Ecological Security Assessment Based on Sensitivity, Connectivity, and Ecosystem Service Value and Pattern Construction: A Case Study of Chengmai County, China

Abstract

Against the backdrop of continuous natural space loss and accelerated urbanization, considerable attention has been directed toward balancing economic development demands with the protection of fragile ecosystems within limited spatial boundaries to achieve regional sustainable development. This study therefore focuses on Chengmai County, a small-scale region prioritizing both green development and ecological conservation. Land-use changes and trends in ecosystem services value (ESV) from 2000 to 2020 were analyzed. An ecological security assessment model was developed, integrating ecosystem services, ecological sensitivity, and landscape connectivity, which enabled the identification of areas with high ecological security value as ecological sources. Ecological corridors and nodes were extracted using the minimum cumulative resistance model and the gravity model, culminating in the construction of Chengmai County’s ecological security pattern through overlay analysis. The main findings are summarized as follows: (1) Construction land expanded rapidly between 2000 and 2020. The ecological sensitivity of Chengmai County displayed a spatial distribution pattern of “high in the south, low in the north,” while ESV exhibited a pattern of “high in the central-south and low in the northeast,” showing an overall increasing trend. (2) The overall ecological security status was relatively favorable. A total of 10 ecological nodes and 45 ecological corridors were identified, including 16 core corridors. (3) Based on these analyses, an ecological security pattern described as “one axis, two belts, and three zones” was established for Chengmai County. This study provides a practical spatial strategy for ecological conservation and sustainable development in Chengmai County and offers a transferable methodological framework for similar coastal regions facing development pressures.

1. Introduction

The ongoing loss of natural spaces, coupled with intensified urbanization, presents a critical challenge for many regions in striking a balance between economic development and ecological conservation [1]. Coastal areas, characterized by dense populations [2,3], robust economic activity, and high ecological sensitivity, host critical ecosystems such as mangroves and coral reefs [4]. Simultaneously, these regions are subjected to multiple pressures, including land-based pollution, coastal erosion, and sea-level rise, which render their ecological security issues particularly pronounced and urgent [5,6].

Within this context, Chengmai County in Hainan Province is selected as a representative case of a developing coastal region confronting distinct and pressing ecological security pressures. As an integral component of the Haikou Economic Circle and a strategic reserve for the development of the free trade port, Chengmai County is undergoing a critical phase of accelerated urbanization and industrialization. This growth is manifested in the rapid expansion of construction land, which increasingly encroaches upon ecological spaces. Concurrently, long-term dependence on coastal resources for developing resource-intensive industries has triggered localized issues, including mangrove degradation, the weakening of coastal ecological functions, and biodiversity impairment. The conflict between anticipated rapid economic growth and the carrying capacity of its fragile ecosystems poses a practical challenge for optimizing the local ecological spatial structure, specifically in curbing ecological fragmentation and maintaining the continuity of key ecological processes. Furthermore, China’s heightened emphasis on ecological and environmental protection is demonstrated by its endorsement of Hainan Island’s designation as a National Ecological Civilization Pilot Zone, which prioritizes ecological conservation and green, low-carbon development. This national policy framework provides a clear mandate and urgent direction for Chengmai County to address its development challenges by establishing an ecological security framework.

The ESP is defined as a spatial network structure established through the identification and protection of key ecological elements to maintain regional ecosystem stability, safeguard ecosystem service functions, and preserve biodiversity [7,8]. This concept originated from the “patch-corridor-matrix” model in landscape ecology and the theory of island biogeography [9]. In 1995, Kongjian Yu introduced the concept of the “landscape security pattern,” which operationalized the security thresholds of ecological processes, thereby laying the foundation for spatial identification and planning. Currently, a mainstream framework has been established in ESP research, focusing on three core components: ecological source identification, resistance surface construction, and ecological corridor extraction [10,11]. Methodologically, ecological sources are typically identified using MSPA and dPC [12,13]. The regional contribution of ecological functions is quantified through an ESV assessment [14], while ecologically sensitive and vulnerable areas are delineated via an ecological sensitivity assessment [15]. Furthermore, ecological flow paths are simulated, and corridors are extracted through the widespread application of the MCR model and circuit theory [16]. However, significant limitations are commonly encountered when these methods are applied individually to small-scale coastal counties. MSPA and dPC are highly dependent on land use classification accuracy, primarily reflecting landscape structural connectivity, while inadequately capturing connectivity in ecological processes [17,18]. ESV assessments, which often rely on static value coefficients [19], exhibit limited responsiveness to internal structural and qualitative differences within local ecosystems [20,21]. For ecological sensitivity modeling, the selection of factors and assignment of their weights involve a degree of subjectivity [22], which potentially compromises the accuracy of the results [23]. Furthermore, the traditional ‘generalized’ resistance surface used in the MCR model fails to account for functional differences among species, thus inaccurately characterizing functional connectivity.

However, the application of these ESP methodologies to small-scale coastal counties is particularly complicated by issues of parameter calibration and scale compatibility [24,25]. This complication arises due to the convergence of inherent ecological fragility, intense development pressure, and complex spatial heterogeneity in such regions. Consequently, traditional ESP models developed at macro scales demonstrate significant limitations when directly applied, specifically in ecological source identification, comprehensive assessment, and corridor prioritization.

Furthermore, a significant imbalance is consistently observed in the existing literature about both research scale and methodological application. Regarding research scale, studies are predominantly concentrated in developed regions [26], large urban agglomerations, or ecologically fragile zones [27,28]. At the same time, insufficient attention has been given to small-scale coastal counties, which are characterized by high ecological sensitivity and substantial development pressure [29]. Methodologically, many studies still emphasize single-method approaches or isolated factor analyses, lacking integrated ESP construction that incorporates multiple methods and indicators [30,31,32]. Case studies of small-scale counties remain relatively scarce, particularly those that systematically integrate landscape connectivity [33,34], ecosystem services, and ecological sensitivity for comprehensive ecological source identification [35]. These regions are typically characterized by limited spatial extent, where the conflict between ecological conservation and economic development is particularly acute. Nevertheless, refined ESP construction methodologies and practical case studies specifically tailored to their scale-specific characteristics remain a notably understudied area in the literature, both domestically and internationally [36]. Therefore, this study is designed to address these research gaps by adopting Chengmai County as a typical case study. It seeks to develop a comprehensive pathway for constructing an ESP that integrates multi-source data and synthesized methodologies. The aim is to provide a transferable framework and offer a reference for refined management solutions to support the sustainable development of analogous coastal areas.

