Aggregate Biophysical Indicators of Environmental State

Aggregate biophysical indicators support a range of 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 — see 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), whilst condition indicators support assessment of SDG target 14.2 on sustainable management and protection of marine and coastal ecosystems.

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.

• 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. The underlying methodology follows SEEA Ecosystem Accounting (2021), Chapter 5
• TG-0.7 Quality Assurance — for quality management principles that apply to indicator construction, including uncertainty documentation and fitness-for-purpose assessment

Within the ocean accounting system, information products are organised in a hierarchical structure sometimes described as an information pyramid. At the base of this pyramid sit data and statistics: the raw observations from monitoring networks, surveys, and remote sensing platforms. Accounts occupy an intermediate level, organising 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. Key indicators at the apex aggregate further to support strategic analysis.

Indicators compiled from accounts are more comparable across space and time than indicators taken directly from raw data, as the accounting process applies standardised classifications, reference conditions, and valuation rules that harmonise otherwise disparate data streams. TG-2.1 addresses the derivation of aggregate biophysical indicators (the upper layers of the information pyramid), whilst the construction of the accounts is addressed in the TG-3.x series (particularly TG-3.1 Asset Accounts), and data production methods in the TG-4.x series.

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). 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). 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. Table 1 below summarises these classes with marine-specific examples and typical data sources:

Combined extent-condition indicators integrate information on both the area and quality of ecosystems to give 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. For example, an “effective coral reef area” indicator might weight reef extent by a condition index. The resulting single measure captures both spatial coverage and ecological quality.

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

3.1.2 Relationship to accounts

The ecosystem condition variable account records raw observations of condition characteristics for each ecosystem type within the accounting area. 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. The normalisation process transforms diverse measurement units into a common scale that enables 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. Compilers should document the aggregation method (arithmetic mean, geometric mean, weighted average, or another approach) together with its implications for the resulting index.

The methods for remote sensing and spatial data that underpin extent and condition measurement are addressed in TG-4.1 Remote Sensing Data, whilst 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. Table 3.1.3 below summarises these principles.

3.1.4 The normalised 0-1 condition index pattern

The normalisation of a raw condition variable against a reference level to produce a dimensionless score on a 0-1 scale is the standard biophysical-indicator pattern used throughout ocean accounting. Compilers should treat this pattern as the default form for any indicator derived from a condition variable, regardless of the ecosystem type, ECT class, or measurement units of the underlying observation.

The pattern has three conceptual components:

The pattern should be applied consistently across all condition-derived indicators in ocean accounts. Section 3.4.1 sets out the formula and parameters used to implement the pattern, including the treatment of variables with inverse polarity (where higher measured values indicate lower condition). Section 3.4.2 addresses the selection and documentation of reference levels. Section 3.4.3 addresses aggregation of normalised indicators into composite indices, which is only well-defined because all contributing indicators share the 0-1 scale established by this pattern.

Some indicators are not derived through normalisation against a reference level and should not be forced onto the 0-1 scale. These include extent indicators expressed in absolute area units, and stock status indicators expressed relative to fishery reference points (B/B_MSY, F/F_MSY). The pattern applies specifically to indicators derived from ecosystem condition variables as defined in the SEEA EA Ecosystem Condition Typology.

3.2 Compilation Procedure for Aggregate Indicators

This section outlines the step-by-step procedure for compiling aggregate biophysical indicators from ocean accounts.

All computed indicator values should be rounded to two decimal places using standard rounding (0.5 rounds up) and this convention applied consistently across all worked tables and published outputs.

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 those summarised in Table 3.2.1 below.

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

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. These links enable 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. For each condition variable, the compiler must:

1. Select the reference condition from the SEEA EA framework in Section 3.4.2 — for a natural ecosystem, an undisturbed or minimally-disturbed, historical, least-disturbed or contemporary condition — and record it separately from the method used to estimate it
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) following the polarity decision protocol described in Section 3.4.1

The strengths and limitations of each reference type for ocean accounting are summarised in Section 3.4.2. Climate-related considerations for reference condition selection are discussed in Section 3.4.2.

Step 4: Compute indicators

Normalise each variable using the min—max rescaling formula described in Section 3.4.1. 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)

Choose an aggregation method following the criteria in Section 3.4.3. The SEEA EA describes aggregation as optional but notes that composite indices are often required for policy communication. 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 full metadata and disseminate them to intended users. Table 3.2.2 below summarises the metadata elements that should accompany every published indicator.

