This is a ‘PRE-RECORDED’ course, lectures are pre-recorded and shared via zoom. The instructors will be available for live help with practicals and to answer any questions. A good internet connection is essential.
TIME ZONE – Multiple timezones – Please email email@example.com for full details or to discuss how we can accommodate you).
The course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology. The umbrella concept of the course is beta diversity, which is the spatial variation of communities. Researchers in spatial ecology, population genetics and landscape genetics will find these methods useful as they are applicable to all types of communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The new methods, collectively referred to as spatial eigen-function analysis, are grounded into techniques commonly used by community ecologists, which will be described first: simple ordination (PCA, CA, PCoA), multivariate regression and canonical analysis, permutation tests. The choice of dissimilarities that are appropriate for community composition data will also be discussed. The focal question is to determine how much of the community variation (beta diversity) is due to environmental sorting and to community-based processes, including neutral processes. Recently developed methods to partition beta diversity in different ways will be presented. Extensions will be made to temporal and space-time data.
Research postgraduates, practicing academics and primary investigators in spatial ecology particularly communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The skills learnt can also be applied by management and environmental professionals in government and industry.
Time Zone –Multiple timezones
Availability – TBC
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 2 ECT’s
Language – English
This course will be a combination of pre-recorded lectures delivered by Prof. Pierre Legendre, Practical sessions with live support via email or video link and final live summary with Q and A at the end of each day Prof Pierre Legendre.
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
The recordings will be aired via zoom. Recordings last for approx. 4 hours. Followed by 3-hour practical. And then an approx. 1 hour (we have more time if needed) Q and A session with Pierre Legendre.
Recordings will be aired to accommodate different time zones listed below in GMT.
Recordings 08:00 – 12:00
Practical 12:30 – 15:30
Recordings 12:00 – 16:00
Practical 16:30 – 19:30
Group 1 and 2
Live Q and A – 20:00 – 21:00 (with Prof. Pierre Legendre)
You can use this link to find a time zone which suites you best.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
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
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 will be credited.
Lesson 1: Ordination in reduced space
Section 1.0. Ordination in reduced space: An introduction
Section 1.1. Principal component analysis (PCA)
Section 1.2. Correspondence analysis (CA)
Section 1.3. Principal coordinate analysis (PCoA)
Section 1.4. Metric ordination methods in ecology (included with 1.3)
Lesson 2: Dissimilarities and transformations
Lesson 3: Tests of statistical significance
Lesson 4: Linear regression
Section 4.1 Multiple linear regression
Section 4.2 Partial regression and variation partitioning
Lesson 5: Canonical analysis
Lesson 6: Beta diversity
Section 6.1. Partitioning beta diversity
Section 6.2. Replacement and richness difference
Section 6.3. Temporal beta diversity
Lesson 7: Spatial modelling
Section 7.1. Origin of spatial structures in ecology
Section 7.2. Spatial eigenfunction modelling
Section 7.3. Space-time interaction
Lesson 8: Mantel test in spatial analysis