# Simulating Ecological Functions of Vegetation Using a Dynamic Vegetation Model
## 2 Materials and Methods
### Data Collection  
In this study, the LPI-GUESS model was used to simulate the spatial pattern and ecological functions of vegetation on the LP. The input data for LPI-GUESS were the PFT data, climate data (monthly average temperature, total precipitation, and the average cloud cover), CO\({}_{2}\) concentration, soil texture, and the eco-physiological parameters of the vegetation [32]. The PFT data were sourced from the MODIS products official website ([[https://lpdaac.usgs.gov/products/mcd12](https://lpdaac.usgs.gov/products/mcd12) clv006/]([https://lpdaac.usgs.gov/products/mcd12](https://lpdaac.usgs.gov/products/mcd12) clv006/)) (accessed on 9 November 2020), and the climate data from 2001 to 2100 were obtained from the National Geographic Resource Science Sub Center, National Earth System Science Data Center, National Science & Technology Infrastructure of China ([[http://gre.geodata.cn](http://gre.geodata.cn)]([http://gre.geodata.cn](http://gre.geodata.cn))) (accessed on 9 November 2020). Specifically, 2001-2020 was the historical period, and 2021-2100 was the future period. The projected climate data for 2021-2100 were based on the newly released IPCC Shared Socio-economic Path scenarios (SSP119 and SSP595), each of which contain three General Circulation Models (GCMs) (EC-Earth3, GFDL-ESM4 and MRI-ESM2-0) that perform well in predicting future climate change in China [32]. These GCMs were constructed by the European Community-Earth Alliance, the American Geophysical Fluid Mechanics Laboratory, and the Japan Meteorological Research Institute. Scenarios SSP119 and SSP585 correspond to a low forcing, low greenhouse gas emission scenario and a high forcing, high greenhouse gas emission scenario, respectively. Therefore, the future ecological functions of vegetation under the SSP119 and SSP585 scenarios can indicate the possible range of future climate change. The simulation results were based on the mean of the three GCMs to reduce possible instability caused by the climate scenarios. Data on current and future CO\({}_{2}\) concentrations came from the Mauna Loa Observatory ([[https://scrippsco2.ucsd.edu/](https://scrippsco2.ucsd.edu/)]([https://scrippsco2.ucsd.edu/](https://scrippsco2.ucsd.edu/))) (accessed on 9 November 2020) and the SSP datasets ([[https://nttcat.iiasa.ac.at/SSPDb](https://nttcat.iiasa.ac.at/SSPDb)]([https://nttcat.iiasa.ac.at/SSPDb](https://nttcat.iiasa.ac.at/SSPDb))) (accessed on 9 November 2020), respectively [32; 33]. Soil texture data were obtained from the Global Soil Data Center ([[http://soilgrids.org](http://soilgrids.org)]([http://soilgrids.org](http://soilgrids.org))) (accessed on 9 November 2020). Vegetation distribution data in the LP and the bioclimatic variables used to determine the LP bioclimatic and eco-physiological parameters were obtained from the Loess Plateau Science Data Center ([[http://loess.geodata.cn/](http://loess.geodata.cn/)]([http://loess.geodata.cn/](http://loess.geodata.cn/))) (accessed on 9 November 2020) and the National Ecological Science Data Center ([[http://www.cnern.org.cn/](http://www.cnern.org.cn/)]([http://www.cnern.org.cn/](http://www.cnern.org.cn/))) (accessed on 9 November 2020).