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Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (2): 167-185    DOI: 10.1007/s40333-022-0058-0
Research article     
Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China
HE Bing, CHANG Jianxia*(), GUO Aijun*(), WANG Yimin, WANG Yan, LI Zhehao
State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China
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Abstract  

The status of regional biodiversity is determined by habitat quality. The effective assessment of habitat quality can help balance the relationship between economic development and biodiversity conservation. Therefore, this study used the InVEST model to conduct a dynamic evaluation of the spatial and temporal changes in habitat quality of the Tarim River Basin in southern Xinjiang Uygur Autonomous Region of China by calculating the degradation degree levels for habitat types that were caused by threat factors from 1990 to 2018 (represented by four periods of 1990, 2000, 2010 and 2018). Specifically, we used spatial autocorrelation analysis and Getis-Ord G* i analysis to divide the study area into three heterogeneous units in terms of habitat quality: cold spot areas, hot spot areas and random areas. Hemeroby index, population density, gross domestic product (GDP), altitude and distance from water source (DWS) were then chosen as the main disturbance factors. Linear correlation and spatial regression models were subsequently used to analyze the influences of disturbance factors on habitat quality. The results demonstrated that the overall level of habitat quality in the TRB was poor, showing a continuous degradation state. The intensity of the negative correlation between habitat quality and Hemeroby index was proven to be strongest in cold spot areas, hot spot areas and random areas. The spatial lag model (SLM) was better suited to spatial regression analysis due to the spatial dependence of habitat quality and disturbance factors in heterogeneous units. By analyzing the model, Hemeroby index was found to have the greatest impact on habitat quality in the studied four periods (1990, 2000, 2010 and 2018). The research results have potential guiding significance for the formulation of reasonable management policies in the TRB as well as other river basins in arid areas.

Key words： habitat quality      biodiversity      InVEST model      spatial heterogeneity      spatial lag model      human activities      Tarim River Basin     
Received: 28 May 2021      Published: 28 February 2022
Corresponding Authors: CHANG Jianxia, GUO Aijun     E-mail: chxiang@xaut.edu.cn;aijunguo619@gmail.com
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HE Bing
CHANG Jianxia
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LI Zhehao
Cite this article:

HE Bing, CHANG Jianxia, GUO Aijun, WANG Yimin, WANG Yan, LI Zhehao. Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China. Journal of Arid Land, 2022, 14(2): 167-185.

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http://jal.xjegi.com/10.1007/s40333-022-0058-0     OR     http://jal.xjegi.com/Y2022/V14/I2/167

