ONLINE COURSE – Stable Isotope Mixing Models using MixSIAR and SIBER (SIMM11) This course will be delivered live
25 March 2025 - 28 March 2025
£480.00
Event Date
Tuesday, March 25th, 2025
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course Details
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. 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. We will then focus on the widely used package MixSIAR and SIBER packages. MixSIAR creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). ‘MixSIAR’ is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. ‘MixSIAR’ incorporates several years of advances since ‘MixSIR’ and ‘SIAR’. SIBER fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. Participants will be taught the advanced features of these packages, which will enable them to produce a richer class of output. Attendees are encouraged to bring their own data sets and problems to study during the round-table discussions.
Intended Audiences
Venue
Course Information
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
Assumed quantitative knowledge
Assumed computer background
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.
Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).
A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
Tuesday 25th
Classes from 09:30 to 17:30
DAY 1
Basic concepts.
Module 1: Introduction; why use a SIMM?
Module 2: An introduction to bayesian statistics.
Module 3: Differences between regression models and SIMMs.
Practical: Revision on using R to load data, create plots and fit statistical models.
Round table discussion: Understanding the output from a Bayesian model.
Wednesday 26th
Classes from 09:30 to 17:30
DAY 2
Understanding and using SIAR.
Module 4: Do’s and Don’ts of using SIAR.
Module 5: The statistical model behind SIAR.
Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots.
Round table discussion: Issues when using simple SIMMs.
Thursday 27th
Classes from 09:30 to 17:30
DAY 3
SIBER and MixSIAR.
Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER).
Module 7: What are the differences between SIAR and MixSIAR?
Practical: Using MixSIAR on real world data sets; benefits over SIAR.
Round table discussion: When to use which type of SIMM.
Friday 28th
Classes from 09:30 to 17:30
DAY 4
Advanced SIMMs.
Module 8: Using MixSIAR for complex data sets: time series and mixed effects models.
Module 9: Source grouping: when and how?
Module 10: Building your own SIMM with JAGS.
Practical: Running advanced SIMMs with JAGS.
Round table discussion: Bring your own data set.
Andrew Jackson
My research interests lie in understanding ecological systems from an evolutionary perspective. I tend to approach these questions by using computational / mathematical models to understand how the nuts and bolts of these systems work. Much of my current research focuses on understanding predator-prey interactions and how large fish use their spatial environment. My interests also extend to community ecology where the challenge is to understand how communities of organisms and species compete and interact with what is often a self-organising and stable system.