Based on this research background, Chengmai County was selected as a typical case study to construct an ESP that integrates multi-source data and comprehensive methods, clarifying the spatial mechanisms for the synergistic optimization of ecological conservation and economic development. The study integrated three assessment methods—ecological sensitivity, ESV, and landscape connectivity—to collectively identify ecological sources, thereby mitigating biases associated with single-method approaches. An integrated resistance surface was subsequently constructed, and multi-level ecological corridors and nodes were extracted using a combination of the MCR model and the gravity model, ultimately forming an integrated ESP of “sources-corridors-nodes.” The specific objectives of this study were defined as follows: (1) To elucidate the spatiotemporal evolution characteristics of land use and ecosystem service value in Chengmai County from 2000 to 2020, and to reveal the spatial differentiation patterns and underlying causes of ecological sensitivity. (2) To identify core ecological sources through the integrated “sensitivity-service-connectivity” framework, construct a comprehensive resistance surface, and extract key corridors and nodes. (3) To establish an integrated “sources-corridors-nodes” ESP for Chengmai County and propose targeted spatial optimization strategies. This study provides a scientific basis for territorial spatial planning and ecological restoration in Chengmai County, while also offering a methodological reference for coordinating conservation and development practices in similar coastal regions.

2. Materials and Methods

2.1. Study Area

Chengmai County, located at 19°23′–20°01′ N and 109°45′–110°15′ E, lies in the northwestern part of Hainan Island (Figure 1). It borders the Qiongzhou Strait to the north, Haikou City, the provincial capital of Hainan, to the east, and Danzhou City, a prefecture-level city, to the west. The county spans 56.25 km from east to west and 70 km from north to south, encompassing a total land area of 2072.97 km² and a marine area of 470.53 km². The region features a tropical monsoon climate, with forest land as the dominant land cover. Topographically, the county is higher in the northwest and lower in the southeast, and it is traversed by several water systems, including the Nandu River, Wencai Stream, and Jiale Stream. With the continuous acceleration of urbanization, the area of construction land increased by 2.89 times between 2000 and 2020, while natural areas have become increasingly fragmented. As a region with pending economic development, Chengmai County faces potential conflicts between high-quality economic growth and high-level ecological conservation [37]. Therefore, the scientific construction of an ESP is essential to balance ecosystem health with sustainable economic development.

2.2. Data Source

To ensure consistent spatial resolution for overlay analysis, all lower-resolution datasets, such as NDVI and rainfall data, were uniformly resampled to a 30-m resolution, aligning with the highest precision land-use and DEM data (

Table 1. Data sources and information.

Type	Resolution (m)	Source	Year
Land-Use Data	30	National Catalogue Service For Geographic Information (https://www.webmap.cn/ (accessed on 16 June 2025))	2000, 2010, 2020
DEM	30	Geospatial Data Cloud (http://www.gscloud.cn/ (accessed on 17 June 2025))	2020
Soil	1000	Soil Dataset (v2.0) from the World Soil Database (HWSD) of the Cold and Arid Regions Scientific Data Centre	2023
Precipitation	1000	Geospatial Data Cloud (http://www.gscloud.cn/ (accessed on 1 June 2025))	2022
Population density data	90	(https://landscan.ornl.gov/ (accessed on 3 June 2025))	2022
NDVI	1000	Geospatial Data Cloud (http://www.Gscloud.cn (accessed on 1 June 2025))	2021
Road and water data body buffer data	-	National Catalogue Service For Geographic Information (https://www.webmap.cn/ (accessed on 20 July 2025))	2021
Socioeconomic data	-	The Hainan Statistical Yearbook, China Statistical Yearbook, and National Agricultural Product Compilation.	2000–2020

). Resampling techniques, including bilinear interpolation and nearest-neighbor assignment, were employed [29,38]. This preprocessing approach optimally preserved the spatial details of high-precision data, thereby ensuring the accuracy of Ecological Source identification and corridor extraction [39,40].

2.3. Methods

The technical framework of this research is presented in Figure 2.

2.3.1. Land Use Data Preprocessing

Based on the National Land Use Remote Sensing Monitoring Classification System, land use data were categorized into six primary types: cultivated land, forest land, grassland, water bodies, construction land, and unutilized land [41]. Following established methodologies, the land use data for Chengmai County for 2000, 2010, and 2020 were divided into 2243 grid cells, each measuring 1 km × 1 km, using the ArcGIS 10.8 Fishnet tool [42]. The area of each land use type within every grid cell was then calculated [43].

2.3.2. Ecosystem Sensitivity Assessment

Ecological sensitivity serves as a comprehensive metric for assessing ecological quality and the rationality of land use, reflecting an ecosystem’s self-regulatory capacity in response to anthropogenic disturbances and natural environmental changes [44]. Lower ecosystem sensitivity indicates a less stable ESP. At the core of ecological sensitivity assessment is the development of an evaluation indicator system [45]. Due to the multitude of potential sensitivity factors and their variability across regions and objectives, no universally standardized protocol exists; therefore, the system must be tailored to the specific conditions of the study area [46,47]. Building upon existing research and considering the natural environmental conditions, data availability, and temporal relevance for Chengmai County, an evaluation indicator system for ecosystem sensitivity was constructed (

Table 2. Ecological Environment Sensitivity Evaluation System.

Evaluation Factors		Low Sensitivity	Moderate Sensitivity	High Sensitivity	Extreme Sensitivity
Soil Erosion	Rainfall Erosivity/(MJ·mm/hm2·h)	<14,074.39	[14,074.39, 14,696.10)	[14,696.10, 15,202.60)	≥15,202.60
	Soil Erodibility/ [t·hm2·h/ (hm2·MJ·mm2)]	<0.010	[0.010, 0.014)	[0.014, 0.018)	≥0.018
	Topographic Roughness/m	<26	[26, 53)	[53, 105)	[105, 272]
Human Disturbance	Population Density	≥240	[50, 240)	[14, 50)	<14
	Road Buffer Zone/m	<100	[100, 150)	[150, 200)	≥200
Habitat Quality	Land Use Type	Construction Land	Unutilized Land	Cultivated Land, Grassland	Forest Land, Water Bodies
	Elevation/m	[0, 62)	[62, 117)	[117, 213)	≥213
	Slope/(°)	[0, 4)	[4, 9)	[9, 17)	≥17
	Aspect	Flat terrain, due north, northwest	Northeast, Due West	East	South, Southeast, Southwest
	Water Body Buffer Zone/m	Buffer distance > 500	Buffer distance 200–500	Buffer distance 50–200	Buffer distance < 50
	NDVI	[0, 0.23)	[0.23, 0.51)	[0.51, 0.74)	[0.74, 1]

). This system was developed with reference to the Guidelines for Resource and Environmental Carrying Capacity Evaluation and Territorial Spatial Suitability Assessment (Trial) issued by the Ministry of Natural Resources in January 2020. The framework encompasses three main dimensions: soil erosion, human disturbance, and habitat quality [48]. The study employed the natural breaks method to classify the ecological sensitivity index, categorizing the sensitivity levels of factors into four grades: Low Sensitivity, Moderate Sensitivity, High Sensitivity, and Extreme Sensitivity. This approach effectively maximizes the differentiation between categories, ensuring the objectivity and rationality of the classification results [49]. The specific formula for calculating the sensitivity model is presented as follows:

$$S=\sum _{i=1}^n\left(W_i\times C_i\right)$$

(1)

In this model, S represents the integrated ecological sensitivity assessment result for the watershed; i denotes the index of an individual evaluation factor; n is the total number of factors considered; W_i is the weight assigned to the i-th evaluation factor; and C_i is the standardized assessment value of the i-th factor.

