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BMME02

Bayesian Multilevel Modelling using brms for Ecologists

Learn applied Bayesian modelling in R with brms for ecological data. Build, fit, and interpret hierarchical and GLMMs in this hands-on workshop.

  • Duration: 5 Days, 7 hours per day
  • Next Date: 1 December, 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.

£450Registration Fee

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5.0

from 200+ reviews

  • Overview
  • Instructors
  • Schedule

Course Description

Have you ever found yourself with a detailed ecological dataset in one hand, and in the other a body of theory you’re excited to test — but struggled to find a statistical procedure to connect the two? Bayesian statistics are exciting for ecologists because they offer us the ability to design a model that meets the full complexity of our dataset and our ecological ideas. An amazing array of models can be built, fit, and assessed using the same flexible toolkit. With modern tools, these models are more accessible than ever.

This workshop will cover the basics of applied Bayesian modelling for ecology. We will discuss some of the theory behind the Bayesian framework and explore some common models using simulated data. We’ll learn how to fit, interpret, diagnose and visualize a Bayesian model using plots, and study a wide variety of models useful to ecologists. These will include Gaussian models for a combination of discrete and continuous predictors, generalized linear models for count data and presence-absence data, as well as hierarchical and zero-inflated models. We’ll look at how to enhance our model’s interpretability and account for variation in sampling effort. We’ll explore these model types using brms, an R package that implements Bayesian Regression Modelling in Stan.

What You’ll Learn

During the course we will cover the following:

  • Understand the foundations of Bayesian inference, including priors, posteriors, and credible intervals.
  • Specify and evaluate meaningful priors for ecological parameters.
  • Diagnose model fit using posterior predictive checks, residuals, and cross-validation (e.g.  WAIC).
  • Fit and interpret linear models, generalised linear models (GLMs) and hierarchical models using the brms package.
  • Model count and binary data with appropriate likelihoods, including overdispersion and zero-inflation.
  • Communicate uncertainty and model results effectively using visualisation and reporting best practices.

 

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 designed for quantitative scientists who work with datasets to test hypotheses, measure causal effects, or make predictions. It assumes both a quantitative and computational background, with prior experience in R required. Participants should have a working installation of brms and a recent version of R, along with foundational skills in R programming—such as managing packages, organizing data with the tidyverse, and generating plots using ggplot2. While introductory knowledge of statistics is recommended, the course will start with basic regression concepts and progress quickly to more advanced topics.

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.

 

A comprehensive list of required packages will also be shared with participants ahead of the course to allow for pre-installation. Participants are required to install brms in advance of the course, and to test it by running an example from the package vignette.

Download R Download RStudio Download Zoom

Dr Andrew MacDonald

Andrew is a community ecologist and entomologist, and an applied statistician also. It is the passion for learning, practicing, and talking about statistics that led him into collaborations with scientists from many different fields. Andrew has extensive experience in collaboration and consulting, and now has experience building statistical models of animal behaviour, community composition, occupancy modelling, tree growth, and survey data from humans – to name just a few. He’s especially passionate about helping ecologists connect their theories and their data through expressive statistical models. His favourite tools to do this with are R, brms, and Stan. His goal is to improve our ability to make predictions for ecological systems, and to build a community of practice around statistical methods in ecology.

 
Education & Career
• PhD in Zoology (Experimental community ecology)
• MSc in Botany (Invasive species ecology)
• Instructor and consultant on a wide range of ecological topics.

 
Teaching & Skills
• Teaching topics including GLMs, nonlinear models, and hierarchical models – usually with a Bayesian perspective.
• Training and advice regarding reproducible workflows and open science.
• Experienced in collaboration and in developing statistical methods to combine theory with a specific dataset.
 

Links
Google scholar

Blog

Session 1- 02:00:00 – What Is Bayesian Statistics?
Introduction to Bayesian vs. frequentist probability; Bayes’ theorem and its components (prior, likelihood, posterior), the role of belief updating and uncertainty quantification in ecological modelling.

Session 2 – 02:00:00 – Priors, Posteriors, and Credible Intervals
Understanding different types of priors, posterior distributions and credible intervals; interpreting Bayesian results with ecological examples.

Session 3 – 02:00:00 – MCMC and the Bayesian Modelling Workflow
Introduction to MCMC algorithms, convergence diagnostics (traceplots, R-hat), overview of the Bayesian modelling cycle and iterative workflow.

Session 4 – 02:00:00 – Introduction to brms
Overview of the brms package; Stan as backend; model formula syntax, specifying families and priors; fitting basic models with brm().

Session 5 – 02:00:00 – Bayesian GLMs for Ecologists
Fitting Poisson, Gaussian, and logistic models in brms; posterior summaries and interpretation; visualising fitted effects and posterior uncertainty.

Session – 02:00:00 – Visualising and Summarising Models
Using bayesplot, ggplot2, and tidybayes to explore posterior distributions and posterior predictions.

Session 7 – 02:00:00 – Why Multilevel Models?
Introducing Pseudo-replication and non-independence in ecological data; random intercepts and partial pooling; visualising multilevel model structure.

Session 8 – 02:00:00 – Fitting Random Intercepts in brms
Syntax for random effects in brms; interpreting group-level effects and variance components; extracting and plotting random intercepts.

Session 9 – 02:00:00- Model Diagnostics for Multilevel Models
Examining traceplots, convergence checks, posterior predictive checks by group; assessing fit and structure of random intercept models.

Session 10- 02:00:00 – Random Slopes and Slope and Intercept Models
Motivation for random slopes, syntax and interpretation, understanding and visualising intercept-slope correlations.

Session 11 – 02:00:00 – Crossed vs. Nested Random Effects
Conceptual and practical distinctions between nested and crossed designs; specifying models in brms; ecological use cases and implications.

Session 12 – 02:00:00 – Centring and Scaling Predictors
Grand mean vs. group mean centring; standardisation for model convergence and interpretation; coding and plotting strategies.

Session 13 – 02:00:00 – Extending Count Models: overdispersion Poisson models with observation-level random effects, Negative Binomial models, and Zero-Inflated models.
Modelling count data in ecology; overdispersion and zero inflation; comparing Poisson and NB models. Overdispersion in proportion models, using the beta-binomial model. ; fitting ZIP/ZINB models in brms.

Session 14 – 02:00:00 – Binary and Proportional Data – accounting for unequal sampling effort
Bernoulli and binomial models; grouped trials and proportion data; predicting and visualising probabilities; accounting for random effects.

Session 15 – 02:00:00 – Posterior Predictive Checks for GLMMs
Assessing model fit; using bayes_R2(), LOO cross-validation, and residual plots to evaluate model performance.

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Frequently asked questions

Everything you need to know about the product and billing.

When will I receive instructions on how to join?

You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.

Do I need administrator rights on my computer?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

When will I receive instructions on how to join?

You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.

Do I need administrator rights on my computer?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

Still have questions?

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1 December 2025 - 5 December 2025
Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
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