Aggregate Biophysical Indicators of Environmental State

1. Outcome

This Circular provides comprehensive guidance on deriving aggregate biophysical indicators of environmental state from ocean accounts. Aggregate indicators synthesise complex, multi-dimensional information about marine and coastal ecosystem condition into tractable summary measures that support policy communication, tracking of conservation targets, and comparison across spatial units and time periods. Building on the ecosystem condition accounting framework established in TG-3.1 Asset Accounts, this Circular explains the conceptual and methodological foundations for constructing extent-based indicators, condition-based indicators, and composite indices for marine ecosystems. Readers will understand the indicator hierarchy from raw condition variables through normalised indicators to aggregate condition indices, the role of reference conditions in indicator derivation, and the specific applications and limitations of aggregate indicators for ocean accounting. The guidance enables compilers to produce policy-relevant summary statistics from detailed ocean accounts while maintaining the transparency and traceability that are essential for scientific credibility.

Aggregate biophysical indicators support a range of critical decision use cases for governments managing ocean resources:

MPA effectiveness monitoring: Indicators derived from extent and condition accounts enable tracking of whether marine protected areas are achieving their conservation objectives. Baseline condition indices established at MPA designation provide benchmarks against which subsequent changes can be measured, supporting adaptive management decisions on zoning, enforcement, and restoration priorities. The application of ocean accounts to MPA assessment is addressed in TG-1.3 OA and Marine Spatial Management.

SDG 14 reporting: Extent indicators align directly with SDG indicator 14.5.1 (marine protected area coverage), while condition indicators support assessment of SDG target 14.2 on sustainable management and protection of marine and coastal ecosystems. Aggregate condition indices provide headline measures for communicating progress toward ecosystem-based management at national and international levels.

Ocean health report cards: Composite condition indices aggregated across multiple ecosystem types produce summary scores suitable for public communication through ocean health scorecards and state-of-environment reports. Such indicators provide accessible entry points for non-technical audiences while maintaining links to underlying accounting data that support scientific scrutiny.

Fisheries management thresholds: Condition indicators for fish nursery habitats (coral reefs, seagrass meadows, mangroves) inform spatial management decisions in fisheries by identifying areas where habitat degradation may threaten recruitment. Combined extent-condition indicators for these ecosystems can inform the setting of precautionary harvest control rules, as addressed in TG-1.5 OA and Fisheries Management.

In practice, these use cases are mutually reinforcing. The same underlying condition accounts can generate indicators for MPA monitoring, SDG reporting, public communication, and fisheries management, ensuring consistency across policy domains and efficient use of monitoring resources.

2. Requirements

This Circular requires familiarity with:

• TG-0.1 General Introduction to Ocean Accounts -- for the conceptual framework and key components of Ocean Accounts
• TG-0.2 Overview of Relevant Statistical Standards -- for the methodological foundations provided by SNA 2025, SEEA CF, and SEEA EA
• TG-3.1 Asset Accounts -- for the structure of ecosystem extent and condition accounts from which indicators are derived
• TG-0.7 Quality Assurance -- for quality management principles that apply to indicator construction, including uncertainty documentation and fitness-for-purpose assessment

3. Guidance Material

Within the ocean accounting system, information products are organised in a hierarchical structure sometimes described as an information pyramid^[1]. At the base of this pyramid sit data and statistics--the raw observations from monitoring networks, surveys, and remote sensing platforms that support the operation of accounts. Accounts occupy an intermediate level, providing a coherent framework that organises data according to internationally agreed classifications and accounting rules. From these accounts, indicators are derived as summary measures that distil complex accounting information into policy-relevant signals. At the apex, key indicators aggregate further to support strategic analysis and high-level decision-making. This layered structure means that the quality of indicators depends fundamentally on the quality and coherence of the underlying data and accounts.

Figure 2.1.1: Information pyramid (adapted from SEEA EA Figure 14.1)^[2]

For biophysical indicators of environmental state, the information pyramid clarifies that indicators can be sourced both directly from data and statistics--where accounts have not yet been compiled--and from structured accounts. Within a comprehensive ocean accounting system, however, the accounts layer provides an essential intermediate structure that ensures consistency across indicators, connects higher-level summary measures with lower-level observations, and enables users to trace any indicator value back through the accounting framework to its source data. Compiling indicators from accounts rather than from raw data alone strengthens their comparability across space and time, because the accounting process applies standardised classifications, reference conditions, and valuation rules that harmonise otherwise disparate data streams.

This framing helps situate the current Circular within the broader Technical Guidance. TG-2.1 addresses the derivation of aggregate biophysical indicators--the upper layers of the information pyramid--while the construction and population of the accounts from which these indicators are drawn is addressed in the TG-3.x series, particularly TG-3.1 Asset Accounts for ecosystem extent and condition accounts. The data production methods that populate the base of the pyramid--remote sensing, surveys, and geospatial integration--are covered in the TG-4.x series. Readers are encouraged to consider the full information chain when designing indicator systems, ensuring that the accounts layer is in place to support robust and traceable indicator derivation.

Ecosystem condition accounts compile detailed information on the biophysical characteristics of ecosystems across multiple dimensions--physical, chemical, compositional, structural, functional, and landscape/seascape^[3]. While this granular information is essential for scientific understanding and detailed analysis, policy-makers and the public often require summary measures that communicate the overall state of ocean ecosystems in accessible terms. Aggregate biophysical indicators serve this communication function while maintaining a rigorous connection to the underlying accounting data.

