Gareth Hughes grew up in Manchester, in northern England, and attended the University of York, where he received both a B.A. in biology in 1972, and a Ph.D. in population genetics and ecology in 1978. He was a lecturer in biometrics at the University of the West Indies, Trinidad, from 1977 to 1981. He returned to the U.K. in 1981 to an appointment as an agricultural systems analyst in the Crop Production Advisory and Development Department of the East of Scotland College of Agriculture, Edinburgh. In 1983, he accepted a position as a lecturer at the University of Edinburgh in the Department of Agriculture, where he has taught quantitative biology, biometrics, and crop science. In 1996, he was promoted to senior lecturer in the University’s Institute of Ecology and Resource Management. An active member of APS, Dr. Hughes has served as chair of the Plant Disease Losses Committee and organized the 1997 APS/ESA symposium on sampling. He is a frequently invited speaker at U.S. and international meetings.
Dr. Hughes’s early career focused on crop ecology, with an emphasis on the determinants of crop yield. His research led to the realization that the spatial pattern of crop plants, and their pests and diseases, were key determinants of yield loss. In a series of pioneering studies, he established the fundamental relationships between crop yield and the spatial heterogeneity of pests using integrated crop physiological and ecological principles and innovative linear and nonlinear statistical modeling. His first contribution to the plant pathology literature was a hallmark paper that demonstrated the importance of patterns of disease in determining crop yield.
In the 1980s, Dr. Hughes realized that existing descriptions of spatial patterns of plant diseases were inadequate both for understanding the spatio-temporal mechanisms of disease progress and for predicting crop loss in relation to disease intensity. The methodology used for quantifying spatial patterns of disease at that time was essentially borrowed from other disciplines such as ecology and entomology, and often had little relevance to the statistical properties of plant disease incidence and severity. For the past decade, Dr. Hughes has committed a major portion of his research effort toward developing the methodology for quantifying patterns of disease incidence, and applying this methodology in basic and applied studies of diseases of fruit crops, especially grapes, pineapple, and citrus.
In collaboration with Dr. Larry Madden, he first showed that the pattern of disease incidence is characterized by a binary power law model, consistent with the use of the beta-binomial probability distribution to describe observed patterns of disease incidence. Then, in a recent series of key papers related to the problem of sampling for disease, Dr. Hughes and colleagues showed that the spatial pattern of disease is fundamental for the determination of the proportion of sampling units that are diseased. They showed that the mean disease incidence and degree of aggregation present at one level in a spatial hierarchy could be used to predict the proportion of sampling units that are diseased at another level, without need for curve-fitting. This relationship was demonstrated empirically for grape downy mildew disease, citrus tristeza, and citrus scab. The great significance of the work is in its integration of quantitative ecology and statistics to show both how aggregation of disease affects epidemic rates across hierarchical scales, and how to predict disease incidence at the lower level in a spatial hierarchy based on observations of disease incidence determined solely at the upper level. The latter point has immense implications for efficient sampling of large areas for disease incidence.
Dr. Hughes and colleagues coined the term “hierarchical sampling” to describe the field implementation of these methods in a cost-efficient approach to determine the incidence of citrus tristeza. This used immunological detection methods and bulked sample analysis to identify focal areas of infection in large citrus groves. This work linked together methodology pertaining to group-sampling and statistical ecology, and Dr. Hughes’s innovative theoretical work on spatial pattern analysis and cluster sampling, to achieve real-world advances in the efficient manage ment of a serious disease problem. This new sampling approach is now standard for Citrus tristeza virus screening in California and elsewhere.
To adapt the hierarchical sampling method to deal with aggregated patterns of disease, Dr. Hughes and Dr. Tim Gottwald developed methods to characterize the effects of aggregation on the relationship between disease incidence at two levels in a spatial hierarchy. The approach links generalized linear modeling and group-sampling theory to provide an empirical disease prediction method based on samples of groups of trees. He recently also worked with Madden to develop an alternative and more general method of prediction of the effects of patterns of disease in hierarchical sampling based on calculation of a new type of effective sample size that is appropriate whenever the beta-binomial distribution describes patterns of disease incidence, but does not require any parameter estimation based on observed data.
During the past decade, Gareth Hughes has used real-world disease problems in fruit crops as model systems for the development of methodology for basic research in epidemiology and plant disease losses. The results of these basic studies have provided new innovative approaches for the management of diseases of citrus, pineapple, and grapevines. For these highly significant contributions, he is clearly deserving of the Lee M. Hutchins Award.