Multi-scale Differences in Landscape Connectivity Evaluation and Protection Strategies: A Case Study of Chongqing, China

doi:10.21203/rs.3.rs-4646135/v1

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Multi-scale Differences in Landscape Connectivity Evaluation and Protection Strategies: A Case Study of Chongqing, China

https://doi.org/10.21203/rs.3.rs-4646135/v1

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In the context of rapid urban expansion, protecting landscape connectivity has become an important means of addressing habitat fragmentation and enhancing biodiversity. Using Chongqing, China, as a case study, this research establishes a multi-scale evaluation framework of "main urban area - main metropolitan area - Chongqing city." By employing landscape connectivity indices and ecological security patterns, the study reveals the scale differences in landscape connectivity from both structural and functional perspectives and proposes protection strategies from a multi-scale viewpoint. The results indicate that in terms of structural connectivity, when the research scale is expanded to medium and large scales, the dPC levels of all core areas within the main urban area (small scale) significantly drop to levels 1–3. At the large scale, the core areas in Jiangjin District, Nanchuan District, and Fuling District within the main metropolitan area (medium scale) exhibit characteristics of significant downgrading (level 8 to level 2), some downgrading (level 9 to level 8), and upgrading (level 7 to level 9), respectively. In terms of functional connectivity, the corridors (394.79 km²) and sources (39.61 km²) in Beibei District, Yubei District, Nan'an District, and Banan District are very important for landscape connectivity protection at small and medium scales; the corridors (11.33 km²) and sources (1180.41 km²) in Nanchuan District and Fuling District are crucial for landscape connectivity protection at medium and large scales. This study provides systematic and comprehensive strategic references for landscape connectivity protection.

Earth and environmental sciences/Ecology

Earth and environmental sciences/Environmental social sciences

urban expansion

landscape connectivity

multi-scale

ecological security pattern

protection strategies

Habitat fragmentation caused by rapid urban expansion is becoming increasingly common (Yang et al., 2020; Huang et al., 2022). The reduction and fragmentation of habitat patches lead to decreased biodiversity and increased risk of species extinction (Wang et al., 2020). Enhancing landscape connectivity is a crucial measure to mitigate the negative impacts of high-intensity land use expansion on habitat patches (Lynch, 2019; Laliberté et al., 2020). Although many practices (such as the construction of overall ecological security patterns and the delineation of ecological protection red lines) focus on protecting landscape connectivity, the issue of urban habitat fragmentation remains severe (Luo et al., 2021). Therefore, effective protection of habitat landscape connectivity in rapidly urbanizing areas is urgently needed.

Landscape connectivity is an important indicator for measuring the ease of species migration or the smoothness of ecological processes (Forman and Godron, 1986; Brooks, 2003). Currently, structural connectivity (the spatial relationship between different landscape components, independent of ecological processes) and functional connectivity (associated with ecological processes such as species migration and energy transformation) are important aspects of evaluating landscape connectivity. Landscape connectivity indices based on graph theory (Urban and Keitt, 2001; Kindlmann and Burel, 2008) and ecological security patterns incorporating percolation theory (Huang et al., 2021; Walpole et al., 2012) are essential tools for assessing structural and functional connectivity, respectively. These studies have been widely applied in environmental impact assessments (Tarabon et al., 2019), land-use planning (Jia and Wang, 2022), and the delineation of biological conservation areas (Zhang et al., 2016), strongly demonstrating the negative impact of urban land expansion on landscape connectivity.

However, previous studies often focus on a single dimension, either structural connectivity or functional connectivity, which makes it difficult to reflect the coupling relationship between ecological processes and landscape patterns (Sun et al., 2021). This is not conducive to formulating accurate and detailed strategies for regional landscape connectivity protection. Focusing only on structural connectivity often fails to simulate real species migration processes, leading to disjointed conservation areas that cannot form a complete protection system (Huang and Liao, 2019). Conversely, focusing solely on functional connectivity makes it difficult to differentiate the importance of various patches, hindering the formulation of graded land planning and ecological management strategies (Zhao et al., 2019). Therefore, this study evaluates regional structural connectivity using landscape connectivity indices, assessing the spatial relationship and morphological association of landscape elements, while distinguishing the importance levels of different patches in landscape connectivity protection. Based on this, ecological security patterns are used to further analyze regional functional connectivity, presenting optimal paths for species migration or energy flow in the environment, and constructing a complete, coherent, and integrated protection spatial system. Ultimately, more precise and systematic identification of key areas for regional landscape connectivity protection is achieved.

Furthermore, current studies mainly explore landscape connectivity at a single scale without considering the differences in evaluation results and optimization strategies at different scales. In other words, the conflicts between human and natural environments often exhibit diverse, multi-layered characteristics at different spatial scales (Lu et al., 2023). Conclusions and optimization strategies obtained at small scales may not be applicable at larger scales (Liu et al., 2024), and vice versa. What are the commonalities and differences in the characteristics of regional landscape connectivity at different spatial scales? What are the focuses of implementing landscape connectivity protection strategies from the standpoint of different scales? How can conflicts of interest be coordinated and managed at different scales? These are scientific questions that urgently need to be addressed. This study conducts a multi-scale landscape connectivity evaluation at the intermediate level of the main metropolitan area (which has the ability to allocate resources across regions and coordinate spatial governance) (Hu et al., 2020), providing a more systematic, comprehensive, and widely applicable integrated solution for regional landscape connectivity protection.

In summary, this paper takes Chongqing, a representative sample with significant internal gradient differences in a typical area of rapid urbanization, as an example. It establishes a multi-scale landscape connectivity evaluation framework of "main urban area - main metropolitan area - Chongqing city." Based on this framework, structural and functional connectivity analyses are conducted using landscape connectivity indices and ecological security patterns, respectively, to clarify the differences in regional landscape connectivity at different scales and to provide systematic, comprehensive, and multi-scale applicable strategic solutions for landscape connectivity protection. The research results will provide references for mitigating natural habitat loss and landscape fragmentation in urban areas.

