Multivariate analysis 2600-MA-GC-1-S2

Classes include the application of advanced methods of environmental data analysis: biodiversty, gradient analysis (models of response to gradients, estimating species optima by weighted averaging, calibration, relating two biotic communities), permutation tests and variation partitioning (Pseudo-F statistics and significance, testing individual constrained axes, tests with spatial or temporal constraints, tests with hierarchical constraints, significance adjustment for multiple tasks), similarity measures and distance-based methods (significance of cases versus similarity of communities, similarity between species in trait values, PCO, constrained PCO – db-RDA, non-metric multidimentional scaling, Mantel test), classification and regression methods, advanced use of ordination (PRC, separating spatial variation, linear discriminant analysis, hierarchical analysis of community variation), visualizing of multivariate data, key studies

Całkowity nakład pracy studenta

Contact hours with teacher: - participation in lecture – 15 hrs - participation in laboratory - 30 hrs - consultations - 10 hrs Self-study hours: - preparation for laboratory - 20 hrs - preparation of final project – 30 hrs Altogether: 105 hrs (5 ECTS)

Efekty uczenia się - wiedza

Student W1: defines a task or problem in the field of his specialty and selects appropriate statistical methods to solve them K_W08

Efekty uczenia się - umiejętności

Student U1: applies advanced knowledge in the field of statistics to the biological data K_U01 U2: is able to use a foreign language to communicate at a basic level in accordance with the requirements of B2 ESOKJ K_U12 U3: has the ability to present results in English, as well as write a report in English K_U14

Efekty uczenia się - kompetencje społeczne

Student K1: demonstrates the ability to use statistical and multivariate methods to develop and present results and analyzes K_K08 K2: can work in a team, both by directing and co-ordinating the team's activities and by performing assigned tasks K_K11

Metody dydaktyczne

Expository teaching methods: conventional lecture, discussion, project, presentation, video / computer, pointer, banners image

Metody dydaktyczne eksponujące

- pokaz

Metody dydaktyczne podające

- wykład problemowy
- wykład konwersatoryjny

Metody dydaktyczne poszukujące

- referatu
- projektu
- studium przypadku
- ćwiczeniowa

Rodzaj przedmiotu

przedmiot obligatoryjny

Wymagania wstępne

Basic knowledge of statistics and multivariate methods

Koordynatorzy przedmiotu

Agnieszka Piernik

Kryteria oceniania

Lecture - presentation based on published research results W1, W2, U1, U2, K1
Laboratory – project in groups W1, W2, U1, U2, K1, K2
61-68% satisfactory, 69-76% satisfactory plus, 77-84 % good, 85- 92% good plus, 93-100% very good

Praktyki zawodowe

not applicable

Literatura

Jongman R.H.G., ter Braak C.J.F., van Tongeren D.F.R., (eds). 1995. Data analysis in community and landscape ecology. Pudoc, Wageningen.
ter Braak C.J.F., 1996. Unimodal models to relate species to environment. DLO-Agrocultura;l Mathematics Group, Wageningen, the Netherlands.
ter Braak C.J.F., Smilauer P., 2012. CANOCO 5.0 Reference Manual User's Guide. Biometrics, Wageningen and Ceske Budejovice.
Smilauer P., Leps J. 2014. Multivariate analysis of ecological data using Canoco 5. Cambridge University Press, Cambridge.

Więcej informacji

Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb:

Strona przedmiotu 2600-MA-GC-1-S2 w USOSweb