Optimization and Construction of Forestland Ecological Security Pattern: A Case Study of the Huai River Source–Dabie Mountains in China

Abstract

In this research, we chose six indicators—soil conservation, water conservation, carbon sequestration, windbreak and sand fixation, biodiversity conservation, and forest recreation—to compute the forestland ecosystem service index for forestland within the study region, utilizing time series data. The outcomes reveal that the aggregate index of forestland ecosystem services exhibits a spatial distribution characterized by higher values in the northeastern part and lower values in the southwestern part, with an upward trend over time. Among these functions, windbreak and sand fixation, water conservation, carbon sequestration, and forest recreation all maintained relatively high growth rates. We selected 10 factors that are closely related to the natural environment and human activities and employed spatial principal component analysis to develop a comprehensive resistance surface. Based on the assessment results of forestland ecosystem functions, in conjunction with morphological spatial pattern analysis (MSPA) as well as landscape connectivity analysis, we optimized the method for identifying ecological source sites and extracted 38 ecological source sites. Subsequently, leveraging circuit theory, we extracted 91 ecological corridors and pinpointed 25 ecological nodes, ultimately constructing a forestland ecosystem security pattern (ESP) in the study area and proposing restoration strategies.

1. Introduction

At present, the global ecological environment is confronted with escalating challenges. Ecological security, as a vital part of national security, plays a pivotal role in safeguarding the health and stability of society. The ecological security pattern serves as a crucial strategy for addressing ecological crises and acts as a fundamental framework for ecological protection and sustainable development. By optimizing the spatial arrangement of key elements, such as ecological source sites and ecological corridors, it ensures the integrity of regional ecosystem structures and enhances the efficiency of their functions, thereby bolstering the ecosystem’s resilience to natural disasters and human activities [1,2]. The ecological security pattern offers a scientific foundation for territorial spatial planning, guiding the rational layout of ecological protection red lines and ecological functional zones, effectively improving ecosystem service functions and promoting the protection of biodiversity and the enhancement of ecological resilience [3,4]. In the contemporary era, research on the ecological security pattern has emerged as a significant approach to tackle challenges such as ecosystem degradation, landscape fragmentation, and biodiversity loss, providing a robust theoretical and practical underpinning for the construction of ecological civilization.

Forests rank among the foremost ecosystems on Earth, and their significance in the ecological environment is unparalleled. Forests not only enrich biodiversity but also fulfill functions such as climate regulation, soil and water conservation, and carbon cycling, which are key factors in maintaining ecological balance [5]. From the strategic perspective of national ecological security, forests constitute the main body of terrestrial ecosystems, the pinnacle of natural ecosystems, and the cornerstone for human survival and development, playing an irreplaceable and crucial role in maintaining ecological balance and national ecological security [6]. Forestland, as the carrier of forests, is a core component of the ecosystem and supports the primary ecological service functions. Hence, constructing an ecological security pattern centered on forestland is of great significance. Through the construction of an ecological security pattern, we can scientifically identify ecological source sites, corridors, and key nodes within forestland, optimize their spatial layout, connect fragmented habitats, protect biodiversity, ensure ecological security, foster harmonious coexistence between humans and nature, and improve social well-being and human health. At the same time, this process offers a vital basis for territorial spatial planning and the demarcation of ecological protection red lines, effectively guiding the implementation of ecological restoration projects and clarifying key areas for forestland protection [4].

The concept of the ecosystem security pattern originated from a profound comprehension of the structure and function of ecosystems. Richard T. T. Forman, a pioneer in landscape ecology [7], significantly advanced the understanding of landscape ecological patterns and contributed to the development of key principles in landscape and regional ecology. These principles, which can be categorized into four aspects—landscapes and regions, patches and corridors, mosaics, and applications—offer valuable guidance for addressing environmental and land use challenges [8]. In 1996, Yu put forward an ESP, which encompasses strategic components and locations in the landscape that are crucial for safeguarding and regulating ecological processes. Utilizing the potential surface derived from landscape resistance, four strategic landscape components and locations were pinpointed: buffers, source-to-source connections, radiating routes, and strategic points [9]. Together with native habitats, these components form ecological security patterns of varying safety levels. Nowadays, constructing an ESP has become a dominant research paradigm, and its construction method based on source resistance and surface corridor node information has been extensively applied. The research objectives of the ESP have evolved from focusing solely on biodiversity conservation to encompassing the comprehensive protection of natural, economic, and social–ecological environments [10]. Compared with Forman’s early work, modern ESP research continues to focus on corridors, ecological networks, connectivity, and ecological corridors as key topics. However, new research foci, such as the ecological security pattern, ecosystem services, and ecological restoration have also emerged. These new areas cover a wide range of themes and scales, including the dynamic assessment and optimization of ecosystem services, landscape patterns, coordinated development of city clusters, and restoration strategies. They place greater emphasis on the overall ecological security within regions, making the ESP an important tool for addressing regional ecological issues and achieving sustainable development by providing more scientific strategies [11]. Scholars have explored the construction of ESPs using diverse approaches. For instance, in a study of the Tehran metropolitan area, researchers applied landscape ecological principles and the patch-corridor-matrix model to develop an urban ecological network, analyzed the spatial structure and functions of the urban area, and proposed recommendations for enhancing the urban landscape structure and functions [12]. In a study in the semi-arid region of central Iran, researchers utilized the maximum entropy model, MSPA, and circuit theory to investigate the habitat of the wild sheep (Ovis orientalis) and construct functional corridors; the results indicated that functional corridors were more effective in demonstrating the migration routes of wild sheep between protected areas [13]. In China, the concept of ecological networks began to garner attention in the early 21st century, particularly after the implementation of a series of national conservation strategies, such as the “Opinions on Strengthening Key Environmental Protection Work” issued by the State Council in 2011 [14]. Cao et al. employed the InVEST model, Conefor software, the integrated gravity model, and the minimum cumulative resistance model (MCRM) to construct an ESP in Xinjiang, offering references for ESP construction and spatial ecological restoration planning in arid and semi-arid regions to bolster regional landscape connectivity and ecosystem functions [15]. Peng et al. applied ecosystem services and circuit theory in Yunnan Province, China, to identify ecological source sites, corridors, and nodes, construct an ESP, and delineate the spatial scope of ecological corridors and key nodes [16].

In the process of constructing an ESP, accurately identifying stable ecological source sites is crucial. It ensures the continuous supply of ecosystem services, maintains the stability of ecosystems, optimizes the construction of ecological corridors, and scientifically guides ecological protection and management, thereby enhancing the regional ecological security level [17]. However, previous similar studies still need optimization in the identification of ecological source sites. First, static identification methods are one-sided. If assessments are based solely on specific states exhibited in particular years, the identification results are often unstable. The interactions between organisms and the environment, and among organisms themselves, are dynamic processes based on spatiotemporal changes, and these interactions are long-term. Therefore, we need to comprehensively consider historical data to identify and evaluate stable ecological source sites. Second, single identification indicators, such as habitat quality, have limited functions and cannot reflect the full picture of ecological functions. Third, in studies focusing on a single land use type, or when the study area contains a large amount of a single land use type, some methods that quantify functions based on land use types are not applicable. For example, methods represented by InVEST model quantification may not work well in such cases. The Huai River Source–Dabie Mountain Ecological Reserve, located at the junction of Henan, Hubei, and Anhui provinces, is an important ecological functional area in central China, with rich forest resources and significant ecological barrier functions. The forest coverage in this area is substantial, with forestland comprising 51.40%–53.85% of the overall land area, and it serves as a vital component in the region’s ecological functions. Therefore, constructing an ecological security pattern framework based on the characteristics of this region is of practical significance.

