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

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.