Phytopathology. 1999 May;89(5):421-33. doi: 10.1094/PHYTO.1999.89.5.421.
ABSTRACT Spatial pattern of the incidence of strawberry leaf blight, caused by Phomopsis obscurans, was quantified in commercial strawberry fields in Ohio using statistics for heterogeneity and spatial correlation. For each strawberry planting, two transects were randomly chosen and the proportion of leaflets (out of 15) and leaves (out of five) with leaf blight symptoms was determined from N = 49 to 106 (typically 75) evenly spaced sampling units, thus establishing a natural spatial hierarchy to compare patterns of disease. The beta-binomial distribution fitted the data better than the binomial in 92 and 26% of the 121 data sets over 2 years at the leaflet and leaf levels, respectively, based on a likelihood ratio test. Heterogeneity in individual data sets was measured with the index of dispersion (variance ratio), C(alpha) test, a standard normal-based test statistic, and estimated theta parameter of the beta-binomial. Using these indices, overdispersion was detected in approximately 94 and 36% of the data sets at the leaflet and leaf levels, respectively. Estimates of the slope from the binary power law were significantly (P < 0.01) greater than 1 and estimates of the intercept were significantly greater than 0 (P < 0.01) at both the leaflet and leaf levels for both years, indicating that degree of heterogeneity was a function of incidence. A covariance analysis indicated that cultivar, time, and commercial farm location of sampling had little influence on the degree of heterogeneity. The measures of heterogeneity indicated that there was a positive correlation of disease status of leaflets (or leaves) within sampling units. Measures of spatial association in disease incidence among sampling units were determined based on autocorrelation coefficients, runs analysis, and a new class of tests known as spatial analysis by distance indices (SADIE). In general, from 9 to 22% of the data sets had a significant nonrandom spatial arrangement of disease incidence among sampling units, depending on which test was used. When significant associations existed, the magnitude of the association was small but was about the same for leaflets and leaves. Comparing test results, SADIE analysis was found to be a viable alternative to spatial autocorrelation analysis and has the advantage of being an extension of heterogeneity analysis rather than a separate approach. Collectively, results showed that incidence of Phomopsis leaf blight was primarily characterized by small, loosely aggregated clusters of diseased leaflets, typically confined within the borders of the sampling units.
摘要 本研究采用统计学方法量化了俄亥俄州商业草莓田中草莓叶枯病（Phomopsis obscurans）的发病率的空间格局。在每个草莓种植园中，随机选择两条样线，从 N = 49 到 106（通常为 75）个均匀间隔的采样单元中确定 15 个小叶片中叶片和 5 个叶片中受叶枯病症状影响的叶片比例，从而建立了一种自然的空间层次结构，用于比较疾病模式。基于似然比检验，在两年的 121 个数据集中，贝塔二项式分布分别比二项式分布更好地拟合了 92%和 26%的叶位和叶位数据。基于方差比（C(alpha)检验）、标准正态检验统计量和贝塔二项式的估计 theta 参数等指标，个体数据集中的异质性在叶位和叶位分别约有 94%和 36%被检测到。在这两年中，叶位和叶位的二元幂律斜率估计值均显著（P < 0.01）大于 1，截距估计值均显著大于 0（P < 0.01），表明异质性程度是发病率的函数。协方差分析表明，品种、时间和采样的商业农场位置对异质性程度的影响很小。异质性度量表明，采样单元内小叶（或叶片）的病害状况存在正相关。基于自相关系数、运行分析和一种称为距离指数空间分析（SADIE）的新类测试，确定了采样单元间疾病发病率的空间关联度量。一般来说，根据使用的测试，9%至 22%的数据集具有显著的非随机空间排列。当存在显著关联时，关联的幅度很小，但在叶位和叶位之间大致相同。比较测试结果发现，SADIE 分析是空间自相关分析的可行替代方法，并且具有作为异质性分析的扩展而不是单独方法的优势。总的来说，结果表明，草莓叶枯病的发病率主要以患病小叶的小而松散聚集的簇为特征，通常局限于采样单元的边界内。
