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Article

Construction and Optimization of the Ecological Security Pattern of Pinglu Canal Economic Zone Based on the InVEST-Circuit Theory Model

by
Zhanhao Dang
1,2,
Baoqing Hu
1,*,
Chunlian Gao
1,2,
Shaoqiang Wen
1,3,
Jinrui Ren
1,2 and
Yunfei Liang
2
1
Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China
2
School of Geographical Sciences and Planning, Nanning Normal University, Nanning 530001, China
3
School of Natural Resources and Surveying, Nanning Normal University, Nanning 530001, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(5), 1103; https://doi.org/10.3390/land14051103
Submission received: 14 April 2025 / Revised: 10 May 2025 / Accepted: 16 May 2025 / Published: 19 May 2025
(This article belongs to the Special Issue Eco-Sensitive Areas: Ecosystem Services, Protected Lands, and Current Challenges (Second Edition))
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Abstract

:
The strategic delineation of ecological corridors and establishment of robust ecological security frameworks constitute fundamental prerequisites for advancing ecologically balanced growth and premium development within the Pinglu Canal Economic Belt. In this study, a comprehensive framework integrating ecological sources, resistance surfaces, and ecological corridors was developed using the InVEST model combined with circuit theory. The framework was then applied to assess the spatial and temporal dynamics of four major ecosystem services over the period from 2000 to 2020. The main findings are as follows: (1) From 2000 to 2020, the values of the four ecosystem services showed an overall declining trend. Spatially, areas with high ecosystem service importance were mainly concentrated in woodland and grassland areas in the southwest and northwest of the region. (2) The resistance values of the study area’s resistance surface ranged from 1 to 4.83. High-resistance areas were primarily located in the central region, corresponding to areas of intense human activity, while low-resistance areas were distributed around the periphery and largely overlapped with ecological source areas, presenting a spatial pattern of “high in the center, low at the edges”. (3) In total, 119 ecological barriers, 28 corridors, 8 critical pinch points, and 16 habitat source areas were identified. Building on these results, an enhanced ecological security layout—defined by the ‘three belts and three zones’ strategy—was formulated to guide restoration efforts and inform ecological management across the Pinglu Canal Economic Region.

