832321 BOKU International wildlife lectures
This page is available under these URLs:
- Lecture and seminar
- Semester hours
- Lecturer (assistant)
- Offered in
- Wintersemester 2019/20
- Languages of instruction
Quantitative decision analysis for wildlife management
Students will have previous exposure to the concepts and methodologies of structured decision making and collaborative decision analysis. Students are expected to be familiar with the deliberative phase that includes working with stakeholders and decision makers to appropriately frame wildlife management decision problems. This includes eliciting and structuring stakeholder values/objectives, identifying decision options and external drivers, and formulating an influence diagram that links management options to objectives. This one-week intensive course will focus on the quantitative modeling phase of decision analysis. Through a capstone project, students will apply diverse methods available for incorporating models into a decision process, including trade-off, value of information, and optimization analyses to identify an optimal course of action. Students will learn how to choose among these methods and tailor them to the context and structure of the decision problem. The course will conclude with an overview of formal adaptive management and discussion of other schools of thought on modern resource management.
- Previous knowledge expected
Basics in Wildlife Biology and Ecology
- Objective (expected results of study and acquired competences)
Students will be able to articulate the relevance and benefit of using quantitative approaches to inform decision making processes for wildlife management. They will understand the various roles of stakeholders in a decision process, the importance of characterizing uncertainty and risk when evaluating decision options, and how to structure complex decision problems to facilitate analysis. Students will recognize discrete classes of decision problems through hands-on exercises. Differentiating types of decision problems will allow them to better anticipate information needs and to select appropriate modeling and trade-off methods given the decision context. Students will be able to develop and implement decision-analytic tools in collaboration with stakeholders, including linear optimization, Simple Multi-Attribute Rating Technique, decision trees, and Bayesian decision networks. Students will learn Value of Information (VOI) methods to identify needs for research or monitoring and principles of formal adaptive management. To this end, they will design and carry out simple stochastic dynamic programming exercises in R.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.