2.1 Study Area

Chongqing (longitude 105°11'–110°11' E, latitude 28°10'–32°13' N) covers an area of 82,402 km² and is the only municipality directly under the central government in western China, serving as a national central city and a megacity. Chongqing features a combination of large urban areas, extensive rural regions, mountainous zones, and vast reservoir areas. It has complex and variable natural geographic conditions, a sensitive and fragile ecological environment, and high-intensity, widespread human activities. These characteristics make it a typical area where rapid urban expansion threatens biodiversity, underscoring the urgency of ecological protection (Liu et al., 2024).Chongqing is also the first city in China to have its national land spatial planning approved by the State Council. Currently, it has delineated an ecological protection red line covering 19,200 km² (Chongqing Planning and Natural Resources Bureau, 2024). As an important ecological barrier in the upper reaches of the Yangtze River and a key ecological function area in the Three Gorges Reservoir Region, the protection of biological habitats in Chongqing is of significant importance.

Additionally, Chongqing exhibits a typical regional differentiation pattern (Chongqing Environmental Protection Bureau, 2019). The outskirts of the Chongqing city are predominantly mountainous, with sparse population distribution and excellent ecological quality, hosting numerous natural protected areas. The main metropolitan area has a large and densely distributed population, serving as a crucial space for human activities. The main urban area is characterized by highly concentrated population and industry, accounting for one-third of the permanent population and over half of the GDP (Liu et al., 2024). Therefore, the urban development characteristics and ecological protection levels of Chongqing city present significant gradient differences, making it a representative sample area for studying multi-scale differences.

2.2 Data Sources and Processing

According to their use in this study, the data are categorized into land use data and data required for constructing the resistance surface (Table 1). Specifically: (1)Land Use Data:As the fundamental data for this study, land use data are sourced from the annual China Land Cover Dataset (CLCD). Verified against ground survey data and high-resolution Google Maps, the overall accuracy of the CLCD reaches 80% (Yang and Huang, 2019), which meets the research requirements. (2)Resistance Surface Data: Elevation, slope, vegetation, roads, and land use types are considered ecological resistance factors. Finally, using resampling methods, all data resolutions are adjusted to be consistent with the land use data (30x30 meters).

Table 1

Data used in this study
Data	Year	Resolution	Database sources	Related used
Land cover data	1990,2000,2010,2020	30X30m	China Land Cover Dataset(CLCD)	Underlying data
DEM	2010,2020	30X30m	http://www.gscloud.cn	Resistance factor
NDVI	2010,2020	1X1km	http://www.nesdc.org.cn	Resistance factor
Road data	2010,2020	Vectorgraph	https://www.openstreetmap.org	Resistance factor

To address the loss of urban natural habitats and landscape fragmentation, this study establishes a multi-scale landscape connectivity evaluation and optimization method at the scales of Chongqing city, the Chongqing main metropolitan area, and the Chongqing main urban area. The specific research process (Fig. 2) is as follows: (1)Using the Morphological Spatial Pattern Analysis (MSPA) method, core areas are extracted. The landscape connectivity index is employed to measure the structural connectivity level of the study area at multiple scales in the year 2020, and these levels are compared with each other. (2)Based on the landscape connectivity evaluation results of the study area in 2020, ecological sources are identified. According to the principles of functional connectivity evaluation, ecological corridors are generated using circuit theory. An ecological network is constructed at multiple scales, and these networks are compared with each other. (3)Based on the multi-scale comparison of structural connectivity and the ecological network, zoning management strategies for optimizing landscape connectivity in the study area are proposed.

3.1 Structural Connectivity Evaluation at Multiple Scales

(1) Core Area Extraction

Morphological Spatial Pattern Analysis (MSPA) is a method that classifies the pixels of binary images based on mathematical morphology. It can extract various ecologically significant areas from the pixel level with advantages such as accuracy, speed, and low data requirements (Vogt et al., 2007; Wickham et al., 2010). In this study, key natural ecological elements such as forests and grasslands are used as the foreground (Lin et al., 2020), while other land types are treated as the background. The resulting binary raster data is imported into the Guidos software, which generates seven non-overlapping types of network structural elements (Fig. 3) (Vogt et al., 2017). Among these, core areas are typically large habitat patches such as wildlife habitats and forest reserves. They are the optimal network structural elements for increasing wildlife dispersal distances and promoting interspecies breeding (Chang et al., 2015), and are the primary focus of this study.

(2) Structural Connectivity Evaluation

The Probability of Connectivity (PC) index assesses the probability of connectivity between any two patches, providing a more precise numerical measure of the structural connectivity level in the study area (Pascual-Hortal and Saura, 2006). The Patch Importance Index (dI) represents the contribution of each patch to habitat connectivity. This is determined by evaluating the change in connectivity when any given patch is removed. A higher index value indicates that the patch supports a richer flow of materials and information (Zhang et al., 2021). The calculation formulas are as follows:

$$\:PC=\frac{\sum\:_{i=1}^{n}\sum\:_{j=1}^{n}{a}_{i}\times\:{a}_{j}\times\:{p}_{ij}^{\ast\:}}{A{L}^{2}}$$

In the equation, PC ranges from 0 to 1 and increases with improved connectivity. Here, n represents the total number of patches in the landscape, ai and aj denote the areas of patches i and j respectively, indicating the probability of species dispersal between patches i and j. AL represents the area of the study area.

$$\:dPC=\frac{PC-{PC}_{remove}}{PC}\ast\:100\%$$

In the equation, dPC represents the change in overall connectivity when a specific patch is removed, used to measure the importance of that patch in maintaining landscape connectivity. PCremove denotes the overall index value of remaining patches after the removal of a single patch.

