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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 2: 306 - 325

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.02.003

Ecosystem Quality and Ecosystem Services

Spatial-temporal Evolution and Driving Factors of Habitat Quality Based on Different Topographic Gradients in Zhaotong City, Yunnan Province

  • CHEN Hongmin , 1 ,
  • LIU Fenglian , 1, 2, * ,
  • YANG Bowen 1 ,
  • LUO Qinqin 1
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  • 1. Institute of Land & Resources and Sustainable Development, Yunnan University of Finance and Economics, Kunming 650221, China
  • 2. Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming 650221, China
* LIU Fenglian, E-mail:

CHEN Hongmin, E-mail:

Received date: 2024-04-15

  Accepted date: 2024-08-10

  Online published: 2025-03-28

Supported by

The Scientific Research Fund Project of Yunnan Education Department(2024J0634)

The Talent Project of Yunnan University of Finance and Economics(2022D13)

The Foundation of Yunnan Key Laboratory of Service Computing(YNSC24305)

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Abstract

Habitat quality plays a crucial role in enhancing the regional ecological environment and safeguarding biodiversity, with topography being a key element influencing the structure and function of ecosystem services. This research aims to assess habitat suitability across various topographic gradients, analyze the spatial heterogeneity of habitat quality between 2000 and 2020, and explore the relationship between influencing factors and habitat quality. InVEST model is used to evaluate the spatial-temporal evolution characteristics of habitat quality in Zhaotong City, focusing on how topographic gradients impact habitat quality distribution. The study also delves into the factors affecting habitat quality in Zhaotong City, including land use patterns, elevation, slope, average annual temperature and other variables. The results indicate three key aspects of this system. (1) During the study period, the land use types were mainly forest land, farmland and grassland, with construction land experiencing the most significant increase. (2) From 2000 to 2020, the quality areas of high and medium habitats in Zhaotong City decreased, while the quality areas of medium-high, medium-low and low habitats increased. (3) The study revealed a notable topographic gradient effect on habitat quality, with the primary driver shifting from GDP to land use type and subsequently to average annual precipitation in Zhaotong City. The transfer between different grades of habitat quality mainly presents the characteristics of “descending” transfer, with medium-low quality habitats typically found on medium-low topographic gradients and medium-high quality habitats on medium-high gradients. Cross-detection results show that land use type exhibited the strongest correlation with other influencing factors. Therefore, this study can provide a scientific basis for policy makers to protect biodiversity, enhance ecosystem services and promote regional economic development.

Key words： habitat quality; InVEST model; topographic gradients; Geo-detector; Zhaotong City

Cite this article

CHEN Hongmin , LIU Fenglian , YANG Bowen , LUO Qinqin . Spatial-temporal Evolution and Driving Factors of Habitat Quality Based on Different Topographic Gradients in Zhaotong City, Yunnan Province[J]. Journal of Resources and Ecology, 2025 , 16(2) : 306 -325 . DOI: 10.5814/j.issn.1674-764x.2025.02.003

1 Introduction

Biodiversity is defined as biodiversity from terrestrial, aquatic and other complex ecosystems, including intraspecific diversity, interspecies diversity and ecosystem diversity (Hong et al., 2021). Biodiversity is the basis for maintaining ecological balance and maintaining the stable operation of ecosystems. According to the EU Biodiversity Strategy 2030: Nature Back to Life, human actions have led to 60% decrease in wild species globally, changed approximately 75% of the Earth’s surface and endangered one million species with extinction (Zhang and Qiao, 2021). The quality of land management is an important factor affecting biodiversity. In most parts of the world, alterations in land use patterns have led to dramatic changes in the landscape, resulting in a series of issues such as reduced biodiversity and ecosystem services. It is a key indicator of ecological security (Wang et al., 2021). Temple found that 82 percent of endangered bird species are at risk due to habitat loss (Temple, 1986). The most crucial factor in protecting biodiversity is to protect habitats, which is also confirmed by some scholar’s study on habitat selection of Golden Eagle’s in Poland (Stój et al., 2024). Biodiversity not only plays an important role in human well-being but also serves as an indicator of ecosystem health (Mattias et al., 2016).
Habitat refers to a location where an ecosystem can offer a suitable living environment and reproductive conditions for a specific individual or population (Riedler and Lang, 2018). Since habitat is a crucial condition for the sustainable survival and development of species, the value of habitat has been widely recognized both socially and ecologically (He et al., 2017). Habitat quality (HQ) refers to the ability of ecosystem to provide suitable and sustainable living conditions for individuals and populations in a general sense (Hall et al., 1997). The habitat quality determines the health of a regional ecosystem, the suitability of a habitat, and the sustainable development of the region (Li et al., 2020). Habitat quality is an important index to evaluate the ecological environment, directly impacting biodiversity conservation and regional sustainable development. Given the rapid decline of global species, safeguarding biodiversity has emerged as a key priority (Zhang et al., 2024). Recently, with rapid economic development and continuous urbanization, a significant amount of agricultural and ecological land has been converted into construction land (Li et al., 2023). Unreasonable land use methods, such as excessive urbanization and large-scale agriculture, have accelerated the degradation of habitat quality (Wu et al., 2023b). The significant movement of human beings has extensively altered the natural landscape, leading to habitat fragmentation, which poses a threat to biodiversity (Seto et al., 2012) and contributes to species decline (Pimm and Raven, 2000). Erik Nelson et al. utilized the InVEST model to assess the biodiversity of the Willamet Basin in the United States, and calculated the species area relationship index under various habitat quality scores to examine the relationship between habitat quality and species diversity (Nelson et al., 2009). The research revealed that higher habitat quality is associated with greater biodiversity. Therefore, analyzing and elucidating the evolutionary characteristics of regional habitat quality and its driving forces can offer a foundation and recommendations for biodiversity conservation. This is crucial for maintaining regional ecological security and promoting sustainable development.
The early studies on habitat quality assessment mainly focused on examining the habitat status of individual species or a few species in the field (Qing et al., 2021) or developing an index to comprehensively assess the quality of these habitats by considering various parameters of habitat quality (Chen et al., 2019; Gou et al., 2023). Early habitat quality assessment methods are typically appropriate for small-scale studies. Despite their advantages in accuracy, field investigations are time-consuming and labor-intensive, incurring high costs in terms of time and labor (Feng et al., 2018), and has a large amount of data, which is difficult to obtain, making it difficult to achieve at the basin and regional scales, and cannot effectively obtain long-term monitoring information. Additionally, they generate a large amount of data that is challenging to obtain, hindering their application at basin and regional scales. Furthermore, these methods are not effective in acquiring long-term monitoring information (Tang et al., 2020). In recent years, with the development of 3S technology, many scholars have studied habitat quality and biodiversity by establishing models, such as the ARIES (Artificial Intelligence for Ecosystem Services) model (Vigerstol and Aukema, 2011), GLOBIO model (Alkemade et al., 2009), InVEST model (Berta Aneseyee et al., 2020), and other ecological models that have been widely used in habitat quality assessment. These models can replace complex methods such as species surveys to rapidly assess habitat status and habitat changes, providing new opportunities for regional and catchment-scale habitat quality assessment. In this study, the InVEST model was used to assess habitat quality. This model is mature and can comprehensively consider the impact of different land use types on habitat and its sensitivity. It is one of the most widely used tools for assessing ecosystem services and habitat quality. Compared with other models, the InVEST model has better applicability and flexibility in dealing with complex terrain and diversified land use (Berta Aneseyee et al., 2020), and the evaluation results can be quantified and visualized (Nematollahi et al., 2020), which is especially suitable for habitat quality assessment under complex terrain conditions such as mountains.
In recent years, the InVEST model has been widely used at all scales. Relevant scholars at home and abroad have applied the Habitat Quality module of InVEST model to explore the relationship between habitat quality and urban expansion, land use, and landscape patterns in river basins (Zhai et al., 2023), nature reserves (Liu et al., 2018a), continental coastal zones (Zhang et al., 2023), and developed cities such as Beijing, Shanghai, and Guangzhou (Feng et al., 2018). Significant progress has been achieved, leading to the formation of numerous valuable research (Sallustio, 2017; Song and Wang, 2017; Chu et al., 2018; Bai et al., 2019; Xu et al., 2019; Zheng et al., 2022; Feng et al., 2023). Abreham Berta Aneseyee et al. found that the main causes of habitat degradation in the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia from 1988 to 2018 were agricultural expansion, population pressure, urbanization, soil erosion, and water pollution (Berta Aneseyee et al., 2020). Li et al. (2023) analyzed the temporal and spatial characteristics of habitat quality in Southwest China during 1990-2015 and explored the key drivers and spatial heterogeneity of habitat quality change in the study area. Wang and Cheng (2022) analyzed the temporal and spatial changes of habitat quality of different land use types in the Altay Region by using InVEST model, and proposed ecological management and protection measures based on the analysis results. Xie and Zhu (2023) evaluated the habitat quality index of Chengdu in 2000, 2010, and 2020, analyzed the effects of urban expansion on habitat quality, and proposed a spatial control zoning strategy. However, previous studies also have certain limitations: 1) From the perspective of research scale, few scholars pay attention to mountainous areas with fragile and sensitive ecosystems and relatively backward economic development, and even fewer studies have explored the changes in habitat quality in mountainous areas with topographic gradients. 2) From the perspective of research drivers, most of the previous studies focused on land use change to explore the drivers of regional habitat quality (Bao et al., 2015; Lu and Li, 2022) and failed to study the drivers of regional habitat quality from the aspects of social economy and the natural environment. As an important factor in the natural environment, topography is not only an important factor influencing the spatial distribution of land use but also a determinant in shaping the spatial patterns of habitat quality. Mountainous regions represent unique national spaces, serving as complex systems that integrate various functions such as biodiversity conservation, provision of ecological goods, water resource preservation, and climate regulation alongside social and economic development (Deng et al., 2015). Mountains are biodiversity hotspots, with habitat quality distribution being more significantly influenced by topographic factors compared to flat areas. Examining the spatial-temporal differentiation of habitat quality in mountainous regions through the lens of topographic gradients can effectively enhance the management of mountain ecosystems and is vital for a comprehensive understanding of the spatial-temporal evolution of habitat quality.
The Master Plan of Zhaotong City Territorial Space (2021-2035) positions Zhaotong City as a new height in the development and opening up of northeast Yunnan, an important ecological security barrier in the upper reaches of the Yangtze River, a comprehensive transportation hub in Yunnan-Sichuan-Guizhou-Chongqing region, a national demonstration city of ethnic unity and progress, a national civilized city, and a pioneer in ecological protection and restoration. Therefore, how the ecological environment can better provide a good spatial carrier for urbanization development and how to achieve reasonable, effective, and high-quality development of habitat quality within the scope of ecological environment carrying capacity has become a key issue for regional sustainable development (Zhou et al., 2021). Solving this key problem will help to promote Zhaotong’s urban characteristic positioning of “integrating mountains and cities and building a solid mountain ecological barrier”, and consolidate the ecological poverty alleviation achievements in Wumeng Mountain area to a certain extent, and provide a reference basis for alleviating the ecological environment pressure in the process of realizing “a river of clean water out of Yunnan”. This study has four objectives: 1) To analyze the spatio-temporal evolution of land use types in Zhaotong City from 2000 to 2020. 2) To evaluate the temporal and spatial evolution characteristics of habitat quality in Zhaotong City from 2000 to 2020. 3) Analyze the topographic distribution characteristics of Zhaotong, and explore the topographic gradient distribution effect of habitat quality based on the topographic factor distribution index. 4) Explore the driving factors of habitat quality from 2000 to 2020.