This study takes forestland as the research object and dynamically assesses its ecosystem services using quantitative analysis methods. This approach enables the accurate identification of key ecological source areas. Additionally, the study integrates morphological spatial pattern analysis, landscape connectivity analysis, and circuit theory to construct a regional forestland ecological security pattern and proposes corresponding restoration strategies. The primary aim is to develop an optimized methodological framework for regions where forestland is the dominant land use type or where large tracts of forests are present. This framework provides a robust foundation for constructing a regional ecological security pattern and can be applied to other similar regions to offer scientific guidance for forest conservation, enhancement of ecosystem services, and regional land development strategies. It serves as a blueprint for balancing ecological protection with urban and economic development.

2. Materials and Methods

2.1. Overview of the Study Area

The Huai River Source–Dabie Mountain Ecological Reserve lies at the convergence of Anhui, Hubei, and Henan provinces in China (Figure 1), with its geographic coordinates extending from 113°00′ E to 116°33′ E longitude and 30°18′ N to 33°40′ N latitude. Situated in the transitional belt between the subtropical and warm temperate zones, the reserve experiences a subtropical continental monsoon climate, marked by pronounced seasonal variations, an average annual temperature hovering around 9–18 °C, and an average annual rainfall of approximately 700–1600 mm. The Huai River, spanning over a thousand kilometers, has its source in this region. Together with the Qinling Mountains, the Huai River delineates the geographical divide between northern and southern China. The Dabie Mountains serve as a natural watershed separating the Yangtze River and the Huai River. The reserve is rich in rare species with large populations and high densities, making it a valuable site for research on climate and environmental changes in China’s subtropical areas [18]. It plays a key role in water conservation, soil and water retention, and biodiversity protection, acting as an important ecological barrier for the Huai River Basin. Additionally, the area is abundant in cultural tourism resources with a profound historical background, having been a significant base during the revolutionary war years. Today, it has become a distinctive tourist destination by integrating ecological and cultural tourism resources [19,20]. The area has been designated as a national key ecological function zone and a priority region for biodiversity conservation through various national policies and plans [21].

2.2. Data Sources and Parameters

The data were preprocessed using ArcGIS 10.8 [22]. The projection coordinates were unified to “WGS 1984/UTM zone 49N”. The Euclidean distance tool was used to process the accessibility factor, and the Kernel density tool was applied to the POI data. A mask extraction was performed on all data, with the cell size set to 30 m × 30 m, resulting in raster data with unified parameters. The environmental data sources are listed in

Table 1. Main data sources and descriptions.

Category	Data Name	Year	Parameter Data Type	Data Source
Basic Data	Land Use/Land Cover (LULC)	2003, 2013, 2023	Resolution 30 m	Zenodo Repository [23]
Natural Factor	Temperature	2003, 2013, 2023	Resolution 1 KM	National Tibetan Plateau Data Center [24]
	Precipitation	2003, 2013, 2023	Resolution 1 KM	National Tibetan Plateau Data Center [24]
	Relative Humidity (RH)	2003, 2013, 2023	Resolution 1 KM	National Tibetan Plateau Data Center [24]
	Wind Speed	2003, 2013, 2023	/	National Climatic Data Center [25]
	NPP	2003, 2013, 2023	Resolution 500 M	Nasa EarthData [26]
	NDVI	2003, 2013, 2023	Resolution 1 KM	Nasa EarthData [26]
	DEM		Resolution 30 m	Geospatial Data Cloud [27]
	Slope		/	Generated from DEM data.
	Topographic Relief (TR)		/	Generated from DEM data.
Social Factor	Population (POP)	The Seventh National Population Census	Resolution 1 KM	Resource and Environmental Science Date Platform [28]
	Gross Domestic Product (GDP)	2023	Resolution 1 KM	Resource and Environmental Science Date Platform [28]
	Nighttime Lights (NTL)	2023	Resolution 1 KM	Resource and Environmental Science Date Platform [28]
Accessibility Factor	Road and railway	2003, 2013, 2023	Shapefile	OSM [29]
	river	2003, 2013, 2023	Shapefile	OSM [29]
Points of Interest (POI)	Scenic spots, intangible cultural heritage, cultural relics, conservation units, and traditional villages	2003, 2013, 2023	Shapefile	Amap [30]

.

2.3. Methods

We identified ecological source areas, constructed a comprehensive resistance surface, and ultimately used circuit theory to construct the forestland ESP (Figure 2).

2.3.1. Forestland Ecosystem Services

The assessment of ecological service functions primarily refers to the following standards and guidelines: the Specifications for Assessment of Forest Ecosystem Services (GB/T 38582-2020) [31], the Technical Specification for Investigation and Assessment of National Ecological Status—Ecosystem Services Assessment (HJ 1173-2021) [32], the Technical Specification for Supervision of Ecological Protection Red Lines—Ecological Function Evaluation (Trial, HJ 1142-2020) [33], and the Technical Guidelines for the Assessment of Resource and Environmental Carrying Capacity and the Suitability of National Territorial Spatial Development (Pilot Edition) [34]. Based on these references, six indicators were selected to calculate the FESI: soil conservation, water conservation, carbon sequestration, windbreak and sand fixation, biodiversity conservation, and forest recreation.

• Soil conservation.

Soil conservation is calculated using a soil and water conservation service model based on the revised universal soil loss equation (RUSLE) [34].

$$SC=C_1-C_2=R\times K\times L\times S\times (1-p)$$

(1)

In the equation for soil conservation and erosion, SC represents the soil conservation amount (t/(hm²·a)), $C_1$ denotes the potential soil erosion amount (t/(hm²·a)), $C_2$ indicates the actual soil erosion amount (t/(hm²·a)), $R$ is the rainfall erosivity factor (MJ·mm/(hm²·h·a)), $K$ is the soil erodibility factor (t·hm²·h/(hm²·MJ·mm)), $L$ is the slope length factor, $S$ is the slope steepness factor, and $p$ is the vegetation cover factor.

• Water conservation.

In this study, the water source conservation amount is calculated using the water balance equation [32].

$$TQ=\left(P-R_i-E{T}_i\right)\times A_i\times {10}^3$$

(2)

TQ denotes the water source conservation amount (m³), $P$ is the average annual precipitation (mm), $R_i$ represents the net surface runoff (mm), $E{T}_i$ is the evapotranspiration (mm), and $A_i$ is the area of the study region (km²).

• Carbon sequestration.

The carbon sequestration of the forestland in the study area is calculated using a net ecosystem production (NEP) estimation model, which is constructed based on the difference between net primary production (NPP) and soil microbial respiration carbon emission ($R_h$, g·C/m²) [35].

$$NEP=NPP-R_h$$

(3a)

$$R_h=0.22 \times \left[exp\right(0.0913 \times T\left)\right]+ln\left(0.3145\times P+1\right) \times 30 \times 46.5\%$$

(3b)

T is the average annual temperature (°C), and $P$ is the average annual precipitation (mm).

