Ecological research is becoming increasingly quantitative, yet students often opt out of courses in mathematics and statistics, unwittingly limiting their ability to carry out research in the future. This course provides a practical introduction to quantitative ecology for students and practitioners who have realised that they need this opportunity.
R is a language and environment for statistical computing and graphics (http://www.r-project.org). R provides a wide variety of statistical (linear and non-linear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques. Many users think of R as a statistics system. Furthermore, it is an environment within which statistical techniques are implemented. R can be extended (easily) via packages, covering a very wide range of modern statistics and much more. It has become the 'lingua franca' among statisticians, and is increasingly being used for data analysis among researchers. Many advanced or recent statistical and graphical/visualisation techniques are only available in R.
Expected learning outcomes
The focus will be on giving the participants practical experience with the software. The course material will be a blend of introductory lectures on R and practical sessions.
The objective is to review the state-of-the-art statistical methods for analysis of ecological data, demonstrating the power of open source statistical software. We will provide hands-on experience for standard data analysis (cookbook), enabling participants to use the software on their own problems (take-home software).
Rspatial. Analysis of vector and raster cartography, also connecting with GRASS and QGIS.
aRtistics. Experimental design. Hypothesis contrast, ANOVA. Basic multivariate statistics.
multivaR. Diversity and multivariate analysis: ordination and gradient analyses, ENFA (Ecological Niche Factor Analysis), habitat suitability maps, metapopulation simulations.
lineaR. Construction, optimization and evaluation of linear models. Representation and spatial interpretation.
modelaRt. Construction, optimization and evaluation of non-linear models. Representation and spatial interpretation.
This course requires some prior experience in statistics and elemental mathematics. Knowing object-oriented programming is not needed but R basic management is required.
This course is divided into 6 theoretical-practical sessions of 4 hours long, including assignments through which you can practice your mastery under supervision.