Bi6590 Statistical analysis of biosystematic and taxonomic data

Faculty of Science
Spring 2013
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: z (credit).
Teacher(s)
Mgr. Petr Šmarda, Ph.D. (lecturer)
Guaranteed by
Mgr. Petr Šmarda, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: Mgr. Petr Šmarda, Ph.D.
Supplier department: Department of Botany and Zoology – Biology Section – Faculty of Science
Prerequisites (in Czech)
( bi2030 High. plant phylog. & divers. && bi2030c High. plant phyl. & div. - pr. )&& bi3110 Scient. present. in bot.&zool.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 15 student(s).
Current registration and enrolment status: enrolled: 0/15, only registered: 0/15, only registered with preference (fields directly associated with the programme): 0/15
fields of study / plans the course is directly associated with
Course objectives
In this course you can learn main statistical methods (mainly those multidimensional) needed by you biosystematical, taxonomical and phylogenetical research. The main objective is learn you (without redundant mathematics) to understand and to actively use these methods by designing of your experiments, analyses, and sampling. You will train each method yourself in practicals following the theoretical lectures. You should learn how to correctly and effectively form hypotheses, ask research questions; how many, why, from where, and which one samples to collect; how many times and what to measure. You will learn to inspect multidimensional data by ordination diagrams (PCA, PCoA, CVA, NMDS), to test mutually various multidimensional data sets (Mantel test, Procrustes analysis), to construct and to test classifications of your samples, and to find out the most proper identification characters, e.g. useful for identification keys (discriminant analysis). You will get knowledge how to build phenetic and evolutionary trees from morphological and molecular/sequence data (UPGMA, NJ trees, LS trees, minimum evolution trees, maximum parsimony), how to understand and interpret trees, and how to test character evolution.
Syllabus
  • 1. Basic classification of methods and data types; basic descriptive statistics 2. Simple statistical tests I, probability, significance 3. Simple statistical tests II, correlation, regression, experimental design, pseudoreplication, hypotheses formulation 4. Similarity coefficients, similarity matrices, testing of matrix data 5. Ordination methods I – basic classification of methods, construction of ordination diagrams 6. Ordination methods II – understanding of ordination diagrams, group testing, comparison of different ordinations 7. Cluster analysis I – classification of methods, agglomerative algorithms, tree building 8. Cluster analysis II – understanding dendrograms, testing of dendrograms reliability, comparison of different dendrograms 9. Discriminant analysis, selection of proper determination characters 10. Evolutionary trees I – phylogenetic approach, phylogenetic terms and the tree description, alignment 11. Evolutionary trees II – tree building methods and criteria (maximum likelihood, parsimony), testing or tree quality and reliability, understanding of trees 12. Evolutionary trees III – testing of character evolution, molecular clocks 13. Statistics on web, graphic presentation of results
Literature
    recommended literature
  • Marhold K, Suda J. (2002): Statitické zpracování mnohorozměrných dat (Fenetické metody). Karolinium, Praha.
  • Sneath PHA, Sokal RR (1973): Numerical taxonomy. W.H. Freeman, San Francisco
  • Webb CO., Ackerly DD., Kembel SW. (2008): Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24: 2098-2100.
  • Garland T. et al. (1992): Procedures for the analysis of comparative data using phylogenetically independant contrasts. Systematic Biology 41: 18-32.
  • Podani J. (2000): Introduction to the exploration of multivariate morphological biological data. Backhuys Publishers, Leiden.
    not specified
  • Legendre P, Legendre L. (1998): Numerical ecology. Elsevir, Amstrdam.
Teaching methods
lectures, individual practical analysis of own and model data
Assessment methods
To get credits, students should prepare an analysis of own or literature data.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2021, Spring 2023, Spring 2025.
  • Enrolment Statistics (Spring 2013, recent)
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