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Journal of Arid Land >

2024 , Vol. 16 >Issue 9: 1183 - 1196

DOI: https://doi.org/10.1007/s40333-024-0107-y

  • ZHENG Guoqiang 1, 2 ,
  • Li Cunxiu 2 ,
  • LI Runjie , 3, * ,
  • LUO Jing 2 ,
  • FAN Chunxia 2 ,
  • ZHU Hailing 2
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收稿日期: 2024-05-11

  修回日期: 2024-08-27

  录用日期: 2024-08-30

  网络出版日期: 2025-08-13

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Spatio-temporal evolution analysis of landscape pattern and habitat quality in the Qinghai Province section of the Yellow River Basin from 2000 to 2022 based on InVEST model

  • ZHENG Guoqiang 1, 2 ,
  • Li Cunxiu 2 ,
  • LI Runjie , 3, * ,
  • LUO Jing 2 ,
  • FAN Chunxia 2 ,
  • ZHU Hailing 2
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  • 1College of Geography, Qinghai Normal University, Xining 810008, China
  • 2Qinghai Engineering Consulting Center Co., Ltd., Xining 810001, China
  • 3Qinghai University, Xining 810016, China
*LI Runjie (E-mail: )

Received date: 2024-05-11

  Revised date: 2024-08-27

  Accepted date: 2024-08-30

  Online published: 2025-08-13

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本文引用格式

ZHENG Guoqiang , Li Cunxiu , LI Runjie , LUO Jing , FAN Chunxia , ZHU Hailing . [J]. Journal of Arid Land, 2024 , 16(9) : 1183 -1196 . DOI: 10.1007/s40333-024-0107-y

Abstract

Habitat quality is an important indicator for evaluating the quality of ecosystem. The Qinghai Province section of the Yellow River Basin plays an important role in the ecological protection of the upper reaches of the Yellow River Basin. To comprehensively analysis the alterations of habitat quality in the Qinghai Province section of the Yellow River Basin, this study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to calculate the habitat quality index and analyze the spatio-temporal variation characteristics of habitat quality in the study area from 2000 to 2022, and calculated seven landscape pattern indices (number of patches, patch density, largest patch index (LPI), landscape shape index (LSI), contagion index (CONTAG), Shannon diversity index, and Shannon evenness index) to research the variation of landscape pattern in the study area. The results showed that the number of patches, patch density, LPI, LSI, Shannon diversity index, and Shannon evenness index increased from 2000 to 2022, while the CONTAG decreased, indicating that the landscape pattern in the Qinghai Province section of the Yellow River Basin changed in the direction of distribution fragmentation, shape complexity, and heterogeneity. The average value of the habitat quality index in the Qinghai Province section of the Yellow River Basin from 2000 to 2022 was 0.90. Based on the value of habitat quality index, we divided the level of habitat quality into five categories: lower (0.00-0.20), low (0.20-0.40), moderate (0.40-0.60), high (0.60-0.80), and higher (0.80-1.00). Most areas were at the higher habitat quality level. The lower habitat quality patches were mainly distributed in Longyang Gorge and Yellow River-Huangshui River Valley. From 2000 to 2022, the habitat quality in most areas was stable; the increase areas were mainly distributed in Guinan County, while the decrease areas were mainly distributed in Xining City, Maqen County, Xinghai County, Qumarleb County, and Darlag County. To show the extent of habitat quality variation, we calculated Sen index. The results showed that the higher habitat quality area had a decrease trending, while other categories had an increasing tendency, and the decreasing was faster than increasing. The research results provide scientific guidance for promoting ecological protection and high-quality development in the Qinghai Province section of the Yellow River Basin.

