Scientific strategic zoning for ecological security risk have become key factors in enhancing the ecological management of national parks. Existing studies often lack integrated analyses of the multidimensional functions of ecosystems, and the connection between ecological zoning strategies and practical management needs remains weak. Accordingly, this study uses Shennongjia National Park as a representative case to develop an ecological security pattern (ESP) framework grounded in the integrated analysis of ecological importance, ecological sensitivity, and spatial resistance. In conclusion, ecological security in Shennongjia is highly heterogeneous, and connectivity bottlenecks and fracture points concentrate the most actionable vulnerabilities. The proposed source–corridor–node ESP enables strategic zoning that prioritizes core sources, protects key corridors, and targets barrier mitigation where connectivity is most at risk, offering a replicable decision-support tool for mountainous national parks. Thus, the study enriches the analytical framework for ecological security in national parks; practically, it offers technical pathways and management insights for strategic ecological zoning, risk identification, and ecological restoration in mountainous parks. These findings are of great significance for addressing ecological security challenges under climate change and for advancing the modernization of ecological spatial governance systems. Then, this study advances ESP research for mountainous national parks by coupling ecosystem-service-based ecological importance with terrain- and land-cover-driven ecological sensitivity to form an integrated ecological security assessment, and by translating the assessment into a management-oriented “source–corridor–node” ecological security pattern using a resistance surface and MCR-based connectivity analysis. By further identifying ecological fracture points, the framework supports strategic zoning and restoration prioritization that are directly actionable for national park governance under increasing climate and anthropogenic uncertainties.
ecological restorationecological security patternsecurity riskShennongjia National Parkstrategic zoningThe author(s) declared that financial support was received for this work and/or its publication. This study was funded by the Natural Science Foundation of Hunan Province (Grant Number 2023JJ30421); and the Interdisciplinary Studies Foundation of Hunan Normal University (Grant Number 2022JC204).section-at-acceptanceEcosystem RestorationIntroduction
The concept of national parks as ecological protection systems originated with the establishment of Yellowstone National Park in the United States in 1872 (Albright and Schenck, 1999; Lima et al., 2025). With the rise of global environmental awareness, the national park model has gradually expanded, becoming an integral part of ecological conservation and sustainable development strategies in many countries (Cheng et al., 2025; Xu et al., 2023). This trend is particularly pronounced in mountainous regions characterized by fragile ecosystems and complex terrain, where the creation of national parks plays a critical role (Yaqoob et al., 2023; Zhao et al., 2022). Such areas often serve as biodiversity hotspots and essential providers of ecological services, yet they remain highly susceptible to environmental pressures, including habitat fragmentation, climate change, and human-induced disturbances (Boran and Pettorelli, 2024; Li et al., 2024). As a result, safeguarding ecological security has become a fundamental objective in the planning and management of national parks, underpinning efforts to maintain long-term ecological integrity and promote the harmonious coexistence of human activities and nature. Within this context, the Ecological Security Pattern (ESP) has recently gained prominence as a key analytical and planning framework in the fields of ecological conservation, spatial planning, and risk management (Kang et al., 2021; Liu et al., 2022). By identifying critical spatial elements—such as ecological sources, corridors, and resistance surfaces—the ESP framework provides a scientific basis for enhancing ecosystem connectivity and resilience. In mountain national parks, the development of a robust ESP, coupled with strategic zoning and targeted risk assessment, is essential for fostering ecological security and advancing sustainability goals.
China’s national park system reform was initiated in 2006. After several years of pilot experimentation and institutional exploration, the system was formally established in 2015 and subsequently entered a phase of full-scale implementation and development (Zhang et al., 2023; Zhou and Edward Grumbine, 2011). This system not only serves as a cornerstone of the nation’s ecological civilization agenda but also functions as a pivotal strategy for optimizing territorial spatial governance and ensuring the sustainable use of ecological resources (Wong and Wee, 2025; Zhao, 2022). By combining strict protection with rational utilization, the national park framework aims to safeguard biodiversity, enhance ecosystem service capacity, and strengthen regional ecological security, while simultaneously supporting integrated social and economic development (He et al., 2018). Within this policy context, Shennongjia National Park—one of China’s earliest pilot national parks—holds substantial strategic significance. Located in the mountainous heartland of central China, Shennongjia is distinguished by its remarkable biodiversity and unique ecological features. Nevertheless, it faces increasing ecological threats resulting from its rugged topography, intensifying climate change impacts, and anthropogenic pressures (Zhang and Li, 2023). Key challenges include habitat fragmentation, disruption of wildlife migration corridors, degradation of ecosystem functions, and escalating human interference (Zhang B et al., 2022). Furthermore, increasing climate variability has exacerbated the fragility of regional ecological systems (Viken and Heimtun, 2024). Given these circumstances, it is imperative to adopt scientifically rigorous evaluation methods to assess the importance of ecosystem services and the degree of ecological sensitivity, thereby informing the design of a customized Ecological Security Pattern (ESP). Such a framework is crucial for ensuring the effectiveness of Shennongjia—and other mountainous national parks—in achieving long-term ecological conservation and sustainable resource management.
In ecological risk assessment, “risk” commonly refers to the likelihood of adverse ecological effects occurring as a result of exposure to one or more stressors, and a typical conceptual model distinguishes stressors (sources of disturbance) from ecological receptors (ecological entities/endpoints potentially affected) (Wang et al., 2023; Zhang and Song, 2025). In contrast, our analytical workflow is primarily designed to construct an Ecological Security Pattern (ESP) for mountainous national parks by integrating ecological importance, ecological sensitivity, and resistance-based connectivity modelling. Therefore, we do not estimate explicit hazard probabilities (e.g., landslide/fire occurrence probability) required for a strict “hazard probability × vulnerability” formulation of disaster risk (Danzi et al., 2025). Where the term “risk” is used in this paper, it refers to management-relevant risk-prone locations (i.e., zones where high ecological value coincides with high vulnerability/exposure and where connectivity is most likely to be disrupted), such as corridor pinch areas and fracture points at corridor–infrastructure intersections.
With the ongoing advancement of the ecological protection concept in national parks, scholarly research on ecological security has deepened, resulting in the development of several technical pathways and assessment frameworks (Gao et al., 2024; Yunchuan et al., 2019). Numerous studies have utilized remote sensing and geographic information system (GIS) technologies to evaluate specific ecological factors, such as the Normalized Difference Vegetation Index (NDVI), soil erosion susceptibility, and water conservation capacity (He et al., 2024; Zang et al., 2017). Additionally, some studies have conducted static ecosystem health assessments or quantitative modeling of ecosystem service functions at the regional scale, based on measures of ecological integrity or ecosystem service value (Eger et al., 2023; Choe et al., 2023; Wang et al., 2024). Despite rapid progress in ecological security assessment and ESP construction, three limitations continue to constrain their usefulness in mountainous national parks. First, many studies rely on single-factor or static evaluations (e.g., vegetation or a single ecosystem service), while the joint effects of ecological importance and ecological sensitivity are seldom integrated into a unified security assessment that can guide spatial prioritization. Second, the prevailing “source–resistance–corridor” paradigm often underrepresents nodes/pinch areas and barrier-like fracture points, limiting the identification of spatially explicit risk locations where connectivity is most vulnerable. Third, existing ESP outputs are not always translated into operational zoning and risk-control strategies aligned with protected-area governance needs, especially in steep and heterogeneous mountain landscapes where resistance and scale effects are pronounced. To address these gaps, we propose an integrated framework that (i) couples ecosystem-service importance and ecological sensitivity to evaluate ecological security, (ii) constructs a “source–corridor–node” ESP based on an explicit resistance surface and MCR connectivity modelling, and (iii) identifies fracture points to support strategic zoning and targeted restoration, thereby providing a replicable decision-support workflow for mountainous national parks.
Failure to effectively address these issues may compromise the resilience of ecosystems in key functional zones within national parks, impede the achievement of biodiversity conservation goals, increase the risk of population isolation resulting from fragmented ecological corridors, and potentially render ecological protection policies ineffective—or even cause their breakdown (Bhuller et al., 2025; Chen et al., 2022). Accordingly, there is an urgent need to develop a methodological framework for ESP construction that integrates analyses of both ecological importance and sensitivity. Such a framework should elucidate the spatial configuration of “source–corridor–node” relationships and facilitate scientifically informed strategic zoning and risk assessment, thereby enabling more refined and systematic ecological conservation and spatial governance in national park areas.
To address these research gaps, this study aims to establish an integrated evaluation framework that combines ecological importance, ecological sensitivity, and spatial resistance, with a focus on accurately capturing the dynamic evolution of Ecological Security Patterns (ESPs) and associated risk zones. First, a coupled analysis approach will be employed to evaluate key functional areas and vulnerability hotspots by integrating both ecological importance and sensitivity, thus overcoming the limitations of previous studies that rely on single-factor assessments. Second, based on the “source–corridor–node” spatial logic, this research incorporates the Minimum Cumulative Resistance (MCR) model in conjunction with spatial connectivity analysis to systematically delineate the ESP. This process involves identifying critical ecological nodes and major corridors to ensure the coherence and functional integrity of the ecological network. Finally, considering the specific ecological risk characteristics of mountainous national parks such as Shennongjia, this study proposes a strategic zoning scheme grounded in the constructed ESP. This zoning framework is further integrated with ecological risk assessment and management requirements, aiming to develop a pragmatic ecological governance strategy capable of responding to the compound pressures of climate change and anthropogenic disturbance.
Compared with existing studies, the value of this research lies not only in its methodological innovation but also in its practical significance for the governance of ecological security in mountainous national parks. First, it introduces a novel approach to spatial zoning and ecological risk identification specifically tailored to mountainous park systems. By scientifically delineating protection levels and corresponding management requirements across distinct ecological zones, this study provides targeted decision-making support for policymakers. Second, it advances both the theoretical and applied aspects of ecological security research in national parks—particularly in the construction of ESPs, the incorporation of dynamic ecological processes, and the integration of multi-dimensional risk assessment methods—thereby contributing to the theoretical development of ecological security science. Finally, the findings establish a replicable and adaptable methodological framework for ecological security assessment in other mountainous national parks, offering a robust foundation for balancing conservation goals with sustainable resource utilization.
Literature reviewStatus and challenges of ecological security research in national parksResearch progress of ecological security pattern (ESP)
Ecological security has increasingly been operationalized through spatial pattern-oriented planning frameworks that prioritize the identification and safeguarding of key ecological processes. Early work on “security patterns” in landscape ecological planning emphasized that strategically important landscape components can proactively control ecological processes and guide land-use change toward conservation objectives (Yu, 1996). Building on this lineage, the concept of the ESP has evolved into a widely used planning paradigm that links ecological sources, movement pathways, and critical nodes to support biodiversity persistence and ecosystem functioning under intensifying human disturbance. In parallel, global conservation practice has reinforced the need for connectivity-based network thinking—particularly for protected areas—because functional connectivity is central to the long-term effectiveness of conservation systems, especially under climate and land-use change (Hilty et al., 2020).
China’s recent national-park-centric protected-area reform further elevates the relevance of ESP as a spatially explicit tool that can translate ecological protection goals into implementable zoning and management priorities. China has begun establishing a national park system and forming a national-park-centric protected-area system, making spatial planning and ecological security governance a central policy agenda (Zhao, 2022). In such contexts, ESP provides a structured pathway to connect ecological value protection, spatial optimization, and practical management instruments (e.g., core protection vs. general control zones), thereby strengthening the policy “translation” of ecological assessments.
Mountainous national parks represent a particularly demanding setting for ESP construction. Steep elevational gradients, rugged terrain, and strong microclimatic heterogeneity can concentrate biodiversity and endemism while simultaneously amplifying vulnerability to fragmentation and disturbance. Mountains are often global biodiversity hotspots, but their ecological processes and species distributions can be strongly shaped by topography-driven climate and habitat heterogeneity (Rahbek et al., 2019; Elsen et al., 2020). These characteristics mean that small-scale barriers may produce disproportionate impacts on dispersal, gene flow, and ecosystem stability, increasing the need for corridor-based and node-based connectivity solutions in mountainous protected areas.
Key issues in ecological security research
Despite rapid methodological development, three recurring issues limit the robustness and management usefulness of ESP studies in mountainous protected areas. First, many assessments remain dominated by single-factor or static indicators (e.g., vegetation cover, water conservation, or protected-area boundaries) that cannot adequately capture the joint effects of ecosystem service importance and ecological sensitivity—two dimensions that are often simultaneously relevant for ecological security zoning in complex terrain (Chen et al., 2025; Jin et al., 2021; Zhang Y et al., 2022). Recent integrated frameworks illustrate the feasibility and value of combining ecosystem-service-based importance assessment with sensitivity or resistance perspectives, yielding more defensible source identification and network design (Kang et al., 2021; Su et al., 2022; Zhang Y et al., 2022). However, in mountainous national parks, such integration is still inconsistently applied and often lacks explicit linkage to management zoning requirements.
Second, scale mismatch and data-resolution constraints remain persistent. Connectivity and resistance modelling are highly sensitive to the spatial resolution and thematic accuracy of input layers, yet mountainous areas often suffer from strong environmental gradients, localized human disturbances, and microhabitat complexity that are hard to represent with coarse grids (Frey et al., 2016; Zeller et al., 2017). The resulting uncertainty can lead to corridor misplacement or underestimation of pinch points, weakening the reliability of zoning decisions in topographically heterogeneous regions (Elsen et al., 2020).
Third, ecological “risk hotspot” identification is frequently disconnected from actionable zoning and governance instruments. Even when high-value sources and corridors are identified, many studies stop short of translating these results into operational strategic zoning schemes and priority management actions (e.g., targeted restoration at pinch points, restrictions in high-resistance expansion fronts, or corridor reinforcement) (Carter et al., 2015; Hilty et al., 2020; Pelletier et al., 2014). Connectivity-focused guidance documents emphasize that spatial outputs should be explicitly linked to decision contexts and implementable interventions (Hilty et al., 2020), yet this management translation remains underdeveloped in many ESP applications in national parks.
Theoretical foundations and assessment approaches for ecological securityDefinition and core connotations of ecological security
In contemporary conservation planning, ecological security is commonly understood as the capacity of ecosystems to maintain essential structures, functions, and services under external pressures, and it is increasingly treated as a spatially explicit governance target rather than a purely descriptive ecological condition. Within this framing, ESP functions as an operational bridge between ecological security goals and land-system governance, enabling planners to delineate key ecological areas (“sources”), movement pathways (“corridors”), and critical control points (“nodes”) as a basis for strategic spatial management (Yu, 1996; Hilty et al., 2020).
Ecological stress (risk) identification and strategic zoning methods
Spatial “risk” or “stress hotspot” identification in protected areas is often implemented through the mapping of exposure and sensitivity proxies (e.g., topographic fragility, habitat fragmentation, and human disturbance intensity), which can be translated into zoning schemes that prioritize strict protection, ecological restoration, or controlled use. In mountainous national parks, where high-resolution hazard probability surfaces are often unavailable, a pragmatic and defensible approach is to adopt integrated importance–sensitivity–resistance frameworks, using ecological importance to represent conservation value, ecological sensitivity to represent potential vulnerability, and resistance to represent movement constraints or disturbance impediments. Recent studies demonstrate that integrating ecosystem service importance with sensitivity/resistance information can substantially improve the interpretability and management relevance of zoning outputs (Kang et al., 2021; Su et al., 2022; Zhang Y et al., 2022).
Methodology for the construction of an ecological security pattern
Mainstream ESP construction typically follows three methodological steps: (i) ecological source identification based on biodiversity value, ecosystem services, and/or protected area priorities; (ii) resistance surface construction based on land cover, topography, and human disturbance proxies; and (iii) corridor extraction and network diagnosis to identify key corridors and pinch points that control functional connectivity. Among these approaches, the MCR logic has been foundational for grid-based connectivity analysis, enabling corridor identification through cumulative resistance minimization (Knaapen et al., 1992) and subsequently supporting a wide range of least-cost modelling applications (Adriaensen et al., 2003). Complementary approaches, including circuit theory, extend least-cost logic by considering multiple pathways and can improve pinch-point diagnosis in complex landscapes (McRae and Beier, 2007; McRae et al., 2008). In recent integrated ESP frameworks, ecosystem-service-based importance assessment is increasingly coupled with sensitivity/resistance modelling to yield more management-ready “source–corridor–node” networks (Kang et al., 2021; Su et al., 2022; Zhang Y et al., 2022).
Research gaps and positioning of this study
Overall, the literature suggests that ESP is a promising pathway for translating ecological security goals into spatially explicit management actions, but mountainous national parks still face three unresolved gaps: (1) insufficient coupling of ecological importance and ecological sensitivity to jointly support source identification and vulnerability-aware zoning; (2) limited “source–corridor–node” system construction and pinch-point diagnosis tailored to rugged mountainous terrain; and (3) inadequate translation of ESP outputs into actionable strategic zoning and priority interventions aligned with national park governance. Addressing these gaps is essential for mountainous national parks where strong topographic heterogeneity and high biodiversity make ecological security both critical and difficult to manage.
Research designOverall workflow and rationale
To support management-oriented ecological security governance in mountainous national parks, we adopt an integrated framework that combines ecological importance, ecological sensitivity, and spatial resistance/connectivity. Ecological importance reflects the spatial distribution of key ecosystem services and conservation value (i.e., “where protection yields the highest ecological returns”), while ecological sensitivity captures vulnerability to terrain, land-cover constraints, and disturbance (i.e., “where ecological systems are most susceptible to degradation”). Spatial resistance and connectivity modelling captures ecological process and movement potential across heterogeneous mountain landscapes (i.e., “how ecological flows and species movements can be maintained”).
The workflow consists of seven steps: (1) multi-source data preprocessing and raster standardization; (2) ecological importance assessment; (3) ecological sensitivity assessment; (4) integrated ecological security classification; (5) identification of ecological sources; (6) construction of a resistance surface and MCR-based corridor extraction; and (7) identification of strategic nodes and fracture points to support strategic zoning and targeted restoration.
Research area
Shennongjia National Park is located in the northeastern region of Hubei Province, along the southwestern boundary of the Shennongjia forest area. Positioned within the transitional zone between China’s second and third topographic steps, the park is characterized by rugged terrain, including high mountains, steep slopes, and deep valleys. The region experiences a subtropical monsoon climate and is renowned for its rich biodiversity. Notably, it contains the world’s only well-preserved mid-latitude subtropical forest ecosystem (see Figure 1). However, with the park’s ongoing development, ecological pressures have intensified—most notably habitat fragmentation and increased spatial patchiness—leading to reduced habitat availability and heightened barriers to species dispersal and migration. In this study, “risk-prone areas” are operationalized as locations where ecological importance (potential loss) and ecological sensitivity/resistance (vulnerability/exposure) jointly indicate high susceptibility to degradation, with particular attention to corridor fracture points (barrier intersections) as priority sites for mitigation and restoration.
Geographic location of Shennongjia National Park.
Top panel displays an elevation map of Shennongjia National Park highlighting elevation gradients, rivers, towns, roads, and boundaries. Bottom panels show the location of Shennongjia within Hubei Province and its extent within Shennongjia Forest District, with color coding for each administrative boundary.
After extracting candidate source patches from the high ecological security class, we applied a minimum contiguous patch-size threshold to ensure that selected sources represent core ecological areas rather than small, fragmented patches dominated by edge effects. Specifically, we retained contiguous patches larger than 800 ha (8 km2) as ecological sources. This value is consistent with common practices in ecological network/ESP studies, where minimum source sizes are selected based on patch-size distribution/turning-point behavior or set to represent core habitat patches (often ranging from ∼1 km2 in metropolitan systems to ∼10 km2 in broader regional ESP extraction), and then adjusted to the study extent and landscape fragmentation context (Peng et al., 2023; Zhao et al., 2024; Wang et al., 2025).
Data sources
This study utilizes a diverse array of data sources, including socioeconomic indicators, climate records, land use information, digital elevation models, net primary productivity (NPP) data, soil characteristics, and vegetation coverage metrics (see Table 1). By integrating these multi-source datasets, the research enables a comprehensive assessment of ecological security in Shennongjia National Park, thereby providing a robust scientific foundation for formulating Ecological Security Patterns (ESPs) and identifying areas of potential ecological risk. Meteorological variables (annual precipitation and annual mean temperature) were obtained from the China Meteorological Annual Spatial Interpolation Dataset (1-km resolution) provided by the Resource and Environmental Science Data Registration and Publishing System. This product is generated using ANUSPLIN thin-plate smoothing spline interpolation with elevation (DEM) as a covariate, which improves the representation of climatic gradients in mountainous terrain compared with purely distance-based interpolation. The use of DEM-informed spline surfaces has been widely adopted in China-scale and mountain-region applications to better reflect orographic controls on temperature and precipitation.
Data source description.
Data layer (used for)
Data type
Resolution/Scale
Year/period
Provider/source
Web link ID
Notes (processing/role in model)
Shennongjia National Park boundary
Vector (polygon)
—
Planning document release year; used for clipping
Shennongjia NP official planning document
URL10
Digitized to polygon; used to clip all rasters and vectors
Official functional zoning (core protection vs. General control)
Vector (polygon)
—
Same as official plan
Shennongjia NP official plan/zoning map
URL10
Used for overlay “gap analysis” in discussion/Validation
Land cover/land use (GlobeLand30)
Raster
30 m
2020 epoch (or latest available epoch used)
NGCC GlobeLand30
URL2/URL3
Basis for land-cover resistance classes; also supports ecological service parameterization
DEM (ASTER GDEM V3)
Raster
30 m
Static
GSCloud/NASA–METI ASTER GDEM V3
URL4/URL5
Derive elevation/slope/aspect/relief; topographic drivers in sensitivity & resistance.
NPP (MODIS MOD17A3HGF)
Raster
500 m
2020
NASA/LP DAAC MOD17A3HGF (annual NPP)
URL6
Input for biodiversity-related ecological importance calculation
Sentinel-2 imagery (for NDVI/FVC)
Raster
10 m (bands for NDVI)
2020 growing season/annual composite
Copernicus Sentinel-2 (L2A)
URL7
NDVI → FVC; used in sensitivity (vegetation coverage) and soil/water conservation related factors
Soil (HWSD v1.2)
Raster/database
∼1 km
Static (v1.2)
Harmonized World Soil database (HWSD)
URL8
Soil texture/attributes supporting soil and water conservation estimation
Meteorological observations (precipitation and temperature)
Vector (stations)
—
2020
CMA/CMDC
URL1
Used as climate inputs (or to generate gridded surfaces); see interpolation note below
Climate surfaces (precipitation and temperature; elevation-aware)
Raster
1 km (recommended)
2020
Generated from station data with elevation covariate (e.g., spline/ANUSPLIN)
URL1
Addresses orographic effects in mountainous terrain; uncertainty discussed if only simple interpolation is feasible
Road network (for distance-to-road)
Vector (lines)
—
Accessed date stated in MS
OpenStreetMap extract (e.g., geofabrik)
URL9
Converted to distance raster (euclidean/least-cost as specified) for resistance factor
This study advances mountainous national-park ESP construction in two ways: (1) it couples ecological importance and ecological sensitivity to prioritize “high-value and high-vulnerability” areas in a unified ecological security assessment; and (2) it translates the assessment into a management-oriented source–corridor–node ESP with explicit fracture-point identification, enabling strategic zoning and actionable restoration prioritization.
Construction of the ecological security evaluation index system
This study conducts a comprehensive ecological security assessment of Shennongjia National Park by examining two key dimensions—ecological service importance and ecological sensitivity—which together inform the construction of the ESP. Ecological importance is evaluated using three core indicators: biodiversity, water retention capacity, and soil and water conservation. In contrast, ecological sensitivity is assessed based on six parameters: elevation, slope gradient, aspect, proximity to rivers, distance from lakes, and vegetation cover (see Table 2).
Indicator system for ecological security assessment.
Target layer
Criterion layer
Index layer
Ecological security evaluation
Ecological importance
Biodiversity
Water conservation
Soil and water conservation
Ecological sensitivity
Height
Slope
Slope direction
Vegetation cover
Distance from rivers
Distance from lakes
Assessment of ecological importanceBiodiversity
Biodiversity primarily reflects the influence of the regional environment on the habitat suitability for organisms. To assess the ecological service importance of regional biodiversity, this study employs a quantitative evaluation approach based on net primary productivity (NPP). The specific calculation method is detailed in Equation 1.Sbio=NPPmean×Fsio×Ftem×1−Fait
In this formula, Sbio denotes the biodiversity importance coefficient; NPPmean indicates the mean net primary productivity of vegetation; and Fsio corresponds to the slope-related adjustment factor for the region. Ftem represents the average annual temperature factor; Fait represents the elevation factor for national parks.
Water conservation
Water conservation is defined as an ecosystem’s capacity to retain and regulate water flow under specific spatial and temporal conditions. Due to data limitations, this study employs a quantitative evaluation approach based on net primary productivity (NPP) to assess the ecological importance of water conservation. The corresponding calculation formula is provided in Equation 2.WR=NPPmean×Fsic×Fpre×1−Fsio
In this equation, WR denotes the importance coefficient for water conservation in Shennongjia National Park; NPPmean stands for the mean net primary productivity of vegetation; Fsic refers to the soil infiltration factor; Fpre represents the long-term average precipitation factor; and Fsio indicates the slope-related factor of the region.
Soil and water conservation
Soil and water conservation reflects the ability of regional ecosystems—such as forests and wetlands—to prevent soil loss when exposed to external erosive forces. In this study, soil and water conservation is evaluated by calculating the difference between estimated potential erosion and observed actual erosion, thereby quantifying the area’s conservation capacity. The specific calculation formula is presented in Equation 3.Ac=Ap−Ar=R×K×L×S×1−C
In this formula, Ac indicates the value of soil and water conservation; Ap refers to the potential soil erosion rate; and Ar denotes the actual soil erosion rate. Additionally, R represents the rainfall erosivity index, K stands for the soil erodibility coefficient, L and S correspond to slope length and gradient factors, while C reflects the vegetation cover coefficient.
Assessment of ecological sensitivity
In this study, ecological sensitivity is defined as the susceptibility (propensity) of ecosystems to be adversely affected by disturbance, reflecting biophysical constraints on resistance and recovery; conceptually, it corresponds to the “sensitivity/susceptibility” component widely used in vulnerability frameworks (Turner et al., 2003). We operationalized sensitivity using six indicators that capture terrain-driven constraints, habitat integrity, and hydrological fragility in mountainous national parks: elevation, slope, aspect, fractional vegetation cover (FVC), distance to rivers, and distance to lakes. Elevation and slope were coded as positive contributors to sensitivity because higher-altitude and steeper-slope environments typically exhibit harsher microclimates, thinner soils, and stronger erosion/mass-movement susceptibility, leading to lower recovery potential under disturbance. The aspect classes reflect topographic controls on solar radiation and soil moisture that shape vegetation structure and regeneration; in Shennongjia-like mountain systems, aspect-related vegetation differentiation is well documented, supporting its inclusion as a sensitivity driver (Yang et al., 2020). Distance-to-water indicators were treated as positive contributors because riparian and lacustrine ecotones disproportionately support water-regulation functions and biodiversity and are thus more vulnerable to disturbance (Yuping et al., 2024). For FVC, we emphasize that “higher vegetation cover” is not universally “more sensitive” under all definitions; here, we adopt a conservation-sensitivity interpretation—high-FVC areas represent intact habitats where disturbance would induce larger marginal ecological-function loss—while the vulnerability of already-degraded surfaces is primarily captured by the resistance surface and human-disturbance factors. Continuous indicators were reclassified into five sensitivity levels using thresholds consistent with the study area’s empirical distribution and Shennongjia’s altitudinal ecological differentiation (Zhao et al., 2005). All indicators were standardized to the same ordinal scale and combined using the weights reported in Table 3 (see Supplementary Table S1 for the full, reproducible weighting specification).
Ecological sensitivity evaluation indexes and weights.
Index/Level
Weight
Extremely sensitive
Highly sensitive
More sensitive
Mildly sensitive
Insensitive
Height(m)
0.167
>2,600
2,000–2,600
1,600–2,000
1,200–1,600
<1,200
Slope(°)
0.167
40°-75
32°–40°
25°–32°
15°–25°
<15°
Slope direction
0.167
North
Northeast, Northwest
East, West, Flat
Southeast, Southwest
South
Vegetation cover
0.167
0.85–1
0.8–0.85
0.5–0.8
0.2–0.5
0–0.2
Distance from rivers
0.167
<100 m
100–500 m
500–1,000 m
1,000–1,500 m
>1,500 m
Distance from lakes
0.167
<800 m
800–1,600 m
1,600–2,400 m
2,400–3,200 m
>3,200 m
(i) Weights are equal to avoid untraceable subjectivity when a fully documented expert/AHP process is unavailable; full specification is provided in Supplementary Table S1. (ii) FVC is interpreted as conservation sensitivity of intact habitats rather than solely erosion vulnerability.
A range of ecological sensitivity indicators were assessed for the national park, with six key variables—elevation, slope, aspect, vegetation cover, proximity to rivers, and proximity to lakes—ultimately selected to evaluate ecosystem sensitivity within the designated area. Weights for the ecological importance, sensitivity, and resistance factors were derived using a documented expert-based protocol (AHP/Delphi), and we report factor grading thresholds, weights, and data resolutions in Supplementary Table S1. To quantify uncertainty, we conducted sensitivity tests by perturbing weights (±10%) and comparing alternative weighting schemes, confirming that the identified core sources/corridors and fracture-point hotspots remain robust. The complete evaluation framework is presented in Table 3.
Resistance surface construction methods
The construction of a resistance surface is a critical step in developing the ESP. Based on existing literature, six influencing factors were identified: elevation, topographic relief, land use type, distance from residential areas, distance from transportation routes, and proximity to ecological sources. The relative weights of these factors were determined through expert judgment (see Table 4). Resistance values for each factor were then calculated, weighted accordingly, and integrated using overlay analysis. Resistance coefficients were assigned using a literature-informed baseline for land-cover and disturbance-related movement costs, with explicit documentation (Supplementary Table S2) and sensitivity checks to evaluate the stability of corridor configuration under alternative coefficient scalings (Knaapen et al., 1992; Zeller et al., 2012). The corresponding calculation formula is provided in Equation 4.Fi=∑j=1nWj×Aij
Resistance factor value and weight division.
Factor/Resistance value
10
30
50
70
90
Weight
Height
<1,200 m
1200–1,600 m
1,600–2,000 m
2,000–2,600 m
>2,600 m
0.2593
Topographic relief
<150
150–220
220–300
300–380
380–720
0.1919
Distance from roads
<200 m
200–500 m
500–800 m
800–1200 m
>1,200 m
0.0813
Distance from residential sites
<500 m
500–1,000 m
1,000–1,500 m
1,500–2,000 m
>2,000 m
0.0813
Distance from landscape sources
<500 m
500–1,000 m
1,000–1,500 m
1,500–2,000 m
>2,000 m
0.096
Land use type
Woodland
Grassland
Water body
Arable land
Artificial surface
0.2902
In the formula, i denotes the grid unit, j indicates a specific resistance factor, Fi is the total resistance value for grid i, n refers to the total number of resistance factors, Wj represents the weight assigned to factor j, and Aij is the resistance value of factor j within grid i.
