PR Statistics is pleased to announce that through its funding scheme it can offer 5 part-funded scholarships for its upcoming course – “Advances in Spatial Analysis of Multivariate Ecological Data: Theory and Practice”.
Scholarships contribute towards tuition fees and accommodation with 5 “All inclusive places” available at £550.00 (Fees have been subsidised by 33% from £820.00).
Applications should be sent to email@example.com and contain the following information:
- Full name
- Institute name
- PhD subject title or Post doc research questions
- Do you hold a funded position
- 150 words why this course would be relevant to your research or how it would help.
Application deadline is Monday 16th March 2017
‘Normal’ places are still available for anyone else interested.
This course will run from 3rd – 7th April at Margam Discovery Centre, Wales.
Full course details are given below and more information can be found on the PR Statistics website
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’
The course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology measured at a genetic, species and family level. There will also be a new additional module on the final day “Is the Mantel test useful for spatial analysis in ecology and genetics?”.
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.
- 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.
- 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.
- 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 enquiries to firstname.lastname@example.org