1. Introduction

With the intensification of global environmental problems and the expansion of human activities, ecosystems are under increasing pressure. The problem of ecological degradation is becoming increasingly serious, resources continue to be depleted, and biodiversity is decreasing. These problems continue to threaten the sustainable development of human society [1,2]. Against this backdrop, establishing a scientifically sound and rational ecological security pattern (ESP) has become a crucial approach for ensuring national ecological security, addressing climate change, and preserving biodiversity. By optimizing land use, enhancing the connectivity of ecological protection areas, and improving ecosystem resilience, an ESP can effectively mitigate ecological risks caused by human activities, provide strong ecological support for socio-economic development, and foster synergistic integration of socioeconomic development and ecosystem functioning [3,4,5].
An ecological security pattern (ESP) delineates fundamental landscape components, including habitat nodes and connectivity pathways, allocates natural resources rationally, protects critical ecological areas, and enhances the stability and connectivity of ecosystems. This ensures the overall health of regional ecosystems, improves their ability to resist ecological risks, and maintains effective ecological services [6,7,8]. The evolution of ESP construction techniques has crystallized into a three-phase operational framework: (1) source node identification; (2) resistance surface modeling; (3) corridor network optimization [9,10,11]. The first step in ESP construction is the identification of ecological sources. These are areas that are highly sensitive to human activities and environmental changes and are of significant importance for regional ecological security, representing the minimum land area required to meet ecological safety needs [12]. The identification of ecological source areas has evolved into three primary methodological branches: (1) quantitative models based on ecosystem service value (ESV) assessments [13,14]; (2) morphological spatial pattern analysis (MSPA) [15]; and (3) landscape network modeling approaches [16]. MSPA and landscape connectivity index methods rely solely on land use data, ignoring environmental factors such as climate, soil, water resources, and vegetation types. In contrast, the ESV assessment models enables a more accurate assessment of multiple functional indicators such as soil and water conservation, carbon storage, water yield, and habitat quality, thereby objectively identifying ecological sources [17,18]. There are two main approaches to constructing resistance surfaces. One is to invert habitat quality as a resistance value [19]; the other involves selecting a set of indicators reflecting human and natural factors and assigning different weights to derive the resistance surface [20,21,22]. The drawback of the first method is its limited consideration of factors, as resistance is influenced by multiple variables. Using only one factor can result in inaccurate outcomes, while a comprehensive index approach that balances various elements offers more reliable identification of ecological sources. Ecological corridors are one of the key components of an ESP, serving as vital channels for ecological flow and species migration [23]. Common methods for corridor identification include the Minimum Cumulative Resistance (MCR) model, circuit theory, and the gravity model [24,25,26]. Among these, using the MCR model to extract corridors and the gravity model to assess their importance is more common [27,28,29], while another method is to apply circuit theory [30,31]. The MCR model assumes that species or ecological flows follow the shortest path with the least resistance from one point to another [32]. However, in reality, species behavior can be random, and this randomness cannot be fully captured by simple cumulative resistance paths. Compared with the first method, the circuit theory model not only effectively extracts ecological corridors but also simulates species migration using the stochastic flow of current over a resistance surface, allowing the identification of ecological pinch points and barriers [33,34].
In summary, although the current ESP research has developed a relatively mature “ecological sources–resistance surface–ecological corridors” framework, there are still limitations in core methodologies. First, ecological source identification tends to overly rely on ecosystem service assessments, which may cause deviations from actual ecological conditions. Second, resistance surface construction often relies on conventional natural factors (e.g., slope, vegetation cover) while neglecting human activity impacts, and the assignment of weights to factors is often subjective. Therefore, this study identifies ecological sources based on ecosystem service assessments and corrects them using nature reserve data. In constructing resistance surfaces, both natural and anthropogenic factors are comprehensively considered, with weights assigned using the analytic hierarchy process (AHP) to reduce subjectivity. By applying the circuit theory model and analyzing current density thresholds, we systematically examined the spatial configuration of the ecological corridor network and quantitatively identified the distribution patterns of critical ecological pinch points and resistance barriers.
Pinglu Canal begins at the mouth of the Pingtang River in Xijin Reservoir, Hengzhou City, Nanning, and enters the Gulf of Tonkin along the Qin River via Luyu Town, Lingshan County, Qinzhou [35]. Pinglu Canal is a networking project to optimize and enhance the national water transport network. In addition, it is an important component to accelerate the construction of the national comprehensive three-dimensional transportation network. It serves as a landmark project for the construction of a new land and sea corridor in the west, is a practical action to implement the new development concept, and is of great strategic significance for the economic and social development of Guangxi and the southwestern region [36]. Currently, the construction of the Pinglu Canal lacks a comprehensive approach to ecosystem integrity, failing to achieve integrated and coordinated management of key ecological components such as mountains, water bodies, forests, farmlands, lakes, and grasslands. This fragmented ecological governance model compromises the stability and resilience of the ecosystem and increases the risk of ecological degradation and environmental hazards. Therefore, establishing a scientifically sound ecological security pattern for the Pinglu Canal region is of critical importance. Such a framework would facilitate the efficient allocation and protection of regional natural resources, enhance ecosystem service functions, and provide institutional support for ecological restoration, biodiversity conservation, and sustainable resource utilization along the canal. Moreover, it serves as a foundational step toward achieving regional ecological civilization goals, safeguarding ecological security baselines, and promoting the harmonious development of the economy, society, and natural environment. Ultimately, it aligns with the overarching strategy of “ecological priority and green development”.
This study aims to construct an ESP in the Pinglu Canal Economic Zone. Specific research objectives include the following: (1) to evaluate four ecosystem services from 2000 to 2020 using the InVEST model and determine ecological sources based on their overall ecological significance; (2) to develop a resistance surface by integrating both natural and anthropogenic factors; and (3) to employ the circuit theory framework to identify ecological corridors, critical nodes, and barrier zones, thereby facilitating the construction and refinement of the ESP for the Pinglu Canal region.

2. Materials and Methods

2.1. Study Area

Pinglu Canal Economic Zone is located in southern Guangxi (20°0′–24°3′ N, 107°19′–110°39′ E), and most of the area is at a low elevation, with the exception of the SW Shiwandashan Mountains and the NW Daming Mountain, which are situated on higher terrain. The core area of the Pinglu Canal Economic Belt involves five cities, including Nanning, Qinzhou, Beihai, Fangchenggang, and Guigang, with an area of about 53,000 km2 (Figure 1). The Pinglu Canal begins in Nanning and enters the Beibu Gulf after passing through Qinzhou City, with a total length of about 135 km. The climate of the Pinglu Canal Economic Zone is a subtropical monsoon climate with high year-round temperatures and plenty of light hours. In summer, there is a lot of rain, which makes it prone to flooding. In winter, the temperature is favorable compared to the north, which makes it a tourist destination for tourists. As a strategic pivot connecting Nanning’s inland shipping hub with the Beibu Gulf International Gateway Port, it has cracked the long-standing water transportation disconnection between the Xijiang River Basin and the Beibu Gulf seaport group. Through the linkage development mode of “port, industry, and city”, it promotes the clustering development of equipment manufacturing, modern logistics, cross-border trade, and other industries and is expected to form a new industrial chain layout covering a population of 50 million people with an annual cargo throughput of over 100 million tons. It is expected to become a key infrastructure leading the China-ASEAN regional economic integration.

