Integrating Ecosystem Services Assessment into Strategic Forest Planning: Frameworks, Methods, and Future Directions

Abstract

This article provides a comprehensive examination of the integration of ecosystem services (ES) assessment into strategic forest planning, addressing a critical gap between scientific research and on-the-ground application. It explores the foundational principles of forest ES, including the social-ecological resilience framework and the critical need to match supply with demand. The review presents a suite of methodological approaches, from interdisciplinary toolkits to participatory modeling and Multi-Criteria Decision Analysis (MCDA), offering practical guidance for implementation. It further tackles troubleshooting and optimization by analyzing common data pitfalls, such as biases in remote sensing, and strategies for overcoming governance and spatial integration barriers. Finally, the article validates these approaches through comparative case studies that demonstrate real-world efficacy in ranking management scenarios and quantifying stakeholder preferences. Aimed at researchers, forest managers, and policy-makers, this synthesis is designed to advance the application of robust, evidence-based ES assessments for sustainable and resilient forest management.

The Foundation of Forest Ecosystem Services: From Core Concepts to Social-Ecological Resilience

Ecosystem services (ES) are the direct and indirect benefits that humans obtain from ecosystems [1]. Forests are critical providers of a vast array of these services, which are fundamental to human well-being, economic development, and ecological stability [2] [3]. The concept of ES bridges the natural environment and human welfare, forming a core content of coupled human-environment system research [4]. The Millennium Ecosystem Assessment (MA) established a foundational framework by categorizing ES into four primary types: Provisioning, Regulating, Cultural, and Supporting services [5]. This classification system aids researchers and policymakers in systematically identifying, quantifying, and valuing the multitude of benefits forests provide, which is essential for strategic forest planning and sustainable management [2].

Categorization of Forest Ecosystem Services

The following sections detail the four categories of forest ecosystem services, providing definitions, specific examples, and their relevance to strategic forest management.

Provisioning Services

Provisioning services are the material goods and products that can be directly extracted or harvested from forest ecosystems [5] [2].

• Definition: These services represent the material or energy outputs from ecosystems [1].
• Key Examples:
  • Food: Forests yield a variety of food, such as berries, mushrooms, herbs, honey, and game meat from hunting [2].
  • Raw Materials: They provide essential raw materials like wood for timber and building, fibers, and resins [2]. Wood fuel is also an important energy source [3].
  • Water: Forests influence the supply of fresh water by storing large amounts of groundwater, which supplies drinking water, irrigation, and industrial process water [2].
  • Genetic Resources: Forest species constitute a vast pool of genetic resources important for biological, chemical, and medical research [2].
  • Medicinal Resources: Plants are a source of compounds for pharmaceutical products, such as Taxol [3].

Regulating Services

Regulating services are the benefits obtained from the natural regulation of ecosystem processes that moderate environmental conditions [5] [3].

• Definition: These are the benefits obtained from the regulation of ecosystem processes [1].
• Key Examples:
  • Climate Regulation: Forests are significant carbon sinks. They sequester CO₂ from the atmosphere through photosynthesis, storing carbon in biomass and soils, thereby mitigating climate change. They also contribute to local air cooling through evapotranspiration [2].
  • Air and Water Purification: Tree crowns filter pollutants from the air (e.g., nitrogen, sulphur), while forest soils filter precipitation, removing nutrients and organic particles, thus purifying water and replenishing groundwater [2] [3].
  • Erosion and Flood Control: The extensive root systems of forests stabilize soils and slopes, reducing erosion and landslides. Upstream forests regulate water flow and infiltration, protecting downstream areas from flooding [2].
  • Pollination: Forests provide crucial habitats for diverse insect populations, supporting the pollination of crops and wild plants [2].

Cultural Services

Cultural services encompass the non-material, intangible benefits that people gain from forests through spiritual enrichment, cognitive development, reflection, and recreation [5] [2].

• Definition: These are the non-material benefits that ecosystems provide to human societies and culture [1].
• Key Examples:
  • Recreation and Ecotourism: Forests are highly valued for activities like hiking, bird-watching, and camping, which contribute significantly to local economies and human well-being [2] [3].
  • Aesthetic, Spiritual, and Inspirational Value: The aesthetic beauty of forests offers artistic and spiritual enrichment. Practices like forest bathing and forest therapy are becoming increasingly popular for mental and physical health [2].
  • Educational and Cultural Heritage: Forests serve as living laboratories for environmental education and scientific discovery, and are often deeply intertwined with local and national cultural identities [3].

Supporting Services

Supporting services are the foundational processes that are necessary for the production of all other ecosystem services [5] [3].

• Definition: These services maintain fundamental ecosystem processes and functions [1].
• Key Examples:
  • Soil Formation: The breakdown of rock and the accumulation of organic matter through leaf litter and root decay are critical for creating and maintaining fertile soils [3].
  • Nutrient and Water Cycling: Forests play a vital role in the cycling of essential nutrients (e.g., carbon, nitrogen, phosphorus) and in the global and local water cycles [3].
  • Photosynthesis: This primary process captures energy from the sun, enabling plant growth and forming the base of food webs that support nearly all life [2] [3].
  • Habitat for Biodiversity: Forests provide the essential living spaces and food resources for a vast diversity of plants and wildlife, which in turn underpin the provision of other services [2].

Table 1: Quantitative Metrics for Forest Ecosystem Service Assessment

Service Category	Specific Service	Example Metrics for Quantification	Common Assessment Methods
Provisioning	Timber Production	Volume (m³/ha/year), biomass, growth rate	Field inventory, yield models, statistical data [6]
	Food & Water	Yield (kg/ha), water quantity (m³), groundwater recharge	Harvest records, hydrological models (e.g., SWAT) [2]
Regulating	Carbon Sequestration	Carbon stocks (t C/ha), sequestration rate (t C/ha/year)	Biome-BGC, LANDIS-II, InVEST models [7]
	Water Purification	Nutrient load reduction (kg N/P retained), sediment retention	InVEST, SWAT, empirical models [8] [4]
Cultural	Recreation	Visitor days, tourism revenue, survey-based preferences	SolVES model, visitor counts, questionnaires [4]
Supporting	Soil Formation	Soil organic matter (%), erosion rate (t/ha/year)	RUSLE, field sampling [9]
	Biodiversity	Species richness, habitat quality/suitability index	Field surveys, InVEST Habitat Quality model [2]

Experimental Protocols for Ecosystem Service Assessment

This section provides detailed methodologies for assessing key forest ecosystem services, integrating contemporary models and field techniques.

Protocol for Assessing Carbon Sequestration Using the LANDIS-II Model

Objective: To project long-term carbon storage and dynamics in forested landscapes under different climate and management scenarios. Background: Carbon sequestration is a critical regulating service for climate mitigation. Process-based models like LANDIS-II are used to simulate forest succession, disturbance, and carbon cycles [7]. Materials & Reagents:

• LANDIS-II Model: A spatially explicit, process-based model simulating forest landscape change.
• Initial Conditions Maps: GIS layers of initial forest composition, age structure, and biomass.
• Climate Projection Data: Downscaled data for future temperature, precipitation, and CO₂ scenarios.
• Soil Data: Maps of soil texture and depth.
• Disturbance Parameters: Data on historical and projected fire, wind, and insect outbreaks.
• High-Performance Computing Cluster: For running complex, multi-decadal simulations.

Procedure:

• Model Domain & Initialization: Define the study landscape and grid cell resolution (e.g., 100m x 100m). Initialize the model with maps of species composition, age, and biomass.
• Parameterization: Calibrate species life history parameters (e.g., longevity, shade tolerance) and ecological processes (e.g., decomposition rates). Input climate and soil data layers.
• Scenario Design: Develop alternative scenarios (e.g., "Business-as-Usual", "Climate Adaptation", "Aggressive Harvest") by modifying model parameters for disturbance regimes and management interventions.
• Simulation Execution: Run the model for a defined period (e.g., 100 years) with multiple replicates (e.g., 5) for each scenario to account for stochasticity.
• Output Analysis: Extract and analyze spatial outputs of aboveground and belowground carbon stocks over time. Use fuzzy logic or statistical models to integrate outputs and identify strategic management zones (e.g., Protect, Adapt, Monitor) based on carbon trajectories [7].

Protocol for Mapping Service Supply and Demand Using the InVEST Model

Objective: To spatially quantify and map the supply of ecosystem services from forest ecosystems and the demand for these services from human populations. Background: Analyzing the supply and demand of ES is crucial for identifying ecosystem service flow, mismatches, and priorities for ecological compensation [8]. Materials & Reagents:

• InVEST Model Suite: A suite of tools for mapping multiple ES (e.g., carbon storage, water purification, habitat quality).
• Land Use/Land Cover (LULC) Map: A high-resolution, classified satellite image.
• Biophysical Table: A CSV file linking each LULC class to its capacity for providing different services (e.g., carbon storage per hectare, nitrogen retention efficiency).
• Demand Data: Spatial data on human demand drivers (e.g., population density for recreation, pollutant loads for water purification).
• GIS Software: For preprocessing spatial data and post-processing model outputs.

Procedure:

• Data Preparation: Prepare a LULC map for the study area. Create a biophysical table based on literature reviews and local field data, assigning service provision coefficients to each LULC class.
• Model Selection & Setup: Select relevant InVEST modules (e.g., "Carbon Storage & Sequestration", "Nutrient Delivery Ratio"). Input the LULC map, biophysical table, and other required parameters (e.g., watershed boundaries, precipitation data).
• Supply Calculation: Run the model to generate maps of ES potential supply. The model uses LULC data and lookup tables to estimate the physical quantity of services provided [8] [4].
• Demand Integration: For services like water purification, map the locations of nutrient and sediment pollution demand (sources). For recreation, map population centers as proxies for demand.
• Spatial Equilibrium Analysis: Overlay supply and demand maps in a GIS to identify spatial mismatches—areas of surplus (high supply, low demand) and deficit (low supply, high demand) [8].

Table 2: Research Reagent Solutions for Ecosystem Service Assessment

Item Name	Function/Application	Example in Protocol
Spatial Simulation Model (LANDIS-II)	Models long-term forest succession, disturbance, and biogeochemical cycles under changing conditions.	Projecting carbon stock dynamics under climate change scenarios [7].
Integrated ES Assessment Model (InVEST)	Maps and values multiple ecosystem services based on land use and land cover data.	Quantifying and mapping service supply (e.g., carbon, water) for spatial equilibrium analysis [8] [4].
GIS (Geographic Information System)	Captures, stores, analyzes, and presents spatial and geographic data; essential for mapping ES.	Preprocessing LULC maps, post-processing model outputs, and overlaying supply-demand maps [9].
Biophysical Table (Lookup Table)	A database assigning quantitative ES provision capacities to different land cover classes.	Serving as a key input for InVEST models to translate land cover into service supply potential [8].
Fuzzy Logic Modeling	A mathematical method for handling imprecise information and providing quantitative support for management strategies.	Classifying landscape areas into management strategies (Monitor, Protect, Adapt, Transform) based on model outputs [7].

Visualization of Ecosystem Service Flows and Assessment Workflow

The following diagrams, generated using Graphviz DOT language, illustrate the logical flow of ecosystem services from nature to people and the generalized workflow for their scientific assessment.

The systematic categorization of forest ecosystem services into provisioning, regulating, cultural, and supporting services provides an indispensable framework for strategic forest planning research. By applying quantitative assessment techniques—including spatial simulation models like LANDIS-II and integrated valuation tools like InVEST—researchers can move beyond theoretical classification to generate actionable, spatially explicit data. This scientific approach, which incorporates supply-demand analysis and scenario forecasting, is critical for navigating the complex trade-offs and synergies between different services. Ultimately, embedding this rigorous assessment of ecosystem services into forest management decisions is fundamental for promoting ecological resilience, securing human well-being, and achieving sustainability goals in a rapidly changing world.

Application Notes

Conceptual and Methodological Foundations

The Social-Ecological Systems Framework (SESF) provides a common diagnostic vocabulary for analyzing linked social and ecological components, particularly in natural resource systems and collective action challenges [10]. The framework organizes first-tier components (e.g., Resource Systems, Governance Systems, Actors, Resource Units) that interact to influence outcomes [10]. A core challenge in its application has been methodological heterogeneity, where researchers must develop their own procedures for variable selection, measurement, and analysis, thus hindering cross-study comparability [10]. Successful application requires navigating several key methodological gaps [10]:

• The variable definition gap: Contextualizing broad SESF variables for a specific case.
• The variable to indicator gap: Selecting measurable indicators for these variables.
• The measurement gap: Choosing methods for data collection.
• The data transformation gap: Processing raw data for analysis.

Applying the SESF is not a method itself but a conceptual guide for focusing research methods on a set of variables with empirical support for shaping collective action and sustainability outcomes [10]. In the context of strategic forest planning, this framework enables a systematic diagnosis of the factors influencing ecosystem services provision and human well-being.

Quantitative Applications and Causal Analysis

Quantitative applications of the SESF are crucial for moving beyond descriptive case studies to testing hypotheses about causal relationships within SES. As demonstrated in a Pyrenees case study, methods such as piecewise structural equation modeling (SEM) and network analysis can quantitatively describe the direct and indirect interactions between water resources, biodiversity, and socioeconomic elements [11]. This approach helps identify key drivers and feedback loops; for instance, revealing how economic dependency on tourism can severely impact water resources and biodiversity, thereby threatening the system's long-term socioeconomic resilience [11]. This methodology addresses a critical need in SES research to move from identifying important variables to modeling how they interact [11].

Table 1: Key Methodological Gaps and Strategies in SESF Application (adapted from Nagel and Partelow, 2022 [10])

Methodological Gap	Description of the Challenge	Recommended Strategy
Variable Definition Gap	Adapting the broad, universal variables of the SESF to a specific case study context.	Clearly define and justify the subset of SESF variables relevant to the forest system being studied.
Variable to Indicator Gap	Selecting specific, measurable indicators that accurately represent the abstract SESF variables.	Identify and document quantifiable proxies for each variable (e.g., canopy cover as an indicator for resource system condition).
Measurement Gap	Choosing appropriate tools and techniques for collecting data on the chosen indicators.	Use a mix of primary (e.g., field surveys, inventories) and secondary (e.g., remote sensing, census data) data sources.
Data Transformation Gap	Processing and converting raw data into a format suitable for statistical analysis and modeling.	Apply appropriate data cleaning, normalization, and aggregation techniques to create a unified dataset for analysis.

A Novel Approach: Hypernetworks for SES Dynamics

Emerging conceptual tools offer new ways to represent SES complexity. Hypernetworks (or hypergraphs) can model non-dyadic (multi-way) interactions that are common in ecology, such as the simultaneous relationship between a pollinator, a plant, and a specific climatic condition [12]. This approach allows an entire ecosystem to be represented by a single, dynamic hypernetwork whose topology changes over time, capturing events like species invasion, land-use change, or policy implementation [12]. For strategic forest planning, this provides a qualitative backbone for modeling regime shifts and long-term ecosystem dynamics under different management scenarios.

Experimental Protocols

Protocol for Quantitative SES Diagnosis using Structural Equation Modeling

This protocol outlines a procedure for diagnosing causal relationships in a social-ecological system, adapted from a Pyrenean case study [11].

Objective

To quantitatively model the direct and indirect interactions among social-ecological variables in a forest system to identify key sustainability challenges and leverage points.

Materials and Equipment

• Statistical software (e.g., R programming environment)
• piecewiseSEM R package (or similar SEM software)
• Data set comprising social, economic, and ecological variables

Procedure

• System Scoping and Variable Identification: Use Ostrom's SESF tiers to identify relevant social-ecological variables for the forest system [11]. The inclusion criteria should be based on the variable's relevance to the system and data availability.
• Data Collection and Compilation: Gather time-series data for the identified variables from public databases, field measurements, and remote sensing. Ensure data covers a meaningful time span (e.g., annually over 20 years).
• A Priori Hypothesis Formulation: Based on literature review and expert knowledge of the system, hypothesize a network of causal relationships (e.g., "Tourist population density directly affects water extraction").
• Model Specification: Statistically test the set of hypothesized paths using piecewise structural equation modeling. Construct the model in a stepwise manner, adding paths based on theoretical support.
• Model Evaluation and Validation: Evaluate model fit using established indices (e.g., Fisher's C, AIC). Modify the model if necessary by adding or removing paths based on statistical and theoretical justification.
• Network Analysis: Use the final path coefficients from the SEM to construct a social-ecological network. Calculate network metrics (e.g., centrality measures) to identify variables that act as key connectors or drivers within the system.
• Interpretation and Scenario Analysis: Interpret the direct, indirect, and total effects of variables on key outcomes (e.g., forest cover, water quality). Use the model to simulate the potential impact of changes in key driver variables.

The workflow for this quantitative diagnostic protocol is summarized in the following diagram:

Protocol for Developing a Conceptual Hypernetwork Model

This protocol describes the process of building a qualitative hypernetwork model to represent the structure and dynamics of a forest social-ecological system [12].

Objective

To create a discrete, qualitative model of a forest SES that can simulate long-term dynamics, including regime shifts and the impact of interventions.

Materials and Equipment

• Qualitative modeling software (e.g., using discrete-event or Boolean network frameworks)
• Knowledge base from literature, expert elicitation, and stakeholder workshops

Procedure

• System Boundary and Component Definition: Define the spatial and temporal boundaries of the forest SES. Identify the key components (e.g., tree species, actor groups, institutions, abiotic factors).
• Identify Interactions and Hyperedges: Document all significant interactions among components. Note which interactions are non-dyadic (involving three or more components) and represent them as hyperedges.
• Construct the Initial Hypernetwork: Assemble the components and interactions into a single hypernetwork, where nodes represent system components and hyperedges represent interactions.
• Define Rules for Dynamics: For each hyperedge, define a set of qualitative rules that determine how the state of components changes over time or in response to events (e.g., "IF invasive species present AND low monitoring effort THEN native species diversity decreases").
• Model Simulation and Validation: Run simulations to explore the system's behavior over time. Compare model outputs with historical data or expert knowledge to validate the model's plausibility.
• Scenario and Intervention Analysis: Use the validated model to test the potential impact of different management interventions (e.g., new policies, conservation actions) or external drivers (e.g., climate change) on the system's trajectory.

Table 2: The Scientist's Toolkit: Key Analytical Reagents for SES Research

Research 'Reagent' (Method/Analysis)	Function in SES Analysis	Application Context
Structural Equation Modeling (SEM)	Tests and quantifies a priori hypothesized networks of direct and indirect causal relationships between social and ecological variables [11].	Uncovering pathways through which governance systems affect forest resource outcomes.
Network Analysis	Identifies key variables (hubs, connectors) within the SES structure and assesses system robustness [11].	Mapping the social-ecological network of an urban forest to identify critical leverage points.
Hypergraph Modeling	Represents complex, non-dyadic (multi-way) interactions within an ecosystem as a single, dynamic network [12].	Modeling the simultaneous interaction between tree health, pest populations, climate stress, and management actions.
PiecewiseSEM R Package	Implements structural equation modeling in a flexible, piecewise manner, suitable for complex ecological data that may not meet strict distributional assumptions [11].	Building an integrated model of forest SES where data come from different sources and have different distributions.
Ostrom's SES Framework (SESF)	Provides a common, systematic vocabulary and structure for diagnosing the relevant variables in a social-ecological system [10] [13].	Conducting a comprehensive and comparable diagnostic of a forest system prior to quantitative modeling.

