ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live

31st March 2025 - 4th April 2025

£485.00

Event Date

Monday, March 31st, 2024

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.

Time Zone

TIME ZONE – Eastern European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course

This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”.

This 5-day course covers R concepts, methods, and tools that can be used to analyse community ecology data using (but not limited to) the R package VEGAN. We will cover :

Fundamentals of community ecology,
Diversity indices,
Methods to transform data and calculate distance measures,
Classifications (i.e., clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram).
Ordinations (i.e., unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes).
Canonical ordinations (i.e., constrained methods) test hypotheses related to multivariate patterns.

In addition the course provides lectures and practices on how to create reproducible workflows and use good programming practices in R.

During the workshops you will follow guided computer coding exercises using either your own data or real empirical datasets to motivate analyses. Exercises include describing patterns along gradients of environmental or anthropogenic disturbance, quantifying the effects of continuous and discrete predictors.

Topics covered during the course include: terrestrial and wetland ecology, microbial ecology, and natural resource management, evolution, palaeoecology.

You are strongly encouraged to use your own datasets (they should be clean and already structured, please contact use if you plan to do this, we will help you to prepare the data). You will benefit from full support in applying multivariate methods to your dataset (defining of the research question, transforming your data, selecting the most appropriate method, carrying out the analysis and interpreting the results).

Intended Audiences

Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines.

Venue

Delivered remotely

Course Details

Availability – 20 places

Duration – 5 days

Contact hours – Approx. 35 hours

ECT’s – Equal to 3 ECT’s

Language – English

Teaching Format

The course will be divided into theoretical lectures to introduce and explain key concepts and theories, and practices with workshop sessions on R.

~2 modules per day, each module consists of ~1h30/2h lecture + coding, break, ~1h30/2h exercises + summary/discussion.

The schedule can be slightly modified according to the interest of the participants.

The course will take place online.

All the sessions will be video recorded and made available immediately on a private video hosting website as soon as possible after each 2hr session.

Assumed quantitative knowledge

We will assume that you are familiar with basic statistical concepts, linear models, and statistical tests (the equivalent of an undergraduate introductory statistics course will be sufficient to follow the course).

Assumed computer background

To take full advantage of this course, minimal prior experience with R is required. Participants should be familiar with basic R syntax and commands, know how to write code in the RStudio console and script editor, load data from files (txt, xls, csv).

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

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