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Predicting variability in biological control of a plant-pathogen system using stochastic models.

机译:使用随机模型预测植物病原体系统生物控制中的变异性。

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摘要

A stochastic model for the dynamics of a plant-pathogen interaction is developed and fitted to observations of the fungal pathogen Rhizoctonia solani (Kühn) in radish (Raphanus sativus L.), in both the presence and absence of the antagonistic fungus Trichoderma viride (Pers ex Gray). The model incorporates parameters for primary and secondary infection mechanisms and for characterizing the time-varying susceptibility of the host population. A parameter likelihood is developed and used to fit the model to data from microcosm experiments. It is shown that the stochastic model accounts well for observed variability both within and between treatments. Moreover, it enables us to describe the time evolution of the probability distribution for the variability among replicate epidemics in terms of the underlying epidemiological parameters for primary and secondary infection and decay in susceptibility. Consideration of profile likelihoods for each parameter provides strong evidence that T. viride mainly affects primary infection. By using the stochastic model to study the dependence of the probability distribution of disease levels on the primary infection rate we are therefore able to predict the effectiveness of a widely used biological control agent.
机译:建立了一种植物-病原体相互作用动力学的随机模型,并将其拟合到在存在和不存在拮抗真菌木霉(木霉)的情况下观察萝卜(Raphanus sativus L.)中真菌病原菌Rhizoctonia solani(Kühn)的现象。前格雷)。该模型结合了主要和次要感染机制的参数,以及表征宿主人群随时间变化的敏感性的参数。参数似然被开发出来并用于使模型适合微观实验的数据。结果表明,随机模型很好地说明了处理过程中和处理之间观察到的变异性。此外,它使我们能够根据主要和次要感染以及易感性下降的潜在流行病学参数,描述重复流行病之间变异性的概率分布的时间演变。对每个参数的分布似然性的考虑提供了有力的证据,证明绿脓杆菌主要影响原发感染。通过使用随机模型研究疾病水平的概率分布对原发感染率的依赖性,因此,我们能够预测广泛使用的生物防治剂的有效性。

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