• Windbreak and sand fixation.

The ecosystem’s windbreak and sand fixation service capacity index is used as the assessment indicator for evaluation [36].

$$W_i=NPP\times K\times F_q\times D$$

(4a)

$$F_q={\displaystyle \frac{1}{100}}{\sum }_{i=1}^{12}{u}^3\left({\displaystyle \frac{ET{P}_i{-P}_i}{ET{P}_i}}\right) \times d$$

(4b)

$$ET{P}_i=0.19\times {(20+T_i)}^2\times (1-R_i)$$

(4c)

$W_i$ is the windbreak and sand fixation (WSF) service function index, D is the surface roughness factor,$u$ is the monthly average wind speed, $ET{P}_i$ is the potential evapotranspiration (mm), $P_i$ is the monthly precipitation (mm), d is the number of days in the month, $T_i$ is the monthly average temperature (°C), and $R_i$ is the monthly average relative humidity (%).

• Biodiversity conservation.

This study employs the NPP (net primary production) correction method, using the biodiversity conservation service capacity index as the assessment indicator [33].

$$B_i={NPP}_{mean}\times P\times T\times (1-F_{alt})$$

(5)

$B_i$ represents the biodiversity conservation service capacity index, ${NPP}_{mean}$ denotes the mean net primary production, $P$ is the average annual precipitation (mm), $T$ is the average annual temperature (°C), and $F_{alt}$ is the altitude factor.

• Forest recreation.

Recreational and entertainment opportunities are closely related to the distribution of regional road accessibility and forest landscape resources. In this study, the Euclidean distance method is used to calculate road accessibility. Meanwhile, various types of forest landscape POIs (scenic areas, intangible cultural heritage sites, traditional villages, and cultural heritage sites) are used as sources for kernel density analysis. The results of this analysis serve as the recreational and entertainment index.

• Forest ecosystem service index.

Normalization is the process of converting indicators with different dimensions and magnitudes into dimensionless indicators with the same magnitude, facilitating comprehensive evaluation. In this study, the total forestland ecosystem service index (FESI) is used as the assessment indicator, which is the sum of the normalized values of various ecosystem service functions [34].

$$FESI={\sum }_{i=1}^n{U}_i$$

(6)

2.3.2. Optimization of Ecological Source Identification

• Identifying ecological source sites by integrating MSPA analysis.

Morphological spatial pattern analysis is a spatial analysis technique rooted in the principles of mathematical morphology; it is primarily used to identify and classify different topological relationships in space. MSPA processes and analyzes raster images through mathematical morphological operations, such as erosion, dilation, opening, and closing [37].

In this study, we used the User Guide of Guidos Toolbox (GTB, Version 2.8) to convert the land use type raster map of the study area into a binary raster map with forestland as the foreground and other land uses as the background. After running the MSPA analysis, we obtained seven landscape types, including core areas, islands, bridges, edges, perforations, rings, and branches. Core areas, which serve as habitats for the majority of species, can be regarded as the sources of various ecological processes.

Step 1: We first classified the total ecosystem service values of forestland for the years 2003, 2013, and 2023 into five categories using the natural breaks method. We selected the medium-to-high value areas and intersected them to obtain the medium-to-high value areas that are stable over time and space as the preliminary ecological source sites (a).

Step 2: Based on the core areas identified by MSPA for the years 2003, 2013, and 2023, we intersected these core areas to obtain the core areas that are stable over time and space as the preliminary ecological source sites (b).

Step 3: Final identification of ecological source sites. Since smaller ecological source sites have limited service capabilities and are more sensitive and unstable to environmental changes and human activities, we need to eliminate smaller, low-impact patches to more accurately identify and evaluate significant habitat patches. We employed cluster analysis to identify key thresholds (Appendix A.1).

Finally, by intersecting and directly selecting individual high-impact patches, we obtained the final ecological source sites, aiming to ensure that the identified source sites possessed good ecological functions and guaranteed scientific validity and stability.

• Classifying source sites based on landscape connectivity.

Landscape connectivity is a concept in ecology and landscape ecology that measures the degree of connection between different habitat patches within a landscape. It is crucial for species conservation, the maintenance of ecosystem functions, and the construction of ecological networks. High connectivity helps to maintain species population sizes and genetic diversity, reducing the risk of species extinction caused by habitat fragmentation. A commonly used quantification index is dPC, which is based on the potential connectivity (PC) index and measures the importance of a patch to overall landscape connectivity [38]. The larger the dPC value, the greater the change in the overall PC index when the patch is absent, indicating that the patch plays a more important role in landscape connectivity.

$$dPC=100\times {\displaystyle \frac{PC-P{C}_{i-remove}}{PC}}$$

(7)

The $PC$ index represents the potential connectivity of the entire landscape when all patches are present. The $P{C}_{i-remove}$ index represents the potential connectivity of the landscape after the removal of habitat patch i. The $dPC$ value indicates the importance of patch i to landscape connectivity.

We used Conefor (Version 2.6) to calculate the $dPC$ index as the basis for source site classification. By applying the natural breaks method, we classified the source sites to identify and assess the importance of different habitat patches to overall landscape connectivity.

2.3.3. Integrated Resistance Surface Construction Method

The ecological resistance surface can intuitively reflect the overall flow of ecological factors within a region and simulate the resistance and risk levels encountered by species when migrating between different landscape units. The construction of ecological resistance surfaces and circuit theory complement each other in the construction of ecological networks. The resistance surface provides information on the resistance to species migration, while circuit theory identifies multiple migration pathways and key nodes by simulating electrical current flow.

We selected 10 factors in total, including 5 natural environment-related factors (DEM, slope, terrain ruggedness, NDVI, and river) and 5 human activity-related factors (land use, population density, nighttime lights, GDP, and road). We then used the spatial principal component analysis tool in ArcGIS 10.8 to calculate the weights of the resistance factors and finally constructed the comprehensive ecological resistance surface using the weighted overlay method. For more information, please refer to Appendix A.2.

2.3.4. Constructing Forestland ESP Using Circuit Theory

The application of circuit theory in the construction of ecological networks is an innovative method. It analogizes ecological landscapes to electrical circuits, where habitat patches serve as nodes and ecological corridors act as the connecting wires. By calculating the resistance values of paths, the migration resistance of species along different paths can be reflected, while the current represents the migration flow of species. This method integrates multiple factors, such as topography, environmental factors, and land use types, to quantitatively analyze ecological connectivity and identify key ecological corridors, thereby providing a scientific basis for ecological protection and restoration. The application of this theory in forest and wetland ecosystems has shown significant effects [39]. In practical applications, we use circuit theory to construct a forestland ESP, with the primary tool being the Linkage Mapper plugin based on ArcGIS. Linkage Mapper is a tool that integrates minimum cumulative resistance (MCR), circuit theory, and graph theory. It can be used to construct ecological corridors (including linear and areal corridors), identify important ecological nodes (such as ecological pinch points and barrier nodes) using circuit theory, and calculate the centrality of ecological sources and corridors using graph theory. Through these functions, Linkage Mapper can effectively support the construction and optimization of ecological networks [40].