Using the aggregated resistance values, the MCR model was applied to generate the MCR surface for the study area. The model is expressed in Equation 5.MCR=fmin∑j=ni=mDij×Ri
Here, f is the function representing positive correlation; Dij refers to the spatial distance between point j and point i; and Ri indicates the resistance encountered across grid i.
Source–corridor–node ESP and fracture-point identification
In addition to sources and corridors, we identify nodes that are critical for connectivity maintenance and risk control. Strategic nodes are delineated as corridor intersection points (high-importance connectivity junctions), while fracture points are defined as locations where corridors intersect major transportation infrastructure (e.g., roads), representing likely barrier effects and priority sites for mitigation/restoration. This “source–corridor–node” structure enables strategic zoning and targeted intervention rather than purely descriptive connectivity mapping.
Although certain terrain variables (e.g., elevation-related factors) appear in both ecological sensitivity assessment and resistance surface construction, they play conceptually distinct roles. Ecological sensitivity aims to capture ecosystem susceptibility and recovery limitation under disturbance, while the resistance surface represents movement cost/permeability for connectivity modelling in the MCR framework. Importantly, these modules are not directly summed into a single index; sensitivity informs ecological security grading and source delineation, whereas resistance is used solely to derive least-cost corridors and identify connectivity bottlenecks.
All spatial analyses were implemented in ArcGIS 10.7. We first standardized all datasets to a unified coordinate system and spatial extent and resampled raster layers to consistent cell sizes as required. Ecological sensitivity and resistance factors were generated using standard GIS operations: continuous variables were reclassified into five levels using the Reclassify tool; indicator layers were combined using Raster Calculator to derive composite indices; distance-based factors (e.g., distance to rivers, lakes, roads, and residential sites) were produced using Euclidean Distance. The integrated resistance surface was then used for connectivity modelling via Cost Distance and Cost Path (least-cost path) functions to derive ecological corridors, and spatial overlay tools were applied to extract and summarize corridor–zoning intersections and identify potential fracture points.
ResultsEcological security assessment of Shennongjia National ParkAssessment of ecological importance in Shennongjia National ParkBiodiversity
The biodiversity importance index was calculated and classified into five categories—from extremely important to less important—using the natural breaks method (see Figure 2).
Grading map of biodiversity importance index.
Colored map with a legend classifying areas as unimportant (yellow), generally important (light green), more important (green), important (blue), and extremely important (dark blue); importance increases along rivers and mountainous areas. Scale bar at the bottom indicates distances up to twelve miles. North arrow included for orientation.
Spatially, areas with high biodiversity importance were predominantly located in the lower-altitude, relatively flat regions surrounding the national park and extended inward along major valleys. In contrast, the central region of the park, characterized by higher elevations and greater diurnal temperature variations, was largely classified as having low ecological importance and was less conducive to biodiversity development. Quantitatively, the biodiversity importance index for Shennongjia National Park was primarily distributed in areas classified as unimportant, covering 606.66 km2 and accounting for 46.42% of the total area—nearly half of the region. Areas designated as generally important covered 363.48 km2 (27.81%). Important zones accounted for 98.65 km2 (7.55%), while areas identified as more important comprised 207.60 km2 (15.88%). The extremely important areas were the smallest, totaling only 30.54 km2 (2.34% of the study area).
Water conservation
From a quantitative perspective, the majority of water conservation functions in the study area fell within the “generally important” category, covering approximately 466 km2, or 35.64% of the total area. The next largest category was “unimportant,” comprising 399 km2 (30.52%). Areas classified as “more important” totaled 259 km2 (19.81%), while “important” zones accounted for 133 km2 (10.2%). The “extremely important” areas were the smallest, occupying only 50 km2, or 3.82% of the region (see Figure 3).
Grading map of water source conservation importance index.
Choropleth map displaying varying levels of importance across a geographic region, with a five-step blue gradient from unimportant to extremely important. A legend clarifies color categories. North arrow and mile scale included.
This distribution largely reflects Shennongjia’s abundant precipitation and dense vegetation, which favor water retention, while the steep and undulating topography imposes constraints on the overall water conservation capacity. Spatially, the significance of water conservation functions increases gradually from the northeast to the southwest, with high-value zones primarily concentrated around western Dajiu Lake and the Duhe River system. The western Dajiu Lake area is characterized by a high density of lakes and the presence of wetland and marsh ecosystems. The southwestern Xiaguping area, with its lower altitude and gentle terrain, serves as the convergence zone for the lower reaches of the Yandu River system, featuring ample water resources and functioning as a critical water conservation base for Shennongjia National Park.
Soil and water conservation
The Soil and Water Conservation Importance Index for Shennongjia National Park was calculated and subsequently classified into five distinct categories using the Jenks natural breaks method (see Figure 4). Areas deemed “extremely important” and “important” for soil and water conservation encompassed 50.29 km2 and 166.06 km2, accounting for 4% and 13% of the total area, respectively. High-importance zones were primarily concentrated in the southern and southwestern regions of the park, including areas such as Taiheshan, Xiaguping, and the Banqiaohe River basin, where rivers like the Yanduhe, Banqiao, and Pingqian exert strong erosive forces.
Grading map of water and soil source conservation importance index.
Color-coded thematic map showing varying levels of importance across a geographic region, with yellow indicating unimportant areas, shades of blue for generally and more important areas, maroon for important, and dark red for extremely important. North is indicated at the top right, and a scale bar is provided in miles.
Areas classified as “more important” and “generally important” covered 317.45 km2 and 445.31 km2, representing 24% and 34% of the total area, and were distributed across the central and western portions of the park. In contrast, “unimportant” zones were concentrated in the park’s northeastern corner, totaling 327.95 km2, or 25% of the total area. This region experiences relatively low annual precipitation and maintains high vegetation coverage, which substantially mitigates the effects of rainfall and surface runoff, thereby enhancing its soil and water conservation capacity.
Ecological sensitivity assessment of Shennongjia National Park
The six factors, including elevation and vegetation cover, were calculated and weighted, and the data for each factor were multiplied by their respective weights and summed to produce a comprehensive sensitivity assessment for Shennongjia National Park (see Figure 5). Ecological sensitivity is generally highest in the central regions of the park and decreases toward the edges. Overall, most areas within the park exhibited sensitivity levels below the medium threshold.
Grading map of comprehensive ecological sensitivity.
Color-coded sensitivity map of a geographic region showing areas classified as insensitive, mildly sensitive, more sensitive, highly sensitive, and extremely sensitive, with a legend using shades of green, yellow, orange, and red.
Zones identified as “extremely sensitive” covered 117.58 km2, accounting for 8.93% of the total area, and were primarily located in the central and northeastern parts of the park, particularly at higher elevations. The central sensitive zone extended northwestward and northeastward along major valleys, with the northwestern section concentrated around areas such as Nantianmen, Monkey Stone, Slate Rock Wall, as well as the Da Shennongjia and Xiao Shennongjia regions. To the northeast, sensitive zones reached Jinhouling, Dalongtan, Xiao Longtan, and other tourist areas. Jinhouling and the Da and Xiao Longtan regions are important habitats for the golden snub-nosed monkey, conferring high conservation value.
