Quantitative Assessment of Carbon Removal Potential of Macroalgae: A Standardized Protocol for Biomass Estimation and Carbon Removal Calculation

Summary

This study presents a standardized, reproducible protocol for estimating macroalgal biomass and calculating its carbon removal potential. The workflow harmonizes sampling, calculation, and reporting to improve cross-study comparability and is demonstrated in representative kelp and sargassum communities across northern and southern China.

Abstract

Macroalgae are an important component of blue carbon systems. However, their carbon removal potential remains difficult to compare across studies because field sampling designs, biomass estimation models, and accounting workflows vary substantially. This study presents a standardized and reproducible protocol for estimating macroalgal biomass and quantifying carbon removal, integrating macroalgal biological traits, marine ecological survey methods, and carbon-cycle accounting principles into a unified workflow. The protocol specifies consistent sampling parameters, harmonized calculation steps, and standardized data recording and reporting to improve cross-study comparability and facilitate regional synthesis. We demonstrate protocol applicability using representative case studies from an offshore kelp community in northern China and a bay sargassum community in southern China, representing contrasting taxa and typical shallow coastal habitats. Using this workflow, within-team relative variability remained below 10%, and cross-team relative error remained below 15%, consistent with established variability thresholds. Overall, the protocol provides an operationally transparent framework for generating comparable macroalgal blue-carbon datasets across sites and research teams, supporting resource surveys and carbon removal assessments.

Introduction

Blue carbon refers to carbon captured and stored in marine ecosystems. It is characterized by long-term storage and high sequestration efficiency, making it an important component of the global carbon cycle and a critical natural mechanism for mitigating climate change¹. Within blue carbon systems, macroalgae are widely distributed across coastal, intertidal, and subtidal zones and fix atmospheric CO₂ into organic carbon via photosynthesis. Part of this production can be exported to deeper or offshore waters, where it may contribute to longer-term carbon storage, while another portion remains in standing biomass².

However, current assessments of macroalgal carbon removal potential are still constrained by limited methodological standardization³. This limitation is mainly reflected in three aspects. First, biomass estimation methods differ substantially across studies. For example, quadrat size and sampling effort range widely (e.g., 0.25 m² to 4 m²), and in some cases, replication is not implemented, leading to large fluctuations in area-based biomass estimates. Second, the blue carbon (or carbon removal) calculation workflow and parameter selection are often inconsistent, yielding divergent accounting outputs even under similar ecological conditions. Third, there is no uniform protocol for data recording and reporting, reducing comparability across research results and limiting regional synthesis⁴.

A standardized approach is necessary for macroalgae because their carbon removal pathway differs from that of rooted coastal plants such as mangroves and seagrasses. Rather than relying primarily on sediment burial at the growth site, macroalgal carbon removal is more closely linked to rapid biomass accumulation and the offshore export of organic matter. This mechanism, together with high productivity and fast turnover, makes methodological consistency especially important when comparing sites and species across regions⁵.

To address this gap, we develop a complete standardized protocol that integrates macroalgal biological characteristics, marine ecological survey methods, and carbon-cycle accounting into a unified workflow. The protocol is organized into five modules: (1) design principles and scope of application, (2) standardized biomass estimation, (3) standardized blue carbon calculation, (4) quality control and data management, and (5) protocol verification. We validate applicability using representative case studies from a northern offshore kelp community and a southern bay sargassum community, illustrating use across species, life histories, and shallow coastal habitats⁶.

In this study, macroalgal carbon uptake is referred to as carbon removal rather than long-term carbon sequestration. Because macroalgae generally grow on hard substrates without direct sediment burial at the site, they do not inherently meet a >100-year storage criterion. Only a fraction of macroalgae-derived carbon may achieve centennial-scale sequestration through export and subsequent burial of detritus.

Protocol

Use this protocol to generate comparable estimates of macroalgal biomass and carbon-removal estimates across sites and research teams. An overview of the complete workflow is shown in Figure 1.

