Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing 48824; and Program in Ecology, Evolutionary Biology and Behavior, Michigan State University.
Plant Dis. 2018 Apr;102(4):708-714. doi: 10.1094/PDIS-06-17-0873-SR. Epub 2018 Feb 14.
The effective control to 50% growth inhibition (EC) is a standard statistic for evaluating dose-response relationships. Many statistical software packages are available to estimate dose-response relationships but, recently, an open source package ("drc") in R has been utilized. This package is highly adaptable, having many models to describe dose-response relationships and flexibility to describe both hormetic relationships and absolute and relative EC. These models and definitions are generally left out of phytopathology literature. Here, we demonstrate that model choice and type of EC (relative versus absolute) can matter for EC estimation using data from Pythium oopapillum and Fusarium virguliforme. For some P. oopapillum isolates, the difference between absolute and relative EC was significant. Hormetic effects changed F. virguliforme EC distributions, leading to higher estimates than when using four- or three-parameter log-logistic models. Future studies should pay careful attention to model selection and interpretation in EC estimation and clearly indicate which model and EC measure (relative versus absolute) was used. We provide guidelines for model choice and interpretation for those wishing to set up experiments for accurate EC estimation.
有效控制生长抑制 50%（EC）是评估剂量-反应关系的标准统计量。有许多统计软件包可用于估计剂量-反应关系，但最近 R 中的一个开源软件包（"drc"）已被广泛使用。该软件包具有高度的适应性，有许多模型可用于描述剂量-反应关系，并具有灵活性来描述 hormesis 关系以及绝对和相对 EC。这些模型和定义通常在植物病理学文献中被忽略。在这里，我们使用 Pythium oopapillum 和 Fusarium virguliforme 的数据证明了模型选择和 EC（相对与绝对）类型对 EC 估计的重要性。对于某些 P. oopapillum 分离株，绝对和相对 EC 之间的差异具有统计学意义。Hormesis 效应改变了 F. virguliforme 的 EC 分布，导致 EC 估计值高于使用四参数或三参数逻辑斯蒂模型时的估计值。未来的研究应在 EC 估计中仔细注意模型选择和解释，并清楚地表明使用了哪种模型和 EC 度量（相对与绝对）。我们为希望进行准确 EC 估计实验的人提供了模型选择和解释的指南。
