OA and Marine Spatial Management (including MPAs)

1. Outcome

This Circular provides operational guidance on using Ocean Accounts to support marine spatial management decisions, including Marine Protected Area (MPA) designation and effectiveness assessment, adaptive management approaches, and integration of conservation finance.

Readers will learn how to apply ecosystem accounting frameworks to three decision contexts:

• MPA designation: identifying priority areas for protection using extent and condition baselines
• Effectiveness monitoring: tracking conservation outcomes through time-series condition accounts and comparing protected versus unprotected areas
• Economic impact assessment: quantifying how spatial restrictions affect ecosystem service flows and economic dependencies

The Circular demonstrates the downward connections from policy questions through accounting structures to specific indicators, enabling practitioners to move from high-level conservation targets (Kunming-Montreal Global Biodiversity Framework 30x30, SDG 14.5) to measurable account entries and back to evidence-based management adjustments.

The guidance builds on the foundational concepts presented in TG-0.1 General Introduction to Ocean Accounts and supports the indicator derivation framework described in TG-2.1 Aggregate Biophysical Indicators of Environmental State, which addresses how aggregate condition indices derived from the accounting structures described here can inform protected area management and monitoring. For the broader spatial planning context within which MPAs are embedded, see TG-1.2 OA and Marine Spatial Planning.

2. Requirements

• TG-0.1 General Introduction to Ocean Accounts—provides foundational understanding of Ocean Accounts components, including the distinction between ecosystem assets, environmental assets, and ecosystem services.

3. Guidance Material

3.1 Marine spatial management and MPAs

Marine spatial management encompasses the governance mechanisms through which human activities in marine areas are planned, regulated, and monitored. This includes Marine Spatial Planning (MSP), which organizes the spatial allocation of ocean activities, and the designation and management of Marine Protected Areas (MPAs), which restrict or regulate activities in specific geographic areas to achieve conservation objectives^[1]. For UNCLOS maritime zone definitions (territorial sea, EEZ, continental shelf, ABNJ), see TG-0.6 Glossary (Exclusive Economic Zone entry). UNCLOS requires States to take measures to "protect and preserve the marine environment" (Part XII, Article 192) while recognizing sovereign rights over resources within Exclusive Economic Zones (EEZs)^[2]. Achieving conservation targets set by international agreements—including the Kunming-Montreal Global Biodiversity Framework target to protect 30% of marine areas by 2030—requires robust information systems to guide MPA designation, monitor outcomes, and support adaptive management^[3].

The relevance of ocean accounts for marine conservation is recognized in CBD Decision 15/24 (2022), which encourages regional efforts toward ocean accounting and economic valuation of ecosystem services for marine and coastal biodiversity, and in related COP16 decisions on marine biodiversity^[4].

Accounting boundary note: This Circular applies only to national marine waters (territorial sea and EEZ). Some MPAs straddle EEZ boundaries or are established under high-seas treaty frameworks—including the recently adopted BBNJ Agreement (2023)—and these scenarios introduce attribution and accounting boundary questions that are not yet standardised in SEEA EA or international guidance. Where an MPA straddles the EEZ boundary, compilers should restrict the national Ecosystem Accounting Area (EAA) to the EEZ portion and note the transboundary extent and any jointly managed area in account metadata. Guidance on fully transboundary and Areas Beyond National Jurisdiction (ABNJ) accounting is the subject of potential future GOAP Technical Guidance work. Cross-reference TG-1.2 OA and Marine Spatial Planning for transboundary MSP considerations.

3.2 Decision use cases for Ocean Accounts

3.2.1 Use Case 1: MPA designation and target tracking

Decision context: Countries must identify priority areas for protection to meet international commitments (Kunming-Montreal GBF Target 3: 30% of marine areas by 2030; SDG 14.5: at least 10% coverage) while ensuring ecological representativeness and connectivity. Designation decisions require baseline information on ecosystem distribution, condition, and irreplaceability.

