832321 BOKU International wildlife lectures

Type
Lecture and seminar
Semester hours
2
Lecturer (assistant)
Windt, Jendrik , Kunz, Florian
Organisation
Wildlife Biology and Game Management
Offered in
Sommersemester 2025
Languages of instruction
Englisch
Content

Sampling animal populations
Wildlife abundance, distribution and space use are fundamental measures in both conservation/management and ecology. In this course, you will learn the fundamental statistical principles and techniques of sampling animal populations and estimating these measures. Statistical models taking into account imperfect detection – the fact that our sampling may miss individuals or species even though they are present – will be at the core of this course. We will start with a general review of basic statistical principles, such as probability and sampling, and generalized linear (mixed) models. With this foundation, we will look at count-based methods like distance sampling and N-mixture modeling to estimate abundance; detection-based models like occupancy models to estimate distribution and space use; and individual-based models such as capture-recapture to estimate abundance, density and population dynamics. Lectures will be accompanied by computer labs, where you will learn how to fit models to data in R and how to interpret and present numerical model output.
Previous knowledge expected

The course requires basic knowledge of R coding (i.e., you must be able use RStudio for scripting, load packages, read in and manipulate data, specifically, data frames, vectors, matrices and lists, and know the basic variable types – numeric, character, logical, factor and missing/NA). Knowledge about generalized linear (mixed) models is beneficial but not required.
Objective (expected results of study and acquired competences)

- Basic knowledge of fundamental statistical principles and techniques of sampling animal populations and estimating these measures
- Skills to develop and understand basic statistical principles, such as probability and sampling, and generalized linear (mixed) models; count-based methods (e.g., distance sampling and N-mixture modeling); detection-based models (e.g., occupancy models); individual-based models (e.g., capture-recapture)
You can find more details like the schedule or information about exams on the course-page in BOKUonline.