Indicator versioning: All published indicators must carry a version number and release date. When a revision is made, the nature of the revision must be classified as:

• Type 1 — data correction (source data corrected after publication)
• Type 2 — reference condition update (reference values revised following new scientific assessment)
• Type 3 — methodology change (normalisation formula, aggregation method, or classification scheme revised)

Revisions that affect trend direction or cross a policy threshold (for example, a change that moves MPA condition from above to below a management target) require a revision note in the metadata and back-casting of the affected time series. This revision classification approach follows standard national statistics revision policy and ensures that changes to published figures can be traced and explained.

Extent-based indicators measure the spatial dimension of marine ecosystems: where they are, how much area they cover, and how coverage is changing over time.

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). 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. 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 Coastal Wetland Accounting, TG-6.3 Seagrass Accounts, and TG-6.5 Pelagic and Open Ocean Accounts.

Beyond net extent change, conversion indicators track the transformation of one ecosystem type into another. 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)

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”. For marine ecosystems, determining when a change in condition constitutes conversion to a different ecosystem type (rather than degradation within the same type) requires careful application of this standard.

3.3.3 Extent indicators for policy targets

Extent indicators align directly with international policy targets. SDG target 14.5 set a 2020 deadline for conserving at least 10% of coastal and marine areas, measured by indicator 14.5.1 (coverage of protected areas in relation to marine areas). The Kunming-Montreal Global Biodiversity Framework Target 3 has since set a more ambitious target of protecting at least 30% of marine areas by 2030. 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, through an approach that integrates extent and condition information. This methodology could be extended to marine ecosystems as “ecosystem degradation neutrality” indicators — the degraded-area classification for GBF Target 2 in TG-2.10 Section 3.3 is the nearest existing guidance for marine contexts.

3.4.1 Deriving condition indicators from variables

Reference condition parameters: The following definitions apply throughout this section and in the normalisation formula:

• Reference condition (general concept) — the benchmark state of the ecosystem used to contextualise current condition. For a natural ecosystem SEEA EA offers an undisturbed or minimally-disturbed, historical, least-disturbed or contemporary condition; anthropogenic ecosystems draw on a separate set ending in best-attainable condition (see Section 3.4.2, which is normative).
• U (upper reference value) — the formula parameter representing good condition. This is the value of the condition variable associated with the chosen reference condition.
• L (lower reference value) — the formula parameter representing degraded condition. This is the minimum acceptable or fully degraded value of the condition variable.

The transformation from condition variables to condition indicators involves normalisation against these reference levels. The SEEA EA describes a linear transformation approach:

Indicator = (Variable value — L) / (U — L)

Where U is the upper reference value (high condition) and L is the lower reference value (degraded condition). The resulting indicator takes values between 0 (fully degraded) and 1 (reference condition). Any reference type (natural, historical, policy target, or best observed) may serve as U, provided the compiler documents the choice in Step 7 metadata.

For some variables, high measured values indicate high condition (e.g., species richness, coral cover), whilst 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. The inverse-polarity formula is:

Indicator = (L — Variable value) / (L — U)

Polarity decision protocol: Compilers must assign a polarity (Normal or Inverse) to each condition variable before computing indicators and record this assignment in Step 7 metadata. The following decision sequence should be applied:

1. If the variable has a clear monotonic relationship with ecosystem health in the target ecosystem type (e.g., coral cover: more = better), assign Normal or Inverse polarity accordingly.
2. If the variable has context-dependent polarity, consult the reference table below for the recommended default and note any override with justification.
3. If polarity cannot be determined from published ecological evidence, flag the variable as requiring expert review before publication.

Example (normal polarity): For a coral reef ecosystem, coral cover percentage might be transformed as follows:

• U (upper reference, high condition): 50% coral cover
• L (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:

• U (upper reference, high condition): 0.1 mg/L (low nutrient level characteristic of unpolluted waters)
• L (lower reference, degraded condition): 1.5 mg/L (eutrophic level)
• Observed value: 0.6 mg/L
• Indicator = (L — Observed) / (L — U) = (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.

Variables measured on a logarithmic scale

The linear transformation above assumes that equal changes in the variable represent equal changes in condition. That assumption fails for variables measured on a logarithmic scale, of which pH is the case most often encountered in ocean accounts. A shift from pH 8.2 to pH 8.0 is a rise of roughly 58 per cent in hydrogen ion concentration, yet the linear transformation registers it as a small deviation and understates the ecological significance of acidification.

For pH, compilers should therefore apply a categorical step deduction in place of the linear formula:

Indicator = 1.00 — (0.10 x n), where D is the deviation in pH units below the reference value and n is the number of complete 0.05-unit steps in D — that is, the largest integer satisfying n x 0.05 <= D.

The indicator is clamped at 0 and capped at 1.00 where observed pH equals or exceeds the reference. The resulting bands are:

A note for implementers. Determine n by comparison in whole hundredths of a pH unit, not by floating-point division. In IEEE-754 double precision 0.15 / 0.05 evaluates to 2.9999999999999996, so a literal floor(D / 0.05) returns 2 and yields 0.80 where the band table gives 0.70 — and D = 0.15 is a value that occurs in the worked example in TG-1.3 Section 3.6.

Example: reference pH 8.2, observed pH 8.03. D = 0.17, which contains three complete 0.05-unit steps (0.15 <= 0.17 < 0.20), so n = 3 and the indicator = 1.00 — 0.30 = 0.70.

Compilers should document the reference pH value, its source (historical baseline, pristine-site comparison, or ecological modelling), and the step size applied. Where a national programme has evidence supporting a different step size, it may be substituted provided the choice is documented and applied consistently across periods.

This treatment is applied to the pH rows of the marine protected area condition accounts in TG-1.3 OA and Marine Spatial Management Section 3.6.

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. SEEA EA Appendix A5.2 separates two decisions that this Circular previously ran together, and they are chosen independently of one another:

• which state is taken as the benchmark — the reference condition; and
• how that state is quantified — the estimation method.

The natural state is the recommended default. SEEA EA takes the reference condition to be the state at which ecological integrity is maximal, and states that using the natural state “is preferred and recommended” (para 5.70). The benchmark is deliberately independent of management objectives: the natural state “may not be related to supply of ecosystem services and may not be the target of current legislation, policy or ecosystem management objectives” (para 5.69). Measuring distance from the natural state is what allows the degree of ecosystem change to be seen at all.

Table 3.4.2: Assessment framework for selecting a reference condition, reproduced from SEEA EA Table A5.2.1

Two consequences follow for marine accounts. First, coral reef, seagrass, mangrove, kelp forest, pelagic and deep-sea systems are natural ecosystems under SEEA EA, so the four natural-ecosystem conditions are the ones available to them; best-attainable condition is not, being confined to anthropogenic systems such as artificial waterbodies and other anthropogenic marine systems. Second, each of the four is a state of the ecosystem, not a management objective.

Table 3.4.3: Methods for estimating a reference condition, reproduced from SEEA EA Table A5.2.2

A mark indicates that the method can be used to estimate that reference condition.

Method 8 is a combination of the above and is not a separate column. SEEA EA directs that methods 1—4 are considered first, as the approaches that best describe and quantify the reference condition and the upper and lower reference levels. Methods 5—7 are alternatives where methods 1—4 cannot be applied, or where policy or legislative drivers require method 5 or 6. Method 7 is noted as particularly relevant to capturing Indigenous knowledge and perspectives; where it is used, the consent, attribution and data-governance requirements in TG-3.6 Traditional Knowledge apply.

The SEEA EA emphasises that reference conditions should be “clearly defined, scientifically based, and consistent with the purpose of the assessment”. 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.

For marine ecosystems subject to climate change, reference conditions may need to incorporate considerations of climate adaptation and novel ecosystem states, and a reference condition that is periodically updated against monitored reference sites is one way SEEA EA contemplates handling this.

Climate change introduces a 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.

Climate adjustment decision box: When a compiler chooses to use an adjusted reference condition to account for climate change, the following specifications apply:

• Recommended scenario: SSP2-4.5 (intermediate emissions pathway) is the default. Where a country has adopted a specific national NDC scenario for official environmental reporting, that scenario may be used as an alternative with documented justification.
• Adjustment time horizon: The baseline-period median projected change to the accounting year, derived from the selected scenario.
• Acceptable data sources: CMIP6 multi-model ensembles are the primary source, whilst downscaled regional climate model outputs are acceptable where available and peer-reviewed.
• Step 7 metadata record (mandatory): The scenario identifier, data source citation, and the numerical adjustment value applied to each reference condition must be recorded and published with the indicator.

Where adjusted reference conditions are used, these specifications must be followed to enable comparison across assessments and time periods.