The following diagram illustrates the logical workflow for applying the SESF, from conceptual diagnosis to quantitative analysis and final outcome assessment, which is fundamental to both protocols described above.

Within the context of strategic forest planning, assessing ecosystem services requires a robust framework to quantify the capacity of forest social-ecological systems (SES) to absorb disturbances while maintaining their essential identity, structure, and functions [14]. The Seven Core Principles of Resilience (7PsR) established by Biggs et al. (2012) provide such a framework, guiding the enhancement of resilience for sustained ecosystem service provision [15]. Social-ecological resilience is defined as the amount of disturbance a system can absorb while maintaining its structure, functions, and feedbacks, and encompasses the capacity for learning, adaptation, and transformation [15]. Forests are quintessential social-ecological systems, where complex interactions between human societies and the natural environment take place [16]. The operationalization of the 7PsR—moving these concepts from theory to measurable indicators—is critical for evidence-based policy and management [15]. This document provides detailed application notes and experimental protocols for researchers applying the 7PsR framework to assess forest system resilience, with a specific focus on sustaining ecosystem services.

The 7PsR Framework and Its Dimensions for Forest Assessment

The seven principles form an interconnected framework for assessing and enhancing forest resilience. The principle of managing slow variables and feedbacks is particularly crucial, as resilience often depends on gradually changing ecosystem components like soil organic matter, water tables, or nutrient cycles, which have significant long-term impacts [17]. Understanding the feedback loops associated with these variables is essential for preventing irreversible regime shifts in forest systems.

Table 1: The Seven Core Principles of Resilience for Forest Systems

Principle Number & Name	Core Concept	Key Dimensions for Forest Assessment
P1: Maintain Diversity and Redundancy	A rich biodiversity provides functional redundancy, acting as an ecological insurance policy [17].	Genetic, species, and functional diversity; landscape heterogeneity; response variety [15].
P2: Manage Connectivity	The degree to which habitats are linked allows movement of organisms, genes, and ecological processes [17].	Landscape permeability; habitat corridor integrity; cross-scale ecological interactions.
P3: Manage Slow Variables & Feedbacks	Resilience depends on slowly changing components (e.g., soil, water tables) that have long-term impacts [17].	Soil organic matter; hydrologic regimes; nutrient cycling rates; disturbance histories.
P4: Foster Complex Adaptive Systems (CAS) Thinking	Acknowledges forests as complex, adaptive systems requiring flexible, multi-scale management [16].	System identity; cross-scale interactions; adaptive cycles; emergence.
P5: Encourage Learning & Experimentation	Builds adaptive capacity through continuous knowledge generation and iterative management [16] [17].	Monitoring & evaluation systems; adaptive co-management; integration of knowledge types.
P6: Broaden Participation	Meaningful involvement of diverse stakeholders leads to more robust and equitable management [17].	Inclusion of local communities; Indigenous knowledge; stakeholder legitimacy.
P7: Promote Polycentric Governance	Multiple, overlapping centers of decision-making enhance the ability to respond at appropriate scales [16] [17].	Multi-level institutions; distributed authority; nested governance systems.

The conceptual relationship between these principles and the core goal of forest resilience is visualized below. The framework positions the forest social-ecological system (SES) at its center, buffered by the application of the seven interconnected principles, which together sustain the flow of ecosystem services.

Quantitative Assessment and Operationalization

A scoping review of how these principles have been operationalized in the literature reveals significant gaps. Of more than 750 articles citing the principles, only 23 attempted to operationalize them, and merely seven of these operationalized all seven principles [15]. This highlights a pressing need for consistent methodologies. In forest studies specifically, the principle of diversity (a sub-criterion of P1) appears in 50% of studies, while social and governance-related principles like learning and experimentation (P5, 7%), participation (P6, 11%), and polycentric governance (P7, 9%) are critically under-represented [16]. This indicates a significant research gap in quantifying these socio-ecological principles in forest systems.

Table 2: Exemplary Quantitative Indicators for Operationalizing the 7PsR in Forests

Principle	Measurable Indicator	Typical Data Source & Method	Representative Value in Literature
P1: Diversity & Redundancy	Tree Species Richness (Shannon Index)	Field plots, Forest Inventories	Found in 50% of forest resilience studies [16]
P2: Manage Connectivity	Patch Cohesion Index; Habitat Connectivity	GIS analysis, Remote Sensing (Landscape Metrics)	-
P3: Slow Variables	Soil Organic Matter (%); Water Table Depth	Soil Cores, Lab Analysis; Piezometers	-
P4: CAS Thinking	Presence of Cross-Scale Management Plans	Policy Document Analysis, Expert Surveys	-
P5: Learning	Existence of Formal Adaptive Mgmt. Feedback Loops	Stakeholder Interviews, Institutional Analysis	Found in 7% of forest resilience studies [16]
P6: Participation	Number & Diversity of Stakeholder Groups in Decision-Making	Meeting Minutes, Stakeholder Analysis	Found in 11% of forest resilience studies [16]
P7: Polycentric Governance	Number of Functioning, Interlinked Governance Centers	Network Analysis of Governance Institutions	Found in 9% of forest resilience studies [16]

Detailed Experimental Protocols for Field Assessment

Protocol for Assessing P1 (Diversity) and P3 (Slow Variables) via Forest Plots

Objective: To quantitatively measure key structural, compositional, and slow variable indicators related to Principles 1 and 3 in a forest stand. Background: This ground-truthing protocol provides foundational data on diversity (species, functional) and critical slow variables like soil chemistry, which underpin ecosystem function and resilience. Materials: Diameter Tape, Clinometer, GPS Unit, Soil Corer, Soil Test Kits (for pH, N, P, K), Soil Drying Oven, Sieve (2mm), Scale, Data Sheets. Workflow Steps:

• Plot Establishment: Using a GPS, randomly or systematically establish permanent (e.g., 30m x 30m) plots within the forest area of interest. Mark plot corners with rebar and record coordinates.
• Floristic Diversity (P1):
  • Conduct a 100% census of all trees >10cm DBH (Diameter at Breast Height) within the plot. Identify species and measure DBH for each.
  • Within nested subplots (e.g., 5m x 5m), survey all saplings and shrubs.
  • Calculate metrics: Species Richness (S), Shannon-Wiener Index (H'), Simpson's Index (D).
• Soil Sampling (P3 - Slow Variable):
  • At 5 predetermined points within the plot (e.g., center and four corners), use a soil corer to extract a sample from 0-15cm depth after removing litter.
  • Composite the 5 sub-samples into a single, representative plot sample.
  • Air-dry soil, sieve to <2mm, and analyze for: Soil Organic Matter (SOM) via loss-on-ignition, pH in water, and available nutrients (e.g., P, K) via standard soil testing kits.
• Data Analysis: Relate tree diversity metrics to soil condition variables to understand diversity-function relationships critical for resilience.

The workflow for this integrated field assessment is outlined below, showing the sequential steps from plot establishment to data synthesis.

Protocol for Assessing P2 (Connectivity) via GIS and Remote Sensing

Objective: To model and quantify landscape connectivity for a focal forest species or process. Background: Connectivity enables species movement and gene flow, which is critical for adaptation. This protocol uses spatial data to assess functional, not just structural, connectivity. Materials: GIS Software (e.g., QGIS, ArcGIS), Land Cover Map, Species Habitat Preference Data, Connectivity Modeling Toolbox (e.g., Linkage Mapper, Circuitscape). Workflow Steps:

• Base Map Preparation: Acquire or create a recent land cover/use map for the study region. Reclassify the map into resistance surfaces, where higher values (e.g., 100) represent greater resistance to movement (e.g., urban areas, cropland), and lower values (e.g., 1-10) represent preferred habitat (e.g., core forest).
• Core Area Delineation: Identify core forest patches of interest, which can be defined by size, conservation status, or species-specific habitat models.
• Model Execution:
  • Least-Cost Path Analysis: Run a model (e.g., in Linkage Mapper) to identify the least-cost pathways for movement between core patches. The output indicates potential corridors.
  • Circuit Theory Analysis: Run a model (e.g., Circuitscape) to calculate current flow across the entire landscape, identifying pinch points and areas of high movement probability.
• Metric Calculation: Calculate key connectivity metrics such as the Patch Cohesion Index, Equivalent Connected Area (ECA), and density of least-cost corridors per unit area.

Protocol for Assessing P5 (Learning) and P7 (Polycentric Governance) via Institutional Analysis

Objective: To characterize the structure and processes of learning and governance in the forest management system. Background: Social and institutional dimensions are critical for adaptive capacity. This protocol uses qualitative and network-based methods. Materials: Interview Guides, Survey Instruments, Recording Device, Transcriptome Software, Network Analysis Software (e.g., Gephi). Workflow Steps:

• Stakeholder Mapping (P6/P7): Identify all organizations and groups involved in or affected by forest management (e.g., state agencies, local communities, NGOs, research institutions).
• Semi-Structured Interviews (P5/P7): Conduct interviews with representatives from identified stakeholder groups. Probe for: existence and function of formal/informal learning feedback loops (P5), how knowledge is shared, presence of monitoring and evaluation systems, and the structure of decision-making authority (P7).
• Document Analysis (P5/P7): Review relevant policy documents, management plans, and meeting minutes to triangulate interview data regarding institutional arrangements and learning mechanisms.
• Network Analysis (P7):
  • Develop a survey asking stakeholders: "With whom do you regularly collaborate on forest management issues?"
  • Use this data to construct a network graph where nodes are organizations and links are collaborations.
  • Calculate network metrics such as network density, centralization, and identify key actors and clusters to objectively describe the polycentric structure.

The Scientist's Toolkit: Essential Reagents & Research Solutions

Table 3: Key Research Reagents and Tools for Forest Resilience Assessment

Tool / Reagent Solution	Function / Application	Key Features for Resilience Research
Soil Testing Kit	Quantifies slow variables (P3) like pH, N, P, K; essential for baseline soil health assessment.	Enables in-situ measurement of critical slow variables influencing nutrient cycles and forest growth.
GPS Unit & GIS Software	Core for mapping forest plots, habitats, and analyzing landscape connectivity (P2).	Fundamental for creating resistance surfaces and modeling functional connectivity and fragmentation.
Machine Learning Systems (e.g., MESOSCAN)	Automated identification and counting of soil mesofauna (mites, springtails) for biodiversity (P1) assessment [18].	Increases processing speed 10x; standardizes biodiversity data collection for soil health, a key slow variable.
Network Analysis Software (e.g., Gephi)	Models and visualizes polycentric governance networks (P7) and knowledge-sharing pathways (P5).	Provides quantitative metrics (density, centrality) to objectively describe complex social-ecological structures.
Structured Interview Guides	Elicits qualitative data on learning processes (P5), participation (P6), and governance (P7) from stakeholders.	Captures critical, non-ecological data on adaptive capacity and institutional arrangements.
Dendrometer Bands / Increment Borers	Measures tree growth rates as a response variable to disturbance and a proxy for ecosystem function.	Provides long-term data on forest recovery (a lagging indicator of resilience) and response to stress.

The 7PsR framework offers a comprehensive, multifaceted approach to assessing forest system resilience, moving beyond purely ecological metrics to integrate vital social and governance dimensions. The protocols and tools outlined here provide a pathway for researchers to operationalize this framework in the context of ecosystem services assessment for strategic forest planning. Successfully applying this framework requires a consistent set of dimensions for each principle combined with contextualized indicators that honor local conditions while enabling global comparative analyses [15]. As the global forest crisis continues, with 8.1 million hectares lost in 2024 alone [19], adopting such a holistic and measurable approach to resilience is not just an academic exercise but a pressing necessity for informing policy and management to halt and reverse forest loss.

Within the discipline of ecosystem services assessment in strategic forest planning, a pronounced and critical gap exists in the integration of social and governance principles. Forests are fundamentally social-ecological systems (SES), where complex interactions between human societies and the natural environment take place [16]. A comprehensive assessment of forest resilience, therefore, must extend beyond ecological metrics to include the social and governance dimensions that fundamentally shape these systems. However, a major scoping review of the literature reveals that the core social and governance principles of resilience—learning and experimentation, broad participation, and polycentric governance—are consistently overlooked [16]. This application note details the quantitative evidence for this gap and provides structured protocols for researchers to integrate these vital principles into their ecosystem services assessments.

Quantitative Analysis of Research Gaps

A systematic scoping review of 330 studies on forest resilience provides compelling quantitative evidence of the research bias towards ecological principles. The following table summarizes the frequency with which the seven core resilience principles (7PsR) appear in the assessed literature [16].

Table 1: Integration of Core Resilience Principles in Forest System Studies (n=330)

Core Resilience Principle	Category	Frequency in Studies
Diversity & Redundancy	Ecological	50%
Managing Connectivity	Ecological	32%
Slow Variables & Feedback	Ecological	25%
Complex Adaptive Systems Thinking	Cross-cutting	18%
Learning & Experimentation	Social	7%
Broadening Participation	Governance	11%
Promoting Polycentric Governance	Governance	9%

The data reveals a stark imbalance. Ecological principles, particularly diversity and redundancy, are integrated in half of all studies. In contrast, social and governance principles are critically neglected; learning and experimentation is addressed in only 7% of studies, while participation and polycentric governance appear in just 11% and 9% of studies, respectively [16]. Furthermore, the review highlights that none of the 330 studies jointly considered all seven resilience principles, emphasizing the fragmented nature of current assessment frameworks.

Experimental Protocols for Assessing Overlooked Principles

To address these gaps, researchers require robust methodologies to quantify and integrate the social and governance dimensions of forest resilience. The following protocols offer detailed, actionable approaches.

Protocol for Assessing Participation in Forest Governance

This protocol evaluates the breadth and depth of stakeholder involvement in forest management decision-making.

1. Research Question: Who participates, and to what extent, in the planning and management of the forest social-ecological system?

2. Data Collection Methods:

• Stakeholder Mapping: Identify all entities with an interest in the forest's ecosystem services. Categorize them as (a) Local Communities, (b) Indigenous Groups, (c) Government Agencies (local, regional, national), (d) Non-Governmental Organizations, and (e) Private Sector.
• Document Analysis: Review forest management plans, meeting minutes, and public consultation records from the last 5-10 years.
• Semi-Structured Interviews: Conduct interviews with representatives from each stakeholder category to understand their perceived level of influence.

3. Quantitative Metrics and Scoring: Develop a scoring system (e.g., 0-3) for the following indicators:

• Inclusivity: Number and diversity of stakeholder groups formally engaged.
• Decision-Making Power: Degree to which stakeholder input influences final decisions (e.g., 0=No mechanism for input, 1=Consultation only, 2=Collaboration, 3=Shared decision-making).
• Frequency of Engagement: How often structured stakeholder engagement occurs.

4. Data Analysis:

• Calculate aggregate scores for each metric to allow for cross-site or temporal comparison.
• Analyze interview data thematically to identify barriers to effective participation.

Protocol for Quantifying Polycentric Governance Structures

This protocol maps the distribution of authority across multiple centers of power in forest governance.

1. Research Question: To what extent is governance of the forest system characterized by polycentricity?

2. Data Collection Methods:

• Policy and Legal Analysis: Identify all formal institutions with jurisdiction or management authority over the forest area.
• Institutional Network Mapping: Survey key informants to document formal and informal relationships (e.g., information sharing, resource transfer, joint planning) between these institutions.

3. Quantitative Metrics and Scoring: Score the following aspects (e.g., on a scale of 0-2):

• Number of Autonomous Decision-Making Centers: Count of institutions with formal authority to make and enforce rules.
• Connectivity: Density of the network of interactions between these centers.
• Functional Overlap: Degree to which the mandates and responsibilities of different centers overlap.

4. Data Analysis:

• Use social network analysis (SNA) to compute metrics like network density and centrality.
• A higher aggregate score indicates a more polycentric governance system, which is theorized to enhance adaptive capacity [16].

Protocol for Evaluating Learning and Experimentation

This protocol assesses the presence of structures that foster adaptive learning and innovation in forest management.

1. Research Question: Are there systematic processes for knowledge generation, reflection, and adaptive management in response to environmental change?

2. Data Collection Methods:

• Management Plan Review: Analyze successive versions of forest management plans for evidence of changes based on monitoring data or past outcomes.
• Key Informant Interviews: Interview forest managers and community leaders about the existence of formal review processes, pilot projects, and the tolerance for "safe-to-fail" experiments.

3. Quantitative Metrics and Scoring: Score the presence (1) or absence (0) of the following:

• Long-Term Monitoring System: Existence of a structured environmental and social monitoring program.
• Formal Review Cycles: Scheduled intervals for reviewing management strategies.
• Documented Adaptive Changes: Evidence of management actions being modified based on monitoring or research.
• Experimental Pilots: Presence of designated areas or projects for testing new management approaches.

4. Data Analysis:

• Sum the scores to create a "Learning & Experimentation Index" (range 0-4).
• Correlate this index with ecological or social outcomes to test its impact on system resilience.

Visualization of Conceptual Workflows

The following diagrams, generated using Graphviz, illustrate the logical relationships and experimental workflows for integrating these principles into research.

Social & Governance Assessment Workflow

Polycentric Governance Network

The Scientist's Toolkit: Essential Methodological Reagents

For researchers embarking on this integrative path, the following "reagents" are essential for robust data collection and analysis.

Table 2: Key Research Reagent Solutions for Social-ecological Forest Research

Research Reagent	Function	Application Example
Structured Interview Guides	Ensures consistent and comparable qualitative data collection across diverse informants.	Used in protocols 3.1 and 3.3 to gather data on participation and learning from different stakeholder groups.
Social Network Analysis (SNA) Software	Quantifies relationships and flows between actors in a network.	Used in protocol 3.2 to analyze connectivity and structure in polycentric governance networks.
Document Analysis Codebook	Provides a systematic framework for extracting and categorizing qualitative data from documents.	Used across all protocols to consistently analyze management plans, meeting minutes, and policy documents.
Quantitative Scoring Rubrics	Transforms qualitative observations into ordinal data for comparison and aggregation.	Central to all three protocols, enabling the creation of indices for participation, polycentricity, and learning.
Geographic Information Systems (GIS)	Integrates and visualizes spatial data on ecological variables and social boundaries.	Can overlay stakeholder territories with forest cover data to analyze spatial equity in access to ecosystem services.