2.2. Data Sources

The data used were obtained from multiple authoritative sources and underwent standardized preprocessing. Land cover datasets from 2000, 2010, and 2020 were derived from the GlobeLand30 land cover database. DEM data were obtained using the geospatial data cloud platform and were used to derive topographic factors such as slope and aspect. Soil data were quoted from the World Soil Database (HWSD v1.2). NDVI data were obtained from the official website of the Loess Plateau SubCenter, and vector data for roads and river networks were extracted from OpenStreetMap. To ensure spatial alignment and model compatibility among heterogeneous multi-source datasets, all data were uniformly projected using the WGS_1984_UTM_Zone_49N coordinate system. Additionally, to eliminate potential spatial scale errors caused by differences in data resolution, all datasets were resampled to a 30-meter spatial resolution using bilinear interpolation. This approach ensured consistency in spatial resolution, the coordinate reference system, and data format across all model inputs, thereby maximizing the scientific rigor and accuracy of the analysis results (Table 1).

2.3. Methods

First, four ES were assessed using the InVEST model. These services were classified using the natural breaks method and integrated with equal weighting to generate a spatial distribution map of comprehensive ecosystem service importance. Subsequently, this map was overlaid with vector data of nature reserves to identify key ecological sources. For the resistance surface, six resistance factors encompassing both natural (e.g., topography, land use) and anthropogenic (e.g., human disturbance) elements were selected and weighted to construct a composite resistance surface. Utilizing the ecological source areas and resistance surface as inputs, the Linkage Mapper tool—rooted in circuit theory—was applied to delineate ecological corridors, identify critical pinch points, and locate dispersal barriers, ultimately delineating the ecological security pattern. Furthermore, targeted ecological restoration strategies and spatial management and control plans were proposed for identified ecological vulnerabilities, aiming to provide scientific guidance for harmonizing regional development and ecological conservation (Figure 2).

2.3.1. Valuation of Ecosystem Services

Ecosystem services (ES) constitute the environmental foundation for human societal development. They refer to the materials and products provided to humans by ecological components and processes, encompassing both the natural processes that sustain life-support systems and the natural resources that can be directly or indirectly utilized by humans [13]. Among them, the setting of the four ES parameters in the InVEST model is a matter of localization and optimization in the light of the actual situation of the Pinglu Canal Economic Zone. This study fully refers to the parameter setting methods used by scholars who have conducted research in the Pinglu Canal Economic Zone or other similar ecological regions and makes localized corrections and optimization adjustments in combination with the actual situation of the Pinglu Canal Economic Zone. The methods used for the four ES and the parameters used in the relevant literature are shown in the following four sub-points [37].
(1)
Soil conservation
The SDR module of the InVEST model quantifies potential and actual soil erosion and soil retention [38,39], providing an important means of assessing soil retention function. The calculation formula is expressed as follows:
A = R K L S − U S L E
R K L S = R × K × L × S
U S L E = R × K × L × S × C × P
In the equation, A represents soil retention (t·hm−2·a−1), where R K L S indicates theoretical erosion under bare soil conditions, and U S L E represents the adjusted soil erosion amount. The rainfall erosivity factor, R (MJ·mm·ha−1·h−1·yr−1), is estimated using mean annual precipitation. The soil erodibility factor, K (t·ha·h·MJ−1·mm−1), quantifies the susceptibility of soil to erosion under rainfall impact, estimated according to soil texture composition and organic carbon content [40]. L and S represent slope length and slope gradient factors. C is the cover management factor reflecting vegetation cover and cropping practices, while P denotes the support practice factor used to account for soil and water conservation measures. Specific parameter values are referenced from the relevant literature [41].
(2)
Water yield
The InVEST model’s Water Yield Module was employed to quantitatively estimate and spatially analyze the annual water yield across the Pinglu Canal Economic Belt. This module is based on a simplified water balance approach and integrates land use/land cover data, climatic variables, and soil characteristics to estimate the water yield capacity of each raster cell over a defined time period. It effectively reflects the spatial pattern of the region’s ES function related to water conservation. The calculation formula is expressed as follows:
Y x = 1 − A E T x P x × P x
where Y x represents yearly water production (mm) for raster unit x. A E T x represents the combined soil-vegetation moisture flux (mm), encompassing both edaphic vaporization and phytogenic water release. P x indicates the total annual rainfall (mm) received by that grid cell.
(3)
Habitat quality
The habitat quality module evaluates ecosystem conditions by spatially overlaying land use types and threat factors, with output values ranging from 0 to 1. The closer the value is to 1, the less human damage to the environment. The closer the value is to 0, the more human damage to the environment [42]. The threat source table and sensitivity table refer to the relevant literature [41]. The calculation formula is as follows:
H Q = H × 1 − D Z D Z + K Z
H Q indicates habitat suitability, D denotes the total threat level, K corresponds to the half-saturation constant, a parameter that determines the point at which the threat effect begins to stabilize, and Z is a fixed parameter, commonly set to 2.5 [43].
(4)
Carbon storage
The carbon storage assessment module quantitatively evaluates regional carbon storage by integrating carbon density parameters corresponding to different land use types, thereby estimating the amount of carbon stored per unit area for each land category, and generates a spatial distribution map of carbon storage. One of the key input datasets for model operation is the carbon density values corresponding to various land use types. The total carbon storage is calculated using the following equation:
C t o t = C a b o v e + C b e l o w + C s o i l + C d e a d
where C t o t stands for the overall carbon storage, C a b o v e signifies the carbon content in aboveground biomass, C b e l o w denotes the carbon density of underground biomass, C s o i l refers to the soil organic carbon concentration, and C d e a d corresponds carbon density of dead organic matter. The carbon density values for each land use category were primarily derived from the existing literature [44] and were adjusted appropriately based on regional ecological characteristics to improve data representativeness and model accuracy.