The study imported core area data obtained from MSPA analysis into Conefor sensinode 2.6 software to calculate PC and dPC values. Subsequently, the dPC values of each patch were visualized in ArcGIS and classified into 9 levels based on the natural breaks method (Fig. 4). Core area patches categorized as "Level 7," "Level 8," and "Level 9" in terms of dPC are crucial for maintaining urban biodiversity and habitat integrity, highlighting regions of particular focus in this study.

(3) Comparison of dPC values of core area patches at different scales

Analyzing at a single large scale may overlook landscape heterogeneity within regions, while analyzing at a single small scale may fail to capture the overall ecological land use pattern of a region (Liu et al., 2024). Understanding the differences in ecological processes across different scales can provide a scientific basis for developing more accurate and detailed ecological conservation plans (Yu et al., 2023). In this study, based on the dPC values of core area patches at three scales: "Chongqing city - main metropolitan area - main urban area," we conducted overlapping analyses to compare the variations among these scales. This approach aims to clarify the emphasis of landscape connectivity conservation strategies among these three scale levels.

3.2 Evaluation of Functional Connectivity at Multiple Scales

(1) Identification of Ecological Source Areas

Ecological source areas are critical regions that ensure biological diffusion and maintain ecological flows, providing essential ecological services and preserving the integrity of landscape patterns (Fu et al., 2020). In this study, core area patches with a dPC ranking higher than "Level 3" and an area greater than 5 km², which are of significant value for maintaining regional landscape connectivity, are identified as ecological source areas (Wang et al., 2021).

(2) Construction of Resistance Surfaces

Ecological resistance refers to the obstacles species must overcome during horizontal migration and ecological flow processes (Cui et al., 2020). This study constructs comprehensive resistance surfaces by combining resistance factors from natural conditions, the degree of human disturbance, and environmental responses. To reduce the impact of subjective human judgment on weight assignment results and eliminate information redundancy caused by potential correlations between resistance factors, the study employs Spatial Principal Component Analysis (SPCA) to determine the weights of each factor (Zou and Yoshino, 2017). The formula is as follows:

$$\:RV=\sum\:_{k=1}^{m}{a}_{ik}{w}_{k}=\:\sum\:_{k=1}^{m}\frac{{a}_{ij}{F}_{k}}{\sum\:_{p=1}^{m}{F}_{k}}$$

In the formula, RV and wk represent the resistance value of the ith evaluation unit and the weight of the kth resistance indicator, respectively; aik is the kth resistance indicator of the ith unit; and Fk is the common factor variance of the kth resistance indicator.

(3) Extraction of Corridors

Ecological corridors are potential pathways for biological migration and the exchange of ecological elements (Huang et al., 2021). The study uses circuit theory to identify the direction and boundaries of ecological corridors in heterogeneous landscapes (Peng et al., 2018). This theory, based on the principles of electrical circuits, is a method for studying dynamic ecological flow trends, assessing specific ecological processes of a species, and quantifying habitat functional connectivity. It considers the random walk characteristics of species and views the landscape as a conductive surface composed of systematic nodes and resistors (McRae et al., 2008; Fan et al., 2021). Here, the nodes are ecological source areas, and the current magnitude represents the probability of species dispersal along a particular path. A higher accumulated current value indicates better connectivity between two ecological source areas in the region, suggesting that more species or a particular species more frequently pass through the area (McRae et al., 2008). The calculation formula is as follows:

$$\:I={V/R}_{eff}$$

In the formula, I represents the current magnitude, indicating the probability of species dispersal along a particular path; V is the voltage between ecological source areas; and Reff is the effective resistance of the conductor, reflecting the degree of isolation between ecological sources. The higher the resistance, the greater the difficulty for species movement.

The study imports ecological source areas and resistance surfaces into Circuitscape 4.0 software and uses the Linkage Mapper module to generate corridors. Subsequently, the scope and boundaries of ecological corridors are identified based on cumulative resistance (Fig. 5).

(4) Comparison of Ecological Networks at Different Scales

Conflicts

between human and natural environments exhibit diverse and hierarchical characteristics across different spatial scales (Lu et al., 2023). Traditional ecological network construction often overlooks the relationships between ecological elements at varying scales, making it challenging to develop systematic solutions (Elmqvist et al., 2013). This study constructs ecological networks at three scales: "Chongqing city - main metropolitan area - main urban area," then conducts overlay analyses to compare the overlapping and distinct parts among them. This approach supports a comprehensive exploration of strategies for protecting and optimizing regional landscape connectivity.

4.1 Multi-Scale Comparison of Structural Connectivity

In this study, the scales of Chongqing city, Chongqing main metropolitan area, and Chongqing main urban area are referred to as large, medium, and small scales, respectively.

The dPC ranks of core area patches within the Chongqing main urban area are compared across large, medium, and small scales. As shown in Fig. 6a, at the small scale, there are six patches with dPC ranks of 7–9 (high-rank dPC patches), covering an area of 107.80 km² (45.62% of the total core area patches). These high-rank dPC patches are mainly located in Beibei District at the northern end (a1) and Banan District at the southern end (a5). This indicates that when prioritizing landscape connectivity protection within the main urban area, the ecological integrity of these regions should be of primary concern. When the study scale is expanded to medium (Fig. 6b) and large (Fig. 6c) scales, the dPC ranks of all core area patches within the main urban area significantly decrease. For example, the core area in Beibei District (a1) has a dPC rank of 9 at the small scale, which decreases to 4 and 1 at the medium and large scales, respectively. Notably, at the large scale, all core area patches within the main urban area have a dPC rank of 1. This demonstrates that patches crucial for landscape connectivity within the main urban area are less important at the scale of Chongqing city, highlighting the scale differences in regional landscape connectivity.