The sustainable management of forest ecosystem services (FES) requires a delicate balance: aligning the supply of services from forest managers with the demand for these services from the broader population. Strategic forest planning research increasingly employs economic valuation methods to quantify this balance, utilizing Willingness-to-Pay (WTP) and Willingness-to-Accept (WTA) as core metrics for understanding stakeholder preferences [20]. WTP measures the maximum amount users are willing to pay for specific FES improvements, reflecting demand-side value. Conversely, WTA represents the minimum compensation forest owners require to implement management practices that provide these services, capturing supply-side constraints [20] [21].

These metrics are particularly crucial in light of economic research showing that ecosystem services face limited substitutability with market goods. Global meta-analyses reveal an income elasticity of WTP for ecosystem services of approximately 0.6, suggesting that as incomes grow, the relative value of FES increases compared to market goods [22]. This necessitates specific valuation approaches rather than relying on generic economic assumptions.

This application note provides researchers with structured protocols for implementing WTP and WTA studies in forest ecosystem services assessment, supported by empirical data and methodological frameworks for integrating findings into strategic forest planning.

Methodological Approaches in Forest Ecosystem Service Valuation

Comparative Analysis of Valuation Methods

Table 1: Methods for Assessing WTP and WTA in Forest Ecosystem Services Research

Method	Application in FES	Key Strengths	Common Metrics	Implementation Context
Choice Experiments	Compares preferences for management alternatives with different attributes [20]	Captures trade-offs between multiple FES simultaneously; Models preference heterogeneity	Marginal WTP for specific FES attributes; Relative importance scores	Swiss forest management studies; Landscape preference assessments
Contingent Valuation	Elicits values for specific FES through hypothetical market scenarios [23]	Applicable to non-use values; Direct approach for policy scenarios	Mean/median WTP for FES bundles; Protest zero responses	Nepalese Siwalik landscapes; Climate-smart forestry assessments [24]
Meta-Analysis	Synthesizes existing valuation studies to derive generalizable parameters [22] [21]	Identifies value determinants; Enables benefit transfer across sites	Income elasticities; Value function coefficients; RPC adjustments	Global ecosystem service values; Forest carbon program design [21]

Key Methodological Considerations

Several critical factors emerge from the literature that must be addressed in research design:

• Spatial and Socioeconomic Heterogeneity: WTP varies substantially based on proximity to forests, wealth levels, and forest management modalities [23]. Research in Nepal's Siwalik landscapes found wealthier households preferred cash payments while poorer households favored labor-based contributions [23].

• Temporal Dynamics: Forest ecosystem services change over time, and valuations must account for these dynamics. Studies in Spain incorporated forest dynamics simulations over 100-year periods to project future FES provision [25].

• Distributional Equity: Normative trade-offs exist between efficiency and equity objectives in FES management. Research in Malawi demonstrated that distribution rules allocating 60% of biomass to poor households and 40% to wealthy households maximized total societal welfare [26].

Experimental Protocols

Protocol 1: Implementing a Choice Experiment for WTP Assessment

Purpose: To quantify population preferences and WTP for forest management alternatives and their associated ecosystem services.

Table 2: Research Reagent Solutions for Choice Experiment Implementation

Research Component	Description & Function	Application Example
Attribute Selection Framework	Defines key FES and management characteristics to be evaluated	Swiss study included forest structure, biodiversity, recreation access [20]
Experimental Design Software	Generates choice sets with balanced statistical properties	Programs like Ngene facilitate efficient experimental designs
Survey Platform	Administration mode for choice experiment implementation	Mail surveys (80.5% of studies) vs. web/telephone modes [21]
Econometric Modeling Package	Analyzes choice data to derive WTP estimates	R, STATA, or NLOGIT with mixed logit, latent class models
Benefit Transfer Protocol	Transfers values from study sites to policy sites	Meta-analysis functions account for income, population differences [22]

Step-by-Step Procedure:

• Attribute Identification: Select 4-6 key forest management attributes that vary across realistic levels (e.g., mixed vs. monoculture forests, permanent vs. rotational management, recreational infrastructure) [20].

• Experimental Design: Develop choice sets using efficient experimental design principles, typically presenting respondents with 8-12 choice tasks, each showing 2-3 alternatives plus a status quo option [20].

• Survey Implementation: Administer to stratified sample of population users, ensuring representation across geographic zones (nearby/distant) and socioeconomic groups (rich/poor) [23].

• Model Estimation: Apply appropriate discrete choice models (mixed logit, latent class) to account for preference heterogeneity:

  Where U_ij is utility of alternative j for individual i, X attributes, β coefficients, and ε error term.

• WTP Calculation: Compute marginal WTP as the ratio of attribute coefficient to price coefficient:

• Validity Testing: Conduct scope tests, analyze protest responses, and assess internal consistency [23].

Protocol 2: Forest Owner WTA Assessment for Carbon Programs

Purpose: To determine compensation requirements for forest owners to adopt climate-smart practices that enhance ecosystem services.

Step-by-Step Procedure:

• Program Definition: Specify contract features including duration (typically 10-50 years), management restrictions, monitoring requirements, and payment structures [21].

• Sampling Framework: Target family forest owners (FFOs) with stratified sampling based on ownership objectives (conservation, passive, production), forest acreage, and region [21].

• Valuation Approach:

  • Contingent Valuation: Use payment card, dichotomous choice, or open-ended formats to elicit minimum acceptance thresholds.
  • Choice Modeling: Present alternatives with varying contract features and payment levels to derive implicit prices for different restrictions.

• Meta-Analytic Benefit Transfer (when primary data collection not feasible):

  • Develop value function from existing studies:

  • Adjust for context using income elasticity of WTP (approximately 0.6) [22].

• Analysis: Model WTA as function of contract attributes, owner characteristics, and property features using robust regression techniques.

Key Findings and Data Synthesis

Empirical WTP and WTA Values Across Studies

Table 3: Synthesis of WTP and WTA Values from Forest Ecosystem Services Studies

Study Context	Valuation Focus	Key Determinants	WTP/WTA Range	Policy Implications
Swiss Forest Zones [20]	Management alternatives	Forest type, biodiversity, recreation access	Substantial variation across zones	Spatial mismatch between WTP and WTA
Siwalik Landscape, Nepal [23]	Regulating & cultural services	Wealth (cash vs labor), distance to forest	Disaggregated by user subgroups	Different payment vehicles for rich vs poor
U.S. Forest Carbon [21]	Carbon sequestration practices	Ownership objectives, contract length, restrictions	Lowest for conservation owners: $X-Y/acre/yr	Diverse contract types needed
Global Meta-Analysis [22]	Generic ecosystem services	Income levels, substitution possibilities	Income elasticity: ~0.6	RPC adjustment ~1.7%/year

Integration into Strategic Forest Planning

The valuation data derived from WTP/WTA studies informs strategic forest planning through several pathways:

• Forest Use Suitability (FUS) Assessment: Research in Spain demonstrated how dynamic FES projections over 100-year horizons can determine optimal forest use allocations (productive, protective, conservation-oriented, social, multifunctional) [25].

• Multi-Criteria Decision Support: The Ecosystem Management Decision Support (EMDS) system integrates FES valuations with spatial planning, enabling trade-off analysis between conflicting objectives [25].

• Payment for Ecosystem Services (PES) Design: Meta-analyses of PES programs show they successfully increase conservation behavior without crowding out intrinsic motivation after payments terminate [27].

The integration of WTP and WTA assessment into strategic forest planning represents a critical advancement in sustainable ecosystem management. These valuation approaches provide empirical foundations for:

• Balancing Supply and Demand: Directly addressing the core challenge of matching forest managers' supply decisions with population demands for ecosystem services [20].

• Informing Equitable Governance: Illuminating distributional consequences of management decisions, particularly for communities dependent on forests for livelihoods [26].

• Future-Proofing Forest Planning: Incorporating dynamic FES projections and relative price changes ensures planning remains relevant under changing ecological and economic conditions [22] [25].

The protocols outlined in this application note provide researchers with standardized approaches for generating comparable, valid valuations that can directly inform forest policy and management decisions within the broader context of ecosystem services assessment in strategic forest planning research.

A Toolkit for Practitioners: Frameworks and Analytical Methods for ES Assessment

Ecosystem services (ES) assessments are critical for sustainable forest management, providing a structured approach to identify, quantify, and value the benefits that forests provide to humanity. In strategic forest planning, these assessments help balance ecological integrity with socio-economic demands, enabling managers to make informed decisions that maintain forest functionality while meeting human needs. The integration of ES assessment into forestry practices has evolved from a conceptual framework to an applied science, employing diverse methodologies from biophysical modeling to economic valuation and participatory planning. This guidance document outlines a comprehensive six-stage process tailored specifically for forest ecosystems, incorporating standardized protocols and decision-support tools to enhance the rigor, applicability, and policy relevance of assessment outcomes.

Forests provide a multitude of ecosystem services, including water purification, carbon sequestration, soil stabilization, timber production, and recreational opportunities. However, managing these services involves navigating complex trade-offs, as enhancing one service may diminish another. A systematic assessment process is therefore essential for identifying these interactions and developing management strategies that optimize the suite of services based on defined priorities. The process described below provides a robust framework for conducting such assessments, with particular emphasis on temporal dynamics, spatial explicitness, and stakeholder engagement.

The Six-Stage Assessment Process

Stage 1: Scoping and Preliminary Identification

Objective: Define assessment boundaries, objectives, and priority ecosystem services relevant to the forest management context.

The initial stage establishes the foundation for the entire assessment by clarifying its purpose, scope, and focal points. This involves engaging key stakeholders to identify management priorities and information needs, which ensures the assessment addresses relevant issues and enhances the likelihood of its findings being utilized. Simultaneously, practitioners should compile and review existing ecological data for the forest area, including forest inventory data, spatial datasets, and previous research findings [25]. Based on stakeholder input and data availability, the assessment team then selects priority ecosystem services for detailed evaluation, focusing on those most critical to decision-making.

• Stakeholder Identification: Determine all relevant parties affected by or influencing forest management decisions, including local communities, industry representatives, government agencies, and non-governmental organizations. Structured approaches like stakeholder analysis can systematically identify these groups and their interests.
• Problem Framing: Collaboratively define the specific forest management decisions the assessment will inform, such as harvest planning, conservation zoning, or restoration prioritization. This establishes a clear decision context that guides subsequent technical work.
• Ecosystem Service Selection: Identify a manageable set of priority services using criteria such as stakeholder relevance, ecological significance, and policy importance. Common classification systems like the Common International Classification of Ecosystem Services (CICS) or the National Ecosystem Services Classification System (NESCS Plus) provide standardized frameworks for this identification [28] [29].

Stage 2: Indicator Selection and Metric Development

Objective: Establish quantifiable, scientifically robust indicators and metrics for assessing the priority ecosystem services identified in Stage 1.

This stage translates conceptual ecosystem services into measurable parameters that can be tracked, modeled, and evaluated. Indicators should capture different aspects of ecosystem service provision, including potential supply, actual flow, and societal demand. The selection process must balance scientific rigor with practical feasibility, prioritizing metrics that can be reliably measured or modeled given available resources and data. For forest ecosystems, it is particularly valuable to distinguish between static metrics (relatively stable over time) and dynamic metrics (changing with forest development and management) to enable temporal analysis [25].

Table 1: Exemplary Ecosystem Service Indicators for Forest Assessments

Ecosystem Service	Potential Indicator	Metric Type	Measurement Unit
Timber Provision	Growing Stock Volume	Dynamic	m³/ha/year
Carbon Sequestration	Aboveground Biomass	Dynamic	tC/ha
Water Regulation	Water Yield	Dynamic	mm/year
Recreation	Accessibility & Scenic Beauty	Static	Composite Index
Soil Retention	Erosion Reduction Potential	Dynamic	t soil retained/ha/year
Biodiversity Conservation	Habitat Quality Index	Static/Dynamic	Species Richness/Index

The development of indicators should follow specific selection criteria, including sensitivity to management changes, reliability, data availability, and relevance to decision-making. For example, a comprehensive forest assessment in Spain utilized 13 metrics to define 11 forest ecosystem services, 9 of which were dynamic metrics derived from forest biophysical variables [25]. The EcoService Models Library (ESML) provides a valuable resource for identifying appropriate modeling approaches for different indicators [28].

Stage 3: Biophysical Assessment and Spatial Modeling

Objective: Quantify the current status, spatial distribution, and, where possible, temporal trends of priority ecosystem services.

The biophysical assessment stage involves data collection, spatial analysis, and modeling to estimate the magnitude and distribution of ecosystem services across the forest landscape. This increasingly relies on geospatial technologies and ecosystem modeling frameworks to handle the complexity of ecological processes and their interactions. Spatially explicit assessments are particularly valuable for forest planning as they enable identification of service hotspots, areas of trade-offs, and targeting of management interventions. The U.S. Environmental Protection Agency's EnviroAtlas provides an example of how diverse ecological data can be organized into accessible spatial layers for ecosystem service assessment [28].

• Data Compilation: Gather relevant biophysical data, including remote sensing imagery, forest inventory plots, soil maps, climate data, and topographic information. National-scale datasets like the Spanish National Forest Inventory have been successfully used as primary data sources for broad-scale assessments [25].
• Model Application: Implement appropriate models to quantify ecosystem services, which may include InVEST, ARIES, or other specialized models for specific services like water yield, carbon storage, or sediment retention. The choice of model should reflect assessment objectives, data availability, and technical capacity.
• Spatial Analysis: Conduct geospatial analysis to map the provision of ecosystem services, identify spatial correlations and trade-offs, and assess connectivity between service provision areas and beneficiaries. This can reveal, for instance, how upstream forest management affects downstream water users.

Stage 4: Scenario Analysis and Projection of Future Conditions

Objective: Evaluate how ecosystem services may change under alternative future scenarios, including different forest management approaches or environmental change pathways.

Forest ecosystems are dynamic, and their capacity to provide services evolves over time due to both natural development and management interventions. This stage employs simulation modeling to project how ecosystem services might change in response to different drivers, providing crucial information for long-term strategic planning. By comparing alternative futures, decision-makers can identify management strategies that maintain or enhance ecosystem services over time. Studies have demonstrated the value of simulating forest dynamics over extended periods (e.g., 100 years) to understand long-term trajectories of service provision [25].

Scenario Development Process:

• Define Scenario Framework: Identify key drivers of change (e.g., climate change, management intensity, market demands) and develop coherent, plausible scenarios representing alternative futures.
• Parameterize Models: Configure forest dynamics models (e.g., FORMES or other empirical individual tree growth, mortality, and ingrowth models) to simulate forest development under each scenario [25].
• Run Simulations: Execute models to project forest conditions and associated ecosystem services over relevant time horizons, typically decades for forest ecosystems.
• Analyze Outcomes: Compare scenario results to identify robust management strategies, potential trade-offs, and tipping points in ecosystem service provision.

Stage 5: Integration and Decision Support

Objective: Synthesize assessment findings to inform management decisions through structured evaluation of options and their consequences.

The integration stage translates complex assessment results into actionable information for forest planning. This involves applying multi-criteria decision analysis to evaluate alternative management options against multiple objectives, often reflecting the diverse values of different stakeholders. Decision-support tools help visualize trade-offs, identify synergies, and prioritize management interventions based on explicit criteria and weights. The Ecosystem Management Decision Support (EMDS) system provides an example of a spatially explicit tool that can handle the complexity of multi-objective decision-making in forest management [25].

Table 2: Forest Use Suitability (FUS) Alternatives and Associated Ecosystem Services

FUS Alternative	Primary Management Objective	Key Ecosystem Services Enhanced
Productive	Maximize economic profitability	Timber production, non-timber forest products
Protective	Mitigate harmful natural or human-induced processes	Erosion control, water regulation, avalanche protection
Conservation-Oriented	Maintain habitat and biodiversity	Habitat provision, genetic diversity, threatened species protection
Social	Enhance non-material values	Recreation, aesthetic value, cultural heritage
Multifunctional	Balance multiple uses	Combination of above services based on local context

The Analytic Hierarchy Process (AHP) is frequently used to derive weights for different ecosystem services based on stakeholder preferences, providing a transparent method for reconciling competing objectives. In the Spanish pine forest assessment, this approach was combined with geospatial logic-based modeling to determine the most suitable forest use for different areas [25]. The output typically includes spatial prioritization maps and management recommendations tailored to different forest stands or planning units.

Stage 6: Implementation and Monitoring

Objective: Incorporate assessment findings into management plans and establish monitoring programs to track outcomes and improve future assessments.

The final stage focuses on translating assessment recommendations into on-the-ground actions and establishing mechanisms to evaluate their effectiveness. This requires close collaboration with forest managers, planners, and policymakers to ensure assessment findings are integrated into relevant decision processes, such as forest management plans, strategic environmental assessments, or policy frameworks. Additionally, implementing monitoring programs allows for adaptive management, where interventions can be refined based on observed outcomes.

• Management Plan Integration: Incorporate assessment results into formal planning documents, specifying management actions, locations, timelines, and responsible parties. This may include environmental and social management plans that outline measures to mitigate project impacts and manage operational dependencies on ecosystems [30].
• Stakeholder Communication: Present findings in accessible formats tailored to different audiences, using visualization tools, summary briefs, and interactive platforms to communicate results effectively.
• Monitoring Framework Development: Establish indicators and protocols for tracking ecosystem service provision over time, creating feedback loops to inform adaptive management. This should include response indicators to track the effects of management actions and pressure indicators to monitor external drivers of change.
• Capacity Building: Enhance the skills and resources of managing institutions to conduct future assessments independently, promoting the institutionalization of ecosystem service thinking in forest planning.

Table 3: Key Research Reagent Solutions for Ecosystem Services Assessment

Tool/Resource	Function	Application Context
National Ecosystem Services Classification System Plus (NESCS Plus)	Standardized framework for tracing links between ecosystems and human benefits	Categorizing final ecosystem goods and services and their beneficiaries [28]
EnviroAtlas	Geospatial data platform with ecosystem services metrics	Accessing and analyzing mapped ecosystem services data at multiple scales [28]
Eco-Health Relationship Browser	Exploring connections between ecosystems and human health	Assessing public health benefits of forest management decisions [28]
FORMES Projection System	Simulating forest dynamics under alternative management scenarios	Projecting long-term changes in forest structure and service provision [25]
Ecosystem Management Decision Support (EMDS) System	Multi-criteria decision support for environmental planning	Evaluating forest use suitability alternatives based on ecosystem services [25]
Common International Classification of Ecosystem Services (CICES)	Standardized classification of ecosystem services	Ensuring consistency in service identification and assessment across studies [29]

The six-stage process for ecosystem services assessment provides a comprehensive, scientifically robust framework for integrating ecological considerations into strategic forest planning. By systematically identifying, quantifying, and valuing forest ecosystem services, this approach enables managers to make more informed decisions that balance multiple objectives and stakeholders. The increasing availability of standardized tools, classification systems, and modeling platforms has significantly advanced our capacity to implement these assessments across diverse forest contexts.

