Analyses of Animal Population Data

Course unit title:

Analyses of animal population data

Degree in:

Master Course in Wildlife Management, Conservation, And Control (WMCC)

Credits:

6

Year:

1

Semester:

2

Type of course unit:

Compulsory

Language of instruction:

English

Learning outcomes of the course unit:

This course is a unique opportunity for students to become familiar with data handling and statistical analyses which will be important prerequisites to deal with more advanced courses and their Master thesis. R is the leading language and environment that allows powerful and fast manipulation of data, offering many statistical and graphical options. This course will focus on entering and manipulating data in R, running statistical analyses, and producing graphs. In the second step of the course, the students will deal the more popular statistical analyses used to assess the wildlife management activities. They will be involved in all steps of the statistical analysis of their field data, including the datasets collected by students during field experiences of other course units.

Course contents:

The lessons will aim at data handling, data exploration, graphics and analysis with R. The specific topics will be:

- Classical parametric and non-parametric tests

- Linear Regression

- Analysis of Variance and Covariance

- Generalized linear models

- Model selection and averaging

- Management of large datasets

- Analysis of animal population census data

 

Hands-on lectures will be deal every day in the computer room.

Recommended or require reading and course materials:

For R see www.r-project.org, where also a wide span of contributed documentations can be found.

Other books and tutorials will be suggested during the course.

 

Mode of delivery:

Frontal lesson and practical lesson in groups

Teaching methods:

Tutorials, exercises.

Assessment methods and criteria:

Students will be graded based on written exam. The exam will take place on the days immediately after the course and last a maximum of 2 hours.