2.3.2. Ecological Source Area Identification

Based on the ecological characteristics of the Pinglu Canal Economic Zone and the relevant literature, the screening conditions for ecological source sites include the following: (1) they should be in areas with high ecosystem service importance levels; (2) due to the existence of fragmented patches in ecological source sites, the area of the nature reserve in the region should be larger than 10 km2 [45]. The natural breakpoint (NB) method is a classification method that relies on distribution characteristics of the dataset itself. It classifies the ES based on the “natural distribution breakpoints” in the ES data rather than setting the intervals artificially, so it reflects the importance distribution of ecosystem services more realistically.
Therefore, the NB method was used in ArcGIS software to classify the significance of four ESs, and indicators were categorized into five levels of importance (general importance, more importance, medium importance, high importance and utmost importance). On this basis, four service functions were integrated using the equal-weight spatial superposition method. The natural breakpoint method was then reapplied to divide the overall ecosystem service value into five importance levels, resulting in a comprehensive assessment of the regional ecosystem service significance. In the process of ecological source area identification, the fourth level (high importance) and fifth level (utmost importance) areas in the integrated importance level were selected as alternative ecological source areas [46]. Ultimately, the alternative ecological source sites were combined with the nature reserve data, and ecological core patches exceeding 10 km2 in area were preserved as the definitive ecological source areas.

2.3.3. Resistance Surface Construction

As a quantitative indicator of landscape connectivity, the resistance surface illustrates relative difficulty of species dispersal between ecological source areas and represents the degree of horizontal resistance experienced by ecological processes across space [47]. In the construction process, the selection of appropriate resistance factors and the assignment of corresponding weights are essential for obtaining accurate results. Drawing on previous studies and integrating relevant findings, this study incorporates both natural and anthropogenic elements. Specifically, six ecological resistance factors were selected: land cover type, NDVI, DEM, slope, distance to roads, and distance to water bodies (Table 2). Each resistance factor was standardized using a five-level classification system ranging from 1 to 5. Using the analytic hierarchy process (AHP) [48], the relative weights of each factor was evaluated. Finally, the resistance surface was generated by performing a weighted overlay of all resistance factors using the raster calculator within a spatial overlay analysis model.

2.3.4. Extraction of Ecological Corridors, Ecological Pinch Points, and Ecological Barrier Points

Ecological corridors, as structural elements of ecological networks, play a crucial role in maintaining ecosystem service functions. By connecting discretely distributed ecological source areas, they provide spatial carriers for species migration and ecological processes, thereby ensuring the transmission efficiency of ecological flows, material flows, and energy flows at a regional scale [49]. Ecological pinch points refer to critical nodes in the topological structure, often acting as temporary habitats with frequent biological activity or intersections of migration paths, and are essential for maintaining ecological connectivity. In contrast, ecological barrier points are spatial units with significant obstructive effects, whose high resistance characteristics weaken corridor connectivity [46].
In this study, ecological corridors were constructed using the Build Network and Map Linkages modules within the Linkage Mapper tool. Based on this, the Pinchpoint Mapper module of the Linkage Mapper tool was employed to identify ecological pinch points. The ‘all-to-one’ option was selected, and a weighted cost distance of 1000 m was set to obtain the cumulative current density. The resulting data were classified into five levels using the NB method in ArcGIS 10.8, with the fourth and fifth levels defined as ecological pinch points. To identify ecological barriers, the ‘Maximum’ mode of the ‘Barrier Mapper’ module in Linkage Mapper was utilized. After multiple trials and adjustments based on regional conditions, the minimum and maximum detection radii were set to 500 m and 1000 m, respectively, to calculate cumulative current density. The results were then classified into five levels using the natural breaks method, with the fourth and fifth levels designated as ecological barrier areas [48].
The current density derived from the Pinchpoint Mapper module represents the total current density passing through a point among all potential paths; the higher the value is, the more critical the area is as a convergence zone for species migration. Meanwhile, the current density in the Barrier Mapper module represents difference between current density of existing landscape and that of a hypothetical landscape without the barrier. A larger difference indicates a more severe obstruction to corridor connectivity caused by the barrier point.