Future developments in ecosystem service assessment will likely focus on enhancing temporal dynamics modeling, improving integration of stakeholder values, and strengthening policy uptake through more targeted and accessible outputs. As research continues to address current gaps in data availability and methodological integration, particularly in complex ecosystems like karst forests, the application of ecosystem service assessments in forest planning is poised to become more sophisticated and impactful [31]. By adopting this structured approach, forest researchers and managers can contribute to more sustainable, multifunctional forest landscapes that simultaneously support biodiversity, human well-being, and economic development.

Ecosystem services (ES) are the benefits humans derive from ecosystems, and the balance between their supply and demand is fundamental to achieving human well-being and a central focus of strategic forest planning research [32]. The spatial mismatch between the supply of ES from natural areas and the demand for these services from human populations is a core challenge in environmental management. Mapping this relationship at a landscape scale is therefore critical for identifying areas of ecological deficit, informing conservation priorities, and aligning forest management with societal needs [33] [34]. This protocol provides a detailed methodology for utilizing publicly available data to quantitatively assess and spatially map ES supply and demand, enabling researchers to identify critical areas for intervention in a forest planning context.

Foundational Concepts and Assessment Framework

Key Definitions

• Ecosystem Service Supply (ES Supply): The capacity of an ecosystem to provide goods and services, influenced by biophysical structures, processes, and land use/land cover (LULC) [34]. For example, a forest's capacity to sequester carbon or regulate water flow.
• Ecosystem Service Demand (ES Demand): The sum of all ecosystem goods and services currently consumed or used in a given area over a given time period, driven by socio-economic activities [33] [34]. This includes the direct use of resources like timber and the indirect reliance on regulatory services like air purification.
• Ecosystem Service Flow (ES Flow): The dynamic process that connects ES supply areas with demand areas, representing the movement of services through biophysical, trade, species, or information pathways [35].
• Spatial Mismatch: The imbalance between the geographical locations of ES supply and demand, often resulting in areas of high demand with low local supply, or vice versa [33].

A Multi-Scale, Social-Ecological Perspective

A robust assessment recognizes that ES supply and demand operate across multiple spatial scales, from local to regional, and exhibit strong scale effects [34]. Furthermore, social-ecological systems are characterized by complex, bidirectional interactions where social factors (e.g., perceptions, values) and ecological factors co-produce ecosystem service patterns [36]. Integrating these perspectives, such as through social-ecological network (SEN) analysis, allows researchers to model the intricate connections and flows between supply and demand nodes across a landscape [35].

A successful mapping project relies on leveraging publicly available datasets. The table below summarizes essential data types and their common public sources.

Table 1: Key Public Data Types and Sources for ES Supply-Demand Mapping

Data Category	Specific Data	Description & Utility	Exemplar Public Sources
Land Use/Land Cover (LULC)	Land cover classification (e.g., forest, agriculture, urban)	Fundamental for modeling ES supply potential based on land cover type.	USGS EarthExplorer, ESA WorldCover, Copernicus Land Monitoring Service
Topography & Hydrology	Digital Elevation Models (DEMs), river networks, watershed boundaries	Crucial for modeling services like water flow regulation, soil erosion, and flood mitigation.	NASA SRTM, ALOS World 3D, USGS National Hydrography Dataset
Climate & Meteorology	Precipitation, temperature, evapotranspiration	Key inputs for models of water yield, crop production, and climate regulation.	WorldClim, TerraClimate, ERA5 (ECMWF)
Soil Properties	Soil type, depth, texture, organic carbon content	Essential for modeling soil formation, erosion control, and agricultural suitability.	SoilGrids, Harmonized World Soil Database
Socio-Economic Data	Population density, income, land use statistics	Proxy for ES demand; higher population density often correlates with greater demand.	WorldPop, NASA Socioeconomic Data and Applications Center (SEDAC)
Biophysical Data	Net Primary Productivity (NPP), Leaf Area Index (LAI)	Indicators of ecosystem productivity and health, informing supply of multiple services.	MODIS (NASA), Sentinel-2 (ESA)

Methodological Protocol: A Step-by-Step Workflow

The following workflow, visualized in the diagram below, outlines the core steps for mapping ES supply and demand.

Figure 1. A generalized workflow for mapping ecosystem service supply and demand using public data, highlighting key inputs and analytical tools.

Step 1: Data Acquisition and Preparation

• Define the Study Area and Scale: Clearly delineate the geographical boundary of your analysis (e.g., a specific forest management unit, watershed, or administrative region). The choice of scale (local, regional) will influence the selection of data resolution and methods [34].
• Data Collection: Download the required public data (see Table 1) for your study area. Ensure all datasets are sourced for the same or similar time periods to maintain temporal consistency.
• Data Pre-processing: Use a Geographic Information System (GIS) to harmonize all datasets. This includes:
  • Reprojection: Converting all spatial layers to a common coordinate system.
  • Resampling: Adjusting the spatial resolution of raster data to a consistent pixel size.
  • Clipping: Masking all data to the exact boundary of your study area.

Step 2: Quantifying Ecosystem Service Supply

The supply of different ES can be quantified using a combination of biophysical models and indicator-based approaches.

• Utilize the InVEST Model: The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite is a publicly available, widely used tool for mapping multiple ES.
  • Water Yield: The InVEST Annual Water Yield model uses data on precipitation, evapotranspiration, soil depth, and LULC to estimate the total annual water yield from a landscape [32].
  • Carbon Storage: The InVEST Carbon Storage and Sequestration model pools carbon across four basic carbon pools (aboveground biomass, belowground biomass, soil, and dead organic matter) based on LULC types to map the distribution of carbon stocks [32].
  • Sediment Retention: The InVEST Sediment Delivery Ratio (SDR) model uses the RUSLE equation with data on rainfall erosivity, soil erodibility, slope, and LULC to model the capacity of ecosystems to prevent soil erosion [32].
  • Habitat Quality: The InVEST Habitat Quality model uses LULC data and information on the location and impact of threats (e.g., urban areas, roads) to produce a relative index of habitat quality and degradation [32].
• Apply the Ecosystem Service Matrix: As a complementary or alternative method, use an ES matrix approach [33] [34]. This is a look-up table that assigns a supply capacity score (e.g., from 0, no supply, to 5, very high supply) for each ES type to every LULC class in your study area. These scores can be derived from literature, expert elicitation, or biophysical measurements.

Step 3: Quantifying Ecosystem Service Demand

Demand can be assessed through consumption-based proxies or socio-economic data.

• Spatial Proxy Indicators: Use publicly available spatial data as proxies for demand.
  • Provisioning Services (e.g., food): Population density can serve as a primary proxy for demand for food, water, and timber [34].
  • Regulating Services (e.g., air purification, flood regulation): Demand can be modeled based on the population and infrastructure located in areas prone to air pollution or flooding [33].
  • Cultural Services (e.g., recreation): Demand can be mapped using data from social media (e.g., geotagged photographs from Flickr) or participatory mapping [33] [36].
• Questionnaire Surveys: For localized studies, primary data collection through questionnaires can provide direct, nuanced information on the demand for various ES, including cultural services [33]. Surveys can quantify how often people use an area for recreation or how they value specific landscape features.

Step 4: Analyzing Supply-Demand Balance and Flow

• Z-score Standardization: To make supply and demand scores comparable, standardize the respective maps using Z-score standardization. This converts the values of each map to a common scale with a mean of 0 and a standard deviation of 1 [33].
• Calculate the Supply-Demand Balance: Create a new raster layer by subtracting the standardized demand map from the standardized supply map (Balance = Z_Supply - Z_Demand). This reveals areas of surplus (balance > 0) and deficit (balance < 0) [33].
• Model Ecosystem Service Flow: To understand how services move from supply to demand areas, employ a Social-Ecological Network (SEN) approach [35] or a spatial interaction model like the Enhanced Two-step Floating Catchment Area (E2SFCA) method [35]. This helps identify the pathways (e.g., river flow, transport networks) and intensity of ES flows, which is critical for managing services like grain production or water provision across a landscape.

Step 5: Identifying Critical Areas and Spatial Mismatches

• Hotspot Analysis: Use spatial statistics (e.g., Getis-Ord Gi* in GIS software) to identify statistically significant clusters of high ES supply ("supply hotspots") and high ES demand ("demand hotspots") [32] [34].
• Spatial Mismatch Typology: Overlay the supply-demand balance map with hotspot maps to classify the landscape into distinct spatial patterns. For example:
  • High Supply - Low Demand (HS-LD): Areas of potential conservation priority.
  • Low Supply - High Demand (LS-HD): Critical areas of deficit requiring targeted restoration or management.
  • Low Supply - Low Demand (LS-LD)
  • High Supply - High Demand (HS-HD) [33]
• Cluster Analysis for Zoning: Use a Self-Organizing Feature Map (SOFM), a type of artificial neural network, to objectively zone the landscape into areas with similar ES supply-demand characteristics, which is highly valuable for targeted management planning [34].

The Scientist's Toolkit: Essential Research Reagents and Models

In the context of ES mapping, "research reagents" refer to the essential software tools, models, and data processing techniques required to conduct the analysis.

Table 2: Key Research Reagent Solutions for ES Supply-Demand Mapping

Tool/Model Name	Type	Primary Function in ES Mapping	Key Inputs
InVEST Suite	Software Model	Spatially explicit modeling of multiple ES supply (e.g., water yield, carbon, habitat).	LULC, DEM, precipitation, soil data [32].
ArcGIS / QGIS	Geographic Information System	Platform for data management, spatial analysis, visualization, and executing many modeling steps.	All geospatial raster and vector data.
Social-Ecological Network (SEN) Analysis	Analytical Framework	Quantifying the direction and intensity of ES flows between supply and demand areas.	Maps of supply areas, demand areas, and potential flow pathways [35].
Self-Organizing Feature Map (SOFM)	Neural Network Algorithm	Unsupervised clustering of regions into distinct ES functional types based on multi-dimensional supply-demand data.	Raster layers of multiple ES supply and demand [34].
Ecosystem Service Matrix	Look-up Table / Index	Rapid assessment of ES supply and demand potential based on land use classes.	LULC map, expert-derived scores for each LULC-ES combination [33] [34].

Visualizing Results for Impact

Effective communication of results is paramount. The supply-demand balance and critical areas should be presented in clear, multi-panel maps. For example, a final output might consist of three maps: (A) the spatial pattern of ES supply, (B) the spatial pattern of ES demand, and (C) the integrated supply-demand balance, color-coded to show surplus, balance, and deficit areas, with overlaid symbols highlighting identified "critical deficit areas" [33] [32]. Adhere to principles of good chart design: use clear titles, descriptive legends, and ensure sufficient color contrast for accessibility [37].

The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making (MCDM) method developed by Thomas Saaty that provides a structured, transparent framework for incorporating diverse stakeholder preferences into complex environmental decisions. Within ecosystem services assessment for strategic forest planning, AHP addresses a critical challenge: reconciling competing ecological, economic, and social priorities among multiple stakeholder groups with often conflicting interests. By decomposing complex decisions into a hierarchical structure and employing pairwise comparisons, AHP enables researchers to systematically quantify stakeholder values and integrate them with biophysical data for more legitimate and contextually appropriate management outcomes [38] [39].

The relevance of AHP to forest planning stems from its capacity to handle the inherent multi-dimensionality of ecosystem services assessment. Forests simultaneously provide provisioning services (e.g., timber, non-timber forest products), regulating services (e.g., carbon sequestration, water purification), cultural services (e.g., recreation, aesthetic value), and supporting services (e.g., soil formation, habitat provision). Different stakeholders—including forest managers, conservationists, local communities, and industry representatives—inevitably prioritize these services differently based on their values, needs, and knowledge systems [38] [25]. AHP provides a rigorous methodological approach to make these often-implicit preferences explicit, quantifiable, and comparable across groups.

Theoretical and Methodological Foundations

Core Principles of AHP

The Analytic Hierarchy Process operates on several key principles that make it particularly suitable for participatory ecosystem services assessment. First, it employs the fundamental concept of hierarchical decomposition, breaking down a complex decision problem into its constituent parts: overall goal, criteria, sub-criteria, and alternatives. This structure mirrors how people naturally process complex decisions and makes the process transparent and accessible to stakeholders with varying levels of technical expertise [38] [40].

Second, AHP utilizes pairwise comparisons rather than absolute assessments. Stakeholders evaluate elements by comparing them two at a time with respect to their parent element in the hierarchy, expressing their preferences on a semantic scale of relative importance (typically 1-9). This approach aligns with human cognitive capabilities, as people are generally more confident and consistent when making relative judgments between two items than when assigning absolute scores to multiple items simultaneously [41].

Third, the method incorporates a mathematical framework for deriving priority scales from the pairwise comparison matrices and checking judgment consistency. The eigenvector method calculates the relative weights of criteria and scores of alternatives, while a consistency ratio (CR) indicates whether the pairwise comparisons are logically coherent. A CR value of ≤0.10 is generally considered acceptable, though higher thresholds may be justified when working with diverse stakeholder groups where perfect consistency is less critical than capturing genuine perspectives [38].

AHP in Environmental Decision-Making Context

When applied to ecosystem services assessment in forest planning, AHP functions as a bridge between quantitative biophysical data and qualitative social values. Ecological models (e.g., InVEST, forest dynamics simulators) generate data on ecosystem service provision under different management scenarios, while AHP translates stakeholder preferences into weights that determine the relative importance of these services in the decision process [42] [25]. This hybrid approach acknowledges that effective environmental management requires both scientific understanding of ecological systems and social understanding of what people value about those systems.

The participatory nature of AHP addresses a critical shortcoming in traditional technical approaches to forest planning, which often fail to secure stakeholder buy-in despite sophisticated analytical methods. For instance, the Australian Regional Forest Agreement process faced criticism that stakeholder involvement represented a mere "information gathering exercise rather than an explicit involvement in decision making" [38]. AHP structurally embeds stakeholder values throughout the decision process rather than treating them as an afterthought, potentially leading to more socially robust and implementable outcomes.

Experimental Protocols and Application Workflows

Structured AHP Protocol for Forest Ecosystem Services

Implementing AHP for integrating stakeholder preferences in forest planning follows a systematic sequence of steps, each with specific methodological considerations:

Table 1: Protocol for AHP Implementation in Forest Ecosystem Services Assessment

Step	Key Activities	Methodological Considerations	Outputs
1. Problem Structuring	- Define decision goal- Identify key decision criteria (ecosystem services)- Identify management alternatives/scenarios- Identify stakeholder groups	- Use literature review, expert consultation, and preliminary stakeholder engagement- Ensure criteria reflect full range of ecosystem services (provisioning, regulating, cultural, supporting)- Consider power dynamics among stakeholder groups	Decision hierarchy with goal, criteria, sub-criteria, and alternatives
2. Stakeholder Identification & Recruitment	- Map relevant stakeholder groups- Determine sampling strategy- Recruit participants representing key perspectives	- Include affected publics, regulatory agencies, scientific experts, industry representatives, NGOs- Aim for diversity rather than statistical representativeness- Consider stratified sampling to ensure all key perspectives included	Stakeholder panel representing key perspectives on forest management
3. Data Collection Instrument Development	- Develop pairwise comparison questionnaires- Pre-test with subject matter experts- Prepare visual aids and explanatory materials	- Structure comparisons according to decision hierarchy- Include consistency checks- Use clear, accessible language avoiding technical jargon- Provide context about ecosystem services being evaluated	Validated data collection instruments and supporting materials
4. Preference Elicitation	- Conduct individual or group sessions- Guide participants through pairwise comparisons- Check consistency ratios- Clarify ambiguities without leading responses	- Use trained facilitators- Allow adequate time for deliberation- Document rationales for choices- Consider electronic meeting systems for efficiency	Completed pairwise comparison matrices from each participant/group
5. Data Analysis	- Calculate priority weights from comparison matrices- Aggregate individual preferences- Check and report consistency measures- Conduct sensitivity analysis	- Use eigenvector method for weight derivation- Apply appropriate aggregation method (aggregate individual judgments vs. aggregate individual priorities)- Explore differences between stakeholder groups	Weighted hierarchy showing relative importance of criteria and performance of alternatives
6. Results Interpretation & Integration	- Identify preferred alternative(s)- Document trade-offs- Communicate results to stakeholders- Incorporate into decision process	- Use clear visualizations to communicate results- Highlight areas of consensus and disagreement- Connect results to management implications	Decision support output for forest planning processes

Workflow Visualization

Case Applications in Forest Planning Context

Forest Use Suitability Assessment in Spain

A comprehensive application of AHP in forest planning demonstrated the method's utility for determining forest use suitability (FUS) across Pinus sylvestris stands in Spain. Researchers combined AHP with forest dynamics simulations to assign appropriate long-term management approaches (productive, protective, conservation-oriented, social, or multifunctional) based on current and future ecosystem service provision [25].

The study employed a multi-stage methodology that integrated biophysical modeling with participatory preference elicitation. First, forest dynamics were simulated over a 100-year period under a no-management scenario using empirical growth models. Next, 13 metrics representing 11 forest ecosystem services were defined, with 9 dynamic metrics projected over time. A participatory AHP process then determined the relative importance of these ecosystem services for different forest use alternatives. The EMDS (Ecosystem Management Decision Support) system was used to spatially assign the most suitable forest use to each plot based on both ecosystem service provision and stakeholder-derived weights [25].

This integrated approach enabled strategic foresight in forest planning by considering not just current forest conditions but projected future states, with stakeholder preferences guiding how trade-offs between different ecosystem services should be balanced when determining optimal forest uses. The results identified protective use as the dominant suitability, followed by productive use, with high levels of multifunctionality across the landscape [25].

Landscape-Level Management in Portugal

Another application in the Vale do Sousa region of northwestern Portugal illustrated how AHP can rank landscape-level management scenarios according to stakeholder preferences. Five management scenarios were developed using linear programming, each maximizing (or minimizing) a single ecosystem service: timber production, carbon sequestration, wildfire resistance, recreation, or biodiversity conservation [43].

Twenty-five stakeholders weighted stand-level forest management models and associated ecosystem services through an AHP survey. These weights were incorporated into a multi-criteria decision analysis using Criterium Decision Plus software. Results demonstrated that stakeholder preferences significantly influenced scenario rankings: the scenario maximizing timber production ranked highest under stakeholder-weight evaluations, whereas maximizing wildfire resistance emerged as top-ranked under equal weighting conditions [43].

This case highlights how AHP reveals value-based priorities that may diverge from technical optimizations, providing critical social context for forest management decisions. The explicit quantification of preferences also allowed for transparent discussion of trade-offs between competing objectives such as timber production and biodiversity conservation.

Agricultural Landscape Optimization in Germany

While not exclusively focused on forests, a study in the Lossa River Basin in Central Germany demonstrated AHP's application in multi-objective land use allocation problems with relevance to agroforestry contexts. Researchers maximized four biophysical objectives—agricultural production, water quality, water quantity, and biodiversity—then used AHP to incorporate preferences from 11 stakeholders representing different backgrounds (water experts, nature conservationists, farmers, etc.) [40].

Stakeholders clearly ranked water quality first, followed by biodiversity, water quantity, and agricultural production. The corresponding optimal land use configurations showed dramatically different management approaches (e.g., less fertilizer application, more linear elements, and conservation tillage) compared to the status quo. This application demonstrated AHP's utility for translating preferences into concrete land management configurations within complex landscapes containing forested elements [40].

