Identification of Ecological Security Patterns for the Qiandongnan Ecotourism Area in Southwest China Using InVEST and Circuit Theory

Abstract

The establishment of ecological security patterns (ESPs) represents a significant paradigm shift in the approach to sustainable development. ESPs aim to reconcile the typically conflicting interests of ecological conservation and economic growth by guaranteeing the sustainability of critical ecosystem services and preserving the ecological integrity of the region while promoting socio-economic development. The primary objective of ESPs is to achieve a balanced and harmonious relationship between human society and the natural environment. The Qiandongnan Ecotourism Area (QEA) located in Southwest China is renowned for its high biodiversity; however, the ecological environment in the region is highly fragile. In light of this, there is an urgent need to establish ESPs for QEA that can promote ecological protection and sustainable economic development. In this study, we used land-use and land-cover change data and human disturbance factors to identify the ESPs of the Qiandongnan Ecotourism Area (QEA), employing the InVEST model and Circuit Theory. Our results revealed that (1) the ecological quality of the study area is relatively high, with high-quality habitat areas covering 19,554.76 km², which account for approximately 64.57% of the study area and the overall ecological environment is in a healthy condition; (2) the total area of ecological sources covers approximately 17,616.27 km², accounting for approximately 58.17% of the study area, primarily distributed in Liping, Rongjiang, and Congjiang, which respectively account for 16.28%, 12.44%, and 11.86% of the total ecological source area; (3) the ESPs are composed of 13 key ecological nodes, 17 ecological corridors (with a length of approximately 1474.47 km), and 21 ecological source clusters. The ecological corridors are distributed in a ring shape, connecting various ecological nodes and sources along mountains, forests, rivers, and valleys. These findings provide a theoretical foundation for the protection of the ecological system’s integrity and the development of social and economic activities in the QEA.

1. Introduction

The rapid expansion of human activities has caused the severe degradation of natural ecosystems, resulting in a loss of biodiversity and a range of environmental and social problems [1,2]. These challenges include climate change, natural disasters, and food and water security issues, which have a significant impact on both human society and the natural environment [3,4,5]. To address these issues, the academic community has recognized the urgent need to establish ecological security patterns (ESPs) [6,7,8,9]. ESPs encompass the spatial arrangements and distribution patterns of ecological components within an ecosystem. These patterns, which include the configuration, connectivity, and arrangement of habitats, species, and ecological processes, are instrumental in upholding the stability, resilience, and functionality of ecosystems [6]. The significance of ecological security patterns lies in their ability to support the conservation and sustainability of biodiversity. By ensuring that habitats are interconnected, ESPs enable the movement of species, facilitates gene flow, and promotes vital ecological interactions. Additionally, these patterns contribute to the continuity of ecosystem processes, such as nutrient cycling, pollination, and the maintenance of natural disturbance regimes [6,7,8,9].

ESPs aim to identify and protect critical ecological zones that are essential for maintaining regional ecosystem functions, structures, and processes [9,10,11]. However, the uncontrolled exploitation of natural resources, such as the overuse of land for construction and the overloading of resource and environmental carrying capacity, have led to the depletion of natural resources and the degradation of ecosystems [12,13]. Consequently, the level of ecological security has drastically declined, further exacerbating the environmental and social problems caused by human activities [14,15]. Therefore, it is essential to implement sustainable practices and policies that prioritize ecological conservation and promote a balance between human development and environmental protection. To address the inherent tension between ecological protection and economic development, scientists have turned their attention towards studying land use, natural resources, and ecological carrying capacity in ecologically fragile regions [16,17]. One approach has been to construct ESPs based on quantitative analysis results. With the advent of 3S technology and ecological models, various models, including InVEST, Circuit Theory, and the Ecological Network Model, have been widely used to identify ESPs [9,18,19,20]. These models are based on the “supply-flow-demand” relationship of ecosystem services, and have proven themselves effective in identifying and protecting critical ecological zones [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. In a previous study conducted in Yunnan Province, China, researchers successfully integrated the InVEST model and Circuit Theory to identify critical areas that play a significant role in providing ecosystem services and maintaining ecological connectivity [9]. The study’s findings emphasize the crucial importance of considering both ecosystem services and landscape connectivity when developing ESPs. These identified patterns offer valuable guidance for decision making and management strategies, with the ultimate aim of striking a balance between ecological conservation and human activities [9].