Ecological corridors refer to linear or strip-shaped areas that serve a connecting function within a landscape. They are typically composed of natural vegetation, water bodies, or other ecological types and provide pathways for species migration and dispersal. We used the ArcGIS 10.8 with the Linkage Mapper toolbox to identify ecological corridors. Specifically, we employed the Linkage Pathways Build Network and Map Linkages tool. During the calculation, we set the truncate cost-weighted distance threshold to 370 km to obtain the ecological corridors. Subsequently, we used the Centrality Mapper tool from the same toolbox to calculate the centrality values and classified them using the natural breaks method.

Ecological nodes refer to areas that play a key connecting role in ecosystems, acting as hubs in a network. They are crucial for maintaining biodiversity, facilitating species migration, and promoting gene flow and are core components of ecological networks. We continued to use the ArcGIS 10.8 with the Linkage Mapper toolbox. Based on the previously created ecological corridors, we invoked the Circuitscape program to identify ecological pinch points and ecological barriers. Specifically, the “all-to-one” mode in the Pinch Point Mapper module was used to extract high-value areas as ecological pinch points. For the Barrier Mapper module, the detection radius parameter was set between 300 and 1500, with a radius step value of 100, and the “Maximum” mode was used to extract high-value areas as ecological barriers. We also used the Intersect tool to extract ecological breakpoints by identifying the intersections of major rivers, roads, and important corridors.

3. Results

3.1. Assessment Results of Forestland Ecosystem Services

The spatial distribution of various ecosystem services in the Huai River Source–Dabie Mountain forest ecosystem has remained relatively stable over different historical periods (Figure 3), although some indicators show certain differences. In terms of changes in quantity (

Table 2. The changes in forestland area and the indicators of ecosystem services.

Indicator Name	2003	2013	2023
$\mathrm{Forestland} \mathrm{Area} (\mathrm{k}{\mathrm{m}}^2$)	23,047.4007	24,149.3256	24,041.1015
Soil Conservation (×109 kg)	11,138.5711	6795.7785	6893.1700
$\mathrm{Water} \mathrm{Conservation} (\mathrm{k}{\mathrm{m}}^3$)	22.9992	24.0495	33.1663
Carbon Sequestration (×109 kg)	11.4621	12.8422	15.0621
Windbreak and Sand fixation (Index, Mean)	293.3546	383.1574	411.6767
Biodiversity Conservation (Index, Mean)	0.1426	0.1514	0.1381
Forest Recreation (Index, Mean)	0.2067	0.6619	0.7337
FESI (Index, Mean)	1.8292	1.8450	1.8567

Note: Results are rounded to four decimal places.

), the forest area increased by 1101.9249 km², or approximately 4.8%, from 2003 to 2013 and remained relatively stable from 2013 to 2023. Other indicators have shown some fluctuations: soil conservation experienced a significant decline in 2013, while water conservation, carbon sequestration, water source conservation service, forest recreation, and the forestland ecosystem service index all showed increasing trends. Biodiversity conservation exhibited minor fluctuations.

Soil Conservation (Figure 3(a1–a3)): The spatial distribution has remained relatively stable, with high-value areas predominantly situated in the eastern and northeastern sections of the study area, whereas low-value areas are found in the western and northwestern sections. The soil conservation value experienced a significant drop of 39% in 2003 compared to the subsequent years, while there were minimal changes between 2013 and 2023.

Water Conservation (Figure 3(b1–b3)): High-value areas in all three periods were predominantly located in the eastern and northeastern sections of the study area, while low-value areas were situated in the western and northwestern sections. The increases in water conservation were 105 million cubic meters and 912 million cubic meters, with growth rates of 12.04% and 37.9%, respectively.

Carbon Sequestration (Figure 3(c1–c3)): The spatial distribution has remained relatively stable, with high-value areas predominantly situated in the eastern and northeastern sections of the study area, while low-value areas are found in the western and northwestern sections. The increases in carbon sequestration were 1.38 × 10⁹ kg and 2.22 × 10⁹ kg, with growth rates of 12% and 17%.

Windbreak and Sand Fixation (Figure 3(d1–d3)): High-value areas in all three periods were predominantly situated in the northeastern and central sections of the study area, while low-value areas were found in the eastern and northwestern sections. The average growth rates of the windbreak and sand fixation index were 30.61% and 7.44%.

Biodiversity Conservation (Figure 3(e1–e3)): There have been certain changes in the spatial and temporal distribution patterns. High-value areas were predominantly situated in the northeastern section, while low-value areas were found in the western and southwestern sections. When comparing the spatial distribution maps of 2003 with those of the subsequent years, a noticeable decline was observed in the eastern section. The biodiversity index exhibited minor fluctuations across the three periods, but overall changes were minimal and had little impact.

Forest Recreation (Figure 3(f1–f3)): High-value areas in all three years were predominantly located in the central region of the study area. In 2003, low-value areas were extensively found in the western and eastern regions, which exhibited a significant upward trend in 2013 and 2023. The forest recreation index saw substantial growth over the decade, with growth rates of 220.28% and 10.85%, respectively. This indicates that the study area has made significant progress in cultural tourism development, with improved landscape development contributing to increased economic revenue for the region.

Forestland Ecosystem Service Index (Figure 3(g1–g3)): The spatial and temporal distribution changes were minimal. High-value areas were predominantly situated in the northeastern and central regions, while low-value areas were found in the western and northwestern regions. The overall trend of the FESI was upward, although the changes were not substantial, indicating that current ecological protection policies and measures are effective. Ecosystem functions have steadily enhanced and are stable, with the comprehensive ecological service value gradually increasing and significant improvements in economic and social benefits.

3.2. Analysis of MSPA Results

In the MSPA analysis results of the study area (Figure 4,

Table 3. The quantity of MSPA analysis results.

Name	$2003 \mathbf{Area} \left(\mathbf{k}{\mathbf{m}}^2\right)$	%	$2013 \mathbf{Area} \left(\mathbf{k}{\mathbf{m}}^2\right)$	%	$2023 \mathbf{Area} \left(\mathbf{k}{\mathbf{m}}^2\right)$	%
Branch	1058.32	4.59	1093.32	4.53	1239.13	5.15
Edge	2208.58	9.58	2376.54	9.84	2850.20	11.86
Islet	711.37	3.09	779.09	3.23	811.46	3.38
Core	15,451.93	67.04	15,956.96	66.08	15,095.30	62.79
Bridge	1597.99	6.93	1708.22	7.07	1723.13	7.17
Loop	979.90	4.25	1037.12	4.29	1092.62	4.54
Perforation	1039.31	4.51	1198.09	4.96	1229.27	5.11

Note: Results are rounded to two decimal places.

), we can observe that the core areas cover 15,451.93 km², 15,956.96 km², and 15,095.30 km², respectively. Each of these core areas accounts for more than 60% of the entire analysis area. These core areas gradually decrease from the southeast to the northwest, with the highest concentration in the eastern and southeastern regions. Within these regions, the core patches are densely packed and relatively contiguous, exhibiting good connectivity. This provides favorable conditions for species migration, reproduction, and other interactions. It also facilitates the implementation of ecological restoration and conservation policies, making it the most ideal region.

