Pierre R.L. Dutilleul

Image by Alex Tran.


Associate Member, Department of Mathematics and Statistics
Associate Member, McGill School of Environment
Adjunct Professor, Département de mathématiques, Université de Sherbrooke

T: 514-398-7870  |  pierre.dutilleul [at] mcgill.ca (Email)  |  Raymond Building, R2-025a  |  Lab website


BSc (Mathematics), MSc (Statistics), DSc (Mathematics)(Université catholique de Louvain, Belgium)

Short Bio

Pierre Dutilleul’s academic background is in Mathematics and Statistics, and his research interests are in statistical inference (estimation and testing) in the temporal and spatial frameworks, in a variety of domains including Environment and Ecology, the soil sciences (e.g., seismology), and phytometry. Accordingly, he is Professor in the Department of Plant Science and Associate Member of the Department of Mathematics and Statistics and of the Bieler School of Environment at McGill University; he is also Adjunct Professor at Université de Sherbrooke. In Google Scholar, Dr. Dutilleul is most known (~900 citations) for his modified t-test for correlation analysis with spatial data, and several extensions of the test have been developed by his group since. Professor Dutilleul is also known for his innovative phytometric research work, in which his group is using a computed tomography (CT) scanner to collect 3-D spatial data on plant structures, and analyzes them statistically; this provided an interview to the Science Magazine and a Radio-Canada Découverte reportage; that work now includes the studies of soil and wood properties. Pierre Dutilleul has authored ~180 peer-reviewed publications and one book (“Spatio-Temporal Heterogeneity: Concepts and Analyses”, Cambridge University Press), and has coordinated from beginning to end the e-book project “Branching and Rooting Out with a CT Scanner” (Nature Publishing Group/Macmillan).

Awards and Recognitions

Appears in Stanford University World Rating of Top 2% Scientists List, December 2020

Active Affiliations

2011 to present: Editor-in-Chief, Environmental and Ecological Statistics, Springer
2009 to present: Member, Board of Directors of Centre SÈVE (http://www.centreseve.org/)

Research Interests

Two main axes of research
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 mean or variance parameter value is likely to change in space or time, or both, or when variability is measured from observations that are partially dependent on each other because they are autocorrelated, there is potential for a heterogeneity analysis (Dutilleul, 2011), which should include sound experimental design to begin with (Dutilleul, 1993a). This opens the door to a lot of interesting situations and problems! Dutilleul’s (1993b) modified t-test provides a valid solution to the problem of assessing the correlation between two autocorrelated spatial processes, and was followed by other modified t-tests and modified F-tests in the contexts of multivariate and multi-scale analyses (Dutilleul and Pinel-Alloul, 1996; Alpargu and Dutilleul, 2006; Dutilleul et al., 2008b; Dutilleul and Pelletier, 2011; 2017). Concerning efficient estimation and the decomposition of the variability contained in multivariate spatial datasets, the articles with geostatistical taste of Pelletier et al. (2004) and Larocque et al. (2007) paved the way to a solution based on the fitting of a linear model of coregionalization by estimated generalized least squares and the development of the method of Co-Regionalization Analysis with a Drift (CRAD; Pelletier et al., 2009a; 2009b) eventually. In a spectral instead of geostatistical approach, the method of multi-frequential periodogram analysis (MFPA; Dutilleul, 2001; 2011, Chapter 6) 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 research interests in point pattern analysis, in planetary science (Dutilleul et al., 2009) and animal behavior (Bonnell et al., 2013), and long-term research interests in multidimensional statistics (Dutilleul, 1999; 2018; Manceur and Dutilleul, 2013). Current and recently completed research projects include works in statistical seismology and time series analysis with copulas.