SEEA Ecosystem Accounting establishes a three-stage hierarchical approach to ecosystem condition measurement^[4]. The first stage records ecosystem condition variables--the raw observations of ecosystem characteristics in their original measurement units. The second stage derives ecosystem condition indicators by normalising variables against reference levels, creating comparable measures on a common scale. The third stage involves optional aggregation into ecosystem condition indices, which combine multiple indicators into summary measures for communication purposes. This Circular focuses primarily on the second and third stages, providing guidance on deriving indicators and indices for marine and coastal ecosystems within the ocean accounting framework.

This Circular supports the indicator derivation and presentation guidance in TG-1.3 OA and Marine Spatial Management, which addresses how aggregate indicators can inform protected area management, and TG-1.5 OA and Fisheries Management, which addresses fish stock indicators within the broader indicator framework described here.

3.1 Indicator Framework

The indicator framework for ocean accounting connects raw biophysical data to policy-relevant summary statistics through a systematic process of classification, normalisation, and aggregation. Understanding this framework is essential for both compiling indicators and interpreting them appropriately.

3.1.1 Types of biophysical indicators

Biophysical indicators in ocean accounting fall into three broad categories based on what they measure:

Extent indicators measure the spatial coverage of marine ecosystem types, expressed in units of area (hectares, square kilometres) or, for certain ecosystems, length (kilometres of coastline) or volume (cubic kilometres of water column)^[5]. Extent indicators include total area of ecosystem types, changes in extent over time, and proportional coverage within accounting areas. Key examples for marine ecosystems include coral reef area, seagrass meadow extent, mangrove forest coverage, and kelp forest distribution.

Condition indicators measure the health and integrity of ecosystems relative to a reference state, typically expressed as dimensionless indices on a standardised scale (commonly 0-1 or 0-100)^[6]. Condition indicators are derived from raw condition variables through normalisation against reference levels. The SEEA Ecosystem Condition Typology (ECT) organises condition characteristics into six classes: physical state, chemical state, compositional state, structural state, functional state, and landscape/seascape context^[7]. The following table summarises these ECT classes with marine-specific examples and typical data sources:

Combined extent-condition indicators integrate information on both the area and quality of ecosystems, providing measures of effective ecosystem capacity. These include weighted indices where condition scores modify extent values, and functional capacity measures that combine area with condition-dependent service delivery potential^[8]. For example, an "effective coral reef area" indicator might weight reef extent by a condition index, providing a single measure that captures both spatial coverage and ecological quality.

Combined extent-condition indicators represent an active area of methodological development for ocean accounting. While SEEA EA does not prescribe a single approach, the combination of extent and condition information addresses an important policy need: distinguishing between situations where a large area of ecosystem exists in degraded condition versus a smaller area in good condition. Compilers are encouraged to present combined indicators alongside their disaggregated components, enabling users to identify whether changes in the combined measure are driven by extent loss, condition decline, or both.

3.1.2 Relationship to accounts

Biophysical indicators derive from and remain linked to the underlying accounting tables. This relationship ensures that aggregate indicators can be traced back to their component data, supporting verification and enabling users to investigate the drivers of observed changes. The conceptual flow from accounts to indicators is illustrated in the following framework.

Figure 2.1.2: General ecosystem accounting framework (adapted from 2025 SNA Figure 35.2)^[9]

The ecosystem condition variable account records raw observations of condition characteristics for each ecosystem type within the accounting area^[10]. For example, a condition variable account for coral reef ecosystems might record coral cover percentage, fish biomass density, water temperature, and species richness. These values are recorded in their native measurement units (percentages, kg/hectare, degrees Celsius, species counts).

The ecosystem condition indicator account derives normalised indicators from variables by applying reference levels^[11]. The normalisation process transforms diverse measurement units into a common scale, enabling comparison across characteristics and aggregation. A coral cover of 35% might become a condition indicator of 0.70 if the reference condition is 50% cover.

The ecosystem condition index aggregates individual indicators into composite measures for each ecosystem type, and potentially across ecosystem types for the entire accounting area^[12]. The aggregation method--arithmetic mean, geometric mean, weighted average, or other approaches--should be documented and its implications understood.

The methods for remote sensing and spatial data that underpin extent and condition measurement are addressed in TG-4.1 Remote Sensing Data, while TG-4.2 Survey Methods covers field survey approaches for condition variable collection.

3.1.3 Principles of indicator design

Effective biophysical indicators for ocean accounting should satisfy several design principles established in international statistical and scientific guidance^[13]:

Relevance: Indicators should measure characteristics that are meaningful for ecosystem integrity and responsive to the pressures or management interventions of interest. For marine ecosystems, indicators should capture characteristics relevant to ocean-specific processes such as productivity, biodiversity, water quality, and habitat structure.

Scientific validity: Indicators should be based on sound scientific understanding of ecosystem function and established relationships between measured characteristics and ecosystem health. Where possible, indicators should align with validated assessment frameworks such as Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs)^[14].

Measurability: Indicators should be based on data that can be collected consistently across space and time using available methods and resources. For many marine ecosystems, this implies prioritising indicators that can be derived from remote sensing, systematic surveys, or monitoring networks with established protocols.

Sensitivity: Indicators should be sensitive to changes in ecosystem state at policy-relevant timescales, neither so volatile as to reflect noise rather than signal, nor so stable as to miss important changes.

Comparability: Indicators should be constructed in ways that enable meaningful comparison across different areas, ecosystem types, and time periods. The use of common reference conditions and standardised normalisation approaches supports comparability.

Transparency: The methods for constructing indicators should be fully documented, enabling independent verification and facilitating understanding of what the indicator does and does not capture.

Uncertainty documentation: Indicators should be accompanied by information on the uncertainty associated with their values, including measurement uncertainty in the underlying data, model uncertainty in the normalisation process, and sensitivity to methodological choices such as reference condition selection and aggregation weights. The quality management principles in TG-0.7 Quality Assurance apply directly to indicator construction and should inform the documentation of confidence levels and data quality ratings for published indicators.