1. Protocol design basics

1. Design principles
   1. Align sampling and biomass estimation with macroalgal biological traits and local ecological context (e.g., growth form, community structure, and nutrient/seasonal dynamics) to ensure measurements reflect natural variability.
   2. Select the sampling window to match the local vigorous growth period of the target macroalgae and avoid sampling during dormancy or decay periods.
   3. Determine carbon content using validated elemental analysis methods. Document sample preparation, analytical settings, and calibration procedures, and apply the same method consistently across all samples⁷.
   4. Estimate carbon removal rate using a predefined calculation framework. Parameterize photosynthesis-related terms using locally appropriate environmental inputs (e.g., light duration and water temperature) and physiological data sources when applicable.
   5. Write each step as an unambiguous operational instruction and specify acceptance criteria (QC thresholds) for field and laboratory measurements to ensure reproducibility across regions and teams⁸.
   6. Use commonly available laboratory and field equipment (e.g., balances, drying oven, elemental analyzer, measuring tape) and report key specifications (e.g., balance resolution, drying temperature).
   7. Streamline procedures without removing QC requirements and prioritize steps that materially affect biomass, carbon content, and calculation outputs.
2. Scope of application
   1. Apply this protocol to attached and floating macroalgae (e.g., kelp and sargassum).
   2. Do not apply this protocol to microalgae or planktonic algae.
   3. Limit application to shallow coastal waters (e.g., nearshore offshore areas, bays, and intertidal zones) where sampling is operationally feasible. Set a target depth limit of <20 m for routine sampling operations.
   4. Do not apply this protocol to deep-sea macroalgal communities where sampling conditions and equipment requirements exceed conventional survey capability⁹.

