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Journal of Arid Land  2023, Vol. 15 Issue (10): 1245-1268    DOI: 10.1007/s40333-023-0070-z
Research article     
Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China
ZHANG Shubao1,2, LEI Jun1,2,*(), TONG Yanjun1,2, ZHANG Xiaolei2,3, LU Danni1,2, FAN Liqin1,2, DUAN Zuliang1
1State Key Laboratory of Desert and Oasis Ecology/Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
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In the Anthropocene era, human activities have become increasingly complex and diversified. The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities, especially in arid and semi-arid areas. In the study, we chose the economic belt on the northern slope of the Tianshan Mountains (EBNSTM) in Xinjiang Uygur Autonomous Region of China as a case study. By collecting geographic data and statistical data from 2010 and 2020, we constructed an ecological resilience assessment model based on the ecosystem habitat quality (EHQ), ecosystem landscape stability (ELS), and ecosystem service value (ESV). Further, we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis, and explored its responses to climate change and human activities using the geographically weighted regression (GWR) model. The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010-2020. The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of "high in the western region and low in the eastern region", and the spatial clustering trend was enhanced during the study period. Desert, Gobi and rapidly urbanized areas showed low level of ecological resilience, and oasis and mountain areas exhibited high level of ecological resilience. Climate factors had an important impact on ecological resilience. Specifically, average annual temperature and annual precipitation were the key climate factors that improved ecological resilience, while average annual evapotranspiration was the main factor that blocked ecological resilience. Among the human activity factors, the distance from the main road showed a negative correlation with ecological resilience. Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions, whereas in the areas with poorer ecological conditions, the correlations were positive. The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

Key wordsecological resilience      ecosystem habitat quality      ecosystem landscape stability      ecosystem service value      spatial autocorrelation analysis      geographically weighted regression model      economic belt on the northern slope of the Tianshan Mountains     
Received: 10 April 2023      Published: 31 October 2023
Corresponding Authors: *LEI Jun (E-mail:
Cite this article:

ZHANG Shubao, LEI Jun, TONG Yanjun, ZHANG Xiaolei, LU Danni, FAN Liqin, DUAN Zuliang. Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China. Journal of Arid Land, 2023, 15(10): 1245-1268.

