# Simulating Ecological Functions of Vegetation Using a Dynamic Vegetation Model
## 2 Materials and Methods
### Model Simulation  
LPJ-GUESS is a multi-scale dynamic vegetation model that simulates the biomass, gross primary productivity (GPP), leaf area index (LAI), and evapotranspiration (ET) of the vegetation in an ecosystem [15]. It can reasonably simulate the continuous evolution of the spatiotemporal patterns of vegetation and its ecological functions under a changing climate and atmospheric CO\({}_{2}\) concentration. Although the development team of the LPI-GUESS model stressed that the proposed parameters could be used to model the vegetation dynamics in different regions worldwide [15], this study aimed to identify the parameters for PFTs to accurately simulate the vegetation spatial distribution of the LP. In this model, 57 ecological and physiological parameters needed to be identified for each PFT. This study used data analysis to directly determine the values for four bioclimatic parameters that determine the distribution of PFTs. The distribution and biological climate data were spatially overlaid to extract bioclimatic parameters for each vegetation type, such as the minimum and maximum temperatures for the coldest and the warmest months, as well as temperatures greater than 5 \({}^{\circ}\)C [32]. Vegetation sampling points were obtained through field investigation. In this study, the ecological physiology parameters analyzed included the maximum canopy area, wood density, the carbon and nitrogen contents of fine root and sapwood, and other eco-physiological parameters referred to the same or similar PFTs in the parameters for existing research models. For the remaining 53 parameters, according to the literature [34], 21 parameters are considered to be sensitive parameters [16] (Table S1). Therefore, in this study, a genetic algorithm was developed in the MATLAB language environment, focusing on the calibration of these sensitive parameters. Considering the integrity of the LPI-GUESSmodel in simulating carbon and water fluxes and the spatial distribution of vegetation, the vegetation parameters were calibrated jointly with multi-source measurements to ensure that they were representative of the study area. The model simulations were carried out one by one on a 1 km resolution grid. The parameter-calibrated LPJ-GUESS model cycled CO\({}_{2}\) concentration and climate data for each geographic grid during 2001-2020, starting with the PFT data for the LP in 2019, to drive the carbon and nitrogen pools of vegetation/soil to a state of dynamic equilibrium. In this state, the carbon and nitrogen values were taken as the starting conditions of each grid and were combined with the CO\({}_{2}\) concentration and climate data (2001-2020, 2021-2100). The biomass, GPP, LAI, and ET of the vegetation were continuously simulated across annual time scales. The vegetation type in each grid was determined by the biomass in the grid, and the detailed classification of the vegetation type in the grid was based on our previous studies [17]. The GPP, ET, and LAI of the vegetation in each grid were used to represent or calculate the carbon stock (CS), soil conservation (SC), and water conservation (WC), respectively. To evaluate the accuracy of the LPJ-GUESS model in simulating the GPP, ET, and LAI of the LP, we selected 51 natural vegetation points through field investigation (Figure 1). The sampling points were extracted from the corresponding MODIS products ([[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/))) (accessed on 5 December 2021) during 2001-2020 according to their geographical location, including the GPP, ET, and LAI values. The annual remote sensing observation data extracted from the MODIS products were fitted linearly with the data simulated from 2001 to 2020 by the LPJ-GUESS model. The average coefficient of determination (r\({}^{2}\)) and the average Nash-Sutcliffe Efficiency (Ens) between the observed and simulated values of GPP, ET, and LAI were 0.77 and 0.53, respectively.