ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM09) This course will be delivered live
25 April 2023 - 28 April 2023
£490.00
Event Date
Tuesday, April 25th, 2023
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 – 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. 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.
Intended Audiences
The course is aimed at biologists with a basic to moderate knowledge in R. The course is aimed at anyone (academic or industry) who research is heavily reliant on analysing stable isotope data. There is a strong association with data on food webs and trophic relationships, but the tools learned can be applied to other systems.
Venue
Delivered remotely
Course Information
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
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.
PLEASE READ – CANCELLATION POLICY
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.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Tuesday 25th
Classes from 09:30 to 17:30
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
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
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
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.
Dr. Andrew Parnell
Works at: Institute or University: Hamilton Institute, Maynooth University
Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science, Nature Communications, and Proceedings of the National Academy of Sciences, and has methodological publications in journals such as Statistics and Computing, Journal of Computational and Graphical Statistics, The Annals of Applied Statistics, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics, time series modelling, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change, 3D printing, and bioinformatics.
Monday 21st
Classes from 09:00 to 17:00
Theory – Introduction to GIS.
Practical – Introduction to GIS with R: Import and plot data.
Theory – Coordinate systems.
Practical – Projecting vectorial & raster files.
Teaches
Introduction to Bayesian Hierarchical Modelling (IBHM)
Time Series Data Analysis Using R (TSDA)
Missing Data Analytics Using R (MDAR)