Course: Advanced ordination methods

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Course title Advanced ordination methods
Course code KBE/052
Organizational form of instruction Lecture + Lesson
Level of course Doctoral
Year of study not specified
Frequency of the course In academic years starting with an even year (e.g. 2016/2017), in the summer semester.
Semester Summer
Number of ECTS credits 3
Language of instruction English
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Šmilauer Petr, doc. RNDr. Ph.D.
Course content
Content of lectures: Summary of work with the Canoco software, Advanced ANOVA models - repeated measurement analysis for individual response variables as well as for multivariate data (3 weeks), principal response curves, co-correspondence analysis, dissimilarity-based ordination methods (PCO, NMDS), analysing distances data with ordination methods (db-MEM, 2 weeks), relating species traits to environment (community-based and species-based methods, 2 weeks) Content of practicals: Complements the lecture topics, before the last week: work on individual miniprojects

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Work with text (with textbook, with book), Demonstration, Projection, E-learning
  • Preparation for classes - 14 hours per semester
  • Preparation for exam - 16 hours per semester
  • Semestral paper - 16 hours per semester
  • Class attendance - 28 hours per semester
Learning outcomes
Students will learn (in English) about the theory of selected advanced approaches in the multivariate statistical analysis and they will learn how to apply such methods to their own data. The methods discussed in depth include repeated measurements analysis of multivariate data, principal response curves (PRC) method, co-correspondence analysis and Procrustes analysis, ordination methods working with spatial autocorrelation (db-MEM), and various approaches to relating species functional traits and environmental factors. Within this course, two weeks are devoted to the work of students on own mini-projects and the presentation of their results.
Students will be able to analyse multivariate data even if they harbour temporal correlations among observations (repeated measures) and/or spatial autocorrelation. They will be also able to analyse the relations between two kinds of biotic communities using co-correspondence analysis and to quantify and test the relations between functional traits of species and the properties of the environment.
Prerequisites
Student should know at the start the basic principles of the analysis of multivariate data (particularly the differences between unconstrained and constrained ordination and the use of covariates) and (s)he should be also able to apply the basic methods to own datasets with the Canoco software. Such requirements can be fulfilled with the course Planning and evaluation of ecological experiments (KBO/332E in English or KBO/332 in Czech language).
KBO/332
----- or -----
KBO/332E

Assessment methods and criteria
Analysis of student's language skills, Seminar work

Towards the end of term, students analyze a dataset with some of the method(s) introduced in this course and in the last practicals they present the results.
Recommended literature
  • P. Legendre, L. Legendre. Numerical Ecology. Third English Edition. Elsevier, Amsterdam, 2012. ISBN 978-0-444-53868-0.
  • Šmilauer P., Lepš J. Multivariate analysis of ecological data using Canoco 5. Cambridge University Press, Cambridge, UK, 2014. ISBN 978-1-107-69440-8.


Study plans that include the course
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Faculty: Faculty of Science Study plan (Version): Ecosystem Biology (1) Category: Ecology and environmental protection - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Science Study plan (Version): Zoology (1) Category: Biology courses - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Science Study plan (Version): Botany (1) Category: Biology courses - Recommended year of study:-, Recommended semester: -
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