The InVEST model is an innovative approach that integrates natural and human factors to assess ecological benefits and facilitate the planning of different spaces, including production, living, and ecological spaces [9,10,11,12,13,14,15,16,17,18,19,20,21,22]. It enables the optimization of ecological benefits in urban areas by evaluating spatial and temporal changes. The model is widely recognized for its practical application in ecological conservation and ecotourism planning [23,24]. Similarly, the Circuit Theory model has shown promise in constructing ESPs by capturing ecological connectivity and identifying areas that support critical ecosystem functions [9,10,11,12,13,14,15]. The InVEST model complements this by incorporating socio-economic and ecological data, enabling stakeholders to make informed decisions in land-use planning. Its flexibility and adaptability make it an ideal tool for policymakers seeking to balance economic growth and environmental conservation [25,26,27]. Both the InVEST model and Circuit Theory model play a significant role in ecological conservation and planning by providing crucial information to decision-makers. They support informed decision-making in land-use planning, promote sustainable development, and contribute to environmental protection [9].

Spatial development based on ecosystem services and socio-economic development is a novel model that aligns with the concept of harmonious coexistence between human and nature, promoting both ecological protection and livelihood development [6,7,8,9]. The spatial pattern construction of ecological security can facilitate a win-win situation by enhancing the stability and recoverability of the ecosystem, mitigating the risk of natural disasters, and providing a solid foundation for sustainable local economic development [9,25,26,27]. Currently, ecological tourism areas are emerging rapidly in China, and the related planning systems for scenic spots are gradually improving. Despite the progress, the tension between ecological protection and economic development persists in the utilization of natural and human resources [28]. In this context, constructing ESPs and scientifically identifying the natural and cultural resources for tourism space development is crucial for the planning of every ecological tourism area. Therefore, constructing ESPs that integrate ecological protection and socio-economic development is essential for achieving sustainable development. The ESPs can be developed based on local ecological characteristics and regional development needs, incorporating socio-economic factors and ecological indicators. This approach ensures the protection of the environment and meets the needs of social and economic development, thus providing a foundation for the long-term and sustainable development of ecological tourism areas.

The Qiandongnan Ecotourism Area (QEA) is a significant ecotourism area in southwest China, boasting rich ecological and cultural resources [29]. However, the QEA is characterized by high ecological vulnerability due to its topography, climate, and human activities. The region is mountainous, with steep slopes and fragile soil, which are susceptible to erosion and landslides [30]. Moreover, the region has a humid subtropical climate with high rainfall, which further exacerbates soil erosion and landslides [31]. Human activities such as agriculture and mining have also contributed to the degradation of the region’s natural resources, including water, soil, and biodiversity. The combined effect of these factors has resulted in significant ecological vulnerability, which has negative impacts on the region’s socio-economic development and the well-being of its inhabitants. In this regard, our research aims to construct ESPs for the QEA based on land-use and land-cover change (LUCC) data and human disturbance data using the InVEST model and Circuit Theory model. By integrating ecological conservation practices and sustainable socio-economic development strategies in the QEA, it is anticipated that ecosystem integrity will be effectively preserved, leading to the enhanced socio-economic well-being and long-term resilience of the region.

2. Materials and Methods

2.1. Study Area

Qiandongnan Miao and Dong Autonomous Prefecture is located in the southeastern part of Guizhou Province, covering a total surface area of approximately 30,283 km² (Figure 1). The region has a subtropical humid monsoon climate, with no severe cold in winter and no extreme heat in summer. It has four distinct seasons, abundant rainfall, and a noticeable three-dimensional climate. As of 2021, the average annual temperature ranges from 14.7 to 18.6 °C, annual precipitation ranges from 1032.5 to 1456.8 mm, annual sunshine hours range from 1068.5 to 1269.6 h, annual average relative humidity ranges from 79 to 83%, and annual frost-free period ranges from 277 to 332 days [29,30]. The forest coverage of the QEA is above 75%, and its biodiversity resources are highly diverse, making it a nationally renowned source of traditional Chinese medicine resources and an important biodiversity hotspot in China. It is rated by the International Union for Conservation of Nature (IUCN) as one of the 200 biodiversity hotspots in the world. The region comprises Kaili city and 15 counties [32]. There are 46 ethnic groups in the jurisdiction, such as Miao, Dong, Buyi, and Tujia. As of the end of 2021, the resident population of the region was 3,740,400, with a household population of 4,898,600. The proportion of minority population in the household population was 81.8%, with Miao and Dong ethnic groups accounting for 43.5% and 30.5%, respectively (The official website of the Qiandongnan Miao and Dong Autonomous Prefecture; http://www.qdn.gov.cn/zjqdn/, accessed on 12 March 2022).