Key words： InVEST model; landscape pattern index; habitat quality; largest patch index; landscape shape index; Shannon evenness index

1 Introduction

A habitat is an area where organisms live and reproduce and consists of living and abiotic environments (Ou et al., 2001). Habitat quality, as the ability of ecological environment to provide suitable survival and development for individuals or groups, is an important indicator for evaluating the degree of regional biodiversity and ecological environment (Jin et al., 2022). High quality of habitat is the basis of ecosystem services, providing humans with significant economic benefits and cultural values (Aneseyee et al., 2020; Lei et al., 2022). The land use/cover change (LUCC) is an important driving factor affecting the changes in habitat quality (Liu et al., 2019; Wu et al., 2023), and can alter the flow of materials and energy between habitat patches, thereby affecting the spatial distribution pattern and quality of regional habitats (Jin et al., 2022; Li et al., 2023b). Improving habitat quality is conducive to protecting and enhancing biodiversity and ensuring regional ecological security (Peng et al., 2019). With the deepening of ecological environmental protection research, which is based on the rational study of landscape pattern, an in-depth exploration of temporal and spatial changes and characteristics of habitat quality provides an important reference for accurate and scientific assessments of ecological service value and is highly important for regional ecological environmental construction.
Landscape pattern is the expression of LUCC (Lü et al., 2019). Spatial correlations between landscape pattern and ecosystem service function, such as the landscape fragmentation index, can change ecosystem structure and affect ecosystem stability (Wang and Zhang, 2023). Landscape pattern research can comprehensively evaluate and analyze regional landscape ecological risk (Huang and Zhang, 2024). However, the analysis of landscape pattern focuses only on changes in surface feature types and cannot be used to accurately determine the degree of environmental advantages and disadvantages. Therefore, the study of habitat quality is particularly important. The landscape pattern affects habitat quality by two ways. On one hand, landscape pattern impacts regional habitat quality by controlling regional climatic factors, thermal and hydrological conditions, and material exchanges (Li et al., 2023a; Yang et al., 2024). On the other hand, the landscape pattern influences regional habitat quality by controlling the flow of regional ecological elements (Paudel and Yuan, 2012; Lin et al., 2022).
Habitat quality assessment has three types according to research perspectives and methods, including key species investigation, evaluation, and ecological model. The key species investigation is only suitable for small-scale habitat quality assessment due to difficult in field surveys and high costs (Fellman et al., 2015; Zeng and Song, 2023; Liu and Qiao, 2024; Qi et al., 2024). The construction of evaluation system requires many evaluation indicators and has problems such as difficult data acquisition, high time costs, diverse evaluation indicators, and difficult comparisons of results (Zhu et al., 2024). Common ecological models include the habitat suitability index (HSI) model (Aneseyee et al., 2020), remote sensing ecological index (RSEI) model (Zhang et al., 2024b), and integrated valuation of ecosystem services and tradeoffs (InVEST) model. The InVEST model, which has mature methods and convenient data integration, has been widely used in research on nature reserves (Yang et al., 2023a), watershed water ecology (Hou et al., 2024; Lai et al., 2024), urban development areas (Mu et al., 2023), carbon storage (Jia and Hu, 2024; Zhi et al., 2024), and other fields.
As the source and trunk area of the Yellow River, Qinghai Province delivers 49.00% of water to downstream every year (Ma et al., 2023), which is an important water conservation area in the upper reaches of the Yellow River Basin and plays an irreplaceable ecological role in the Yellow River Basin. However, there have been few studies on ecological quantitative research in this area. Most studies focused on the region-wide ecology of the Yellow River Basin or qualitative countermeasures for ecological protection and restoration in the Qinghai Province section of the Yellow River Basin (Chen et al., 2021; Wang and Sun, 2024; Zhang et al., 2024a). Analyses of the correlation between habitat quality and regional landscape features are lacking. In recent years, owing to the increasing intensity of human activities and ecological fragility and the complexity of the Qinghai-Xizang Plateau and Loess Plateau, the ecological environment in the basin has undergone significant changes, leading to prominent ecological problems (Wang et al., 2009; Liu et al., 2023b).
In this study, the Qinghai Province section of the Yellow River Basin, which has an important ecological security barrier function, was selected as the study area, and land use data from 2000 to 2022 were selected. The landscape pattern index and InVEST model were applied to evaluate and analyze the spatio-temporal evolutionary characteristics of landscape pattern and habitat quality in the study area. This paper provides a basic reference for the effective formulation of ecological protection and high-quality development policies and measures in the Qinghai Province section of the Yellow River Basin.