The Scientist's Toolkit: Essential Research Reagents

Table 2: Essential Methodological Components for AHP in Ecosystem Services Research

Component Category	Specific Elements	Function & Application Notes
Stakeholder Mapping Tools	- Stakeholder analysis matrices- Power-interest grids- Institutional network maps	Identify relevant stakeholder groups, their interests, interrelationships, and appropriate engagement strategies
Preference Elicitation Instruments	- Structured pairwise comparison questionnaires- Interview protocols- Focus group guides- Electronic meeting systems	Systematically collect stakeholder judgments in consistent, comparable format
Decision Modeling Software	- Expert Choice- Criterium Decision Plus- Super Decisions- R packages (ahp, decisionSupport)	Implement AHP calculations, check consistency, visualize results, conduct sensitivity analyses
Ecosystem Service Assessment Models	- InVEST model suite- i-Tree ecosystem- ARIES modeling platform- Forest simulation models (FORMES, SORTIE)	Quantify biophysical provision of ecosystem services under alternative scenarios
Spatial Analysis Platforms	- GIS software (ArcGIS, QGIS)- EMDS (Ecosystem Management Decision Support)- Integrated modeling environments	Spatially explicit assessment and mapping of ecosystem services and decision outcomes
Data Integration Frameworks	- Multi-Criteria Decision Analysis (MCDA) frameworks- Geodesign platforms- Participatory GIS approaches	Integrate stakeholder preferences with biophysical data for spatially explicit decision support

Advanced Methodological Integration

Hybrid Decision-Support Framework

The most robust applications of AHP in forest planning combine it with complementary methods in hybrid decision-support frameworks. A representative integration scheme shows how AHP connects with other analytical components:

This integrated approach was exemplified in a Portuguese forest planning study that combined optimization-based scenario generation with AHP-based stakeholder preference elicitation [43]. Linear programming generated efficient management scenarios, while AHP weighted the evaluation criteria based on stakeholder values, and multi-criteria decision analysis ranked the scenarios according to these weighted preferences.

Addressing Temporal Dynamics

An advanced consideration in forest ecosystem services assessment is the incorporation of temporal dynamics into AHP-based decision processes. Forests change over time through growth, succession, disturbances, and management interventions, causing fluctuations in ecosystem service provision. A Spanish study addressed this by simulating forest dynamics over a 100-year period and using these projections to inform AHP-based forest use suitability assessments [25].

This temporal dimension introduces methodological complexities, as stakeholders may value both current and future ecosystem services differently. Approaches to address this include:

• Temporal weighting: Explicitly considering time preferences in the AHP hierarchy
• Scenario-based assessment: Evaluating alternatives under different future scenarios
• Adaptive management frameworks: Incorporating monitoring and periodic AHP reassessments

The integration of stakeholder preferences through AHP represents a significant advancement in ecosystem services assessment for strategic forest planning. By providing a structured, transparent, and replicable method for quantifying diverse values, AHP helps bridge the gap between technical forest management and societal priorities. The case applications demonstrate its flexibility across different forest contexts, from Mediterranean pine forests to Atlantic landscapes, and its compatibility with various modeling approaches.

Important research frontiers include developing more dynamic AHP approaches that better accommodate changing forest conditions and evolving social values, improving methods for handling uncertainty in both ecological projections and stakeholder preferences, and enhancing digital participatory platforms that can engage broader stakeholder groups while maintaining methodological rigor. As forest management increasingly confronts climate change impacts and competing societal demands, AHP and related participatory multi-criteria methods will remain essential for developing legitimate, effective, and adaptable forest strategies that balance multiple ecosystem services.

Within strategic forest planning, the ecosystem services (ES) framework is essential for balancing ecological, economic, and social priorities [43] [44]. Forest management decisions inherently involve multiple, conflicting criteria, such as trading off timber production against biodiversity conservation or recreational value [45] [46]. Multi-Criteria Decision Analysis (MCDA) provides a structured, transparent suite of methods to support these complex decisions by integrating objective biophysical data with subjective stakeholder preferences [47] [48]. This protocol outlines the application of MCDA for ranking landscape-level forest management scenarios, framed within a broader thesis on ecosystem services assessment. We present a synthesized methodology from contemporary research, detailed experimental protocols, and essential tools for researchers and scientists engaged in environmental management and strategic planning.

Theoretical Framework and Key Concepts

MCDA is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making [45]. In forest management, "solving" an MCDA problem typically does not yield a single optimal solution but identifies a set of non-dominated solutions—alternatives where no criterion can be improved without worsening another [45]. The process is fundamentally normative, indicating what decision should be made if the decision-maker is consistent with their stated preferences, rather than describing how decisions are actually made [47].

The ES concept, as classified by systems like the Millennium Ecosystem Assessment (MEA) or the Common International Classification of Ecosystem Services (CICES), provides a critical framework for defining evaluation criteria in forest management MCDA [44] [46]. However, practitioners must be vigilant against double-counting of benefits, particularly when supporting services are conflated with final services [46].

Application Note: Comparative Analysis of MCDA Case Studies in Forestry

Recent research demonstrates the versatile application of MCDA for ranking forest management scenarios. The following table synthesizes quantitative findings and methodologies from key case studies.

Table 1: Summary of MCDA Applications in Landscape-Level Forest Management

Case Study Location & Reference	Primary Management Objectives	MCDA Method(s) Employed	Scenarios or Alternatives Evaluated	Key Findings & Highest-Ranked Scenario
Vale do Sousa, Portugal [43] [49]	Maximize income; mitigate wildfire risk; enhance biodiversity & cultural services.	Analytic Hierarchy Process (AHP); Linear Programming (LP); Multi-Criteria Decision Analysis in Criterium Decision Plus (CDP).	Five scenarios, each maximizing/minimizing a single ES (e.g., timber, wildfire resistance).	Under stakeholder weights, timber production scenario ranked highest. Under equal weights, wildfire resistance scenario ranked highest.
Monte Morello, Central Italy [44]	Improve wood production, climate change mitigation, and recreational opportunities.	Multi-Criteria Decision Analysis (MCDA) with stakeholder surveys.	1. Baseline2. Selective Thinning3. Thinning from Below	Selective thinning scenario was optimal for increasing recreational attractiveness and wood production.
Spanish Pinus sylvestris Stands [25]	Assign Forest Use Suitability (FUS): Productive, Protective, Conservation, Social, Multifunctional.	Ecosystem Management Decision Support (EMDS) system; Delphi & AHP for participatory planning.	Five FUS alternatives based on current and simulated future ES provision.	Dominant FUS was protective, followed by productive. Exhibited high multifunctionality.

The case studies reveal that MCDA outcomes are highly sensitive to the incorporation of stakeholder preferences. The Portuguese case demonstrates that the top-ranked scenario can shift entirely based on whether expert-derived or equal weights are applied [43]. Furthermore, structuring the decision problem is critical. The Portuguese study used a cognitive mapping approach to identify the relevant criteria and sub-criteria at the outset, ensuring the model reflected the actors' mental frameworks [49].

Experimental Protocols

This section provides a detailed, step-by-step protocol for implementing an MCDA process to rank landscape-level forest management scenarios, synthesizing methodologies from the cited research.

Protocol 1: Problem Structuring and Criteria Definition

Objective: To define the decision context, identify stakeholders, and establish a hierarchical set of evaluation criteria based on ecosystem services.

Materials:

• Stakeholder list (e.g., land owners, government agencies, NGOs, local community representatives, researchers).
• Facilitation materials (whiteboards, sticky notes, etc.) or specialized software for cognitive mapping.
• Relevant biophysical, social, and economic data for the landscape.

Procedure:

• Stakeholder Identification and Engagement: Identify and convene a representative group of decision-stakeholders. Their preferences will be formally elicited in the MCDA [47].
• Cognitive Mapping: In a facilitated workshop, guide stakeholders to create a cognitive map. This involves:
  • Brainstorming all relevant objectives for forest management in the area.
  • Linking these objectives to identify cause-and-effect relationships and cluster related concepts [49].
• Develop a Decision Hierarchy: Convert the cognitive map into a structured decision tree. The top level represents the overall goal (e.g., "Select Optimal Forest Management Scenario"). Subsequent levels contain:
  • Criteria: Broad categories of concern (e.g., "Provisioning Services," "Regulating Services").
  • Sub-criteria: Specific, measurable ecosystem services or objectives (e.g., "Timber Production," "Carbon Sequestration," "Recreational Attractiveness") [49] [46].
• Define Metrics and Scales: For each lowest-level sub-criterion, define a quantitative or qualitative scale for performance measurement. Prefer dynamic metrics (e.g., biomass, tree density) that can be simulated over time to forecast ES provision [25].

Protocol 2: Scenario Generation and Performance Matrix Construction

Objective: To define management alternatives and quantify their performance against all criteria.

Materials:

• Forest simulation software (e.g., FORMES, other forest dynamics models).
• Geospatial data and GIS software.
• Field measurement data (e.g., from Forest Inventories) for model calibration.

Procedure:

• Scenario Formulation: Develop a set of mutually exclusive management scenarios. These can be created via:
  • Optimization: Using Linear Programming (LP) to generate scenarios that each maximize a single ES [43].
  • Pre-defined Silvicultural Systems: Defining scenarios based on different management practices (e.g., selective thinning vs. thinning from below) [44].
• Biophysical Modeling and ES Quantification:
  • For each scenario, use a forest dynamics model (e.g., FORMES) to simulate forest development over a long-term horizon (e.g., 100 years) under a no-management and the proposed management scenarios [25].
  • Link model outputs (e.g., biomass, volume, species composition) to the defined ES metrics.
  • Calculate the performance of each scenario for each quantitative ES criterion (e.g., timber volume in m³/ha, carbon stock in t/ha).
• Social and Cultural ES Assessment:
  • For qualitative criteria like recreational attractiveness, employ methods like visitor surveys to assess the impact of different silvicultural treatments [44].
• Construct the Performance Matrix: Create a matrix where rows represent scenarios, columns represent criteria, and cells contain the quantified performance of each scenario against each criterion. Check the matrix for dominance, where one alternative outperforms another across all criteria [47].

Objective: To elicit the relative importance (weights) of criteria from stakeholders.

Materials:

• Structured questionnaire.
• AHP software (e.g., Criterium Decision Plus, 1000minds, or similar).

Procedure:

• Design the Elicitation Instrument: Develop a questionnaire based on the decision hierarchy. For the Analytic Hierarchy Process (AHP), this involves creating pairwise comparison matrices for criteria and sub-criteria at each level of the hierarchy [43] [49].
• Administer the Survey: Conduct the questionnaire with stakeholders. In pairwise comparisons, stakeholders indicate on a standardized scale (e.g., 1-9) how much more important one criterion is than another [48].
• Aggregate Preferences: For group decision-making, aggregate individual responses. This can be done by computing the geometric mean of all individual pairwise comparisons or by using a Delphi survey technique to iteratively build consensus [49] [50].
• Calculate Criteria Weights: Process the aggregated pairwise comparison matrices using the AHP algorithm to derive a normalized weight for each criterion, ensuring the sum of all weights equals 1 [48]. Calculate the consistency ratio to ensure that stakeholder judgments are logically coherent.

Protocol 4: Model Synthesis, Ranking, and Sensitivity Analysis

Objective: To compute overall scores for each scenario, produce a ranking, and test the robustness of the results.

Materials:

• MCDA software (e.g., Criterium Decision Plus, 1000minds, EMDS).
• Spreadsheet software (e.g., Microsoft Excel).

Procedure:

• Apply the MCDA Model: Use a weighted-sum model or other aggregation method to compute a total score for each management scenario. The score is the sum of its performance on each criterion multiplied by the criterion's weight [48].
• Rank Scenarios: Rank the scenarios from highest to lowest total score.
• Conduct Sensitivity Analysis: Systematically vary the criteria weights to assess how changes in stakeholder preferences affect the final ranking. This identifies which weights are most critical to the outcome and tests the stability of the top-ranked scenario(s) [47] [50].
• Report Results: Present the ranking, along with the results of the sensitivity analysis, to decision-makers. This provides a transparent and defensible basis for selecting a management scenario [47].

Workflow Visualization

The following diagram illustrates the integrated, iterative workflow for applying MCDA to rank forest management scenarios, as described in the protocols.

The Scientist's Toolkit: Essential Research Reagents and Solutions

The successful application of MCDA in forest planning relies on a suite of methodological tools and software solutions.

Table 2: Essential Tools for MCDA in Forest Ecosystem Services Research

Tool Category	Specific Tool/Technique	Primary Function in the MCDA Process
Stakeholder Preference Elicitation	Analytic Hierarchy Process (AHP)	Structures complex decisions using pairwise comparisons to derive precise criterion weights [43] [49].
	Delphi Method	Facilitates iterative, anonymous expert feedback to build consensus on criteria and weights in group settings [49] [25].
Forest & ES Modeling	Forest Dynamics Models (e.g., FORMES)	Simulates long-term forest growth and development under different scenarios, providing data for dynamic ES metrics [25].
	Geospatial Information Systems (GIS)	Maps and analyzes spatial data on ES provision, landscape vulnerability, and priority areas [51] [25].
MCDA Software & Platforms	Criterium Decision Plus (CDP)	Implements AHP and other MCDA methods for scoring, weighting, and ranking alternatives [43].
	Ecosystem Management Decision Support (EMDS)	A spatially-focused decision support system that integrates logic and value-based reasoning for multi-criteria assessment [25].
	1000minds	Online tool for implementing MCDA and conjoint analysis via pairwise comparisons for ranking and prioritization [48].

Navigating Challenges: Data Biases, Spatial Integration, and Governance Barriers

Accurate forest data is the cornerstone of informed decision-making in forest policy and management. The shift from traditional field surveys to remote sensing (RS) for data production, while providing comprehensive resource maps, introduces significant uncertainties. These uncertainties, comprising both random and systematic errors, pose a substantial challenge for long-term strategic planning, particularly within the critical context of ecosystem services assessment. Systematic errors, especially those stemming from regression towards the mean—where small true values are overestimated and large true values are underestimated—can lead to flawed assumptions about forest conditions, such as perceiving a forest as conforming to average conditions due to artificially reduced variability [52]. This application note details the impacts of these biased predictions and provides protocols to mitigate their effects on strategic forest planning.

Quantified Impact of Biased Remote Sensing Predictions

The reliance on biased remote sensing predictions has direct, measurable consequences on strategic forest planning outcomes, particularly affecting core components of ecosystem services such as provisioning (e.g., timber harvest) and regulating (e.g., carbon sequestration) services. The table below summarizes the quantified differences between expected and realized outcomes from a study evaluating inventories based on Airborne Laser Scanning (ALS) and optical satellite imagery, set against a business-as-usual scenario with biodiversity and carbon sink targets [52].

Table 1: Quantified Impacts of Remote Sensing Biases on Forest Planning Outcomes

Planning Metric	Impact of Satellite-Based RS	Impact of ALS-Based RS	Primary Cause
Harvest Levels	10% to 12% overestimation [52]	Less severe overestimation	Regression to the mean & general uncertainty
Economic Return (Net Present Value)	Decrease of -6% to -9% [52]	Less severe decrease	Overestimated harvest and flawed planning
Carbon Stocks	8% to 24% unintentional reduction [52]	Less severe reduction	Systematic prediction errors
Biodiversity Stability	Difficulty achieving stable development for biologically valuable forests [52]	Difficulty achieving stable development	Reduced variability in predictions

These results underscore that satellite-based inventories, which are more impacted by general uncertainty and regression towards the mean, perform worse than ALS. However, both methods struggle to support stable development for biologically valuable forests, highlighting a critical challenge for biodiversity conservation within ecosystem services frameworks [52].

Experimental Protocols for Assessing & Mitigating RS Bias

To robustly integrate remote sensing data into ecosystem services assessment, researchers must adopt protocols that explicitly account for and quantify uncertainty. The following detailed methodology outlines the key steps.

Protocol: Evaluation of RS Prediction Bias in a Planning Model

1. Research Question Formulation: Define the specific ecosystem services under investigation (e.g., timber provision, carbon storage, biodiversity habitat) and the corresponding planning objectives and targets.

2. Data Acquisition and Preparation:

• Remote Sensing Data: Acquire resource maps (e.g., forest volume, species composition, height) generated from ALS and/or optical satellite imagery.
• Field Validation Data: Collect a representative dataset of ground-truthed measurements from the same area covered by the RS maps. This serves as the reference for accuracy assessment.
• Planning Model Setup: Define the long-term strategic forest planning model, including all constraints, targets, and objective functions (e.g., maximizing net present value while maintaining carbon stocks).

3. Uncertainty and Bias Quantification:

• Conduct a statistical analysis comparing RS predictions to field validation data.
• Quantify systematic errors (bias) and random errors (general uncertainty).
• Specifically test for and measure the effect of regression towards the mean.

4. Scenario-Based Planning Analysis:

• Baseline Scenario: Run the planning model using the field validation data to establish a benchmark for expected outcomes.
• RS-Informed Scenarios: Run the planning model separately using the ALS-based and satellite-based predictions.
• Comparative Analysis: Compare the outcomes (harvest levels, NPV, carbon stocks, biodiversity indicators) of the RS-informed scenarios against the baseline scenario to quantify the realized impact of biases.

5. Cost-Plus-Loss Analysis (Advanced): Introduce a cost-plus-loss analysis into the hierarchical planning environment. This evaluates the cost of obtaining more accurate data (e.g., via intensified field sampling or superior RS technology) against the expected loss (e.g., financial, carbon) incurred by using biased data [52].

Workflow: Integrating Uncertainty Assessment in Forest Planning

The following diagram illustrates the logical workflow for the experimental protocol, from data preparation to decision support.

The Scientist's Toolkit: Essential Research Reagents & Solutions

This section details key methodological tools and concepts essential for conducting research on data uncertainty in forest ecosystem services.

Table 2: Key Methodological Tools for Uncertainty Research in Forest Planning

Tool / Concept	Function & Application in Research
Airborne Laser Scanning (ALS)	Provides high-resolution 3D data on forest structure; used as a higher-accuracy RS method to compare against lower-cost optical imagery [52].
Regression Towards the Mean	A statistical phenomenon that is a major source of bias in RS models; understanding it is crucial for interpreting predictions and correcting biases [52].
Cost-Plus-Loss Analysis	A decision-support framework that quantifies the trade-offs between the cost of improved data and the economic loss from decisions based on poor-quality data [52].
Strategic Forest Planning Model	A computerized model (e.g., Heureka System) used to simulate long-term forest development and evaluate trade-offs between different ecosystem services under various scenarios [52].
Multi-Criteria Decision Analysis (MCDA)	A framework for balancing different, often conflicting, forest values (e.g., timber production, biodiversity, carbon sinks) in a single planning process [52].

