Advanced techniques in environmental data analysis

Basic knowledge of statistics and multivariate methods.

przedmiot obligatoryjny

Contact hours with teacher:

- participation in lecture – 10 hrs

- participation in laboratory - 30 hrs

- consultations - 10 hrs

Self-study hours:

- preparation for laboratory - 20 hrs

- preparation of final project – 30 hrs

Altogether: 100 hrs (4 ECTS)

Student

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

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

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

Expository teaching methods:

conventional lecture, discussion, presentation, video / computer, pointer, banners image

- pokaz

- opis
- wykład informacyjny (konwencjonalny)
- wykład problemowy

- projektu
- referatu
- seminaryjna

- metody odnoszące się do autentycznych lub fikcyjnych sytuacji
- metody oparte na współpracy
- metody służące prezentacji treści
- metody wymiany i dyskusji

Introduction to advanced methods and applications of environmental data analysis and research planning

Classes include the application of advanced methods of research design and environmental data analysis: experimental design (completely randomized design, randomized complete blocks, pseudoreplicates, latin squer design ect.), 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 (mixed effect models, classification and regression trees- CART), interpreting community composition with functional traits (two approaches in traits – environmental studies, community based approach) advanced use of ordination (PRC, separating spatial variation, linear discriminant analysis, hierarchical analysis of community variation, partitioning diversity indices into alpha and beta components, predicting community compositions), visualizing of multivariate data, key studies

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.

Lecture - written test 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

not applicable