Fig. 1 Overview of the Tarim River Basin (TRB). ARB, Aksu River Basin; YRB, Yarkant River Basin; HRB, Hotan River Basin; CRB, Cherchen River Basin; WRB, Weigan-Kuche River Basin; KRB1, Kaxgar River Basin; KRB2, Keriya River Basin; KRB3, Kaidu-Konqi River Basin.
Fig. 2 Temporal and spatial distributions of land use types in the TRB in 1990 (a), 2000 (b), 2010 (c) and 2018 (d)
Threat source factor (R) drmax (km) Weight Spatial decay function
Cultivated land 0.35 0.60 Linear
Construction land 8.00 0.40 Exponential
Unused land 3.00 0.50 Linear
Table 1 Attribute table of habitat threat source factors
Code Land use type Sensitivity
Habitat suitability Cultivated land Construction land Unused land
10 Cultivated land 0.30 0.00 0.90 0.50
20 Forestland 1.00 0.60 0.80 0.20
30 Grassland 0.90 0.80 0.50 0.30
40 Water body 1.00 0.50 0.40 0.50
50 Construction land 0.00 0.00 0.00 0.00
60 Unused land 0.10 0.10 0.30 0.00
Table 2 Sensitivity of land use types to each threat source factor
Cultivated land Forestland Grassland Water body Construction land Unused land
HI 0.7500 0.5500 0.5000 0.3000 0.9500 0.7200
Table 3 Hemeroby coefficient (HI) corresponding to each land use type
Fig. 3 Spatial distribution of habitat quality in the TRB in 1990 (a), 2000 (b), 2010 (c) and 2018 (d)
Sub-basin Habitat quality
1990 2000 2010 2018 Value change (1990-2018)
ARB 0.4476 0.4362 0.4292 0.4146 -0.0330
KRB1 0.4530 0.4552 0.4156 0.4097 -0.0433
YRB 0.4410 0.4391 0.4282 0.4257 -0.0153
HRB 0.3605 0.3521 0.3729 0.3724 0.0119
KRB2 0.3597 0.3533 0.2894 0.2896 -0.0701
CRB 0.2878 0.2895 0.2715 0.2713 -0.0165
KRB3 0.3649 0.3634 0.3327 0.3327 -0.0322
WRB 0.4268 0.4290 0.3649 0.3584 -0.0684
Mainstream 0.4968 0.4828 0.4471 0.4459 -0.0509
Taklimakan Desert 0.1409 0.1365 0.1203 0.1205 -0.0204
Kumtag Desert 0.1384 0.1371 0.1351 0.1342 -0.0042
TRB 0.2981 0.2950 0.2766 0.2746 -0.0235
Table 4 Changes in habitat quality in the sub-basins of the TRB from 1990 to 2018
Altitudinal gradient Habitat quality
1990 2000 2010 2015 Value change (1990-2018)
Gradient 1 0.2266 0.2222 0.1991 0.1980 -0.0330
Gradient 2 0.3377 0.3381 0.3311 0.3317 -0.0433
Gradient 3 0.5238 0.5229 0.4802 0.4743 -0.0204
Gradient 4 0.3727 0.3697 0.3795 0.3775 -0.0042
TRB 0.2981 0.2950 0.2766 0.2746 -0.0235
Table 5 Habitat quality changes in the TRB at different altitudinal gradients from 1990 to 2018
Fig. 4 Global Moran's I index statistics of habitat quality in the TRB in 1990 (a), 2000 (b), 2010 (c) and 2018 (d)
Fig. 5 Spatial distribution of cold spots and hot spots of habitat quality in the TRB in 1990 (a), 2000 (b), 2010 (c) and 2018 (d)
Fig. 6 Distribution of Hemeroby index degree levels in the TRB in 1990 (a), 2000 (b), 2010 (c) and 2018 (d)
Altitudinal gradient Hemeroby index
1990 2000 2010 2018
Gradient 1 0.6791 0.6810 0.6906 0.6905
Gradient 2 0.6337 0.6331 0.6439 0.6486
Gradient 3 0.5519 0.5522 0.5772 0.5820
Gradient 4 0.6905 0.6486 0.5820 0.5950
TRB 0.6459 0.6472 0.6586 0.6608
Table 6 Hemeroby index at different altitudinal gradients in 1990, 2000, 2010 and 2018
Fig. 7 Results of variance inflation factor (VIF) in collinearity diagnostics for the main disturbance factors. Cold, cold spots; Hot, hot spots; Random, random areas. H, Hemeroby index; PD, population density; GDP, gross domestic product; DWS, distance from water source.
Fig. 8 Relationship between habitat quality and disturbance factors in cold spot areas, hot spot areas and random areas. *, statistically significant at the 5% level; **, statistically significant at the 1% level.
Heterogeneous unit Statistic 1990 2000 2010 2018
Cold spot areas R2 0.9343 0.9259 0.5232 0.4690
logL 198,929.0 198,940.0 59,972.3 50,485.7
AIC -397,845.0 -397,868.0 -119,933.0 -100,959.0
SC -397,791.0 -397,813.0 -119,877.0 -100,904.0
Moran’s I (error) 68.9** 72.0** 347.3** 388.0**
LM (lag) 556.4** 415.0** 84,837.9** 116,775.9**
RLM (lag)
LM (error)
RLM (error)		 1.2
4736.6**
4181.4**		 51.6**
5182.9**
4819.5**		 94.3**
120,561.3**
35,817.7**		 1128.1**
150,482.0**
34,834.2**
Hot spot areas R2 0.8447 0.8367 0.3661 0.3367
logL 47,430.2 45,911.6 17,201.2 15,911.7
AIC -94,848.3 -91,811.2 -34,390.4 -31,811.3
SC -94,796.2 -91,759.1 -34,338.4 -31,759.4
Moran’s I (error) 245.0** 242.3** 192.2** 188.8**
LM (lag) 3771.4** 4116.7** 20,683.1** 21,503.1**
RLM (lag)
LM (error)
RLM (error)		 3638.0**
59,975.4**
59,842.0**		 3501.3**
58,627.1**
58,011.8**		 1402.6**
36,908.3**
17,627.7**		 778.3**
35,592.3**
14,867.5**
Random areas R2 0.8928 0.8826 0.4390 0.3832
logL 73,152.8 72,003.3 30,106.2 13,218.4
AIC -146,294.0 -143,995.0 -40,200.4 -26,424.8
SC -146,239.0 -143,940.0 -40,147.8 -26,374.4
Moran’s I (error) 312.3** 319.7** 220.1** 175.3**
LM (lag) 23,710.9** 26,155.5** 30,901.5** 20,351.3**
RLM (lag)
LM (error)
RLM (error)		 635.5**
97,456.1**
74,380.7**		 615.1**
102,120.4**
76,580.0**		 219.4**
48,391.0**
17,709.0**		 148.4**
30,678.2**
10,475.3**
Table 7 Comparison of statistical test goodness of the spatial regression models in different heterogeneous units in 1990, 2000, 2010 and 2018
Heterogeneous unit Variable 1990 2000 2010 2018
Cold spot areas Constant 1.8829** 1.8855** 1.2565** 1.1626**
H -2.4739** -2.4775** -1.5976** -1.4406**
PD 0.0013 0.0051** 0.0014** 0.0043**
GDP 0.0068** 0.0349** 0.0571** 0.0158**
Altitude -0.0068** -0.0069** -0.0016** -0.0017**
DWS -0.0073** -0.0075** -0.0019** -0.0041**
Hot spot areas Constant 1.6167** 1.6592** 1.3339** 1.3222**
H -1.901** -1.8900** -1.2942** -1.2416**
PD -0.0028** -0.0086 -0.0045** -0.0057**
GDP 0.0169** 0.0580** 0.0876** 0.0303**
Altitude -0.0025** -0.0026** -0.0017** -0.0021**
DWS -0.0019** -0.0021** -0.0034** -0.0043**
Random areas Constant 1.8317** 1.8260** 1.2711** 1.2653**
H -2.3133** -2.2993** -1.4637** -1.3628**
PD 0.0117** 0.0097** 0.0048** 0.0071**
GDP 0.0146** 0.0652** 0.0935** 0.0103**
Altitude -0.0016** -0.0016** -0.0047** -0.0016**
DWS -0.0023** -0.0025** -0.0074** -0.0010**
Table 8 Spatial regression results of habitat quality with disturbance factors in different heterogeneous units in 1990, 2000, 2010 and 2018 based on the spatial error model
Fig. 9 Area percentages of land use types in the three heterogeneous units. The arrow direction and years indicated that the circles from outside to inside were 1990, 2000, 2010 and 2018, respectively. Values in the table represented the area percentages of land use types. The colors for area percentages of land use types were consistent with those of years.
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