2.2 Environmental variables
We collected 29 environmental variables for this study, which can be
roughly divided into four categories: bioclimate, topography, habitat,
and human impact, source of all variables show in Table S2. In order to
avoid model overfitting, all collected variables were processed by the
band collection statistics function in ArcGIS 10.2. Remove the
environment variables with high correlation (r>0.7). After
screening, Bio3, Bio5, Bio9, Bio14, Bio15, slope, Veg, NDVI, POP, GDP
and HI were used for this study. Bio3, Bio5, Bio9, Bio14, Bio15, slope,
Veg, NDVI, POP, GDP and HI. Since variables other than future
bioclimatic factors cannot be predicted and change relatively little
over a short period of time, we assume that they remain constant and use
their projections of the future distribution of B. gargarizans(Yang et al., 2020).
In order to reduce the uncertainty of model prediction, we chose four
internationally recognized GCMs (BCC-CSM1-1, HadGEM2-ES, IPSL-CM5A-LR,
MIROC5) and four RCPs (RCP2.6, 4.5, 6.0 and 8.0) published in the IPCC
fifth Assessment report for prediction in 2050 and 2070.
2. 3
Model optimization and parameter setting
To establish the most stable and reliable model, we choose model
calibration, which includes regularization multiplier (0.5 to 6,
interval 0.5) and combination of basic feature classes and from one
different sets of all layers. The best parameters were selected
based on statistical significance (partial ROC), predictive ability
(miss rate E=5%) and complexity level (AICc) by useing R package kuenm
(Cobos, Peterson, Barve, & Osorio-Olvera, 2019). After the model
parameters were determined, chose the data output format of mathematical
logic to ensure that the output data was between 0 and 1. And the
jackknife method was selected to evaluate the contribution of
environmental variables. Then the distribution data ofB.gargarizans were randomly divided into two groups: 25% were
randomly selected as the test set, and the remaining 75% were used as
the training set, repeat Bootstrap replicates 10 times in MaxEnt.3.4.1.
Other model parameters are selected by system default. Finally, the
predictive performance of the model was validated by using the area
under the receiver-operating characteristic (ROC) curve (AUC) and the
mean omission error. The higher the AUC value, the more accurate the
model performance. Normally, AUC > 0.9 represents excellent
prediction performance of the model. The ensemble threshold of model was
calculated according to the Maximizing Sensitivity and Specificity (MSS)
by using the dismo package in R. The MSS method is commonly used in
presence only kind of occurrence data(Hijmans, Phillips, Leathwick, &
Elith, 2017; Liu, White, Newell, & Pearson, 2013).
2.4Distribution
barycenter migration under different scenarios of future global climate
change
We divided the study area into small grids of 0.1°×0.1° to evaluate the
distribution barycenter migration of B. gargarizans in China.
Assuming that our research area is composed of N small grids, the
proportion of the kth grid is
GK=PK×SK, and
PK represents the probability of the species appearing
in the kth small grid, and SKrepresents the area of the kth small grid. The
coordinates of each grid are obtained by Arcgis10.2. The following
formula is used to obtain the coordinates of the barycenter:
X= \(\frac{\sum_{k=1}^{k}G_{k}X_{k}}{\sum_{k=1}^{k}G_{k}}\);
Y=\(\frac{\sum_{k=1}^{k}G_{k}Y_{k}}{\sum_{k=1}^{k}G_{k}}\)
Here, X and Y represent the longitude and latitude of the
kth grid, respectively(He et al., 2011).
The results of four GCMs under four RCPS were averaged to obtain
Pf, using △P(△P=Pf-Pc,
Pc represents the current distribution probability ofB. gargarizans ) to evaluate the change trend of B.
gargarizans distribution in China in 2050 and 2070:
-0.1<ΔP≤0.1 indicated that the habitat suitability changes
were not obvious. 0.1<ΔP≤0.3 , 0.3<ΔP≤0.5 and
0.5<ΔP≤0.7 represented slight, moderate and severe increasein
in habitat suitability, respectively. -0.3<ΔP≤-0.1,
-0.5<ΔP≤-0.3 and -0.7 <ΔP≤ -0.5 represented slight,
moderate and severe declines in habitat suitability,
respectively.