## 4 Results
### Spatial Autocorrelation Analysis of Vegetation Dynamics in the ARB
#### 4.3.1 Regional Impacts of Climate Changes on Vegetation
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Independent**} & \multicolumn{2}{c}{**Beta Coefficient**} & \multicolumn{4}{c}{**VIP**} \\ \cline{2-13}  & **LAI** & **FVC** & **GPP** & **Model** & **LF 1** & **LF 2** & **LF 3** & **LF 4** & **LF 5** \\ \hline Pcp & 0.0011 & 0.0002 & 0.4972 & 1.5346 & 1.6776 & 1.6435 & 1.5819 & 1.5751 & 1.5715 \\ Tavg & 0.0048 & \(-0.0006\) & 4.6085 & 0.7952 & 0.6384 & 0.7849 & 0.7568 & 0.7552 & 0.7535 \\ Tmax & 0.0078 & \(-0.0009\) & 6.4948 & 0.8564 & 0.7404 & 0.8523 & 0.8283 & 0.8274 & 0.8263 \\ Tmin & 0.0020 & \(-0.0006\) & 2.8969 & 0.7935 & 0.6432 & 0.7820 & 0.7528 & 0.7496 & 0.7481 \\ Forest & 0.6001 & 0.1132 & 237.3714 & 1.6577 & 1.7452 & 1.6676 & 1.7356 & 1.7357 & 1.7317 \\ Pasture & \(-0.6216\) & \(-0.1269\) & \(-270.6097\) & 1.4005 & 1.4170 & 1.5180 & 1.5098 & 1.5025 & 1.5008 \\ Wetland & \(-0.6399\) & \(-0.0686\) & \(-272.2829\) & 0.5943 & 0.5958 & 0.5673 & 0.5588 & 0.5665 & 0.5786 \\ Crop & \(-0.2707\) & \(-0.0376\) & \(-84.2908\) & 0.6461 & 0.4925 & 0.5442 & 0.5552 & 0.5981 & 0.6009 \\ Residential & \(-0.9260\) & \(-0.2014\) & \(-384.5357\) & 0.4913 & 0.4026 & 0.3803 & 0.4589 & 0.4680 & 0.4679 \\ Water & \(-0.6076\) & \(-0.1382\) & \(-221.1102\) & 0.5024 & 0.3042 & 0.2963 & 0.5232 & 0.5220 & 0.5233 \\ Range & \(-0.6574\) & \(-0.1851\) & \(-237.5839\) & 0.4536 & 0.4024 & 0.3998 & 0.4715 & 0.4755 & 0.4807 \\ SURQ & \(-0.0052\) & \(-0.0001\) & \(-2.0949\) & 1.2230 & 1.2454 & 1.1747 & 1.2252 & 1.2290 & 1.2262 \\ GWQ & 0.0020 & 0.0006 & 0.7943 & 0.8109 & 0.7918 & 0.7407 & 0.7975 & 0.7985 & 0.7967 \\ LATQ & 0.0825 & 0.0617 & \(-32.9155\) & 0.8351 & 0.8570 & 0.8210 & 0.8140 & 0.8138 & 0.8234 \\ ET & 0.0071 & 0.0008 & 4.4259 & 0.7231 & 0.4901 & 0.7509 & 0.7228 & 0.7193 & 0.7207 \\ SW & 0.0018 & 0.0003 & 1.1370 & 1.2293 & 1.3418 & 1.2865 & 1.2371 & 1.2401 & 1.2440 \\ Snowmelt & \(-0.0006\) & 0.0018 & \(-0.8766\) & 1.2590 & 1.3398 & 1.2543 & 1.2591 & 1.2552 & 1.2529 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Variable importance in the projection (VIP) values and beta coefficients of environmental factors in the PLSR model analysis. Argun River Basin and the Shilka River Basin. Areas with relatively low coefficient values were found to be mostly distributed in the northeastern part of the basin, near the border of the Sanjiang Plain in China and in Khabarovsk Krai and the Jewish Autonomous Oblast in Russia. The area of negative Pcp regression coefficient values spread over time, or the intensity of the negative effect of Pcp increased. Figure 6: The spatial distribution of the regression coefficients for Pcp and Tmax with the LAI ((**a–d**) present the coefficients for Periods 1–4, respectively), FVC ((**e–h**) show the coefficients for Periods 1–4, respectively), and GPP ((**i–l**) exhibit the coefficients for Periods 1–4, respectively) GTWR models for four studied periods. The spatial distributions of the regression coefficients for Tmax with the LAI, FVC, and GPP GTWR models for the four studied periods are exhibited in the lower part of Figure 6. The effects of Tmax on LAI and GPP showed similar spatial and temporal patterns and, unlike the effects of Pcp, were roughly concentrated in high-value areas in the north and low-value areas in the south of the basin. The impact of Tmax on FVC, in contrast, had a distinct area of low values in the central part of the study area.