Data Presentation and Structuring for Analysis

Effective data analysis requires well-structured data. Data should be organized in a table format with rows and columns, where each row represents a unique record (e.g., a specific forest stand) and each column represents a variable or attribute of that record (e.g., predicted volume, measured volume, species) [53]. Understanding the granularity—what each row represents—is fundamental to correct analysis and avoiding aggregation errors [53]. For presenting comparative results, bar charts are ideal for showing differences in aggregated metrics like total harvest or NPV between scenarios, while line charts effectively illustrate fluctuations in these metrics over time [54]. Tables, however, remain superior for presenting precise numerical values for deeper scrutiny [55].

Table 3: Example Structure for Holding RS Prediction and Validation Data

Stand ID	RSPredictedVolume	FieldMeasuredVolume	Bias	Species	Planning_Scenario
S_001	245	280	-35	Pine	Satellite-Based
S_002	180	165	+15	Spruce	ALS-Based
...	...	...	...	...	...

Regression toward the mean (RTM) is a pervasive statistical bias in remote sensing (RS) that compromises the accuracy of ecosystem services assessments. This phenomenon causes models to overestimate small true values and underestimate large true values, artificially reducing variability in datasets and leading to flawed strategic forest planning [56]. Strategic forest management decisions based on these biased maps can result in overestimated harvest levels, reduced economic returns, and unintended depletion of carbon stocks [56].

This document provides application notes and protocols to identify, quantify, and mitigate RTM biases in airborne and satellite data, enabling more reliable assessment of forest ecosystem services.

The Impact of RTM on Ecosystem Services Assessment

Biased RS predictions directly impact the quality of strategic forest planning. A 2025 study quantified these discrepancies by comparing plans based on ALS and optical satellite imagery with high-quality field surveys [56].

Table 1: Realized Impacts of RTM Bias on Strategic Forest Planning Outcomes

Planning Metric	Satellite-Based Map Impact	ALS-Based Map Impact	Primary Cause
Harvest Levels	10% to 12% overestimation [56]	Less severe overestimation [56]	Overestimation of low-volume areas, underestimation of high-volume areas
Net Present Value	Decrease of -6% to -9% [56]	Less severe decrease [56]	Suboptimal harvest scheduling and resource allocation
Carbon Stocks	8% to 24% overestimation, leading to unintended reduction [56]	More accurate than satellite [56]	Faulty baseline data impairs carbon flux projections
Biodiversity Stability	Difficult to achieve stable development [56]	Difficult, but better than satellite [56]	Inaccurate identification of high conservation-value forests

These systematic errors are classified as Berkson-type errors, where the error correlates with the true value rather than the prediction, making them particularly challenging to detect and correct without reference data [56].

Calibration Techniques and Protocols

Statistical Bias Correction: Classical Calibration

Classical calibration is a direct statistical method to correct RTM bias in model predictions.

• Principle: This technique adjusts biased RS predictions using a regression model trained on a sample of ground truth data, effectively rescaling the predictions to match the distribution of the reference data [56].
• Protocol:
  • Collect Reference Data: Obtain a probability sample (e.g., simple random, stratified) of high-quality ground truth observations. The sample must be representative of the population and the RS map's variability [57] [58].
  • Develop Calibration Model: For each variable of interest (e.g., stem volume), fit a linear regression model where the ground truth measurement is the dependent variable and the RS prediction is the independent variable.
  • Apply Correction: Use the fitted regression model (slope and intercept parameters) to adjust all RS predictions across the entire map.
• Application: Lindgren et al. (2022) demonstrated that this method effectively mitigates the effects of regression toward the mean in forest resource maps [56].

Advanced Inference: Prediction-Powered Inference (PPI)

PPI is a modern framework that uses a small ground truth sample to debias a much larger set of RS predictions without requiring a full statistical calibration model for each variable.

• Principle: PPI uses a small, randomly-sampled set of ground truth data (n points) to correct for bias in a much larger set of machine learning-generated RS predictions (N points, where N >> n). It provides unbiased parameter estimates (e.g., regression coefficients) and confidence intervals [57] [58].
• Protocol:
  • Data Collection:
    • Acquire the full RS-based map product (N data points).
    • Collect a separate, randomly sampled set of ground truth observations for the response variable and covariates (n data points). This can be done via fieldwork or manual image labeling [57].
  • Model Application:
    • The PPI algorithm combines the two data sources to produce a bias-corrected point estimate and confidence interval for the parameter of interest, which is more precise than using ground truth data alone [58].
  • Validation:
    • The method provides bootstrap-based confidence intervals, ensuring statistical reliability for downstream analyses and decision-making [57] [58].
• Application: PPI has been successfully applied to estimate the relationship between US forest cover and elevation, correcting the biased estimate from the map-only analysis and achieving a 17-fold effective sample size increase compared to using ground truth data alone [58].

Data Integration: Histogram Matching and Imputation

These techniques focus on correcting the distribution of RS predictions rather than individual pixel values.

• Principle: The distribution (histogram) of the RS predictions is adjusted to match the distribution of a reference dataset, which can be a high-quality ground truth sample or an alternative RS product [56].
• Protocol:
  • Obtain Reference Distribution: Use data from a comprehensive source, such as National Forest Inventory (NFI) plots, which provide an unbiased representation of the forest variable's distribution across the landscape [56].
  • Apply Matching Algorithm: Use a k-Nearest Neighbour (k-NN) imputation or similar algorithm to find RS pixels whose predictions can be replaced with values from the reference dataset that have a similar feature space but come from the target distribution [56].
  • Generate Corrected Map: The output is a wall-to-wall map where the statistical distribution of values, and thus the variance, is preserved and aligned with the reference data [56].

Technical Calibration for Radiometric Repeatability

Accurate calibration for RTM requires that the underlying radiometric measurements are consistent over time. Temporal radiometric repeatability (T_r) is defined as the consistency of optical signals from the same object across multiple time points [59].

• Principle: T_r is calculated to quantify variability introduced by the imaging system, sun parameters, and atmospheric conditions. It is essential for classifications to capture true variation and stochastic noise [59].
• Protocol:
  • Acquire Data: Conduct repeated flight missions over stable calibration targets (e.g., white Teflon, colored panels) across different days and times.
  • Apply Calibration Methods: Compare different radiometric calibration methods:
    • Empirical Line Method (ELM): Uses physical calibration targets in the scene [59].
    • Atmospheric Radiative Transfer Model (ARTM): Uses a drone-mounted down-welling sensor to measure irradiance [59].
    • ARTM+: Combines modeled sun parameters and weather variables with down-welling irradiance data [59].
  • Calculate Repeatability: For each spectral band, calculate T_r using the formula: T_r = 100 - { (Confidence Interval_95 / Average Signal) * 100 } [59].
  • Select Optimal Method: Research indicates ARTM+ calibration markedly attenuates loss of radiometric repeatability, especially in spectral bands beyond 900 nm, making it the recommended approach for maximizing temporal consistency [59].

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials and Data Sources for Calibration Research

Item / Solution	Function / Application	Key Characteristics
Airborne Laser Scanning (ALS) Data	Provides 3D structural information on forests (e.g., canopy height, volume) [56].	Higher spatial resolution; less impacted by RTM than optical data [56].
Optical Satellite Imagery (e.g., Landsat)	Provides spectral information for wall-to-wall forest resource mapping [56].	More susceptible to RTM bias; requires robust calibration [56].
National Forest Inventory (NFI) Plots	Serve as unbiased ground truth for calibration and imputation [56].	High-quality, geo-positioned field measurements; statistical robustness.
Heureka PlanWise	A decision support system for simulating long-term forest management scenarios [56].	Used to quantify the economic and ecological impacts of using biased data.
Stable Calibration Tarps	Used for Empirical Line Method (ELM) calibration of airborne sensors [59].	High reflectance stability (e.g., white Teflon); known reflectance properties.
Drone-Mounted Down-Welling Sensor	Measures incident irradiance for ARTM calibration during flights [59].	Enables conversion of radiance to reflectance without ground targets.

Workflow and Decision Pathway

The following diagram synthesizes the core problem of RTM and the available solution pathways into a single, coherent workflow for researchers.

Integrating robust calibration techniques is no longer optional but essential for credible ecosystem services assessment and strategic forest planning. Techniques like classical calibration, Prediction-Powered Inference, and histogram matching provide a methodological toolkit to correct RTM bias, transforming remotely sensed data from a potentially misleading input into a reliable foundation for decision-making. As forest policy increasingly relies on remote sensing for monitoring, adopting these protocols ensures that investments in nature are guided by accurate data, ultimately supporting the resilience of both ecosystems and the communities that depend on them.

The integration of Ecosystem Services (ES) knowledge into spatial planning represents a paradigm shift for achieving sustainable development, particularly in strategic forest planning. This transition, however, faces significant implementation hurdles between research and practice [60]. These Application Notes and Protocols provide a structured framework for researchers and planning professionals to instrumentally apply ES assessments in land-use decisions, bridging the gap between theoretical assessment and practical spatial planning applications. The protocols are framed within the context of strategic forest planning research, emphasizing quantitative data analysis and spatially explicit evaluation methods to ground policies and plans in robust ecological evidence.

Quantitative Data Analysis Methods for ES Assessment

Effective ES integration requires appropriate quantitative analysis methods to transform raw ecological data into actionable planning intelligence [61]. The table below summarizes core quantitative techniques relevant to ES assessment in spatial planning contexts.

Table 1: Quantitative Data Analysis Methods for Ecosystem Services Assessment

Analysis Method	Primary Function in ES Assessment	Application Example in Spatial Planning	Key ES Indicators
Descriptive Statistics [61] [62]	Summarizes central tendency and dispersion of ES data	Characterizing baseline conditions of forest carbon storage or recreation potential	Mean, median, mode, standard deviation of ES provision
Cross-Tabulation [61]	Analyzes relationships between categorical variables	Assessing connections between forest types and provision of specific ES	Frequency distributions of ES across land cover categories
Gap Analysis [61]	Compares actual performance to potential or targets	Identifying spatial mismatches between ES supply and demand	Disparities between current and optimal ES provision levels
Regression Analysis [61] [62]	Examines relationships between dependent and independent variables	Modeling how forest management intensity affects water purification services	Correlation coefficients, predictive models of ES flows
Hypothesis Testing [61] [62]	Assesses assumptions about populations from sample data	Evaluating whether protected forest areas significantly enhance habitat quality	Statistical significance of planning interventions on ES

Experimental Protocol: Spatial Cross-Tabulation for ES-Land Use Relationships

Purpose: To quantitatively analyze relationships between forest types and ecosystem service provision using cross-tabulation methods [61].

Materials:

• Geospatial data on forest composition and structure
• ES quantification metrics (e.g., habitat quality scores, carbon storage values)
• Statistical software (R, SPSS, or Python with Pandas)
• GIS software for spatial analysis

Methodology:

• Data Preparation: Compile spatial datasets on forest characteristics and ES indicators for the planning region. Ensure consistent spatial resolution and coordinate systems.
• Categorization: Classify forest areas into distinct types based on species composition, age structure, and management regime.
• ES Quantification: Calculate relevant ES indicators for each spatial unit using established models (e.g., InVEST, ARIES).
• Contingency Table Construction: Create cross-tabulation tables comparing forest types with levels of ES provision [61].
• Statistical Analysis: Apply chi-square tests or Cramer's V to determine significance and strength of relationships.
• Interpretation: Identify which forest characteristics are associated with high provision of specific ES to inform planning priorities.

Spatial Planning Workflow for ES Integration

The following diagram illustrates the comprehensive workflow for integrating ES knowledge into spatial planning processes, with particular emphasis on forest planning contexts.

Data Visualization Approaches for ES Communication

Effective visualization of quantitative ES data is essential for communicating complex relationships to diverse stakeholders in planning processes [63] [61]. The table below presents appropriate visualization techniques for different ES data types.

Table 2: Data Visualization Methods for Ecosystem Services Communication in Planning

Visualization Type	Best Use Cases in ES Planning	Data Requirements	Accessibility Considerations
Bar Charts [61] [62]	Comparing ES values across different forest management scenarios	Categorical variables with associated numerical values	Minimum 4.5:1 contrast ratio for text [64]
Line Graphs [63]	Depicting trends in ES provision over time or across spatial gradients	Time series or continuous distribution data	Distinct line styles for colorblind accessibility
Scatter Plots [63] [62]	Exploring relationships between ES indicators and driving factors	Paired numerical observations for two or more variables	Adequate marker size and shape differentiation
Box and Whisker Plots [63]	Showing distribution of ES values across different planning zones	Multiple data sets with sufficient observations	Clear labeling of median and outlier values
Maps [60]	Spatial representation of ES supply, demand, and flows	Georeferenced ES data with appropriate resolution	Colorblind-friendly palettes with sufficient contrast

Experimental Protocol: Participatory Scenario Modeling with Bayesian Networks

Purpose: To implement participatory scenario modeling for exploring development pathways under alternative spatial planning options, adapted from methodologies applied in small island contexts [65].

Materials:

• Stakeholder engagement framework
• Bayesian network software (e.g., Netica, GeNIe)
• Land use change models
• Multi-criteria impact assessment framework

Methodology:

• Stakeholder Identification: Engage key stakeholders (forest managers, local communities, policymakers) in defining development pathways.
• Conceptual Model Development: Co-create Bayesian network structures representing cause-effect relationships between planning decisions and ES outcomes.
• Parameterization: Quantify conditional probabilities based on empirical data, expert elicitation, and literature review.
• Scenario Definition: Develop distinct planning scenarios (e.g., business-as-usual, conservation priority, sustainable development).
• Model Simulation: Run land use models under different regulatory frameworks to project spatial patterns of change.
• Impact Assessment: Evaluate tradeoffs across environmental, social, and economic criteria for each scenario.
• Policy Analysis: Compare outcomes with and without spatial regulations to quantify their effectiveness in maintaining ES provision [65].

Research Reagent Solutions for ES Assessment

The following toolkit outlines essential materials and analytical solutions for implementing robust ES assessments in spatial planning research.

Table 3: Essential Research Toolkit for Ecosystem Services Assessment in Spatial Planning

Tool/Category	Specific Solution	Function in ES Assessment	Application Context
Statistical Analysis	R Programming [62]	Advanced statistical computing and ES modeling	Habitat quality analysis, spatial regression models
Geospatial Analysis	GIS Software	Spatial analysis and ES mapping	Identifying ES provision hotspots, connectivity analysis
Participatory Methods	Bayesian Networks [65]	Modeling complex decision scenarios with uncertainty	Participatory planning under uncertain future conditions
Data Visualization	ChartExpo [61]	Creating accessible visualizations of quantitative ES data	Communicating trade-offs to decision-makers
Modeling Platforms	InVEST Suite	Integrated valuation of ecosystem services	Mapping and valuing multiple ES simultaneously

Green Infrastructure Planning Protocol

Purpose: To define the spatial structure of Green Infrastructure (GI) and establish conditions for effective operation as spatial networks providing multiple ES [60].

Materials:

• High-resolution land cover data
• Species distribution models
• ES quantification tools
• Landscape connectivity software

Methodology:

• Spatial Assessment: Map current ES provision using habitat quality, outdoor recreation, and agricultural production indicators [60].
• Network Analysis: Identify ecological corridors using species movement resistance models [60].
• Multi-functionality Assessment: Evaluate potential for simultaneous provision of multiple ES across the landscape.
• Intervention Planning: Prioritize areas for conservation, restoration, and creation of green spaces.
• Integration: Embed GI network into formal spatial plans and development regulations.
• Implementation: Establish management plans and monitoring protocols for GI elements.

Diagram: Green Infrastructure Planning and Implementation Framework

The protocols and methodologies presented herein provide a robust framework for overcoming the spatial planning hurdle through instrumental use of ES knowledge. By implementing these structured approaches—incorporating quantitative data analysis, participatory scenario modeling, green infrastructure planning, and effective data visualization—researchers and planning professionals can significantly enhance the integration of ES considerations into land-use decisions. This application-oriented framework supports the development of more sustainable, resilient, and equitable spatial planning outcomes, particularly in forest landscape contexts where balancing conservation and development objectives remains a critical challenge.

Integrating scientific knowledge into effective forest policy requires structured collaboration between researchers, policymakers, and diverse stakeholders. This integration is particularly critical within strategic forest planning research, where assessing ecosystem services provides a vital evidence base for decision-making. Science-policy collaboration enables the translation of complex ecological data into actionable management strategies, balancing multiple forest functions including productive, protective, conservation-oriented, and social uses [25]. The participatory governance approaches examined in this protocol are designed to enhance the environmental standard of governance outputs by systematically incorporating diverse knowledge systems and values [66] [67]. Environmental awareness serves as both a foundation and outcome of these processes, ensuring that decisions regarding forest ecosystem services are grounded in robust science and broad societal engagement.

Quantitative Evidence: Linking Participation to Environmental Outcomes

Empirical evidence demonstrates that well-designed participatory processes significantly influence environmental governance outcomes. A meta-analysis across 22 Western democracies, encompassing 305 cases of public environmental decision-making, provides robust quantitative support for this relationship [66].

Table 1: Key Design Features of Participation and Their Impact on Environmental Outcomes

Design Feature	Impact on Environmental Outcomes	Contextual Considerations
Power Delegation to Participants	Most reliable predictor of strong environmental outputs [66]	Requires institutional commitment to shared decision-making [67]
Communication Intensity	Predicts conservation-related output standards [66]	Less reliably predicts environmental health-related standards [66]
Representation of Environmental Stance	Strong predictor, but varies considerably across contexts [66]	Depends on participants' pre-existing environmental orientation [66]
Dialogical Interaction	Fosters collective learning and integrative solutions [67]	Requires skilled facilitation and safe discursive spaces [67]
Conflict Resolution Mechanisms	Enhances implementation through increased acceptance [67]	Particularly vital in contexts with historical stakeholder conflicts [67]

The effectiveness of these design features is moderated by contextual conditions and the specific environmental goals of government agencies. Furthermore, the intensity of communication among participants and the representation of different stakeholder groups constitute additional dimensions that require careful design to maximize environmental benefits [66].

Application Notes: Protocol for Integrating Stakeholder Participation in Forest Ecosystem Service Assessment

Conceptual Framework and Workflow

The following diagram visualizes the sequential and iterative workflow for integrating stakeholder participation into forest ecosystem service assessment, from preparation to policy integration.

Stage 1: Preparatory Phase – Stakeholder Mapping and Process Design

Objective: To identify all relevant stakeholders and define the participatory process parameters, including power delegation levels.

• Stakeholder Identification and Analysis:

  • Method: Conduct a stakeholder mapping exercise to identify groups affected by or able to influence forest management decisions. Categorize stakeholders by their interest, influence, and knowledge regarding forest ecosystem services [68] [25].
  • Protocol: Employ a "breadth of involvement" framework to ensure representation across government agencies, scientific communities, local communities, private sector, and civil society organizations [67].

