Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01) – Applications for Evolutionary Biology
 https://prstats.org/course/joint-species-distribution-modelling-jsdm01/

Delivered by Dr. Bastien Parisy, a community ecologist at the University of Helsinki whose research focuses on plant–soil interactions, community ecology, and environmental change. His work combines field ecology with advanced statistical methods, including Joint Species Distribution Models (JSDMs), Hierarchical Modelling of Species Communities (HMSC), and ecological network analysis.

Learn how to analyse complex biological communities using Joint Species Distribution Models (JSDMs) and Hierarchical Modelling of Species Communities (HMSC) in R, with methods that are highly applicable to evolutionary biology.

Evolutionary processes shape community composition through adaptation, niche evolution, phylogenetic history, species interactions, and environmental filtering. Traditional species distribution models treat species independently, often overlooking these important evolutionary relationships. Joint Species Distribution Models provide a powerful hierarchical framework for modelling entire communities simultaneously, allowing researchers to incorporate phylogenetic relationships, functional traits, environmental responses, and residual species associations to better understand the evolutionary processes underlying community assembly. This course provides comprehensive, hands-on training in HMSC, one of the leading frameworks for modern community and evolutionary ecology.

What you'll gain

- A thorough understanding of Joint Species Distribution Modelling (JSDMs) and the HMSC framework
- Practical experience building hierarchical community models in R
- Skills in incorporating environmental variables, species traits, phylogenies, and spatial structure into community models
- Understanding of species co-occurrence, evolutionary relationships, and community assembly processes
- Confidence interpreting JSDM outputs and evaluating model performance

Course format

- Live, instructor-led online training
- Hands-on coding using real biological community datasets
- Interactive practical exercises throughout
- Strong focus on applied, research-ready workflows

Who is this course for?

- Evolutionary biologists and evolutionary ecologists
- Community ecologists
- Population and quantitative geneticists
- Researchers investigating trait evolution, adaptation, and phylogenetic community structure
- PhD students and quantitative life scientists

Why take this course?

Evolutionary biology increasingly seeks to understand how historical diversification, adaptation, ecological interactions, and environmental filtering combine to shape biological communities. JSDMs provide an integrated framework for investigating how species traits, shared evolutionary history, environmental gradients, and residual associations contribute to patterns of biodiversity. By incorporating phylogenetic information directly into community models, researchers can test hypotheses about niche conservatism, adaptive divergence, trait evolution, and the evolutionary drivers of species distributions.
This course equips you with the practical skills needed to construct, fit, interpret, and validate Joint Species Distribution Models using HMSC in R. Whether you're studying adaptive radiations, phylogenetic community structure, trait evolution, genotype–environment relationships, macroevolutionary patterns, or biodiversity responses to environmental change, you'll gain the analytical toolkit needed to confidently analyse complex biological communities using state-of-the-art hierarchical models.

Learn more & enrol

PR Stats course page for Joint Species Distribution Modelling (JSDM) using HMSC: A Hierarchical Modelling Approach (JSDM01)
https://prstats.org/course/joint-species-distribution-modelling-jsdm01/

Questions?
Email: oliver@prstats.org

Oliver Hooker
Managing Partner
Advanced Training for Researchers in the Life Sciences
coursesinfo@prstats.org | www.prstats.org

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