Dear reviewer,

We appreciate and are grateful for the time to make the revision and the constructive and useful comments provided to improve our manuscript.

Please find below these lines our responses to each comment.

This manuscript presents an interesting study on the ecosystem services provided by an unintentional artificial reef (a submarine environmental monitoring station installed off the southwestern coast of Tenerife (Canary Islands)). The study is novel in that it represents the first assessment of "blue carbon" development and associated ecosystem services in this oligotrophic coastal region of the Canary Archipelago. The authors provide an 18-month dataset of physical, chemical, and biological parameters, quantify benthic biomass and organic carbon accumulation, document 46 species across 11 phyla, and apply the CICES framework to identify ecosystem services.

The topic is relevant to ongoing marine restoration efforts, particularly within the Horizon Europe OCEAN CITIZEN project, and addresses current gaps in understanding blue carbon potential in oligotrophic, subtropical environments. However, the manuscript has several limitations that should be addressed before it can be considered for publication. These include methodological constraints (single, non-replicated installation; sampling losses; sensor issues), statistical shortcomings, limited temporal and spatial contextualisation, and a lack of comparison with appropriate control sites or baseline data.

Methodological Limitations

Single Installation and Lack of Replication

The study relies on a single monitoring station with no replication. This severely limits the statistical power and generalizability of the results. While the authors acknowledge this limitation in the discussion, it is not adequately addressed in the study design or conclusions. The authors should consider whether the results can be considered representative of the broader SW Tenerife coastal zone (as suggested in lines 456-457) given the lack of spatial replication.

The SW coastline of Tenerife is affected by regional-scale events such as calima and upwelling filaments detached from the NW African upwelling, as well as for local-scale underwater sewage discharges. It is true that the study is based on a single installation; however, considering the spatial extent of calima events (e.g. fertilizing consequences), the consistent pattern of the continental shelf bathymetry and the current direction and speed measured at the study site along 18 months, this group of factors leaded us to suggest that the observed blue carbon growth conditions along the entire SW coastline of Tenerife are likely to be similar to those observed at the study site. On line with the reviewer’s comment, we only suggested the potential of the SW coast of Tenerife to experience similar conditions of blue carbon development as those observed in the present study site (line 460 to 463 in the original manuscript).

As explained in the original manuscript in lines 558 to 560 and 573 to 577, the analyses of organic carbon development, among other parameters studied at the installation, were triggered after 7 months of observations on an underwater station that was originally planned to operate as a submarine environmental observatory not as a blue carbon development site; nevertheless, the vigorous blue carbon growth observed on such a short period -7 month-, invited us to study the process. This is why there were no replicas; nevertheless, a second sampling line, RT₁₂, on the body of the sediment trap starting on month 12 provided basis to, at least, corroborate the timing for blue carbon development and to propose a time frame to reach a steady state condition.  In any case, this is not the first time that this type of studies, analyzing blue carbon development with no replicas, has been carried out (Castellan et al., 2026; Villafranca-Sánchez et al., 2025).

Sample Loss and Data Quality Issues

The authors report significant sample losses during collection (due to buoyancy and water currents) and transport (leakage). While the resulting biomass values are presented as "minimum values" (lines 138-140), this introduces considerable uncertainty. The authors should provide quantitative estimates of expected losses or, at minimum, discuss how these losses might affect the interpretation of temporal trends. 

We cannot provide an accurate estimate of sample loss due to the light characteristics of the sample and the difficult sampling conditions (e.g., strong currents and sample buoyancy). Further, we have no parameter or base line to contrast the losses with. We estimate that the amount of biomass produced in T₁₂ and T₁₈ was greater than the results provided in the manuscript attending to the coverage results provided in Figure 9. Therefore, the reported biomass represents the minimum values. The biomass coverage in T₁₂ and T₁₈ was 14.71% and 33.75% greater than in T₇ (Figure 9), respectively. According to these calculations, considering the collected biomass at T₇ as a maximum value, e.g., no evident biomass loss, and using the biomass sampled with the 20x20 cm square in T₇ (0.12 g) and the coverage in T₇ (43.2%), our estimation of the biomass expected in T₁₂ and T₁₈ according to the coverages reported in Figure 9 and assuming a linear biomass growth would be 0.16 g for T₁₂ and 0.21 g for T₁₈. This range of maximum and minimum values would represent the quantifiable uncertainty in this experiment. The present calculations have been included in the results section (new section 3.6) and discussion in the revised version of the manuscript (lines 606-609).

Sensor and Data Gaps

The study experienced several sensor failures: salinity data only from September 2024 (line 244-246), dissolved oxygen data missing for multiple periods (lines 256-258), and current meter failure from September 2024 (line 275). These gaps weaken the temporal continuity of the environmental characterisation. The authors should more thoroughly discuss how these gaps affect the interpretation of the biological data.