• Participatory Process Design:

  • Power Delegation Specification: Clearly define the degree of decision-making power allocated to participants, from consultation to co-decision making. Empirical evidence identifies power delegation as the most stable predictor of strong environmental outputs [66].
  • Communication Intensity Planning: Design the frequency, format, and deliberative quality of interactions. This includes structuring dialogues to ensure all perspectives are heard and to foster collective learning [67].

Stage 2: Participatory Assessment of Forest Ecosystem Services (FES)

Objective: To collaboratively assess the supply, demand, and value of forest ecosystem services, linking them to broader policy frameworks.

• Semi-Quantitative FES Assessment:

  • Method: Facilitate workshops where stakeholders rank the importance of different FES for achieving specific policy goals, such as the Sustainable Development Goals (SDGs) [68].
  • Protocol: Use structured matrices where stakeholders assign scores (e.g., 0-5) to represent the perceived importance of each FES (e.g., water regulation, carbon sequestration, recreation) for specific targets under the SDGs. This semi-quantitative approach makes implicit perceptions explicit and comparable [68].

• Mapping FES Interactions:

  • Method: Conduct cross-impact matrix analysis to identify trade-offs and synergies between different FES.
  • Protocol: Guide stakeholders in assessing how the enhancement of one FES (e.g., timber production) might positively (synergy) or negatively (trade-off) affect other FES (e.g., biodiversity conservation or water purification). This reveals complex system dynamics and potential conflicts [68].

Stage 3: Multi-Criteria Decision Support and Planning

Objective: To synthesize diverse inputs and define the most suitable forest management strategies.

• Forest Use Suitability (FUS) Analysis:

  • Method: Implement a multi-criteria decision support system, such as the Ecosystem Management Decision Support (EMDS) system, to evaluate alternative forest uses [25].
  • Protocol:
    • Define Indicators: Select dynamic (e.g., biomass growth, water yield) and static (e.g., soil type, slope) metrics for FES provision.
    • Simulate Forest Dynamics: Project forest development and associated ES supply over a long-term horizon (e.g., 100 years) using forest growth models.
    • Assign FUS Alternatives: Calculate suitability scores for different forest uses (productive, protective, conservation-oriented, social, multifunctional) based on current and future ES provision and stakeholder-weighted priorities [25].

• Participatory Prioritization:

  • Method: Apply structured decision-making techniques like the Analytic Hierarchy Process (AHP) to elicit stakeholder preferences on evaluation criteria [25].
  • Protocol: Stakeholders pairwise compare the relative importance of FES (e.g., climate regulation vs. wood production) to derive priority weights for the FUS analysis, ensuring the outcomes reflect collective values.

Stage 4: Policy Integration and Adaptive Management

Objective: To translate collaborative assessments into actionable policies and management plans.

• Science-Policy-Practice Dialogue:

  • Method: Establish permanent or long-term forums for ongoing discussion between researchers, policymakers, practitioners, and other stakeholders [68].
  • Protocol: Use the outputs from the FUS analysis and FES-SDG mapping to inform the development of integrated forest management plans. These plans should explicitly address trade-offs and leverage synergies identified in the participatory process [68] [25].

• Implementation and Monitoring:

  • Method: Develop mechanisms for monitoring the outcomes of implemented decisions and for adapting management based on new information.
  • Protocol: The conflict resolution and acceptance built through participatory processes enhance implementation. Collaborative networks established during the process provide the social infrastructure for long-term adaptive management [67].

The Scientist's Toolkit: Key Reagents and Methodologies

Table 2: Essential Methodologies and Tools for Participatory Forest Ecosystem Service Research

Tool or Method	Primary Function	Application Context
Stakeholder Mapping Matrix	Systematically identify and categorize stakeholders based on interest, influence, and knowledge [67]	Preparatory phase of any participatory process
Semi-Quantitative Cross-Impact Matrix	Map perceived synergies and trade-offs between different ecosystem services [68]	Eliciting and visualizing complex system interactions during stakeholder workshops
Analytic Hierarchy Process (AHP)	Structure participatory prioritization and weight assignment to decision criteria [25]	Multi-criteria decision analysis in Forest Use Suitability (FUS) assessment
Ecosystem Management Decision Support (EMDS)	Spatially explicit, logic-based modeling to evaluate management alternatives [25]	Integrating biophysical data and stakeholder preferences for strategic forest planning
Forest Dynamics Model (e.g., FORMES)	Simulate future forest structure and ecosystem service provision under different scenarios [25]	Providing long-term projections for strategic decision-making
Photovoice Walks	Visually capture stakeholder preferences and perceptions of landscape values [69]	Qualitative data collection on cultural ecosystem services and aesthetic values

The structured protocols for stakeholder participation outlined in this document provide a replicable framework for enhancing the relevance and impact of forest ecosystem services research. By systematically integrating diverse forms of knowledge and values through power delegation, dialogical interaction, and multi-criteria analysis, these approaches bridge the gap between scientific assessment and policy implementation. The resulting environmental awareness and shared understanding among stakeholders are fundamental for developing resilient and socially supported forest management strategies. The rigorous application of these protocols ensures that strategic forest planning is not only scientifically sound but also democratically legitimate and effective in achieving sustained ecological and social benefits.

Forest management has evolved from a primarily timber-production focus toward a multi-objective paradigm that harmonizes ecological, economic, and sociocultural values [70]. This shift necessitates strategic planning approaches that can explicitly address the complex relationships between competing forest ecosystem services. Key among these relationships is the trade-off between timber production and regulating services such as biodiversity conservation and carbon sequestration, alongside the increasing threat of wildfires to forest ecosystems [71] [72]. Contemporary forest planning must therefore integrate sophisticated decision-support systems to balance these objectives under changing climatic conditions [73]. This Application Note provides researchers and forest managers with structured protocols and analytical frameworks for quantifying, analyzing, and optimizing these critical forest ecosystem services within a strategic planning context.

Quantitative Data on Ecosystem Service Relationships and Trade-offs

Tabulated Ecosystem Service Relationships

Table 1: Documented trade-offs and synergies between key forest management objectives.

Primary Objective	Interacting Objective	Relationship Type	Quantitative Evidence	Source Context
Maximizing Carbon Stock	Maximizing Net Present Value	Trade-off	Results in lower net present value	[71]
Timber Production	Biodiversity Conservation	Trade-off	Negative effect when wood is left for conservation	[44]
Carbon Sequestration	Timber Production	Synergistic	Transfer of carbon stocked from forest to long-lived wood products	[44]
Plantation Management	Wildfire Resistance	Trade-off (Temperate)	Temperate plantations twice as likely to experience stand-replacing wildfire	[72] [74]
Spatial Constraints	Ecosystem Service Magnitude	Impact	Affects level of service provision rather than trade-off patterns	[70]

Global Wildfire Risk in Production Forests

Table 2: Comparative stand-replacing wildfire impacts on natural production forests versus plantations (2015-2022).

Forest Type	Global Area Burned (Mha)	Representative Countries with High Burn Area	Relative Burn Risk (Temperate Regions)	Key Risk Factors
Natural Production Forests	15.7 (14.7–16.7)	Brazil (3.69 Mha), USA (3.08 Mha), Australia (2.85 Mha)	Baseline (1x)	Climate, weather extremes, proximity to anthropogenic activities
Timber Plantations	1.40 (1.26–1.64)	USA (0.20 Mha), Australia (0.16 Mha), Portugal (0.13 Mha)	2x higher (100% increase)	Low diversity, uniform stand structure, dense planting of species

Experimental Protocols for Ecosystem Service Assessment

Protocol 1: Multi-Criteria Decision Analysis for Forest Restoration

Application: Assessing effects of silvicultural treatments on multiple ecosystem services.

Methodology Summary: Based on a Central Italy case study evaluating wood production, climate change mitigation, and recreational opportunities [44].

• Define Forest Restoration Scenarios:

  • Baseline: No active management or current standard practices.
  • Selective Thinning: Removal of trees across multiple crown classes to achieve specific structural goals.
  • Thinning from Below: Removal of trees from lower crown classes to reduce stand density.

• Quantify Ecosystem Service Indicators:

  • Wood Production: Estimate harvested wood volumes and value using local market prices.
  • Climate Change Mitigation: Quantify carbon stock and sequestration changes in all carbon pools (above-ground biomass, below-ground biomass, soil organic carbon) using field measurements and allometric models.
  • Recreational Opportunities: Conduct face-to-face questionnaire surveys (e.g., n=200 visitors) to assess perceived recreational attractiveness and willingness-to-pay before and after treatments.

• Perform Multi-Criteria Analysis:

  • Assign weights to each ecosystem service based on stakeholder input or policy priorities.
  • Apply a multi-criteria decision method (e.g., Analytical Hierarchy Process - AHP) to compare scenarios and identify the optimal restoration practice that maximizes overall ecosystem service supply.

Protocol 2: Pareto Frontier Approach for Spatial Trade-off Analysis

Application: Analyzing trade-offs between multiple ecosystem services in large, spatially constrained forest landscapes [70].

• Problem Decomposition:

  • Subdivide a large forest landscape into smaller, non-adjacent sub-problems to manage computational complexity.
  • Include a separate sub-problem for all remaining stands that share borders.

• Define Management Prescriptions and Simulate Outcomes:

  • For each homogeneous stand (unit), define a set of possible management prescriptions (e.g., rotation ages, thinning regimes, species conversion options) through stakeholder engagement.
  • Simulate the provision of target ecosystem services (e.g., wood, carbon, biodiversity, fire resistance, erosion protection) for each prescription over the planning horizon.

• Generate Pareto Frontiers:

  • Use linear programming-based techniques to generate the Pareto frontier for each sub-problem, identifying non-dominated solutions where improving one service necessitates reducing another.
  • The solution for the sub-problem with bordering stands is constrained by the solutions obtained for the independent sub-problems.

• Trade-off Analysis:

  • The combined Pareto frontiers provide decision-makers with the production possibilities of the landscape, highlighting the rate of trade-off between services (e.g., how much carbon stock must be sacrificed to achieve a given level of wood production).

Protocol 3: Dynamic Assessment of Forest Use Suitability

Application: Determining the long-term suitable use of a forest based on simulated future provision of ecosystem services [25].

• Define Ecosystem Service Indicators and Metrics:

  • Select dynamic metrics derived from forest biophysical variables (e.g., biomass, diameter, height) that can be projected over time. Static metrics (e.g., soil type) are used directly.

• Simulate Forest Dynamics:

  • Use a forest dynamics model (e.g., empirical individual tree growth, mortality, and ingrowth models) to project forest characteristics for a defined period (e.g., 100 years) under a no-management or other baseline scenario.

• Assess Forest Use Suitability (FUS):

  • Define FUS alternatives: Productive, Protective, Conservation-Oriented, Social, and Multifunctional.
  • Use a multi-criteria decision support tool (e.g., Ecosystem Management Decision Support - EMDS system) to calculate a suitability score for each FUS alternative for every stand, based on current and future simulated ES provision and stakeholder-derived weights.

• Robustness Analysis and Allocation:

  • Prioritize primary and secondary FUS for each stand based on the performance scores and the differences between them. This identifies the most suitable long-term forest use to guide management strategy development.

Visualization of Methodological Workflows

Multi-Objective Forest Planning Optimization Workflow

Figure 1: A generalized workflow for optimizing multiple objectives in forest management planning, integrating data collection, simulation, and multi-criteria decision analysis.

Forest Use Suitability Assessment Logic

Figure 2: Logical workflow for assigning Forest Use Suitability (FUS) based on dynamic simulations of ecosystem service provision.

The Scientist's Toolkit: Key Reagents & Research Solutions

Table 3: Essential analytical tools, models, and frameworks for multi-objective forest ecosystem service research.

Tool/Solution Category	Specific Tool/Model	Primary Function in Research	Application Context
Decision Support Systems	Ecosystem Management Decision Support (EMDS)	Multi-criteria spatial analysis and planning for assigning Forest Use Suitability	[25]
Multi-Criteria Decision Methods	Analytical Hierarchy Process (AHP)	Structuring participatory planning and weighting ecosystem service priorities	[44] [25]
Optimization Modeling Frameworks	Generalized Disjunctive Programming (GDP)	Formulating complex discrete/continuous problems (e.g., management schedule assignment)	[71]
Optimization Modeling Frameworks	Mixed Integer Linear Programming (MILP)	Solving forest planning problems with binary decisions (e.g., entire stand treatment)	[71] [70]
Optimization Modeling Frameworks	Pareto Frontier Method	Identifying non-dominated solutions and visualizing trade-offs between competing objectives	[70]
Forest Simulation Models	FORMES Projection System	Simulating individual tree growth, mortality, and ingrowth for forest dynamics	[25]

Evidence and Efficacy: Validating Approaches Through Case Studies and Comparative Analysis

Ecosystem services assessment has become a cornerstone of strategic forest planning, enabling managers to quantify the multiple benefits forests provide, from carbon sequestration and biodiversity to water regulation and timber production [75]. Remote sensing (RS) technologies are indispensable for providing the spatially explicit, repeatable data required for these assessments over large landscapes. Among these technologies, Airborne Laser Scanning (ALS) and optical satellite imagery represent two of the most prominent data sources. This document provides a structured, comparative analysis of these two methods, quantifying their performance across key forest ecosystem service indicators. It further offers detailed application protocols to guide researchers and forest managers in selecting and implementing the appropriate technology based on specific assessment goals, leveraging their complementary strengths for a holistic view of forest structure and function [76].

The performance of ALS and optical satellite imagery varies significantly across different forest metrics. The table below provides a comparative summary of their capabilities, synthesizing findings from recent research.

Table 1: Quantitative Performance Comparison of ALS and Optical Satellite Imagery for Forest Ecosystem Service Assessment

Forest Metric	Data Modality	Reported Performance (R²)	Key Strengths	Key Limitations
Aboveground Biomass (AGB)	ALS + Optical Satellite Fusion	0.97 (Max reported) [76]	Highly accurate; captures 3D structure; mitigates saturation [77] [76]	High cost of ALS; model complexity
	Optical Satellite (Sentinel-2) Alone	~0.85 [78]	Large area coverage; cost-effective; good for rough estimates [77]	Signal saturation in dense canopies [77] [76]
	SAR (ALOS-2 PALSAR-2 L-band)	0.12 [79]	Penetrates cloud/vegetation; weather independent	Lower accuracy; sensitive to moisture
Tree Species Classification	ALS + Multispectral/Hyperspectral	Overall Accuracy up to 95% [76]	Combines structural and spectral information for high discrimination	Requires data fusion; can be costly
	Optical Satellite Alone	Good performance with high-resolution data	Direct access to spectral signatures of species	Limited by canopy structure complexity
Urban Forest Variables	ALS	R² 0.83 - 0.92 [76]	Detailed vertical structure mapping for ecosystem service valuation	Limited to airborne campaigns
Forest Volume & Structure	ALS (Height Metrics)	Very High (up to 0.97 for volume) [76]	Direct measurement of height and vertical structure; high precision [80] [81]	No inherent spectral information; cost
	Optical Satellite	Moderate	Correlative models using vegetation indices	Lacks 3D information; indirect estimates [76]

The fusion of ALS and optical data consistently yields the highest performance for estimating critical forest parameters like aboveground biomass and tree species identity [82] [77] [76]. Optical data alone provides a good baseline for large-area assessments but is prone to saturation in high-biomass forests. ALS excels in delivering precise, three-dimensional structural data but at a higher cost and with less frequent coverage than satellites.

Experimental Protocols for Ecosystem Service Assessment

Protocol 1: Aboveground Biomass Estimation via Data Fusion

This protocol outlines a method for achieving high-accuracy AGB estimation by fusing ALS and satellite imagery, ideal for carbon stock assessment.

Table 2: Research Reagents and Essential Materials for AGB Estimation

Item Category	Specific Examples	Function in Protocol
Remote Sensing Data	ALS Point Clouds, Sentinel-2 MSI, Landsat 8-9 OLI, GF2	ALS provides structural metrics; optical data provides spectral information.
Field Data (Calibration)	Terrestrial Laser Scanning (TLS), Unmanned Aerial Vehicle Laser Scanning (ULS), Field Calipers, Hypsometers	Provides ground-truthed tree parameters (DBH, Height) for allometric models and model validation [77].
Allometric Models	Species-specific equations (e.g., from Chave et al.)	Convert tree parameters (DBH, H) into individual tree and plot-level AGB [77].
Software & Computing	GIS Software (e.g., ArcGIS, QGIS), Statistical Software (e.g., R, Python), Point Cloud Processing Tools (e.g., LAStools, FUSION)	Data preprocessing, variable extraction, regression modeling, and spatial analysis.

Step-by-Step Workflow:

• Field Data Collection and AGB Calculation:

  • Establish sample plots (e.g., 15m x 15m or 15m radius) within the study area [77].
  • Collect field measurements using TLS and ULS to accurately derive Diameter at Breast Height (DBH) and tree height (H) for individual trees [77].
  • Calculate plot-level AGB using existing, species-specific allometric equations. This serves as the dependent variable for model training [77].

• Remote Sensing Data Acquisition and Preprocessing:

  • Acquire ALS data and coincident optical satellite imagery (e.g., Sentinel-2, Landsat) for the study area.
  • Preprocess ALS data: Classify ground points, generate a Digital Terrain Model (DTM), and normalize the point cloud to create a height-normalized point cloud [77].
  • Preprocess optical data: Perform atmospheric and radiometric correction to obtain surface reflectance. Pansharpening may be applied to enhance spatial resolution [77].

• Predictor Variable Extraction:

  • From ALS: Calculate structural metrics from the normalized point cloud for each plot. Key metrics include height percentiles (e.g., p95, p99), mean and maximum height, canopy cover, and intensity metrics [82] [76].
  • From Optical Imagery: Extract spectral metrics for each plot, such as reflectance values per band, and vegetation indices (e.g., NDVI, EVI).

• Model Development and Validation:

  • Combine the field-based AGB (from Step 1) with the extracted ALS and optical variables (from Step 3) into a single dataset.
  • Employ regression methods (e.g., Random Forest, Support Vector Machines, or Multiple Linear Regression) to build a model predicting AGB from the remote sensing variables [77].
  • Validate the model using a hold-out subset of the field data or cross-validation, reporting metrics like R² and Root Mean Square Error (RMSE).

• Wall-to-Wall Prediction:

  • Apply the trained model to the entire area covered by the ALS and satellite imagery to generate a continuous, wall-to-wall map of AGB.

Diagram 1: AGB estimation workflow via data fusion.

Protocol 2: Forest Habitat and Biodiversity Assessment

This protocol uses ALS-derived structural metrics to infer habitat complexity and biodiversity, a key support and regulatory ecosystem service.

Step-by-Step Workflow:

• Define Ecological Hypothesis: Link forest structure to a specific biodiversity component (e.g., bird species richness, deadwood density, vertical complexity) [82] [80].

• ALS Data Processing:

  • Process ALS data to generate a Canopy Height Model (CHM) and derive a suite of structural metrics. Key metrics include:
    • Height distribution metrics: Standard deviation, skewness, and kurtosis of canopy heights.
    • Canopy density metrics: Calculated as the proportion of returns above height thresholds at different vertical strata [80] [76].
    • Horizontal heterogeneity: Textural metrics derived from the CHM.