3. Results

3.1. Spatial and Temporal Evolution of Ecosystem Services

The results indicate a clear spatial differentiation in water yield services. The southwestern region exhibits the highest value distribution, including Fangchenggang City, southwestern Qinzhou City, and western Beihai City. The northern and eastern parts of the Pinglu Canal Economic Zone exhibit predominantly low values, covering Nanning City, Guigang City, Qinzhou City, and Beihai City. The Shiwandashan Mountain Range, known for its rich biodiversity and extensive vegetation cover, supports a diverse range of flora and fauna, which contributes to its relatively high water yield. A distinct spatial trend is observed, with elevated water production in the southwest and reduced levels in adjacent areas (Figure 3). With inter-annual changes, the water yield service shows a decreasing trend, from 925.2 mm in 2000 to 833.1 mm in 2010 and 789.1 mm in 2020. The habitat quality services in the spatial distribution of the Fangchenggang Shiwandashan Mountains are significantly higher than those in other places, where habitat quality values are relatively low (Figure 4). From a temporal perspective, habitat quality exhibited a fluctuating downward trend: it was 0.096 in 2000, increased to 0.104 in 2010, but declined to 0.084 by 2020. Overall, the values remain low, with the mean below 0.1, indicating poor ecological conditions and the urgent need for enhanced environmental management efforts. High-value areas of soil retention services are primarily concentrated in the northern parts of Nanning and Guigang, as well as in Fangchenggang, while the central region generally shows lower values (Figure 5). The soil and water conservation services decrease from 2000 to 2020, with values of 650.8 t/hm2, 578.9 t/hm2, and 550.0 t/hm2. The area with the most serious decrease in soil and water conservation services is located in Nanning City, where the rapid development of real estate as well as urbanization and the ever-expanding construction land have led to a continuous decrease in the vegetation cover of its development areas. This triggers the problem of soil and water erosion. The most significant reduction in soil retention services was observed in Nanning City. This decline is primarily attributed to the rapid expansion of construction land driven by real estate development and urbanization, which has led to a continuous decrease in vegetation cover and subsequently triggered issues such as soil erosion. In terms of spatial distribution, carbon storage services exhibit higher values in the southern part of the Pinglu Canal Economic Zone, while lower values are observed in the northern regions, demonstrating a spatial pattern characterized as “high in the south and low in the north”. The low value zone is located in construction land and bare land, especially in Nanning City and Guigang City, and the values of carbon storage in and around the urban agglomerations are low (Figure 6). From the time dimension, the carbon stock also shows a decreasing trend, with 27.1 t/hm2 in 2000, 26.7 t/hm2 in 2010, and 26.2 t/hm2 in 2020. This is different from the soil and water conservation service, which is decreasing at a more moderate rate, and the value does not change much (Figure 7).
Regarding the spatial pattern of overall ecosystem service significance, areas of moderate and relatively high importance are primarily located in Nanning, Guigang, and Beihai. Meanwhile, the southwestern and central parts of Fangchenggang City were marked by clusters of highly and extremely important areas, including the northern region of Nanning and the northwest section of Guigang (Figure 8a). The spatial distribution of ES importance levels in the Pinglu Canal Economic Zone is characterized as “lower in the center and higher at the periphery”.
Finally, ecological source area was mainly located in Fangchenggang, Nanning, and Guigang, with an area of 1687.6 km2, i.e., most of the area was located in Fangchenggang Shiwandashan Nature Reserve and Nanning Daming Mountain Nature Reserve, which was consistent with areas of high and very high ecosystem service importance in the comprehensive ecosystem service importance (Figure 8b).

3.2. Resistance Analysis

Based on the figure, the ecological resistance values within the Pinglu Canal Economic Belt range from 1 to 4.83 (Figure 9). Overall, the spatial distribution of resistance values exhibits a decreasing trend from the central areas toward the periphery. High-resistance zones are primarily concentrated in densely built-up urban areas and regions with intense human activity. Notably, the cores of major cities exhibit significantly higher resistance values compared to surrounding areas. These elevated resistance levels are attributed to rapid urbanization and extensive land development, which have severely disrupted natural ecological processes and impeded species migration and ecological flows. In contrast, low-resistance areas are mainly distributed along the outer edges of the study area and largely coincide with key ecological source locations. These zones are predominantly located in ecologically favorable regions, including Fangchenggang, the northern part of Nanning, and the northeastern part of Qinzhou. Characterized by extensive ecological forest land and low levels of land development, these areas maintain strong ecological connectivity and facilitate the continuity of ecological processes and species dispersal.

3.3. Ecological Corridor Identification

The results indicate that the identified ecological corridors span across Nanning, Guigang, Qinzhou, and Fangchenggang (Figure 10), with a total of 28 corridors extracted, amounting to a combined length of 1426.4 km. Multiple corridors connect each ecological source, significantly enhancing species mobility and overall ecological connectivity. Specifically, the corridors linking ecological sources in Nanning and Guigang primarily extend along the boundary of the study area, while those between Guigang and Fangchenggang run approximately parallel to the Pinglu Canal. The land use along these corridors is predominantly cropland and forest, which helps to mitigate the barrier effect of urban construction land on species migration and supports the functional integrity of the corridors. Overall, the ecological corridors in the study area exhibit a triangular spatial pattern, providing stable and continuous pathways for species migration and dispersal among ecological sources in Nanning, Guigang, and Fangchenggang.

