A Novel Approach to Modeling Vegetation Distributions and Analyzing Vegetation Sensitivity Through Trait-Climate Relationships In China

Abstract:

There is increasing evidence that current DGVMs have suffered insufficient realism and hard to improve, particularly because they are built on plant functional type (PFT)-climate schemes. It is urgent to develop new approaches, like plant trait-based methods (FTs), to replace of PFT schemes when predicting the distribution of vegetation and investigating the vegetation sensitivity. In this research, we proposed a novel approach to modeling vegetation distributions and analyzing the vegetation sensitivity through trait-climate relationship in China. First, we aggregated data on three key FTs, including leaf mass per area (LMA), area-based leaf nitrogen (N_area), and mass-based leaf nitrogen (N_mass), from the available literatures. In addition, one structural trait of plant communities, leaf area index (LAI), was extracted from MODIS products across China. Second, we derived and developed trait-climate relationships and used different trait combinations in a Gaussian Mixture Model (GMM) to model vegetation distribution. Finally, the GMM trained by the LMA-N_mass-LAI combination was applied to investigate the climate sensitivity of vegetation. The results demonstrated the following: (1) all four traits captured well the relationships between climate variables and traits, as well as effectively predicted vegetation distribution and helped analyzing environmental sensitivity; (2) the LMA-N_mass-LAI combination yielded an accuracy of 72.05% for simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem function, and was therefore selected for training GMMs; and (3) a sensitivity analysis indicated that increasing temperatures shifted the boundaries of most vegetation northward and westward. Because the forests in these regions are well adapted to growth under rainy conditions, increasing precipitation is predicted to expand the boundaries of forests compared with the baseline vegetation distribution. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analyzing climatic sensitivity, it sheds new light on developing the next generation of trait-based DGVMs.