• Field Validation of Habitat Indicators:

  • Conduct field surveys in sample plots to measure the target habitat variable. This could include:
    • Deadwood assessment: Mapping and quantifying deadwood volume and distribution [82] [80].
    • Avian point counts: Surveying bird species richness and abundance [82].
    • Horizontal structure mapping: Measuring canopy gaps and closure.

• Modeling and Prediction:

  • Statistically model the relationship between the field-measured habitat variable and the ALS-derived structural metrics.
  • Use this model to predict the habitat variable across the entire forest area, creating maps of habitat quality, deadwood potential, or biodiversity indices [80].

Diagram 2: Forest habitat and biodiversity assessment workflow.

The Scientist's Toolkit: Key Research Reagents and Materials

This table details essential tools and data sources for conducting comparative remote sensing studies in forestry.

Table 3: Essential Research Reagents and Materials for Forest Remote Sensing

Category	Item	Specification/Example	Primary Function in Research
Data Sources	Airborne Laser Scanning (ALS)	Discrete-return, multi-return systems	Provides direct, 3D measurements of forest structure (height, volume, canopy density) [80] [81].
	Optical Satellite Imagery	Sentinel-2 (10-60m), Landsat (15-30m), PlanetScope (~3m)	Provides spectral information for species ID, health assessment, and land cover classification [78] [81].
	Synthetic Aperture Radar (SAR)	ALOS/PALSAR-2 (L-band), Sentinel-1 (C-band)	Penetrates clouds and vegetation; provides data on moisture and structure; complements optical/ALS [77] [78] [79].
Field Equipment	Terrestrial Laser Scanner (TLS)	RIEGL VZ-400, etc.	Highly detailed 3D scans of plot structure for calibrating ALS and validating models [77] [80].
	Unmanned Aerial Vehicle (UAV) with LiDAR/Photography	Altura AT8, etc.	Bridges gap between field and satellite/airborne data; high-resolution 3D and spectral data [77].
	Field Dendrometry Kit	Hypsometer, Calipers, GPS	Measures DBH, height, and tree location for allometric equations and ground truthing.
Software & Models	Allometric Models	Species-specific equations (e.g., from Jenkins et al., Chave et al.)	Converts field-measured tree parameters (DBH, H) into biomass and carbon stock estimates [77].
	Point Cloud Processing	LAStools, FUSION, CloudCompare	Processes, classifies, and analyzes ALS and TLS point cloud data.
	Geospatial & Statistical	R, Python (with scikit-learn, Pandas), QGIS, ArcGIS	Data analysis, model development, and map production.

ALS and optical satellite imagery are not mutually exclusive but are powerfully complementary technologies for ecosystem service assessment. ALS provides unparalleled structural detail for accurate biomass estimation and habitat complexity analysis, while optical data offers frequent, wide-area coverage for monitoring vegetation health and land cover change. The highest accuracies are consistently achieved through data fusion, which integrates the vertical structure from ALS with the spectral information from optics [82] [76]. The protocols and tools outlined herein provide a framework for researchers to strategically deploy these technologies, thereby generating robust data to inform strategic forest planning and management in the face of global environmental change.

Forests are critical social-ecological systems that provide vital habitat for biodiversity and essential ecosystem services, including wood production and carbon sequestration [83]. However, potential trade-offs and synergies between these services remain unclear, especially within the context of uncertain climate and socio-economic developments [83]. Strategic forest planning requires a nuanced understanding of how management decisions simultaneously influence timber production, carbon dynamics, and biodiversity conservation [84] [85]. This application note synthesizes current research findings and provides detailed protocols for assessing forest management scenarios against these three critical metrics, supporting evidence-based decision-making in forest management and policy.

Quantitative Outcomes of Forest Management Scenarios

Research across diverse forest systems reveals consistent patterns in how management interventions influence timber, carbon, and biodiversity outcomes. The table below synthesizes key findings from recent studies.

Table 1: Management impacts on timber, carbon, and biodiversity metrics across forest ecosystems

Management Scenario	Timber Production	Carbon Stocks	Carbon Sequestration Rate	Biodiversity Response	Geographic Context
Intensive Harvest	Increased sustainable yield in some systems [83]	Significant decrease [86]	Short-term increase post-harvest [86]	Species composition changes relatively abruptly [85]	Central Adirondacks, USA [86]; Germany [85]
Extended Rotation/Reduced Harvest	Variable (decreases to minor changes) [83]	Significant increase [84]	Decreased in some systems [86]	More gradual compositional changes [85]; enhanced habitat structures [84]	Finland [84]; Europe-wide case studies [83]
Improved Forest Management (IFM)	Maintained with sustainable practices [87]	Increased through specific practices [87]	Optimized through silvicultural adjustments [87]	Maintained or increased through appropriate practices [87]	Certification standards (FSC, SFI) [87]
Structural Complexity Enhancement	Potential moderate reduction	Increased storage [86]	Variable response	Increased fungal richness [86]; supports specialist species [84]	Northern hardwood forests [86]
Set-Aside/No Harvest	No production	Highest stocks [84]	Sustained in old forests [86]	Highest potential for sensitive species [84]	Finland [84]; Multiple European case studies [83]

Key Relationships and Trade-offs

Carbon Stock-Sequestration Trade-off

A fundamental trade-off exists between managing forests for high carbon stocks versus high carbon sequestration rates. Studies in northern hardwood forests demonstrate that areas with higher management intensity exhibit higher decadal rates of carbon sequestration but lower carbon stocks [86]. This occurs because reducing stand density through management releases growing space, potentially increasing growth rates of residual trees, but simultaneously reduces the total standing biomass.

Management Intensity-Biodiversity Relationship

Forest management intensity shows variable effects across taxonomic groups. Intensive management significantly influences insects and tree-related microhabitats, while carbon stocks exert stronger influence on bats, birds, and vascular plants [85]. The share of non-native tree species in a stand (a component of management intensity) particularly affects most taxonomic groups [85].

Spatial and Temporal Dynamics

Ecosystem service bundles—sets of services that consistently co-occur across space or time—provide a valuable framework for understanding landscape-level management outcomes [88]. Different areas within a landscape can be characterized by distinct ecosystem service bundles, allowing for targeted management approaches that align with regional strengths and constraints [88].

Protocol for Assessing Management Scenarios

Experimental Design and Setup

Define Management Scenarios and Simulation Parameters

• Harvest Intensity Gradients: Establish scenarios ranging from no harvest to intensive harvest, with intermediate steps (e.g., low harvest, business-as-usual) [84]
• Management Strategies: Incorporate alternative approaches including rotation length variations, thinning intensity, tree species selection, and structural complexity enhancement [84] [86]
• Spatial Allocation: Consider different configurations of protected areas, set-asides, and multi-purpose forestry [84]
• Temporal Framework: Simulate forest development over extended periods (typically 50-100 years) to capture long-term dynamics [25] [83]

Select Appropriate Indicators and Metrics

• Timber Production: Sustainable harvest levels, standing volume, mean annual increment [83]
• Carbon Metrics: Aboveground biomass carbon, root carbon, soil organic carbon, carbon sequestration rates [85] [86]
• Biodiversity Indicators: Taxonomic group richness (birds, bats, insects, plants), tree-related microhabitats, deadwood volume, habitat suitability indices [84] [85]

Data Collection and Assessment Workflow

The following diagram illustrates the integrated assessment workflow for evaluating management scenarios:

Analytical Methods

Forest Simulation Modeling

Utilize appropriate forest growth simulators adapted to regional conditions (e.g., PREBAS for Finland [84], FORMES for Spanish pine stands [25]). Models should incorporate:

• Tree-level growth and mortality functions
• Management intervention responses
• Climate change projections [83]
• Soil and nutrient dynamics where available

Statistical Analysis

• Multi-variate approaches to identify ecosystem service bundles [88]
• Threshold detection to identify potential tipping points along management gradients [85]
• Relative importance analysis to determine which management factors most strongly influence different metrics [85]
• Trade-off analysis to quantify relationships between timber, carbon, and biodiversity outcomes [83] [86]

Participatory Assessment

Implement iterative participatory processes with stakeholders to:

• Co-define relevant management scenarios [88]
• Select meaningful indicators for local context [88]
• Interpret trade-offs and synergies from multiple perspectives [88]
• Enhance legitimacy and utility of assessment outcomes [88]

Table 2: Essential resources for forest management scenario assessment

Resource Category	Specific Tools/Methods	Application/Function	Examples/References
Forest Simulation Models	PREBAS model	Forest growth simulator for Nordic conditions	Finnish national assessments [84]
	FORMES system	Individual tree-based projection for multi-objective planning	Spanish Pinus sylvestris stands [25]
	Various DSSs	Case study-specific forest simulation	European landscape studies [83]
Biodiversity Assessment	Habitat Suitability Indices (HSI)	Model species presence based on habitat features	Fennoscandian avian and mammal species [84]
	Tree-related microhabitats	Structural indicators for forest biodiversity	Temperate montane forests [85]
	Multi-taxonomic surveys	Direct biodiversity measurement	Birds, bats, insects, plants [85]
Decision Support Frameworks	Ecosystem Management Decision Support (EMDS)	Spatial decision support for multi-criteria assessment	Spanish forest use suitability [25]
	2012 USFS Planning Rule Framework	Ecosystem service assessment in federal planning	US National Forests [89]
	Participatory Bayesian Networks	Integrate scientific and local knowledge	Helge å catchment, Sweden [88]
Carbon Stock Assessment	Allometric equations	Biomass estimation from tree measurements	Northern North American species [86]
	Soil carbon analysis	Belowground carbon quantification	Various forest ecosystems [85]
	Harvested wood products accounting	Carbon storage in forest products	Improved Forest Management protocols [87]

Evaluating forest management scenarios through integrated assessment of timber, carbon, and biodiversity metrics provides critical insights for strategic forest planning. The protocols outlined here enable researchers and forest managers to quantify trade-offs and synergies, identify management strategies that align with multiple objectives, and support decision-making that balances competing demands on forest ecosystems. The incorporation of both biophysical and social dimensions through participatory approaches enhances the relevance and applicability of assessment outcomes, ultimately supporting more sustainable forest governance.

This application note details the methodology and key findings from a Swiss Choice Experiment (CE) designed to quantify and compare the Willingness to Pay (WTP) and Willingness to Accept (WTA) for forest ecosystem services (ES) across diverse geographic and societal contexts. The research provides a standardized protocol for assessing the economic value of non-market forest benefits, addressing the critical disparity between public demand and forester supply-side preferences. This comparative analysis is instrumental for developing efficient, context-sensitive policies in strategic forest planning and conservation financing.

Forest ecosystems provide a multitude of services, from avalanche protection and carbon sequestration to cultural and recreational benefits [90] [91]. However, many of these services are non-market public goods, leading to their potential underestimation in policy decisions. Sustainable forest management requires robust economic valuation to balance the often-competing demands on forest resources [91]. The Willingness to Pay (WTP), defined as the maximum amount a beneficiary is willing to pay to obtain a specific ES, and the Willingness to Accept (WTA), the minimum compensation a provider requires to relinquish it, are two fundamental measures of economic value [92].

Standard economic theory suggests WTP and WTA should be comparable, but a substantial body of empirical evidence consistently demonstrates that WTA values often significantly exceed WTP for the same good or service [93]. This disparity, with WTA/WTP ratios frequently reaching 2:1 or higher, is a critical anomaly in environmental economics [93] [94]. A recent systematic review in healthcare found a median WTA/WTP ratio of 1.61, confirming the persistence of this phenomenon across different fields [94]. The reasons are multifaceted, encompassing psychological factors like the endowment effect (where individuals value goods they own more highly) and loss aversion, as well as economic constraints such as income effects and substitution possibilities [93]. Failing to account for this disparity can lead to systematically biased policy decisions that undervalue environmental losses [93].

Experimental Protocol: Conducting a Comparative WTP/WTA Choice Experiment

This protocol is adapted from interdisciplinary valuation studies in the Swiss Alps [90] [20].

Research Design and Preparation

• Objective Definition: Clearly define the forest ES to be valued (e.g., avalanche protection, biodiversity, recreation) and the policy or management alternatives to be tested.
• Stakeholder Identification: Define and segment the target populations. For a comprehensive analysis, this includes:
  • Demand Side: The general public, differentiated by geographic criteria (e.g., urban vs. rural residents, proximity to specific forest zones).
  • Supply Side: Professional foresters and forest owners, differentiated by region and management type.
• Survey Instrument Development:
  • Choice Sets: Construct a series of hypothetical choice scenarios. In each scenario, respondents are presented with several management alternatives, each defined by a bundle of attributes (e.g., level of avalanche protection, biodiversity status, recreational access, cost or compensation level). An example is shown in Table 1.
  • Attribute and Level Selection: Attributes should be policy-relevant, understandable, and vary across realistic levels. The monetary attribute (tax for WTP, compensation for WTA) is essential for valuation.
  • Visualization Aids: To enhance respondent comprehension, employ virtual reality visualization or detailed visual aids, particularly for complex ES like avalanche protection or landscape aesthetics [90].
  • Questionnaires: Include sections on socio-demographics, attitudes towards forests and conservation, and knowledge of forest ES.

Data Collection and Sampling

• Sampling Strategy: Employ stratified random sampling to ensure representation across pre-defined forest zones (e.g., Jura, Plateau, Pre-Alps, Alps) and settlement areas (urban, peri-urban, rural) [20].
• Survey Administration: Distribute the survey to both the public (WTP) and forester/owner (WTA) samples. Modes can include online surveys, mailed questionnaires, or in-person interviews.

Data Analysis

• Econometric Modeling: Use discrete choice models (e.g., conditional logit, mixed logit) to analyze the responses. The model estimates the utility derived from each attribute.
• WTP/WTA Calculation: Calculate the marginal WTP (from the public survey) and marginal WTA (from the forester survey) for a unit change in each ES attribute. This is derived as the negative ratio of the attribute's coefficient to the monetary attribute's coefficient.
• Comparative Analysis: Statistically compare WTP and WTA estimates across different forest zones and societal groups to identify significant spatial and social heterogeneities.

Table 1: Example Choice Set Presented to Respondents (Public - WTP Perspective)

Attribute	Alternative A (Status Quo)	Alternative B (Policy Change)	Alternative C
Avalanche Risk Reduction	Current level	30% reduction	50% reduction
Recreational Access	Standard trails	Enhanced trail network	Standard trails
Bird Species Diversity	50 species	50 species	70 species
Annual Tax Increase	€0	€50	€100
Your Choice	☐	☐	☐

Conceptual Workflow

The following diagram illustrates the logical workflow for conducting a comparative WTP/WTA analysis, from study design to policy application.

Key Data and Findings from Swiss Context

The following table synthesizes the core quantitative findings and their implications from the Swiss Choice Experiment, illustrating the spatial and societal heterogeneity in ecosystem service valuation.

Table 2: Comparative Summary of WTP and WTA Findings from Swiss Choice Experiments

Analysis Dimension	Key Finding	Quantitative Insight	Implication for Forest Planning
Overall WTP vs. WTA	A significant disparity exists between the two measures.	WTA values are consistently higher than WTP, with ratios often >2:1 [93].	Using WTP to value losses (e.g., from development) systematically underestimates costs [93].
Public Preferences (WTP)	The public demonstrates a strong preference for mixed, permanent forests over monocultures [20].	N/A (Qualitative finding)	Forest management promoting diversity aligns with public demand for multifunctional forests.
Spatial Heterogeneity (WTP)	WTP for forest ES varies substantially across different geographic regions and settlement types [20].	WTP is higher in areas more directly dependent on or impacted by specific ES (e.g., avalanche protection in alpine zones) [90] [20].	Conservation funding and policy should be spatially targeted to areas where ES are most valued.
Supply-Demand Matching	Divergence exists between what the public is willing to pay for and what foresters are willing to provide for the same compensation [20].	Foresters' WTA may exceed public WTP for certain management practices, creating a policy gap.	Voluntary conservation programs require careful calibration of compensation levels to bridge the supply-demand gap cost-effectively.

The Scientist's Toolkit: Research Reagent Solutions

This section outlines the essential "research reagents" – the key methodological tools and concepts – required to conduct a rigorous WTP/WTA analysis in environmental economics.

Table 3: Essential Methodological Tools for WTP/WTA Choice Experiments

Research 'Reagent'	Function / Role in the Experiment
Choice Experiment (CE)	The core stated-preference method for eliciting values by presenting respondents with repeated trade-offs between multi-attribute alternatives [20].
Contingent Valuation Method (CVM)	A survey-based technique to directly ask individuals their WTP or WTA in a hypothetical market [95] [94].
Discrete Choice Models	A class of econometric models (e.g., Logit) used to analyze the choices made in the CE, revealing the implicit utility and value of each attribute [95].
Virtual Reality (VR) Visualization	A tool to enhance the realism and comprehension of complex or risk-based ES (e.g., avalanche protection), reducing hypothetical bias [90].
Payment Card	A survey format used to elicit WTA, presenting respondents with a range of possible compensation amounts from which they choose their minimum acceptable value [95].
Cost-Benefit Analysis (CBA)	The overarching analytical framework into which WTP and WTA estimates are fed to evaluate the economic efficiency of policies and projects [90] [92].

The Swiss Choice Experiment demonstrates that an integrative assessment approach is paramount for accurately valuing forest ecosystem services [91]. By simultaneously analyzing WTP and WTA across different forest zones and societal groups, this methodology provides nuanced, high-resolution data for strategic forest planning. The persistent WTA-WTP disparity underscores that losses are valued more highly than gains, a fundamental insight that must be incorporated into environmental policy to avoid inefficient outcomes [93].

For policymakers, these findings enable the design of more efficient and equitable conservation programs. Understanding spatial heterogeneity allows for the targeted allocation of scarce conservation funds. Recognizing the gap between public WTP and forester WTA informs the design of payment for ecosystem service (PES) schemes that are both financially viable and attractive enough to ensure landowner participation [20] [95]. Ultimately, this comparative WTP/WTA analysis serves as a powerful tool for aligning forest management with societal preferences, ensuring the sustainable provision of vital ecosystem services for future generations.

Conclusion

The integration of ecosystem services assessment into strategic forest planning is no longer a theoretical ideal but an operational necessity for sustainable management. This synthesis demonstrates that a successful approach is inherently interdisciplinary, combining biophysical, socio-cultural, and economic analyses within robust frameworks like the seven resilience principles. While powerful methodological toolkits and decision-support systems are available, their effectiveness hinges on addressing persistent challenges: improving the accuracy of foundational data, particularly from remote sensing; proactively integrating ES into spatial planning processes; and strengthening often-neglected governance principles like polycentricity and broad participation. Future efforts must focus on closing the identified research gaps, especially in quantifying all resilience principles jointly and fostering the science-policy collaborations needed to translate assessment results into management actions that ensure the long-term resilience of forest social-ecological systems.

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