Biostatistics (mainly ANOVAs and Regression analysis).
The course has a practical approach to the use of the different tools for data analysis. After the theoretical explanation of how each method works, students have to solve exercises in which to respond to the attached questions they have to use the different tools they have learned. In the final evaluation they will have to solve an exercise with several questions associated and where the application of different tools and the decisions taken in each analysis project must be clearly justified in order to evaluate whether students understood the modus operandi of these tools.
This course aims to provide students with advanced knowledge indispensable to the level of treatment of biological and environmental data in ecology, through the understanding and application of different methods of univariate and multivariate tools, promoting and encouraging their scientific skills and their critical abilities so they can understand, work and find appropriate solutions to the issues related to the integrated analysis of ecological and environmental data.
1. Introduction to techniques or multivariate analysis: Terminology used; Brief description of existing techniques
2. Ordination techniques I: rrpresenting the underlying structure if the data: Principal Component Analysis (PCA); . Correspondence Analysis (CA); “Non-Metric Multidimentional Scalling” (NMDS); Principal Coordinate Analysis (PCoA)
3. Ordination techniques II: Discrimination between experimental units: Canonocal Discriminant Analysis (DA); Similarity Analysis (ANOSIM)
4. Ordination techniques III: Relation between response and explanatory variables: Redundancy Analysis (RDA) and distance based Redundancy Analysis (dbRDA); Canonical Correspondence Analysis (CCA); Selection of explanatory variables using permutation methods
5. Regression techniques: Multiple Linear Regression (MR); Generalized Linear Models (GLM): Poisson; Bionomial; GAM for count data and binary data
6. Ordination techniques IV :Variance decomposition techniques; Principal response curves (PRC).
Resolution Problems: 100.0%
1. Jongman, R.H.G.; Ter Braak, C..J.F. e Van Tongeren, O.F.R. (Eds.) (1995) Data analysis in community and landscape ecology. Cambridge University Press, Cambridge. 299 pp.
2. Leps, J. e Smilauer, P. (2003) Multivariate analysis of ecological data using Canoco. Cambridge University Press, Cambridge. 269 pp.
3. Maroco, J. (2003) Análise estatística com utilização do SPSS (2ª ed.). Edições Sílabo, Lisboa. 508 pp.
4. Quinn, G.P. e Keough, M.J. (2002) Experimental design and data analysis for biologists. Cambridge University Press, Cambridge. 537 pp.
5. Zuur, A.F.; Ieno, E.N. & Smith, G.M. (2007) Analysing Ecological Data. Springer, NewYork, U.S.A. 685 pp.
6. Zuur, A.F.; Ieno, E.N.; •Walker, N.J.; Saveliev, A.A. & Smith, G.M. (2009) Mixed Effects Models and Extensions in Ecology with R. Springer. 549 pp.