2 Materials and methods

2.1 Study area

The Qinghai Province section of the Yellow River Basin (33°06′-38°17′N, 95°52′-103°04′E; Fig. 1) is located in southeastern part of Qinghai Province, China, covering 35 cities, districts, and counties in 6 prefectures. The length of the main stream of the Yellow River in Qinghai Province is 1694 km, accounting for 31.00% of the total length of the Yellow River; and the annual average outbound water volume is 2.64×1010 m3, accounting for 49.40% of the runoff of the whole basin. It is the largest water-producing and water-conservation area in the Yellow River Basin (Sui et al., 2007; Yang et al., 2023b). The terrain of the Qinghai Province section of the Yellow River Basin is dominated by plateau mountains, and is undulating, with a decreasing trend from southeast to northwest as a whole. The average altitude is approximately 4000 m a.s.l. (Sui et al., 2007). The area has a plateau continental climate, with drought and little rain, large temperature differences between day and night, and long sunshine times. In the Qinghai Province section of the Yellow River Basin, the land use types include grassland, forest, cultivated land, wetland, water body, unused lands, etc., of which grassland has the greatest area, accounting for 88.27% of the whole area (Guo et al., 2020).
Fig. 1 Location of the Qinghai Province section of the Yellow River Basin, China. 1, Datong Hui and Tu Autonomous County; 2, Huzhu Tu Autonomous County; 3, Huangyuan County; 4, Huangzhong County; 5, Xining City; 6, Pingan District; 7, Minhe Hui and Tu Autonomous County; 8, Hualong Hui Autonomous County; 9, Jainca County; 10, Xunhua Salar Autonomous County; 11, Henan Mongolian Autonomous County; 12, Baima County.

2.2 Data sources

The land use data of the study area from 2000 to 2022 were collected from the Wuhan University's annual China Land Cover Dataset (CLCD) (https://zenodo.org/record/5816591), with a resolution of 30 m. On the basis of 5463 samples of visual interpretation and third-party inspection, the overall accuracy is 79.31% (Yang and Huang, 2021). The digital elevation model (DEM) data were obtained from the Geospatial Data Cloud (http://www.gscloud.cn/), with a resolution of 30 m.

2.3 Landscape pattern index

The landscape pattern index has been used to describe and quantify the structural composition and spatial characteristics of surface landscapes (Zang et al., 2019; Zhang et al., 2019; Wang et al., 2023a; Liu and Qiao, 2024). Different types of landscape pattern indices reflect the changing characteristics of landscape structure. To further analyze the changing characteristics of landscape pattern in the Qinghai Province section of the Yellow River Basin, we used seven landscape pattern indices, including the number of patches, patch density, largest patch index (LPI), landscape shape index (LSI), contagion index (CONTAG), Shannon diversity index, and Shannon evenness index (Table 2). The changing characteristics of the landscape pattern in the Qinghai Province section of the Yellow River Basin from 2000 to 2022 were calculated and analyzed via FragStats software (University of Massachusetts, Amherst, Massachusetts, USA).

2.3.1 Patch density

The patch density, which can reflect the degree of differentiation and spatial heterogeneity of landscape, is calculated by the formula as follows (Duan et al., 2024):
PD = NP A ,
where PD is the patch density that is the number of a certain type of patch per unit area; NP is the number of patches; and A is the total area of landscape (m2). The higher the value of patch density, the higher the degree of fragmentation, the greater the degree of spatial heterogeneity, and vice versa.

2.3.2 LPI

The LPI can reflect the abundance of dominant and interior species in the landscape. Changes in LPI can alter the intensity and frequency of disturbances, reflecting the direction and strength of human activities. The formula is as follows (Cai et al., 2004):
LPI = α max A × 100 ,
where LPI is the proportion of the area of the largest patch to the total area of landscape (%); and αmax is the area of the largest patch (m2). The larger the value of LPI, the stronger the discrete effect on the landscape.

