Stable Isotope Mixing Models Using SIBER, SIAR, MixSIAR
Master stable isotope mixing models (SIMMs) using SIBER, SIAR, and MixSIAR in R. Analyse food webs and trophic relationships using real-world ecological datasets.
Duration: 32 hours
Format: Recorded ‘on-demand’ Format
£450Registration Fee
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- Overview
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Course Description
This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions.
What You’ll Learn
During the course will cover the following
- Explain the purpose of Stable Isotope Mixing Models (SIMMs) and their applications in ecological and biological research.
- Understand the principles of Bayesian statistics and how they underpin SIMMs.
- Distinguish between standard regression models and SIMMs, and identify when each is appropriate.
- Confidently use R to load, manipulate, and visualise stable isotope data, and fit basic statistical models.
- Apply SIAR to real-world datasets, interpret model outputs, and create clear graphical summaries.
- Compare and contrast SIAR and MixSIAR, recognising the advantages and limitations of each.
- Use MixSIAR to analyse complex datasets, including time series and mixed effects models, and apply source grouping techniques effectively.
- Build and run advanced custom SIMMs using JAGS, extending analysis beyond standard software packages.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is aimed at biologists with a basic to moderate knowledge of R, as well as researchers in both academia and industry whose work relies heavily on the analysis of stable isotope data. While the course has a strong focus on applications to food webs and trophic relationships, the tools and approaches introduced can be applied to a wide range of systems. Participants should have a basic understanding of statistical concepts, including generalised linear regression models, statistical significance, and hypothesis testing. Familiarity with R is required, including the ability to import and export data, manipulate data frames, fit basic statistical models, and generate simple exploratory and diagnostic plots.
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.
Prof. Andrew Parnell
Andrew is a statistician and professor working at the intersection of statistics, machine learning, and real-world scientific applications. His research focuses on developing and applying statistical methods for large, structured datasets, with applications spanning climate science, 3D printing, bioinformatics, and more. He works with a wide array of techniques, including Bayesian hierarchical models, time series analysis, and modern machine learning tools.
Andrew holds the Hamilton Professorship of Statistics at the Hamilton Institute, Maynooth University. He has co-authored over 90 peer-reviewed publications in high-impact journals such as Science, Nature Communications, and PNAS, as well as in leading statistical journals including Statistics and Computing, The Annals of Applied Statistics, JCGS, and JRSS Series C. He has extensive experience teaching Bayesian statistics, statistical learning, and applied modelling across undergraduate, postgraduate, and doctoral levels.
Education & Career
• Hamilton Professor of Statistics, Hamilton Institute, Maynooth University
• PhD in Statistics (Bayesian Methods for Complex Data)
• Internationally published researcher with over 90 peer-reviewed papers
• Active collaborator with interdisciplinary teams in science and engineering
Research Focus
Andrew’s work is centred on statistical methodology and its integration with machine learning for complex, structured data. He is particularly interested in how Bayesian inference and scalable modelling techniques can enhance data-driven research in the natural sciences, engineering, and public policy.
Current Projects
• Hierarchical Bayesian models for environmental and ecological datasets
• Machine learning methods for analysing high-dimensional, structured data
• Time series modelling for dynamic systems in science and industry
• Statistical approaches to reproducible, transparent modelling practices
Professional Consultancy
Andrew collaborates widely across disciplines, providing expert statistical advice on model development, uncertainty quantification, and data analysis pipelines. His applied consulting includes climate modelling, bioinformatics, additive manufacturing, and data-driven public health initiatives.
Teaching & Skills
• Instructor in Bayesian statistics, time series modelling, and machine learning
• Strong advocate for reproducibility, open-source tools, and accessible education
• Skilled in R, Stan, JAGS, and statistical computing for large datasets
• Experienced mentor and workshop leader at all academic levels
Links
• ResearchGate
• Google Scholar
• ORCID
• LinkedIn
• GitHub
Session 1 – 02:00:00 – Introduction; why use a SIMM?
Session 2 – 02:00:00 – An introduction to bayesian statistics.
Session 3 – 02:00:00 – Differences between regression models and SIMMs.
Session 4 – 02:00:00 – Practical: Revision on using R to load data, create plots and fit statistical models.
Session 5 – 02:00:00 – Do’s and Don’ts of using SIAR.
Session 6 – 02:00:00 – The statistical model behind SIAR.
Session 7 – 02:00:00 – Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots.
Session 8 – 02:00:00 – Creating and understanding Stable Isotope Bayesian Ellipses (SIBER).
Session 9 – 02:00:00 – What are the differences between SIAR and MixSIAR?
Session 10 – 02:00:00 – Practical: Using MixSIAR on real world data sets; benefits over SIAR.
Session 11 – 02:00:00 – Using MixSIAR for complex data sets: time series and mixed effects models.
Session 12 – 02:00:00 – Source grouping: when and how?
Session 13 – 02:00:00 – Building your own SIMM with JAGS.
Session 14 – 02:00:00 – Practical: Running advanced SIMMs with JAGS.
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?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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