## 3 Results and Discussion
### The Relationship between Ecospatial Network Topology Index and NPP
#### 3.3.2 Correlation Analysis between Topological Indices and NPP of Different Land Cover Types  
Since the topological indices values of ecological nodes in different land cover types in the study area are different, and their correlation and regression relationship with NPP is also significantly different, this paper divides the ecological source patches into three basic land areas: woodland, grassland, and water. The ecological nodes extracted from these three ecological source patches are also divided into three types. The topological indices of these three types of ecological nodes are correlated and regressed with NPP, respectively. Firstly, the Pearson correlation coefficients and significance levels of the degree and clustering coefficients of nodes with different land cover types and NPP were calculated separately using SPSS, where the significance test results less than 0.05 are usually considered to mean that the results are significant, i.e., the results are not due to sampling error and are statistically significant. The results are shown in Table 3.  
Figure 13: NPP spatial distribution. The results show that only the ecological nodes in woodland and grassland had a significant correlation between NPP and degree (\(p<0.01\)), among which there is a strong negative correlation between the degree of nodes in forest land and NPP with a correlation of \(-0.444\); the negative correlation between the degree of nodes in the grassland and NPP is lower with a correlation of \(-0.217\). There is no significant correlation between the degree of ecological nodes in the waters and NPP, as well as the clustering coefficient of ecological nodes in the three land cover types and NPP (\(p>0.05\)), and the absolute values of the correlation coefficient are also low, indicating that the correlation is low and the results might be caused by accidental factors and are not statistically significant. Since there is a significant correlation between the degree of ecological nodes in woodland and grassland and NPP, further regression analysis was applied to these two sets of data. In order to evaluate the fitting ability of the model and select the optimal regression model, this paper used the goodness of fit index (R\({}^{2}\)). R\({}^{2}\) indicates the goodness of fit of the model and the stability of model validation; the closer R\({}^{2}\) is to 1, the better the dependent variable can be explained by the independent variable, and the better the model fits [46, 47]. SPSS was used to calculate R\({}^{2}\) and the significance level of the linear regression model and each curve regression model for comparison. The results are shown in Table 4.  
The results show that all curve models are correlated at the significance level of 0.01. In the regression model between the degree of woodland nodes and NPP, the logarithmic function curve has an R\({}^{2}\) value closest to 1, and the best goodness of fit; in the regression model between the degree of grassland nodes and NPP, the R\({}^{2}\) value of logarithmic function curve is also the closest to 1, and the goodness of fit is the best. Therefore, the logarithmic function curve is selected for fitting. The fitting results are shown in Figure 14. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regression** & \multicolumn{3}{c}{**Woodland**} & \multicolumn{2}{c}{**Grassland**} \\ \cline{2-5}
**Model** & **R\({}^{2}\)** & **\(p\)-Value \({}^{*}\)** & **R\({}^{2}\)** & **\(p\)-Value \({}^{*}\)** \\ \hline Linear model & 0.197 & \(<\)0.01 & 0.047 & \(<\)0.01 \\ \hline Logarithmic function curve & 0.210 & \(<\)0.01 & 0.089 & \(<\)0.01 \\ \hline Power function curve & 0.105 & \(<\)0.01 & 0.047 & \(<\)0.01 \\ \hline Exponential function curve & 0.096 & \(<\)0.01 & 0.024 & \(<\)0.01 \\ \hline \hline \end{tabular} \({}^{*}\)\(p\)-value is the abbreviation of significance level, which is the result of the significance test. \(p\)-Value \(<0.01\) means significance at the 0.01 level.  
\end{table} 
Table 4: Comparison of the goodness of fit of regression models.