Subjective weighting methods are often characterized by significant subjectivity and cognitive bias. Although objective weighting methods avoid human subjective bias, they may assign inappropriately low weights to important indicators with small data dispersion, resulting in substantial deviations from actual conditions. To overcome the limitations of single-weighting approaches, a combined weighting method integrating the AHP and entropy weight methods was employed in this study, as detailed in

Table 3. Weights of the evaluation indicators. Note: w_Ai represents the subjective weight determined by the AHP; w_Bi represents the objective weight determined by the Entropy Weight Method; w_i represents the final combined weight calculated via the multiplicative integration method.

Weight Type	DEM	Slope Gradient	Slope Aspect	Water Body Buffer Zone	NDVI	Land Use Type	Rainfall Erosive Power	Soil Erodibility	Terrain Undulation	Population Density	Road Buffer Zone
wAi	0.06	0.07	0.05	0.09	0.22	0.08	0.08	0.12	0.08	0.10	0.05
wBi	0.09	0.09	0.09	0.09	0.09	0.09	0.08	0.08	0.09	0.12	0.08
wi	0.06	0.07	0.05	0.09	0.22	0.08	0.07	0.11	0.08	0.13	0.04

. The multiplicative synthesis formula was utilized for combination scoring. Within the AHP framework, ten domain experts were invited to evaluate the relative importance of factors at the same hierarchy level using the 1–9 scale method. Expert evaluations were integrated through the geometric mean method to construct a comprehensive judgment matrix, from which initial factor weights were calculated and consistency was verified. The multiplicative synthesis formula is presented below:

$$w_j={\displaystyle \frac{W_{s,j}\cdot W_{e,j}}{{\sum }_{k=1}^{11}{W}_{s,k}\cdot W_{e,k}}}$$

(2)

In the formula, wj denotes the combined weight of the j-th indicator; Ws,j represents the subjective weight of the j-th indicator, derived from the AHP; We,j indicates the objective weight of the j-th indicator, obtained through the Entropy Weight Method; and k is the total number of indicators.

2.3.3. Ecosystem Service Value Assessment

(1)

Ecosystem Service Value Assessment

Based on recent studies on ESV and the equivalent value table for Chinese terrestrial ecosystems established by Xie Gaodi et al. [50] the economic value of one standard equivalent factor for ecosystems in China was determined to be 3406.50 yuan per hectare. The average actual grain yield in Chengmai County during the study period was 5192.26 kg/ha, compared to the national average of 5578.71 kg/ha [43]. This ratio yielded a correction coefficient of 0.9307 for the ecosystem service equivalent value in the study area. Accordingly, the economic value of one standard equivalent factor was adjusted to 3077.36 yuan/ha for Chengmai County. Furthermore, by incorporating insights from studies on specific regions of Hainan Island conducted by Lei Jinrui et al. [43], the ESV equivalents for Chengmai County were finalized. The value equivalent for built-up land was set to zero. The ESV coefficient for the study area was derived based on these adjusted value equivalents and the spatial distribution of each land use type (

Table 4. Ecosystem service value coefficients per unit area for land-use types (yuan/hm²).

Ecosystem Service	Secondary Type	Cultivated Land	Forest Land	Grassland	Water Bodies	Construction Land	Unutilized Land
Provisioning Services	Food Production	3169.77	800.14	892.46	2031.12	0	30.77
	Raw Material Production	892.46	1846.47	1323.3	1138.66	0	92.32
	Water Supply	−2800.48	954.01	738.59	16,741.33	0	61.55
Regulating Services	Gas Regulation	2523.51	6093.35	4646.95	4123.78	0	307.75
	Climate Regulation	1323.3	18,218.5	12,309.8	9078.48	0	276.97
	Purification of the Environment	369.29	5323.99	4062.23	14,094.72	0	892.46
	Hydrological Regulation	3477.52	11,909.73	9016.93	194,617.94	0	584.72
Supporting Services	Soil Conservation	2061.89	7416.65	5662.51	4985.47	0	369.29
	Nutrient Cycling Maintenance	430.84	553.94	430.84	400.07	0	30.77
	Biodiversity	492.39	6739.62	5139.34	16,033.51	0	338.52
Cultural Services	Aesthetic Landscape	215.42	2954.35	2277.31	10,186.36	0	153.87

). The total ESV was calculated using the following formula:

$$ESV=\sum _{i=1}^n{A}_i\times V{C}_i$$

(3)

$$ES{V}_f=\sum _{i=1}^n{A}_i\times V{C}_{fi}$$

(4)

$$V{C}_i=\sum _{j=1}^{k}E{C}_{ij}\times E_a$$

(5)

In the equation, ESV represents the total value of ecosystem services; A_i is the area of the i-th land use type; VC_i is the ESV coefficient for the i-th land use type; ESV_f denotes the value of the f-th ecosystem service; VC_fi is the ESV coefficient for the f-th ecosystem service of the i-th land use type; EC_f represents the value equivalent of the f-th ecosystem service for a given land use type; and Ea is the standard equivalent ESV for the study area, which is 3077.36 yuan/hm².

(2)

Ecosystem Service Change Index (ESCI)

The ESCI was employed to quantify relative gains or losses in ecosystem services. To comply with measurement accuracy and presentation standards, all ESCI values utilized for mapping and analysis were uniformly rounded to two decimal places. The calculation formula is as follows:

$$ESC{I}_x={\displaystyle \frac{E{S}_{CU{R}_x}-E{S}_{HI{S}_x}}{E{S}_{HI{S}_x}}}$$

(6)

In the equation, ESCI_x represents the change index of a single ecosystem service; ES_CURx denotes the ESV at the final state; and ES_HISx indicates the ESV at the initial state.

2.3.4. Landscape Connectivity Assessment

MSPA is an image-processing method based on mathematical morphology principles, designed to evaluate the morphological connectivity of landscapes. This method identifies patches with significant ecological functions within land use types by measuring, segmenting, and analyzing raster images. Through this process, seven landscape elements are distinguished: core, edge, bridge, islet, perforation, loop, and branch, which clarify the spatial topological relationships between target pixel sets and structural elements. MSPA thus enables the identification of ecologically functional patches and individual pixel units, providing a more scientific basis for selecting ecological sources [51].