2.2. Data Sources

The land use data for the QEA in 2020 were obtained from the GlobeLand30 database, developed by the National Center for Basic Geographic Information of China (http://www.globallandcover.com/, accessed on 18 March 2022). The GlobeLand30 dataset, developed using a POK method based on multispectral images with 30 m spatial resolution, is the first global land cover data with the highest resolution worldwide. Its accuracy was verified using a large number of samples, and the Landscape Shape Index sampling model was used to distribute the data points, totaling more than 2.3 × 10⁵ samples. The overall accuracy of GlobeLand30-2020 was 85.72%, with a Kappa coefficient greater than 0.8. This dataset includes ten first-class land types, namely, cultivated land, forest, grass, shrub, wetland, water, tundra, artificial surface, bare land, and ice, making it an excellent resource for researchers studying comparative regional spatial and temporal changes [33,34].

2.3. Habitat Quality Assessment and Ecological Source Identification

We employed the Habitat Quality Module of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model to analyze the spatial distribution characteristics of habitat quality for the QEA. The InVEST model is a highly utilized tool in spatial planning, biodiversity conservation, ecological compensation, and other environmental management decisions at the global scale [25,26,27]. The main objective of habitat quality analysis is to identify potential threats emanating from different land use types, evaluate the correlation between habitat quality and these threats, and quantify the adverse impacts of these threats on habitats [35]. One way to describe the reduction in threats to ecological sources over distance is through a distance attenuation function. This function may take the form of a linear or exponential relationship:

$$\mathrm{If} \mathrm{linear}, \mathrm{it} \mathrm{is} {\mathit{i}}_{\mathit{rxy}}=\mathbf{1}-\left(\frac{{\mathit{d}}_{\mathit{xy}}}{{\mathit{d}}_{\mathit{rmax}}}\right)$$

(1)

$$\mathrm{If} \mathrm{exponential}, \mathrm{it} \mathrm{is} {\mathit{i}}_{\mathit{rxy}}=\mathit{exp}\left[-\left(\frac{2.99}{{\mathit{d}}_{\mathit{rmax}}{}_{{}_{}}}\right){\mathit{d}}_{\mathit{xy}}\right]$$

(2)

The linear distance between habitat grid x and y is represented by d_xy, while d_rmax denotes the maximum effective distance for threats associated with artificial surfaces, bare land and cultivated land. The quality of habitat was calculated according to the following formula:

$${\mathit{Q}}_{\mathit{xj}}={\mathit{H}}_{\mathit{j}}\left[1-\left(\frac{{{\displaystyle \mathit{D}}}_{\mathit{xj}}^{\mathit{z}}}{{{\displaystyle \mathit{D}}}_{\mathit{xj}}^{\mathit{z}}+{\mathit{K}}^{\mathit{z}}}\right)\right]$$

(3)

Habitat quality suitability for grid j is denoted by H_j. The semi-saturated property constant K is typically set to half the maximum value of D_xj. The default value for the parameter z is typically 2.5. To run the InVEST model, four sets of data need to be inputted, including LUCC (raster data), threat source (raster data), the quantitative value of threat source (CSV format), and the quantitative value of sensitivity of LUCC to each ecological threat source (CSV format). In this study, the parameters in the two CSV files were obtained from the InVEST model user’s guide and previous studies [25,26,27,35] (

Table 1. Influence range and weight of threat factors.