2 Materials and methods

2.1 Study area

Yunnan Province is situated in the southwestern part of China, and Zhaotong City is positioned in the northeast corner of Yunnan Province (Figure 1). Zhaotong City features a typical plateau mountain structure, with mountainous terrain covering 96% of the area. Situated on a low latitude plateau, Zhaotong City experiences a monsoon climate characterized by a unique dimensional climate that combines subtropical and warm temperate zones. The average annual temperature in the central city ranges from 11.7 ℃ to 20.9 ℃, with extreme maximum and minimum temperatures occurring at Qiaojia Station and Dasanbao Station, which are 44.4 ℃ and -16.8 ℃ respectively. The annual frost-free period is about 220 days. The average annual sunshine ranges from 834 to 2105 hours, and the average annual precipitation ranges from 675 to 1093 mm, with uneven distribution in the north and south, showing a spatial distribution (Du et al., 2021; Li, 2021). Zhaotong is situated in the Wumeng Mountainous Area, which serves as a crucial ecological security barrier in the upper reaches of the Yangtze River. Given its unique ecological status, Zhaotong plays a vital role in the ecological security barrier of the upper reaches of the Yangtze River and the ecological security pattern of “three barriers and two belts” in Yunnan Province. It is characterized by a delicate ecological environment, marked by a relatively prominent contradiction between “people and land” and holds a substantial responsibility for ecological protection. The city boasts a widespread water system distribution, primarily consisting of dendritic rivers that are predominantly recharged by rainwater (Li, 2022).
Figure 1 Location of the study area

2.2 Data sources

Land use type is fundamental data for assessing regional habitat quality. Data on land use and vegetation cover in Zhaotong City for the years 2000, 2010, and 2020 were obtained from the Data Center for Resources and Environ- mental Sciences, Chinese Academy of Sciences (http://www.resdc.cn, accessed November 15, 2023). The data source is derived from US Landsat TM/ETM remote sensing images with a spatial resolution of 30 m. These images were processed using supervised classification and human-computer interactive interpretation, making it the most precise land use remote sensing monitoring data product in China. To enhance interpretation accuracy and timeliness, remote sensing data from the same season with less than 5% cloud cover were selected as the base data, ensuring an interpretation accuracy of over 85%. Considering the study area’s actual conditions and following the national land use/land cover classification system, land use types were categorized into six primary groups: farmland, forest land, grassland, water area, construction land, and unused land.
DEM data is derived from the geospatial data cloud (https://www.gscloud.cn, accessed on November 23, 2023), with a spatial resolution of 30 m×30 m, and the unit is meters. The elevation of the research area is obtained using the concatenation and clipping tools in ArcGIS 10.7.
Based on DEM data, the 3D Analyst tool of ArcGIS 10.7 was used to calculate the slope of the study area. Initially, the Spatial Analyst tool of ArcGIS 10.7 was employed for neighborhood analysis to perform focus statistics and obtain the maximum and minimum elevation values of the study area. Subsequently, the raster calculator was used to subtract the maximum elevation value from the minimum elevation value to determine the topographic relief in meters.
NPP (Net Primary Productivity) is the material basis for the survival and reproduction of other members of the ecosystem. NDVI (Normalized Difference Vegetation Index) is closely related to transpiration, sunlight interception, photosynthesis, and net primary productivity of the land surface. Both NDVI and NPP data are from MODIS datasets, in which the spatial resolution of NDVI is 1 km and that of NPP is 500 m.
The annual average temperature data was obtained from the National Tibetan Plateau Scientific Data Center. The annual average temperature grid was derived from the monthly average temperature grid data with a resolution of 1 km for the years 2000, 2010, and 2020. This was achieved by aggregating the monthly average temperature grid data for all 12 months of each respective year, maintaining a spatial resolution of approximately 1 km.
The annual average precipitation data is sourced from the ERA5-Land dataset. The original data consists of monthly average precipitation raster data for the years 2000, 2010, and 2020. Utilizing the original raster data, the average value of 12-month average precipitation was computed using a raster computing tool to derive the annual average precipitation raster data with a spatial resolution of 1 km and unit mm.
GDP (Gross Domestic Product) is the fundamental indicator of national economic accounting and a crucial measure of a country’s or region’s economic status and development level. The data sources for GDP and population density are the Yunnan Statistical Yearbooks for 2001, 2011, and 2021. The unit for GDP is 100 million yuan, and the unit for population density is persons km-2.
The administrative boundary of Zhaotong City was derived from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn; accessed on December 1, 2023) with a spatial resolution of 30 m×30 m.
Road data for 2000, 2010, and 2020 were obtained from Amap.

2.3 Methods

2.3.1 Dynamic degree of land use

The concept of dynamic land use is introduced to analyze the rate and extent of change in specific land use types and all land use types within a defined time. Dynamic land use encompasses both the dynamic degree of single land use and the comprehensive dynamic degree of land use (Han et al., 2015). Its expression is as follows:
K=$\frac{{{N}_{q}}-{{N}_{p}}}{{{N}_{p}}}$×$\frac{1}{T}$×100%
LC=$\frac{\sum\limits_{e,f=1}^{n}{\Delta {{S}_{e-f}}}}{2\sum\limits_{e=1}^{n}{{{S}_{e}}}}\times \frac{1}{T}$×100%
where K is single land use dynamic degree. It aims to study the dynamic degree of a specific type of land use; Np, Nq are the quantity of a specific land use type at the beginning and end of the study period; T is the selected research period; where LC is comprehensive land use dynamic degree; It aims to study the overall area change amplitude of all land use types during the study period. Se is the area of e type of land use during the study period. ΔSe-f is the absolute value of the total area of e type land use converted to f type land use during the study period.