The central and western regions also have a certain proportion of core areas, but human influence is increasing, and vegetation is becoming sparser. Compared to the eastern and southeastern parts, these areas have lower concentration and connectivity. The northwestern region has the fewest core patches, which are scattered and have the poorest connectivity. This leads to weakened ecological functions and difficulties in species interactions.

The edge zones, which act as the buffer between core areas and non-ecological land uses, are primarily located in the central and western regions of the study area. Their areas are 2208.58 km², 2376.54 km², and 2850.20 km², comprising 9.58%, 9.84%, and 11.86% of the total area, respectively. The upward trend in edge zones signals shifts in the fragmentation of ecological land use, moving toward a smoother and more continuous transition between ecological and non-ecological land uses. This aids in providing better safeguarding of the ecological processes of the core areas. It also impacts landscape connectivity and stability, bolstering landscape resilience and diminishing the risk of ecosystem collapse.

Other areas, such as bridge zones and loop zones, with relatively high proportions (6.93%–7.17% and 4.25%–4.54%, respectively), indicate that the ecological network in this region has good connectivity. This effectively promotes species migration and the continuity of ecological processes. The presence of islet patches and perforations (3.09%–3.38% and 4.51%–5.11%, respectively) shows that despite overall good connectivity, there is still a certain degree of patch fragmentation. These fragmented areas may impact the long-term viability of species and the coherence of ecosystems, but they can also serve as areas for ecological restoration and conservation, helping to maintain biodiversity and ecosystem stability. The proportion of branch zones (4.53%–5.15%) indicates that there are some auxiliary connection paths in the ecological network. These paths can bolster the robustness and flexibility of the ecological network, mitigating the risks associated with damage to main corridors.

3.3. Construction of Forestland ESP

• Final Confirmation of Ecological Source Areas

We combined forest ecosystem services with the outcomes of MSPA to determine the final ecological source areas through landscape connectivity grading (Figure 5).

In preliminary source site one, identified based on FESI (Figure 5a), we can observe that the preliminary source sites are predominantly located in the eastern and southeastern regions of the study area, with sporadic distribution in the central region, while the western and northwestern parts have very few sites, with only sporadic areas. In preliminary source site two, identified based on MSPA-Core (Figure 5b), there is also a large distribution in the eastern and northeastern parts, but slightly smaller compared to Figure 5a, and there are several patchy distributions in the western and northwestern parts, with a greater number than in Figure 5a. A total of 38 ecological source sites were ultimately identified, comprising 34 ecological source sites identified by us using the intersect function, and four core areas selected by MSPA that were retained during the intersection process. They are the Shiman Tan National Forest Park in the northwestern section of the study area (Figure 5c 38), Tongshan Lake National Forest Park (Figure 5c 37), Boshan National Forest Park (Figure 5c 36), and the Huai River Source National Forest Park in the west (Figure 5c 31). These national forest parks are scenic, with a wide variety of plant species and rich wildlife resources. They are important bases for ecological education, and each has its own characteristics. For example, Shiman Tan National Forest Park is home to many nationally protected bird species, Boshan National Forest Park is known as the “Water Park” for its rich water-based landscapes, and Huai River Source National Forest Park contains the source of the Huai River. They integrate ecological, cultural, tourism, scientific research, and educational values [41].

Combining the landscape connectivity analysis results from Conefor (Version 2.6) and using the natural breaks method, the 38 source sites were classified (

Table 4. The results of source area classification.

Name	Quantity	Area (km2)	%
First-level source site	1	5320.94	56.40
Second-level source site	4	1637.28	17.35
Third-level source site	33	2476.58	26.25
Sum	38	9434.80	100.00

Note: Results are rounded to two decimal places.

). We can observe that there is one first-level source site, namely Figure 5c 20, situated in the eastern region of the study area, covering an area of 5320.94 km², which constitutes 56.40% of the total source site area. There are four second-level source sites, namely Figure 5c 2, 18, 19, and 28, with three positioned in the central region of the study area and one in the southeastern region, collectively accounting for 17.35% of the area. The remaining 33 third-level source sites, which make up 26.25% of the area, are predominantly located in the western, northwestern, central, and southeastern regions of the study area.

2.

Construction of Comprehensive Resistance Surface

Using ArcGIS 10.8, the spatial principal component analysis method was employed to determine the weights of ten factors, thereby constructing a comprehensive resistance surface (Figure 6).

The results indicate that areas with high resistance are primarily distributed in the western, northwestern, and central north–south sides of the study area. These regions are predominantly characterized by land use types other than forests and are significantly influenced by human activities. The high resistance values are mainly derived from factors closely related to human activities, such as population (POP), GDP, and road networks.

In contrast, the central block-like areas with low resistance values, as well as the eastern and southeastern regions, benefit from better environmental conditions and are less affected by human activities, resulting in relatively lower resistance. The resistance in these areas mainly originates from topographic factors, such as DEM, topographic relief (TR), and slope.

3.

Corridor Extraction and Classification

Based on circuit theory, we utilized the Linkage Mapper tool to identify a total of 91 ecological corridors (Figure 7), with a combined length of 2299.07 km.

After calculating the centrality values, we applied the natural breaks method for classification, resulting in the following:

• Fifteen first-level corridors, totaling 110.22 km in length;
• Forty-two second-level corridors, totaling 800.78 km in length;
• Thirty-four third-level corridors, totaling 1388.07 km in length.

Spatially, the first-level corridors predominantly link major medium to high-value ecological source areas and are characterized by their short distances, which contribute to high ecological connectivity. This significantly enhances the flow of materials, energy, and information among various source areas, reducing the cost of movement and improving the overall ecological connectivity and functionality of the watershed. These corridors serve as the primary pathways for species migration and genetic exchange and are the most critical, also constituting the core of the ecological network.

The second-level corridors, in terms of spatial distribution within the study area, connect numerous local ecological source areas. They play a role in facilitating species exchange and dispersion, providing habitats and ecological services, and aiding in the construction of the ecological network. They enhance regional ecological connectivity and act as supplementary elements within the ecological network, contributing to the overall enhancement and stability of the ecosystem’s functionality.

Regarding the third-level corridors, although they primarily connect relatively lower-value source areas within the study area and have relatively longer routes, which might seem to indicate weaker functionality, they still hold significance from the perspective of ecological landscape functions. By leveraging river tributaries, main roads, greenways, and connecting landscapes, parks, cultural resources, and other characteristic elements along the route, they can fulfill certain ecological service functions and contribute to cultural and tourism comprehensive values. They also provide passages for smaller animals and invertebrates, making them an indispensable part of the overall ecological network.

4.

Ecological Nodes

• Pinch points

Using the “all-to-one” model, we derived the current density distribution throughout the research region (Figure 8A), with values spanning from approximately 0 to 0.5677. Based on this distribution, we pinpointed regions with high current density as ecological pinch points (Figure 8B), identifying a total of 13 ecological pinch points that encompass an area of 7.11 km², with the largest individual area reaching 1.8 km². These pinch points are primarily situated in areas with dense vegetation or bodies of water, where the current density is high and resistance is low, indicating superior ecological connectivity. As a result, these areas are designated as ecological pinch points. By integrating remote sensing imagery, we selected several distinct and representative pinch points for further analysis.

