Description: This module introduces students to fundamental and novel approaches in forest ecology and allows them to apply their forecasting skills to typical challenges in quantitative forest ecology. Students explore themes including forest growth and regeneration dynamics, tree demography, forest structure, forest management, species diversity, and interactions of trees with forest-associated organisms. The course comprises short lectures, a seminar on climate-smart forestry research, fieldwork on forest monitoring principles, and a practical data analysis and modelling section. Example forecasting tasks include inverse modelling of seed dispersal kernels, data assimilation for growth trend modelling, and species detection using camera traps, soundscapes, and citizen-science data.
Learning outcomes:
Explain the ecological and biophysical processes underlying forest dynamics and biodiversity.
Apply ecological forecasting techniques to analyse and predict forest growth, structure, and species interactions.
Collect, process, and integrate eld and remote-sensing data relevant to forest monitoring and modelling.
Evaluate the role of uncertainty in forest forecasts and communicate the implications for sustainable management.
Synthesize theoretical, empirical, and modelling perspectives to inform transformative, climate-smart and adaptive forest
management.
Module group: Themes Module
Credits: 5 ECTS
Lecturers: Prof. Dr. Claus Bässler