2.3.2 Habitat quality assessment methods

InVEST model is a system developed by the US Natural Capital Project to assess ecosystem services and their economic value, and to aid in ecosystem management and decision-making (Deng et al., 2018). The model includes a variety of ecosystem service function evaluation modules, among which Habitat Quality module can better reflect the spatial evolution characteristics of habitat quality in the study area over a certain times series (Zheng et al., 2023). Consequently, it is widely used in the evaluation of regional habitat quality spatiotemporal evolution analysis (Huang et al., 2023). The Habitat Quality module in InVEST model can combine the threat intensity of different land use types to the outside world and their sensitivity to various threat factors, so as to obtain the spatial distribution of habitat quality in the study area (Feng et al., 2018). The higher the value of habitat quality, the richer the biodiversity of the study area, and the stronger the potential ability of the ecosystem to provide suitable survival and reproduction for species. The formula for habitat quality is as follows (Bao et al., 2015):
${{Q}_{xj}}={{H}_{j}}\times \left( 1-\frac{D_{xj}^{z}}{D_{xj}^{z}+{{k}^{z}}} \right)$
where Qxj is the habitat quality of grid x in land use type j; Hj is the habitat attribute of land use type j; z is the normalized constant, usually 2.5; k is the semi-saturation sum parameter, usually taking the value 0.05; Dxj is the degree of habitat degradation, calculated as follows (Zhong and Wang, 2017):
${{D}_{xj}}=\underset{r=1}{\overset{R}{\mathop \sum }}\,\underset{y=1}{\overset{{{Y}_{r}}}{\mathop \sum }}\,\left( \frac{{{w}_{r}}}{\sum\limits_{r=1}^{R}{{{w}_{r}}}} \right){{r}_{y}}\times {{i}_{rxy}}\times {{\beta }_{x}}\times {{S}_{jr}}$
${{i}_{rxy}}=1-\frac{{{d}_{xy}}}{{{d}_{r-\text{max}}}}$ (Linear decay)
$~{{i}_{rxy}}=\text{exp}\left( \frac{-2.99{{d}_{xy}}}{{{d}_{r-\text{max}}}} \right)$ (Exponential decay)
where R is the number of threat factors; Yr is the number of grids occupied by threat factor r in the layer of land use type; wr the weight of threat factor r, the value ranges from 0 to 1; ry is the number of threat factors on each grid in the layer of land use type; βx is the degree of legal protection; Sjr is the sensitivity degree of land use type j to threat factor r; irxy the influence degree of threat factor r in grid y on grid x, the calculation formulas of linear decay and exponential decay are different, which are formula (5) and (6) respectively, where dxy is the linear distance between grid x and grid y, and dr-max is the maximum influence distance of stress factor.
Among the six types of land use in Zhaotong City, farmland and construction land are the most disturbed by human activities, while roads are the essential for urban development. Therefore, farmland, construction land, national highway, and provincial highway are identified as threat factors in this study. To ensure that the model parameters reflect the actual situation of Zhaotong City, this paper established the threat factors and habitat sensitivity parameters by integrating existing studies (Wang et al., 2023; Wu et al., 2023a) in relevant areas, the current situation surveys, expert opinions, and the InVEST model user manual. This process yielded the threat factor weights and maximum impact distances (Table 1) and assessed habitat suitability and sensitivity to threat factors for different land use types (Table 2).
Table 1 Threat factor weights and maximum impact distances
Stress factors Maximum stress distance Weight Spatial attenuation mode
Farmland 5 0.7 Linear decay
Construction land 10 1.0 Exponential decay
National highway 7 0.5 Linear decay
Provincial highway 4 0.4 Linear decay
Table 2 Habitat suitability and sensitivity to threat factors for different land use types
Land use type Habitat suitability Stress factor
Farmland Construction land National highway Provincial highway
Farmland 0.4 0 0.8 0.2 0.2
Forest land 1 0.4 0.7 0.4 0.45
Grassland 0.75 0.4 0.6 0.4 0.6
Water area 0.9 0.7 0.8 0.35 0.5
Construction 0 0 0 0 0
Unutilized land 0 0 0 0 0

2.3.3 Topographic gradient distribution index

(1) Topographic position index
Terrain is a key factor affecting land use patterns and their spatial distribution patterns (Liao et al., 2023). The topographic position index is a composite topographic factor that comprehensively analyzes the elevation and slope of any point in a specific region. It effectively describes the distribution and complexity of topographic conditions in the region (Tian et al., 2022). Its calculation formula is:
$T=\ln \left[ \left( \frac{E}{{\bar{E}}}+1 \right)\times \left( \frac{S}{{\bar{S}}}+1 \right) \right]$
where E and S represent the elevation value and slope value of any grid in the study area respectively; $\bar{E}$ and $\bar{S}$ represent the mean elevation value and the mean slope value of the whole research area respectively. T represents topographic position index, the lower the elevation value and slope value, the smaller the topographic position index, and the larger the vice versa.
(2) Topographic distribution index
Topographic distribution index represents the distribution frequency of different habitat quality grades in different topographic intervals (Zang et al., 2019; Liao et al., 2023), and can be used to solve the problem of dimensional differences between habitat quality areas under different topographic gradients (Zeng et al., 2023). Its calculation formula is as follows:
$P=\frac{{{S}_{ij}}}{{{S}_{i}}}/\frac{{{S}_{j}}}{A}$
where P is terrain distribution index; A represents the total area of the study area; Si represents the total area of Class i habitat quality in the study area; Sj represents the total area of Class j topographic gradient in the study area; Sij represents the area of Class i habitat quality within Class j topographic gradient in the study area. If P>1, it means that the habitat quality in Class i is superior in Class j topographic gradient, and vice versa.
Based on the actual situation in Zhaotong, the elevation, slope, topographic relief, and topographic position index were classified into five grades (I, II, III, IV, and V) using the natural break point grading method in ArcGIS (Table 3).
Table 3 Standards of elevation, slope, topographic relief and topographic position index
Grade Elevation (m) Slope (°) Topographic relief (m) Topographic position index
$\text{I}$ [260, 1111.35] [0, 10.81] [0, 19] [0.06, 0.42]
II (1111.35, 1610.42] (10.81, 19.94] (19, 36] (0.42, 0.53]
$\text{III}$ (1610.42, 2050.77] (19.94, 29.40] (36, 56] (0.53, 0.63]
IV (2050.77, 2535.16] (29.40, 40.89] (56, 88] (0.63, 0.74]
V (2535.16, 4003] (40.89, 86.17] (88, 1020] (0.74, 1.16]

2.3.4 Driving force detection model

In this study, qualitative and quantitative analyses were used to explore the driving factors of habitat quality change in the study area. Eight natural environment indicators and two socioeconomic indicators were qualitatively selected. The natural environment indicators included X1 (average annual temperature), X2 (average annual precipitation), X5 (elevation), X6 (slope), X7 (NDVI), X8 (NPP), X9 (land use type), and X10 (topographic relief). Socioeconomic indicators include X3 (GDP) and X4 (population density). In quantitative analysis, single-factor and two-factor interaction detection were carried out to study the main driving factors affecting the habitat quality change in the study area. The geographical detect model proposed by Wang Jinfeng is a statistical method that can detect spatial differentiation characteristics and reveal its driving factors. Its unique advantage is to detect the interaction of two factors on the dependent variable (Wang and Xu, 2017). The basic principle of geographic detector is to take the study area as a total sample, and divide the total sample into several subsamples, and then calculate and compare the variance size. If the total sample variance is greater than the sum of the variance of the subsamples, there is spatial differentiation (Tian et al., 2022). If there is a statistical correlation between the two, it means that the spatial distribution of the two variables tends to be consistent (Wang and Xu, 2017). Factor detection mainly analyzes the degree of influence (Wang et al., 2022), and the q value is generally used to reflect the explanatory power of driving factors on the spatial and temporal distribution of habitat quality. The calculation formula is as follows:
$q=1-\frac{\sum\limits_{h=1}^{L}{{{N}_{h}}\delta _{h}^{2}}}{N{{\delta }^{2}}}$
where the value of q ranges from 0 to 1. The closer the value of q is to 1, the stronger the interpretation is; N is the habitat quality of the total number of samples in the study area, h is the number of subsamples in the study area (zoning index), h=1,2,... L; ${{\delta }^{2}}$,$\delta _{h}^{2}$ represent discrete variances of habitat quality in the study area and subarea, respectively.
Detecting factors interaction can explore the influence of the interaction of two independent variables X on the dependent variable Y (Jin et al., 2022), and judge the form of the interaction according to the size of the interaction result (Table 4).
Table 4 Types of two-factor interactions
Basis of judgment Interaction
q(X1$\cap$X2)<min[q(X1),(X2)] Nonlinearity attenuation
min[q(X1),(X2)]<q(X1$\cap$X2)<max[q(X1),(X2)] Reduced single-factor nonlinearity
q(X1$\cap$X2)> max[q(X1),(X2)] Two-factor enhancement
q(X1$\cap$X2)= q(X1)+q(X2) Independent
q(X1$\cap$X2)> q(X1)+q(X2) Nonlinear enhancement