3.2 Compilation Procedure for Aggregate Indicators

This section outlines the step-by-step procedure for compiling aggregate biophysical indicators from ocean accounts. Understanding this workflow is essential for translating condition and extent data into policy-relevant summary measures while maintaining transparency and traceability.

Step 1: Identify indicator set and policy requirements

The compilation process begins with identifying which indicators are needed to serve the intended policy use cases. This involves reviewing policy commitments (SDG targets, national conservation strategies, MPA management plans) to determine reporting requirements, consulting with decision-makers and stakeholders to understand their information needs, and assessing data availability and quality to ensure feasibility.

For ocean accounting, priority indicator sets typically include:

• Extent change indicators for ecosystem types covered by international targets (mangroves, coral reefs, seagrass meadows)
• Condition indicators spanning all six ECT classes to provide comprehensive assessment
• Combined extent-condition indicators for ecosystems that deliver critical services (coastal protection, fish nursery habitat, carbon sequestration)
• Headline composite indices for high-level communication in state-of-environment reports and ocean health scorecards

The selection process should be documented in a technical specification that records the rationale for each indicator, its intended use, and the accounts from which it will be derived.

Step 2: Source data from accounts

With the indicator set identified, the next step is to extract the required data from the underlying asset accounts compiled following TG-3.1 Asset Accounts. For extent indicators, this involves extracting opening and closing extent values from ecosystem extent accounts, calculating net change, and expressing change as both absolute values (hectares) and relative values (percentage change from opening extent).

For condition indicators, the process involves extracting condition variable values from condition variable accounts for opening and closing periods, retrieving reference condition values for each variable, and checking that measurement units are consistent across accounting periods. The data extraction should preserve links to the spatial units (ecosystem assets or BSUs) from which the data originate, enabling disaggregated analysis when needed.

Data quality ratings from TG-0.7 Quality Assurance should be carried forward from the accounts to the indicators, documenting the fitness-for-purpose of the underlying data.

Step 3: Apply reference conditions

Reference conditions establish the benchmark against which current ecosystem state is measured^[15]. The choice of reference condition is one of the most consequential methodological decisions in condition indicator construction, as it determines both the absolute value of the indicator and its interpretation.

For each condition variable, the compiler must:

1. Select reference type (natural reference, historical baseline, policy target, or current best condition)
2. Justify the choice based on data availability, scientific understanding, and policy requirements
3. Document the reference value with its source and any assumptions
4. Establish polarity (whether high measured values indicate high or low condition)

For marine ecosystems, establishing natural reference conditions is particularly challenging due to the long history of human fishing pressure and the "shifting baseline syndrome" whereby each generation accepts the degraded state they first observe as normal^[16]. Where natural reference data are unavailable, compilers may use:

• Historical reconstruction from paleoecological records or early survey data
• Space-for-time substitution using remote or protected areas as proxies
• Expert elicitation from marine ecologists familiar with the ecosystem type
• Policy targets from conservation plans or recovery objectives

The SEEA EA notes that reference conditions should be "clearly defined, scientifically based, and consistent with the purpose of the assessment"^[17]. Different reference types are appropriate for different indicator applications, as summarised in the following table:

Climate change introduces a fundamental methodological challenge for reference condition selection. Fixed reference conditions (whether natural or historical) enable tracking of total change in ecosystem state, including climate-driven change, and support assessment of cumulative human impact. Adjusted reference conditions that account for inevitable climate-driven shifts enable assessment of the additional impact of local pressures beyond background climate change. In practice, compilers may present both fixed and adjusted reference conditions in parallel, allowing users to distinguish between total change and locally manageable change. Where adjusted reference conditions are used, the climate scenario and adjustment methodology should be documented transparently to enable comparison across assessments and time periods.

Step 4: Compute indicators

With reference conditions established, the next step is to normalise each condition variable into a dimensionless indicator. The SEEA EA describes a linear transformation approach^[18]:

Indicator = (Variable value - Lower reference) / (Upper reference - Lower reference)

Where the upper reference represents the value associated with high (or reference) condition and the lower reference represents the value associated with low (or degraded) condition. The resulting indicator takes values between 0 (fully degraded) and 1 (reference condition).

For variables where high measured values indicate low condition (inverse polarity)--such as pollutant concentrations or invasive species abundance--the transformation is adjusted to:

Indicator = (Lower reference - Variable value) / (Lower reference - Upper reference)

This ensures consistent interpretation where indicator values approaching 1 always represent better condition.

The computation should be performed for each condition variable, for each ecosystem asset or spatial unit, and for both opening and closing accounting periods. The results populate the ecosystem condition indicator account, enabling comparison across variables, ecosystem types, and time periods.

Step 5: Validate indicators

Before aggregation, individual indicators should be validated to ensure they produce reasonable results. Validation checks include:

• Range check: All indicator values should fall within [0, 1] unless deliberately allowed to exceed reference condition
• Consistency check: Indicators derived from the same underlying process (e.g., coral cover and reef structural complexity) should show correlated patterns
• Sensitivity check: Indicators should respond to known pressures or management interventions in expected directions
• Comparison with external assessments: Where available, indicators should be compared with independent condition assessments from scientific literature or management reports

Indicators that fail validation checks should be investigated before aggregation. Common issues include incorrect reference condition selection, data entry errors, or inappropriate choice of variable for the ecosystem type.

Step 6: Aggregate into composite indices (optional)

The final step is to aggregate individual condition indicators into composite measures that summarise overall ecosystem health. The SEEA EA describes aggregation as optional but notes that composite indices are often required for policy communication^[19].

Three aggregation approaches are commonly used:

Arithmetic mean: Gives equal weight to all indicators, calculated as (1/n) × Sum of all indicator values. This is the simplest approach and is transparent, but assumes substitutability between indicators--that improvement in one characteristic can compensate for decline in another.

Geometric mean: Calculated as (Product of all indicator values)^(1/n). This approach emphasises low values, such that a very low score on any indicator substantially reduces the overall index, reflecting the view that ecosystem health requires adequate performance across all characteristics.

Weighted mean: Assigns different weights to indicators based on their importance for ecosystem function, data quality, or policy relevance. Weights should be transparent, justified, and sum to 1. Weighting can be based on expert elicitation, statistical approaches (principal components analysis), or ecosystem modelling.

For ocean accounting, the choice of aggregation method should reflect the ecological relationships among the characteristics being measured. For ecosystems with strong functional dependencies--where multiple characteristics must all exceed thresholds for the ecosystem to persist--geometric mean aggregation may be more appropriate than arithmetic mean. The SEEA EA emphasises that "where aggregation is undertaken, a clear link should be established to information on movements in individual indicators"^[20], ensuring that composite indices do not obscure important changes in component variables.

Aggregation can occur at several levels: across indicators within the same ECT class (e.g., a composite structural state index for coral reefs), across all ECT classes for a single ecosystem type (e.g., an overall coral reef condition index), or across ecosystem types for the entire accounting area (e.g., a national marine ecosystem condition index). Higher levels of aggregation increase communication efficiency but reduce transparency and may mask critical thresholds in specific characteristics or ecosystem types.

Step 7: Disseminate with metadata

The final compilation step is to package indicators with comprehensive metadata and disseminate them to intended users. Metadata should document:

• Source accounts: Which extent and condition accounts provided the underlying data
• Reference conditions: What reference type and values were used for each variable
• Normalisation method: The formula and parameters used to transform variables to indicators
• Aggregation method: If composite indices were created, how indicators were weighted and combined
• Uncertainty: Confidence intervals, data quality ratings, and sensitivity to methodological choices
• Limitations: What the indicator does and does not capture, and appropriate uses

Indicators can be disseminated through multiple channels: integration into national statistical releases, publication in dedicated ocean accounts reports, upload to data portals with API access for researchers and developers, and presentation in accessible formats (dashboards, infographics) for public audiences. The dissemination strategy should ensure that indicators reach their intended users in formats suited to their needs, while maintaining access to underlying data and documentation for transparency and verification.

3.3 Extent-Based Indicators

Ecosystem extent accounts provide the foundation for indicators that measure the spatial dimension of marine ecosystems--where they are, how much area they cover, and how coverage is changing over time. Extent-based indicators are among the most fundamental outputs of ocean accounting, providing essential context for condition and service accounts.

3.3.1 Ecosystem extent change

The primary extent indicator is change in ecosystem extent over the accounting period, measured in absolute terms (hectares gained or lost) or relative terms (percentage change from opening extent)^[21]. For marine ecosystems, extent change indicators capture:

• Loss of ecosystem area due to conversion, degradation, or sea-level change
• Gain of ecosystem area due to expansion, restoration, or natural succession
• Net change combining losses and gains

The SEEA EA recommends recording extent changes through a structured account that distinguishes managed and natural drivers of change^[22]. The ecosystem extent account structure records opening extent, additions (managed expansion, natural expansion), reductions (managed reduction, natural reduction), and closing extent. This structure enables indicators to distinguish anthropogenic drivers (e.g., coastal development converting mangroves) from natural processes (e.g., storm damage to coral reefs).

For ocean accounting, priority extent indicators include:

Thematic circulars provide detailed guidance on extent accounting for specific ecosystem types: TG-6.1 Coral Reef Accounts, TG-6.2 Mangrove and Wetland Accounts, TG-6.3 Seagrass Accounts, and TG-6.5 Pelagic and Open Ocean Accounts.

3.3.2 Ecosystem conversion

Beyond net extent change, conversion indicators track the transformation of one ecosystem type into another^[23]. The ecosystem type change matrix records flows between ecosystem types, enabling construction of indicators such as:

• Area of natural ecosystems converted to artificial or managed types (e.g., mangrove to aquaculture pond)
• Area restored from degraded to natural condition
• Area of coastal land converted to marine area (coastal erosion) or vice versa (land reclamation)

Conversion indicators are particularly important for understanding the drivers of extent change and the implications for ecosystem services. Conversion of mangroves to aquaculture ponds, for example, involves loss of coastal protection and carbon storage services even if total coastal ecosystem extent is maintained through expansion elsewhere. These service implications are addressed in TG-2.4 Ecosystem Goods and Services.

The SEEA EA defines ecosystem conversion as occurring when "for a given location, there is a change in ecosystem type involving a distinct and persistent change in ecological structure, composition and function"^[24]. For marine ecosystems, determining when a change in condition constitutes conversion to a different ecosystem type (versus degradation within the same type) requires careful application of classification criteria. Guidance on marine ecosystem classification is provided in TG-0.7 Quality Assurance.

3.3.3 Extent indicators for policy targets

Extent indicators align directly with international policy targets. SDG 14.5 targets conservation of at least 10% of coastal and marine areas, measured by indicator 14.5.1 (coverage of protected areas in relation to marine areas)^[25]. The Kunming-Montreal Global Biodiversity Framework Target 3 calls for protection of at least 30% of marine areas by 2030^[26]. Extent accounts for marine protected areas, combined with condition accounts assessing protection effectiveness, provide the data foundation for national reporting against these targets. The application of ocean accounts to MPA assessment is addressed in detail in TG-1.3 OA and Marine Spatial Management.

SDG 15.3.1 measures land degradation, including coastal areas, with methodology that integrates extent and condition information^[27]. This approach has been applied to derive measures of "land degradation neutrality" and could be extended to marine ecosystems as "ecosystem degradation neutrality" indicators combining extent loss with condition decline. The adaptation of land degradation neutrality concepts to marine contexts is an emerging area where ocean accounting can contribute methodological innovation. Key considerations include the selection of sub-indicators analogous to the terrestrial triad (land cover, land productivity, soil organic carbon), such as habitat extent, primary productivity, and benthic condition for coastal ecosystems. Compilers exploring this approach should document adaptations and their rationale, contributing to the evolving evidence base for marine degradation neutrality assessment.

3.4 Condition-Based Indicators

While extent indicators measure how much ecosystem area exists, condition indicators measure how healthy or functional those ecosystems are. Ecosystem condition is "the quality of an ecosystem measured in terms of its abiotic and biotic characteristics"^[28]. Condition accounts and derived indicators provide essential information on whether marine ecosystems retain the structure and function necessary to support biodiversity and deliver ecosystem services.

3.4.1 Deriving condition indicators from variables

The transformation from condition variables to condition indicators involves normalisation against reference levels. The SEEA EA describes a linear transformation approach^[18:1]:

Indicator = (Variable value - Lower reference) / (Upper reference - Lower reference)

Where the upper reference represents the value associated with high (or reference) condition and the lower reference represents the value associated with low (or degraded) condition. The resulting indicator takes values between 0 (fully degraded) and 1 (reference condition).

For some variables, high measured values indicate high condition (e.g., species richness, coral cover), while for others, high measured values indicate low condition (e.g., pollutant concentration, invasive species abundance). The polarity of the transformation must be adjusted accordingly to ensure consistent interpretation of indicator values^[29].

Example: For a coral reef ecosystem, coral cover percentage might be transformed as follows:

• Upper reference (high condition): 50% coral cover
• Lower reference (degraded condition): 5% coral cover
• Observed value: 35% coral cover
• Indicator = (35 - 5) / (50 - 5) = 30 / 45 = 0.67

This indicator value of 0.67 indicates the coral reef is at 67% of reference condition based on the coral cover characteristic.

Example (inverse polarity): For a coastal water body, dissolved inorganic nitrogen concentration is an inverse indicator--higher values indicate lower condition. The transformation adjusts polarity accordingly:

• Upper reference (high condition): 0.1 mg/L (low nutrient level characteristic of unpolluted waters)
• Lower reference (degraded condition): 1.5 mg/L (eutrophic level)
• Observed value: 0.6 mg/L
• Indicator = (Lower reference - Observed) / (Lower reference - Upper reference) = (1.5 - 0.6) / (1.5 - 0.1) = 0.9 / 1.4 = 0.64

This indicator value of 0.64 indicates the water body is at 64% of reference condition based on nutrient concentration, with the inverse polarity ensuring that lower pollutant levels correspond to higher indicator scores.

3.4.2 Reference conditions

The choice of reference condition is one of the most consequential methodological decisions in condition indicator construction. Reference conditions establish the benchmark against which current ecosystem state is measured^[15:1]. The SEEA EA discusses several approaches to setting reference conditions:

Natural reference condition: The state of the ecosystem in the absence of significant human influence, representing its natural or "pristine" state. For marine ecosystems, this may be estimated from historical data, comparison with protected or remote areas, or ecological modelling^[30]. Natural reference conditions support assessment of anthropogenic impact and distance from naturalness.

Historical baseline: The state of the ecosystem at a specified historical date, which may represent conditions before major human impacts or simply provide a consistent temporal reference point. Historical baselines enable tracking of change over time but do not necessarily represent ecologically optimal conditions.

Policy target: A desired future state specified in policy, legislation, or management plans. Policy targets may be more or less ambitious than natural reference conditions, reflecting practical constraints or socio-economic considerations. Using policy targets as reference enables tracking of progress toward management objectives.

Current best condition: The best observed condition within a region or population of comparable ecosystem types, representing an achievable benchmark even if natural reference conditions are unknown or considered unattainable.

The following table summarises the strengths and limitations of each reference type for ocean accounting applications:

The SEEA EA emphasises that reference conditions should be "clearly defined, scientifically based, and consistent with the purpose of the assessment"^[17:1]. For marine ecosystems, establishing natural reference conditions is particularly challenging due to the long history of human fishing pressure and the "shifting baseline syndrome" whereby each generation accepts the degraded state they first observe as normal^[16:1].

Table A5.2.2 of SEEA EA provides a summary of methods for estimating reference condition for natural and managed ecosystems, including historical reconstruction, space-for-time substitution, ecological modelling, and expert elicitation^[31]. For marine ecosystems subject to climate change, reference conditions may need to incorporate considerations of climate adaptation and novel ecosystem states.

Climate change introduces a fundamental methodological challenge for reference condition selection. Fixed reference conditions (whether natural or historical) enable tracking of total change in ecosystem state, including climate-driven change, and support assessment of cumulative human impact. Adjusted reference conditions that account for inevitable climate-driven shifts enable assessment of the additional impact of local pressures beyond background climate change. In practice, compilers may present both fixed and adjusted reference conditions in parallel, allowing users to distinguish between total change and locally manageable change. Where adjusted reference conditions are used, the climate scenario and adjustment methodology should be documented transparently to enable comparison across assessments and time periods.

3.4.3 Condition indices

The connections between the different ecosystem accounts--extent, condition, services (physical and monetary), and monetary asset accounts--illustrate why condition indices occupy a central position in the accounting framework. Condition accounts both depend on extent information and inform the measurement of ecosystem services, creating an integrated chain of measurement.

Figure 2.1.3: Connections between ecosystem accounts (adapted from 2025 SNA Figure 35.3)^[32]

Ecosystem condition indices aggregate individual condition indicators into composite measures that summarise overall ecosystem health^[19:1]. Index construction involves decisions about which indicators to include, how to weight them, and what aggregation function to apply.

Arithmetic mean: The simplest aggregation approach, giving equal weight to all included indicators. An ecosystem condition index calculated as the arithmetic mean of n indicators would be:

ECI = (1/n) x Sum of all indicator values

This approach is transparent and easy to calculate but assumes substitutability between indicators--that improvement in one characteristic can compensate for decline in another.

Geometric mean: Gives more emphasis to low values, such that a very low score on any indicator reduces the overall index substantially. This approach reflects the view that ecosystem health requires adequate performance across all characteristics, with weak-link characteristics constraining overall condition.

Weighted mean: Assigns different weights to indicators based on their importance for ecosystem function, data quality, or policy relevance. Weighting can be based on expert elicitation, statistical approaches (e.g., principal components analysis), or ecosystem modelling. Weights should be transparent and justified^[33].

The structure of ecosystem condition accounting described in SEEA EA allows for aggregation in several ways: across indicators within the same ECT class, across classes of characteristics in the ECT, or across ecosystem types^[34]. For example:

• An aggregate index for "structural state" of coral reefs (combining coral cover, rugosity, colony size distribution)
• An overall condition index for coral reefs (combining structural, compositional, physical, chemical, functional, and seascape indicators)
• A national marine ecosystem condition index (combining condition indices for coral reefs, seagrass, mangroves, and other marine ecosystem types, weighted by area)

The SEEA EA notes that "where it is undertaken, a clear link should be established to information on movements in individual indicators"^[20:1], ensuring that aggregate indices do not obscure important changes in component variables.

3.5 Worked Example: Coastal Ecosystem Condition Indicators

This section presents a worked example demonstrating how to compile and aggregate biophysical indicators for a hypothetical coastal area. The example uses synthetic data for two marine ecosystem types (mangroves and coral reefs) to illustrate the compilation procedure described in Section 3.2 and the normalisation and aggregation methods presented in Sections 3.3 and 3.4.

Scenario description

The accounting area is a coastal zone containing 450 km² of mangrove forest and 180 km² of coral reef ecosystems. The accounting period is calendar year 2025. Condition monitoring data were collected at 25 field sites (15 mangrove, 10 reef) distributed across the accounting area, with quarterly sampling throughout the year.

Step 1: Source data from condition accounts

Condition variable data were extracted from the ecosystem condition variable accounts compiled following TG-3.1 Asset Accounts. Table 1 presents selected condition variables for mangroves, with values at the opening (January 2025) and closing (December 2025) of the accounting period.

Table 1: Ecosystem condition variables for mangroves, 2025

Table 2 presents condition variables for coral reefs:

Table 2: Ecosystem condition variables for coral reefs, 2025

Step 2: Apply reference conditions and compute indicators

For each variable, indicators were computed using the linear normalisation formula described in Section 3.4.1. The reference value represents the upper reference (high condition), while the lower threshold represents the lower reference (degraded condition).

For variables with normal polarity (higher values = better condition): Indicator = (Observed - Lower) / (Reference - Lower)

For variables with inverse polarity (higher values = worse condition, such as sedimentation rate and water temperature): Indicator = (Reference - Observed) / (Reference - Lower)

Table 3: Mangrove condition indicators, 2025

Table 4: Coral reef condition indicators, 2025

Interpretation: Both ecosystem types show declining condition across most indicators during 2025. Mangrove condition declined moderately, with the largest decreases in sedimentation rate (increased sediment loading) and soil salinity (increased salinity stress). Coral reef condition also declined, with a substantial decrease in the temperature stress indicator reflecting warming water temperatures that approached bleaching thresholds.

Step 3: Aggregate into composite condition indices

Composite condition indices were calculated for each ecosystem type using three aggregation methods to illustrate their differences:

Arithmetic mean: Simple average of all six indicators Geometric mean: nth root of the product of all indicators Weighted mean: Weighted average with structural state (B2) receiving double weight (reflecting its importance for ecosystem services)

Table 5: Composite ecosystem condition indices for mangroves and coral reefs, 2025

Interpretation: All three aggregation methods produce similar results, indicating robust condition decline for both ecosystem types. The geometric mean produces slightly lower values than the arithmetic mean, reflecting its emphasis on low-scoring indicators. The weighted mean that emphasises structural state produces slightly higher values for coral reefs (because structural state is performing relatively better than other characteristics) and similar values for mangroves.

Step 4: Calculate area-weighted national marine condition index

A national-level marine condition index was calculated by weighting the ecosystem-type indices by their relative extent:

National Marine ECI = (Extent_mangroves × ECI_mangroves + Extent_coral × ECI_coral) / Total Marine Extent

Using the arithmetic mean indices and extent values:

• Mangroves: 450 km² × 0.62 = 279
• Coral reefs: 180 km² × 0.51 = 92
• Total: (279 + 92) / (450 + 180) = 371 / 630 = 0.59

The national marine condition index of 0.59 indicates that marine ecosystems in the accounting area are at 59% of reference condition. This single headline number can be reported in state-of-environment reports and ocean health scorecards, while the disaggregated indices by ecosystem type and the underlying indicator values remain available for detailed analysis.

Step 5: Link to extent indicators and policy targets

The worked example can be extended to link condition indicators with extent indicators to assess progress toward policy targets. If the accounting area's marine protected area (MPA) coverage target is 30% by 2030 (following the Kunming-Montreal Global Biodiversity Framework Target 3), and current MPA coverage is 25% (158 km² of the 630 km² total marine area), the question arises: is the protected area in good condition?

