Description: This module focuses on evidence-based biodiversity conservation and the application of ecological forecasting to inform
adaptive management and policy decisions. Students learn how forecasting tools, modern monitoring techniques, and decision-support frameworks can be combined to enhance conservation outcomes. The course introduces monitoring methods such as acoustic sampling, automated image classification, and environmental DNA (eDNA) analysis, as well as predictive approaches including species distribution modelling and social media analytics. Beyond technical skills, students engage with the institutional, political, and societal dimensions of conservation, examining structured decision-making under uncertainty, systematic reserve design, optimisation approaches, and connectivity modelling for biodiversity protection.
Learning outcomes:
Explain the principles of evidence-based and adaptive biodiversity conservation.
Apply modern monitoring techniques and data sources to inform conservation decisions.
Use predictive modelling tools such as species distribution models and optimisation algorithms to support conservation planning.
Integrate ecological forecasting with structured decision-making and adaptive management under uncertainty.
Critically assess the social, political, and institutional contexts influencing conservation practice and policy implementation.
Module group: Themes Module
Credits: 5 ECTS
Lecturers: Prof. Dr. Manuel Steinbauer