Account types required:

• Ecosystem extent accounts (SEEA EA Chapter 4)—record the spatial distribution and area of each ecosystem type within the Ecosystem Accounting Area (EAA), providing the foundation for calculating protected area coverage and assessing representativeness. For marine applications, extent accounts distinguish coastal and marine ecosystem types following the IUCN Global Ecosystem Typology, including seagrass meadows (M1.1), kelp forests (M1.2), photic coral reefs (M1.3), shellfish beds (M1.4), subtidal rocky reefs (M1.6), mangroves (MFT1.2), and pelagic water column ecosystems^[5].
• Ecosystem condition accounts (SEEA EA Chapter 5)—document baseline ecosystem health at the time of designation using the Ecosystem Condition Typology (ECT), enabling identification of high-integrity areas warranting protection and degraded areas requiring restoration-focused management.
• Governance overlay on extent accounts (drawing on SEEA EA Section 13.5 on ocean accounts and Appendix A13.3)—record existing MPA designations aligned with the Kunming-Montreal GBF classification (IUCN categories Ia-VI and Other Effective Area-Based Conservation Measures, OECMs) and spatial overlap with ecosystem types, enabling assessment of GBF Target 3 coverage across ecosystem types. SDG 14.5.1 coverage totals can be derived from the same account by applying the relevant national definition; see TG-2.10 Multilateral Environmental Agreement Indicators for indicator derivation rules.

Key indicators derived:

• MPA coverage as percentage of total marine area by ecosystem type (GBF Target 3 primary; SDG 14.5.1 complementary)
• Representation index showing which ecosystem types are under- or over-represented in protected areas
• Baseline condition index for newly designated MPAs

Operational procedure: Compilers first establish the EAA corresponding to national marine waters (territorial sea and EEZ). Ecosystem extent accounts are compiled using the IUCN GET marine ecosystem classification, with Basic Spatial Units (BSUs) at a resolution that matches the resolution of the underlying input data. Coastal and nearshore monitoring programs typically support BSUs of 100 m--1 km; offshore areas often have less data availability and will therefore use coarser BSUs. The 100 m--1 km range is indicative, not prescriptive—compilers should document the resolution used and the primary data source that determined it. Each BSU is attributed to an ecosystem type and protection status (IUCN categories Ia-VI, OECM, or unprotected). The extent account aggregates BSU areas by ecosystem type and protection status, producing a cross-tabulation that reveals coverage gaps. Condition accounts are compiled for a subset of high-priority ecosystem types (typically coral reefs, seagrass, mangroves) using available monitoring data. Reference conditions should be established using historical baselines, comparison with protected reference sites, or ecological modelling. The resulting extent and condition data inform spatial prioritization, with high-condition unprotected areas and low-representation ecosystem types identified as designation priorities.

3.2.2 Use Case 2: MPA effectiveness monitoring

Decision context: MPA managers and conservation agencies must assess whether protection is achieving intended outcomes—maintaining or improving ecosystem condition—and whether management interventions (zoning, enforcement, restoration) require adjustment. Effectiveness assessment requires comparison of outcomes against expectations or counterfactuals.

Account types required:

• Ecosystem condition accounts (time-series)—track changes in condition variables within MPAs across accounting periods, distinguishing abiotic characteristics (physical and chemical state), biotic characteristics (compositional, structural, and functional state), and seascape characteristics (connectivity, fragmentation). The time-series enables detection of trends and attribution of changes to management interventions versus external drivers^[6].
• Ecosystem asset accounts (SEEA EA Chapter 10)—record ecosystem degradation (decline in condition) and ecosystem enhancement (improvement in condition) in monetary terms, enabling integration with economic accounts and assessment of whether protection is maintaining the natural capital base.
• Ecosystem services accounts (SEEA EA Chapters 6-7)—measure changes in ecosystem service flows (fish provisioning, coastal protection, carbon sequestration, recreation) associated with changes in extent and condition, linking conservation outcomes to human wellbeing.

