Pierre Dutilleul’s background is in mathematics and statistics, and his research interests are in statistical inference (estimation and testing) and applied statistics, the domains of application including plant science, ecology and the environmental sciences, agronomy and crop science, forestry and dendrochronology, soil science and seismology. Accordingly, he is Professor in the Department of Plant Science and Associate Member of the Department of Mathematics and Statistics and of the McGill School of Environment. In Google Scholar, Dr. Dutilleul is most known (600+ citations) for his modified t-test for correlation analysis with spatial data. Professor Dutilleul is also known for his innovative phytometric research work, in which his group is making use of a computed tomography (CT) scanner to collect 3-D spatial data on plant structures and analyzing them statistically (see, e.g., his interview to the Science magazine in February 2006 and the Radio-Canada Découverte reportage made in February 2007); this work has since been extended to the studies of soil and wood structures. Pierre Dutilleul has authored ~150 peer-reviewed publications and one book (“Spatio-Temporal Heterogeneity”, Cambridge University Press, 2011) and has coordinated from beginning to end the e-book project “Branching and Rooting Out with a CT Scanner” (Nature Publishing Group/Macmillan, 2016).

Spatio-temporal heterogeneity analysis: In statistical sense and simply put, heterogeneity may concern the mean or variance parameter of the distribution of a random variable, or be related to the autocorrelation function of a stochastic process. When the value of the mean or the variance is susceptible to change, or variability is measured from observations that are partially dependent on each other because they are autocorrelated, in time or space, there is potential for a heterogeneity analysis, starting with the experimental design (Dutilleul, 1993a, Ecology). This opens the door to a lot of interesting situations and problems! A modified t-test (Dutilleul, 1993b, Biometrics) provides a solution to the problem of assessing validly the correlation between two autocorrelated spatial processes, and was followed by a modified F-test and other modified t-tests in the contexts of multivariate and multi-scale analyses (e.g., Dutilleul et al., 2008a; Dutilleul and Pelletier, 2011). Concerning efficient estimation and the decomposition of the variability contained in multivariate spatial datasets, the series of geostatistical articles including Pelletier et al. (2004, 2009a, 2009b) and Larocque et al. (2007) provide solutions based on the fitting of the linear model of coregionalization by estimated generalized least squares and the development of the method of coregionalization analysis with a drift (CRAD) eventually. In a spectral instead of geostatistical approach, the method of multi-frequential periodogram analysis (MFPA; Dutilleul, 2001) allows the decomposition of a time series, univariate or multivariate, into a number of periodic components, the number of periodic components as well as the period values being estimated in a stepwise procedure. I also have rising research interests in point pattern analysis (Dutilleul et al., 2009; Bonnell et al., 2013) and long-term research interests in multi-dimensional statistics (Dutilleul and Pinel-Alloul, 1996; Dutilleul, 1999), actually back to my doctoral studies. Modern phytometry: My research work in this area has started before that, via the search and finding of an improved quantification of the structural complexity of crop canopies (e.g. Foroutan-pour et al., 2001), but it was really boosted with the creation of the CT Scanning Laboratory for agricultural and environmental research at Macdonald Campus of McGill, thanks to an NSERC Major Equipment grant (PI: Dutilleul) and the portion of a CFI grant (PI: Fortin) for the equipment of a computer room, both in 2000. Since the official opening of the facility in Fall 2003, our research group developed new procedures for the graphical, quantitative and statistical analyses of CT scan data in a broad range of applications other than the medical one for which the CT scanning equipment was designed originally. This includes fractal analysis of branching patterns of conifers, with McGill collaborators (Dutilleul et al., 2008b), and multi-fractal analysis of soil macropore networks, with U. Laval collaborators (Lafond et al., 2012).