Landscape connectivity refers to the degree to which a landscape facilitates or impedes movement between patches. Stronger connectivity supports ecosystem stability. A commonly used metric for assessing connectivity is the integral connectivity index, dPC, which is calculated as follows:

$$dPC={\displaystyle \frac{PC-P{C}_{i\_remove}}{PC}}$$

(7)

In the formula, dPC represents the patch importance index. Pc_remove denotes the likelihood of connectivity in the study area after the specific patch and all other corresponding data are removed.

In this study, areas of high ecological value, such as forest land, grassland, and water bodies, were defined as foreground data, while all other areas were classified as background [52]. Foreground connectivity was assigned a value of 1. Using the Guidos Toolbox with an eight-neighbor analysis method, seven landscape types were delineated: core, bridge, edge, loop, branch, islet, and perforation, as shown in Section 3.4.2. Core areas were emphasized due to their high morphological connectivity within the foreground and were subsequently used as the primary basis for selecting potential ecological sources [53]. Patches larger than 1 km² within the core areas were then selected. Connectivity distance was set to 2 km, and the connectivity probability was set to 0.5. Inter-patch connectivity levels were calculated using Conefor 2.6. Finally, the landscape connectivity of Chengmai County was categorized into three levels, low, medium, and high, based on the resulting dPC values.

2.3.5. Ecological Security Pattern Construction

(1)

Resistance Surface Construction

A resistance surface represents the cumulative resistance that ecological elements must overcome when moving between sources and serves as a fundamental component in constructing an ESP. Higher resistance coefficients indicate greater resistance, thereby increasing the “cost” of ecological land expansion. Based on relevant studies and considering the unique physiographical characteristics of the study area, six factors were selected as resistance coefficients: elevation, slope, NDVI, land use type, distance from water bodies, and distance from roads. These coefficients were assigned values ranging from 1 to 9, with higher values corresponding to greater resistance levels (

Table 5. Ecological resistance surface evaluation system.

Resistance Type	Resistance Value					Weight
	1	3	5	7	9
Land Use Type	Forest land, Water bodies	Grassland	Cultivated land	Unutilized land	Construction land	0.43
Elevation/m	<47	47–87	87–135	135–227	>227	0.06
Slope/°	<3	3–7	7–12	12–20	>20	0.11
NDVI	>0.45	0.36–0.45	0.24–0.36	0.08–0.24	0–0.08	0.17
Distance from Road/m	>5867	3224–5867	1881–3224	850–1881	<850	0.12
Distance from Water Dodies/m	<50	50–200	200–500	500–800	>800	0.11

). The resistance surface indicators were allocated using ArcGIS 10.8, and the Analytic Hierarchy Process was implemented. In this process, five domain experts were invited to assess the relative importance of factors within the same hierarchy level using the 1–9 scale method. The expert evaluations were subsequently integrated through the geometric mean method to construct a comprehensive judgment matrix. Initial weights for each factor were then calculated and subjected to consistency verification. Based on the determined weights of each indicator, a comprehensive resistance surface for Chengmai County was generated [54].

(2)

Potential Corridor Extraction

The MCR model is a GIS-based spatial analysis tool that calculates the minimum cost distance for landscape elements moving from a source to a target. This model identifies optimal pathways with the least resistance or lowest cost between ecological elements, thereby facilitating the identification of potential points of ecological connectivity [55]. The formula is expressed as follows:

$$MCR=f_{min}\sum _{j=n}^{i=m}{D}_{ij}\times R_i$$

(8)

In the formula, D_ij denotes the spatial distance between ecological source i and ecological source j; R_i represents the resistance coefficient of ecological source i to species movement; and m and n are the numbers of ecological sources i and j, respectively.

(3)

Significant Corridor Identification

The gravity model assesses the relative importance of ecological corridors by quantifying the intensity of interaction between different ecological patches. Corridors with higher interaction forces are considered more important. Identifying key corridors using this model provides valuable guidance for establishing conservation priorities [56]. The equation for the gravity model is presented below:

$$G_{ab}={\displaystyle \frac{N_a{N}_b}{D_{ab}^2}}={\displaystyle \frac{L_{\max }^2\ln \left(S_a\right)\ln \left(S_b\right)}{L_{ab}^2{P}_a{P}_b}}$$

(9)

In the equation, G denotes the magnitude of the gravitational force; N is the weight value; D represents the standardized value of the potential corridor resistance between patches; P is the patch resistance value; S denotes the patch area; L represents the cumulative resistance value; and L_max is the maximum cumulative resistance of the regional corridors.

3. Results

3.1. Land Use Analysis

According to the remote sensing interpretation results for Chengmai County presented in Figure 3, significant changes in land use structure were observed between 2000 and 2020 (

Table 6. Land use changes in Chengmai County from 2000 to 2020.

Land Use Type		Cultivated Land	Forest Land	Grassland	Water Bodies	Construction Land	Unutilized Land
2000	Area (km2)	710.06	1252.50	24.39	45.14	29.45	0.00
	Proportion/%	34.44	60.76	1.18	2.19	1.43	0.00
2010	Area (km2)	618.05	1356.40	12.15	40.07	34.85	0.00
	Proportion/%	29.98	65.80	0.59	1.94	1.69	0.00
2020	Area (km2)	595.88	1276.52	12.07	62.32	114.81	0.00
	Proportion/%	28.90	61.92	0.59	3.02	5.57	0.00
Area Change (2000–2020)/km2		−114.18	24.02	−12.32	17.18	85.36	0.00
Area Change Rate (2000–2020)/%		−16.08	1.92	−50.51	38.05	289.81	0.00

). The landscape was dominated by forest land, predominantly distributed in the western and southern regions, while cultivated land was primarily concentrated in the eastern areas. Collectively, these two land cover types accounted for nearly 90% of the total area. Driven by policies such as the Hainan International Tourism Island initiative, construction land underwent rapid expansion with an area increase of 289.81%. This urban growth was primarily achieved through the conversion of cultivated land and other ecological spaces, resulting in significantly negative impacts on regional ecological connectivity. Concurrently, influenced by ecological restoration policies including the Grain for Green Program, cultivated land area was reduced by 114.18 km², with substantial portions being converted to forest land and water bodies. Although this land use transformation affected food production capacity, it contributed to the overall enhancement of ecological regulation and service functions. The land use change pattern in Chengmai County reveals a distinct spatial divergence between ecological land and urban development land. A stable ecological barrier function is maintained in the central-western region, while significant urban expansion is observed in the northeast. This spatial pattern poses a potential threat to regional ecological security and requires coordination through ecological spatial optimization.