2. Standardized process of biomass estimation

1. Preliminary preparation
   1. Determine sampling periods based on local environmental conditions and species-specific phenology rather than calendar months.
   2. Define the "vigorous growth period" as the period of maximum biomass accumulation and photosynthetic activity, based on local observations or regional evidence.
   3. Select sampling timing using site-specific phenology information in low- and high-latitude systems. Document the basis for timing selection (e.g., local monitoring records or regional studies)¹⁰.
2. Quadrat setting and sampling
   1. Place the quadrat frame stably on the substrate at each sampling point (Figure 2). Ensure the vertical height difference within the frame is <5 cm (verify using a ruler or fixed reference).
   2. Select sampling sites to represent the spatial heterogeneity of the macroalgal community within the study area. For intertidal zones, place quadrats within 1 hour after low tide to minimize desiccation and air-exposure-related effects on biomass and carbon measurements.
   3. Establish at least 3-5 sites (or blocks) per study area, depending on habitat extent and heterogeneity. At each site, sample 3 replicate quadrats (minimum total: 9-15 quadrats per study area).
   4. Set quadrat area according to species morphology and growth form (e.g., 1 m² for kelp-dominated communities; 2 m² for floating or sparsely distributed macroalgae)¹¹.
   5. Conduct an initial variability check using the first three quadrats at each site. If the coefficient of variation (CV) of dry biomass per unit area exceeds 10%, add quadrats until the CV falls to ≤10%. Key standardized sampling parameters and acceptance thresholds are summarized in Table 1.
   6. Place collected samples on ice immediately after harvest. Maintain transport temperature at 5-10 °C to reduce thermal degradation.
   7. Deliver samples to the laboratory within 24 h after collection. If transport exceeds 24 h, maintain samples in seawater matching site salinity and monitor sample condition regularly.
3. Biomass parameter measurement
   1. Fresh weight measurement
      1. Remove samples from bags and rinse gently with deionized water 2-3 times to remove attached sediment and salts. Limit total rinsing time to ≤30 s to reduce tissue water loss. Collect biomass from each quadrat consistently (including holdfasts, stipes, and blades unless the study design specifies otherwise).
      2. Blot surface moisture using absorbent paper. Replace paper 2-3 times until no visible water remains on the sample surface.
      3. Place the sample into a pre-weighed container (e.g., beaker or Petri dish). Weigh using an electronic balance with ≥0.01 g precision. Record gross mass after stabilization and calculate net fresh weight (g) by subtracting container mass.
      4. Weigh each sample three consecutive times. Use the average as the final fresh weight. If any two readings differ by >2%, check sample condition (e.g., dripping water or unstable placement) and repeat weighing until the criterion is met.
   2. Dry weight measurement
      1. After fresh-weight measurement, randomly select three representative sub-samples from each quadrat sample. Ensure each sub-sample weighs >10 g. Dry sub-samples at 60 °C. After each drying interval, cool sub-samples to room temperature in a desiccator before weighing.
      2. Store remaining material for carbon content analysis. If analysis occurs within 24 h, refrigerate at 4-10 °C in sealed, labeled containers and keep samples in the dark. For longer storage, freeze at −20 °C.
      3. Continue drying until constant weight is reached. Define constant weight as a difference of ≤0.05 g between two consecutive measurements. Record sub-sample dry weight (g).
      4. Calculate the dry-to-fresh ratio (R_df) as (dry weight) / (fresh weight) for each sub-sample. If the CV of R_df is ≤5%, use the mean R_df to convert total fresh weight to total dry weight for that quadrat. If the CV exceeds 5%, increase the sub-sample number to five, and re-estimate R_df.
   3. Plant height and coverage measurement
      1. Randomly select 10 representative individuals (sporophytes/fronds) in each quadrat. Measure thallus height (cm) from the holdfast attachment point to the uppermost intact living tissue (excluding broken/eroded tips). Record individual heights and calculate the quadrat mean height (Havg).
      2. For clustered algae, select 3-5 clusters, measure mean height per cluster, and compute overall Havg.
      3. Measure coverage using the grid method. Subdivide the quadrat into 100 equal grids (10 cm × 10 cm).
      4. Count the number of grids in which target-algae coverage exceeds 50% (N). Calculate coverage as C (%) = (N/100) × 100, and report to one decimal place²¹. Where N is the number of grids out of 100.
4. Biomass calculation model
   1. Calculation of biomass per unit area: All equations, variables, and reporting units used in the biomass calculation workflow are summarized in Table 2.
      1. Calculate fresh weight per unit area (FW_per, g/m²):    (1)
      2. Calculate dry weight per unit area (DW_per, g/m²):    (2)
         Where A is the quadrat area (m²), FW_tot is the total fresh weight of the quadrat (g).
         NOTE: If multiple replicates exist at a sampling point, calculate FW_per and DW_per for each replicate and report the mean as the sampling-point value
   2. Population biomass estimation
      1. Determine the population distribution area (S, m²). If remote sensing imagery is available, combine field surveys with remote sensing images to obtain boundary coordinates, delineate the population boundary in GIS software, and calculate S accordingly¹². If remote sensing images are unavailable, measure population length and width with a tape measure. Estimate S using the rectangular area formula, and partition irregular areas into multiple rectangles for separate calculation before summing to obtain the total S.
      2. Calculate total population biomass (B, t):    (3)
         Where 10⁶ converts grams to metric tonnes. (t; 1 t = 10³ kg).
      3. If biomass differs across population subregions (e.g., marginal vs. core areas), calculate DW_per for each partition separately, multiply by the area of each partition, and sum the results to obtain B.
   3. Dynamic calculation of biomass
      1. Calculate growth rate (r, day^-1):    (4)
         Where, DW_end is the dry weight per unit area at the end of the period (g/m²), DW_start is the dry weight per unit area at the start of the period (g/m²), and t is the growth time (days).
      2. Calculate annual average biomass (DW_{avg_year}, g/m²) as the average of DW_i from all sampling events within one year:
            (5)
         Where, n is the number of samples per year (dimensionless), referring to the total number of independent sampling events conducted within one year (e.g., seasonal or monthly surveys), i is an index representing each sampling time within a year used to compute the annual average biomass, and DW_i is the dry weight per unit area of the i-th sampling event (g/m²).

