New Applications of Statistical Tools in Plant Pathology

Garrett, K. A., Madden, L. V., Hughes, G., and Pfender, W. F. 2004. New applications of statistical tools in plant pathology. Phytopathology 94:999-1003. The series of papers introduced by this one address a range of statistical applications in plant pathology, including survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. Here we present an overview of additional applications of statistics in plant pathology. An analysis of variance based on the assumption of normally distributed responses with equal variances has been a standard approach in biology for decades. Advances in statistical theory and computation now make it convenient to appropriately deal with discrete responses using generalized linear models, with adjustments for overdispersion as needed. New nonparametric approaches are available for analysis of ordinal data such as disease ratings. Many experiments require the use of models with fixed and random effects for data analysis. New or expanded computing packages, such as SAS PROC MIXED, coupled with extensive advances in statistical theory, allow for appropriate analyses of normally distributed data using linear mixed models, and discrete data with generalized linear mixed models. Decision theory offers a framework in plant pathology for contexts such as the decision about whether to apply or withhold a treatment. Model selection can be performed using Akaike’s information criterion. Plant pathologists studying pathogens at the population level have traditionally been the main consumers of statistical approaches in plant pathology, but new technologies such as microarrays supply estimates of gene expression for thousands of genes simultaneously and present challenges for statistical analysis. Applications to the study of the landscape of the field and of the genome share the risk of pseudoreplication, the problem of determining the appropriate scale of the experimental unit and of obtaining sufficient replication at that scale. The disciplines of plant pathology and statistics continue to develop, offering new opportunities for the application of statistics in the biological sciences and new demands for statistical approaches in plant pathology. This series of papers, including this introductory article, offers several perspectives on the contributions of new statistical theory and newly available statistical programs. The papers introduce and highlight statistical methods that are relatively little used in phytopathological research at present, but that have potential for improving the analysis of data from many types of experiments. Taken collectively, the experimental situations appropriate for the statistical tools presented are quite common in plant pathology. Each of the contributed papers provides the context and vocabulary to enable readers to evaluate the utility of the tool to their research, and the references for further exploration. Examples from plant pathology are used to illustrate the analyses, and caveats about inappropriate applications are given. It is common in plant pathology to estimate the relationship between disease responses and a number of environmental and other predictor variables. Sanogo and Yang (40) provide an overview of multivariate analysis techniques, and provide details about several selected as having greatest utility in plant disease epidemiology research: discriminant analysis, multivariate analysis of variance (ANOVA), correspondence analysis, and canonical correlation analysis.