The dPC ranks of core area patches within the Chongqing main metropolitan area are compared across large and medium scales. As shown in Fig. 7a, at the medium scale, there are four high-rank dPC patches, covering an area of 1530.95 km² (58.10% of the total core area patches). These high-rank dPC patches are mainly located in Jiangjin District (b5), Qijiang District (b6), Nanchuan District (b7), and Fuling District (b8) in the southeast. When the study scale is expanded to the large scale (Fig. 7b), the dPC rank of the core area patch in Jiangjin District (b5) drops significantly from 8 to 2. This indicates that while these areas are crucial for landscape connectivity protection at the medium scale, they are of lesser importance at the large scale. The dPC rank of the core area patch in Nanchuan District (b7) shows a slight decrease from 9 to 8, indicating its importance for landscape connectivity protection at both the medium and large scales. Notably, the dPC rank of the core area patch in Fuling District (b8) increases from 7 to 9, suggesting that while this area is important for landscape connectivity protection at the medium scale, it is even more critical at the large scale. This illustrates that scale differences in landscape connectivity impact the focus of regional protection strategies.

4.2 Multi-Scale Comparison of Functional Connectivity

Due to the lack of overlap between ESPs (Ecological Source Patches) at the large scale and the small scale, a comparative analysis is not feasible. Therefore, the ESPs within the Chongqing main urban area are compared at the medium and small scales only. As shown in Fig. 8a, at the small scale, there are 17 source patches with an area of 194.91 km² (3.48% of the main urban area), mainly located in Beibei District at the northern end (a1) and Banan District at the southern end (a5). There are 34 corridors covering an area of 986.52 km² (17.62% of the main urban area), primarily passing through Yubei District (a6), Banan District (a5), Jiulongpo District (a3), and Shapingba District (a2). When the study scale is expanded to the medium scale (Fig. 8b), the number of source patches (3) and their area (42.92 km²) significantly decrease, with source patches only located in Beibei District at the northern end (a1). The number of corridors (8) also decreases substantially, but their area (1063.94 km²) increases. An overlay analysis of ESPs at the small and medium scales (Fig. 8c) shows that the intersecting source patches number 3, with an area of 39.61 km², concentrated in Beibei District (a1) at the northern end. The intersecting corridors number 21, with an area of 394.79 km², mainly passing through Yubei District (a6), Nan'an District (a8), and Banan District (a5). This indicates that for both the main urban area and the main metropolitan area, the identified ESP regions are crucial for landscape connectivity protection.

The ESPs within the Chongqing main metropolitan area are compared at the medium and large scales. As shown in Fig. 9a, at the medium scale, there are 21 source patches with an area of 1900.26 km² (6.47% of the main metropolitan area), mainly located in Jiangjin District (b5), Qijiang District (b6), Nanchuan District (b7), and Fuling District (b8) in the south. There are 40 corridors covering an area of 2797.67 km² (9.53% of the main metropolitan area), primarily passing through Jiangjin District (b5), Bishan District (b12), Banan District (a5), Qijiang District (b6), Nanchuan District (b7), Fuling District (b8), and Yubei District (a6). When the study scale is expanded to the large scale (Fig. 9b), the number of source patches (6) and their area (1196.39 km²) significantly decrease, with source patches only located in Nanchuan District (b7) and Fuling District (b8) in the southeast. The number of corridors (3) and their area (103.07 km²) also decrease substantially. An overlay analysis of ESPs at the medium and large scales (Fig. 9c) shows that the intersecting source patches number 5, with an area of 1180.41 km², concentrated in Nanchuan District (b7) and Fuling District (b8) in the southeast. The intersecting corridors number 4, with an area of only 11.33 km², mainly passing through Nanchuan District (b7) and Fuling District (b8) in the southeast. This indicates that, for the landscape connectivity protection of Chongqing city, most ESPs within the main metropolitan area are not significantly effective.

5.1 Fine-Grained and Networked Identification of Key Areas for Landscape Connectivity Protection

Using the MSPA method, the study identified core patches in Chongqing city that possess good landscape connectivity, covering an area of 28,170.11 km², which is 33.57% of Chongqing city. However, the available land for future urban economic development is extremely limited, making it impractical to implement uniform protection measures over such a large area (Zhao et al., 2019). It is crucial to classify core patches based on their importance and apply hierarchical protection. This study further extracted high-level dPC patches within Chongqing's core areas, covering 17,583.88 km², or 20.95% of Chongqing city, which are vital for maintaining regional landscape connectivity. Unfortunately, although these areas meet the requirements for hierarchical management, they are still identified based on structural connectivity evaluation and fail to simulate specific species migration processes or diffusion patterns of specific ecological processes (Kong et al., 2010; Foltête et al., 2014), making them less applicable in real situations. Moreover, key areas identified based on structural connectivity evaluation often consist of scattered, isolated landscape patches (Li et al., 2018; Huang et al., 2019), making it difficult to form a complete system (Huang and Liao, 2019), which is not conducive to integrated protection of regional ecological resources. In this regard, an ecological security pattern, which uses ecological corridors to organically connect key areas ensuring ecological processes, forms a complete network system (Peng et al., 2018; Huang et al., 2022). This study further evaluated Chongqing’s functional connectivity using ecological security patterns, identifying key protection area networks that are practically valuable for landscape connectivity protection. These include source areas covering 19,827.82 km² (23.63% of Chongqing city) and corridors covering 3,491.62 km² (4.16% of Chongqing city). This has positive implications for protecting habitat integrity and ensuring migration corridor connectivity (Peng et al., 2018; Huang et al., 2021). In summary, this study couples structural and functional connectivity evaluations, providing a paradigm for the fine-grained and networked identification of key areas for landscape connectivity protection.