3.4. Ecological Security Pattern Construction

The cumulative current density of ecological pinch points ranges from 0 to 53.8, primarily distributed across western Guigang and the forested northern slopes of the Shiwandashan Mountains in Fangchenggang. These areas are predominantly covered by forest and grassland, providing favorable conditions for ecological connectivity (Figure 11). In contrast, ecological barrier points exhibit a cumulative current density ranging from 0 to 6.5 and are concentrated in the central and northeastern parts of Nanning, as well as the western urbanized regions of Guigang. Owing to the high ecological resistance in these areas, the formation and connectivity of ecological corridors are impeded, thereby weakening the overall functionality of the regional ecological network (Figure 11). The ESP constructed consists of three key components: ecological corridors, pinch points, and barrier points (Figure 12). Sixteen ecological sources were recognized, covering an area of 1687.6 km2. Additionally, 28 ecological corridors were delineated, totaling 1426.4 km in length. Eight ecological pinch points were extracted, occupying 44.1 km2. In total, 119 ecological barrier points were identified, occupying 685.7 km2. Overall, ecological sources show a generally unbalanced layout, with Nanning, Fangchenggang, and Guigang, emerging as a major cluster area, while no sources were identified in Qinzhou and Beihai. As a result, ecological corridors are mainly distributed among the aforementioned three cities, forming a distinct “triangular” spatial configuration.

4. Discussion

This study employed the InVEST model in conjunction with circuit theory to develop the ESP of the Pinglu Canal Economic Zone. It is worth noting that the habitat quality is poor in most areas of the Pinglu Canal Economic Zone, which also indicates the relatively low vegetation cover. The poor habitat quality also indirectly affects the water yield service, soil and water conservation service, and carbon storage service, which leads to a decreasing trend. The four ecosystem services share the common feature of being higher in the forested and grassland areas and lower in the urban agglomerations and watershed areas. In addition, soil and water conservation, habitat quality, water yield, and carbon storage are also affected by multiple factors such as soil properties, climatic conditions, and human activities. Therefore, corresponding measures should be taken to protect and restore them. First, land use should be rationally planned to reduce unreasonable agricultural and industrial development. Particularly in regions facing a high risk of soil erosion, strategies like reforestation of farmland and restoration of pastures to grassland should be adopted to enhance soil and water conservation capacity in the Pinglu Canal Economic Zone. Second, a monitoring and assessment system for ecosystem services should be established so that protection and restoration measures can be adjusted in a timely manner. In the southeastern part of the study area—coinciding with the location of the Pinglu Canal—ecological corridors are notably absent. Therefore, greater emphasis should be placed on ecological protection during the canal’s construction, including the artificial restoration of grassland and forest ecosystems within the affected areas. The presence of ecological pinch points plays a critical role in enhancing corridor connectivity and is vital for conserving biodiversity. Accordingly, ecological protection efforts should be strengthened, particularly for grasslands and forests, to expand the spatial extent of pinch points and further improve the functional connectivity of ecological corridors.
Based on the identified ESP and from the perspective of ecological restoration, protection, and development, this study proposes an ecological optimization framework characterized by the “three belts and three zones”, with priority conservation areas as the foundation (Figure 13). Specifically, the “three belts” refer to (1) the ecological security protection belt connecting the Damingshan ecological source area in western Nanning with the Shiwandashan ecological source area; (2) the ecological barrier belt linking Damingshan in Nanning with Pingtian Mountain in Guigang; and (3) the ecological construction belt connecting Pingtian Mountain and Shiwandashan. The ecological safety protection belt in the west and the ecological barrier belt in the north may expand the area of ecological safety buffer zones for the ecological corridors in the west and the north. The construction of animal-specific corridors and fish migration facilities can be used to solve the problem of animal migration and increase the ability of cross-regional exchange of organisms. Constructing an eastern ecological corridor construction zone can increase the number of ecological corridors between the central part of the Pinglu Canal Economic Zone and the southern and northern regions, increasing ecological connectivity. In terms of measures, additional ecological conservation zones can be set up to form a “multi-corridor and multi-zone” ecological pattern. The “three zones” refer to ecological protection zones, ecological conservation development zones, and ecological obstacle restoration zones. Among them, the establishment of ecological protection zones in places where ecological sources are located can provide stable living space for plants and animals in the area. Biodiversity monitoring stations are set up to regularly assess the changes in the number of plants and animals in the ecological reserve, so as to dynamically adjust the protection measures. Since the habitats in northwestern Nanning and Beihai are of relatively low quality and dominated by woodlands and grasslands, the establishment of ecological conservation and development zones around the urban construction sites will contribute to ecological restoration. Regular monitoring of the vegetation cover of the Pinglu Canal Economic Zone can be implemented using remote sensing satellites such as Gaofen-1. When the vegetation cover is greatly reduced, the area of ecological damage can be quickly identified. Areas such as the central part of Nanning City and the northern part of the Pinglu Canal Economic Zone are the main concentration areas of ecological obstacle points. The ecological obstacle restoration area is constructed, and animal activity sensors, cameras, and acoustic recorders are deployed in areas with large ecological obstacle points to grasp the migration of animals in real time, so as to manage the ecological environment.
This study constructed the ESP of the Pinglu Canal Economic Belt. However, the following shortcomings exist: (1) Although the ecological corridors in the Pinglu Canal Economic Zone have been identified using the relevant tools of circuit theory (Linkage Mapper plug-in), the connectivity of the extracted ecological corridors cannot be accurately verified owing to the absence of sustained ecological observation records (e.g., animal migratory paths and biodiversity data) in the Pinglu Canal Economic Zone. (2) The current research only conducted a superficial analysis of four ecological service impact factors and failed to further identify key driving forces. (3) During the construction of the resistance surface, its weight assignment was determined using hierarchical analysis. However, the measured resistance value in the Pinglu Canal Economic Zone could not be obtained, and the results of the construction of the resistance surface could not be verified. (4) No prediction of the future evolution of the ESP was made because the lack of soil data from past years and DEM data prevented the input of accurate and relevant parameters into the prediction model, which led to the inability to derive and validate the accurate results of the ESP in future scenarios. In future studies, better models and methods should be used to predict future ESPs.

