Gilligan C A
Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK.
New Phytol. 1994 Nov;128(3):539-553. doi: 10.1111/j.1469-8137.1994.tb03000.x.
The effects and interactions of inoculum density (Iρ), rate of fungal growth (kf) and the maximum distances for primary (w ) and secondary (cross) (w ) infection on the temporal progress of infection of the take-all fungus, Gaeumannomyces graminis, in small populations of contiguous wheat plants are examined in factorial combination using a stochastic simulation model. The effects are analyzed in relation to infection progress curves for the percentage infected roots, the total length of infected root, the density of separate infection and the mean length of separate infections as well as the densities of primary and cross infections. Linear models are used to analyze the effects of changing parameter values on components of the infection progress curves. Non-linear, logistic models are used to summarize infection progress curves and to map the effects of changing simulation model parameters onto the parameters of the simpler, growth curve function. The proportion of infected roots was most influenced by Iρ and w with a negligible effect due to w . The density and length of infections on roots was principally controlled by kf. Changes in infection length were non-monotonic. The average length of infections increased towards a temporary maximum, following the first wave of primary infections, dipped as infections overlapped and then increased rapidly as root colonization progressed. The first wave of cross infection occurred 7 d after the initiation of primary infection. The density of primary infections was controlled principally by Iρ and w . The density of cross infections was controlled not only by kf and w , which are directly involved in cross infection, but also by kf and w which affect the amount of infected and susceptible tissue. The asymptotic parameter of the logistic model for the infection progress curves was the most frequently affected parameter, followed by the delay parameter, with relatively few changes affecting the rate parameter. Some problems in the use of complex, stochastic simulation models to simulate experimental conditions are discussed.
利用随机模拟模型，以析因组合的方式研究了接种密度（Iρ）、真菌生长速率（kf）以及初次感染（w）和二次（交叉）感染（w）的最大距离对小麦全蚀病菌（Gaeumannomyces graminis）在相邻小麦小群体中感染时间进程的影响及相互作用。针对感染根百分比、感染根总长度、单独感染密度、单独感染平均长度以及初次感染和交叉感染密度的感染进程曲线，分析了这些影响。采用线性模型分析参数值变化对感染进程曲线各组成部分的影响。使用非线性逻辑模型总结感染进程曲线，并将模拟模型参数变化的影响映射到更简单的生长曲线函数参数上。感染根的比例受Iρ和w影响最大，而w的影响可忽略不计。根上感染的密度和长度主要由kf控制。感染长度的变化是非单调的。随着初次感染的第一波，感染平均长度朝着一个临时最大值增加，在感染重叠时下降，然后随着根部定殖的进展迅速增加。交叉感染的第一波在初次感染开始7天后出现。初次感染的密度主要由Iρ和w控制。交叉感染的密度不仅受直接参与交叉感染的kf和w控制，还受影响感染和易感组织数量的kf和w控制。感染进程曲线逻辑模型的渐近参数是最常受影响的参数，其次是延迟参数，相对较少的变化影响速率参数。讨论了使用复杂随机模拟模型模拟实验条件时的一些问题。
