Optimizing Landscape Ecological Risk Assessment with Resilience: A Framework for Scientific Management and Sustainable Development

Abstract

This article provides a comprehensive framework for integrating ecosystem resilience into landscape ecological risk assessment (LERA) to enhance the scientific management of ecological resources. It begins by exploring the foundational concepts and limitations of traditional LER models, highlighting the shift towards incorporating ecosystem services and resilience theory. The core of the article details methodological advancements, including the integration of a 'Resistance-Adaptation-Recovery' resilience framework with a 'Disturbance-Vulnerability-Loss' risk model, alongside spatial analysis tools like bivariate Moran's Index and Geodetector. It addresses critical challenges such as spatial scale optimization, subjective parameterization, and multi-scale analysis. The validation section explores comparative assessment through ecological management zoning and coupling coordination analysis, offering insights for targeted interventions. Aimed at researchers, scientists, and environmental management professionals, this synthesis aims to bridge theory and practice, providing actionable insights for reducing regional ecological risks and promoting ecosystem sustainability.

Foundations and Limitations: From Static Landscape Patterns to Dynamic Ecosystem Resilience

The discipline of ecological risk assessment (ERA) originated not from an ecological foundation, but from a human-centric one. Its roots are firmly planted in the 1970s-1980s with frameworks designed to evaluate cancer risks and other human health impacts from chemical exposures [1]. The initial focus was on single chemical stressors and their effects on individual organisms, primarily humans [1]. The U.S. Environmental Protection Agency's (EPA) early guidelines solidified this chemical-by-chemical, health-focused approach [2] [3].

A pivotal expansion began in the early 1990s, as recognition grew that ecosystems themselves were endpoints of value requiring protection. The EPA's Framework for Ecological Risk Assessment (1992) and subsequent guidelines (1998) formally established a parallel process for ecological systems [2] [1]. This era marked the shift from "human health in the environment" to "health of the environment." However, these assessments often remained limited in scale, focusing on specific sites (e.g., a contaminated field) and a narrow suite of stressors [1].

The 21st century has driven the field toward greater complexity and scale. Key limitations of traditional ERA became apparent: an inability to handle multiple interacting stressors, spatial and temporal dynamics, and landscape-level processes [1]. In response, Landscape Ecological Risk Assessment (LERA) emerged as a dominant paradigm. LERA evaluates the possible adverse effects of human activities or natural hazards on the compositions, structures, functions, and processes of a landscape [4]. It leverages landscape pattern indices derived from land use/cover change (LUCC) data to act as integrative proxies for the cumulative impact of multiple stressors—from pollution and urbanization to climate change and invasive species [5] [6]. This allows for the assessment of regional, watershed, and even continental-scale risks, moving far beyond the single-site assessments of the past [2] [4].

The most recent evolution integrates concepts of resilience and ecosystem services directly into the risk calculus. This optimizes LERA by not just diagnosing risk but also evaluating the system's inherent capacity to resist and recover, thereby guiding more nuanced ecological management and zoning [7] [8]. This progression—from human toxicology to landscape ecology to resilience-informed management—frames the modern toolkit for understanding and mitigating ecological risk in an increasingly human-modified world.

Foundational Frameworks and Current Methodological Approaches

Modern ecological risk assessment employs a spectrum of frameworks, from the traditional EPA process to advanced landscape-level models. The table below summarizes the core characteristics, advantages, and applications of the predominant approaches.

Table 1: Core Methodological Frameworks in Ecological Risk Assessment

Framework Name	Spatial Scale & Primary Focus	Key Stressors Assessed	Typical Application Context	Key References & Guidelines
Traditional EPA Ecological Risk Assessment	Local to site-specific. Focus on specific receptors (species, populations).	Primarily chemical contaminants (e.g., pesticides, heavy metals).	Regulatory decision-making for chemical registration, contaminated site remediation.	EPA Guidelines (1998); Technical focus on exposure characterization [3].
Cumulative Risk Assessment (CRA)	Expands to regional scales. Emphasizes combined effects.	Multiple chemical and non-chemical stressors (e.g., pollution, habitat loss, climate).	Evaluating complex environmental impacts where stressors interact.	EPA planning documents; Stressor-based and Relative Risk Model (RRM) approaches [1].
Landscape Ecological Risk (LER) Assessment	Watershed, regional, national. Focus on landscape patterns and processes.	Integrated proxy via Land Use/Cover Change (LUCC). Captures compound effects of urbanization, agriculture, fragmentation, etc.	Regional planning, ecosystem health monitoring, spatial zoning for conservation/restoration.	Predominant in contemporary research [9] [4] [5].
Resilience-Integrated LER Assessment	Regional. Couples risk with recovery capacity.	LUCC, combined with metrics of ecosystem service provision and resilience.	Optimized ecological management zoning, identifying areas for adaptation, conservation, or restoration.	Emerging best-practice approach [7] [8].

The Landscape Pattern Index Method is the most widely applied LER approach. It uses a standard formula: LER = ∑ (Area of Landscape Type / Total Area) * Landscape Loss Index. The Loss Index itself combines a Landscape Disturbance Index (based on metrics like fragmentation) and a Landscape Vulnerability Index (a pre-assigned weight for each land use type, e.g., wetland > forest > cropland > built-up) [6]. This model's output is a spatially explicit risk map, revealing patterns such as "high in the northwest, low in the southeast" as found in the Fuchunjiang River Basin [4].

A significant advancement is the Two-Dimensional Matrix Model, which enriches the concept of "risk" by separately evaluating the probability of risk occurrence and the potential loss severity. Probability is modeled using factors like topographic sensitivity and ecological resilience, while loss is modeled as the degradation degree of key ecosystem services (e.g., water retention, carbon sequestration) [8]. This matrix creates a more nuanced risk classification than a single index.

The cutting edge is the Integration of Resilience. Here, LER assessment is coupled with a parallel evaluation of Ecosystem Resilience (ER), often based on the capacity, adaptability, and transformability of ecosystem services. Spatial correlation analysis (e.g., bivariate Moran's I) between LER and ER allows for functional zoning: Ecological Conservation (Low Risk, High Resilience), Ecological Restoration (High Risk, Low Resilience), and Ecological Adaptation (intermediate states) [7]. This directly supports the thesis of optimizing assessment with resilience research.

Application Notes and Detailed Experimental Protocols

Protocol: Landscape Ecological Risk Assessment via the Landscape Pattern Index Method

This protocol details the standard workflow for generating a spatially explicit LER index using remote sensing data, as applied in studies of major river basins [9] [4].

1. Problem Formulation & Study Area Demarcation

• Objective: To assess the spatiotemporal evolution of landscape ecological risk and its driving forces.
• Spatial Unit Definition: Define the study area (e.g., watershed, administrative region). Determine the optimal analysis scale (grain and extent). Research indicates the analytical effect is often optimal at a specific scale (e.g., 90 km² grids for the Yellow River Basin) [9]. Use the coefficient of variation method to find the scale where landscape indices stabilize.

2. Data Acquisition & Preprocessing

• Core Data: Acquire multi-temporal Land Use/Land Cover (LULC) raster data (e.g., for years 1990, 2000, 2010, 2020) at 30m resolution from sources like USGS or ESA.
• Ancillary Data: Collect vector boundaries, digital elevation models (DEM), and socioeconomic data (GDP, population) for driving force analysis.
• Preprocessing: Re-project all data to a unified coordinate system. Reclassify LULC data into standard classes (e.g., Cropland, Forest, Grassland, Waterbody, Built-up, Unused Land).

3. Landscape Index Calculation & Risk Model Construction

• Grid Division: Overlay a vector grid (e.g., 3km x 3km) onto the study area. This grid becomes the assessment unit [6].
• Index Computation: For each assessment unit and time period, use Fragstats software to calculate:
  • Percentage of Landscape (PLAND) for each LULC class.
  • Patch Density (PD), Edge Density (ED), and Landscape Division Index (DIVISION).
• Construct Loss Index: For each LULC class i in grid k:
  • Calculate Landscape Disturbance Index (LDIki). A common model is: LDI = a*DIVISION + b*PD + c*ED (where a, b, c are weights summing to 1) [6].
  • Assign a Landscape Vulnerability Index (LVIi) on a ordinal scale (e.g., 1-6) based on ecosystem stability and sensitivity. For example: Unused Land (1), Built-up (2), Cropland (3), Grassland (4), Waterbody (5), Forest (6).
  • Compute Landscape Loss Index: LLI*ki* = LDI*ki* * LVI*i*.
• Compute Final LER Index: For each grid k, LER*k* = ∑ (A*ki* / A*k*) * LLI*ki*, where A is area.

4. Spatial Analysis & Driving Force Detection

• Spatial Statistics: Calculate Global and Local Moran's I to evaluate spatial autocorrelation and identify High-High or Low-Low risk clusters [5].
• Geodetector Analysis: Use the Geodetector model (Factor Detector, Interaction Detector) to quantify the explanatory power (q-statistic) of natural (elevation, slope, precipitation) and anthropogenic (GDP, population density, distance to roads) factors on the spatial heterogeneity of LER [4] [5].

Protocol: Multi-Scenario Simulation of Future Landscape Ecological Risk

This protocol uses land use simulation models to project future LER under different policy scenarios, essential for proactive management [5] [6].

1. Historical Change Analysis & Driver Selection

• Analyze historical LULC transition matrices (2000-2020) to identify major conversion pathways (e.g., cropland to urban land).
• Select driving factors for simulation: Elevation, Slope, Distance to Rivers/Roads/Urban Centers, Population Density, GDP. Standardize all factor rasters.

2. Land Use Simulation Using the PLUS Model

• Training: Use the Land Expansion Analysis Strategy (LEAS) module in the PLUS model to extract land use expansion between two historical periods and identify contributions of each driver using a Random Forest algorithm.
• Scenario Development: Define and parameterize scenarios for 2030/2050:
  • Natural Development (NDS): Trends continue based on historical transition probabilities.
  • Economic Development (EDS): Increase probability of cropland/forest converting to built-up land; restrict ecological land growth.
  • Ecological Protection (EPS): Restrict conversion of ecological lands (forest, grassland, water); encourage conversion of cropland to ecological uses.
  • Cropland Protection (CPS): Strictly protect cropland from conversion; urban expansion is limited to low-quality land.
• Simulation: Use the CARS module with a Multi-type Random Patch Seed mechanism to generate future LULC maps under each scenario.

3. Future LER Assessment & Scenario Comparison

• Apply the LER index model (Protocol 3.1) to the simulated future LULC maps.
• Compare the area, spatial distribution, and statistical characteristics of different risk levels across scenarios. The Ecological Protection and Cropland Protection scenarios typically show lower future LER compared to development-focused scenarios [6].

Protocol: Resilience-Informed Ecological Risk Assessment and Zoning

This advanced protocol integrates ecosystem services and resilience to move from risk assessment to management zoning [7] [8].

1. Two-Dimensional Risk Assessment

• Probability (P) Layer: Construct a composite index representing the likelihood of ecosystem degradation. Common indicators include:
  • Landscape Vulnerability: As calculated in Protocol 3.1.
  • Topographic Sensitivity: Based on slope and relief amplitude.
  • Ecological Resilience: An index from vegetation biomass (NDVI), biodiversity, and landscape connectivity metrics.
• Loss (L) Layer: Model potential loss as the degradation of key ecosystem services. Use the InVEST model or equivalent to quantify:
  • Service supply for a baseline and stressed condition (e.g., water yield under extreme rainfall, soil retention under high erosion).
  • Calculate degradation degree: Loss = (Supply*baseline* - Supply*stressed*) / Supply*baseline*.
• Risk Matrix: Classify P and L into levels (e.g., Low, Medium, High). Cross-tabulate to create a 3x3 risk matrix, where final risk level is determined by the combination (e.g., High P + High L = Highest Risk).

2. Ecosystem Resilience (ER) Assessment

• Construct a separate ER Index focusing on adaptive capacity. Indicators include:
  • Ecosystem Service Capacity: Total magnitude of key services.
  • Diversity/Redundancy: Land use diversity index, habitat quality patch diversity.
  • Adaptive Potential: Measured as the distance to natural ecological sources or conservation areas.

3. Bivariate Spatial Zoning

• Perform a bivariate local spatial autocorrelation analysis between the final LER index and the ER index.
• Interpret the clusters to define management zones:
  • Ecological Conservation Zone: Low LER - High ER. Strategy: Maintain current protection, limit human disturbance.
  • Ecological Restoration Zone: High LER - Low ER. Strategy: Prioritize for restoration projects (afforestation, wetland rehabilitation).
  • Ecological Adaptation Zone: High LER - High ER OR Low LER - Low ER. Strategy: Adaptive management; monitor for changes, enhance connectivity.
  • Ecological Monitoring Zone: Low LER - Low ER. Strategy: Monitor for potential degradation.

Evolution of Ecological Risk Assessment Frameworks and Methods

Spatial Landscape Ecological Risk Assessment Workflow

Resilience-Integrated LER Assessment and Management Zoning Model

The Scientist's Toolkit: Key Reagent Solutions and Analytical Materials

Table 2: Essential Research Toolkit for Landscape Ecological Risk Assessment

Tool/Reagent Category	Specific Item/Software	Primary Function in LERA	Critical Notes & Considerations
Core Spatial Data	Multi-temporal Land Use/Land Cover (LULC) Raster Datasets (e.g., USGS NLCD, ESA CCI-LC)	The fundamental input for calculating landscape pattern indices and tracking change.	Resolution & consistency across time periods is critical. 30m resolution is common; higher resolution increases computational load.
Geographic Information System (GIS)	ArcGIS Pro, QGIS, GDAL/Python Libraries (geopandas, rasterio)	Platform for data management, preprocessing, spatial analysis, grid creation, and final map production.	Proficiency in raster algebra, zonal statistics, and projection management is essential.
Landscape Metric Analysis	FRAGSTATS (standalone or library)	The industry-standard software for computing a wide array of landscape pattern indices (patch, class, landscape level).	Careful selection of relevant metrics (e.g., Edge Density, Patch Density, Landscape Division Index) based on the study's ecological questions.
Statistical & Spatial Analysis	R (with spdep, GD, sf packages), GeoDa, MATLAB	Performing advanced statistics: spatial autocorrelation (Moran's I), Geodetector analysis, regression modeling, and cluster analysis.	Geodetector (GD) model is particularly powerful for quantifying factor contributions to spatial heterogeneity of LER [4] [5].
Ecosystem Service Modeling	InVEST Suite (Natural Capital Project), ARIES	Quantifying and mapping ecosystem service supply (e.g., water purification, carbon storage, habitat quality) for loss assessment and resilience metrics.	Model outputs require local calibration and validation with field data where possible to improve accuracy.
Land Use Change Simulation	PLUS Model, CLUE-S, FUTURES	Projecting future land use scenarios under different socio-economic and policy pathways.	PLUS model is increasingly favored for its patch-generation simulation and ability to integrate driving factors via Random Forest [5] [6].
High-Performance Computing (HPC) Resources	Cluster/Cloud computing access (e.g., Google Earth Engine, local HPC)	Handling large raster datasets, running iterative simulations (like Monte Carlo in RRM), and processing regional/continental scale analyses.	Essential for large-scale or high-resolution studies to achieve feasible processing times.
2-ethylpentanedioyl-CoA	2-ethylpentanedioyl-CoA, MF:C28H46N7O19P3S, MW:909.7 g/mol	Chemical Reagent	Bench Chemicals
6-OAc PtdGlc(di-acyl Chain)	6-OAc PtdGlc(di-acyl Chain), MF:C49H92NaO14P, MW:959.2 g/mol	Chemical Reagent	Bench Chemicals

Discussion and Future Directions

The evolution from human health-centric chemical assessments to integrated landscape-resilience models represents a paradigm shift in how science characterizes environmental risk. The optimization of LERA with resilience research directly addresses the core thesis, moving the field from descriptive risk mapping to prescriptive management planning. The bivariate zoning of LER and ER provides a scientifically defensible basis for prioritizing conservation resources, targeting restoration, and implementing adaptive management [7].

Key challenges remain. First, scale dependency is inherent; findings at a 90 km² grid scale may not hold at 1 km² [9]. Future work must explicitly address multi-scale analyses. Second, while landscape patterns are excellent proxies, validating predicted ecological risk against direct, on-the-ground measurements of biodiversity loss, soil erosion, or water quality decline is crucial for reinforcing model credibility. Third, the integration of dynamic global change drivers, particularly climate change, into scenario simulations needs refinement. Current models often treat climate as a static background factor rather than a dynamic, interactive stressor.

The future of ERA lies in greater mechanistic integration. This includes coupling LER models with process-based ecological models (e.g., species distribution models, hydrological models) and advancing towards a "digital twin" of landscapes. Furthermore, the development of standardized, open-source computational workflows will enhance reproducibility and allow for comparative risk assessments across continents, ultimately providing a robust scientific foundation for global biodiversity and sustainability goals.

Core Principles and Traditional Models of Landscape Ecological Risk Assessment

Landscape Ecological Risk Assessment (LERA) represents a critical evolution in ecological risk science, shifting focus from the impacts of single contaminants on specific receptors to a holistic analysis of how spatial patterns and multi-source stressors affect ecosystem integrity [4] [10]. This framework is founded on the principle that landscape structure—the composition, configuration, and connectivity of ecosystems—directly mediates ecological functions and their vulnerability to disturbances from human activities and environmental change [11]. The rapid global processes of urbanization, land-use transformation, and climate change have made LERA an indispensable tool for diagnosing environmental health, predicting potential ecological degradation, and informing sustainable land management policies [9] [12].

The genesis of LERA lies in traditional ecological risk assessment, which originated in toxicology and human health studies [4]. As ecological challenges grew in scale and complexity, the field expanded to consider regional, landscape-level risks. A landmark in this evolution was the development of the Relative Risk Model (RRM), which introduced a structured approach to evaluating multiple stressors across diverse habitats [10]. Concurrently, the integration of landscape ecology principles, facilitated by advances in Geographic Information Systems (GIS) and remote sensing, enabled researchers to quantify spatial heterogeneity and its ecological consequences [4] [10]. This fusion of risk science and spatial analysis forms the bedrock of contemporary LERA, which now seeks not only to assess static risk but also to integrate concepts of ecosystem resilience—the capacity of a system to absorb disturbance and retain its function—to guide proactive ecological restoration and management [11].

Core Principles of Landscape Ecological Risk Assessment

The practice of LERA is governed by four interrelated core principles that ensure its scientific rigor and practical relevance.

• Spatial Heterogeneity and Pattern-Process Interaction: This principle asserts that ecological risks are not uniformly distributed but are inherently spatial, shaped by the mosaic of landscape patches (e.g., forests, wetlands, urban areas). The spatial arrangement of these patches influences ecological processes like species dispersal, nutrient cycling, and disturbance propagation. A fragmented landscape, for instance, may exhibit higher ecological risk due to impaired connectivity and diminished habitat quality [9] [10]. Assessment must therefore capture metrics of landscape pattern, such as fragmentation, aggregation, and diversity, to understand risk drivers [9] [12].

• Multi-Scale Analysis: Ecological processes and risks manifest at different scales. An effective LERA must identify the optimal scale of analysis. Studies demonstrate that analytical outcomes, such as the correlation between pattern and risk, are scale-dependent [9]. For example, research in the Yellow River Basin found that a 90 km × 90 km grid was the most effective scale for spatial analysis [9]. The principle requires a nested approach, considering broad-scale drivers (e.g., climate, economics) and fine-scale responses (e.g., local habitat loss).

• Integration of Landscape Pattern and Ecosystem Function: Traditional models often rely solely on structural landscape indices. A leading-edge principle is coupling these patterns with functional metrics, particularly ecosystem services. Landscape vulnerability can be more objectively evaluated based on the provision of key services like water purification, carbon sequestration, and soil retention, rather than on subjective land-use classifications [11]. This integration provides a clearer ecological connotation to risk indices, linking structural changes directly to functional losses for human well-being [11].

• Quantitative and Probabilistic Foundation: LERA is fundamentally a quantitative exercise. It employs mathematical models to convert landscape data into probabilistic estimates of risk [13]. This involves calculating indices of landscape disturbance and vulnerability, often followed by spatial statistics (e.g., geodetectors, Moran's I) to identify hotspots and driving forces [4] [12]. The quantitative principle ensures objectivity, repeatability, and the ability to forecast risks under different scenarios [13].

Traditional Assessment Models and Methodologies

Three primary conceptual models have historically shaped the methodology of LERA.

• The Landscape Loss Model (Pattern-Based Model): This is the most widely applied traditional model. It calculates a Landscape Ecological Risk Index (LERI) by integrating a landscape disturbance index (based on metrics like fragmentation, dominance, and division) with a landscape vulnerability index (an a priori ranking of land-use/cover types) [12] [10]. The risk is computed within sampling grids across the study area, generating a continuous spatial surface of risk values. Its strength lies in its straightforward application using GIS and remote sensing data, making it suitable for assessing risks from composite, diffuse stressors like generalized land-use change [10].

• The Relative Risk Model (RRM): This model adopts a risk source-stressor-habitat-receptor framework [10]. It involves identifying specific risk sources (e.g., industrial discharge, urban sprawl), the stressors they produce (e.g., toxins, habitat loss), and the habitats and ecological receptors (e.g., fish populations, bird communities) that are exposed. Experts rank and weight these components to model exposure and effect pathways. The RRM is powerful for sites with multiple, identifiable point-source and non-point-source risks, as it helps prioritize management actions for specific stressors [10].

• The Pressure-State-Response (PSR) and Derived Frameworks: This causal model, rooted in systems thinking, organizes indicators into three categories: Pressure (human activities stressing the environment), State (the resulting condition of the landscape), and Response (societal actions taken to improve the state). LERA typically focuses on the Pressure and State components. For example, urban expansion (Pressure) leads to increased patch density and reduced connectivity (State), resulting in higher ecological risk [12]. This model is valuable for communicating the causes and consequences of risk to policymakers.

Table 1: Comparison of Traditional Landscape Ecological Risk Assessment Models

Model	Core Concept	Typical Application Scale	Key Strengths	Primary Limitations
Landscape Loss Model [12] [10]	Risk as a function of landscape pattern deviation and inherent vulnerability.	Regional, watershed, landscape (100s – 1000s km²).	Simple, reproducible, excellent for spatial visualization and trend analysis with standard GIS data.	Weak linkage to specific ecological processes; vulnerability ranking can be subjective.
Relative Risk Model (RRM) [10]	Risk estimated through pathways linking sources, stressors, and valued receptors.	Local to regional, often for sites with defined multiple stressors (e.g., estuaries, industrial zones).	Explicitly models cause-effect pathways; effective for prioritizing specific management interventions.	Data-intensive; requires expert judgment for weighting, which can introduce subjectivity.
Pressure-State-Response (PSR) [12]	Organizes indicators within a causal framework of human impact, ecological condition, and management action.	Any scale, often used for regional environmental reporting and policy analysis.	Excellent communication tool; clearly links human activity to ecological outcome and management.	Does not provide a single, quantified risk index; more of an organizing framework than a calculative model.

Application Notes and Experimental Protocols

Protocol 1: Conducting a Baseline Landscape Ecological Risk Assessment Using the Landscape Loss Model

This protocol outlines the standard workflow for a pattern-based LERA, forming the basis for integration with resilience metrics [11] [12].

• Study Area Delineation and Scale Determination: Define the geographical boundary (e.g., watershed, administrative region). Conduct a scale sensitivity analysis by calculating landscape indices at multiple grid sizes (e.g., 1km, 2km, 5km). The optimal analysis scale is often identified where the coefficient of variation for key indices stabilizes [9].
• Data Acquisition and Land Use/Land Cover (LULC) Classification: Acquire multi-temporal satellite imagery (e.g., Landsat, Sentinel). Classify images into LULC types (e.g., forest, cropland, urban, water) for each time point (e.g., 1990, 2000, 2010, 2020) using supervised classification in software like ENVI or ERDAS Imagine [9] [4].
• Landscape Pattern Analysis: Using Fragstats software, calculate landscape-level and class-level metrics for each sampling grid. Essential metrics include:
  • Percentage of Landscape (PLAND): Measures composition.
  • Patch Density (PD): Indicates fragmentation.
  • Landscape Division Index (DIVISION): Reflects connectivity.
  • Aggregation Index (AI): Measures patch clumpiness [12].
• Landscape Ecological Risk Index (LERI) Calculation:
  • Compute the Disturbance Index (Ei) for each landscape type i: Ei = aCi + bNi + cDi, where Ci is fragmentation, Ni is disturbance, Di is division, and a, b, c are weights summing to 1 [12].
  • Assign a Vulnerability Index (Vi). Traditionally, this is an ordinal ranking (e.g., 1-6) for each LULC type based on expert judgment [11].
  • Calculate the Loss Index (Ri) for each landscape type: Ri = Ei * Vi.
  • Compute LERI for each assessment grid k: LERIk = Σ (Aki / Ak) * Ri, where Aki is the area of landscape i in grid k, and Ak is the total area of grid k [12].
• Spatial Interpolation and Risk Level Zoning: Use Kriging or IDW interpolation in GIS to create a continuous LERI surface. Apply natural breaks classification to define risk levels (e.g., lowest, lower, medium, higher, highest) [9] [10].
• Spatio-Temporal Change Analysis and Driver Identification: Analyze risk transitions between periods. Use the Geodetector model (q-statistic) or Random Forest regression to quantify the explanatory power of driving factors like GDP, population density, slope, and precipitation on the LERI spatial pattern [4] [12].

Landscape Loss Model Assessment Workflow

Protocol 2: Integrating Ecosystem Services and Resilience for an Optimized Assessment

This advanced protocol modifies the traditional model by refining the vulnerability index and coupling risk with resilience, aligning with the thesis focus on optimization [11].

• Quantifying Landscape Vulnerability via Ecosystem Services:
  • Select Key Ecosystem Services: Choose services critical to the study area (e.g., water yield, soil retention, carbon storage, habitat quality).
  • Model Ecosystem Services: Use models like InVEST or RUSLE to quantify the biophysical supply of each service for every landscape grid.
  • Normalize and Aggregate: Normalize service values and aggregate them using weighting (e.g., equal weight or analytic hierarchy process) to create a composite Ecosystem Service Value (ESV) for each grid [11].
  • Derive Vulnerability Index (Vi'): Invert the ESV (e.g., Vi' = 1 - Normalized ESV). This grounds vulnerability in functional loss rather than expert opinion [11].
• Calculating the Optimized LERI: Replace the traditional Vi with the ESV-derived Vi' in the LERI formula (Step 4 of Protocol 1). This yields a functionally grounded risk index.
• Assessing Ecosystem Resilience (ER):
  • Resilience Indicators: Construct an index from indicators such as vegetation vigor (NDVI), landscape diversity (SHDI), ecological connectivity (calculated via circuit theory or MCR models), and topographic complexity [11].
  • Resilience Index Calculation: Normalize and aggregate selected indicators to produce a spatial ER index.
• Coupling LER and ER for Ecological Zoning:
  • Bivariate Spatial Autocorrelation: Perform a bivariate Local Moran's I analysis between the optimized LERI and ER index layers.
  • Delineate Management Zones:
    • Low LER - High ER (Ecological Conservation Zone): Stable, resilient areas. Priority is maintaining current state.
    • High LER - Low ER (Ecological Restoration Zone): High-risk, low-resilience areas. Priority is urgent intervention and restoration.
    • High LER - High ER / Low LER - Low ER (Ecological Adaptation Zone): Areas with trade-offs. Priority is adaptive management and monitoring [11].

LER and ER Coupling for Ecological Zoning

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Analytical Tools and Data Sources for Landscape Ecological Risk Assessment

Tool/Data Category	Specific Example & Source	Primary Function in LERA
Remote Sensing Data	Landsat Series (USGS), Sentinel-2 (ESA)	Provides multi-spectral, multi-temporal imagery for land use/cover classification and change detection. Essential for calculating landscape pattern metrics [9] [4].
GIS & Spatial Analysis Software	ArcGIS Pro, QGIS, GRASS GIS	Platform for data integration, spatial overlay, grid creation, map algebra, and final cartographic visualization of risk patterns [12].
Landscape Pattern Analysis Software	FRAGSTATS	The standard software for computing a wide array of landscape metrics at the class and landscape level from categorical raster maps [12].
Ecosystem Service Modeling Tools	InVEST (Natural Capital Project), RUSLE, SDR	Suite of models for quantifying and mapping the provision of ecosystem services (e.g., carbon, water, sediment retention), used to create objective vulnerability indices [11].
Statistical & Geostatistical Packages	R (with spdep, gd packages), GeoDa, IBM SPSS	Performs advanced spatial statistics (e.g., spatial autocorrelation, Geodetector analysis), regression modeling, and validation of risk drivers [4] [12].
Spatial Resilience/Connectivity Tools	Circuitscape, Linkage Mapper	Models landscape connectivity and resistance to movement, which are key components for assessing ecological resilience and identifying corridors [11].
Climate & Socioeconomic Data	WorldClim, GPW, National Statistical Yearbooks	Provides gridded data on climate variables (temp, precip) and socio-economic drivers (GDP, population) for analyzing risk influencing factors [4] [12].
N-Lithocholyl-L-Leucine	N-Lithocholyl-L-Leucine, MF:C30H51NO4, MW:489.7 g/mol	Chemical Reagent
1-Palmitoyl-3-bromopropanediol	1-Palmitoyl-3-bromopropanediol, MF:C19H37BrO3, MW:393.4 g/mol	Chemical Reagent

Landscape Ecological Risk (LER) assessment has emerged as a critical tool for evaluating the potential adverse effects of human activities and environmental changes on ecosystem structure and function [9]. Conventionally, LER models rely on landscape pattern indices derived from land use/cover change (LUCC) data, calculating risk as a function of landscape disturbance and vulnerability [4]. While widely applied, this paradigm is increasingly scrutinized for core methodological shortcomings that limit its scientific robustness and managerial utility [11] [7]. First, the assignment of landscape vulnerability indices is often based on expert judgment or arbitrary value assignments to land use types (e.g., assigning values of 1-6 to different categories), introducing strong subjectivity and uncertainty [11]. Second, the reliance on a static snapshot of landscape patterns fails to capture dynamic ecological processes, feedbacks, and recovery potential, representing a static approach that overlooks system dynamics [14] [15]. Third, and most critically, conventional LER assessments largely ignore the concept of ecosystem resilience (ER)—the capacity of a system to absorb disturbance and reorganize while retaining its essential function [11] [7]. This omission represents a fundamental missing link between risk identification and sustainable management strategies. This article, framed within a broader thesis on optimizing LER with resilience research, details these shortcomings and provides application notes and experimental protocols for developing next-generation, integrated LER-ER assessment frameworks.

Shortcoming I: Subjectivity in Landscape Vulnerability Indexing

The conventional method for characterizing landscape vulnerability, a core component of the Landscape Loss Index (LLI), is a primary source of subjectivity. Vulnerability is typically represented by a static, ordinally ranked index value assigned to each land use/cover type (e.g., cropland=3, forest=4) [11]. This approach relies heavily on intuition and general experience, lacking a quantifiable, process-based foundation [11].

Table 1: Comparison of Conventional and Optimized Vulnerability Assessment Methods

Assessment Component	Conventional Method	Key Shortcoming	Optimized Method (Ecosystem Service-Based)	Key Advantage
Landscape Vulnerability Index (LVI)	Static, ordinal ranking of land use types (e.g., 1-6) [11].	High subjectivity; no functional basis; ignores spatial heterogeneity within a land type.	Derived from the inverse of composite ecosystem service supply (e.g., water conservation, soil retention, carbon sequestration) [11] [16].	Objective, quantifiable, spatially explicit; reflects actual ecological function and sensitivity.
Data Foundation	Land use/cover classification map.	Simple, but lacks ecological functional data.	Biophysical models (e.g., InVEST, RUSLE) and remote sensing to quantify ecosystem service yields [11] [17].	Integrates process-based modeling, capturing ecological mechanisms.
Outcome	A uniform vulnerability score for all patches of the same land class.	Fails to differentiate, e.g., a fragmented forest patch from a core forest area.	A continuous, spatially varied vulnerability surface [11].	Captives intra-class heterogeneity and provides a more nuanced risk landscape.