5. Conclusions

(1) The values of the four ecosystem services were relatively high in the southwestern, northwestern, and northeastern regions of the Pinglu Canal Economic Zone but showed an overall declining trend for 2000–2020. Highly and extremely important ecosystem service zones were mainly distributed in woodland and grassland areas in southwestern and central Fangchenggang, northern Nanning, and northwestern Guigang, while moderately and generally important zones were concentrated in urban and construction land areas.
(2) The resistance values of the resistance surface range from 1 to 4.83. The high resistance area is located in the urban agglomeration area, while the low resistance area is located in the boundary of the study area. Most of the ecological source sites overlap, and the overall presentation exhibits a “center of the high, the edge of the low” spatial distribution pattern.
(3) A total of 16 ecological sources (1687.6 km2), 28 ecological corridors (1426.4 km), 8 ecological pinch points (44.1 km2), and 119 ecological barriers (685.7 km2) were identified in the study area, with ecological corridors exhibiting a roughly triangular spatial distribution. Based on ecological conditions, restoration needs, and development dynamics, an optimized ecological security pattern comprising “three belts” (ecological protection belt, ecological barrier belt, ecological construction belt) and “three zones” (ecological protection zone, ecological maintenance and development zone, ecological barrier restoration zone) is proposed to support the balance between ecological integrity and economic development in the Pinglu Canal Economic Zone.

