OA and Marine Spatial Management (including MPAs)

TG-1.3 Section 1: Using Ocean Accounts in Decision-Making Applied Draft

Circular ID	TG-1.3
Version	7.0
Badge	Applied
Status	Draft
Last Updated	May 2026

1Framework Position

2TG-1.3 applies the foundational Ocean Accounts concepts established in TG-0.1 to the specific governance context of marine spatial management and Marine Protected Areas. It bridges the Section 1 policy-decision guidance with the indicator derivation work in Section 2, demonstrating how extent and condition accounts compiled under SEEA EA methodology can directly inform MPA designation, effectiveness monitoring, and conservation finance decisions.

1. Outcome

1This 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.

2Readers will learn how to apply ecosystem accounting frameworks to three decision contexts:

• 3MPA designation: identifying priority areas for protection using extent and condition baselines
• 4Effectiveness monitoring: tracking conservation outcomes through time-series condition accounts and comparing protected versus unprotected areas
• 5Economic impact assessment: quantifying how spatial restrictions affect ecosystem service flows and economic dependencies

6The 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.

7The 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

• 1TG-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

1Marine 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 organises 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 objectives¹. 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) whilst recognising 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 reliable information systems to guide MPA designation, monitor outcomes, and support adaptive management³.

2The relevance of ocean accounts for marine conservation is recognised in CBD Decision 15/24 (2022), which encourages regional efforts towards ocean accounting and economic valuation of ecosystem services for marine and coastal biodiversity, and in related COP16 decisions on marine biodiversity⁴.

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

1Decision 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) whilst ensuring ecological representativeness and connectivity. Designation decisions require baseline information on ecosystem distribution, condition, and irreplaceability.

2Account types required:

• 3Ecosystem 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 ecosystems⁵.
• 4Ecosystem 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.
• 5Governance 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.

6Key indicators derived:

• 7MPA coverage as percentage of total marine area by ecosystem type (GBF Target 3 primary; SDG 14.5.1 complementary)
• 8Representation index showing which ecosystem types are under- or over-represented in protected areas
• 9Baseline condition index for newly designated MPAs

10Operational 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 are established from historical baselines, comparison with protected reference sites, or ecological modelling. The resulting extent and condition data inform spatial prioritisation, with high-condition unprotected areas and low-representation ecosystem types identified as designation priorities.

3.2.2 Use Case 2: MPA effectiveness monitoring

1Decision 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.

2Account types required:

• 3Ecosystem 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 drivers⁶.
• 4Ecosystem 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.
• 5Ecosystem 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.

6Key indicators derived:

• 7Ecosystem Condition Index (ECI) trends for protected versus unprotected areas
• 8Area under effective protection (combining extent with condition criteria)
• 9Change in ecosystem service capacity attributable to protection

10Operational 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 normalised 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, and where declining, as degradation. Reliable effectiveness assessment rests on counterfactual comparison: compilers should establish comparable condition accounts for unprotected control sites matched to MPA characteristics.

3.2.3 Use Case 3: Economic impact assessment of spatial restrictions

1Decision 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.

2Account types required:

• 3Ecosystem 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.
• 4Monetary 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.
• 5Extended 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.

6Key indicators derived:

• 7Ecosystem service value per hectare by protection status
• 8Economic dependency ratio (ocean industry GDP / ecosystem service value)
• 9Distributional incidence of MPA restrictions across sectors and communities

10Operational 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.

11Where primary valuation data (travel cost surveys, contingent valuation studies) are not available, compilers should apply the following tiered framework:

• 12Tier 1 — Primary valuation study available: Apply stated methods directly (exchange values, avoided damage cost, travel cost, contingent valuation).
• 13Tier 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.
• 14Tier 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.

15For detailed guidance on ecosystem services valuation, see TG-2.4 Environmental (including Ecosystem) Goods and Services.

3.3 Ocean accounts for monitoring ecosystem health

1The accounting approach rests on three properties: 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

1When 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.

2Condition 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).

3Table 1.3.1: Marine ecosystem condition variables relevant to MPA baselines

Class	Variables
Abiotic physical state	Water temperature, salinity, turbidity, current patterns, bathymetric characteristics.
Abiotic chemical state	Dissolved oxygen, pH (ocean acidification), nutrient concentrations, pollutant levels.
Biotic compositional state	Species richness, community composition, abundance of indicator species, presence of threatened species.
Biotic structural state	Coral cover percentage, seagrass canopy height and density, kelp forest biomass, mangrove stem density.
Biotic functional state	Primary productivity, fish recruitment rates, trophic structure, decomposition rates.
Seascape characteristics	Habitat patch size distribution, connectivity between reef systems, fragmentation of coastal wetlands.

4SEEA 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.

5Table 1 illustrates a simplified ecosystem condition account structure for an MPA.

6Table 1: Illustrative ecosystem condition account for a marine protected area

Condition Variable	Indicator	Reference Value	Baseline (Year 1)	Year 5	Condition Index
Abiotic: Physical
Water clarity	Secchi depth (m)	25	18	21	0.84
Abiotic: Chemical
Ocean pH	pH units	8.2	8.05	8.03	0.70
Biotic: Compositional
Fish species richness	Species count	120	85	102	0.85
Biotic: Structural
Coral cover	% cover	40	22	28	0.70
Seagrass density	Shoots/m2	800	450	580	0.73
Seascape
Habitat connectivity	Index (0-1)	0.9	0.6	0.65	0.72

3.3.1.1 pH condition indicator: categorical approach

1pH 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:

• 2pH within ±0.05 units of reference = 1.00
• 3Each 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)
• 4Indicator is clamped at 0 (cannot be negative)
• 5If observed pH exceeds the reference value, the indicator is capped at 1.00

6This 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).

7Example (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

1Individual condition variables can be aggregated into composite condition indices to provide summary assessments of ecosystem health. An ECI value of 1 indicates reference condition, whilst values below 1 indicate degradation⁹.

2The 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.

3Figure 1.3.1 sets out the four sequential steps by which heterogeneous condition variables are measured, normalised against baseline condition, compiled and aggregated to a composite ECI, and then tracked across accounting periods.

Figure 1.3.1 Four panels show how measured condition variables become a composite MPA Ecosystem Condition Index tracked against management thresholds. Values are illustrative. Scale is 0–1 against each variable's own baseline, not min-max across variables. Source: SEEA EA 2024 ch. 5 (ecosystem condition accounts, variables, indicators and reference levels); IUCN-WCPA protected-area management-effectiveness framework. See TG-1.3 (MPA condition assessment) and TG-2.1 (aggregate biophysical indicators). Adapted from: SEEA EA 2024 §5.69 and Table 5.1 (condition indicators, reference levels, categorical abiotic variables).

4For 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.

3.4 Measuring MPA effectiveness

3.4.1 Accounting for ecosystem degradation and enhancement

1SEEA 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), whilst 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)¹⁰. These accounting entries enable tracking of whether ecosystems within MPAs are improving (net enhancement), stable (no net change), or declining despite protection (net degradation).

2The 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.

3Where 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. Recording the change without attribution 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.

4Where 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

1Changes 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.

2Table 3.4.2: Ecosystem service effectiveness indicators for MPA assessment

Ecosystem Service	Effectiveness Indicator
Fish provisioning	Monitoring fish biomass and catch per unit effort in areas adjacent to no-take zones to assess spillover effects
Coastal protection	Measuring changes in wave attenuation capacity of reef systems or mangrove forests within the MPA
Carbon sequestration	Quantifying carbon storage in seagrass meadows, mangroves, and other blue carbon ecosystems
Recreation and tourism	Recording visitor numbers, satisfaction levels, and economic contributions from MPA-based tourism

3For 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

1Credible 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.

2Three principal counterfactual designs are applicable:

• 3Before-After-Control-Impact (BACI): Strengthens the effectiveness estimate by controlling for regional trends that affect both sites where baseline data from before designation exist.
• 4Matching-based approaches: Applicable where baseline data from before designation are unavailable but a pool of candidate control sites with comparable characteristics exists.
• 5Regression discontinuity designs: Applicable where a continuous spatial or temporal running variable at the MPA boundary can be defined.

6Each 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

1The 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”¹¹. 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.”

2Ecosystem extent accounts can directly inform reporting on protected area coverage, whilst 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

1The 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”¹². The Framework explicitly calls for integration of biodiversity values into national accounting processes (Target 14), and related CBD decisions have recognised the role of ocean accounts in supporting marine conservation⁴.

2The 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

1The 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 organisations 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

1Accounts 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.

2For MPA managers, accounts can inform decisions such as:

• 3Zoning adjustments based on observed changes in ecosystem condition
• 4Resource allocation priorities based on identification of degraded areas requiring restoration
• 5Stakeholder engagement supported by clear communication of ecosystem trends
• 6Investment decisions supported by assessment of ecosystem services benefits

3.6 Worked Example: MPA Effectiveness Assessment

1This 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

1A 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.

2The 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)

1At 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:

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

ECT Class	Variable	Unit	Reference	MPA Baseline	Control Baseline
Physical state	Water clarity	Secchi depth (m)	20	14	13
Chemical state	pH	units	8.2	8.05	8.04
Chemical state	DIN	mg/L	0.1	0.4	0.5
Compositional	Fish biomass	kg/ha	600	180	170
Structural	Coral cover	%	40	22	20
Structural	Seagrass density	shoots/m2	750	400	380

3Notes: 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. 4.¹³

3.6.3 Normalising condition indicators

1Each condition variable is normalised 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:

2Indicator = Observed / Reference

3For inverse variables where lower values indicate better condition (DIN), the polarity is reversed using:

4Indicator = 1 - (Observed - Reference) / (Degraded - Reference)

5where 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.

6If 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.

7For pH, the categorical step-deduction method described in Section 3.3.1.1 is used in place of the inverse-variable formula.

8This example uses arithmetic mean aggregation consistent with the default method described in Section 3.3.2.

9Table 3: Baseline condition indicators (2018)

Variable	MPA Indicator	Control Indicator
Water clarity	0.70	0.65
pH	0.70	0.70
DIN	0.67	0.56
Fish biomass	0.30	0.28
Coral cover	0.55	0.50
Seagrass density	0.53	0.51
Mean ECI	0.58	0.53

3.6.4 Monitoring results (2024)

1After six years of protection and enforcement, condition monitoring was repeated at both sites using identical protocols.

2Table 4: Monitoring results and condition indicators (2024)

Variable	MPA 2024	MPA Indicator	Control 2024	Control Indicator	Change (MPA)	Change (Control)
Water clarity (m)	17	0.85	12	0.60	+0.15	-0.05
pH	8.00	0.6014	7.98	0.6014	-0.10	-0.10
DIN (mg/L)	0.25	0.83	0.55	0.50	+0.16	-0.06
Fish biomass	340	0.57	160	0.27	+0.27	-0.01
Coral cover (%)	30	0.75	18	0.45	+0.20	-0.05
Seagrass (shoots/m2)	520	0.69	350	0.47	+0.16	-0.04
Mean ECI		0.72		0.48	+0.14	-0.05

3.6.5 Effectiveness interpretation

1The MPA site recorded improvement in condition (ECI increased from 0.58 to 0.72), whilst the control site recorded 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

1The 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.

2For this illustrative example, a perpetuity assumption is applied. The calculation proceeds as follows:

1. 3

   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. 4

   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. 5

   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

1This worked example simplifies several aspects of real-world MPA assessment:

• 2Attribution: 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.
• 3Baseline comparability: Perfect matching of control sites is rarely achievable; residual differences in baseline characteristics may confound treatment effects.
• 4Spatial heterogeneity: Within-MPA variation in condition may be substantial; the example presents aggregate figures but operational assessments should examine spatial patterns.
• 5Temporal 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).
• 6Condition-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

1The 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. So that managers and reporting agencies can distinguish habitat-specific trends from overall MPA condition, sub-type ECIs should be reported separately alongside the aggregate MPA ECI — not only as components of the aggregate.