3 Results

3.1 Spatial-temporal evolution analysis of land use types

Land use types in Zhaotong mainly consist of farmland, forest land, and grassland (Figure 2). As depicted in Figure 3, forest land is the predominant land use type in Zhaotong, representing over 44.74% of the total land area. Over the past 20 years, the forest land area initially increased and then decreased, with an overall net increase. Farmland is the second largest land use type in Zhaotong, accounting for 30.00%, 28.62%, and 28.17% in 2000, 2010, and 2020 respectively, indicating a declining trend. Grassland as the third largest land use type in the region, with its area showing a consistent decrease over the past two decades. Water bodies, construction land, and unutilized land cover relatively small areas, with water bodies and construction land areas increasing, while unutilized land area is decreasing.
Figure 2 Land use in Zhaotong City in 2000, 2010, 2020
Figure 3 Area proportion of land use types in Zhaotong City
In order to further explore the change amplitude of each land use type in Zhaotong City, dynamic degree analysis was introduced (Table 5). The dynamic degrees of comprehensive land use in 2000-2010 and 2010-2020 were 0.15% and 0.08% respectively, indicating that the total land use in the study area changed dramatically in the first 10 years. From the perspective of single land use dynamic degree, farmland, grassland, and unutilized land showed a decreasing trend, with dynamic degrees of -0.46%, -0.03%, and -1.36% respectively, while forest land, construction land, and water area showed an increasing trend, with dynamic degrees of 0.12%, 58.98%, and 0.94% respectively from 2000 to 2010. Among them, the increase rate of construction land was the most drastic, as China entered the stage of new heavy industrialization and urban development continued to grow. In response to the state’s call, Zhaotong City developed heavy industry, realized urban expansion, and had a huge demand for construction land. From 2010 to 2020, farmland, forest land, grassland, and unutilized land showed a decreasing trend, with dynamic degrees of -0.16%, -0.03%, -0.10%, and -1.13% respectively, while construction land and water area showed an increasing trend, with dynamic degrees of 3.87% and 8.44% respectively. Water area had the largest growth rate, attributed to the construction of a hydropower station requiring the establishment of a reservoir area to impound water in Zhaotong City during this stage. The barrage in the reservoir area intercepts the flow, slowing down the river’s flow velocity and diffusing the circulation capacity to the surrounding area, leading to an increase in the water area.
Table 5 Dynamic degree of land use types in Zhaotong City
Land use types 2000-2010 2010-2020 2000-2020
Range of
change (km2)		 Single dynamic degree (%) Range of change (km2) Single dynamic degree (%) Range of
change (km2)		 Single dynamic
degree (%)
Farmland -309.13 -0.46 -102.29 -0.16 -411.42 -0.31
Forest land 116.04 0.12 -25.53 -0.03 90.51 0.05
Grassland -15.76 -0.03 -55.57 -0.10 -71.32 -0.06
Construction land 201.80 58.98 91.29 3.87 293.09 42.83
Water area 9.38 0.94 92.35 8.44 101.73 5.08
Unutilized land -2.30 -1.36 -1.66 -1.13 -3.96 1.17
Comprehensive dynamic degree (%) 0.15 0.08 0.11

3.2 Spatial-temporal evolution analysis of habitat quality

In this study, Habitat Quality module in InVEST model was selected to explore the temporal and spatial variation characteristics of habitat quality in Zhaotong City. The results of birth environment quality were calculated according to formulas (3) to (6), and the results showed that the habitat quality of Zhaotong City in 2000, 2010, and 2020 were 0.7257, 0.6699, and 0.6595 respectively. Overall, the habitat quality grade of study area was high but showed a downward trend during the study period. Based on the classification methods used by Zeng et al. (2023), Zheng et al. (2022), and Zhang et al. (2023) in the analysis of habitat quality results, this study used the equal interval method to classify the habitat quality of Zhaotong City from 0 to 1 into low (0, 0.2], medium-low (0.2, 0.4], medium (0.4, 0.6], medium-high (0.6, 0.8], and high (0.8, 1]. The spatial distribution map of habitat quality was compiled using the reclassification tool in ArcGIS 10.7 (Figure 4). The low and medium-low habitat quality is mainly distributed in the areas of low elevation and gentle slope where human activities are more intense. Medium-high and high habitat quality were mainly distributed in sparsely populated areas with higher vegetation coverage. The spatial and temporal transfer of habitat quality of different levels in Zhaotong City from 2000 to 2020 is as follows: the decrease area of high habitat quality was mainly concentrated in the eastern and southwestern areas of Zhaotong City, while the increase area of medium-high habitat quality was mainly concentrated in the decrease area of high habitat quality, and the increase area of medium-low habitat quality and low habitat quality were mainly concentrated in Zhaoyang District.
Figure 4 Spatial distribution of habitat quality in Zhaotong City
According to the habitat quality classification calculations, the ArcGIS 10.7 analysis tool was utilized to determine the distribution of habitat quality grades in Zhaotong City in 2000, 2010, and 2020 (Figure 5). Over the two-decade period, high-quality habitats predominated in Zhaotong City. In 2000, high-quality habitats accounted for 44%, decreasing to 35% by 2020. Medium-high and medium-low habitat qualities followed, representing 25% and 18% in 2000, and increasing to 30% and 28% respectively by 2020. Medium-quality habitats exhibited a declining trend, decreasing from 13% in 2000 to 5% in 2020. Additionally, low-quality habitats were relatively scarce, comprising less than 1% of the total in 2000 and increasing to 2% in 2020, mainly concentrated in Zhaoyang District.
Figure 5 Habitat quality at different grades
From the perspective of different grades of habitat quality transfer (Figure 6), between 2000 and 2010, the main types of habitat quality transfer were medium and high habitat quality, with transfer areas of 2438.76 km2 and 2184.21 km2, respectively. This was followed by medium-high and medium-low habitat quality, with transferred areas of 1135.45 km2 and 370.88 km2, respectively. During this period, high habitat quality mainly transitioned to medium-high habitat quality, medium-high habitat quality mainly transitioned to medium habitat quality, medium habitat quality mainly transitioned to medium-low habitat quality, and medium-low habitat quality mainly transitioned to low habitat quality. It is evident that the transfer area of each grade of habitat quality in this stage is substantial, with the primary transformation being This suggests that the area experiencing habitat quality degradation was larger than that experiencing habitat quality improvement. From 2010 to 2020, the area of habitat quality transfer decreased compared to the previous time interval, but habitat quality degradation still exceeded habitat quality improvement, indicating an overall declining trend in habitat quality.
Figure 6 Sankey map of habitat quality transfer

3.3 Topographic gradient effect of habitat quality

3.3.1 Analysis of topographic features

The DEM data was downloaded from the geospatial data cloud, and the elevation and slope data of Zhaotong City were extracted using ArcGIS. The topographic position index of the study area was calculated using formula (7), and the topographic feature map was finally generated (Figure 7). As shown in Figure 6, the elevation generally increases from the northeast to the southwest. The lowest and highest elevations are 260 m and 4003 m, respectively. The steepest slope reaches 86.17°, and the topographic relief reaches 1020 m. The lowest value of the topographic position index is 0.06, mainly concentrated in Zhaoyang District, where the slope is gentle. The maximum value of the topographic position index is 1.16, indicating high elevation and steep slopes in Zhaotong City, primarily distributed in Qiaojia County and Yongshan County.
Figure 7 Terrain feature of Zhaotong City

