OEKB002104 Wildlife under global change

Type
course with continuous assessment
Semester hours
3
Lecturer (assistant)
Mattsson, Brady
Organisation
Wildlife Biology and Game Management
Offered in
Wintersemester 2025/26
Languages of instruction
Englisch
Content

Students prepare a semester project by working alone or with another student, supported by short lectures by the instructor on the scientific method and statistical analysis. Each student or pair presents preliminary results from their work and addresses feedback from the instructor and other students.

Each pair of students develops a research question/hypotheses and focuses on a unique response variable like species richness, abundance, reproduction, and body condition. They then select the appropriate data to represent the response variable (e.g., number of species captured, count of individuals for a given species captured, number of young fledged, weight, clutch size, nestling success, nesting success). For the response variable, they use existing data from the Konrad Lorenz Institute of Ethology, eBird, GBIF, MAPS, or the North American Breeding Bird Survey. They then regress the response variable with at least one covariate representing land cover (CORINE) and one representing weather. When relevant, they incorporate as predictors intrinsic variables such as animal age or sex. The study sites should ideally represent a gradient from low to high human footprint with similar topography, soils, and elevation.
Previous knowledge expected

Students must have either taken the following courses or get approval from the instructor to demonstrate equivalent knowledge from other sources:
Both of these: 832313 Basics in wildlife ecology; 851303 Advanced statistical methods for wildlife research.
One of these: 832309 Current contributions in wildlife research and wildlife management; 832333 Behavioural and population ecology.
Objective (expected results of study and acquired competences)

Following this course, students understand the importance and theory of linking environmental changes to spatiotemporal patterns in wildlife communities and populations. They also can generate and apply relevant research questions, hypotheses, and predictions, along with identifying and using appropriate statistical models and data sets within R Studio. In particular, students learn about generalized linear mixed models using the Gaussian, binomial, and Poisson distributions. Students gain skills in developing and giving oral presentations describing their investigation.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.