# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 3 Results
### Spatial Heterogeneity of Human Impacts on FEF  
In order to compare the spatial differences between the driving factors, we calculate the mean regression coefficients in different physical geographic regions and visualize them using ArcGIS 10.8. Similarly, the coefficients are all classified into five grades with natural breaks classification, as is shown in Figure 4.  
We find that human activities exert a substantial impact on FEF, and the coefficients and significance levels of each factor show marked spatial heterogeneity. After controlling the interference from natural factors, the core human activity factors affecting FEF are caught in five of the eight natural geographic regions, as shown in Table 4. The results and discussion are carried out according to physical geographic subdivisions in the following section. Figure 3: Regression error probability distribution. The combined analysis of Figure 4 and Table 4 shows that in the source area of YRB (Region I), the expansion of productive land (PDUC) is the major inhibitor of FEF. Economic development (EDS) exhibits both positive and negative impacts on FEF, while other human activities mentioned contribute positively to the FEF. In Region II, occupied mainly by the Qinghai Lake and the Gonghe Basins, economic development, especially agricultural growth (AGRI), and encroachments on ecological land are detrimental to the FEF, which can be improved by population agglomeration (PDS) and forest protection (PROT). The \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{8}{c}{**Coefficient**} \\ \cline{2-9}  & **I** & **II** & **III** & **IV** & **V** & **VI** & **VII** & **VIII** \\ \hline ECO & 0.047 & \(-\)0.175 & \(-\)0.631 *** & \(-\)0.021 & \(-\)0.079 & \(-\)0.068 & \(-\)0.073 & \(-\)0.059 \\ AGRI & 0.051 & \(-\)0.133 & \(-\)0.327 *** & \(-\)0.170 ** & \(-\)0.179 * & \(-\)0.108 * & \(-\)0.097 & \(-\)0.021 \\ INDS & 0.116 & 0.008 & 0.162 & \(-\)0.094 & \(-\)0.037 & 0.044 & 0.030 & 0.105 \\ PDS & 1.138 & 0.136 & \(-\)0.212 & \(-\)0.282 & \(-\)0.082 & \(-\)0.085 & 0.075 & 0.579 \\ URB & 0.067 & 0.100 & 0.246 & 0.010 & \(-\)0.083 & \(-\)0.021 & 0.056 & 0.273 ** \\ PDUC & \(-\)0.198 & \(-\)0.086 & \(-\)0.730 *** & 0.050 & 0.101 & \(-\)0.033 & 0.065 & 0.166 ** \\ LIVE & 0.281 & \(-\)0.186 & \(-\)0.343 & 0.026 & 0.157 & 0.098 * & 0.072 & \(-\)0.108 \\ PROT & 0.122 & 0.090 & 0.024 & 0.211 * & 0.058 & 0.022 * & \(-\)0.014 & \(-\)0.016 \\ TEMP & 0.043 & \(-\)0.016 & \(-\)0.652 *** & 0.246 & 0.511 ** & 0.516 *** & 0.092 & \(-\)0.159 \\ PREC & 0.315 & 0.308 ** & 0.508 *** & 0.328 ** & 0.174 & 0.347 *** & 0.030 & \(-\)0.015 \\ ELEV & 0.020 & \(-\)0.097 & \(-\)0.943 *** & 0.304 ** & 0.694 ** & 0.825 *** & 0.204 & 0.001 \\ SLOP & 0.005 & 0.124 & 0.231 ** & 0.165 ** & 0.107 & 0.180 * & 0.236 ** & 0.193 \\ SOIL & \(-\)0.041 & \(-\)0.202 & \(-\)0.397 *** & \(-\)0.125 & \(-\)0.012 & 0.055 & 0.034 & \(-\)0.007 \\ \hline \hline \end{tabular} Note: ***, **, and * indicate significance at 0.01, 0.05, and 0.1 levels, respectively (two-tailed). Refer to Table 2 for definitions of the acronyms.  
\end{table} 
Table 4: The core influencing factors on FEF in different physical geographic regions. Figure 4: Spatial distribution of mean regression coefficients for each influencing factor. Refer to Table 2 for definitions of the acronyms. development of the secondary sector (INDS) and the urbanization of permanent residents (URB) reveal both positive and negative impacts on FEF. Regarding Region III, the wetlands of West Sichuan and East Tibet Plateau, EDS, AGRI, PDUC and LIVE are significantly constraining the FEF. We find that changes in FEF are very sensitive to the above activities compared to others. Region IV is the most livable in the upper reaches of the Yellow River, where the more densely populated counties have better FEF. The protection of forests also greatly benefits the improvement of FEF.