Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China.

ABSTRACT: Dynamic global vegetation models (DGVMs) suffer insufficiencies in tracking biochemical cycles and ecosystem fluxes. One important reason for these insufficiencies is that DGVMs use fixed parameters (mostly traits) to distinguish attributes and functions of plant functional types (PFTs); however, these traits vary under different climatic conditions. Therefore, it is urgent to quantify trait covariations, including those among specific leaf area (SLA), area-based leaf nitrogen (N _area), and leaf area index (LAI) (in 580 species across 218 sites in this study), and explore new classification methods that can be applied to model vegetation dynamics under future climate change scenarios. We use a redundancy analysis (RDA) to derive trait-climate relationships and employ a Ga

SUBMITTER: Yang Y 

PROVIDER: S-EPMC6640191 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature