Ecological Security Pattern Construction in the Yellow River Water Replenishment Area of Gannan, China

Abstract

The northeastern margin of the Qinghai–Tibet Plateau is an ecologically fragile region that faces severe habitat fragmentation, which directly threatens regional biodiversity conservation and ecological security. To address this challenge, this study constructed a hierarchical “source-corridor-node” ecological network for the Gannan Tibetan Autonomous Prefecture by integrating Morphological Spatial Pattern Analysis (MSPA), the Minimum Cumulative Resistance (MCR) model, landscape connectivity assessment, and gravity modeling. The key results are as follows: (1) The Gannan Yellow River Water Source Replenishment Area contains 11 core ecological source regions, which are predominantly located in the southeastern regions of Diebu County and Zhouqu County, covering a total area of 4237.81 km²; (2) Ecological resistance analysis identifies high-resistance zones concentrated in anthropogenically active river valleys and urban belts (e.g., Hezuo urban area, Awanzang Town, and the G213 corridor). Low-resistance zones are predominantly situated in protected ecological enclaves (e.g., Zhagana Geopark and Gahai Wetland Reserve); (3) A total of 55 ecological corridors were identified, with a total length of 4355.77 km. Among these, 26 were classified as key ecological corridors, primarily distributed in Diebu and Zhouqu counties in the eastern part of Gannan Prefecture. These areas feature relatively concentrated ecological sources, and the key corridors play a critical role in connecting isolated ecological patches and maintaining regional ecological connectivity. (4) Across the entire territory of Gannan Prefecture, a total of 81 first-level ecological nodes and 53 second-level ecological nodes were delineated. As the core hub of the regional ecological network in Gannan Prefecture, Diebu County encompasses 60 First-level and 41 Second-level ecological nodes, respectively. The hierarchical “source-corridor-node” ecological network constructed in this study effectively enhances the overall landscape connectivity of the area. This progressive analytical framework—integrating source identification, corridor extraction, and node diagnosis—provides a scientific basis for biodiversity conservation, territorial ecological restoration, and sustainable development in high-altitude ecologically fragile zones.

1. Introduction

Ecological security is the foundation of human survival and the guarantee of sustainable development [1,2]. With the intensification of global climate change and continuous increases in human activity, the vulnerability of regional ecosystems has significantly increased, and ecological degradation risks exacerbated [3,4,5]. This not only threatens biodiversity but also severely endangers regional sustainable development [6,7]. Maintaining regional ecological balance and ensuring regional ecological security have become urgent priorities for regional sustainable development. Accordingly, the construction of ecological security patterns has emerged as a key research focus in the field of ecological protection and strategic planning [8,9,10,11,12].

Research on ecological security dates back at least to 1941, with an early focus on biodiversity conservation [13]. Subsequently, European countries proposed nature-oriented ecological networks, integrating ecological sources, corridors, and nodes into early “Point-Line-Area” ecological security pattern (ESP) models [14]. In the late 1990s, Chinese scholar Yu Kongjian proposed the theory of establishing ecological security patterns internationally [15,16]. Over years of development, this theory has gradually formed a construction methodology of “source identification-resistance surface construction-corridor extraction” [17,18]. This method can effectively enhance the identification of sources and corridors and strengthen the scientific rigor of ecological security pattern construction, and it has become a fundamental paradigm. For source identification, existing research often relies on natural reserves [19], network analysis [20], landscape connectivity indices [21], and Morphological Spatial Pattern Analysis (MSPA) [22], among others. The construction of a comprehensive ecological resistance surface primarily involves the selection, classification, and correction of multiple ecological resistance factors [23]. Various methods exist for extracting ecological corridors, including minimum resistance [24], circuit theory [25], graph theory [26,27], and agent-based modeling [28]. The Minimum Cumulative Resistance (MCR) model is a commonly used method in recent years, often combined with the gravity model [29] to screen key corridors and comprehensively construct the ecological network. Most existing research focuses on macro-scale ecological security pattern construction. However, traditional resistance surface construction often overlooks the terrain–vegetation coupling effect in high-altitude areas, and the grading system for ecological nodes lacks sufficient alignment with regional conservation needs.

The Gannan Tibetan Autonomous Prefecture, as an important ecological barrier on the northeastern edge of the Qinghai–Tibet Plateau, China, faces growing ecological and environmental challenges from accelerating urbanization. With infrastructure construction and tourism development, natural habitats within the region are gradually fragmenting. Problems such as the degradation of alpine meadows and the decreasing wetland area are becoming increasingly prominent, which pose threats to the water conservation function of the upper Yellow River and biodiversity conservation [30]. Excessive human activities, such as unregulated grazing, road network expansion, and tourism facility construction, further exacerbate ecosystem vulnerability [31] and hinder the overall improvement of regional ecological quality.

Research on ecological security patterns has largely focused on low-altitude regions, particularly urban areas and nature reserves [32,33]. In contrast, high-altitude fragile water recharge zones (critical for both local and downstream ecosystems) have received comparatively little attention. To address this imbalance, this study developed an integrated methodological framework that combined Morphological Spatial Pattern Analysis (MSPA), landscape connectivity analysis, spatial principal component analysis, the Minimum Cumulative Resistance (MCR) model, and the gravity model. This multi-model approach, forming a systematic “source identification—resistance mapping—corridor extraction—network construction” workflow, overcomes the limitations inherent in single-model applications. The framework not only enabled the accurate delineation of an ecological security pattern for the study area but also provided a replicable technical reference for ecological network planning in other high-altitude fragile water recharge regions.

2. Study Area and Methods

2.1. Overview of the Study Area

Gannan Tibetan Autonomous Prefecture (hereafter referred to as Gannan Prefecture) is located in the southwest of Gansu Province, China (Figure 1), spanning the transition zone between the Qinghai–Tibet Plateau and the Loess Plateau (33°06′–36°10′ N, 100°46′–104°44′ E). It borders Qinghai and Sichuan provinces, with an elevation ranging from 1100 to 4900 m, with a mean elevation exceeding 3000 m, exhibiting a topographic gradient decreasing from northwest to southeast [28].

Gannan Prefecture features complex topography, encompassing three major natural zones: the southern Mindie mountainous area, the eastern hilly and mountainous area, and the northwestern alpine meadow grassland. As a transitional zone from the Loess Plateau to the Longnan mountainous area, the region is marked by overlapping mountains and crisscrossing valleys. The region is characterized by diverse zonal plant associations, including subalpine coniferous forests, alpine shrubs, and alpine meadows. Forests dominated by spruce, fir, and birch are widely distributed in mountainous areas, while cold-tolerant grasses and sedges form extensive meadows in the northwestern high-altitude zones [34]. It has a continental monsoon climate, with an average annual temperature of about 3 °C and significant regional temperature differences. Annual precipitation ranges from 400 to 800 mm, mainly concentrated in summer. Water resources are abundant, including the Yellow River, Tao River, Daxia River, Bailong River, and over 120 tributaries, which belong to the source areas of both the Yellow River and Yangtze River basins. Vegetation coverage exceeds 60%, dominated by grassland (approximately 7.72 × 10⁵ hectares, accounting for 67.64% of the total area) and forest land, with usable grassland accounting for 94.2% [35]. It is an important pastoral area and ecological barrier in China [36].

2.2. Data Sources and Preprocessing

(1)

Land use data for Gannan Prefecture in 2023 were obtained from the annual China Land Cover Dataset (CLCD; http://doi.org/10.5281/zenodo.4417809) produced by Huang Ting’s team at Wuhan University, with a spatial resolution of 30 m. Administrative boundary data were collected from the 1:1,000,000 National Fundamental Geographic Information Database. Using ArcGIS 10.8, land use was reclassified into five landscape types: cropland, forest, shrub, grassland, and others.

(2)

Boundaries of Gannan Prefecture and its towns, as well as road and water system distributions, were obtained from BIGEMAP GIS Office (http://www.bigemap.com/) with a spatial resolution of 30 m. Road data include main roads, highways, and railways.

(3)

Remote sensing images and digital elevation data were obtained from the Geospatial Data Cloud (http://www.gscloud.cn/search), accessed on 15 July 2025, at a spatial resolution of 30 m. Slope was derived using the slope analysis tool in ArcGIS. Vegetation quality was represented by the Normalized Difference Vegetation Index (NDVI).

(4)

Forest cover and plant functional type data were obtained from the 1 km annual forest cover and plant functional type dataset for China from 1981 to 2023 (https://doi.org/10.5194/essd-18-1103-2026).

2.3. Research Methodology

This study proposed a comprehensive method for constructing an ecological network for high-altitude fragile ecosystems in the Gannan Yellow River Water Supply Zone, from the perspective of landscape patches. The research framework is illustrated in Figure 2. Based on the county scale, this study constructed an ecological network for Gannan Prefecture by identifying ecological sources, building ecological corridors, and dividing ecological nodes using the MSPA-Conefor-MCR model. By comprehensively identifying network elements, the ecological network was constructed, and the key areas for network construction in each township were analyzed.

2.3.1. Identification of Ecological Sources Based on MSPA and Conefor

MSPA can identify the spatial topological relationships between target pixel sets and structural elements [37]. In this study, forest land was used as the foreground data. The MSPA analysis tool in Guidos Toolbox was employed to identify seven distinct and non-overlapping landscape types within the study area, including core, islet, perforation, and edge, to determine ecological sources and construct the ecological network. In Conefor, the Probability of Connectivity (PC) and Integral Index of Connectivity (IIC) were analyzed. Ecological sources were identified by comprehensively considering source area size and the relative importance index [38].

2.3.2. Comprehensive Ecological Resistance Evaluation System and Spatial Principal Component Analysis

Based on previous studies [22,39,40,41] and the actual conditions of Gannan Prefecture, 6 indicators were selected from ecological attributes and human disturbance aspects as comprehensive ecological resistance evaluation factors: elevation, slope, land use, NDVI, distance from water systems, and distance from roads. Resistance values were assigned to establish a comprehensive ecological resistance evaluation index system (

Table 1. Comprehensive ecological resistance evaluation index system for the study area.

Type	Resistance Factor	Resistance Assignment
		1	2	3	4	5
Ecological Property	Altitude (m)	0–1067	1067–2135	2135–3203	3203–4271	4271–5339
	Slope (°)	0–17	17–35	35–53	53–71	71–89
	NDVI	0.6–1	0.2–0.6	−0.2–0.2	−0.6–0.2	−0.6–1
Human interference	Land use	Forest	Shrub	Grassland	Cropland	Others
	Distance from water (m)	0–6289	6289–12,579	12,579–18,869	18,869–25,159	25,159–31,449
	Distance from road (m)	10,691–19,472	6644–10,690	3743–6643	1605–3742	0–1604

). To eliminate interference from multicollinearity among factors, the Spatial Principal Component Analysis tool in ArcGIS was used to determine the weights of the resistance factors. By calculating the eigenvalues, contribution rates, and load matrix of each principal component, the weights for the ecological resistance indicator factors were obtained. The Weighted Overlay tool in ArcGIS was then used to construct the comprehensive ecological resistance surface.

2.3.3. Minimum Cumulative Resistance (MCR) Model

The Minimum Cumulative Resistance model primarily reflects passage accessibility by quantifying the cumulative cost incurred by animals when traversing different landscape types. The widely applied MCR model was optimized by Yu Kongjian [15] based on the work of Knaapen et al. [42]. The formula is as follows:

$$MCR = f \times \min \sum _{j=n}^{i=m}\left(D_{ij}\times R_i\right)$$

(1)

where MCR represents the minimum cumulative resistance value for diffusion from ecological source patch j to a certain point in space; f is a function of the product between MCR and the variable (D_ij × R_i); D_ij represents the spatial distance traveled from target source patch j to other source patch i; R_i represents the diffusion resistance coefficient of source patch i in a certain direction in space; and min represents the minimum accumulated resistance of the evaluation unit for different sources.

2.3.4. Gravity Model

The gravity model (GM) was used to quantify the importance of potential ecological corridors between ecological sources extracted by the MCR model and then identify important potential corridors and form the ecological security network. The larger the area of ecological sources and the lower the resistance, the higher the importance of the corridor. The gravity model calculation formula [43] is as follows:

$$G_{ab}={\displaystyle \frac{N_a·N_b}{D_{ab}^2}}={\displaystyle \frac{\left[\frac{1}{P_a}\times \ln S_a\right]\left[\frac{1}{P_b}\times \ln S_b\right]}{{\left(\frac{L_{ab}}{L_{max}}\right)}^2}}={\displaystyle \frac{L_{max}^2·\ln S_a·\ln S_b}{L_{ab}^2·P_a·P_b}}$$

(2)

where g_ab is the interaction force between ecological source patches a and b; N_a, N_b are the weight coefficients of ecological source patches a and b; D_ab is the standardized resistance value of the potential ecological corridor between patches a and b, respectively; P_a, P_b are the overall resistance values of source patches P_a, P_b, respectively; S_a, S_b are the areas of source patches S_a and S_b, respectively; L_ab is the cumulative resistance value of the potential ecological corridor between patches a and b; and L_max is the maximum cumulative resistance value among all potential ecological corridors between ecological source patches.

2.3.5. Ecological Node Classification

Ecological nodes represent critical structural elements within ecological networks, governing the flow of ecological processes among habitat patches [44,45]. Operationally, these nodes are identified as intersections of least-cost paths, convergence zones of multiple corridors, or junctions connecting corridors of different hierarchical levels. Due to their spatial configuration and functional significance, ecological nodes are typically situated in the most vulnerable segments of corridors, predominantly within ecologically fragile areas where landscape connectivity is highly susceptible to anthropogenic disturbance.

In this study, ecological nodes were identified by constructing a network feature dataset in ArcGIS. Intersections among important corridors, as well as between important and general corridors, were classified as first-level ecological nodes, while intersections between general corridors were designated as second-level ecological nodes [28]. Targeted management of these nodes can substantially mitigate the impacts of human disturbance on species migration, enhance connectivity among ecological sources, and contribute to the improvement of regional ecological environmental quality.

3. Results

3.1. Identification of Ecological Sources

Ecological sources refer to key habitat patches that serve as origins of species dispersal and ecological flows within the regional ecological network. These patches are characterized by high habitat quality, strong landscape connectivity, and critical ecosystem service functions (e.g., biodiversity conservation, water retention) [46].

Based on the current land use status of Gannan Prefecture, forest and water bodies were used as foreground data, with others as background data. MSPA was applied to classify the foreground elements into seven landscape types: core, islet, perforation, edge, etc. (

Table 2. Statistics of landscape types based on MSPA.

Landscape Type	Foreground (Forest)			Foreground (Water)
	Area (km2)	Proportion of Forest (%)	Proportion of the Study Area (%)	Area (km2)	Proportion of Water (%)	Proportion of the Study Area (%)
Core	4237.81	49.31	11.58	20.90	22.70	0.06
Islet	341.07	3.97	0.93	19.96	21.68	0.05
Loop	214.43	2.50	0.59	0.28	0.30	0.00
Bridge	1545.69	17.99	4.22	13.21	14.35	0.04
Perforation	310.86	3.62	0.85	2.29	2.49	0.01
Edge	640.17	7.45	1.75	8.28	8.99	0.02
Branch	516.25	6.01	1.41	9.35	10.16	0.03

). As the key region for ecological source distribution and a vital component of ecological network operation, the core area covered a total area of 4237.81 km², accounting for 11.58% of the study area. The bridge area, as a type with structural corridor characteristics in the ecological network, accounted for only 4.22% of the study area. This indicated a lack of connectivity between patches and hindered species migration. There were 14 forest land core areas with areas greater than 50 km² (Figure 3). These sources were further screened using Conefor 2.6 software, with a connection distance threshold of 500 m and a connection probability of 0.5. Ultimately, 11 core areas with an area greater than 50 km² and a relative importance index greater than 1 were determined as ecological sources (

Table 3. Ranking of the importance index of core area.

Ecological Source	Area (km2)	Importance Index		Relative Importance Index
		Integral Index of Connectivity	Probability of Connectivity
1	689.35	75.39	78.37	76.88
2	244.37	10.22	8.84	9.53
3	183.63	18.88	21.87	20.38
4	142.82	5.97	5.17	5.57
5	118.41	11.90	19.31	15.60
6	116.64	2.33	2.36	2.35
7	114.79	1.42	1.23	1.33
8	109.39	1.29	1.12	1.21
9	85.80	0.80	0.69	0.74
10	79.58	0.68	0.59	0.64
11	68.27	1.36	1.53	1.45

).

The spatial distribution of core ecological sources across the prefecture exhibited distinct hierarchical characteristics (as shown in Table 3). Ecological core area No. 1 had the largest area (689.35 km²), with the highest importance index (75.39) and overall connectivity index (76.88). This area corresponds to Zhouqu County, serving as a key carrier for water regulation in the Bailong River source area. Its relative importance index (78.37) confirmed the strategic position of this region within the ecological barrier on the northeastern edge of the Qinghai–Tibet Plateau. Ecological sources No. 2 (244.37 km²) and No. 3 (183.63 km²) were located in the karst landform zone of Lijie Town, Zhouqu County, and the alpine meadow area of Mu’er Town, Zhuoni County, respectively. Their probability of connectivity indices were 9.53 and 20.38, respectively, reflecting that Lijie Town, affected by terrain fragmentation, required enhanced corridor construction. In contrast, Mu’er Town, with its high meadow integrity (landscape index 0.91), has become a core transit station for animal migration. Our research showed that the top three ecological sources (No. 1–3) contributed 87.6% of the total connectivity value. Notably, source No. 5 (118.41 km²), although smaller in area, had a significantly higher probability of connectivity index (15.60) compared to source No. 4 (5.57), indicating that the planned 46.5 km ecological corridor in Shubu Township, Lintan County, had effectively enhanced the connection efficiency between this area and the Tao River water system.

3.2. Construction of the Comprehensive Ecological Resistance Surface

The ecological environment of Gannan Tibetan Autonomous Prefecture is inherently fragile. Under the combined stress of global environmental change and human activities, ecological degradation is increasingly severe. To address these challenges, this study incorporated both natural ecological attributes and human disturbance factors into a comprehensive ecological resistance model (Figure 4). At the level of ecological attributes, key considerations included the sensitivity of alpine wetland systems, the continuity of forest vegetation, and water conservation capacity, which were directly related to the stability of the regional ecosystem. For instance, wetland degradation weakens its functions in regulating runoff and purifying water quality, while fragmentation of forest patches can block wildlife migration corridors. At the level of human disturbance, the intensity of livestock activities, the density of transportation infrastructure, and the scope of tourism development were the main resistance factors. In particular, the construction of national and provincial highways traversing the plateau not only fragmented continuous habitats but also introduced external disturbances to impair ecological corridor functions.

According to the results of spatial principal component analysis (SPCA) on the ecological resistance evaluation factors in Gannan Prefecture (

Table 4. Principal component load matrix, cumulative contribution rate, and weight of each factor.

Resistance Factor	Principal Component(PC)
	PC1	PC2	PC3	PC4	PC5	PC6	Weight
DEM	0.546	0.221	−0.241	−0.154	0.005	0.756	0.163
LULC	0.452	−0.409	0.206	0.117	0.747	−0.122	0.173
NDVI	−0.435	0.299	−0.599	−0.021	0.601	0.028	0.168
Slope	−0.290	0.444	0.734	−0.133	0.284	0.285	0.173
Distance from water system	0.264	0.528	0.009	0.786	−0.026	−0.182	0.154
Distance from road	0.392	0.468	−0.030	−0.572	0.027	−0.547	0.169
Principal component eigenvalue	2.046	1.489	0.826	0.692	0.504	0.443	/
Cumulative contribution rate (%)	34.1	58.1	72.7	84.2	92.6	100.0	/

), the first six principal components collectively accounted for 100% of the total variance. The first principal component (PC1) contributed 34.1%, the second (PC2) 24.8% (cumulative 58.9%), and the third to sixth principal components contributed 13.8%, 11.5%, 8.4%, and 7.4%, respectively. According to the principal component load matrix, the weight values of each resistance factor indicated that topographic slope (0.173), LULC type (0.173), and distance from roads (0.169) had the most significant impact on ecological resistance. The load value for topographic slope in PC3 was as high as 0.734, indicating its deep regulatory effect on the regional ecological pattern.

Principal component analysis showed that the distance from roads exhibited significant correlations in both PC2 (load 0.468) and PC6 (load −0.547). Although its weight value was not the highest, the spatial distribution characteristics revealed a significant cutting effect of the road network on the continuity of alpine meadows. The density of fracture points along the G213 National Highway corridor positively correlated with the PC2 load value (r = 0.62), confirming the interference mechanism of transportation infrastructure on habitat connectivity. Furthermore, the Digital Elevation Model (DEM) had a load value of 0.756 in PC6 and a weight of 0.163, reflecting that elevation gradients indirectly regulate the ecological resistance pattern by influencing vegetation distribution.

Among natural factors, distance from water systems (weight 0.154) had a load value of 0.786 in PC4, indicating that the ecological sensitivity of river buffer zones was closely related to their spatial attenuation characteristics. The vegetation cover index (NDVI) had a load value of 0.601 in PC5 and a weight of 0.168, revealing the coupling relationship between declining NDVI in degraded grassland areas and increasing grazing pressure. Research confirmed that the ecological resistance surface constructed based on Table 4 can accurately identify key management units such as the core area of the Maqu Wetland (PC1 load 0.546) and the steep slope zone of the Tao River Canyon (PC3 load 0.734), providing a quantitative basis for prioritizing spatial ecological restoration projects.

Based on the ecological resistance factor system of Gannan Prefecture, the MCR model was employed. Combined with core resistance factors including the Digital Elevation Model (DEM), Land Use/Land Cover (LULC), NDVI, topographic slope, distance from water systems, and distance from roads (Table 4), the comprehensive ecological resistance surface was constructed using ArcGIS spatial analysis tools (Figure 4). The spatial distribution of ecological resistance across the prefecture showed a distinct pattern of high values in the northwest and low values in the southeast, aligning well with the topographic gradient from the Qinghai–Tibet Plateau to the Loess Plateau transition zone.

High-resistance areas are concentrated in valley zones with intensive human activity and urban development axes, such as the urban area of Hezuo City, Awanzang Town in Maqu County, and areas along National Highway G213. These regions were at a critical stage of synergistic development between agriculture/animal husbandry and tourism. Disturbances like road network expansion and overgrazing have exacerbated the fragmentation of alpine meadow patches, significantly degrading wetland ecosystem service functions. Low-resistance areas were mainly located in core ecological conservation zones such as the Zhagana Geopark in Diebu County and the Gahai Wetland Nature Reserve in Luqu County. Benefiting from strict protection policies and natural topographic barriers, these areas experienced lower human disturbance intensity, and the vegetation continuity and water conservation capacity were maintained in a good state.

The spatial differentiation characteristics in Figure 4 further revealed that high resistance in the northwest was directly related to terrain fragmentation in the Loess Plateau transition zone. However, low resistance in the southeast benefited from contiguous primary spruce forests and wetland systems. This resistance gradient difference highlighted the urgency of strengthening the construction of a protected area system. Especially in the ecotone between agriculture and animal husbandry, it was necessary to balance the relationship between ecological protection and community development and gradually enhance the overall connectivity of the regional ecological network through ecological compensation mechanisms and sustainable industrial models.

3.3. Extraction of Ecological Corridors

The gravity model is used to obtain the strength of interactions between ecological source areas. The higher the intensity, the higher the connection level between the ecological source sites. Referring to the research methodology of An Yi [47], potential ecological corridors in Gannan Prefecture were comprehensively constructed based on cost distance and cost path. Using the gravity model, an interaction heatmap was obtained (Figure 5), identifying a total of 55 ecological corridors. Corridors with an interaction force between patches greater than 1 were classified as important corridors, with a total of 26. Corridors with an interaction force not exceeding 1 were classified as general corridors, with a total of 29. Based on this, redundant and duplicate corridors were removed, and combined with the current situation in Gannan, a comprehensive ecological corridor was constructed.

Among them, the interaction strength between ecological source areas 1 and 2 was the highest. It basically covered the forest area and had high connectivity, making it a key distribution area for ecological corridors. Due to the relatively stable ecological environment, good vegetation continuity, and unimpeded species migration in this area, it should be given priority protection in ecological corridor planning to ensure that connectivity is not disrupted. Simultaneously, this area could serve as a core node of the ecological network to further enhance the stability and connectivity of ecological sources. Relatively speaking, the interaction strength between ecological source areas 4 and 11 was only 0.04 (threshold for important corridors = 1), indicating low connectivity. Spanning construction land, the landscape was severely fragmented, and species migration encountered greater resistance within this range. Frequent human activity disturbances, obvious vegetation fragmentation, and isolation effects led to weaker interaction forces between ecological sources. In constructing the ecological network, it was necessary to increase ecological nodes or adopt measures like green planting in this area to alleviate species migration barriers and enhance corridor connectivity. Additionally, through optimized corridor design, combined with the actual conditions of Gannan Prefecture, ecological nodes could be rationally arranged and suitable green plants selected for ecological restoration to improve the overall connectivity and stability of the ecological network. In addition, the interaction strength between ecological source areas 5 and 7 was 5.02, located in the ecological transition zone, with strong connectivity. However, due to complex terrain variations, there was a certain ecological risk. In the optimization process of the Gannan ecological network, corridor width could be increased or composite vegetation introduced to strengthen ecological connections in this area. Meanwhile, the interaction strength between ecological source areas 8 and 10 was 3.10, indicating a high degree of connectivity. These areas were primarily located at the edge of the nature reserve and were suitable as important nodes in the ecological corridor. They could serve as corridor connectors and ecological buffers within the overall network.

Overall, the ecological network layout of Gannan Prefecture should prioritize enhancing the ecological connectivity of fragmented areas while ensuring the connectivity of core areas. It should rationally arrange ecological nodes and corridors to facilitate organic connections between ecological source areas, thereby establishing a stable and efficient ecological corridor system.

3.4. Identification of Ecological Nodes

Ecological nodes are parts of the ecological network that require strengthened ecological management and control, generally located at intersections in ecologically weak areas. Combined with corridor analysis, a total of 81 first-level ecological nodes and 53 second-level ecological nodes were identified in the study area. Their spatial distribution exhibited significant regional differences, reflecting the ecological function positioning and protection needs of different counties and districts.

Diebu County was the core hub of Gannan’s ecological network, distributing 60 first-level ecological nodes (accounting for 74.1% of the prefecture’s total first-level ecological nodes) and 41 second-level ecological nodes (accounting for 77.4% of the prefecture’s total second-level ecological nodes). The county boasted a dense convergence of ecological corridors, ensuring strong habitat connectivity. It served as a core transitional zone linking the high-altitude pastoral area with the river valley agricultural area. The dense node layout strengthened the regional ecological barrier function, providing stable migration corridors for plateau species such as Panthera uncia and Pseudois nayaur. At the same time, it ensured water conservation and soil retention functions.

Auxiliary node distribution areas were Lintan County and Zhouqu County. The remaining 21 first-level ecological nodes and 12 second-level ecological nodes were all distributed in Lintan and Zhouqu counties. These two counties jointly bore the auxiliary support function of the ecological network. Relying on the intersection points of ecological corridors, Lintan County focused on strengthening ecological management and control in the agro-pastoral ecotone, aiming to reduce the impact of human activities on habitat fragmentation. Zhouqu County, as a transitional area between valley agriculture and mountainous forests, focused its node layout on mitigating geological disaster risks and enhancing ecological resilience. Through corridor connections between nodes, these two counties compensated for the weak links in the peripheral ecological network of Diebu County, ensuring the continuity of regional ecological flows (matter, energy, and species). The highly concentrated distribution of ecological nodes in Diebu County highlighted its irreplaceable ecological strategic value. The auxiliary support role of Lintan and Zhouqu counties provided a fundamental guarantee for regional ecological security.

4. Discussion

4.1. Spatial Pattern Optimization and Management Suggestions

The protection and restoration of ecological sources must consider both patch integrity and landscape connectivity, as ecological networks depend on continuous spatial linkages to sustain material and energy flows [48]. Our findings revealed that habitat connectivity between valley urban belts and plateau ecological conservation areas was severely constrained. Ecological corridors in the Tao River basin exhibited multiple fragmentation points due to tourism infrastructure development, potentially impeding species migration. To address these challenges, we proposed that Gannan Prefecture establish a hierarchical “source-corridor-node” ecological network [49], while optimizing ecological spatial structure based on natural ridgelines and river buffer zones [50].

This study confirmed that the resistance surface method based on the weighting system presented in Table 4 enabled accurate identification of differentiated management units [51]. The periphery of Gahai Wetland (a low-resistance area) and Kecai Town in Xiahe County (a high-resistance area) provided scientific support for zoning strategies, consistent with the differentiated governance approach adopted in the Hexi Region [50].

Based on the distribution of the ecological network and variations in resource endowments, the development positioning of counties in Gannan Prefecture can be categorized into three types. Type 1: Diebu County as an Ecological Conservation Core Area. This county hosted 60 first-level and 41 second-level ecological nodes, accounting for 74.1% and 77.4% of the prefecture’s totals, respectively, and served as a critical hub connecting the Yellow River water conservation area with the Minshan Mountains biodiversity hotspot. Strict protection of ecological source integrity is essential, and large-scale development should be prohibited [52], whereas natural landscapes such as Zhagana can support low-intensity ecotourism [33]. Type 2: Lintan–Zhouqu Ecological Economy Synergy Zone. Lintan County should focus on managing the agro-pastoral ecotone by promoting grassland-livestock balance systems [53], while Zhouqu County should strengthen ecological restoration in areas prone to geological disasters and promote the integrated development of ecological agriculture and rural tourism [50]. Type 3: Hezuo–Maqu Urban Construction and Restoration Zone. Hezuo City should embed green corridors and small-scale green spaces into urban construction to restore patches fragmented by road infrastructure [49], whereas Maqu County should prioritize grazing bans and wetland restoration projects to rebuild key nodes for waterbird migration [54].

4.2. Promoting Multi-Scale Ecological Network Construction

Current research on ecological networks often adopts a single spatial scale (e.g., administrative scale or landscape scale), which fails to capture the connectivity and heterogeneity inherent across multi-level scales (province–city–district–town), leading to disconnects between scales [55]. To address this gap, this study analyzed the spatial distribution of Gannan ecological network, enhanced cross-scale transmission, and implemented ecological network elements at the township level. By identifying key construction areas and potential ecological node sites, this study offered practical guidance for ecological restoration and conservation. This multi-scale downscaling approach is consistent with the framework of operationalizing ecological network optimization across administrative levels to improve planning feasibility [51].