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Fig. 1 Overview of the economic belt on the northern slope of the Tianshan Mountains (EBNSTM) and spatial distribution of land use types in 2020
Data Unit Resolution Data source
Land use / 1 km Resource and Environment Science and Data Center
TMP 1 km National Earth System Science Data Center
PRE mm 1 km National Earth System Science Data Center
ETP mm 1 km National Earth System Science Data Center
Road network / / Open Street Map
NLI / 1 km Global Change Research Data Publishing & Repository
Crop sown area hm2 / Statistic Bureau of Xinjiang Uygur Autonomous Region
Grain yield kg / Statistic Bureau of Xinjiang Uygur Autonomous Region
Agricultural product price CNY/kg / National Bureau of Statistics
Table 1 Geographic data and statistical data used in this study as well as their sources
First-level class Second-level class
Cultivated land Paddy field and dryland
Forestland Forest, shrubbery, open forestland and other forestland
Grassland High coverage grassland, medium coverage grassland and low coverage grassland
Water body River canal, lake, reservoir pond, glacier and shoaly land
Construction land Urban land, rural residential land and other construction land
Unused land Desert, Gobi, saline and alkaline land, marsh, bare land, bare rock land and other unused land
Table 2 First-level classes and second-level classes of land use types
Fig. 2 Spatial distribution of PRE (a1 and a2), TMP (b1 and b2) and ETP (c1 and c2) in the EBNSTM in 2010 and 2020. PRE, annual precipitation; TMP, average annual temperature; ETP, average annual evapotranspiration.
Fig. 3 Spatial distribution of MRD (a1 and a2), NLI (b1 and b2) and PM2.5 concentration (c1 and c2) in the EBNSTM in 2010 and 2020. MRD, distance to the main road; NLI, night light index.
Fig. 4 Spatial distribution of EHQ (a1 and a2), ELS (b1 and b2) and ESV (c1 and c2) in the EBNSTM in 2010 and 2020. EHQ, ecosystem habitat quality; ELS, ecosystem landscape stability; ESV, ecosystem service value.
Land use type Habitat suitability Sensitivity to threat factors
Cultivated land Urban land Rural residential land Other construction land
Paddy field 0.5 0.3 0.8 0.6 0.7
Dryland 0.5 0.3 0.8 0.6 0.7
Forest 1.0 0.8 0.9 0.8 0.8
Shrubbery 1.0 0.5 0.8 0.6 0.6
Open forestland 0.8 0.7 0.9 0.8 0.8
Other forestland 1.0 0.8 0.9 0.8 0.8
High coverage grassland 0.9 0.5 0.7 0.6 0.6
Medium coverage grassland 0.8 0.6 0.7 0.6 0.6
Low coverage grassland 0.7 0.6 0.8 0.7 0.7
River canal 0.9 0.5 0.9 0.7 0.8
Lake 1.0 0.5 0.9 0.7 0.8
Reservoir pond 0.9 0.5 0.9 0.7 0.8
Glacier 0.1 0.2 0.3 0.2 0.2
Shoaly land 0.6 0.6 0.9 0.8 0.9
Urban land 0.0 0.0 0.0 0.0 0.0
Rural residential land 0.0 0.0 0.0 0.0 0.0
Other construction land 0.0 0.0 0.0 0.0 0.0
Desert 0.1 0.2 0.3 0.2 0.2
Gobi 0.1 0.2 0.3 0.2 0.2
Saline and alkaline land 0.1 0.2 0.3 0.2 0.2
Marsh 0.6 0.6 0.9 0.8 0.9
Bare land 0.1 0.2 0.3 0.2 0.2
Bare rock land 0.1 0.2 0.3 0.2 0.2
Other unused land 0.1 0.2 0.3 0.2 0.2
Table 3 Habitat suitability and sensitivity to the threat factors for the 24 second-level classes of land use types
Threat factor Weight Maximum impact distance (km) Decay type
Cultivated land 0.6 6 Exponential
Urban land 1.0 10 Exponential
Rural residential land 0.6 8 Exponential
Other construction land 0.7 9 Exponential
Table 4 Threat factors and their weights as well as the maximum impact distance
First-order indicator Second-order indicator Landscape index Weight
Connectivity Overall connectivity CONTAG 0.3
Forestland cohesion COHESION 0.2
Grassland cohesion COHESION 0.2
Heterogeneity Landscape diversity SHDI 0.3
Table 5 Evaluation index system of ecosystem landscape stability (ELS) used in this study
Ecosystem service Equivalent coefficient of ESV
First-class type Second-class type Cultivated land Forestland Grassland Water body Glacier Unused land
Food production 0.85 0.23 0.23 0.66 0.00 0.01
Raw material production 0.40 0.54 0.34 0.37 0.00 0.03
Water supply 0.02 0.28 0.19 5.44 2.16 0.02
Gas regulation 0.67 1.76 1.21 1.34 0.18 0.11
Climate regulation 0.36 5.27 3.19 2.95 0.54 0.10
Purify environment 0.10 1.57 1.05 4.58 0.16 0.31
Hydrological regulation 0.27 3.81 2.34 63.24 7.13 0.21
Soil conservation 1.03 2.14 1.47 1.62 0.00 0.13
Nutrient cycling 0.12 0.16 0.11 0.13 0.00 0.01
Biodiversity 0.13 1.95 1.34 5.21 0.01 0.12
Cultural service Aesthetic landscape 0.06 0.86 0.59 3.31 0.09 0.05
Table 6 Equivalent coefficient of ecosystem service value (ESV) for the 6 first-level classes of land use types in the EBNSTM
Ecosystem service ESV (CNY/hm2)
First-class type Second-class type Cultivated land Forestland Grassland Water body Glacier Unused land
Food production 3253.29 893.06 893.06 2506.95 0.00 38.27
Raw material production 1530.96 2054.04 1314.07 1397.00 0.00 114.82
Water supply 76.55 1058.91 727.21 20,821.05 8267.18 76.55
Gas regulation 2564.36 6736.22 4618.39 5109.58 688.93 421.01
Climate regulation 1377.86 20,157.63 12,209.40 11,271.69 2066.80 382.74
Purify environment 382.74 5996.26 4031.53 17,510.35 612.38 1186.49
Hydrological regulation 1033.40 14,582.39 8943.35 242,025.52 27,289.35 803.75
Soil conservation 3942.22 8203.39 5626.28 6200.39 0.00 497.56
Nutrient cycling 459.29 625.14 433.77 478.42 0.00 38.27
Biodiversity 497.56 7476.18 5115.96 19,940.74 38.27 459.29
Cultural service Aesthetic landscape 229.64 3278.80 2258.16 12,668.69 344.47 191.37
Table 7 ESV of the 6 first-level classes of land use types in the EBNSTM
Fig. 5 Quantitative changes of ecological resilience levels in the EBNSTM from 2010 to 2020. The value in the parenthesis represents the number of evaluation units at each ecological resilience level for each year, and the proportion represents the percentage of evaluation units with each ecological resilience level in the total. The thickness of the line indicates the amount of change at each level from 2010 to 2020; the thicker the line, the greater the change.
Fig. 6 Spatial distribution of ecological resilience (a1 and a2) and LISA (b1 and b2) in the EBNSTM in 2010 and 2020. LISA, the global spatial autocorrelation index (Moran's I) of the local spatial autocorrelation, showing the spatial heterogeneity of ecological resilience.
Parameter 2010 2020
OLS model GWR model OLS model GWR model
Sigma 0.173 0.118 0.172 0.110
AICc -3374 -7050 -3449 -7743
R2 0.513 0.787 0.500 0.807
Radj2 0.512 0.773 0.499 0.794
Table 8 Comparison of parameters between the ordinary least squares (OLS) model and geographically weighted regression (GWR) model in 2010 and 2020
Influencing factor Regression coefficient
Minimum Maximum Mean Standard deviation
2010 2020 2010 2020 2010 2020 2010 2020
TMP -14.16 -10.95 9.39 5.62 0.76 0.57 3.26 3.06
PRE -11.19 -8.60 28.78 60.66 0.37 1.27 3.93 6.41
ETP -5.55 -5.55 6.55 7.02 -1.58 -1.36 1.94 1.98
MRD -1.62 -1.97 1.06 0.57 -0.36 -0.61 0.50 0.36
NLI -28.64 -35.40 119.54 58.89 5.01 1.92 14.96 12.99
PM2.5 concentration -4.21 -3.25 2.77 3.40 0.17 -0.26 1.12 1.18
Table 9 Statistical results of regression coefficients between ecological resilience and influencing factors from the GWR model in 2010 and 2020
Fig. 7 Spatial distribution of regression coefficients between ecological resilience and climate factors in 2010 and 2020. (a1 and a2), correlation between ecological resilience and TMP; (b1 and b2), correlation between ecological resilience and PRE; (c1 and c2), correlation between ecological resilience and ETP.
Fig. 8 Spatial distribution of regression coefficients between ecological resilience and human activity factors in 2010 and 2020. (a1 and a2), correlation between ecological resilience and MRD; (b1 and b2), correlation between ecological resilience and NLI; (c1 and c2), correlation between ecological resilience and PM2.5 concentration.
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