3.2. Ecological Sensitivity

The spatial patterns of individual ecological sensitivity factors are displayed in Figure 4a–k, revealing pronounced variations in the spatial distribution across different sensitivity types. As summarized in

Table 7. Single-factor Ecological Sensitivity Areas in Chengmai County.

Evaluation Factors		Area\km2
		Low Sensitivity	Moderate Sensitivity	High Sensitivity	Extreme Sensitivity
Habitat Quality	Elevation	871.15	763.54	366.25	60.65
	Aspect	647.74	468.68	233.31	703.79
	Slope	1002.08	685.56	284.55	81.34
	Land Use Type	114.81	0.00	607.95	1338.83
	Water Body Buffer Zone	1488.02	216.41	232.35	124.75
Human Disturbance	Population Density	0.35	32.35	71.75	1956.66
	Road Buffer Zone	125.62	59.67	57.83	1818.42
Soil Erosion	Soil Erodibility	579.83	1191.25	19.23	264.99
	Rainfall Erosivity	114.77	259.97	384.70	1302.10
	NDVI	214.60	288.04	638.19	920.55
	Topographic Roughness	1091.90	829.13	160.38	59.48

, both Elevation and Slope sensitivity were closely correlated with mountain distribution, with Extreme Sensitivity areas predominantly located in higher elevation zones. Specific Aspect orientations and Land Use Types dominated areas of Extreme Sensitivity. The Water Body Buffer Zone exhibited a distinct ribbon-like distribution along watersheds, while High Sensitivity and Extreme Sensitivity categories predominated in the NDVI and Rainfall Erosivity factors, indicating the substantial influence of vegetation cover and rainfall erosion on the ecological environment. Extreme Sensitivity areas demonstrated overwhelming dominance in both Population Density and Road Buffer Zone distributions, reflecting the pervasive pressure exerted by human activities on ecosystems. In summary, topographic factors, vegetation coverage extent, and human activities were all identified as exerting considerable influence on ecological sensitivity.

The comprehensive ecological sensitivity distribution presented in Figure 4l was generated through an overlay analysis of individual sensitivity factors and classified using the natural breaks method into four categories: Low Sensitivity, Moderate Sensitivity, High Sensitivity, and Extreme Sensitivity, as detailed in

Table 8. Comprehensive Ecological Sensitivity Areas in Chengmai County.

	Comprehensive Evaluation Index	Area\km2	Proportion/%
Low Sensitivity	1.12–2.18	206.08	10.06
Moderate Sensitivity	2.18–2.61	539.87	26.34
High Sensitivity	2.61–2.96	791.69	38.63
Extreme Sensitivity	2.96–3.73	511.81	24.97

. This integrated assessment revealed a clear “high in the south, low in the north” spatial distribution pattern, with High Sensitivity and Extreme Sensitivity areas collectively accounting for 63.60% of the total study area. The predominance of higher sensitivity levels suggests that Chengmai County’s ecological environment is more vulnerable to external environmental changes. Driven by complex topography and extensive vegetation coverage, the central-southern region displayed a distinct high ecological sensitivity pattern. This pattern signifies that the ecosystem in this region is particularly vulnerable and susceptible to degradation under external disturbances, which warrants its designation as a core protection area with decisive importance for maintaining regional ecological security. This spatial distribution reveals that the southern region of Chengmai County is characterized by steeper topography, denser vegetation coverage, and reduced human disturbance. These characteristics contribute to higher ecological sensitivity, identifying this area as a core zone for Soil Conservation and Biodiversity maintenance. In contrast, the northern low-sensitivity areas show substantial overlap with urban construction and agricultural reclamation zones. This spatial correspondence reflects the suppressive effect of intensive human activities on the self-regulatory capacity of ecosystems.

3.3. Spatiotemporal Distribution of Ecosystem Service Value

3.3.1. Ecosystem Service Value Changes

From 2000 to 2020, the total ecosystem services value in Chengmai County demonstrated a consistent upward trend, increasing from 10.078 billion yuan to 10.502 billion yuan, with a net gain of 424 million yuan as documented in

Table 9. Changes in Ecosystem Service Values by Land Use Type in Chengmai County, 2000–2020 (Unit: 10⁸ Yuan).

Land Use Type		Cultivated Land	Forest Land	Grassland	Water Bodies	Construction Land	Unutilized Land	Total
2000	Area (km2)	8.63	78.67	1.13	12.34	0.00	0.00	100.78
	Proportion/%	8.56	78.06	1.13	12.25	0.00	0.00	100.00
2010	Area (km2)	7.51	85.20	0.56	10.96	0.00	0.00	104.23
	Proportion/%	7.21	81.74	0.54	10.51	0.00	0.00	100.00
2020	Area (km2)	7.24	80.18	0.56	17.04	0.00	0.00	105.02
	Proportion/%	6.90	76.34	0.53	16.22	0.00	0.00	100.00
ESV Changes (2000–2020)		−1.39	1.51	−0.57	4.70	0.00	0.00	4.24
ESV Change Rate (2000–2020)/%		−16.08	1.92	−50.51	38.05	0.00	0.00	4.21

. Among different land use types, water bodies exhibited the most substantial ESV increase of 470 million yuan, representing a growth rate of 38.05%. Forest land also showed an ESV increment of 151 million yuan, while grassland and cultivated land experienced substantial ESV declines, indicating degradation in their ecological functions. No ESV changes were recorded for Construction Land and unutilized land throughout the study period. Regarding ESV composition, forest land consistently constituted the dominant component, maintaining a share exceeding 76%. The proportion contributed by water bodies increased from 12.25% to 16.22%, with this expansion driven by ecological restoration projects directly resulting in significantly enhanced ESV. This transformation influenced the regional ecological baseline, specifically strengthening key service functions including Hydrological Regulation and Biodiversity maintenance, while creating new spatial clusters of exceptionally high ESV. Analysis of phased changes revealed pronounced ESV growth from 2000 to 2010, primarily attributable to the increase in forest land ESV. Between 2010 and 2020, the growth rate moderated, although substantial ESV gains from water bodies compensated for declines in forest land and cultivated land. In summary, the expansion of water bodies was identified as the principal driver behind the increased total ecosystem services value in Chengmai County. At the same time, reductions in cultivated land and grassland harmed ESV. A distinct spatial pattern of Ecosystem Services Value was observed in Chengmai County, characterized by higher values in the central and southern regions and lower values in the northeast (Figure 5). This distribution pattern was found to closely align with the spatial extent of forest land and water bodies. The consistent spatial correspondence highlights the crucial role of these ecological substrates in sustaining regional ecosystem service functions.