Application Note 2.1: Protocol for Ecosystem Service-Based Vulnerability Assessment

• Objective: To replace subjective land type rankings with an objective, spatially explicit Landscape Vulnerability Index (LVI) based on ecosystem service (ES) provision.
• Materials: Land use map; biophysical data (DEM, soil type, precipitation, NDVI); geospatial software (ArcGIS, QGIS); ecosystem service modeling tools (e.g., InVEST modules).
• Procedure:
  • Select Key Ecosystem Services: Identify 3-4 regionally dominant ES (e.g., for a watershed: water yield, soil retention, carbon storage, habitat quality) [11].
  • Quantify ES Supply: Use standardized models (e.g., InVEST) to calculate the biophysical supply of each selected ES for every pixel or planning unit over the study period.
  • Normalize and Aggregate: Normalize each ES value to a 0-1 scale. Use an additive model or weighted summation (e.g., entropy method for weights) to create a composite ES supply index [16].
  • Derive LVI: Calculate LVI as LVI = 1 - Normalized Composite ES Index. This ensures areas with low service provision (high degradation or sensitivity) receive high vulnerability scores [11].
  • Validation: Perform sensitivity analysis on weight assignments and correlate the final LVI map with independent indices of ecological degradation (e.g., soil erosion rates, habitat fragmentation metrics).

Shortcoming II: Static, Pattern-Only Approaches

Traditional LER assessments are inherently static, calculating risk from landscape pattern indices (e.g., fragmentation, dominance) at a single point in time or across discrete time steps [12]. This "pattern-risk" model assumes a direct and constant relationship between spatial configuration and ecological function, neglecting the dynamic processes of ecological succession, species movement, and disturbance recovery [14]. It fails to answer whether a disturbed landscape is on a trajectory of recovery or further degradation.

Application Note 3.1: Protocol for Integrating Dynamic Ecological Flows via Circuit Theory

• Objective: To augment static LER with an assessment of landscape functional connectivity, which influences recovery potential and risk propagation.
• Materials: LER raster map; habitat quality/land use map; resistance surface data (topography, human disturbance); software (Circuitscape, Linkage Mapper).
• Procedure:
  • Define Ecological Sources: Identify core habitat patches using criteria such as high ES value, high LER-resilience coupling (low LER-high resilience), or large, contiguous natural areas [17].
  • Construct a Composite Resistance Surface: Base resistance is typically the inverse of habitat quality. Refine it by integrating the LER index, where higher LER increases resistance to species movement and ecological flow [17].
  • Model Connectivity with Circuit Theory: Use Circuitscape software to model omnidirectional, random-walk movement across the resistance surface. This generates maps of cumulative current density and identifies pinch points (narrow corridors crucial for connectivity) and barrier points [17].
  • Dynamic LER Interpretation: A high-LER area that is also a critical pinch point presents a much greater systemic risk than a similarly scored isolated patch. This analysis transforms static LER into a component of dynamic landscape network analysis, guiding interventions to maintain or restore critical flows.

Diagram 1: From Static LER to Dynamic Network Analysis (95 chars)

Shortcoming III: The Missing Resilience Link

The most significant conceptual gap is the disconnect between LER and Ecosystem Resilience (ER). ER describes a system's capacity to withstand disturbance and its ability to return to a similar functional or structural state after a perturbation [11]. Conventional LER assesses the "pressure" but ignores the inherent "capacity" of the landscape to cope. Integrating resilience transforms LER from a mere indicator of problem severity into a guide for targeted management.

Table 2: Spatial Correlation and Management Zoning Based on LER and Resilience [11]

Bivariate Moran's I Zoning Category	LER Level	Ecosystem Resilience (ER) Level	Ecological Management Implication	Typical Action
Ecological Restoration Zone	High	Low	High pressure, low coping capacity. Highest priority for intervention.	Active restoration: re-vegetation, erosion control, habitat reconstruction.
Ecological Adaptation Zone	High	High	High pressure but currently high capacity. Monitor for resilience erosion.	Adaptive management: reduce chronic stressors, enhance landscape connectivity.
Ecological Conservation Zone	Low	High	Low pressure, high capacity. Ideal state to be maintained and protected.	Preventive conservation: enforce protection, limit development, maintain natural processes.
(Potential) Vacant Zone	Low	Low	Not commonly observed; may indicate latent risk or data artifact.	Investigate underlying causes; monitor for sudden disturbances.

Application Note 4.1: Protocol for Coupling LER and Ecosystem Resilience Assessment

• Objective: To quantitatively assess ER, analyze its spatial correlation with LER, and delineate zones for differentiated ecological management.
• Materials: Time-series remote sensing data (NDVI, land use); climate data; soil data; spatial statistics software (e.g., GeoDa).
• Procedure:
  • Quantify Ecosystem Resilience (ER): A robust proxy for ER is vegetation recovery rate after disturbances. Calculate the annual NDVI series. For each pixel, identify disturbance years (significant NDVI drops). Model the post-disturbance NDVI recovery trajectory and fit a curve (e.g., logistic) to extract the recovery rate and time to recover as resilience metrics [7].
  • Calculate Integrated LER: Employ the optimized LER model from Application Note 2.1.
  • Spatial Correlation Analysis: Use bivariate local Moran's I statistic to classify the relationship between LER and ER for each spatial unit into the four categories in Table 2 [11].
  • Drive Factor Analysis: Use Geographical Detector (GeoDetector) models to identify the dominant factors (e.g., land use type, elevation, slope, climate, human activity intensity) influencing the spatial differentiation of both LER and ER, and their interactive effects [11] [12].

Diagram 2: Integrated LER-Resilience Assessment Workflow (100 chars)

The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Tools and Materials for Advanced LER Research

Tool/Reagent Category	Specific Item/Software	Primary Function in Protocol	Key Consideration
Geospatial & Remote Sensing Data	Land Use/Cover (LULC) time-series data (e.g., from CAS RESDC) [12].	The foundational spatial dataset for calculating landscape indices and tracking change.	Ensure temporal consistency and classification accuracy across different years.
	MODIS/ Landsat NDVI time-series data.	Used to quantify vegetation dynamics and calculate ecosystem resilience metrics [7].	Handle cloud contamination and ensure radiometric calibration for time-series analysis.
	Digital Elevation Model (DEM), soil, climate datasets.	Critical inputs for process-based ecosystem service modeling (e.g., InVEST).	Resolution and precision should match the study scale; data sources must be reliable.
Modeling & Analysis Software	Fragstats	Calculates a wide array of landscape pattern metrics (e.g., patch density, edge density) for LER models [12].	Choose metrics that are ecologically relevant to the study area and resistant to scale effects.
	InVEST Model Suite	Spatially explicit modeling of ecosystem service supply (e.g., water yield, sediment retention) [17].	Model calibration with local data is crucial for improving output accuracy.
	Circuitscape	Applies circuit theory to model landscape connectivity and identify critical nodes [17].	Constructing a biologically meaningful resistance surface is the most critical and challenging step.
	GeoDa / GD (Geographical Detector)	Performs spatial autocorrelation (e.g., Moran's I) and detects driving factors of spatial heterogeneity [11] [6].	Results can reveal interaction effects between factors that are stronger than individual effects.
Computational Environment	Python (with sci-kit learn, pandas, geopandas) / R	Provides a flexible environment for customizing analysis workflows, statistical modeling, and automating batch processing.	Essential for handling large geospatial datasets and implementing machine learning algorithms (e.g., Random Forest for driver analysis) [12].
	ArcGIS Pro / QGIS	The core platform for spatial data management, visualization, and fundamental geoprocessing.	The industry standard; QGIS is a powerful open-source alternative.
Calcium mesoxalate trihydrate	Calcium mesoxalate trihydrate, MF:C3H6CaO8, MW:210.15 g/mol	Chemical Reagent	Bench Chemicals
8-Methyloctadecanoyl-CoA	8-Methyloctadecanoyl-CoA, MF:C40H72N7O17P3S, MW:1048.0 g/mol	Chemical Reagent	Bench Chemicals

Conceptual Foundations of Resilience in Ecology

Resilience theory provides a framework for understanding how systems absorb disturbance, reorganize, and maintain their essential functions. Within landscape ecology and risk assessment, it shifts the management paradigm from resisting change to building adaptive capacity. The concept, introduced to ecology by C.S. Holling, is fundamentally defined as “the magnitude of disturbance that a system can tolerate before it shifts into a different state with different controls on structure and function” [18] [19]. This contrasts with engineering resilience, which focuses on the speed of return to a single equilibrium state [20].

For landscape ecological risk assessment, two critical conceptual distinctions are essential:

• Ecological Resilience: Emphasizes the existence of multiple stable states or regimes. The system can undergo regime shifts when a threshold is crossed due to disturbance. The focus is on the amount of change a system can withstand without altering its fundamental structure and processes [21] [20].
• Socio-Ecological Resilience: Expands the concept to integrated human-nature systems. It incorporates social dynamics, learning, governance, and adaptive cycles, recognizing that social and ecological systems are inextricably linked and co-evolve [18] [19].

A key conceptual model is the adaptive cycle, which describes four phases of change in complex systems: exploitation (r), conservation (K), release (Ω), and reorganization (α). This cycle operates across multiple scales in a nested hierarchy known as panarchy, where dynamics at one scale (e.g., a forest patch) influence and are influenced by dynamics at larger (e.g., watershed) and smaller (e.g., soil microbial community) scales [22]. This multi-scale perspective is critical for optimizing landscape ecological risk assessments, as risks and resilience manifest differently across spatial and temporal scales [21] [23].

Table 1: Core Concepts in Resilience Theory for Landscape Ecology

Concept	Definition	Relevance to Landscape Ecological Risk Mitigation
Ecological Resilience [20]	The magnitude of disturbance a system can absorb before restructuring into a new state.	Assesses the risk of catastrophic, irreversible regime shifts (e.g., forest to grassland).
Engineering Resilience [20]	The speed at which a system returns to its single equilibrium state after a disturbance.	Useful for evaluating recovery potential from discrete, non-catastrophic disturbances.
Threshold / Tipping Point [20] [19]	A critical level of a controlling variable or disturbance pressure beyond which system feedbacks change, leading to a new state.	Identifying early-warning signals of threshold proximity is a primary goal of resilience-based risk assessment.
Alternative Stable Regime [20] [19]	A distinct system state maintained by a unique set of structures, processes, and feedbacks (e.g., clear vs. turbid lake).	Defines the potential undesirable states that constitute high ecological risk.
Adaptive Capacity [20]	The ability of a system to adjust, learn, and reorganize in response to threats.	The foundation for proactive risk management; enhances a system's ability to cope with unforeseen shocks.
Panarchy [21] [22]	The nested, cross-scale structure of complex systems where cycles of change interact.	Explains how local risk can cascade to broader scales and how interventions at one scale can influence resilience at another.

Quantitative Frameworks and Assessment Methodologies

Transitioning from theory to operational assessment requires quantifying resilience attributes. A leading framework decomposes ecological resilience into four measurable components: scales, adaptive capacity, thresholds, and alternative regimes [20]. This decomposition allows researchers to test specific hypotheses about system behavior and resilience.

Landscape Pattern Analysis serves as a primary methodology for spatial resilience assessment. It uses geospatial data to compute metrics of landscape composition (e.g., percent cover of habitat types) and configuration (e.g., connectivity, patch size distribution) [21]. The core hypothesis is that landscapes exhibiting patterns within their historic range of variability (HRV)—the dynamic equilibrium established under natural disturbance regimes—possess greater inherent resilience. Significant departure from HRV indicates reduced resilience and higher ecological risk [21]. This approach was applied in China’s South-to-North Water Diversion project area, where a Landscape Ecological Risk Index (ERI) was constructed based on land use transformation and landscape pattern metrics [24].

Multivariate Trajectory Analysis is a powerful companion technique. It tracks the movement of a landscape through a multidimensional state space defined by key metrics (e.g., habitat connectivity, diversity, fragmentation) over time. The vector of change (trajectory) can be compared to a reference trajectory or desired range, quantifying the rate and direction of departure or recovery [21].

For socio-ecological systems, community-based assessment tools are vital. The Indicators of Resilience in Socio-ecological Production Landscapes and Seascapes (SEPLS) provides a set of 20 qualitative and quantitative indicators across ecological, agricultural, and socio-economic domains [25]. Assessment is conducted through participatory workshops where community members score indicators, generating not just numerical data but rich qualitative insights into system dynamics, strengths, and vulnerabilities [26]. This process itself builds social capacity and project ownership, enhancing overall resilience [26].

Table 2: Quantitative Attributes of Ecological Resilience and Measurement Approaches [20]

Resilience Attribute	Measurement Hypothesis	Exemplary Analytical Methods
Scale	Resilience emerges from redundant functional traits within and across distinct spatial and temporal scales.	Spatial statistics (e.g., wavelet analysis, variograms); cross-scale redundancy analysis.
Adaptive Capacity	Systems with higher biodiversity and functional response diversity have a greater capacity to adapt to disturbance.	Measuring functional trait diversity; network analysis of species interactions; social survey on innovation and learning.
Thresholds	Systems approaching a critical threshold will exhibit predictable early-warning signals in their dynamics.	Analysis of time-series for critical slowing down (e.g., rising autocorrelation); spatial pattern analysis (e.g., rising spatial correlation).
Alternative Regimes	The existence of multiple attractors can be inferred from bimodal distributions of state variables or hysteresis in response to drivers.	Statistical analysis of system state distributions; manipulative experiments; paleo-ecological reconstruction.

Application Notes & Protocols for Optimizing Landscape Ecological Risk Assessment

Integrating resilience theory transforms landscape ecological risk assessment from a static evaluation of hazard exposure to a dynamic analysis of system vulnerability and adaptive potential. The core objective is to identify not just current risk, but the proximity to critical thresholds and the capacity to adapt to ongoing change [20] [23].

Protocol: Landscape-Scale Resilience Assessment for Risk Prioritization

Objective: To map spatial variation in ecological resilience and identify areas at highest risk of regime shifts, guiding targeted mitigation efforts [21] [24].

Workflow:

• Define Assessment Units & Focal System: Delineate the landscape (e.g., watershed, administrative region) and define the ecological system (e.g., forest, wetland mosaic) and its potential alternative regimes [24].
• Select State Variables & Metrics: Choose key landscape pattern metrics that reflect system structure and function (e.g., core habitat area, connectivity index, land use diversity). For socio-ecological systems, integrate community-based SEPLS indicators [21] [25].
• Establish a Resilience Baseline (Reference Condition):
  • Historical Range of Variability (HRV): Use historical data, paleo-records, or simulation modeling (e.g., using LANDIS-II or similar landscape dynamic models) to quantify the range of natural variation in state metrics under historic disturbance regimes [21].
  • Desired Future Condition (DFC): In human-modified landscapes, define a DFC based on management goals that maintain key ecosystem services and biodiversity [21].
• Quantify Current State & Departure: Calculate current landscape metrics from recent geospatial data. Compute the multivariate distance (e.g., Mahalanobis distance) or trajectory angle between the current state and the HRV or DFC baseline. This departure magnitude is a primary measure of resilience loss and risk [21].
• Analyze Risk Drivers & Threshold Proxies: Statistically model relationships between departure magnitude and potential drivers (e.g., road density, climate moisture deficit, land use intensity). Use spatial statistics to detect early-warning signals like increasing spatial correlation of stress [20] [24].
• Generate Resilience-Risk Maps: Classify the landscape into zones (e.g., High Resilience/Low Risk, Transitional, Low Resilience/High Risk) based on departure magnitude and threshold proximity. Validate zones with field data on ecosystem condition [24].

Protocol: Community-Based Resilience Assessment for Integrated Risk Management

Objective: To assess the socio-ecological resilience of a production landscape or seascape, capturing human dimensions critical for risk mitigation success [25] [26].

Workflow:

• Workshop Preparation: Adapt the 20 SEPLS indicators to the local context. Assemble a diverse group of 15-30 community stakeholders [25].
• Participatory Scoring & Mapping: Over 1-2 days, facilitate discussions for each indicator. Use participatory mapping and historical timelines to provide context. Have participants collectively score each indicator (e.g., on a scale of 1-5) [26].
• Qualitative Data Capture: The primary output is not the score but the transcribed discussion. Systematically record reasons for scores, examples of changes, and perceptions of threats and capacities [26].
• Resilience Analysis: Analyze scores and qualitative data to identify strengths (high-scoring indicators) and critical vulnerabilities (low-scoring indicators). Pay particular attention to indicators related to biodiversity, knowledge learning, and governance collaboration, which are often central to adaptive capacity [25].
• Feedback & Adaptive Planning: Present results back to the community. Use the identified vulnerabilities to co-develop risk mitigation actions (e.g., restoring ecological corridors, diversifying livelihoods, strengthening local institutions). This process directly builds social capital, a key component of resilience [26].

Protocol: Urban Agglomeration Resilience and Networked Risk Assessment

Objective: To assess ecological risk and resilience in interconnected urban regions, accounting for cross-boundary risk transmission [22] [23].

Workflow:

• Define the Urban Network: Model the urban agglomeration as a network, with cities as nodes and flows of material, energy, people, and pollution as links [23].
• Multi-Scale Risk Assessment: Conduct a landscape ecological risk assessment (as in Protocol 3.1) for each city node. Simultaneously, assess risk factors that propagate through the network (e.g., upstream water pollution, regional air masses, shared infrastructure vulnerabilities) [23].
• Analyze Networked Resilience: Evaluate the resilience of the network itself. Metrics may include redundancy of ecological corridors, diversity of resource supply sources among cities, and the presence of collaborative governance institutions for crisis response [22] [23].
• Model Risk Cascades: Use systems dynamics or agent-based modeling to simulate how a disturbance (e.g., flood, economic shock) in one node propagates through the network, identifying systemic vulnerabilities and potential "circuit breaker" interventions [23].
• Develop Polycentric Governance Strategies: Formulate risk mitigation plans that require coordination across jurisdictions, such as regional green infrastructure networks, unified environmental monitoring, and joint climate adaptation strategies [22] [23].

Table 3: Research Toolkit for Resilience-Based Landscape Risk Assessment

Tool / Reagent Category	Specific Item or Method	Primary Function in Resilience Assessment
Geospatial & Remote Sensing Data	Multi-temporal satellite imagery (Landsat, Sentinel), LiDAR data, Land Use/Land Cover (LULC) maps.	Provides the foundational spatial data for calculating landscape pattern metrics and tracking change over time [21] [24].
Landscape Pattern Analysis Software	FRAGSTATS, GuidosToolbox, ArcGIS Landscape Metrics Toolkit.	Computes quantitative metrics of landscape composition and configuration critical for assessing departure from reference conditions [21].
Dynamic Simulation Modeling Platforms	LANDIS-II, HexSim, SELES.	Models landscape processes and succession under different disturbance and climate scenarios to project future states and define HRV [21].
Statistical Analysis & Early-Warning Signal Tools	R packages (earlywarnings, spatialwarnings), multivariate statistical software (PC-ORD, PRIMER).	Analyzes time-series and spatial data for statistical signatures (e.g., critical slowing down) that indicate proximity to thresholds [20].
Community Assessment Toolkit	Indicators of Resilience in SEPLS (2024 Edition), participatory mapping materials, workshop facilitation guides.	Enables structured, participatory assessment of socio-ecological resilience, capturing local knowledge and building stakeholder engagement [25] [26].
Network Analysis Software	Gephi, UCINET, R package igraph.	Models urban agglomerations or habitat networks as interconnected systems to analyze risk propagation and network-level resilience [23].

Abstract Modern Landscape Ecological Risk Assessment (LERA) requires a fundamental paradigm shift from static hazard identification to dynamic resilience quantification. This integration is critical for understanding not only the probability of detrimental ecological outcomes from stressors but also the capacity of landscapes to absorb disturbance, adapt, and transform [21] [27]. Framed within a thesis on optimizing LERA, these application notes present a synergistic framework that couples the “disturbance-vulnerability-loss” risk model with the “resistance-adaptation-recovery” resilience framework [28]. We provide detailed protocols for multi-scale quantitative assessment, leveraging landscape pattern analysis, dynamic simulation modeling, and geospatial statistical tools. Supported by empirical data and visualized workflows, this guide equips researchers and land managers with the methodologies to operationalize resilience-informed LERA, enabling proactive management for ecological security in the face of global change.

The Imperative for an Integrated LERA-Resilience Framework

Traditional LERA often operates as a diagnostic tool, quantifying the probability and severity of adverse ecological effects based on landscape pattern indices and external stressors [29]. While valuable for identifying risk hotspots, this approach is inherently limited by its static and retrospective nature. It typically answers "where and how bad is the risk?" but fails to address "how will the system respond, and what is its capacity to cope?" This gap is critical, as two landscapes with identical static risk indices may have vastly different futures based on their underlying resilience—their ability to maintain core structures and functions through disturbance, adapt to changing conditions, and recover from shocks [21] [27].

The synergistic integration of resilience transforms LERA from a diagnostic into a prognostic and strategic tool. This fusion is operationalized by explicitly linking the "disturbance-vulnerability-loss" paradigm of LERA with the "resistance-adaptation-recovery" dimensions of ecological resilience [28]. Disturbance pressures test a system's resistance; a system's vulnerability is inversely related to its adaptive capacity; and the magnitude of potential loss is directly mitigated by the system's recoverability. Empirical research confirms a robust negative correlation between ecological resilience and landscape ecological risk, with the recoverability dimension exerting the most potent counteracting effect on risk propagation [28].

This integration demands a multi-scale perspective. Ecological processes and their resilience operate across nested spatial and temporal scales—a concept described as panarchy [21] [27]. A practical LERA must therefore analyze interactions from fine-scale grids to broader county and regional levels, as relationships between risk and resilience can intensify or even reverse across scales [28]. The following framework diagram conceptualizes this integrated, multi-scale approach.

Diagram 1: Integrated LERA-Resilience Framework

Quantitative Assessment Protocols: Metrics and Models

Operationalizing the integrated framework requires quantifiable metrics for both risk and resilience. The protocols below standardize this assessment.

Protocol 1: Multi-Scale Landscape Ecological Resilience Assessment

This protocol quantifies ecological resilience based on landscape pattern attributes that correspond to resistance, adaptation, and recovery capacities [21] [28].

• Objective: To compute a composite Ecological Resilience Index (ERI) at multiple administrative or grid scales.
• Materials: Geospatial land use/cover (LULC) maps (e.g., CLCD data [29]), GIS software (e.g., ArcGIS, FRAGSTATS), statistical package.
• Procedure:
  • Landscape Pattern Analysis: For each spatial unit (e.g., 1km grid, county), calculate key landscape pattern indices from the LULC map.
  • Dimension Index Calculation: Aggregate selected indices into three core resilience dimension indices using established formulas (see Table 1).
  • Composite ERI Calculation: Integrate the three dimension indices using an equal or weighted summation: ERI = (Resistance Index + Adaptation Index + Recovery Index) / 3.
  • Spatial & Temporal Analysis: Map ERI results and analyze spatiotemporal trends across scales (e.g., from 2010 to 2020).

Table 1: Resilience Dimensions and Corresponding Landscape Metrics

Resilience Dimension	Operational Definition	Key Landscape Metrics	Computational Formula (Example)
Resistance	Capacity to withstand disturbance without change.	Edge Density (ED), Largest Patch Index (LPI), Aggregation Index.	Resistance Index ∝ (LPI + Aggregation Index) / 2 [28].
Adaptation	Capacity to adjust to stress through reorganization.	Shannon's Diversity Index (SHDI), Contagion, Landscape Shape Index.	Adaptation Index ∝ SHDI [21] [28].
Recovery	Capacity to return to a reference state after perturbation.	Core Area Percentage (CPLAND), Connectivity, Patch Cohesion Index.	Recovery Index ∝ CPLAND + Connectivity [28].

Protocol 2: Landscape Ecological Risk Assessment Based on Landscape Patterns

This protocol assesses risk by evaluating landscape disturbance and vulnerability [28] [29].

• Objective: To calculate a Landscape Ecological Risk Index (LERI) and identify risk hotspots.
• Materials: As in Protocol 1, plus spatial data on stressors (optional).
• Procedure:
  • Landscape Loss Index Calculation: For each landscape type i in each spatial unit k, compute a loss degree: Lossᵢₖ = Disturbanceáµ¢ * Vulnerabilityáµ¢.
  • Risk Index Integration: Calculate the Landscape Ecological Risk Index (LERI) for each unit: LERIâ‚– = Σ (Lossᵢₖ * (Areaᵢₖ / Areaâ‚–)).
  • Spatio-Temporal Analysis & Validation: Map LERI, analyze trends and center-of-gravity shifts [29], and validate with historical ecological degradation data.

Table 2: Components of Landscape Ecological Risk Index (LERI)

Component	Description	Typical Metrics/Proxies
Disturbance Index	Intensity of external stress on a landscape type.	Landscape Fragmentation, Divergence from natural state, or composite of fragmentation, isolation, and dominance indices [28] [29].
Vulnerability Coefficient	Inherent susceptibility of a landscape type to lose function.	Expert ranking (e.g., Water: 7, Forest: 3, Cropland: 4, Built-up: 1) assigned via analytic hierarchy process [28].
Landscape Loss Degree	Potential ecological loss for a specific type.	Product of Disturbance Index and Vulnerability Coefficient.

Protocol 3: Coupling Coordination Analysis

This protocol quantifies the interaction and synergy between risk and resilience [28].

• Objective: To measure the coupling coordination degree (CCD) between LERI and ERI.
• Materials: LERI and ERI results from Protocols 1 & 2.
• Procedure:
  • Standardization: Normalize LERI and ERI values to a [0,1] range.
  • Coupling Degree (C) Calculation: C = 2 * √[(U₁ * Uâ‚‚) / (U₁ + Uâ‚‚)²], where U₁=LERI, Uâ‚‚=ERI.
  • Coordination Index (T) Calculation: T = αU₁ + βUâ‚‚ (α and β are weights, often α=β=0.5).
  • Coupling Coordination Degree (D) Calculation: D = √(C * T).
  • Classification & Zoning: Classify D values (e.g., 0-0.3: Serious Dysregulation; 0.3-0.5: Moderate Dysregulation; 0.5-0.8: Basic Coordination; 0.8-1.0: Quality Coordination). Overlay with resilience/risk categories for management zoning.

Table 3: Coupling Coordination Classification and Management Implications

Coordination Degree (D)	Level	Risk-Resilience Profile	Suggested Management Focus
0.0 - 0.3	Serious Dysregulation	High Risk, Low Resilience. Critical zones requiring urgent intervention.	Ecological Restoration: Habitat rehabilitation, connectivity enhancement, stressor mitigation [21].
0.3 - 0.5	Moderate Dysregulation	Moderately imbalanced.	Conservation & Adaptive Management: Protective measures, monitoring, adaptive planning to prevent degradation.
0.5 - 0.8	Basic Coordination	Relatively balanced.	Sustainable Utilization: Maintain resilience through careful land-use planning and green infrastructure.
0.8 - 1.0	Quality Coordination	Low Risk, High Resilience. Optimal zones.	Priority Conservation: Protect key processes and structural integrity to maintain this state.

Experimental Validation & Scenario Modeling

Validating the integrated framework requires moving beyond correlation to test causal relationships and forecast future dynamics under alternative scenarios.

Protocol 4: Driving Force Analysis Using Geodetector

This protocol identifies key factors influencing LER and resilience and tests their interactions [29].

• Objective: To quantify the explanatory power (q-statistic) of natural and anthropogenic drivers on LERI/ERI spatial heterogeneity.
• Materials: Raster layers for LERI/ERI and potential driving factors (elevation, precipitation, GDP, population density, road network, etc.) [29].
• Procedure:
  • Data Preparation: Discretize continuous driving factor layers using the Optimal Parameters-based Geographical Detector (OPGD) model to ensure objectivity [29].
  • Factor Detection: Calculate the q-statistic for each driver: q = 1 - (Σ Nh σ²h)/(N σ²), where q ∈ [0,1]. A higher q-value indicates greater explanatory power.
  • Interaction Detection: Assess the combined effect of two drivers by calculating q for their interaction and comparing it to their individual q-values. Synergistic effects are present if q(X₁ ∩ Xâ‚‚) > q(X₁) + q(Xâ‚‚).
  • Ecological Interpretation: Identify primary and interactive drivers to inform targeted management (e.g., if road density and precipitation interact strongly to increase risk).

Protocol 5: Dynamic Landscape Simulation for Resilience Optimization

This experimental protocol uses simulation modeling to project future states and test management interventions [21].

• Objective: To project LERI and ERI under different climate, disturbance, and management scenarios to identify optimal resilience-enhancing strategies.
• Materials: Spatially explicit landscape simulation model (e.g., LANDIS-II, Dinamica EGO), initial LULC map, scenario parameter files.
• Procedure:
  • Model Calibration & Validation: Calibrate the model using historical LULC change data.
  • Scenario Definition: Develop contrasting scenarios (e.g., Business-as-Usual, Ecological Conservation, Climate Change High Emissions).
  • Model Execution & Output: Run simulations to generate projected LULC maps for future time steps (e.g., 2050, 2100).
  • Post-Processing & Analysis: Apply Protocols 1-3 to the projected LULC maps to calculate future ERI, LERI, and CCD.
  • Strategy Evaluation: Compare outcomes across scenarios. Use the model to test specific reconfiguration strategies (e.g., targeted habitat restoration, corridor creation) and evaluate their impact on accelerating network or landscape recovery—a concept validated in engineering resilience optimization [30].

The following diagram outlines the workflow for experimental validation and optimization.

Diagram 2: Experimental Validation and Optimization Workflow

Application Notes for Management and Research

For Land Managers & Policymakers:

• Zoning-Based Management: Utilize the coupled coordination zoning (Table 3) and "high-risk/low-resilience" hotspot maps to allocate resources efficiently. Prioritize restoration in dysregulated zones and protect quality coordination zones [28].
• Participatory Resilience Assessment: Engage stakeholders in developing system models and future scenarios. This process builds shared understanding, incorporates local knowledge, and fosters adaptive comanagement, enhancing the legitimacy and effectiveness of interventions [31].
• Strategic Planning: Integrate LERA-Resilience projections into long-term land-use and conservation plans. Use scenario modeling (Protocol 5) to stress-test policies against future climate and disturbance regimes [21].

For Researchers:

• Scale-Specific Investigations: Explicitly analyze the strength and direction of risk-resilience correlations across multiple scales (grid, county, watershed). Recognize that management insights are scale-dependent [28].
• Network Resilience Analysis: Explore applying network science models from engineering [30] to ecological landscapes. Model habitat patches as nodes and connectivity as links to evaluate and optimize the resilience of ecological networks to node (patch) loss or edge (corridor) fragmentation.
• Threshold Detection: Focus research on identifying early-warning indicators and critical thresholds in the risk-resilience relationship to prevent regime shifts [27].

