546-0115/02 – Multicriterial analyses CANOCO (CANOCO)
Gurantor department | Department of Environmental Engineering | Credits | 2 |
Subject guarantor | Ing. Hana Švehláková, Ph.D. | Subject version guarantor | Ing. Hana Švehláková, Ph.D. |
Study level | undergraduate or graduate | Requirement | Compulsory |
Year | 1 | Semester | summer |
| | Study language | English |
Year of introduction | 2019/2020 | Year of cancellation | |
Intended for the faculties | HGF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Students will be able to work in CANOCO 5 environment, especially analysis, synthesis and interpretation of data - ie. ecological data collection, control, transformation, classification and ordination of including visualization of results.
Teaching methods
Lectures
Tutorials
Summary
Within the course the students will be acquainted with basic methods of multidimensional data analysis in CANOCO 5 environment. Emphasis will be placed on the use of biocenological data, their collection, control, transformation, classification and ordination, studies design, hypothesis creation and their testing.
Compulsory literature:
Šmilauer, P., Lepš, J. Multivariate analysis of Ecological Data using CANOCO 5.Cambridge University Press 2014.
teer BRAAK, C. J. F., ŠMILAUER, P: CANOCO 5. Reference manual and user’s guide to Canoco for Windows: Software for Ordination. Microcomputer Power, Ithaca, New York, USA.2012
Digby, P.G.N., Kempton, R.A Multivariate analysis of ecological communities. Chapman and Hall, London – New York. 1987
Legendre, P., Legendre, L. Numerical Ecology (Third English Edition). Elsevier 2012. Amsterdam.
Zuur, A.F., Ieno, E.N ,Smith, G.M. Analysing Ecological Data. Springer 2007
Recommended literature:
McGarigal, K., Cushman, S. & Stafford, S.G., Multivariate Statistics for Wildlife and Ecology Research, Springer, New York. 2000.
Gotelli, N.J., Ellison, A.M. A Primer of Ecological Statistics. Sinauer Associates 2004.
Oksanen, J. Multivariate Analysis in Ecology, Lecture Notes. 2004.
http://cc.oulu.fi/~jarioksa/opetus/metodi/notes.pdf
Palmer, M. Ordination methods for ecologists. http://ordination.okstate.edu/
Legendre, P., Legendre, L. Numerical Ecology, 2nd Engl. Ed., Elsevier, Amsterdam, ISBN 0444892494. 1998.
Way of continuous check of knowledge in the course of semester
Presentation of semestral work
Final test
E-learning
Other requirements
Presentation of semestral work, final test.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Working with data. Types of data. Data Collection. Primary data. Data transcription and control. EDA. CDA. Data transformation
2. Ecological data and its use. Ecological similarity. Biodiversity indices. Ellenberg\'s indication values. Traits
3. Basic terminology of multicriterial statistical methods
4. Regression. Linear models. Regression curves
5. Ordination analysis. Models of species response on environment gradient. Basic ordination techniques and methods
6. Indirect gradient analysis. PCA (Principal Components Analysis). CA (Correspondence Analysis). DCA (Detrended Correspondence Analysis)
7. Direct gradient analysis. RDA (redundancy analysis). CCA (canonical correspondence ananlysis)
8. Null hypothesis. Monte Carlo permutation test. Testing statistics
9. Case study
10. Classification methods. Nonhierarchical classification
11. Classification methods. Hierarchical classification. Divisive classification
12. Case study
13. Design of experiments - manipulation vs. natural experiments
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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