Areas classified as “highly sensitive” totaled 255.4 km2, representing 19.4% of the study area, and were mainly situated around the extremely sensitive regions, with some portions near the Dajiu Lake area. The surroundings of Dajiu Lake, notable for their high ecological sensitivity, support dense populations and rare subalpine peat moss wetland ecosystems. Zones identified as “moderately sensitive” were more sporadically distributed throughout the park, covering 374.16 km2 (28.42%). “Mildly sensitive” areas represented the largest extent, amounting to 357.09 km2 (27.12%), typically serving as transitional zones between insensitive and more sensitive areas. The least sensitive areas were mainly distributed along the park’s periphery, totaling 212.32 km2, or 16.13% of the entire landscape.
Evaluation results of ecological security of Shennongjia National Park
The data representing ecological service importance and ecological sensitivity were standardized to a 0–1 scale. The three importance indicators were then integrated and overlaid with the ecological sensitivity values to produce a composite ecological security assessment dataset for Shennongjia National Park. Using the natural breaks classification method, the results were divided into five categories: safe, relatively safe, marginally safe, relatively unsafe, and unsafe. The corresponding ecological security assessment outcomes are presented in Figure 6 and Table 5.
The evaluation results of land ecological security.
Color-coded map shows safety zones, with areas shaded green as safe, yellow as critically safe, orange as less safe, and red as unsafe; unsafe regions dominate the center and northeast.
The proportion of ecological safety evaluation.
Security level
Safe
Relatively safe
Critically safe
Less safe
Unsafe
Total
Area
73
369
314
273
288
1,317
Percentage
5.54%
28.02%
23.84%
20.73%
21.87%
1
The combined area of the safe, relatively safe, and marginally safe zones totaled 756 km2, accounting for 57.4% of Shennongjia National Park, indicating that over half of the region maintains a relatively secure ecological status. Specifically, the safe zone covered 73 km2 (5.54%), while the relatively safe zone encompassed 369 km2 (28.02%). These two categories closely correspond to the spatial distribution of the “extremely important” and “important” areas identified in the ecological importance assessment, primarily located in the southwestern section of the park and extending northwestward in a belt-like pattern. The marginally safe zone comprised 314 km2, representing 23.84% of the area.
In contrast, the less secure portions of the park included 288 km2 (21.87%) classified as unsafe and 273 km2 (20.73%) as relatively unsafe. These areas were concentrated in the central and northeastern regions, corresponding to the zones previously identified as extremely or highly sensitive in the ecological sensitivity analysis.
Construction of the ESP in Shennongjia National ParkProcess of ESP developmentIdentification of ecological source areas
Based on the ecological security assessment results, areas classified as “safe” and “relatively safe” were designated as candidate ecological source zones. To minimize the influence of small, fragmented patches, only contiguous areas exceeding 800 ha were retained. Following spatial filtering and refinement, 21 ecological source areas were identified, collectively covering 262.6 km2—approximately 20% of the park’s total land area. Spatially, these source areas were primarily concentrated in the western and southern regions of the park, whereas the central and northern regions exhibited a more scattered distribution (see Figure 7).
Spatial distribution characteristics of ecological sources areas.
Outline map with green areas representing ecological sources distributed throughout the region, scale in miles at the bottom right, legend at the bottom left, and a north arrow in the top right corner.Establishment of resistance surface
A composite resistance surface map was generated by calculating individual resistance values for each factor within the MCR model framework and conducting a weighted overlay analysis (see Figure 8). The integrated resistance values for Shennongjia National Park ranged from 10 to 74.146, exhibiting a spatial pattern of higher ecological resistance in the central and western regions, and relatively lower resistance in the southern and northwestern areas. The elevated resistance in the central zone is primarily attributed to complex topography, while the abundance of lakes in the western region restricts ecological flows, resulting in similarly high resistance values. In contrast, areas such as Xiaguping in the south, Dongxi in the north-northwest, as well as Muyu Town, Shennongtan, and Honghuaping are characterized by lower resistance due to relatively flat terrain.
Spatial distribution of comprehensive resistance surface.
Color-coded topographic map illustrating comprehensive resistance values, with a gradient from low resistance in blue and green areas to high resistance in orange and red regions; a scale bar and north arrow are shown.
Based on these synthesized resistance values, the MCR surface for the study area was subsequently modeled (see Figure 9). The results indicated that MCR values in Shennongjia National Park ranged from 0 to 249,888, with the highest resistance areas located in the central and northeastern regions. Resistance values generally decreased from the interior toward the periphery.
Spatial distribution of MCR surface.
Heatmap illustrating minimum cumulative resistance values across a geographic area, with a spectrum from blue (low resistance) to red (high resistance), and a labeled scale bar ranging from zero to eight miles.Buffer zone discrimination
Buffer zones function both as priority areas for the outward expansion of ecological source regions and as key targets for habitat restoration. The natural breaks method was used to classify the study area into three distinct zones, reflecting the spatial heterogeneity of MCR values: ecological source buffer zones, low-resistance zones, and high-resistance zones (see Figure 10). Among these, the ecological source buffer zone was the most extensive, encompassing 595.5 km2, or 45.29% of the total area, indicating that Shennongjia National Park possesses substantial potential for ecological growth and enhancement. The low-resistance zone covered 274.85 km2 (20.87%), while the high-resistance zone accounted for 183.07 km2 (13.9%). These latter two zones formed a ring-like spatial pattern, primarily distributed in the central and northeastern regions of the park.
Spatial distribution of ecological buffer zone.
Map illustration showing ecological zones with four categories: ecological sources in dark green, sources buffer zone in light green, low resistance zone in green, and high resistance zone in dark blue. Scale bar and north arrow included.Ecological corridor construction
Using MCR values, cost distance and backlink cost paths were calculated between each ecological source and all other sources. An origin–destination (OD) matrix was then applied to determine the shortest paths among the ecological source areas. This process was repeated iteratively until all potential ecological corridors connecting the various sources were identified. After vectorization, a total of 441 preliminary ecological corridors were generated within the study area. Following the removal of redundant paths, the final set of ecological corridors for Shennongjia National Park was delineated (see Figure 11).
Distribution of potential ecological corridors.
Map displaying a region’s ecological spatial structure with green areas marking ecological sources, purple lines indicating ecological corridors, and a black outline for the regional boundary. Scale provided in miles and a north arrow included.Ecological risk node identification
Ecological strategy points serve as key nodes in ecosystem material cycles and as temporary stopover sites for organisms during migration and movement. In this study, ten ecological strategic nodes were identified at critical intersections of ecological corridors within the park. Ecological fracture points were defined as locations where ecological corridors intersect major transportation infrastructure, such as highways and railways—areas subject to substantial anthropogenic disturbance and thus prioritized for restoration. A total of 23 such intersection sites were identified as ecological fracture points (see Figure 12). Spatially, strategic nodes were concentrated around Muyu Town and Tianshengqiao, while most ecological fracture points were situated where two major northwest–southeast transportation routes intersect ecological corridors in Shennongjia National Park.
Ecological strategy point and ecological fracture point plan distribution.
Map displays ecological corridors, roads, boundaries, and ecological sources within a region. Fracture points are marked with black triangles, strategy points with red dots, and green areas indicate ecological sources.Results of ESP construction in Shennongjia National Park
By overlaying the identified ecological sources, buffer zones, corridors, strategic points, and fracture points, a comprehensive map of the existing ESP in Shennongjia National Park was generated (see Figure 13). The finalized ESP framework comprises 21 ecological source areas, three hierarchical buffer zones, 20 ecological corridors, and 33 ecological nodes.
Esp construction of Shennongjia National Park.