2.3.3 LSI

The LSI reflects the degree of regularity in patches and the complexity of patch edges. The formula is as follows (Zhang et al., 2007; Wu et al., 2024):
LSI = 0.25 C A ,
where LSI is the weighted average value of the ratio of patch circumference to the circumference of circular patch with same area; and C is the total length of all landscape patches (m). The larger the value of LSI, the more complex the shape of corresponding type of patch.

2.3.4 CONTAG

The CONTAG has been used to describe the degree of clustering (or diffusion) of different patches in a landscape. The formula is as follows (He and Zhang, 2009; Wu et al., 2024):
CONTAG = 1 + ∑ i = 1 m ∑ j = 1 m P i j ln P i j 2 ln m × 100 ,
where CONTAG is the degree of aggregation or extension trend of patch types in the landscape; m is the total number of patch types; and Pij is the probability that two randomly selected neighboring raster cells belong to patch types i and j. A higher value of CONTAG indicates that the dominant patch types in the landscape form good connections, while a lower value indicates a higher degree of fragmentation in landscape.

2.3.5 Shannon diversity index

The Shannon diversity index represents the change in the number of landscape elements, reflecting the diversity of the landscape. The formula is as follows (Li et al., 2014):
H = – ∑ a = 1 b ln P a × P a ,
where H is the proportion of area occupied by each landscape element; Pa is the proportion of the area occupied by landscape type a; and b is the number of landscape types.

2.3.6 Shannon evenness index

The Shannon evenness index represents the degree of distributional homogeneity of different ecosystems in the landscape. The formula is as follows (Li et al., 2014):
E = – ∑ a = 1 b ln P a × P a ln b ,
where E is the distribution uniformity of different landscapes, ranging from 0 to 1.

2.4 Calculation of habitat quality index via InVEST model

The habitat quality index can be calculated by identifying the intensity of external threats to various landscapes and the sensitivity of various landscapes to threats (Liu et al., 2024). The range of habitat quality index is 0-1, and the closer the index is to 1, the higher the quality of the habitat is. The habitat quality module in InVEST model combines the LUCC situation with biodiversity threat factors, and the calculation is as follows (Nelson et al., 2009):
Q x c = H c 1 – ( D x c z D x c z + k 2 ) ,
where Qxc is the habitat quality of grid x in the land use type c; Hc is the habitat suitability of land use type c; Dxc is the habitat degradation degree of grid x in the land use type c; z is the normalization constant, and usually takes the value 2.5; and k is the half-saturation constant, usually set to 0.5 by default at first, and then take the half of Dxc. The calculation of Dxc is as follows:
D x c = ∑ 1 R ∑ 1 y w r ∑ r = 1 R w r r y i r x y β x S c r ,
where r is the threat factor; R is the number of threat factors; wr is the weight of threat factor r, which represents the relative destructive power of a certain threat factor to each habitat, with a value range of 0-1; ry is the number of threat factors on gird y; irxy is the threat level of land use type c to threat factor r; βx is the accessibility level of grid x, with a value range of 0-1; and Scr is the sensitivity of land use type c to threat factor r, with a value range of 0-1 (Wang and Chen, 2022).
In this study, construction land, cultivated land, and unused land were regarded as the threat factors (Hou et al., 2024; Shi et al., 2024). The parameters that were input to InVEST model include the maximum impact distance, weight, and sensitivity to each threat factor (Tables 1 and 2) (Zhu et al., 2020; Pan et al., 2022; Wang et al., 2023b; Yan et al., 2024).
Table 1 Threat factors of habitat quality selected by this study
Threat factor Maximum impact distance (km) Weight Type of recession Reference
Construction land 10 1.0 Linear Hou et al. (2024)
Cultivated land 8 0.7 Exponential Pan et al. (2022)
Unused land 5 0.3 Exponential Wang and Sun (2024)
Table 2 Habitat suitability and sensitivity to threat factors for different land use types
Land use type Habitat suitability Habitat sensitivity
Cultivated land Construction land Unused land
Cultivated land 0.40 0.00 0.40 0.40
Forest 1.00 0.70 0.80 0.50
Grassland 1.00 0.70 0.75 0.60
Water body 0.80 0.65 0.70 0.30
Glacier 1.00 0.00 0.80 0.30
Unused land 0.00 0.00 0.00 0.00
Construction land 0.10 0.00 0.00 0.00
Wetland 1.00 0.70 0.90 0.40