5.2 Multi-Scale Differences in Landscape Connectivity Evaluation

Ecological processes exhibit diverse and multi-tiered characteristics across different spatial scales (Lu et al., 2023). This study explores the relationships of landscape connectivity across different scales—city, main metropolitan area, and main urban area—benefiting the provision of scientific basis for systematic and meticulous ecological protection planning (Liu et al., 2024). Multi-scale comparisons of structural connectivity show that at the large scale, the dPC levels of core patches within the main urban area all drop to level 1. Shi et al. (2024) also confirmed this difference between large and small scales in their study of the Shiyang River Basin in Gansu, China. The reason is that Chongqing has typical regional differentiation patterns (Chongqing Environmental Protection Bureau, 2019); the peripheral areas of the city are mainly large forested areas, with an ecological foundation significantly better than that of the ecological spaces within the main urban area. Compared to the large scale, core patches in Jiangjin District, Nanchuan District, and Fuling District within the main metropolitan area show significant downgrades, moderate downgrades, and upgrades in dPC levels, respectively. The reason is that core patches in the west of the main metropolitan area (Nanchuan District, Fuling District) are directly connected to large natural habitats on the periphery of the city, and their connectivity value, linked to these high-quality habitats, does not significantly decrease and may even increase. In contrast, core patches in the east of the main metropolitan area (Jiangjin District) are close to the ecological spaces of the main urban area, and their connectivity value is significantly lowered due to the influence of these low-quality habitats. Changes brought by this “edge effect” and “radiation effect” are also reflected in Wang's study (2022). Multi-scale comparisons of functional connectivity show that ecological spaces in Beibei District, Yubei District, Nan’an District, and Banan District are very important for landscape connectivity protection in both the main urban area and the main metropolitan area. Ecological spaces in Nanchuan District and Fuling District are crucial for landscape connectivity protection in both the main metropolitan area and Chongqing city. Therefore, in the overall ecological security pattern of “three belts, four screens, multiple corridors, and multiple points” constructed by Chongqing in recent years (Chongqing Planning and Natural Resources Bureau, 2024), these areas are included as important ecological function zones, establishing a long-term ecological protection red line management mechanism.

5.3 Implications for Context-Specific Management Strategies within a Multi-Scale Evaluation Framework

Differences exist in landscape connectivity evaluation results at different spatial scales, making it necessary to propose corresponding land planning and ecological management strategies from the perspective of different administrative divisions. Based on multi-scale comparisons of structural connectivity, at the large scale, all core patches within the main urban area and most core patches in the west of the main metropolitan area have dPC levels of 1–3. Therefore, when decision-makers focus on the landscape connectivity protection of Chongqing city, these areas can all be included in the environmental admission zone, adopting a “environment-friendly, quality development” regulation concept to create a diverse and flexibly arranged characteristic industrial town system (Liu and Yu, 2023). However, when focusing on the landscape connectivity protection of the main metropolitan area or the main urban area, the core patches in Jiangjin District (important patches at the medium scale) and Beibei District, Banan District (important patches at the small scale) should be designated as key restoration areas, adhering to the “ecological restoration, green development” regulation concept, enhancing comprehensive agricultural space management, and creating new rural complexes integrating agriculture, culture, and tourism (Zhan and Zhu, 2023). Core patches in Nanchuan District and Fuling District show high importance across multiple scales, so decision-makers from different perspectives should treat them as strictly protected areas, insisting on the “ecological conservation, strict development control” regulation concept, strictly following the ecological protection red line management approach, and strengthening the protection of national parks and forest parks (Luo et al., 2021). Based on multi-scale comparisons of functional connectivity, for landscape connectivity protection from the perspective of both the main metropolitan area and the main urban area, special attention should be paid to the corridors and source areas in Beibei District, Yubei District, Nan’an District, and Banan District, establishing buffer zones at the interface with urban construction land to reduce the interference of human activities on wildlife population dispersal (Gao et al., 2021). Additionally, core zones in these areas should be designated as exclusion zones, constructing multi-tiered vertical ecological conservation forests (Fergus, 2019). For landscape connectivity protection from the perspective of both Chongqing city and the main metropolitan area, special attention should be paid to the corridors and source areas in Nanchuan District and Fuling District, setting up ecological stepping stones at the outer edges of these areas (Synes et al., 2020), and incorporating engineering measures such as eco-bridges and underground culverts (Liu et al., 2021; Zhang et al., 2023) to further enhance habitat connectivity and aggregation. Additionally, relying on river networks and shelterbelts, a blue-green ecological network should be woven to promote population dispersal and increase genetic diversity (Gippet et al., 2017).

5.4 Limitations and Future Research Directions

This study establishes a multi-scale landscape connectivity evaluation framework of “main urban area - main metropolitan area - city,” evaluating Chongqing's landscape connectivity from both structural and functional perspectives and identifying key areas for landscape connectivity protection. This provides a more systematic and comprehensive solution for regional landscape connectivity protection.

However, there are still some uncertainties and limitations in this study. On the one hand, the study is based on a static perspective, recognizing the characteristics of regional landscape connectivity through current ecological elements, ignoring the potential impacts of future land expansion dynamics on landscape connectivity (Gao et al., 2022). Therefore, combining land use simulations to conduct dynamic evaluations of landscape connectivity is a direction worthy of further exploration. On the other hand, precise setting of thresholds for ecological sources and resistance surfaces often requires collecting specific species information. However, research focusing on specific species is extremely limited, and such information is difficult to observe and obtain (Wu et al., 2022). Although some threshold settings in this study are based on previous research, subjectivity cannot be avoided.

Compared to previous studies, this study combines structural and functional connectivity to provide a safeguard for the fine-grained and networked identification of key areas for landscape connectivity protection. Additionally, the multi-scale evaluation framework of “main urban area - main metropolitan area - city” created by this study, compared to traditional single-scale research, can provide more systematic, comprehensive, and coordinated strategies for landscape connectivity protection. The specific results are as follows:

(1)The area of high-level dPC patches in Chongqing’s main urban area is 107.80 km² (45.62% of all core areas). When the study scale is expanded to medium and large scales, the dPC levels of all core patches within the main urban area drop significantly.

(2)The area of high-grade dPC patches in the main metropolitan area of Chongqing city is 1530.95 km², accounting for 58.10% of the core area. When the scale of study expands to the large scale, the core area patches in Jiangjin District, Nanchuan District, and Fuling District within the main metropolitan area show significant degradation, some degradation, and improvement in dPC grades, respectively.

