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Model-Based Multivariate Analysis Of Abundance Data Using R (MBMVPR)

1st January 2030

Model-Based Multivariate Analysis Of Abundance Data Using R (MBMVPR)

Course Format

Pre Recorded

About This Course
This course will provide an introduction to modern multivariate techniques, with a special focus on the analysis of abundance or presence/absence data. Multivariate analysis in ecology has been changing rapidly in recent years, with a focus now on formulating a statistical model to capture key properties of the observed data, rather than transformation of data using a dissimilarity-based framework. In recent years, model-based techniques have been developed for hypothesis testing, identifying indicator species, ordination, clustering, predictive modelling, and use of species traits as predictors to explain interspecific variation in environmental response.  These techniques are more interpretable than alternatives, have better statistical properties, and can be used to address new problems, such as the prediction of a species’ spatial distribution from its traits alone.
Intended Audiences

PhD students, research postgraduates, and practicing academics as well as persons in industry working with multivariate data, especially when recorded as presence/absences or some measure of abundance (counts, biomass, % cover, etc).

Course Details

Last Up-Dated – 12:02:2021

Duration  – Approx. 30 hours

ECT’s – Equal to 3 ECT’s

Language – English

Teaching Format

A mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

Assumed quantitative knowledge

An understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Assumed computer background

Previous experience with data analysis using R is required. 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. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom


The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
£ 350.00


Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com


Day 1 – approx. 3 hours
Revision of key “Stat 101” messages.

Day 2 – approx. 3 hours
Revision of (univariate) regression analysis: the linear model, generalised linear model.
Main packages: lme4.

Day 3 – approx. 3 hours
Linear mixed models, the parametric bootstrap, permutation tests and the bootstrap.
Main packages: lme4, mvabund.

Day 4 – approx. 3 hours
Model selection, classical multivariate analysis.
Main packages: glmnet.

Day 5 – approx. 3 hours
Multivariate abundance data: hierarchical models, key properties, hypothesis testing.
Main packages: mvabund.

Day 6 – approx. 3 hours
Multivariate abundance data: design-based inference for dependent data, indicator species.
Main packages: mvabund.

Day 7 – approx. 3 hours
Compositional data, explaining cross-species patterns using traits.
Main packages: mvabund.

Day 8 – approx. 3 hours
Classifying species based on environmental response, predictive models
Main packages: Speciesmix, mvabund, lme4.

Day 9 – approx. 3 hours
Model-based ordination and inference
Main packages: gllvm.

Day 10 – approx. 3 hours
Inferring interactions form co-occurrence data
Main packages: gllvm, ecoCopula.

Course Instructor

Prof. David Warton

Personal website

Work Webpage



David is an ecological statistician who advances methodology for data analysis in ecology to improve the ability of ecologists to answer important research questions with a focus on developing and translating modern statistical approaches to important ecological problems.

His cross-disciplinary research involves evaluating the methods for data analysis currently used in ecology, and where necessary, developing new methodologies to assist ecologists answer key research questions. This has led to contributions to current practice in ecology in multivariate analysis, allometric line-fitting and the analysis of presence-only data.


1st January 2030
Event Category:


United Kingdom + Google Map


The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
£ 350.00