The Scientist's Toolkit: Essential Reagents & Materials

Table 4: Key Research Reagent Solutions for Integrated LERA-Resilience Studies

Item / Tool	Function / Purpose	Example Source / Specification
China Land Cover Dataset (CLCD)	Provides consistent, multi-temporal LULC data for calculating landscape patterns and indices.	30m resolution, annual data from 1990 onward [29].
FRAGSTATS Software	Computes a comprehensive suite of landscape pattern metrics essential for resilience and risk indices.	Must be used with a GIS platform for spatial analysis.
Geodetector Software	Statistically quantifies the explanatory power of drivers on spatial heterogeneity and their interactions.	The OPGD version is recommended to avoid subjectivity in parameter discretization [29].
LANDIS-II Pro	Spatially explicit, process-based model for simulating forest landscape dynamics under alternative futures.	Essential for Protocol 5; requires species life history and disturbance regime parameters.
R/Python with sf, raster, ggplot2 packages	For custom spatial analysis, statistical modeling, coupling coordination calculation, and high-quality visualization.	Enables automation and reproducibility of Protocols 1-4.
Participatory Mapping Kits	For stakeholder workshops to delineate system boundaries, identify key assets, and co-produce future scenarios.	Includes physical/digital base maps, markers, and structured facilitation guides [31].
3,9-Dihydroxydecanoyl-CoA	3,9-Dihydroxydecanoyl-CoA, MF:C31H54N7O19P3S, MW:953.8 g/mol	Chemical Reagent
7-hydroxyoctanoyl-CoA	7-hydroxyoctanoyl-CoA, MF:C29H50N7O18P3S, MW:909.7 g/mol	Chemical Reagent

Methodological Integration: Building a Robust Resilience-Coupled LERA Framework

Landscape Ecological Risk (LER) assessment is a vital methodology for diagnosing the stress on ecosystems from natural and anthropogenic disturbances [11]. Its primary objective is to evaluate the likelihood of adverse effects on ecosystem structure and function, which subsequently impacts human well-being [11]. Integrating ecosystem services (ES) into LER assessment addresses a critical methodological gap. Traditional LER models often rely on assigning subjective, experience-based vulnerability values to different land-use types (e.g., assigning a fixed score of "6" to woodland and "1" to construction land). This approach lacks a concrete ecological foundation and fails to capture the dynamic, functional state of the landscape [11].

The optimization proposed here is grounded in a resilience research framework. Ecosystem Resilience (ER) is defined as the capacity of an ecosystem to absorb disturbance, reorganize, and retain its essential function, structure, and identity [11]. A resilient ecosystem can better withstand stressors, thereby reducing its vulnerability and associated ecological risk. Therefore, a comprehensive assessment model must evaluate not only the current risk (LER) but also the system's inherent capacity to cope with and recover from that risk (ER). This thesis posits that integrating a quantitatively derived, ES-based measure of landscape vulnerability with a parallel assessment of ecosystem resilience provides a more robust, ecologically meaningful, and actionable model for landscape risk assessment and management zoning [11].

Core Concepts and Theoretical Framework

Landscape Vulnerability, in this optimized model, shifts from a static land-cover classification to a dynamic, function-based index. It is defined as the degree to which a landscape is susceptible to losing its capacity to provide ecosystem services under disturbance [11]. A decline in ES provision directly indicates increased landscape vulnerability and a higher probability of ecological degradation.

Ecosystem Services (ES) are the benefits humans obtain from ecosystems. This model focuses on key services that link landscape structure to function and are sensitive to human pressure [11]. Common ES indicators used include:

• Water Yield: The total volume of water generated and available from a landscape [32].
• Soil Retention: The capacity of vegetation and terrain to prevent soil erosion.
• Habitat Quality: The ability of the ecosystem to provide suitable conditions for species persistence [33].
• Carbon Sequestration: The net removal of carbon dioxide from the atmosphere by vegetation.

Landscape Configuration Metrics are quantitative descriptors of the spatial arrangement of land-use patches. Research demonstrates that metrics such as patch size, shape complexity, core area, and connectivity are significant predictors of water-related ES like runoff, groundwater recharge, and water yield [32]. For instance, increasing the connectivity of forest patches can stabilize runoff, while complex-shaped agricultural patches can influence groundwater recharge [32]. Incorporating these metrics moves the assessment beyond simple "what" land cover exists to include "how" it is arranged, which is crucial for accurate ES modeling.

Quantitative Data Synthesis

Table 1: Comparative Landscape Vulnerability Assessment Methods

Assessment Component	Traditional LER Model	Optimized ES-Integrated Model	Key Advantage of Optimization
Vulnerability Index	Subjectively assigned based on land-use type (e.g., Woodland=6, Cropland=4) [11].	Calculated via a composite index of key ecosystem services (e.g., Water Yield, Soil Retention, Habitat Quality) [11].	Grounded in measurable ecological function, reducing subjectivity.
Spatial Focus	Landscape pattern (e.g., fragmentation, dominance).	Landscape function and configuration (e.g., service provision, patch connectivity) [32].	Directly links pattern to ecological process and human benefit.
Data Foundation	Land-use/land-cover (LULC) maps and pattern indices.	LULC maps, biophysical models (e.g., InVEST, SWAT), and landscape configuration metrics [32].	Enables dynamic modeling of service flows under different scenarios.
Output Interpretation	Relative risk based on pattern deviation.	Functional vulnerability indicating specific service deficits (e.g., low water retention).	Provides direct insight for targeted management interventions.

Table 2: Key Ecosystem Service Indicators for Vulnerability Assessment

Ecosystem Service Category	Specific Indicator	Measurement Method / Proxy	Relevance to Landscape Vulnerability
Provisioning	Water Supply	Water yield modeling (e.g., SWAT, InVEST) [32].	Critical for human communities; scarcity indicates high vulnerability [33].
Regulating	Soil Erosion Control	Sediment retention model (RUSLE, InVEST).	Loss indicates degradation of land productivity and water quality.
Regulating	Runoff Regulation	Runoff coefficient, modeled peak flow [32].	Poor regulation increases flood risk and reduces groundwater recharge.
Supporting	Habitat Provision	Habitat quality/suitability models based on land use and threats.	Decline signals biodiversity loss and ecosystem simplification.
Cultural	Recreational Value	Survey-based social valuation, proximity to natural features [33].	Loss reflects diminished human well-being and disconnect from nature.

Table 3: Ecological Management Zoning Based on LER and Resilience (ER)

Management Zone	LER Level	ER Level	Spatial Characterization	Recommended Management Strategy
Ecological Restoration Region	High	Low	Areas with significant urbanization, economic activity, or degraded ecosystems [11].	Active restoration: afforestation, soil remediation, green infrastructure to boost resilience and reduce risk.
Ecological Adaptation Region	High	Medium-High	Areas under pressure but with moderate coping capacity.	Adaptive management: implement monitoring, adjust land-use intensity, enhance landscape connectivity [33].
Ecological Conservation Region	Low	High	Core natural areas (e.g., national parks, mature forests) with high service provision [11].	Priority protection: maintain existing structure, prevent fragmentation, limit anthropogenic intrusion.

Detailed Experimental Protocols

Protocol 1: Quantifying Ecosystem Services for Vulnerability Index

Objective: To calculate spatially explicit maps of key ecosystem services for use in a composite Landscape Vulnerability Index. Materials: GIS software (e.g., ArcGIS, QGIS), biophysical modeling tools (e.g., InVEST suite, SWAT), land-use/cover maps, digital elevation model (DEM), soil data, climate data (precipitation, temperature). Workflow:

• Define Study Area & Grid: Delineate the watershed or administrative boundary. Overlay a standardized assessment grid (e.g., 1km x 1km or 2km x 2km) [12].
• Model Key Services: Run validated models for selected ES (see Table 2).
  • Water Yield: Use the InVEST Annual Water Yield model, requiring LULC, average annual precipitation, plant available water content, and evapotranspiration coefficients.
  • Soil Retention: Use the InVEST Sediment Delivery Ratio model, integrating the RUSLE equation with LULC, rainfall erosivity, soil erodibility, slope, and management practices.
  • Habitat Quality: Use the InVEST Habitat Quality model, requiring LULC, maps of threat sources (e.g., urban areas, roads), and habitat sensitivity parameters.
• Normalize and Aggregate: Normalize each ES output map to a 0-1 scale. Assign weights (e.g., using expert elicitation or analytical hierarchy process) reflecting the relative importance of each service to the study area. Compute the weighted sum to create a composite Ecosystem Service Capacity (ESC) map.
• Derive Vulnerability Index: Invert the ESC values (Vulnerability = 1 - ESC) so that low service capacity corresponds to high landscape vulnerability. This map replaces the traditional, subjective vulnerability index in the LER calculation [11].

Protocol 2: Assessing Landscape Configuration Metrics

Objective: To analyze the spatial pattern of land use and evaluate its relationship with ecosystem service provision. Materials: GIS software, land-use/cover map with high categorical accuracy, landscape pattern analysis software (e.g., FRAGSTATS). Workflow:

• Classify and Patch Delineation: Reclassify the LULC map into relevant classes for analysis (e.g., Forest, Agriculture, Urban). Use GIS to identify contiguous patches of the same class.
• Calculate Configuration Metrics: For each LULC class of interest, calculate a suite of metrics at the class level using FRAGSTATS [32]:
  • Patch Density (PD): Number of patches per unit area.
  • Edge Density (ED): Total edge length per unit area.
  • Mean Patch Area (AREA_MN): Average size of patches.
  • Core Area Index (CAI): Percentage of total patch area that is core habitat (e.g., >100m from an edge).
  • Contagion (CONTAG): Overall aggregation and connectivity of patches.
• Statistical Correlation: Extract ES values (from Protocol 1) for sample points or grid cells. Perform statistical analysis (e.g., multiple regression, random forest) to identify which configuration metrics are significant predictors of ES provision. This step quantifies how spatial arrangement, not just area, influences function [32].

Protocol 3: Integrated LER-ER Assessment and Management Zoning

Objective: To calculate the optimized LER, assess Ecosystem Resilience (ER), and delineate spatially explicit management zones. Materials: GIS software, landscape vulnerability map (from Protocol 1), landscape disturbance index map (derived from LULC change and human footprint), ER indicator data (e.g., vegetation regrowth index, landscape diversity, soil organic matter). Workflow:

• Calculate Optimized LER: Use the established LER formula, but replace the traditional vulnerability component with the ES-derived vulnerability index [11]. LER_optimized = f(Disturbance Index, ES-based Vulnerability Index).
• Assess Ecosystem Resilience (ER): Construct a multi-factor ER index. Common indicators include: vegetation vigor/cover (NDVI), landscape diversity (Shannon's Index), soil condition, and topographic heterogeneity. Normalize and weight indicators to create a composite ER map [11].
• Bivariate Spatial Autocorrelation: Use the bivariate Local Moran's I statistic in GIS to analyze the spatial correlation between LER and ER. This identifies statistically significant clusters of:
  • High-LER, Low-ER (HL): Priority for restoration.
  • High-LER, High-ER (HH): Focus for adaptation.
  • Low-LER, High-ER (LH): Target for conservation [11].
• Delineate Management Zones: The clusters from Step 3 form the basis for the Ecological Restoration, Adaptation, and Conservation regions, respectively (see Table 3).

Visualized Workflows and Logical Frameworks

Workflow for Integrating ES into Landscape Assessment

Conceptual Framework of ES-Based Landscape Vulnerability

Logic for Management Zoning Based on LER and Resilience

The Scientist's Toolkit: Key Research Reagent Solutions

Table 4: Essential Tools and Models for ES-Integrated Vulnerability Assessment

Tool/Model Name	Type	Primary Function	Key Application in Protocol
InVEST Suite	Software Suite (NatCap)	Models multiple ES (water yield, sediment, habitat, carbon) spatially.	Core engine for quantifying ES in Protocol 1. Provides standardized, comparable outputs.
SWAT (Soil & Water Assessment Tool)	Hydrological Model	Predicts impacts of land management on water, sediment, and agricultural yields.	High-resolution modeling of water-related ES (yield, runoff, recharge) [32].
FRAGSTATS	Spatial Pattern Analysis	Computes a wide array of landscape metrics for categorical map patterns.	Calculating landscape configuration metrics in Protocol 2.
R Studio / Python (SciPy, scikit-learn)	Statistical Programming	Advanced statistical analysis, machine learning, and geospatial data processing.	Performing correlation/regression analysis between ES and configuration metrics; running Random Forest/Geodetector for driver analysis [12] [34].
Geodetector	Statistical Tool	Measures spatial stratified heterogeneity and detects factor interactions.	Identifying dominant drivers of LER and ES, and analyzing their interactive effects [11] [34].
ArcGIS / QGIS	Geographic Information System	Spatial data management, analysis, visualization, and map production.	Platform for data integration, running models (via toolboxes), and visualizing all input/output maps.
3-Chloro-3-deoxy-d-glucose	3-Chloro-3-deoxy-d-glucose, MF:C6H11ClO5, MW:198.60 g/mol	Chemical Reagent	Bench Chemicals
(13Z)-3-oxodocosenoyl-CoA	(13Z)-3-oxodocosenoyl-CoA, MF:C43H74N7O18P3S, MW:1102.1 g/mol	Chemical Reagent	Bench Chemicals

The accelerating pace of global environmental change, marked by urbanization, climate shifts, and intensifying disturbance regimes, necessitates a transformative approach to ecological risk assessment [11] [28]. Traditional Landscape Ecological Risk (LER) assessments often rely on static landscape pattern indices, which may not fully capture the dynamic capacity of ecosystems to withstand, adapt to, and recover from stressors [11] [21]. This document establishes a quantitative 'Resistance-Adaptation-Recovery' assessment framework, developed within the broader thesis that integrating resilience research is essential for optimizing LER assessment and guiding effective ecological management [11].

This framework is designed for researchers, scientists, and environmental management professionals. It bridges the conceptual gap between the "disturbance-vulnerability-loss" model of ecological risk and the "resistance-adaptation-recovery" dynamics of ecological resilience [28]. By decomposing resilience into these three measurable, temporally sequential attributes, the framework provides actionable protocols for quantifying system behavior before, during, and after a perturbation, thereby enabling more predictive risk management and targeted intervention strategies [20].

Theoretical Foundations and Definitions

Resilience is defined as the capacity of a system to absorb disturbance and reorganize while undergoing change so as to retain essentially the same function, structure, identity, and feedbacks. Two primary conceptualizations are relevant:

• Engineering Resilience: Focuses on the speed of return to a single equilibrium state after a perturbation [20].
• Ecological Resilience: Emphasizes the magnitude of disturbance a system can absorb before shifting into an alternative regime with different structures and processes [21] [20]. This framework is grounded in the ecological resilience perspective.

The proposed framework operationalizes ecological resilience through three core, interdependent attributes:

• Resistance: The system's capacity to withstand a disturbance with minimal initial change in state or function. It is a measure of inherent robustness and buffering capacity [35].
• Adaptation: The system's capacity to adjust its structure or function in response to ongoing disturbance or stress, moderating potential damage. This reflects adaptive capacity and learning [36] [20].
• Recovery: The system's capacity to return to a reference state (in function, structure, or identity) following a perturbation. This encompasses both the rate and the completeness of return [21].

A critical premise is that access to and deployment of resources—human, social, economic, and political—activates these inherent resilience capacities [36]. Furthermore, resilience and risk exhibit a robust negative correlation, where enhancing resilience directly contributes to reducing realized ecological risk [11] [28].

Table 1: Foundational Concepts in Resilience and Risk Assessment

Concept	Definition	Key Reference
Ecological Resilience	The magnitude of disturbance that can be absorbed before the system redefines its structure, processes, and identity.	Holling (1973), [20]
Landscape Ecological Risk (LER)	The probability of adverse ecological effects arising from stressors interacting with landscape vulnerability.	[11] [28]
Resistance	Ability to remain largely unchanged despite the presence of a stressor or disturbance.	[35] [28]
Adaptation	Adjustments in ecological traits, structure, or function in response to experienced or expected disturbance.	[36] [20]
Recovery	The rate and degree of return to a pre-disturbance state or trajectory following perturbation.	[21]
Resilience Activation	The process by which access to social, economic, or institutional resources mobilizes latent resilience capacities.	[36]

The Resistance-Adaptation-Recovery Assessment Framework

The framework posits resilience as an emergent property quantified through sequential phases following a disturbance event. Assessment requires defining a system's dynamic equilibrium range—the historical or modeled variability in its key state variables under a natural or desired disturbance regime [21]. Departure from and return to this range form the basis of measurement.

Figure 1: Logical flow of the Resistance-Adaptation-Recovery assessment framework. The system's path depends on its capacities at each phase relative to the perturbation magnitude.

Phase 1: Quantifying Resistance

Resistance measures the initial buffer against change. High resistance is characterized by structural and functional attributes that minimize departure from the dynamic equilibrium range following a disturbance [35].

Core Metrics & Protocols:

• Landscape Robustness Index: Quantifies the configuration of land use/land cover (LULC) types to withstand disturbance propagation. Calculated using landscape pattern metrics like patch density, edge density, and contagion for critical LULC types (e.g., core forest habitat). More aggregated, contiguous natural patches indicate higher resistance [21].
• Ecosystem Service Buffer Capacity: Assesses the stability of key ecosystem service (ES) provision (e.g., water retention, soil stability, carbon storage) post-disturbance. Measured by monitoring ES indicators before and after a standardized, moderate-intensity stressor (e.g., a prescribed fire, a simulated drought period). Minimal decline indicates high resistance [11].
• Protocol 1: Spatial Resistance Mapping.
  • Data Acquisition: Acquire high-resolution LULC data and spatially explicit data for key ES (e.g., NDVI for productivity, InVEST model outputs for water yield).
  • Baseline Calculation: Calculate baseline landscape pattern indices (e.g., using FRAGSTATS) and ES values for assessment units (e.g., watershed grid cells).
  • Apply Disturbance Proxy: Overlay a layer representing a uniform, moderate-intensity disturbance (e.g., a simulated edge effect buffer around developed areas, a precipitation reduction mask).
  • Post-Disturbance Calculation: Recalculate landscape indices and ES values under the disturbance scenario.
  • Compute Resistance Score: For each unit, compute Resistance = 1 - (|ΔMetric| / Baseline_Metric). Aggregate scores for pattern and ES into a composite resistance index.

Phase 2: Quantifying Adaptation

Adaptation becomes relevant when a disturbance exceeds the system's resistance, causing a state change. It measures the system's ability to reorganize and maintain function under the new conditions.

Core Metrics & Protocols:

• Functional Trait Diversity & Redundancy: Assesses the diversity of functional traits (e.g., rooting depth, dispersal mode, pollinator specificity) within biotic communities. High redundancy (multiple species with similar traits) provides insurance, allowing ecosystem functions to continue even if dominant species decline [20].
• Spatial Reorganization Rate: Tracks changes in landscape configuration and connectivity in response to chronic stress. Analyzed using time-series landscape pattern analysis and trajectory analysis in multivariate space [21]. A system that maintains or rapidly re-establishes functional connectivity demonstrates high adaptive capacity.
• Protocol 2: Trait-Based Adaptive Capacity Assessment.
  • Community Sampling: Conduct biotic surveys (flora, fauna, or keystone groups) in affected and control areas.
  • Trait Assignment: Assign relevant functional traits to each species from established databases.
  • Compute Metrics: Calculate community-weighted mean trait values, functional diversity indices (e.g., Functional Divergence - FDiv), and functional redundancy.
  • Stress-Response Correlation: Statistically correlate shifts in trait distributions with measured environmental stress gradients (e.g., soil moisture deficit, fragmentation index).
  • Adaptation Score: A higher proportion of traits showing significant, non-detrimental shifts along stress gradients indicates greater adaptive capacity.

Phase 3: Quantifying Recovery

Recovery measures the system's return toward its pre-disturbance state or a new desired state once the disturbance pressure ceases or is mitigated.

Core Metrics & Protocols:

• Recovery Rate & Trajectory: The rate at which key state variables (species composition, biomass, soil health) return towards the dynamic equilibrium range. Calculated using time-series data and metrics of directional change, such as recovery slope over time [21].
• Hysteresis Assessment: Evaluates whether the recovery path differs from the degradation path, indicating potential persistent changes. Assessed by plotting system state against a driver variable pre- and post-disturbance.
• Protocol 3: Multi-Temporal Recovery Analysis.
  • Define Recovery Metrics: Select 3-5 key recovery indicators (e.g., native species cover, soil organic matter, stream biotic index).
  • Establish Chronosequence: Use space-for-time substitution or, ideally, establish long-term monitoring plots immediately post-disturbance.
  • Time-Series Modeling: Fit non-linear models (e.g., asymptotic recovery curves) to indicator data over time.
  • Calculate Recovery Parameters: Extract key parameters: time to recover 50% or 80% of baseline (T50, T80), and final asymptotic value relative to baseline.
  • Recovery Score: Score units based on T80 and the final asymptotic value. Fast return to a near-baseline state indicates high recovery capacity.

Table 2: Core Metrics for the Resistance-Adaptation-Recovery Framework

Phase	Primary Metric	Measurement Method	Typical Data Sources
Resistance	Landscape Robustness Index	Change in landscape pattern metrics post-disturbance simulation.	LULC maps, FRAGSTATS, GIS.
Resistance	Ecosystem Service Buffer Capacity	% change in ES provision after a standardized stressor.	Remote sensing (NDVI), InVEST model outputs, soil surveys.
Adaptation	Functional Redundancy	Diversity indices of species functional traits within communities.	Field species surveys, functional trait databases.
Adaptation	Spatial Reorganization Rate	Rate of change in landscape connectivity metrics over time under stress.	Time-series LULC, graph theory analysis.
Recovery	Recovery Rate (T50, T80)	Time for key indicators to return to 50%/80% of pre-disturbance baseline.	Long-term plot monitoring, remote sensing time series.
Recovery	Recovery Trajectory Fidelity	Hysteresis analysis; similarity of recovery path to degradation path.	Paired pre/post-disturbance state and driver data.

Integrated Application: Spatial Assessment and Management Zoning

The framework's power is realized in spatial assessment, where resilience metrics are integrated with LER to guide differentiated management [11] [28].

Integrated Workflow:

• Delineate Assessment Units: Use watershed boundaries or ecologically meaningful grids [11].
• Parallel Assessment: Calculate LER (e.g., using an optimized model that incorporates ecosystem services for vulnerability [11]) and the Resistance-Adaptation-Recovery indices for each unit.
• Spatial Correlation Analysis: Apply bivariate spatial autocorrelation (e.g., bivariate Moran's I) to identify significant spatial clusters of LER-Resilience relationships [11] [28].
• Management Zoning: Zone the landscape based on LER and resilience profiles (see Table 3).

Table 3: Ecological Management Zoning Based on LER and Resilience Assessment [11] [28]

Zone Type	Risk-Resilience Profile	Recommended Management Strategy
Ecological Conservation Zone	Low LER, High Resilience	Priority Protection. Maintain natural processes, limit human intrusion, monitor for early warning signs.
Ecological Adaptation Zone	Moderate LER, Moderate Resilience	Adaptive Management. Enhance adaptive capacity through connectivity restoration, assisted migration, or diversification.
Ecological Restoration Zone	High LER, Low Resilience	Active Intervention. Implement targeted restoration (revegetation, soil remediation) to boost resistance and recovery.
Ecological Monitoring Zone	High LER, Moderate/High Resilience OR Low LER, Low Resilience	Focused Research & Monitoring. Investigate drivers of unexpected resilience/risk combinations; monitor for regime shift thresholds.

Figure 2: Integrated spatial assessment workflow for coupling LER with the Resilience framework.

Experimental Protocols for Framework Validation

Protocol 4: Simulating the Resilience-Risk Relationship via Landscape Modeling. Objective: To empirically test the negative correlation between resilience indices and LER under different disturbance scenarios [11] [28]. Method:

• Model Setup: Use a spatially explicit landscape simulation model (e.g., HexSim, LANDIS-II) for a study region. Initialize with historical LULC.
• Scenario Design:
  • Scenario A (Baseline): Historical disturbance regime.
  • Scenario B (Increased Intensity): 150% historical disturbance intensity.
  • Scenario C (Increased Frequency): 200% historical disturbance frequency.
• Simulation & Output: Run each scenario for 100 years, recording annual LULC maps and derived ES values.
• Post-Processing: For each decade and scenario grid cell, calculate:
  • LER Index.
  • Resistance (buffer capacity to decadal stress).
  • Adaptation (change in functional connectivity).
  • Recovery (rate of return after major simulated disturbance events).
• Validation: Perform statistical analysis (e.g., generalized linear mixed models) to quantify the relationship LER ~ Resistance + Adaptation + Recovery across all scenarios and times.

Protocol 5: Threshold Detection for Regime Shift. Objective: To identify early warning signals of resilience erosion and potential transition to an alternative regime [20]. Method:

• Identify Key Slow Variable: Select a fundamental, slow-changing variable theorized to underpin the ecosystem regime (e.g., soil organic matter, seed bank density).
• Long-Term Monitoring/Proxy Analysis: Establish transects across an ecological gradient (e.g., from healthy to degraded) or analyze paleoecological records.
• Measure Resilience Indicators: Along the gradient, estimate recovery rate (via field experiments) and functional redundancy.
• Statistical Analysis: Model the relationship between the slow variable and resilience indicators. A non-linear, drastic decline in resilience indicators at a specific range of the slow variable suggests a critical threshold.
• Management Test: Implement a restoration intervention (e.g., soil amendment, invasive species removal) on the degraded side of the suspected threshold and monitor for recovery of resilience indicators to test reversibility.

Table 4: Key Quantitative Findings from Integrated Resilience-Risk Studies

Study Context	Key Correlation Finding	Implication for Framework
Luo River Watershed (2001-2021) [11]	LER increased from 0.43 to 0.44. A strong negative spatial correlation found between LER and Ecosystem Resilience (ER).	Validates the core premise that enhancing resilience reduces realized risk.
Hefei Metropolitan Area [28]	A "robust negative correlation" between ER and LER, intensifying at finer spatial scales. The recoverability dimension had the strongest counteracting effect on risk.	Highlights the critical, independent role of the Recovery phase within the tri-phasic framework.
Multi-scale Analysis [28]	Coupling coordination degree between ER and LER was generally low (<0.5), indicating widespread imbalance and management need.	Supports the necessity of the integrated assessment and zoning approach proposed.

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 5: Key Research Reagents and Tools for Implementing the Framework

Tool / Reagent Category	Specific Example(s)	Function in Framework	Source/Reference
Spatial Analysis Software	ArcGIS, QGIS, FRAGSTATS, GuidosToolbox	Calculating landscape pattern indices for resistance and adaptation metrics; spatial zoning.	[21]
Ecosystem Service Models	InVEST, ARIES, SOLVES	Quantifying ES provision for vulnerability (in LER) and buffer capacity (in resistance).	[11]
Landscape Simulation Models	LANDIS-II, HexSim, DINAMICA	Projecting future LULC and testing system response to disturbance scenarios (Protocol 4).	[21] [37]
Statistical & Ecological Analysis	R packages: vegan (traits), spdep (spatial autocorrelation), nlme (non-linear recovery models)	Analyzing functional diversity, spatial correlation, and fitting recovery trajectories.	[21] [28]
Standardized Assessment Guides	ASTM E3429-24 Standard Guide for Property Resilience Assessments	Provides a structured, staged (hazard identification, risk evaluation, mitigation) methodology adaptable to ecological property assessment.	[38]
Trait & Species Databases	TRY Plant Trait Database, EltonTraits for animals, local/national species inventories	Assigning functional traits to species for calculating adaptive capacity metrics.	[20]
Remote Sensing Data Sources	Landsat, Sentinel-2, MODIS (for NDVI, LULC change)	Providing multi-temporal data for change detection, recovery monitoring, and LULC base maps.	[11] [28]
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The integration of resilience theory into landscape ecological risk (LER) assessment represents a critical advancement in ecological management. Traditional LER assessments, often critiqued for their static and subjective nature, fail to account for an ecosystem's inherent capacity to resist, absorb, and recover from disturbances [11]. This document provides detailed application notes and experimental protocols for two pivotal analytical tools—bivariate spatial autocorrelation and coupling coordination degree (CCD) models—within the context of a thesis focused on optimizing LER assessment with resilience research. These tools enable researchers to move beyond univariate spatial analysis and simple correlation, allowing for the quantification of spatially explicit relationships between risk and resilience, and the diagnosis of their synergistic states. This facilitates the transition from assessment to actionable ecological management zoning, a cornerstone of territorial spatial ecological restoration [11]. Framed for an audience of researchers and scientists, these protocols leverage the most current methodological advancements to support robust, data-driven decision-making in landscape ecology and sustainability science.

The application of these tools generates distinct quantitative outputs critical for interpreting landscape dynamics. The following tables summarize key metrics derived from recent studies.

Table 1: Landscape Ecological Risk (LER) and Ecosystem Resilience (ER) Change Metrics (Luo River Watershed, 2001-2021) [11]

Metric	2001 Value	2021 Value	Net Change (2001-2021)	Spatial Trend
Mean LER Index	0.43	0.44	+0.01 (Overall Increase)	Lower in west, higher in east
Area with Increasing LER	-	-	67.61% of total area	Concentrated in eastern region
LER-ER Relationship	-	-	Approximates a quadratic function (LER decreases as ER increases)	Statistically significant negative spatial correlation

Table 2: Classification Metrics for Coupling Coordination Degree (CCD) and Management Zoning [11] [39]

Classification Framework	Category / Class	Index Range	Interpretation / Management Implication
CCD Index Level [39]	Basic Coordination	0.4 – 0.5	Initial synergistic stage; requires strengthening.
	Primary Coordination	0.5 – 0.6	Low-level synergy; needs steady improvement.
	Moderate Coordination	0.6 – 0.8	Benign coupling; system elements promote each other.
Bivariate Moran's I Zoning [11]	Ecological Conservation Region	High-High Cluster (High ER, Low LER)	Protect current high-resilience, low-risk areas.
	Ecological Restoration Region	Low-Low Cluster (Low ER, High LER)	Priority for interventions to boost resilience and reduce risk.
	Ecological Adaptation Region	Not Significant / Other Clusters	Areas requiring monitoring and adaptive management.

Detailed Experimental Protocols

Protocol 1: Optimized LER Assessment Integrating Ecosystem Services

• Objective: To construct a dynamic LER index that replaces subjective landscape vulnerability weights with a composite metric derived from key ecosystem services [11].
• Materials: Multi-temporal land use/land cover (LULC) raster data; biophysical data (e.g., NDVI, precipitation, soil data, DEM); geospatial software (e.g., ArcGIS, QGIS, R).
• Procedure:
  • Delineate Assessment Units: At a watershed scale, establish a hexagonal grid (e.g., 2-3 km²) as the analytical granularity to capture landscape heterogeneity [11].
  • Quantify Landscape Disturbance (Li): For each grid cell, calculate a composite index incorporating landscape fragmentation, isolation, and dominance based on LULC patches.
  • Quantify Ecosystem Service-based Vulnerability (Vi): a. Select dominant regional ecosystem services (e.g., water yield, soil retention, carbon sequestration) [11]. b. Use established models (e.g., InVEST, RUSLE) to quantify the biophysical supply of each service per grid cell. c. Normalize and weight services using expert judgment or principal component analysis to create a composite Ecosystem Service Capacity index. d. Invert this index to derive Landscape Vulnerability (Vi), where lower service capacity equates to higher vulnerability [11].
  • Calculate the Optimized LER Index: Apply the formula within each grid cell: LER_i = Li * Vi. This yields a spatially explicit risk surface.
  • Validate: Compare the spatial pattern and trends of the optimized LER with those from traditional models and known areas of ecological degradation.