(3)There is a relatively large overlap in ESP between the main urban area and the main metropolitan area. Ecological source areas (39.61 km²) and ecological corridors (394.79 km²) in Beibei District, Yubei District, Nan'an District, and Ba'nan District are crucial for landscape connectivity conservation at small and medium scales.

(4)The overlap in ESP between the main metropolitan area and Chongqing city is minimal. Ecological source areas (1180.41 km²) and ecological corridors (11.33 km²) in Nanchuan District and Fuling District are crucial for landscape connectivity conservation at medium and large scales.

Author Contribution

Xianlong Rao: Conceptualization, Methodology, Software Writing - Original draft preparation, Language polishing. Jue Li: Data analysis, Visualization, Framework revision, content check. Jiayan Li: Organizing framework, Methodology, Proofread, check for errors, Writing - Reviewing and Editing.

Acknowledgement

We would like to express our profound gratitude to Professor Yang Jie and Professor Huang Xin of Wuhan University for the CLCD dataset. This data set provides the basic data of land use for this study.

Data Availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Brooks, C. (2003). A scalar analysis of landscape connectivity. Oikos, 433-439. http://www.jstor.org/stable/3548048
Chang, Q., Liu, X., Wu, J., He, P. (2015). MSPA-based urban green infrastructure planning and management approach for urban sustainability: case study of Longgang in China. Journal of Urban Planning and Development, 141(3). https://doi.org/10.1061/(ASCE)UP.1943-5444.0000247
Chongqing planning and Natural Resources Bureau. (2024). Anchor the bottom line of space for high-quality development -- Focusing on the delineation and management of ecological protection redlines in Chongqing. Resources Guide, (09): 56-57. (in Chinese)
Cui, L., Wang, J., Sun, L., & Lv, C. (2020). Construction and optimization of green space ecological networks in urban fringe areas: A case study with the urban fringe area of Tongzhou district in Beijing. Journal of Cleaner Production, 276, 124266. https://doi.org/10.1016/j.jclepro.2020.124266
Elmqvist, T., Fragkias, M., Goodness, J., Güneralp, B., Marcotullio, P. J., McDonald, R. I., Parnell, S., Schewenius, M., Sendstad, M., Seto, K. C., & Wilkinson, C. (2013). Urbanization, biodiversity and ecosystem services: challenges and opportunities: a global assessment. Springer Nature, https://link.springer.com/book/10.1007/978-94-007-7088-1
Fan, F., Liu, Y., Chen, J., & Dong, J. (2021). Scenario-based ecological security patterns to indicate landscape sustainability: a case study on the Qinghai-Tibet Plateau. Landscape Ecology, 36(7), 2175-2188. https://doi.org/10.1007/s10980-020-01044-2
Fergus C. (2019). Make a Home for Wildlife: Creating Habitat on Your Land Backyard to Many Acre. Essex: Stackpole Books.
Foltête, J. C., Girardet, X., & Clauzel, C. (2014). A methodological framework for the use of landscape graphs in land-use planning. Landscape and Urban Plannin, 124, 140–150. https://doi.org/10.1016/j.landurbplan.2013.12.012
Forman, R., & Godron, M. (1986). Landscape Ecology. New York: John Wiley & Sons.
Fu, Y., Shi, X., He, J., Yuan, Y., & Qu, L. (2020). Identification and optimization strategy of county ecological security pattern: A case study in the Loess Plateau, China. Ecological Indicators, 112, 106030. https://doi.org/10.1016/j.ecolind.2019.106030
Gao, J., Du, F., Zuo, L., & Jiang, Y. (2021). Integrating ecosystem services and rocky desertification into identification of karst ecological security pattern. Landscape Ecology, 36(7), 2113-2133. https://doi.org/10.1007/s10980-020-01100-x
Gao, L., Tao, F., Liu, R., Wang, Z., Leng, H., & Zhou, T. (2022). Multi-scenario simulation and ecological risk analysis of land use based on the PLUS model: A case study of Nanjing. Sustainable Cities and Society, 85, 104055. https://doi.org/10.1016/j.scs.2022.104055
Gippet, J.M.W., Mondy, N., Diallo-Dudek, J., Bellec, A., Dumet, A., Mistler, L., & Kaufmann, B. (2017). I’m not like everybody else: urbanization factors shaping spatial distribution of native and invasive ants are species-specific. Urban Ecosyst, 20, 157-169. https://doi.org/10.1007/s11252-016-0576-7
Hu, M., Gong, P., Chen, H., & Yang, Z. (2020). The Key to High-Quality Development of China's City Clusters in the 14th Five-Year Plan Period: Building the Modern Metropolitan Areas. Administration Reform, (12): 19-29. (in Chinese) https://doi.org/10.14150/j.cnki.1674-7453.2020.12.002
Huang, A., Xu, Y., Liu, C., Lu, L., Zhang, Y., Sun, P., Zhou, G., Du, T., & Xiang, Y. (2019). Simulated town expansion under ecological constraints: A case study of Zhangbei County, Heibei Province, China. Habitat International, 91, 101986. https://doi.org/10.1016/j.habitatint.2019.05.005
Huang, L., Wang, J., & Cheng, H. (2022). Spatiotemporal changes in ecological network resilience in the Shandong Peninsula urban agglomeration. Journal of Cleaner Production, 339, 130681. https://doi.org/10.1016/j.jclepro.2022.130681
Huang, L., Wang, J., Fang, Y., Zhai, T., & Cheng, H. (2021). An integrated approach towards spatial identification of restored and conserved priority areas of ecological network for implementation planning in metropolitan region. Sustainable Cities and Society, 69, 102865. https://doi.org/10.1016/j.scs.2021.102865
