EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improving ecosystem model development with machine learning: a full hybrid approach

Phillip Papastefanou1, Adriane Esquivel-Muelbert2, Susanne Suvanto3, Stefan Olin4, Thomas Crowther5, Mart-Jan Schelhaas6, and Thomas Pugh4
Phillip Papastefanou et al.
  • 1Max Planck Institute for Biogeochemistry, Jena, Germany
  • 2University of Birmingham, School of Geography, Earth and Environmental Sciences, Birmingham, United Kingdom
  • 3Natural Resources Institute Finland, Helsinki, Finland
  • 4Lund University, Lund, Sweden
  • 5ETH Zurich, Zurich, Switzerland
  • 6Wageningen University and Research, Wageningen, Netherlands

Making projections of ecological systems under environmental change is central to many disciplines. Process-based models aim to represent core ecological mechanisms governing ecosystem dynamics, which can then be valuable for projecting change under novel environmental conditions. Yet, as our ecological understanding evolves, updating parameter information can be challenging. In addition, classical statistical approaches to fitting functional relationships often miss the complexity of interacting, non-linear dynamics, which can limit the predictive capacity of models. As such, a growing body of work suggests that the integration of modern machine learning might help to improve the representation of key ecological dynamics within such process-based models.  Here, we present a case study, using machine learning to identify key relationships between relative growth, biomass and mortality compared to classical regression methods. Our results suggest that the inclusion of a deep neural network (DNN) into a “theory-driven” process based global vegetation model can greatly improve model predictions of patch level forest structure and vegetation dynamics. This hybrid approach offers both the benefits of interpretability and physically-realistic structure, combined with the depth of information contained in big datasets and the flexibility of model machine learning.

How to cite: Papastefanou, P., Esquivel-Muelbert, A., Suvanto, S., Olin, S., Crowther, T., Schelhaas, M.-J., and Pugh, T.: Improving ecosystem model development with machine learning: a full hybrid approach, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9948,, 2023.