3.3.2. Spatial Distribution of Ecosystem Service Change Index

An analysis of the ESCI on a 1 km × 1 km grid system was conducted, revealing significant alterations in the spatial pattern of ESV in Chengmai County from 2000 to 2020, as illustrated (Figure 6). A continuous expansion of high-value zones was observed alongside a noticeable contraction of low-value zones. Newly established water bodies formed multiple punctate high-value centers, collectively creating a distribution pattern characterized as “high in the central-south, low in the northeast,” which closely corresponded with topographic features and dominant land cover types. Analysis of the ESV hierarchical structure, as documented in

Table 10. Changes in the distribution of ecosystem service value levels in Chengmai County from 2000 to 2020.

Value Level		Low Value	Medium Value	High Value	Extreme Value	Total
2000	Area (km2)	535.88	684.52	808.52	32.67	2061.59
	Proportion/%	25.99	33.20	39.22	1.58	100.00
2010	Area (km2)	469.90	634.37	913.30	44.00	2061.57
	Proportion/%	22.79	30.77	44.30	2.13	100.00
2020	Area (km2)	448.74	623.21	919.97	69.66	2061.58
	Proportion/%	21.77	30.23	44.62	3.38	100.00

, revealed a reduction of 87.14 km² in low-value areas, a steady decrease in medium-value zones, and significant increases in both high-value and extremely high-value categories. These changes reflect the positive impacts of ecological restoration and water body expansion on regional ecological quality. The spatial distribution of ESCI further revealed that significant ESV loss areas were concentrated in urban built-up zones, including Jinjiang Town and Laocheng Town, as well as along major transportation corridors, primarily resulting from the conversion of forest land to Construction Land. Conversely, significant ESV gain areas were distributed in zones of water body expansion and ecological restoration, such as the Lisha River and Nandu River, demonstrating the effectiveness of ecological conservation measures.

3.4. Landscape Connectivity Assessment

3.4.1. MSPA Analysis

The landscape pattern analysis employing MSPA methods, as detailed in Figure 7 and

Table 11. Area of mspa Landscape Types in Chengmai County.

Type	Edge	Branch	Islet	Core	Loop	Perforation	Bridge	Total
Area/km2	103.89	17.17	3.77	1150.33	5.74	59.51	6.01	1346.41
Area of foreground/%	7.72	1.28	0.28	85.44	0.43	4.42	0.45	100
Proportion of total area/%	5.04	0.83	0.18	55.8	0.28	2.89	0.29	65.31

, demonstrated that the ecological landscape foreground in Chengmai County covered 1346.41 km², accounting for 65.31% of the total area. Core areas constituted 55.80% of the total landscape, representing 85.44% of the total ecological landscape area, which indicates well-connected natural habitats and a robust ecosystem foundation throughout the county. Edge areas accounted for 7.72% of the foreground area, providing essential buffering and protection for the core zones. However, bridges and loops were minimally represented, reflecting insufficient structural connectivity among core patches. Islets accounted for merely 0.28%, further indicating a certain degree of ecosystem fragmentation risk. The overall landscape was characterized by core dominance with weak connectivity, rendering core areas vulnerable to external disturbances. Comprehensive analysis indicated that the landscape pattern in Chengmai County was predominantly shaped by natural topography and vegetation coverage, forming a core-dominated configuration that provides complete habitat environments for organisms and exerts positive influences on maintaining regional Biodiversity. However, this configuration is constrained by a severe deficiency in landscape connectivity elements due to human-induced habitat fragmentation, which impedes ecological flows between patches and, without intervention, will generate long-term negative consequences for species migration and gene flow.

3.4.2. Landscape Connectivity Analysis

A connectivity analysis conducted using Conefor 2.6 software, as presented in

Table 12. Area of Landscape Connectivity in the Core Zone of Chengmai County.

Type	High Connectivity	Medium Connectivity	Low Connectivity	Total
Area/km2	917.32	53.74	119.26	1090.32
Core area coverage/%	84.13	4.93	10.94	100

and Figure 7, demonstrated high overall connectivity within core areas, with a maximum dPC value of 98.66, indicating the hub functionality of specific patches. High-connectivity zones covered 84.13% of the core area and were predominantly distributed in the southern and western regions of Chengmai County. These patches serve as critical habitats for various species, providing secure, low-resistance migration corridors for birds, reptiles, and other wildlife. They facilitate gene flow among populations, maintain genetic diversity, ensure the continuity of ecological processes, and enhance ecosystem stability and resilience, thereby enabling the entire system to buffer disturbances and achieve recovery. These ecologically favorable areas provide a solid foundation for the subsequent selection of Ecological Sources. Low-connectivity areas were primarily located in the northern and eastern regions, dominated by cultivated land and Construction Land, where high landscape resistance impedes species migration and material flows. Natural ecological conditions and the intensity of human activity jointly drove the formation of the landscape connectivity pattern. In the southern and western regions, the continuous distribution of forest land and minimal human disturbance facilitated the development of highly connected ecological networks. Conversely, in the northern and eastern areas, rapid urbanization and agricultural expansion accelerated landscape fragmentation, constraining ecological flow processes and ultimately leading to significant degradation of ecological service functions. The high connectivity coverage of 84.13% within core areas signifies the establishment of a structurally intact and functionally stable ecological matrix in central-southern Chengmai County. This configuration provides crucial support for species migration, gene flow, and the circulation of matter and energy within ecosystems.

3.5. Ecological Security Pattern Construction

3.5.1. Resistance Surface

The comprehensive resistance surface presented in Figure 8a was constructed, revealing significant spatial heterogeneity in ecological resistance across Chengmai County, with resistance values ranging from 1.24 to 8.33. Areas of high resistance were concentrated in the northeastern and central regions, dominated primarily by Construction Land, where severe landscape fragmentation impedes ecological flows. In contrast, low resistance areas were distributed throughout the western and southeastern regions, characterized by forest land and grassland, which exhibit favorable connectivity and are identified as potential distribution zones for Ecological Sources. These results elucidate the interaction between human activities and ecological processes. The high resistance zones correspond precisely to areas that have experienced the most rapid expansion of Construction Land over the past two decades, as well as the highest concentrations of population and economic activity, as detailed in Section 3.1 and Figure 3. This spatial coupling demonstrates that rapid urbanization and infrastructure development serve as the primary drivers of ecological space fragmentation and connectivity impairment, representing key conflicts that require prioritized coordination in future planning. The resistance pattern revealed a clear directional preference for ecological flows in Chengmai County. Natural pathways for ecological functional flows were identified primarily in forest and grassland areas of the western and southeastern regions. In contrast, the concentration of Construction Land in the northeast was identified as the primary barrier to ecological connectivity.