2.5 Trend analysis

The Theil-Sen median trend method was used to analyze the variation trend of habitat quality in the Qinghai Province section in the Yellow River Basin during the study period. The Theil-Sen median estimation is a nonparametric analysis method (Long et al., 2023). It does not rely on the data distribution hypothesis, which can effectively avoid the loss of data in the time series and the impact of data distribution on the results during calculation, and has strong resistance to data errors. It is suitable for various types of data, and the analysis results are more scientific and credible (Chen et al., 2019; Long et al., 2023). The formula is as follows:
S e n = median index f – index g f – g ,
where indexf and indexg are the habitat quality time series; and Sen is the trend of index. The Sen<0 represents that the index shows a decreasing trend, while Sen>0 represents that the index shows an increasing trend. Referring to existing research findings (Jiang et al., 2015), we divided the variation trend of habitat quality into five categories (significant decrease, non-significant decrease, no change, non-significant increase, and significant increase).

3 Results

3.1 Landscape pattern analysis

The landscape pattern index highly condenses the information of landscape patterns, which is a simple quantitative index reflecting the characteristics of the structural combination and spatial configuration of landscapes. As shown in Table 3, the number of patches in the Qinghai Province section of the Yellow River Basin increased from 47,016 to 47,518 from 2000 to 2022, mainly due to the increase in the number of patches of cultivated land, construction land, and forest. The patch density increased from 0.3111 to 0.3144 patches/hm2. As an index reflecting landscape fragmentation, the results of the above two indices reflected that the landscapes in the study area developed in the direction of fragmentation under the influence of human activities. The LPI increased from 87.73% to 88.01%, indicating that the dominance of largest patch in the study area increased and the degree of disturbance decreased.
Table 3 Change in landscape pattern index in the Qinghai Province section in the Yellow River Basin from 2000 to 2022
Year Number of patches Patch density (patches/hm2) LPI (%) LSI CONTAG Shannon diversity index Shannon evenness index
2000 47,016 0.3111 87.73 81.5409 80.7507 0.5137 0.2470
2005 46,040 0.3046 88.05 78.9123 81.1027 0.5068 0.2437
2010 47,825 0.3164 87.77 80.4385 80.5832 0.5214 0.2507
2015 45,658 0.3021 87.95 79.1291 80.9037 0.5130 0.2467
2020 46,268 0.3061 87.65 82.6330 80.3702 0.5248 0.2524
2022 47,518 0.3144 88.01 82.5149 80.4614 0.5210 0.2505

Note: LPI, largest patch index; LSI, landscape shape index; CONTAG, contagion index.

The LSI of the study area increased from 81.5409 to 82.5149 from 2000 to 2022, indicating that the landscape shape tended to be discrete, severe, and complicated under the influence of human activities. The CONTAG decreased from 80.7507 to 80.4614, indicating that landscape accessibility decreased and the degree of fragmentation increased in the study area.
The Shannon diversity index increased from 0.5137 to 0.5210, and the Shannon evenness index increased from 0.2470 to 0.2505 from 2000 to 2022. The two indices represent the diversity and complexity of landscape. The changes in the above two indices showed an increasing trend, which indicated that the landscape types in the study area were complex and diverse and that the landscape types were interleaved.