Threat Factor	Maximum Influence Distance (km)	Weight	Spatial Attenuation Type
Cultivated land	3	0.7	Linear
Bare land	2	0.6	Linear
Artificial surfaces	5	0.9	Exponential

and

Table 2. Sensitivity of different habitats to threat factors.

Land Use Type	Habitat Suitability	Cultivated Land	Bare Land	Artificial Surfaces
Cultivated land	0.3	0	0.5	0.25
Forest	0.9	0.5	0.45	0.7
Grass land	0.6	0.5	0.55	0.35
Shrub	0.8	0.5	0.3	0.8
Wetland	0.7	0.8	0.5	0.5
Water body	0.8	0.65	0.4	0.55
Artificial surfaces	0	0	0	0

). To visualize the spatial distribution of habitat quality, the output data from the InVEST model were classified into five categories using ArcGIS 10.6 (ESRI Inc., Redlands, CA, USA). The InVEST model employs a continuous scale from 0 to 1 to represent habitat quality, with values closer to 1 indicating higher quality habitat, less intense land development and utilization, and more abundant natural landscape resources. Hence, these categories included unsuitable (0–0.2), low (0.2–0.4), medium (0.4–0.6), high (0.6–0.8), and optimal (0.8–1) habitat quality. Grids with a habitat quality index greater than 0.8 and a patch area greater than 10 km² were selected as ecological sources [35].

2.4. Resistance Analysis and Ecological Corridor Construction

Ecological corridors are critical for the conservation and restoration of biodiversity by facilitating the movement of species and maintaining genetic diversity [6,7,8,9]. However, constructing effective ecological corridors requires a thorough understanding of the resistance levels of different landscapes to species movement. Consequently, identifying habitat landscape resistance surfaces has become a critical research area for studying ESPs [6]. The Habitat Suitability Index (HSI) is a widely used metric to evaluate the quality of habitats for species survival and reproduction [36]. However, to incorporate HSI into ecological corridor planning, it is essential to relate it to the degree of movement resistance of different landscapes. In this regard, this study proposes a negative exponential transformation function to convert HSI into resistance values. This transformation approach was adopted because it is commonly used in landscape resistance modeling and can effectively convert continuous HSI values into discrete resistance categories. The resistance values obtained from the transformation of HSI values were then integrated with LUCC data to produce habitat landscape resistance surfaces. These surfaces depict the degree of movement resistance for different landscapes, thus enabling the identification of suitable locations for ecological corridor construction [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37]. The calculation formula is as follows:

If HSI > Threshold → Suitable habitat → Resistance = 1

If HSI < Threshold → Non-suitable habitat/Matrix → Resistance = ${\mathrm{e}}^{\frac{\ln \left(0.001\right)}{\mathrm{threshold}} \times \mathrm{HSI}} \times 1000$

HSI is calculated by the InVEST model and used to distinguish between suitable and non-suitable habitats. A threshold value of 0.8 is applied, with HSI values equal to or above this threshold indicating a suitable habitat and values below it indicating a non-suitable habitat.

The HSI maps generated using the InVEST model were combined with Circuit Theory (https://circuitscape.org/, accessed on 10 May 2022) to identify ecological corridors within the study area. Ecological nodes were the intersection points of more than two ecological corridors. The Circuit Theory model utilizes circuit and random walk theory by incorporating landscape as a resistance layer, and simulating movement patterns of random walkers between source and target image elements of the landscape to determine current patterns. This approach is commonly used to model wildlife migration and gene flow, and can identify ecologically important connectivity areas that can be targeted for conservation and management, including ecological corridor identification, wildlife corridor design, and gene flow simulation. In the Circuit Theory model, patches with ecological processes that promote them are assigned low resistance values, while those that hinder them are assigned high resistance values.

3. Results

3.1. Spatial Distribution Characteristics of Habitat Quality

By using the Habitat Quality Module of the InVEST model, a distribution map of habitat quality for the QEA was generated (Figure 2). Currently, the QEA has 261.42 km² of unsuitable habitat, 7541.98 km² of low-quality habitat, 2443.43 km² of medium-quality habitat, 481.01 km² of high-quality habitat, and 19,554.76 km² of optimal habitat, accounting for 0.86%, 24.91%, 8.07%, 1.59%, and 64.57% of the study area, respectively. The spatial pattern of habitat quality shows that the north, east, and south regions have higher quality habitats, with generally high levels of habitat quality throughout. When compared to other administrative regions, Liping exhibited a higher habitat quality, with optimal habitats covering 15.33% of the total optimal habitat area. Conversely, Kaili and Majiang had a relatively low habitat quality, with optimal habitats accounting for only 2.8% and 2.61% of the total optimal habitat, respectively (

Table 3. Distribution characteristics of habitat quality in different administrative regions of the Qiandongnan Ecotourism Area in Southwest China.

