Kelp Forest and Temperate Reef Accounting

This Circular provides guidance on compiling ecosystem accounts for kelp forests and temperate rocky reef ecosystems. These systems are classified as Emerging because methodological development remains less advanced than for tropical marine ecosystems, so guidance should be treated as indicative rather than prescriptive. The extent and condition methods build on TG-3.1 Asset Accounts, and ecosystem services follow TG-2.4 Ecosystem Goods and Services and TG-1.9 Valuation. For the foundational accounting framework, see TG-0.1 General Introduction to Ocean Accounts. For comparison with other biogenic marine ecosystems, see TG-6.1 Coral Reef Accounts and TG-6.3 Seagrass Accounts.

Kelp restoration prioritisation: Extent and condition accounts identify degraded areas suitable for restoration. Condition indicators tracking canopy density, urchin barrens extent, and species richness provide baseline data for restoration site selection and success monitoring. The kelp-to-barren transition dynamics documented in Section 3.1 inform restoration feasibility assessment.

Urchin barren remediation: Condition accounts tracking urchin density and predator abundance support culling and reintroduction decisions. Phase shift dynamics and hysteresis effects are described in Section 3.1.

Temperate reef fisheries support: Extent accounts quantifying reef area and connectivity inform fisheries management plans for reef-associated species (rock lobster, abalone, temperate reef fish). Condition indicators for herbivore biomass and rugosity provide early warning of trophic cascade effects. Integration with TG-6.7 Fisheries Accounting enables assessment of habitat-fisheries linkages.

Marine protected area effectiveness: Condition accounts compiled inside and outside MPAs support quantitative evaluation of protection effectiveness, analogous to the approach described in TG-6.1 Coral Reef Accounts Section 3.2.

This Circular requires familiarity with TG-0.1 General Introduction to Ocean Accounts and TG-3.1 Asset Accounts. Compilers will also benefit from TG-4.1 Remote Sensing Data for subtidal mapping approaches, TG-2.1 Biophysical Indicators for condition indicator derivation, and TG-0.7 Quality Assurance for uncertainty documentation.

Readiness and sequencing. Whether a country is positioned to compile this account at all — mandate, data architecture, capacity and the order in which to build — is assessed under TG-0.8 Implementation Readiness Assessment, and is not restated here.

Kelp forests (IUCN GET M1.2) are characterised by dense canopies of large brown macroalgae (Order Laminariales). Key genera include Macrocystis (giant kelp), Laminaria, Ecklonia, Lessonia, and Eisenia. Some kelps (notably Macrocystis) can grow to over 30 metres in length with growth rates up to 0.5 metres per day. Kelp forests occur on hard rocky substrates in the photic zone, typically to depths of 30 metres, and are limited to temperate and polar waters, absent from warm tropical waters except in upwelling zones off Peru, the Galapagos, Namibia, Oman, and Cape Verde.

Subtidal rocky reefs (IUCN GET M1.6) are characterised by hard minerogenic substrates supporting communities of macroalgae, sessile invertebrates, and associated mobile fauna. Unlike kelp forests, rocky reefs lack dense macroalgal canopies, with algal growth kept in check by herbivory, storm disturbance, and depth-related light limitation.

For accounting purposes, the boundary between kelp forests and rocky reefs may be dynamic as kelp canopies expand or contract in response to environmental conditions and herbivore populations. Compilers should establish clear canopy density thresholds or classification rules, document them, and record the rationale for any reclassification decisions.

Subtidal location. Subtidal kelp and rocky reef habitats cannot be directly observed from above the water surface in most conditions, limiting the applicability of conventional aerial and satellite remote sensing.

Water column effects. Remote sensing of benthic habitats requires correction for water column effects, which adds uncertainty to extent estimates. The SEEA EA notes that marine ecosystems extend “throughout the water column and include the underlying sediment and seabed”. The resulting three-dimensional complexity may not be fully captured by area-based extent measures.

Canopy structure variability. Some kelps form floating surface canopies (e.g., Macrocystis), whilst others have subsurface canopies (e.g., Laminaria, Ecklonia), requiring different detection approaches.

Seasonal dynamics. Kelp biomass can vary substantially across seasons. To address this in annual accounting, compilers should standardise the observation period (for example, recording extent at the late summer maximum) or apply multi-temporal averaging. The chosen approach must be documented and held constant across accounting periods (see Step 2.4).

Tier 1 (basic): Use existing global or regional habitat maps (e.g., UNEP-WCMC Ocean Data Viewer or national marine habitat mapping programmes).