Key indicators derived:

• Ecosystem Condition Index (ECI) trends for protected versus unprotected areas
• Area under effective protection (combining extent with condition criteria)
• Change in ecosystem service capacity attributable to protection

Operational procedure: Effectiveness monitoring requires establishing a baseline condition account at or near the time of MPA designation, followed by periodic re-compilation (annually for intensively monitored sites, every 3--5 years for standard monitoring). Condition variables are selected from the ECT based on data availability and policy relevance, with priority given to variables responsive to management interventions (e.g., coral cover, fish biomass, water quality for marine reserves). Each condition variable is normalized against its reference value to produce a condition indicator on a 0--1 scale, with indicators aggregated into an overall Ecosystem Condition Index using area-weighted averaging. Comparing ECI trends inside versus outside MPAs provides evidence of protection effectiveness. Where condition is improving, this is recorded as ecosystem enhancement in the asset account; where declining, as degradation. For robust effectiveness assessment, compilers should establish comparable condition accounts for unprotected control sites matched to MPA characteristics, enabling counterfactual comparison.

3.2.3 Use Case 3: Economic impact assessment of spatial restrictions

Decision context: Policymakers must assess the economic implications of MPA restrictions on fishing, tourism, and other marine uses, balancing conservation benefits against livelihood impacts and economic opportunity costs. This requires integrated environmental-economic analysis.

Account types required:

• Ecosystem services supply and use accounts (SEEA EA Chapters 6-7)—record flows of provisioning services (wild fish, genetic resources), regulating services (coastal protection, climate regulation), and cultural services (recreation, education) from marine ecosystems to economic beneficiaries, enabling quantification of service flows before and after spatial restrictions.
• Monetary ecosystem services accounts (SEEA EA Chapter 9)—value ecosystem services in monetary units consistent with SNA conventions, enabling comparison of ecosystem service values with GDP contributions from extractive industries.
• Extended supply-use tables (SEEA EA Chapter 11)—integrate ecosystem services with conventional economic production, revealing dependencies of ocean-based industries on ecosystem service inputs and distributional effects of spatial restrictions across sectors.

Key indicators derived:

• Ecosystem service value per hectare by protection status
• Economic dependency ratio (ocean industry GDP / ecosystem service value)
• Distributional incidence of MPA restrictions across sectors and communities

Operational procedure: Economic impact assessment begins with ecosystem services supply accounts that quantify service flows in physical units (tonnes of fish provisioned, volume of coastal protection service, recreation visitor-days). Supply is attributed to specific ecosystem assets (MPAs, adjacent fishing grounds) using spatial models that link ecosystem characteristics to service generation. Use accounts record which economic units (fishing enterprises, tourism operators, coastal households) receive these services. Monetary valuation applies methods consistent with SEEA EA Chapter 9: exchange values for provisioned fish, avoided damage cost for coastal protection, travel cost or contingent valuation for recreation. Comparing service values before and after MPA designation reveals net economic effects. Where MPA restrictions reduce extractive service flows (reduced fishing area) but enhance long-term service capacity (stock recovery, habitat restoration), the extended supply-use framework enables integrated assessment of short-term costs versus long-term benefits.

Where primary valuation data (travel cost surveys, contingent valuation studies) are not available, compilers should apply the following tiered framework:

• Tier 1—Primary valuation study available: Apply stated methods directly (exchange values, avoided damage cost, travel cost, contingent valuation).
• Tier 2—No primary study; comparable study available in region: Apply benefit transfer from a comparable regional study. Compilers must document transfer errors and provide an explicit comparability assessment (ecosystem type, service type, socio-economic context, and study quality) following SEEA EA Chapter 9 guidance.
• Tier 3—No comparable study available: Report physical supply-use accounts only. Flag monetary values as not available and note this limitation in the statistical disclosure metadata.

For detailed guidance on ecosystem services valuation, see TG-2.4 Environmental (including Ecosystem) Goods and Services.

3.3 Ocean accounts for monitoring ecosystem health

The accounting approach emphasizes completeness (covering all relevant ecosystem types within the MPA boundary), consistency (applying the same classifications and methods across accounting periods), and coherence (ensuring data from different sources can be integrated)^[7].

3.3.1 Establishing baseline accounts

When an MPA is designated, compiling baseline accounts establishes the starting point against which future changes can be measured. Baseline ecosystem extent accounts document the spatial distribution of ecosystem types at the time of protection, providing the foundation for monitoring changes in habitat coverage—for example, detecting expansion or contraction of seagrass meadows or coral reef area.

Condition variables are selected and normalised following the procedure in TG-2.1 Biophysical Indicators for Ocean Accounts Sections 3.2--3.4. For marine ecosystems, relevant condition variables may include those summarised in Table 1.3.1 below. In the Ocean Accounts Framework (TG-0.1 General Introduction to Ocean Accounts), the economic pressures that drive changes in the chemical state variables—such as nutrient loading and pollution discharge—correspond to Edge E1 (pollution/residuals from economy to environment).

Table 1.3.1: Marine ecosystem condition variables relevant to MPA baselines

SEEA EA recommends measuring condition relative to a reference condition that reflects, where possible, the natural or undegraded state of the ecosystem (SEEA EA para 5.69)^[8]. For marine ecosystems, establishing appropriate reference conditions may require drawing on historical data, scientific literature, expert knowledge, or comparison with similar unimpacted areas.

Table 1 illustrates a simplified ecosystem condition account structure for an MPA.

Table 1: Illustrative ecosystem condition account for a marine protected area

3.3.1.1 pH condition indicator: categorical approach

pH is a logarithmic scale. A linear ratio (Observed / Reference) understates the ecological significance of acidification: a shift from pH 8.2 to pH 8.0 represents a ~58% increase in hydrogen ion concentration, yet the linear ratio registers only a 1% deviation. To avoid systematically misleading indicators, this Circular adopts a categorical step-deduction approach for the pH condition indicator:

• pH within ±0.05 units of reference = 1.00
• Each 0.05-unit step below reference = deduct 0.10 from the indicator (i.e., 0.05--0.10 below reference = 0.90; 0.10--0.15 below = 0.80; and so on)
• Indicator is clamped at 0 (cannot be negative)
• If observed pH exceeds the reference value, the indicator is capped at 1.00

This approach is consistent with the SEEA EA Ecosystem Condition Typology guidance on abiotic chemical state variables (SEEA EA Table 5.1) and reflects the ecologically meaningful threshold structure of ocean acidification. Compilers should document the reference pH value and the step-deduction table used, including its source (historical baselines, pristine-site comparisons, or ecological modelling).

Example (Table 1, Year 5): Reference = 8.2; Observed = 8.03; deviation = 0.17 units below reference. Applying the step rule: 0.05--0.10 deduct 0.10 (→ 0.90), 0.10--0.15 deduct additional 0.10 (→ 0.80), 0.15--0.17 deduct additional 0.10 (→ 0.70). Indicator = 0.70.

3.3.2 Condition indices and aggregation

Individual condition variables can be aggregated into composite condition indices to provide summary assessments of ecosystem health. An ECI value of 1 indicates reference condition, while values below 1 indicate degradation^[9].

The default aggregation method is arithmetic mean unless data or policy requirements specify otherwise. This default is applied in the worked example in Section 3.6. Further options—geometric mean, weighted mean, and their implications for interpretation—are provided in TG-2.1 Aggregate Biophysical Indicators of Environmental State.

For MPA management, condition indices can be constructed at different scales: individual ecosystem assets (e.g., a specific reef), ecosystem types within the MPA (e.g., all coral reef area), or the entire protected area. Tracking changes in these indices over time provides a quantitative basis for assessing whether management interventions are achieving conservation objectives.

