total number of daughter lesions produced to the number of original mother lesions. This occurs when the transport efficiency of inoculum from infected to susceptible host is strongly dependent on the spatial density of plant tissue. Thus, crop growth may have a major impact on the development of plant disease epidemics occurring during the vegetative phase of crop growth. The effects that these crop growth-related factors have on plant disease epidemics spread by airborne spores are evaluated using mathematical models and their importance is discussed. In particular, plant disease epidemics initiated by the introduction of inoculum during this stage of development are shown to be relatively insensitive to the time at which inoculum is introduced.     

A perspective on the measurement of time in plant disease epidemiology

The growth and development of plant pathogens and their hosts generally respond strongly to the temperature of their environment. However, most studies of plant pathology record pathogen/host measurements against physical time (e.g. hours or days) rather than thermal time (e.g. degree-days or degree-hours). This confounds the comparison of epidemiological measurements across experiments and limits the value of the scientific literature.