[R-sig-eco] SpatialAnalysisOfMultivariateEcologicalData.PierreLegendre.3-7April2017.UK

[Oliver Hooker]

“Advances in Spatial Analysis of Multivariate Ecological Data: Theory 
and Practice”

http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp02/

This course is being delivered by Prof. Pierre Legendre who is a leading 
expert in numerical ecology and author of the book titled ‘Numerical 
ecology’

This course will run from 3rd – 7th April at Margam Discovery Centre, 
Wales.

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
Day 1
•	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 
analyses.

Day 2
•	Measures of similarity and distance, especially for community 
composition data.
•	Multiple linear regression. R-square, adjusted R-square, AIC, tests 
of significance.
•	Polynomial regression.
•	Partial regression and variation partitioning.

Day 3
•	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.

Day 4
•	Origin of spatial structures.
•	Beta diversity partitioning and LCBD indices
•	Replacement and richness difference components of beta diversity.

Day 5
•	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 oliverhooker at prstatistics.com

or visit our website www.prstatistics.com

or to book online 
http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp02/

Please feel free to distribute this material anywhere you feel is 
suitable

Upcoming courses - email for details oliverhooker at prstatistics.com

1.	ADVANCING IN STATISTICAL MODELLING USING R (December 2016, April 
2017, December 2017
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr05/
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr06/
2.	SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017)
http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-spae05/
3.	STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R 
(February 2017)
http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/
4.	GENETIC DATA ANALYSIS USING R (TBC)
5.	BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017)
http://www.prstatistics.com/course/bioinformatics-for-geneticists-and-biologists-bigb02/
6.	APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS 
(November 2017)
7.	INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017)
http://www.prstatistics.com/course/introduction-to-statistics-and-r-for-biologists-irfb02/
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 
2017)
http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp02/
11.	ADVANCES IN DNA TAXONOMY (TBC)
12.	INTRODUCTION TO BIOINFORMATICS USING LINUX (TBC)
13.	INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING
http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/
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 
(January 2017)
http://www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-data-using-r-mbmv01/
17.	ADVANCED PYTHON FOR BIOLOGISTS (February 2017)
http://www.prstatistics.com/course/advanced-python-biologists-apyb01/
18.	NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March)
http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/
19.	GEOMETRIC MORPHOMETRICS USING R (June)
http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/
20.	INTRODUCTION TO METHODS FOR REMOTE SENSING (July 2017)
21.	ECOLOGICAL NICHE MODELLING (October 2017)
22.	ANIMAL MOVEMENT ECOLOGY (TBC)

-- 
Oliver Hooker PhD.
PR statistics

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128 Brunswick Street
Glasgow
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+44 (0) 7966500340

www.prstatistics.com
www.prstatistics.com/organiser/oliver-hooker/