An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling
2021
Thrippleton, Timothy | Hülsmann, Lisa | Cailleret, Maxime | Bugmann, Harald | Department of Environmental Systems Science [ETH Zürich] (D-USYS) ; Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich) | Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Universität Regensburg - University of Regensburg (UR) | Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER) ; Aix Marseille Université (AMU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Swiss National Science Foundation project ‘Advanced Tree MOrtality MOdeling’, ATMO 2 , Project Number 163250. LH received funding by the Bavarian Ministry of Science and the Arts in the context of the Bavarian Climate Research Network (bayklif).
International audience
显示更多 [+] 显示较少 [-]英语. Tree mortality is key for projecting forest dynamics, but difficult to portray in dynamic vegetation models (DVMs). Empirical mortality algorithms (MAs) are often considered promising, but little is known about DVM robustness when employing MAs of various structures and origins for multiple species. We analysed empirical MAs for a suite of European tree species within a consistent DVM framework under present and future climates in two climatically different study areas in Switzerland and evaluated their performance using empirical data from old-growth forests across Europe. DVM projections under present climate showed substantial variations when using alternative empirical MAs for the same species. Under climate change, DVM projections showed partly contrasting mortality responses for the same species. These opposing patterns were associated with MA structures (i.e. explanatory variables) and occurred independent of species ecological characteristics. When comparing simulated forest structure with data from old-growth forests, we found frequent overestimations of basal area, which can lead to flawed projections of carbon sequestration and other ecosystem services. While using empirical MAs in DVMs may appear promising, our results emphasize the importance of selecting them cautiously. We therefore synthesize our insights into a guideline for the appropriate use of empirical MAs in DVM applications.
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