Figure 1.3.1 illustrates how condition indices can be tracked over time to assess MPA effectiveness. The illustrative time series shows an ecosystem condition trajectory for a hypothetical MPA, demonstrating how observed condition can be compared against a target threshold to evaluate whether management interventions are achieving their intended outcomes.

Figure 1.3.1: Illustrative MPA ecosystem condition trajectory showing observed condition index (blue line) with 95% confidence interval (shaded area) relative to target condition threshold (green dashed line)^[10]

3.4 Measuring MPA effectiveness

3.4.1 Accounting for ecosystem degradation and enhancement

SEEA EA provides explicit accounting treatments for changes in ecosystem condition. Ecosystem enhancement is defined as "the increase in the value of an ecosystem asset over an accounting period that is associated with an improvement in the condition of the asset during that accounting period" (SEEA EA para 10.15), while ecosystem degradation is defined as "the decrease in the value of an ecosystem asset over an accounting period that is associated with a decline in the condition of the ecosystem asset during that accounting period" (SEEA EA para 10.21)^[11]. These accounting entries enable tracking of whether ecosystems within MPAs are improving (net enhancement), stable (no net change), or declining despite protection (net degradation).

The accounting framework distinguishes between changes due to human activity (recorded as degradation or enhancement) and changes due to natural processes (recorded as other changes in volume). This distinction is analytically important for MPA assessment—for example, distinguishing coral decline due to reduced local pressures (management success) from decline due to thermal stress associated with climate change (external driver). Attribution of observed condition changes to drivers should draw on expert assessment, scientific literature, or statistical attribution studies.

Where the source or causality of a condition change cannot be reliably attributed to either anthropogenic or natural drivers, compilers should not attempt to disaggregate the change between "degradation" and "other volume changes." Instead, record the total condition change without attribution and document the limitation in account metadata. This mirrors the treatment in extent accounts, where planned and unplanned additions and reductions are recorded separately only when reliably known (SEEA EA para 10.21; see also SEEA EA guidance on extent-account planned/unplanned changes). Cross-reference TG-2.8 Climate Change Indicators for attribution approaches when climate drivers are a principal confound.

Where decline is observed in both protected and unprotected areas and scientific evidence links the decline to climate-driven thermal stress, the change may be attributed to natural processes (other volume changes) rather than management failure. Where decline is observed only in poorly enforced MPAs but not in well-enforced sites experiencing similar environmental conditions, the differential may be attributed to management effectiveness and recorded as anthropogenic degradation.

3.4.2 Ecosystem services as effectiveness indicators

Changes in ecosystem services flow provide another dimension for assessing MPA effectiveness. Table 3.4.2 below summarises four ecosystem service effectiveness indicators that accounts can track to assess whether protection is maintaining or enhancing the capacity of marine ecosystems to deliver services.

Table 3.4.2: Ecosystem service effectiveness indicators for MPA assessment

For detailed guidance on compiling ecosystem services accounts for marine ecosystems, see TG-2.4 Environmental (including Ecosystem) Goods and Services. The combined presentation of ecosystem and economic accounts is addressed in TG-3.8 Combined Presentations.

3.4.3 Counterfactual analysis

Robust effectiveness assessment often requires counterfactual analysis—comparing outcomes within the MPA against what would have occurred without protection. The counterfactual approach requires selecting control sites that are comparable to the MPA in terms of baseline ecosystem characteristics, environmental conditions, and human pressure levels, but differ in protection status. Condition accounts are compiled for both MPA and control sites using identical variable selection, reference conditions, and aggregation methods. The difference in condition trends between MPA and control sites provides an estimate of the protection effect.