Author Contributions

Z.D. and B.H. conceived the study; Z.D. developed the methodology and implemented the software; validation was performed by Z.D., C.G. and S.W.; Z.D., J.R. and Y.L. conducted the formal analysis; Z.D. carried out the investigation and curated the data; resources were provided by Z.D.; the original draft was written by Z.D.; Z.D., C.G. and B.H. contributed to the review and editing of the manuscript; visualizations were prepared by Z.D.; B.H. supervised the work; Z.D. managed the project; funding was secured by B.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Guangxi Science and Technology Major Project, grant number AA24263011; Guangxi Science and Technology Major Project, grant number AA23062039-2; and National Natural Science Foundation of China, grant number 42071135.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 14 01103 g001
Figure 1. Schematic of the Pinglu Canal Economic Zone. (a) Guangxi Geographical Location; (b) Geographical location of the study area; (c) DEM spatial distribution of Pinglu Canal Economic Zone.
Figure 1. Schematic of the Pinglu Canal Economic Zone. (a) Guangxi Geographical Location; (b) Geographical location of the study area; (c) DEM spatial distribution of Pinglu Canal Economic Zone.
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Figure 2. Framework for the study of ecological security patterns.
Figure 2. Framework for the study of ecological security patterns.
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Figure 3. Spatial distribution of water yield, 2000–2020. (a) Spatial distribution of water yield in 2000; (b) Spatial distribution of water yield in 2010; (c) Spatial distribution of water yield in 2020.
Figure 3. Spatial distribution of water yield, 2000–2020. (a) Spatial distribution of water yield in 2000; (b) Spatial distribution of water yield in 2010; (c) Spatial distribution of water yield in 2020.
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Figure 4. Spatial distribution of habitat quality, 2000–2020. (a) Spatial distribution of habitat quality in 2000; (b) Spatial distribution of habitat quality in 2010; (c) Spatial distribution of habitat quality in 2020.
Figure 4. Spatial distribution of habitat quality, 2000–2020. (a) Spatial distribution of habitat quality in 2000; (b) Spatial distribution of habitat quality in 2010; (c) Spatial distribution of habitat quality in 2020.
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Figure 5. Spatial distribution of soil conservation, 2000–2020. (a) Spatial distribution of soil conservation in 2000; (b) Spatial distribution of soil conservation in 2010; (c) Spatial distribution of soil conservation in 2020.
Figure 5. Spatial distribution of soil conservation, 2000–2020. (a) Spatial distribution of soil conservation in 2000; (b) Spatial distribution of soil conservation in 2010; (c) Spatial distribution of soil conservation in 2020.
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Figure 6. Spatial distribution of carbon stock, 2000–2020. (a) Spatial distribution of carbon stock in 2000; (b) Spatial distribution of carbon stock in 2010; (c) Spatial distribution of carbon stock in 2020.
Figure 6. Spatial distribution of carbon stock, 2000–2020. (a) Spatial distribution of carbon stock in 2000; (b) Spatial distribution of carbon stock in 2010; (c) Spatial distribution of carbon stock in 2020.
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Figure 7. Changes in four ecosystem services in the Pinglu Canal Economic Zone, 2000–2020.
Figure 7. Changes in four ecosystem services in the Pinglu Canal Economic Zone, 2000–2020.
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Figure 8. Integrating ecosystem service importance and ecological source. (a) Spatial distribution of ecosystem service importance; (b) Spatial distribution of ecological source.
Figure 8. Integrating ecosystem service importance and ecological source. (a) Spatial distribution of ecosystem service importance; (b) Spatial distribution of ecological source.
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Figure 9. Resistance surface.
Figure 9. Resistance surface.
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Figure 10. Schematic diagram of the ecological corridor.
Figure 10. Schematic diagram of the ecological corridor.
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Figure 11. Spatial arrangement of ecological pinch points and ecological barrier points. (a) Ecological pinch point extraction results; (b) Ecological barrier point extraction results.
Figure 11. Spatial arrangement of ecological pinch points and ecological barrier points. (a) Ecological pinch point extraction results; (b) Ecological barrier point extraction results.
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Figure 12. Pinglu Canal ecological security patterns.
Figure 12. Pinglu Canal ecological security patterns.
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Figure 13. Optimization of the ESP of the Pinglu Canal Economic Zone.
Figure 13. Optimization of the ESP of the Pinglu Canal Economic Zone.
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Table 1. Data source and description.
Table 1. Data source and description.
DataDescriptionsSource
2000/2010/2020 land use dataResolution of 30 mhttp://loess.geodata.cn (accessed on 20 August 2024)
2000/2010/2020 precipitation data1 km resolution monthly mean precipitation dataset
DEM dataASTER GODEM elevation data with 30 m resolutionhttps://www.gscloud.cn/ (accessed on 20 August 2024)
Rainfall erosion factorReflecting rainfall intensity in the study areaPrecipitation data are obtained by applying the formula
Soil dataUsed to calculate soil erosion factors and plant utilization of waterhttps://gaez.fao.org/pages/hwsd (accessed on 21 August 2024)
NDVI dataFor building resistance surfaceshttp://loess.geodata.cn (accessed on 21 August 2024)
Road data/river network dataCalculation of distances to roads and riversOpenStreetMap Data extract (https://www.openstreetmap.org) (accessed on 22 August 2024)
Railroad dataCalculate the distance to the railroad
Table 2. Resistance values and weights.
Table 2. Resistance values and weights.
Resistance FactorResistance ValueWeights
12345
LULCWaterForestGrasslandCroplandArtificial surface0.06
NDVI0.67–0.860.60–0.670.52–0.600.39–0.520.04–0.390.34
DEM703–1740399–703219–39998–2190–980.11
Slope32–7321–3212–215–120–50.09
Distance to the road (m)>27,37016,572–27,3709683–16,5714468–9682<44680.18
Distance to the water (m)<22752275–51495150–88628863–14,851>14,8510.22
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Dang, Z.; Hu, B.; Gao, C.; Wen, S.; Ren, J.; Liang, Y. Construction and Optimization of the Ecological Security Pattern of Pinglu Canal Economic Zone Based on the InVEST-Circuit Theory Model. Land 2025, 14, 1103. https://doi.org/10.3390/land14051103

AMA Style

Dang Z, Hu B, Gao C, Wen S, Ren J, Liang Y. Construction and Optimization of the Ecological Security Pattern of Pinglu Canal Economic Zone Based on the InVEST-Circuit Theory Model. Land. 2025; 14(5):1103. https://doi.org/10.3390/land14051103

Chicago/Turabian Style

Dang, Zhanhao, Baoqing Hu, Chunlian Gao, Shaoqiang Wen, Jinrui Ren, and Yunfei Liang. 2025. "Construction and Optimization of the Ecological Security Pattern of Pinglu Canal Economic Zone Based on the InVEST-Circuit Theory Model" Land 14, no. 5: 1103. https://doi.org/10.3390/land14051103

APA Style

Dang, Z., Hu, B., Gao, C., Wen, S., Ren, J., & Liang, Y. (2025). Construction and Optimization of the Ecological Security Pattern of Pinglu Canal Economic Zone Based on the InVEST-Circuit Theory Model. Land, 14(5), 1103. https://doi.org/10.3390/land14051103

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