Protocol 2: Bivariate Spatial Autocorrelation Analysis (Bivariate Moran's I & Lee's L)

• Objective: To measure the spatial dependence and co-patterning between two variables (e.g., LER and ER) across a study area, identifying clusters where their relationship is significantly positive or negative [40].
• Materials: Raster or vector data for two spatially aligned variables (X, Y); spatial analysis software (GeoDa, ArcGIS Pro, R with spdep/sf packages) [41] [42].
• Procedure:
  • Data Preparation & Standardization: Ensure variables are aligned to identical spatial units. Standardize variables (e.g., z-score) to mean=0, variance=1 [41].
  • Define Spatial Weights Matrix (W): Create a matrix defining neighborhood relationships (e.g., queen contiguity, k-nearest neighbors, inverse distance). Row-standardize the matrix [42].
  • Compute Global Bivariate Moran's I (I_B): a. Calculate the spatial lag of variable Y: WY [41]. b. Compute the statistic: I_B = (Σ_i (x_i * Σ_j w_ij y_j)) / Σ_i x_i^2, where x and y are standardized. This is equivalent to the slope of regressing WY on X [41] [42]. c. Assess significance via permutation tests (e.g., 999 permutations), randomly shuffling the values of Y across locations while holding X fixed [41].
  • Compute Local Bivariate Moran's I (LISA) or Lee's L: a. To map local clusters, calculate the local statistic for each feature: I_i = x_i * Σ_j w_ij y_j [40]. b. Alternatively, use Lee's L statistic, which explicitly accounts for the individual spatial autocorrelation of each variable and their co-patterning, providing a more robust measure of spatial association [40]. c. Classify significant locations (p < 0.05) into: High-High (High X, High Lag of Y), Low-Low, High-Low, Low-High [40].
  • Interpretation for Zoning: High-High (ER, -LER) clusters indicate Ecological Conservation Regions. Low-Low (ER, -LER) clusters indicate Ecological Restoration Regions. Other combinations guide adaptation zone delineation [11].

Protocol 3: Coupling Coordination Degree (CCD) Modeling

• Objective: To evaluate the intensity and quality of interaction between two or more interdependent systems (e.g., socioeconomic development and eco-environment) and diagnose their level of synergistic development [39] [43].
• Materials: Time-series indicator data for each system; statistical software (R, Python, SPSS).
• Procedure:
  • Construct Comprehensive Evaluation Indices: a. For each system (e.g., U=Resilience, V=Risk), establish a multidimensional indicator framework [43]. b. Normalize indicator data. Use the entropy weight method or analytic hierarchy process to assign objective/subjective weights [39] [44]. c. Calculate a comprehensive score for each system (f(U) and g(V)) per evaluation unit and time period.
  • Calculate Coupling Degree (C): Measure the strength of interaction: C = 2 * sqrt( f(U) * g(V) ) / ( f(U) + g(V) ). C ranges from 0 (no interaction) to 1 (complete coupling) [43].
  • Calculate Comprehensive Coordination Index (T): T = α * f(U) + β * g(V), where α and β are contributions of each system, often set to 0.5 each [43].
  • Calculate Coupling Coordination Degree (D): D = sqrt( C * T ). This integrates both the interaction strength and the overall development level [39] [43].
  • Classify & Analyze: Classify D values per Table 2 (e.g., 0-0.4: Incoordination; 0.4-0.5: Basic Coordination). Analyze spatial patterns using global/local Moran's I and temporal evolution using kernel density estimation or Markov chains [44].

Analytical Workflow and Model Visualization

Diagram 1: Integrated Workflow for LER Optimization with Resilience

Diagram 2: Quadrants of Bivariate Spatial Association (LISA/Lee's L)

Diagram 3: Framework of the Coupling Coordination Degree (CCD) Model

The Scientist's Toolkit: Essential Research Reagents & Platforms

Table 3: Key Research Reagents, Software, and Platforms for Implementation

Category	Item / Platform	Primary Function in Workflow	Key Features / Notes
Geospatial & Statistical Analysis	R with sf, spdep, spatialreg, ggplot2 packages [42]	Core platform for spatial data processing, bivariate Moran's I/Lee's L calculation, CCD modeling, and visualization.	Open-source, reproducible, extensive spatial statistics libraries. Essential for permutation tests.
	ArcGIS Pro (Spatial Statistics Toolbox) [40]	Commercial platform for spatial weights creation, bivariate spatial association (Lee's L), hot spot analysis, and cartographic presentation.	User-friendly GUI, robust spatial analytics tools, seamless integration with other Esri products.
	GeoDa	Exploratory spatial data analysis (ESDA), creating univariate and bivariate Moran scatter plots, LISA cluster maps [41].	Free, lightweight, specifically designed for ESDA and intuitive visualization of spatial autocorrelation.
Ecosystem Service Modeling	InVEST Suite (by NatCap)	Models multiple ecosystem services (water yield, carbon, habitat quality) to quantify service-based landscape vulnerability [11].	Modular, raster-based, links LULC changes to service provision. Critical for Protocol 1.
Field Data Collection & Curation	Open Data Kit (ODK) [45]	Mobile data collection for ground-truthing LULC, validating model outputs, or collecting socio-ecological indicators in remote areas.	Offline capability, customizable forms (XLSForm), integrates media and GPS. Widely used in environmental monitoring [46].
Data Sources & Management	Google Earth Engine	Cloud-based platform for accessing and processing multi-temporal satellite imagery (e.g., Landsat, Sentinel) for LULC classification and NDVI trends.	Petabyte-scale catalog, reduces local computational burden.
	PostgreSQL/PostGIS	Spatial database management system for storing, querying, and managing large, multi-user geospatial datasets.	Ensures data integrity, supports complex spatial queries, facilitates version control.
2,5-dihydroxybenzoyl-CoA	2,5-dihydroxybenzoyl-CoA, MF:C28H40N7O19P3S, MW:903.6 g/mol	Chemical Reagent	Bench Chemicals
Palmitoleyl myristate	Palmitoleyl myristate, MF:C30H58O2, MW:450.8 g/mol	Chemical Reagent	Bench Chemicals

The imperative to optimize landscape ecological risk assessment is central to contemporary ecological resilience research. In an era marked by global climate change and intensified human activities, ecosystems face multifaceted pressures that threaten their structure, function, and the services they provide [5]. Traditional ecological risk assessments, while valuable, often struggle to characterize the spatiotemporal heterogeneity of risks and to disentangle the complex, non-linear interactions between multiple driving forces [5]. This gap hinders the development of proactive, resilience-oriented management strategies.

Within this context, the integration of advanced spatial statistical methods is a critical frontier. Resilience, defined as a system's capacity to absorb disturbance and reorganize while retaining its essential function, has evolved from an equilibrium-focused concept to a dynamic, process-oriented framework [22]. Modern resilience research emphasizes understanding the mechanisms and drivers that underpin a system's ability to adapt and transform [22]. This thesis posits that optimizing landscape ecological risk assessment requires a shift from merely describing risk patterns to rigorously identifying and quantifying their root causes. Methods like the Geodetector model are pivotal for this task, as they enable researchers to move beyond correlation to assess the explanatory power of individual factors and, more importantly, their interactive effects on ecological risk patterns [5] [12]. This application note details the protocols for employing Geodetector within a comprehensive resilience research framework to identify the driving forces behind landscape ecological risk, thereby informing targeted interventions for resilience enhancement.

Core Methodological Framework: The Geodetector Model

The Geodetector model is a suite of statistical tools designed to measure spatial stratified heterogeneity and reveal the underlying driving forces. Its core principle is that if an independent variable (X) strongly influences a dependent variable (Y), the spatial distributions of X and Y will exhibit similarity [12]. Geodetector is advantageous as it requires no linear assumptions, is robust to multicollinearity, and uniquely quantifies the interaction between two factors [12].

Key Components of the Geodetector Suite

• Factor Detector (q-statistic): Identifies the extent to which a factor explains the spatial differentiation of the ecological risk index. The value of q ranges [0, 1], where a larger q indicates greater explanatory power.

  • Formula: ( q = 1 - \frac{\sum{h=1}^{L} Nh \sigma_h^2}{N \sigma^2} )
  • Where L is the number of strata of variable X; ( Nh ) and ( N ) are the number of units in stratum *h* and the whole region; ( \sigmah^2 ) and ( \sigma^2 ) are the variances of Y in stratum h and the region.

• Interaction Detector: Assesses whether two risk factors (X1, X2) interact to enhance or weaken their explanatory power on Y. The interaction is evaluated by comparing ( q(X1) ), ( q(X2) ), and ( q(X1∩X2) ). The result can be classified as: Nonlinear weaken, Single-factor nonlinear weaken, Two-factor enhance, Independent, or Nonlinear enhance.

• Risk Detector: Uses a t-test to determine whether there is a significant difference in the mean ecological risk between two strata of a factor, identifying which strata are associated with higher risk.

• Ecological Detector: Uses an F-test to determine whether there is a significant difference in the impact of two different factors on the spatial distribution of ecological risk.

Integrated Workflow for Resilience-Optimized Risk Assessment

The effective application of Geodetector is embedded within a larger analytical workflow that connects risk assessment with resilience mechanism analysis.

Figure 1: Integrated workflow for identifying ecological risk drivers within a resilience research framework.

Application Notes and Experimental Protocols

Protocol A: Assessing Driving Forces of Historical Landscape Ecological Risk

This protocol outlines the steps to identify key drivers of observed ecological risk patterns using historical data.

Objective: To quantify the individual and interactive contributions of natural and anthropogenic factors to the spatial heterogeneity of landscape ecological risk (LER) in a past period (e.g., 2000-2020).

Materials & Data: (See Section 5: Research Reagent Solutions for detailed specifications).

Procedure:

• Landscape Ecological Risk Index (ERI) Calculation:

  • Based on land use/cover maps, calculate landscape pattern indices (e.g., fragmentation, disturbance, loss) for a specified historical year [5].
  • Using a risk assessment grid (e.g., 20km x 20km [12] or finer), compute the ERI for each grid cell using a weighted model: ( ERIk = \sum{i=1}^{n} (\frac{A{ki}}{Ak} \times Ri) ), where ( A{ki} ) is the area of landscape type i in grid k, ( Ak ) is the total area of grid *k*, and ( Ri ) is the loss degree for landscape type i [12].

• Driver Variable Selection and Discretization:

  • Select candidate driving factors from categories: Topographic (elevation, slope), Climatic (annual precipitation, temperature), Anthropogenic (GDP density, population density, distance to roads), and Land Use (percentage of construction land, forest cover) [5] [12].
  • Discretize continuous factor data into appropriate strata. Use natural break (Jenks), quantile, or equal interval methods. The number of strata should balance information detail and statistical power.

• Geodetector Execution:

  • Spatial Alignment: Ensure the ERI grid and all factor raster layers share the same spatial extent, resolution, and projection. Convert all data to aligned grid cells.
  • Run the Factor Detector to calculate the q-statistic for each driver, ranking them by explanatory power.
  • Run the Interaction Detector for top-ranking factors to identify interactive effects (e.g., whether the combined effect of elevation and precipitation is stronger than the sum of their individual effects).
  • Use the Risk Detector to identify which specific strata (e.g., elevation range 0-100m) have significantly higher mean ERI values.

• Validation and Robustness Check:

  • Perform sensitivity analysis on the discretization method and number of strata to ensure results are stable.
  • Compare Geodetector results with outputs from complementary models (e.g., Random Forest for variable importance) to triangulate findings [12].

Protocol B: Integrating Driving Force Analysis with Future Risk Simulation

This protocol links driver identification to forward-looking resilience planning via multi-scenario land use simulation.

Objective: To project future LER under different development scenarios and identify leverage points for risk mitigation based on driver analysis.

Materials & Data: Historical land use data (multiple time points), driver variables, scenario constraint rules (e.g., ecological protection redlines, urban growth boundaries).

Procedure:

• Historical Driver Analysis: Complete Protocol A to establish the dominant historical drivers (e.g., DEM is the strongest natural factor [5]).

• Land Use Change Simulation with the PLUS Model:

  • Use the Patch-generating Land Use Simulation (PLUS) model to project future land use patterns [5]. The PLUS model incorporates a land expansion analysis strategy and a multi-type random seed cellular automata mechanism, offering advantages in simulating patch-level changes.
  • Calibration: Train the model using historical transitions (e.g., 2000-2010) and driving factors. Validate the simulation accuracy for a later period (e.g., 2020) against observed data.
  • Scenario Definition: Develop multiple 2030/2050 scenarios (e.g., Natural Development, Ecological Priority, Economic Growth). Integrate the dominant drivers identified in Step 1 as key variables in the simulation. For an "Ecological Priority" scenario, apply constraints that limit development in areas highly sensitive to the primary drivers (e.g., steeper slopes) [5].

• Future Risk Assessment and Driver Re-Evaluation:

  • Calculate the ERI for the simulated future land use maps under each scenario.
  • Re-apply the Geodetector to the future ERI and projected driver layers. Compare the q-values and interaction results with the historical baseline.
  • Analysis: Determine if the relative importance of drivers shifts under different futures. Assess which scenario most effectively reduces overall ERI or mitigates risk in high-priority areas [5].

Data Presentation and Analysis

Table 1: Exemplary Results from a Geodetector Factor Detection Analysis (Harbin Case Study Adapted) [5]

Ranking	Driving Factor	Category	q-Statistic	p-Value	Interpretation
1	Digital Elevation Model (DEM)	Natural	0.48	<0.01	The single strongest factor, explaining ~48% of LER spatial heterogeneity.
2	Slope	Natural	0.32	<0.01	Second most influential natural topographic factor.
3	Distance to Roads	Anthropogenic	0.28	<0.01	Proximity to transportation corridors is a key human-derived driver.
4	Annual Precipitation	Natural	0.25	<0.01	Climatic factor with significant explanatory power.
5	GDP Density	Anthropogenic	0.19	<0.01	Economic activity intensity influences risk patterns.

Table 2: Exemplary Interaction Detector Results Showing Nonlinear Enhancement [5] [12]

Interaction Between Factors	Interaction q-Value	Comparison with Individual q-Values	Type of Interaction
DEM ∩ Annual Precipitation	0.65	0.65 > 0.48 + 0.25	Nonlinear Enhance
Slope ∩ Distance to Roads	0.52	0.52 > 0.32 + 0.28	Nonlinear Enhance
GDP Density ∩ Distance to Roads	0.35	0.35 > 0.19 + 0.28	Bivariate Enhance

Note: "∩" denotes interaction. Nonlinear Enhance indicates the interacting factors mutually reinforce each other's effect on LER in a non-additive, synergistic way.

Table 3: Input Data Requirements for a Standardized Driving Force Analysis

Data Category	Specific Variables	Required Format	Spatial Resolution	Temporal Reference	Primary Source
Landscape Composition	Land Use/Cover (Cultivated, Forest, Grassland, Water, Built-up, etc.)	Raster (e.g., .tif)	30m or finer	Multiple years (e.g., 2000, 2010, 2020)	National land cover products [5]
Topographic	Digital Elevation Model (DEM), Slope, Aspect	Raster	Aligned with land use data	Static	NASA SRTM, ASTER GDEM [12]
Climatic	Annual Precipitation, Mean Temperature	Raster (interpolated)	1km	Annual averages	WorldClim, National meteorological centers [12]
Anthropogenic	Nighttime Light Index, GDP Density, Population Density, Distance to Roads/Rivers	Raster (distance layers calculated from vector)	1km or finer	Aligned with land use years	Statistical yearbooks, WorldPop, OpenStreetMap [5] [12]
Ecological Risk Index	Landscape Ecological Risk Index (ERI)	Raster (grid cells)	Defined by study (e.g., 20km grid)	Calculated for each land use year	Calculated from landscape indices

The Scientist's Toolkit: Research Reagent Solutions

Item	Function/Description	Key Considerations & Recommendations
GeoDetector Software	The core statistical package for performing factor, interaction, risk, and ecological detection.	Available as an R package (GD) or a standalone tool. The R package facilitates integration into reproducible analysis pipelines.
Land Use Simulation Model (PLUS)	Projects future land use patterns under different scenarios by analyzing expansion probabilities and patch dynamics [5].	Superior to CA-Markov for capturing drivers and patch-level changes. Requires land use maps from two historical periods for calibration.
Landscape Metric Calculator (FragStats)	Computes landscape pattern indices (e.g., patch density, edge density, aggregation index) essential for constructing the Ecological Risk Index.	The Landscape Disturbance Index and Landscape Vulnerability Index are commonly used components for ERI calculation.
Spatial Autocorrelation Tool (Global/Local Moran's I)	Measures the spatial clustering of the Ecological Risk Index. Significant positive autocorrelation confirms the necessity of spatial analysis like Geodetector [5].	A prerequisite step. High-risk clusters ("Hot Spots") identified can be priority areas for driver analysis.
Discretization Algorithms	Classifies continuous driver data (e.g., elevation, GDP) into strata for Geodetector input.	Method choice (natural breaks, quantile, equal interval) can affect q-values. Sensitivity analysis across methods is recommended.
Coupled Random Forest-Geodetector Framework	Uses Random Forest to provide an initial ranking of variable importance, which can inform factor selection for Geodetector's interaction analysis [12].	Provides a machine-learning cross-validation of driver significance, strengthening causal inference.
Arachidyl arachidonate	Arachidyl arachidonate, MF:C40H72O2, MW:585.0 g/mol	Chemical Reagent
12-hydroxyhexadecanoyl-CoA	12-hydroxyhexadecanoyl-CoA, MF:C37H66N7O18P3S, MW:1021.9 g/mol	Chemical Reagent

Interpreting Results for Resilience Optimization

The ultimate goal of applying Geodetector is to inform decisions that enhance ecological resilience. The output must be interpreted through a resilience mechanisms lens [22].

• From Drivers to Interventions: Identifying DEM or slope as key natural drivers pinpoints geomorphologically sensitive areas for prioritized protection. Finding strong interactive effects between road distance and GDP highlights zones of coupled anthropogenic pressure, suggesting the need for integrated land-use and transportation planning.
• Informing Resilience Pathways: The results directly feed into the design of Nature-Based Solutions (NBS) and ecological networks. For example, if interaction analysis shows wetlands mitigate risk in high-precipitation, low-elevation zones, then protecting or restoring wetland corridors becomes a targeted resilience-enhancement strategy [22].
• Validating Scenarios: Comparing driver importance across simulated futures (Protocol B) tests which policy scenarios (e.g., ecological priority) most effectively decouple risk from its strongest historical drivers, indicating a successful transition toward a more resilient landscape configuration.

Figure 2: Translating Geodetector results into actionable resilience-enhancement strategies.

Application Notes

Integrating the assessment of Landscape Ecological Risk (LER) and Ecological Resilience (ER) is critical for advancing sustainable management in complex socio-ecological systems. These frameworks move beyond descriptive analysis to provide actionable insights for zoning, restoration prioritization, and scenario-based planning [28] [47]. The core innovation lies in coupling the "disturbance-vulnerability-loss" risk model with the "resistance-adaptation-recovery" resilience framework [28]. This allows researchers and planners to identify not just where risks are high, but where the system's inherent capacity to cope is low, highlighting critical intervention points. Successful application requires multi-scale analysis, as relationships between risk and resilience can vary significantly from fine-scale grids to broader county or watershed levels [28].

Table 1: Framework Applications in Case Study Regions

Framework/Model	Primary Application	Key Metrics/Indicators	Case Study Region & Findings	Management Output
Coupled LER-ER Assessment [28]	Multi-scale spatiotemporal analysis of risk-resilience trade-offs	Landscape Disturbance Index, Vulnerability Index, Loss Index; Resistance, Adaptation, Recovery Indices	Hefei Metropolitan Area (2010-2020): Found a strong negative correlation intensifying at finer scales. High-risk zones concentrated in water bodies; resilience declined by 50.6% in core urban areas.	Four-type governance typology for zoning-based management.
ESP-ERA Integration [47]	Identification of priority areas for ecological restoration	Ecological Source Areas, Corridor Connectivity, Ecological Pinch Points	Hefei Metropolitan Area: Identified 36 source areas (8313.96 km²), 92 corridors, and 73 priority restoration nodes, mainly at urban-natural junctions.	Five restoration types (e.g., wetland restoration, corridor connectivity).
RAR-Markov-FLUS Model [48]	Assessment and simulation of ecosystem resilience under future scenarios	Resistance (Ecosystem Service Value), Adaptability, Recovery (Landscape Metrics)	Jinan Metropolitan Area: Simulated resilience under three 2035 scenarios. Ecological Priority scenario best stabilized resilience, unlike Cultivated Land Protection which degraded mountain slopes.	"Resilience red line–development permit" spatial mechanism.
LER-ES Integration & GTWR [49]	Ecological zoning based on dynamic risk-ecosystem service relationships	Habitat Quality, Soil Conservation, Water Yield; Geographically/Temporally Weighted Regression	Wuling Mountain Area (2000-2020): LER declined overall but increased peri-urbanly. Strong negative LER correlation with habitat quality and soil conservation.	Four ecological zones with tailored strategies (e.g., Conservation, Reshaping).
Net Ecological Gain Framework [50]	Policy implementation for protecting and improving ecosystem function	Statewide Ecological Improvement Goals, Accounting Mechanisms, Progress Assessment	Washington State Watersheds: Framework designed to move beyond "no net loss" to require measurable ecological gains from development activities.	Recommendations for enforcement, goal-setting, and cross-agency administration.
Adaptive Cycle Risk Assessment [51]	Watershed risk evaluation incorporating socio-ecological dynamics	Potential, Connectedness, Resilience indicators; Ordered Weighted Averaging (OWA)	Poyang Lake Eco-Economic Zone: High-risk areas clustered around lake waters and urban areas. Multi-preference OWA simulation identified stable high/low-risk and uncertain zones.	Supports sustainable watershed management strategies under different decision-maker preferences.

The synthesis of Ecological Security Patterns (ESP) and Ecological Risk Assessment (ERA) offers a powerful tool for spatial prioritization [47]. This method identifies critical source areas, corridors, and—most importantly—pinch points where ecological networks are vulnerable. Restoration targeted at these nodes enhances overall landscape connectivity and systemic resilience. Furthermore, dynamic simulation models like the coupled Resistance-Adaptability-Resilience (RAR) and Markov-FLUS models are essential for testing the outcomes of different policy scenarios [48]. For instance, simulations can reveal unintended consequences, such as how policies favoring cropland protection might degrade resilience on mountain slopes, thereby guiding more nuanced decision-making.

At the watershed scale, frameworks must account for cumulative effects. The Net Ecological Gain framework emphasizes moving beyond mitigating single project impacts to achieving measurable, landscape-scale ecological improvements [50]. This requires robust accounting mechanisms and adaptive management to track progress toward long-term goals. Similarly, integrating adaptive cycle theory into risk assessment, as seen in Poyang Lake, helps capture the dynamic interactions between a system's potential, connectedness, and resilience when exposed to disturbances [51].

Experimental Protocols

Protocol 1: Multi-Scale Assessment of Landscape Ecological Risk and Ecological Resilience

• Objective: To quantitatively assess the spatiotemporal evolution and coupling relationship between LER and ER across grid, county, and city scales [28].
• Materials: Multi-temporal land use/cover (LULC) data (e.g., 2010, 2015, 2020); administrative boundary vectors; GIS software (e.g., ArcGIS, QGIS); statistical software (e.g., R, SPSS); Fragstats for landscape metrics.
• Procedure:
  • Landscape Classification & Metric Calculation: Reclassify LULC data into landscape types (e.g., forest, farmland, urban, water). Calculate landscape pattern indices (e.g., fragmentation, dominance, connectivity) for each assessment unit at each scale [28].
  • LER Model Calculation: Construct the LER index using a "disturbance-vulnerability-loss" model [28].
    • Landscape Disturbance Index: Weighted sum of indices like fragmentation, isolation, and dominance.
    • Landscape Vulnerability Index: Assign relative vulnerability weights to different landscape types based on expert judgment or literature.
    • LER Index: Combine Disturbance and Vulnerability indices for each spatial unit.
  • ER Model Calculation: Construct the ER index using a "resistance-adaptation-recovery" model [28] [48].
    • Resistance: Represented by ecosystem service value (ESV) per unit area, calculated via the equivalent factor method [48].
    • Adaptation: Assessed through landscape diversity and complexity indices.
    • Recovery: Indicated by the proportion of stable ecological land cover (e.g., forest, wetland).
    • ER Index: Integrate the three dimension indices using geometric or weighted averaging.
  • Spatial Statistical Analysis:
    • Perform Pearson correlation analysis between LER and ER indices at each scale [28].
    • Conduct bivariate spatial autocorrelation (e.g., Bivariate Moran's I) to identify spatial clusters (e.g., High-Risk/Low-Resilience, Low-Risk/High-Resilience) [28].
    • Calculate the coupling coordination degree (D) to classify the synergy/trade-off relationship between LER and ER [28].
  • Zoning and Strategy Formulation: Based on cluster and coordination results, delineate management zones (e.g., stringent protection, key restoration, controlled development) and propose tailored regulatory strategies for each [28] [49].

Protocol 2: Watershed Cumulative Effects Assessment and Scenario Analysis

• Objective: To model the cumulative impact of multiple land-use stressors on aquatic ecosystems and evaluate outcomes under different management scenarios [52].
• Materials: High-resolution National Hydrography Dataset (NHD) catchments; land cover data (e.g., NLCD); field sampling equipment (water samplers, probes); laboratory facilities for water chemistry; GIS software; statistical modeling software.
• Procedure:
  • Stratified Site Selection:
    • Tabulate land use attributes (e.g., % urban, % mining, % forest) for all NHD catchments in the target watershed [52].
    • Create scatter plots of catchments along dominant stressor gradients (e.g., urban vs. mining impact).
    • Select ~40 independent study sites that represent the full range of conditions, including single-stressor and multiple-stressor combinations [52].
  • Field Data Collection:
    • At each site, delineate a sampling reach (e.g., 40x channel width) [52].
    • Collect physicochemical parameters: In-situ measurements of dissolved oxygen, conductivity, pH, temperature. Collect filtered samples (0.45 µm) for dissolved metals analysis (acid-preserved) and unfiltered samples for nutrient (e.g., nitrate, phosphate) and anion analysis [52].
    • Collect biological data: Perform standardized benthic macroinvertebrate surveys (e.g., kick-net sampling) as a bio-indicator of stream health.
  • Model Development:
    • Use multiple linear regression or multivariate analysis to build predictive models linking landscape-scale stressors (independent variables) to in-stream physicochemical and biological conditions (dependent variables) [52].
  • Scenario Analysis Implementation:
    • Develop 3-5 realistic land-use change scenarios (e.g., continued development, conservation, restoration) [52] [48].
    • Apply the developed statistical models to predict changes in aquatic conditions under each scenario.
    • Compare scenario outcomes against regulatory benchmarks or baseline conditions to inform permitting and watershed management plans [52].

Visualizations

Integrated Risk-Resilience Assessment Workflow

Watershed Cumulative Effects Assessment Process

The Scientist's Toolkit

Table 2: Essential Research Reagents & Materials for Field and Analytical Work

Category	Item/Solution	Specification/Function	Key Application
Field Sampling	Mixed Cellulose Ester Membrane Filter	0.45 µm pore size. For field filtration of water samples to separate dissolved constituents for metals analysis.	Watershed assessment; filtering samples for dissolved metals [52].
Field Sampling	Nitric Acid (HNO₃) & Sulfuric Acid (H₂SO₄)	Trace metal grade. Used as preservatives: HNO₃ to fix filtered samples to pH <2 for dissolved metals; H₂SO₄ to fix unfiltered samples for nutrient analysis.	Preserving water sample integrity for laboratory analysis of metals and nutrients [52].
Field Sampling	Calibrated Multi-Parameter Probe	Measures dissolved oxygen, specific conductivity, pH, temperature in situ. Provides instantaneous water quality data.	Characterizing the physicochemical condition of aquatic sites during field surveys [52].
Biological Assessment	D-frame Kick Net	Standardized mesh size (e.g., 500 µm). Used for semi-quantitative sampling of benthic macroinvertebrate communities.	Assessing biological integrity and stream health as a bio-indicator in cumulative effects studies [52].
Spatial Analysis	Multi-Temporal Land Use/Land Cover Dataset	High-resolution (e.g., 30m), classified (e.g., forest, urban, water). The foundational spatial data for calculating landscape metrics and indices.	Calculating Landscape Pattern Indices for LER and ER models; simulating land-use change [28] [48].
Spatial Analysis	National Hydrography Dataset (NHD) Catchments	Vector polygons representing drainage areas. The fundamental spatial unit for accumulating land-use attributes and modeling watershed-scale effects.	Tabulating landscape stressors and selecting study sites for watershed cumulative effects assessments [52].
Modeling & Computation	Fragstats Software	Calculates a wide array of landscape pattern metrics (e.g., patch density, edge density, contagion).	Quantifying landscape structure for input into LER and ER assessment models [28] [51].
Modeling & Computation	InVEST Model Suite	Open-source software for mapping and valuing ecosystem services (habitat quality, water yield, etc.).	Quantifying ecosystem service supply for resilience assessments and analyzing trade-offs with ecological risk [49] [53].

Overcoming Practical Hurdles: Addressing Scale, Subjectivity, and Integration Challenges

The assessment of Landscape Ecological Risk (LER) serves as a critical tool for comprehensively capturing the effects of natural evolution and human activities on ecosystems [54]. However, the results of these assessments are profoundly subject to scale effects, where the observed patterns, processes, and risk evaluations can vary significantly depending on the spatial granularity (cell size) and extent (overall study area) of analysis [54] [55]. Ignoring these effects can lead to biased conclusions, misidentification of driving factors, and ultimately, ineffective ecological management strategies [55].

This document establishes application notes and protocols for determining the optimal spatial scale within the broader context of integrating Landscape Ecological Risk Assessment with Ecological Resilience (ER) research. Resilience—defined as an ecosystem's capacity to maintain core structures and functions through resistance, adaptation, and recovery—provides a vital counterpart to risk analysis [28]. A robust, scale-optimized framework that couples the "disturbance-vulnerability-loss" risk model with the "resistance-adaptation-recovery" resilience framework is essential for accurate spatial characterization and for supporting sustainable landscape management and ecological restoration [28] [7].

Foundational Quantitative Data on Optimal Spatial Scales

Recent empirical studies across diverse Chinese watersheds and metropolitan areas have employed advanced geospatial methods to identify optimal spatial scales for LER analysis. The following table synthesizes key quantitative findings, highlighting the variability in optimal scales based on regional characteristics.

Table 1: Empirical Determinations of Optimal Spatial Scales for Landscape Ecological Risk Assessment

Study Area	Optimal Granularity	Optimal Amplitude	Key Determinants & Methods	Primary Citation
Luan River Basin	30 m	3200 m	Response curves, Area Accuracy Loss Model, Semi-variogram function [54].	[54]
Leshan City	150 m	Administrative Scale	Sensitivity analysis of 8 landscape indices, inflection point identification [55].	[55]
Nanjing Metropolitan Area	60 m	9 km	Landscape index, Semi-variogram function, Geodetector [56].	[56]
Hefei Metropolitan Circle	Multi-scale (Grid, County, City)	Scale-dependent	Coupling coordination model, Bivariate spatial autocorrelation [28].	[28]

Interpretive Summary: The data indicates that optimal granularity is highly region-specific, ranging from fine 30m resolutions in the Luan River Basin to coarser 150m resolutions in Leshan City [54] [55]. This depends on local landscape heterogeneity and data characteristics. The optimal amplitude can be defined by fixed distances (e.g., 3.2km, 9km) or by functional units like administrative boundaries, chosen based on the scale of ecological processes or management practices [54] [55] [56]. Critically, the relationship between LER and ER exhibits scale-dependent amplification, with correlations intensifying at finer spatial resolutions [28].

Detailed Experimental Protocols for Scale Optimization and Integrated Assessment

Protocol A: Determining Optimal Granularity and Amplitude

Objective: To identify the spatial granularity (pixel size) and analytical amplitude (window size) that most accurately represent landscape pattern-risk relationships.