Tier 2 (intermediate): Combine satellite remote sensing of surface-canopy kelp with acoustic surveys or diver transects for subsurface habitats. For remote sensing data acquisition and processing, see TG-4.1 Remote Sensing Data.

Tier 3 (advanced): Develop high-resolution benthic habitat maps using multibeam sonar, underwater video transects, and species distribution modelling, integrated with satellite time series.

The extent account structure, including the reclassification row block, is set out in TG-3.1 Asset Accounts Section 3.5.1 (Tables 2a and 2b) and is not restated here. Compile one column per ecosystem type present — typically kelp forests (M1.2) and subtidal rocky reefs (M1.6), and a third for urchin barrens where the national classification recognises them as a distinct type.

The reclassification block matters more for kelp than for most ecosystems, because the kelp-to-urchin-barren transition is the dominant form of loss and it is a change of type, not a reduction: the area stays in the accounting area. Kelp lost to storm damage where the substrate is exposed but not yet colonised is the opposite case and is an unmanaged reduction. A worked illustration of the three available classification approaches is at Box 1 in Section 3.6.

Transitions between ecosystem types. Kelp forests can transition to “urchin barrens” (rocky substrates dominated by sea urchins with minimal macroalgal cover) when herbivore populations increase following predator loss. These trophic cascades are ecosystem conversions and should be recorded in extent accounts. Three classification approaches are available (see Box 1 in Section 3.6 for a worked illustration). Compilers should document their chosen approach and its rationale.

The kelp-to-urchin-barren transition is characterised by nonlinear dynamics and hysteresis: conditions required to trigger degradation differ from those required for recovery.

Table 2: Kelp-urchin phase shift characteristics

Ecosystem condition accounts record the quality of kelp forest and temperate reef assets using indicators of biotic and abiotic characteristics. For the general condition accounting methodology, see TG-3.1 Asset Accounts Section 3.5.2. For deriving biophysical indicators and connecting them to policy frameworks, see TG-2.1 Biophysical Indicators. The measurement protocols for the variables themselves — how each is observed, at what frequency and to what tolerance — are owned by TG-4.8 Physical Condition Measurement and TG-4.9 Biological Condition Measurement; this Section selects and interprets them for kelp forests and temperate reefs.

The chosen tier should be documented. For data quality and tiered reporting frameworks, see TG-0.7 Quality Assurance.

Ocean warming. Kelp forests have truncated thermal niches and are sensitive to elevated temperatures. Marine heatwaves have caused widespread kelp losses, including documented decline of Ecklonia radiata forests in Western Australia following the 2011 marine heatwave. For the relationship between ecosystem condition and climate-related pressures, see TG-2.1 Biophysical Indicators.

Ocean acidification. Reduced pH can affect calcifying organisms such as coralline algae, sea urchins, and shellfish that are integral components of reef communities.

Storm regime changes. Increased storm intensity can dislodge kelp and create gaps in canopy cover. Recovery depends on recruitment success and may be impaired if environmental conditions have shifted.

Condition indicators should be expressed relative to a reference level representing ecosystem integrity. Establishing appropriate reference conditions for kelp forests is challenging for three reasons: long-term baselines are often lacking, many systems have been affected by fishing pressure, pollution, and climate change for decades, and natural variability is high. Compilers should document the reference conditions applied and their rationale. Historical reconstructions, minimally impacted reference sites, or modelled baselines may each be appropriate depending on context.

Condition accounts should track the area of kelp forests in degraded (barren) versus healthy states, as this provides baseline data for restoration targeting and MPA effectiveness assessment.

For the general methodology of ecosystem service measurement and valuation, see TG-2.4 Ecosystem Goods and Services and TG-1.9 Valuation.

Wild-harvested products. Kelp is harvested in some regions for food, fertiliser, animal feed, and industrial products. Sea urchins harvested from rocky reefs are a valuable food product. These should be recorded as provisioning services with physical flows measured in tonnes and valued at market prices where available.

Fisheries habitat. Kelp forests and rocky reefs provide habitat for commercially important fish and invertebrate species, including rockfish, sea bass, abalone, lobster, and bivalves. For accounting purposes, this is treated as a habitat or nursery maintenance service (an intermediate service) following the SEEA EA classification. Habitat and nursery services are intermediate services. Their monetary value cannot equal the full resource rent of an associated fishery without an attribution model, and only the share attributable to the habitat (via a habitat dependency coefficient, dose-response function, or similar) should be recorded as the service value. For the intermediate-versus-final service distinction, see TG-2.4 Ecosystem Goods and Services.