Administrative Regions	Unsuitable		Low		Medium		High		Optimal
	Area /km2	Percentage /%	Area /km2	Percentage /%	Area /km2	Percentage /%	Area /km2	Percentage /%	Area /km2	Percentage /%
Kaili	81.27	31.09	631.58	8.37	171.02	7.00	138.23	28.74	547.04	2.80
Liping	37.88	14.49	772.81	10.25	588.66	24.09	17.74	3.69	2997.63	15.33
Congjiang	18.70	7.15	748.17	9.92	157.89	6.46	58.79	12.22	2243.7	11.47
Rongjiang	15.02	5.75	942.02	12.49	26.81	1.10	29.65	6.16	2300.43	11.76
Huangping	13.83	5.29	667.79	8.85	414.06	16.95	5.53	1.15	556.44	2.85
Cengong	13.09	5.01	409.65	5.43	28.42	1.16	30.97	6.44	991.02	5.07
Jinping	11.62	4.44	190.49	2.53	163.53	6.69	35.51	7.38	1214.00	6.21
Zhenyuan	10.46	4.00	518.04	6.87	104.14	4.26	27.12	5.64	1233.85	6.31
Taijiang	9.97	3.81	254.49	3.37	54.01	2.21	10.98	2.28	753.40	3.85
Danzhai	9.39	3.59	354.47	4.70	18.00	0.74	4.07	0.85	558.39	2.86
Tianzhu	8.51	3.26	364.65	4.83	166.02	6.79	35.25	7.33	1631.86	8.35
Shibin	7.47	2.86	384.08	5.09	215.13	8.80	51.42	10.69	889.47	4.55
Majiang	7.12	2.72	390.93	5.18	45.93	1.88	1.18	0.25	510.85	2.61
Leishan	6.28	2.40	329.45	4.37	41.61	1.70	1.80	0.37	812.41	4.15
Sanhui	5.60	2.14	184.97	2.45	105.03	4.30	2.67	0.56	731.61	3.74
Jianhe	5.21	1.99	398.39	5.28	143.17	5.86	30.10	6.26	1582.66	8.09
Total	261.42	100	7541.98	100	2443.43	100	481.01	100	19,554.76	100

).

3.2. Spatial Distribution Characteristics of Habitat Resistance and Ecological Sources

The western region of the QEA showed significantly higher habitat landscape resistance values compared to other areas (Figure 3). The patches with high resistance were primarily concentrated in and around Kaili, where human activities are more prevalent, leading to severe habitat fragmentation and low connectivity among patches. The distribution of core ecological sources in the QEA showed considerable variation and is similar to the spatial pattern of habitat quality, with higher values observed in the north, east, and south than in the northwest. The total area covered by ecological sources in the study area was 17,616.27 km², accounting for approximately 58.17% of the total area. The ecological environment was generally found to be in good condition, with the majority of ecological sources distributed in Liping, Rongjiang, and Congjiang, accounting for 16.28%, 12.44%, and 11.86% of the total ecological source area, respectively (Figure 4;

Table 4. The spatial distribution characteristics of ecological resources in different administrative regions of the Qiandongnan Ecotourism Area in Southwest China.

Administrative Regions	Ecological Resources
	Area/km2	Percentage/%
Liping	2868.44	16.28
Rongjiang	2191.76	12.44
Congjiang	2088.8	11.86
Tianzhu	1552.34	8.81
Jianhe	1537.10	8.73
Jinping	1179.45	6.70
Zhenyuan	1115.94	6.33
Cengong	894.42	5.08
Leishan	768.77	4.36
Shibin	731.35	4.15
Taijiang	723.38	4.11
Sanhui	708.49	4.02
Sanzhai	491.13	2.79
Kaili	354.64	2.01
Majiang	284.74	1.62
Huangping	125.52	0.71
Total	17,616.27	100.00

). The high-current patches are mainly situated in the mountainous regions in the east and south, where the forest coverage is relatively high, the ecological landscape is less disturbed, and the ecosystem is relatively integrated (Figure 5).

