“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 [log in to unmask]
or visit our website www.prstatistics.com
or to book online http://prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice/
Please feel free to distribute this material anywhere you feel is suitable
Upcoming courses - email for details [log in to unmask]
1. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January
2017) #MBMV
http://www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-data-using-r-mbmv01/
2. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) #APYB
http://www.prstatistics.com/course/advanced-python-biologists-apyb01/
3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R
(February 2017) #SIMM
http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/
4. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) #NTWA
http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/
5. ADVANCES IN MULTIVAIRAITE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April
2017) #MVSP
http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp02/
6. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017) #IRFB
http://www.prstatistics.com/course/introduction-to-statistics-and-r-for-biologists-irfb02/
7. ADVANCING IN STATISTICAL MODELLING USING R (April 2017) #ADVR
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr05/
8. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (May 2017) #IBHM
http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/
9. GEOMETRIC MORPHOMETRICS USING R (June) #GMMR
http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/
10. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017) #MASE
http://www.prstatistics.com/course/multivariate-analysis-of-spatial-ecological-data-using-r-mase01/
11. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) #BIGB
http://www.prstatistics.com/course/bioinformatics-for-geneticists-and-biologists-bigb02/
12. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAE
http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-spae05/
13. ECOLOGICAL NICHE MODELLING (October 2017) #ENMR
http://www.prstatistics.com/course/ecological-niche-modelling-using-r-enmr01/
14. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS
(November 2017)
http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-epidemiologists-abme03/
15. GENETIC DATA ANALYSIS USING R (October TBC) #GDAR
16. INTRODUCTION TO BIOINFORMATICS USING LINUX (October TBC) #IBUL
17. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November TBC)#LNDG
18. PHYLOGENETIC DATA ANALYSIS USING R (November TBC) #PHYL
19. INTRODUCTION TO METHODS FOR REMOTE SENSING (December 2017 TBC) #IRMS
20. ADVANCING IN STATISTICAL MODELLING USING R (December 2017 TBC) #ADVR
21. INTRODUCTION TO PYTHON FOR BIOLOGISTS (December 2017 TBC) #IPYB
22. DATA VISUALISATION AND MANIPULATION USING PYTHON (December 2017TBC) #DVMP
23. ANIMAL MOVEMENT ECOLOGY USING R (TBC) #ANME
24. INTRODUCTION TO MIXED MODELS USING R (TBC) #IMMR
25. STRUCTURAL EQUATION MODELLING USING R (TBC) #SEMR
26. TIMESERIES MODELS FOR ECOLOGISTS AND CLIMATOLOGISTS (TBC) TSME#
email [log in to unmask] for details
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