Huang, X., Wang, H., Shan, L., & Xiao, F. (2021). Constructing and optimizing urban ecological network in the context of rapid urbanization for improving landscape connectivity. Ecological Indicators, 132, 108319. https://doi.org/10.1016/j.ecolind.2021.108319
Huang, Y., & Liao, T.J. (2019). An integrating approach of cellular automata and ecological network to predict the impact of land use change on connectivity. Ecological Indicators, 98, 149–157. https://doi.org/10.1016/j.ecolind.2018.10.065
Jia, Y., & Wang, Y. (2022). "From Single to Diverse: A Planning Approach to Biodiversity Conservation in Plain Agricultural Area---The Case of Heishan County, Liaoning Province." Chinese Landscape Architecture, 38, 26-31. https://link.cnki.net/doi/10.19775/j.cla.2022.07.0026
Kindlmann, P., & Burel, F. (2008). Connectivity measures: a review. Landscape Ecology, 23, 879-890. https://doi.org/10.1007/s10980-008-9245-4
Kong, F., Yin, H., Nakagoshi, N., & Zong, Y. (2010). Urban green space network development for biodiversity conservation: identification based on graph theory and gravity modeling. Landscape and Urban Planning, 95, 16–27. https://doi.org/10.1016/j.landurbplan.2009.11.001
Laliberté, J., & St-Laurent, M-H. (2020). Validation of functional connectivity modeling: The Achilles’ heel of landscape connectivity mapping. Landscape and Urban Planning, 202, 103878. https://doi.org/10.1016/j.landurbplan.2020.103878
Li, F., Wang, L., Chen, Z., Clarke, K. C., Li, M., & Jiang, P. (2018). Extending the SLEUTH model tointegrate habitat quality into urban growth simulation. Journal of environmental management, 217, 486-498. https://doi.org/10.1016/j.jenvman.2018.03.109
Lin, P., Yang, L., & Zhao, S. (2020). Urbanization effects on Chinese mammal and amphibian richness: a multi-scale study using the urban-rural gradient approach. Environmental Research Communications, 2(12), 125002. https://doi.org/10.1088/2515-7620/abd1c5
Liu, J., Tao, P., Yang, G., Pu, Y., He, Y., Zhang, B., Zhang, Y., & Xiao, H. (2024). Ecological Protection and Restoration Zoning in Land Space of Mountainous Urban: A Case Study of Chongqing. Ecology and Environmental Monitoring of Three Gorges, 9(01): 38-48.(in Chinese) https://doi.org/10.19478/j.cnki.2096-2347.2024.01.05
Liu, K., & Yu, Q. (2023). The layout evolution and coordination optimization of innovation and entrepreneurship space in emerging industries in the Yangtze River Delta. Geography and Geo-Information Science, 39(05): 74-81. (in Chinese)
Liu, W., Shi, C., Ma, Y., Li, H., & Ma, X. (2021). Land use and land cover change-induced changes of sediment connectivity and their effects on sediment yield in a catchment on the Loess Plateau in China. Catena, 207, 105688. https://doi:10.1016/j.catena.2021.105688
Liu, W., Wang, H., Li, H., Zhao, Y., & Xu, H. (2024). Analysis on Landscape Patterns of Regional Ecological Land in Nanjing Metropolitan Area on Multiple Spatial Scales. Journal of West China Forestry Science, 53(01): 178-187. (in Chinese) https://doi.org/10.16473/j.cnki.xblykx1972.2024.01.022
Lu, J., Jiao, S., Hu, J., Cai, Y., & Ou, L. (2023). Construction and hierarchical optimization of Changsha ecological network based on multi-scale collaboration. Acta Ecologica Sinica, 43( 15) : 6332-6344. (in Chinese)https://doi.org/10.5846/stxb202207262138
Luo, Y., Wu, J., Wang, X., & Peng, J. (2021). Using stepping-stone theory to evaluate the maintenance of landscape connectivity under China’s ecological control line policy. Journal of Cleaner Production, 296, 126356. https://doi.org/10.1016/j.jclepro.2021.126356
Lynch, A.J. (2019). Creating effective urban greenways and stepping-stones: four critical gaps in habitat connectivity planning research. Journal of Cleaner Production, 34, 131–155. https://doi.org/10.1177/0885412218798334
McRae, B. H., Dickson, B. G., Keitt, T. H., & Shah, V. B. (2008). Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology, 89(10), 2712-2724. https://doi.org/10.1890/07-1861.1
Pascual-Hortal, L., & Saura, S. (2006). Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation. Landscape ecology, 21, 959–967. https://doi.org/10.1007/s10980-006-0013-z
Peng, J., Yang, Y., Liu, Y., Hu, Y. n., Du, Y., Meersmans, J., & Qiu, S. (2018). Linking ecosystem services and circuit theory to identify ecological security patterns. Science of The Total Environment 644, 781-790. https://doi.org/10.1016/j.scitotenv.2018.06.292
Shi, J., Shi, P., Li, X., Wang, Z., & Xu, A. (2024). Spatial and temporal variability of ecosystem services in the Shiyang River Basin and its multiscale influencing factors. Progress in Geography, 43(2): 276-289. (in Chinese) https://doi.org/10.18306/dlkxjz.2024.02.006
Sun, R., Sun, L., Su, X., & Chen, L. (2021). Coupling Research on Landscape Pattern and Ecological Process: Inheritance and Innovation. Acta Ecologica Sinica, 41(1): 415-421.(in Chinese) https://doi.org/1.5846/stxb201912102684
Synes, N. W., Ponchon, A., Palmer, S.C.F., Osborne, P.E., Bocedi, G., Travis, J.M.J., & Watts, K. (2020). Prioritising conservation actions for biodiversity: lessening the impact from habitat fragmentation and climate change. Biological Conservation, 252, 108819. https://doi.org/10.1016/j.biocon.2020.108819
Tarabon, S., Bergès, L., Dutoit, T., & Isselin-Nondedeu, F. (2019). Environmental impact assessment of development projects improved by merging species distribution and habitat connectivity modelling. Journal of Environmental Management, 241, 439-449. https://doi.org/10.1016/j.jenvman.2019.02.031
