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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (3): 406-428    DOI: 10.1016/j.jaridl.2026.03.004     CSTR: 32276.14.JAL.20250502
Research article     
Spatiotemporal evolution of ecosystem services and ecological connectivity optimization in arid Northwest China
HE Jing1,2, YU Yang1,2,3,*(), SUN Lingxiao1,2, LI Chunlan1,2, GUO Zengkun1,3, LU Yuanbo1,3, Ireneusz MALIK2, Malgorzata WISTUBA2
1State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Polish-Chinese Centre for Environmental Research, Institute of Earth Sciences, University of Silesia in Katowice, Katowice 40-007, Poland
3University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Northwest China serves as a critical ecological barrier region for maintaining national water, energy, and food security, as well as transboundary ecological governance. However, under the dual pressures of climate change and human activities, ecosystem services (ESs) are facing severe challenges in this region. Based on multi-source remote sensing and statistical data during 2000-2020, this study investigated the spatiotemporal evolution characteristics of four key ESs (water yield, habitat quality, carbon storage, and food provisioning) in Northwest China using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Integrating morphological spatial pattern analysis (MSPA) and circuit theory, we identified ecological sources, corridors, pinch points, and barriers, and further designed three optimization scenarios (bottleneck optimization, high-resistance corridor buffering, and barrier removal optimization) to enhance landscape connectivity. The results revealed that ES supply and demand exhibited marked spatial heterogeneity, with high-supply areas concentrated in the southeastern sectors. Ecological sources primarily distributed in the southeastern and northern sectors, and ecological resistance surfaces continuously intensified. Water yield and habitat quality demands were increasing, food provisioning demand was decreasing, and carbon storage demand was surging. A total of 61 ecological sources (8% of the study area), 142 ecological corridors (24,957 km in total length), 237 ecological pinch points, and 89 barrier zones were identified. Among the three optimization scenarios, barrier removal achieved optimal connectivity improvement across all distance thresholds, with the probability of connectivity index improvement reaching up to 4%. This study provides scientific foundations and spatial decision support for ecological network optimization and sustainable governance in arid and semi-arid areas.

Key words： ecosystem services (ESs)      landscape connectivity      Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model      morphological spatial pattern analysis (MSPA)      circuit theory      barrier removal scenario     
Received: 09 October 2025      Published: 31 March 2026
Corresponding Authors: *YU Yang (E-mail: yuyang@ms.xjb.ac.cn)
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HE Jing
YU Yang
SUN Lingxiao
LI Chunlan
GUO Zengkun
LU Yuanbo
Ireneusz MALIK
Malgorzata WISTUBA
Cite this article:

HE Jing, YU Yang, SUN Lingxiao, LI Chunlan, GUO Zengkun, LU Yuanbo, Ireneusz MALIK, Malgorzata WISTUBA. Spatiotemporal evolution of ecosystem services and ecological connectivity optimization in arid Northwest China. Journal of Arid Land, 2026, 18(3): 406-428.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.03.004     OR     http://jal.xjegi.com/Y2026/V18/I3/406