3.2 Habitat quality analysis

3.2.1 Evolution of spatio-temporal pattern of habitat quality

To further explore and analyze the spatio-temporal variation characteristics of habitat quality in the Qinghai Province section of the Yellow River Basin, this study used the reclassification method based on ArcGIS software (Eris, Redlands, California, USA) to divide the operation results of InVEST model into five categories: lower habitat quality (0.00-0.20), low habitat quality (0.20-0.40), moderate habitat quality (0.40-0.60), high habitat quality (0.60-0.80), and higher habitat quality (0.80-1.00). The habitat quality index of the Qinghai Province section of the Yellow River Basin from 2000 to 2022 was stable at approximately 0.90, with slight improvement. As seen from the analysis in Table 4, the area of lower habitat quality patches increased by 0.64%; the area of low habitat quality patches increased by 0.18%; the area of moderate habitat quality patches decreased by 2.24%; the area of high habitat quality patches increased by 0.63%; and the area of higher habitat quality patches decreased by 1.22%.
Table 4 Area of different levels of habitat quality in the Qinghai Province section of the Yellow River Basin from 2000 to 2022
Year Lower
habitat quality		 Low
habitat quality		 Moderate
habitat quality		 High
habitat quality		 Higher
habitat quality
Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)
2000 3608 2.38 5094 3.36 5412 3.56 6351 4.18 131,362 86.52
2005 3426 2.23 4956 3.23 5372 3.50 6541 4.26 133,272 86.78
2010 3213 2.11 5115 3.36 5046 3.32 6318 4.15 132,385 87.05
2015 3867 2.52 5095 3.32 4987 3.25 6704 4.37 132,914 86.55
2020 4738 3.12 5103 3.36 4899 3.23 7001 4.61 130,085 85.68
2022 4572 3.02 5351 3.54 5019 3.32 7271 4.81 128,930 85.30
According to the spatial distribution, from 2000 to 2022, the overall habitat quality of the study area was at higher habitat quality level (Fig. 2). The high and higher habitat quality patches were mainly distributed in areas where grasslands, water bodies, and wetlands were concentrated, such as Hainan Tibetan Autonomous Prefecture, Golog Tibetan Autonomous Prefecture, and Yushu Tibetan Autonomous Prefecture; of which the areas with higher habitat quality were mainly distributed in the Zhaling Lake and Eling Lake and surroundings, where experienced fewer human activities and maintained the original ecosystem. The low and lower habitat quality patches were distributed in areas where unused land and construction land were concentrated, particularly in the Longyang Gorge and Yellow River-Huangshui River Valley.
Fig. 2 Spatial distribution of habitat quality in the Qinghai Province section of the Yellow River Basin in 2000 (a), 2005 (b), 2010 (c), 2015 (d), 2020 (e), and 2022 (f)

3.2.2 Extent of change in habitat quality

The area of lower habitat quality patches had an increasing trend until 2020, with a Sen index of 0.082, indicating that the landscape type had an increasing trend throughout the study period (Table 5). The area of low habitat quality patches fluctuated significantly at the five-year stage, with a Sen index of 0.053, showing an overall increasing trend. The areas of moderate and high habitat quality patches both first decreased and then increased, with Sen index of 0.046 and 0.059, respectively, also showing an overall increasing trend. The area of higher habitat quality had an increasing trend from 2000 to 2010 and then a decreasing trend after 2010, with Sen index of -0.242, showing an overall decreasing trend. The rate of decrease was larger than the magnitude of the others categories. Taken together, the area of lower habitat quality patches was increasing and the area of higher habitat quality patches was decreasing, and the rate of decrease was faster from 2000 to 2022.
Table 5 Change of area of different levels of habitat quality in the Qinghai Province section of the Yellow River Basin from 2000 to 2022
Period Percentage of area change (%)
Lower habitat quality Low habitat quality Moderate habitat quality High habitat quality Higher habitat quality
2000-2005 -0.15 -0.13 -0.06 0.08 0.26
2005-2010 -0.12 0.13 -0.18 -0.11 0.27
2010-2015 0.41 -0.04 -0.07 0.22 -0.50
2015-2020 0.60 0.04 -0.02 0.24 -0.87
2020-2022 -0.10 0.18 0.09 0.20 -0.38
Sen index 0.082 0.053 0.046 0.059 -0.242

Note: Positive value means increase in area and negative value means decrease in area.