Implementing ecological network construction at the county level facilitates the clarification of management responsibilities, enhances planning applicability, and supports sustainable development at the township level. This finding aligns with the conclusion that precise zoning management based on administrative scales strengthens the effectiveness of ecological security pattern implementation [56]. Future research should incorporate multi-scale landscape ecology principles to advance the integrated development of a “province–city–district–town” ecological network, supported by tiered management strategies [57,58].

4.3. Coupling Between Ecological Security Pattern and Forest Functional Type Heterogeneity

Current research on ecological security patterns has predominantly focused on structural identification and connectivity optimization, often overlooking the matching relationship between ecological network elements and underlying vegetation patterns. This oversight may lead to inconsistencies between the spatial configuration of ecological security patterns and actual ecosystem characteristics [59]. To address this gap, this study analyzed the compositional differences in forest plant functional types surrounding ecological sources, corridors, and nodes in Gannan Prefecture and further revealed the spatial coupling between key ecological network positions and vegetation transition zones (Figure 6). By identifying the spatial concentration of ecological nodes and critical corridors, this study clarified the relationship between ecological network distribution and vegetation heterogeneity, thereby providing scientific support for ecological restoration and conservation. This vegetation-coupled identification approach is consistent with the framework that integrates vegetation functional traits into ecological security pattern optimization to enhance ecological rationality [60].

The observed divergence in plant functional type composition across hierarchical ecological elements facilitates the validation of the identified ecological security pattern and strengthens both the scientific foundation and practical applicability of the ecological network. This finding supports the conclusion that incorporating vegetation functional characteristics into ecological security pattern identification improves the effectiveness of ecosystem conservation and restoration efforts [61]. Future research should integrate long-term vegetation dynamics and climate change scenarios to further explore the stability and adaptability of ecological networks in alpine transition zones, thereby informing targeted strategies for biodiversity maintenance and ecological flow enhancement [62,63].

4.4. Prospects and Limitations

This study integrated Morphological Spatial Pattern Analysis (MSPA), connectivity analysis, and the Minimum Cumulative Resistance (MCR) model to construct the ecological network of Gannan Prefecture, following the mainstream technical framework widely adopted in current research [18,33]. By strengthening source protection, corridor construction, and ecological management, it provided a scientific basis for biodiversity conservation and ecological security pattern optimization in high-altitude regions, echoing the research value demonstrated by Sun et al. in the Hexi high-altitude region [50].

Limitations are closely related to the adopted methods, which are common in similar studies. First, uncertainties exist in model operation and parameter configuration (e.g., no unified standard for MSPA threshold selection, and MCR model resistance factor weights rely on expert experience), which affect result reliability [50]. Second, the study used single-period static data without exploring the dynamic evolution of the ecological network, as static data can only capture the current state of ecological networks and fail to reflect long-term changes driven by land-use and climate dynamics [49]. Third, the cross-provincial characteristics of the study area also reduce the accuracy of connectivity analysis and simulation, a limitation also encountered in ecological network research in the Hexi Region [50].

Future research could prioritize optimizing the MSPA threshold standards and resistance factor weights in the MCR model by introducing objective quantitative methods such as Monte Carlo simulations to reduce subjective bias [50]; incorporating multi-period dynamic data to capture the spatiotemporal evolution of ecological networks [64]; and designing targeted migration pathways based on species-specific migration characteristics [33], as demonstrated in the habitat-specific corridor planning for egrets in Haikou. Such efforts will enhance the practicality of ecological networks and provide precise support for high-altitude regional biodiversity conservation.

5. Conclusions

In this study, an ecological network was constructed for the Gannan Tibetan Autonomous Prefecture in Gansu Province, China. Ecological sources were identified using the MSPA method, covering a total area of 4237.81 km², or 11.58% of the study region, whereas bridging areas accounted for only 4.22%, indicating limited patch connectivity. Based on classification and evaluation of ecological sources using Conefor 2.6 software, 81 first-level and 53 second-level ecological nodes were identified. Potential ecological corridors were generated from a comprehensive ecological resistance surface, and their relative importance was quantitatively assessed via the gravity model, resulting in the delineation of 26 key corridors and 29 general corridors. The overall ecological resistance exhibited a spatial pattern characterized by higher values in the northwest and lower values in the southeast, with human activities significantly intensifying ecological resistance.

This study enriched the methodological framework of ecological security pattern construction in high-altitude ecologically fragile areas and advanced landscape connectivity theory by clarifying the hierarchical structure of ecological networks and the functional role of nodes. These findings provide a theoretical reference for future research on ecological network optimization in similar regions.