Urban, D., & Keitt, T. (2001). Landscape connectivity: a graph‐theoretic perspective. Ecology, 82(5), 1205-1218. https://doi.org/10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2
Vogt, P., Riitters, & K.J.E.J.o.R.S. (2017). GuidosToolbox: universal digital image object analysis. European Journal of Remote Sensing, 50, 352-361. https://doi.org/10.1080/22797254.2017.1330650
Vogt, P., Riitters, K. H., Estreguil, C., Kozak, J., Wade, T. G., & Wickham, J.D. (2007). Mapping Spatial Patterns with Morphological Image Processing. Landscape Ecology, 22(2), 171-177. https://doi.org/10.1007/s10980-006-9013-2
Walpole, A. A., Bowman, J., Murray, D. L., & Wilson, P. J. (2012). Functional connectivity of lynx at their southern range periphery in Ontario, Canada. Landscape Ecology, 27, 761-773. https://doi.org/10.1007/s10980-012-9728-1
Wang, K., Li, Z., Zhang, J., Wu, X., Jia, M., & Wu, L. (2020). Built-up land expansion and its impacts on optimizing green infrastructure networks in a resource-dependent city. Sustainable Cities and Society, 55, 102026. https://doi.org/10.1016/j.scs.2020.102026
Wang, T., Li, H., & Huang, Y. (2021). The complex ecological network’s resilience of the Wuhan metropolitan area. Ecological Indicators, 130, 108101. https://doi.org/10.1016/j.ecolind.2021.108101
Wickham, J. D., Riitters, K. H., Wade, T. G., & Vogt, P. (2010). A national assessment of green infrastructure and change for the conterminous United States using morphological image processing. Landscape and Urban Planning, 94(3), 186-195. https://doi.org/10.1016/j.landurbplan.2009.10.003
Wu, Y., Wu, J., Bi, X., Li, Y., & Xiao, L. (2022). "Application of the Least Cost Distance Model and the Circuit Theory Model in the Evaluation of Wetland Landscape Connectivity in the Yellow River Delta". Acta Ecologica Sinica, 42, 1315-1326. (in Chinese)
Yang, J., & Huang X. (2019). The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth System Science Data, 13(8), 3907-3925. https://doi.org/10.5194/essd-13-3907-2021
Wang, Y., Li, W., Zen, X., Li, P., & Xue, B. (2022). The Spatial Multi-scale Mechanism of Land Use Elemental Conflicts in Shenyang Economic Zone. Economic Geography, 42(03): 231-239.(in Chinese) https://doi.org/10.15957/j.cnki.jjdl.2022.03.024
Yang, Y., Nan, Y., Liu, Z., Zhang, D., & Sun, Y. (2020). Direct and indirect losses of natural habitat caused by future urban expansion in the transnational area of Changbai Mountain. Sustainable Cities and Society, 63, 102487. https://doi.org/10.1016/j.scs.2020.102487
Yu, H., Tang, J., & Chen, R. (2023). Spatiotemporal Evolution Characteristics and Multi-scale Spatial Balance and Matching Analysis of Ecosystem Service Supply and Demand in Hubei Province. Environmental Science, 1-18[2024-06-17](in Chinese). https://doi.org/10.13227/j.hjkx.202312019
Zhan, L., & Zhu, Q. (2023). Coupling coordination research on agricultural modernization and rural tourism in the Beijing-Tianjin-Hebei urban agglomeration. Journal of Agricultural Resources and Environment, 40(5): 1146-1155.(in Chinese) https://doi.org/10.13254/j.jare.2023.0118
Zhang, H., Liu, Y., Li, X., Feng, R., Gong, Y., Jiang, Y., Guan, X., & Li, S. (2023). Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China. Journal of Environmental Management, 325, 116533. https://doi.org/10.1016/j.jenvman.2022.116533
Zhang, R., Zhang, L., Zhong, Q., Zhang, Q., Ji, Y., Song, P., & Wang, Q. (2021). An optimized evaluation method of an urban ecological network: The case of the Minhang District of Shanghai. Urban Forestry & Urban Greening, 62, 127158. https://doi.org/10.1016/j.ufug.2021.127158
Zhang, Y., Li, L., Gong, F., Zhou, Y., Tan, S., & Wang, X. (2016). "Analysis of Landscape Connectivity of the Yunnan Snub-nosed Monkeys (Rhinopithecus bieti) based on Habitat Patches" (in Chinese). Acta Ecologica Sinica, 36, 51-58.
Zhao, S., Ma, Y., Wang, J., & You, X. (2019). Landscape pattern analysis and ecological network planning of Tianjin City. Urban Forestry & Urban Greening, 46: 16188667. https://doi.org/10.1016/j.ufug.2019.126479
Zou, T., & Yoshino, K. (2017). Environmental vulnerability evaluation using a spatial principal components approach in the Daxing’anling region, China. Ecological Indicators, 78, 405-415. https://doi.org/10.1016/j.ecolind.2017.03.039

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Multi-scale Differences in Landscape Connectivity Evaluation and Protection Strategies: A Case Study of Chongqing, China

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Abstract

Figures

1. Introduction

2. Study Area and Data Sources

2.1 Study Area

2.2 Data Sources and Processing

3. Research Methodology

3.1 Structural Connectivity Evaluation at Multiple Scales

3.2 Evaluation of Functional Connectivity at Multiple Scales

4. Results and Analysis

4.1 Multi-Scale Comparison of Structural Connectivity

4.2 Multi-Scale Comparison of Functional Connectivity

5. Discussion

5.1 Fine-Grained and Networked Identification of Key Areas for Landscape Connectivity Protection

5.2 Multi-Scale Differences in Landscape Connectivity Evaluation

5.3 Implications for Context-Specific Management Strategies within a Multi-Scale Evaluation Framework

5.4 Limitations and Future Research Directions

6. Conclusion

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

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