3.5.2. Source Distribution

The integration of ecosystem service importance, ecological sensitivity, and landscape connectivity assessments revealed favorable overall ecological security conditions. A High-Security Zone covering 437.27 km² was identified, representing 21.21% of the study area and primarily located in the central-southern forest land regions, as illustrated in Figure 8b. Through the final screening of core nodes, ten Core Ecological Sources were identified based on the actual ecological conditions of the study area, as shown in Figure 8c. The remaining areas with lower security were designated as Potential Ecological Sources. The spatial distribution of these Ecological Sources exhibited three distinct characteristics. Firstly, a patchy distribution pattern was observed, predominantly characterized by forest land as the primary Land Use Type, with additional distributions in grassland and water bodies. Secondly, Ecological Sources were primarily concentrated in forested areas with favorable ecological conditions, including Lingao Ridge, Jinshi Ridge, and agricultural regions, as well as surrounding water bodies such as Fushan Reservoir, Huachang Bay Mangrove Reserve, Nandu River Basin, and Nanfang Reservoir. Thirdly, overlay analysis between the identified Potential Ecological Sources and Chengmai County’s ecological protection redlines and nature reserves demonstrated that most nature reserves, water sources, and ecological protection boundaries fall within the area of the Potential Ecological Sources, as depicted in Figure 8d. This spatial consistency validates the scientific basis of the methodology and supports the reasonableness of the evaluation results, establishing a foundation for subsequent ecological security pattern analysis. The distribution of Ecological Sources was found to be highly concentrated around key Ecological Nodes. This spatial pattern validates the scientific rationale underlying the existing nature reserve system in Chengmai County. Simultaneously, it offers precise spatial guidance for optimizing the ecological protection network.

3.5.3. Corridor Extraction

Forty-five Potential Corridors with a total length of 1186.91 km were extracted using the MCR model and the gravity model. Based on an evaluation of interaction intensity among sources as presented in

Table 13. Importance of Ecological Corridors in Chengmai County (* indicates interaction > 430).

Node	1	2	3	4	5	6	7	8	9	10
1	0	3373.98 *	67.16	104.78	345.54	2913.64 *	3514.74 *	593.30 *	471.46 *	122.87
2		0	100.34	163.70	646.55 *	3019.94 *	1490.31 *	435.35 *	310.72	103.28
3			0	479.44 *	275.74	89.31	103.87	63.53	36.78	31.47
4				0	901.69 *	131.41	179.06	99.23	51.25	40.30
5					0	403.74	694.78 *	303.91	124.63	84.56
6						0	1065.36 *	344.60	522.79 *	120.23
7							0	2558.81 *	526.64 *	230.75
8								0	352.69	276.32
9									0	125.39
10										0

, sixteen corridors with a total length of 193.12 km were identified as Core Corridors, primarily connecting ecologically functional and low-resistance sources. At the same time, the remaining twenty-nine were classified as Potential Corridors as shown in Figure 8d. These sixteen Core Corridors form an elongated network configuration that facilitates ecological connectivity across the landscape. The corridor system effectively interconnects various Ecological Sources, forming an ecological network with complementary functions. This integrated network serves as a critical spatial safeguard for mitigating habitat fragmentation and maintaining regional Biodiversity and ecological security.

4. Discussion

4.1. The Necessity of Research

Through systematic analysis of the spatiotemporal evolution of land use and ESV between 2000 and 2020, rapid construction land expansion and widespread distribution of highly sensitive areas were revealed, demonstrating that Chengmai County is experiencing concurrent ecological preservation and development tensions. The ecological environment was shaped by inherent topographic and ecosystem distributions, which established a baseline of high sensitivity. In contrast, rapid urbanization introduced intensive human disturbance as an external stressor. The superposition of these factors substantially diminished the system’s buffering capacity. Within this context, the constructed ESP essentially represents a spatial planning intervention strategy designed to enhance ecological resilience and preemptively mitigate the negative impacts of development. By identifying and protecting key ecological hubs and corridors, the capacity of ecosystems to reorganize their core functions following disturbances can be maintained or restored. This approach aligns closely with the principles of landscape resilience theory, emphasizing the preservation of diversity, connectivity, and modularity. Consequently, the value of this research extends beyond ecological pattern construction to provide an actionable framework integrating theoretical and practical approaches for enhancing ecosystem sustainability in vulnerable regions through spatial means, while also establishing early warning mechanisms for small-scale areas under development pressure.

4.2. Ecological Security Pattern and Optimization Recommendations

An ESP characterized by one axis, two belts, and three zones was constructed for Chengmai County based on ecological sources and resistance surfaces, through the extraction of ecological corridors and integration of ecological functional zones, as illustrated in Figure 9. This framework comprises 10 ecological nodes, 16 core corridors, and 29 potential corridors [57].

The one axis represents a core ecological corridor for water conservation and biodiversity protection, extending north–south across the county. It connects key ecological nodes, including the Huachang Bay Mangrove Reserve, Fushan Reservoir, and the Nandu River Basin, serving as the primary pathway that links core ecological sources and facilitates the flow of energy and materials. This axis is critical for ensuring regional water resource security, promoting natural forest restoration, and enhancing ecosystem stability. The two belts consist of a central water conservation belt and a southern mountain forest conservation belt. The water conservation belt encompasses aquatic ecological patches centered on reservoirs such as Jiatan, Jiaju, and Shilang, functioning as vital zones for water supply and conservation [58]. The mountain forest conservation belt encompasses northern mountainous regions, including forested areas such as Wuda Ridge and Fuzhong Ridge, as well as contiguous farmlands, providing critical areas for soil and water conservation and serving as habitats for Biodiversity. Supported by the ecological axis, these two belts traverse east–west ecological sources, forming key passages that effectively connect these sources.

The three zones correspond to protection and restoration divisions within the ESP, including ecological barrier zones, ecological restoration zones, and ecological conservation zones. Tailored optimization strategies were developed for each zone based on its specific ecological characteristics, ensuring a coordinated approach to conservation, restoration, and sustainable land use.

The ecological barrier zone comprises potential ecological sources within the region, primarily distributed in key areas such as the mangrove nature reserve, forest protection zones, and Fushan Reservoir. These areas exhibit high ecological flow intensity and function as core nodes and strategic pivots for maintaining regional ecological processes. The region is characterized by abundant forest resources and favorable ecological conditions, primarily attributable to the integrity of existing ecological resources and the level of Biodiversity. Consequently, the sustained functioning of ecological processes is maintained as the foundation of regional ecological security. At the same time, human disturbance is recognized as the principal threat to ecosystem stability and the degradation of ecological service capacity. Minimizing human disturbance and promoting biodiversity recovery are essential to consolidate its role as a regional ecological barrier and a hub for ecological radiation. The ecological restoration zone primarily encompasses areas with high ecological resistance, predominantly located in regions with limited potential corridors, such as Laocheng Town and Renxing Town. Here, cultivated land and construction land dominate, human activities are intensive, vegetation cover is low, and ecological disturbances are significant. The regular expression of ecological functions is constrained by high ecological resistance in this region. In contrast, the combined effects of ecological degradation and agricultural activities further exacerbate the obstruction of ecological processes. The ongoing expansion of Construction Land potentially intensifies this trend. Therefore, the key to ecological improvement in this area is considered to be the regulation of land use patterns, with the ecosystem’s self-recovery capacity enhanced through the optimization of relationships between human activities, natural resources, and land use arrangements. The ecological conservation zone primarily consists of areas with low to moderate ecological resistance, located mainly in the central part of the study area. It serves as a peripheral buffer zone for core ecological sources [59]. These areas experience relatively light ecological disturbance and maintain generally good environmental quality. Conservation efforts should focus on building ecological infrastructure to enhance the flow and connectivity of natural elements, thereby promoting the health and resilience of ecosystems. These low-resistance areas provide favorable conditions for the flow of natural elements and the continuity of ecological processes. In contrast, locally distributed, ecologically degraded patches may compromise the integrity and functional stability of the buffer zone. Moderate intervention in degraded areas is recommended to facilitate the gradual restoration of ecosystems, thereby strengthening the supportive role of the entire buffer zone in maintaining regional ecological security.