Cross-tabulating MPA coverage with ecosystem condition reveals:

Table 6: MPA coverage by ecosystem type and condition status

Interpretation: The accounting area has achieved 25% MPA coverage, approaching the 30% target. Ecosystems within MPAs show better condition than those outside (average ECI 0.65 vs 0.56), suggesting that protection is having a positive effect. However, even protected ecosystems are well below reference condition (0.65 vs 1.0), indicating that MPA designation alone is insufficient to maintain ecosystem health--broader pressures such as climate change and upstream pollution also require management. This analysis demonstrates how aggregate indicators derived from accounts can inform policy discussions about the quality of protection, not just the quantity of area protected.

Analytical insights

This worked example illustrates several key features of aggregate indicator compilation:

1. Transparency: Every indicator value can be traced back through the normalisation formula to the raw condition variable value and reference condition in the underlying accounts.

2. Comparability: The normalisation process enables comparison across diverse characteristics (physical, chemical, biological) and ecosystem types (mangroves, coral reefs) that would be impossible using raw variable values.

3. Aggregation sensitivity: Different aggregation methods produce similar but not identical results, highlighting the importance of documenting and justifying the chosen approach.

4. Policy relevance: The linked analysis of MPA coverage and condition demonstrates how aggregate indicators can directly inform questions about conservation effectiveness and target achievement.

5. Communication efficiency: The single national marine ECI of 0.59 provides an accessible headline for non-technical audiences, while the full indicator set supports detailed technical analysis for researchers and managers.

3.6 Aggregation Approaches

Aggregation across space, time, and ecosystem types is often necessary to produce indicators relevant to policy questions at national or regional scales. This section addresses the methodological considerations for spatial, temporal, and thematic aggregation.

3.6.1 Spatial aggregation

Spatial aggregation combines condition indicators from individual ecosystem assets or grid cells into summary measures for larger areas such as management zones, provinces, or entire countries. Key considerations include:

Area weighting: Larger ecosystem assets should typically contribute more to aggregate indicators than smaller assets. The standard approach weights each unit's condition score by its area relative to the total area of the ecosystem type^[35]:

Aggregate ECI = Sum(Area_i x ECI_i) / Total Area

This area-weighted approach ensures that the aggregate indicator reflects conditions across the full spatial extent of the ecosystem type.

Representativeness: Aggregate indicators are only meaningful if the underlying spatial units adequately represent the full range of ecosystem conditions within the accounting area. Sampling bias--for example, if monitoring sites are concentrated in accessible or well-managed areas--can distort aggregate indicators. The SEEA EA recommends that "hierarchical aggregation schemes should contain a description of how missing indicators or subindices are handled"^[36].

Connectivity: For some purposes, spatially connected ecosystem assets may warrant different aggregation treatment than fragmented assets. Landscape/seascape indicators that capture connectivity and fragmentation can complement simple area-weighted aggregation.

Spatial considerations for marine ecosystems are addressed in more detail in TG-4.1 Remote Sensing Data and TG-4.3 Geospatial Data Integration.

3.6.2 Temporal aggregation

Temporal aggregation produces indicators for periods longer than the basic accounting period or enables comparison across non-contiguous time points. Considerations include:

Time series averaging: Multi-year averages smooth short-term variability, which may obscure important trends but provides more robust indicators when inter-annual variation is high due to natural cycles or measurement noise.

Trend indicators: Change over time may be more policy-relevant than absolute condition levels. Trend indicators express the rate of improvement or decline in condition, often calculated using statistical approaches such as linear regression over the available time series.

Baseline comparison: Indicators expressed relative to a baseline year enable tracking of progress (or regress) from a defined starting point, which is often required for policy reporting.

3.6.3 Aggregation across ecosystem types

For summary indicators at the national or regional level, aggregation across different marine ecosystem types may be desirable. The SEEA EA provides guidance on creating an overall ecosystem condition index where "aggregation can take the form of a condition index applied to each ecosystem type, weighted by the area of the ecosystem type within the EAA, then summed for all ecosystem types in the EAA to derive an overall ecosystem condition index"^[37].

This approach produces a single number summarising ecosystem condition across all marine ecosystem types, which can be compelling for high-level communication. However, such highly aggregated indicators also carry risks:

• Loss of information about which ecosystem types are improving or declining
• Masking of critical thresholds in particular ecosystem types
• Implicit assumptions about substitutability between different ecosystem types
• Sensitivity to the choice of weights and aggregation method

The SEEA EA recommends maintaining clear links between aggregate indices and the underlying disaggregated information, so that users can investigate the composition and drivers of aggregate indicator values^[38].

Combined presentations that integrate biophysical indicators with economic accounts are addressed in TG-3.8 Combined Presentations.

3.7 Marine-Specific Applications

Marine and coastal ecosystems present specific challenges and opportunities for aggregate biophysical indicators. This section addresses applications for priority marine ecosystem types and ocean-specific condition characteristics.

3.7.1 Coral reef health indicators

Coral reefs are among the most biodiverse and economically valuable marine ecosystems, and their condition is sensitive to multiple stressors including warming, acidification, nutrient pollution, and overfishing^[39]. Priority condition variables for coral reef accounts include:

Aggregate reef condition indices combine these variables with appropriate weighting. The Reef Check methodology, for example, produces summary indicators from standardised survey protocols^[40]. The Ocean Health Index includes a "biodiversity" subgoal that incorporates habitat condition for coral reefs and other marine habitats^[41].

Detailed guidance on coral reef accounting, including extent measurement and condition indicators, is provided in TG-6.1 Coral Reef Accounts.