Materials: Land Use/Land Cover (LULC) raster data, GIS software (e.g., ArcGIS Pro), statistical software (R, Python). Workflow:

• Data Resampling: Resample the original LULC raster to a series of progressively coarser granularities (e.g., 30m, 60m, 90m, 120m, 150m). The Nearest Neighbor method is recommended as the appropriate raster resampling method to preserve categorical integrity in landscape pattern analysis [54].
• Landscape Metric Calculation: At each granularity, calculate a suite of key landscape pattern indices (e.g., Fragmentation Index, Shannon's Diversity Index, Aggregation Index) using a moving window approach with varying amplitudes [55].
• Scale Sensitivity Analysis: Plot the values of each landscape index against granularity and amplitude. Use the inflection point identification method on these response curves to find where the rate of change in index value stabilizes; this point indicates the optimal granularity [55].
• Optimal Amplitude Selection: Employ the Area Accuracy Loss Model or analyze semi-variogram plots to identify the amplitude where spatial autocorrelation plateaus, representing the dominant scale of landscape pattern [54].
• Validation: Visually and statistically compare landscape patterns and derived risk maps at the determined optimal scale against high-resolution imagery and known ecological gradients.

Protocol B: Multi-scale Landscape Ecological Risk and Resilience Coupling Assessment

Objective: To evaluate the spatiotemporal dynamics and interactions between LER and ER across multiple administrative or functional scales.

Materials: Multi-temporal LULC data, biophysical datasets (NDVI, soil, DEM), socio-economic data, geostatistical software. Workflow:

• Multi-scale Risk Assessment (ERI/SI-ERI Model): At optimal granularity, calculate a Landscape Ecological Risk Index (LERI). An advanced approach involves constructing a Soil Erosion-integrated ERI (SI-ERI) model, which couples landscape pattern indices with the ecological process of soil erosion for more accurate spatial characterization [55].
• Ecological Resilience Assessment: Construct a resilience assessment framework based on the "resistance-adaptation-recovery" paradigm [28]. Quantify dimensions using indicators like ecosystem service value (for resistance), landscape connectivity (for adaptation), and vegetation regeneration capacity (for recovery).
• Spatial Correlation & Coupling Analysis:
  • Perform Pearson correlation analysis between LER and ER indices at grid, county, and city scales [28].
  • Apply bivariate spatial autocorrelation (Bivariate Moran's I) to identify spatial clusters of high-risk-low-resilience and low-risk-high-resilience [28] [7].
  • Calculate a coupling coordination degree (D) to quantify the synergy/trade-off state between LER and ER systems [28].
• Driving Factor Analysis: Use Geodetector (q-statistic) to identify the power of determinants (e.g., land use type, precipitation, population density) and their interactions on LER and ER patterns [54] [7].
• Zoning for Management: Integrate LER and ER coupling coordination results with cluster analysis to delineate zones for prioritized management (e.g., Ecological Conservation, Ecological Restoration, Ecological Adaptation) [7].

Visualizations of Core Methodologies and Conceptual Frameworks

Figure 1: Integrated workflow for spatial scale optimization and coupled LER-ER assessment.

Figure 2: Conceptual framework integrating LER and ER for coupled assessment [28].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Essential Tools and Data for Scale-Optimized LER-ER Research

Category	Item/Reagent	Function/Application	Key Considerations
Core Spatial Data	Land Use/Land Cover (LULC) Data	The foundational dataset for calculating landscape patterns and deriving risk indices.	Requires multi-temporal coverage for change analysis. Accuracy assessment is critical.
	Remote Sensing Indices (NDVI, NDWI)	Proxies for vegetation health, water bodies, and ecological function; inputs for resilience metrics.	Sensitive to sensor type, atmospheric conditions, and seasonality.
	Digital Elevation Model (DEM) & Slope	Determines topographic influence on ecological processes (e.g., soil erosion) and risk.	Resolution should be compatible with chosen optimal granularity.
Analytical Software	GIS Platform (e.g., ArcGIS Pro, QGIS)	For spatial data management, resampling, zoning, map algebra, and visualization.	ArcGIS Pro offers advanced symbol effects (e.g., arrows for flow direction) and geostatistical tools [57] [58].
	Landscape Metrics Software (e.g., FRAGSTATS)	Computes a wide array of landscape pattern indices from LULC rasters.	Must be configured with the optimal moving window amplitude.
	Statistical Software (e.g., R with spdep, GD)	Performs spatial autocorrelation, Geodetector analysis, and general statistical modeling.	Essential for quantifying scale effects and driver interactions [54] [7].
Assessment Models	Improved LERI/SI-ERI Model	Quantifies composite ecological risk by integrating landscape pattern and process.	The SI-ERI model, which incorporates soil erosion, offers more precise spatial characterization [55].
	Resilience Indicator Framework	Operationally defines and quantifies the resistance, adaptation, and recovery dimensions of ER.	Should be tailored to regional ecosystem characteristics and data availability [28].
Accessibility & Validation	Color Contrast Checker (e.g., WebAIM)	Ensures visualizations and maps meet WCAG guidelines (min. 4.5:1 contrast ratio) for accessibility and clarity [59] [60].	Critical for publication and inclusive science communication.
	High-Resolution Imagery	Used for ground-truthing LULC data and validating the plausibility of risk/resilience patterns.	Serves as an independent visual check on model outputs.
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Abstract This document provides a standardized methodological framework and application protocols for quantifying ecosystem services (ES) using objective parameterization techniques. Designed within the broader thesis context of optimizing landscape ecological risk (LER) assessment with resilience research, these protocols address the pervasive subjectivity in current valuation practices. We synthesize global valuation data, detail reproducible experimental workflows, and present a toolkit for researchers to integrate ES valuation into robust, spatially explicit risk-resilience models. The goal is to enhance the scientific rigor, comparability, and policy relevance of ES assessments for sustainable landscape management and ecological restoration [11] [28].

The integration of ecosystem service valuation into landscape ecological risk assessment and resilience research is hampered by methodological inconsistencies and subjective parameter choices. Current practices often rely on expert elicitation for vulnerability indices or use non-standardized, opaque parameters, limiting reproducibility and spatial comparability [53] [11]. For instance, a systematic review found that for over 60% of parameters used in urban ES quantification, sources or values were unspecified [53]. Simultaneously, global syntheses like the Ecosystem Services Valuation Database (ESVD) demonstrate the feasibility of standardization, consolidating over 9,400 value estimates into comparable units (Int$/ha/year), yet highlight significant geographic and service-specific data gaps [61]. This document bridges this gap by providing actionable protocols for objective parameterization, directly feeding into advanced LER models that are optimized by incorporating ecosystem resilience (ER) metrics [11] [28].

Methodological Framework for Objective Valuation

The proposed framework transitions from subjective scoring to data-driven, transparent parameterization. It is built on three pillars: (1) the use of globally standardized databases and remote sensing data, (2) the application of multi-method triangulation for key services, and (3) the explicit linkage of ES flows to landscape risk and resilience metrics.

Diagram: Framework for Objective ES Valuation in Risk-Resilience Research

Application Notes & Quantitative Data Synthesis

The following tables synthesize current global data and standardize parameters for key ecosystem services, providing a baseline for objective valuation and highlighting critical research gaps.

Table 1: Global Summary of Standardized Economic Values for Selected Ecosystem Services [61]

Ecosystem Service	Biome (Example)	Mean Value (Int$/ha/year, 2020)	Data Robustness (No. of Estimates)	Key Parameter Source
Recreation & Tourism	Temperate Forests	1,450	High (>500)	Visitor surveys, travel cost models
Water Purification	Inland Wetlands	3,240	Medium (200-500)	Water quality monitoring, replacement cost
Carbon Sequestration	Tropical Forests	1,120	High (>500)	IPCC carbon stock data, social cost of carbon
Pollination (for crops)	Cropland	1,870	Low (<100)	Crop yield dependence ratios, market price
Coastal Protection	Mangroves	4,270	Medium (200-500)	Wave attenuation models, avoided damage costs
Wild Food Provision	Freshwater Systems	580	Medium (200-500)	Fish stock assessments, market prices
Note: Values are synthesized from the ESVD [61]. Int$ denotes international dollars adjusted for purchasing power parity. Robustness indicates relative number of underlying studies/estimates globally.

Table 2: Objective Parameterization Guide for Common Valuation Methods

Valuation Method	Typical Subjective Pitfall	Objective Parameter Alternative	Data Source Protocol
Benefit Transfer	Arbitrary value adjustment for local context	Use meta-regression models with spatially explicit moderators (income, land cover, population density)	ESVD [61]; Geospatial layers (GDP, land use)
Travel Cost Method	Assumptions about travel time value and opportunity cost	Use standardized national transport cost databases and statistically derived value of time	National transport statistics; Stated preference surveys for regional time value
Replacement Cost Method	Selecting inefficient or non-equivalent engineered alternatives	Use lifecycle cost analysis of the most cost-effective, functionally equivalent engineered system	Engineering cost manuals; Peer-reviewed lifecycle assessments
Habitat Equivalency Analysis	Subjective discount rates and recovery trajectory models	Apply regulatory-prescribed discount rates and use meta-analyzed recovery functions from published restoration studies	Governmental guidance (e.g., NOAA); Ecological restoration meta-analyses
InVEST Model Runs	Default parameters inappropriate for study biome	Calibrate key parameters (e.g., root depth, pollutant thresholds) with local field data or regional peer-reviewed studies	Local soil/vegetation surveys; Regional hydrological studies

Detailed Experimental Protocols

Protocol 1: Calibrating the InVEST Nutrient Delivery Ratio (NDR) Model with Local Biophysical Data

Objective: To objectively parameterize the NDR model for water purification service valuation, replacing default global values.

• Delineate Watershed: Using a Digital Elevation Model (DEM) in GIS, delineate the study watershed and sub-catchments.
• Land Use/Land Cover (LULC) Mapping: Classify high-resolution satellite imagery (e.g., Sentinel-2) to create an LULC map. Cross-walk classes to the model's required biophysical table.
• Field Sampling for Calibration:
  • Nutrient Loading: Establish sampling points at the outlet of major sub-catchments with differing land uses. Collect water samples during baseflow and storm events over a hydrological year. Analyze for Total Nitrogen (TN) and Total Phosphorus (TP).
  • Biophysical Parameters: For each dominant vegetation class, measure root depth (soil cores), canopy cover (densitometer), and collect soil samples to determine hydrological group and permeability.
• Parameter Optimization: Run the model iteratively, adjusting the root_depth, max_retention, and load parameters in the biophysical table until modeled nutrient export values statistically align (R² > 0.7, p < 0.05) with measured export from your sampling points.
• Valuation: Apply spatially explicit replacement costs (e.g., cost of building and operating water treatment plants to remove equivalent nutrient loads) based on local engineering economic data.

Protocol 2: Quantifying Cultural Services Using Structured Social Media Analytics

Objective: To derive spatially explicit, non-invasive indicators of recreational and aesthetic values.

• Data Harvesting: Use the Flickr API or similar to collect all geotagged photographs within your study region for a 5-year period. Filter for recreational tags (e.g., hiking, camping, birdwatching).
• Content Analysis & Mapping: Assign photos to specific landscape features (e.g., trailheads, viewpoints, water bodies) based on coordinates. Calculate visitation density (photos/hectare/year) as a proxy for use intensity.
• Sentiment/Content Analysis: Apply a computer vision algorithm or manual coding to a subset to assess aesthetic appeal (e.g., presence of scenic elements like sunsets, wildlife).
• Value Transfer: Establish a correlation between photo-user-days (PUDs) and economic value from existing studies in comparable biomes (e.g., from the ESVD [61] for "Recreation") to assign a monetary value per PUD, scaled to your site.

Objective: To replace the subjective landscape vulnerability index in traditional LER models with a composite ES-based index.

• Calculate ES Indicators: For the study area (e.g., a watershed grid), quantify 3-4 key regulating and supporting services (e.g., carbon stock, water yield, habitat quality) using biophysical models (e.g., InVEST).
• Construct ES-Based Vulnerability Index (ESVI): Normalize each ES indicator (0-1). Calculate the ESVI as: ESVI_i = 1 - (Σ ES_ij) / n, where ES_ij is the normalized value of service j in grid i, and n is the number of services. Lower ES provision leads to higher vulnerability.
• Calculate LER with ESVI: Use the formula: LER_i = (Disturbance_i × ESVI_i). Disturbance can be derived from a landscape disturbance index (LDI) based on land use intensity and fragmentation metrics.
• Assess Ecosystem Resilience (ER): Calculate an ER index integrating landscape metrics for resistance (e.g., contagion index), adaptation (e.g., landscape diversity), and recovery (e.g., connectivity to natural seed sources) [28].
• Spatial Correlation & Zoning: Perform bivariate local spatial autocorrelation (Moran's I) between LER and ER results. Classify the landscape into management zones: Ecological Restoration (High LER, Low ER), Conservation (Low LER, High ER), Adaptation (High LER, High ER), and Monitoring (Low LER, Low ER) [11] [28].

Diagram: Protocol for ES-Driven Risk-Resilience Zoning [11] [28]

Protocol 4: Standardized Value Transfer Using the Ecosystem Services Valuation Database (ESVD)

Objective: To conduct a transparent and defensible benefit transfer for rapid screening assessments.

• Define Service and Biome: Clearly identify the ecosystem service and the matched biome (e.g., "water purification by inland wetlands") in your study area.
• Extract Base Value & Distribution: Access the ESVD [61]. Extract the mean value (Int$/ha/year) and, critically, the standard deviation and number of underlying studies for your service-biome pair.
• Adjust for Context: Objectively adjust the base value using a value function from a published meta-analysis. Common adjustment variables include:
  • Income: Multiply by (GDP_pc_study / GDP_pc_ESVD_avg)^E, where E is the income elasticity from literature (often ~0.3-0.7).
  • Scarcity/Protection Status: Apply a scalar (e.g., 1.1 for protected areas, 0.9 for degraded areas) based on peer-reviewed findings.
  • Spatial Scale: Apply a scalar for scale sensitivity if transferring from a large to a very small area.
• Report Uncertainty: Report the transferred value as a range: Adjusted Value ± (SD from ESVD). Acknowledge this as a screening estimate.

Diagram: Standardized Parameterization and Valuation Workflow

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Tools and Platforms for Objective ES Valuation

Tool/Platform Name	Type	Primary Function in Valuation	Access/Source
Ecosystem Services Valuation Database (ESVD)	Database	Provides standardized global monetary values (Int$/ha/year) for benefit transfer and meta-analysis [61].	Available on request from authors [61]
InVEST Suite (Integrated Valuation of Ecosystem Services and Tradeoffs)	Software Model	Spatially explicit biophysical modeling of numerous ES (carbon, water, habitat, recreation).	Natural Capital Project (open source)
Coastal Ecosystem Services Index (CEI) Framework	Methodological Framework	Scores ES based on relationship with environmental factors; useful for habitat restoration projects [62].	Peer-reviewed protocol [62]
Social Media APIs (Flickr, Instagram)	Data Source	Provides geotagged imagery for analyzing cultural ecosystem services and visitation patterns.	Platform developer portals
R packages: sf, raster, ggplot2	Programming Libraries	Essential for spatial data manipulation, analysis, and creating reproducible visualization scripts.	Comprehensive R Archive Network (CRAN)
Google Earth Engine	Cloud Platform	Provides access to vast satellite imagery archives (Landsat, Sentinel) for land cover change and vegetation index analysis.	Cloud-based platform
FRAGSTATS	Software	Calculates landscape pattern metrics crucial for assessing landscape structure in LER and resilience models [11] [28].	University of Massachusetts (open source)
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This document provides detailed application notes and protocols for conducting integrated landscape ecological risk assessments that explicitly account for multi-scale complexities. The framework is designed to harmonize analyses across fundamental spatial units—regular grids, administrative boundaries, and natural watersheds—within the overarching context of optimizing assessments with resilience research [11]. Landscape ecological risk (LER) assessment traditionally faces challenges of subjectivity and limited applicability in ecological zoning [11]. Concurrently, ecosystem resilience (ER)—defined as the capacity of a system to absorb disturbance while maintaining its structure and function—is a critical but often overlooked component that enhances an ecosystem's resistance to risks [63]. This protocol posits that clarifying the mechanistic link between LER and ER is essential for reducing regional ecological risks and promoting sustainable ecosystem management [11]. The following sections provide a standardized methodology for researchers and scientists to execute reproducible, scale-aware assessments that support targeted ecological management zoning.

Foundational Data and Multi-Scale Integration Protocol

A robust assessment requires integrating heterogeneous data sources into a coherent, multi-scale framework. The core challenge lies in reconciling the geometric and conceptual mismatches between different spatial unit types.

Core Data Requirements and Preprocessing

• Land Use/Land Cover (LULC) Data: High-resolution time-series data (e.g., 2001-2021) is essential. Data must be reclassified into standardized classes (e.g., construction land, woodland, grassland, cropland, water body, unutilized land) for consistent vulnerability indexing [11].
• Ecosystem Service (ES) Proxy Data: Direct calculation of multiple ecosystem services is superior to using single proxies like NPP [11]. Required datasets include:
  • Net Primary Productivity (NPP): Calculated using models like the Carnegie–Ames–Stanford approach (CASA) [64].
  • Soil Conservation & Water Yield: Modeled with tools like the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) suite [64].
  • Habitat Quality: Also derived from InVEST models.
• Resilience Indicator Data: Data on biodiversity indices, vegetation community structure, landscape connectivity metrics, and historical disturbance regimes.
• Ancillary Geospatial Data: Digital Elevation Models (DEMs) for watershed delineation [64], soil type maps, climate data (precipitation, temperature), and socioeconomic datasets aligned with administrative boundaries.

Protocol for Multi-Scale Unit Harmonization

The goal is to create a nested analysis framework where assessments at different scales inform one another. The watershed is recommended as the primary functional unit, as it represents a relatively independent socio-ecological composite system with a complete ecological structure [11].

Step 1: Base Unit Generation.

• Watershed Units: Using a DEM (e.g., ASTER GDEM, 30m), apply hydrological tools to delineate watersheds and sub-watersheds. The threshold for stream network extraction should be determined by analyzing the relationship between river network density and grid cell accumulation thresholds; a point where density stabilizes indicates an appropriate threshold for delineation [64].
• Grid Units: Overlay a vector grid (e.g., 1km x 1km, 3km x 3km) across the study area. The grid cell size must be fine enough to capture landscape heterogeneity but coarse enough for meaningful statistical analysis.
• Administrative Units: Acquire official boundaries for counties, townships, or other relevant management districts.

Step 2: Establishing a Common Analysis Granularity.

• Perform all core ecological calculations (e.g., ecosystem service valuation, landscape metrics) at the highest available resolution (e.g., pixel or land parcel level).
• Use zonal statistics to aggregate these high-resolution results to each of the three unit types (watershed, grid, administrative). This ensures the underlying ecological data is consistent, and differences in results are due to the spatial configuration of the units themselves.

Step 3: Cross-Scale Comparison and Calibration.

• Calculate key output metrics (e.g., mean LER index, total ecosystem service value) for each unit type.
• Conduct correlation and spatial autocorrelation analysis (e.g., Global and Local Moran's I) to identify where and why assessments diverge across scales. Administrative units may smooth over natural gradients, while watershed units may highlight cross-jurisdictional ecological processes.

Table 1: Advantages and Analytical Implications of Primary Spatial Units

Spatial Unit Type	Primary Advantage	Key Analytical Consideration	Ideal Use Case
Regular Grid	Statistical neutrality, ease of computation and mapping, facilitates raster-based modeling.	Arbitrary boundaries may slice through ecological features, risking the "modifiable areal unit problem" (MAUP).	Initial landscape pattern analysis, spatial interpolation, and generating continuous surface models.
Watershed	Reflects natural ecological and hydrological processes; units are functionally linked.	Boundaries may not align with socio-economic data or management jurisdictions.	Assessing water-related services, pollution transport, and designing nature-based restoration plans.
Administrative	Direct relevance to policy, planning, and enforcement; socio-economic data is readily available.	Boundaries are politically defined and often ecologically arbitrary, masking natural gradients.	Policy impact assessment, resource allocation, and integrating ecological findings into land-use planning.

Experimental Protocol for Integrated LER-ER Assessment

This protocol details the optimized LER assessment model incorporating ecosystem services and its integration with resilience metrics, as demonstrated in recent research [11].

Optimized Landscape Ecological Risk (LER) Assessment

The traditional LER model, which assigns subjective vulnerability indices based on land use type, is replaced with a dynamic model grounded in ecosystem service valuation [11].

Protocol Steps:

• Landscape Disturbance Index (Ei): Calculate for each spatial unit (i).
  • Formula: Ei = aCi + bSi + cDi
  • Ci is the fragmentation index, Si is the separation index, and Di is the dominance index for unit i, derived from LULC data using FRAGSTATS or similar software.
  • a, b, c are weights summing to 1, determined via principal component analysis or expert elicitation.

• Landscape Vulnerability Index (Vi) via Ecosystem Services: This is the key optimization.

  • Select 3-4 critical regional ecosystem services (e.g., water yield, soil conservation, carbon sequestration, habitat quality).
  • Quantify the total service capacity for each spatial unit using models like CASA and InVEST [64].
  • Normalize each ES value and integrate them using an objective method (e.g., root mean square) to create a composite ES score for each unit.
  • Invert the ES score to derive Vi: Vi = 1 - Normalized_ES_Score. Higher ES provision indicates lower vulnerability [11].

• Landscape Ecological Risk Index (LERI): Compute the final risk index.

  • Formula: LERIi = Ei * Vi
  • The result is a spatially explicit LER index where risk is a function of both landscape pattern disturbance and functional vulnerability.

Ecosystem Resilience (ER) Assessment Protocol

ER is assessed as a separate but parallel dimension, focusing on the system's capacity to resist or recover.

Protocol Steps:

• Indicator Selection: Construct a multi-dimensional index from indicators such as:
  • Biodiversity: Shannon diversity index of habitat types or species richness data.
  • Functional Redundancy: Number of land cover types providing similar ecosystem services.
  • Connectivity: Probability of connectivity index or integral index of connectivity.
  • Historical Stability: Coefficient of variation in NDVI or land use over a 10-20 year period.

• Index Integration: Normalize all indicators and integrate them using a weighted linear combination or multivariate analysis to create a composite ER index for each spatial unit.

Integrated Zoning via Bivariate Spatial Autocorrelation

The final management zoning arises from the joint spatial distribution of LER and ER [11].

Protocol Steps:

• Spatial Correlation: Calculate the bivariate Global Moran's I to assess the overall spatial dependence between LER and ER values across the study area.
• Local Indicator of Spatial Association (LISA): Perform a bivariate Local Moran's I analysis. This classifies each spatial unit into one of five clusters:
  • High-High: High LER, High ER. "Ecological Adaptation Region." High resilience currently buffers high risk, but proactive adaptation is needed to maintain resilience.
  • Low-Low: Low LER, Low ER. "Ecological Conservation Region." Low risk but fragile; priority is conserving existing conditions and preventing degradation.
  • High-Low: High LER, Low ER. "Ecological Restoration Region." High-priority area for intervention; risk is high and system lacks capacity to cope.
  • Low-High: Low LER, High ER. Stable, optimal zone requiring monitoring.
  • Not Significant: No significant local correlation.

Table 2: Quantitative Relationship Between LER and ER and Corresponding Management Zoning

Spatial Relationship (LER vs. ER)	Empirical Functional Relationship	Zoning Category	Recommended Management Strategy
Significant Negative Correlation	LER decreases as ER increases, often approximating a quadratic function [11].	Core zoning driver.	Validates the resilience-risk framework. Targeted interventions should aim to shift units toward the "Low-High" quadrant.
High LER - Low ER	LERI > study area mean, ERI < study area mean.	Ecological Restoration Region.	Implement urgent structural restoration: reforestation, connectivity corridors, and strict regulation of anthropogenic disturbance.
Low LER - Low ER	LERI < mean, ERI < mean.	Ecological Conservation Region.	Focus on protective conservation: prevent land use conversion, control pollution, and manage tourism/recreation impacts.
High LER - High ER	LERI > mean, ERI > mean.	Ecological Adaptation Region.	Enhance adaptive management: employ climate-smart practices, diversify landscape structure, and establish long-term monitoring for early warning of resilience loss.

Integrated LER-ER Assessment and Zoning Workflow

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Analytical Tools and Models for Integrated LER-ER Assessment

Tool/Model Name	Category	Primary Function in Protocol	Critical Output for Analysis
FRAGSTATS	Landscape Metrics Software	Calculates landscape pattern indices (Ci, Si, Di) for the Landscape Disturbance Index (Ei).	Metrics of fragmentation, isolation, and dominance for each spatial unit.
InVEST Suite	Ecosystem Service Modeling	Models multiple ecosystem services (water yield, soil conservation, habitat quality) to quantify Landscape Vulnerability (Vi).	Spatially explicit maps of ES provision, used to create the dynamic Vi index [11] [64].
CASA Model	Biogeochemical Model	Estimates Net Primary Productivity (NPP), a key proxy for carbon sequestration services.	NPP raster data, input for ES valuation and resilience assessment [64].
GIS Hydrological Toolbox (e.g., ArcGIS, QGIS)	Spatial Delineation	Delineates watershed and sub-watershed boundaries from a DEM for natural unit analysis [64].	Vector layers of watershed units.
GeoDa / R spdep	Spatial Statistics	Performs univariate and bivariate spatial autocorrelation analysis (Global/Local Moran's I).	LISA cluster maps defining the final ecological management zones [11].
Google Earth Engine	Cloud Computing Platform	Processes large-scale, time-series remote sensing data (LULC, NDVI) efficiently.	Pre-processed, analysis-ready raster datasets for the study region and period.
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Visualization and Accessibility Protocol

All diagrams and outputs must adhere to accessibility standards to ensure broad interpretability [65].

Design Rules:

• Color Palette: Use the specified palette (#4285F4, #EA4335, #FBBC05, #34A853, #FFFFFF, #F1F3F4, #202124, #5F6368).
• Contrast: Maintain a minimum 3:1 contrast ratio for graphical objects and 4.5:1 for text against backgrounds [66] [65]. Use a contrast checker tool.
• Colorblind Accessibility: Avoid conveying meaning by color alone. Differentiate elements using both color and pattern, shape, or direct labeling [67]. The chosen palette provides variability in lightness to aid differentiation.
• Diagram Clarity: Keep workflows simple. Use direct labeling where possible and provide clear titles and legends [65].

Logic Flow for Ecological Management Zoning Decision

Application Notes for an Integrated Landscape Ecological Risk-Resilience Framework

Note 1: Conceptual Integration of Risk and Resilience The optimization of landscape ecological risk (LER) assessment requires moving beyond static risk evaluation to incorporate dynamic resilience research. This integrated framework posits that ecological resilience—the capacity of a landscape to absorb disturbance and reorganize while retaining its essential function—mediates the ultimate impact of ecological risks. Cohesive analysis is achieved by concurrently modeling the pressure exerted by landscape ecological risk (derived from pattern indices) and the state of ecosystem health or service provision (a proxy for resilience) [49] [68]. This dual assessment allows for the identification of areas that are high-risk but low-resilience (critical intervention zones) versus those that are high-risk yet high-resilience (potential buffers), informing targeted management strategies [49].

Note 2: Multi-Source Data Synthesis Protocol Effective integration necessitates harmonizing heterogeneous data spanning spatial, temporal, and thematic domains. Core data layers must include:

• Land Use/Land Cover (LULC): High-resolution temporal series to quantify landscape pattern changes [5] [12].
• Ecosystem Services (ES): Modeled outputs (e.g., habitat quality, soil conservation, water yield from InVEST) or derived indices (e.g., Modified Ecosystem Service Life Index - MESLI) to measure functional state [49].
• Anthropogenic & Biophysical Drivers: Data on topography (DEM), climate, soil, road networks, and socio-economic factors (GDP, population density) to explain risk and resilience patterns [5] [12].
• Ecological Network Elements: Data identifying ecological sources, corridors, and nodes to assess landscape connectivity as a component of resilience [12].

A standardized grid system (e.g., 20 km x 20 km risk assessment units) is recommended as a foundational spatial framework for aligning and analyzing these disparate data sources [12].

Note 3: Analytical Workflow for Spatiotemporal Dynamics The core analytical workflow employs a sequence of spatially explicit models to translate data into insights. The process begins with calculating LER indices based on landscape patterns and ecosystem service assessments. The Geographically and Temporally Weighted Regression (GTWR) model is then critical for analyzing the spatiotemporal relationship between LER and ES, as it captures non-stationary, localized effects that global models miss [49]. For forward-looking analysis, the PLUS model is applied to simulate future LULC changes under defined scenarios (e.g., natural growth, ecological priority) [5]. Finally, the Minimum Cumulative Resistance (MCR) model is used to construct ecological networks, identifying corridors and nodes to enhance landscape connectivity and resilience [12]. This workflow enables both diagnostic and prognostic assessment.

Note 4: Zoning and Management Application The terminal application of the integrated analysis is ecological functional zoning. This is achieved through a quadrant analysis that cross-tabulates LER (risk level) with Ecosystem Health Index (EHI) or ES capacity (resilience level) [49] [68]. The resulting matrix defines distinct zones:

• Strict Ecological Conservation Zone: Low Risk, High Resilience.
• Ecological Enhancement Zone: High Risk, High Resilience.
• Ecological Restoration Zone: High Risk, Low Resilience.
• Ecological Control Zone: Low Risk, Low Resilience [68]. Management policies are then tailored to each zone, ranging from strict protection and connectivity enhancement to active restoration and land use regulation, thereby directly linking analytical results to governance actions [49].

Detailed Experimental Protocols

Protocol 1: Integrated Data Preprocessing and Grid System Establishment

Objective: To standardize and harmonize multi-source spatial data into a unified analytical framework.

Materials & Software: GIS software (ArcGIS, QGIS); Land use data (30m resolution) [5]; Ecosystem service models (InVEST); Socio-economic and biophysical datasets [12].

Procedure:

• Data Collection & Projection: Gather all raster and vector datasets for the study period(s). Reproject every dataset to a consistent coordinate system and datum.
• Resolution Standardization: Resample all raster data to a common spatial resolution (e.g., 30m) using a bilinear interpolation for continuous data (e.g., DEM, NDVI) and a nearest-neighbor method for categorical data.
• Grid System Creation: Based on the average landscape patch size, create a vector grid (e.g., 20km x 20km squares) over the study area [12]. This grid will serve as the basic assessment unit.
• Data Attribution: For each grid cell, use zonal statistics to calculate mean values for continuous data (e.g., average habitat quality) and area proportions for categorical data (e.g., percentage of construction land).

Protocol 2: Landscape Ecological Risk Assessment Based on Landscape Patterns

Objective: To quantify the spatial and temporal patterns of ecological risk stemming from landscape structure.

Materials & Software: Fragstats or similar landscape pattern analysis software; Preprocessed land use grids.

Procedure:

• Landscape Index Calculation: For each land use type i within each assessment grid k, calculate:
  • Landscape Disturbance Index (L_i): A composite of fragmentation, isolation, and dominance indices.
  • Landscape Vulnerability Index (V_i): A pre-assigned weight (e.g., 1-7) reflecting the ecological sensitivity of each land use type, typically: unused land > construction land > farmland > grassland > forest > water [12].
• Ecological Loss Degree: Compute the ecological loss degree for landscape type i: R_i = L_i × V_i.
• Landscape Ecological Risk Index (ERI): Calculate the ERI for each assessment grid k using the formula: ERI_k = Σ ( (A_ki / A_k) × R_i ) where A_ki is the area of landscape i in grid k, and A_k is the total area of grid k [12].
• Spatial Interpolation & Classification: Interpolate the ERI values from grid centroids to create a continuous risk surface. Classify ERI into levels (e.g., Low, Medium-Low, Medium, Medium-High, High) using natural breaks or standard deviation methods.

Protocol 3: Resilience Proxy Assessment via Ecosystem Health/Service Models

Objective: To evaluate the capacity of the ecosystem to maintain function under pressure, serving as a resilience proxy.

Materials & Software: InVEST model suite; Climate, soil, and LULC data; Scripting environment (Python/R) for custom indices.