To more intuitively reflect the trends in spatial evolution of habitat quality in the Qinghai Province section of the Yellow River Basin, this study utilized the natural breakpoint method to reclassify the results of changes in habitat quality from 2000 to 2022 into five levels (significant decrease, non-significant decrease, no change, non-significant increase, and significant increase) via ArcGIS software, and a grid calculator to carry out simple subtraction calculations. The results of the changes in habitat quality in the Qinghai Province section of the Yellow River Basin from 2000 to 2022 were obtained.
In terms of the variation of spatial distribution, there were significant spatial differences in the distribution pattern of habitat quality in the Qinghai Province section of the Yellow River Basin, with significant changes in the northeastern part of the study area (Fig. 3). The significant decrease areas are mainly distributed Xining City, Gonghe County, Maqen County, Xinghai County, Qumarleb County, and Darlag County. The non-significant decrease areas were mainly distributed in Haiyan County, Huangyuan County, Huangzhong County, Pingan District, Hualong Hui Autonomous County, and Xunhua Salar Autonomous County. The significant increase and non-significant increase areas were mainly distributed in Longyang Gorge and its surrounding areas in Guinan County, and Huzhu Tu Autonomous County, Datong Hui and Tu Autonomous County, and northern part of Huangzhong County. The no change areas were mainly distributed in Baime County, Zekog County, Tongren City, and Gade County.
Fig. 3 Spatial distribution of habitat quality transition process in the Qinghai Province section of the Yellow River Basin from 2000 to 2022. 1, Datong Hui and Tu Autonomous County; 2, Huzhu Tu Autonomous County; 3, Huangyuan County; 4, Huangzhong County; 5, Xining City; 6, Pingan District; 7, Minhe Hui and Tu Autonomous County; 8, Hualong Hui Autonomous County; 9, Jainca County; 10, Xunhua Salar Autonomous County; 11, Henan Mongolian Autonomous County; 12, Baima County.

4 Discussion

4.1 Spatio-temporal evolution of habitat quality

In this study, we used InVEST model to analyze the habitat quality of the Qinghai Province section of the Yellow River Basin from 2000 to 2022, and found that the average value of the habitat quality index was more than 0.90, with an overall upward trend. The results showed that the habitat quality of the Qinghai Province section of the Yellow River Basin from 2000 to 2022 was dominated by higher habitat quality level, which was closely related to China's vigorous implementation of ecological protection project in Qinghai Province. The areas with high and higher habitat quality were mainly distributed in Huangnan Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, Yushu Tibetan Autonomous Prefecture, and Guoluo Tibetan Autonomous Prefecture. The areas with low and lower habitat quality were mainly distributed in the Gonghe Basin and the Yellow River-Huangshui River Valley. The differences in water and heat patterns on the Qinghai-Xizang Plateau create unique habitat conditions, and there are differences in water and heat conditions among different grassland types. The soil moisture conditions and humus content of alpine meadows are greater than those of alpine grasslands, resulting in greater vegetation coverage (Zhang et al., 2023) and improved habitat quality. In addition, the imbalance of regional economic development is also an important reason for spatial heterogeneity of habitat quality. Comparing with other areas, the habitat quality of Gonghe Basin and Yellow River-Huangshui River Valley experienced the most obvious decrease from 2000 to 2022, due to large population size and high level of social development. Besides, the large consumption and disturbance of resources such as arable land, construction land, etc., make the surrounding habitat degradation more serious (Zhu et al., 2020; Yan et al., 2024).

4.2 Influence of policy on habitat quality

Habitat quality bears the burden of biodiversity development and is the basis for ensuring regional ecological security (Zhang et al., 2024c). The Qinghai Province section of the Yellow River Basin, as the core component of the ecological functional plate of the Three-River Source, Qilian Mountains, Qinghai Lake, and eastern arid mountains, is a typical area with a concentrated and interlacing distribution of forest, grassland, wetland, and desert ecosystems and is a key ecological functional area and a water conservation area. Protecting the ecological environment of the Yellow River Basin is related not only to the development of Qinghai Province but also to the ecological environment of the Yellow River Basin. Against the background of Qinghai Province as an "ecological province", policy factors have a significant impact on ecosystem services. On one hand, under the implementation of major ecological protection and restoration projects in the key Yellow River ecological zone (including the ecological barrier of the Loess Plateau), the ecological protection and restoration efforts in the Yellow River-Huangshui River Valley, Qilian Mountains, Gonghe Basin and the area around the lake have achieved remarkable achievements, mitigating the deterioration of habitat in these regions (Liu et al., 2023a; Yang et al., 2023b). On the other hand, major projects in the fields of infrastructure, industrial development, livelihood, education and so on have occurred, resulting in many lands constantly changing to construction land. In this regard, this study proposed the following countermeasures and suggestions (Wu et al., 2015; Yang et al., 2021; Zhang et al., 2024a). To improve the habitat quality, we should optimize the pattern of territorial development and protection and compensate for the loss of main interest in permanent basic farmland reserves and ecological protection areas. The ecological protection projects must be continuously implemented in the study area, especially in the Yellow River-Huangshui River Valley.