Modern phytometry:

My research activities in this domain started before 2003, with the development of a fractal dimension estimation procedure to quantify the structural complexity of plant branching patterns and thereby improve plant light interception models (Foroutan-pour et al., 2001), but they were boosted with the creation of the CT Scanning Laboratory for agricultural and environmental research on the Macdonald Campus of McGill University in Fall 2003, 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. The CT scanning equipment was recently renewed and expanded thanks to two CFI 9 grants, and there is now a macro-CT scanning section (https://www.mcgill.ca/ecp3/fr/imagerie-multi-echelles/equipement/macro-ct-scanner; CFI 9 grant PI: Geitmann) and a micro-CT scanning section (https://micro-ct.lab.mcgill.ca/; CFI 9 grant PI: Ghoshal).

Since the creation of the facility, my research group, generally in collaboration with other research groups for the applications, developed new procedures for the graphical, quantitative and statistical analyses of CT scanning data in a broad range of domains other than the medical one for which the macro-CT scanning equipment was originally designed. This research first included pioneering work (Dutilleul et al., 2005; 2008a; Lontoc-Roy et al., 2006; Han et al., 2008) and then, diversified and sophisticated works (Lafond et al., 2012; 2015; Dutilleul et al., 2014; 2015; Subramanian et al., 2015; Han et al., 2017), where “fractal” has become a keyword in two forms: mono-fractal and multi-fractal. Collaborating research groups are from McGill, U. de Sherbrooke, U. Laval, U. de Montréal, UQÀM, Canadian governmental departments (Agriculture and Agri-Food, Natural Resources), and Spain, Scotland outside Canada.

Current Research

  • NSERC, Individual Discovery Grant, Mathematics and Statistics Group (2021-26): “Spatial, Temporal and Multidimensional Statistics: Estimation, testing, and applications in the environmental sciences”
  • FRQNT, Regroupements stratégiques Grant (2017-23): “SÈVE: Centre de recherche en sciences du végétal” by Carole Beaulieu et al.; Pierre Dutilleul (Co-Applicant), specific group project (2019-2021): “Applications de la tomodensitométrie assistée par ordinateur (macro et micro) pour l’étude de structures végétales, de l’hétérogénéité structurelle du sol, et de structures matérielles naturelles”
  • FRQNT, Projet de recherche en équipe Grant (2021-24): “Caractérisation de l’assise génétique de l’architecture racinaire chez le soja et son rôle dans la résistance aux changements climatiques” by François Belzile (U. Laval) and Pierre Dutilleul (McGill)

Examples of current and recently completed research projects:

In Spatio-Temporal Statistics, I continue to “develop and apply”: develop, when there is a need for a new statistical method or model; apply, to extract and distill the fuzzy information hidden in a dataset.

  • I continue to work with Prof. Yves Carrière (The University of Arizona) and American entomologists. The results obtained in our latest collaborative research project, with contributors from a resilience foundation and a multinational company, were published in PNAS (Carrière et al., 2020). I was in charge of the statistical analysis of spatio-temporal data, based on a mixed model for covariate analysis.
  • My sabbatical leave in the Department of Statistics at UC Berkeley in 2013 gave me the opportunity to meet with Prof. Roland Bürgmann (Earth and Planetary Science) and Dr. Christopher Johnson. Among other things, our fruitful collaboration produced three articles (Dutilleul et al., 2015; 2020; 2021). The MFPA method plays a key role in our work and we were able to resolve several periodicities in Central California earthquake catalogs that reveal external periodic forcing.
  • My adjunct professorship in the Département de mathématiques at U. de Sherbrooke allows me to co-supervise Master’s students with Prof. Taoufik Bouezmarni; two thesis projects were completed on the topic of time series analysis with copulas and two are under way/in preliminary stage. A first publication can be reported (Bégin et al., 2020).
  • My long-term research collaboration with Prof. Joann Whalen (Natural Resource Sciences, McGill) reached a new milestone with the development of the spatio-temporal concept of persistence and its quantification in soil science in the Ph.D. thesis project of Tian Tian (Tian et al., 2021).