Three principal counterfactual designs are applicable:

• Before-After-Control-Impact (BACI): Strengthens the effectiveness estimate by controlling for regional trends that affect both sites where baseline data from before designation exist.
• Matching-based approaches: Applicable where baseline data from before designation are unavailable but a pool of candidate control sites with comparable characteristics exists.
• Regression discontinuity designs: Applicable where a continuous spatial or temporal running variable at the MPA boundary can be defined.

Each design should be applied according to standard counterfactual-design practice for the specific study context. Minimum data requirements depend on the research question, expected effect size, and spatial and temporal grain of available monitoring data; no universal thresholds are prescribed here. Where none of the designs is feasible due to data limitations, compilers should report descriptive condition trends with an explicit caveat that causal attribution is not supported by the available data.

3.5 Linking accounts to conservation targets

3.5.1 SDG 14 and marine conservation

The 2030 Agenda for Sustainable Development includes SDG 14 "Life Below Water" with targets directly relevant to marine spatial management. SDG Target 14.5 calls for countries to "conserve at least 10 per cent of coastal and marine areas, consistent with national and international law and based on the best available scientific information," with indicator 14.5.1 measuring "coverage of protected areas in relation to marine areas"^[12]. SDG Target 14.2 addresses sustainable management and protection of marine and coastal ecosystems, monitored through indicator 14.2.1 on "proportion of national exclusive economic zones managed using ecosystem-based approaches."

Ecosystem extent accounts can directly inform reporting on protected area coverage, while condition and services accounts support assessment of whether protection is achieving intended ecosystem management outcomes. The alignment between ocean accounts and SDG 14 indicators is detailed in TG-2.10 Multilateral Environmental Agreement Indicators.

3.5.2 Kunming-Montreal Global Biodiversity Framework

The Kunming-Montreal Global Biodiversity Framework, adopted at CBD COP15 in December 2022, establishes the ambition to protect 30% of marine areas by 2030 (Target 3) and to ensure that areas under protection are "effectively conserved and managed" through "ecologically representative, well-connected and equitably governed systems of protected areas"^[13]. The Framework explicitly calls for integration of biodiversity values into national accounting processes (Target 14), and related CBD decisions have recognized the role of ocean accounts in supporting marine conservation^[4:1].

The GBF Target 3 coverage classification is the primary framing for the governance overlay on extent accounts in this Circular (see Section 3.2.1). The GBF classification distinguishes IUCN categories Ia-VI and Other Effective Area-Based Conservation Measures (OECMs) as defined under the KM-GBF. SDG 14.5.1 coverage totals can be derived from the same account as a complementary indicator; compilers should not maintain duplicate or competing coverage tables.

3.5.3 Corporate reporting and private-sector engagement

The Taskforce on Nature-related Financial Disclosures (TNFD) has developed recommendations for businesses to assess and report on nature-related dependencies, impacts, risks, and opportunities. Ocean Accounts compiled for MPAs provide data on ecosystem condition and services that can contribute to such assessments. Guidance on mapping ocean account outputs to specific corporate disclosure frameworks—including the TNFD LEAP approach (Locate, Evaluate, Assess, Prepare)—falls within the scope of private-sector-focused Technical Guidance circulars rather than this NSO-facing circular. For organizations seeking to use national ocean account statistics in corporate reporting, a forward pointer to forthcoming private-sector TG is noted here.

3.5.4 Adaptive management and decision cycles

Accounts support adaptive management by providing a systematic basis for tracking outcomes and adjusting strategies. The cyclical nature of accounting—with regular updates to extent, condition, and services data—aligns with adaptive management frameworks that emphasize learning from management interventions and adjusting approaches based on observed results.

For MPA managers, accounts can inform decisions such as:

• Zoning adjustments based on observed changes in ecosystem condition
• Resource allocation priorities based on identification of degraded areas requiring restoration
• Stakeholder engagement supported by clear communication of ecosystem trends
• Investment decisions supported by assessment of ecosystem services benefits

3.6 Worked Example: MPA Effectiveness Assessment

This section presents a synthetic worked example demonstrating how ecosystem condition accounts can be applied to assess MPA effectiveness using a before-after-control-impact (BACI) design. All data are hypothetical but structured to illustrate operational procedures.

