Ecological Forecasting, Master of Science (M.Sc.)

The Master’s program in Ecological Forecasting is part of the Elite Graduate Programs of the Elite Network of Bavaria. It is designed to equip students with the skills required to forecast how ecosystems, the services they provide, and the biodiversity they harbour are changing. Reliable forecasts are essential for the adaptive management of ongoing ecological change.

As society increasingly turns to ecologists for guidance, many ecologists still lack formal training in forecasting. The field of ecological forecasting addresses this gap by predicting ecological responses, advancing ecological understanding, and informing decision-making, while explicitly accounting for multiple sources of uncertainty. The forecasting process therefore represents a novel interface that links traditional scientific approaches, such as hypothesis testing, with adaptive management.

Aligned with the mission of the Elite Network of Bavaria, the program aims to educate excellent young scientists who are prepared for leading roles in research, policy, and management across a wide range of sectors that rely on ecological forecasts.

The program draws on content from ecology, ecological modelling, statistics, artificial intelligence, remote sensing, philosophy, and economics. Teaching modules are designed to build upon one another in a structured and coordinated manner. The Foundations in Ecological Forecasting modules introduce core concepts that are reinforced and expanded in the subsequent Themes in Ecological Forecasting modules, ensuring a cohesive learning experience and a gradual progression of skills and knowledge.

Foundations in Ecological Forecasting (40 ECTS)

The compulsory Foundations in Ecological Forecasting modules introduce the core concepts required by all ecological forecasters. Modules cover the foundations of ecology, scientific programming, probability theory, deep learning, statistical modelling, remote sensing, and the philosophical and behavioural-economic aspects of decision-making.

Themes in Ecological Forecasting (20–30 ECTS)

Concepts introduced in the Foundations in Ecological Forecasting modules are reinforced and expanded in the Themes in Ecological Forecasting modules. These modules allow students to apply foundational methods to develop forecasts relevant to forest, open-ecosystem, aquatic, and systems ecology, as well as to evolutionary and conservation biology. Students also apply forecasting approaches in the context of developing innovation projects for sustainability.

Pilot Study (5–15 ECTS)

In the Pilot Study modules, students individually undertake internships in which they acquire specialised methods and gain exposure to specific forecasting domains. Pilot Study internships may be completed with external regional, national, or international organisations, or within the research group of a faculty member.

Forecasting Challenge (5 ECTS)

The Forecasting Challenge module provides students with hands-on experience in collaborative and interdisciplinary research focused on producing clearly defined ecological forecasts.

Research Support (10 ECTS)

The Research Proposal and Peer Review modules (5 ECTS each) support students in planning their Master’s project while receiving structured feedback from their peers. An iterative review process allows for multiple rounds of feedback and improvement. Embedded in a research-based teaching framework, these modules emulate the peer-review process that is central to scientific practice.

Master’s Project (30 ECTS)

The Master’s project is conducted under the supervision of a faculty member from the program. It overlaps in time with the Peer Review module, allowing peer feedback to be directly incorporated and used to improve the quality of the submitted Master’s thesis.

• Participate in a globally unique study program dedicated to the emerging field of ecological forecasting.
• Benefit from the combined expertise of leading ecology and data-science groups at the Universities of Bayreuth and Würzburg.
• Enjoy a high supervisor-to-student ratio, ensuring close mentoring, tailored support, and rapid skill development.
• Engage in an immersive, cohort-driven learning structure that accelerates the learning process.
• Gain hands-on research experience through pilot studies, field campaigns, and forecasting challenges.
• Access specialized laboratories, field stations, remote-sensing platforms, and advanced computational research facilities at both institutions.
• Connect with strong national and international partners, including the German Aerospace Center (DLR), the Bavarian Forest National Park, the Skukuza Science Leadershp Initiative and the Ecological Forecasting Initiative.

Graduates can pursue careers either as forecast makers —engaged in fundamental and applied research in ecological forecasting— or as forecast interpreters, applying ecological forecasts in decision-making, ecosystem management, and policy contexts. The program prepares graduates for careers in a wide range of sectors that rely on ecological forecasts, including:

• Ecological and environmental research
• Governmental agencies and environmental authorities
• Non-governmental organizations (NGOs) focused on biodiversity, conservation, or global change
• Environmental and sustainability consulting
• Forestry, agriculture, and landscape planning
• Industry sectors such as insurance, renewable energy, and environmental risk analysis
• Data science and environmental informatics

The program provides comprehensive preparation for a PhD in ecology, conservation biology, Earth system science, data science, or related fields. Its strong research orientation, combined with a solid foundation in quantitative methods and a balanced mix of individual and team-based work, offers an excellent basis for academic and research-oriented careers.

• Part of the Elite Network of Bavaria
• Fully taught in English
• Joint program between the University of Bayreuth and JMU Würzburg
• Block-style modules enabling immersive and focused learning
• Deep integration with active research projects
• High proportion of fieldwork, modelling, and real-data applications
• Strong commitment to Open Science standards

Further information

Please be aware that all international applicants must undergo a fee-based procedure and request the issuance of the document "preliminary review documentation (Vorprüfungsdokumentation (VPD))" via uni-assist. Please find all necessary information about this on the respective University of Bayreuth website: VPD uni-assist 

Semester fee

The University of Bayreuth does not charge tuition fees. However, every student must pay the semester fee. This includes the fees for the Studentenwerk Oberfranken and the semester ticket for the use of public transport in the cities of Bayreuth and Kulmbach and in many parts of the regional transport system.