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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (5): 752-773    DOI: 10.1016/j.jaridl.2026.05.002     CSTR: 32276.14.JAL.20250303
Research article     
Anthropogenic activities amplify spatiotemporal variations in regional ecological security patterns dominated by natural factors: Evidence from the West Liaohe River Basin, China
LYU Xin1, LI Xiaobing1,2,*(), WANG Kai1, CAO Wanyu1, ZHANG Chenhao1
1 State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2 School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
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Abstract  

Ecological security patterns (ESPs) represent an effective way to maintain regional ecological security and promote regional sustainable development. This study investigated the spatiotemporal variations of ESPs in the West Liaohe River Basin (WLRB), China during 2000-2020 on the basis of five key ecosystem services (net primary production, soil conservation, habitat quality, water retention, and soil loss by wind). On the basis of the Geodetector model, we initially measured the explanatory rates of various natural and anthropogenic factors on the spatial differentiation of ecological sources and ecological corridors. The Geographically and Temporally Weighted Regression (GTWR) model was subsequently used to elucidate the driving mechanism of ESPs at the interannual scale. During 2000-2020, a "fan-shaped" ESP of "two zones, three belts, and many branches" formed in the WLRB. Natural factors dominated the spatial distribution of ESPs, and the average spatial explanation rate for ecological sources and ecological corridors was 23.86%, which was higher than that of anthropogenic activities (13.29%). However, anthropogenic activities amplified the spatiotemporal variations in ESPs. On this basis, this study proposed an ecological security protection and regulation strategy from three aspects, namely, regional priority, suitability analysis, and risk regulation, which might provide a working direction for regional practical management. This study extends the paradigm of ESP research and offers an important theoretical basis for regional ecological security, from "passive management" to "active management".

Key words： ecological security patterns (ESPs)      ecosystem services      anthropogenic activities      Geodetector      Geographically and Temporally Weighted Regression (GTWR) model      West Liaohe River Basin     
Received: 02 July 2025      Published: 31 May 2026
Corresponding Authors: *LI Xiaobing (E-mail: xbli@bnu.edu.cn)
About author: Author contributions

Conceptualization: LYU Xin; Methodology: LYU Xin; Formal analysis: LYU Xin, WANG Kai; Writing - original draft preparation: LYU Xin, WANG Kai; Writing - review and editing: LYU Xin, LI Xiaobing, WANG Kai, CAO Wanyu, ZHANG Chenhao; Funding acquisition: LYU Xin, LI Xiaobing; Resources: LI Xiaobing; Supervision: LI Xiaobing. All authors approved the manuscript.

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LYU Xin
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LYU Xin, LI Xiaobing, WANG Kai, CAO Wanyu, ZHANG Chenhao. Anthropogenic activities amplify spatiotemporal variations in regional ecological security patterns dominated by natural factors: Evidence from the West Liaohe River Basin, China. Journal of Arid Land, 2026, 18(5): 752-773.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.05.002     OR     http://jal.xjegi.com/Y2026/V18/I5/752

Fig. 1 Distribution of land use/land cover (LULC) types in the West Liaohe River Basin (WLRB) in 2020. The percentages following the LULC types correspond to the area proportions.
Fig. 2 Research framework of this study. NDVI, normalized difference vegetation index; DEM, digital elevation model; ESPs, ecological security patterns; GRP, gross regional product; GP, grain production; GD, grazing density; PD, population density; DNS, distance to the nearest settlement; DNR, distance to the nearest road; ESs, ecosystem services; GTWR, Geographically and Temporally Weighted Regression.
Type Interaction relationship Criterion
1 Nonlinear weakening q(X1∩X2)<Min(q(X1), q(X2))
2 Single-factor nonlinear weakening Min(q(X1), q(X2))<q(X1∩X2)<Max(q(X1), q(X2))
3 Two-factor enhancement q(X1∩X2)>Max(q(X1), q(X2))
4 Independent q(X1∩X2)=q(X1)+q(X2)
5 Nonlinear enhancement q(X1∩X2)>q(X1)+q(X2)
Table 1 Classification of the interaction relationships between two factors
Fig. 3 Spatial distribution of ESPs in the WLRB in 2000 (a), 2005 (b), 2010 (c), 2015 (d), and 2020 (e). A "fan-shaped" ESP with "two zones, three belts, and many branches" is shown in Figure 3e.
Fig. 4 Driver analysis of ecological sources. (a), q statistics of impact factors; (b), difference analysis of the mean q values of impact factors in ecological sources and non-ecological sources; (c), interaction effects between impact factors. The values in Figure 5c represent the q-statistics when the two factors interact.
Fig. 5 Driver analysis of ecological corridors. (a), q-statistics of impact factors; (b), difference analysis of the mean q values of impact factors between ecological corridors and non-ecological corridors; (c), interaction effects between impact factors. The values in Figure 5c represent the q-statistics when the two factors interact.
Fig. 6 Spatial variations in the regression coefficients of precipitation (a1-a5), PD (b1-b5), GRP (c1-c5), GP (d1-d5), and GD (e1-e5) as ecological source drivers in the WLRB at the subbasin scale during 2000-2020
Fig. 7 Spatial variations in the regression coefficients of precipitation (a1-a5), PD (b1-b5), GRP (c1-c5), GP (d1-d5), and GD (e1-e5) as ecological corridor drivers in the WLRB at the subbasin scale during 2000-2020
Fig. 8 Strategy analysis for watershed ecological security protection. (a), regional priority; (b), suitability analysis of ecological protection; (c), suggested areas for protection; (d), risk regulation.
Fig. S1 Spatial distributions of the driving factors of ecological security patterns (ESPs). (a), digital elevation model (DEM); (b), slope; (c), aspect; (d), temperature; (e), precipitation; (f), population density (PD); (g), distance to the nearest road (DNR); (h), distance to the nearest settlement (DNS); (i), gross regional product (GRP); (j), grain production (GP); (k) grazing density (GD).
Fig. S2 Threshold effect analysis for driving factors of ESPs. (a), aspect; (b), DEM; (c), GRP; (d), GP; (e), GD; (f), precipitation; (g), PD; (h), DNS; (i), DNR; (j), slope; (k), temperature. The x-axis represents the number of categories of the independent variables.
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