3. Standardized process of blue carbon calculation

1. Determination of carbon content
   1. Place the dried samples that have already reached constant weight (i.e., the reserved material for carbon content analysis) into a crusher and grind for 2-3 min to homogenize the material. Use a mechanical grinder or a mortar and pestle to obtain uniform particles suitable for carbon analysis.
   2. Sieve the homogenized material through a 100-mesh sieve (aperture size ≈150 µm). Collect the undersieve fraction and transfer it into pre-numbered, pre-dried vials.
   3. Store the pretreated samples in a desiccator to minimize moisture uptake. Complete the carbon content determination within 7 days.
   4. Check for moisture uptake during storage and apply a dry-mass correction when needed. Weigh an aliquot of the stored pretreated sample as m_before, then oven-dry the aliquot again at 60 °C to constant weight and weigh as m_after.
   5. Calculate the moisture uptake rate (MR, %) as:    (6)
      When reporting carbon content, express values on a true dry-mass basis using MR when moisture uptake is non-negligible.
2. Benchmark method: Elemental analysis
   1. Calibrate the elemental analyzer using a certified reference material before sample determination. Ensure calibration error is <0.5%.
   2. Set the operating conditions: combustion temperature of 950 °C, reduction temperature of 650 °C, and a carrier gas flow rate of 200 mL/min.
   3. Weigh 5-10 mg of the pretreated sample into a pre-weighed tin capsule, then fold and compact the capsule to prevent leakage during combustion.
   4. Load the capsule into the elemental analyzer and allow the instrument to complete combustion, reduction, separation, and detection automatically.
   5. Repeat the determination three times for each sample. Use the average value as the final carbon content (C, %)
         (7)
      Where, m_C is the mass of carbon in the sample (mg), m_sample is the total mass of the analyzed sample (mg).
   6. If the relative standard deviation (RSD, %) of the three measurements exceeds 1%, re-measure until the acceptance criterion is met. Calculate RSD as:
         (8)
      ​Where SD is the standard deviation of the three C measurements and C̄ is their mean.
3. Alternative method: Burning (loss-on-ignition)
   1. Use this method only when an elemental analyzer is unavailable. Clearly indicate this method in the report and apply the correction procedure described in 3.3.5.
   2. Place 5-10 g of pretreated dried sample in a crucible. Dry at 60 °C to constant weight, then combust in a muffle furnace at 550 °C for 4-6 h. Cool in a desiccator and weigh the residue.
   3. Calculate C (%) as:    (9)
      Where m₀ is the dry mass of the sample before burning (mg), m_ash is the mass of the residue after burning (mg).
   4. For each batch, determine C for 3-5 representative samples using elemental analysis. Fit a correction equation between combustion-derived C and elemental-analysis C and apply the correction equation to all combustion-derived results from the same batch.