The next two projects are at the interface with Modern Phytometry

  • With Prof. Louis-Paul Rivest and Dr. Nishan Mudalige (U. Laval), we are re-analyzing statistically some of the tree branching patterns presented in Dutilleul et al. (2015).
  • Prof. Kunio Shimizu (Keio U.), Prof. Imoto Tomoaki (U. of Shizuoka) and I are collaborating on the development of a new statistical approach to modeling tree growth from wood CT scanning data.

In Modern Phytometry, research collaborations with Centre SÈVE members continue to occupy an important place. See, for example:

  • the recent results obtained in the Biochar CT Scanning project with Prof. Donald Smith (Smith) and Dr. Ondrej Masek (The University of Edinburgh) and published or to be published (Han et al., 2020; Srocke et al., 2021);
  • the start of the FRQNT Projet de recherche en équipe with Prof. François Belzile (U. Laval), after the completion of our Centre SÈVE Nouvelles Initiatives project;
  • the ongoing Iceberg Lettuce CT Scanning project, conducted with Dr. Mamadou Lamine Fall (AAFC) and Ph.D. candidate Azza Larafa.


AEMA 310 Statistical Methods 1 3 Credits
    Offered in the:
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AEMA 403 Environmetrics Stage 3 Credits
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AEMA 411 Experimental Designs 01 3 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

AEMA 414 Temporal&Spatial Statistics 01 3 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

AEMA 611 Experimental Designs 1 3 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

AEMA 614 Temporal&Spatial Statistics 1 3 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer


View a list of current publications

Selected Publications

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 Pinel-Alloul, B. 1996. A doubly multivariate model for statistical analysis of spatio-temporal environmental data. Environmetrics 7:551-566.

Dutilleul, P. 1999. The MLE algorithm for the matrix normal distribution. Journal of Statistical Computation and Simulation 64:105-123.

Foroutan-pour, K., Dutilleul, P., and Smith, D. L. 1999. Advances in the implementation of the box-counting method of fractal dimension estimation. Applied Mathematics and Computation 105:195-210.

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.

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.

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.

Alpargu, G. and Dutilleul, P. 2006. Stepwise regression in mixed quantitative linear models with autocorrelated errors. Communications in Statistics – Simulation and Computation 35:79-104.

Cattaneo, M. G., Yafuso, C., Schmidt, C., Olson, C., Huang, C., Rahman, M., Ellers-Kirk, C., Orr, B., Marsh, S., Antilla, L., Dutilleul, P., and Carrière, Y. 2006. Farm-scale evaluation of the impacts of transgenic cotton on biodiversity, pesticide use, and yield. Proceedings of the National Academy of Sciences of the USA 103:7571-7576.

Lontoc-Roy, M., Dutilleul, P., Prasher, S. O., Han, L., Brouillet, T., and Smith, D. L. 2006. Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3-D space. Geoderma 137:231-241.

Larocque, G., Dutilleul, P., Pelletier, B., and Fyles, J. W. 2007. Characterization and quantification of uncertainty in coregionalization analysis. Mathematical Geology 39:263-288.

Dutilleul, P., Han, L., and Smith, D. L. 2008a. Plant light interception can be explained via computed tomography scanning: Demonstration with pyramidal cedar (Thuja occidentalis, Fastigiata). Annals of Botany 101:19-23.

Dutilleul, P., Pelletier, B., and Alpargu, G. 2008b. Modified F-tests for assessing the multiple correlation between one spatial process and several others. Journal of Statistical Planning and Inference 138:1402-1415.

Han, L., Dutilleul, P., Prasher, S. O., Beaulieu, C., and Smith, D. L. 2008. Assessment of common scab-inducing pathogen effects on potato underground organs via computed tomography scanning. Phytopathology 98:1118-1125.

Dutilleul, P., Haltigin, T. W., and Pollard, W. H. 2009. Analysis of polygonal terrain landforms on Earth and Mars through spatial point patterns. Environmetrics 20:206-220.