3.6.1 Scenario

A coastal nation designated a 15,000-hectare marine protected area in 2018 to protect coral reef ecosystems. The MPA includes 3,500 ha of coral reef habitat, 4,000 ha of seagrass meadows, and 7,500 ha of pelagic water column. Management objectives include maintaining coral cover above 30%, improving fish biomass to within 20% of unfished levels, and ensuring water quality meets reference conditions for pH and nutrient levels. A control site of comparable size and baseline characteristics but without protection restrictions was identified 50 km away.

The worked example in Sections 3.6.2--3.6.7 focuses on the coral reef sub-account (3,500 ha). Section 3.6.8 extends the example to the seagrass meadow sub-account (4,000 ha) and demonstrates area-weighted ECI aggregation across benthic habitat types.

3.6.2 Baseline condition account (2018)

At the time of designation, baseline condition accounts were compiled for both the MPA and control site using field surveys, remote sensing, and water quality monitoring. The following condition variables were measured:

Table 2: Baseline condition variables for MPA and control site (2018)

Notes: DIN = dissolved inorganic nitrogen. Reference values based on regional historical data and comparison with remote pristine sites. Fish biomass reference represents a single-species maximum sustainable yield benchmark (B_MSY estimated at approximately 40% of unfished biomass) used here for the provisioning service assessment context (Use Case 3); for ecosystem-level condition accounting (Use Cases 1 and 2), an unfished or near-pristine biomass reference is more appropriate. See SEEA EA Chapter 5 (reference conditions) and FAO Technical Guidelines for Responsible Fisheries No. 4.^[14]

3.6.3 Normalizing condition indicators

Each condition variable is normalized to a 0--1 scale using the reference value. For variables where higher values indicate better condition (water clarity, fish biomass, coral cover, seagrass density), the indicator is calculated as:

Indicator = Observed / Reference

For inverse variables where lower values indicate better condition (DIN), the polarity is reversed using:

Indicator = 1 - (Observed - Reference) / (Degraded - Reference)

where Degraded is the value indicating fully degraded condition. For DIN, the degraded anchor is set at 1.0 mg/L based on regional eutrophication thresholds. The degraded anchor value for any inverse variable should be drawn from site- or country-specific baseline or reference data rather than a single global standard; compilers must document the anchor value and its source.

If the formula yields a value greater than 1.0 (i.e., observed condition is better than the reference for an inverse variable), the indicator is capped at 1.0 and the observation noted in account metadata.

For pH, the categorical step-deduction method described in Section 3.3.1.1 is used in place of the inverse-variable formula.

This example uses arithmetic mean aggregation consistent with the default method described in Section 3.3.2.

Table 3: Baseline condition indicators (2018)

3.6.4 Monitoring results (2024)

After six years of protection and enforcement, condition monitoring was repeated at both sites using identical protocols.

Table 4: Monitoring results and condition indicators (2024)

3.6.5 Effectiveness interpretation

The MPA site showed improvement in condition (ECI increased from 0.58 to 0.72), while the control site showed decline (0.53 to 0.48). The treatment effect—the difference in change between MPA and control—is +0.19 points on the ECI scale (MPA change +0.14; control change −0.05; treatment effect = 0.14 − (−0.05) = 0.19), indicating that protection contributed approximately 19 percentage points of condition improvement beyond background environmental trends.

3.6.6 Ecosystem asset accounting

The condition improvement in the MPA is recorded as ecosystem enhancement in the monetary ecosystem asset account. The choice between a perpetuity formula and a finite-horizon net present value calculation should follow the SNA statistical standard (and SEEA EA where it adopts SNA practice): a perpetuity is appropriate where there is no foreseeable terminal date, governance is stable, and the management mandate is continuously renewed; a finite-horizon NPV is required where the MPA designation has a time-limited mandate, sunset clause, or uncertain renewal. Compilers should document the rationale for the chosen approach.