Colored map visualizing ecological corridors, roads, fracture points, and strategy points within boundaries, showing ecological sources, source buffer zones, low resistance zones, and high resistance zones per the legend in the bottom left corner.Weighting procedure and robustness
To ensure reproducibility and to avoid introducing undocumented subjective tuning, ecological importance (EI) and ecological sensitivity (ES) were aggregated using an equal-weight linear combination after indicator standardization (Supplementary Table S1). Specifically, each EI sub-index (biodiversity importance, water conservation, soil and water conservation) was min–max normalized to [0,1] and averaged with equal weights; ES factors were first reclassified into graded sensitivity scores (defined in Table 3), then normalized and averaged with equal weights (Supplementary Table S1). Because EI/ES weights are equal by design, AHP consistency ratio (CR) is not applicable to these two modules. For connectivity modelling, we constructed a composite resistance surface R using literature-consistent resistance assignments for terrain, land cover, and disturbance proxies, and applied the resistance-factor weights already specified in the manuscript (Table 4; reproduced in Supplementary Table S1 for completeness). As a robustness check, we additionally conducted a simple weight-perturbation test (±10% around baseline weights with re-normalization) and confirmed that the locations of key ecological sources/corridors and identified fracture-point hotspots were stable, with only minor shifts in class boundaries; this indicates that the main conclusions are not driven by small variations in weighting assumptions.
DiscussionScientific foundation of the ESP constructionValidity of the ESP framework
This study establishes the ESP of Shennongjia National Park by integrating ecological importance and sensitivity through a synergistic analytical approach. This methodology provides both a novel theoretical perspective and a practical reference for the ecological governance of mountainous national parks. Its scientific rigor is demonstrated by the comprehensive consideration of both ecosystem functionality and vulnerability, offering an effective tool for the precise identification of ecological risks and critical areas. By combining the dimensions of ecological importance and ecological sensitivity, the study enables a more holistic assessment of ecological vulnerability and ecosystem service significance within Shennongjia National Park, thereby facilitating the scientific identification of conservation priorities.
In developing the ESP, this research adopts a “source–corridor–node” framework, which offers distinct advantages. Unlike the traditional “source–corridor–surface” model, this approach places greater emphasis on the strategic role of ecological nodes—such as key connectivity points—in maintaining ecological flows and enhancing overall network coherence. This consideration is particularly crucial in mountainous national parks like Shennongjia, where rugged terrain and fragmented habitats increase the importance of identifying and protecting these functional nodes (Xiang et al., 2024). By delineating ecological sources, corridors, and nodes, the proposed ESP not only improves connectivity among habitat patches but also mitigates disruptions to ecological processes, thereby promoting greater ecosystem stability.
The spatial configuration of the constructed ESP reveals that ecological source areas are predominantly located in zones of high ecological importance. The integration of ecological corridors has substantially strengthened connectivity among these core areas. This spatial arrangement enhances the adaptive capacity and resilience of the ecosystem—particularly in response to climate variability and anthropogenic pressures—thus providing a more robust foundation for long-term ecological sustainability.
The value of applying spatial identification and zoning strategies for ecological risk
The spatial identification and zoning strategy for ecological risk presented in this paper provides practical management support for Shennongjia National Park. Through spatial analysis of ecological sensitivity, this study identifies high-risk areas and develops targeted protection measures for these zones. The practical value of spatial ecological risk identification lies in its ability to assist policymakers in pinpointing ecological functional zones that require urgent protection—typically, areas characterized by high ecological vulnerability or those providing critical ecosystem services. A scientifically grounded zoning strategy ensures that limited resources and management efforts are directed toward the most vulnerable areas, thereby enhancing the efficiency and effectiveness of ecological protection.
Furthermore, this study introduces a differentiated ecological zoning strategy, enabling more precise management interventions tailored to the ecological security status of each zone. For example, regions with higher ecological security can accommodate relatively flexible management approaches that support ecotourism and sustainable utilization, while areas with lower security require stricter protection policies and limitations on human activities to prevent ecological degradation. This nuanced approach optimizes resource allocation within national parks and improves the effectiveness of on-the-ground ecological protection efforts.
Comparison and linkage with existing research
Our study builds on the “security pattern” tradition in landscape ecological planning, which emphasizes identifying strategically important landscape components for safeguarding ecological processes (Yu, 1996). While recent ESP studies commonly employ resistance surfaces and MCR/least-cost modelling to delineate ecological sources and corridors, many applications remain centered on “source–corridor–surface” outputs and provide limited guidance on where connectivity is most vulnerable and which locations should be prioritized for intervention (Xu et al., 2019; Kang et al., 2021; Huang et al., 2023).
Relative to this mainstream pathway, our work differs in three key respects. First, rather than relying on single indicators or static ecological indices, we explicitly couple ecosystem-service-based ecological importance with terrain- and land-cover-driven ecological sensitivity to identify areas that are simultaneously high-value and high-vulnerability—an aspect that is particularly critical in mountainous national parks where steep gradients amplify ecological fragility and disturbance impacts. Second, we operationalize ESP as a management-oriented “source–corridor–node” structure. Although some recent studies acknowledge the role of ecological nodes (e.g., stepping stones or pinch areas), node identification is not consistently integrated into zoning logic and management actions (Fan et al., 2022). Third, beyond sources/corridors/nodes, we identify fracture points where corridors intersect transport infrastructure, translating corridor mapping into targeted risk-control and restoration priorities (e.g., mitigation at barrier points). This responds to a well-recognized limitation that corridor outputs alone may not indicate where connectivity is most constrained (Chen et al., 2025; Li et al., 2025).
These differences strengthen the academic value of the study in two ways. Methodologically, the framework integrates value–vulnerability–process (importance–sensitivity–resistance/connectivity) into a single workflow, improving the interpretability and implementability of ESP for complex mountain systems. Empirically, applying this workflow to Shennongjia National Park demonstrates how ecological security assessment can be translated into strategic zoning and actionable intervention points, improving the alignment between spatial modelling outputs and protected-area governance needs.
Gap analysis with official zoning
To strengthen the management relevance of our strategic zoning, we conduct a gap analysis by comparing the ESP-based outputs with the legally operative two-zone control framework widely implemented in China’s national parks (core protection area vs. general control area). In principle, the core protection area prioritizes ecosystem integrity and typically restricts most human activities, whereas the general control area accommodates limited, strictly regulated uses and community-related needs (Zhuang et al., 2021). Building on this governance logic, we overlay (i) ecological sources, (ii) least-cost corridors, and (iii) fracture/pinch points with the official control zoning boundary to quantify spatial consistency and identify potential mismatches.
The overlay provides an interpretable validation layer: high ecological security assets are expected to concentrate within the core protection area, while any corridor bottlenecks or fracture points in the general control area indicate risk-exposed segments where human disturbance and infrastructure pressures may compromise connectivity. In Shennongjia, this comparison enables a concrete policy diagnosis—pinpointing where corridor restoration, road-crossing mitigation, visitor-use management, or micro-zoning refinement would yield the largest ecological-security gains. The approach aligns with recent calls for improving the operability of national park zoning by linking spatial ecological priorities with differentiated control rules, rather than treating zoning as a purely administrative delineation (Wang et al., 2021; Ye and Zhang, 2023).
Research contributions
The theoretical contribution of this study lies in its integration of ESP construction with spatial management practices, thereby advancing a novel framework for ecological security assessment. This approach emphasizes not only the spatial configuration and connectivity of ecological components but also thoroughly explores the coupling between ecosystem service functions and ecological vulnerability. As a result, it introduces a more holistic and dynamic methodology for evaluating ecological security (Li et al., 2024). Traditional models of ecological security research often focus on isolated indicators, overlooking the multifaceted and complex nature of ecosystems. In contrast, this paper adopts a synergistic analytical framework specifically tailored to the ecological characteristics and sensitivity of Shennongjia National Park. This not only enhances the precision of the evaluation but also ensures that the resulting ESP more accurately reflects regional realities and is better suited to address the dual pressures of environmental change and anthropogenic impacts.
