Ecological Security Patterns (ESP): Foundational Concepts, Methodologies, and Future Directions for Sustainable Ecosystem Management

Abstract

This article provides a comprehensive exploration of Ecological Security Patterns (ESP), a critical spatial approach for balancing ecological conservation and socio-economic development. Tailored for researchers, environmental scientists, and land-use planners, it systematically unpacks ESP's core principles, tracing its origins in landscape ecology and its ‘source-corridor-network' paradigm. The content details advanced methodological frameworks for ESP construction, including the integration of Minimum Cumulative Resistance (MCR) models and circuit theory, and addresses key challenges such as ecological source homogenization and resistance surface setting. Further, it validates ESP's application across diverse ecosystems like the Yellow River Basin and China's black soil regions, comparing its efficacy with related concepts like Green Infrastructure (GI) and Ecological Networks (EN). The synthesis offers a forward-looking perspective on integrating dynamic simulations and multi-scale assessments to enhance ESP's role in achieving global sustainability and ecological security goals.

What Are Ecological Security Patterns? Unpacking the Core Concepts and Historical Evolution

Ecological Security Patterns (ESPs) are spatial networks composed of ecological sources interconnected through ecological corridors and strategic points, which are critical for maintaining the health and safety of ecological processes such as species migration and ecosystem service flows [1]. In the Anthropocene, we face serious ecological challenges, including accelerated landscape fragmentation and unprecedented biodiversity loss [1]. The construction of ESPs has emerged as a key landscape ecological approach to spatial conservation planning, providing a strategic framework for balancing ecological conservation with economic development needs [1] [2]. By identifying and protecting these critical landscape elements, ESPs help safeguard biodiversity, ensure the continuous provision of vital ecosystem services, and maintain ecological connectivity across increasingly human-dominated landscapes.

The fundamental premise of ESPs is that ecological conservation requires more than just protecting isolated natural areas; it necessitates a systematic approach that secures the functional connections between these areas to maintain ecological processes and services [1]. This approach represents a shift from unchecked expansion-focused planning to more constrained and sustainable spatial planning that respects ecological thresholds [1]. ESP implementation has seen wide applications, particularly in China, though its fundamental concepts and principles are universally applicable for addressing global ecological challenges [1].

Theoretical Foundations and Core Components

Conceptual Framework of ESPs

Ecological Security Patterns integrate concepts from landscape ecology, conservation biology, and sustainability science to form a comprehensive framework for spatial conservation. The patch-corridor-matrix model and the pattern-process-scale paradigm from landscape ecology provide important theoretical foundations for understanding and designing ESPs [1]. These patterns support ecological processes that maintain biodiversity and ecosystem services, including species dispersal, nutrient cycling, and hydrological flows [1] [2].

It is essential to distinguish ESPs from related concepts such as green infrastructure and ecological networks. While these terms are related and sometimes used interchangeably, they have distinct focuses and applications. ESPs specifically emphasize the security and maintenance of critical ecological processes, with a stronger focus on identifying minimal spatial elements necessary to ensure ecological security [1]. The concept also recognizes the difference between ecological security and environmental security, with the former focusing more directly on ecosystem structures and functions that support biological diversity and ecological processes [1].

Core Components of ESPs

Table 1: Core Components of Ecological Security Patterns

Component	Definition	Ecological Function
Ecological Sources	Landscape elements that are key to species survival and serve as starting points for diffusion [2]	Provide habitat for biodiversity; serve as origins for ecological flows [1] [2]
Ecological Corridors	Linear landscape elements that connect ecological sources for material, energy, and information flows [1]	Facilitate species migration, gene flow, and ecosystem service transfer between sources [1] [2]
Strategic Points	Locations within corridors that have special significance for maintaining or impeding connectivity [1]	Pinch points: Areas with high movement probability; Barrier points: Areas hindering species movement [1] [2]
Buffer Zones	Areas surrounding core ESP components that provide additional protection	Mitigate edge effects; absorb external pressures from human activities

Graph 1: Conceptual Framework of Ecological Security Patterns (ESP). The diagram illustrates the core components of ESPs and their relationships to ecological processes and ultimate security outcomes.

Methodological Framework for ESP Construction

The development of Ecological Security Patterns follows a systematic workflow that integrates spatial analysis, modeling, and field validation. This process involves identifying critical ecological elements through standardized procedures that assess ecosystem services, habitat quality, and landscape connectivity. The methodological framework ensures that ESPs are scientifically grounded and practically implementable for spatial conservation planning [1] [2].

Graph 2: Methodological Workflow for Constructing Ecological Security Patterns. The flowchart outlines the four-phase process from data collection to planning integration.

Ecological sources represent the most critical habitats that support biodiversity and provide key ecosystem services. Rather than simply designating protected areas as sources, modern ESP construction uses rigorous spatial analysis to identify areas with high ecological functionality [2]. The InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) integrates multiple levels of ecosystem services—including water conservation, biodiversity, soil conservation, and carbon sequestration—providing a more accurate approach for quantifying ecosystem connectivity and integrity than single-factor identification methods [2].

Table 2: Primary Methods for Identifying Ecological Sources

Method	Key Indicators	Applications	Advantages
Ecosystem Service Importance Assessment	Biodiversity maintenance, water conservation, carbon sequestration, soil conservation [2]	Regional ESP planning; Hainan Island case study [2]	Integrates multiple ecosystem functions; identifies areas with comprehensive ecological value
Habitat Quality Assessment	Habitat rarity, integrity, vulnerability	Urban ESP construction; sensitive ecosystem protection	Evaluates habitat capacity to support species; identifies high-quality habitats
Landscape Connectivity Analysis	Probability of connectivity, integral index of connectivity	Species-focused conservation; fragmented landscapes	Identifies patches critical for maintaining landscape connectivity
Composite Index Method	Ecological security index (ESI) combining multiple dimensions [3]	Urban agglomerations; socio-ecological systems [3]	Incorporates social, economic, and ecological factors; comprehensive assessment

Constructing Resistance Surfaces

Landscape resistance surfaces represent the impedance that ecological flows encounter when moving across different landscape types [2]. These surfaces are typically constructed by assigning resistance values based on land use types, supplemented by adjustments according to topography, vegetation cover, and human activity intensity [2]. Higher resistance values represent greater difficulty for species movement and ecological flow.

The circuit theory model has emerged as a powerful approach for analyzing connectivity across resistance surfaces. This model simulates species migration using the random walk characteristics of electrical current, intuitively providing feedback on corridor importance and node significance according to current strength [2]. This approach overcomes the limitation of other models in reflecting the continuous nature of ecological flows and information exchange [2].

Delineating Corridors and Strategic Points

Ecological corridors are identified as potential pathways for species movement and ecological flows between sources. The Circuit Theory model and Minimum Cumulative Resistance model are commonly used for this purpose [2]. The Circuit Theory model offers advantages in exploring corridor width and accurately identifying node locations, providing both structural and functional connectivity assessments [2].

Within these corridors, strategic points play disproportionate roles in maintaining or impeding connectivity:

• Ecological pinch points: Areas with high current density in connectivity models, indicating a high probability of species movement through these locations during migration. These represent priority areas for conservation and management in ecological protection planning [2].
• Ecological barrier points: Locations where species movement between ecological sources is hindered. Identifying and improving or removing these barriers can significantly reduce resistance to ecological processes and enhance landscape connectivity [2].

Technical Protocols and Analytical Tools

Experimental Protocols for ESP Construction

Protocol 1: Integrated Ecosystem Service Assessment for Source Identification

This protocol describes the comprehensive assessment of multiple ecosystem services to identify ecological sources, as applied in the Hainan Island case study [2].

• Data Collection Phase

  • Collect land use/land cover data with minimum 30m resolution
  • Acquire soil data (soil type, texture, depth)
  • Gather meteorological data (precipitation, temperature, evaporation)
  • Obtain digital elevation models (DEMs)
  • Compile biodiversity data (species distributions, habitat maps)

• Ecosystem Service Modeling

  • Implement the InVEST model suite:
    • Run the InVEST Habitat Quality module to assess biodiversity maintenance capacity
    • Execute the InVEST Seasonal Water Yield module to model water conservation service
    • Apply the InVEST Sediment Retention module to calculate soil conservation service
    • Use the InVEST Carbon Storage module to estimate carbon sequestration capacity

• Spatial Overlay Analysis

  • Normalize all ecosystem service maps to a common scale (0-1)
  • Apply spatial overlay to identify areas with high values across multiple services
  • Extract contiguous patches exceeding threshold values as preliminary ecological sources

• Validation and Refinement

  • Conduct field surveys to verify ecological significance of identified sources
  • Compare with existing protected areas and species occurrence data
  • Adjust source boundaries based on landscape context and expert knowledge

Protocol 2: Circuit Theory Analysis for Corridor Identification

This protocol outlines the application of circuit theory to identify ecological corridors and strategic points [2].

• Resistance Surface Development

  • Classify land use types and assign base resistance values
  • Incorporate modifiers based on road density, human disturbance, and topographic complexity
  • Validate resistance values using species occurrence or movement data where available

• Circuit Theory Implementation

  • Input ecological sources as nodes in the circuit
  • Apply the resistance surface as the electrical resistance grid
  • Use software such as Circuitscape or Omniscape to calculate current flow:
    • Set all-to-one mode for landscape-level connectivity
    • Apply pairwise mode for specific source connections
  • Generate cumulative current density maps representing movement probability

• Corridor and Strategic Point Delineation

  • Extract corridors following high current density pathways between sources
  • Identify pinch points as areas with consistently high current density
  • Locate barrier points where current density is unexpectedly low despite connection potential
  • Apply GIS spatial analysis to vectorize and map the identified elements

• Field Validation

  • Conduct species presence surveys in identified corridors
  • Use camera traps or acoustic monitors to detect movement
  • Assess habitat condition and permeability at strategic points

Table 3: Essential Analytical Tools and Data Resources for ESP Research

Tool/Resource	Type	Primary Function	Application Context
InVEST Model Suite	Software ecosystem	Modeling and mapping ecosystem services [2]	Quantifying ecosystem services for ecological source identification [2]
Circuitscape	Landscape connectivity software	Modeling connectivity using circuit theory [2]	Identifying corridors and pinch points based on ecological resistance [2]
ArcGIS Pro	Geographic Information System	Spatial data management, analysis, and visualization	Multi-criteria evaluation; resistance surface development; map production
Google Earth Engine	Cloud computing platform	Processing satellite imagery and geospatial datasets	Large-area habitat monitoring; time-series analysis of landscape change
OBIS (Ocean Biodiversity Information System)	Data portal	Access to marine species distribution data [4]	Marine ESP construction; biodiversity assessments in coastal areas [4]
GBIF (Global Biodiversity Information Facility)	Data portal	Species occurrence records across taxa	Validating habitat models; understanding species distributions
Darwin Core Standard	Data standard	Standardized biodiversity data format [4]	Ensuring interoperability of biological data across systems [4]

Case Study Applications and Validation

Hainan Island ESP Implementation

A comprehensive ESP was constructed for Hainan Island, China, demonstrating the practical application of the methodologies described previously [2]. The study identified 65 large ecological sources with a total area of 8,238.23 km², concentrated in biodiversity and water conservation areas in the central mountainous region [2]. These sources were connected by 138 ecological corridors (73 primary and 65 secondary) forming a spider web-like network connecting all important ecological patches [2].

The implementation identified 222 ecological pinch points and 198 ecological barrier points, providing clear spatial priorities for conservation and restoration interventions [2]. The findings indicated that ecological pinch points should be managed as natural conservation areas supplemented by anthropogenic restoration, while ecological barrier points demand equal attention for both anthropogenic restoration and nature conservation [2]. This systematic approach provided clear guidance for alleviating the contradiction between land use and economic development on Hainan Island [2].

Urban Application: Guangdong-Hong Kong-Macao Greater Bay Area

In the highly urbanized Greater Bay Area (GBA), researchers employed an extended DPSIR-S framework (Driver-Pressure-State-Impact-Response-Structure) coupled with an obstacle degree model to assess ecological security levels [3]. This approach integrated natural, social, and economic dimensions of ecological security, addressing a critical gap in conventional ESP methodologies that often overlook socio-economic drivers [3].

The study revealed that response level was a significant factor determining urban ecological security, with environmental protection investment share, GDP, population density, and GDP per capita identified as the main obstacle factors [3]. The proposed ecological infrastructure network increased ecological space by 10.5%, incorporating 121 ecological nodes and 227 ecological corridors, significantly improving connectivity of fragmented ecological sources and optimizing the urban landscape [3].

Discussion and Future Directions

Key Research Challenges

Despite advances in ESP research, several scientific questions remain unresolved. Key challenges include understanding the construction mechanisms of effective ESPs, optimizing ESP configurations to enhance efficiency, and quantitatively evaluating the ecological benefits of implemented ESPs [1]. These unresolved issues undermine ESP's theoretical underpinnings and limit its broader acceptance and influence [1].

Future research needs to address seven key issues: (1) coupling spatial patterns and ecological processes; (2) determining reasonable range thresholds for ESP elements; (3) multi-scale ESP construction and scaling; (4) ESP optimization under changing environments; (5) balancing ecological and socioeconomic benefits; (6) validating ESP effectiveness; and (7) translating ESP research into policy and practice [1]. These issues are closely interconnected, with advancements in one area often dependent on progress in others [1].

Integration with Emerging Technologies

The future of ESP research lies in greater integration with emerging technologies and data sources. Natural Language Processing shows promise for analyzing policy documents and planning frameworks to better align ESPs with governance structures [3]. Advanced remote sensing technologies, including hyperspectral imaging and LiDAR, offer enhanced capabilities for habitat mapping and monitoring. Environmental DNA methods provide new opportunities for biodiversity assessment across ESP elements.

The Marine Biodiversity Observation Network demonstrates the importance of standardized data practices and the FAIR principles (Findable, Accessible, Interoperable, and Reusable) for enabling large-scale biodiversity assessments and ESP construction [4]. Similar approaches should be adopted for terrestrial ESP applications to enhance data sharing and comparative analyses.

Ecological Security Patterns provide a robust spatial framework for addressing the interconnected challenges of biodiversity conservation, ecosystem service maintenance, and landscape connectivity. By integrating landscape ecological principles with advanced spatial analysis and modeling techniques, ESPs identify the minimal spatial elements necessary to ensure ecological security across scales. The methodological protocols and case studies presented in this technical guide demonstrate the practical application of ESP construction, from ecological source identification through corridor delineation to strategic point localization.

As human pressures on ecosystems continue to intensify, ESP approaches offer a scientifically-grounded basis for spatial planning that balances conservation and development objectives. The ongoing refinement of ESP methodologies, particularly through better integration of socio-economic dimensions and emerging technologies, will enhance their effectiveness as tools for achieving ecological sustainability in human-dominated landscapes.

The escalating challenges of biodiversity loss and ecosystem degradation have necessitated the development of robust conceptual frameworks for ecological conservation and planning. Within this context, Ecological Security Patterns (ESP) have emerged as a critical approach for identifying and safeguarding ecologically strategic landscapes that are essential for maintaining regional ecological security and sustainability. ESP serves as an integrative framework that synthesizes elements from related ecological concepts, primarily Ecological Networks (EN) and Green Infrastructure (GI). While these terms are sometimes used interchangeably in policy and planning discourse, they represent distinct but complementary approaches with different emphases and applications. This technical guide provides a comprehensive comparative analysis of ESP, EN, and GI, clarifying their conceptual foundations, methodological applications, and synergistic relationships within ecological planning and research.

Conceptual Foundations and Definitions

Core Concepts and Terminology

• Ecological Security Patterns (ESP): ESP refers to the strategic spatial arrangement of landscape elements that are critical for maintaining regional ecological security and sustainability. These patterns consist of security points (crucial ecological nodes), lines (corridors linking nodes), and networks that together form a defensive structure against ecological degradation. ESP aims to ensure the long-term provision of ecosystem services and maintain biodiversity integrity by identifying and protecting key landscape components [5] [6].

• Ecological Networks (EN): EN represents the interconnected system of habitats and corridors that facilitate species movement, ecological flows, and biodiversity conservation. This concept focuses explicitly on the functional connectivity between ecosystem patches, emphasizing the role of corridors in enabling species dispersal and genetic exchange. The term is sometimes confused with food webs in ecological literature, but in spatial planning contexts, it specifically refers to physical landscape connectivity [7].

• Green Infrastructure (GI): GI encompasses a strategically planned network of natural and semi-natural areas designed to deliver a wide range of ecosystem services [8]. This multifunctional approach integrates ecological, social, and cultural objectives, where ecological issues "rub shoulders with, and are even sometimes surpassed by, social and cultural issues (nature recreation, landscape aesthetics)" [7]. GI includes both natural areas and engineered structures that mimic natural processes, particularly in urban stormwater management contexts [8].

Comparative Analysis of Conceptual Frameworks

Table 1: Conceptual Comparison of ESP, EN, and GI

Aspect	Ecological Security Patterns (ESP)	Ecological Networks (EN)	Green Infrastructure (GI)
Primary Focus	Identifying and securing critical elements for regional ecological security	Maintaining functional connectivity for biodiversity conservation	Providing multifunctional ecosystem services through networked natural areas
Spatial Emphasis	Strategic points, lines, and networks crucial for ecological security	Habitat patches and connecting corridors	Network of hubs, links, and sites of ecological and social value
Key Objectives	Ensuring ecosystem service provision, maintaining biodiversity integrity	Facilitating species movement, preventing habitat fragmentation	Delivering diverse ecosystem services, including social and cultural benefits
Functionality	Defensive, protective framework against ecological degradation	Connectivity-focused for ecological flows	Multifunctional, integrating ecological, social, and cultural services
Scope	Broad regional ecological security	Specifically biodiversity and habitat connectivity	Comprehensive ecosystem services including stormwater management, recreation

Methodological Approaches and Analytical Techniques

ESP Assessment Methodologies

ESP assessment typically involves identifying ecological sources, constructing resistance surfaces, analyzing connectivity, and extracting critical spatial patterns. The methodological framework integrates multiple analytical approaches:

Land Use/Land Cover (LULC) Analysis: Monitoring ESP dynamics utilizing open-source LULC data and expert assessment methods enables long-term tracking of metropolitan ESP [6]. This procedure has been applied across European capital metropolitan areas, revealing significant declines in ESP due to LULC alterations between 2006, 2012, and 2018.

Ecological Network Robustness Analysis: This approach evaluates ESP vulnerability to species losses by simulating extinction scenarios within food webs [5]. The methodology quantifies both food web robustness and ecosystem service robustness, finding they are highly correlated (rs[36] = 0.884, P = 9.504e-13) but vary across services depending on their trophic level and redundancy.

Expert Matrix Methodology for ESP Assessment

The expert matrix method provides a practical approach for converting LULC data into ESP assessments. This relative weighting procedure among ecosystem types utilizes normalized expert opinions to quantify ES potential based on physical properties of landscapes [6]. The methodology has been refined since its initial development by Burkhard et al. to improve reliability and has been successfully applied in urban contexts.

Table 2: Experimental Protocol for Expert-Based ESP Assessment

Protocol Step	Description	Application Example
1. Ecosystem Service Selection	Identify relevant ES for assessment (e.g., biodiversity integrity, drinking water provision, flood protection, air quality, water purification, recreation)	ECMA study assessed 6 ES: biodiversity integrity, drinking water provision, flood protection, air quality, water purification, and recreation & tourism [6]
2. Land Use/Land Cover Classification	Utilize detailed LULC data (e.g., Urban Atlas data for metropolitan areas)	Analysis of 38 European Capital Metropolitan Areas using Urban Atlas data for 2006, 2012, and 2018 [6]
3. Expert Elicitation	Engage interdisciplinary experts to score ES potential of different LULC types	Five interdisciplinary workshops engaging 46 academic experts to identify linkages between 22 ES and 14 GI types in New York City context [8]
4. Matrix Population	Create matrix with LULC types as rows and ES as columns, populated with expert scores	Use of Likert scales or numerical ratings (e.g., 0-5) to evaluate linkages between each GI type and each ES [8]
5. ESP Calculation	Compute aggregate ESP scores based on weighted LULC areas and expert scores	Relative assessment approach bringing all cases on comparable common ground based on universal qualitative values of ESP [6]

Green Infrastructure Assessment Protocols

GI assessment requires specialized methodologies to evaluate its performance in delivering multiple ecosystem services:

Stormwater GI Assessment: For engineered GI systems, quantification focuses on stormwater benefits using models such as EPA SWMM, MUSIC, SUSTAIN, iTree-Hydro, and WinSLAMM [8]. Performance metrics for co-benefits like air pollution reduction, carbon sequestration, and aesthetic benefits are increasingly incorporated but remain challenging to fully define and assess.

Matrix Model Approach: This method evaluates different GI types (rows) against an array of ecosystem services (columns) [8]. The approach is particularly valuable given the "pragmatic dilemma between the urgency of enacting ES-informed planning and uncertainty around the effectiveness of ecological systems."

Visualization of Conceptual Relationships and Methodological Workflows

Conceptual Relationship Between ESP, EN, and GI

ESP Assessment Methodology Workflow

Table 3: Essential Research Resources for ESP, EN, and GI Studies

Research Tool Category	Specific Tools/Methods	Function/Application
Geospatial Data Sources	Urban Atlas (UA) Data, Corine Land Cover (CLC) Data	Provides standardized LULC data for ESP monitoring and change detection across metropolitan areas [6]
Ecological Network Analysis	Food Web Robustness Analysis, Extinction Scenario Simulation	Quantifies indirect risks to ecosystem services through secondary species losses; assesses vulnerability to species losses [5]
Expert Elicitation Framework	Matrix Model Approach, Interdisciplinary Workshops	Harnesses expert opinion to link ecosystem services to different GI types; provides qualitative insight where quantitative data is limited [8]
Ecosystem Service Assessment	Expert Matrix Method, CICES, TEEB, MEA Frameworks	Converts LULC into ES potential using relative weighting procedures; classifies and values ecosystem services [6] [8]
Hydrological Modeling	EPA SWMM, MUSIC, SUSTAIN, iTree-Hydro	Quantifies stormwater benefits of GI; assesses performance of engineered structures in urban contexts [8]

Research Implications and Future Directions

The integration of ESP, EN, and GI frameworks offers promising approaches for addressing complex ecological challenges at multiple spatial scales. Research demonstrates that ESP potential has declined significantly across European capital metropolitan areas due to LULC alterations, with particularly high impact reductions observed in Fennoscandinan ECMA like Helsinki, Stockholm, and Oslo [6]. Correlation analyses suggest that "inattentive urbanization processes impact ESP more than population growth," highlighting the critical importance of strategic spatial planning.

Future research should focus on developing more robust quantitative metrics for ESP assessment, particularly for cultural ecosystem services which were identified as "those most universally provided by GI" [8]. The expansion of expert elicitation methodologies to include non-academic experts and other urban stakeholders could provide more comprehensive qualitative insights. Furthermore, standardized protocols for monitoring ESP dynamics across different biogeographical regions would enhance comparative analyses and support evidence-based policy interventions for ecological security.

The 'Pan- European Ecological Network (PEEN)' and 'Green Infrastructure' as International Precedents

The conceptual foundation of Ecological Security Patterns (ESP) relies on identifying and safeguarding key ecological structures that are essential for maintaining biodiversity, ecosystem services, and overall landscape resilience. This approach spatializes ecological processes by mapping core areas, ecological corridors, and strategic nodes that together form a functional network. Within this theoretical context, international large-scale conservation initiatives provide critical precedents for ESP implementation, demonstrating practical methodologies for continental-scale ecological planning. The Pan-European Ecological Network (PEEN) and the European Union's Green Infrastructure (GI) strategy represent two of the most advanced and policy-relevant frameworks of this kind. PEEN emerged as a key component of the Pan-European Biological and Landscape Diversity Strategy (PEBLDS), developed under the auspices of the Council of Europe to effectively implement the Convention on Biological Diversity (CBD) at the European level [9]. Simultaneously, the EU's Green Infrastructure strategy, defined by the European Commission as a "strategically planned network of natural and semi-natural areas," seeks to enhance ecosystem health and connectivity while delivering a wide range of environmental benefits to human societies [10]. These frameworks provide invaluable models for ESP development, offering tested approaches for integrating ecological network concepts into broad-scale environmental policy and land-use planning.

The Pan-European Ecological Network (PEEN): A Foundational Precedent

Historical Development and Strategic Objectives

The development of PEEN was pursued through three sequential subprojects that collectively covered the European continent: Central and Eastern Europe (completed 2002), South-eastern Europe (completed 2006), and Western Europe (completed 2006) [9]. This phased approach allowed methodological adaptation to regional variations in data availability, ecological conditions, and conservation priorities. The primary objective was to create a coherent vision and spatial framework for biodiversity conservation across political and biogeographic boundaries, addressing habitat fragmentation through the identification and connection of key ecological areas. PEEN explicitly aims to guide the development of trans-European ecological corridors and ensure international coherence among the numerous agencies responsible for biodiversity conservation throughout Europe [9].

Methodological Framework and Technical Implementation

The methodological approach for PEEN development involved harmonizing disparate national habitat datasets to create comparable maps across regions, a significant challenge given Europe's diverse ecological classification systems. While the general methodology was broadly comparable across the three subprojects, technical implementations varied in response to geographical differences and data constraints [9]. The mapping process identified:

• Core Areas: Regions of high ecological value requiring conservation.
• Ecological Corridors: Landscape elements enabling species movement and genetic flow between core areas.
• Restoration Zones: Degraded areas with high potential for ecological recovery.

Table: PEEN Subproject Characteristics and Regional Variations

Subproject Region	Completion Year	Primary Connectivity Challenge	Key Methodological Adaptation
Central & Eastern Europe	2002	Moderate fragmentation	Integration of post-socialist ecological data
South-eastern Europe	2006	Data standardization	Harmonization of Balkan region habitat classifications
Western Europe	2006	High fragmentation	Emphasis on corridor functionality in intensively used landscapes

The resulting maps revealed significant geographical variation in ecological connectivity needs. In Central and Western Europe, where landscape fragmentation is severe, ecological corridors were identified as essential for maintaining functional connectivity. Conversely, in Northern, Eastern, and South-eastern Europe, larger contiguous natural areas still exist, allowing for different conservation prioritization focused on maintaining these extensive coherent natural ecosystems [9].

EU Green Infrastructure Strategy: A Policy-Driven Approach

Conceptual Foundation and Policy Integration

The European Union's Green Infrastructure strategy represents a policy-driven evolution of ecological network concepts, explicitly linking ecological connectivity with human well-being through the provision of ecosystem services. The European Commission defines GI as a "strategically planned network of natural and semi-natural areas" designed to deliver a wide range of environmental, social, and economic benefits [10]. This approach moves beyond traditional conservation by emphasizing the multifunctionality of ecological systems, positioning healthy ecosystems as essential infrastructure comparable to traditional gray infrastructure. The EU's Biodiversity Strategy for 2030 strongly promotes investments in green and blue infrastructure and their systematic integration into urban planning, representing a mainstreaming of ESP principles into sectoral policies [10].

Implementation Framework and Synergies with Other Policies

The EU GI strategy facilitates implementation through its integration into multiple policy instruments, including spatial planning tools, Environmental Impact Assessment (EIA), and Strategic Environmental Assessment (SEA) [10]. This cross-sectoral integration creates powerful synergies with various EU policy domains:

• Agriculture and Forestry: Promoting GI elements in working landscapes
• Climate Change Adaptation: Utilizing natural systems for flood management and urban cooling
• Disaster Prevention: Implementing natural water retention measures for flood risk reduction
• Transport and Energy: Mitigating infrastructure fragmentation through ecological corridors
• Public Health: Providing accessible green spaces for physical and mental well-being

Table: Green Infrastructure Benefits and Ecosystem Services Delivery

GI Benefit Category	Specific Ecosystem Services	Relevant Policy Integration
Biodiversity Conservation	Habitat provision, Genetic diversity maintenance	Habitats Directive, Natura 2000, CBD
Climate Adaptation	Urban heat island mitigation, Flood water retention	EU Climate Adaptation Strategy, Disaster Risk Reduction
Human Health & Well-being	Recreational space, Air and water purification, Noise reduction	Public Health Policies, Urban Development Strategies
Economic Benefits	Increased property values, Tourism opportunities, Reduced infrastructure costs	Regional Development Funds, Agricultural Policy

Comparative Analysis: PEEN versus EU Green Infrastructure

Conceptual Orientation and Implementation Focus

While both frameworks address ecological connectivity, they differ in primary orientation and implementation mechanisms. PEEN operates primarily as a conservation-oriented scientific framework focused specifically on biodiversity preservation through habitat connectivity. In contrast, EU Green Infrastructure adopts a multifunctional service-oriented approach that explicitly links ecological health with human well-being and economic benefits [9] [10]. This fundamental difference manifests in their implementation: PEEN provides a spatial vision to guide national and transboundary conservation planning, while GI is integrated into sectoral policies and project-level assessments through instruments like EIA and SEA [10].

Spatial Configuration and Functional Relationships

The European Green Belt initiative—the strip of valuable habitats along the former Iron Curtain—exemplifies the practical convergence of these frameworks, serving as both a central component of PEEN and an EU-level Green Infrastructure project [11]. This demonstrates how both concepts can spatially align in practice, with the Green Belt functioning as the "backbone" of the Pan-European ecological network while delivering multiple ecosystem services characteristic of GI [11].

Relationship Between ESP, PEEN, GI & European Green Belt

Methodological Protocols for Ecological Network Development

PEEN Development Methodology

The technical development of PEEN followed a systematic spatial planning process that can be adapted for ESP development in various contexts. The methodology involved sequential stages:

• Data Collection and Harmonization: Gathering and standardizing national habitat data across European countries, requiring identification of "common denominators" for habitat classification despite significant differences in national databases and technical approaches [9].

• Core Area Identification: Selecting ecologically valuable areas based on criteria including size, ecological quality, and representativeness of habitat types, with specific parameters varying regionally based on geographical conditions [9].

• Connectivity Analysis: Assessing landscape permeability and identifying potential ecological corridors using spatial modeling techniques, with particular emphasis on connectivity needs in highly fragmented regions of Central and Western Europe [9].

• Gap Analysis: Evaluating the completeness of the existing protected area network and identifying critical areas for future conservation action and international cooperation [9].

Green Infrastructure Planning Protocol

The methodology for implementing Green Infrastructure emphasizes multifunctionality and stakeholder engagement:

• Ecosystem Service Assessment: Mapping and evaluating the distribution of key ecosystem services, including provisioning (water, food), regulating (climate, flood), and cultural services (recreation, aesthetics) [10].

• Spatial Network Design: Creating interconnected systems of natural and semi-natural areas that maximize service provision while maintaining ecological connectivity, incorporating elements ranging from large wilderness areas to urban green roofs [11] [10].

• Integration Mechanism Development: Establishing procedures for incorporating GI into spatial planning, EIA, and SEA processes to ensure mainstreaming across sectors [10].

• Stakeholder Engagement: Actively involving multiple sectors and local communities in the planning and management of GI elements to ensure social relevance and long-term sustainability [10].

