Capture-Recapture Methods Professors and Supervisors in Canada

Frances StewartView profile

Assistant Professor

Wilfrid Laurier UniversityCanada

Frances Stewart is an Assistant Professor and Canada Research Chair in Northern Wildlife Biology at Wilfrid Laurier University's Faculty of Science, Department of Biology. She leads the WILDlab research group focusing on applied wildlife biology in northern regions through field projects, modeling, and community collaboration. She holds a PhD in Environmental Studies from the University of Victoria, and BScH and MSc degrees in Ecology from the University of Guelph. Her research specializes in quantifying wildlife responses to landscape changes using camera trapping, ecological forecasting, and spatial modeling. Current projects investigate species co-occurrence, habitat selection, and biodiversity conservation in Canada's rapidly changing northern ecosystems, with emphasis on integrating Indigenous knowledge. Publications (2019-2024) demonstrate strong focus on wildlife conservation, climate change impacts, and ecological modeling. Dominant themes include boreal caribou conservation, landscape connectivity, anthropogenic disturbance effects, and predictive ecology frameworks. Methodologically, her work combines field data with advanced statistical approaches to inform conservation decisions. Awards and Honors: Canada Research Chair in Northern Wildlife Biology Wilburforce Leader in Conservation Science Canadian Society of Ecology and Evolution Conference Presentation Award (2018) Advising and Grants: Supervises 2 postdoctoral fellows, 3 PhD candidates, and 4 MSc students Secured funding from NSERC, Weston Foundation, POLAR, and MITACS Emphasizes professional development through conference attendance and publication support Lab and Team: Leads the WILDlab (Wildlife Integration for Land use Decisions Lab) with active field projects across northern Canada. The team collaborates with government agencies (Natural Resources Canada), Indigenous communities, and international researchers to address wildlife management challenges in changing northern ecosystems.

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Pengfei LiView profile

Professor

University of WaterlooCanada

Roles & Affiliations Professor at Department of Statistics and Actuarial Science , University of Waterloo since 2012. Serves as Associate Editor for Canadian Journal of Statistics and Metrika . Previously held roles at University of Alberta (2008-2011) and University of British Columbia (2008 postdoc). Education PhD in Statistics (2007) - University of Waterloo MA in Statistics (2004) - Nankai University BSc in Statistics (2001) - Nankai University Research Focus Specializes in statistical methodologies including finite mixture models, empirical likelihood, experimental design, biased sampling problems, capture-recapture data, and density ratio models. Current interests include statistical inference under constraints and analysis of non-probability survey samples. Awards CRM-SSC Prize in Statistics (2022) Institute of Mathematical Statistics Fellow (2022) Multiple awards for teaching and research excellence from University of Waterloo Students & Grants Advised over 10 PhD/Master’s students. Active grants include NSERC funding since 2009 and startup grants from University of Waterloo. Supervises postdocs in statistical methodology development.

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Audrey BéliveauView profile

Associate Professor

University of WaterlooCanada

Audrey Béliveau is an Associate Professor at the University of Waterloo, affiliated with the Department of Statistics and Actuarial Science. She can be reached at audrey.beliveau@uwaterloo.ca and is located in M3 4232. Her research focuses on Bayesian statistical methods, network meta-analysis, and environmental and population dynamics modeling. She has contributed to software development, notably the Bugsnet R package for Bayesian network meta-analyses. Her work spans interdisciplinary applications, including estimating homeless population sizes, methane emissions quantification, and genetic mark-recapture methods for ecological studies. She also explores error propagation in medical treatment comparisons and Bayesian approaches for item response modeling in online assessments. Béliveau’s research emphasizes methodological rigor and practical tools, such as automated R packages for reproducible statistical analyses. She regularly publishes in top-tier journals, addressing both theoretical and applied challenges in statistics.

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Gracia Yunruo DongView profile

