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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (11): 1518-1541    DOI: 10.1007/s40333-025-0111-x    
Research article     
Enhancing ecological network connectivity in semi-arid mountain areas through minimal landscape restructuring
PAN Yilu1,2, YANG Xia1,2, FANG Yuxuan1,2, PAN Hongyi1,2,*(), ZHANG Wen3
1Faculty of Geography and Resources Sciences, Sichuan Normal University, Chengdu 610066, China
2Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Ministry of Education, Chengdu 610066, China
3School of Civil Engineering and Water Resources, Qinghai University, Xining 810016, China
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Abstract  

Increasing human disturbance and climate change have threatened ecological connectivity and structural stability, especially in semi-arid mountain areas with sparse vegetation and weak hydrological regulation. Large-scale ecological restoration, such as adding ecological sources or corridors, is difficult in such environments and often faces poor operability and high implementation costs in practice. Taking the southern slope of the Qilian Mountains in China as the study area and 2020 as the baseline, this study integrated weighted complex network theory into the "ecological source-resistance surface-corridor" framework to construct a heterogeneous ecological network (EN). Circuit theory was integrated with weighted betweenness to identify critical barrier points for locally differentiated restoration, followed by assessment of the network optimization effects. The results revealed that 494 ecological sources and 1308 ecological corridors were identified in the study area. Fifty-one barrier points with restoration potential were identified along key ecological corridors and locally restored. After optimization, the network gained 11 additional ecological corridors, and the total ecological corridor length increased by approximately 1143 km. Under simulated attacks, the decline rates of maximum connected subgraph (MCS) and network efficiency (Ne) slowed compared with pre-restoration conditions, indicating improved robustness. These findings demonstrate that targeted local restoration can enhance network connectivity and stability while minimizing disturbance to the overall landscape pattern, providing a practical pathway for ecological restoration and sustainable management in semi-arid mountain areas.

Key words： ecological networks      circuit theory      complex networks      gravity model      ecosystem services      ecological source      ecological barrier point     
Received: 05 June 2025      Published: 30 November 2025
Corresponding Authors: *PAN Hongyi (E-mail: panhongyi80@sicnu.edu.cn)
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PAN Yilu
YANG Xia
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ZHANG Wen
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PAN Yilu, YANG Xia, FANG Yuxuan, PAN Hongyi, ZHANG Wen. Enhancing ecological network connectivity in semi-arid mountain areas through minimal landscape restructuring. Journal of Arid Land, 2025, 17(11): 1518-1541.

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http://jal.xjegi.com/10.1007/s40333-025-0111-x     OR     http://jal.xjegi.com/Y2025/V17/I11/1518

Fig. 1 Elevation (a) and land use type (b) of the southern slope of the Qilian Mountains
Data Type Resolution Source
Land cover Raster 30 m Resource and Environmental Science and Data Platform (https://www.resdc.cn)
Digital elevation model (DEM) Raster 30 m Geospatial Data Cloud (https://www.gscloud.cn)
Slope Raster 90 m Geospatial Data Cloud (https://www.gscloud.cn)
Normalized difference vegetation index (NDVI) Raster 30 m National Aeronautics and Space Administration (NASA) Earth Observation Data (https://www.earthdata.nasa.gov)
Net primary productivity (NPP) Raster 1 km NASA Earth Observation Data (https://www.earthdata.nasa.gov)
Precipitation Raster 1 km National Earth System Science Data Center (http://www.geodata.cn)
Road network Vector - OpenStreetMap (https://www.openstreetmap.org)
Administrative boundary Vector - Resource and Environmental Science and Data Platform (https://www.resdc.cn)
Table 1 Detailed description of data used in this study
Fig. 2 Framework for construction, optimization, and evaluation of ecological networks (ENs). MSPA, morphological spatial pattern analysis; MCR, minimum cumulative resistance.
Fig. 3 Schematic diagram of restoration strategy
Fig. 4 Spatial distribution of provisioning (a), regulating (b), supporting (c), and cultural (d) service functions and the composite ecosystem service (e) in the southern slope of the Qilian Mountains in 2020. (a1), food production; (a2), raw material production; (a3), water supply; (b1) gas regulation; (b2), climate regulation; (b3), waste treatment; (b4), hydrological regulation; (c1), soil conservation; (c2) nutrient cycling maintenance; (c3) biodiversity maintenance.
Land use type Cropland Forest land Grassland Water body Construction land Barren land
CS 0.0260 0.0987 0.0595 0.8147 0.0000 0.0013
Table 2 Coefficient of sensitivity (CS) of ecosystem service for different land use types
Fig. 5 Spatial distribution of MSPA landscape types in the southern slope of the Qilian Mountains in 2020
Fig. 6 Spatial distribution of ecological sources in the southern slope of the Qilian Mountains in 2020
Fig. 7 Spatial distribution of individual resistance factor after uniform weighting process in the southern slope of the Qilian Mountains in 2020. (a), digital elevation model (DEM); (b), slope; (c), distance to road; (d), normalized difference vegetation index (NDVI); (e), land use type; (f), habitat quality.
Fig. 8 Spatial distribution of integrated resistance surface of the southern slope of the Qilian Mountains in 2020
Fig. 9 Spatial distribution of ecological corridors in the southern slope of the Qilian Mountains in 2020
Fig. 10 Weighted complex network and corridor priority classification in the southern slope of the Qilian Mountains in 2020. The size of node reflects the influence of the node within the overall network; the bigger the node is, the higher importance the ecological source is. Numbers in the figure represent the ecological sources identified by this study.
Fig. 11 Spatial distribution of ecological barriers in the southern slope of the Qilian Mountains in 2020
Fig. 12 Spatial distribution of barrier points on the key ecological corridors in the southern slope of the Qilian Mountains in 2020. (a), Barrier Points 1-6; (b), Barrier Points 7-27; (c), Barrier Points 33 and 36-40; (d), Barrier Points 28, 32, 34, 35, 41-45, and 47-53; (e), Barrier Points 29-31; (f), Barrier Point 46; (g), Barrier Points 54-59; (h), Barrier Points 81 and 85-87; (i), Barrier Points 61, 62, 64, 65, 67, 71, and 73; (j), Barrier Points 60, 63, and 66; (k), Barrier Points 68-70, 74, 76, 78-80, 82, and 88-91; (l), Barrier Points 96-99; (m), Barrier Points 72, 75, and 84; (n), Barrier Points 77, 83, and 92-95.
Fig. 13 Integrated visualization of land use types (a) and stability of selected barrier points as restoration sites across different buffer distances (b) in the southern slope of the Qilian Mountains in 2020
Fig. 14 Spatial distribution of the ecological resistance surface before (a) and after optimization (b) in the southern slope of the Qilian Mountains in 2020
Fig. 15 Spatial distribution of ecological corridors after optimization
Fig. 16 Changes in robustness of ENs before (a and b) and after (c and d) optimization under different attack methods in the southern slope of the Qilian Mountains in 2020. MCS, maximum connected subgraph; Ne, network efficiency.
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