2.2.1 Environmental factors
Using 22 environmental factors (including climate, soil and biology) to
study the changes in the distribution of T. chinense (Table S1).
In order to match the environmental factors with the current climate
scenario, based on the monthly temperature and precipitation data
provided by the Worldclimate database (v2.1), we calculated 19 climate
data in the time range of 2000-2018 through the “biovar” package. The
two soil factors (T_OC and T_pH_H2O) were downloaded from Harmonized
World Soil Database (HWSD, http://www.fao.org). As a semi-parasitic
plant, vegetation (host) is the prerequisite for the growth of T.
chinense . Therefore, we included NDVI factors that can reflect
vegetation growth in the ensemble species modeling. NDVI data was
downloaded in MODIS (https://www.earthdata.nasa.gov). We averaged the
NDVI data from 2000 to 2020 according to the month of the growth period
(May to July) of T. chinense , and finally obtained layer was
required for the species distribution model modeling.
The high correlation between environmental factors may lead to the
species distribution model overfitting. Therefore, we first built the
initial model (10 repeated modeling) with MAXENT 3.4.4 software without
adjusting the parameters, and removed the environmental factors that
contributed less than 1%. The correlation function in ENMTools software
was then used to analyze the remaining environmental factors’
correlation (Warren et al., 2010). The two environmental factors with
| r | ≥ 0.8 were screened, and the one with relatively
small contribution rate was removed. After the above steps, we reserved
11 environmental factors for the final modeling.
To estimate the distribution changes of T. chinense in different
periods in the future (2050s: 2041-2060, 2070s: 2061-2080, 2090s:
2081-2100), we selected three widely used atmospheric circulation models
(MIROC-ES2L, CNRM-CM6-1, MRI-ESM2-0) to build species distribution model
in the future. Each atmospheric circulation model includes four shared
socio-economic pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5,
with 12 climate scenario combinations. Among the four selected shared
socio-economic pathways, the low to high radiative forcing scenarios
range from SSP1-2.6 to SSP5-8.5 (Jiang et al., 2020). ArcMap10.5
software was used to average the data of three climate models in the
same carbon emission scenario and year. The spatial resolution of
environmental data was 2.5 min.