Assistant Professor

University of TorontoCanada

Gracia Yunruo Dong is an Assistant Professor in the Teaching Stream at the University of Toronto, affiliated with the Department of Cell & Systems Biology and the Department of Statistical Sciences. She also collaborates with Island Health on healthcare equity research. PhD, MMath, and BMath in Statistics from the University of Waterloo CANSSI Distinguished Postdoctoral Fellow (2022–2024) at University of Toronto and University of Victoria Her research spans Computational Statistics , Healthcare Equity , Quasi-Monte Carlo Methods , and Population Health , with a focus on marginalized populations. She develops capture-recapture models for homeless populations, applies case-crossover designs to environmental health, and explores quasi-random number generation for high-dimensional integration. Her publications reflect interdisciplinary collaboration, combining Bioinformatics , Public Health , and Statistical Computing . Recent work addresses gaps in healthcare access, overdose prediction, and simulation experiments. CANSSI Distinguished Postdoctoral Fellowship NSERC funding for SMMEID network Dong teaches courses such as Epidemiology of Health & Disease and The Practice of Statistics I , integrating real-world applications into her instruction. She collaborates with researchers like Patrick Brown and Laura Cowen, applying statistical methods to ecological and human health challenges. Her affiliations with Vancouver Island Health Authority’s Applied Health Data Analytics team highlight her commitment to addressing health disparities through data-driven solutions.

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Julie ParéView profile

Associate Professor

University of MontrealCanada

Julie Paré is an Associate Professor at the University of Montreal’s Faculty of Veterinary Medicine, affiliated with the Department of Pathology and Microbiology. She holds a professional role as an Epidemiologist at the Canadian Food Inspection Agency (CFIA). Her research focuses on epidemiological applications in veterinary diagnostics and surveillance, particularly regarding zoonotic diseases and public health. She is a member of the GREZOSP research group, specializing in epidemiology of zoonoses and veterinary public health. Dr. Paré supervises graduate students, including master’s theses on topics such as West Nile virus distribution in horses and Mycobacterium avium paratuberculosis transmission in dairy herds. Her publications address critical issues in veterinary microbiology, infectious disease control, and livestock population analysis. She contributes to advancing methodologies in environmental sampling, risk factor analysis, and disease surveillance in animal populations. Her work bridges academic research and applied public health, emphasizing evidence-based approaches to mitigate zoonotic threats and improve veterinary healthcare systems.

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Simon BonnerView profile

Associate Professor

Western UniversityCanada

Simon Bonner is an Associate Professor in Environmetrics in the Department of Statistical and Actuarial Sciences at The University of Western Ontario. Previously, he held positions at the University of Kentucky (Assistant Professor), University of British Columbia (Postdoc), and completed his PhD at Simon Fraser University. His research focuses on developing statistical models for ecological data, particularly Bayesian hierarchical methods applied to wildlife population monitoring via mark-recapture experiments. He has contributed to studies on salamanders impacted by mountaintop removal mining, predator-prey dynamics, and conservation challenges in diverse ecosystems. Education: PhD in Statistics, Simon Fraser University Postdoc, University of British Columbia Research Interests: Ecological statistics, Bayesian modeling, and hierarchical models Applications in wildlife population dynamics, conservation, and environmental impact studies Methodological advancements in capture-recapture and occupancy modeling Advising & Grants: Supervises PhD/MSc students in statistical ecology and applied data science Focus on interdisciplinary projects with collaborators in biology and environmental science Labs & Teams: Lead of the Ecological Statistics and Applied Data Science Laboratory at Western University Collaborations with researchers on salamander ecology, predator behavior, and conservation biology

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Gracia DongView profile

Assistant Professor

University of TorontoCanada

Gracia Dong is an Assistant Professor, Teaching Stream, at the University of Toronto, jointly appointed with the Human Biology Program and the Department of Statistical Sciences. She is affiliated with the Vancouver Island Health Authority (Island Health) and focuses on healthcare equity and vulnerable population modeling. PhD, MMath, BMath in Statistics from the University of Waterloo Her research spans computational statistics, quasi-Monte Carlo methods, and population health, with applications in healthcare access disparities and statistical education. Current projects involve Bayesian capture-recapture models for estimating COVID-19 prevalence in homeless populations using electronic health records. Recent peer-reviewed work includes methodological advancements in quasi-Monte Carlo sampling, service utilization analysis for marginalized groups, and hierarchical survival modeling for overdose prediction. She has received the CANSSI Distinguished Postdoctoral Fellowship (2022–2024) and collaborates with researchers at the University of Victoria and University of Toronto. CANSSI Distinguished Postdoctoral Fellowship Gracia has taught courses in statistics and human biology at the University of Toronto, University of Victoria, and University of Waterloo, emphasizing interdisciplinary applications and data-driven problem-solving.

