Bi7542 Data analysis in community ecology

Faculty of Science
Autumn 2023
Extent and Intensity
1/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
Mgr. Irena Axmanová, Ph.D. (lecturer)
Mgr. Kryštof Chytrý (lecturer)
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
Timetable
Tue 10:00–10:50 D31/238, Tue 13:00–14:50 B09/316
Prerequisites
( Bi5560 Basics of statistics for biol. || Bi6050 Introduction to Biostatistics ) && ! Bi7540 Data anal. commun. ecology
Students need to be familiar with the R software including basic data manipulation and analysis, and graph plotting. Knowledge of at least basic statistics (ANOVA, simple linear regression) 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
Course objectives
The course introduces methods of statistical analysis of data on species composition of plant or animal communities, irrespective of their taxonomic delimitation. The principal course topics include advanced data manipulation techniques, analyses of diversity, ordination methods, and numerical classification. At the end of this course, students should be able to apply the methods discussed in the R environment.
Learning outcomes
Choose an appropriate statistical method to address questions concerning diversity and species composition of ecological communities;
Apply these methods;
Interpret the results;
Produce the graphical output illustrating the results;
Incorporate the analysis in a scientific text;
Syllabus
  • Data types in community ecology (community composition data, univariate community parameters, environmental condition) Data preparation for analysis, data formats and their conversions, exploratory data analysis.
  • Diversity indices and their dependence on environmental conditions (multiple regression), species accumulation curve, and rarefaction.
  • Ecological similarity (indices of ecological similarity and distance between samples)
  • Ordination methods (linear vs unimodal, distance-based, constrained vs unconstrained, ordination diagrams, permutation tests, variance partitioning, covariates)
  • Designs of ecological experiments (observations vs. manipulative experiments)
  • Numerical classification (hierarchical vs nonhierarchical, agglomerative vs divisive)
  • Practicals will consist of the analysis of real-world data in the software R.
Literature
    recommended literature
  • Oksanen J. Vegan vignetes https://cran.r-project.org/web/packages/vegan/vignettes/
  • ŠMILAUER, Petr and Jan LEPŠ. Multivariate Analysis of Ecological Data using CANOCO 5. 2nd ed. Cambridge: University Press. xii, 362. ISBN 9781107694408. 2014. info
  • BORCARD, Daniel, François GILLET and Pierre LEGENDRE. Numerical ecology with R. New York: Springer. xi, 306. ISBN 9781441979759. 2011. info
Teaching methods
Theoretical lessons with additional computer practicals; online student participation possible.
Assessment methods
For the exam, students will prepare a short essay in which they analyze their own or demonstration data using the statistical approaches discussed in the course. The essay should have a form of methods and results of a scientific paper. Subsequently, students present the essays at a colloquium. The grade is based on the essay quality, presentation, and discussion at the colloquium.
Language of instruction
English
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2024.
  • Enrolment Statistics (recent)
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