3.3.2 Distribution of habitat quality based on topographic position index

In mountainous areas, slope and elevation are the dominant factors determining land resource utilization (Zang et al., 2019). Topographic position index can comprehensively reflect the geographical situation of a study area by combining elevation and slope data. Firstly, the average of habitat quality in 2000, 2010, and 2020, the differences in habitat quality between 2000-2010, 2010-2020, and 2000-2020, and the gradient of the topographic position index were superimposed and analyzed to produce Figure 8. The habitat quality difference was calculated by subtracting the habitat quality at the end of the study period from the habitat quality at the beginning of the study period. Subsequently, the habitat quality grade of each phase in Zhaotong City was overlaid with elevation, slope, topographic relief, and topographic position index gradients to obtain the distribution of different habitat quality grade indexes on each topographic gradient.
Figure 8 Average value and difference of HQ at different topographic gradients
As shown in Figure 8, from 2000 to 2020, habitat quality increased with the increase in topographic gradient grade, demonstrating a pattern where low topographic gradient corresponded to low habitat quality and high topographic gradient correlated with high habitat quality. The disparity in habitat quality decreased as the gradient grade increased from 2000 to 2010 and from 2010 to 2020, suggesting a gradual reduction in the variation of habitat quality with the rise in gradient grade. The decline in habitat quality in high gradient grades slowed down from 2000 to 2010 and 2010 to 2020, indicating that human activities were more intense in areas with low gradient grades, resulting in deeper ecological damage, while the ecological damage was relatively less severe in areas with high topographic gradient grades.
In terms of habitat quality distribution along different elevation gradients (Figure 9), low habitat quality has a distribution advantage on gradient I and III, with a greater advantage on gradient I, having a distribution index of 2.13 in 2000. The distribution of low-medium habitat quality decreased as the elevation gradient grade increased, with the largest distribution index of 1.60 on gradient I in 2000. There were variations in the distribution of medium habitat quality along the elevation gradient in 2000, 2010, and 2020, with distribution advantages on gradient IV and V in 2000, and on gradient I in 2010 and 2020. The medium-high habitat quality exhibited a distribution advantage on gradient I, IV, and V, with a greater advantage on gradient V. High habitat quality initially increased and then decreased with the elevation gradient grade.
Figure 9 Distribution of habitat quality on elevation gradients
In terms of habitat quality distribution on slope gradient grade (Figure 10), the low and medium-low habitat quality decreased with the increase of slope gradient grade, and both had significant distribution advantages on gradient grade I. The medium habitat quality decreased with the increase of slope gradient grade in 2000, and the distribution indexes on gradients I and II had a distribution advantage. However, in 2010 and 2020, the medium habitat quality first decreased then increased. The high and medium-high habitat quality increased with the increase of slope gradient grade. The medium-high habitat quality in 2000 had distribution advantages on gradients IV and V, while in 2010 and 2020, it had distribution advantages on gradients II, III, IV, and V. The high habitat quality had distribution advantages on gradients III, IV, and V from 2000 to 2020, indicating that the steeper the gradient, the less human damage to the environment, and the better the habitat quality.
Figure 10 Habitat quality distribution on slope gradients
According to the distribution of habitat quality on the topographic relief gradient (Figure 11), the distribution index of habitat quality was consistently low during the study period. However, the curve representing habitat quality at all grades on the topographic relief gradient fluctuated significantly. It was observed that habitat quality at all grades exhibited a strong preference for topographic relief but had limited adaptability to changes in relief.
Figure 11 Distribution of habitat quality on topographic relief gradients
According to the distribution of habitat quality at the gradient grade of topographic position index (Figure 12), the low habitat quality decreased with the increase of topographic gradient level. The medium-low habitat quality increased first and then decreased at the topographic gradient level in 2000, while in 2010 and 2020, the medium-low habitat quality decreased with the increase of topographic gradient, and there was a distribution advantage on gradients I and II. The medium habitat quality increased with the increase of the topographic gradient grade in 2000, while in 2010 and 2020, the medium habitat quality decreased with the increase of topographic gradient grade. The distribution index of medium-high and high habitat quality increased with the increase of topographic gradient grade from 2000 to 2020.
Figure 12 Distribution of habitat quality at topographic position index gradients
In conclusion, topography is an important factor affecting the spatial distribution pattern of habitat quality in Zhaotong City. The differences in elevation, slope, topographic relief, and landform influence the spatial distribution pattern of land use types. The changes in land use types can reflect the intensity of human activities, thus resulting in different spatial distributions of habitat quality (Liu et al., 2018b). Therefore, in future ecological planning and habitat protection, the government should fully consider the impact of topographic factors such as elevation, slope, topographic relief, and geomorphic form.

3.4 Driving force analysis of habitat quality

Combined with the actual situation of the ecological environment in Zhaotong City, this study selected 10 factors from the aspects of the natural environment and social economy as driving factors to study the driving force of habitat quality change in Zhaotong from 2000 to 2020. These factors include X1 (average annual temperature), X2 (average annual precipitation), X3 (GDP), X4 (population density), X5 (elevation), X6 (slope), X7 (NDVI), X8 (NPP), X9 (land use type), and X10 (topographic relief). The data for 2000, 2010, and 2020 were selected for all 10 driving factors, and the q-values of each time node were obtained by using formula (9) to create Table 6. The q-values of different years all passed the significance test at the 1% level. From the table of detection results of impact factors at three different time nodes, the explanatory power of each factor to habitat quality varied, and the dominant driving factors of habitat quality at each time node differed. In 2000, GDP, land use type, and NPP were the top three factors in explaining habitat quality, with GDP being the leading factor. In 2010, the order of importance for explaining habitat quality was: land use type > NPP > population density > average annual temperature > average annual precipitation > GDP > elevation > NDVI > topographic relief > slope, with land use type as the leading factor. In 2020, the order of explanation intensity for habitat quality was: average annual precipitation > NDVI > population density > GDP > land use type > average annual temperature> NPP > slope > topographic relief > elevation, with average annual precipitation as the dominant factor. From 2000 to 2020, the dominant factor affecting the distribution of habitat quality in Zhaotong changed from GDP to land use type, and then to average annual precipitation.
Table 6 Detection results of factors affecting HQ
Driving factors q value
2000 2010 2020
Average annual temperature 0.4282 0.4411 0.4508
Average annual precipitation 0.3662 0.4397 0.5935
GDP 0.8157 0.3793 0.5037
Population density 0.3539 0.5073 0.5639
Elevation 0.2089 0.3080 0.3033
Slope 0.4989 0.2141 0.3822
NDVI 0.4551 0.2465 0.5693
NPP 0.5385 0.5885 0.3844
Land use type 0.5614 0.8362 0.4615
Topographic relief 0.4165 0.2410 0.3587
From the beginning to the end of the study period, the analysis of average annual temperature, average annual precipitation, elevation, NDVI, and population density on habitat quality in Zhaotong revealed a consistent upward trend. This suggests that the habitat quality in the study area was influenced not by a single natural or socio-economic factor, but by a complex interplay of both natural and socio-economic factors.
The data for each driving factor in Zhaotong City in 2000, 2010 and 2020 were selected to calculate the average value. The interaction of the driving factors was explored to generate Figure 13. It can be seen that the interaction results of each driving factor in Zhaotong City exhibited nonlinear enhancement or double-factor enhancement. The most significant interaction interpretation was that land use type intersected with elevation, NDVI, NPP, population density, GDP, average annual temperature, topographic relief. Additionally, NPP intersected with average annual precipitation, and the degree of interaction interpretation of these factors was above 0.99. NDVI $\cap$ slope (0.4725), NDVI $\cap$ elevation (0.4718), and elevation $\cap$ average annual temperature (0.5001) were weak interpreted results of the interactive detection. In conclusion, among the driving factors of habitat quality in Zhaotong, the correlation between land use type and other driving factors is strong. The influence degree after interaction is more significant, indicating a high correlation.
Figure 13 Interactive detection results of habitat quality

4 Discussion

4.1 Changes of land use types in counties/districts

From 2000 to 2020, the main land use types in Zhaotong included forest land, farmland, and grassland. The land use growth rate of construction land was the highest, aligning with the spatial-temporal dynamic changes in land use types in Northern Yunnan as studied by Chen et al (Chen et al., 2022). This study utilized the regional statistical tool in ArcGIS to analyze the changes and dynamic trends of farmland, forest land, grassland, and construction land in each county/district of Zhaotong City from 2000 to 2020 (Table 7). The largest change in farmland area was observed in Ludian county, showing a decreasing trend. Qiaojia county had the largest change in forest land and grassland area, both exhibiting an increasing trend. All counties/districts in Zhaotong City experienced an increasing trend in construction land, with Zhaoyang District showing the largest increase in area. According to Wang et al. (Wang et al., 2007), Zhaotong City is characterized by a challenging natural environment, a large population, and extensive poverty. Consequently, there is an urgent development need in Zhaotong City, leading to a strong demand for construction land and an overall increase in construction land across districts and counties. The sig- nificant increase in construction land area in Zhaoyang district can be attributed to its relatively flat terrain and high usability.
Table 7 Land use change and dynamic degree in Zhaotong City from 2000 to 2020
County/District Farmland Forest land Grassland Construction land
Area of change (km2) Dynamic
degree (%)		 Area of change (km2) Dynamic
degree (%)		 Area of change (km2) Dynamic
degree (%)		 Area of change (km2) Dynamic
degree (%)
Ludian -120.70 -1.10 -36.51 -0.32 84.19 1.91 25.47 4.72
Zhaoyang -53.11 -0.28 21.05 0.16 74.16 -0.35 69.62 4.46
Zhenxiong -70.60 -0.26 -1.19 0.00 69.42 0.06 61.85 4.70
Yiliang 5.62 0.04 -18.84 -0.06 -24.46 0.04 9.26 4.53
Weixin -28.88 -0.29 -4.06 -0.03 24.83 0.22 24.48 4.66
Daguan -18.38 -0.20 5.73 0.03 24.10 0.06 5.87 4.45
Yanjin 6.62 0.09 -7.89 -0.04 -14.51 -0.25 16.69 4.37
Yongshan -44.22 -0.32 34.75 0.16 78.96 -0.24 16.38 3.91
Shuifu -14.35 -0.60 1.09 0.02 15.44 -0.31 7.44 3.50
Suijiang -25.37 -0.47 10.29 0.15 35.66 -0.70 10.41 3.46
Qiaojia -46.56 -0.33 86.04 0.36 132.60 -0.37 45.59 4.79