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Laura CowenView profile

Professor

University of VictoriaCanada

Laura Cowen is a Professor in the Department of Mathematics and Statistics at the University of Victoria, specializing in ecological statistics with a focus on capture-recapture methods. Her work addresses real-world challenges in wildlife, fisheries, and public health, including estimating population parameters for species like seabirds and fishery populations, and modeling hidden human populations such as injection drug users and homeless individuals. She leads the Cowen Lab, committed to interdisciplinary collaboration and equity, diversity, and inclusion (EDI) initiatives. Her research integrates statistical methods with ecological and public health applications, including tag loss modeling, hidden Markov models, and large-scale data analysis. She collaborates with diverse fields such as microbiology, sociology, and anthropology. Key projects include estimating salmon exploitation rates, California sea cucumber movement, and lemur population viability in fragmented habitats. Cowen received her PhD from Simon Fraser University and has held roles in initiatives like the VADA (Visualization and Data Analytics) team and the WHALE program at SFU. She was awarded the 2025 Bob Riffenburgh Award for bridging statistical ecology and public health applications, including pandemic modeling and serotype occupancy analysis. Her lab emphasizes fostering an inclusive environment and supports Indigenous student initiatives through AISES chapter formation and travel awards.

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Marie Auger-MéthéView profile

Associate Professor

University of British ColumbiaCanada

Marie Auger-Méthé is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), holding a Canada Research Chair (CRC Tier 2). Her research integrates statistical methods with ecological questions to study animal behavior, movement, health, and space use. She develops advanced statistical tools to address complex ecological datasets, particularly focusing on marine species in temperate and polar ecosystems. Her work emphasizes conservation applications, such as creating solutions for species at risk. She is affiliated with UBC's Vancouver Campus, specifically in the Earth Sciences Building. Her research spans statistical ecology, including habitat selection modeling, animal tracking data analysis, and the incorporation of Indigenous Knowledge into ecological studies. She collaborates on projects involving polar bears, whales, penguins, and seabirds, addressing topics like migration patterns, foraging strategies, and climate change impacts. She has pioneered methods for hidden Markov models, spatial capture-recapture, and Bayesian approaches to integrate diverse datasets. Dr. Auger-Méthé's work is published in high-impact journals and has been featured in media outlets. She actively contributes to statistical ecology education and interdisciplinary collaborations, aiming to bridge ecological theory and applied conservation challenges.

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Andrew DerocherView profile

Professor

University of AlbertaCanada

Andrew Derocher is a Professor in the Department of Biological Sciences at the University of Alberta, specializing in Arctic ecology and conservation biology. His research focuses on polar bears and large carnivores, particularly their response to climate change and human activities. He has been studying polar bears in western Hudson Bay for over four decades, contributing to conservation policies and management strategies. His work integrates field studies, telemetry, and population modeling to understand ecological dynamics and threats to Arctic species. Education: PhD in Zoology, University of Alberta (details not explicitly provided in text). His research spans ecology, conservation genetics, and wildlife management, with a strong focus on Arctic ecosystems. He collaborates with government agencies and international organizations like the IUCN/SSC Polar Bear Specialist Group. Research Interests: Ecology and conservation of polar bears and other Arctic mammals Climate change impacts on Arctic habitats and species Behavioral ecology of large carnivores Human-wildlife interactions and conservation policy Advising and Grants: Supervised numerous graduate students, including Nick Paroshy (MSc), Anna Jovtoulia (MSc), and Camille Jodouin (MSc). Involved in projects funded by Canadian government agencies and international collaborations, focusing on polar bear population monitoring and ecosystem dynamics. Labs/Teams: Leads a research group at the University of Alberta, collaborating with institutions globally on polar bear and Arctic conservation initiatives.

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Martin LysyView profile

Associate Professor

University of WaterlooCanada

Martin Lysy is an Associate Professor and Director of the Statistical Consulting and Survey Research Unit at the University of Waterloo. His primary affiliation is with the Department of Statistics, and he is actively involved in the fields of Statistics and Quantitative Finance. His research focuses on developing advanced statistical methodologies with applications in biostatistics, environmental science, neuroscience, and machine learning. Lysy’s work often involves creating robust computational tools and software packages to address complex problems in these domains. Key research interests include Bayesian inference, spatial statistics, stochastic processes, and the statistical analysis of biological systems such as microrheology and neural spike trains. He has contributed to the development of R packages like SpatialGEV for spatial extremes and nichROVER for ecological niche modeling. His recent work emphasizes methodological innovations in capture-recapture methods for population estimation, environmental data analysis, and computational efficiency in Bayesian frameworks. Notable contributions include studies on the lockdown effects on air quality in Ontario, Canada, and interdisciplinary projects integrating machine learning with systems pharmacology. Lysy’s research bridges theoretical statistics with real-world applications, ensuring practical relevance across diverse scientific disciplines.

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