## Materials and Methods
### Research Method
#### 2.2.4 Green Space Ecological Network Construction  
(1) Resistance factor weight calculation  
Based on a combination of vegetation cover, land use, and slope factors, this study selected factors such as soil erosion sensitivity, land use type, vegetation coverage, and resistance surface patterns derived from comprehensive ecological security evaluations. Resistance values were assigned based on related studies [44]. ArcGIS 10.8.2 facilitated the grading and normalization of each impact factor, followed by assigning weights to each index based on the AHP, employing the geometric average technique for weight integration, and finally obtaining the detailed weights. (2) Resistance factor weight consistency test  
To maintain the logic behind the judgment matrix, it was necessary to determine the comparative significance of each component in the matrix in relation to its highest factor, as depicted in the formula:  
\[C_{I}=\frac{\lambda_{max}}{m-1} \tag{6}\]  
In Formula (6), \(\lambda_{max}\) represents the highest eigenvalue in the judgment matrix, while \(C_{I}\) denotes the index of consistency, and \(m\) represents the order of the target judgment matrix. If this formula were satisfied, it would pass the consistency test; otherwise, it was invalid and required revision, as shown in the following formula:  
\[C_{R}=\frac{C_{I}}{R_{I}}<0.1 \tag{7}\]  
In Formula (7), \(C_{I}\) represents the index of consistency, \(R_{I}\) denotes the index of random consistency and \(C_{R}\) signifies the ratio of random consistency. In this paper, the consistency test was evaluated according to \(C_{R}\) and \(R_{I}\). By comparing the ratio \(C_{R}\) to 0.1 of the consistency test of each matrix, if \(C_{R}\) a value of less than 0.1; the matrix was deemed to have successfully met the consistency criteria. (3) Habitat source extraction (landscape connectivity analysis)  
First, the core area patches identified by MSPA were extracted in ArcGIS. Subsequently, the core areas were allocated the habitat quality index derived from the InVEST model. Patches with higher habitat quality were identified as habitat sources [45]. The research focused on choosing the Integral Index of Connectivity (dIIC) and the Probability of Connectivity (dPC), calculating their relative importance (dl) to prioritize habitat sources. Areas with higher dl values were selected as core habitat sources [46; 47]. Quantification of the indicators was conducted through Conefor 2.6, setting the patch connectivity distance limit at 500 m and the likelihood of connectivity at 0.5. The primary formula for calculation is outlined as follows:  
1. Global connectivity index \[dIIC=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}\frac{a_{i}a_{j}}{1+l_{ij}}}{A_{L}^{2}}\] (8) In Formula (8), \(n\) signifies the aggregate count of patches in the terrain, with \(a_{i}\) and \(a_{j}\) indicating the respective areas of patches \(i\) and \(j\), and \(l_{ij}\) denoting the connections between patches \(i\) and \(j\). When \(0<dIIC<1\), a \(dIIC\) value of 0 indicates no relationship between habitat patches. Conversely, a \(dIIC\) figure of 1 signifies that the total region encompassed by the calculation constitutes a habitat patch. 2. Possible connectivity index \[dPC=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}p_{ij}^{*}a_{i}a_{j}}{A_{L}^{2}}\] (9) In Equation (9), \(n\) signifies the aggregate count of habitat patches, \(p_{ij}^{*}\) denotes the highest likelihood of species dispersal between habitat patch \(i\) and \(j\), \(a_{i}\) indicates the expanse of habitat patch \(i\), and \(a_{j}\) denotes the area of habitat patch \(j\). The PC value ranges between 0 and 1. 3. Relative importance of both \[dI=0.5 dIIC+0.5 dPC\] (10) In Formula (10), \(dI\) symbolizes the potential connectivity index within the landscape, while \(dPC\) denotes the potential connectivity index after patch removal. 4.