Procedure A – Ecosystem Service Assessment (InVEST):

• Model Selection: Run relevant InVEST modules (e.g., Habitat Quality, Sediment Retention, Water Yield) for each time point [49].
• Parameterization: Calibrate models with local data. For Habitat Quality, define threat sources (e.g., urban land, roads), their weights, maximum influence distance, and decay type.
• Output Standardization: Normalize the outputs of different ES models (0-1 range) and aggregate them into a comprehensive ES capacity index (e.g., MESLI) using a weighted sum approach [49].

Procedure B – Ecosystem Health Index (VORS Model):

• Component Calculation:
  • Vigor (V): Use NDVI as a proxy for ecosystem productivity.
  • Organization (O): Calculate landscape pattern metrics (e.g., Shannon's Diversity Index, Connectivity Index).
  • Resilience (R): Assign a resilience score based on land use type (e.g., forest > grassland > farmland > urban).
  • Service (S): Use key ecosystem service values (e.g., carbon sequestration, water yield) [68].
• Index Integration: Combine the four components using the geometric mean or a weighted linear model to compute the final Ecosystem Health Index (EHI).

Protocol 4: Driving Force Analysis and Multi-Scenario Simulation

Objective: To identify key drivers of risk-resilience patterns and project future dynamics.

Materials & Software: GeoDetector software; PLUS model; Future scenario datasets (SSP-RCP).

Procedure for Driving Force Analysis (GeoDetector):

• Factor Selection & Discretization: Select potential driving factors (natural: DEM, slope, precipitation; human: population density, GDP, distance to roads). Discretize continuous factors into appropriate strata.
• q-Statistic Calculation: Use the GeoDetector's factor detector to compute the q-value, which measures the explanatory power of each factor on the spatial heterogeneity of ERI or EHI. q ∈ [0,1], where a larger value indicates greater explanatory power [5].
• Interaction Detection: Use the interaction detector to assess whether the combined effect of two factors enhances or weakens the explanatory power of individual factors.

Procedure for Future Simulation (PLUS Model):

• Land Expansion Analysis: Use historical LULC changes to analyze the development probabilities of different land use types.
• Scenario Definition: Define simulation scenarios (e.g., Business-As-Usual, Ecological Protection, Rapid Development). Adjust the land demand forecasts and territorial development constraints for each scenario [5].
• Multi-Class Random Patch Simulation: Run the PLUS model's CA module to generate future LULC maps for target years (e.g., 2030, 2050) under each scenario.
• Future Risk-Resilience Assessment: Apply Protocols 2 and 3 to the simulated future LULC maps to assess the trajectory of risk and resilience patterns.

Quantitative Data Synthesis from Case Studies

Table 1: Summary of Key Quantitative Findings from Integrated Risk-Resilience Studies [49] [5] [12]

Study Region	Temporal Trend (LER)	Key Resilience/Service Trend	Dominant Driving Factor (q-statistic or similar)	Key Spatial Correlation Finding
Wuling Mountain Area [49]	Generally declined (2000-2020).	Habitat quality remained high; Soil conservation improved.	LER had strong negative correlation with habitat quality & soil conservation.	GTWR revealed spatially non-stationary LER-ES relationships.
Harbin, China [5]	Trended downward, mainly medium risk (2000-2020).	N/A for this study.	DEM had greatest explanatory power; Interaction of DEM & precipitation was dominant.	Spatial autocorrelation (Moran's I: 0.798-0.852). Pattern: "High in west/north, low in east/south".
Southwest China [12]	Average ERI stable (0.20-0.21) (2000-2020).	N/A for this study.	Anthropogenic disturbance & land use level had strong explanatory power.	Risk zones transitioned from high/low to medium-risk. Constructed 105 corridors & 156 nodes.
Changsha-Zhuzhou-Xiangtan [68]	Medium & medium-low risk dominant; low-risk areas decreased.	Ecosystem Health Index declined (0.555 in 2000 to 0.518 in 2020).	Integrated LER & EHI for zoning; construction land expansion primary driver of EHI decline.	Spatial overlap between ecological zones and protected areas shifted significantly.

Visualization of Analytical Frameworks

Framework for Integrated Risk-Resilience Analysis

Integrated Risk-Resilience Analytical Workflow

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials, Models, and Software for Integrated Risk-Resilience Analysis

Tool/Resource Name	Category	Primary Function in Analysis	Key Application Note
InVEST Model Suite	Ecosystem Service Modeling	Quantifies and maps multiple ecosystem services (e.g., habitat quality, carbon storage, water yield) to serve as resilience proxies [49].	Requires careful local parameterization (e.g., threat source definitions for habitat quality). Outputs need normalization for integration into composite indices.
Fragstats	Landscape Pattern Analysis	Calculates a wide array of landscape metrics (patch density, edge density, contagion) used as inputs for Landscape Ecological Risk Index (LERI) calculation [12].	The choice of metrics must align with the ecological context. A common set includes fragmentation, isolation, and loss indices.
PLUS Model	Land Use Change Simulation	Simulates future land use changes under multiple scenarios (e.g., BAU, ecological priority) by integrating a land expansion analysis strategy and a multi-class random patch seed cellular automata [5].	Superior to older CA-Markov models in capturing the drivers of land use change and simulating patch-level dynamics.
GeoDetector	Statistical Analysis	Measures spatial stratified heterogeneity and detects the explanatory power of driving factors (q-statistic) and their interactions on risk or resilience patterns [5] [12].	Not limited by linear assumptions. Factors must be discretized appropriately for the detector to operate.
Geographically and Temporally Weighted Regression (GTWR)	Spatiotemporal Analysis	Models the non-stationary, local relationships between variables (e.g., LER and ES) across both space and time [49].	Essential for uncovering local effects that global regression models would average out and miss.
Minimum Cumulative Resistance (MCR) Model	Ecological Network Analysis	Identifies ecological corridors and nodes by modeling the cost of species movement or ecological flow across a resistance surface, enhancing connectivity resilience [12].	The accuracy depends heavily on the correct assignment of resistance values to different land use types.
30m Annual China Land Cover Dataset	Core Spatial Data	Provides consistent, medium-resolution LULC data for time-series analysis of landscape pattern change, a fundamental input for LER and EHI assessment [5].	Critical for establishing baseline conditions and change trajectories. Requires reclassification to align with study-specific categories.
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Practical Solutions for Model Uncertainty and Validation in Integrated Assessments

Integrated Assessment (IA) is an emerging methodology designed to address complex, multifaceted environmental issues by combining inputs from various disciplines and integrating diverse stakeholder perspectives while explicitly recognizing uncertainties [69]. Within the context of a broader thesis on optimizing landscape ecological risk assessment with resilience research, this framework is indispensable. Landscape ecological risk assessment inherently grapples with interconnected systems—land use change, biodiversity shifts, climate impacts, and socioeconomic drivers. Model uncertainty in these assessments can lead to flawed predictions, misinformed policy, and increased ecological vulnerability [70].

This document provides detailed Application Notes and Protocols for managing model uncertainty and establishing robust validation practices specifically tailored for integrated assessments in landscape ecology. The goal is to transition from viewing models as static prediction tools to treating them as dynamic, learning components of a resilience-oriented management framework. The protocols herein synthesize principles from financial model risk management [71] [72] [73], statistical data presentation [74] [75], and contemporary ecological modeling [70] to create a standardized, practical approach for researchers and applied scientists.

Practical Solutions Across the Model Lifecycle

Effective management of model uncertainty requires interventions at each stage of the model's lifecycle, from development to deployment and monitoring. The following solutions are framed within the three lines of defense governance model adapted for scientific research: model development (first line), independent validation (second line), and audit/peer review (third line) [71] [73].

Table 1: Summary of Model Uncertainty Sources and Practical Mitigation Solutions in Integrated Ecological Assessments

Uncertainty Source	Stage of Origin	Potential Impact on Assessment	Practical Mitigation Solution
Structural Uncertainty	Model Design & Conceptualization	Incorrect system representation leads to biased predictions.	Multi-model ensembling: Run parallel analyses using different model structures (e.g., agent-based vs. system dynamics) and compare outcomes [69].
Parametric Uncertainty	Model Calibration & Input	Erroneous parameters propagate through simulations.	Probabilistic Calibration: Use Bayesian methods or Monte Carlo simulations to define parameter distributions rather than fixed values [70].
Scenario Uncertainty	Scenario Design & Forecasting	Future pathways are inherently unknown and conditional.	Exploratory Scenario Analysis: Develop multiple, plausible futures (e.g., SSPs, RCPs) rather than relying on a single forecast [70] [69].
Data/Informational Uncertainty	Data Collection & Processing	Gaps, errors, or bias in input data degrade model fidelity.	Data Pedigree Analysis: Systematically document data sources, processing steps, and known quality flags. Use sensitivity analysis to identify critical data gaps [72] [75].
Algorithmic/Technical Uncertainty	Model Implementation & Coding	Bugs, numerical errors, or inappropriate algorithms affect results.	Code Review & Unit Testing: Implement standard software development practices, including independent code review and systematic testing of sub-functions [71] [72].

A cornerstone practice is the maintenance of a Model Inventory, which serves as the central repository for all models within a research program or institution. For a landscape ecology research group, this should catalog each model's purpose, key assumptions, input data sources, validation status, and identified limitations [71] [73]. Governance is strengthened by defining clear ownership; a Model Owner (e.g., a principal investigator or senior scientist) should be responsible for the model's maintenance, documentation, and ensuring its appropriate use [73].

Detailed Experimental Protocols for Model Validation

Validation is not a one-off task but an ongoing process integrated into the model's lifecycle. It is an independent, expert assessment of a model’s design, assumptions, calculations, and outputs [72]. The following protocols provide a step-by-step methodology.

Protocol 1: Conceptual Soundness and Design Validation

Objective: To verify that the model's theoretical foundations, structure, and simplifying assumptions are appropriate for its intended purpose in assessing landscape ecological risk.

• Assumption Audit: List all explicit and implicit assumptions (e.g., "species dispersal is unlimited," "market responses are linear"). Classify each as critical or non-critical based on potential impact.
• Peer Review Workshop: Convene a panel of domain experts (ecologists, economists, social scientists) not involved in the model's build. Present the model's structure and assumptions for critical debate and challenge [69].
• Comparison to Established Theory: Document how the model aligns with or diverges from accepted theoretical frameworks (e.g., metabolic theory in ecology, diffusion of innovations in socio-economic models).

Protocol 2: Input Data and Calibration Validation

Objective: To ensure the input data is accurate, complete, and appropriate, and that the calibration process is robust and transparent [72].

• Data Provenance Tracing: For each key input dataset (e.g., land cover maps, species occurrence records, climate projections), document source, resolution, temporal coverage, known uncertainty, and any preprocessing steps [75].
• Benchmarking: Compare input values and statistical summaries against independent data sources or published benchmarks.
• Calibration Sensitivity Analysis: Use global sensitivity analysis (e.g., Sobol indices) to identify which parameters most influence model outputs. Focus validation efforts on high-sensitivity parameters. Report the calibrated parameter ranges, not just best-fit values.

Protocol 3: Operational and Numerical Validation

Objective: To verify that the model's calculations are implemented correctly and perform reliably across expected conditions.

• Extreme Value & Stress Testing: Input extreme but plausible values (e.g., 100-year flood events, catastrophic habitat loss) to test if the model behaves reasonably or breaks down [72].
• Challenger Model Comparison: Develop a simpler, independent model ("challenger") that replicates the core function of the primary model. Compare outputs for a standard set of scenarios. Significant deviations require investigation [72].
• Software Unit Testing: If custom code is written, implement a suite of automated tests that verify individual components (functions, modules) produce expected results for given inputs.

Protocol 4: Predictive Performance Validation (Back-Testing)

Objective: To assess the model's accuracy by comparing its forecasts against observed historical outcomes.

• Historical Split: Reserve a portion of historical data (e.g., the most recent 20% of a time series) not used during model calibration.
• Initialize and Run: Initialize the model with conditions from the starting point of the validation period and run it forward.
• Quantitative Comparison: Compare model predictions against actual observations using a suite of metrics (see Table 2). This is a direct test of predictive skill [72].

Table 2: Quantitative Metrics for Model Performance Validation [74] [70] [75]

Metric	Formula / Description	Interpretation (Ideal Value)	Applicable Model Output Type
Root Mean Square Error (RMSE)	$$RMSE = \sqrt{\frac{1}{n}\sum{i=1}^{n}(yi - \hat{y}_i)^2}$$	Measures average error magnitude (0)	Continuous (e.g., ecosystem service value, population size)
Mean Absolute Percentage Error (MAPE)	$$MAPE = \frac{100\%}{n}\sum_{i=1}^{n}\left	\frac{yi - \hat{y}i}{y_i}\right	$$	Average percentage error (0%)	Continuous, non-zero data
Kappa Coefficient (K)	$$K = \frac{po - pe}{1 - p_e}$$	Agreement between categorical maps, corrected for chance (1)	Categorical (e.g., land use/cover classes)
Nash-Sutcliffe Efficiency (NSE)	$$NSE = 1 - \frac{\sum{i=1}^{n}(yi - \hat{y}i)^2}{\sum{i=1}^{n}(y_i - \bar{y})^2}$$	Relative magnitude of residual variance vs. data variance (1)	Hydrological, continuous ecological models
Coefficient of Determination (R²)	Proportion of variance in observed data explained by the model.	Goodness of fit (1)	Any linear relationship assessment

Application in Landscape Ecology: A Case Study Framework

The following workflow applies the above protocols to a typical integrated assessment of landscape ecological risk, drawing on the example of assessing uncertainties based on predictions of land use change and ecosystem service evolution [70].

Case Study Aim: To project landscape ecological risk to 2050 under multiple socio-economic scenarios and evaluate the uncertainty associated with land use change predictions.

Experimental Workflow:

• Scenario Definition (Addresses Scenario Uncertainty): Define three distinct scenarios: Business-as-Usual (BAU), Ecological Protection (EP), and Rapid Urbanization (RU). Each must articulate clear, quantitative narratives for policy, economic growth, and conservation investment [70].
• Land Use Change Modeling: Use a model like the PLUS (Patch-generating Land Use Simulation) model to project land use transitions from a baseline year to 2050 under each scenario [70].
  • Validation Step: Perform back-testing by running the model from 1990 to 2020 and comparing simulated 2020 maps to actual 2020 land cover. Calculate Kappa Coefficient and per-class accuracy metrics (Table 2).
• Ecosystem Service Valuation (ESV): Apply established value coefficients to the projected land use maps to calculate total and spatial ESV [70].
  • Validation Step: Conduct sensitivity testing on value coefficients. Vary coefficients within their published confidence intervals and quantify the effect on total ESV (Protocol 3).
• Ecological Risk Index (ERI) Calculation: Compute a landscape ERI based on landscape pattern indices (e.g., fragmentation, loss) and ESV change [70].
  • Uncertainty Propagation: Use Monte Carlo simulation to propagate uncertainty from land use prediction accuracy and value coefficients through to the final ERI, producing a distribution of possible risk outcomes rather than a single map.
• Multi-Model Comparison (Addresses Structural Uncertainty): Repeat steps 2-4 using a different land use change model (e.g., a CA-Markov model). Compare the range and central tendency of ERI outcomes from the two model structures.

Table 3: Example Scenario Results from an Integrated Assessment (Inspired by [70])

Scenario	Key Driver Assumptions	Projected Change in Key Land Use (2030 vs. 2020)	Projected Δ in Total ESV	Dominant Source of Uncertainty
Business-as-Usual (BAU)	Current policies continue; moderate growth.	Forest: -1.5%; Built-up: +2.3%	-4.2% (±1.8%)	Parametric: Future economic growth rate.
Ecological Protection (EP)	Strict conservation laws; payments for ecosystem services.	Forest: +3.1%; Farmland: -0.8%	+5.7% (±2.1%)	Scenario: Political feasibility of strict laws.
Rapid Urbanization (RU)	Deregulated planning; high economic growth.	Forest: -4.5%; Built-up: +6.7%	-12.4% (±3.5%)	Structural: Model's representation of urban sprawl dynamics.

Visualization of Key Processes and Workflows

Integrated Assessment Workflow for Landscape Ecological Risk

Model Validation and Uncertainty Assessment Framework

The Scientist's Toolkit: Key Research Reagent Solutions

Table 4: Essential Tools and Platforms for Integrated Assessment Modeling & Validation

Tool/Reagent Category	Specific Example(s)	Primary Function in IA	Key Considerations for Selection
Land Use/Cover Change (LUCC) Simulation Models	PLUS Model [70], SLEUTH, CA-Markov	Projects spatial patterns of land use change under different scenarios.	Ability to integrate multiple drivers, transparency of transition rules, computational efficiency for large landscapes.
Ecosystem Service Valuation (ESV) Models	InVEST, ARIES, benefit transfer methods [70]	Quantifies the biophysical and economic value of ecosystem services.	Alignment with study region's ecosystems, inclusion of relevant services (provisioning, regulating, cultural), uncertainty handling.
Statistical & Sensitivity Analysis Software	R (with sensitivity, sp packages), Python (SALib, NumPy), SimLab	Performs global sensitivity analysis, uncertainty propagation, and statistical validation.	Support for complex model integration, variety of SA methods (e.g., Sobol, Morris), and robust visualization capabilities.
Model Risk Management (MRM) & Inventory Platforms	Custom databases (SQL), commercial MRM solutions [73]	Maintains the model inventory, tracks validation status, documents assumptions and limitations.	Flexibility for scientific (non-financial) models, support for version control, ease of linking to documentation and results.
Geographic Information System (GIS) Platforms	QGIS, ArcGIS Pro, GRASS GIS	Manages, analyzes, and visualizes all spatial data inputs and model outputs.	Compatibility with model I/O formats, scripting/automation capabilities (e.g., PyQGIS, ArcPy), 3D/spatio-temporal analysis tools.
High-Performance Computing (HPC) Resources	Local clusters, cloud computing (AWS, Google Cloud)	Enables running large multi-model, multi-scenario, Monte Carlo simulation ensembles.	Cost, scalability, support for necessary software and parallel processing frameworks (e.g., MPI).
Model Documentation & Version Control	Git/GitHub/GitLab, LaTeX, Jupyter Notebooks	Ensures reproducibility, tracks model evolution, and creates audit trails for all changes.	Integration with analysis workflows, support for collaborative writing, and ability to handle both code and manuscript assets.
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Validation, Zoning, and Comparative Analysis for Targeted Management

Abstract This application note establishes a standardized protocol for the integrated assessment of landscape ecological risk (LER) and ecological resilience (ER), validating their spatial correlation and inherent negative relationship. Framed within the broader thesis of optimizing landscape ecological risk assessment through resilience research, it synthesizes contemporary methodologies from recent studies in diverse ecosystems [54] [76] [77]. The note details a multi-scale analytical workflow combining landscape pattern indices, resilience dimension quantification, spatial autocorrelation, and geographical detector analysis. We provide validated experimental protocols for data processing, index calculation, and statistical validation, supported by quantitative findings that demonstrate a robust negative correlation (e.g., Pearson’s r ranging from -0.3 to -0.7) [77] [28], which intensifies at finer spatial scales [28]. The integration of a “disturbance-vulnerability-loss” risk model with a “resistance-adaptation-recovery” resilience framework enables the identification of synergistic and trade-off zones, offering actionable insights for targeted ecological management and zoning [76] [28].

Landscape Ecological Risk (LER) and Ecological Resilience (ER) are two foundational but historically distinct concepts in environmental security science. LER quantifies the probability and magnitude of adverse ecological effects arising from natural or anthropogenic stressors on landscape patterns [54] [28]. In contrast, ER defines the capacity of an ecosystem to absorb disturbances, undergo adaptation, and maintain its fundamental structure and functions [78] [28]. The central thesis posits that optimizing traditional LER assessments requires integration with ER research, moving from static hazard identification to a dynamic understanding of a system's coping capacity.

Recent empirical advances demonstrate a quantifiable, spatially explicit negative relationship between these constructs. Landscapes with higher resilience consistently exhibit lower ecological risk, and vice-versa [77] [28]. This correlation is scale-dependent, becoming more pronounced at finer spatial resolutions (e.g., grid-level analysis compared to city-level), highlighting the critical importance of multi-scale assessment [28]. The validation of this negative relationship provides a powerful diagnostic tool. It shifts management paradigms from purely risk mitigation to resilience enhancement, enabling the identification of areas that are not only high-risk but also possess low innate capacity for recovery—priority zones for intervention [76] [28].

Table 1: Core Conceptual Constructs in Integrated Risk-Resilience Assessment

Construct	Operational Definition	Key Dimensions/Indicators	Thesis Relevance
Landscape Ecological Risk (LER)	The potential for negative ecological consequences due to landscape pattern changes under stressors [54] [28].	Disturbance (e.g., fragmentation), Vulnerability (e.g., landscape sensitivity), Potential Loss [54] [28].	The traditional assessment target; requires optimization via resilience linkage.
Ecological Resilience (ER)	The capacity to resist, adapt to, and recover from disturbances while retaining core functions [78] [28].	Resistance, Adaptation, Recoverability [28].	Provides the dynamic counterbalance to risk; key to optimization.
Spatial Correlation	The degree to which LER and ER values co-vary across geographic space.	Global metrics (Pearson’s r), Local metrics (Bivariate Local Moran’s I) [77] [28].	Validates the theoretical negative relationship empirically.
Coupling Coordination	The state of balanced and synergistic development between the risk and resilience systems.	Coupling Coordination Degree (D) [28].	Informs zoning strategies by identifying synergistic vs. dysregulated areas.

Experimental Protocols

Protocol 2.1: Multi-Scale Data Acquisition and Preprocessing

Objective: To acquire and standardize spatial data for consistent multi-temporal, multi-scale analysis.

• Data Sources: Collect multi-temporal (e.g., 2000, 2010, 2020) Landsat or Sentinel series satellite imagery. Acquire ancillary data: Digital Elevation Model (DEM), climate datasets (precipitation, temperature), soil data, and socioeconomic data (population density, GDP) [54] [79].
• Land Use/Land Cover (LULC) Classification: Perform image preprocessing (radiometric calibration, atmospheric correction). Classify imagery using supervised methods (e.g., Support Vector Machine) into categories like forest, grassland, cropland, wetland, built-up land, and bare land [79]. Validate classification accuracy with ground truth points; ensure Kappa coefficient > 0.75 [79].
• Scale Optimization & Resampling: Determine the optimal spatial grain (e.g., 30m [54]) and extent for analysis using response curves and area accuracy loss models [54]. Use the Nearest Neighbor resampling method for landscape pattern indices to preserve categorical integrity [54].
• Grid Partitioning: Overlay a scalable grid system (e.g., 1km x 1km, 3km x 3km, county administrative boundaries) onto the study area. This creates assessment units for subsequent index calculation and multi-scale comparison [28].

Protocol 2.2: Quantitative Index Calculation

Objective: To compute standardized indices for LER and ER within each assessment unit.

A. Landscape Ecological Risk Index (LERI) Calculation [54] [76] [79]:

• Calculate Landscape Metrics: For each LULC type i in each grid k, compute:
  • Fragmentation Index (Fi): F_i = n_i / A_i (where n is number of patches, A is total area).
  • Dominance Index (Di): Based on patch density and frequency.
  • Isolation Index (Ii): Average distance between patches of the same type.
  • Vulnerability Coefficient (Vi): Assign an ordinal weight (1-6) based on ecosystem sensitivity, typically: Wetland (6) > Forest (5) > Grassland (4) > Cropland (3) > Bare land (2) > Built-up (1) [76].
• Compute Landscape Loss Index (L_k): L_k = ∑ (A_{ki} / A_k) * (F_{ki} + D_{ki} + I_{ki}) * V_i where A_{ki}/A_k is the area proportion of landscape i in grid k.
• Normalize to LERI: LERI_k = L_k / max(L_k) to standardize values between 0-1. Higher LERI indicates higher risk.

B. Ecological Resilience Index (ERI) Calculation [78] [28]:

• Select Proxy Indicators: Use landscape patterns as proxies for resilience dimensions:
  • Resistance: Represented by Connectivity (e.g., Patch Cohesion Index).
  • Adaptation: Represented by Diversity (e.g., Shannon’s Diversity Index).
  • Recoverability: Represented by Proportion of Natural Landscapes (e.g., combined area of forest, grassland, wetland) [28].
• Standardize and Weight Indicators: Normalize each indicator to a 0-1 scale. Assign weights (e.g., via Principal Component Analysis or expert judgment). Studies suggest recoverability may exert the strongest counteracting effect on risk [28].
• Compute ERI: ERI_k = w_R * Resistance_k + w_A * Adaptation_k + w_{Rec} * Recoverability_k where w denotes dimension weights.

Protocol 2.3: Spatial Correlation and Statistical Validation

Objective: To quantify and validate the spatial relationship between LERI and ERI.

• Global Correlation Analysis: Calculate Pearson’s correlation coefficient between the LERI and ERI values across all grids for each study year. A significant negative correlation is hypothesized [77] [28].
• Spatial Autocorrelation Analysis:
  • Univariate Moran’s I: Assess the spatial clustering of LERI and ERI individually [54] [79].
  • Bivariate Local Moran’s I (LISA): Identify localized spatial correlation types [28]. This maps four significant cluster types:
    • High Risk - Low Resilience (HL): Priority intervention zones.
    • Low Risk - High Resilience (LH): Conservation priority zones.
    • High Risk - High Resilience (HH): Areas with high stress but good buffer capacity.
    • Low Risk - Low Resilience (LL): Potentially stable but vulnerable areas.
• Scale-Dependency Test: Repeat steps 1-2 for assessment grids at different scales (e.g., 1km, 3km, county). Analyze how correlation strength and cluster patterns change with scale [28].
• Driving Factor Analysis with Geodetector:
  • Factor Detection: Use the q-statistic in Geodetector to quantify the explanatory power of natural (e.g., slope, precipitation, NDVI) and anthropogenic (e.g., population density, land use intensity) factors on LERI and ERI spatial heterogeneity [54] [79].
  • Interaction Detection: Determine whether driving factors interact to enhance or weaken each other’s influence on risk and resilience [54] [79].

Data Presentation and Validation

Table 2: Quantitative Findings from Empirical Case Studies

Study Area	Key Quantitative Finding (LER)	Key Quantitative Finding (ER)	Validated Risk-Resilience Correlation	Primary Drivers Identified
Luan River Basin [54]	ILERI values: 0.242 (2000), 0.234 (2022). Medium-low risk areas increased to 70.55% (2022).	Not assessed independently.	Not assessed.	Precipitation, population density, primary industry output. Interaction of factors > single factor effect.
Puding County (Karst) [76]	LER mostly low; pattern "high in middle, low around". Lower & moderate risk zones cover ~64% of area.	ESV fluctuated (15.11% overall change). High ESV in northeast (shrubland).	Low ESV - Low LER zone was most common spatial coupling type.	Land use conversion (dry land/paddy → construction land).
Shenyang (Winter City) [77]	ERI "Very Low" zones decreased by 12.78%; "Low" zones increased by 13.21% (2000-2020).	Total ESV decreased from 273.97 to 270.38 × 10⁸ CNY (2000-2020).	Significant spatial correlation identified. High ESV & high ERI co-located in northeastern hills.	EVP/NDVI interaction (for ESV); SSD/DEM interaction (for ERI).
Hefei Metropolitan Circle [28]	"Stable overall, localized differentiation". High-risk zones concentrated in high water-body-ratio areas.	Overall low & slightly declining. High-resilience area reduced by 50.6% (2010-2020).	Robust negative correlation, strengthening at finer scales. Recoverability dimension had strongest counteracting effect on risk.	Urban expansion, landscape pattern changes.
Saihanba Forest Farm [79]	High-risk area proportion dropped from 72.30% (1987) to localized clusters (2020).	Forest landscape proportion increased from 23.19% to 74.55% (1987-2020).	Not directly assessed, but forest gain implies resilience increase coupled with risk decrease.	Landscape type (strongest driver), temperature, vegetation coverage.

Table 3: Scale-Dependency of Risk-Resilience Correlation [28]

Spatial Analysis Scale	Pearson Correlation Coefficient (r)	Characteristics of Spatial Clustering	Implication for Management
Grid Scale (e.g., 1km)	-0.65 to -0.72 (Strong Negative)	Clear, fine-grained patterns of HL and LH clusters. Highly heterogeneous.	Enables precise, targeted intervention at the patch level.
County Scale	-0.45 to -0.55 (Moderate Negative)	Clusters become more aggregated; patterns reflect administrative averages.	Suits policy formulation and resource allocation at the jurisdictional level.
City/Regional Scale	-0.30 to -0.40 (Weak to Moderate)	Broad regional trends evident; local heterogeneity is masked.	Useful for strategic, macro-level planning and cross-regional comparison.

The Scientist’s Toolkit: Essential Research Reagent Solutions

Table 4: Key Software, Data, and Analytical Tools

Item Name	Function/Description	Application in Protocol
Landsat/Sentinel Imagery	Provides multi-spectral, multi-temporal land surface data.	Primary data source for LULC classification and change detection [54] [79].
Geographic Information System (GIS) Software (e.g., ArcGIS, QGIS)	Platform for spatial data management, analysis, and cartographic visualization.	Grid creation, spatial overlay, map algebra, and final map production [78].
Fragstats	Computes a wide array of landscape pattern metrics.	Calculating fragmentation, dominance, and isolation indices for LERI [76].
R or Python (with spdep, GD, matplotlib libraries)	Statistical computing and spatial analysis environment.	Performing spatial autocorrelation, Geodetector analysis, correlation tests, and data visualization [77] [79].
Geodetector Software	Specifically designed for spatial stratified heterogeneity analysis and factor detection.	Quantifying the driving power (q-statistic) of natural and human factors on risk and resilience [54] [79].
Normalized Difference Vegetation Index (NDVI)	Remote-sensing derived indicator of vegetation vigor and cover.	Proxy for ecosystem health and a key input for resilience assessment and driver analysis [79].
Digital Elevation Model (DEM)	Represents topographic elevation.	Used for deriving slope/aspect and as a covariate in driver analysis [79].
(2R)-Ethylmalonyl-CoA	(2R)-Ethylmalonyl-CoA, MF:C26H42N7O19P3S, MW:881.6 g/mol	Chemical Reagent
(S)-3-Hydroxyoctacosanoyl-CoA	(S)-3-Hydroxyoctacosanoyl-CoA, MF:C49H90N7O18P3S, MW:1190.3 g/mol	Chemical Reagent

Visualization of Frameworks and Workflows

Discussion and Integrated Application

The validated negative spatial correlation provides an evidence-based mechanism for optimizing LER assessment. It confirms that resilience is not merely a conceptual buffer but a measurable, spatially variable property that directly offsets risk. The scale-dependency of this relationship is critical: finer-scale analyses reveal stronger negative correlations and more precise cluster patterns, enabling targeted micro-interventions, while broader scales inform regional policy [28].

Integration of this assessment into the broader thesis enables predictive zoning. By classifying areas into the four LISA cluster types, managers can prioritize actions:

• High Risk - Low Resilience (HL) Zones: Require immediate, intensive restoration (e.g., ecological engineering, strict land-use control).
• Low Risk - High Resilience (LH) Zones: Priority for conservation and preventative protection.
• High Risk - High Resilience (HH) Zones: Focus on reducing the disturbance pressure, leveraging the system's inherent capacity to cope.
• Low Risk - Low Resilience (LL) Zones: May require monitoring and gentle enhancement of natural capital.

The use of Geodetector moves the analysis beyond correlation to causality, identifying the key levers (e.g., vegetation cover, landscape configuration) that simultaneously mitigate risk and enhance resilience [54] [79]. This integrated, validated framework transforms landscape ecological risk assessment from a reactive accounting of threats into a proactive science of system stewardship, directly supporting the optimization thesis by embedding resilience as the core diagnostic and prescriptive component.

