E5980 Statistical Evaluation of Biodiversity

Faculty of Science
Autumn 2023
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Danka Haruštiaková, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Thu 9:00–10:50 F01B1/709
Prerequisites
Prerequisite for this course is basic knowledge on principles of statistical testing and calculating estimation. Knowledge on population biology and ecology is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 7 fields of study the course is directly associated with, display
Course objectives
The aim of the course is the introduction of specific area of biostatistical analysis - evaluation of data of biological communities using the biodiversity analysis methodology. The presented methods ranges from the simple biodiversity indices and biological communities through theoretical distributions to multivariate analysis of biological communities data.
Learning outcomes
At the end of the advanced course the student is able to:
- prepare dataset for analysis of biodiversity;
- visualize structure of biological communities and their biodiversity;
- describe biodiversity using biodiversity indices and their variability;
- apply species abundance models including niche oriented models;
- make decisions about correct usage of multivariate analysis of biodiversity data;
- interpret results of biodiversity analysis both from the computational and ecological point of view;
- have overview of available software for analysis of biodiversity data.
Syllabus
  • 1. Introduction to biodiversity. Biodiversity in data. Biodiversity data visualization.
  • 2. Indices of biodiversity and equitability, rarefaction.
  • 3. Species abundances models. Mathematical and niche oriented models.
  • 4. Transformation of species data. Association coefficients. Introduction to multivariate methods. Direct and indirect gradient analysis.
  • 5. Biodiversity assessment using cluster analysis methods. Hierarchical and non-hierarchical cluster analysis.
  • 6. Biodiversity assessment using ordination methods. Principal component analysis (PCA), correspondence analysis (CA), detrendend correspondence analysis (DCA).
  • 7. Biodiversity assessment using nonmetric multidimensional scaling (NMDS).
  • 8. Evaluation of relationships between biological communities and environmental characteristics using canonical ordination methods. Canonical correspondence analysis (CCA).
  • 9. Analysis of similarity (ANOSIM).
Literature
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • KREBS, Charles J. Ecological Methodology. New York: Harper Collins Publishers, 1989, 654 s. ISBN 0060437847. info
  • Magurran A.E. (1988) Ecological Diversity and Its Measurement. Cambridge University Press, UK.
  • Jongman, Ter Braak and Van Tongeren (1995). Data analysis in community and landscape ekology, Cambridge University Press, Cambridge
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of principles in diversity data analysis, their prerequisites and application.
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
http://www.iba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2022.
  • Enrolment Statistics (recent)
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