1. Term Two and a Word about Multi-variate Statistical Methods ….
LEM4001
Term Two and a Word about Multi-variate Statistical Methods ….

2. Term two plan Today….. IRP feedback
(Even) more on data analysis and multivariate techniques, (use of R?)
Individual tutorials for project implementation
Coming up…..
Thesis expectations
Critical thinking and reasoning
Developing your writing style - becoming a published author
Designing academic posters and visual abstracts

3. The research cycle

4. Multi-variate analyses / ordination methods
Inter-relationships amongst variables are often complex, so a univariate association becomes obsolete.
Tests termed multi-variate because they examine the pattern of relationships between several variables simultaneously.
Q. Why bother?
Classify.
Identify environmental gradients.
(Try and) distil meaning from (infinitely) complex data.
Data exploration.

5. Fundamental concepts Ordination is used to order multivariate data.
Methods typically organise data along hypothetical environmental gradients denoted as axes.
Indirect vs direct gradient analyses.
Methods: CA, DCA, Factor Analysis, PCA, CCA, Cluster Analysis.
Basic interpretation - points close together are similar, those far apart are dissimilar.
Can uncover underlying structure in your data.
Vegetation analysis, crop trials, tree data……there is no escape! 1 2 3 4
Light
1,2
Dry
Wet 4 3
Shade

6. An Example…..
Source: Van Tongeren (1995)

7. Interpretation
Eigenvalue = the measure of importance of the ordination axis (shows the maximum dispersion of scores on the ordination axis).
The first axis has the highest eigenvalue and therefore accounts for the most variation in the data.

8. Tasks
Analyse supplementary datasets supplied (basic and advanced techniques).
Planning the next step – discuss with JL
Biological Conservation 116 (2004) 289–303

9. More information
Multi-variate Analysis Jongman, R., Ter Braak, C. & van Tongeren, O. (1995) Data Analysis in Community and Landscape Ecology. Cambridge University Press. Kent, M. (2012) Vegetation Description and Data Analysis: A Practical Approach. Wiley-Blackwell.