4.3 Limitations and future research

Habitat quality assessment is an important sub-module of InVEST model (Zhong and Wang, 2017). The advantages of InVEST model in terms of spatial representation, dynamics, and biomass valuation improve the shortcomings of traditional ecosystem service valuation methods (Wang et al., 2024), however, the model itself has limitations. For habitat quality estimates, which is estimated by accumulating the effects of each threat factor on habitat quality in InVEST model (Bao et al., 2015); however, the simple summation of the effects of each threat factor on habitat quality is not fully equivalent to the combined effects of each threat factor on habitat quality (Wang et al., 2020). Therefore, the model still needs to be further refined in the application of habitat quality estimates. The accuracy of habitat quality estimates was also limited by the precision of land use data (Xu et al., 2024). In addition, this study only explored the habitat quality from the perspective of land use (Deng et al., 2024; Xu et al., 2024), the effects of vegetation growth within different land use types on habitat quality were unconsidered. The effects of vegetation growth on habitat quality should be taken into account.

5 Conclusions

The Qinghai Province section of the Yellow River Basin has multiple tributaries and is crucial to the sustainable development of Qinghai Province. As a result of urbanization and policy implementation, the habitat quality of the Qinghai Province section of the Yellow River Basin has changed considerably. All landscape pattern indices (number of patches, patch density, LPI, LSI, Shannon diversity index, and Shannon evenness index) increased from 2000 to 2022 except CONTAG, indicating that the landscape pattern of the study area exhibited a tendency of distribution fragmentation, shape complexity, and heterogeneity. The habitat quality index of the Qinghai Province section of the Yellow River Basin from 2000 to 2022 was stable (0.90), indicating that most areas of the study area maintain a stable ecological environment. Most areas belonged to higher habitat quality level (0.80-1.00), which was mainly distributed in the Hainan Tibetan Autonomous Prefecture, Golog Tibetan Autonomous Prefecture, and Yushu Tibetan Autonomous Prefecture; whereas the lower habitat quality patches were mainly distributed in the Longyang Gorge and Yellow River-Huangshui River Valley, where existed frequent human activities. From 2000 to 2022, the area of higher habitat quality exhibited a declining trend, while the areas of other types slowly increased, and the rate of decline was much greater than the remaining rates of increase. The overall habitat quality was largely unchanged, the increase aareas were mainly distributed in Guinan County, while the significant decrease and non-significant decrease areas were mainly distributed in Xining City, Maqen County, Xinghai County, Qumarleb County, and Darlag County. The findings of this study provide a scientific decision-making basis for ecological environmental protection in the Qinghai Province section of the Yellow River Basin.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the Demonstration Project of Integrated Ecological Rehabilitation Technology for Key Soil and Water Erosion Areas in the Yellow River Valley (2021-SF-134).

Author contributions

Data curation: ZHENG Guoqiang, LUO Jing, FAN Chunxia; Methodology: ZHENG Guoqiang, LI Runjie, ZHU Hailing; Formal analysis: ZHENG Guoqiang; Writing - original draft preparation: ZHENG Guoqiang, LI Cunxiu; Writing - review and editing: ZHENG Guoqiang, LI Cunxiu, LI Runjie; Funding acquisition: ZHENG Guoqiang: LU Qing. All authors approved the manuscript.

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