Ecosystem accounts connect extent, condition, services in physical and monetary terms, and monetary asset accounts. Those connections 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 shows the connections between ecosystem accounts (adapted from 2025 SNA Figure 35.3).

Ecosystem condition indices aggregate individual condition indicators into composite measures that summarise overall ecosystem health. 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: improvement in one characteristic can compensate for decline in another.

Geometric mean: Gives more emphasis to low values, such that a 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. For a set of n indicators with weights w_i summing to 1, the weighted mean ECI is:

ECI = Sum of (w_i x Indicator_i) for i = 1 to n

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. 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”. That link ensures 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. All indicator values are rounded to two decimal places using standard rounding.

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 with U (upper reference) and L (lower reference) as defined in Table 1 and Table 2. The exception is pH, which takes the categorical step deduction in Section 3.4.1 against the reference pH of 8.20: D = 0.15 at opening and 0.17 at closing, so n = 3 in both periods and the indicator is 0.70 in both.

Interpretation: Both ecosystem types record 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 the largest decrease in the temperature stress indicator, where warming water temperatures 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), giving weight vector w = [1/7, 1/7, 1/7, 2/7, 1/7, 1/7] for the six indicators in the order A1, A2, B1, B2, B3, C.

The weighted mean ECI for each ecosystem type and period is computed as:

ECI = (w_A1 x I_A1) + (w_A2 x I_A2) + (w_B1 x I_B1) + (w_B2 x I_B2) + (w_B3 x I_B3) + (w_C x I_C)

Interpretation: All three aggregation methods produce similar results. Each indicates a condition decline for both ecosystem types. The geometric mean produces slightly lower values than the arithmetic mean, as it places more emphasis on low-scoring indicators. The weighted mean that emphasises structural state produces values equal to or slightly higher than the arithmetic mean and is consistent with the stated weight vector.

Step 4: Calculate realm-specific and area-weighted condition indices

Under the IUCN Global Ecosystem Typology v2.1, mangroves belong to the Marine Freshwater-Terrestrial (MFT) realm and coral reefs belong to the Marine realm. Because the SEEA EA (para 5.87) cautions against aggregation across ecosystem types from different realms, compilers should present condition indices by realm as their primary outputs. Cross-realm aggregation requires explicit documentation of purpose and limitations.

Separate realm indices (primary output):

• MFT realm (mangroves only): Opening ECI = 0.68, Closing ECI = 0.62
• Marine realm (coral reefs only): Opening ECI = 0.62, Closing ECI = 0.55

Cross-realm combined index (illustrative; for use only when explicitly documented):

When a single national marine summary is required for state-of-environment reporting, an area-weighted aggregate may be computed alongside the separate realm indices:

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

Using the arithmetic mean indices and extent values:

• Mangroves: 450 km² x 0.62 = 279
• Coral reefs: 180 km² x 0.55 = 99
• Total: (279 + 99) / (450 + 180) = 378 / 630 = 0.60

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

Note on derivation of Table 6 condition values: The average condition within and outside MPAs is computed by spatially disaggregating the condition variable accounts to MPA polygon boundaries and computing separate area-weighted condition indices for each spatial stratum. The “Avg Condition in MPAs” values therefore require that condition data are available at a spatial resolution finer than the MPA boundary. For coral reefs, the area-weighted check is: (43 x 0.60 + 137 x 0.53) / 180 = (25.80 + 72.61) / 180 = 98.41 / 180 = 0.55, consistent with the whole-ecosystem closing ECI.

Interpretation: The accounting area has achieved 25% MPA coverage, approaching the 30% target. Ecosystems within MPAs exhibit better condition than those outside (average ECI 0.66 vs 0.58). This gap points to a positive effect of protection. Even protected ecosystems, however, remain well below reference condition (0.66 vs 1.0): MPA designation alone is insufficient to maintain ecosystem health, and 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, and not area alone.

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

1. Transparency and comparability: Normalised indicators improve both transparency and cross-ecosystem comparability by exposing the underlying reference conditions and aggregation choices, enabling traceability from headline index to source condition variable.

2. Aggregation sensitivity: Different aggregation methods produce similar but not identical results. That divergence points to the importance of documenting and justifying the chosen approach.

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

4. Communication efficiency: The separate realm indices and the illustrative combined national ECI of 0.60 together serve different audiences: technical analysts can investigate realm-specific trends whilst non-technical audiences receive an accessible headline figure.

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 (area-weighting formula: Section 3.5 Step 4). This area-weighted approach ensures that the aggregate indicator reflects conditions across the full spatial extent of the ecosystem type.

Default aggregation method: Where data or policy requirements do not indicate otherwise, the arithmetic mean of the component indicators is the default method for combining condition indicators within an ecosystem type. Section 3.4.3 and the worked example in Section 3.5 Step 3 set out the geometric and weighted alternatives and their implications for interpretation. Compilers should state which method has been applied wherever a composite index is published.

Sub-account aggregation within a single accounting area: Where an accounting area contains more than one habitat type — for example a marine protected area holding coral reef, seagrass and pelagic sub-accounts — the area-level index is the area-weighted mean of the sub-type indices:

Area ECI = Sum over sub-types of (Sub-type ECI x Sub-type area) / Total area of the sub-types included

Sub-type indices should be published alongside the area-level aggregate, not only as inputs to it. An aggregate can conceal divergent trends between habitat types that a manager needs to act on separately, and the sub-type series is the one that supports habitat-specific intervention. Where a habitat present in the accounting area is excluded from the aggregate for want of data, the excluded area and the reason should be stated with the index.

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 can distort aggregate indicators, for example if monitoring sites are concentrated in accessible or well-managed areas. The SEEA EA recommends that “hierarchical aggregation schemes should contain a description of how missing indicators or subindices are handled”.

Missing data protocol: Compilers will routinely encounter spatial units with no condition monitoring data (for example, remote offshore reefs or areas with monitoring gaps). Where condition data are unavailable for some spatial units, the following protocol applies:

Scenario analysis in planning and appraisal circulars requires a projected condition index, not only an observed one. Accounts do not record the immediate effect of a management action: the efficacy of a decision becomes visible over successive accounting periods as the condition variables are re-measured, and for many marine ecosystems a change in the composite index takes three to five years or more to emerge. Projection is therefore a modelling step layered on the accounts, and its outputs must be labelled as projections rather than account entries.

The procedure is:

Variables that respond on different timescales should not be projected on a common rate. Structural complexity and apex predator recovery lag compositional measures substantially, and a composite projected from a single recovery rate will overstate near-term improvement.

This procedure supports the scenario comparison in TG-1.2 OA and Marine Spatial Planning Section 3.5 and the sustainability scenarios in TG-1.4 OA and Sustainable Ocean Planning Section 3.6.

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 reliable 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.

3.6.4 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”.

Combining condition indices for ecosystem types that belong to different IUCN GET v2.1 realms is discouraged unless compilers explicitly document the purpose and limitations. The primary published outputs should be separate condition indices by realm or ecosystem type. Where a cross-realm combined index is presented, compilers must state the realm classification of each contributing ecosystem type, explain why a combined figure is required, and note the limitation that the aggregate may conceal divergent trends across realms. This requirement reflects SEEA EA para 5.87, which cautions against aggregation across fundamentally different ecosystem types.

Within a single realm, aggregation across ecosystem types follows the area-weighted approach described in Section 3.5 Step 4, with the same requirements for documentation, quality flags, and linkage to disaggregated component indices.

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

3.7.1 Coral reef health indicators

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. The Ocean Health Index includes a “biodiversity” subgoal that incorporates habitat condition for coral reefs and other marine habitats.

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. The SEEA EA treats fish stocks primarily as non-cultivated biological resources recorded in asset accounts. Compilers should apply the following decision boundary when assigning fish-related variables to account categories:

Decision boundary — fish stock and fish community indicators:

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.

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”.

SDG indicator 14.4.1 measures the proportion of fish stocks within biologically sustainable levels. 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.

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. Table 3.7.3 below summarises priority water quality variables for marine condition accounts.

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. 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 spanning physics (temperature, salinity, currents), biogeochemistry (nutrients, oxygen, pH), and biology/ecosystems (phytoplankton, zooplankton, fish abundance, marine habitat). Alignment between ocean accounts and EOV frameworks enhances data interoperability.

Ocean Health Index (OHI): The OHI assesses ocean health across ten goals reflecting the diverse benefits people derive from the ocean. 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. Its indicators of marine species diversity and community composition can inform the compositional state component of ecosystem condition accounts.

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. Ocean accounts can provide standardised condition indicators suitable for corporate nature-related disclosure — see TG-2.2 Macro-economic Dependencies and TG-1.7 OA and Multilateral Development Finance.

3.7.5 Climate-related marine indicators

Climate-related indicators bridge the boundary between pressure indicators (measuring driving forces) and state indicators (measuring ecosystem response). 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.

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).