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Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM05)
10 June 2019 - 14 June 2019£275.00 - £500.00
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.
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 – PR statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map
Availability – 30 places
Duration – 5 days
Contact hours – Approx. 30 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments and welcome meal Monday evening.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, refreshments and welcome dinner Monday evening. Self-catering facilities are available in the accommodation. Accommodation is multiple occupancy (max 4 people) single sex en-suite rooms. Arrival Sunday 9th June (between 17:00-21:00) and departure Friday 14th June (accommodation must be vacated by 09:15).
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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 firstname.lastname@example.org. 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 (and accommodation fees if booked through PR statistics) will be credited. However, PR statistics will not be held responsible/liable for any travel fees, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of this PR statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economical viable.
A mixture of lectures and hands-on practicals. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of statistical concepts. Such as regression modelling and generalised linear models. Some understanding of Bayesian Statistics is recommended but will be covered during the introductory sessions.
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/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
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Meet at Flat 2/1, 43 Cook Street, Glasgow G5 8JN between 17:00-21:00
Monday 10th – Classes from 09:30 to 17:30
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.
Tuesday 11th – 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.
Wednesday 12th – 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.
Thursday 13th – Classes from 09:30 to 17:30
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.
Friday 14th – Classes from 09:30 to 16:00
Bring your own data set.