Fig. 1 Overview of the study area based on elevation data (a) and land use data in 2020 (b). The boundary is based on the standard map (GS(2023)2767) of the Map Service System (https://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the boundary has not been modified.
Data Temporal coverage Original resolution Source Official URL
Land use 1990, 2000, 2010, and 2020 30 m Resource and Environmental Science Data Platform https://www.resdc.cn/DOI/DOI.aspx?DOIID=54
Population density (persons/km2) 1990-2020 1 km WorldPop Project, University of Southampton https://hub.worldpop.org/geodata/summary?id=39793
Administrative boundaries 2020 - National Geographic Information Resource Catalog Service System http://www.webmap.cn
Elevation (m) 2020 30 m Geospatial Data Cloud, CNIC, Chinese Academy of Sciences http://www.gscloud.cn
Soil 2020 1 km FAO Soils Portal http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/en/
Precipitation (mm) 1990-2020 1 km National Earth System Science Data Center https://www.geodata.cn
Temperature (°C) 1990-2020 1 km National Qinghai Tibet Plateau Science Data Center https://cstr.cn/18406.11.Meteoro.tpdc.270961
Carbon emission inventory 1990-2020 1 km Carbon Emission Accounts & Datasets https://www.ceads.net/news/20211262.html
Road network 2020 1 km OpenStreetMap Contributors http://www.openstreetmap.org
GDP
(104 CNY/km2)		 1990-2020 1 km Resource and Environmental Science Data Platform https://www.resdc.cn/DOI/DOI.aspx?DOIID=33
Table 1 Datasets used in this study
Land use type Habitat suitability Susceptibility
Urban land Bare land Cropland Road
Cropland 0.3 0.9 0.5 0.2 0.0
Forestland 1.0 0.8 0.2 0.5 0.3
Grassland 0.9 0.5 0.3 0.2 0.3
Urban land 0.0 0.0 0.0 0.0 0.6
Bare land 0.1 0.3 0.2 0.1 0.0
Water bodies 1.0 0.6 0.5 0.4 0.0
Table 2 Sensitivity of land use types to habitat threat factors
Land use type Cabove (t/hm2) Cbelow (t/hm2) Csoil (t/hm2) Cdead (t/hm2)
Cropland 6.40 17.92 88.50 2.13
Forestland 89.50 21.48 148.70 18.80
Grassland 4.00 13.80 86.70 2.70
Water bodies 0.01 0.00 0.00 0.00
Urban land 5.72 0.00 57.72 2.00
Bare land 0.00 0.65 15.99 0.00
Table 3 Carbon density for different land use types
Resistance factor Feature Ecological resistance value Weight interval
(2000-2020)
Land use Forestland 1 0.1352-0.1402
Grassland and water bodies 20
Cropland 50
Bare land 80
Urban land 100
Elevation (m) <1000 1 0.3690-0.0443
1000-2000 20
2000-3000 50
3000-4000 80
>4000 100
Slope (°) 0-5 1 0.1521-0.1601
5-15 20
15-25 50
25-35 80
>35 100
Normalized Difference Vegetation Index (NDVI) >0.7 1 0.0500-0.0632
0.6-0.7 20
0.4-0.6 50
0.2-0.4 80
<0.2 100
Population density (persons/km2) <2000 1 0.2148-0.2183
2000-8000 20
8000-20,000 50
20,000-50,000 80
50,000-150,000 100
Distance to highways (m) >8000 1 0.1838-0.1883
4000-8000 20
2000-4000 50
1000-2000 80
0-1000 100
Distance to railroads (m) >8000 1 0.1998-0.2071
4000-8000 20
2000-4000 50
1000-2000 80
0-1000 100
Table 4 Ecological resistance factors and weights in Northwest China
Fig. 2 Spatiotemporal variation in ecosystem service (ES) supply in Northwest China from 2000 to 2020. (a1-a3), WY; (b1-b3), HQ; (c1-c3), CS; (d1-d3), FP. WY, water yield; HQ, habitat quality; CS, carbon storage; FP, food provisioning. FP was calculated and visualized at the prefecture-level city scale.
Fig. 3 Spatiotemporal variation in ES demand in Northwest China from 2000 to 2020. (a1-a3), WY; (b1-b3), HQ; (c1-c3), CS; (d1-d3), FP. All ES demand indicators were mapped at the prefecture-level city scale.
Fig. 4 Spatiotemporal variation in ES supply-demand ratio (ESDR) for four ES types from 2000 to 2020. (a1-a3), WY; (b1-b3), HQ; (c1-c3), CS; (d1-d3), FP. All ESDR were mapped at the prefecture-level city scale.
Fig. 5 Spatial distribution of ecological sources in Northwest China in 2000 (a), 2010 (b), and 2020 (c)
Fig. 6 Spatial distribution of ecological resistance in Northwest China in 2000 (a), 2010 (b), and 2020 (c)
Fig. 7 Spatial distribution of ecological corridors in Northwest China in 2000 (a), 2010 (b), and 2020 (c)
Fig. 8 Spatial distribution of ecological security pattern in Northwest China in 2000 (a), 2010 (b), and 2020 (c)
Fig. 9 Ecological connectivity analysis across different CWD thresholds under different optimization scenarios. (a), EC(PC) and EC(IIC); (b), PC-based improvement rate. EC(PC) and EC(IIC) are the equivalent connectivity (EC) values for the probability of connectivity (PC) and integral index of connectivity (IIC), respectively. CWD, cost‑weighted distance; Sce1, bottleneck optimization; Sce2, high-resistance corridor buffering; Sce3, barrier removal optimization.
Fig. 10 Spatial distribution of cumulative current density for the original resistance surface (baseline) (a) and optimization scenarios Sce1 (b), Sce2 (c), and Sce3 (d) as well as the difference distribution between Sce2 and baseline (e) and between Sce3 and baseline (f)
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