EGU23-6385, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Investigating the spatial and temporal variation of plants traits across flux sites using a trait-based dynamic vegetation model 

Dushyant Kumar1, Simon Scheiter2, Liam Langan2, Sujan Koirala1, Mirjam Pfeiffer3, Carola Martens2, Ulrich Weber1, and Nuno Carvalhais1,4,5
Dushyant Kumar et al.
  • 1Max Planck Institute for Biogeochemistry Jena, Hans-Knoell-Strasse 10, 07745 Jena, Germany (
  • 2Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325 Frankfurt am Main, Germany
  • 3Oeko-Institut e.V., Rheinstrasse 95, 64295 Darmstadt, Germany
  • 4Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
  • 5ELLIS Unit Jena, 07745, Jena, Germany

The study of plant trait variability is critical for understanding ecosystem dynamics and predicting the response of vegetation to varying climatic conditions. Understanding the factors controlling the spatial and temporal variability in vegetation traits is key for addressing the ecosystem responses and feedbacks to changes in climate. In this study, we used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate the temporal evolution and spatial distribution of plant traits across a wide range in edapho-climatic conditions. For such, we select locations of existing different ecosystem types and where in situ meteorological and eddy covariance flux measurements are taken.

We forced the aDGVM2 with FAO soil and flux site climate data, extended until 2020 and gap-filled with ERA5 data. To ensure that the simulated vegetation had sufficient time to adapt to prevailing local environmental conditions we conducted simulations for 500 years, split into a 400-year spin-up phase and a 100-year transient phase. For the spin-up phase, we randomly sampled years of the first 30 years of daily climate. Stochasticity in the selection-driven assembly of plant communities within the model can lead to multiple potential state; therefore, 10 replicate runs were conducted for each site with same model configuration.

We examine the differences in the 25 simulated trait values across sites, replicates and time via an analysis of variance (ANOVA). The analysis shows significant differences in trait values between sites, with some traits showing higher variability than others. In particular, we find that traits related to plant structural support (height, stem counts) were highly variable across sites, while traits related to resource acquisition (e.g., specific leaf area, leaf nitrogen content) are more stable. These results provide important insights into the factors that influence trait variability in space, and will be valuable for predicting the response of terrestrial ecosystems to environmental change. Further understanding the factors driving trait variability is of essential value in the design of mitigation and adaptation strategies and guide conservation efforts in the face of a rapidly changing world.

How to cite: Kumar, D., Scheiter, S., Langan, L., Koirala, S., Pfeiffer, M., Martens, C., Weber, U., and Carvalhais, N.: Investigating the spatial and temporal variation of plants traits across flux sites using a trait-based dynamic vegetation model , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6385,, 2023.