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Advances in Spatial Analysis of Multivariate Ecological Data: Theory and Practice (MVSP03)
7th May 2018 - 11th May 2018
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.
Research postgraduates, practicing academics and primary investigators in spatial ecology particularly communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The skills learnt can also be applied by management and environmental professionals in government and industry.
Venue – PR statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map
Availability – 30 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, welcome dinner Monday evening, farewell dinner Thursday evening, refreshments and accommodation. Self-catering facilities are available in the accommodation. Accommodation is approximately a 6-minute walk from the PR statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 6th May (after 5pm) and departure Friday 11th May (accommodation must be vacated by 9am).
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Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PR~statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PR~statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
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Meet at Orford Musique approx. 18:30
Monday 7th – Classes from 09:00 to 17:00
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.
Tuesday 8th – Classes from 09:00 to 17:00
Measures of similarity and distance, especially for community composition data.
Multiple linear regression. R-square, adjusted R-square, AIC, tests of significance.
Partial regression and variation partitioning.
Wednesday 9th – Classes from 09:00 to 17:00
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.
Thursday 10th – Classes from 09:00 to 17:00
Origin of spatial structures.
Beta diversity partitioning and LCBD indices
Replacement and richness difference components of beta diversity.
Friday 11th – Classes from 09:00 to 16:00
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?
“Attending the Advances in Spatial Analysis of Multivariate Ecological Data course really helped me to both understand and apply multivariate stats to my data. I joined the course hoping for guidance and tools to analyse my complex data set for my PhD research on biodiversity trends. The course exceeded my expectations both in terms of how much I learned and how creative and innovative statistics can be!
Pierre Legendre and Olivier Gauthier are truly excellent statisticians but more importantly they are also excellent teachers. I thought I knew the basic theory of ordination covered in the first few days of the course, but the way Pierre explained it shed new light and understanding on these complicated concepts. Although I am not a mathematician, I was able to follow the logic of every analysis, such that I could adapt the methods as needed to my data.
The overall format of the course was well thought out and crammed a lot of information in, starting at the very beginning with basic principles and reaching extremely complex level over just a few days. Each lecture session was followed by a step by step practical session in the afternoon, allowing us to immediately repeat and apply the theories learned in the morning. I found this an excellent way to learn.
The other great aspect was the range and diversity of the course attendants. We were a very nicely balanced mix of students, researchers, data managers and Professors of all ages, covering a range of marine and terrestrial habitats, and from so many different parts of the world! It made the social aspect really enjoyable and dynamic. In addition the location at Margam Discovery Center was absolutely stunning and provided much needed fresh air for short breaks and fabulous scenery for long walks in the evenings!
It was very clear that Oliver and his team invest a huge amount of time and effort into perfecting every angle of the course from the teaching quality, specialisation of the topics covered, to the accommodation and location. I am confident that all of the courses conducted by PR Statistics are of similar high standard and enjoyability. I really had great fun learning and would not be able to finish my PhD without this course!” Tania Bird, PhD candidate Ecology, Ben Gurion University, Israel (Attended MVSP course)