The innovativeness of the Shennongjia National Park coupling analysis method proposed in this study—relative to traditional single-factor assessments—is reflected in several key aspects. First, conventional ecological security assessments typically rely on single ecological factors, rendering the results insufficient to capture the true condition of the ecosystem, particularly in diverse mountain environments, and often neglecting the ecosystem’s complexity and dynamics (Zhang B et al., 2022). In contrast, the method presented here improves the accuracy and spatial adaptability of risk assessment by integrating multiple ecological dimensions and combining comprehensive evaluations of Shennongjia National Park with ecological sensitivity analyses. Second, this approach enhances ecological risk identification, optimizes resource allocation, and informs spatial planning. Specifically, the coupling analysis enables the identification of ecologically vulnerable areas and the development of more targeted protection and management measures based on the specific characteristics of these areas. Finally, the proposed methodology is highly scalable and can be adapted to different spatial scales, providing decision-making support for ecological management across various administrative levels.
From a practical standpoint, the findings of this research offer a robust and operable framework for ecological security assessment and management, tailored to Shennongjia and other mountainous national parks with similar ecological contexts. By establishing a scientifically grounded ESP, this study supports the formulation of more targeted and refined management strategies for Shennongjia—particularly for safeguarding critical areas such as ecological sources, corridors, and strategic nodes (Liu et al., 2022). The approach not only enhances landscape connectivity and strengthens ecosystem functioning but also facilitates optimized resource allocation and mitigates anthropogenic disturbances through zoned management schemes. The ecological security assessment framework and management pathway developed here can serve as a valuable reference for other mountainous national parks in designing scientific conservation and sustainable resource utilization plans. Accordingly, the results presented hold not only direct practical value for Shennongjia’s ecological governance but also broader applicability for ecological management in mountain protected areas worldwide.
Management implications and strategic zoning recommendations
To improve policy operability, we translate the ESP outputs into a zone-based management toolbox tailored to mountainous national parks. Strategies are organized by the spatial elements most relevant to governance—sources (core habitats), corridors (connectivity pathways), nodes/fracture points (bottlenecks/barriers), and surrounding support zones—and aligned with the park’s differentiated control logic.
First, source-oriented strategy: strengthen “core habitat integrity” management. For ecological sources identified in the high-security class, the primary goal is to maintain intact forest ecosystems and biodiversity refugia. Management should prioritize strict disturbance control, ecological restoration of localized degraded patches, and long-term biodiversity monitoring. In the general control area, source-adjacent zones should be managed with low-impact access rules and ecological buffer measures to prevent edge effects from expanding into core habitats.
Second, corridor-oriented strategy: implement “connectivity safeguarding” in mountainous terrain. Key corridors should be managed as functional ecological infrastructure. In Shennongjia-type mountain systems, corridors are often constrained by steep terrain and valley–ridge structures, making them vulnerable to fragmentation. Management actions should therefore focus on: (i) maintaining continuous vegetation cover along corridor routes, (ii) restricting new linear infrastructure within corridor buffers, and (iii) coordinating tourism route planning to avoid corridor pinch areas. For corridor segments located in general control zones, enhanced use regulation and seasonal access control can reduce peak disturbance.
Third, fracture-point strategy: targeted mitigation at corridor–infrastructure intersections. Fracture points identified at corridor crossings with roads/settlements are the most actionable “high-leverage” locations for reducing connectivity disruption. Priorities include retrofitting wildlife passage structures (e.g., culverts/overpasses where feasible), roadside fencing guidance, speed management, and vegetation restoration around crossings. These measures are particularly applicable where corridors overlap with transport corridors in the general control area, providing a practical entry point for ecological risk-control investments.
Finally, adaptive monitoring and regional co-management strategy. Given climatic and human-use uncertainties in mountainous national parks, zoning should be implemented with adaptive monitoring. We recommend a monitoring set that directly corresponds to our indicators and ESP elements: (i) NDVI/FVC trends for habitat condition, (ii) disturbance proxies (road density/POI growth) for human pressure, and (iii) corridor continuity metrics (connectivity indices, repeated least-cost mapping) for network health. Governance-wise, implementation should combine park administration, local communities, and relevant sectoral agencies (transportation, tourism, forestry) to ensure corridor protection and fracture-point mitigation can be executed within existing administrative responsibilities.
Although the above strategies are tailored to Shennongjia, the decision logic is generalizable to other mountainous national parks: identify sources–corridors–nodes, quantify mismatch with official zoning, and then apply a standard intervention toolbox (core protection, corridor safeguarding, fracture-point mitigation, and adaptive monitoring) with site-specific parameter calibration.
Limitations and prospects
While this research has yielded valuable methodological and practical results, several limitations remain. First, in terms of data accuracy, although high-resolution remote sensing data have been utilized, certain ecologically high-risk zones remain difficult to monitor due to climatic constraints, rugged terrain, and other environmental factors. These challenges may affect the spatial precision of the ecological security assessment. Second, the current study is primarily conducted at the regional scale and lacks a comprehensive multi-scale analysis spanning global, regional, and local dimensions, thereby limiting the scope and granularity of ecological pattern optimization.
Although elevation-dependent spline interpolation improves the realism of climatic surfaces in complex terrain, uncertainty may persist in high-elevation or microclimatically heterogeneous areas due to uneven station density and localized orographic precipitation. Such uncertainty can propagate into climate-sensitive ecosystem-service layers (e.g., water conservation-related factors). Future work can further reduce this uncertainty by incorporating denser local station networks, lapse-rate constrained downscaling, and/or satellite–gauge merged precipitation products to strengthen climate inputs for mountainous national parks.
Future research should seek to address these limitations in the following ways. On one hand, the integration of high-precision remote sensing and advanced GIS technologies should be further strengthened. Additionally, the development of dynamic, multi-source data processing methods will be essential for improving the spatiotemporal resolution of ecological security evaluations. On the other hand, advancing cross-scale research is crucial; future work should overcome existing scale limitations, integrate global change scenarios with regional management practices, and establish a dynamically evolving ESP assessment and response system.
Conclusion
Using Shennongjia National Park as a representative case, this study develops an ecological security assessment framework based on the integrated analysis of ecological importance and sensitivity. By applying the “source–corridor–node” model, a comprehensive ESP was constructed, yielding the following key findings.
First, the ecological security evaluation reveals pronounced spatial heterogeneity across the park. In terms of ecological importance, core ecological source areas were identified through the combined assessment of essential ecosystem service functions, including water conservation, carbon sequestration, and biodiversity maintenance. These findings underscore significant spatial variation in the provision of ecosystem services. For ecological sensitivity, vulnerable areas were delineated by integrating physical and biological factors such as elevation, slope, and NDVI, with high-altitude and wetland regions exhibiting particularly low ecological stability. Overall, the assessment indicates a marked spatial imbalance in ecological security: core protected zones demonstrate relatively high security levels, while marginal zones, fragmented areas, and regions impacted by human activities are subject to greater ecological risk.
Second, the construction of the ESP significantly enhances both ecosystem connectivity and the precision of management interventions. Through the “source–corridor–node” framework, 21 major ecological sources and 20 ecological corridors were identified as critical for maintaining uninterrupted ecological processes and supporting species migration. Additionally, 23 ecological breakpoints—representing weak links within the ecological network—were identified as restoration priorities. The spatial arrangement of ecological strategic nodes further supports the development of a resilient and adaptive ecological security system, thereby strengthening the ecosystem’s overall capacity to withstand disturbances.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
QZ: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft, Writing – review and editing. JL: Conceptualization, Data curation, Formal Analysis, Investigation, Resources, Writing – original draft. JH: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing. YC: Conceptualization, Data curation, Investigation, Software, Writing – original draft, Writing – review and editing. FX: Conceptualization, Data curation, Validation, Investigation, Resources, Writing – original draft, Writing – review and editing. XX: Conceptualization, Supervision, Validation, Visualization, Writing – review and editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. During the preparation of this work the author(s) used ChatGPT 5 in order to improve readability and language. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1737561/full#supplementary-material
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Edited by:Enxiang Cai, Henan Agricultural University, China
Reviewed by:Liang Yuan, China Three Gorges University, China
Xinchen Gu, South China University of Technology, China