Ecological Management Zoning (EMZ) represents a spatially explicit, strategic framework for administering land and resource management to achieve specific ecological objectives. Framed within the critical context of optimizing landscape ecological risk (LER) assessment with resilience research, EMZ provides a science-based methodology to partition landscapes into distinct functional units—Adaptation, Conservation, and Restoration Regions [11]. This approach moves beyond static protection to embrace dynamic management strategies that enhance ecosystem capacity to absorb disturbance, reorganize, and sustain function—a core tenet of ecological resilience [20]. The integration of Landscape Ecological Risk assessment, which evaluates the potential adverse impacts of landscape pattern changes on ecosystem structure and function, with quantitative resilience metrics, offers a robust foundation for zoning [11] [5]. This synthesis allows researchers and land managers to proactively identify areas where ecosystems are vulnerable (high LER), assess their innate ability to cope with stress (resilience), and prescribe targeted interventions. For drug development professionals, understanding these ecological dynamics is increasingly relevant for assessing environmental impacts of operations, ensuring sustainable sourcing of natural materials, and comprehending large-scale ecological drivers that influence regional environmental health.

Core Concepts and Assessment Foundations

Landscape Ecological Risk (LER) Assessment

Landscape Ecological Risk assessment is a pattern-based approach that evaluates potential ecological consequences arising from the spatial configuration and composition of landscapes under natural or anthropogenic stress [11]. Traditional LER models often rely on landscape pattern indices (e.g., fragmentation, isolation, dominance) which can be subjective and lack a direct functional ecological link [11]. Contemporary optimized frameworks address this by grounding assessments in ecosystem services (ES). Here, landscape vulnerability is derived from the capacity of an area to provide key services like water retention, soil conservation, and carbon sequestration, rather than arbitrary land-use classifications [11]. A declining capacity to provide these services indicates higher vulnerability and, consequently, higher ecological risk. The general formula for an ecosystem service-informed LER index is:

LER_i = f(S_i, V_i(ES)) where for spatial unit i, LER is a function of a landscape disturbance/structure index (S_i) and a vulnerability index (V_i) quantified via ecosystem services [11].

Table 1: Key LER Assessment Models and Their Applications

Model Name/Approach	Core Components	Advantages	Primary Application Context	Key Citations
Ecosystem Service-Based LER	Landscape pattern indices + Vulnerability from ES valuation (e.g., water yield, soil retention, NPP)	Direct ecological relevance; reduces subjectivity; links risk to human well-being.	Watershed management; regional ecological security assessment.	[11]
Production-Living-Ecological Space (PLES) Risk Index	LER assessment based on functional land use categories (production, living, ecological spaces).	Integrates socio-economic function; suitable for urban-peri-urban landscapes with high human pressure.	Urban agglomeration planning; national land spatial planning.	[12]
Risk Source-Sink Model	Identifies risk sources (e.g., pollution, erosion), receptors, and exposure pathways.	Effective for specific, known stressors like chemical pollution or point-source degradation.	Local-scale contamination studies; habitat impact assessments.	[80]

Ecosystem Resilience (ER) Quantification

Ecological resilience, defined as the capacity of a system to tolerate disturbance without shifting to an alternative state with different structure and function, is a complementary metric to LER [20]. Quantifying ER is central to resilience research and involves measuring key attributes:

• Adaptive Capacity: The system's ability to adjust to stress through processes like genetic diversity or functional redundancy among species [20].
• Thresholds & Alternative Regimes: Identifying tipping points and potential alternative stable states the system may flip into [20].
• Scale: Recognizing that resilience operates across nested spatial and temporal scales [20].

Operationally, resilience can be indexed using metrics like vegetation recuperation rate after disturbance, landscape connectivity, or the diversity and redundancy of functional traits within ecosystems [11] [20]. Spatially, ER and LER often exhibit a non-linear, inverse relationship; areas with high resilience typically demonstrate a greater capacity to buffer against and recover from risks [11].

Table 2: Metrics for Assessing Ecosystem Resilience (ER) Attributes

Resilience Attribute	Measurable Indicators/Proxies	Data Sources & Methods	Interpretation for Zoning
Adaptive Capacity	Functional trait diversity & redundancy; habitat connectivity; genetic diversity indices.	Species surveys, remote sensing (NDVI, land cover), circuit theory or graph-based connectivity models.	High scores indicate systems better able to reorganize post-disturbance; suitable for Conservation/Adaptation zones.
Threshold Proximity	Stressor response curves; early-warning signals (e.g., rising autocorrelation, variance).	Long-term monitoring data, spatial-temporal statistical analysis.	Areas near thresholds are priority for intervention (Restoration or intensive Adaptation).
Scale-Specific Recovery	Rate of return to baseline function after drought/fire; patch dynamics.	Time-series remote sensing (MODIS, Landsat), historical disturbance maps.	Fast recovery indicates high engineering resilience; informs management timelines.

Integrating LER and ER for Zoning

The synergy between LER and ER forms the analytical core of modern EMZ. LER pinpoints where ecosystems are under pressure, while ER diagnoses why they are vulnerable (low resilience) or robust (high resilience). This integration is typically achieved through bivariate spatial autocorrelation analysis (e.g., Local Moran's I), which classifies the landscape into spatial clusters based on the dual status of LER and ER [11] [81]. For instance, a High-LER / Low-ER cluster signifies a high-risk, low-resilience area urgently requiring restoration, while a Low-LER / High-ER cluster indicates a healthy, robust system ideal for conservation [11]. Advanced studies also employ causal analysis models, like the Geographically Convergent Cross-mapping Model (GCCM), to determine the directionality of influence between LER and ecosystem service value, further refining zoning logic [81].

Application Notes: Protocols for Data Acquisition and Analysis

Protocol 1: Ecosystem Service-Based LER Assessment

This protocol details the steps to compute a spatially explicit LER index grounded in ecosystem services [11].

Objective: To map Landscape Ecological Risk by integrating landscape pattern disturbance with vulnerability assessed via key ecosystem services. Materials: Multi-temporal land use/cover (LULC) maps (30m resolution recommended), digital elevation model (DEM), soil data, climate data (precipitation, temperature), and boundary of the study area. Software: GIS software (e.g., ArcGIS, QGIS), FragStats or similar landscape pattern analysis tool, and ecosystem service modeling tools (e.g., InVEST, RUSLE).

Procedure:

• Data Preparation & Grid Creation: Unify all spatial data to a common coordinate system and resolution. Overlay a grid of assessment units (e.g., 2-5 times the average patch size) on the study area [11] [12].
• Landscape Disturbance Index (S_i) Calculation: For each grid cell, calculate landscape pattern indices using LULC data:
  • Fragmentation (C_i): Number of patches / total area.
  • Isolation (N_i): A function of distance and area between patches of the same type.
  • Dominance (D_i): Degree to which the landscape is dominated by one or few patch types.
  • Synthesize into a composite S_i using weighted summation (e.g., weights of 0.5, 0.3, 0.2 for C, N, D respectively) [80].
• Ecosystem Service-Based Vulnerability (V_i) Assessment:
  • Select 3-5 regionally critical ES (e.g., water conservation, soil retention, carbon storage, habitat quality).
  • Model the biophysical capacity for each ES using tools like InVEST.
  • Normalize and weight ES values (e.g., via expert Delphi method or principal component analysis) to create a composite ES Capacity score per grid.
  • Derive V_i as the inverse of the ES Capacity score (i.e., low service provision = high vulnerability) [11].
• LER Index Computation & Mapping: For each grid cell, compute LER_i = S_i * V_i. Classify the resulting continuous LER values (e.g., using natural breaks) into discrete risk levels (Low, Medium, High) and generate a spatial map.

Protocol 2: Spatial Coupling of LER and Ecosystem Resilience

This protocol guides the integration of LER and ER assessments for preliminary zoning [11] [81].

Objective: To identify spatially correlated clusters of LER and ER as the basis for defining Adaptation, Conservation, and Restoration regions. Materials: Raster maps of LER index and ER index (from Protocol 1 and resilience modeling), statistical software (e.g., R, GeoDa). Software: GIS software, spatial statistics software/packages (e.g., spdep in R, GeoDa).

Procedure:

• Data Standardization: Ensure LER and ER raster layers have identical extents, resolutions, and cell alignment. Convert layers to a consistent numeric scale (e.g., Z-score normalization).
• Bivariate Local Spatial Autocorrelation:
  • Perform a Bivariate Local Moran's I analysis using the LER index as the primary variable and the ER index as the secondary variable.
  • This analysis will classify each grid cell into one of five significant spatial cluster types:
    • High-High (HH): High LER, High ER.
    • Low-Low (LL): Low LER, Low ER.
    • High-Low (HL): High LER, Low ER.
    • Low-High (LH): Low LER, High ER.
    • Not Significant.
• Zone Delineation:
  • Restoration Region: Primarily derived from High-Low (HL) clusters (high risk, low resilience). These are priority areas for active, interventionist restoration.
  • Conservation Region: Primarily derived from Low-High (LH) clusters (low risk, high resilience). The focus is on preserving existing structure and function.
  • Adaptation Region: Encompasses High-High (HH) and Low-Low (LL) clusters, as well as non-significant areas. HH areas have inherent resilience to manage risk, requiring adaptive monitoring; LL areas are stable but fragile, needing measures to build resilience [11].

The Ecological Management Zoning Framework

Based on the integrated assessment of LER and ER, landscapes can be partitioned into three primary management regions, each with distinct goals, strategies, and intervention intensities.

Adaptation Region

• Definition: Areas experiencing significant ecological risk where natural ecosystem resilience is either moderately high or actively being compromised. The primary goal is not to revert to a historical state but to facilitate the ecosystem's transition to a new, desired state that maintains function under changing conditions [20].
• Characteristics: Moderate to High LER; Variable (Low to High) ER. Often corresponds to ecological frontiers facing climate change impacts or moderate, chronic human pressure.
• Management Strategies: Adaptive management is key [82]. Strategies include assisted migration of key species, managing connectivity to facilitate species movement, implementing low-tech, process-based restoration (e.g., beaver dam analogues for riparian areas), and proactive monitoring for early-warning signals of threshold crossings [20] [82].

Conservation Region

• Definition: Core areas of high ecological integrity and resilience with currently low levels of ecological risk. The objective is to preserve and protect these functional ecosystems, which often serve as critical refugia and sources for ecosystem services.
• Characteristics: Low LER; High ER. Typically includes large, contiguous patches of natural vegetation (e.g., core forests, intact wetlands), protected areas, and regions with high biodiversity value.
• Management Strategies: Preventative protection. Strengthen legal safeguards, control access, prevent fragmentation, and mitigate external threats. The U.S. Bureau of Land Management's policy emphasizes "protect[ing] the most intact, functioning landscapes" as a top conservation priority [82]. Management focuses on maintaining natural processes with minimal intervention.

Restoration Region

• Definition: Areas suffering from high ecological risk and degraded resilience, often exhibiting clear signs of ecosystem degradation or dysfunction. The goal is active intervention to recover ecological structure, function, and services.
• Characteristics: High LER; Low ER. Commonly associated with highly fragmented landscapes, severely eroded or contaminated sites, and abandoned industrial or agricultural lands.
• Management Strategies: Active, targeted restoration. Actions must "address causes of degradation" and be "designed, implemented, and monitored at appropriate spatial and temporal scales" [82]. This includes reforestation, erosion control, invasive species removal, hydrological restoration, and reintroduction of native species. The focus is on rebuilding adaptive capacity and moving the system away from a degraded threshold.

Table 3: Zoning Criteria, Objectives, and Intervention Examples

Zone	LER/ER Profile	Primary Objective	Example Interventions	Monitoring Focus
Adaptation	Moderate-High LER / Variable ER	Facilitate functional ecosystem transitions under change.	Assisted migration; managed connectivity; climate-smart practices.	Early-warning indicators; rate of change in ES provision.
Conservation	Low LER / High ER	Preserve intact ecosystem integrity and processes.	Legal protection; threat buffer zones; anti-poaching patrols.	Keystone species populations; habitat connectivity metrics.
Restoration	High LER / Low ER	Recover ecological structure, function, and resilience.	Revegetation with natives; erosion control; contaminant remediation; dam removal.	Survival/growth rates; return of indicator species; soil health indices.

Case Studies and Practical Implementation

Case Study 1: Watershed Management (Luo River, China)

A study in the Luo River Watershed optimized LER assessment using ecosystem services (water yield, soil retention, carbon storage) and integrated it with a resilience index [11]. Bivariate zoning revealed:

• Restoration Regions: Concentrated in the eastern, urbanizing areas with high LER and low ER, guiding targeted reforestation and soil conservation projects.
• Conservation Regions: Located in the western forested highlands (low LER, high ER), leading to strengthened protection of source water areas.
• Adaptation Regions: Identified in transitional zones, prompting plans for developing ecological agriculture to adapt to seasonal climate variability. This case demonstrates the model's utility in guiding differentiated, watershed-scale management.

Case Study 2: Regional Biodiversity Security (Yellow River Basin)

Research in the ecologically fragile Yellow River Basin used LER assessment to construct an ecological security pattern [80]. Key steps included:

• Identifying ecological sources (low-risk, high-quality patches).
• Building a resistance surface based on LER levels (higher risk = higher resistance to species movement).
• Using the Minimum Cumulative Resistance (MCR) model to delineate ecological corridors and nodes. This network of sources, corridors, and nodes directly informed a zoning plan: core sources became Conservation Regions, degraded corridors requiring rehabilitation were classified as Restoration Regions, and the broader matrix with moderate risk was designated for Adaptation measures to improve landscape permeability [80].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 4: Key Research Reagents, Tools, and Datasets for EMZ Implementation

Item Category	Specific Item/Software	Function in EMZ Research	Critical Specifications/Notes
Spatial Data	Multi-temporal Land Use/Land Cover (LULC) data (e.g., FROM-GLC, ESA CCI)	The foundational layer for calculating landscape pattern indices and tracking change.	Resolution (≥30m recommended), temporal consistency, and classification accuracy are critical.
Biophysical Data	Digital Elevation Model (DEM), soil type/texture grids, climate datasets (precip, temp)	Essential inputs for modeling ecosystem services (e.g., soil retention, water yield) and assessing resilience drivers.	DEM resolution should match LULC; climate data should be spatially interpolated.
Analysis Software	FragStats, GuidosToolbox	Calculates landscape pattern metrics (patch density, edge, connectivity) for the LER disturbance index.	Batch processing capability for multi-temporal data is highly beneficial.
Ecosystem Service Models	InVEST Suite, RUSLE/SDR models	Quantifies the biophysical supply of key ecosystem services to derive vulnerability and assess benefits.	Model selection must match regional context and data availability.
Spatial Statistics Tools	GeoDa, R with spdep, sf packages	Performs univariate and bivariate spatial autocorrelation (Local Moran's I) to identify LER-ER clusters for zoning.	Capacity to handle large raster/vector datasets efficiently.
Resilience Metrics Tools	R with vegan, SDMTools packages; Time-series analysis software	Calculates biodiversity indices, connectivity metrics, and analyzes recovery rates from time-series data.	Flexibility to integrate custom scripts for novel resilience proxies.
Scenario & Planning Tools	PLUS, CLUE-S land-use change models; MCR model in GIS	Simulates future land-use scenarios under different policies; identifies ecological corridors and restoration priorities.	Calibration with historical transition data is required for reliable simulation.
Palmitoleyl palmitoleate	Palmitoleyl palmitoleate, MF:C32H60O2, MW:476.8 g/mol	Chemical Reagent	Bench Chemicals
2-Hydroxytetracosanoyl-CoA	2-Hydroxytetracosanoyl-CoA, MF:C45H82N7O18P3S, MW:1134.2 g/mol	Chemical Reagent	Bench Chemicals

Evaluating the Effectiveness of Different Management Strategies Across Risk-Resilience Zones

The integration of landscape ecological risk assessment with resilience research represents a critical advancement in sustainable ecosystem management. This synthesis moves beyond static risk evaluation to understand a landscape's dynamic capacity to absorb disturbance, adapt, and transform [83]. Effective management requires zoning landscapes based on their unique Risk-Resilience (R-R) profile—the specific interplay between exposure to ecological threats (risk) and the intrinsic capacity to withstand them (resilience) [10] [76]. Drawing parallels from frontier fields like artificial intelligence, where models are classified into green (manageable), yellow (controlled), and red (suspended) deployment zones based on risk thresholds, this article establishes a similar zoning framework for landscape management [84]. We posit that management strategies are only effective when they are precisely calibrated to the R-R zone of a given landscape unit. This article provides detailed application notes and experimental protocols for researchers to implement this integrated assessment framework, define R-R zones, and test the efficacy of targeted interventions, thereby contributing to the optimization of landscape ecological risk assessment.

Methodology for Integrated Risk-Resilience Assessment and Zoning

A robust, multi-step methodology is required to classify landscapes into actionable Risk-Resilience Zones. The following workflow, synthesized from recent research, provides a standardized protocol.

Integrated Risk-Resilience Assessment and Zoning Workflow

Diagram 1: Workflow for integrated risk-resilience assessment and zoning.

Protocol 2.1: Calculating the Landscape Ecological Risk Index (ERI)

• Objective: Quantify the spatial heterogeneity and potential ecological loss due to landscape pattern changes.
• Procedure:
  • Landscape Classification & Grid Establishment: Using land use/cover maps (e.g., from Landsat), classify the landscape into types (e.g., forest, grassland, cropland, urban). Overlay a uniform grid (e.g., 1km x 1km or 100m x 100m) across the study area. Each grid cell is the basic assessment unit [10] [76].
  • Calculate Landscape Metrics per Grid Cell: For each grid cell i, use software like Fragstats to compute:
    • Landscape Disturbance Index (LDIi): A weighted sum of the area and disturbance coefficient of each landscape type within the cell. LDI_i = Σ (A_{ij} * D_j) / A_i. A_{ij} is the area of landscape type j in cell i, D_j is the assigned disturbance coefficient for type j (higher for artificial landscapes), and A_i is the total area of the cell.
    • Landscape Fragmentation Index (LFIi): Often derived from metrics like edge density, patch density, or the splitting index within the cell.
  • Compute ERI per Grid Cell: ERI_i = LDI_i * LFI_i. Normalize ERI values to a 0-1 scale for comparison across time and space [10].

Protocol 2.2: Calculating the Ecological Resilience Index (ER)

• Objective: Assess the ecosystem's intrinsic capacity to maintain function against disturbance, based on its structural configuration.
• Procedure:
  • Select Core Landscape Metrics: Calculate for each grid cell i [83]:
    • Habitat Suitability (HSi): Assign a coefficient (0-1) based on the dominant land use type's naturalness (e.g., forest=1, grassland=0.8, urban=0.1).
    • Landscape Connectivity (LCIi): Use a metric like the probability of connectivity (PC) or connectance index.
    • Landscape Diversity (SHDI_i): Use the Shannon's Diversity Index based on the area proportions of landscape types within the cell.
  • Compute ER per Grid Cell: ER_i = HS_i * SHDI_i * LCI_i. Standardize all three components and the final ER to a 0-1 scale [83].

Protocol 2.3: Defining Risk-Resilience Zones

• Objective: Create a spatially explicit zoning map to guide differentiated management.
• Procedure:
  • Standardize and Overlay: Perform Z-score standardization for the ERI and ER grids. Spatially overlay the two standardized grids.
  • Apply Risk-Resilience Matrix: Classify each grid cell into one of four zones based on the joint classification of ERI and ER percentiles (e.g., above/below median) [76]:
    • Zone I: High Risk-Low Resilience (Critical Restoration): High external pressure, low coping capacity.
    • Zone II: High Risk-High Resilience (Key Supervision): Under pressure but currently robust; focus on maintaining resilience.
    • Zone III: Low Risk-Low Resilience (Function Enhancement): Stable but vulnerable to future change; needs capacity building.
    • Zone IV: Low Risk-High Resilience (Conservation Maintenance): Healthy and stable; core conservation area.
  • Spatial Aggregation: Dissolve adjacent grid cells of the same zone to create contiguous management polygons.

Application Notes: Key Findings and Data Synthesis

Application of this framework in diverse regions yields quantitative insights into risk-resilience dynamics and zone-specific management outcomes.

Table 1: Characteristics and Area Distribution of Risk-Resilience Zones in Case Studies

Risk-Resilience Zone	Primary Characteristics	Key Landscape Indicators	Area % (Northern Qinghai) [83]	Area % (Puding Karst) [76]	Recommended Management Focus
I: High Risk-Low Resilience	Severe fragmentation, low habitat quality, high disturbance.	High ERI, Low HS & LCI.	20.09% (Key Ecological Restoration)	~18% (Ecological Risk Improvement)	Priority restoration: Habitat rehabilitation, connectivity corridors, strict disturbance regulation.
II: High Risk-High Resilience	Under pressure but structurally diverse and connected.	High ERI, High SHDI & LCI.	38.77% (Key Supervision)	~22% (Ecological Comprehensive Restoration)	Supervision & maintenance: Monitor for resilience loss, enforce protection, manage human activity intensity.
III: Low Risk-Low Resilience	Stable but homogeneous, vulnerable to change.	Low ERI, Low SHDI.	(N/A - classified differently)	~40% (Most prevalent - Low ESV/Low LER)	Function enhancement: Ecological engineering, diversification of landscape mosaic, sustainable livelihood programs.
IV: Low Risk-High Resilience	Optimal landscape health, high integrity and function.	Low ERI, High ER.	40.94% (Prevention and Protection)	~20% (Ecological Conservation)	Conservation & prevention: Strengthen protected areas, prohibit destructive activities, long-term monitoring.

Table 2: Simulated Effectiveness of Management Scenarios Across Zones (Northern Qinghai Example) [83]

Management Scenario	Core Policy Instruments	Projected Impact on Ecological Resilience (by 2030 vs. 2020)	Implied Effectiveness per Zone
Inertia Development (ID)	Unrestricted urban/agricultural expansion, no new protection.	-23.38% overall decline. Largest decreases in Zones I & III.	Ineffective: Accelerates degradation in vulnerable Zones I & III. Fails to sustain Zone II resilience.
Ecological Protection (EP)	Strict protection of Zones IV & II, restoration in Zone I, sustainable use in Zone III.	-14.28% overall decline. Mitigates loss, preserves core resilient areas.	Targeted & Effective: Slows decline by focusing interventions on zones where they are most strategic and cost-effective.
Gap from Optimal	N/A	A 9.10-percentage-point improvement from ID to EP.	Demonstrates the tangible value of zone-differentiated strategy over a one-size-fits-all or laissez-faire approach.

Experimental Protocol for Scenario Simulation and Strategy Testing

Evaluating future management strategies requires spatially explicit simulation models. The Future Land Use Simulation (FLUS) model is a robust tool for this purpose.

FLUS Model Workflow for Scenario Testing

Diagram 2: FLUS model workflow for simulating management scenarios.

Protocol 4.1: Configuring the FLUS Model with R-R Zone Constraints

• Objective: Project future land use under different management scenarios that respect R-R zoning.
• Procedure:
  • Model Calibration: Use historical land use maps (e.g., 2000, 2010, 2020) and spatial drivers (topography, climate, infrastructure) to train a Back-Propagation Neural Network (BP-ANN). This generates a base suitability probability map for each land use type [83].
  • Define Scenario-Specific Parameters:
    • Inertia Development (ID) Scenario: Set land use demand based on historical trends. Apply minimal conversion restrictions.
    • Ecological Protection (EP) Scenario:
      • Demand: Limit expansion of built-up land, increase demand for forest/grassland.
      • Zoning Constraints: Integrate the R-R Zone map. Set extremely high conversion costs for changes within Zone IV (Conservation). Set high costs for conversion of natural land in Zone II (Supervision). Set moderate to low costs for restorative transitions (e.g., barren to grassland) in Zone I (Restoration) [83].
  • Run Simulation: The FLUS model uses an adaptive inertia mechanism and a roulette wheel selection to allocate future land use changes iteratively until the projected demands are met, respecting the defined suitability and zoning constraints.
  • Validation: Use a historical period (e.g., simulate 2010 from 2000) to validate model accuracy via Kappa coefficient before running future projections.

Protocol 4.2: Evaluating Management Strategy Effectiveness

• Objective: Quantify the impact of different zoning-informed strategies on future ecological security.
• Procedure:
  • Calculate Future Indices: Compute the Landscape Ecological Risk Index (ERI) and Ecological Resilience Index (ER) for the simulated land use maps of 2030 under both ID and EP scenarios.
  • Conduct Comparative Analysis:
    • Calculate the percentage change in total area-weighted ERI and ER from the baseline (2020) to each scenario (2030).
    • Map the spatial transition of R-R Zones between scenarios.
    • Statistically compare the mean ERI and ER values within each R-R Zone across scenarios using paired t-tests or Mann-Whitney U tests.
  • Effectiveness Metrics: Key outcome metrics include: (a) the reduction in overall ERI increase, (b) the mitigation of overall ER decline, and (c) the stability or improvement in zone classification, particularly the reduction in area of Zone I (High Risk-Low Resilience).

The Scientist's Toolkit: Essential Research Reagent Solutions

Tool / Solution Category	Specific Product/Platform	Primary Function in R-R Assessment	Key Benefit / Application Note
Geospatial Data & Analysis	Google Earth Engine, USGS EarthExplorer	Cloud-based access and processing of multi-temporal satellite imagery (Landsat, Sentinel).	Enables large-scale, long-term land use change analysis without local computational bottlenecks.
Landscape Metric Calculation	Fragstats 4.2 (https://fragstats.org)	Computes a comprehensive suite of landscape pattern metrics (patch, class, landscape level).	Industry-standard for deriving LFI, SHDI, LCI, and other indices for ERI and ER calculations [83].
Land Use Change Modeling	GeoSOS-FLUS Model	Simulates future land use scenarios under complex spatial constraints and competition.	Superior for integrating R-R zoning rules as spatial constraints via conversion costs; includes BP-ANN for suitability [83].
Spatial Statistics & Zoning	ArcGIS Pro / QGIS with R/Python integration	Spatial overlay, Z-score standardization, map algebra, and spatial clustering (e.g., Getis-Ord Gi*).	Essential for creating the final R-Zone map and performing hotspot analysis of risk and resilience.
Risk Zone Visualization Standards	ANSI/ISO Safety Color Palette (Red, Yellow, Green, Blue)	Provides a cognitively intuitive scheme for mapping risk (Red/Orange) and safe/resilient (Green/Blue) zones.	Using standardizes colors (e.g., #EA4335 for High Risk, #34A853 for High Resilience) ensures clear, accessible communication of findings [85].
trans-2-decenedioyl-CoA	trans-2-decenedioyl-CoA, MF:C31H50N7O19P3S, MW:949.8 g/mol	Chemical Reagent	Bench Chemicals
Trifunctional Sphingosine	Trifunctional Sphingosine, MF:C34H48N4O6, MW:608.8 g/mol	Chemical Reagent	Bench Chemicals

Discussion: Synthesis and Strategic Recommendations

The integrated framework demonstrates that management effectiveness is intrinsically tied to the spatial targeting of interventions based on the R-R Zone. The 9.10-percentage-point improvement in resilience outcome from the EP scenario over the ID scenario (Table 2) provides compelling quantitative evidence for this approach [83]. This mirrors findings from financial and infrastructure sectors, where forward-looking, zone-based risk assessment (e.g., for climate bonds or AI deployment) is key to resilient investment and governance [84] [86].

Strategic Recommendations for Zone-Specific Management:

• Zone I (High Risk-Low Resilience): Implement priority, intensive restoration. Strategies include reforestation, wetland restoration, and engineering solutions to mitigate specific hazards (e.g., soil erosion control). This zone offers the highest potential return on investment for improving overall landscape security.
• Zone II (High Risk-High Resilience): Focus on supervision and proactive maintenance. Policies should restrict further fragmentation, protect key connectivity elements, and employ sustainable resource management to prevent a transition to Zone I.
• Zone III (Low Risk-Low Resilience): Promote ecological function enhancement. Incentivize agroforestry, diversify agricultural landscapes, and establish community-based conservation to build ecological redundancy and socio-ecological resilience.
• Zone IV (Low Risk-High Resilience): Enforce strict conservation and protection. Designate as ecological redlines or core protected areas. Management should focus on monitoring and preventing encroachment or external pollution.

Optimizing landscape ecological risk assessment requires its fusion with resilience research through a dynamic zoning framework. By calculating discrete Landscape Ecological Risk and Ecological Resilience indices, landscapes can be classified into four actionable Risk-Resilience Zones. As demonstrated, the effectiveness of management strategies—whether restorative, preventative, or conservative—is maximized when they are precisely calibrated to the specific risk-resilience profile of each zone. The protocols for assessment, zoning, and scenario simulation using tools like Fragstats and the FLUS model provide researchers and land managers with a replicable methodology. This integrated, zone-differentiated approach offers a scientifically rigorous pathway for translating landscape ecological theory into targeted, effective action for long-term ecosystem sustainability.

This analysis provides detailed application notes and protocols for differentiating natural and anthropogenic drivers within landscape ecological risk assessment (LERA). Framed within the broader thesis of optimizing LERA with resilience research, these methodologies are designed to dissect the complex interactions between human-induced pressures and natural environmental variability [12]. The core objective is to equip researchers with precise tools to quantify driving forces, thereby supporting the development of targeted strategies that enhance ecological resilience and inform sustainable landscape management.

Quantitative Data Synthesis of Driving Factors

The following tables synthesize key quantitative findings on the influence and interaction of driving factors from recent landscape ecological risk studies.

Table 1: Dominant Driving Factors of Landscape Ecological Risk in Selected Basins This table compares the primary natural and anthropogenic factors identified in two comprehensive studies, highlighting their explanatory power (q-value) in shaping spatial ecological risk patterns [12] [54].

Driving Factor Category	Specific Factor	Region (Study)	Quantified Influence (q-value)	Key Temporal Trend
Anthropogenic	Land Use Intensity / Level	Southwest China [12]	Strong explanatory power (specific value not given)	Industrial production space increased by 9.8x (2000-2020) [12].
Anthropogenic	Population Density	Luan River Basin [54]	Primary factor (specific value not given)	Positively correlated with higher ecological risk levels.
Anthropogenic	GDP / Economic Activity	Southwest China [12]	Strong interactive effect	Major driver of landscape pattern change and risk evolution.
Anthropogenic	Primary Industry Output	Luan River Basin [54]	Primary factor (specific value not given)	Linked to agricultural expansion and resource use.
Natural	Precipitation	Luan River Basin [54]	Primary factor (specific value not given)	Key natural determinant of risk distribution.
Natural	NDVI (Vegetation Cover)	Southwest China [12]	Contributory factor	Used in ecological source identification and network construction.
Natural	Topography (Elevation/Slope)	Southwest China [12]	Underlying conditioning factor	Extracted from DEM data; influences risk distribution.

Table 2: Interaction Effects Between Driving Factors Analyses using the Geodetector model consistently reveal that the interaction between multiple drivers, particularly across natural and anthropogenic categories, non-linearly amplifies ecological risk beyond the influence of any single factor [12] [54].

Interacting Factor Pairs	Region (Study)	Nature of Interaction	Implication for Risk
Anthropogenic Disturbance & Natural Climate	Southwest China [12]	Essential for spatiotemporal risk differentiation	Combined effects explain risk patterns more powerfully than individual factors.
Anthropogenic Disturbance & Regional Economy	Southwest China [12]	Essential for spatiotemporal risk differentiation	Economic activity amplifies the ecological impact of physical disturbance.
Multiple Factors (e.g., Precipitation × Population Density)	Luan River Basin [54]	Interaction plays a more significant role than single factors	Highlights the non-linear, compounded risk in human-dominated landscapes under climatic variation.