3.3. Spatial Distribution Characteristics of Ecological Security Patterns

According to the Circuit Theory model, the ESPs in the QEA consisted of several ecological nodes, corridors, and sources, interconnected by ecological corridors in a radial pattern along mountains, forests, rivers, and valleys. This ESPs included 13 key ecological nodes, 17 ecological corridors (totaling approximately 1474.47 km in length), and 21 clusters of ecological sources (Figure 6). The ecological nodes were primarily situated at the intersections of the south-central and eastern ecological corridors, with fewer nodes observed in the north. The ecological corridors were distributed in a circular pattern, linking various core ecological source sites along the mountainous and river valley areas in the north, west-central, south-central, and southeast.

4. Discussion

To construct Ecological Security Patterns (ESPs), the identification of optimal ecological patterns is of utmost importance. These patterns are designed to address regional ecological and environmental issues comprehensively. By establishing thresholds and security levels based on natural ecological processes and ecosystem functions, ESPs aim to create a spatial pattern that effectively maintains and controls ecological processes while enhancing ecosystem functions [9,22,38]. The variations observed in ESPs hold significant implications for various aspects of ecosystem dynamics, biodiversity conservation, and ecosystem services. Understanding and considering these variations is critical for making informed decisions and implementing effective environmental management strategies [6]. In our study, we specifically identified spatial variations in habitat quality characteristics and ecological corridors within the QEA. This provides a scientific foundation for the construction of ESPs tailored to the region. The establishment of ESPs becomes essential in mitigating conflicts between economic development and ecological protection, ensuring a balance between these two aspects [39,40]. The observed variations in ESPs have wide-ranging implications. Firstly, they influence ecosystem dynamics by shaping the spatial arrangements and distribution patterns of ecological components, thereby affecting the stability and functioning of ecosystems. Secondly, these variations have direct consequences for biodiversity conservation, as they determine habitat suitability and connectivity, which are critical for the survival and movement of species. Lastly, variations in ESPs impact ecosystem services, as they influence the delivery of essential benefits such as pollination, water purification, and climate regulation. By considering and incorporating these variations in decision-making processes and environmental management strategies, stakeholders can promote sustainable development practices while safeguarding ecological integrity and preserving ecosystem services.

The combination of the InVEST and Circuit Theory models has become increasingly popular and effective in identifying ESPs globally [6,7,8,9]. This integrated approach allows for a more comprehensive understanding of ecosystem dynamics, enabling the identification of critical ecological nodes, corridors, and sources that are essential for maintaining ecosystem functions and services [41]. The use of both models together provides a powerful tool for policymakers and researchers in developing strategies for sustainable land use and conservation. The results indicated that the habitat quality in the QEA is generally high, with variations in spatial patterns, including the north, east, and south regions having slightly higher habitat quality compared to the northwest. The density and continuity of ecological nodes, corridors, and sources that constitute the ESPs differ in different regions, mainly due to natural environment and human activity interference. In eastern mountainous areas with larger forested areas and rich biodiversity, ecological nodes and corridors are denser, ecological sources are larger, ecosystems are more continuous, and the ecological safety level is higher [29]. On the other hand, the regions with lower ecological security levels are mainly located in the western region, with Kaili and its surrounding areas being the most typical. This area has a high population density, highway density, and urban agglomeration density, frequent farming activities, and a relatively homogeneous ecosystem and surface vegetation, making it more vulnerable to external disturbance factors.