Coastal protection. Kelp canopies can dampen wave energy, but the magnitude of this service is less well documented than for coral reefs or mangroves. Empirical studies report wave-height reductions of approximately 7—60% depending on species, canopy density, bathymetry, and wave period, but kelp-specific attenuation coefficients are not yet validated for accounting use. Any estimate should be reported at low confidence with an explicit uncertainty range. See Step 6.4 for the methodological approach and TG-6.1 Coral Reef Accounts for the analogous coral reef method.

Water quality regulation. Kelp forests take up nutrients from surrounding waters and can help mitigate eutrophication effects in nutrient-enriched coastal areas.

Recreation and tourism. Kelp forests and temperate reefs are popular destinations for recreational diving and snorkelling.

Scientific and educational value. Long-term monitoring sites such as those established by the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) provide valuable scientific data.

Cultural and spiritual significance. Where these services are recorded, the consent, attribution and data-governance requirements in TG-3.6 Traditional Knowledge apply and are not restated here. Kelp forests and rocky reefs may have cultural significance for coastal Indigenous communities, supporting traditional fishing practices, food security, and cultural identity.

For Emerging-status ecosystems, quantification faces particular challenges: limited spatial extent data, less-developed ecological production functions, poorly documented beneficiary populations, and fewer monetary valuation studies to support benefit transfer. Compilers should document methods and assumptions, and communicate uncertainty to users. See TG-0.7 Quality Assurance.

The procedure follows the asset accounting framework from TG-3.1 Asset Accounts and data quality protocols from TG-0.7 Quality Assurance.

Step 1: Define ecosystem accounting area (EAA)

Define the spatial boundary of the accounting area, typically aligned with national marine waters or a regional marine planning area. Document the coordinate system, vertical datum for depth measurements, and seaward boundary (territorial sea, EEZ, or other jurisdictional limit).

1.1 Spatial disaggregation within the EAA. Decision use cases in Section 1 (MPA effectiveness, restoration targeting) require accounts at the management unit level (individual MPAs, bioregions, depth bands) as well as the EAA-wide aggregate. Sub-unit accounts must be additive to the national total. Compilers should adopt the MPA comparison approach described in TG-6.1 Coral Reef Accounts Section 3.2, noting that kelp distribution is typically patchier than coral reef distribution and sub-unit boundaries should reflect ecological structure where possible.

Step 2: Delineate ecosystem assets

2.1 Assemble spatial data layers:

• Bathymetric data (depth)
• Substrate classification (rocky vs. soft sediment)
• Water quality data (turbidity, nutrient levels)
• Existing habitat maps (national, regional, or global datasets)

2.2 Apply ecosystem type criteria:

• M1.2 Kelp forests: Rocky substrate + depth 3-30m + kelp canopy present
• M1.6 Temperate reefs: Rocky substrate + depth <50m + no kelp canopy or sparse kelp

2.3 Map ecosystem extent using the Tier approach described in Section 3.1.

2.4 Address seasonal variation:

• Select 1-2 variables per ECT class (A1 physical, A2 chemical, B1 compositional, B2 structural, B3 functional, C1 landscape)
• Aim for 6-10 variables total
• Prioritise variables with available data and policy relevance
• ECT class coding affects how accounts are aggregated and compared internationally. Brief justifications for each class assignment should be documented.

3.2 Recommended minimum set (with ECT class justifications):

• V_good: Value at reference condition (high integrity)
• V_bad: Value at degraded/collapsed state
• For “normal” (direct-direction) variables (higher values = better condition), V_good > V_bad numerically.
• For “inverse” variables (lower values = better condition, e.g., urchin barrens extent, temperature anomaly), V_good < V_bad numerically.

4.3 Example for kelp canopy density (Ecklonia radiata, temperate Australia):

• V_good = 14 plants/m² (upper reference for dense canopy)
• V_bad = 1 plant/m² (functional threshold below which recruitment fails)

These values are specific to Ecklonia radiata in temperate Australia and should not be applied without recalibration to other species or regions. Indicative reference ranges for other major kelp genera are in Table 3.

Normal variable — kelp canopy density (V_good = 14, V_bad = 1, V = 8.5): Indicator = (8.5 — 1) / (14 — 1) = 7.5 / 13 = 0.58

Inverse variable — urchin barrens extent (V_good = 2%, V_bad = 60%, V = 22%): Indicator = (22 — 60) / (2 — 60) = (—38) / (—58) = 0.66

Both cases yield values in [0, 1] without sign reversal.