4. Carbon removal rate calculation

1. Carbon removal rate based on growth rate
   1. Calculate carbon removal rate (CR, g C m^-2d^-1):    (10)
      ​Where, ΔDW is biomass growth per unit area (DW_end−DW_start, g m⁻²). C is carbon content (in decimal, e.g., 35.2% = 0.352), t is growth time (days).
      NOTE: Use the same start and end time nodes used for DW_start and DW_end to define t and record the sampling dates and interval length for reproducibility.
2. Carbon removal rate based on primary productivity (light-dark bottle method)
   1. Prepare paired incubation bottles (light and dark). Rinse bottles three times with site seawater, then fill them with site seawater, minimize headspace, and label bottles in advance.
   2. Place representative macroalgal material into each bottle. Use a biomass amount that avoids crowding and allows the algae to maintain a natural orientation during incubation.
   3. Calibrate the dissolved oxygen (DO) meter according to the manufacturer's instructions using air-saturated water as a reference. Confirm measurement error is ≤0.1 mg L⁻¹.
   4. Measure initial DO in the light bottle and dark bottle and record values (DO₀,_white and DO₀,_black, mg L⁻¹).
   5. Incubate the bottles for 24 h under near-natural temperature and light conditions at the sampling site. Keep bottles undisturbed during incubation and avoid overheating from direct solar exposure or shading artifacts.
   6. Measure final DO in the light bottle and dark bottle (DO₁,_white and DO₁,_black, mg L⁻¹).
   7. Calculate DO changes as:    (11)
         (12)
   8. Calculate net oxygen production as:    (13)
      ​Where V is seawater volume (L).
   9. Convert oxygen production to carbon fixation (C_fix, mg C):    (14)
      Where O_net is net oxygen production (mg O₂), 0.375 is the coefficient (1 mg O₂ corresponds to 0.375 mg C fixed)
   10. Calculate carbon removal rate based on primary productivity (CR_productivity, g C m⁻² d⁻¹) as:
          (15)
       where 1000 converts mg to g, A is the sample (projected) area (m²), and t is the incubation time (days).
       ​NOTE: During incubation, ensure algae remain in a natural state and avoid physical disturbance. Record seawater temperature and approximate light conditions during incubation for reporting consistency.
3. Integration and correction of the two methods
   1. Calculate the average CR as:    (16)
   2. Calculate relative error (RE) as:    (17)
      NOTE: If RE < 10%, use CRavg as the final CR. If RE ≥10%, verify time nodes and operational consistency (e.g., matching growth interval definition, incubation timing, and sample area estimation), and repeat the measurement or calculation steps that contribute most to the discrepancy.
   3. Calculate corrected CR (CR_corr, g C m⁻² d⁻¹) as:    (18)
      Where CR_meas is the measured carbon removal rate (g C m⁻² d⁻¹), T_light is the measured light duration (h). T_{light_stand} is the standard light duration (h).

5. Conversion of carbon storage and carbon flux

1. Calculate carbon storage (C_storage, t C):    (19)
   Where B_{avg_year} is the annual average population biomass (t), and C is carbon content (decimal)
2. Define annual carbon flux (Fc, t C yr-1) as corrected long-term carbon sink (CS_{long_corr}):
      (20)
3. Convert Fc to CO₂ equivalent:    (21)
   where 1 t C = 3.67 t CO₂. The relationships among carbon storage, carbon removal rate, carbon sink metrics, and flux conversions used in this protocol are summarized in Figure 3.