The scale of ecotourism development in China is rapidly growing; however, blind development has generated many ecological and environmental problems [42]. In the process of regional development and utilization, it has become a major challenge for ecologically fragile areas planning in ecotourism areas to simultaneously enhance ecological, economic, and social benefits, maintain a virtuous cycle of ecosystems, and promote green economic development [42]. One of the key paths to synergistically enhancing ecological protection and economic development is through the scientific identification of ESPs [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. The use of ecological models in assessing habitat quality is more scientific, objective, and avoids interference from subjective factors in determining the core ecological protection range and potentially exploitable natural landscape resource space [25,26,27]. The construction of ESPs and the identification of an ecological priority-protection area based on 3S, ecological models and spatial measurement tools has been accepted and recognized by the academic community as scientifically sound. By constructing an ESP, key initiatives can help alleviate the contradiction between local ecological protection, and economic and social development, promoting sustainable development and ecosystem coordination in the QEA. When comparing our results to previous studies [6,7,8,9], we can observe certain similarities and differences. For example, in studies focusing on other regions facing similar ecological challenges, such as rapid urbanization or habitat degradation, the identification of key ecological nodes, corridors, and sources has proven crucial for conservation planning.

Our study utilized an integrated approach by combining the InVEST and Circuit Theory models to identify ESPs. This integration enabled us to comprehensively analyze and evaluate the connectivity, habitat quality, and spatial distribution of key ecological elements within the study area. By leveraging the strengths of both models, we successfully assessed the ecological benefits and prioritized conservation efforts. The InVEST model played a crucial role in providing valuable insights into ecosystem services and their spatial distribution [43]. It facilitated the quantification and mapping of various ecosystem services [43]. On the other hand, the Circuit Theory model contributed significantly to identifying critical ecological nodes and corridors, offering an understanding of the connectivity and functional importance of different ecological elements [9]. Nevertheless, it is essential to acknowledge the limitations associated with these methods. The accuracy of the results relies heavily on the quality of input data, which may inherently contain uncertainties. In addition, the models make simplifying assumptions and utilize parameters that may not capture the full complexity of real-world ecological systems. Furthermore, the quantitative values of threat sources and the sensitivity of land use types to each ecological threat source were based on subjective scoring by experts’ experience. Although this approach may introduce some errors, the overall assessment results of habitat quality remain largely unaffected.

To enhance the accuracy of identifying ecological security patterns and exploitable natural landscape resources, future studies should consider collecting data on threat factors beyond the study area and conduct field surveys to obtain actual data. These efforts will contribute to refining the assessment process and ensuring the robustness and reliability of the conclusions. While our study has certain limitations, it provides valuable insights into habitat quality within the study area. Future research endeavors should aim to incorporate additional data sources and employ more objective methods to improve the accuracy of assessments. By addressing these considerations, we can further enhance the reliability and applicability of ecological security patterns for effective conservation and management strategies.

5. Conclusions

Based on the integration of the InVEST and Circuit Theory models, this study successfully identified critical ecological sources, nodes, and corridors, leading to the development of ESPs for the QEA. The analysis revealed that forests played a predominant role as ecological sources, while radial ecological corridors connected these sources through mountainous regions, forests, rivers, and valleys. The resulting ESPs consisted of 13 key ecological nodes, 17 ecological corridors, and 21 clusters of ecological sources, visually demonstrating the spatial heterogeneity of these components. These findings have important implications for urban development, emphasizing the need to integrate ecosystem protection plans within economic strategies. For effective implementation in the QEA, several recommendations are proposed. Firstly, it is crucial to enhance management and protection efforts for the identified 21 large ecological sources. Secondly, prioritizing the establishment and management of an ecological corridor network system is essential, safeguarding the 17 identified ecological corridors and 13 key ecological nodes to facilitate species dispersal and gene exchange within the QEA’s ecological zones. Lastly, controlling rocky desertification in the identified ecological source areas should be a focus, as it contributes to overall ecological security by preventing land degradation, promoting vegetation growth, improving soil quality, and enhancing water retention. These actions can effectively mitigate erosion risks, reduce the likelihood of natural disasters, and support biodiversity conservation, all of which are integral to maintaining a healthy and resilient ecosystem. It is important to acknowledge the limitations of this study. Firstly, the methodology employed, consisting of the integration of the InVEST and Circuit Theory models, carries inherent uncertainties and assumptions that may impact the accuracy of the results. Additionally, the study focused specifically on the QEA, and the generalizability of the findings to other regions may vary. Future research should aim to address these limitations and further refine the methodology to enhance the reliability and applicability of the ecological assessment.