Pelletier, B., Dutilleul, P., Larocque, G., and Fyles, J. W. 2009a. Coregionalization analysis with a drift for multi-scale assessment of spatial relationships between ecological variables 1. Estimation of drift and random components. Environmental and Ecological Statistics 16:439-466.

Pelletier, B., Dutilleul, P., Larocque, G., and Fyles, J. W. 2009b. Coregionalization analysis with a drift for multi-scale assessment of spatial relationships between ecological variables 2. Estimation of correlations and coefficients of determination. Environmental and Ecological Statistics 16:467-494.

Dutilleul, P. 2011. Spatio-Temporal Heterogeneity: Concepts and Analyses. Cambridge: Cambridge University Press (official date of publication in Europe: May 19, 2011; in the Americas: June 30, 2011; reviews: see, e.g., Biometrics, June 2013 issue).


Dutilleul, P. and Pelletier, B. 2011. Tests of significance for structural correlations in the linear model of coregionalization. Mathematical Geosciences 43:819-846.

Carrière, Y., Ellers-Kirk, C., Hartfield, K., Larocque, G., Degain, B., Dutilleul, P., Dennehy, T. J., March, S. E., Crowder, D. W., Li, X., Ellsworth, P. C., Naranjo, S. E., Palumbo, J. C., Fournier, A., Antilla, L., and Tabashnik, B. E. 2012. Large-scale, spatially-explicit test of the refuge strategy for delaying insecticide resistance. Proceedings of the National Academy of Sciences of the USA 109:775-780.

Lafond, J. A., Han, L., Allaire, S. E., and Dutilleul, P. 2012. Multifractal properties of porosity as calculated from computed tomography (CT) images of a sandy soil, in relation to the relative soil gas diffusion coefficient. European Journal of Soil Science 63:861-873.

Bonnell, T. R., Dutilleul, P., Chapman, C. A., Reyna-Hurtado, R., Sengupta, R., and Sarabia, U. 2013. Analysing small-scale aggregation in animal visits in space and time: the ST-BBD method. Animal Behaviour 85:483-492.

Manceur, A. M. and Dutilleul, P. 2013. Unbiased modified likelihood ratio tests for simple and double separability of a variance-covariance structure. Statistics and Probability Letters 83:631-636.

Dutilleul, P., Han, L., and Beaulieu, J. 2014. How do trees grow? Response from the graphical and quantitative analyses of computed tomography scanning data collected on stem sections. Comment les arbres poussent-ils ? Réponse des analyses graphique et quantitative de données de tomodensitométrie pour des sections de la tige. Comptes Rendus Biologies (Académie des Sciences de Paris) 337:391-398.

Dutilleul, P., Han, L., Valladares, F., and Messier, C. 2015. Crown traits of coniferous trees and their relation to shade tolerance can differ with leaf type: A biophysical demonstration using computed tomography scanning data. Frontiers in Plant Science 6:172 (published online on March 24, 2015; DOI: 10.3389/fpls.2015.00172).

Dutilleul, P., Johnson, C. W., Bürgmann, R., Wan, Y., and Shen, Z.-K. 2015. Multi-frequential periodogram analysis of earthquake occurrence: An alternative approach to the Schuster spectrum, with two examples in central California. Journal of Geophysical Research – Solid Earth 120:8494-8515.

Subramanian, S., Han, L., Dutilleul, P., and Smith, D. L. 2015. Computed tomography scanning can monitor the effects of soil medium on root system development: An example of salt stress in corn. Frontiers in Plant Science 6:256 (published online on April 28, 2015; DOI: 10.3389/fpls.2015.00256).

Lafond, J. A., Han, L., and Dutilleul, P. 2015. Concepts and analyses in the CT scanning of root systems and leaf canopies: A timely summary. Frontiers in Plant Science 6:1111 (published online on December 24, 2015; DOI: 10.3389/fpls.2015.01111).

