This book places an emphasis upon statistical methodology, data interpretation, and futureproofing with the intention of advancing statistics for the environment, ecology, and environmental health, in addition to environmental theory and practice, via the application of reliable statistics.
With a focus on advances in statistical methodology and application within the environmental sciences, the overarching purpose of this volume is to illuminate current trends, stimulate a focus on, and connect multidisciplinary domains originating from and within statistical analysis, development, and research. Given that the contributions consist of current improvements and new innovations in climate and environmental science research that are based on statistical theory, researchers can derive inspiration for future advancements or similar analyses on other environmental data.
Authored by internationally renowned scholars, this book is organized into three parts: Part I on Supervised and Unsupervised Learning, Part II on Directional Statistics, and Part III on Spatial and Temporal Modeling. Primarily intended as a reference book for academic researchers and graduate-level students in statistics as well as multidisciplinary domains, the chapters reflect a shared commitment to advancing methodological rigor while addressing real-world environmental concerns. They illustrate how environmental complexity drives the evolution of statistical thinking―and how statistical insight, in turn, informs meaningful action.
Key Features
- Emphasizes the ongoing necessity to progress basic statistical theory and explores its relevance to environmental research
- Employs multidisciplinary approaches and applications, appealing to a broader audience beyond solely theoretical statistics
- Enhances the collective understanding of the ecosystem’s diverse perspectives to ensure the welfare of present and future generations
- Written by renowned subject matter experts and researchers, making it appealing to scholars from diverse fields
- The statistical framework is not limited to a single methodology based on data complexity but promotes different techniques
Professor Andriėtte Bekker is an emeritus professor and former Head of the Department of Statistics at the University of Pretoria (2012–2022). A recipient of the S2A3 Medal for scientific achievement, she is internationally recognised for her contributions to multivariate and matrix variate distribution theory, with expertise spanning directional statistics, model-based clustering, and graphical network modelling. She has authored over 130 peer-reviewed publications and edited volumes advancing statistical methodology and computation. Professor Bekker is an elected member of the International Statistical Institute and leads the Statistical Theory and Applied Statistics focus area within the DSTI-NRF Centre of Excellence in Mathematical and Statistical Sciences. Her recent accolades include the University of Pretoria’s Exceptional Academic Achiever Award (2023), a fellowship from the South African Statistical Association (2024), and an NRF rating as a researcher of international standing.
Dr. Priyanka Nagar is a Senior Lecturer in Statistics and Actuarial Science at Stellenbosch University. She holds a PhD in Mathematical Statistics from the University of Pretoria. Her research focuses on statistical learning theory, directional statistics, and copula-based modelling, with applications in environmental, biomechanical, and energy domains. Dr Nagar’s work advances statistical methodologies for complex environmental systems. Drawing on experience in both academia and industry, she brings a rigorous and applied perspective to statistical analysis. She is actively engaged in mentoring, supervision, and strengthening statistical capacity within environmental statistics research across South Africa.
Johan Ferreira is a Professor in the School of Statistics and Actuarial Science at the University of the Witwatersrand, and previously served as the Assistant Focus Area Coordinator for the Statistical Theory and Applied Statistics focus area of the Centre of Excellence in Mathematical and Statistical Science, based at the University of the Witwatersrand in Johannesburg. He is an ASLP 4.1/4.2 fellow of Future Africa and was identified as one of the Top 200 South Africans under the age of 35 by the Mail & Guardian newspaper in the Education category. Johan regularly published in peer-reviewed, accredited journals, and his research interests include the probabilistic modelling of entropy, meaningful mixture modelling, directional statistics, and topics in educational statistics.
Professor Barend Erasmus is an ecologist with broad experience in climate change adaptation. His publication record reflects his interests in interdisciplinary work. His doctoral degree at the University of Pretoria on assessing impacts of climate change impacts on biodiversity in South Africa, remains relevant in international literature. Over time, his research interests expanded from climate change impacts, to broader sustainability issues across a wide range of sectors. His current academic work is on exploring the risks and opportunities of rapidly developing climate science for business and industry. He is passionate about postgraduate training, and students are deeply embedded in collaborative and interdisciplinary research programmes.
Professor Abel Ramoelo is an Executive Director of the Earth Observation Programme at the South African National Space Agency (SANSA) and an Extraordinary Professor at the University of Pretoria. He has a PhD in Geoinformation Science and Earth Observation from the University of Twente, the Netherlands. He leads a dynamic team focused on developing earth intelligence to address the societal challenges we face today. He previously worked at the CSIR, advancing from junior to principal researcher, at SANParks as a regional ecologist/ remote sensing specialist, and at the University of Pretoria as an associate professor and Director of the Centre for Environmental Studies in the Department of Geography, Geoinformatics, and Meteorology.