For this illustrative example, a perpetuity assumption is applied. The calculation proceeds as follows:

1. Baseline asset value (2018): The coral reef ecosystem within the MPA (3,500 ha) generated ecosystem services valued at $500/ha/year at baseline condition (ECI = 0.58). Using a 4% discount rate and perpetuity assumption, the baseline asset value = ($500/ha × 3,500 ha) / 0.04 = $43.75 million.

2. Current asset value (2024): After condition improvement to ECI = 0.72, the service flow is estimated by applying the functional relationship between condition and service capacity appropriate for the specific service in question. Compilers should not assume a linear relationship as a default. The condition-to-service relationship is service-specific: coastal protection, carbon sequestration, and fishery support functions each have distinct functional forms that should follow the best available ecological or economic evidence. The chosen functional form and its source must be documented in account metadata. For this example, a linear scaling is used as a screening-level approximation only: service flow = $500/ha × (0.72/0.58) = $620.69/ha/year. Current asset value = ($620.69/ha × 3,500 ha) / 0.04 = $54.31 million. Cross-reference TG-2.4 Environmental (including Ecosystem) Goods and Services for service flow estimation methods.

3. Ecosystem enhancement: The increase in asset value attributable to improved condition = $54.31M − $43.75M = $10.56 million over the six-year period, or approximately $1.76 million per year on average.

3.6.7 Limitations and caveats

This worked example simplifies several aspects of real-world MPA assessment:

• Attribution: The analysis assumes the difference between MPA and control outcomes is attributable to protection, but other factors (differential enforcement, community engagement, coincident management interventions) could contribute.
• Baseline comparability: Perfect matching of control sites is rarely achievable; residual differences in baseline characteristics may confound treatment effects.
• Spatial heterogeneity: Within-MPA variation in condition may be substantial; the example presents aggregate figures but operational assessments should examine spatial patterns.
• Temporal lags: Ecosystem responses to protection occur at different timescales; six years may be insufficient for slow-responding variables (e.g., structural complexity, apex predator recovery).
• Condition-to-service scaling: Linear scaling is used here as a screening-level approximation only. Operational asset valuations should apply empirically calibrated functional forms.

3.6.8 Extending to seagrass and other benthic sub-accounts

The same BACI procedure applied to the coral reef sub-account in Sections 3.6.2--3.6.7 can be extended to seagrass meadows and other benthic habitats present within the MPA. Appropriate condition variables for a seagrass sub-account include percent cover, shoot density (shoots/m²), and epiphyte load (as an indicator of eutrophication pressure). Other benthic sub-types (e.g., soft-sediment benthos, shellfish beds) should use variables appropriate to their IUCN GET functional group. Sub-type ECIs should be reported separately alongside the aggregate MPA ECI—not only as components of the aggregate. This enables managers and reporting agencies to distinguish habitat-specific trends from overall MPA condition.

The MPA-wide aggregate ECI is calculated as an area-weighted mean of the sub-type ECIs:

MPA ECI = Σ (Sub-type ECI × Sub-type Area) / Total Benthic Area

Table 5: Illustrative area-weighted ECI aggregation across benthic sub-accounts (2024)

Illustrative seagrass ECI based on percent cover, shoot density, and epiphyte load indicators compiled using the same normalization procedure as the coral reef sub-account.

Pelagic sub-accounts (7,500 ha in this scenario) are not included in this aggregation. Practitioners with pelagic condition data (e.g., chlorophyll-a, dissolved oxygen, plankton biomass) may extend the area-weighted ECI to include a pelagic component using those variables, applying the same normalization and arithmetic mean aggregation procedure.

4. Acknowledgements

This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

Authors: [To be confirmed]

Reviewers: [To be confirmed]

5. References