Dr Pierre R. L. Dutilleul
Professor, Department of Plant Science
Associate Member, Department of Mathematics and Statistics
Associate Member, McGill School of Environment
pierre [dot] dutilleul [at] mcgill [dot] ca (Email)
General research interests
Throughout my research career, I have developed links between statistics and the life sciences. I regard this "bridge building" as the main motivation of my research work. I first became aware of the potential value of applying efficient and valid statistical methodology to the agricultural and environmental sciences during my doctoral studies in mathematics. This interest stimulated me to work to develop statistical models appropriate to ecology during my post-doctoral fellowship. I have continued to elaborate, expand and assess statistical models and methods of data analysis since I joined the faculty of McGill. My D.Sc. thesis project was in temporal statistics, my post-doctoral fellowship has been in spatial statistics, and my research work in applied statistics at McGill incorporates both components. The fields of application are agricultural, biological and environmental sciences, with animal, earth and plant sciences, chronobiology and dendrochronology, forest ecology and limnology, quantitative genetics and wood technology as subfields of application. Key words are autocorrelation, complexity, heterogeneity, heteroscedasticity, nonstationarity, periodicity, scale, and structure.
Training and collaborative research network
In large part, my research work in applied statistics is devoted to developing new statistical methods and to improving existing methods by providing more accurate and efficient estimates of the parameters of interest and/or more valid testing of the relevant hypotheses. Accordingly, I train graduate students and post-doctoral fellows in the development and assessment of statistical methods and in the application of modern statistics in general. As an applied statistician, I do believe that one cannot develop new statistical methods without applications to real data, even if simulation is used as a complement when a mathematical proof is not possible. Such an approach requires collaborative efforts with colleagues in other disciplines. My location on Macdonald Campus, my associate membership of the McGill School of Environment and my membership of the Centre SÈVE (FQRNT, Regroupements Stratégiques) help foster such collaborations. My associate membership with the McGill Department of Mathematics and Statistics ensures a balance between statistical methodology and applications. I also entertain close links with The Statistical Society of Canada and The International Environmetrics Society.
Examples of current projects
In more direct relation to plant science research, I am using a computed tomography (CT) scanner to study the structural complexity of plant canopies and root systems. I supervise very original projects of graduate students in this area at the CT Scanning Laboratory for agricultural and environmental research, a unique research facility for this type of plant science applications. In environmental research, key words are environmetrics, spatial and spatio-temporal statistics, and environmental impact. Together with ecologists, we are developing a new statistical method of multi-scale spatial analysis for application to multivariate data sets. In the frame of a USDA/RAMP grant project, I am working with entomologists in the Southwestern U.S. to design and implement field and landscape level reduced-risk management strategies for Lygus bug in Western cropping systems. In the frame of a CSA grant project, I am working with McGill geographers and CSA and NASA researchers, to analyse and compare spatial patterns formed by polar desert ice wedge polygons and Mars analogue landforms. Last but not least, I am writing a book on spatio-temporal heterogeneity analysis, to be published by Cambridge University Press.
Dutilleul, P. 1993a. Modifying the t test for assessing the correlation between two spatial processes. Biometrics 49:305-314.
Dutilleul, P. 1993b. Spatial heterogeneity and the design of ecological field experiments. Ecology 74:1646-1658.
Dutilleul, P. and Potvin, C. 1995. Among-environment heteroscedasticity and genetic autocorrelation: Implications for the study of phenotypic plasticity. Genetics 139:1815-1829.
Dutilleul, P. and Pinel-Alloul, B. 1996. A doubly multivariate model for statistical analysis of spatio-temporal environmental data. Environmetrics 7:551-566.
Wu, T., Mather, D. E., and Dutilleul, P. 1998. Application of geostatistical and neighbor analyses to data from plant breeding trials. Crop Science 38:1545-1553.
Dutilleul, P., Stockwell, J. D., Frigon, D., and Legendre, P. 2000. The Mantel test versus Pearson's correlation analysis: Assessment of the differences for biological and environmental studies. Journal of Agricultural, Biological and Environmental Statistics 5:131-150.
Foroutan-pour, K., Dutilleul, P., and Smith, D. L. 2001. Inclusion of the fractal dimension of leafless plant structure in the Beer-Lambert law. Agronomy Journal 93:333-338.
Dutilleul, P. 2001. Multi-frequential periodogram analysis and the detection of periodic components in time series. Communications in Statistics - Theory and Methods 30:1063-1098.
Pelletier, B., Dutilleul, P., Larocque, G., and Fyles, J. W. 2004. Fitting the linear model of coregionalization by generalized least squares. Mathematical Geology 36:323-343.
Dutilleul, P., Lontoc-Roy, M., and Prasher, S. O. 2005. Branching out with a CT scanner. Trends in Plant Science 10:411-412.