2The MPA-wide aggregate ECI is calculated as an area-weighted mean of the sub-type ECIs:

3MPA ECI = Σ (Sub-type ECI × Sub-type Area) / Total Benthic Area

4Table 5: Illustrative area-weighted ECI aggregation across benthic sub-accounts (2024)

Sub-account	Area (ha)	ECI (2024)	Area × ECI
Coral reef	3,500	0.72	2,520
Seagrass meadow	4,000	0.68*	2,720
Total / Aggregate	7,500	0.70	5,240

5Illustrative seagrass ECI based on percent cover, shoot density, and epiphyte load indicators compiled using the same normalisation procedure as the coral reef sub-account.

6Pelagic 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 normalisation and arithmetic mean aggregation procedure.

4. Acknowledgements

1This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

2Authors: [To be confirmed]

3Reviewers: [To be confirmed]

5. References

Footnotes

1. 1

   Gacutan, J., et al. (2021). Marine spatial planning and ocean accounting: Synergistic tools enhancing integration in ocean governance. Marine Policy, 104936. ↩

2. 2

   United Nations. (1982). United Nations Convention on the Law of the Sea. Part XII: Protection and Preservation of the Marine Environment. ↩

3. 3

   Convention on Biological Diversity. (2022). Kunming-Montreal Global Biodiversity Framework. CBD/COP/15/L.25. ↩

4. 4

   Convention on Biological Diversity. (2022). CBD Decision 15/24 on ocean accounting for conservation and sustainable management of marine and coastal biodiversity; and related COP16 decisions on marine biodiversity. ↩ ↩²

5. 5

   Keith, D.A., Ferrer-Paris, J.R., Nicholson, E. and Kingsford, R.T. (eds.) (2020). The IUCN Global Ecosystem Typology 2.0: Descriptive profiles for biomes and ecosystem functional groups. Gland, Switzerland: IUCN. ↩

6. 6

   United Nations. (2021). System of Environmental-Economic Accounting — Ecosystem Accounting. Chapter 5: Ecosystem condition accounts; Table 5.1: Ecosystem Condition Typology (ECT). ↩

7. 7

   United Nations. (2021). System of Environmental-Economic Accounting — Ecosystem Accounting. Chapter 2: Principles of ecosystem accounting. ↩

8. 8

   United Nations. (2021). System of Environmental-Economic Accounting — Ecosystem Accounting. Para 5.69: Reference conditions. ↩

9. 9

   United Nations. (2021). System of Environmental-Economic Accounting — Ecosystem Accounting. Section 5.4: Ecosystem condition indices. ↩

10. 10

    United Nations. (2021). System of Environmental-Economic Accounting — Ecosystem Accounting. Chapter 10: Ecosystem asset accounts, paras 10.15 (enhancement) and 10.21 (degradation). ↩

11. 11

    United Nations. (2017). Global Indicator Framework for the Sustainable Development Goals. A/RES/71/313. ↩

12. 12

    Convention on Biological Diversity. (2022). Kunming-Montreal Global Biodiversity Framework. Target 3. ↩

13. 13

    FAO. (1997). FAO Technical Guidelines for Responsible Fisheries No. 4: Fisheries Management. Rome: FAO. See also FAO Fisheries Technical Paper 347 (García, 1994) for biological reference points including B_MSY. ↩

14. 14

    pH 2024 MPA = 8.00, deviation = 0.20 units below reference (8.2): four steps → indicator = 1.00 - 4×0.10 = 0.60. Control pH = 7.98, deviation = 0.22 units below reference (8.2): bands 0—0.05 (step 1), 0.05—0.10 (step 2), 0.10—0.15 (step 3), 0.15—0.20 (step 4) → four full steps → indicator = 1.00 - 4×0.10 = 0.60. All pH indicators calculated using the categorical step-deduction method (Section 3.3.1.1). Control mean ECI = (0.60 + 0.60 + 0.50 + 0.27 + 0.45 + 0.47) / 6 = 2.89 / 6 = 0.482, rounded to 0.48. Rounding convention: condition indicators are reported to two decimal places; the final mean ECI is rounded to two decimal places after summing the six individual indicators. ↩ ↩²