Online Courses

VGNR08

Multivariate Analysis of Ecological Communities Using VEGAN

Name: Multivariate Analysis of Ecological Communities Using VEGAN
Start: 2025-12-08T00:00:00+00:00
End: 2025-12-12T23:59:59+00:00
Location: Delivered remotely (United Kingdom)

Analyse ecological community data in R using VEGAN. Learn ordination, clustering, and multivariate statistics with real datasets.

Duration: 5 Days, 7 hours per day
Next Date: December 8-12, 2025
Format: Live Online Format

TIME ZONE

UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.

^£485Registration Fee

Overview
Instructors
Schedule

Course Description

This 5-day course provides a comprehensive introduction to analysing ecological community data using R, with a focus on the VEGAN package. Designed for intermediate R users, the course blends foundational theory with practical, hands-on sessions that will help you apply multivariate methods to real-world ecological datasets.

Throughout the course, we will work with empirical datasets from a range of ecological fields including terrestrial and wetland ecology, microbial ecology, natural resource management, evolution, and palaeoecology. Analyses will focus on detecting patterns along environmental or anthropogenic gradients and quantifying relationships with continuous and categorical predictors.

This course includes two live practical sessions scheduled to accommodate participants from different time zones. Recordings will be made available the same day, and we can also provide access to last year’s recordings in advance if that would be helpful.

What You’ll Learn

You will learn how to process and analyse complex multivariate data typical in community ecology, covering topics such as:

Diversity indices and community metrics.
Data transformation and distance measures.
Clustering and classification techniques.
Hypothesis testing using canonical ordinations.
Ordination methods, both unconstrained and constrained (e.g., PCA, NMDS, RDA, CCA) .
Reproducible workflows and best practices in R programming.

Participants are strongly encouraged to bring their own datasets. These should be clean, structured, and ready for analysis (see our guidelines: “Recommendations if participating with your data”). We offer full support in helping you apply the methods to your own data, including refining your research question, choosing suitable transformations and analyses and interpreting and presenting your results.

Whether you’re working in ecological research, conservation, or natural resource management, this course will give you the tools to conduct robust multivariate analyses and build reproducible, high-quality analytical workflows in R.

Course Format

Interactive Learning Format

Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.

Global Accessibility

All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.

Collaborative Discussions

Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.

Comprehensive Course Materials

All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.

Personalized Data Engagement

Participants are encouraged to bring their own data for discussion and practical application during the course.

Post-Course Support

Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.

Who Should Attend / Intended Audiences

This course is ideal for researchers—including PhD and MSc students, postdoctoral researchers, and principal investigators—as well as environmental professionals interested in applying best practices and state-of-the-art methods for modelling species distributions and ecological niches. Applications span a variety of fields such as biogeography, spatial ecology, biodiversity conservation, and related disciplines.
Participants should have a basic understanding of statistical concepts, including linear models and statistical tests—roughly equivalent to an undergraduate introductory statistics course.

To make the most of the course, some prior experience with R is required. You should be comfortable with basic R syntax and commands, using the RStudio console and script editor and importing data from common file formats (e.g., .txt, .xls, .csv)

Equipment and Software requirements

A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.

A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.

While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.

All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.

Download R Download RStudio Download Zoom

Dr. Antoine Becker-Scarpitta

Antoine is a community and forest ecologist currently working as a researcher at the French Agricultural Research and International Cooperation Organization (CIRAD), which is dedicated to supporting sustainable development in tropical and Mediterranean regions.

Antoine earned his PhD in Biology/Ecology from the University of Sherbrooke (Canada) and holds a degree in Conservation Biology from the University of Paris-Sud-Orsay. He has held postdoctoral research positions at the University of Helsinki and the Institute of Botany at the Czech Academy of Sciences.

His research focuses on the temporal dynamics of biodiversity, with a particular interest in forest ecosystems and Arctic vegetation. Antoine has extensive experience teaching topics such as community and plant ecology, evolutionary biology, and both linear and multivariate statistical methods using R.

Education & Career

PhD in Biology/Ecology, University of Sherbrooke, Canada
BSc in Conservation Biology, University of Paris‑Sud‑Orsay
Postdoctoral research at University of Helsinki and the Institute of Botany, Czech Academy of Sciences

Research Focus

Antoine studies the temporal dynamics of biodiversity, with a strong focus on forest ecosystems and Arctic vegetation. His work combines field surveys and advanced modelling to unravel ecological changes over time, especially along environmental gradients.

Current Projects

EDENE (2023–2026) – Ecological Dynamics behind species Extinctions in Novel Ecosystems (ANR-funded), investigating species loss and community shifts in emerging ecosystems.
ALT (2022–2025) – Amazonian Landscapes in Transition (ANR), examining changes in forest composition and underlying drivers in Amazonia.
GUARDEN (2022–2025) – safeGUARDing BiodivErsity aNd critical ecosystem services across sectors and scales(EU Horizon Europe), focused on conserving ecosystem services through cross-scale strategies.
GAMBAS (2019–2023) – Generating Advances in Modeling Biodiversity And ecosystem Services, enhancing and applying joint species distribution models in forest ecology.
METRADICA Fieldwork (ongoing) – Conducting soil sampling in tropical forests of French Guiana to map soil textures and water availability, linking them to tree species distributions under climate change scenarios.

Professional Consultancy

Antoine has provided scientific expertise to organisations such as FAO, WWF, and various national environmental agencies. His contributions include developing soil mapping tools for FAO and building deforestation models tailored to Madagascar.

Teaching & Skills

Delivers courses in community and plant ecology, evolutionary biology, and both linear and multivariate statistical methods—all with integrated R programming.
Champions reproducible workflows and robust analytical practices in ecological research.

Links

Session 1 – 01:30:00 – Introduction to community data analysis, basics of programming in R

Session 2 – 01:30:00 – Diversity analysis, species-abundance distributions

Break

Practical – 02:00:00 – Practical session for Europe (Eastern time zones)

Break