6. Quality control system

1. Quality control in the sampling stage
   1. Provide unified training to sampling personnel to ensure standardized operations for quadrat setting, sample collection, and parameter recording (refer to Table 1).
   2. Administer an assessment after training. Require quadrat layout error to be <5% and fresh-weight measurement error to be <2%. Allow personnel to participate in field sampling only after they have passed the assessment.
   3. Calibrate electronic balances using standard weights (e.g., 100 g or 500 g) before each sampling session. Record the calibration results in the instrument calibration table. Recalibrate the balance if it is moved during sampling.
   4. Calibrate GPS locators at a location with known coordinates before sampling. Ensure positioning error is <5 m. If measurements are taken under shaded or poor-signal conditions, record the deviation and correct coordinates during data processing¹³.
   5. Calibrate water thermometers and salinity meters daily using standard solutions. Ensure water temperature calibration error is <0.2 °C and salinity calibration error is <0.2.
   6. Measure water temperature and salinity in ambient seawater at each sampling site at the time of collection and record values in the database.
   7. Set three replicate quadrats for each sampling point. If the coefficient of variation of DW_per across the three quadrats exceeds 10%, add two more replicate quadrats and repeat until the coefficient of variation is <10%.
   8. Retain all collected samples under refrigerated conditions (5-10 °C) until laboratory processing is completed to enable additional replicate determination when required.
   9. Set three parallel determinations for fresh weight, dry weight, and carbon content for each quadrat sample. Require the relative standard deviation to be <5% and repeat the determination if the criterion is not met.
2. Quality control in the experimental analysis stage
   1. Conduct sample pretreatment and analysis in a clean laboratory environment to minimize contamination from dust and organic reagents.
   2. Wipe and disinfect experimental surfaces daily using alcohol, and dry/sterilize reusable equipment in advance¹⁴.
   3. Check the carrier-gas pressure of the elemental analyzer before each use and ensure it exceeds 0.5 MPa. Start measurement only when the combustion furnace temperature fluctuation is <5 °C.
   4. Clean the combustion tube after completing each batch of measurements and after the furnace cools to a safe operating temperature to minimize cross-sample contamination¹⁵.
   5. Zero electronic balances before use. Measure fresh weight and dry weight three times. Require relative standard deviation to be ≤2% for fresh weight and ≤5% for dry weight. Determine carbon content at least three times per sample and apply the acceptance criterion specified in Section 3 (carbon content determination).
   6. Perform primary productivity incubations at least in duplicate per site and repeat incubations when measurement error thresholds are exceeded.
3. Data review
   1. Assign two reviewers to audit experimental data. Require the first reviewer to check the completeness of original records (sampling metadata, instrument parameters, and measurement results).
   2. Require the second reviewer to check calculation accuracy (e.g., R_df, DWper, CR, and subsequent conversions).
   3. Record issues identified during audit and implement re-measurement or recalculation where necessary.
   4. Record instrument calibration logs, relative standard deviation of parallel determinations, and abnormal data handling notes as QC evidence, and link these QC records to each sampling batch in the database¹⁶.

7. Data management

1. Establish a standardized database template before fieldwork and use the same template across sites and teams.
2. Record sampling information, including study area name, block number, GPS coordinates of sampling points, sampling date, sampling personnel, and weather conditions.
3. Record environmental parameters, including water temperature, salinity, water depth, light duration (T_light), and dissolved oxygen concentration when available.
4. Record biomass data, including quadrat area (A), quadrat fresh weight (FW_tot), quadrat dry weight (DW_tot), dry-fresh ratio (R_df), biomass per unit area (DW_per), population distribution area (S), and total population biomass (B).
5. Record blue carbon metrics, including carbon content (C), carbon removal rate (CR), short-term carbon sinks (CS_short), corrected long-term carbon sinks (CS_{long_corr}), carbon storage (C_storage), and carbon flux (Fc).
6. Record quality-control data, including instrument calibration records, relative standard deviation of parallel determinations, and abnormal data processing instructions¹⁶.
7. Document growth-stage labels consistently (e.g., vigorous vs. non-vigorous growth period) and record the month/year for each sampling campaign.

Results

Case 1: Assessment of the carbon removal potential of the northern offshore kelp population

Study area and sampling periods: Field sampling was conducted in the offshore kelp aquaculture area of Qingdao, Shandong Province (water depth: 5-8 m). Measurements were collected during a vigorous growth period (May 2022) and a non-vigorous growth period (November 2022).

Sampling layout: Wit...

Discussion

The core strength of this protocol is that it directly addresses methodological variation in existing assessments of macroalgal carbon removal potential. In practice, differences in quadrat specifications, measurement approaches, and calculation models can lead to carbon sink estimates that vary by a factor of two to three even within the same coastal system¹⁷. By standardizing the operational sequence, parameter definitions, and calculation logic, the protocol improves cross-site comparability an...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Electronic balance	METTLER TOLEDO	ML204T	For weighing samples
Elemental analyzer	Thermo Fisher	FlashSmart	For carbon content analysis
Oven	Memmert	UN55	For drying samples
GPS device	Garmin	GPSMAP 65s	For recording locations
Dissolved oxygen meter	YSI	ProODO	For measuring DO levels