Areas a and c have current densities of 0.2785 and 0.2822, respectively. These regions boast favorable ecological conditions, with lush forests and flat terrains, making them highly suitable for species migration and movement. Areas D and E have current densities of 0.3000 and 0.2975, respectively. These pinch points are adjacent to water bodies and surrounded by dense forest environments, providing abundant water and food resources. Analysis of the surrounding land use types and human activity distribution revealed that areas d and e are less impacted by human activities, making them ideal for the migration and habitat of birds and other wildlife.

Area b has a current density of 0.2857. This region consists of several mountains and lakes, with complex terrain and high current density, indicating that it represents an area with a higher likelihood of species passage. Area f has a current density of 0.4476. This region features undulating terrain with numerous ravines and a central agricultural strip. Although agricultural areas are typically associated with human activity, this area has the highest current density, suggesting the presence of topographical barriers. The central strip is a critical passage for species migration, with no alternative routes available.

• Barriers

Using the Barrier Mapper module, we delineated the spatial variation in the resistance values throughout the research region (Figure 9A), with values ranging from 0 to 2.993. Based on this resistance distribution, we identified 12 high-resistance areas as ecological barriers (Figure 9B), which cover a total area of 267.39 km², with the largest single area measuring 53 km². These areas have a high resistance, which typically poses significant obstacles to the dispersal and migration of organisms, thus affecting the connectivity of the ecosystem. By integrating analysis with remote sensing imagery, we selected six distinct and representative barrier points for further analysis. Among them, areas a and d have resistance values of 1.6500 and 1.6114, respectively. Their high resistance values are primarily influenced by complex terrain, which is characterized by mountains, ravines, high elevations, steep slopes, and significant topographical variations, making it difficult for animals to navigate.

Areas e and f have resistance values of 2.5758 and 1.7184, respectively. These areas are in close proximity to human activity zones with substantial built-up areas and extensive road networks, leading to landscape fragmentation and significant ecological impacts. Areas b and c have resistance values of 1.9069 and 2.0353, respectively, and they exhibit a combination of the characteristics of the aforementioned areas, being both close to human activity zones and influenced by complex terrain.

In conclusion, the ecological barriers within the study area are categorized into different types based on their features. These barriers significantly impede the dispersal and migration of organisms and should be considered as focal areas for intervention in future ecological conservation and land use planning.

3.4. Restoration Strategies for Forestland ESP

• Forestland ESP

Following the identification of key ecological elements, such as source areas, corridors, and nodes, we constructed the ecological security pattern for the Huai River Source–Dabie Mountain forestland (Figure 10).

The ESP comprises 38 ecological source sites, 91 ecological corridors, 13 ecological pinch points, and 12 ecological barrier points. Furthermore, we marked the intersections of the primary first- and second-level corridors with major roads and rivers as ecological breakpoints on the map. These breakpoints either fully or partially sever the connectivity between landscape patches, posing threats to the free and safe movement and migration of species.

• Restoration strategies

Based on the distribution of forestland in the Huai River Source–Dabie Mountain Ecological Conservation Area, the ecological function indices of the forestland, and the established ecological security pattern, along with land use and transportation networks, we proposed a restoration strategy of “One Belt, Three Axes, Five Zones, and Multiple Nodes” (Figure 11).

“One Belt” refers to a forest conservation belt that traverses the study area, incorporating the primary ecological corridors. This belt should prioritize natural regeneration and ecological conservation strategies. It ensures the exchange among species and the connectivity between source sites and the landscape, which is crucial for maintaining the integrity of the regional ecosystem and biodiversity.

“Three Axes” refers to urban development axes centered around major transportation routes and economic belts. These axes serve not only as the city’s main transportation channels but also as conduits for significant economic development. By connecting different regions and functional areas along key transportation routes, they facilitate the rational allocation of resources and coordinated economic growth, playing a pivotal role in the overall urban planning and functional zoning. In ecological restoration, these development axes should focus on balancing ecological conservation with economic development to prevent damage to ecological corridors.

The “Five Zones” consist of one key forest conservation area, one secondary forest conservation area, one ecological urban development area, and two ecological tourism development areas, corresponding to the differently colored and patterned regions on the map. The eastern part features the largest and most critical ecological source sites, with the strongest ecological functions and numerous mountains, making it the most suitable as a key forest conservation area. The secondary forest conservation area is primarily composed of multiple second- and third-level ecological source sites. The northwest part of the research region has sparser forestland and relatively weaker ecological functions but has more urban plains, making it suitable for an ecological urban development area. According to the forest leisure function distribution map, areas with higher indices are located on the central north–south sides, with numerous points of interest and abundant cultural tourism resources, making them ideal for ecological tourism development areas. By rationally dividing functional zones, a balance between ecological protection and economic development can be achieved, enhancing the overall benefits of the ecosystem.

The “Multiple Nodes” are composed of one main urban development node, several primary ecological nodes, and several secondary urban development nodes, as indicated by the red and dark red circles on the map. Xinyang City, with its advantageous geographical location, well-developed transportation, proximity to various zones and key nodes, beautiful environment, and rich resources, can act as the main urban development node, providing foundational support for the reserve’s development and improving overall benefits. Several nearby cities can serve as secondary urban development nodes to assist in the restoration tasks of the ecological reserve. The primary ecological nodes are critical areas that impede ecological connectivity and should be removed or restored to enhance connectivity between ecological source sites and reduce resistance encountered during biological activities.

4. Discussion

4.1. Discussion on the Functions of Forestland Ecosystem Services

When evaluating the forestland ecosystem services in the Huai River Source–Dabie Mountain area using individual indicators, water conservation, carbon sequestration, windbreak and sand fixation, and forest recreation all show increasing trends, with respective growth rates of 44.21%, 31.41%, 40.33%, and 255.02% by 2023. Biodiversity conservation shows minimal change, while soil conservation experienced a significant decline of 38.99% between 2003 and 2013, with little change thereafter. Spatially, the overall index is higher in the southeast and lower in the northwest, due to the higher density of mountainous areas and vegetation in the southeast, which are less disturbed by human activities. In contrast, the northwest has lower altitudes, fewer mountains, more plains, and sparser vegetation, leading to a decline in ecosystem services. Over the past two decades, the forestland ecosystem service index has shown a slow upward trend. This is primarily because the region was designated as a key protection area by the national government early on. The Dabie Mountain Nature Reserve, established in the 1980s, was upgraded from a provincial to a national reserve. Under strong policy protection, regional development plans have increasingly focused on balancing economic development with the protection of forest ecosystems. With policy support, financial investment, and project implementation, the Huai River Source–Dabie Mountain area has significantly enhanced ecological protection outcomes. The national and local governments have introduced multiple policies emphasizing ecological space management and ecosystem protection and restoration [42]. Central and provincial finances have increased support for the Dabie Mountain area through ecological compensation and transfer payments. By 2023, ecological compensation funds allocated to Lu’an County alone had reached a cumulative total of CNY 1.8 billion. In terms of project implementation, initiatives such as natural forest protection, water conservation, soil and water conservation, and biodiversity conservation projects have been carried out. Infrastructure construction in nature reserves has been advanced, and a biodiversity conservation and sustainable utilization project for the Huai River Source has been launched. These measures have significantly safeguarded the ecological environment within the region, fostering forest resource growth and ecosystem stability [43].

