## Materials and Methods
### Research Method
#### 2.2.3 Habitat Quality Assessment  
The InVEST framework served as an all-encompassing assessment model for measuring diverse ecosystem service operations. Data on habitat quality mainly originated from the InVEST model, evaluating an ecosystem's capacity to create conducive environments for species' survival and reproduction. The index for assessing habitat quality ranged between 0 and 1, with higher index values indicating an ecosystem with greater resilience and improved habitat conditions [40]. Habitat degradation reflected the degree of change in habitat caused by human activities, specifically the level of degradation due to threat factors. A higher habitat degradation value indicated a greater level of threat to the habitat. The assessment of habitat quality could evaluate habitat degradation across different regions and serve as an indirect indicator of biodiversity. This metric was crucial in assessing an ecosystem's ability to offer appropriate living environments for different species [41]. First, we preprocessed the land use data in ArcGIS. No-data values were reclassified as 0, while the remaining land use data were categorized into seven types: wetlands, water bodies, grasslands, arable land, forests, barren land, and built-up areas. We then input land use data, threat factors, and sensitivity to those factors, calculating a habitat quality index ranging from 0 to 1 on a raster grid basis. The model parameters were set based on the suggested figures from the InVEST model manual and pertinent scholarly works [42; 43], adjusted for the specific conditions of the study area (Tables 4 and 5). The specific formula for evaluating habitat quality by using habitat quality index is outlined below:  
\[Q_{xj}=H_{j}\Bigg{(}1-\frac{D_{xj}^{z}}{D_{xj}^{z}+k^{z}}\Bigg{)} \tag{2}\]  
In Formula (2), \(Q_{xj}\) is the habitat quality of grid \(j\) in certain land use type \(j\); \(k\) is a half-full sum constant; \(H_{j}\) represents the appropriateness of the habitat for land use type \(j\); \(D_{xj}\) is the habitat stress level of grid \(x\) in a habitat type \(j\), represented by land use. \(D_{xj}\) satisfies the following formula:  
\[D_{xj}=\sum_{\tau=1}^{R}\sum_{y=1}^{\gamma_{r}}\Big{(}W_{r}/\sum_{\tau=1}^{R}W _{r}\Big{)}r_{y}i_{rxy}\beta_{x}S_{jr} \tag{3}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Stress Factor**} & \multicolumn{2}{c}{**Maximum Impact Distance/km**} & \multirow{2}{*}{**Weight**} & \multirow{2}{*}{**Attenuation Type**} \\ \cline{1-1} \cline{5-5}  & & & \\ \hline \multicolumn{1}{c}{Plowland} & 5 & 0.5 & linearity \\ \multicolumn{1}{c}{Construction land} & 8 & 1.0 & exponent \\ \multicolumn{1}{c}{Unutilized} & 6 & 0.5 & linearity \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Resistance factors and their maximum influence distance, weight, and attenuation type. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Land Use Types**} & \multirow{2}{*}{**Habitat Suitability**} & \multicolumn{2}{c}{**Plowland (Stress Factor)**} & \multirow{2}{*}{**Construction Land (Stress Factor)**} & \multirow{2}{*}{**Unutilized (Stress Factor)**} \\ \hline Wet land & 0.7 & 0.4 & 0.5 & 0.3 \\ Waters & 0.6 & 0.3 & 0.4 & 0.2 \\ Meadow & 0.8 & 0.7 & 0.6 & 0.5 \\ Flowland & 0.1 & 0.2 & 0.8 & 0.5 \\ Forest land & 1 & 0.6 & 0.4 & 0.2 \\ unutilized & 0.4 & 0.5 & 0.4 & 0.2 \\ Construction land & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Resistance factor parameters of different land types. In Formula (3), \(R\) represents the count of stress-related factors, and \(W\), represents the weight of stress factor \(r\). \(Y_{r}\) is the number of grids of stress factor layers in land use data. \(r_{y}\) represents the count of stress elements present in each category of the land use grid. \(\beta_{x}\) represents the degree of accessibility for grid \(x\), with a maximum value of 1, indicating maximum reachability.