Methodological Approaches: PEEN vs. Green Infrastructure

Research and Implementation Toolkit

Successful ESP development requires specific analytical tools and data resources, as demonstrated by both PEEN and GI implementation:

Table: Essential Research Tools for Ecological Security Pattern Development

Tool/Resource Category	Specific Function in ESP Development	Exemplification in Precedents
Geographic Information Systems (GIS)	Spatial data integration, analysis, and visualization	PEEN mapping across European regions; GI spatial planning [9] [10]
Habitat Classification Systems	Standardization of ecological data across jurisdictions	PEEN's development of "common denominators" for European habitats [9]
Landscape Permeability Models	Assessment of resistance to species movement and corridor functionality	PEEN connectivity analysis; GI network design [9] [11]
Ecosystem Service Assessment Tools	Quantification and mapping of services provided by ecosystems	GI multifunctional benefit analysis [10]
Remote Sensing & Spatial Data	Land cover mapping and change detection	Monitoring of European Green Belt and other GI elements [11]

Implementation Mechanisms and Governance Tools

Translating ecological network designs into practical implementation requires specific governance mechanisms:

• International Cooperation Frameworks: PEEN implementation necessitates coordination among "over 100 European-wide agencies responsible for biodiversity conservation," requiring sophisticated governance arrangements for transboundary ecological coordination [9].

• Policy Integration Instruments: The EU GI strategy specifically promotes the use of spatial planning tools, Environmental Impact Assessment, and Strategic Environmental Assessment as mechanisms for implementing green infrastructure across sectors [10].

• Economic Valuation Methods: Demonstrating the economic benefits of GI, including cost savings from natural disaster reduction, improved public health outcomes, and increased property values, provides crucial arguments for policymakers [11] [10].

• Stakeholder Engagement Protocols: Successful implementation requires involvement of diverse stakeholders, including private sector actors like those in the Dow Chemical Company, Shell, and Unilever consortium that evaluated business cases for green infrastructure [12].

The Pan-European Ecological Network and EU Green Infrastructure strategy provide complementary international precedents for the development of robust Ecological Security Patterns. PEEN offers a scientifically-grounded methodology for identifying and connecting core ecological areas across vast geographical scales, demonstrating techniques for overcoming data heterogeneity and political boundaries in continental-scale conservation planning [9]. The EU GI strategy contributes a policy-oriented, multifunctional approach that explicitly links ecological connectivity with human well-being through ecosystem service provision, providing models for mainstreaming ESP concepts into diverse sectoral policies [10]. Together, these frameworks demonstrate that effective ecological security planning requires both scientific rigor in spatial ecology and policy integration across governance levels and sectors. The continuing development of PEEN through national ecological networks and trans-European ecological corridors, combined with the EU's systematic promotion of Green Infrastructure, provides an evolving laboratory for ESP refinement and implementation that can inform ecological security initiatives globally [9] [10].

Ecological Security Patterns (ESPs) provide a strategic framework for landscape planning and biodiversity conservation, designed to mitigate the effects of habitat fragmentation and ecosystem degradation. These patterns form an interconnected network that safeguards ecological processes and maintains ecosystem services essential for sustainable development [13]. The construction of ESPs is based on the identification and interconnection of key landscape components, including ecological sources, corridors, nodes, and the assessment of landscape resistance [14] [13]. This methodology enables researchers and policymakers to prioritize conservation efforts, enhance landscape connectivity, and improve ecosystem resilience against anthropogenic pressures and climate change.

The theoretical foundation of ESPs originates from landscape ecology and conservation biology, integrating concepts of island biogeography and spatial ecology to address the challenges of habitat fragmentation. By understanding and applying the core terminology of ESPs, conservation professionals can develop scientifically-grounded strategies for maintaining ecological integrity across increasingly human-modified landscapes.

Core Terminology and Concepts

Ecological sources (or source areas) represent the foundation of ecological security patterns. These are landscape patches characterized by high-quality habitat, exceptional biodiversity value, or critical ecosystem functions that support species persistence and ecological processes [13]. Sources serve as primary habitats for native species and act as starting points for dispersal throughout the landscape.

Sources are typically identified through assessments of:

• Ecosystem service value: Areas providing essential services like water conservation, carbon sequestration, or soil retention [13]
• Ecological sensitivity: Regions with low disturbance tolerance or high conservation priority [13]
• Habitat quality: Patches with superior vegetation structure, resource availability, and minimal anthropogenic disturbance
• Biodiversity significance: Areas supporting rare, threatened, or endemic species concentrations

In practice, ecological sources often include protected areas, large natural vegetation patches, core wetland areas, and regions with high ecological integrity that can sustain viable populations of target species.

Ecological Corridors

Ecological corridors are geographically defined spaces that maintain or restore ecological connectivity by facilitating the movement of organisms, genetic exchange, and the flow of natural processes between ecological sources [15] [16]. These linear landscape elements counteract the negative effects of habitat fragmentation by allowing species to access resources, find mates, establish new territories, and respond to environmental changes.

Corridors function through multiple mechanisms:

• Daily movements: Enabling access to seasonal resources
• Seasonal migration: Supporting cyclical movement patterns
• Genetic exchange: Facilitating interbreeding between populations
• Range shifts: Allowing species distribution adjustments in response to climate change

Table 1: Classification of Ecological Corridors by Scale and Function

Corridor Type	Width Range	Primary Functions	Target Species
Local	<50 meters	Connecting remnant patches of gullies, wetlands, ridge lines	Small mammals, reptiles, amphibians, invertebrates
Sub-regional	>300 meters	Connecting larger vegetated landscape features like ridge lines and valley floors	Medium-sized mammals, forest birds
Regional	>500 meters	Connecting major ecological gradients such as migratory pathways	Large mammals, wide-ranging species, migratory animals

Corridors can be continuous strips of habitat or consist of "stepping stone" patches that provide temporary refuge during movement [16]. Their effectiveness depends on proper design considering target species' ecology, including width, vegetation structure, and connectivity to source areas.

Ecological Resistance

Ecological resistance refers to the inherent property of ecosystems, communities, or species to withstand disturbance without significant alteration to their fundamental structure or function [17]. In landscape ecology, resistance is quantified through resistance surfaces that represent the degree to which landscape features impede or facilitate movement between habitat patches.

Key principles of ecological resistance:

• Species-specific variation: Landscape elements present different resistance levels to different species
• Spatial heterogeneity: Resistance varies across landscapes based on topography, land use, and vegetation
• Behavioral influence: Animal movement decisions affect perceived resistance

Resistance is inversely related to sensitivity – highly resistant ecosystems or species demonstrate low sensitivity to disturbances, while sensitive systems show significant changes when subjected to environmental stress [17]. Understanding resistance is crucial for modeling ecological connectivity and identifying optimal corridor locations.

Ecological Nodes

Ecological nodes are strategic points within an ecological network that perform critical functions in maintaining or enhancing connectivity [14]. These include:

• Pinch points: Narrow passageways where corridors constrict, creating potential bottlenecks
• Intersection points: Locations where multiple corridors converge
• Stepping stones: Smaller habitat patches that facilitate movement between larger sources
• Barrier-crossing points: Strategic locations where wildlife crossings overcome anthropogenic barriers

Nodes serve essential roles in:

• Enhancing network connectivity by facilitating multi-path movement options
• Identifying priority areas for ecological restoration
• Providing supplementary habitat for corridor-dwelling species
• Serving as key locations for monitoring species movement

Recent research emphasizes the importance of considering both the external connectivity and internal structure of ecological nodes, as their functionality depends on size, shape, and habitat quality [14].

Methodological Framework

Technical Approaches for ESP Component Identification

Table 2: Methodologies for Identifying ESP Components

ESP Component	Identification Methods	Key Data Requirements	Output Metrics
Ecological Sources	Ecosystem service value assessment, Ecological sensitivity analysis, Habitat quality modeling	Land cover/use maps, Species distribution data, Remote sensing imagery	Source area, Spatial distribution, Ecosystem service capacity
Landscape Resistance	Resistance surface modeling, Expert elicitation, Species movement data	Land use, Topography, Human footprint, Road density, Vegetation coverage	Resistance values, Cost distances, Permeability maps
Ecological Corridors	Minimum Cumulative Resistance (MCR) model, Circuit theory, Least-cost path analysis	Resistance surfaces, Source locations	Corridor pathways, Width recommendations, Connectivity priority
Ecological Nodes	Circuit theory, Connectivity threshold analysis, Network centrality measures	Pinch point analysis, Barrier identification, Current flow maps	Node location, Priority rank, Restoration urgency

Experimental Protocols and Analytical Workflows

Protocol 1: Ecological Source Identification through Ecosystem Service Assessment

• Data Collection: Gather spatial data on land use/cover, vegetation indices, species distributions, and environmental factors
• Ecosystem Service Valuation: Quantify key services (water conservation, soil retention, carbon sequestration, biodiversity maintenance) using standardized valuation methods
• Ecological Sensitivity Analysis: Evaluate sensitivity to disturbance using factors like slope, vegetation fragility, and erosion risk
• Spatial Overlay Analysis: Combine ecosystem service and sensitivity assessments to identify priority areas
• Threshold Determination: Establish quantitative criteria for source selection (e.g., top 20% of ecosystem service value)
• Validation: Ground-truth identified sources through field surveys and species occurrence data

Protocol 2: Corridor Delineation Using MCR Model and Circuit Theory

• Resistance Surface Development: Assign resistance values to landscape features based on species-specific permeability
• Source Input Preparation: Define ecological sources as movement origins and destinations
• MCR Calculation: Compute cumulative resistance costs between sources using GIS algorithms: MCR = fmin ∑ (Dij × Ri) where Dij is the distance through landscape grid cell i, and Ri is the resistance value of cell i
• Least-Cost Path Identification: Determine optimal corridor routes between source pairs
• Circuit Theory Application: Model movement patterns as electrical current flow to identify multiple potential pathways and pinch points
• Corridor Validation: Use field surveys, camera traps, or genetic markers to confirm species use

Protocol 3: Node Identification and Prioritization

• Pinch Point Analysis: Use circuit theory to identify areas where movement probability is concentrated
• Connectivity Threshold Assessment: Determine minimum node size and habitat quality requirements for target species
• Barrier Analysis: Locate points where anthropogenic features disrupt connectivity
• Network Centrality Calculation: Apply graph theory metrics (betweenness, degree centrality) to identify critical network elements
• Restoration Priority Ranking: Develop multi-criteria decision framework for node prioritization

Research Applications and Implementation

Case Study: ESP Construction in Black Soil Regions

A 2025 study on China's black soil region demonstrated dynamic changes in ESP components from 2002-2022, revealing:

• Ecosystem service functions exhibited a spatial pattern of higher values in the east and lower values in the west
• Ecological sensitivity decreased annually despite ongoing environmental pressures
• The number of ecological source areas decreased, but their total area increased due to consolidation
• Ecological corridor numbers decreased, but length fluctuated, with stepping-stone nodes significantly increasing [13]

This research employed a "point-line-polygon-network" optimization strategy, constructing ecological belts, strengthening ecological barriers, and restoring connectivity of ecological nodes and corridors to improve regional ecosystem stability.

Case Study: Wu'an Resource-Based City

In Wu'an, China, researchers identified 247 ecological sources totaling 60,829.90 hectares (33.43% of the study area), mainly distributed in the northwest and southwest regions [14]. The study focused on optimizing the internal structure of ecological nodes through land-use adjustments, demonstrating that targeted modifications within nodes could significantly enhance overall network connectivity without large-scale land acquisition.

Global Conservation Applications

Major international corridor initiatives illustrate ESP implementation at scale:

• Yellowstone to Yukon Conservation Initiative: Connecting protected areas across North America [15] [16]
• Mesoamerican Biological Corridor: Enhancing connectivity throughout Central America [16] [18]
• European Green Belt: Following the former Iron Curtain as an ecological network [16]
• Eastern Himalayan Corridor: Connecting protected areas across international borders [16]

The Scientist's Toolkit: Research Reagents and Essential Materials

Table 3: Essential Research Tools for ESP Analysis

Tool Category	Specific Tools/Platforms	Primary Function	Application Context
Geospatial Analysis	ArcGIS, QGIS, GRASS GIS	Spatial data processing, Resistance surface creation, corridor mapping	Core platform for spatial analysis and visualization
Connectivity Modeling	Circuitscape, Linkage Mapper, Conefor	Circuit theory analysis, Least-cost path modeling, Network connectivity metrics	Specialized connectivity analysis and corridor optimization
Remote Sensing Data	Landsat, Sentinel, MODIS, LiDAR	Land cover classification, Vegetation monitoring, Habitat mapping	Source identification, Change detection, Habitat quality assessment
Field Validation	Camera traps, GPS collars, Hair snares, Genetic sampling	Species presence confirmation, Movement tracking, Population monitoring	Corridor use verification, Node functionality assessment
Statistical Analysis	R, Python, FRAGSTATS	Landscape pattern metrics, Statistical modeling, Data visualization	Quantitative analysis, Model validation, Result interpretation

Conceptual Relationships in ESP Framework

The conceptual framework illustrates how ESP components interact: ecological sources anchor the network, landscape resistance shapes corridor configuration, corridors intersect at strategic nodes, and together these elements form a coherent security pattern that enhances ecosystem resilience and functionality.

The systematic framework of ecological sources, corridors, resistance, and nodes provides conservation professionals with a powerful methodology for addressing the escalating challenges of habitat fragmentation and biodiversity loss. By integrating these concepts through standardized protocols and analytical tools, researchers can develop robust ecological security patterns tailored to specific landscapes and conservation objectives. The continued refinement of ESP methodologies—particularly through dynamic temporal analysis and improved integration of ecological processes—represents a critical frontier in conservation science with profound implications for global biodiversity protection and ecosystem service maintenance.

Building Ecological Security Patterns: A Step-by-Step Guide to Frameworks and Real-World Applications

Ecological Security Patterns (ESP) represent a strategic spatial framework essential for biodiversity conservation, environmental quality maintenance, and regional sustainable development [19]. The concept, which systematically integrates habitat patches, ecological corridors, and buffer zones, provides robust theoretical support and practical guidance for biological conservation and landscape planning [19]. The construction of ESP has been elevated to a national strategic level in China, forming a crucial component of the country's ecological civilization construction and offering a feasible implementation path to mitigate conflicts between economic development and ecological protection [19]. The standard workflow of "Source Identification - Resistance Surface - Corridor Extraction" has evolved into a fundamental and core research approach for ESP development, establishing a strong theoretical foundation and research framework for ecological environmental protection and restoration [19]. This methodology is particularly vital for ecologically vulnerable areas, including karst regions and major river basins, where it provides a scientific basis for targeted restoration strategies and spatial boundary control of urban development [20] [21] [19].

Foundational Concepts and Theoretical Framework

The theoretical foundation of ESP construction originates from landscape ecology and regional ecosystem development strategies. Forman's "patch-corridor-matrix" model of landscape ecology laid the groundwork for studying landscape patterns and their ecological functions [20]. This model conceptualizes landscapes as interconnected systems where ecological flows move between patches through corridors within a broader matrix. The ESP approach further integrates principles from landscape ecology, ecological thresholds, and connectivity to ensure ecosystem integrity and landscape sustainability [22].

The "source-sink" theory provides another critical theoretical foundation, where "sources" represent high-quality habitat patches that facilitate ecological processes, while "sinks" refer to landscapes that impede these processes [20]. This theory helps in understanding the spatial dynamics of ecological flows and identifying key areas for conservation and restoration. The comprehensive ESP framework extends beyond basic ecological elements to incorporate "source-resistance surface-corridor-node-network space" as an integrated system [19], providing a more holistic approach to spatial conservation planning.

Table 1: Key Theoretical Concepts in ESP Construction

Concept	Definition	Role in ESP Framework
Ecological Sources	High-quality habitat patches that support biodiversity and ecological processes	Serve as starting points for ecological flows and core areas for conservation
Resistance Surface	A spatial layer representing landscape resistance to species movement and ecological flows	Determines the ease or difficulty of movement between sources
Ecological Corridors	Linear landscape elements that facilitate connectivity between ecological sources	Enable species movement, gene flow, and ecological processes across the landscape
Ecological Nodes	Strategic locations along corridors that are critical for maintaining connectivity	Serve as stepping stones or identify areas requiring restoration or protection

The Standard ESP Construction Workflow

Stage 1: Ecological Source Identification

The first critical step in ESP construction involves identifying ecological sources - areas of significant ecological value that serve as primary habitats for species and core zones for ecological processes. The methodology for source identification has evolved to incorporate multiple approaches, including ecological importance assessment, nature reserve integration, and advanced spatial analysis techniques [21] [19].

The Morphological Spatial Pattern Analysis (MSPA) model has emerged as a powerful tool for scientifically and accurately identifying ecological sources based on landscape connectivity and pattern analysis [19]. This method analyzes binary raster images (typically land use/land cover classifications) to categorize landscape patterns into seven morphological types: core, islet, perforation, edge, loop, bridge, and branch. The core areas often serve as potential ecological sources, particularly when combined with landscape connectivity index analysis to evaluate their functional importance in maintaining ecological networks [19].

Complementary approaches include integrating results from ecological importance assessment with data on existing nature reserves [21]. This combined method ensures that designated protected areas are incorporated as fundamental components of the ecological security pattern. Additionally, the perspective of "natural-social" multi-source elements has been advocated to create a more comprehensive assessment system that considers both ecological and human dimensions [20].

Table 2: Methodological Approaches for Ecological Source Identification

Method	Key Indicators	Application Context
MSPA Model	Core areas, connectivity indices, landscape metrics	Forest ecosystems, karst desertification control forests [19]
Ecological Importance Assessment	Ecosystem services, sensitivity, vulnerability	Regional planning, watershed management [21]
"Natural-Social" Multi-source Elements	Landscape pattern indices, social development factors	Basin landscapes, urban-rural interfaces [20]
Landscape Ecological Risk Assessment	Risk indices, spatial distribution patterns	Areas with significant human disturbance, river basins [20]

Stage 2: Constructing the Ecological Resistance Surface

The construction of an ecological resistance surface represents a crucial intermediate step that quantifies the landscape's impediment to species movement and ecological flows. This surface is built based on various resistance factors that influence movement through different landscape types. The resistance values typically range from 1 (lowest resistance) to 100 (highest resistance), with water bodies and forests generally offering lower resistance, while urban areas and bare lands present higher resistance to ecological flows [20].

Contemporary approaches construct comprehensive resistance surfaces by integrating both natural factors (elevation, slope, vegetation coverage, etc.) and human factors (land use type, distance to roads, population density, etc.) [19]. The Fraction Vegetation Coverage (FVC) serves as a particularly important indicator, with higher vegetation coverage generally correlating with lower resistance values [20]. In the Lower Yellow River affected area, for instance, water bodies were assigned a resistance value of 1, while construction lands received values as high as 100, with forestlands (20), shrublands (30), and cultivated lands (60) occupying intermediate positions [20].

Advanced methodologies have also incorporated landscape ecological risk assessment (LERA) results into resistance surface construction. This integrated approach recognizes that areas with higher ecological risk present greater resistance to ecological flows. The LERA system typically evaluates risks from three perspectives: "natural environment - human society - landscape pattern," creating a more nuanced understanding of resistance dynamics across the landscape [20].

Diagram 1: Ecological Resistance Surface Construction Workflow

Stage 3: Corridor Extraction and Network Optimization

The final stage involves extracting ecological corridors and identifying ecological nodes to form a complete ecological security network. The Minimum Cumulative Resistance (MCR) model is the most widely used method for corridor extraction, calculating the path of least resistance between ecological sources [20] [21]. The MCR model is based on the formula:

MCR = fmin Σ(Dij × Ri)

Where Dij represents the distance from source j to spatial unit i, and Ri is the resistance coefficient of spatial unit i [20]. This model effectively identifies the optimal pathways for ecological flows between sources, forming the basic corridor network.

More recently, circuit theory has emerged as a promising alternative approach that addresses some limitations of the MCR model. While MCR determines direction and location effectively, it overlooks the random movement behavior of species. Circuit theory models landscape connectivity by analogizing the landscape as an electrical circuit, where current flow represents the probability of species movement [19]. This method is particularly effective for identifying pinch points and barriers in the landscape, which correspond to critical ecological nodes requiring conservation attention or restoration intervention [19].

The integration of MSPA and MCR models has proven highly effective, as it quantifies key ecological nodes and simulates optimal path networks, effectively bridging the gap between pattern characterization and process simulation in integrated studies [19]. This combined approach has been successfully applied in various contexts, including desertification control forests in South China Karst, where it identified 108, 68, and 113 ecological corridors across three different research areas [19].

Diagram 2: Corridor Extraction and Network Optimization Process

Experimental Protocols and Methodological Details

Comprehensive Protocol for ESP Construction

A standardized experimental protocol for ESP construction involves sequential steps with specific methodological considerations at each stage:

Phase 1: Data Collection and Preprocessing

• Acquire land use/land cover (LULC) data from satellite imagery (Landsat, Sentinel) with a minimum classification accuracy of 85%
• Collect topographic data (Digital Elevation Model) with resolution appropriate to study scale (typically 30m or finer)
• Obtain vegetation indices (NDVI, FVC) from multispectral satellite data
• Compile anthropogenic data layers including road networks, population density, and protected areas

Phase 2: Ecological Source Identification

• Apply MSPA using GUIDOS Toolbox or similar software with 8-pixel connectivity rule
• Calculate landscape connectivity indices (dPC, IIC) using CONEFOR software
• Integrate ecological importance assessment results using spatial overlay analysis
• Select final ecological sources based on combined criteria: core area > minimum patch threshold, high connectivity value, and ecological significance

Phase 3: Resistance Surface Development

• Select appropriate resistance factors through literature review and expert consultation
• Normalize factor values to consistent measurement scale (1-100)
• Determine factor weights using Analytical Hierarchy Process (AHP) or similar method
• Validate resistance values through field verification or species occurrence data when available

Phase 4: Corridor Extraction and Validation

• Implement MCR model using GIS software with cost distance tools
• Apply circuit theory using Circuitscape software with 8-neighbor connection scheme
• Identify ecological nodes as areas with high current density or convergence
• Field validate corridor locations through ground truthing and species presence surveys

Advanced Analytical Techniques

Recent methodological advances have incorporated more sophisticated analytical approaches:

Landscape Ecological Risk Assessment (LERA) Integration The LERA system establishes a comprehensive assessment framework based on "natural environment - human society - landscape pattern" dimensions [20]. This involves calculating landscape pattern indices including Shannon's Evenness Index (SHEI), Contagion Index (CONTAG), and Fraction Vegetation Coverage (FVC) to create a holistic risk profile that informs resistance surface development [20].

Spatial Pattern Analysis The SPCA (Spatial Principal Component Analysis) method is used to analyze the spatial distribution of landscape ecological risk, revealing patterns such as the uniform distribution observed in the Lower Yellow River affected area, where risks were "slightly higher in the northeast than the southwest" [20]. This analytical approach helps identify priority areas for intervention within the ESP framework.

Network Optimization Analysis After initial corridor extraction, network analysis methods evaluate and optimize the ESP structure. This includes assessing connectivity metrics before and after optimization to ensure improved stability of the network structure, as demonstrated in the AALYR study where this approach enhanced the overall robustness of the ecological network [20].

Table 3: Key Analytical Methods and Their Applications in ESP Construction

Analytical Method	Primary Function	Software/Tools	Key Output Metrics
MSPA	Identifies ecological patterns and core areas	GUIDOS Toolbox	Core areas, bridges, branches
Landscape Connectivity Analysis	Evaluates functional connectivity between patches	CONEFOR, Conefor 2.6	Probability of Connectivity (PC), Integral Index of Connectivity (IIC)
MCR Model	Calculates least-cost paths and corridors	ArcGIS, QGIS, Linkage Mapper	Cost paths, cumulative resistance values
Circuit Theory	Models movement probability and identifies pinch points	Circuitscape	Current density, pinch points, barriers
Spatial Statistics	Analyzes spatial patterns and distributions	Fragstats, R packages	Spatial autocorrelation, hotspot analysis

The construction of ESP requires specialized "research reagents" in the form of spatial data, analytical tools, and methodological frameworks. The table below details essential components for implementing the standard ESP workflow:

Table 4: Research Reagent Solutions for ESP Construction

Category	Specific Tool/Data	Specifications	Primary Application
Remote Sensing Data	Landsat 8/9 OLI	30m spatial resolution, 16-day revisit	Land use/land cover classification
	Sentinel-2 MSI	10-20m resolution, 5-day revisit	Vegetation indices (NDVI, FVC)
	MODIS Vegetation Indices	250m-1km resolution, daily	Large-scale vegetation monitoring
Spatial Data	SRTM DEM	30m resolution digital elevation model	Topographic analysis, slope calculation
	OpenStreetMap	Vector data for roads, settlements	Anthropogenic resistance factors
	WorldPop	100m resolution population data	Human activity intensity assessment
Software Tools	ArcGIS Spatial Analyst	Cost distance, path analysis	MCR modeling, corridor extraction
	GUIDOS Toolbox	MSPA implementation	Ecological source identification
	Circuitscape	Circuit theory implementation	Corridor modeling, node identification
	CONEFOR 2.6	Landscape connectivity analysis	Source importance evaluation
Methodological Frameworks	"Source-Resistance-Corridor"	Standard ESP workflow	Overall ESP construction
	Landscape Ecological Risk Assessment	"Natural-social" multi-dimensional	Risk-informed resistance surfaces
	Network Optimization	Graph theory applications	ESP structure evaluation and improvement

The standard ESP construction workflow of "Source Identification - Resistance Surface - Corridor Extraction" provides a robust, methodical approach to spatial conservation planning. This framework has proven effective across diverse contexts, from the river basins [20] to karst desertification control forests [19], and from urban regions [21] to agricultural hubs [20]. The integration of advanced methodologies like MSPA, landscape connectivity analysis, MCR modeling, and circuit theory has significantly enhanced the precision and ecological relevance of ESP constructions.

Future research priorities include addressing critical dimensions that have received insufficient attention, such as underlying ecological mechanisms, spatiotemporal dynamics, and spillover effects [22]. Particularly important is developing the ESP framework to better incorporate China's specific ecological needs and national context [19], and expanding applications to outflow-type watersheds and ecologically vulnerable areas that have been understudied [19]. The continued refinement of this standardized workflow will strengthen its scientific foundation and promote broader international acceptance [22], ultimately contributing to more effective ecological conservation and restoration planning worldwide.

Ecological Security Patterns (ESPs) provide a spatial framework essential for maintaining regional ecological stability and biodiversity amidst rapid global change [23]. Identifying ecological sources—the core patches sustaining key ecological processes—forms the foundational step in constructing robust ESPs. This process strategically integrates the assessment of ecosystem services (ESs) to quantify ecological functionality and ecological sensitivity to understand inherent vulnerability [24] [23]. This technical guide details the methodologies for conducting these assessments, framing them within the integrated "supply-demand-corridor-node" research paradigm crucial for modern ecological planning [23].

Theoretical Framework and Core Concepts

The identification of ecological sources transcends the simple selection of green patches; it involves pinpointing areas critical to regional ecological health based on their capacity to supply services and their resilience to stress. Ecosystem services represent the tangible benefits human populations derive from ecosystems, including provisioning (e.g., water, food), regulating (e.g., climate, flood regulation), supporting (e.g., soil formation), and cultural services [25]. The ESs supply is the ecosystem's capacity to provide these benefits.

Ecological sensitivity, alternatively referred to as ecological vulnerability, is the degree to which an ecosystem's structure and function are susceptible to, and unable to cope with, adverse effects of external stressors, including both natural and anthropogenic factors [24] [25]. It is a product of the ecosystem's inherent properties and the external pressures it faces. Highly sensitive areas are more prone to degradation and require prioritized conservation. The synergy between these concepts is clear: high-quality ecological sources exhibit a high supply capacity of key ESs and low ecological sensitivity, ensuring their long-term stability and functionality [23].

Table 1: Core Concepts in Ecological Source Identification

Concept	Definition	Role in ESP Development
Ecological Source	Core patches of landscape that are crucial for maintaining ecological processes and biodiversity.	Serves as the foundation and starting point for building ecological networks and security patterns.
Ecosystem Service (ES) Supply	The capacity of an ecosystem to provide goods and services beneficial to human well-being.	Used to identify and rank potential ecological sources based on their functional importance.
Ecological Sensitivity/ Vulnerability	The susceptibility of an ecosystem's structure and function to alteration or degradation from external stressors.	Identifies fragile areas requiring conservation and areas unsuitable for development, refining source selection.
Ecological Security Pattern (ESP)	A spatial configuration of landscape elements designed to ensure the continuity of ecological processes and the integrity of regional ecosystems.	The ultimate framework for guiding regional ecological conservation and sustainable spatial planning.

Methodologies for Assessing Ecosystem Services

Quantifying the supply of ecosystem services is critical for identifying which areas function as significant ecological sources. The following protocols outline robust methods for this assessment.

Integrated ESs Assessment Using the InVEST Model

The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model suite is a widely adopted tool for spatially explicit ESs quantification [23].

Experimental Protocol:

• Objective: To map and quantify the spatial supply of key ecosystem services (e.g., carbon storage, soil conservation, water yield, habitat quality) for ecological source identification.
• Data Requirements:
  • Land Use/Land Cover (LULC) Data: High-resolution raster datasets (e.g., 30m) categorized into types like forest, grassland, cropland, wetland, urban, and barren land.
  • Biophysical Data: This includes a carbon pool table (for carbon storage), soil depth and texture (for soil conservation), precipitation and evapotranspiration data (for water yield), and threat sources and their impact distances (for habitat quality).
  • Topographic Data: Digital Elevation Model (DEM) to derive slope, which is crucial for soil conservation calculations.
• Workflow:
  • Data Preprocessing: Standardize all raster datasets to a common resolution and projection. Assign relevant values to the LULC classes in the biophysical tables required by each InVEST module.
  • Model Execution: Run the respective InVEST models (e.g., Carbon Storage, Sediment Delivery Ratio, Annual Water Yield, Habitat Quality) independently.
  • Output Standardization: Rescale the raw output maps of each ES to a comparable scale (e.g., 0-1) using max-min normalization.
  • Synthesis: Combine the normalized layers using an arithmetic mean or a weighted linear combination based on regional conservation priorities to generate a comprehensive ESs supply map.

The diagram below illustrates this integrated workflow.

The Remote Sensing Ecological Index (RSEI)

For a more rapid, satellite-based assessment, the Remote Sensing Ecological Index (RSEI) provides a valuable alternative. It integrates four key indicators derived from satellite imagery: greenness (e.g., NDVI), humidity (from tasseled cap transformation), heat (Land Surface Temperature), and dryness (NDBSI, combining built-up and bare soil indices) [24].

Experimental Protocol:

• Data Source: Acquire satellite imagery, such as Landsat or MODIS (e.g., MOD13Q1 for vegetation indices).
• Indicator Calculation: Compute the four component indices (NDVI, Wetness, LST, NDBSI) from the preprocessed satellite images.
• Principal Component Analysis (PCA): Perform PCA on the four indicator layers. The first principal component (PC1) typically captures the majority of the variance and information, representing the integrated RSEI.
• Interpretation: The RSEI value ranges from 0 to 1, where higher values indicate better ecological quality [24].

Methodologies for Assessing Ecological Sensitivity

Ecological sensitivity assessment evaluates an ecosystem's susceptibility to external disturbances. The Sensitivity-Resilience-Pressure (SRP) model is a comprehensive framework for this purpose [25].