The significant decline in soil conservation in 2003 was found to be due to extremely uneven precipitation distribution in China that year. During the summer, water levels in the main Huai River and its tributaries rose significantly, with the average precipitation during the flood season being the second highest in nearly 70 years, only surpassed by that in 1954. In the autumn, precipitation in the northern regions reached a new high, with areas in North China and the Huang-Huai region breaking records set since 1961 [44]. The abnormal precipitation in 2003 was mainly caused by an El Niño event leading to abnormal atmospheric circulation, with the western Pacific subtropical high being stronger and in an anomalous position. Meanwhile, global warming has amplified the frequency and severity of extreme weather events. These factors collectively led to flooding in northern China, while southern regions experienced high temperatures and droughts due to stable control by the subtropical high [45]. The average annual precipitation data for the study area in 2003, 2013, and 2023 were 1501.37 mm, 1071.02 mm, and 1036.59 mm, respectively. In the universal soil loss equation, rainfall erosivity is a pivotal factor influencing soil erosion. In years with heavy precipitation, rainfall intensity and concentration significantly increase, leading to a substantial rise in rainfall erosivity. This can lead to an escalation in potential soil erosion, which may cause soil conservation to be higher than in years with normal precipitation. This phenomenon reflects how ecosystems can more effectively conserve soil under high-precipitation conditions, with large areas of vegetation cover playing a role in soil erosion prevention. Compared to 2013 and 2023, soil conservation has returned to a normal level. This also indicates that static data from a single year cannot accurately judge ecological indicators; historical trends must be considered for comprehensive analysis. Therefore, dynamic and continuous research and monitoring are necessary for effective management and optimization strategies.

The significant fluctuations in the forest recreation index are primarily due to changes in the points of interest within the study area, including A-level scenic spots, cultural heritage sites, intangible cultural heritage, and traditional villages. The quantity of POIs within the study area rose from 8 in 2003 to 95 in 2013 and 324 in 2023. Additionally, the road network in 2023 was more developed than in 2003, providing more opportunities for forest recreation. First, considering the development background of the study area and various economic, social, and policy factors, the national and local governments have prioritized ecological conservation and tourism growth in the Dabie Mountain region, introducing a series of policies to support eco-tourism, cultural heritage protection, and the construction of traditional villages. For example, policy documents such as the “Revitalization Development Plan for the Dabie Mountain Revolutionary Old District” and the “Huai River Ecological Economic Belt Development Plan” have provided policy support for regional tourism development [42,46]. Second, the improvement of tourism infrastructure and significant enhancement of accessibility have driven the development and utilization of more POIs. Third, the study area is rich in cultural and tourism resources, with beautiful ecological environments. The rise of the development model integrating eco-tourism and culture has increased the attractiveness of POIs [47]. Fourth, economic growth and increased disposable income have driven market demand for tourism, leading to more scenic spot development and the declaration and confirmation of cultural heritage sites, traditional villages, and intangible cultural heritage projects. In summary, the significant increase in the forest recreation index is the result of multiple factors, including ecological protection policies, tourism infrastructure construction, the integration of ecology and culture, and market demand.

It is also important to note that although forest recreation is not a disorderly expanding indicator, it may have negative impacts. For example, poorly managed recreational activities can damage the ecological environment of ecological source sites, excessive visitors can lead to vegetation trampling and soil erosion, littering can pollute the environment, and improper construction and operation of entertainment facilities can destroy the integrity of the ecosystem. Unscientific road network planning can create numerous breakpoints in ecological corridors. However, eco-tourism should not be simply regarded as a negative activity. It is part of the forest ecosystem services, meeting people’s spiritual needs while enhancing ecological protection awareness, driving regional economic development, and improving the overall benefits of ecological source sites. A study using data from 32 countries and the European Union, employing various cointegration and clustering convergence techniques, found a certain coordinated relationship between tourism growth and environmental sustainability [48]. Therefore, through scientific planning and rational management, such as controlling the number of visitors, setting up dedicated recreational areas, and strengthening environmental protection publicity, the negative impacts of forest recreation on ecological source sites can be effectively reduced, achieving a balance between ecological protection and reasonable human utilization.

4.2. Construction and Restoration of Forestland ESP

In constructing the forestland ESP of the Huai River Source–Dabie Mountain area, the identification of ecological source sites is an extremely important step and forms the basis for the entire network pattern. In previous studies, various methods were used to identify ecological source sites. For example, in a study on constructing the ecological security pattern of the Huang-Huai-Hai Plain, the InVEST model-based ecosystem services (including water yield, soil conservation, carbon storage, and habitat quality) were primarily used to determine ecological source sites [49]. This method of identifying sources is simple and easy to operate, and it integrates multiple ecological functions. However, it also has limitations. The InVEST model mainly classifies and calculates based on land use types [50], and the indicators show the same values for the same land use type in space, failing to differentiate the ecological service quantities of the same land use type. Therefore, the source identification results are generally based on a single land use type, usually forests, but do not clearly identify key areas. In our study of the construction of the forestland ESP, we cannot adopt this method. The choice of source identification method will lead to significant differences in the results when constructing the ESP later, affecting the identification of corridors and, consequently, the entire ESP construction. In previous studies in this region, Ge mainly used the InVEST model ecosystem services (including soil retention, water yield, and carbon sequestration) to construct the ecological security system of the Dabie Mountain area, identifying 12 source sites and 52 corridors [51]. We integrated the six major ecological services of the forest area (carbon sequestration, soil conservation, water conservation, windbreak and sand fixation, biodiversity conservation, and forest recreation), combined with MSPA analysis, and considered the dynamics of different periods to optimize the method of extracting ecological source sites. The extracted source sites are more stable, and the method used is more conducive to grading based on differences, distinguishing key areas, and accurately identifying source sites.

We identified 38 ecological source sites and classified them based on landscape connectivity, extracted 91 ecological corridors, and graded them based on corridor centrality. Among the extracted ecological source sites, there is one first-level source site located in the eastern part of the study area, accounting for 56.40% of the total source site area; four second-level source sites, accounting for 17.35% of the total area, are distributed in the central region. These areas are situated within the primary conservation zones of the restoration strategy, with main ecological corridors passing through them. Not only do these areas have high vegetation cover, they also have good ecological environments and are key regions for biodiversity conservation. They provide habitats for species and are connected to other areas via ecological corridors, facilitating species migration and gene flow. These areas are the core regions of the ESP network. Therefore, in the future, human activities should be minimized, and natural regeneration and ecological conservation should be the main approaches. For example, measures such as closing mountains for forest regeneration, converting closed areas to other uses, and combining closure with nurturing can be implemented to protect the natural reproduction and growth of plants through closure. The scope and functional zoning of nature reserves should be scientifically delineated to establish a protected area system. Ecological restoration techniques should be employed to restore ecosystems to a reference state through artificial intervention, thereby enhancing their natural recovery capacity. Focusing on key ecological projects with such measures can restore vegetation and improve ecosystem services, significantly enhancing ecological benefits [52].