4.2 Topographic gradient effects on habitat quality

Zhaotong City is one of the mountainous cities in southwest China. Southwest China is suited on the second and third ladder, characterized by steep terrain, alpine forest, and fragile ecology. The irrational development and utilization in this area are more harmful to the ecological environment than that in the plain areas. This study used the Habitat Quality module of InVEST model to explore the temporal and spatial evolution characteristics of habitat quality in Zhaotong City. It introduced topographic position index to study the distribution of habitat quality across different topographic gradients. This study aimed to determine whether there are distinct gradient distribution characteristics of habitat quality based on various topographic factors. Similar to the distribution patterns of typical ecosystem services in Bailongjiang Watershed of Gansu province studied by Xu Caixian et al. (Xu et al., 2020), four topographic factors including elevation, slope, topographic relief, and topographic position index, were selected for analysis and divided into five gradients to assess the distribution advantages and disadvantages of habitat quality at each grade on elevation, slope, and topographic position index gradients. In this study, statistical tools in ArcGIS were used to obtain the average habitat quality of Zhaotong City at different topographic position grades (Table 8). From the beginning of the study period to the end of the study period, the habitat quality of each topographic position grade showed varying degrees of decline, with the largest decline observed in grade I. As the topographic position index grade increased, the decline in habitat quality slowed down. At the beginning, middle, and end of the study period, the habitat quality of Zhaotong City increased with the rise of the topographic position index grade, indicating a significant topographic gradient effect on habitat quality in the study area. This finding is consistent with the results of the topographic gradient effect on the habitat quality of the middle Yangtze River economic belt as studied by Liu Yuan et al. (Liu et al., 2019).
Table 8 Average value of habitat quality at different topographic locations in Zhaotong City
Topographic position index grade Average habitat quality 2000-2020 change value
2000 2010 2020
I 0.6066 0.5345 0.5213 -0.0852
II 0.6852 0.6265 0.6156 -0.0696
III 0.7357 0.6825 0.6725 -0.0632
IV 0.7775 0.7266 0.7165 -0.0610
V 0.8114 0.7633 0.7555 -0.0559
In areas with a high topographic position index, the forest coverage rate is high, the terrain is complex, and development is challenging. The predominant land use types are forests and grasslands (refer to Table 9), resulting in relatively high habitat quality. To enhance regional habitat quality and ecosystem service capacity in these areas, it is essential to enhance the connectivity of habitat patches. Conversely, in regions with a low topographic position index, human activities are more intense, leading to greater development and increased ecological damage. The primary land use types in these areas are farmland and construction land, resulting in relatively low habitat quality. Therefore, in regions with a low topographic position index, it is crucial to intensively utilize farmland and construction land, allocate land resources rationally, optimize and adjust land use structures, protect biodiversity, and subsequently enhance regional habitat quality.
Table 9 Structural characteristics of land use types at different topographic locations
Topographic position index grade Proportion by land use type 2000/2010/2020 (%)
Farmland Forest land Grassland Water area Construction land Unutilized land
I 20.53/20.23/19.53 12.17/12.67/12.59 7.44/7.66/7.32 0.87/1.04/1.91 0.34/1.74/2.68 0.05/0.06/0.05
II 30.54/30.50/30.78 32.20/33.82/34.12 17.49/18.79/18.84 0.31/0.32/0.68 0.11/1.10/1.48 0.08/0.08/0.07
III 27.21/27.36/27.73 43.98/46.54/47.19 24.57/26.00/26.30 0.14/0.14/0.32 0.03/0.53/0.64 0.07/0.06/0.06
IV 16.22/16.40/16.52 40.20/42.76/43.52 21.21/21.98/22.31 0.06/0.07/0.13 0.02/0.24/0.29 0.03/0.02/0.02
V 5.49/5.50/5.44 20.53/22.27/22.78 11.27/11.26/11.42 0.05/0.05/0.06 0.00/0.07/0.08 0.01/0.01/0.01

4.3 Effects of land use type change on habitat quality

During the study period, the area of habitat quality degradation was larger than the area of habitat quality improvement. Specifically, from the beginning to the middle of the study period, habitat quality degradation accounted for 24.34%, habitat quality improvement for 3.00%, and habitat quality stability for 72.66%. From the middle to the end of the study period, habitat quality degradation was 5.14%, improvement was 2.22%, and stability was 92.64%. Overall, from the beginning to the end of the study period, habitat quality degradation accounted for 27.57%, improvement for 4.05%, and stability for 68.38%. The degradation of habitat quality exceeded the improvement, indicating a declining trend in habitat quality over time. The temporal and spatial changes in habitat quality revealed a consistent downward trend in Zhaotong’s habitat quality throughout the study period. The habitat quality grades displayed a pattern of “degradation” transfer, with an increase in low-quality habitat areas. This trend was attributed to the expansion of construction land, signifying a weakening of Zhaotong’s ecosystem service capacity due to intensive human economic activities. These findings align with the research conducted by Han et al. (2023) in the Dongting Lake Basin.
In this paper, geographic detectors were used to study the driving factors of habitat quality in Zhaotong. Among them, land use type was the dominant factor of habitat quality change in 2010, and the interaction results with other driving factors were highly explanatory, which was consistent with the conclusions of Li et al. (2023) in their study on the driving factors of spatial differentiation of habitat quality in Puding County, Guizhou Province. Therefore, this study further combined with the temporal and spatial changes of land use types in Zhaotong, summarized the driving factors of habitat quality decline, and calculated the contribution rate of land use type change leading to habitat quality decline in Zhaotong by using the ecological contribution rate of landscape change used by Hu et al. (2016) when studying the regional ecological environment effect in some areas of central Guizhou. As can be seen from Table 10, in terms of land use type change in Zhaotong, the main driving factors leading to the decline of habitat quality are farmland and construction occupation, whose contribution degrees are 0.1294% and 0.0883% respectively, and the transferred area is 438.1236 km2 and 298.7478 km2 respectively. It can be seen that the conversion of regional ecological land into farmland is the dominant factor leading to the decline of habitat quality in Zhaotong, and the expansion of construction land and occupation of other land use types also bring great threats to the decline of habitat quality, which is different from the results of the habitat contribution degree of land functional space transformation in Qujing studied by Wu et al. (2023a). Although Zhaotong is adjacent to Qujing, there are some differences in geomorphological terrain, ecological environment, and social economy, which lead to differences in the research results.
Table 10 Main land use changes and contribution rates leading to HQ degradation in Zhaotong City
Driving factors Type of land use transfer Total transfer area (km2) Contribution (%)
Construction occupancy Farmland→Construction land 298.7478 -0.0883
Forest land→Construction land
Grassland → Construction land
Water area → Construction land
Unutilized land → Construction land
Reclamation (conversion of regional ecological land to farmland) Forest land → Farmland 438.1236 -0.1294
Grassland → Farmland
Water area → Farmland
Land degradation Farmland → Unutilized land 1.6803 -0.0005
Forest land → Unutilized land
Grassland → Unutilized land
Water area → Unutilized land
In addition, deforestation and agricultural expansion are the main changes in land use in the Alabaha Basin (Hong et al., 2021). And Polasky et al. (2011) compared the effects of different land use change scenarios on ecosystem provision and found that agricultural expansion scenarios were responsible for the decline in habitat quality. Strengthening land management and implementing interventions in mixed landscape areas, both in China and in other countries around the world, is essential to conserve biodiversity and improve habitat quality, as seen in Alabaha River Watershed located in southeastern Georgia (Upadhaya and Dwivedi, 2019). Land conservation presents an opportunity to reduce biodiversity loss while achieving other human benefits (Watson et al., 2019).

5 Conclusions

Based on various topographic gradients, the Habitat Quality module of the InVEST model was utilized to investigate the distribution characteristics and evolution of habitat quality in the study area. The driving factors of habitat quality were examined through geographic detectors, considering aspects of the natural environment and social economy. While these models and methods are not novel in research, the outcomes of this study can serve as a foundation for decision-makers to conduct long-term scientific and professional surveys on the status and characteristics of landforms, ecosystems, biodiversity, and landscapes. A scientific and systematic evaluation of regional habitat quality can assist decision-makers in promptly identifying areas where habitat quality is deteriorating, understanding the causes of degradation, and recognizing the sensitivity of habitats to biodiversity threats. This information can guide the implementation of appropriate measures to safeguard biodiversity, restore the ecological environment, and promote sustainable use. Policymakers should evaluate the impact of land use on biodiversity in advance, particularly when selecting areas with varying levels of habitat degradation for development, and should only permit low-impact development in these areas.
During the 20-year study period, the main land use types in Zhaotong are forest land, farmland, and grassland. The most significant change occurred in construction land, with the expansion area showing a trend of habitat quality degradation. The amount of unutilized land is relatively small, indicating a high utilization rate of land resources. However, this also highlights the scarcity of reserve land resources in Zhaotong City to some extent. Using a series of ArcGIS tools, the hierarchical distribution of habitat quality was examined at various elevation, slope, topographic relief, and topographic position index gradient levels. The results revealed significant topographic gradient effects on habitat quality distribution in Zhaotong City. Furthermore, the study explored the spatial and temporal patterns of land use change and their impact on habitat quality. The methods employed in this research can be combined with existing data to promptly evaluate land use dynamics and biodiversity. Consequently, similar studies can be conducted on a larger scale to deepen our understanding of how land use patterns influence habitat quality at different topographic gradient levels and other factors.
The findings of this paper can improve international indicators for quantitative biodiversity assessment, provide scientific and evidence-based guidance for strengthening biodiversity policy-making, and support the global vision of living in harmony with nature. It is of great significance to realize multiple win-win situation of economic, social and ecological protection in the study area.
Among a series of methods to evaluate habitat quality, the use of InVEST model to evaluate regional habitat quality can improve the visibility of results. Habitat quality is assessed by understanding the impact of habitat threats and their distribution, assessing habitat types that best reflect natural conditions in the study area. In this study, cultivated land, construction land, national road and provincial road were selected as threat factors. The InVEST habitat quality model requires the weight of the maximum effective distance and the weight of each ecological threat factor. Therefore, quality research usually relies on the InVEST model data set parameters and the knowledge or opinions of experts, which is subjective to a certain extent. InVEST habitat quality models can be heavily influenced by expert judgment, which is one of the limitations and potential for future improvement of models. In the future, the model can be used to set up some human-made threat sources that are difficult to quantify, and field investigation can be carried out to verify the research results, which will make the research results more secure. However, in contrast to the GLOBIO method, the InVEST model does not base the equation for the relationship between environmental drivers and biodiversity on average species abundance. Explore common assessment indicators of biodiversity, such as the Mean Species Abundance, Living Planet Index, and National Biodiversity Index, do not qualitatively consider biodiversity as a basis for formulating biodiversity strategies. In terms of the selection of driving factors, this study selects 8 factors in terms of natural environment: land use type, elevation, slope, topographic relief, NDVI, NPP, average annual temperature and average annual precipitation, and 2 factors in terms of social economy: GDP and population density. Although factor detection has been selected from the aspects of natural environment and social economy, this study did not optimize the parameters, which may lead to differences in the degree of explanation of habitat quality driving factors at different spatial scales, and deviations in explanatory power.
Development strategy in Zhaotong is shifting from poverty alleviation to rural revitalization. It is essential to develop tourism based on geographical characteristics of Zhaotong to improve economic income. However, in the process of rural revitalization, the concept of “clear waters and green mountains are gold hills and silver mountains” should be implemented, ecological priority and green development should be adhered to, and major protection should be carried out instead of large-scale development. In the future, the explanatory power of driving factors at different spatial scales should be compared in the application of geographical detectors, and the spatial scale with the greatest explanatory power should be selected for research, so as to make the research results more reliable, and provide reference direction for realizing the coordinated development of ecology and economy in Zhaotong and formulating targeted ecological protection and restoration strategies.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Alkemade R, Van Oorschot M, Miles L, et al. 2009. GLOBIO3: A framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems, 12(3): 374-390.