Experimental Protocol:

• Objective: To create a spatial map of ecological sensitivity/vulnerability by integrating environmental and anthropogenic pressure factors.
• Indicator System Construction: Develop a multi-layered indicator system. A sample framework with 15 indicators, as used in arid regions, is shown below [24].
• Data Standardization: Normalize all indicator data to a uniform scale (e.g., 0-1) to ensure comparability. The standardization direction (higher score = higher sensitivity) must be consistent.
• Weight Assignment and Calculation: Assign weights to each indicator and sub-criterion, often using expert judgment or analytical methods like the Analytic Hierarchy Process (AHP). The Ecological Vulnerability Index (EVI) is then calculated using the weighted sum method [25]: EVI = (Sensitivity Index × W_S) + (Resilience Index × W_R) + (Pressure Index × W_P) Higher EVI values indicate greater ecological sensitivity.

Table 2: Example Indicator System for Ecological Sensitivity Assessment

Criterion Layer	Indicator Layer	Measurement Proxy & Rationale
Topographic Sensitivity	Slope	DEM-derived slope gradient; steeper slopes are more sensitive to erosion.
	Elevation	DEM-derived elevation; influences temperature and precipitation patterns.
Climatic Sensitivity	Annual Precipitation	Interpolated meteorological data; water availability is a key stressor.
	Land Surface Temperature	MODIS-derived LST; heat stress affects ecosystem function [24].
Ecological Vitality	Fractional Vegetation Cover (FVC)	NDVI-derived; indicates ecosystem health and resilience [25].
	Habitat Quality	Modeled (e.g., via InVEST); reflects biodiversity support capacity.
Human Pressure	Land Use Intensity Index	Classified LULC with assigned intensity weights; direct anthropogenic impact.
	Population Density	Socio-economic census data; proxy for human activity pressure [25].
	Distance to Roads	GIS buffer analysis; proximity to infrastructure increases disturbance.

Integration for Ecological Source Identification and ESP Construction

The final stage integrates the ESs supply and ecological sensitivity assessments to precisely delineate ecological sources and integrate them into a full ESP.

Ecological sources are typically identified as areas simultaneously exhibiting high ESs supply and low ecological sensitivity (or low EVI) [25] [23]. The workflow involves:

• Reclassification: The comprehensive ESs supply map and the EVI map are reclassified into distinct levels (e.g., high, medium, low).
• Overlay Analysis: The two reclassified maps are overlaid using GIS tools.
• Source Selection: Patches categorized as "High ESs supply" and "Low EVI" are selected as primary candidate ecological sources. Additional criteria, such as a minimum patch area threshold, are applied to finalize the ecological source map.

Constructing the Full Ecological Security Pattern

With ecological sources defined, the ESP is constructed using the "supply-demand-corridor-node" framework [23].

• Resistance Surface: A resistance surface, representing the cost or difficulty for ecological flows to cross the landscape, is created. This surface should be informed by both land use types and the demand for ESs, which reflects socio-economic pressures that can impede ecological flows [23].
• Corridor Identification: Using models like Linkage Mapper or Circuitscape based on circuit theory, ecological corridors are identified as the least-cost paths for movement between ecological sources [23].
• Node Identification: Circuit theory further helps pinpoint key areas: ecological pinch points (narrow, crucial corridors requiring protection) and ecological barrier points (degraded areas blocking connectivity, requiring restoration) [23].

The following diagram visualizes this comprehensive, integrated process from data to final ESP.

The Scientist's Toolkit: Essential Reagents and Data Solutions

Successful implementation of these methodologies relies on specific, high-quality data and analytical tools. The following table catalogs the essential "research reagents" for this field.

Table 3: Essential Research Reagents and Data Solutions for ESP Development

Category	Item/Software	Primary Function & Application
Core Data Resources	Landsat/Sentinel-2 Imagery	Provides medium-to-high resolution multispectral data for LULC classification and index calculation (e.g., NDVI).
	MODIS Products (e.g., MOD13Q1, MOD16A3)	Offers long-term, frequent global data for vegetation (NDVI), evapotranspiration, and other phenological metrics.
	SRTM DEM	Provides global topographic data for slope, aspect, and elevation analysis.
	Soil Databases (HWSD)	Supplies critical soil parameters (texture, depth, organic content) for modeling soil erosion and water yield.
	National Meteorological Datasets	Source for precipitation, temperature, and other climatic variables for spatial interpolation and climate sensitivity analysis.
Analytical & Modeling Software	InVEST Model Suite	The primary tool for spatially explicit modeling and mapping of multiple ecosystem services.
	ArcGIS / QGIS	The central platform for all geospatial data management, processing, analysis, and cartographic output.
	R / Python with geospatial libraries	Provides advanced statistical analysis, data manipulation, and custom script-based geospatial computations.
	Linkage Mapper Toolkit	A GIS toolset for designing wildlife corridors and regional connectivity networks between core habitats.
	Circuitscape	An advanced circuit theory-based model for predicting connectivity and identifying pinch points and barriers.
Specialized Indices & Models	Remote Sensing Ecological Index (RSEI)	A rapid, satellite-based index for integrated ecological quality assessment using four key indicators.
	Sensitivity-Resilience-Pressure (SRP) Model	A conceptual and quantitative framework for constructing a comprehensive ecological vulnerability index (EVI).

Ecological Security Patterns (ESPs) provide a strategic spatial framework essential for maintaining regional ecological security and promoting sustainable development by ensuring the stability and connectivity of ecosystems [26]. The construction of ESPs relies on a triad of core components: the identification of ecological sources, the modeling of ecological resistance surfaces, and the extraction of ecological corridors and nodes [27]. Within this framework, the ecological resistance surface is a foundational spatial model that quantifies the potential impediments to ecological flows, such as species movement and energy exchange, across a landscape [28] [26]. Accurately modeling this resistance is critical, as it directly influences the identification of optimal corridor pathways and the overall functionality of the ecological network.

Traditional approaches to constructing resistance surfaces often rely on a limited set of land use types or expert opinion to assign resistance values, which can lack objectivity and fail to capture the complex, multi-faceted pressures facing ecosystems [26]. This technical guide advances a more robust and scientifically-grounded methodology by integrating two powerful analytical paradigms: Landscape Ecological Risk (LER) and the Human Footprint Index (HFI).

LER assessment moves beyond single-source risks to evaluate the potential negative impacts of landscape pattern changes on ecosystem structure and function, reflecting a landscape's anti-interference capacity and inherent vulnerability [29] [30]. Simultaneously, the HFI provides a direct, quantifiable measure of the intensity and spatial distribution of human interference, incorporating factors such as built infrastructure, land transformation, and population density [31] [32] [33]. The integration of LER (an ecological response) and HFI (an anthropogenic pressure) into a composite resistance surface provides a holistic representation of the socio-ecological barriers to ecological connectivity, thereby enabling the construction of more resilient and effective ESPs for ecological management and restoration [28] [33] [26].

Theoretical and Conceptual Foundations

Landscape Ecological Risk (LER) in Resistance Modeling

Landscape Ecological Risk assessment is predicated on the understanding that the spatial configuration of a landscape—its pattern—exerts a profound influence on ecological processes and, consequently, on the ecosystem's vulnerability to disturbances. The core premise is that landscapes with higher ecological risk pose greater resistance to ecological flows. This risk is typically conceptualized through a model incorporating landscape disturbance, vulnerability, and loss.

The LER index (LERI) is often calculated using a weighted combination of landscape metrics that capture pattern characteristics linked to ecosystem stability. A standard formulation is:

LERI = Landscape Disturbance Index × Landscape Vulnerability Index

Where the Landscape Disturbance Index is frequently derived from metrics like fragmentation, separation, and dominance of landscape types, while the Landscape Vulnerability Index is assigned based on the inherent susceptibility of different land use/cover types (e.g., water bodies and forests may be assigned higher vulnerability than built-up land) [30] [26]. The resulting LERI provides a spatially explicit measure of ecological fragility, where higher values indicate areas less conducive to ecological flows and thus warranting higher resistance values.

Human Footprint Index (HFI) as a Direct Pressure Metric

The Human Footprint Index offers a complementary perspective by directly quantifying the cumulative pressure of human activities on the environment. It translates various anthropogenic stressors into a coherent spatial layer that defines the difficulty biological flows encounter when moving through a human-modified landscape.

Modern HFI assessments, especially high-resolution ones, move beyond coarse proxies like nighttime lights. They integrate a suite of direct and observable variables, including:

• Land cover transformation: Proportion of area converted to artificial surfaces, croplands, or pastures [31] [32].
• Infrastructure density: Distance to and density of roads, railways, and other linear infrastructures that create barriers and increase mortality [31] [28].
• Building density: Used as a high-resolution proxy for human population density and activity intensity [31].
• Accessibility: Proximity to navigable waterways and coastlines, which can facilitate human access and resource extraction [32].

These variables are normalized, weighted, and summed to create a composite HFI layer, where higher values signify more intense human modification and, therefore, higher resistance to ecological movement [31] [33].

The Synergy of Integrated Resistance

Integrating LER and HFI creates a resistance surface that encapsulates both the ecological consequence of landscape pattern (LER) and the anthropogenic pressure causing it (HFI). This synergy is critical because a landscape might exhibit high ecological risk due to its intrinsic configuration (e.g., a fragmented forest patch) even with a moderate immediate human presence, or it might be subject to intense human pressure (e.g., urban periphery) whose ecological consequences are still unfolding. The combined model captures both scenarios, leading to a more anticipatory and comprehensive resistance surface for ESP construction [28] [33].

Table 1: Core Data Requirements for Constructing an Integrated Resistance Surface

Data Category	Specific Datasets	Spatial Resolution	Primary Use
Land Use/Land Cover (LULC)	Sentinel-2, Landsat, or national LULC products (e.g., MapBiomas) [31]	10m - 30m	LER calculation (landscape metrics), HFI (land cover class)
Human Infrastructure	OpenStreetMap, official national transport databases [31]	Vector lines	HFI (distance to roads/railways)
Built Environment	Microsoft/Google Building Footprints [31]	Vector points/polygons	HFI (building density)
Topography	SRTM DEM, ASTER GDEM [30]	30m	Underlying resistance factor; slope influences human access & ecological flow
Ecosystem Services	NPP, Habitat Quality, Soil Erosion (from InVEST, etc.) [29]	30m - 100m	Refining LER or landscape vulnerability
Population & Activity	WorldPop, Nighttime Light Data (e.g., VIIRS) [30]	100m - 1km	HFI (proxy for human activity intensity)

Methodological Protocols

This section provides a detailed, step-by-step workflow for constructing the integrated LER-HFI resistance surface.

The following diagram visualizes the end-to-end methodological pipeline for this approach.

Protocol 1: Quantifying Landscape Ecological Risk (LER)

Objective: To compute a spatially explicit Landscape Ecological Risk Index (LERI) that reflects the vulnerability and disturbance of the landscape.

• Landscape Metric Calculation: Using Fragstats or similar software, calculate the following key landscape pattern metrics for each landscape type (e.g., forest, grassland, cropland) within pre-defined assessment units (e.g., 20km x 20km grids or H3 hexagons [30] [34]):

  • Fragmentation (Ci): Ci = ni / ai, where ni is the number of patches and ai is the total area of landscape type i.
  • Separation (Si): Si = (1/2) * √(ni / A) * (1 - Ai), where A is the total landscape area and Ai is the area proportion of landscape type i.
  • Dominance (Di): Measured by the deviation from the expected proportional representation of the landscape type.

• Landscape Disturbance Index (Ei): Combine the above metrics for each landscape type i using a weighted sum. A common weighting is [26]: Ei = 0.5 * Ci + 0.3 * Si + 0.2 * Di

• Landscape Vulnerability Index (Vi): Assign a relative vulnerability weight to each landscape type based on its ecological function and stability. For example, on a normalized scale of 0-1, assign weights such as: water body (1.0), forest (0.8), grassland (0.6), cropland (0.4), barren land (0.2), construction land (0.1) [30] [26]. Advanced Approach: This can be refined using ecosystem service values (e.g., NPP, habitat quality, soil retention) to replace subjective assignments, making the index more ecologically meaningful [29].

• Landscape Ecological Risk Index (LERI): Calculate the final LERI for each assessment unit k using the formula [30] [26]: LERIk = ∑ (Aki / Ak) * (Eki * Vki) Where Aki is the area of landscape type i in unit k, and Ak is the total area of unit k. The result is a raster layer where each cell/polygon holds a LERI value.

Protocol 2: Calculating the Human Footprint Index (HFI)

Objective: To produce a high-resolution raster layer representing the cumulative intensity of human pressure.

• Variable Selection and Data Preparation: Select and prepare the core HFI variables as rasters at a consistent resolution (e.g., 10m-30m). Key variables include [31] [32]:

  • Land use/cover (e.g., reclassified into levels of human impact)
  • Distance from roads and railways
  • Building footprint density (using kernel density on point/polygon data)
  • Distance from navigable waterways

• Variable Normalization: Normalize each variable to a common scale (e.g., 0-100 or 0-1) to ensure comparability. For distance variables, this typically involves a negative exponential transformation where resistance decreases with distance.

• Weight Assignment and Integration: Assign weights to each variable based on its relative impact on ecological permeability. Weights can be derived from expert opinion, statistical methods like Spatial Principal Component Analysis (SPCA) [28], or Random Forest analysis. The composite HFI is then calculated as: HFI = (w1 * Var1) + (w2 * Var2) + ... + (wn * Varn) Where w are the weights and Var are the normalized variable rasters.

Table 2: Example HFI Variable Weights Derived from SPCA [28]

Human Footprint Variable	Example Weight	Justification
Land Use Type (e.g., urban vs. forest)	0.35	Directly represents physical habitat loss and transformation.
Distance from Major Roads	0.25	Major barrier effect, source of noise, pollution, and mortality.
Nighttime Light Intensity	0.20	Proxy for general human activity and energy use.
Building Density	0.15	Direct measure of urbanization and impervious surfaces.
Vegetation Cover (NDVI)	0.05	Inverse proxy; lower cover often correlates with higher disturbance.
Total	1.00

Protocol 3: Integrating LER and HFI into a Composite Resistance Surface

Objective: To fuse the LER and HFI layers into a single, normalized resistance surface.

• Spatial Resampling and Alignment: Ensure the LER and HFI rasters have the same cell size, extent, and coordinate system.

• Data Normalization: Reclassify both the LER and HFI rasters to a consistent resistance scale, typically 1-100, where 1 represents the lowest resistance and 100 the highest. This can be done using a linear stretch or based on quantile breaks.

• Weighted Integration: Combine the normalized layers using a weighted overlay. The weights should reflect the relative importance of inherent ecological risk versus direct human pressure in your study context. A balanced starting point is a 50/50 weighting. Composite Resistance = (Weight_LER * Normalized_LER) + (Weight_HFI * Normalized_HFI)

• Validation and Calibration: Ideally, validate the composite resistance surface against independent data, such as known animal movement paths or plant dispersal data [31]. If such data is available, use logistic regression or MaxEnt to calibrate the weights of LER and HFI to best predict the observed movement. Without validation data, the surface remains a theoretically informed model.

This section catalogs the key data, tools, and models required to implement the described methodology.

Table 3: Essential Reagents and Resources for Resistance Surface Construction

Category/Item	Function/Description	Example Sources/Platforms
Geospatial Data
Sentinel-2 Imagery	Base data for 10m land cover classification and change detection.	ESA Copernicus Open Access Hub
Microsoft Building Footprints	High-resolution vector data for building density calculation in HFI.	Microsoft GitHub [31]
OpenStreetMap	Crowdsourced data for roads, railways, and other infrastructure.	OpenStreetMap [31]
SRTM DEM	Topographic data for deriving slope, which can be a base resistance factor.	USGS EarthExplorer
Software & Platforms
ArcGIS Pro / QGIS	Primary GIS platforms for spatial data management, analysis, and cartography.	Esri, QGIS.org
Fragstats	Computes landscape pattern metrics essential for LER assessment.	UMASS Landscape Ecology Lab
R / Python with GDAL	For scripting complex spatial analyses, statistical weighting, and customization.	R-project.org, Python.org
Analytical Models
InVEST Habitat Quality	Alternative/model validation; can inform landscape vulnerability in LER.	Natural Capital Project
Circuit Theory (Linkage Mapper)	Uses the final resistance surface to model ecological corridors and pinch points.	Circuitscape [28] [27]
Minimum Cumulative Resistance (MCR)	Alternative to circuit theory for deriving least-cost paths and corridors.	Built into most GIS software [26] [27]

Application in Ecological Security Pattern Construction

The primary application of the integrated LER-HFI resistance surface is in the construction of robust ESPs. Once the resistance surface is generated, it is used in conjunction with ecological sources (core habitat areas identified through methods like MSPA or ecosystem service valuation) within models like circuit theory or the Minimum Cumulative Resistance (MCR) model [28] [26] [27].

Circuit theory, implemented through tools such as Linkage Mapper and Circuitscape, treats the landscape as an electrical circuit. Ecological sources represent "nodes" of voltage, the resistance surface defines the "resistance" (or conductance) of each pixel, and the resulting "current" flow maps predict the probability of movement and the location of corridors, pinch points, and barriers [28] [27]. This process, visualized below, pinpoints critical areas for protection and restoration.

The integration of Landscape Ecological Risk and Human Footprint indices represents a significant methodological advancement in the construction of ecological resistance surfaces. This approach moves beyond simplistic land-cover-based assignments by dynamically incorporating both the inherent ecological fragility of the landscape and the external pressures of human activity. The resultant composite surface provides a more ecologically realistic and spatially nuanced representation of the barriers to connectivity, which is the bedrock of effective Ecological Security Pattern planning. By following the detailed technical protocols, utilizing the essential tools, and applying the validation frameworks outlined in this guide, researchers and conservation practitioners can develop superior ESPs. These patterns are critical for mitigating ecological risks, enhancing landscape resilience, and guiding sustainable spatial planning in an increasingly human-dominated world.

Corridor Delineation with the Minimum Cumulative Resistance (MCR) Model and Circuit Theory

The construction of Ecological Security Patterns (ESP) is a pivotal spatial strategy for reconciling ecological conservation with economic development, ensuring the stability and sustainability of regional ecosystems [35]. As an integral component of ESPs, ecological corridors serve as connected carriers of material flow and energy flow between isolated ecological source patches [36]. The delineation of these corridors has evolved from qualitative planning to quantitative research, with the MCR model and circuit theory emerging as leading methodologies for identifying potential connectivity pathways and key areas for restoration [13] [37].

The standard framework for ESP construction involves "source identification, resistance surface construction, and corridor extraction" [13]. This process begins with identifying core ecological source areas, which are critical patches that support regional ecological security by maintaining the integrity of ecological processes and functions [36]. These sources are then connected through ecological corridors, which are extracted using models that simulate the movement of species and ecological flows across landscapes characterized by varying degrees of resistance [35].

Conceptual Foundations of MCR and Circuit Theory

The Minimum Cumulative Resistance (MCR) Model

The Minimum Cumulative Resistance (MCR) model quantifies the resistance that species encounter when migrating between ecological source areas by calculating the least-cost path [13] [36]. This model simulates the potential trend of biological spatial movement by computing the minimum cumulative resistance and the shortest cost path for species migration and dispersal among different ecological source sites [36]. The core assumption is that organisms select pathways that minimize their energy expenditure or perceived risk when moving through a landscape.

The MCR model has significant advantages in the spatial optimization of ecological networks and is effective for identifying optimal ecological corridors and habitat connectivity [13]. However, a primary limitation is that it assumes species migration follows a single optimal path, overlooking the influence of environmental heterogeneity on multi-path selection and the randomness inherent in dispersal processes [13].

Circuit Theory

Circuit theory represents an alternative, process-driven approach to modeling connectivity, drawing analogies from electrical circuit theory [37]. In this conceptual framework, the landscape is represented as a conductive surface, where habitats with low resistance correspond to conductors with high conductivity. When electrical current is applied between ecological source areas, it flows through all possible pathways, with the current density reflecting the probability of movement or gene flow through each location [37].

This approach provides a robust way to quantify movement across multiple possible paths in a landscape, not just a single least-cost path [37]. Circuit theory can identify critical 'pinch points' where movement pathways constrict, as well as barriers that impede connectivity [37]. The theoretical foundation of "isolation by resistance" explains that genetic distance among subpopulations can be estimated by representing the landscape as a circuit board, where gene flow occurs via all possible pathways linking them [37].

Comparative Analysis of Model Characteristics

Table 1: Comparative analysis of MCR and circuit theory characteristics

Feature	MCR Model	Circuit Theory
Theoretical Basis	Cost-path analysis; Least-cost distance	Electrical circuits; Random walk theory
Path Selection	Single optimal path	Multiple potential pathways
Key Outputs	Least-cost corridors, Cumulative resistance values	Current density maps, Pinch points, Barriers
Connectivity Metric	Minimum cumulative resistance	Effective resistance, Current flow
Data Requirements	Resistance surface, Source locations	Resistance surface, Source locations
Computational Efficiency	Relatively efficient	More computationally intensive, especially for large landscapes [13]
Primary Applications	Identifying optimal corridor routes	Modeling gene flow, Identifying movement networks and critical nodes

Integrated Methodological Framework for Corridor Delineation

The integration of MCR and circuit theory leverages the strengths of both approaches, creating a comprehensive framework for corridor delineation. The MCR model optimizes the identification of core corridors, while circuit theory supplements multi-path analysis and key node identification [13]. This synergistic approach helps overcome the limitations of single-model applications, improving the accuracy and reliability of ecological security pattern construction [13].

Diagram 1: Integrated methodological framework for corridor delineation illustrating the sequential steps combining MCR and circuit theory approaches, with methodological components highlighted in yellow and relationships shown in red.

Ecological Source Identification

The first critical step in corridor delineation involves identifying ecological source areas, which are patches of high ecological quality that serve as origins for ecological flows and destinations for biodiversity [35]. Multiple approaches exist for source identification:

• Ecosystem Service Assessment: Evaluating the importance of ecosystem services from aspects such as water conservation, soil and water conservation, and habitat quality [36]. This approach considers the ecosystem service level of ecological sources and reflects the degree of ecosystem response to natural environmental changes and human disturbance.

• Morphological Spatial Pattern Analysis (MSPA): Effectively identifying and distinguishing various landscape types, highlighting structural connections, and quantifying landscape complexity and dynamics [35]. This method enhances the scientific rigor associated with ecological source area identification.

• Landscape Connectivity Analysis: Using tools like Conefor to evaluate the functional connectivity between habitat patches based on their spatial configuration and species dispersal capabilities [36].

In practice, integrated approaches that combine multiple methods often yield the most robust results. For example, one study on the black soil region of Northeast China identified ecological source areas through integrated analyses of ecosystem service value and ecological sensitivity at multiple time points (2002, 2012, and 2022) [13].

Resistance Surface Construction

The construction of an ecological resistance surface represents the difficulty species face when migrating and spreading between ecological sources [36]. This surface is typically developed based on land use types and modified using relevant factors such as topography, nighttime light data, and human disturbance indicators.

Table 2: Common resistance factors and their typical values

Resistance Factor	Low Resistance (1-3)	Medium Resistance (4-6)	High Resistance (7-9)	Data Sources
Land Use/Land Cover	Core forests, Natural wetlands	Agricultural lands, Shrublands	Urban areas, Bare ground	Landsat imagery, National land cover databases
Topography	Gentle slopes (<5°)	Moderate slopes (5-15°)	Steep slopes (>15°)	Digital Elevation Models (DEM)
Human Footprint	Protected areas, Wilderness	Rural settlements, Light agriculture	Urban centers, Industrial zones	Nighttime light data, Population density maps, Road networks
Hydrological Features	River buffers, Natural water bodies	Intermittent streams	Major river crossings	Hydrography datasets, Euclidean distance to water
Vegetation Coverage	Dense native vegetation	Moderate vegetation cover	Sparse vegetation	NDVI from remote sensing imagery

The resistance surface is typically constructed through weighted overlay analysis, where different factors are assigned weights based on their relative importance to species movement. For example, in the Fujiang River Basin study, researchers constructed the basic resistance surface based on natural factor indicators and corrected it using population density, night lighting, road traffic, and landscape ecological risk conditions [36].

MCR Model Application

The application of the MCR model involves calculating the cumulative resistance encountered when moving from each ecological source across the resistance surface. The fundamental formula for MCR is:

MCR = f(min Σ (Dij × Rij))

Where:

• MCR is the minimum cumulative resistance value
• Dij represents the distance through which ecological flows move from source j to landscape unit i
• Rij is the resistance coefficient of landscape unit i to ecological flows from source j
• f is an increasing monotonic function reflecting the positive correlation between cumulative resistance and distance [13] [21]

In practice, GIS tools are used to compute cumulative resistance surfaces from each ecological source, and the least-cost paths between sources are identified as potential ecological corridors. For example, a study in Jining City successfully identified 51 ecological corridors using the MCR-gravity model [13].

Circuit Theory Application

Circuit theory application involves using software such as Circuitscape to simulate current flow across the resistance surface between ecological sources [37]. Key steps include:

• Preparing the resistance surface raster and designating source locations
• Running pairwise or advanced connectivity models to calculate current flow
• Generating current density maps that visualize the probability of movement across all possible pathways

Circuit theory produces several key metrics:

• Current Density: Estimates the net movement probabilities of random walkers through a given grid cell [37]
• Effective Resistance: A pairwise distance-based measure of isolation among populations or sites [37]
• Pinch Points: Areas where movement pathways constrict, representing critical bottlenecks
• Barriers: Areas where connectivity is significantly impeded

In the black soil region study, researchers combined the MCR model with circuit theory to identify ecological corridors and then used circuit theory to supplement multi-path analysis and identify key nodes [13]. The number of ecological restoration nodes extracted by circuit theory in the Jining City study was 3.6 times that identified by the MCR model alone [13].

Corridor Classification and Validation

Following corridor extraction, it is essential to classify and prioritize corridors based on their connectivity importance. The gravity model is commonly used for this purpose, quantifying the interaction strength between ecological sources based on their quality and connectivity [38].

For example, in the Qin River Basin cultural heritage corridor study, researchers applied the gravity model to classify corridors, ultimately identifying 4 primary, 5 secondary, and 12 tertiary corridors [38]. This classification helps prioritize conservation efforts and resource allocation.

Validation of delineated corridors can involve:

• Field surveys to verify habitat connectivity and species presence
• Genetic analysis to confirm gene flow patterns match predicted corridors
• Camera trapping or other wildlife monitoring techniques to document animal movement

Technical Implementation and Tools

Software and Computational Tools

Table 3: Essential tools for corridor delineation implementation

Tool Name	Primary Function	Implementation Requirements	Key Features
ArcGIS	Spatial data management, resistance surface construction, MCR calculation	Commercial license, Spatial Analyst extension	Comprehensive geoprocessing tools, Raster calculator, Cost distance and path functions
Circuitscape	Circuit theory implementation, current flow mapping	Open-source, MATLAB or Julia versions available	Pairwise and advanced connectivity models, Pinch point identification, Integration with GIS
Linkage Mapper	Corridor identification and mapping	Free toolbox for ArcGIS	Automated corridor delineation, Centrality metrics, Barrier identification
Conefor	Landscape connectivity analysis	Free, command-line or GUI interface	Probability of connectivity index, Importance of individual patches for connectivity
R (gdistance package)	Connectivity analysis in R environment	Open-source R statistical platform	Least-cost path computation, Circuit theory applications, Customizable analysis pipelines

Research Reagent Solutions

Table 4: Essential research reagents and data sources for corridor delineation

Research Reagent	Function/Application	Key Characteristics	Exemplary Sources
Land Use/Land Cover Data	Base layer for resistance surface construction	Thematic classification of landscape types; Determines baseline resistance values	National Land Cover Database (NLCD), CORINE Land Cover, Regional satellite imagery classification
Digital Elevation Models (DEM)	Topographic resistance factor	High-resolution elevation data; Derived slope and aspect for resistance modeling	SRTM, ASTER GDEM, LiDAR datasets, National elevation datasets
Nighttime Light Data	Proxy for human disturbance and urbanization	Quantifies light pollution and human activity intensity; Modifies base resistance surface	DMSP-OLS, SNPP-VIIRS, NASA's Black Marble dataset
Species Occurrence Data	Validation of model predictions; Source identification	Field observations, camera traps, genetic samples; Confirms model accuracy	GBIF, eBird, iNaturalist, Regional wildlife databases, Original field surveys
Remote Sensing Imagery	Land cover classification, vegetation monitoring	Multi-spectral, multi-temporal data; Enables change detection and time-series analysis	Landsat, Sentinel-2, MODIS, Commercial high-resolution satellites

Case Studies and Applications

Black Soil Region of Northeast China

A comprehensive study of China's black soil region employed both MCR and circuit theory at three time points (2002, 2012, and 2022) to analyze dynamic changes in the ecological security pattern [13]. Researchers identified ecological source areas through analyses of ecosystem service value and ecological sensitivity, then constructed ecological corridors by combining the MCR model and circuit theory [13].

Key findings included:

• Ecosystem service functions exhibited a spatial pattern of higher values in the east and lower values in the west
• Although the number of ecological source areas decreased over time, their total area increased
• The number of ecological corridors decreased, but their length fluctuated, and the number of stepping stones significantly increased [13]

Based on these results, the researchers proposed a "point-line-polygon-network" optimization strategy, including constructing ecological belts, strengthening ecological barriers, and restoring the connectivity of ecological nodes and corridors [13].

Fujiang River Basin

In the Fujiang River Basin, researchers used the importance evaluation results of ecosystem services and the MCR model to construct an ecological security network with ecological sources as "key patches," ecological corridors as "axis," and ecological nodes as "hub" [36]. The study identified 23 ecological sources with a total area of 7638.88 km² and ecological corridors distributed in a "cobweb" pattern with a total length of 2249.32 km [36].

A significant outcome was the identification of priority areas for ecological restoration based on the spatial superposition of ecological security networks and negative interference surfaces [36]. This approach allowed for targeted ecological restoration strategies to ensure connectivity between ecological sources and the integrity and stability of ecological networks.

Qin River Basin Cultural Heritage Corridors

This study demonstrated the application of these methods beyond pure ecological conservation, using the MCR model and circuit theory to develop cultural heritage corridor networks [38]. Researchers integrated heritage "source" raster data with resistance surfaces to generate 53 potential corridors with a total length of 578.48 km [38].

The gravity-based classification yielded 4 primary, 5 secondary, and 12 tertiary corridors, creating a macro-level network with a "two vertical-one horizontal" pattern centered on Runcheng Town and Qinyang City [38]. At the micro-level, the study constructed a multi-dimensional "corridor-station-source" system to connect heritage nodes through corridors and use key areas as stations [38].

The integration of MCR and circuit theory provides a powerful methodological framework for corridor delineation within ecological security pattern construction. While the MCR model excels at identifying optimal corridor routes, circuit theory complements this by revealing multiple pathways and critical nodes [13]. This synergistic approach enables more robust and comprehensive corridor delineation that better reflects the complexity of ecological flows across heterogeneous landscapes.

Future methodological developments should focus on:

• Dynamic corridor modeling that incorporates temporal changes in resistance surfaces due to land use change, climate change, and restoration activities
• Multi-species approaches that account for the varying habitat requirements and movement capabilities of different taxonomic groups
• Integration with genetic data to validate and refine resistance surfaces based on empirical gene flow patterns
• Improved computational efficiency for applying circuit theory to large-scale landscapes [13]

As demonstrated across diverse applications, the combined use of MCR and circuit theory provides researchers and conservation practitioners with a robust toolkit for maintaining and restoring ecological connectivity in an era of rapid global change.

Ecological Security Patterns (ESP) are spatial frameworks essential for biodiversity conservation, environmental quality maintenance, and regional sustainable development [39]. The foundational concept of ESP involves identifying priority conservation areas through a structured approach of recognizing ecological sources, constructing resistance surfaces, and extracting ecological corridors and nodes [40] [41]. Traditional ESP methodologies have primarily emphasized the supply-side of ecosystem services, focusing on the capacity of ecological patches to provide services such as water conservation, soil retention, and habitat provision [40]. However, this unilateral approach fails to address the complex interplay between ecological systems and human societal needs.

The integration of supply, demand, and sensitivity dimensions represents a paradigm shift in ESP construction, moving beyond simple ecological supply assessment to a more holistic framework that acknowledges socio-ecological complexities [40]. This advanced approach recognizes that important ecological patches should not only have high ecosystem service supply capacity but must also be strategically positioned to fulfill human demands while maintaining resilience to anthropogenic pressures [40]. The Ordered Weighted Averaging (OWA) model serves as a critical mathematical framework within this integrated approach, enabling researchers to evaluate conservation efficiency, decision-making risks, and tradeoff levels across multiple scenarios and weighting schemes [40]. This technical guide provides a comprehensive methodology for implementing this advanced ESP framework, with specific applications for researchers and scientists working at the intersection of landscape ecology and spatial planning.

Theoretical Foundations: Supply-Demand-Sensitivity in ESP

Conceptual Framework and Principles

The integrated supply-demand-sensitivity framework for ESP identification operates on three fundamental principles that distinguish it from conventional approaches. First, important ecological patches must demonstrate higher supply of ecosystem services than non-ecological patches, ensuring significant ecological functionality [40]. Second, these patches should be strategically distributed where they can effectively fulfill human demands, maximizing accessibility to ecological benefits for surrounding populations [40]. Third, important ecological patches must possess high resistance to disturbance, indicating robust resilience capabilities despite anthropogenic pressures [40].

This tripartite framework addresses critical limitations in traditional ESP methodologies by explicitly incorporating the spatial flow of ecosystem services and their societal relevance. The supply component quantifies the capacity of ecosystems to provide essential services, typically measured through indicators like soil conservation capacity, water retention, or habitat quality [40]. The demand component represents the human requirement for these ecosystem services, often spatially explicit and varying according to population density, land use type, and socio-economic factors [40]. The sensitivity component functions as a critical adjustment mechanism, evaluating ecosystem vulnerability to human activities and environmental stressors, thus serving as an indicator of ecological stability and resilience [40].

The Role of OWA in Multi-Scenario Decision Making

Ordered Weighted Averaging (OWA) provides a flexible multi-criteria decision-making framework essential for handling the complex tradeoffs inherent in supply-demand-sensitivity analyses. The OWA operator enables researchers to evaluate ecological sources under different weighting scenarios, systematically exploring how varying priorities assigned to supply, demand, and sensitivity criteria affect ESP outcomes [40]. This capability is particularly valuable for identifying ecological patches with high conservation efficiency and low decision-making risks across multiple potential futures [40].

Through OWA, decision-makers can generate a spectrum of possible ESP configurations based on different risk attitudes and conservation priorities, from optimistic scenarios that emphasize potential benefits to pessimistic scenarios that prioritize risk aversion [40]. This multi-scenario approach directly addresses the inherent uncertainties in ecological planning and provides a robust foundation for spatial conservation strategies resilient to changing priorities and conditions. The application of OWA in ESP construction represents a significant advancement over single-scenario approaches, allowing for the identification of conservation priorities that remain effective across various decision-making contexts.

Table 1: Core Components of the Supply-Demand-Sensitivity Framework

Component	Definition	Measurement Indicators	Spatial Consideration
Supply	Capacity of ecosystems to provide services	Soil conservation, water yield, habitat quality	Based on biophysical characteristics and ecosystem type
Demand	Human needs for ecosystem services	Population density, land use type, infrastructure	Spatial distribution of beneficiaries and service areas
Sensitivity	Vulnerability to human disturbance	Land use intensity, proximity to stressors, ecological fragility	Spatial variation in anthropogenic pressure and resilience

Methodological Framework: Implementing the Integrated Approach

Ecological Source Identification

The initial phase in ESP construction involves identifying ecological sources through the integrated supply-demand-sensitivity framework coupled with fuzzy multi-criteria decision-making. Ecological sources constitute the fundamental spatial elements maintaining ecological processes and providing key ecosystem services [40]. The methodological workflow begins with quantifying supply capacity using biophysical models such as the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) or similar tools to map ecosystem service provision [40]. Subsequently, demand assessment employs spatial demographic and land use data to identify areas where human populations require ecosystem services, creating a demand surface that reflects spatial variation in need [40].

The sensitivity analysis evaluates ecosystem vulnerability through indicators of resistance to disturbance, often derived from land use intensity, proximity to anthropogenic pressures, and ecological fragility metrics [40]. The OWA model then integrates these three dimensions through a scenario-based approach, applying different weight combinations to generate ecological source candidates under varying decision-making preferences [40]. This process identifies ecological patches that exhibit high conservation efficiency and low decision-making risks while minimizing tradeoff levels across multiple scenarios [40]. The final selection of ecological sources represents a compromise solution that balances supply capacity, demand fulfillment, and disturbance sensitivity under various weighting schemes.

Resistance Surface Development and Connectivity Analysis

Following ecological source identification, constructing an accurate ecological resistance surface is crucial for modeling ecological flows and connectivity. The resistance surface represents the landscape permeability to species movement and ecological processes, with higher values indicating greater resistance or cost to movement [39]. Habitat quality often serves as the foundational metric for resistance surface construction, with additional adjustments for human disturbance intensity and landscape fragmentation patterns [40].

Circuit theory provides a powerful methodological approach for analyzing ecological connectivity once sources and resistance surfaces are established [40] [41] [39]. Unlike least-cost path methods that identify single optimal routes, circuit theory models ecological flow as random movement behavior, simulating multiple potential pathways and identifying areas with high probability of ecological movement [39]. This approach enables the identification of ecological corridors as broad pathways rather than single lines, as well as ecological pinch points (areas where movement is constricted) and barrier points (areas that disrupt connectivity) [41]. The application of circuit theory thus provides a more robust and realistic representation of ecological flows compared to traditional corridor identification methods.

Diagram Title: ESP Supply-Demand-Sensitivity Workflow

Corridor Delineation and Node Identification

The integration of circuit theory with the supply-demand-sensitivity framework enables sophisticated identification of ecological corridors and strategic nodes. Ecological corridors are extracted by modeling ecological flows between identified sources across the resistance surface, resulting in a connectivity network that facilitates species movement and ecological process maintenance [41] [39]. These corridors are typically categorized hierarchically based on their connectivity importance, distinguishing between primary and secondary corridors to guide conservation prioritization [39].

Circuit theory further enables the identification of ecological pinch points (areas where movement is constricted and conservation action would have high impact) and ecological barrier points (areas that disrupt connectivity and would benefit from restoration) [41]. In the Fuzhou City case study, researchers identified 98 ecological corridors extending over 2500.55 km, alongside 100 ecological pinch points and 146 ecological barrier points [41]. This precise spatial identification of strategic locations allows for targeted interventions that maximize conservation effectiveness while minimizing resource investment. The integration of recreational spatial patterns with ecological networks further enhances the practical implementation of ESP by addressing both conservation and human wellbeing objectives [41].

Technical Implementation: Data Requirements and Analytical Procedures

Data Collection and Preprocessing

The implementation of the integrated ESP framework requires comprehensive spatial data covering biophysical, ecological, and socio-economic dimensions. Core datasets include land use/land cover (LULC) classifications, normalized difference vegetation index (NDVI) time series, digital elevation models (DEM), soil data, hydrological data, and socio-economic datasets such as population density and infrastructure distribution [40] [39]. Remote sensing data from platforms like Landsat, Sentinel, and MODIS typically provide the foundational layers for ESP construction, supplemented by field surveys and statistical yearbooks for validation and parameterization [39].

Data preprocessing follows a standardized workflow including projection standardization to ensure consistent spatial reference systems, resampling to harmonize spatial resolutions, and categorization for categorical variables. For the Yellow River Basin case study, researchers processed data covering approximately 79.46 × 10⁴ km² with complex topography ranging from -106 to 6248 meters above sea level [40]. This extensive spatial domain required particular attention to scale issues and regional differentiation in parameter development. The preparation of accurate and consistent spatial datasets forms the critical foundation for all subsequent analytical stages in ESP construction.

Table 2: Core Data Requirements for Integrated ESP Analysis

Data Category	Specific Datasets	Spatial Resolution	Primary Application
Land Surface	LULC, NDVI, DEM, Soil Type	10-100m	Supply capacity assessment, resistance surface
Ecological	Species Distribution, Habitat Quality, Protected Areas	30-1000m	Ecological source identification, connectivity
Climate/Hydrology	Precipitation, Temperature, River Networks	100-1000m	Ecosystem service modeling, corridor delineation
Socio-economic	Population Density, Road Networks, Land Use Intensity	100-10000m	Demand assessment, sensitivity analysis

OWA Model Configuration and Scenario Development

The OWA model implementation requires careful parameterization to reflect different decision-making scenarios and risk attitudes. The fundamental OWA operator can be represented as: OWA(x₁, x₂, ..., xₙ) = Σⱼ(wⱼ × zⱼ), where zⱼ is the j-th largest element of the input set, and wⱼ is the associated weight [40]. Weights are determined based on the decision-maker's risk attitude, ranging from optimistic (emphasizing potential benefits) to pessimistic (emphasizing risk avoidance) scenarios [40].

Scenario development typically includes balanced scenarios with equal weights assigned to supply, demand, and sensitivity criteria, supply-emphasis scenarios prioritizing ecosystem service provision, demand-emphasis scenarios focusing on human needs fulfillment, and sensitivity-emphasis scenarios prioritizing ecological stability [40]. In the Yellow River Basin application, researchers implemented seven distinct scenarios representing different combinations of criteria weights, with irrigation management zone No. 3 achieving the best ranking values in five of the seven scenarios [40]. This multi-scenario approach enables identification of robust ecological sources that perform well across various decision-making contexts, enhancing the practical utility and resilience of the resulting ESP.

Circuit Theory Implementation

Circuit theory implementation begins with converting the ecological resistance surface into a conductance surface by inversely relating conductance to resistance [39]. The Circuitscape software package or similar tools then simulate random walkers moving between ecological sources across this conductance surface, modeling ecological flows as electrical current would flow through a circuit [39]. This process generates current density maps that visualize patterns of ecological connectivity, with higher current density indicating greater probability of movement and higher conservation priority [39].

The analytical outputs include corridor networks showing connectivity pathways between sources, pinch points identified as areas where current density is high relative to corridor width, and barrier points identified as areas that strongly impede current flow [39]. In the South China Karst application, researchers extracted varying numbers of ecological corridors (108, 68, and 113) and nodes (67, 20, and 40) across three different study areas, reflecting the landscape-specific nature of connectivity patterns [39]. These circuit theory outputs provide the final spatial elements needed to construct a comprehensive ESP that integrates ecological sources with connectivity elements.

Case Study Applications and Validation

Yellow River Basin Implementation

The Yellow River Basin case study demonstrates the practical application of the integrated supply-demand-sensitivity framework at a regional scale. Researchers identified significant spatial variation in ecosystem service supply, with important areas for soil conservation covering 40,195.91 km² (4.96% of the basin) dominated by grasslands, forests and cropland [40]. These high-supply areas were distributed mainly in the transition zone between the Qinghai-Tibet Plateau and Loess Plateau, particularly in high-elevation areas, and the Qinling Mountains [40]. In contrast, areas with low supply capacity covered 517,913.42 km² (63.94% of the basin), primarily distributed in the northern and western regions with lower precipitation [40].

The integration of demand and sensitivity analyses revealed critical spatial mismatches between supply and demand, with high-demand areas concentrated in urban centers and agricultural regions [40]. The OWA-based multi-scenario analysis identified ecological sources that balanced high conservation efficiency with low decision-making risks, resulting in an ESP that addressed both ecological protection and human wellbeing objectives [40]. The successful implementation in this complex large-scale basin demonstrates the scalability and practical utility of the integrated framework for regional environmental planning and ecosystem management.

Karst Desertification Control Forest Application

The South China Karst application addressed the specific ecological challenges of karst desertification control forests, characterized by severe fragmentation and degradation [39]. Researchers employed Morphological Spatial Pattern Analysis (MSPA) to identify ecological sources based on structural connectivity, then integrated these with functional connectivity analysis using circuit theory [39]. The results revealed significant differences in ESP configuration across forests with varying desertification severity, highlighting the need for targeted restoration strategies adapted to local conditions [39].

The study established a hierarchical ESP with "one core, five districts, six corridors, and seven wedges" that specifically addressed the unique surface-subsurface binary structure of karst landforms [39]. This application demonstrated how the general ESP framework could be adapted to specific ecological contexts and challenges, providing valuable insights for ecological restoration in fragile karst ecosystems. The research further contributed to understanding the structure-function relationships in desertification control forests, offering scientific foundation for targeted restoration strategies [39].

Diagram Title: ESP Technical Implementation Framework

Table 3: Research Reagent Solutions for ESP Implementation

Tool/Category	Specific Examples	Primary Function	Application Context
Spatial Analysis	ArcGIS, QGIS, GRASS	Geoprocessing, spatial statistics	All spatial analysis and mapping stages
ESP-Specific Tools	Circuitscape, Linkage Mapper	Connectivity analysis, corridor design	Circuit theory implementation, corridor mapping
Statistical Analysis	R, Python (Pandas, NumPy)	Data analysis, model implementation	OWA calculations, statistical validation
Remote Sensing	Google Earth Engine, ENVI	Image processing, classification	Land use mapping, vegetation monitoring
Ecosystem Service Models	InVEST, ARIES, SOLVE	Biophysical modeling	Supply capacity quantification

The integration of OWA models with supply-demand-sensitivity analysis represents a significant methodological advancement in ecological security pattern construction. This approach moves beyond traditional supply-focused methodologies to incorporate critical socio-ecological dimensions, resulting in more robust and practically relevant conservation planning outcomes. The multi-scenario capability of OWA models directly addresses decision-making uncertainties and enables the identification of conservation priorities that remain effective across various weighting schemes and potential futures [40].

Future research directions should focus on enhancing the dynamic aspects of ESP construction, incorporating temporal dimensions to address landscape and climate change impacts [39]. Further methodological refinement is needed in quantifying ecological flows and integrating multi-species perspectives to enhance the biodiversity conservation effectiveness of ESPs [39]. The integration of cultural ecosystem services and recreational functions, as demonstrated in the Fuzhou City case study, represents another promising direction for creating multifunctional ecological networks that simultaneously address conservation and human wellbeing objectives [41]. As ESP methodologies continue to evolve, their capacity to support sustainable landscape planning and biodiversity conservation in increasingly human-dominated landscapes will become ever more critical.

Ecological Security Patterns (ESPs) represent a strategic spatial framework essential for balancing ecosystem protection with economic development, particularly in critical agricultural zones [35]. The construction of ESPs provides a practical pathway to maintain the balance between local environmental protection and economic growth, which has become a hot research topic globally [23]. In China, black soil regions are of paramount importance to national food security, often described as the "bedrock" of food security for the nation [42]. These regions face severe ecological threats, including soil erosion, salinization, and structural degradation, which directly threaten sustainable agricultural development and regional ecological security [43]. This case study examines the application of ESP frameworks in China's black soil regions, focusing on methodologies for identifying, constructing, and optimizing these patterns to safeguard food security while addressing ecological challenges.

Background: The Ecological Crisis in China's Black Soil Regions

Significance and Degradation of Black Soils

The black soil region of Northeast China is one of only four major black soil regions worldwide, alongside the Mississippi River Basin in North America, the Pampas of South America, and the Russia-Ukraine Great Plain [42]. These fertile lands occupy one-sixth of the world's arable land and play a disproportionately important role in global food production systems. In China, the black soil administrative region encompasses Liaoning Province, Jilin Province, Heilongjiang Province, and Eastern Inner Mongolia Autonomous Region, covering approximately 1.03 million km² [42].

The strategic importance of these regions to China's food security is demonstrated by production statistics: the area sown with grain in Northeast China has increased from 1.55 × 10⁷ ha in 1980 to 2.85 × 10⁷ ha in 2020, while total grain output has surged from 3.70 × 10⁷ tons to 17.35 × 10⁷ tons during the same period [42]. This dramatic intensification of agricultural production has come at significant ecological cost, manifesting through several degradation pathways:

• Soil Organic Matter Depletion: Research indicates that during the first 50 years of black soil cultivation, the organic matter content in Bei'an City decreased at a rate of 0.3–2.6% per year [42]. The annual decreases in organic matter and nitrogen contents of black soil in Bei'an City are currently approximately 0.17% and 0.18%, respectively [42].
• Structural Deterioration: Compared with natural black soil, the soil bulk density of the 0–30 cm layer increased by 7.59%, 34.18%, and 59.49% after cultivation for 20, 40, and 80 years respectively, while total porosity decreased by 1.91%, 13.25%, and 22.68% over the same periods [42].
• Erosion Vulnerability: Statistics indicate that soil erosion mainly occurs on farmland with a slope of 3°–10°, which accounts for 46.39% of the total area of eroded black soil in Northeast China [42].

Policy Context and the 2025 Soil Survey

Recognizing the severity of soil degradation, China is currently engaged in a comprehensive national soil census scheduled for completion in 2025, updating the previous survey conducted forty years prior [44]. This initiative reflects growing governmental concern about soil health and its implications for food security and environmental sustainability. The 2014 revelation that pollutants exceeded warning levels in 16% of soil samples highlighted the urgent need for updated comprehensive data to inform protection strategies [44].

The pressing nature of these challenges is underscored by the assessment that "Chinese people enjoy a better quality of life now, but the soil has paid a heavy price" [44]. This new survey is considered essential for understanding and protecting soils, helping to ensure food security and progress toward China's peak carbon and carbon neutrality targets [44].

Methodological Framework for ESP Construction

The construction of Ecological Security Patterns follows a systematic methodology that integrates landscape ecology principles with ecosystem service assessment. This section outlines the core technical framework for ESP development in black soil regions.

The ESP construction process follows a sequential "supply-demand-corridor-node" framework that integrates both ecological and socioeconomic dimensions [23]. This comprehensive approach ensures that ESPs reflect not only natural ecosystem functions but also human influences and requirements.

The following diagram illustrates the integrated workflow for constructing Ecological Security Patterns in black soil regions:

Ecological Source Identification

Ecological sources are critical zones fundamental to essential ecosystem functions and overall ecological integrity [35]. These areas serve as the primary foundation for ESP construction and are identified through multiple analytical approaches:

• Ecosystem Service Value Assessment: Quantitative evaluation of key ecosystem services including carbon storage, soil conservation, water yield, and habitat quality using models like InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) [23] [43]. This approach identifies regions with high capacity for providing essential ecological functions.
• Ecological Sensitivity Analysis: Assessment of ecosystem vulnerability to external disturbances, including evaluation of soil erosion sensitivity, salinization risk, and pollution vulnerability [43]. This analysis helps prioritize areas requiring protection.
• Morphological Spatial Pattern Analysis (MSPA): This method effectively identifies and distinguishes various landscape types, highlighting structural connections and quantifying landscape complexity and dynamics [35]. MSPA enhances the scientific rigor of ecological source identification by analyzing the structural characteristics of landscape patterns.

In the black soil region, dynamic changes show that although the number of ecological source areas has decreased over time (2002-2022), their total area has increased, indicating successful consolidation and protection efforts [43].

Resistance Surface Construction

The resistance surface quantifies and represents various obstacles that ecological flows encounter when dispersing from source locations through the landscape [35]. Construction of this surface integrates multiple factors:

• Land Use-Based Resistance: Different land use types (forest, farmland, urban area) are assigned resistance values based on their permeability to ecological flows.
• Socioeconomic Factors: Human demand for ecosystem services creates resistance to ecological flows, as urbanization and economic activities fragment landscapes [23]. This represents "a potential consumption of ESs" during service delivery from ecological sources to other regions [23].
• Natural Geographic Constraints: Topography, slope, and hydrological features influence species movement and ecological processes.
• Habitat Risk Assessment: Evaluation of threats from human activities, pollution, and infrastructure development.

Recent research emphasizes that "the expansion of ESs from the source to other units would be influenced by factors such as accessibility, cost, and the degree of economic and social development, which are frequently reflected in the human demand for ESs" [23].

Ecological Corridor Extraction

Ecological corridors function as passages for ecological flow, ecological processes, and ecological functions within the region [23]. These linear landscape elements maintain ecological stability and ensure continuity of ecological processes [23]. Several methods are employed for corridor extraction:

• Minimum Cumulative Resistance (MCR) Model: This approach quantifies the energy expenditure associated with species migration from source to target habitats, simulating various ecological flow patterns [35]. The MCR model is effective for identifying optimal pathways but has limitations in delineating specific spatial boundaries [35].
• Circuit Theory: This method simulates biological flow within ecosystems using electrical current analogs, identifying key nodes and areas within the ecological network based on current intensity [35]. Circuit theory effectively addresses MCR limitations by identifying ecological pinch points and barrier points [23].
• Linkage Mapper Tool: An open-source tool that applies circuit theory to identify corridors and key connectivity areas [23].

In Chinese black soil regions, research findings indicate that "the number of ecological corridors has decreased, but their length has fluctuated, and the number of stepping stones has significantly increased" [43].

Key Node Identification

Critical areas within the ESP are identified as key nodes, which require targeted management interventions:

• Ecological Pinch Points: Areas where ecological flows are concentrated, representing critical connectivity zones. Identification of ecological pinch points "can prevent the degradation or change of ecological sources" [23]. These are prioritized as key protection areas.
• Ecological Barrier Points: Regions where organism migration between ecological sources is hindered. "Eliminating these spaces will improve communication among ecological sources" [23]. These areas are designated for ecological restoration.

A study in Fuzhou City (as a methodological reference) identified "100 ecological pinch points and 146 ecological barrier points" through this approach [35].

Technical Implementation and Data Requirements

Successful implementation of ESP methodology requires specific technical tools, datasets, and analytical procedures. This section details the practical requirements for constructing robust ecological security patterns.

Research Reagent Solutions and Essential Materials

Table 1: Essential Research Tools and Data Requirements for ESP Construction

Category	Specific Tool/Model	Primary Function	Data Requirements	Application in Black Soil Context
Spatial Analysis Tools	Linkage Mapper Tool	Identifies ecological corridors and connectivity	Resistance surfaces, source locations	Maps migration pathways across agricultural landscapes [23]
	Circuitscape	Pinpointing key nodes via circuit theory	Resistance raster, source points	Identifies pinch points and barriers in black soil regions [23] [35]
	InVEST Model	Quantifies ecosystem service supply	Land use, DEM, soil, precipitation data	Assesses soil conservation, carbon storage in black soils [23]
Data Sources	Multi-source Economic-Social Data	Characterizes ES demand and resistance	Population, GDP, infrastructure maps	Quantifies human pressure on black soil ecosystems [23]
	Satellite Imagery	Land use classification, change detection	Multi-temporal remote sensing images	Monitors black soil erosion and land use change [45]
	Soil Survey Data	Baseline soil characteristics	Organic matter, nutrients, contamination	Provides fundamental data for 2025 national survey [44]
Analytical Models	Gray Forecast Model	Predicts soil property changes	Historical soil nutrient data	Projects OM and N content decline in black soils [42]
	MCR Model	Calculates cumulative resistance	Resistance values, source locations	Models ecological flow paths in agricultural landscapes [43]
	MSPA Model	Identifies structural landscape patterns	High-resolution land cover data	delineates core, bridge, and edge habitats [35]

Experimental Protocols for ESP Component Analysis

Ecosystem Service Assessment Protocol

Objective: Quantify the supply capacity of key ecosystem services in black soil regions to identify ecological sources.

Methodology:

• Data Collection: Gather land use/cover data, digital elevation models (DEMs), soil maps, precipitation records, and agricultural management information.
• Service Quantification:
  • Carbon Storage: Apply InVEST Carbon module using aboveground, belowground, soil, and dead organic matter carbon pools.
  • Soil Conservation: Utilize InVEST SDR (Sediment Delivery Ratio) module with inputs including rainfall erosivity, soil erodibility, topography, and land management factors.
  • Water Yield: Implement InVEST Water Yield module based on precipitation, reference evapotranspiration, soil depth, and plant available water content.
  • Habitat Quality: Employ InVEST Habitat Quality module incorporating threat sources (urban areas, roads), sensitivity of land types, and relative impact distances.
• Spatial Overlay: Combine individual service assessments using weighted overlay or multivariate statistics to identify areas of high overall ecosystem service value.

Research in black soil regions has shown that "ecosystem service functions exhibit a spatial pattern of higher values in the east and lower values in the west" [43].

Ecological Sensitivity Evaluation Protocol

Objective: Assess ecosystem vulnerability to external disturbances in black soil regions.

Methodology:

• Factor Selection: Identify key sensitivity factors including soil erosion sensitivity, salinization risk, pollution vulnerability, and habitat fragmentation.
• Indicator Standardization: Normalize indicators using min-max normalization or z-score standardization to ensure comparability.
• Weight Assignment: Apply analytical hierarchy process (AHP) or entropy method to determine relative importance of factors.
• Sensitivity Index Calculation: Combine weighted factors using weighted overlay or fuzzy logic approaches.
• Classification: Categorize areas into low, medium, high, and very high sensitivity classes using natural breaks or quantile classification.

Studies in black soil regions note that "ecological sensitivity decreased annually" from 2002 to 2022, suggesting improving conditions in some areas [43].

Soil Degradation Forecasting Protocol

Objective: Project changes in black soil quality to identify priority intervention areas.

Methodology:

• Soil Sampling: Collect soil samples from representative locations across different cultivation histories and management practices.
• Laboratory Analysis: Determine organic matter content (via loss-on-ignition or Walkley-Black method), nitrogen content (Kjeldahl method), soil texture, bulk density, and pH.
• Temporal Series Establishment: Organize data according to years under cultivation and management histories.
• Gray Model Development: Apply GM(1,1) gray forecasting model with the following core formulation:
  • Let ( x^{(0)} = (x^{(0)}(1), x^{(0)}(2), ..., x^{(0)}(n)) ) be the original data sequence
  • Generate accumulated sequence ( x^{(1)} = (x^{(1)}(1), x^{(1)}(2), ..., x^{(1)}(n)) ) where ( x^{(1)}(k) = \sum_{i=1}^k x^{(0)}(i) )
  • Establish gray differential equation: ( \frac{dx^{(1)}}{dt} + ax^{(1)} = b )
  • Solve for predicted values: ( \hat{x}^{(1)}(k+1) = (x^{(0)}(1) - \frac{b}{a})e^{-ak} + \frac{b}{a} )
• Model Validation: Calculate mean absolute percentage error (MAPE) to evaluate forecast accuracy.

Research applying this methodology projected that "the annual decreases in the organic matter and nitrogen contents of black soil in Bei'an City were determined as 0.17% and 0.18%, respectively" [42].

ESP Optimization Strategies for Black Soil Regions

Based on comprehensive analysis of ecological sources, corridors, and nodes, specific optimization strategies can be implemented to enhance ecological security in black soil regions.

Integrated "Point-Line-Polygon-Network" Optimization

A comprehensive optimization strategy has been proposed for black soil regions that includes multiple spatial components [43]:

• Point-level Interventions: Focus on protecting ecological pinch points and restoring ecological barrier points identified through circuit theory. These strategic locations represent the most efficient areas for intervention to enhance overall landscape connectivity.
• Line-level Interventions: Strengthen ecological corridors through targeted restoration, establishing vegetative buffers along watercourses, and reducing fragmentation from infrastructure.
• Polygon-level Interventions: Protect and expand ecological source areas through conservation measures, sustainable land management, and restoration of degraded areas.
• Network-level Interventions: Integrate point, line, and polygon elements into a cohesive ecological network that supports biodiversity movement, ecosystem service flow, and landscape resilience.

Conservation Tillage and Soil Management Practices

Implementation of appropriate soil conservation practices is essential for maintaining black soil health within ESP frameworks:

Table 2: Black Soil Conservation Practices and Their Efficacy

Conservation Practice	Impact on Soil Properties	Effect on Crop Yield	Ecological Benefits	Implementation Considerations
No Tillage	Reduces soil erosion, improves water infiltration	Variable effects; may decrease in some conditions	Reduces nitrogen losses by 19.03 kg compared to conventional tillage [42]	Requires residue management; may need specialized equipment
Reduced Tillage	Moderate improvement in soil structure	Generally positive effects on yield	Reduces nitrogen losses by 6.33 kg compared to conventional tillage [42]	Balance between soil protection and yield objectives
Straw Mulching	Increases soil moisture, adds organic matter	Enhanced yield stability in drought conditions	Improves soil carbon sequestration, reduces evaporation	Ensure proper carbon:nitrogen ratio to avoid nutrient immobilization
Ridge Tillage	Reduces water erosion, improves drainage	Beneficial in specific topographic conditions	Controls runoff, conserves soil moisture	Particularly suitable for sloped farmland in black soil regions
Integrated Conservation System	Comprehensive improvement across physical, chemical, biological properties	Maximizes long-term productivity	Provides multiple ecological services including carbon storage, water regulation	Requires adaptive management based on local conditions

Ecological and Recreational Function Integration

Modern ESP frameworks increasingly recognize the importance of integrating ecological protection with recreational functions to enhance public support and multifunctional landscape benefits [35]. This integrated approach involves:

• Trade-off Matrix Analysis: Creating a systematic framework to assess and balance ecological protection needs with recreational development opportunities. This approach "delineates functional zones, and identifies strategic points" for optimized management [35].
• Multifunctional Corridor Design: Developing ecological corridors that simultaneously serve biodiversity conservation and recreational needs, enhancing public access to nature while maintaining ecological functionality.
• Zoning Strategies: Categorizing landscapes into different functional zones such as "ecological core zone, ecological important zone, eco-recreation key trade-off zone, recreational core zone" [35] to guide appropriate management interventions.

Research in Fuzhou City identified "95 key strategic points and 475 sub-strategic points" through this integrated approach, demonstrating its practical applicability [35].

The following diagram illustrates the strategic optimization framework for ESP implementation in black soil regions:

The construction of Ecological Security Patterns in China's black soil regions represents a scientifically-grounded approach to addressing the critical challenge of balancing agricultural productivity with ecological sustainability. The methodologies outlined in this case study—incorporating ecosystem service assessment, ecological sensitivity analysis, corridor identification, and node optimization—provide a robust framework for safeguarding these vital agricultural landscapes.

The ongoing 2025 national soil survey [44] will provide crucial updated data to refine ESP construction and identify priority intervention areas. Future research directions should focus on:

• Dynamic Monitoring Systems: Developing integrated monitoring that tracks changes in both ecological security patterns and soil health indicators over time.
• Climate Resilience Integration: Enhancing ESP methodologies to specifically address climate change impacts on black soil ecosystems.
• Policy Implementation Mechanisms: Strengthening the connection between ESP identification and practical land management policies that incentivize conservation practices.
• Cross-Regional Learning: Facilitating knowledge exchange between China's black soil region and other major black soil areas worldwide facing similar challenges.

The successful implementation of ESP frameworks in black soil regions offers promise for achieving the dual objectives of food security and ecological sustainability, providing a replicable model for other critical agricultural regions worldwide facing similar pressures of intensification and degradation.

Overcoming ESP Challenges: Solutions for Source Identification, Dynamic Modeling, and Network Optimization

Addressing Ecological Source Homogenization with Deep Learning and Self-Organizing Maps

Ecological Security Patterns (ESPs) represent a critical framework in landscape ecology and conservation science, designed to safeguard ecological processes and maintain biodiversity through interconnected ecological networks. Traditional ESP construction methodologies have consistently relied on an "overlay analysis method" for identifying ecological sources. This conventional approach, while systematic, suffers from a significant limitation: it tends to produce "homogenous ecological sources" that fail to capture the complex, multidimensional nature of ecosystems [46]. This phenomenon, termed ecological source homogenization, occurs when the overlay process disregards the original information contained within various ecological factors, ultimately simplifying ecosystem complexity into generalized spatial units that may not accurately represent actual ecological functions or biodiversity distributions [46].

The homogenization problem presents substantial challenges for effective ecological conservation planning. When ecological sources are identified through simplistic overlay methods, they often overlook critical nuances in ecosystem structure and function, potentially leading to inefficient allocation of conservation resources and inadequate protection of biodiversity hotspots. Furthermore, homogenized sources may fail to capture the true connectivity requirements for species movement and ecological flow, resulting in ESPs that appear robust theoretically but prove ineffective in practical conservation applications [46] [47].

Recent technological advancements offer promising solutions to this persistent challenge. The integration of deep learning approaches and Self-Organizing Maps (SOMs) presents a paradigm shift in how researchers can identify and characterize ecological sources, moving beyond homogenization toward more nuanced, data-driven classifications that preserve the original complexity of ecological systems [46] [48]. These computational intelligence techniques can process multidimensional ecological data while maintaining the intrinsic relationships between variables, thereby producing more ecologically meaningful source identifications for ESP construction.

Theoretical Foundations and Limitations of Conventional Methods

The Traditional ESP Framework and Its Technical Shortcomings

The standard approach to constructing Ecological Security Patterns typically follows a three-stage methodology: (1) ecological source identification, (2) resistance surface development, and (3) ecological corridor extraction [13] [23]. In conventional practice, ecological sources—representing core habitat areas with high ecological value—are identified through overlay analysis of multiple ecological factors. This process typically involves stacking layers of ecological importance, such as areas with high ecosystem service value, biodiversity significance, or ecological sensitivity, and selecting regions that surpass predetermined thresholds [47].

The inherent limitation of this approach lies in its simplistic indicator selection and the information loss that occurs during the overlay process [46]. When multiple continuous ecological variables are converted to binary layers (suitable/unsuitable) and combined, the subtle gradients and complex interactions between factors are inevitably flattened. This creates homogenized ecological sources that may share similar composite scores but differ significantly in their actual ecological composition and function. The problem is further exacerbated when using uniform weighting schemes for different ecological factors, which fails to account for their varying importance in supporting biodiversity and ecological processes [46] [47].

Consequences of Homogenization for Ecological Connectivity

The practical implications of ecological source homogenization extend throughout the ESP construction process. When source areas are homogenized, the resulting resistance surfaces derived from them may inaccurately represent the actual landscape permeability for species movement. Similarly, ecological corridors identified between homogenized sources may not align with actual wildlife movement pathways or genetic flow patterns [23]. This fundamental misrepresentation can lead to ESPs that appear connected in theory but remain fragmented in ecological function.

Research demonstrates that homogenized sources typically result in oversimplified corridor networks that fail to account for the diverse habitat requirements of different species [23]. Furthermore, conservation priorities identified based on homogenized sources may overlook critical areas that serve specialized ecological functions or support unique species assemblages. The problem is particularly pronounced in heterogeneous landscapes where ecosystem characteristics transition gradually rather than abruptly, as the binary classification inherent in overlay analysis cannot capture these ecological gradients effectively [46] [47].

Deep Learning Approaches for Multidimensional Ecological Pattern Recognition

Architectural Foundations for Ecological Data Processing

Deep neural networks (DNNs) offer a powerful alternative to conventional methods for ecological pattern identification through their capacity to model complex, nonlinear relationships in multidimensional ecological data. A DNN's structure consists of multiple layers of computational units (neurons) that progressively learn hierarchical representations of input data [49]. In initial layers, the network learns basic features from raw ecological data inputs, which are subsequently combined in deeper layers to identify increasingly complex and abstract ecological patterns [49]. This hierarchical learning capability enables DNNs to detect ecological sources based on intricate combinations of environmental variables that would be impossible to capture through traditional overlay analysis.

The application of deep learning to ESP construction typically employs multispecies distribution models that jointly model the distributions of hundreds or thousands of species based on citizen science data and environmental predictors [50]. These models can incorporate high-resolution environmental data alongside temporal components, such as seasonal variations, allowing for dynamic ESP construction that accounts for phenological shifts and other temporal ecological patterns [50]. Unlike conventional methods that require manual variable selection and weighting, DNNs automatically learn the relative importance of different environmental predictors through the training process, reducing human bias in ecological source identification.

Implementation Framework for Deep Learning in ESP Construction

Table 1: Deep Neural Network Configuration for Ecological Source Identification

Component	Specification	Ecological Application
Network Architecture	Multiple hidden layers with nonlinear activation functions (ReLU, sigmoid, tanh)	Hierarchical learning of ecological patterns from raw environmental data
Input Parameters	Seasonal predictors (sine-cosine mapping of day of year), environmental variables (temperature, precipitation, topography, land cover)	Capturing spatiotemporal dynamics of ecological processes
Cost Functions	Cross-entropy loss (CEL), Normalized Discounted Cumulative Gain (NDCG)	Optimizing species distribution predictions and community composition accuracy
Training Data	Citizen science observations (e.g., 6.7M plant observations in Switzerland), remote sensing data, field surveys	Learning from large-scale, real-world ecological data with spatial and temporal biases
Output Interpretation	Observation probabilities, habitat suitability indices, species richness estimates	Deriving ecological importance scores for source identification

The implementation of deep learning for ESP construction begins with data compilation and preprocessing, where multiple environmental variables are assembled into a standardized geospatial database. Subsequent training of the DNN involves iterative presentation of labeled examples (known species occurrences or habitat areas) to adjust the network's internal parameters until it can accurately predict ecological patterns across the landscape [50]. Once trained, the network generates prediction surfaces representing the probability of species occurrences or the value of ecological functions, which can then be used to identify ecological sources based on areas of high predicted ecological value.

A key advantage of this approach is its capacity to handle the spatial sampling biases inherent in ecological observation data [50]. Unlike conventional methods that require spatial thinning of observations to reduce bias, DNNs can jointly model distributions of multiple species, making them comparatively robust to variations in sampling intensity across the landscape [50]. This capability allows researchers to leverage the full complement of available ecological data rather than sacrificing observations through thinning procedures.

Self-Organizing Maps for Nonlinear Ecological Data Classification

Theoretical Basis of SOMs in Ecological Applications

Self-Organizing Maps (SOMs), introduced by Kohonen, represent a specialized class of artificial neural networks designed for unsupervised pattern recognition and data visualization [48] [51]. Unlike deep learning approaches that typically require labeled training data, SOMs operate through unsupervised learning to identify inherent structures within multidimensional ecological datasets without prior knowledge of class identities [48]. This capability makes SOMs particularly valuable for exploring complex ecological systems where the number and nature of distinct ecological source types may not be known in advance.

The SOM algorithm operates through a process called vector quantization, where it maps high-dimensional input data onto a low-dimensional (typically two-dimensional) grid of nodes while preserving the topological relationships of the original data [51]. This means that similar ecological configurations in the multidimensional input space are positioned close together on the resulting map, while dissimilar configurations are placed farther apart. This topological preservation provides a powerful visualization tool that allows researchers to understand the continuous gradients and discrete transitions in ecological conditions across a landscape [48] [52].

SOM Implementation for Ecological Source Differentiation

Table 2: Self-Organizing Map Application for Ecological Source Identification

Implementation Phase	Technical Procedure	Ecological Outcome
Data Preparation	Standardization of multiple ecological variables (soil properties, vegetation indices, topographic factors, climate data)	Comparable multidimensional input vectors for pattern recognition
Map Initialization	Definition of map size and geometry (rectangular/hexagonal grid); random initialization of weight vectors	Framework for organizing ecological patterns
Training Iteration	Repeated presentation of input vectors; competitive learning; neighborhood function adaptation	Identification of distinct ecological source types with similar multidimensional characteristics
Visualization	Component planes, U-matrix visualization, cluster identification	Interpretation of ecological gradients and classification of source types
Validation	Comparison with known ecological patterns, statistical assessment of cluster separation	Confirmation of ecological relevance for identified source types

The application of SOMs to ecological source identification begins with the compilation of a comprehensive ecological dataset representing the key environmental factors influencing ecosystem structure and function. In a study examining soil pollution patterns, researchers successfully used SOMs to analyze 25 elements measured in soil samples, identifying seven distinct clusters based on elemental associations that reflected both natural pedological processes and anthropogenic influences [48]. This demonstrates how SOMs can differentiate ecological sources based on complex multivariate signatures rather than simplified composite scores.

A significant advantage of SOMs in addressing ecological source homogenization is their capacity to handle nonlinear relationships between ecological variables [48]. Traditional linear methods like Principal Component Analysis (PCA) may fail to capture complex ecological patterns, whereas SOMs can identify and visualize these nonlinear structures, providing a more accurate representation of ecological reality. Furthermore, the visualization capabilities of SOMs through component planes and U-matrices allow researchers to interpret how different ecological factors contribute to the identified patterns, maintaining transparency in the classification process [48] [51].

Integrated Methodological Framework: Combining Deep Learning and SOMs

Synergistic Workflow for Comprehensive ESP Construction

The integration of deep learning and Self-Organizing Maps creates a powerful methodological framework that leverages the strengths of both approaches to address ecological source homogenization. In this synergistic workflow, SOMs serve as an initial pattern discovery tool that identifies distinct ecological configurations within multidimensional environmental data, while deep learning models provide predictive capabilities to extrapolate these patterns across the broader landscape and model their dynamics over time.

The complementary nature of these approaches addresses different aspects of the homogenization problem. SOMs excel at exploratory data analysis and identifying the fundamental ecological units that exist within a landscape based on their multivariate characteristics [48] [52]. Deep learning models, conversely, excel at predictive mapping of these ecological units across spatial and temporal gradients based on limited sample data [50]. When combined, they form a comprehensive framework for ecological source identification that preserves the original information content of ecological variables while providing robust predictive capabilities.

Diagram 1: Integrated workflow combining SOMs and deep learning for ecological source identification. The process begins with multidimensional data collection and progresses through pattern discovery to predictive modeling.

Experimental Protocol for Implementing the Integrated Framework

The implementation of this integrated framework follows a systematic protocol designed to maximize ecological pattern detection while minimizing homogenization:

• Data Compilation and Preprocessing: Assemble a comprehensive geodatabase incorporating multisource ecological data, including remote sensing products, field surveys, climate records, and topographic information. Standardize all variables to consistent spatial and temporal resolutions.

• SOM-Based Ecological Typology: Apply Self-Organizing Maps to identify distinct ecological types present within the study area. The SOM training process involves:

  • Determining appropriate map size based on data complexity
  • Implementing iterative training with decreasing learning rates
  • Visualizing results through component planes and U-matrices
  • Interpreting ecological meaning of identified types

• Deep Learning Model Development: Train deep neural networks to predict the distribution of SOM-identified ecological types across the landscape. This involves:

  • Architecting network structure with multiple hidden layers
  • Implementing appropriate cost functions for ecological data
  • Training with spatial cross-validation to prevent overfitting
  • Generating prediction surfaces for each ecological type

• Ecological Source Identification: Select areas representing high-probability occurrences of ecologically significant types as ecological sources for ESP construction. This selection considers both the conservation value and the spatial configuration of high-probability areas.

• Corridor Delineation and Network Optimization: Apply circuit theory or minimum cumulative resistance models to identify ecological corridors between source areas, incorporating resistance surfaces derived from the deep learning predictions.

This protocol was successfully implemented in the Poyang Lake Ecological Urban Agglomeration, where researchers used an "adaptive generation approach utilizing deep learning, specifically the self-organizing mapping neural network model, to overcome the traditional homogenisation problem" [46]. This approach identified various types of ecological sources by integrating multi-sourced data, addressing the issue of original information loss caused by overlay analysis [46].

Technical Toolkit for Implementation

Table 3: Essential Research Reagent Solutions for Implementation

Tool Category	Specific Solutions	Technical Function in ESP Research
Deep Learning Frameworks	TensorFlow, PyTorch, Keras	Building and training neural network architectures for ecological pattern recognition
SOM Implementations	SOM Toolbox (Python), kohonen package (R), Somoclu	Performing unsupervised clustering of multidimensional ecological data
Spatial Analysis Platforms	ArcGIS, QGIS, GRASS GIS	Processing geospatial data, constructing resistance surfaces, mapping ecological networks
Ecological Modeling Tools	InVEST, Circuitscape, Linkage Mapper	Quantifying ecosystem services, modeling connectivity, identifying corridors
Data Processing Environments	R, Python (pandas, scikit-learn), MATLAB	Data cleaning, standardization, and analysis prior to model implementation

Successful implementation of deep learning and SOM approaches for addressing ecological source homogenization requires appropriate computational resources. For regional-scale analyses, workstations with substantial memory (64+ GB RAM) and advanced graphics processing units (GPUs) are recommended to handle the computational demands of training deep neural networks on large spatial datasets [49] [50]. For continental-scale analyses or high-resolution data, high-performance computing clusters may be necessary to complete model training within practical timeframes.

The software environment typically integrates multiple specialized tools through scripting frameworks, with Python and R serving as the most common integration platforms. These environments allow researchers to combine specialized spatial analysis libraries with machine learning implementations, creating reproducible workflows for ESP construction. Version control and containerization (e.g., Docker) are recommended to maintain consistency across analytical environments, particularly when models are developed collaboratively or intended for long-term monitoring applications.

Data Requirements and Preparation Protocols

The effectiveness of deep learning and SOM approaches in addressing ecological source homogenization depends fundamentally on data quality and comprehensiveness. Implementation requires both ecological response data (species occurrences, habitat quality measurements, ecosystem service indicators) and environmental predictor data spanning the full spectrum of ecological gradients in the study area.

Critical data layers include:

• Topographic variables: Elevation, slope, aspect, terrain roughness
• Climatic data: Temperature, precipitation, evapotranspiration, climate extremes
• Soil properties: Texture, pH, organic matter, elemental composition
• Land cover and vegetation: Remote sensing indices (NDVI, EVI), land use classifications
• Hydrological features: Distance to water, flow accumulation, wetland locations
• Anthropogenic influences: Urban areas, infrastructure, nighttime light emissions

Data preparation follows a rigorous protocol of spatial standardization, gap-filling, and multicollinearity assessment. Unlike traditional approaches that may use simplified data representations, the deep learning and SOM framework preserves the original continuous nature of these variables, allowing the models to detect nuanced ecological patterns that would be lost in categorical simplifications [48] [50].

Validation and Performance Assessment Framework

Comparative Metrics for Methodological Evaluation

The performance of deep learning and SOM approaches in addressing ecological source homogenization must be rigorously validated against both conventional methods and independent ecological data. Quantitative assessment typically employs multiple metrics to evaluate different aspects of model performance:

• Predictive Accuracy: Measured through area under the curve (AUC) statistics, precision-recall curves, and top-k accuracy metrics comparing predicted species distributions or ecological types against independent validation data [50]
• Community Composition Accuracy: Assessment of how well the modeled ecological sources represent actual community composition using site-by-site AUC metrics [50]
• Connectivity Effectiveness: Evaluation of whether corridors identified based on the derived sources align with independent wildlife movement data or genetic flow patterns
• Conservation Efficiency: Measurement of how well the identified ecological sources capture biodiversity features compared to random selections or conventional approaches

In a comprehensive comparison, deep neural networks demonstrated "distinctly higher performance" than traditional species distribution models in predicting both species distributions and community composition [50]. The median site-by-site AUC for community composition prediction was 0.976 for DNN ensembles compared to 0.964 for traditional approaches, representing a statistically significant improvement in capturing ecological patterns [50].

Ecological Relevance Assessment

Beyond statistical metrics, the ecological relevance of identified sources must be assessed through expert evaluation and field validation. This involves examining whether the differentiated ecological sources correspond to meaningful ecological units with distinct species assemblages, ecosystem functions, or environmental characteristics. The SOM approach has demonstrated particular strength in this regard, with research showing that identified clusters "are controlled by chemical and mineralogical factors of the study area parent material and by soil-forming processes, but with some exceptions linked to anthropogenic input" [48].

This validation of ecological meaning is crucial for ensuring that the technical differentiation of ecological sources corresponds to actual functional differences relevant to conservation planning. Field surveys, biodiversity inventories, and expert workshops provide essential ground-truthing to confirm that the computationally identified sources represent ecologically distinct units rather than statistical artifacts.

The integration of deep learning and Self-Organizing Maps presents a transformative approach to addressing the persistent challenge of ecological source homogenization in ESP research. By moving beyond the simplistic overlay methods that have dominated conventional practice, these advanced computational techniques can preserve the original information content of ecological data while identifying complex, multidimensional patterns that more accurately represent ecological reality. The resulting ESPs therefore offer enhanced capacity to protect biodiversity, maintain ecological processes, and promote landscape connectivity.

Implementation success depends on careful attention to data quality, appropriate computational resources, and rigorous validation protocols. As these methodologies continue to evolve, their integration with emerging technologies like remote sensing platforms and citizen science initiatives will further enhance their capacity to support effective conservation planning in an era of rapid environmental change. By embracing these advanced analytical approaches, researchers and practitioners can develop ecological security patterns that truly reflect the complexity of natural systems, moving beyond homogenization toward more effective and nuanced conservation strategies.

Ecological Security Patterns (ESPs) provide a strategic spatial blueprint for balancing ecological conservation with socioeconomic development, forming a foundational concept in landscape ecology and sustainability science. The construction of ESPs typically follows a paradigm of identifying ecological sources, constructing resistance surfaces, and extracting ecological corridors [53]. Within this framework, the ecological resistance surface is a spatially explicit model that quantifies the impedance that landscapes pose to ecological flows, such as animal movement or seed dispersal. The accuracy of this resistance surface directly determines the reliability of identified ecological corridors and nodes [13].

Traditional approaches to building resistance surfaces have relied heavily on land-use and land-cover classifications, assigning fixed resistance values to broad categories such as "forest," "agricultural land," or "urban area" [53]. This method, while computationally straightforward, suffers from significant limitations as it treats all patches within a land-use category as homogeneous, ignoring the substantial variation in ecological permeability within these classes. For instance, the resistance value assigned to "urban" typically fails to distinguish between a densely built city center and a suburban area with green spaces, leading to oversimplified corridor predictions [54].

Recognizing these limitations, contemporary research has shifted toward data-integrated approaches that incorporate continuous variables such as nighttime light data and impervious surface indices. These datasets capture the intensity and footprint of human activity with far greater precision, enabling researchers to create more nuanced and biologically realistic resistance surfaces. This technical guide details the methodologies, data sources, and analytical frameworks for moving beyond basic land-use types to refine resistance surfaces for robust ESP construction and optimization.

Foundational Concepts: From Traditional to Enhanced Resistance Modeling

Limitations of Land-Use Type Classifications

Traditional resistance surfaces based solely on land-use types operate on a categorical model that masks critical intra-class variability. This approach abstracts complex landscapes into homogeneous units, failing to capture:

• Graduated intensity of human disturbance: A low-density residential area and a high-density industrial zone may both be classified as "built-up land" but present vastly different levels of resistance to species movement.
• Edge effects and fragmentation: The permeability of a habitat patch is influenced not only by its classification but also by its spatial configuration, proximity to human infrastructure, and internal fragmentation—dimensions poorly represented in categorical maps.
• Context-dependent resistance: The resistance value of a pixel may depend on its surrounding landscape matrix, a relational characteristic that land-use classifications cannot encode.

These limitations ultimately compromise the effectiveness of conservation planning, potentially misdirecting corridor protection and restoration efforts.

Theoretical Basis for Enhanced Variables

The integration of nighttime light data and impervious surface coverage addresses these limitations by providing continuous, quantitative measures of anthropogenic pressure. The theoretical foundation rests on several key principles:

• Human Footprint Hypothesis: The cumulative impact of human activities directly correlates with landscape resistance to ecological flows. Nighttime light intensity and impervious surface percentage serve as robust proxies for this human footprint [53].
• Edge Effect Quantification: These continuous variables naturally capture gradations in human influence, effectively modeling the softening or hardening of habitat edges that significantly impact species movement.
• Behavioral Ecology: Many species exhibit avoidance behaviors correlated with human disturbance levels, which are more accurately reflected in light pollution and urbanization metrics than in simple land-use categories.

Using these proxies, resistance surfaces transition from static categorical representations to dynamic, behaviorally-informed models that better predict actual species movement and gene flow.

Nighttime Light (NTL) Data

Nighttime light data captures artificial illumination, serving as a powerful proxy for human activity intensity, energy consumption, and urbanization density. Different NTL datasets offer varying temporal coverage, spatial resolution, and sensor characteristics, as summarized in Table 1.

Table 1: Characteristics of Major Nighttime Light Datasets

Dataset Name	Spatial Resolution	Temporal Coverage	Key Features and Applications	Data Sources
DMSP/OLS	~1 km	1992-2013	Foundational for long-term trend analysis; suffers from saturation in bright areas [55].	[55]
NPP/VIIRS	~500 m	2012-present	Reduced saturation issues; better detection of low-light areas; superior for urban ecological studies [55].	[56]
Black Marble	~500 m	2012-present	Calibrated version of VIIRS with improved atmospheric correction; recommended for precise analysis [55].	[55]
DVNL	Varies	2013-2019	Fusion product using neural networks to bridge DMSP and VIIRS; addresses continuity issues [55].	[55]

Recent advancements have specifically targeted improved monitoring of Low-Light Areas (LLAs), which constitute approximately 80% of the Earth's surface and include critical ecological zones such as protected areas, biodiversity hotspots, and rural landscapes with important ecosystem functions [55]. When selecting NTL data, researchers should consider temporal consistency, sensitivity in LLAs, and compatibility with other spatial datasets in their workflow.

Impervious Surface Data

Impervious surfaces—including buildings, roads, and pavements—directly fragment habitats, alter hydrological regimes, and create barriers to species movement. Impervious surface data is typically derived from satellite imagery through spectral analysis, with key characteristics:

• Spatial Resolution: Available at various resolutions (e.g., 30 m from Landsat, 10 m from Sentinel-2), with higher resolution providing greater detail for local-scale ESP analysis.
• Temporal Frequency: Annual or periodic updates allow for tracking urbanization impacts on ecological connectivity over time.
• Measurement Scale: Represented as percentage coverage per pixel (e.g., 0-100%) or as binary classifications, though continuous values are preferable for resistance modeling.

Impervious surface data complements NTL by providing a direct physical measure of urbanization and infrastructure development, unlike the proxy measure of artificial light. In combination, these datasets capture both the physical and functional dimensions of human landscape modification.

Methodological Framework: Constructing Enhanced Resistance Surfaces

Workflow for Resistance Surface Generation

The process of creating an enhanced resistance surface integrates traditional land-use data with continuous human footprint variables through a structured workflow, illustrated in the following diagram:

Diagram Title: Workflow for Enhanced Ecological Resistance Surface Construction

This workflow begins with a base resistance surface derived from land-use data, which is then systematically modified using continuous variables representing human footprint, ultimately producing a refined resistance surface for ESP construction.

Technical Protocols for Data Integration

Base Resistance Surface Creation

Begin by assigning resistance values based on land-use/land-cover data:

• Data Preparation: Obtain a recent land-use/land-cover classification map for your study area. Reclassify the map into broad categories (e.g., forest, agriculture, urban, water).
• Resistance Assignment: Assign preliminary resistance values to each category based on literature review and ecological expertise. Typically, natural habitats receive low resistance values (e.g., 1-10), agricultural areas moderate values (e.g., 20-50), and urban areas high values (e.g., 80-100).
• Spatial Alignment: Ensure the base resistance raster is aligned with other datasets in terms of coordinate system, extent, and cell size.

Incorporating Nighttime Light Data

Integrate NTL data using the following protocol:

• Data Preprocessing:

  • Download NTL data for your study area and time period.
  • Reproject and resample to match the base resistance surface.
  • Apply any necessary inter-annual calibration to ensure consistency if using multi-year data.

• Resistance Modification:

  • Reclassify NTL values into resistance weights. For example:
    • DN = 0: Weight = 1.0 (minimal modification)
    • DN = 1-10: Weight = 1.2
    • DN = 11-30: Weight = 1.5
    • DN > 30: Weight = 2.0
  • Multiply the base resistance values by these weights to create a NTL-modified resistance surface.

This approach acknowledges that even in the same land-use category, areas with higher nighttime light intensity typically present greater resistance to species movement.

Incorporating Impervious Surface Data

Integrate impervious surface data using this protocol:

• Data Acquisition:

  • Obtain impervious surface data (percentage per pixel) for your study area.
  • Ensure spatial alignment with other datasets.

• Resistance Modification:

  • Create a resistance multiplier based on impervious percentage:
    • 0% impervious: Weight = 1.0
    • 1-20% impervious: Weight = 1.3
    • 21-50% impervious: Weight = 1.7
    • 51-100% impervious: Weight = 2.2
  • Apply these weights to the base resistance surface.

The specific weight values should be calibrated based on the sensitivity of target species to human disturbance and the regional context.

Integrated Resistance Modeling

Combine the modified surfaces to create a final integrated resistance surface. Two primary approaches exist:

• Multiplicative Model:

  This approach assumes compounding effects of different resistance factors.

• Weighted Linear Combination:

  where w1 + w2 + w3 = 1, allowing for differential weighting of factors based on their perceived ecological importance.

The multiplicative model generally produces more realistic resistance surfaces, as anthropogenic factors typically have synergistic rather than additive effects on landscape resistance.

Analytical Approaches: From Resistance Surfaces to Ecological Security Patterns

Corridor Delineation Using Circuit Theory and MCR

With the refined resistance surface, ecological corridors can be identified using established landscape ecological models. The relationship between these models and their application is illustrated below:

Diagram Title: Analytical Models for ESP Construction from Resistance Surfaces

The Minimum Cumulative Resistance (MCR) model calculates the path of least resistance between ecological sources, identifying optimal corridor locations [13]. The model is expressed as:

where Dij represents the distance through pixel i to j, and Ri is the resistance value of pixel i.

Circuit theory complements this approach by simulating ecological flow as electrical current, identifying not only optimal corridors but also alternative pathways and pinch points [13]. This synergy leverages the MCR model's efficiency in identifying core corridors while utilizing circuit theory's strength in multi-path analysis and key node identification [13].

Validation and Optimization Techniques

Validating the predictive accuracy of resistance surfaces is essential. Several methods can be employed:

• Field Validation: Collect actual species movement data (e.g., from GPS tracking, camera traps, or genetic analysis) to verify whether predicted corridors align with observed movement patterns.
• Independent Data Comparison: Compare corridor predictions with independent ecological data, such as species occurrence records or roadkill hotspots.
• Sensitivity Analysis: Systematically vary resistance values to test the stability of predicted corridors and identify thresholds where corridor patterns change significantly.

Optimization involves iterative refinement of resistance values and weights based on validation results, gradually improving the model's biological realism.

Table 2: Research Reagent Solutions for Enhanced Resistance Surface Modeling

Tool/Category	Specific Examples	Function and Application	Key Features
Nighttime Light Data	VIIRS Black Marble, DMSP/OLS, DVNL	Quantifies human activity intensity; models light pollution effects on connectivity [55].	Corrected for atmospheric effects; compatible across sensors [55].
Impervious Surface Data	Global Man-made Impervious Surface (GMIS)	Measures physical urbanization footprint; refines resistance in built-up areas [53].	Annual composites available; global coverage.
GIS Analysis Platforms	ArcGIS, QGIS, GRASS GIS	Spatial data processing; resistance surface generation; corridor modeling.	Supports raster calculator; circuit theory plugins.
Connectivity Modeling Software	Circuitscape, Linkage Mapper	Corridor identification; pinch point analysis; network connectivity assessment.	Implements circuit theory; user-friendly interfaces.
Land Use/Land Cover Data	MODIS Land Cover, ESA CCI Land Cover	Provides base resistance values; context for human modification intensity.	Annual global products; consistent classification schemes.

The integration of nighttime light data and impervious surface information represents a significant advancement in ecological resistance modeling. By moving beyond simplistic land-use classifications, researchers can create more biologically realistic representations of landscape permeability that better capture the graduated nature of human impact. This refined approach enables more accurate identification of ecological corridors and critical connectivity areas, ultimately supporting more effective conservation planning and landscape management.

The continuous evolution of remote sensing technologies, particularly the improved sensitivity to low-light areas and higher resolution impervious surface mapping, promises even greater precision in future resistance surfaces. As these methodologies become more accessible and standardized, they will increasingly inform regional planning and environmental policy, helping to maintain essential ecological flows in increasingly human-dominated landscapes.

The construction of Ecological Security Patterns (ESP) is a critical strategic initiative for ecological civilization, balancing protection and development to explore harmonious coexistence between humans and nature [46]. The integrated "MCR-Circuit Theory" framework addresses limitations of single-method approaches by leveraging the directional, cost-path optimization of the Minimum Cumulative Resistance (MCR) model and the stochastic, multi-path connectivity analysis of circuit theory [57] [58]. This hybrid methodology provides a more robust and realistic identification of ecological corridors and nodes, which are indispensable conduits linking ecological sources and upholding ecosystem stability [57] [19]. This technical guide delineates the core concepts, detailed protocols, and practical applications of this integrated approach, providing researchers with a advanced toolkit for spatial ecological optimization within ESP research.

Ecological security constitutes an essential component of national security, providing the foundation for other forms of security [19]. The ESP serves as an integrated spatial decision-making framework for ecological protection and restoration, incorporating key spatial elements such as ecological redlines and restoration zones [19]. The prevailing research paradigm for establishing ESPs adheres to the fundamental model of "source identification - construction of resistance surfaces - extraction of corridors" [57].

Within this paradigm, ecological corridors serve as indispensable conduits linking ecological source sites [57]. The Minimum Cumulative Resistance (MCR) model outlines the fundamental principles that govern the flow of ecological elements and provides the theoretical foundation for identifying these corridors and nodes [58]. It effectively determines the direction and location of corridors with the least cost [19].

Conversely, circuit theory perceives species migration as akin to the erratic movement of charges in an electrical circuit [57]. This concept models the movement of matter and energy in the biosphere as electron flow and ecological resistance as electrical resistance [58]. This approach prevents the animals from knowing the surrounding landscape features in advance and is more in line with their random movement behavior, making the identified ecological corridor structures exhibit greater alignment with real-world conditions [57].

Comparative Framework: MCR vs. Circuit Theory

The table below summarizes the core characteristics, strengths, and limitations of each model, highlighting their complementary nature.

Table 1: Comparative Analysis of the MCR Model and Circuit Theory

Feature	Minimum Cumulative Resistance (MCR) Model	Circuit Theory
Core Concept	Calculates the least-cost path from a source to a destination across a resistance surface [58].	Models ecological flows as random walkers, simulating movement in all possible directions across a landscape of resistance [57] [58].
Primary Strength	Directional, cost-path optimization; identifies the single most efficient corridor between two sources [57].	Identifies all potential connection pathways, including zones of high flow (pinch points) and areas of low connectivity (barriers) [58].
Key Outputs	Least-cost corridors and paths [57].	Pinch points, barriers, and cumulative current flow maps [58].
Limitation	Over-simplifies ecological flows as deterministic processes, overlooking random walk behavior and the multiplicity of potential pathways [57] [19].	Does not inherently identify a single "optimal" corridor; can produce complex, diffuse patterns of connectivity [58].
Theoretical Analogy	Least-cost path analysis in GIS [57].	Electrical circuit analysis with current, voltage, and resistance [58].

The integration of these two models effectively captures the impact of landscape pattern changes on ecological processes, bridging the gap between pattern characterization and process simulation [19]. The following diagram illustrates the synergistic workflow of combining these methods.

Integrated Methodology: A Step-by-Step Experimental Protocol

Stage 1: Prerequisite Data Collection and Preprocessing

The foundational step involves assembling the necessary geospatial data. The specific datasets required are detailed in the table below.

Table 2: Essential Research Reagents & Data for ESP Construction

Item/Reagent	Function in ESP Construction	Key Characteristics & Examples
Land Use/Land Cover (LULC) Data	Serves as the primary base layer for identifying ecological sources and assigning resistance values [58] [19].	High-resolution (e.g., 30m) classified raster data (e.g., from CLCD, ESA CCI) [58].
Multispectral Remote Sensing Imagery	Used for calculating ecological indices (e.g., NDVI, RSEI) to assess vegetation health and ecological quality for source identification [58] [19].	Landsat, Sentinel-2 imagery; often processed via platforms like Google Earth Engine (GEE) [58].
Topographic Data	Digital Elevation Model (DEM) used to derive slope, which functions as a factor in the ecological resistance surface [58].	30m resolution DEM (e.g., from SRTM, ASTER) [58].
Anthropogenic Disturbance Vectors	Represents human activities (roads, railways, mining districts) that increase ecological resistance and fragment habitats [57] [58].	Spatial point/line data for roads, railways, and mining districts from national geographic databases [58].
Morphological Spatial Pattern Analysis (MSPA)	A tool for identifying core ecological patches based on their shape, connectivity, and morphological pattern, used to refine ecological sources [19].	Software: GuidosToolbox; input: binary LULC raster (e.g., forest/non-forest) [19].

Ecological sources are areas of high habitat quality that act as origins for ecological flows. A robust method combines multiple perspectives:

• Ecosystem Service Assessment: Evaluate the capacity of ecosystems to provide services like water conservation, soil retention, and biodiversity maintenance [46] [57].
• MSPA and Landscape Connectivity: Use MSPA to identify core habitat patches from a land cover map. Then, evaluate their importance using landscape connectivity indices (e.g., the probability of connectivity, dPC) to select the most crucial patches as final ecological sources [19].
• Remote Sensing Ecological Index (RSEI): Develop a comprehensive index using remote sensing data (greenness, wetness, dryness, heat) to rapidly assess regional ecological quality and identify source areas [58].

Stage 3: Construction of the Ecological Resistance Surface

The Ecological Resistance Surface (ERS) quantifies the landscape's impedance to species movement. Construction involves:

• Select Resistance Factors: Choose factors based on the study context, including land use type, slope, elevation, and distance from anthropogenic disturbances (roads, residential areas, mining districts) [57] [58].
• Assign Relative Resistance Values: Assign resistance values to each class of these factors, typically using the Analytic Hierarchy Process (AHP) to weigh expert knowledge [58]. For example, forest land may have the lowest resistance (e.g., 1), while built-up areas and mining zones have the highest (e.g., 100-500) [58].
• Generate the Composite ERS: Use a weighted overlay function in GIS to combine all factor layers into a single, continuous resistance surface. The formula is often expressed as: ERS = f(Land_Use_Resistance, Slope_Resistance, Distance_to_Roads_Resistance, ...)

Stage 4: Integrated Corridor and Node Identification

This is the core integrative stage, leveraging both MCR and circuit theory.

• MCR Model Execution: The MCR model is calculated between all pairs of ecological sources to identify the least-cost paths (LCPs), which represent the most efficient potential corridors [57]. The formula is: MCR = f_min ∑ (D_ij * R_ij) Where D_ij is the distance and R_ij is the resistance of grid cell ij [57]. These LCPs form the skeleton of the ecological network.

• Circuit Theory Application: Using the same ecological sources and resistance surface, apply circuit theory software such as Circuitscape or Linkage Mapper. This analysis produces two critical types of elements [58]:

  • Pinch Points: Areas where movement flow is funneled into a narrow area, making them critically important and sensitive to disruption.
  • Barriers: Areas with high resistance that severely impede ecological flow, indicating priority locations for restoration efforts.

Table 3: Key Outputs from an Integrated Analysis in Chenzhou [58]

Analysis Method	Ecological Element Identified	Quantitative Results
Ecological Source Identification	Primary Ecological Sources	2,903 km²
	Secondary Ecological Sources	1,735 km²
MCR & Circuit Theory Integration	Total Ecological Corridors	90 corridors (1,183.66 km total length)
	Inactive Corridors	22 corridors (983.37 km total length)
	Major River Corridors	3 corridors
	Pinch Points	68 points
	Barriers	80 areas

The relationship between the core elements of an ESP and the methods used to identify them is synthesized in the following conceptual diagram.

Discussion: Applications and Optimization Strategies

The integrated "MCR-Circuit Theory" framework facilitates advanced spatial optimization and targeted ecological management. The resulting ESP allows for the proposal of zoning strategies, such as classifying areas into ecological conservation, ecological enhancement, ecological restoration, and ecological control zones [57].

For example, pinch points located near towns or mining areas represent critical bottlenecks where ecological flow is concentrated; these areas require strict protection, potentially through the construction of artificial ecological corridors or the expansion of ecological channel boundaries [58]. Conversely, barrier areas, often coinciding with urban centers, mining zones, and farmland, require active restoration measures to lower resistance and reconnect fragmented habitats [58].

This methodology is highly adaptable. It has been successfully applied in diverse contexts, from the desertification control forests of the South China Karst [19] to resource-based cities like Chenzhou, where integrating specific mining district data into the ERS was crucial for accurately reflecting localized impacts [58]. This integrated approach provides a scientific foundation for territorial spatial planning, natural resource management, and achieving sustainable development goals [58] [19].

Ecological Security Patterns (ESPs) are a crucial strategy for coordinating ecological and economic development while optimizing sustainable land use [13]. They form a complete ecological network comprising ecologically important areas and the corridors that connect them, designed to maintain ecological processes, ensure the provision of ecosystem services, and promote regional ecological sustainability [59]. In the context of China's black soil regions, which are vital for national food security, constructing and optimizing ESPs is particularly urgent due to severe ecological challenges like soil erosion, salinization, and biodiversity loss that threaten sustainable agriculture [13].

The dynamic evolution of ecosystems necessitates a multi-temporal analytical approach to ESP research. Traditional studies focusing on single time points fail to capture the spatial and temporal dynamics of ecosystem degradation and restoration trends [13]. This technical guide provides a comprehensive framework for conducting multi-temporal ESP analysis from 2002 to 2022 in black soil areas, offering researchers detailed methodologies for tracking changes and optimizing ecological security patterns over time.

Methodological Framework for Multi-Temporal ESP Analysis

The standard framework for constructing ESPs follows a "source identification-resistance surface construction-corridor extraction" sequence [13]. For multi-temporal analysis, this process is repeated at each time node (2002, 2012, 2022) to enable comparative assessment of changes.

Table 1: Core Components of Multi-Temporal ESP Analysis Framework

Component	Description	Key Methods/Models
Ecological Source Identification	Areas with high ecological importance and low vulnerability that serve as primary habitat patches	Ecosystem service value assessment, Ecological sensitivity analysis [13] [60]
Resistance Surface Construction	Landscape matrix that impedes or facilitates species movement and ecological flows	Habitat quality assessment, Anthropogenic activity influence, Nighttime light data modification [61] [59]
Corridor and Node Extraction	Pathways connecting ecological sources and critical areas for maintaining connectivity	Minimum Cumulative Resistance (MCR) model, Circuit theory, Least-cost path analysis [13] [59]

The multi-temporal approach overcomes limitations of single-time-point evaluations by revealing spatial-temporal evolution of ecological source areas, water conservation, and carbon sequestration over extended periods [13]. This provides a more comprehensive examination of ecological degradation and restoration trends, offering a scientific basis for ecological protection and restoration that avoids the limitations of single-point decision-making.

Ecological Source Identification Across Time Series

Ecological sources are identified through integrated assessment of ecosystem service value and ecological sensitivity at each time node [13]. In black soil regions, these typically include water areas, forestland, and certain cropland areas with high ecological functionality [59] [60].

Ecosystem Service Value Assessment quantifies the ecological importance of different areas through multiple indicators:

• Biodiversity: Assessed through habitat quality models or spatial overlay of vegetation coverage and slope [59]
• Carbon Storage: Modeled using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model [59]
• Water Yield: Evaluated using the InVEST model or similar hydrological modeling approaches [59]

Ecological Sensitivity Analysis identifies areas vulnerable to human activities or natural disasters through:

• Habitat Sensitivity: Response of different ecosystems to anthropogenic disturbances
• Soil Erosion Sensitivity: Assessed using models like the Chinese Soil Loss Equation [59]
• Water Sensitivity: Vulnerability of water bodies and surrounding areas to pollution and degradation

Areas with both high ecosystem service value and high ecological sensitivity are designated as ecological sources. In the black soil region of Northeast China, studies have identified a spatial pattern of higher ecosystem service values in the east and lower values in the west [13].

Constructing Dynamic Resistance Surfaces

Ecological resistance surfaces represent the landscape matrix's impedance to species movement and ecological flows. These surfaces are constructed using a multi-indicator approach that considers:

Table 2: Factors for Ecological Resistance Surface Construction

Factor Category	Specific Indicators	Data Sources
Topography	Slope, altitude	Digital Elevation Models (DEMs) [60]
Eco-Environment	Land cover types, distance from water bodies, vegetation coverage	Landsat imagery, MODIS data [62] [60]
Human Disturbance	Distance from roads, nighttime light data, population density	Nighttime light datasets, road networks, census data [13] [59]

Higher resistance values are assigned to urban land and areas with intensive human activity, while lower values are assigned to wetlands, grasslands, and forests [59]. The resistance surface can be modified using nighttime lighting data to better reflect anthropogenic pressure [59].

Corridor and Critical Node Extraction

Ecological Corridors are extracted using the Minimum Cumulative Resistance (MCR) model, which identifies optimal pathways for species movement between ecological sources [13]. The MCR model quantifies resistance to species migration by calculating the minimum cost path between source areas [13]. The formula for MCR is:

[ MCR = f{min} \sum{j=1}^{n} (D{ij} \times Ri) ]

Where ( D{ij} ) is the distance through landscape grid i, ( Ri ) is the resistance value of grid i, and ( f_{min} ) indicates the minimum cumulative resistance over all paths between source j and target areas.

Circuit Theory complements the MCR model by simulating current diffusion processes of biological flow, supporting multi-path migration simulation that aligns with the randomness and diversity of species dispersal [13]. This approach helps identify:

• Pinch Points: Areas where ecological flows are concentrated and particularly vulnerable to disruption [61]
• Barriers: Locations with high improvement potential that impede ecological connectivity [61]

The combination of MCR and circuit theory leverages the advantages of both approaches: MCR optimizes identification of core corridors, while circuit theory supplements multi-path analysis and key node identification [13].

Experimental Protocols and Technical Procedures

Data Collection and Preprocessing

Multi-temporal ESP analysis requires consistent data collection across all time nodes (2002, 2012, 2022). Key data sources include:

Remote Sensing Data:

• Landsat-5/7/8/9 time-series images with cloud coverage <10% and spatial resolution of 30m [62] [60]
• MODIS data for NPP (Net Primary Productivity) and NDVI (Normalized Difference Vegetation Index) with 500m spatial resolution [60]
• Nighttime light data from DMSP-OLS and SNPP-VIIRS satellites [13]

Climate Data:

• Precipitation and temperature datasets from meteorological stations and reanalysis products [13] [60]
• Potential evapotranspiration data from scientific data centers [13]

Topography and Soil Data:

• Digital Elevation Models (DEMs) for slope and altitude analysis [60]
• Soil data from Harmonized World Soil Database (HWSD) [60]

All raster data should be unified to the same coordinate system, with spatial resolution resampled to a consistent grid (typically 30m) to ensure comparability across time periods [13] [60].

Land Use/Land Cover Classification

Land use and land cover (LULC) classification forms the foundation for ESP analysis. The recommended procedure includes:

• Image Acquisition: Download Landsat images for each time node with minimal cloud cover
• Supervised Classification: Use classification algorithms (e.g., in ENVI software) to categorize land into cultivated land, forestland, grassland, water area, construction land, and unexploited land [60]
• Accuracy Assessment: Verify classification accuracy using ground truth data and high-resolution imagery (target accuracy >85%) [60]

Advanced classification approaches incorporate Decision Tree (DT) algorithms with water frequency (WF) and vegetation frequency (VF) metrics calibrated with field observations to improve accuracy [62].

Dynamic Change Detection Analysis

Tracking ESP changes across time nodes involves:

• Spatial Overlay Analysis: Comparing spatial patterns of ecological sources, corridors, and nodes across different years
• Metric Quantification: Calculating changes in number and area of ecological sources, length and pattern of corridors, and identification of critical nodes
• Trend Analysis: Identifying patterns of improvement or degradation in ecological connectivity

In black soil regions, this analysis has revealed that although the number of ecological source areas decreased from 2002 to 2022, their total area increased, suggesting consolidation of important habitat patches [13].

Case Study: Black Soil Region of Northeast China (2002-2022)

Application of the multi-temporal ESP analysis framework to China's black soil region reveals significant changes in ecological security patterns over two decades.

Table 3: Dynamic Changes in ESP Components in Black Soil Region (2002-2022)

ESP Component	2002 Status	2012 Status	2022 Status	Trend Analysis
Ecological Sources	Number and area at initial state	Transitional state	Decreased number but increased total area	Consolidation and expansion of key habitat patches
Ecological Corridors	Baseline number and length	Fluctuating state	Decreased number but fluctuating length	Structural simplification with maintained connectivity
Stepping Stones	Initial count	Increasing	Significant increase	Enhanced landscape connectivity
Ecosystem Service Function	Higher values in east, lower in west	Transitional pattern	Maintained east-west gradient	Spatial pattern persistence
Ecological Sensitivity	Initial sensitivity level	Decreasing trend	Continued decrease	Improving ecosystem resilience

The findings demonstrate a general improvement in ecosystem health in the black soil region from 2000 to 2020, with the ecosystem health index increasing from 0.49 to 0.51, attributed to increases in forestland area and precipitation [60]. However, some suburban areas experienced degradation, highlighting the need for targeted restoration measures.

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Analytical Tools for Multi-Temporal ESP Research

Tool/Category	Specific Examples	Function in ESP Analysis
Remote Sensing Platforms	Google Earth Engine (GEE), Landsat 5/7/8/9, MODIS	Large-scale spatial-temporal data processing and analysis [62]
Ecological Modeling Software	InVEST, Circuit Theory, MCR Model	Quantifying ecosystem services and modeling ecological flows [13] [59]
GIS Analysis Tools	ArcGIS, QGIS	Spatial analysis, resistance surface construction, corridor mapping [13] [60]
Climate Data Sources	Peng's Dataset, NCEI, China Meteorological Data	Providing climate variables for ecological process modeling [13] [60]
Vegetation Indices	NDVI, NPP	Measuring ecosystem vigor and productivity [60]

Multi-temporal analysis of ESPs in black soil regions from 2002 to 2022 reveals dynamic changes in ecological sources, corridors, and connectivity. Based on the identified patterns, a "point-line-polygon-network" optimization strategy is recommended:

• Point Restoration: Focus on critical pinch points and barriers identified through circuit theory to address key connectivity impediments [13] [61]
• Linear Corridors: Strengthen and protect identified ecological corridors, particularly those with high improvement potential [13]
• Polygonal Sources: Enhance protection of consolidated ecological source areas, particularly those with high ecosystem service value [13]
• Network Connectivity: Implement comprehensive ecological networks that connect sources through corridors and stepping stones [13]

This optimization approach requires integrating ecological conservation with agricultural practices in black soil regions, ensuring both food security and ecological sustainability. The methodological framework presented enables researchers to track ESP dynamics over time, identify degradation hotspots, and prioritize interventions for maintaining ecological security in vulnerable ecosystems.

Ecological Security Patterns (ESPs) provide a spatial blueprint for balancing ecological conservation with socioeconomic development, ensuring the stability and sustainability of ecosystems. These patterns are composed of strategically planned ecological components—sources, corridors, and nodes—that work in concert to maintain critical ecological processes. The construction of ESPs typically follows an established paradigm of "identifying ecological sources, constructing resistance surfaces, extracting corridors, and identifying ecological nodes" [63]. Within this paradigm, optimization frameworks are essential for translating identified ecological elements into actionable and effective spatial configurations for conservation planning. This technical guide explores two advanced optimization frameworks: the "point-line-polygon-network" strategy and the "one ring, two corridors" model, detailing their methodologies, applications, and implementation protocols for researchers and planning professionals.

The "Point-Line-Polygon-Network" Optimization Framework

The "point-line-polygon-network" framework is a comprehensive strategy for enhancing the connectivity and functionality of ecological networks. Its application in China's black soil regions has demonstrated significant effectiveness in improving regional ecosystem stability [13].

Core Components and Functions

• Point (Ecological Nodes): These are strategic locations that facilitate ecological flow. They include:
  • Stepping Stones: Small habitat patches that act as relays for species movement between larger core areas [13].
  • Ecological Barrier Points: Pinch-points in the landscape where ecological flow is constricted and requires intervention [54].
• Line (Ecological Corridors): These linear landscape elements connect ecological sources, allowing for the flow of species, energy, and information. Their optimization often involves restoring connectivity at critical junctions and barriers [13].
• Polygon (Ecological Sources): These are the core areas of high ecological quality that serve as primary habitats and source populations. Optimization strategies here focus on strengthening ecological barriers and expanding core areas to enhance their service provision [13].
• Network (Integrated Ecological Security Pattern): This represents the final, optimized spatial structure formed by the synergistic connection of points, lines, and polygons. It is designed to maximize ecological connectivity and stability across the entire landscape [13].

Quantitative Foundation from Case Studies

Table 1: Quantitative Changes in ESP Elements Following "Point-Line-Polygon-Network" Optimization in a Black Soil Region [13]

ESP Element	Metric	Temporal Change (2002-2022)
Ecological Sources	Number of Patches	Decreased
	Total Area	Increased
Ecological Corridors	Number	Decreased
	Total Length	Fluctuated
Stepping Stones (Points)	Number	Significantly Increased

Detailed Experimental Protocol for Implementation

The following workflow outlines the core methodological steps for constructing an ESP suitable for the "point-line-polygon-network" optimization.

Figure 1. Workflow for constructing and optimizing an Ecological Security Pattern (ESP). The process begins with identifying core ecological sources, models landscape resistance to species movement, extracts potential corridors and nodes, and finally applies the "point-line-polygon-network" framework for spatial optimization.

• Ecological Source Identification:

  • Objective: To delineate core habitat patches with high ecological value.
  • Protocol: Combine analyses of ecosystem service value (e.g., water conservation, carbon sequestration, soil retention) and ecological sensitivity (e.g., to soil erosion, desertification) [13] [47]. Areas with persistently high values across a time series (e.g., 2002, 2012, 2022) are identified as stable ecological sources [13].
  • Tools: GIS software (e.g., ArcGIS, QGIS) for spatial overlay and analysis.

• Resistance Surface Construction:

  • Objective: To model the landscape's impedance to species movement and ecological flow.
  • Protocol: Develop a composite resistance surface using factors such as land use/cover, topography, and human disturbance indices (e.g., distance to roads, nighttime light data) [13]. Weights can be assigned using methods like the Analytical Hierarchy Process (AHP) to reflect the relative importance of each factor [64].

• Corridor and Node Extraction:

  • Objective: To identify the least-cost paths for connectivity and key intervention points.
  • Protocol: Apply the Minimum Cumulative Resistance (MCR) model to delineate potential ecological corridors between sources [13] [65] [63]. Supplement this with circuit theory to model random-walk dispersal paths, identify pinch-points, and locate strategic stepping stones and barrier points for restoration [13] [64].

The "One Ring, Two Corridors" Optimization Framework

The "one ring, two corridors" framework represents a more synthesized and policy-oriented spatial model for regional planning, as demonstrated in the Poyang Lake Ecological Urban Agglomeration (PLEUA) [46].

Spatial Configuration and Interpretation

• One Ring: This typically refers to a core ecological zone or a continuous greenbelt that encircles a key ecological or urban asset. It functions as a primary buffer and stabilizer for the core ecosystem [46].
• Two Corridors: These are two major, strategic ecological corridors that serve as the main arteries for ecological flow across the region. They are critical for connecting disparate habitat patches and ensuring longitudinal and latitudinal connectivity [46].
• Supporting Elements: The framework is often part of a larger pattern, which can include additional components like "two zones" (e.g., core conservation and coordinated development zones) and "multiple cores" (key ecological source areas) to form a complete ESP [46].

Quantitative Foundation from Case Studies

Table 2: Ecological Network Elements in the Poyang Lake "One Ring, Two Corridors" Case Study [46]

ESP Element	Identified Quantity	Spatial Function
Ecological Sources	20	Form the "multiple cores" of the pattern
Ecological Corridors	30	Constitute the "two corridors" and other key linkages
Ecological Nodes	61	Critical connection and intervention points within the network

Detailed Experimental Protocol for Implementation

• Advanced Source Identification:

  • Objective: To overcome the homogenization of ecological sources and identify diverse source types.
  • Protocol: Utilize deep learning models, such as a self-organizing mapping (SOM) neural network, to integrate multi-sourced data from perspectives of ecosystem health, integrity, and services association (characterized by "contribution-sensitivity-vigour-organization") [46]. This method clusters ecological sources based on inherent data structures, preserving original information often lost in simple overlay analyses.

• Network Construction and Gravity Modeling:

  • Objective: To map the functional connections between ecological sources.
  • Protocol: After extracting corridors via the MCR model, use a gravity model to quantify the interaction strength between source patches [46] [63]. This helps prioritize the most significant corridors for conservation, which form the backbone of the "two corridors" in the final framework.

• Spatial Synthesis for Planning:

  • Objective: To translate the ecological network into a clear, actionable spatial plan.
  • Protocol: Synthesize the configuration of core sources, primary corridors, and the central ecological zone into the "one ring, two corridors, two zones, and multiple cores" spatial pattern. This provides a simplified yet powerful model for policymakers to integrate into land development optimization and environmental management plans [46].

The Scientist's Toolkit: Key Analytical Models and Reagents

Table 3: Research Reagent Solutions for ESP Construction

Tool/Model Name	Type	Primary Function in ESP	Key Advantage
MCR Model	Analytical Model	Identifies the least-cost path for ecological corridors and species movement [13] [65]	Intuitive; effectively models landscape resistance [63]
Circuit Theory	Analytical Model	Models multi-path connectivity and identifies pinch-points & barriers [13] [64]	Captures randomness of species dispersal; identifies critical nodes [64]
InVEST Model	Software Suite	Evaluates habitat quality and ecosystem services (e.g., water yield, carbon storage) [47] [63]	Quantifies and maps ecosystem services for source identification
Self-Organizing Map (SOM)	Deep Learning Model	Classifies heterogeneous ecological sources from multi-sourced data [46]	Overcomes homogenization problem in traditional source identification
PLUS Model	Simulation Model	Simulates future land use change scenarios under various development pathways [54]	Enables dynamic ESP assessment and future scenario optimization
RSEI Index	Spectral Index	Rapidly assesses regional ecological quality using remote sensing (dryness, greenness, wetness, heat) [66]	Objective and efficient for large-scale ecological quality monitoring

The "point-line-polygon-network" and "one ring, two corridors" frameworks represent two powerful, complementary approaches to optimizing Ecological Security Patterns. The former provides a granular, component-based methodology for strengthening an ecological network at every structural level, from strategic points to an integrated system. The latter offers a synthesized, policy-friendly spatial model that distills complex ecological data into a clear plan for regional land-use optimization. Employing advanced tools like circuit theory, MCR models, and deep learning is crucial for the robust identification of ecological network elements. Ultimately, integrating these optimization strategies into territorial spatial planning is fundamental for achieving ecological civilization and sustainable development goals.

Ecological Security Patterns (ESPs) provide a strategic spatial framework for balancing ecological conservation with socio-economic development. Within these networks, certain critical elements—pinch points, barriers, and stepping stones—disproportionately influence landscape connectivity and ecological flow. Pinch points are narrow, constricted areas in ecological corridors where movement is funneled, making them highly sensitive to disruption. Barriers are landscape elements that impede ecological flows, fragment habitats, and reduce connectivity. Stepping stones are smaller, often isolated patches of habitat that facilitate species movement between larger core areas by providing temporary refuge and resources.

Recent research underscores that targeted intervention at these critical points can dramatically enhance ESP effectiveness. A 2024 study demonstrated that restoring specific barrier areas increased landscape connectivity by up to 28.6%, while establishing stepping stones boosted connectivity by 13.9% [67]. Another 2025 study in China's black soil region highlighted a significant increase in the number of stepping stones over a 20-year period, correlating with improved ecosystem resilience [13]. This technical guide provides researchers and conservation professionals with advanced methodologies to identify, analyze, and restore these critical elements, enabling more efficient and cost-effective ecological conservation.

Foundational Concepts and Ecological Significance

The Role of Critical Points in Ecological Security Patterns

Critical points function as the leverage points of ecological networks. Their primary roles can be summarized as follows:

• Pinch Points: These areas represent the most vulnerable sections of ecological corridors. Their identification is crucial for corridor stability assessment and conservation prioritization. When pinch points are disrupted, the entire corridor's functionality can be compromised, effectively isolating ecological sources.
• Barriers: Typically characterized by high-resistance land uses (e.g., urban areas, major highways, intensive agriculture), barriers fragment habitats and disrupt ecological processes. Barrier restoration aims to reduce resistance and re-establish connectivity, often with substantial returns on investment [67].
• Stepping Stones: Unlike continuous corridors, stepping stones facilitate long-distance dispersal and genetic exchange for various species, particularly birds and flying insects. They are vital in heavily fragmented landscapes where continuous corridors are impossible to maintain [67].

Theoretical Framework: Circuit Theory and Network Analysis

The identification and analysis of these critical points are grounded in robust theoretical frameworks:

• Circuit Theory: This approach, adapted from electrical circuit theory, models landscape connectivity by simulating "current flow" across resistance surfaces. It excels at identifying pinch points (areas of concentrated current flow) and barriers (areas blocking current) [67] [13].
• Minimum Cumulative Resistance (MCR) Model: This model calculates the least-cost path for species movement between ecological sources. It is fundamental for corridor identification but typically assumes a single optimal path, unlike circuit theory's multi-path approach [13].
• Graph Theory: Applied to ecological networks, graph theory uses metrics (e.g., connectivity probability index) to quantify the importance of individual nodes (stepping stones) and links (corridors), predicting the landscape-scale impact of restoring or losing specific elements [67].

Table 1: Comparative Analysis of Critical Points in Ecological Security Patterns

Critical Point Type	Primary Ecological Function	Key Identification Characteristics	Sensitivity to Human Disturbance
Pinch Points	Funneling ecological flows through corridors	Narrow topography, concentrated movement pathways	Very High - disruption can sever entire corridors
Barriers	Impeding ecological flows and processes	High-resistance land uses (urban, agriculture)	Moderate-High - restoration can yield significant connectivity gains
Stepping Stones	Facilitating dispersal between core habitats	Small habitat patches in fragmented landscapes	High - loss can collapse dispersal routes

Methodological Framework for Identification and Analysis

Data Requirements and Pre-processing

A multi-layered spatial dataset is fundamental to this analytical process. The core data requirements include:

• Land Use/Land Cover (LULC) Data: High-resolution (e.g., 1-30m) data to construct resistance surfaces.
• Remote Sensing Indices: NDVI (Normalized Difference Vegetation Index) and NPP (Net Primary Productivity) to assess ecosystem vigor [68].
• Ecosystem Service Maps: Quantified data on habitat quality, water conservation, soil retention, and carbon sequestration [47].
• Topographic Data: Digital Elevation Models (DEMs) to account for slope-based resistance.
• Anthropogenic Data: Road networks, population density, and nighttime light data to model human impact [13].

Data pre-processing should ensure all raster layers share consistent spatial resolution, projection, and extent. Resistance values (e.g., 1-100) should be assigned to each LULC class, where higher values represent greater permeability to species movement.

Technical Protocols for Identifying Critical Points

Protocol for Pinch Point Analysis Using Circuit Theory

Table 2: Key Software and Tools for Critical Point Analysis

Tool Name	Primary Application	Strengths	Limitations
Circuitscape	Pinch point and barrier identification	Models random walk connectivity; identifies multiple pathways	Computationally intensive for large landscapes [13]
Linkage Mapper	Corridor and pinch point mapping	GIS-based toolbox; user-friendly interface	Less nuanced than circuit theory for complex flow [67]
Conefor	Graph theory-based connectivity metrics	Quantifies node importance; efficient for large networks	Requires pre-defined nodes and links [67]

Objective: To identify areas where ecological flow is concentrated and vulnerable to disruption. Workflow:

• Define Ecological Sources: Identify core habitat patches using criteria such as high ecosystem service value, large patch size, and high ecosystem health [68] [47].
• Construct Resistance Surface: Create a raster where each cell's value represents the cost of movement for a focal species or general ecological flow. Calibrate using expert knowledge or species-specific data.
• Execute Circuit Theory Analysis: Use software like Circuitscape to model connectivity. The analysis treats the landscape as an electrical circuit, with sources as nodes and the resistance surface determining conductivity.
• Map Current Flow: The output is a cumulative current density map. Areas with highest current density represent pinch points.

The following workflow diagram illustrates this analytical process:

Protocol for Barrier Identification and Restoration Prioritization

Objective: To locate landscape elements that most significantly impede ecological connectivity and prioritize them for restoration. Workflow:

• Calculate Cumulative Resistance: Using the same resistance surface from pinch point analysis, calculate the minimum cumulative resistance between ecological sources.
• Model Ecological Corridors: Extract corridors using least-cost paths or circuit theory.
• Identify Barrier Areas: Within and between corridors, identify areas with high resistance values that, if improved, would significantly enhance connectivity. The MCR model can be used here [13].
• Prioritize Restoration: Rank barriers using the Probability of Connectivity (dPC) index or similar graph theory metrics to quantify the potential connectivity improvement from restoring each barrier [67].

Protocol for Stepping Stone Identification and Network Design

Objective: To locate optimal positions for small habitat patches that enhance landscape connectivity. Workflow:

• Analyze Connectivity Gaps: Identify areas between ecological sources where resistance is high and dispersal is unlikely.
• Map Potential Stepping Stones: Identify existing small patches (e.g., woodlots, wetlands) that could serve as stepping stones.
• Evaluate Strategic Locations: Using graph theory, identify locations where new stepping stones would provide the greatest increase in overall connectivity. The gravity model can be applied to assess the interaction strength between potential stepping stones and ecological sources [46].
• Design Stepping Stone Network: Create a network where stepping stones are spaced according to the dispersal capabilities of target species.

Restoration and Management Protocols

Barrier Restoration Techniques

Barrier restoration focuses on reducing resistance to movement. A 2024 study in the Dongting Lake Basin quantitatively evaluated barrier restoration, finding it more effective for connectivity enhancement than stepping stone establishment [67].

Specific Restoration Methods:

• Vegetation Restoration: Convert high-resistance land covers (e.g., cropland) to native vegetation, prioritizing areas that create bottlenecks [67] [13].
• Ecological Engineering: Construct wildlife crossings (overpasses/underpasses) across significant linear barriers like highways.
• Low-Impact Land Use: Promote permeable matrices through agroforestry or silvopasture systems in strategic locations.

Stepping Stone Establishment and Management

Stepping stones are crucial in fragmented landscapes where continuous corridors are not feasible.

Implementation Protocols:

• Site Selection: Choose locations that minimize the effective distance between major habitats for target species.
• Habitat Creation: Develop small wetlands, woodlots, or grassland patches in strategic positions. A study in the Poyang Lake Ecological Urban Agglomeration identified 61 key ecological nodes that function as stepping stones [46].
• Network Optimization: Ensure stepping stones form a functional network rather than isolated patches. The optimal spatial configuration depends on target species' dispersal ecology.

Table 3: Comparison of Connectivity Enhancement Strategies

Strategy	Typical Implementation Cost	Connectivity Enhancement Potential	Key Performance Indicators
Barrier Restoration	Moderate-High	High (up to 28.6% increase) [67]	Reduction in MCR value; increased dPC index
Stepping Stone Establishment	Low-Moderate	Moderate (13.9% increase) [67]	Increased network connectivity; higher species dispersal success
Pinch Point Protection	Low (conservation) to High (restoration)	Critical for maintaining existing connectivity	Maintenance of corridor width; prevention of further constriction

Research Reagent Solutions

Table 4: Essential Analytical Tools for Critical Point Research

Tool/Category	Specific Examples	Research Application
Spatial Analysis Software	ArcGIS, QGIS, GRASS	Base platform for spatial data management, analysis, and cartography
Connectivity Modeling	Circuitscape, Linkage Mapper, UNICOR	Pinch point and corridor modeling using circuit theory and least-cost paths
Graph Theory Analysis	Conefor, Graphab	Quantifying network connectivity and node importance
Ecosystem Service Assessment	InVEST model	Mapping ecosystem services to identify ecological sources [47]
Remote Sensing Data	Landsat, Sentinel, MODIS	Land cover classification and vegetation vigor assessment (NDVI) [68]

Advanced Analytical Approaches

Emerging methodologies are refining critical point identification:

• Deep Learning Applications: Self-organizing mapping (SOM) neural networks overcome traditional limitations in identifying heterogeneous ecological sources, providing more nuanced classification [46].
• Time-Series Analysis: Evaluating ESP dynamics over multiple time points (e.g., 2002, 2012, 2022) reveals evolutionary trends and conservation priorities, as demonstrated in black soil region studies [13].
• Multi-Species Approach: Developing resistance surfaces for different functional species groups (e.g., forest specialists, grassland species) provides a more comprehensive conservation strategy.

The following diagram illustrates the integrated approach to ESP optimization:

Targeted management of critical points represents a paradigm shift in ecological conservation—from protecting vast, continuous areas to strategically intervening at leverage points that disproportionately impact landscape connectivity. The methodologies outlined herein enable researchers to precisely identify pinch points, barriers, and stepping stones, then implement the most effective restoration strategies. As research advances, integrating dynamic time-series analysis [13] with multi-species approaches will further refine our ability to maintain and enhance ecological security in human-modified landscapes. This scientific approach ensures that limited conservation resources are deployed where they will yield the greatest ecological returns, ultimately creating more resilient and functional ecosystems.

Measuring ESP Success: Effectiveness Evaluation, Policy Integration, and Comparative Frameworks

The DPSIR-S Framework (Driver-Pressure-State-Impact-Response-Security) represents an advanced evolution of the traditional DPSIR model, specifically tailored for the comprehensive assessment of ecological security patterns (ESPs). This framework provides a systematic structure for organizing indicators to enable feedback to policymakers on environmental quality and the resulting impact of political choices [69]. Originally developed by the European Environment Agency, the DPSIR framework assumes a chain of causal links starting with 'driving forces' through 'pressures' to 'states' and 'impacts' on ecosystems, eventually leading to political 'responses' [70]. The DPSIR-S extension explicitly incorporates ecological security as a central outcome, addressing the sustainability of ecosystems and focusing on reducing the probability or risk of ecological disaster caused by human-induced stresses [71].

The integration of security elements addresses a critical gap in traditional environmental assessment frameworks by emphasizing the maintenance of ecosystem functions and services essential for ecological integrity and human wellbeing. Ecological security, in this context, aims to conserve ecosystems in an unthreatened state while still experiencing human activity, and to provide the necessary resources to maintain their health and ability to adapt to environmental changes [71]. The DPSIR-S framework enables researchers and environmental managers to identify key causal pathways, assess ecological and social impacts, and design effective responses specifically geared toward enhancing ecological security [72].

Theoretical Foundations and Evolution

Historical Development of DPSIR

The DPSIR framework has its roots in several environmental reporting frameworks that emerged throughout the late 20th century. It was built upon the Pressure-State-Response (PSR) framework developed by the Organization for Economic Co-operation and Development (OECD) in 1993, which itself was an extension of Rapport and Friend's Stress-Response (SR) framework from 1979 [70]. The PSR framework simplified environmental problems and solutions into variables that stress the cause-effect relationship between human activities that exert pressure on the environment, the state of the environment, and society's response to the condition. A significant limitation was its failure to effectively factor natural variability into the pressure category, leading to the development of the expanded Driving Force-State-Response (DSR) framework by the United Nations Commission on Sustainable Development (CSD) in 1997 [70].

The refined DPSIR model emerged in 1999 from the European Environment Agency (EEA), addressing shortcomings of its predecessors by incorporating root causes of human activities that impact the environment, incorporating natural variability as a pressure, and addressing responses to the impact of changes in state on human well-being [70]. Unlike PSR and DSR, DPSIR was conceived not as a model but as a means of classifying and disseminating information related to environmental challenges. The subsequent DPSIR-S framework further extends this foundation by explicitly integrating ecological security as a measurable outcome, creating a more holistic approach to ecosystem assessment that connects landscape patterns with ecological processes [73].

Conceptual Framework of DPSIR-S

The DPSIR-S framework introduces a structured approach to understanding the complex relationships between human activities and environmental change, with specific emphasis on security outcomes. The framework's components form a causal chain that links socioeconomic drivers with environmental outcomes and management responses:

• Drivers: Social, demographic, and economic developments that influence human activities with direct impacts on the environment [70]
• Pressures: Stresses that human activities place on the environment, which can be endogenic managed pressures (from within the system) or exogenic unmanaged pressures (from outside the system) [70] [74]
• State: The physical, chemical, and biological condition of the environment [74]
• Impact: The effects of changes in the state of the system on human well-being and ecosystem functions [70]
• Response: Actions taken by society to address environmental problems [74]
• Security: The sustainability of ecosystems and their ability to maintain functions while reducing disaster risk [71]

The framework is particularly valuable for linking evidence to decision-making, encouraging integrated responses across environmental, social, and economic dimensions [72]. Recent enhancements to traditional DPSIR frameworks have introduced key elements such as iteration; risk, uncertainty, and analytical bias; stakeholder engagement; integration across scales; and adaptive management, making the approach more robust and applicable to complex environmental policy challenges [72].

Integration with Ecological Security Patterns

The DPSIR-S framework integrates Ecological Security Patterns (ESPs) to evaluate ecosystem integrity and security, particularly in fragile ecosystems. ESPs combine landscape patterns with ecological processes, evaluating the significance of various landscape patches for specific ecological processes and services [73]. The ESP research paradigm focuses on "ecological sources–resistance surface–ecological corridors" to identify key ecological patches and pathways crucial for species dispersal and ecological function flows, thereby enhancing landscape connectivity and ecological process management.

This integration addresses a critical limitation of previous assessment models, which primarily focused on static ecosystem structures and failed to effectively capture dynamic ecosystem processes [73]. By incorporating ESPs, the DPSIR-S framework comprehensively considers the structure and function of ecosystems, socioeconomic regime considerations, and connectivity that encourages material, energy, and information flows within and between ecosystems. This combined approach provides both theoretical and practical implications for assessing and managing ecological security, particularly in vulnerable regions such as alpine grasslands [73].

Core Components of the DPSIR-S Framework

Drivers (D) - Root Causes of Change

In the DPSIR-S framework, drivers represent the fundamental social, demographic, and economic developments that influence human activities with direct impacts on the environment [70]. These can be categorized as primary driving forces (technological and societal actors that motivate human activities) and secondary driving forces (human activities triggering "pressures" and "impacts") [70]. Drivers can also be classified based on their characteristics as underlying or immediate, physical or socio-economic, and natural or anthropogenic.

Table 1: Classification of Drivers in the DPSIR-S Framework

Driver Category	Description	Examples
Socioeconomic Drivers	Social and economic developments driving human activities	GDP growth, urbanization rate, industrial added value, natural population growth rate [73]
Production System Drivers	Human activities related to resource utilization and production	Livestock numbers, fertilizer application rate, land-use changes [73]
Natural System Drivers	Environmental factors influencing ecosystem conditions	Temperature, precipitation, soil organic carbon, pH value [73]

Drivers are particularly important in the context of ecological security as they represent the root causes that must be addressed through policy interventions and management responses. In practical applications, such as the assessment of alpine grassland ecosystems, drivers include both socioeconomic factors (e.g., GDP, urbanization rate, industrial added value, fertilizer application rate, natural population growth rate, livestock number) and environmental drivers (e.g., soil organic carbon, temperature, pH value, land-use and land cover change, total nitrogen, precipitation, NDVI, DEM, GPP, slope, and soil texture) [73].

Pressures (P) - Direct Stresses on Ecosystems

Pressures represent the consequences of driving forces, which directly affect the state of the environment [70]. They are typically depicted as negative influences based on the concept that any change in the environment caused by human activities is damaging and degrading, though this perspective can be context-dependent [70]. Pressures can have effects on the short run (e.g., deforestation) or the long run (e.g., climate change), and can be both human-induced (e.g., emissions, waste generation) and natural processes (e.g., solar radiation, volcanic eruptions) [70].

A critical distinction in the DPSIR-S framework is between endogenic managed pressures (stemming from within the system and can be controlled, such as land claim or power generation) and exogenic unmanaged pressures (stemming from outside the system and cannot be controlled, such as climate change or geomorphic activities) [70]. This distinction is crucial for designing appropriate management responses, as it helps identify which pressures can be directly managed versus those that require adaptation strategies.

In the context of ecological security assessment, pressures manifest as specific stresses on ecosystem integrity. For example, in lake ecosystems, pressure indicators measure the impacts that human activities exert on water quality and quantity, including pollutant emissions from various sources, total water-resource utilization, water-quantity ratio of inputs and outputs, per capita water-resource use, and ecological water demand [71]. Understanding these pressures is essential for identifying intervention points to enhance ecological security.

State (S) - Condition of the Ecosystem

The state component describes the physical, chemical, and biological condition of the environment or observable temporal changes in the system [70]. It may refer to natural systems (e.g., atmospheric CO2 concentrations, temperature), socio-economic systems (e.g., living conditions of humans, economic situations of an industry), or a combination of both (e.g., number of tourists, size of current population) [70]. The state is not intended to be just static but to reflect current trends as well, such as increasing eutrophication and changes in biodiversity [70].

In ecological security assessment, state indicators reflect ecological health and include both abiotic and biotic factors. For aquatic ecosystems, these include water quality parameters obtained from conventional monitoring and water-ecology indicators that reveal community and system status [71]. Specific state indicators might include phytoplankton biomass (Bp), zooplankton biomass (Bz), large zooplankton biomass (Bmacroz), small zooplankton biomass (Bmicroz), ratio of zooplankton and phytoplankton biomass (Bz/Bp), and ratio of large zooplankton and small zooplankton biomass (Bmacroz/Bmicroz) [71].

The state component serves as a critical link between pressures and impacts, representing the measurable condition of the ecosystem that results from the interplay of drivers and pressures. Accurate assessment of state variables is essential for evaluating ecological security and determining appropriate management responses.

Impact (I) - Consequences of State Changes

Impact refers to how changes in the state of the system affect human well-being and ecosystem functions [70]. In traditional environmental assessments, impacts are often measured in terms of damages to the environment or human health, such as migration, poverty, and increased vulnerability to diseases [70]. However, in the context of DPSIR-S, impacts can also be identified and quantified without positive or negative connotations by simply indicating changes in environmental parameters [70].

Impacts can be ecological (e.g., reduction of wetlands, biodiversity loss), socio-economic (e.g., reduced tourism), or a combination of both [70]. The definition of impact may vary depending on the discipline and methodology applied. In biosciences, it typically refers to effects on living beings and non-living domains of ecosystems (e.g., modifications in the chemical composition of air or water), whereas in socio-economic sciences, it is associated with effects on human systems related to changes in environmental functions (e.g., physical and mental health) [70].

In the specific context of ecological security assessment for lakes, impact indicators represent the variation in lake services (e.g., fishery output and tourism) caused by changes in the lake's status [71]. Understanding these impacts is crucial for demonstrating the societal relevance of ecological security and justifying management interventions.

Response (R) - Management Interventions

Response refers to actions taken to correct problems identified in the previous stages by adjusting drivers, reducing pressure on the system, bringing the system back to its initial state, and mitigating impacts [70]. Responses can be associated uniquely with policy action or with different levels of society, including groups and/or individuals from the private, government, or non-governmental sectors [70]. They are mostly designed and/or implemented as political actions of protection, mitigation, conservation, or promotion [70].

A mix of effective top-down political action and bottom-up social awareness can also be developed as responses, such as eco-communities or improved waste recycling rates [70]. In the context of ecological security, responses are specifically geared toward maintaining or enhancing security through targeted interventions. These might include the establishment of protected areas, implementation of pollution control measures, restoration of degraded habitats, or development of sustainable resource management practices.

The effectiveness of responses depends on their ability to address the root causes (drivers) and direct stresses (pressures) identified in the assessment. A key advantage of the DPSIR-S framework is its ability to trace the causal pathway from responses back through the system, allowing for the evaluation of response effectiveness and adaptation of management strategies based on monitoring results.

Security (S) - Ecological Security Outcomes

The security component represents the ultimate outcome of interest in the DPSIR-S framework – the achievement of ecological security defined as the sustainability of ecosystems and their ability to maintain functions while reducing disaster risk [71]. This component integrates elements from traditional risk assessment with broader concepts of ecosystem health and resilience.

In practical applications, security is often quantified through Ecological Security Indices (ESI) that evaluate a system's deviation from a background or reference condition [71]. For lakes, this might include assessment of the risk of algal blooms through specific indicators such as bloom duration, time, area, and the population or biomass affected through drinking water or exposure to the bloom [71]. Security indicators can be calculated using formulas that incorporate multiple parameters, such as the risk of cyanobacterial blooms based on temperature and nutrient thresholds [71].

The security component also incorporates the concept of Ecological Security Patterns (ESPs), which integrate landscape patterns with ecological processes to evaluate ecosystem integrity and security [73]. ESPs are identified through a research paradigm focusing on "ecological sources–resistance surface–ecological corridors", which pinpoints key ecological patches and pathways crucial for species dispersal and ecological function flows [73].

Methodological Approach for DPSIR-S Assessment

Indicator Selection and Data Screening

The selection of appropriate indicators is critical to the success of DPSIR-S assessments. Indicators should meet specific criteria to ensure their usefulness and reliability [71]:

• Relevancy: Indicators should reflect changes in ecological quality, status, management, or other security domains.
• Availability: Indicator data should be available, accessible, and consistent within the period of analysis.
• Independence: Indicators must be independent of each other to eliminate multicollinearity (statistical redundancy or duplicity).
• Representativeness: Each indicator must represent a category or phenomenon of its own and provide superior information to other indicators in a similar category.

To address these criteria, particularly independence and representativeness, statistical methods such as correlation analysis and Principal Component Analysis (PCA) can be employed [71]. Correlation tests evaluate the independence of datasets, while PCA helps select the most significant ecological indicators when statistical redundancy occurs among correlated indicators.

Table 2: Example DPSIR-S Indicators for Lake Ecological Security Assessment

DPSIR-S Component	Indicator Category	Specific Indicators
Drivers (D)	Socioeconomic	Regional population, population density, GDP, per capita income, Gini coefficient [71]
Pressures (P)	Pollution Load	Pollutant emissions from various sources, water resource utilization [71]
State (S)	Ecological Health	Water quality parameters, phytoplankton biomass, zooplankton biomass, energy quality [71]
Impact (I)	Ecosystem Services	Fishery output, tourism revenue, recreational opportunities [71]
Response (R)	Management Actions	Protection measures, mitigation efforts, conservation programs [70]
Security (S)	Risk Assessment	Bloom duration, area, frequency; ecological security index [71]

Data Normalization and Index Calculation

The calculation of an Ecological Security Index (ESI) is a key output of DPSIR-S assessments, providing a quantitative measure of a system's deviation from a reference condition. The process involves data normalization followed by index calculation [71].

Data are first standardized using normalization equations to transform DPSIR-S data into "the-higher-the-better" numbers. For indicators where values reflect improved conditions relative to a baseline:

For indicators where values reflect worsening conditions relative to a baseline:

where i denotes the year and j denotes the ecological indicators.

The index of each DPSIR-S component is calculated using an averaging formula:

where k = 1, 2, 3, 4, 5, 6 denoting the six components of the DPSIR-S model, and wk,j is the weighting for the j-th indicator of the k-th component [71]. The overall Ecological Security Index (ESI) can then be derived from the component indices to provide a comprehensive measure of ecological security.

Integration of Ecological Security Patterns

The DPSIR-S framework incorporates Ecological Security Patterns (ESPs) to address dynamic ecosystem processes and landscape connectivity [73]. The methodological approach for ESP integration includes:

• Identification of Ecological Sources: Through protected areas, key species habitats, or quantitative evaluation indices to pinpoint ecologically important areas [73]. Ecological sources can be refined by superimposing high-importance ecological service patches onto morphological spatial pattern analysis (MSPA) results, integrating ecosystem services with regional landscape structures [73].

• Development of Resistance Surfaces: Based on land use types, human disturbance, and other factors that influence species movement and ecological flows.

• Delineation of Ecological Corridors: Using circuit theory or minimum cumulative resistance (MCR) to identify essential pathways for species movement and transfer of ecosystem services between source sites [73]. Circuit theory focuses on the stochastic movement of organisms and provides key information regarding ecological barriers, corridor widths, and pinch points [73].

The integration of ESPs with the traditional DPSIR framework creates a more comprehensive assessment approach that captures both structural and functional aspects of ecological security.

Experimental Protocols and Assessment methodologies

Field Assessment Protocols

Comprehensive field assessment is fundamental to implementing the DPSIR-S framework. Standardized protocols ensure consistent data collection across temporal and spatial scales. For terrestrial ecosystem assessment, particularly in fragile regions like alpine grasslands, the following field measurements are essential:

• Vegetation Surveys: Conducted using quadrat methods along transects to assess species composition, cover, height, and aboveground biomass. Belowground biomass is sampled using soil cores.
• Soil Sampling: Collection of soil samples from multiple depths (typically 0-10 cm, 10-20 cm, 20-30 cm) for analysis of physicochemical properties including soil organic carbon, total nitrogen, pH, and soil texture.
• Microclimate Monitoring: Installation of automated weather stations to record temperature, precipitation, solar radiation, and wind speed at key locations.
• Land Use/Land Cover Mapping: Using GPS technology to ground-truth remote sensing data and validate land cover classifications.

For aquatic ecosystems, such as lakes and reservoirs, assessment protocols include:

• Water Quality Sampling: Collection of water samples from multiple depths and locations for analysis of nutrient concentrations (total nitrogen, total phosphorus), chlorophyll-a, suspended solids, and other relevant parameters.
• Biological Community Assessment: Sampling of phytoplankton, zooplankton, and macroinvertebrate communities using appropriate gear (e.g., plankton nets, benthic grabs).
• Hydrological Measurements: Monitoring of water levels, inflow and outflow rates, and residence times.

All field data should be collected following standardized protocols with appropriate quality assurance/quality control measures, including field blanks, duplicates, and standard reference materials.

Laboratory Analytical Methods

Laboratory analysis of field-collected samples follows established analytical methods to ensure data quality and comparability:

• Soil Analysis: Soil organic carbon determined using the Walkley-Black method or elemental analyzer; total nitrogen measured using Kjeldahl digestion or combustion methods; soil texture analyzed by hydrometer method or laser diffraction.
• Water Analysis: Nutrient concentrations (TN, TP) determined using colorimetric methods following persulfate digestion; chlorophyll-a measured fluorometrically following acetone extraction.
• Biological Samples: Phytoplankton and zooplankton identification and enumeration using microscopy; biomass estimation through measuring biovolume or dry weight.

Molecular techniques, such as DNA barcoding, may be employed for difficult-to-identify taxa or to assess microbial community composition. Stable isotope analysis (δ¹³C, δ¹⁵N) can provide insights into food web structure and nutrient cycling.

Remote Sensing and Spatial Analysis

Remote sensing provides valuable data for assessing ecosystem state and changes over large spatial scales. Key applications in DPSIR-S assessments include:

• Vegetation Indices: Calculation of NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and other spectral indices from satellite imagery to assess vegetation vigor and productivity.
• Land Cover Mapping: Classification of land cover types using supervised classification algorithms applied to multispectral imagery.
• Landscape Pattern Analysis: Calculation of landscape metrics (e.g., patch density, edge density, contagion, connectivity) using FRAGSTATS or similar software.
• Ecosystem Service Modeling: Application of models like InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) to map and quantify ecosystem services such as water yield, carbon storage, and soil retention.

Spatial analysis techniques, including Geographic Information Systems (GIS) and geostatistics, are employed to analyze spatial patterns and interpolate point data across landscapes.

The Scientist's Toolkit: Research Reagent Solutions

Implementation of the DPSIR-S framework requires specialized tools, reagents, and methodologies for comprehensive ecological security assessment. The following table details essential research solutions and their applications in DPSIR-S studies.

Table 3: Essential Research Reagent Solutions for DPSIR-S Implementation

Research Tool/Reagent	Application in DPSIR-S Framework	Specific Function
Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST)	Spatial modeling of ecosystem services for State and Impact assessment	Quantifies and maps ecosystem services such as water yield, carbon storage, soil retention, and habitat quality [73]
Morphological Spatial Pattern Analysis (MSPA)	Identification of ecological sources for ESP delineation	Analylandscape connectivity and identifies core habitat areas, bridges, and branches critical for ecological security [73]
Circuit Theory Applications	Delineation of ecological corridors and connectivity analysis	Models landscape connectivity and identifies corridors, pinch points, and barriers for species movement [73]
Principal Component Analysis (PCA)	Data screening and indicator selection	Identifies independent and representative indicators by reducing multicollinearity in dataset [71]
Term Frequency-Inverse Document Frequency (TF-IDF)	Text mining of environmental complaints for Pressure identification	Extracts keywords from environmental complaints to identify priority concerns and pressures [75]
Normalized Difference Vegetation Index (NDVI)	Assessment of ecosystem State and vigor	Measures vegetation health and productivity through remote sensing [73]
Revised Universal Soil Loss Equation (RUSLE)	Quantification of soil erosion as a Pressure indicator	Models soil erosion rates based on rainfall, soil type, topography, land cover, and management practices [73]
Ecological Security Index (ESI)	Integrated assessment of Security component	Provides comprehensive measure of ecological security through multi-indicator integration [71]

These research tools enable the quantitative assessment of each DPSIR-S component and facilitate the integration of ecological security patterns into the framework. The combination of biophysical measurements, spatial analysis, and statistical tools provides a comprehensive approach to ecological security assessment that addresses both structural and functional aspects of ecosystems.

Case Study Applications

Alpine Grassland Ecosystem Assessment

The DPSIR-S framework has been successfully applied to assess the health of alpine grassland ecosystems on the Qinghai-Tibet Plateau, a critical natural barrier for ecological security of the Yellow and Yangtze River Basins [73]. This region provides essential ecosystem services including water conservation, soil retention, carbon sequestration, and biodiversity maintenance, but faces significant ecological fragility due to harsh natural conditions and increasing anthropogenic activity [73].

In this application, researchers integrated a revised DPSIR framework with ecological security patterns to create a comprehensive assessment model. The study revealed that nearly 90% of natural grasslands in the Gannan alpine grassland region were degraded to varying degrees, with grassland desertification covering 285.4 km² and aboveground biomass decreasing by approximately 34% during 2000-2019 [73]. The integration of ESPs helped identify key areas for conservation and restoration, with ecological sources predominantly located in mountainous regions with higher vegetation coverage, and ecological corridors following river valleys and connecting large habitat patches [73].

The assessment identified both environmental and socioeconomic drivers of ecosystem change, including GDP, urbanization rate, industrial added value, fertilizer application rate, natural population growth rate, livestock number, temperature, precipitation, NDVI, and landscape structure metrics such as contagion index [73]. The results provided valuable insights for targeted management interventions to enhance ecological security in this fragile region.

Lake Ecological Security Assessment

The DPSIR framework has been adapted for lake ecological security assessment through the Driver-Pressure-State-Impact-Risk (DPSIR) model, applied to Dianchi Lake in China [71]. This approach incorporated risk assessment specifically focused on algal bloom events, a critical security concern for eutrophic lakes.

In this application, driving force indicators described social and economic developments in communities surrounding the lake, including regional population, population density, GDP, per capita income, and Gini coefficient [71]. Pressure indicators measured impacts on water quality and quantity, while state indicators reflected ecological health through water quality parameters and biological community metrics. Impact indicators represented variations in lake services such as fishery output and tourism, and risk indicators expressed the chance of ecological disasters like algal blooms [71].

The study employed rigorous data screening using correlation and principal component analysis to select independent and representative indicators, eliminating multicollinearity and ensuring information efficiency [71]. The ecological security index revealed a V-shaped trend over the assessment period (1988-2007), providing insights into the effectiveness of management interventions and the persistence of challenges. This approach demonstrated the practical utility of the DPSIR framework for diagnosing ecological security issues in aquatic ecosystems.

Marine and Coastal Ecosystem Management

The DPSIR framework has been widely applied in marine and coastal ecosystems, where it helps structure complex problems and connect scientific research with policy and stakeholder engagement [72]. A structured literature review of DPSIR applications in coastal systems highlighted its potential to bridge science and policy, while identifying a gap in participatory use of the framework [72].

In marine applications, the framework has been reworked into expanded versions such as D(A)PSI(W)R(M) - the "Butterfly" model, which integrates socio-ecological complexities and ecosystem services into a more holistic assessment tool for sustainable decision-making [72]. These applications emphasize the importance of clear definitions for drivers and pressures to improve communication between science and management at international levels.

The flexibility of the DPSIR framework allows its adaptation to various marine and coastal contexts, from fisheries management to pollution control, making it a valuable tool for addressing the complex challenges facing marine ecosystems in an era of increasing human impacts and climate change.

Future Directions and Framework Evolution

The DPSIR-S framework continues to evolve in response to emerging challenges and methodological advances. Several promising directions for future development include:

• Temporal Dynamics Integration: The recent introduction of the temporal DPSIR (tDPSIR) framework incorporates temporal dynamics into the traditional model, accounting for time lags between environmental pressures and policy responses [72]. This enhancement aims to improve the timing and effectiveness of environmental governance by recognizing that responses may take years to manifest measurable effects on ecosystem state.

• Equity and Inclusive Development: A 2020 paper highlighted how DPSIR frameworks often overlook social justice and equity concerns, proposing a structured approach to integrating equity and gender considerations [72]. This evolution recognizes that environmental policies ignoring justice aspects risk being exclusive and unsustainable, emphasizing the need for integrated approaches that address both ecological and social dimensions of security.

• Enhanced Stakeholder Engagement: Next-generation applications of DPSIR emphasize improved stakeholder engagement processes to ensure that assessments incorporate local and indigenous knowledge [72]. This addresses criticisms that traditional applications often privilege formal scientific knowledge over valuable contextual understanding held by local communities.

• Integration with Emerging Technologies: Advancements in remote sensing, sensor networks, and data analytics offer opportunities to enhance DPSIR-S assessments through higher resolution spatial and temporal data, automated monitoring, and real-time assessment capabilities.

• Cross-scale Integration: Modern environmental challenges operate across multiple scales, from local to global. Future DPSIR-S applications need to explicitly address these cross-scale interactions, particularly for transboundary environmental issues such as climate change, biodiversity loss, and water resource management.

The continued refinement of the DPSIR-S framework will enhance its utility as a comprehensive model for assessing ecological security levels, providing policymakers and resource managers with robust tools for addressing the complex interplay between human activities and ecosystem integrity in an increasingly uncertain world.

The construction of Ecological Security Patterns (ESP) represents a critical strategic initiative for ecological civilization, essential for balancing protection and development and exploring a harmonious coexistence between humans and nature [46]. This technical guide provides an in-depth analysis of ESP implementation within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a highly urbanized region facing significant ecological challenges. The study is framed within broader thesis research on ESP foundational concepts, offering quantitative methodologies and empirical findings relevant for researchers and scientists working at the intersection of landscape ecology, spatial planning, and sustainable development. The GBA case study exemplifies how advanced analytical frameworks incorporating policy quantification, deep learning, and ecosystem service assessment can generate actionable solutions for regional ecological management amid rapid urbanization pressures [76] [77].

Quantitative Analysis of Ecological Changes in the GBA

Comprehensive analysis of the GBA's ecosystem reveals significant changes between 2000 and 2020, establishing a baseline for ESP construction. Researchers quantified key ecosystem services (ESs) using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model to understand spatio-temporal dynamics [77]. The findings demonstrate concerning declines in several critical ecological indicators alongside one notable improvement, highlighting the complex interplay between urbanization and ecosystem functioning.

Table 1: Temporal Changes in Key Ecosystem Services (2000-2020)

Ecosystem Service Indicator	Change Percentage (2000-2020)	Significance for ESP Construction
Natural Habitat	-5.5% decrease	Directly reduces ecological source areas and connectivity
Water Retention	-7.9% decrease	Impacts hydrological regulation and regional water security
Carbon Sequestration	-3.9% decrease	Affects climate regulation capacity and carbon neutrality goals
Soil Conservation	+63.7% increase	May reflect improved land management practices in specific areas

Beyond these ecosystem service metrics, the ESP construction identified specific spatial components through GIS-based modeling and corridor analysis [76]. The spatial configuration comprised 1,993 km² of ecological sources, 23 key ecological corridors, 58 general ecological corridors, and 31 ecological nodes, forming the physical backbone of the regional security pattern. This configuration supports an optimized pattern described as "one circle, two bays, three rivers, multiple areas, corridors, and nodes" that guides regional spatial optimization and environmental management [76].

Methodological Framework for ESP Construction

Integrated Technical Approach

The ESP construction methodology follows a "Patch-matrix-corridor" framework that creates spatially explicit ecological security patterns [76]. This approach addresses limitations of traditional research methods that use overly simplistic indicators and overlay analysis, which often result in homogenous ecological sources and loss of original information contained in various factors [46]. The innovative framework for regional sustainable development incorporates perspectives of ecosystem health, integrity, and ecosystem services association, characterized by "contribution-sensitivity-vigour-organization" metrics.

Policy Quantification Analysis (PQA)

A groundbreaking aspect of the GBA case study involves the integration of Policy Quantification Analysis (PQA) with ecological data [76]. This methodology transforms policy texts into reusable research data using machine learning techniques, which then serves as a foundation for indicator selection and ecological security pattern optimization. The research demonstrated that policy adaptability had a substantial impact on environmental security in the GBA, though the ecosystem service index maintained the highest influence overall. Through factor analysis and Random Forest (RF) algorithms, the impact of each indicator on ecological security was thoroughly examined, providing substantial support for policy development and ESP construction planning.

Advanced Ecological Source Identification

To overcome traditional homogenization problems in ecological source identification, the study employed an adaptive generation approach utilizing deep learning, specifically the self-organizing mapping neural network model [46]. This technical innovation integrated multi-sourced data to address the issue of original information loss caused by overlay analysis and homogenization of eco-sources. The approach enabled identification of various types of ecological sources rather than homogeneous ones, significantly improving the precision of the resulting security pattern.

Corridor and Node Delineation

Building upon ecological source identification, the study utilized the minimum cumulative resistance model and gravity model to extract eco-corridors and nodes [46]. This technical process involved calculating connectivity pathways between ecological sources while considering landscape resistance, ultimately constructing a comprehensive ESP consisting of 20 ecological sources, 30 ecological corridors, and 61 ecological nodes for the research area.

Experimental Protocols and Technical Procedures

Ecosystem Services Assessment Protocol

Objective: Quantify spatio-temporal changes of key ecosystem services to establish baseline conditions for ESP construction.

Materials and Equipment:

• Land use/land cover data for 2000, 2010, 2020
• Climate data (precipitation, temperature)
• Soil data sets
• Topographic maps
• GIS software with InVEST model capabilities

Procedure:

• Data Preparation: Collect and preprocess land use data from 2000-2020 with consistent classification schema. Gather precipitation, soil, and topographic data at appropriate spatial resolutions.
• Model Parameterization: Configure InVEST model parameters for each ecosystem service module:
  • Water Retention: Apply the water yield module with precipitation, evapotranspiration, and soil depth parameters
  • Carbon Sequestration: Utilize carbon storage pools based on land use classifications
  • Soil Conservation: Apply USLE equation within the sediment retention module
  • Habitat Quality: Input threat factors and sensitivity parameters based on land use types
• Temporal Analysis: Execute models for each time period (2000, 2010, 2020) using consistent parameters to ensure comparability.
• Change Detection: Calculate difference between time periods to identify trends and hotspots of change.
• Validation: Conduct field verification at randomly selected sites to validate model outputs.

Analysis: Quantify percentage changes for each service between time periods. Identify spatial clusters of significant change using hotspot analysis (Getis-Ord Gi* statistic).

Ecological Source Identification Protocol

Objective: Identify and categorize ecological sources using advanced deep learning approaches to overcome homogenization limitations.

Materials and Equipment:

• Multi-source spatial data (ecological, social, economic)
• Deep learning framework with self-organizing mapping capabilities
• High-performance computing resources
• GIS software for spatial analysis

Procedure:

• Indicator System Development: Establish comprehensive evaluation system based on ecosystem health, integrity, and services association characterized by "contribution-sensitivity-vigour-organization" [46].
• Data Integration: Compile and standardize multi-source data including remote sensing imagery, ecosystem service maps, habitat quality assessments, and human disturbance indices.
• Self-Organizing Map Training:
  • Normalize all input data to common scale
  • Initialize SOM neural network with appropriate architecture
  • Train network using iterative process until convergence
  • Validate clustering results with independent ecological data
• Ecological Source Classification: Identify heterogeneous ecological sources based on SOM clustering results, categorizing by ecological function and importance.
• Spatial Delineation: Convert classified sources to spatial polygons with minimum mapping unit appropriate to study scale.

Analysis: Calculate spatial metrics (size, connectivity, shape complexity) for each ecological source type. Assess representation of different ecosystem types within identified sources.

Policy Quantification Analysis Protocol

Objective: Transform policy texts into quantifiable research data for integration into ESP construction.

Materials and Equipment:

• Comprehensive policy document database
• Natural language processing tools
• Machine learning algorithms (Random Forest)
• Statistical analysis software

Procedure:

• Policy Corpus Construction: Collect regional environmental policies, development plans, and regulatory documents from 2000-2025.
• Text Preprocessing: Apply tokenization, stop-word removal, and stemming to prepare text for analysis.
• Feature Extraction:
  • Identify policy targets, implementation measures, and enforcement mechanisms
  • Extract policy intensity indicators (funding levels, regulatory strictness)
  • Quantify policy coherence across different governance levels
• Machine Learning Application:
  • Train Random Forest classifier to identify policy themes relevant to ecological security
  • Develop regression models to quantify policy implementation intensity
  • Validate models with expert assessment of policy strength
• Spatial Explicit Policy Mapping: Georeference policy indicators to create spatial layers of policy influence across the GBA.

Analysis: Integrate policy quantification with ecological data to identify relationships between policy interventions and ecosystem outcomes. Use factor analysis to determine relative influence of policy versus ecological factors.

Research Reagent Solutions: Essential Analytical Tools

Table 2: Key Research Tools and Models for ESP Construction

Research Tool	Primary Function	Application in GBA Case Study
InVEST Model	Ecosystem services quantification	Quantified spatio-temporal changes in habitat quality, water retention, carbon sequestration, and soil conservation [77]
Self-Organizing Mapping Neural Network	Pattern recognition and clustering	Overcame traditional homogenization problems in ecological source identification through deep learning [46]
Random Forest Algorithm	Feature importance analysis and prediction	Analyzed impact of various indicators on ecological security and supported policy development [76]
Future Land Use Simulation (FLUS) Model	Land use change modeling	Simulated land use change scenarios considering ecosystem conservation priorities [77]
Minimum Cumulative Resistance Model	Landscape connectivity analysis	Identified optimal pathways for ecological corridors between source areas [46]
Gravity Model	Interaction strength assessment	Quantified potential ecological flows between patches to prioritize corridor importance [46]
Policy Quantification Framework	Text analysis and metric development	Transformed policy documents into quantifiable data for ESP optimization [76]

The GBA case study demonstrates that effective ESP construction requires integrated approaches that combine traditional ecological assessment with innovative policy analysis and advanced computational methods. The research findings were beneficial for achieving regional high-quality development and sustainable development through rational urban system and landscape management [77]. The proposed ESP framework serves as a crucial reference for achieving regional spatial optimization and provides actionable solutions for the international community facing growing ecological challenges [76].

The study further provides insights and ideas for other cities undergoing rapid urbanization to coordinate the interactions between human activities and the ecological security of natural resources during urban expansion, promoting a healthy and sustainable urban expansion process [46]. The multi-scale analysis employed in the research enabled tailored ecological management policy recommendations for each city in the GBA, illustrating the importance of context-specific interventions within regional planning frameworks [76].

Ecological Security Patterns (ESP) represent a paradigm shift in spatial ecology planning, moving from static, restrictive boundaries to dynamic, functional networks. This technical guide provides an in-depth analysis contrasting ESP with two established spatial management frameworks: Urban Growth Boundaries (UGB) and Ecological Control Lines (ECL). Through quantitative comparison, detailed methodological protocols, and specialized research tools, we demonstrate how ESP's network-based approach fundamentally differs in conceptual foundation, technical implementation, and conservation outcomes. Framed within broader ecological security research, this whitepaper equips researchers and environmental professionals with the analytical frameworks necessary to evaluate and implement these distinct approaches for sustainable urban-regional planning.

Rapid global urbanization has triggered unprecedented natural habitat destruction and landscape fragmentation, threatening ecosystem integrity and functionality worldwide [78] [79]. In response, spatial planning policies have evolved various regulatory frameworks to reconcile urban development with ecological preservation. Ecological Security Patterns (ESP) emerge as an integrative conceptual framework that identifies and protects strategically important ecological areas to maintain landscape connectivity and ecosystem service flows [79]. Unlike conventional approaches, ESP explicitly models ecological processes to construct networked spatial structures.

Urban Growth Boundaries (UGB) represent long-established planning instruments that delineate explicit separations between urban development areas and surrounding natural/agricultural lands [80] [81]. Originally conceptualized in Howard's garden city concept, UGBs aim primarily to contain urban sprawl, promote compact development, and protect greenbelts from dispersed development [80]. These boundaries have been implemented extensively in Western countries and increasingly adopted in developing nations experiencing rapid urbanization.

China's Ecological Control Lines (ECL) constitute another significant policy mechanism, defined as "a series of closed artificial spatial boundaries for preventing urban sprawl and ensuring the integrity and stability of regional ecosystems" [78]. Implemented in numerous Chinese cities including Shenzhen, Wuhan, and Xiamen, ECLs enforce strict development restrictions within designated ecological zones through regulatory measures that typically prohibit all construction except critical public infrastructure [78].

This technical analysis examines the fundamental distinctions between these frameworks through quantitative comparison, experimental validation methodologies, and specialized research tools essential for ecological security research.

Conceptual Framework Comparison

The theoretical foundations and operational objectives of ESP, UGB, and ECL reveal fundamentally different approaches to managing urban-ecological systems.

ESP employs a functional connectivity paradigm based on landscape ecology principles, specifically designing spatial networks to maintain ecological flows between habitat patches [79]. This approach conceptualizes ecological space as an interconnected system where nodes (core habitats) connect through corridors (linkage pathways) embedded within a matrix of varying resistance. The theoretical basis draws from Island Biogeography Theory, which recognizes large ecological patches as "species pools" that generate radiation effects to surrounding areas through stepping-stone connections [79]. ESP implementation focuses on identifying critical connectivity elements—including ecological sources, corridors, and strategic stepping stones—that collectively maintain landscape-level ecological processes despite fragmentation pressures.

In contrast, UGB operates primarily through a containment paradigm that establishes a binary division between urban and non-urban territories [80] [81]. The primary mechanism involves restricting urban development to a defined zonal boundary, thereby directing growth inward and preserving exterior landscapes. While modern UGB implementations increasingly incorporate ecological considerations, the fundamental operating principle remains spatial separation rather than functional integration.

ECL functions as a regulatory protection paradigm that establishes inviolable boundaries around ecologically significant areas [78]. Unlike ESP's emphasis on connectivity, ECL prioritizes the integrity of designated ecological zones through stringent development controls. The policy mechanism operates through absolute restrictions, with permitted exceptions requiring rigorous environmental impact assessments and feasibility studies [78].

Table 1: Fundamental Conceptual Differences Between Frameworks

Dimension	Ecological Security Patterns (ESP)	Urban Growth Boundaries (UGB)	Ecological Control Lines (ECL)
Primary Objective	Maintain ecological processes and connectivity	Contain urban sprawl; separate urban from rural	Protect designated ecological areas from development
Theoretical Basis	Landscape ecology; Circuit theory; Island Biogeography	Smart growth; Garden city concept	Ecological conservation zoning; Regulatory control
Satial Conceptualization	Functional network (nodes, corridors, matrix)	Binary division (urban/non-urban)	Protected area boundaries
Implementation Flexibility	Adaptive based on ecological processes	Fixed period (e.g., 20 years) with revisions	Typically permanent with strict alterations
Connectivity Emphasis	Explicit, quantitative focus on ecological flows	Indirect, through containment	Incidental, through area protection

Methodological Approaches and Experimental Protocols

The technical methodologies for delineating and implementing these frameworks reveal their underlying conceptual differences, particularly in handling landscape connectivity.

Ecological Security Patterns (ESP) Methodology

ESP construction employs a multi-step analytical process integrating landscape ecology principles with spatial modeling:

Step 1: Ecological Source Identification

• Protocol: Identify core ecological patches using multi-criteria evaluation combining ecosystem service importance and habitat quality assessment [78] [79].
• Technical Execution:
  • Quantify key ecosystem services (water retention, soil conservation, carbon sequestration, biodiversity maintenance) using models like InVEST
  • Conduct habitat suitability modeling for focal species using BP artificial neural networks with environmental predictors
  • Overlay ecosystem service importance with high-quality habitats to identify ecological sources
• Validation: Evaluate model predictive accuracy using Area Under Curve (AUC) statistics, with values >0.85 indicating high reliability [79]

Step 2: Resistance Surface Modeling

• Protocol: Develop landscape resistance surfaces representing mobility costs for ecological flows between sources [78].
• Technical Execution:
  • Classify land use/cover types and assign relative resistance values based on permeability
  • Incorporate topographic, human disturbance, and vegetation quality factors
  • Validate resistance values through empirical movement data or literature review

Step 3: Connectivity Analysis and Corridor Delineation

• Protocol: Apply circuit theory or least-cost path models to identify potential connectivity corridors and pinch points [78].
• Technical Execution:
  • Implement circuit theory models using software like Circuitscape to simulate random walk movement
  • Calculate current flow densities and identify areas with high movement probability
  • Delineate corridors connecting ecological sources based on cumulative current flow values
  • Identify critical stepping stones that facilitate connectivity between major patches

Step 4: ESP Integration and Validation

• Protocol: Synthesize ecological sources, corridors, and strategic points into comprehensive security patterns [79].
• Technical Execution:
  • Overlay all connectivity elements to form integrated network
  • Validate patterns using independent species occurrence data or genetic markers
  • Prioritize elements based on connectivity importance and threat vulnerability

Urban Growth Boundary (UGB) Methodology

UGB delineation typically combines two complementary approaches:

Forward Expansion Modeling

• Protocol: Project urban growth patterns using simulation models to define logical expansion limits [82] [80].
• Technical Execution:
  • Employ Cellular Automata (CA) models like FLUS or PLUS incorporating socioeconomic drivers
  • Calibrate models using historical land use change data
  • Simulate multiple scenarios (trend, ecological protection, compact development)
  • Generate urban growth probability surfaces

Reverse Constraint Analysis

• Protocol: Identify ecological constraints that should limit urban expansion [80].
• Technical Execution:
  • Conduct ecological suitability evaluation identifying sensitive areas
  • Develop ecological security patterns identifying critical areas for protection
  • Overlay expansion projections with constraint areas
  • Delineate boundaries that accommodate growth needs while protecting sensitive areas

Ecological Control Line (ECL) Methodology

ECL delineation employs a regulatory zoning approach:

Protocol: Identify and demarcate ecologically critical areas requiring strict protection [78].

• Technical Execution:
  • Evaluate ecosystem functions, ecological sensitivity, and landscape connectivity
  • Classify ecological importance using multi-factor analysis
  • Establish closed boundaries around designated ecological areas
  • Implement strict regulatory controls prohibiting development with minimal exceptions

Quantitative Framework Comparison

Empirical implementation of these frameworks reveals substantial differences in spatial outcomes, connectivity performance, and ecological effectiveness.

Table 2: Quantitative Performance Indicators Across Frameworks

Performance Metric	ESP	UGB	ECL	Measurement Protocol
Habitat Connectivity	42-68% improvement in ecological flow [78]	Incidental benefit only	15-30% connectivity maintenance [78]	Circuit theory analysis; current density maps
Protected Area Efficiency	25-40% more efficient than area-based approaches [79]	Variable based on boundary placement	High within boundaries; limited outside	Species persistence per unit area
Implementation Scale	Regional to landscape scale	Metropolitan to municipal scale	Municipal to regional scale	Typical jurisdictional application
Stakeholder Participation	Multi-stakeholder; interdisciplinary	Primarily government-led planning	Top-down regulatory approach	Number and diversity of involved parties
Adaptive Capacity	High; dynamic boundary adjustment	Moderate; periodic revision	Low; fixed boundaries	Flexibility for boundary modification
Carbon Storage Impact	Explicit optimization for carbon sequestration [82]	Indirect benefit through containment	Direct protection of carbon-rich ecosystems [82]	Carbon storage modeling (InVEST)

Application of these frameworks in Chinese cities demonstrates their distinctive outcomes. In Wuhan, ESP approaches employing multi-objective programming (MOP) combined with patch-level land use simulation (PLUS) models generated 762 solution sets on a Pareto surface, explicitly optimizing for carbon storage, economic benefits, and ecological preservation simultaneously [82]. This sophisticated modeling enabled scenario analysis identifying optimal boundary configurations balancing multiple objectives.

In Shenzhen, ECL implementation protected 52.7% of municipal territory as ecological space [79]. However, network analysis revealed that this area-based protection approach left critical connectivity elements vulnerable, with 29.5% of ecological groups and 24.6% of connectivity pathways located outside protected boundaries [79]. This demonstrates the limitation of uniform zonal protection versus strategic network conservation.

UGB implementations show different outcomes based on delineation methodologies. Traditional UGBs focusing primarily on containment often fail to maintain functional connectivity, while newer approaches integrating ecological security patterns demonstrate improved performance in preserving landscape integrity [80].

Research Tools and Technical Implementation

Implementation of these frameworks requires specialized analytical tools and research reagents spanning spatial analysis, modeling software, and ecological assessment techniques.

Table 3: Essential Research Reagents and Analytical Tools

Tool Category	Specific Solutions	Technical Function	Framework Application
Spatial Modeling Software	Circuitscape	Circuit theory modeling for connectivity analysis	ESP: Critical for corridor identification [78]
	InVEST (Integrated Valuation of Ecosystem Services)	Ecosystem service quantification and mapping	ESP, ECL: Ecological source identification [78]
	FLUS/PLUS Models	Land use change simulation under scenarios	UGB: Urban expansion projection [82]
GIS Analytical Tools	ArcGIS Urban	Suitability analysis; development impact assessment	UGB: Growth management planning [83]
	Linkage Mapper	Corridor and connectivity network design	ESP: Ecological corridor delineation
Statistical & Modeling Platforms	R Programming (gdistance, raster packages)	Landscape resistance analysis; statistical validation	All frameworks: Analytical implementation
	Python (Scikit-learn, PySAL)	Machine learning for habitat modeling	ESP, ECL: Predictive ecological modeling
Field Validation Equipment	GPS Wildlife Tracking Collars	Animal movement data for corridor validation	ESP: Empirical connectivity verification
	Environmental DNA (eDNA) Sampling	Biodiversity assessment across habitat patches	All frameworks: Ecological effectiveness monitoring

Discussion: Integrated Implementation and Future Directions

While this analysis highlights conceptual and methodological distinctions between ESP, UGB, and ECL, optimal urban-ecological planning often involves strategic integration of these approaches. The most effective implementations recognize the complementary strengths of each framework:

• ESP provides the scientific foundation for identifying critical ecological elements requiring protection
• UGB offers the policy mechanism for containing urban expansion and directing growth
• ECL delivers the regulatory authority for enforcing protection of priority areas

Advanced implementations, as demonstrated in Wuhan, combine multi-objective optimization with patch-level simulation models to delineate boundaries that simultaneously address urban growth management and ecological conservation goals [82]. These integrated approaches generate Pareto-optimal solutions balancing economic development, carbon storage, and ecological preservation through sophisticated trade-off analysis.

Future methodological development should focus on dynamic boundary adjustment mechanisms that respond to changing ecological conditions and urban growth pressures. Additionally, enhanced integration of ecosystem service quantification with connectivity modeling will strengthen the scientific basis for spatial planning decisions. The evolving regulatory landscape, including recent updates to the National Environmental Policy Act (NEPA) implementation guidelines [84], may create new opportunities for incorporating ESP principles into mandated environmental review processes.

For researchers and practitioners, selection of appropriate frameworks depends on specific planning objectives, jurisdictional contexts, and ecological priorities. ESP offers the most ecologically sophisticated approach for maintaining landscape functionality, while UGB and ECL provide more straightforward regulatory mechanisms for growth containment and area-based protection, respectively.

Ecological Security Patterns (ESP) provide a strategic framework for conserving biodiversity and maintaining critical ecosystem services by identifying and connecting ecologically significant areas. The ESP framework is primarily composed of ecological sources, ecological corridors, and ecological pinch points [85]. Ecological sources are patches of high ecosystem service value that support biodiversity and key ecological processes. Ecological corridors are linkages that facilitate movement and genetic exchange between source areas. Pinch points represent areas within these corridors that are particularly crucial for maintaining connectivity. The construction of an ESP typically involves quantifying multiple ecosystem services—such as habitat quality, water retention, and carbon storage—to identify source areas, developing resistance surfaces based on landscape features, and using models like circuit theory or Least Cost Path to delineate corridors and critical nodes [85]. This scientific approach allows land managers and policymakers to prioritize conservation efforts in the face of climate change and land use pressures.

Core Principles and Methodologies of ESP

The implementation of Ecological Security Patterns relies on a sequence of rigorous technical steps, from assessing fundamental ecosystem components to modeling complex spatial relationships. The workflow progresses from basic data collection to advanced spatial modeling, ensuring the resulting pattern reflects ecological reality.

Foundational Workflow for ESP Construction

Quantitative Assessment of Ecosystem Services

The construction of a robust ESP begins with the quantitative assessment of key ecosystem services. Researchers typically evaluate multiple services to capture different aspects of ecological function, which are then integrated to identify ecological source areas [85].

• Habitat Quality: This measures the ability of an ecosystem to support populations of native species, considering the conditioning influence of environmental factors and the destructive impact of threats like human development. High-quality habitats are prime candidates for designation as ecological sources.
• Soil Erosion: Often calculated using the Revised Universal Soil Loss Equation (RUSLE), this service is vital for maintaining agricultural productivity and preventing sedimentation in water bodies. Areas with low erosion rates contribute to regional ecological security [85].
• Water Retention: This refers to the capacity of a landscape to capture, store, and regulate the flow of water. It is crucial for flood mitigation, water supply, and drought resistance.
• Carbon Storage: This service quantifies the sequestration and storage of carbon in both vegetation and soil, playing a critical role in climate regulation. Models like InVEST are commonly used for its estimation.

These assessed services are often combined using methods like the entropy weight method to create a comprehensive map of ecosystem service value, which forms the basis for identifying ecological source areas [85].

Methodologies for Corridor and Pinch Point Identification

Once ecological sources and a resistance surface are defined, connectivity models are applied.

• Circuit Theory: This model treats the landscape as an electrical circuit, with sources as nodes and the resistance surface defining the conductivity of each pixel. It simulates multiple random walk paths between sources, revealing corridors with high flow density and pinpointing critical pinch points that are vital for maintaining connectivity [85].
• Least Cost Path (LCP) and Minimum Cumulative Resistance (MCR): The LCP model identifies the single route between two sources that incurs the lowest cumulative travel cost. The MCR model measures the effective distance between sources, reflecting the potential for ecological flows. While useful, MCR alone does not specify key pinch points within the corridors [85].

Table 1: Core Ecosystem Services for ESP Assessment

Ecosystem Service	Ecological Function	Common Assessment Model	Role in ESP Construction
Habitat Quality	Supports viable populations of native species	Habitat assessment within InVEST	Primary basis for identifying ecological sources
Soil Erosion	Maintains soil productivity, prevents sedimentation	Revised Universal Soil Loss Equation (RUSLE)	Identifies areas vulnerable to degradation
Water Retention	Regulates hydrological cycles, mitigates floods	Water Yield model in InVEST	Highlights key areas for watershed protection
Carbon Storage	Sequesters carbon, mitigates climate change	Carbon Storage model in InVEST	Identifies significant carbon sinks

ESP Application in a North American Context

The application of ESP principles in North American forest management demonstrates a shift towards ecosystem-based management, moving away from practices focused solely on timber production. This approach is guided by key ecological considerations aimed at sustaining long-term forest health [86].

Foundational Principles for Management

Scientific consensus, as reported by the Ecological Society of America, outlines several core principles for managing national forests as integrated ecosystems [86]:

• Maintenance of Soil Quality: This requires adjusting harvest rates and leaving large woody debris on sites to protect nutrient cycles, which are fundamental to future productivity.
• Protection of Aquatic Systems: This involves mitigating the impacts of logging roads and preserving undisturbed riparian buffers to maintain water quality and reduce flooding.
• Landscape-Level Planning: This is essential to address macro-level ecological concerns like biodiversity conservation and habitat fragmentation, which cannot be managed at the stand level alone.
• Biodiversity Conservation: This necessitates reducing fragmentation caused by clearcuts and roads, avoiding harvest in vulnerable areas (e.g., old-growth stands), and restoring structural complexity.

Scientific Evidence on Key Ecological Assumptions

Research has evaluated and often challenged the assumptions underlying traditional forest management [86]:

• Natural vs. Managed Forests: Natural forest reserves are proven to be more likely to sustain full biological diversity than lands managed for timber. They are not inherently more vulnerable to disturbances like wildfire and pests; in many cases, they show greater resistance.
• Timber Harvesting as a Substitute: Scientific evidence indicates that timber harvesting cannot duplicate the ecological effects of natural disturbances. However, new techniques that retain trees and woody debris can more closely mimic these processes.
• Silvicultural Limitations: While management can help restore forests toward more natural conditions, science does not support the claim that silviculture can create ecosystems equivalent to natural old-growth forests.

Europe's Natura 2000 Network as a Regional ESP

The Natura 2000 network is the cornerstone of the European Union's biodiversity conservation policy and represents a massive, legally mandated implementation of an Ecological Security Pattern. It is the largest coordinated network of protected areas in the world, extending across all 27 EU Member States and covering both land and sea [87].

Legal Foundation and Structural Components

The network is established under two key European directives: the Birds Directive (1979, amended 2009) and the Habitats Directive (1992). Each directive creates specific types of protected areas that together form the network [87]:

• Special Protection Areas (SPAs): Designated under the Birds Directive for the protection of 194 threatened bird species and all migratory birds.
• Sites of Community Importance (SCIs) & Special Areas of Conservation (SACs): The Habitats Directive identifies SCIs for approximately 230 habitat types and 900 species. SCIs must subsequently be designated as SACs by member states. SPAs and SCIs/SACs together constitute the Natura 2000 network [87].

A critical legal tool for the network's protection is Article 6 of the Habitats Directive. It requires member states to establish conservation measures for Natura 2000 sites to maintain or restore habitats and species to a favourable conservation status. It also mandates an appropriate assessment for any plan or project likely to have a significant effect on a site, only allowing projects to proceed after confirming they will not adversely affect the site's integrity [87].

Marine Ecosystem Integration and Recent Developments

The application of the Natura 2000 framework to marine environments has been a key focus. To date, more than 3,000 marine Natura 2000 sites have been designated, covering over 9% of the EU's marine waters [88] [89]. These sites are a major contribution towards the EU target of legally protecting at least 30% of its marine area by 2030 [89].

Recognizing that fishing is one of the most significant pressures on marine ecosystems, the European Commission adopted new guidance in October 2025 to help member states manage the interaction between fishing activities and marine Natura 2000 sites [89]. The guidance provides a clear process to:

• Assess if fishing activities threaten protected habitats and species.
• Evaluate impacts against site-specific conservation objectives.
• Implement measures to prevent habitat deterioration and significant species disturbance [89].

Table 2: Quantitative Overview of the Natura 2000 Network (Key Figures)

Metric	Terrestrial & Marine	Marine Specific
Network Size	Largest coordinated network of protected areas in the world [87]	>3,000 sites [88] [89]
Spatial Coverage	Extends across all 27 EU Member States [87]	>9% of EU marine waters [88] [89]
Legal Basis	Birds Directive (SPAs) & Habitats Directive (SCIs/SACs) [87]	Habitats Directive (9 marine habitats, 16 species) & Birds Directive (60 bird species) [88]
Key Policy Link	Contribution to EU Biodiversity Strategy for 2030 [87]	Support for EU Marine Action Plan & 30% protection target [89]

Comparative Analysis: Methodologies and Implementation

A direct comparison reveals how the general scientific framework of ESP is adapted to different regulatory and geographical contexts in North America and Europe. The following diagram illustrates the distinct logical pathways of each approach.

Key Distinctions and Commonalities

• Basis for Designation: The most significant difference lies in the initial basis for identifying the network. Model-driven ESPs (as in Northeast China and advocated for in North America) use quantitative assessments of ecosystem services and spatial modeling to dynamically identify ecological sources and corridors [85]. In contrast, the Natura 2000 network is primarily a policy-driven ESP where sites are legally designated for specific habitat types and species listed in the annexes of the Birds and Habitats Directives [87].
• Approach to Connectivity: Both systems recognize the critical importance of ecological connectivity. The model-driven approach explicitly maps corridors and pinch points using resistance surfaces and connectivity models like circuit theory [85]. Natura 2000 addresses connectivity through Article 10 of the Habitats Directive, which encourages member states to manage landscape features that improve the ecological coherence of the network, though it does not typically map formal corridors in the same way [87].
• Management and Monitoring: Natura 2000 has a robust, legally embedded management and assessment process defined by Article 6, which requires conservation measures and impact assessments for plans and projects [87]. The management of model-driven ESPs is often guided by ecological principles, such as maintaining soil quality and reducing fragmentation, but may not be backed by the same overarching legal framework [86].

The Scientist's Toolkit: Research Reagent Solutions

This section details key datasets, models, and analytical tools essential for constructing and analyzing Ecological Security Patterns.

Table 3: Essential Research Tools for ESP Construction

Tool Category	Specific Tool/Data	Function in ESP Research
Spatial Data	Land Use/Land Cover (LULC) data	Base layer for assessing ecosystem services and modeling resistance.
	Climate Data (WorldClim)	Provides precipitation, temperature for soil erosion, water yield models [85].
	Soil Data (HWSD)	Critical input for RUSLE and carbon storage models [85].
	Digital Elevation Model (DEM)	Used for topographic analysis and hydrological modeling.
Software & Models	Fragstats	Calculates landscape metrics to quantify habitat fragmentation [85].
	InVEST (Integrated Valuation of Ecosystem Services)	Suite of models for mapping and valuing ecosystem services.
	GIS with Circuit Theory plugins	Platforms (e.g., ArcGIS, R, Circuitscape) for modeling connectivity and corridors [85].
Analytical Frameworks	Revised Universal Soil Loss Equation (RUSLE)	Empirically-based model for predicting soil erosion rates [85].
	Least Cost Path (LCP) / Minimum Cumulative Resistance (MCR)	Identifies the optimal pathway between sources based on a resistance surface [85].
	Shared Socio-economic Pathways (SSPs)	Scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) for projecting future land use and climate impacts [85].

Conclusion

Ecological Security Patterns (ESP) have emerged as a vital, actionable framework for safeguarding ecological integrity amidst growing anthropogenic pressures. This exploration affirms that ESP's core strength lies in its spatially explicit 'source-corridor-node' approach, which is increasingly refined by integrating multi-sourced data, advanced modeling techniques like MCR and circuit theory, and dynamic time-series analysis. The future of ESP development hinges on overcoming key challenges: establishing critical ecological thresholds, implementing robust multi-scale and cross-boundary effectiveness evaluations, and deepening the coupling of spatial patterns with underlying ecological processes. As global challenges of habitat fragmentation and biodiversity loss intensify, the continued evolution and international adoption of ESP principles offer a promising pathway for guiding strategic land-use planning, enhancing ecosystem resilience, and ultimately achieving sustainable development goals worldwide. Future research should prioritize the development of standardized evaluation metrics and the exploration of market-based compensation mechanisms, such as eco-banking, to support ESP implementation.

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• 89. Commission adopts guidance on Natura 2000 and fishing [https://environment.ec.europa.eu/news/commission-adopts-guidance-natura-2000-and-fishing-2025-10-17_en]