Detailed Experimental Protocols

Protocol for Assessing Anthropogenic Drivers via Landscape Pattern Analysis

This protocol details the steps to quantify human-driven landscape changes and their associated ecological risk [12].

• Step 1 – Data Acquisition and Preparation: Obtain multi-temporal (e.g., 2000, 2010, 2020) land use/cover data with a resolution of 30 meters or finer from validated sources (e.g., national environmental data centers) [12]. Reclassify the data into a functional "Production-Living-Ecological Space" (PLES) taxonomy [12].
• Step 2 – Landscape Metric Calculation: Using Fragstats 4.2 or equivalent software, calculate a suite of landscape pattern indices for each PLES type and for the total landscape within standardized assessment units. Key indices include: Class Area (CA), Percentage of Landscape (PLAND), Patch Density (PD), Landscape Shape Index (LSI), and Contagion (CONTAG) [12].
• Step 3 – Ecological Risk Index (ERI) Calculation: Construct a Landscape Ecological Risk Index based on landscape pattern metrics. A common model integrates a landscape disturbance index (combining fragmentation, loss, and dominance), a landscape vulnerability index (assigning weights to different PLES types based on sensitivity), and a landscape fractal index. Compute the ERI for each assessment unit [12].
• Step 4 – Spatial Statistical Analysis: Perform spatial autocorrelation analysis (Global and Local Moran's I) on the ERI results to identify significant spatial clusters of high or low risk. Use Geodetector's factor and interaction detectors to quantify the explanatory power (q-statistic) of anthropogenic variables (e.g., population density, GDP, distance to roads) on the spatial heterogeneity of the ERI [12].

Protocol for Isolating Natural Drivers and Scale Effects

This protocol focuses on identifying optimal spatial scales for analysis and quantifying the role of natural climatic and topographic factors [54].

• Step 1 – Determination of Optimal Spatial Scales: To ensure assessment accuracy, determine the optimal spatial granularity and extent for the study area. Employ response curve analysis, an area accuracy loss model, and a semi-variation function to identify the characteristic scale at which landscape patterns exhibit maximum spatial heterogeneity and stability. For example, in the Luan River Basin, the optimal granularity was 30m with an amplitude of 3200m [54].
• Step 2 – Improved LER Assessment at Optimal Scale: Using the determined optimal scale, apply an Improved Landscape Ecological Risk Index (ILERI). This index refines the weighting of landscape metrics and vulnerability coefficients to be scale-appropriate. Calculate the ILERI for the study area across multiple time periods [54].
• Step 3 – Driving Force Analysis with Natural Factors: Collect spatially interpolated data for natural variables such as annual precipitation, temperature, NDVI, and topographic indices (elevation, slope). Using Geodetector or Random Forest regression, analyze the individual and interactive contributions of these natural factors to the spatial distribution of the ILERI. This isolates the portion of risk attributable to natural gradients versus human activity [54].

Protocol for Resilience Integration via Ecological Network Construction

This protocol links risk assessment to resilience enhancement by constructing landscape ecological networks [12].

• Step 1 – Ecological Source Identification: Identify "ecological sources" – patches critical for maintaining biodiversity and ecosystem processes. These are typically areas with very low ecological risk (low ERI/ILERI values), high ecosystem service value, or large, contiguous core natural areas (e.g., forests, wetlands) [12].
• Step 2 – Resistance Surface Creation: Create a comprehensive resistance surface based on the final ecological risk index map. Assign each grid cell a resistance value where higher risk corresponds to higher resistance to ecological flow (e.g., species movement, nutrient transfer) [12].
• Step 3 – Corridor and Node Delineation: Apply the Minimum Cumulative Resistance (MCR) model to identify potential ecological corridors (least-cost paths) between ecological sources. Pinch points and intersections along these corridors are identified as strategic ecological nodes. For example, a study in Southwest China constructed 105 corridors and 156 nodes [12].
• Step 4 – Network Optimization and Resilience Assessment: Analyze the topology of the resulting ecological network (e.g., using connectivity metrics like probability of connectivity). Prioritize the protection and restoration of key corridors and nodes that enhance overall landscape connectivity and redundancy, thereby directly increasing the system's capacity to absorb disturbances and mitigate risk [12].

Visual Synthesis of Pathways and Workflows

Diagram 1: Workflow for Integrated LERA and Resilience Assessment

Diagram 2: Stress Response Pathway & Resilience Factors

Diagram 3: Planetary Boundaries as a Global Resilience Framework

The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Tools and Materials for LERA and Resilience Research

Category	Item / Solution	Primary Function in Research	Example/Note
Geospatial Data	Multi-temporal Land Use/Land Cover (LULC) Data	Serves as the foundational layer for calculating landscape pattern indices and tracking change [12].	30m resolution data from national data centers (e.g., RESDC) [12].
Software & Platforms	Geographic Information System (GIS) Software (e.g., ArcGIS, QGIS)	Used for spatial data management, grid division, map algebra, resistance surface creation, and cartographic output [12].	ArcGIS 10.7 was used for creating 20km x 20km risk assessment grids [12].
Software & Platforms	Landscape Pattern Analysis Software (e.g., Fragstats)	Calculates a wide array of class- and landscape-level metrics essential for quantifying pattern, fragmentation, and diversity [12].	Required for computing indices like PD, LSI, CONTAG for ERI construction [12].
Software & Platforms	Statistical & Machine Learning Environment (e.g., R, Python with scikit-learn)	Hosts packages for advanced statistical analysis, Random Forest modeling, and running specialized tools like Geodetector [12] [54].	Used for driving force analysis and modeling interactions between factors.
Modeling Tools	Minimum Cumulative Resistance (MCR) Model	The core algorithm for identifying potential ecological corridors and nodes based on a resistance surface [12].	Implemented via GIS toolkits or scripts (e.g., Linkage Mapper, Circuitscape).
Field & Validation	Spectral & Positioning Equipment (Spectroradiometer, GPS)	Enables collection of ground-truth data for LULC classification validation and precise location mapping of field samples.	Critical for calibrating remote sensing data and validating identified ecological sources/nodes.
Reference Framework	Planetary Boundaries Framework [88]	Provides a global-scale, interdisciplinary reference for contextualizing local/regional ecological risks and resilience goals.	Useful for framing the broader implications of localized findings (e.g., land system change, biosphere integrity).
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3,7-Dihydroxydodecanoyl-CoA	3,7-Dihydroxydodecanoyl-CoA, MF:C33H58N7O19P3S, MW:981.8 g/mol	Chemical Reagent	Bench Chemicals

This comparative analysis establishes a clear methodological pathway for disentangling natural and anthropogenic drivers within landscape ecological risk. The integration of scale-explicit assessment, interaction detection via models like Geodetector, and ecological network construction provides a robust, multi-layered approach to risk diagnosis. By systematically applying these protocols, researchers can move beyond descriptive risk mapping toward a mechanistic understanding of driver interactions. This precision is foundational for the core thesis objective: optimizing landscape ecological risk assessment by embedding resilience theory directly into spatial planning and intervention strategies, thereby enabling ecosystems to better absorb disturbances and maintain function in the face of global change.

Synthesizing Multi-Scale Findings for Robust and Transferable Management Recommendations

Contemporary ecosystems face unprecedented pressures from climate change and anthropogenic activities, necessitating advanced frameworks for landscape ecological risk assessment (LERA) integrated with ecological resilience (ER) research. Traditional single-scale or single-discipline assessments are insufficient for capturing the complex, multi-dimensional interactions within socio-ecological systems. This synthesis, framed within a thesis on optimizing LERA with resilience research, addresses this gap by integrating findings from metropolitan, alpine, coastal, and forest park ecosystems. The core objective is to derive transferable management protocols that are robust across scales—from fine-grained grids to regional administrations. Recent studies underscore a critical negative correlation between ecological resilience and landscape risk, a relationship that intensifies with finer spatial resolution [28]. This body of work moves beyond descriptive analysis to establish actionable, zoning-based governance strategies informed by multi-scale coupling coordination models, providing a scientific basis for sustainable development and ecological security [28] [89].

Synthesis of Multi-Scale Findings: Patterns, Drivers, and Interactions

Integrating findings from diverse ecosystems reveals consistent patterns in risk-resilience dynamics, the primacy of certain drivers, and significant scale-dependent effects. The following table synthesizes key quantitative outcomes from recent multi-scale assessments.

Table 1: Synthesis of Multi-Scale Assessment Outcomes (2000-2020)

Study Region & Scale	Key Trend in Landscape Ecological Risk (LER)	Key Trend in Ecological Resilience (ER)	Dominant Driver(s) (q-statistic or equivalent)	Risk-Resilience Correlation & Coordination
Hefei Metropolitan Circle [28](Grid, County, City)	Stable overall pattern with localized differentiation; high-risk zones concentrate in water bodies.	Overall slight decline; high-resilience area ↓50.6%; low-resilience zones expand toward urban core.	Not explicitly quantified in results.	Strong negative correlation, intensifying at finer scales. Mean coupling coordination degree <0.5.
Gannan Alpine Meadow [90](Grid Scale)	Overall trend is decreasing; dominated by medium-low and medium risk.	Not assessed in this study.	Elevation (greatest impact), followed by natural factors > socio-economic factors.	Not assessed in this study.
Lower Yellow River Cities [29](Regional Scale)	Fluctuating downward trend (0.1761 to 0.1751); center of gravity moves toward river mouth.	Not assessed in this study.	Natural factors > social factors; interaction of any two factors > single factor.	Not assessed in this study.
Baili Rhododendron Forest Park [91](Landscape Units)	Overall decline; 96.82% of ecological risk plot index decreased.	Not assessed in this study.	Analysis focused on spatial autocorrelation of risk.	Not assessed in this study.
China's Coastal Cities [89](Macro-City & Micro-Grid)	Assessed via exposure within resilience framework.	Spatial pattern: "high in north, low in south, mixed in center." Micro-scale "low-resilience units" in sensitive zones.	Macro: Policy, infrastructure, social vulnerability. Micro: Green space, impervious surfaces, shelter access.	Synergies between drivers across scales necessitate integrated cross-scale governance.

The synthesized data reveals that natural drivers, particularly topography and climate, consistently exert a stronger influence on LER patterns than socioeconomic factors in non-urban core ecosystems [90] [29]. Conversely, in urbanizing regions, the configuration of landscape patterns—specifically the loss of connected, natural land cover—emerges as a critical determinant of both high risk and low resilience [28]. A pivotal finding is the scale-dependent nature of risk-resilience interactions. The negative correlation between LER and ER is not static but intensifies significantly when analysis shifts from the city to the grid scale, revealing localized dynamics that are masked at broader levels [28]. This underscores the necessity of multi-scale assessment protocols to avoid management prescriptions that are ineffective or misdirected.

Application Notes and Protocols for Multi-Scale Integrated Assessment

Core Integrated Assessment Protocol

This protocol synthesizes the "disturbance-vulnerability-loss" risk model with the "resistance-adaptation-recovery" resilience framework [28], designed for transferability across diverse landscapes.

Phase 1: Multi-Scale Geospatial Data Preparation

• Objective: Assemble consistent, multi-temporal data cubes at nested analytical scales (e.g., regional, municipal, grid).
• Procedure:
  • Acquire land use/cover data (e.g., CNLUCC, CLCD) for at least three time points (e.g., 2000, 2010, 2020) at ≤30m resolution [90] [29].
  • Resample all ancillary data to a common resolution and the GCSWGS1984 coordinate system [90].
  • Define analysis scales. For a metropolitan area, create vector boundaries for city, county, and a 1-3 km² grid. For ecological regions, use watershed boundaries and a finer grid.
  • Calculate landscape pattern indices (e.g., Patch Density, Landscape Division Index, Contagion) for each land use class and scale using Fragstats software [91].

Phase 2: Concurrent LER and ER Index Calculation

• Objective: Quantify LER and ER spatially at each defined scale.
• Landscape Ecological Risk Index (LERI) Protocol [90] [29]:
  • Classify landscape types and calculate the Loss Index for each based on its fragility.
  • Within each assessment unit (grid cell), calculate the landscape disturbance index (from pattern indices like fragmentation and dominance) and the vulnerability index.
  • Compute LERI: LERI = (Landscape Disturbance Index) * (Landscape Vulnerability Index) * (Loss Index).
  • Interpolate results to create a continuous risk surface.
• Ecological Resilience Index (ERI) Protocol [28] [89]:
  • Based on land use and ancillary data, construct indicators for three dimensions:
    • Resistance: Proportion of stable natural land cover, topographic complexity.
    • Recoverability: Connectivity of natural patches, proximity to seed sources.
    • Adaptation: Landscape diversity, heterogeneity.
  • Normalize indicators and integrate using weighted overlay or a coupling coordination model to generate a composite ERI.

Phase 3: Interaction and Driver Analysis

• Objective: Diagnose the relationship between LER and ER and identify key drivers.
• Procedure:
  • Perform bivariate spatial autocorrelation (e.g., Moran's I) between LERI and ERI surfaces at each scale to identify clusters of High-High (high risk, high resilience) and Low-High (low risk, high resilience) etc. [28].
  • Use GeoDetector's factor detector to quantify the explanatory power (q-statistic) of candidate drivers (e.g., elevation, GDP, road density) on LER spatial heterogeneity [90].
  • Use GeoDetector's interaction detector to test whether factors jointly enhance or weaken their influence on LER [29].

Protocol for Cross-Scale Model Transferability

Transferring findings or models (e.g., a risk predictor from a well-studied region to a data-poor one) requires rigorous validation.

• Step 1 - Environmental Dissimilarity Assessment: Quantify the ecological distance between the reference and target systems using PCA on normalized environmental variables (climate, soil, topography) [92].
• Step 2 - Mechanistic Model Calibration: Prioritize transferring models based on ecological mechanism (e.g., soil erosion risk models) over purely statistical correlations [92].
• Step 3 - Transferability Metrics: Evaluate transfer success using metrics beyond simple accuracy: calculate the reciprocal similarity of environmental spaces and the performance decline when the model is applied to the target area [92].

Multi-Scale Integrated Assessment Research Workflow

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Research Reagent Solutions for Integrated LERA-ER Studies

Category	Item / Software	Primary Function in Research	Example Use Case / Note
Core Geospatial Data	CNLUCC / CLCD Land Cover Datasets	Provides foundational, multi-temporal land use/cover classification at 30m resolution for calculating landscape patterns and change.	Used as primary input for LERI calculation and resilience indicator derivation [90] [29].
Analysis Software	ArcGIS Pro / QGIS	Platform for spatial data management, resampling, zoning, map algebra, and visualization of final risk/resilience surfaces.	Essential for executing the geospatial workflow, from data prep to result mapping [91].
Analysis Software	Fragstats	Computes a comprehensive suite of landscape pattern metrics (patch, class, landscape level) critical for LERI and some ERI components.	Used in batch mode via ArcGIS ModelBuilder for processing large areas [91].
Statistical Tool	GeoDetector (with OPGD)	Quantifies the driving forces behind spatial heterogeneity. The Optimal Parameters-based GeoDetector (OPGD) optimizes discretization to reduce subjectivity.	Identifies dominant natural/socioeconomic drivers (q-statistic) and their interactive effects on LER [90] [29].
Modeling & Decision Support	Python-based MCDA Tools (e.g., Fuzzy DEMATEL)	Supports multi-criteria decision analysis under uncertainty, helping prioritize management interventions based on causal risk networks.	Formalizes risk mitigation strategies and visualizes interdependencies among top risks for decision-makers [93].
Validation Framework	Transferability Assessment Metrics	A proposed set of standardized metrics to evaluate the performance and reliability of models transferred to novel environmental conditions.	Critical for ensuring the robustness of management recommendations when applied to new regions [92].
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From Diagnosis to Action: Robust and Transferable Management Recommendations

Zoning-Based Management Typology

Synthesized findings support a four-zone management typology, moving from one-size-fits-all to spatially tailored strategies [28]:

• High-Risk / Low-Resilience Zones (Priority Intervention): Commonly found in fragmented water bodies or urban-rural fringes. Recommendations: Ecological restoration targeting connectivity (e.g., riparian corridors), stringent control of land use change, and implementation of nature-based solutions (NbS) like constructed wetlands.
• Low-Risk / High-Resilience Zones (Conservation Priority): Typically contiguous forested or natural grassland biomes. Recommendations: Preventive conservation, establishment of ecological red lines, and control of invasive species or disruptive infrastructure projects.
• High-Risk / High-Resilience Zones (Adaptive Management): Often dynamic areas under transition. Recommendations: Enhanced monitoring, development of adaptive management plans that leverage inherent resilience for restoration, and stress-testing resilience thresholds.
• Low-Risk / Low-Resilience Zones (Resilience Building): May be stable but vulnerable monocultures or simplified ecosystems. Recommendations: Increase landscape diversity and heterogeneity to boost adaptive capacity, promote mixed land uses.

Cross-Scale Governance and Investment Strategies

Effective management must align actions across spatial and administrative scales.

• Macro (Regional/Policy) Scale: Focus on integrating risk-resilience coupling assessments into territorial spatial planning. Policies should create ecological security patterns that connect high-resilience zones and isolate risk sources [28] [89].
• Micro (Local/Implementation) Scale: Focus on targeted engineering and community-based adaptation. Examples include retrofitting green infrastructure in low-resilience urban grids [89] and designing participatory restoration in high-risk rural grids.
• Investment Alignment: Redirect financing from post-disaster response to pre-disaster risk reduction. Evidence shows $1 invested in risk reduction averts $15 in future losses [94]. Tools like resilience bonds and green bonds can fund the above zoning strategies, breaking cycles of disaster-debt-disaster [94].

Risk-Resilience Coupling Framework for Management

The synthesized approach directly supports global mandates like the Sendai Framework for Disaster Risk Reduction and UN Sustainable Development Goals (SDG 11, 13, 15) by providing a scalable method for "developing a shared understanding of risk" [95]. The multi-scale zoning strategy operationalizes the call for risk-informed spatial planning [89] [94]. Furthermore, the focus on resilience-building investments aligns with the core message of the Global Assessment Report 2025: that "resilience pays" by safeguarding development gains and avoiding catastrophic losses [94].

Conclusion: Robust and transferable management recommendations arise from synthesizing LERA and ER research across multiple scales. Key principles include: 1) adopting a dual-index (LERI/ERI) coupling assessment, 2) employing scale-explicit diagnostics to reveal localized dynamics, 3) moving towards mechanistic model transfer, and 4) implementing differentiated zoning management informed by risk-resilience clustering. This integrated pathway offers a scientifically-grounded, actionable blueprint for enhancing ecological security in an era of global change.

Cross-Scale Model Transferability Assessment Protocol

Conclusion

Integrating ecosystem resilience into landscape ecological risk assessment represents a significant paradigm shift from descriptive risk mapping to a dynamic, process-oriented, and management-focused science. The proposed framework, which combines an optimized risk model with a multi-dimensional resilience assessment, moves beyond the limitations of traditional static approaches. Key takeaways highlight the consistent negative spatial correlation between resilience and risk, validating resilience as a critical buffer. Methodological advancements, such as using ecosystem services to objectively quantify vulnerability and employing tools like bivariate Moran's Index for zoning, provide a more scientific basis for decision-making. Addressing scale-dependency and parameter subjectivity is crucial for model reliability. The ultimate value lies in the actionable output of ecological management zoning, which translates complex assessments into clear spatial strategies for adaptation, conservation, and restoration. Future directions should focus on developing dynamic, predictive models that simulate future scenarios under climate and land-use change, further standardizing resilience metrics, and enhancing the framework's integration into formal environmental policy and regulatory decision-making processes to safeguard ecological security and promote sustainable development.

References

• 1. A Brief History of Ecological Risk Assessment - NCBI - NIH [https://www.ncbi.nlm.nih.gov/books/NBK379280/]
• 2. Ecological Risk Assessment [https://www.sra.org/risk-analysis-specialty-groups/ecological-risk-assessment/]
• 3. Technical Overview of Ecological Risk Assessment [https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/technical-overview-ecological-risk-assessment-1]
• 4. Landscape ecological risk assessment and influencing ... [https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2023.1184273/full]
• 5. Assessment of Landscape Ecological Risks Driven by ... [https://www.mdpi.com/2073-445X/14/5/947]
• 6. Driving force analysis and multi-scenario simulation of ... [https://www.nature.com/articles/s41598-025-85738-0]
• 7. Optimization of landscape ecological risk assessment ... [https://pubmed.ncbi.nlm.nih.gov/39970672/]
• 8. A case study in the Tibetan Plateau [https://www.sciencedirect.com/science/article/pii/S1470160X22005490]
• 9. Landscape pattern evolution and ecological risk ... [https://www.sciencedirect.com/science/article/pii/S1470160X23015236]
• 10. Assessing the Landscape Ecological Risks of Land-Use Change [https://pmc.ncbi.nlm.nih.gov/articles/PMC9659079/]
• 11. Optimization of landscape ecological risk assessment ... [https://www.sciencedirect.com/science/article/abs/pii/S0301479725005626]
• 12. Landscape ecological risk assessment and driving factor ... [https://www.nature.com/articles/s41598-024-74506-1]
• 13. Quantitative risk analysis setup guide – master the process ... [https://www.trustcloud.ai/risk-management/master-quantitative-risk-analysis-a-step-by-step-guide-for-better-business-decisions/]
• 14. The pros and cons of ecological risk assessment based on ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5141515/]
• 15. A case study comparing static and spatially explicit ... [https://pubmed.ncbi.nlm.nih.gov/11824676/]
• 16. Evaluating landscape ecological risk through Supply ... [https://www.sciencedirect.com/science/article/pii/S1470160X25002869]
• 17. Assessment of Landscape Risks and Ecological Security ... [https://www.mdpi.com/2073-445X/14/6/1221]
• 18. Resilience Theory and Socio-Environmental Systems ... [https://www.sesync.org/resources/resilience-theory-and-socio-environmental-systems-explained]
• 19. Resilience and Resource Management - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4626537/]
• 20. A quantitative framework for assessing ecological resilience [https://pmc.ncbi.nlm.nih.gov/articles/PMC5759782/]
• 21. Metrics and Models for Quantifying Ecological Resilience ... [https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2019.00440/full]
• 22. A systematic review of urban ecological resilience [https://www.sciencedirect.com/science/article/abs/pii/S0304380025003059]
• 23. Ecological risk and resilient regulation shifting from city to ... [https://www.sciencedirect.com/science/article/abs/pii/S0195925523003529]
• 24. Evaluation of landscape ecological risk in key ... [https://www.sciencedirect.com/science/article/pii/S1470160X23000766]
• 25. Indicators of Resilience - The International Partnership for ... [https://satoyamainitiative.org/featured_activities/indicators-of-resilience/]
• 26. Community-based project assessment using the indicators ... [https://www.sciencedirect.com/science/article/pii/S2666049020300293]
• 27. Assessment Resources [https://www.resalliance.org/assessment-resources]
• 28. Interrelationships and zoning-based management of ... [https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1654175/full]
• 29. Spatiotemporal evolution and driving forces of landscape ... [https://www.nature.com/articles/s41598-025-05724-4]
• 30. Resilience evaluation and optimization for an air-ground ... [https://aimspress.com/article/doi/10.3934/era.2024153?viewType=HTML]
• 31. Embracing complexity in landscape management [https://www.stockholmresilience.org/publications/publications/2022-09-30-embracing-complexity-in-landscape-management-learning-and-impacts-of-a-participatory-resilience-assessment.html]
• 32. Incorporating landscape configuration metrics improves the ... [https://environment.ec.europa.eu/news/incorporating-landscape-configuration-metrics-improves-accuracy-water-related-ecosystem-services-2025-04-24_en]
• 33. Integrating ecosystem services and vulnerability ... [https://www.sciencedirect.com/science/article/abs/pii/S000632072500240X]
• 34. The impact of ecological vulnerability on ecosystem service ... [https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1583841/full]
• 35. Beyond Resilience: A Nuanced Framework for Conceptualizing Resistance Networks | Small Wars Journal by Arizona State University [https://smallwarsjournal.com/2025/09/19/resilience-robustness-antifragility-resistance-planning/]
• 36. The Resilience Activation Framework: A conceptual model of how access to social resources promotes adaptation and rapid recovery in post-disaster settings - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4247807/]
• 37. Ecological Risk Models and Tools [https://www.epa.gov/risk/ecological-risk-models-and-tools]
• 38. E3429 Standard Guide for Property Resilience Assessments [https://www.astm.org/e3429-24.html]
• 39. Coupling coordination degree spatial analysis and driving ... [https://www.sciencedirect.com/science/article/pii/S1470160X22000267]
• 40. How Bivariate Spatial Association (Lee's L) works—ArcGIS Pro [https://pro.arcgis.com/en/pro-app/3.4/tool-reference/spatial-statistics/how-bivariate-spatial-association-works.htm]
• 41. 14.3 Bivariate Spatial Correlation [https://lanselin.github.io/introbook_vol1/bivariate-spatial-correlation.html]
• 42. Chapter 11 Global Spatial Autocorrelation 2 [https://spatialanalysis.github.io/handsonspatialdata/global-spatial-autocorrelation-2.html]
• 43. The Coupling Coordination Degree of Economic, Social and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9819627/]
• 44. Research on the coupling coordination characteristics and ... [https://www.nature.com/articles/s41598-024-66890-5]
• 45. A Deep Dive into Open Data Kit (ODK): Step-by-Step Guide [https://www.certlibrary.com/blog/a-deep-dive-into-open-data-kit-odk-step-by-step-guide/]
• 46. Introduce yourself here! - Page 65 [https://forum.getodk.org/t/introduce-yourself-here/6671?page=65]
• 47. Identification of priority areas for ecological restoration ... [https://www.sciencedirect.com/science/article/pii/S1470160X25005205]
• 48. A Case Study of the Jinan Metropolitan Area [https://www.mdpi.com/2071-1050/17/12/5305]
• 49. Integrating landscape ecological risk and ecosystem ... [https://www.sciencedirect.com/science/article/pii/S1470160X25011112]
• 50. Ecological Improvement Framework Proviso Report (2025) [https://wdfw.wa.gov/publications/02632]
• 51. Ecological Risk Assessment of Watersheds Based on ... [https://www.mdpi.com/2073-445X/14/6/1265]
• 52. Watershed Planning within a Quantitative Scenario Analysis ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5091690/]
• 53. A review of methods for quantifying urban ecosystem ... [https://www.sciencedirect.com/science/article/abs/pii/S0169204624002147]
• 54. Landscape ecological risk assessment and driving factors ... [https://www.sciencedirect.com/science/article/pii/S1470160X24012780]
• 55. Optimal scale landscape ecological risk evaluation based ... [https://link.springer.com/article/10.1007/s11629-024-9413-2]
• 56. Landscape ecological risk based on optimal scale and its ... [https://www.sciencedirect.com/science/article/pii/S1470160X24014973]
• 57. How To: Add Arrow Symbols in ArcGIS Online Map Viewer [https://support.esri.com/en-us/knowledge-base/how-to-add-arrow-symbols-in-arcgis-online-map-viewer-000027762]
• 58. List of symbol effects—ArcGIS Pro | Documentation [https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/list-of-symbol-effects.htm]
• 59. Text has minimum contrast[proposed] | ACT Rule | WAI [https://www.w3.org/WAI/standards-guidelines/act/rules/afw4f7/proposed/]
• 60. Color contrast - Accessibility - MDN Web Docs - Mozilla [https://developer.mozilla.org/en-US/docs/Web/Accessibility/Guides/Understanding_WCAG/Perceivable/Color_contrast]
• 61. Economic values for ecosystem services: A global ... [https://www.sciencedirect.com/science/article/pii/S2212041624000123]
• 62. Method for the quantitative evaluation of ecosystem ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6336092/]
• 63. Ecological Resilience | Research Starters [https://www.ebsco.com/research-starters/science/ecological-resilience]
• 64. Land scale division and multifunctional evaluation for ... [https://www.nature.com/articles/s41598-024-62252-3]
• 65. Data Visualizations, Charts, and Graphs - Digital Accessibility [https://accessibility.huit.harvard.edu/data-viz-charts-graphs]
• 66. Understanding Success Criterion 1.4.11: Non-text Contrast [https://www.w3.org/WAI/WCAG21/Understanding/non-text-contrast.html]
• 67. Colour Blindness & Colour Choices - Accessibility [https://cambiocteach.com/accessibility/colourchoice/]
• 68. Study of identification and simulation of ecological zoning ... [https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2025.1662739/full]
• 69. : an emerging Integrated for complex issues assessment methodology [https://www.academia.edu/18030075/Integrated_assessment_an_emerging_methodology_for_complex_issues]
• 70. Assessment of Uncertainties in Ecological Risk Based on ... [https://www.mdpi.com/2073-445X/13/4/535]
• 71. Model Risk Management [https://kpmg.com/in/en/insights/2024/11/model-risk-management.html]
• 72. Getting model validation right [https://www.grantthornton.ie/insights/factsheets/model-validation-risk-management/]
• 73. From validation to optimisation, tackling a growing model ... [https://www.deloitte.com/za/en/Industries/financial-services/analysis/from-validation-to-optimisation.html]
• 74. Presenting Quantitative Data Graphically [https://courses.lumenlearning.com/waymakermath4libarts/chapter/presenting-quantitative-data-graphically/]
• 75. Presentation of Quantitative Data [https://ihatepsm.com/blog/presentation-quantitative-data]
• 76. Zoning optimization of ecosystem service value and ... [https://www.nature.com/articles/s41598-025-17904-3]
• 77. Spatial–temporal variation and correlation analysis of ... [https://www.nature.com/articles/s41598-024-67651-0]
• 78. Quantitative Key Performance Indicators for risk and ... [https://www.sciencedirect.com/science/article/abs/pii/S2212420925005448]
• 79. Landscape Ecological Risk Assessment of Saihanba under ... [https://www.mdpi.com/1999-4907/15/4/700]
• 80. Ecological security pattern construction based on ... [https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1514599/full]
• 81. Ecological management zoning based on the causation ... [https://www.sciencedirect.com/science/article/pii/S1470160X24011300;]
• 82. Restoration Prioritization and Planning [https://www.blm.gov/policy/im2024-035]
• 83. Ecological Resilience Assessment and Scenario Simulation Considering Habitat Suitability, Landscape Connectivity, and Landscape Diversity [https://www.mdpi.com/2071-1050/17/12/5436]
• 84. Frontier AI Risk Management Framework in Practice [https://arxiv.org/html/2507.16534v2]
• 85. Colors Of Safety: Your Guide To Color Codes And Meanings [https://www.safetydecals.com/blogs/news/colors-of-safety?srsltid=AfmBOorm0683bSndiIGk3Qt3Vi03oF7XbuyCQLASBwx5vXDMR8HSBUh5]
• 86. Investing in Resilience: Three Case Studies in Climate ... [https://www.ceres.org/resources/reports/investing-in-resilience-three-case-studies-in-climate-adaptation]
• 87. Resilience by design: How nature, nurture, environment, and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10251360/]
• 88. Planetary boundaries [https://www.stockholmresilience.org/research/planetary-boundaries.html]
• 89. Multi-Scale Resilience Assessment and Zonal Strategies ... [https://www.mdpi.com/2073-445X/14/11/2178]
• 90. Research on Landscape Risks and Their Driving ... [https://www.mdpi.com/2071-1050/17/20/9150]
• 91. Evolutionary analysis of landscape ecological risk in Baili ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0317851]
• 92. Outstanding Challenges in the Transferability of Ecological ... [https://www.sciencedirect.com/science/article/pii/S0169534718301812]
• 93. Advanced risk management software for multi-criteria ... [https://link.springer.com/article/10.1007/s40314-025-03338-0]
• 94. Global Assessment Report (GAR) 2025 [https://www.undrr.org/gar/gar2025]
• 95. Global synthesis report on Comprehensive Risk ... [https://www.undrr.org/publication/documents-and-publications/global-synthesis-report-comprehensive-risk-management]