Until now, the process descriptions in many dynamic vegetation models (DVMs), in particular the representation of tree survival, lack empirical justification. Since tree death can be described quite precisely as ‘stochastic, rare and irregular’, its investigation is hampered by limited data and high variability. Tree mortality thus remains one of the least understood processes in the simulation of stand dynamics. This is particularly problematic since projections of future forest dynamics are highly sensitive to mortality formulations. Consequently, improved concepts for the modeling of tree mortality are urgently needed.
We are interested in how the representation of tree mortality in DVMs can be improved by using growth-based predictors and by strengthening the connection of data and simulations. To this end, we use direct and inverse parameterizations of mortality models, particularly in the forest gap model ForClim.
- Hülsmann, L., Bugmann, H., Cailleret, M. & Brang, P. (2018). How to kill a tree: Empirical mortality models for eighteen species and their performance in a dynamic forest model. Ecological Applications. 28, 522-540. [journal]
- Hülsmann, L., Bugmann, H., Meyer, P. & Brang, P. (2018). Natürliche Baummortalität in Mitteleuropa: Mortalitätsraten und -muster im Vergleich. Schweizerische Zeitschrift für Forstwesen. 169, 166-174. [journal]
- Hülsmann, L., Bugmann, H. & Brang, P. (2017). How to predict tree death from inventory data – lessons from a systematic assessment of European tree mortality models. Canadian Journal of Forerst Research, 47, 890-900. [journal]
- Hülsmann, L. (2016). Tree mortality in Central Europe: Empirically-based modeling using long-term datasets. ETH Zürich, PhD Thesis. p. 219. [ethz]
- Hülsmann, L., Bugmann, H., Commarmot, B., Meyer, P., Zimmermann, S. & Brang, P. (2016). Does one model fit all? Patterns of beech mortality in natural forests of three European regions. Ecological Applications, 26, 2463-2477. [journal]