Dutilleul, P. and Lafond, J. A., Eds. 2016. Branching and Rooting Out with a CT Scanner: The Why, the How, and the Outcomes, Present and Possibly Future. Nature Publishing Group/Macmillan Publishers Limited (e-book; published online on April 13, 2016; DOI: 10.3389/978-2-88919-791-0).


Dutilleul, P. and Pelletier, B. 2017. A valid parametric test of significance for the average R2 in redundancy analysis with spatial data. Spatial Statistics 19:21-41.

Han, L., Whalen, J. K., and Dutilleul, P. 2017. A high-resolution surface mapping procedure for soil cores using computed-tomography scanning data. Vadose Zone Journal 16:6 (published online on June 27, 2017; DOI: 10.2136/vzj2016.09.0076).

(featured in CSA News magazine on October 19, 2017; https://dl.sciencesocieties.org/ publications/csa/tocs/62/11)

Schwinghamer, T. D., Backer, R., Smith, D. L., and Dutilleul, P. 2017. Block-recursive path models for rooting-medium and plant-growth variables measured in greenhouse experiments. Agronomy Journal 109:870-882.

Dutilleul, P. 2018. Estimation and testing for separable variance-covariance structures. Wiley Interdisciplinary Reviews – Computational Statistics 10:4 (DOI: 10.1002/ wics.1432).

Pelletier, B. and Dutilleul, P. 2018. Coregionalization Analysis with a Drift (CRAD): A methodological review with companion testing procedures, software and applications. Environmental and Ecological Statistics 25:5-29.

Bégin, É., Dutilleul, P., Beaulieu, C., and Bouezmarni, T. 2020. M-Vine decomposition and VAR(1) models. Statistics and Probability Letters 158:108660 (DOI:

Carrière, Y., Brown, Z., Aglasan, S., Dutilleul, P., Carroll, M., Head, G., Tabashnik, B. E., Jørgensen, P. S., and Carroll, S. P. 2020. Crop rotation mitigates impacts of corn rootworm resistance to transgenic Bt corn. Proceedings of the National Academy of Sciences of the USA 117:18385-18392 (DOI: 10.1073/pnas.2003604117).

Dutilleul, P., Johnson, C. W., and Bürgmann, R. 2020. Marked spatio-temporal point patterns, periodicity analysis and earthquakes: An analytical extension including hypocenter depth. Environmental and Ecological Statistics 27:689-708 (DOI: 10.1007/s10651-020-00470-4).

Han, L., Srocke, F., Mašek, O., Smith, D. L., Lafond, J. A., Allaire, S., and Dutilleul, P. 2020. A Graphical-User-Interface application for multifractal analysis of soil and plant structures. Computers and Electronics in Agriculture 174:105454 (DOI: 10.1016/j.compag.2020.105454).

Van der Heyden, H., Dutilleul, P., Charron, J.-B., Bilodeau, G. J., and Carisse, O. 2020. Factors influencing the occurrence of onion downy mildew (Peronospora destructor) epidemics: Trends from 31 years of observational data. Agronomy 10:738 (DOI: 10.3390/agronomy10050738).

Dutilleul, P., Johnson, C. W., and Bürgmann, R. 2021. Periodicity analysis of earthquake occurrence and hypocenter depth near Parkfield, California, 1994-2002 versus 2006-2014. Geophysical Research Letters 48:e2020GL089673 (DOI: 10.1029/2020GL 089673).

Tian, T., Whalen, J. K., and Dutilleul, P. 2021. Macroaggregate persistence: Definition and applications to describe soil surface dynamics. Geoderma 397:115096 (DOI: 10.1016/j.geoderma.2021.115096).

Srocke, F., Han, L., Dutilleul, P., Xiao, X., Smith, D. L., and Mašek, O. Synchrotron X-ray microtomography and multifractal analysis for the characterization of pore structure and distribution in softwood pellet biochar. Biochar (in press).


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