[2]
Bai L M, Xiu C L, Feng X H, et al. 2019. Influence of urbanization on regional habitat quality: A case study of Changchun City. Habitat International, 93: 102042. DOI: 10.1016/j.habitatint.2019.102042.

[3]
Bao Y B, Liu K, Li T, et al. 2015. Effects of land use change on habitat based on InVEST model—Taking Yellow River Wetland Nature Reserve in Shaanxi Province as an example. Arid Zone Research, 32(3): 622-629. (in Chinese)

[4]
Berta Aneseyee A, Noszczyk T, Soromessa T, et al. 2020. The InVESThabitat quality model associated with land use/cover changes: A qualitative case study of the Winike Watershed in the Omo-gibe Basin, southwest Ethiopia. Remote Sensing, 12(7): 1103. DOI: 10.3390/rs12071103.

[5]
Chen C, Li Y Q, Yang D H, et al. 2022. Spatiotemporal dynamic changes of land use and vegetation coverage in northern Yunnan. Journal of Northeast Forestry University, 50(10): 101-107. (in Chinese)

[6]
Chen M, Su X L, Huang H M, et al. 2019. Assessment of river habitat quality in the Three Gorges Reservoir Region. Acta Ecologica Sinica, 39(1): 192-201. (in Chinese)

[7]
Chu L, Sun T, Wang T, et al. 2018. Evolution and prediction of landscape pattern and habitat quality based on CA-Markov and InVEST model in Hubei section of Three Gorges Reservoir area (TGRA). Sustainability, 10(11): 3854. DOI: 10.3390/su10113854.

[8]
Deng W, Dai E F, Jia Y W, et al. 2015. Spatiotemporal coupling characteristics, effects and their regulation of water and soil elements in mountainous area. Mountain Research, 33(5): 513-520. (in Chinese)

[9]
Deng Y, Jiang W G, Wang W J, et al. 2018. Urban expansion led to the degradation of habitat quality in the Beijing-Tianjin-Hebei Area. Acta Ecologica Sinica, 38(12): 4516-4525. (in Chinese)

[10]
Du J S, Fu J Y, Hao M M. 2021. Analyzing the carbon metabolism of “Production-Living-Ecological” space based on ecological network utility in Zhaotong. Journal of Natural Resources, 36(5): 1208-1223. (in Chinese)

[11]
Feng J M, Feng Y F, Li C, et al. 2023. Evolution of habitat quality and landscape pattern in the towns along the Yellow River floodplain under the boundary of river regime. Acta Ecologica Sinica, 43(16): 6798-6809. (in Chinese)

[12]
Feng S, Sun R H, Chen L D. 2018. Spatio-temporal variability of habitat quality based on land use pattern change in Beijing. Acta Ecologica Sinica, 38(12): 4167-4179. (in Chinese)

[13]
Gou M M, Liu C F, Li L, et al. 2023. Spatiotemporal variations and scenario simulation of habitat quality in a typical basin of the Three Gorges Reservoir Area. Chinese Journal of Ecology, 42(1): 180-189. (in Chinese)

DOI

[14]
Hall L S, Krausman P R, Morrison M L. 1997. The habitat concept and a plea for standard terminology. Wildlife Society Bulletin, 25(1): 173-182.

[15]
Han H R, Yang C F, Song J P. 2015. The spatial-temporal characteristic of land use change in Beijing and its driving mechanism. Economic Geography, 35(5): 148-154, 197. (in Chinese)

[16]
Han Y, Mao Y F, Yang L, et al. 2023. Dynamic responses of habitat quality to LUCC in the Dongting Lake Basin. Journal of Central South University of Forestry & Technology, 43(6): 148-157. (in Chinese)

[17]
He J H, Huang J L, Li C. 2017. The evaluation for the impact of land use change on habitat quality: A joint contribution of cellular automata scenario simulation and habitat quality assessment model. Ecological Modelling, 366: 58-67.

[18]
Hong H J, Kim C K, Lee H W, et al. 2021. Conservation, restoration, and sustainable use of biodiversity based on habitat quality monitoring: A case study on Jeju Island, South Korea (1989-2019). Land, 10(8): 774. DOI: 10.3390/land10080774.

[19]
Hu F, An Y L, Zhao H B. 2016. Research on characteristics of ecological environment effect on a “semi-karst” Region based on land use transition: A case in central Guizhou Province, China. Earth and Environment, 44(4): 447-454. (in Chinese)

[20]
Huang L, Chen Q J, Feng J Z, et al. 2023. Spatial-temporal characteristics and driving mechanisms analysis of habitat quality in Shenfu Mining Area based on Geodetector. Journal of Xi'an University of Technology, 39(4): 451-463. (in Chinese)

[21]
Jin H X, Tian H W, Zhang X X, et al. 2022. Spatial-temporal evolution of habitat quality and driving factors along the Yellow River: A case study of 19 counties in Shanxi. Yellow River, 44(10): 89-94, 100. (in Chinese)

[22]
Li H X. 2022. Analysis and optimization research on territorial space pattern of Zhaotong. Diss., Kunming, China: Yunnan Normal University. (in Chinese)

[23]
Li S P, Liu J L, Lin J, et al. 2020. Spatial and temporal evolution of habitat quality in Fujian Province, China based on the land use change from 1980 to 2018. Chinese Journal of Applied Ecology, 31(12): 4080-4090. (in Chinese)

[24]
Li T, Bao R, Li L, et al. 2023. Temporal and spatial changes of habitat quality and their potential driving factors in southwest China. Land, 12(2): 346. DOI: 10.3390/land12020346.

[25]
Li Y, Feng X, Wu L H, et al. 2023. Spatial-temporal evolution and quantitative attribution of habitat quality in typical Karst Counties of Guizhou Plateau. Environmental Science, 45(5): 2793-2805. (in Chinese)

[26]
Li Y J. 2021. Ecological security evaluation of urbanization process in Zhaotong City, Yunnan Province, China. Diss., Shanghai, China: East China Normal University. (in Chinese)

[27]
Liao Y M, Yin L J, Lan A J, et al. 2023. Analysis of terrain gradient effect on land use change under different landforms in poor mountainous areas of northwest Guizhou. Ecological Science, 42(2): 111-118. (in Chinese)

[28]
Liu C F, Wang C, Liu L C. 2018a. Spatio-temporal variation on habitat quality and its mechanism within the transitional area of the three natural zones: A case study in Yuzhong County. Geographical Research, 37(2): 419-432. (in Chinese)

[29]
Liu S L, Liu L M, Wu X, et al. 2018b. Quantitative evaluation of human activity intensity on the regional ecological impact studies. Acta Ecologica Sinica, 38(19): 6797-6809. (in Chinese)

[30]
Liu Y, Zhou Y, Du Y T. 2019. Study on the spatio-temporal patterns of habitat quality and its terrain gradient effects of the middle of the Yangtze River Economic Belt based on InVEST model. Resources and Environment in the Yangtze Basin, 28(10): 2429-2440. (in Chinese)

[31]
Lu Y F, Li H B. 2022. Temporal and spatial dynamic evolution of habitat quality based on land use change from 2000 to 2020—Taking Wuhan Metropolitan region as an example. Research of Soil and Water Conservation, 29(6): 391-398. (in Chinese)

[32]
Mattias G, Vassilis G. A, Elena G, et al. 2016. Land use change effects on ecosystem services of river deltas and coastal wetlands: Case study in Volan-Mesola-Goro in Po River Delta (Italy). Wetlands Ecology and Management, 25: 67-86.