5.2 Calculate indicators for each variable:

• Opening indicators (t0)
• Closing indicators (t1)
• Change in indicators

5.3 Aggregate to composite index (optional):

• Equal-weighted arithmetic mean across indicators
• Or apply ECT-weighted approach if ecological rationale supports differential weighting

• Globally consistent mapping of kelp forest extent remains incomplete
• Subsurface kelp species are poorly captured by current satellite-based approaches
• Temporal coverage is insufficient to distinguish long-term trends from natural variability
• Standardised protocols for classifying transitions between kelp forests, rocky reefs, and urchin barrens are lacking

Development priorities:

• Investment in satellite-based monitoring capable of detecting subsurface macroalgae
• Integration of acoustic survey methods with remote sensing
• Development of change detection algorithms suited to dynamic kelp systems
• Engagement with international research networks such as the Kelp Ecosystem Ecology Network (KEEN). The Emerging badge may be revisited if major mapping advances emerge from these initiatives.

Priority gaps:

• Research to quantify carbon sequestration pathways
• Development of accounting protocols that reflect current scientific understanding
• Coordination with IPCC and blue carbon initiatives

Priority gaps:

• Economic valuation studies for kelp and temperate reef services are limited compared to tropical systems
• Resource rent estimates for kelp-associated fisheries are rarely compiled
• Benefit transfer approaches lack locally calibrated studies

Development priorities:

• Primary valuation studies in the North Pacific, Southern Ocean, and North Atlantic
• Integration of kelp forest values into broader natural capital assessments

This worked example demonstrates the compilation of kelp forest and temperate reef ecosystem accounts for a hypothetical coastal system in a southern Australian setting. Given the Emerging status of this Circular, methods and values below should be treated as indicative rather than prescriptive.

Setting: A national ecosystem accounting area (EAA) containing 6,500 hectares of kelp forest (M1.2, dominated by Ecklonia radiata) and 2,500 hectares of subtidal temperate rocky reef (M1.6), totalling 9,000 hectares.

Step 1: Extent account (year t to t+1)

Note: Net change = Total additions + Net reclassification - Total reductions. For kelp: 50 + (-250) - 105 = -305. For rocky reefs: 15 + 250 - 5 = +260. The 250 hectares of kelp that transitioned to urchin barrens are recorded as a reclassification from M1.2 to M1.6, consistent with the phase shift dynamics in Section 3.1. An additional 100 hectares were lost to marine heatwave damage without transitioning to urchin barrens (recorded as natural reduction).

Step 2: Condition account

Condition indicators are derived from diver transect surveys and satellite monitoring, using pre-2010 baselines as reference conditions and the normalisation formula from TG-2.1 Biophysical Indicators for Ocean Accounts Section 3.4.1 (see also Step 5.1):

Note: For inverse variables (urchin barrens extent, temperature anomaly), V_good < V_bad numerically. The unified formula handles both cases without sign reversal. For urchin barrens: (22 — 60) / (2 — 60) = —38 / —58 = 0.66.

Composite condition index (equal weights): (0.58 + 0.66 + 0.60 + 0.60) / 4 = 0.61

This worked example uses four of the six minimum-set variables recommended in Step 3.2. Rugosity (B2) and connectivity (C1) are excluded for illustrative simplicity. Operational composite indices must cover the full minimum set, and omissions should be justified and documented.

Interpretation: A composite condition index of 0.61 indicates the ecosystem is at 61% of reference condition, reflecting canopy density reduction, expanding urchin barrens, and elevated water temperatures.

Step 3: Ecosystem services (annual flows)

Note: The fisheries values shown are total resource rents of reef-associated fisheries, not the share attributable to the kelp/reef habitat as an intermediate service. The carbon sequestration estimate is provisional and presented with explicit uncertainty so its effect on downstream valuation can be quarantined. The coastal protection figure is reported at low confidence given the wide range of empirical wave-attenuation values for kelp (Step 6.4 and Section 3.3).

Step 4: Asset valuation

Applying a 4% social discount rate over a 25-year projection horizon under the stable-flows assumption (Step 7.3) and using PVAF = 15.62 (Step 7.4):

Asset value (including provisional carbon) = 17,130,000 x 15.62 = approximately USD 267,500,000 Asset value (excluding provisional carbon) = 16,350,000 x 15.62 = approximately USD 255,400,000

The two estimates bracket the influence of the provisional carbon service. The difference (approximately USD 12,200,000, or about 4.6% of gross asset value) is the value the carbon row contributes when capitalised over 25 years at 4%. Compilers should report both figures and avoid embedding the provisional carbon component in a single headline number.