3.7.2 Fish stock indicators

Fish stock condition provides indicators of both individual environmental asset status (treated in TG-3.1 Asset Accounts) and ecosystem compositional state. Stock status indicators include:

Biomass relative to reference points: Current spawning stock biomass (B) relative to the biomass at maximum sustainable yield (B_MSY) or unfished biomass (B_0). Stocks with B > B_MSY are considered within biologically sustainable limits^[42].

Exploitation rate indicators: Fishing mortality (F) relative to the fishing mortality at maximum sustainable yield (F_MSY). Stocks with F < F_MSY are not experiencing overfishing.

Trophic level indicators: Mean trophic level of fish communities, tracking changes in ecosystem structure that may indicate "fishing down the food web"^[43].

SDG indicator 14.4.1 measures the proportion of fish stocks within biologically sustainable levels^[44]. National ocean accounts can directly support reporting on this indicator by tracking the status of assessed fish stocks relative to MSY-based reference points. At global level, FAO estimates that the proportion of stocks fished within biologically sustainable levels declined from 90% in 1974 to 65.8% in 2017^[45].

For detailed guidance on integrating fish stock assessment with ocean accounts, see TG-1.5 OA and Fisheries Management and TG-6.7 Fisheries Accounting: Integrating Stock Assessment.

3.7.3 Water quality indicators

Water quality condition is fundamental to marine ecosystem function and is directly connected to land-based sources of pollution. Priority water quality variables for marine condition accounts include:

Nutrient concentrations: Dissolved nitrogen and phosphorus levels, which at elevated concentrations can cause eutrophication, algal blooms, and hypoxia^[46].

Dissolved oxygen: Oxygen levels in the water column, with low oxygen (hypoxia) indicating degraded condition and threat to marine life.

Chlorophyll concentration: A proxy for primary productivity and phytoplankton abundance, which at excessive levels may indicate eutrophication.

Ocean acidification: Sea water pH and carbonate saturation state, tracking the impacts of atmospheric CO2 absorption on marine chemistry.

Pollutant levels: Concentrations of heavy metals, persistent organic pollutants, plastics, and other contaminants.

The SDG framework includes indicator 14.1.1 (Index of coastal eutrophication and floating plastic debris density), which aggregates water quality and pollution variables into a composite measure^[47]. National ocean accounts can provide the underlying data and support disaggregation of this aggregate indicator.

Links between water quality indicators and flows from the economy to the environment are addressed in TG-3.4 Flows Economy to Environment.

3.7.4 Integrated marine indicators

Several existing indicator frameworks integrate multiple dimensions of marine ecosystem condition:

Essential Ocean Variables (EOVs): Defined by the Global Ocean Observing System (GOOS), EOVs provide a standardised set of variables for ocean observation and monitoring. EOVs span physics (temperature, salinity, currents), biogeochemistry (nutrients, oxygen, pH), and biology/ecosystems (phytoplankton, zooplankton, fish abundance, marine habitat)^[48]. Alignment between ocean accounts and EOV frameworks enhances data interoperability.

Ocean Health Index (OHI): The OHI is a composite index that assesses ocean health across ten goals reflecting the diverse benefits people derive from the ocean^[49]. While broader than condition alone (including economic and social dimensions), the OHI incorporates condition indicators for coral reefs, seagrass, mangroves, and other habitats within its "biodiversity" and "carbon storage" goals.

Marine Biodiversity Observation Network (MBON) indicators: MBON promotes standardised biodiversity observation, providing indicators of marine species diversity and community composition that can inform the compositional state component of ecosystem condition accounts^[50].

Taskforce on Nature-related Financial Disclosures (TNFD): The TNFD framework recommends disclosure of metrics on ecosystem condition relevant to business dependencies and impacts, including marine ecosystems^[51]. Ocean accounts can provide standardised condition indicators suitable for corporate nature-related disclosure. The connection between ocean accounts and TNFD disclosure is also discussed in TG-1.3 OA and Marine Spatial Management.

3.7.5 Climate-related marine indicators

Climate change exerts cross-cutting pressure on marine ecosystems, affecting physical, chemical, and biological condition across all ecosystem types. Climate-related indicators bridge the boundary between pressure indicators (measuring driving forces) and state indicators (measuring ecosystem response), and are increasingly important for ocean accounting.

Ocean warming indicators: Sea surface temperature anomalies, marine heatwave frequency and intensity, and thermal stress accumulation (degree heating weeks) provide condition indicators for the physical state class of the ECT. These indicators are relevant across all marine ecosystem types and directly inform coral bleaching risk assessment and species distribution shifts.

Ocean acidification indicators: Sea water pH decline and aragonite saturation state track the chemical impacts of atmospheric CO2 absorption. These indicators are particularly consequential for calcifying organisms--corals, molluscs, and planktonic foraminifera--and can be incorporated into the chemical state class of condition accounts.

Sea level indicators: Relative sea level change affects coastal ecosystem extent directly, driving landward migration of intertidal ecosystems and inundation of low-lying habitats. Sea level indicators connect to both extent accounts (through ecosystem conversion) and condition accounts (through salinity and inundation stress).

Extreme event indicators: The frequency and severity of marine heatwaves, tropical cyclones, and hypoxic events can be recorded as condition-relevant pressures. Where these events cause measurable changes in ecosystem condition variables, they contribute to explaining temporal patterns in condition indicators.

Compilers are encouraged to include climate-related variables within the relevant ECT classes of their condition accounts, rather than treating climate indicators as a separate category. This approach maintains consistency with the SEEA EA framework while ensuring that climate-driven changes are captured within the standard indicator hierarchy.

4. Acknowledgements

Authors: Mikael JA Maes (GOAP Secretariat)

Reviewers: Jessica Andrews

5. References