[33]
Nelson E, Mendoza G, Regetz J, et al. 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment, 7(1): 4-11.

[34]
Nematollahi S, Fakheran S, Jafari A, et al. 2020. Landscape planning for conservation, based on the InVEST Model of habitat quality and ecological impact assessment of road network in Chaharmahal & Bakhtiari Province. Iranian Journal of Applied Ecology, 8(4): 67-81.

[35]
Pimm S L, Raven P. 2000. Extinction by numbers. Nature, 403(6772): 843-845.

[36]
Polasky S, Nelson E, Pennington D, et al. 2011. The impact of land-use change on ecosystem services, biodiversity and returns to landowners: A case study in the state of Minnesota. Environmental and Resource Economics, 48(2): 219-242.

[37]
Qing L, Yong Z, Cunningham M A, et al. 2021. Spatio-temporal changes in wildlife habitat quality in the middle and lower reaches of the Yangtze River from 1980 to 2100 based on the InVEST Model. Journal of Resources and Ecology, 12(1): 43-55.

DOI

[38]
Riedler B, Lang S. 2018. A spatially explicit patch model of habitat quality, integrating spatio-structural indicators. Ecological Indicators, 94: 128-141.

[39]
Sallustio L, De Toni A, Strollo A, et al. 2017. Assessing habitat quality in relation to the spatial distribution of protected areas in Italy. Journal of Environmental Management, 201: 129-137.

DOI PMID

[40]
Seto K C, Güneralp B, Hutyra L R. 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the USA, 109(40): 16083-16088.

[41]
Song G, Wang P P. 2017. Spatial pattern of land use along the terrain gradient of county in Songnen High Plain: A case study of Bayan County. Scientia Geographica Sinica, 37(8): 1218-1225. (in Chinese)

DOI

[42]
Stój M, Kruszyk R, Zawadzka D, et al. 2024. Habitat impacts on the golden eagle’s foraging ecology and nest site selection in Poland. Diversity, 16(2): 123. DOI: 10.3390/d16020123.

[43]
Tang F, Fu M, Wang L, et al. 2020. Land-use change in Changli County, China: Predicting its spatio-temporal evolution in habitat quality. Ecological Indicators, 117: 106719. DOI: 10.1016/j.ecolind.2020.106719.

[44]
Temple S A. 1986. The problem of avian extinctions. Current Ornithology, 3: 453-485.

[45]
Tian J B, Du Z, Jia Q J, et al. 2022. Study on the spatial differentiation of habitat quality of different terrain gradients in Taihang Mountains and its influencing factor—Taking Fuping County in Hebei Province as an example. Forestry and Ecological Sciences, 37(4): 426-436. (in Chinese)

[46]
Upadhaya S, Dwivedi P. 2019. Conversion of forestlands to blueberries: Assessing implications for habitat quality in Alabaha River watershed in Southeastern Georgia, United States. Land Use Policy, 89: 104229. DOI: 10.1016/j.landusepol.2019.104229.

[47]
Vigerstol K L, Aukema J E. 2011. A comparison of tools for modeling freshwater ecosystem services. Journal of Environmental Management, 92(10): 2403-2409.

DOI PMID

[48]
Wang B X, Cheng W M. 2022. Effects of land use/cover on regional habitat quality under different geomorphic types based on InVEST model. Remote Sensing, 14(5): 1297. DOI:10.3390/rs14051279.

[49]
Wang C, Chang Y, Hou X Y, et al. 2021. Temporal and spatial evolution characteristics of habitat quality in Jiaodong Peninsula based on changes of land use pattern. Journal of Geo-Information Science, 23(10): 1809-1822. (in Chinese)

[50]
Wang C S, Yang X G, Zhao H Y, et al. 2007. Functional regionalization of ecological shelter zone reconstruction on Jinsha River Basin of Yangtze River—A case in Zhaotong, Yunnan. Mountain Research, (3): 309-316. (in Chinese)

[51]
Wang J F, Xu C D. 2017. Geodetector: Principle and prospective. Acta Geoaphica Sinica, 72(1): 116-134. (in Chinese)

[52]
Wang Q K, Wu W, Yang X Q, et al. 2022. Spatial-temporal changes and driving factors of habitat quality in Shaanxi Province during the past 20 years. Arid Zone Research, 39(5): 1684-1694. (in Chinese)

DOI

[53]
Wang Y L, Lan A J, Fan Z M, et al. 2023. Spatial-temporal differentiation and driving factors of habitat quality in the Chishui River basin based on InVEST model. China Rural Water and Hydropower, (1): 17-23. (in Chinese)

[54]
Watson K B, Galford G L, Sonter L J, et al. 2019. Effects of human demand on conservation planning for biodiversity and ecosystem services. Conservation Biology, 33(4): 942-952.

DOI PMID

[55]
Wu K X, Shui W, Xue C Z, et al. 2023a. Spatiotemporal responses of habitat quality to land use changes in the source area of Pearl River, China. Chinese Journal of Applied Ecology, 34(1): 169-177. (in Chinese)

[56]
Wu Y X, Liu F N, Chen B T. 2023b. Spatial and temporal evolution and drivers of habitat quality of urban agglomeration in Lower Yellow River Basin. Bulletin of Soil and Water Conservation, 43(4): 396-404. (in Chinese)

[57]
Xie X L, Zhu Q. 2023. Research on the impact of urban expansion on habitat quality in Chengdu. Sustainability, 15(7): 6271. DOI: 10.3390/su15076271.

[58]
Xu C X, Gong J, Yan L L, et al. 2020. Spatial distribution characteristics of typical ecosystem services based on terrain gradients of Bailongjiang Watershed in Gansu. Chinese Journal of Ecology, 40(5): 1397-1408. (in Chinese)

[59]
Xu L T, Chen S, Xu Y, et al. 2019. Impacts of land-use change on habitat quality during 1985-2015 in the Taihu Lake Basin. Sustainability, 11(13): 3513. DOI: 10.3390/su11133513.

[60]
Zang Y Z, Liu Y S, Yang Y Y. 2019. Land use pattern change and its topographic gradient effect in the mountainous areas: A case study of Jinggangshan City. Journal of Natural Resources, 34(7): 1391-1404. (in Chinese)

[61]
Zeng Z, Ai J W, Chen L Y, et al. 2023. Research on the temporal and spatial evolution of habitat quality and terrain gradient effect in mountainous city: Taking the urban area of Sanming as an example. Journal of Nanjing Normal University (Natural Science Edition), 46(1): 100-109. (in Chinese)

[62]
Zhai Y X, Zhang F Y, Ma L N. 2023. Evolution and prediction of habitat quality in Bosten Lake Basin based on production-living-ecological space. Arid Land Geography, 46(11): 1792-1802. (in Chinese)

[63]
Zhang H T, Li J L, Tian P, et al. 2023. Spatio-temporal evolution of habitat quality in the East China Sea continental coastal zone based on land use changes. Acta Ecologica Sinica, 43(3): 937-947. (in Chinese)

[64]
Zhang M, Qiao J Y. 2021. EU Biodiversity Strategy for 2030: Main contents and positions on COP15. Environment and Sustainable Development, 46(6): 52-56. (in Chinese)

[65]
Zhang W W, Zhao S J, Yang X, et al. 2024. Effects of land cover on the taxonomic and functional diversity of the bird communities on an urban subtropical mountain. Diversity, 16(2): 107. DOI:10.3390/d16020107.

[66]
Zheng J C, Xie B G, You X B. 2022. Evolution of habitat quality and its influencing factors in the different terrain gradient of the Guangdong-Hong Kong-Macao Greater Bay area from 1980 to 2020. Economic Geography, 42(8): 41-50. (in Chinese)

DOI

[67]
Zheng Y P, Zhang J H, Tian H W, et al. 2023. Spatio-temporal characteristics of habitat quality and natural-human driven mechanism in Dabie Mountain Area. Environmental Science, 45(4): 2268-2279. (in Chinese)

[68]
Zhong L N, Wang J. 2017. Evaluation on effect of land consolidation on habitat quality based on InVEST model. Transactions of the Chinese Society of Agricultural Engineering, 33(1): 250-255. (in Chinese)

[69]
Zhou L, Tang J J, Liu X K, et al. 2021. Effects of urban expansion on habitat quality in densely populated areas on the Loess Plateau: A case study of Lanzhou, Xi’an-Xianyang and Taiyuan, China. Chinese Journal of Applied Ecology, 32(1): 261-270. (in Chinese)

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