“Advances in Spatial Analysis of Multivariate Ecological Data: Theory
This course is being delivered by Prof. Pierre Legendre who is a leading
expert in numerical ecology and author of the book titled ‘Numerical
This course will run from 3rd – 7th April at Margam Discovery Centre,
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. These methods 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.
Course content is as follows
• Introduction to data analysis.
• Ordination in reduced space: principal component analysis (PCA),
correspondence analysis (CA), principal coordinate analysis (PCoA).
• Transformation of species abundance data tables prior to linear
• Measures of similarity and distance, especially for community
• Multiple linear regression. R-square, adjusted R-square, AIC, tests
• Polynomial regression.
• Partial regression and variation partitioning.
• Statistical testing by permutation.
• Canonical redundancy analysis (RDA) and canonical correspondence
analysis (CCA). Multivariate analysis of variance by canonical analysis.
• Forward selection of environmental variables in RDA.
• Origin of spatial structures.
• Beta diversity partitioning and LCBD indices
• Replacement and richness difference components of beta diversity.
• Spatial modelling: Multi-scale modelling of the spatial structure of
ecological communities: dbMEM, generalized MEM, and AEM methods.
• Community surveys through space and time: testing the space-time
interaction in repeated surveys.
• Additional module depending on time – Is the Mantel test useful
for spatial analysis in ecology and genetics?
Please email any inquiries to [hidden email]
or visit our website www.prstatistics.com
or to book online
Please feel free to distribute this material anywhere you feel is
Upcoming courses - email for details [hidden email]
1. ADVANCING IN STATISTICAL MODELLING USING R (December 2016, April
2017, December 2017
2. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017)
3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R
4. GENETIC DATA ANALYSIS USING R (TBC)
5. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017)
6. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS
7. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017)
8. INTRODUCTION TO PYTHON FOR BIOLOGISTS (TBC)
9. TIME SERIES MODELS FOR ECOLOGISTS AND CLIMATOLOGISTS (TBC)
10. ADVANCES IN MULTIVAIRAITE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April
11. ADVANCES IN DNA TAXONOMY (TBC)
12. INTRODUCTION TO BIOINFORMATICS USING LINUX (TBC)
13. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING
14. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (TBC)
15. PHYLOGENETIC DATA ANALYSIS USING R (TBC)
16. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R
17. ADVANCED PYTHON FOR BIOLOGISTS (February 2017)
18. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March)
19. GEOMETRIC MORPHOMETRICS USING R (June)
20. INTRODUCTION TO METHODS FOR REMOTE SENSING (July 2017)
21. ECOLOGICAL NICHE MODELLING (October 2017)
22. ANIMAL MOVEMENT ECOLOGY (TBC)
Oliver Hooker PhD.
128 Brunswick Street
+44 (0) 7966500340
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