Ecological pinch points and ecological barriers identified through circuit analysis represent high-connectivity and high-resistance areas in the ecological network, respectively. It is not just the ecological barriers that require optimization through ecological restoration measures. In this study, some of the identified ecological pinch points, when observed in satellite imagery, are located near areas with signs of human activity, such as visible buildings and croplands. Ecological pinch points are not necessarily areas with high ecosystem service values; their formation is often due to the compression by surrounding high-resistance patches, leading to the concentration of species migration pathways in these areas. Therefore, these regions may be ecologically vulnerable and subject to human disturbance, and the areas where these ecological nodes are located need to be prioritized for attention and restoration to ensure the connectivity and stability of the ecological network. Ecological breakpoints, on the other hand, are intersections between major corridors and transportation routes. These breakpoints are areas in the ecosystem where ecological connectivity is interrupted due to human activities or natural factors, which can hinder species migration and gene flow, exacerbate landscape fragmentation, weaken ecosystem functions, reduce biodiversity, and threaten regional ecological security. Measures such as the construction of ecological passages [53], like culverts under roads and “green bridges” over roads, should be implemented to mitigate the impact of transportation on ecological corridors.

The ESP is not static; it undergoes dynamic changes influenced by natural factors and human activities. For instance, rapid urbanization leads to changes in land use patterns, which may cause the overall contraction of the ESP and alterations in the number and distribution of ecological corridors and source sites [54]. The emergence and restoration of ecological breakpoints also impact the configuration of the ESP. Ecological barriers and breakpoints, often caused by human activities such as the construction of transportation infrastructure, disrupt the connectivity of ecological corridors, exacerbate landscape fragmentation, and pose threats to species migration and ecological functions. However, ecological restoration measures, such as the construction of ecological passages and the restoration of ecological corridors, can effectively improve the connectivity of the ESP and thus optimize its patterns. Therefore, changes in the ESP are subject to disturbances from natural and human activities and rely on scientifically sound and rational ecological restoration strategies to achieve sustainable development. Future research should further focus on the long-term trends in the changes in the ESP and their impacts on ecosystem functions and services. The construction of the forest ESP not only aids in ecological protection and enhances ecosystem service functions but also promotes biodiversity conservation and provides ecological support for regional economic development. For example, urban development planning and tourism area planning should fully consider the ESP to achieve a win–win situation for ecological protection and economic development.

This study provides a comprehensive analysis of the forest ecosystem services and ecological security pattern in the Huai River Source–Dabie Mountain Ecological Reserve. However, its significance extends beyond this specific region. The methods employed in this study are widely applicable to research areas where forestland is the primary land use type or areas with extensive forest cover. Specifically, the indicators selected are among the most widely recognized forest service indicators, thereby minimizing potential controversy. The dynamic assessment of ecosystem services using time series data allows for a more accurate understanding of the ecosystem’s adaptive capacity to environmental changes, rather than being influenced by values exhibited in particular years. The quantitative analysis approach also enables the identification of differentiated areas within the same land use type, facilitating the accurate identification of key ecological source sites and corridors. This study integrates morphological spatial pattern analysis with circuit theory to provide a robust methodological framework for constructing ecological security patterns. This framework can be applied to different ecological contexts to optimize the spatial layout of ecological networks and enhance landscape connectivity. The restoration strategies proposed in this study, such as the construction of ecological corridors, protection of core areas, restoration of ecological nodes, and regional land development strategies, can serve as blueprints for balancing ecological conservation with urban and economic development. By adopting similar approaches, researchers and policymakers can develop more effective strategies for urban development, economic growth, and forest conservation and sustainable development in other similar regions.

4.3. Limitations and Future Prospects

• Limitations

First, the forest ecosystem service system established in this study, although covering the main functions, does not fully display all functions. For example, due to the lack of data support, the functions of forest nutrient retention and air purification were not included in the assessment system. This may lead to an incomplete coverage of ecosystem service functions.

Second, the ESP constructed in this study is mainly for the current period and does not include a comparative analysis of the ESP from a historical perspective. This may result in a lack of understanding of the evolutionary trends of the ESP and an inability to fully assess the long-term impacts of human activities and natural factors on the ESP.

• Future Prospects

First, in response to the limitations of ecosystem service function assessment, future research should further improve data collection. On the one hand, supplementing data support for key functions such as forest nutrient retention and air purification can provide a more scientific basis for ecological compensation and ecological restoration.

Second, to address the shortcomings in the historical study of ecological corridors, future research should combine historical land use data with ecosystem service assessment models to conduct spatiotemporal evolution analyses of the ESP in the study area. By comparing ESPs across different periods, the dynamic changes and driving factors of ecological corridors and nodes can be revealed, providing scientific references for the optimization and restoration of the ESP.

5. Conclusions

This study took the Huai River Source–Dabie Mountain Ecological Reserve as the research object. Through dynamic assessment of forest ecosystem service functions, combined with morphological spatial pattern analysis and landscape connectivity analysis, it scientifically explores the optimization of ecological source site extraction and constructs a forest ESP. The study found that the forestland ecosystem service index shows a spatial distribution that is higher in the northeast and lower in the southwest, and it has shown an increasing trend over time. Some indicators, such as windbreak and sand fixation, water conservation, forest carbon sequestration, and forest recreation, are all on the rise, indicating that the ecological protection measures in the region have achieved significant results. Through MSPA, 38 ecological source sites were identified; they are mainly distributed in the eastern and southeastern parts of the study area, with patchy distribution in the central part, and there are fewer in the western and northwestern parts. These source sites are the core areas of the regional ecosystem and are of great significance for maintaining biodiversity and the stability of the ecosystem. Spatial principal component analysis was used to construct a comprehensive resistance surface, and 91 ecological corridors were identified based on circuit theory. These corridors spatially connect the main ecological source sites, facilitating species migration and gene flow and enhancing the connectivity of the regional ecosystem. In addition, 13 ecological pinch points and 12 ecological barriers were identified. These nodes play a key role in the ecological network, with ecological pinch points being high-connectivity areas and ecological barriers being high-resistance areas that need to be optimized through ecological restoration measures. Through dynamic assessment of forest ecosystem service functions, we can more accurately understand the trends and current status of the regional ecosystem, providing data support for the formulation of scientific and rational ecological protection strategies. Identifying ecological source sites and constructing an ESP helps optimize the ecological space configuration and enhance the connectivity and stability of the ecosystem, and thus better protect biodiversity and enhance the ecosystem’s resilience to natural disasters and human activities.

This study proposes a “1 belt, 3 axes, 5 zones, and multiple nodes” restoration strategy, providing a scientific basis for regional ecological restoration. These strategies include natural regeneration, ecological conservation, and ecological passage construction, aiming to restore and enhance ecosystem functions through scientific and rational measures. Additionally, the study provides scientific guidance for regional ecological protection, urban development planning, and tourism development, promoting the coordination of ecological protection and economic development. By reasonably dividing functional zones, a balance between ecological protection and economic development can be achieved, enhancing the overall benefits of the ecosystem.

Despite the achievements of this study, there are still some limitations. Future research should further improve data collection and analysis methods and strengthen the understanding of the evolutionary trends of the ecological security pattern. This will enable better understanding and optimization of the ESP, providing more scientific guidance for regional ecological protection and sustainable development.