4.3. Innovativeness and Rationality

This study employed an integrated approach to address common limitations in current ESP construction, such as excessive subjectivity and insufficient comprehensiveness. Ecological sources were identified through a synthesis of ecological sensitivity, ESV, and landscape connectivity. The MCR and gravity models were then applied in combination, overcoming biases inherent in traditional single-method approaches and enhancing the systematic and objective identification of ecological sources. During corridor extraction, the continuity of key ecological connections was ensured, and a graded assessment of their functional importance was achieved, strengthening the hierarchical structure and operational applicability of the ESP network. Moreover, many existing studies on ESP construction are limited to a single time point and do not investigate spatiotemporal changes in land use and ESV. However, significant land use changes can alter ESV trajectories and reshape ecosystem patterns [60], an aspect that has been overlooked mainly in previous research. By systematically analyzing the spatiotemporal evolution of land use and ESV from 2000 to 2020 [60], this study clarifies the causes and trends of ecological changes in Chengmai County, providing insight into the intensity and spatial distribution of human disturbance on ecological processes [61]. This approach supports the quantitative calibration of the ecological resistance surface, offering a dynamic perspective for early warning of ecosystem degradation and the rapid expansion of cultivated land.

In Chengmai County, ecological sources covering 437.27 km² were identified, showing a high degree of overlap with existing ecological protection redlines and nature reserves. This overlap validates the rationality of the current protected area system and underscores the scientific rigor of the study. Additionally, 45 ecological corridors and 10 key ecological nodes were extracted, collectively forming an ESP characterized as “one axis, two belts, and three zones.” This pattern delineates clear boundaries between conservation and development, helps safeguard the ecological baseline under high-intensity development pressure, and provides a replicable framework for similar small-scale coastal regions in China. Particularly in the context of Free Trade Port construction, the Chengmai case demonstrates a potential pathway for less economically developed areas to achieve ecological primacy alongside green development.

4.4. Limitations and Prospects

While this study addresses several important issues, certain limitations should be acknowledged. First, due to data accessibility constraints, the influence of areas beyond Chengmai County’s administrative boundaries was not incorporated in the ESP construction. Future research should be conducted across different regional ecological contexts. Second, the spatial data utilized in this study exhibited varying resolutions. Although resampling techniques were applied to standardize all data to a standard analytical grid, ensuring computational feasibility, this processing could not fundamentally alter the inherent precision of the original datasets [62]. Additionally, while the constructed ecological security pattern demonstrates high spatial consistency with officially protected areas, a significant limitation remains the absence of direct empirical validation based on field surveys of species distribution, biodiversity hotspots, or ecological process monitoring [63]. Third, the model complexity prevented the incorporation of influences from investment plans and development intensity on the modeling outcomes. Fourth, the analysis primarily relied on spatiotemporal distributions and current environmental conditions, without addressing the driving factors of ecological change or simulating ESP evolution under dynamic land use scenarios. Fifth, the ESP construction involved a complex multi-method framework. Although combined weighting methods were employed to enhance parameter objectivity, systematic sensitivity analysis and uncertainty quantification of key parameters were not performed. Finally, regardless of the methodology used for ESP construction, its effectiveness requires evaluation and subsequent optimization based on feedback.

Future research will be expanded in the following aspects: First, extensive connectivity between ESPs at different scales will be considered, extending beyond the current study area. Second, the introduction of higher-resolution remote sensing data integrated with species distribution surveys and ecological monitoring data will be prioritized for empirical validation, thereby verifying and refining the pattern construction results at finer scales [64]. Third, subsequent studies will incorporate socio-economic data, including territorial spatial planning and key project construction, directly integrating specific investment plans and development intensity into future policy scenarios to enhance the foresight and policy relevance of ESPs [65]. Fourth, building on ESV and land use spatiotemporal distributions, driving factor analysis and multi-scenario simulations will be combined to assess the underlying causes of ecological pattern changes and evaluate ESP stability and vulnerability under different development policies, enabling predictive adjustment of ecological networks through dynamic conservation concepts [66,67]. Fifth, systematic uncertainty quantification and sensitivity analysis will be implemented using methods such as Monte Carlo simulations to comprehensively assess model sensitivity, advancing the research from static pattern identification to dynamic reliability assessment. Sixth, methods for evaluating ESP effectiveness will be developed. These directions represent the primary focus for subsequent research efforts.

5. Conclusions

An integrated ecological security assessment framework, combining ecosystem service importance, ecological sensitivity, and landscape connectivity, was applied to evaluate the ecological security of Chengmai County. Based on this framework, areas with high ecological security were identified as ecological sources, and ecological corridors and nodes were extracted using the MCR and gravity models. The ESP of Chengmai County was subsequently constructed through overlay analysis. The key findings are summarized as follows:

(1)

Rapid expansion of construction land was observed, particularly after 2010, with an overall increase of 289.81%. Ecological sensitivity exhibited a spatial pattern of “high in the south, low in the north,” indicating that southern regions are more vulnerable to environmental changes. ESV displayed significant spatial differentiation, with higher values in the central–southern region and lower values in the northeast, showing an overall increase of 424 million Chinese Yuan.

(2)

The overall ecological security status was relatively favorable. Potential ecological sources covering 437.27 km² were identified, showing a high degree of overlap with ecological protection red lines and nature reserves. A total of 10 ecological nodes and 45 ecological corridors were extracted, with a cumulative length of 1186.91 km. Among these, 16 core corridors, totaling 193.12 km, were prioritized for conservation due to their ecological significance.

(3)

An integrated ESP described as “one axis, two belts, and three zones” was established. This pattern provides a practical framework for guiding ecological conservation and regional development planning. Furthermore, the ecological security assessment framework developed in this study offers a novel and transferable approach for ESP research in small-scale coastal areas undergoing development.