PřF:Bi7540 Data anal. commun. ecology - Course Information
Bi7540 Data analysis in community ecologyFaculty of Science
- Extent and Intensity
- 2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
- doc. RNDr. Jakub Těšitel, Ph.D. (lecturer)
- Guaranteed by
- doc. RNDr. Jakub Těšitel, Ph.D.
Department of Botany and Zoology - Biology Section - Faculty of Science
Contact Person: doc. RNDr. Jakub Těšitel, Ph.D.
Supplier department: Department of Botany and Zoology - Biology Section - Faculty of Science
- Mon 1. 3. to Fri 14. 5. Tue 14:00–16:50 online_B6
- Bi5040 Biostatistics - basic course || Bi5560 Basics of statistics for biol.
The lecture expects a basic statistical knowledge in the extent of the basic Bi5040 Biostatistics course, namely correlation analysis, and (generalized) linear models. It is therefore recommended that students enroll this class after the Biostatistics course. The practicals will be taught in the R software. Previous basic knowledge of work in R is also recommended. Such knowledge may be achieved in the Bi7560 Introduction to R course.
- 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
- Course objectives
- The course deals with basic methods of statistical analysis of the data on species composition of plant and animal communities, irrespective of their taxonomic delimitation. The focus is on numerical classification and ordination methods and analysis of the relationships between species composition and environmental factors.
At the end of this course, students should be able to apply particular methods, using a popular program R.
- Learning outcomes
- Students should be able to:
Choose an appropriate multidimensional method to solve a given ecological problem
Apply this method
Interpret the results
Accompany the results with an illustrative graphical output
Incorporate the analysis into a scientific text
- Pre-analysis data preparation (data cleaning, outliers, transformation, standardization, exploratory data analysis), types of data (categorical vs quantitative, abundances, frequencies)
- Ecological similarity (indices of ecological similarity and distance between samples)
- Ordination (linear vs unimodal, constrained vs unconstrained, ordination diagrams, permutation tests, variance partitioning, forward selection, case studies)
- Numerical classification (hierarchical vs nonhierarchical, agglomerative vs divisive, supervised vs unsupervised)
- Use of species functional traits or species indicator values in multivariate analysis (functional traits, Ellenberg indicator values, community-weighted mean, fourth-corner)
- Diversity indices (alpha, beta and gamma diversity, accumulation curves and rarefaction curves)
- Case studies demonstrating the use of particular analytical methods
- Design of ecological experiments (manipulative vs natural experiments)
Practicals will consist of the analysis of real-world data in the software R.
- recommended literature
- LEPŠ, Jan a Petr ŠMILAUER. Mnohorozměrná analýza ekologických dat. 2001. http://regent.jcu.cz/skripta.pdf
- HERBEN, Tomáš and Zuzana MÜNZBERGOVÁ. Zpracování geobotanických dat v příkladech. Část I. Data o druhovém složení. http://www.natur.cuni.cz/~botanika/, 2001. info
- not specified
- ŠMILAUER, Petr and Jan LEPŠ. Multivariate Analysis of Ecological Data using CANOCO 5. 2nd ed. Cambridge: University Press, 2014. xii, 362. ISBN 9781107694408. info
- BORCARD, Daniel, François GILLET and Pierre LEGENDRE. Numerical ecology with R. New York: Springer, 2011. xi, 306. ISBN 9781441979759. info
- ZUUR, Alain F., Elena N. IENO and Graham M. SMITH. Analysing Ecological Data. Springer-Verlag New York, 2007. 672 pp. ISBN 978-0-387-45967-7. doi:10.1007/978-0-387-45972-1. URL info
- Teaching methods
- theoretical lessons with additional computer labs
- Assessment methods
- For exam, students will prepare a short study, in which they analyze their own or demonstration data, using the statistical approaches discussed in the lecture. The study should have a form of short scientific paper - more details about its structure will be published on the class website. The exam is oral discussion about the study, with additional questions targeting theoretical background of used methods.
- Language of instruction
- Further Comments
- Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
- Teacher's information
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/sci/spring2021/Bi7540