According to the salinity data collected from September 2024 to March 2025, the values were within the expected range of shallow coastal waters and between the values of the surface water mass affecting the Canary Islands (Casanova-Masjoan et al., 2020; Pastor et al., 2008), with the exception of the irregular, discontinuous drops (a few hours long) in December 2024 and early January 2025, which were most probably due to underwater sewage discharges (e.g. fresh water intrusions). The irregular and punctual drops in salinity, along with the consistent values throughout the rest of the dataset, leaded us to conclude that the gaps in salinity do not affect the interpretation of the biological data. Similarly, despite its record discontinuity, the dissolved oxygen data showed diurnal variations and similar average monthly values (see Supplement Table S1) in all three periods, as expected for well-oxygenated waters such as those in shallow coastal environments, where the action of waves and wind maintain a well-ventilated water column. As can be seen in the Supplement (Table S1B, Figures S7a and S7b, and Figure 6), the current speed did not follow a seasonal pattern, and showed little variation in its average monthly values. The current direction remained constant throughout the monitoring period.  Based on the entire dataset, we concluded that the data gaps did not affect the interpretation of the biological data. As defended in the manuscript, the entire collection of data demonstrates that, at least all the measured parameters at the station are no limiting factors for blue carbon development, which is the main message of the section.

Statistical Analysis

Weak Correlation Testing

The Spearman correlation analysis between current speed and turbidity (rho = -0.014) and between chlorophyll-a and turbidity (rho = 0.761) is presented with minimal interpretation (lines 350-356). The claim that the chlorophyll-a-turbidity correlation "should be considered" due to sensor housing (line 354) suggests these sensors may share instrumental artefacts, yet this is not adequately explored. The authors should provide more detailed statistical analyses, including seasonal variations in correlations and potential lag effects (e.g., the 2-5 day delay between calima and chlorophyll peaks mentioned in lines 533-534).

The statistical analysis performed in this study was used to establish a relationship between the entire current speed and turbidity dataset as well as among chlorophyll-a and turbidity. Although, the scope of the statistical analysis was not to establish a weekly or seasonal-scale correlation, we expanded the analysis to provide numerical reinforcement of the hypothesis that calima may affect chlorophyll-a abundance and a potential lag effect among the calima and chlorophyll-a peaks mentioned in the text. The section 2.6 was modified (lines 189 to 195 in the revised version), the results section was extended (lines 407 to 414 and table S3 and S4 in the Supplement). The new results were used in the discussion (lines 567 to 575).

On the other hand, the comment regarding the housing of the sensors, the reviewer comment is appropriate and this hypothesis has been removed from the text (lines 381-383 in the original manuscript) because it is rather speculative. At the time of the deployment both sensors were new and factory calibrated; therefore, the is no reason for voltage (technical) failures.

Contextualization and Comparison

Limited Comparison with Other Blue Carbon Ecosystems

The authors compare their carbon sequestration values (0.24-0.40 g C m⁻²) with seagrass meadows, salt marshes, mangroves, and Mediterranean gorgonians (lines 589-598). This comparison is valuable but would be strengthened by: (1) more detailed discussion of why the authors chose these particular comparators; (2) consideration of other oligotrophic benthic ecosystems (e.g., coralligenous assemblages, rhodolith beds); and (3) discussion of whether the study site's values are truly "among the lowest globally" (line 601) or comparable to other similar oligotrophic environments.

Undoubtedly, the blue carbon generation values in the studied site are among the lowest globally because they are orders of magnitude smaller than in the largest blue carbon ecosystems (BCE, e.g., salt marshes, seagrass meadows and mangroves). In the manuscript, the comparation with the main BCE and gorgonian forests was based on the abundant information regarding carbon production, stocks and the spatial extent of these ecosystems, which is lacking for other less productive ecosystems. Therefore, we considered the BCE as the most suitable reference for a study of this kind. Information on oligotrophic ecosystems with similar characteristics as those in the present study is still scarce relative to BCE. Nevertheless, reviewer’s comment is adequate and we have included information on other oligotrophic ecosystems such as rhodolith beds and coralligenous assemblages to provide a more robust frame to allocate the values in the present study. This information is included in lines 641 to 643 in the revised version of the manuscript.

Need for Baseline Data

The authors note that "there is no information on blue carbon development in the area to compare" (lines 563-564). This is understandable given the exploratory nature of the work, but the manuscript could include comparison with: (1) nearby natural rocky substrates; (2) other artificial structures of similar age in the Canary Islands; or (3) modelled estimates based on environmental parameters. The "control station" (nearest rock, approximately 2 m away) mentioned in the biodiversity methodology (line 160) is not described in the biomass or blue carbon methods, representing a missed opportunity for comparative assessment.

The nearest rock or nearby rocky substrates were not considered as a control station of blue carbon because the T0 was not known. Also, there’s no artificial installation with the similar age and characteristics in Canary Islands to compare with, neither studies with quantifiable observations. The blue carbon assessment in the present study was done based on the T₀ (the day of deployment) of the station itself and a subsequent systematic sample collection to evaluate the biomass growth (and therefore, the blue carbon developed during the time of the study of 18 months). The monitoring of biomass growth on this artificial structure provided useful information on blue carbon development and ecosystem services provision on oligotrophic environments and their potential for successful benthic ecosystem restoration efforts. In our opinion the study itself represents a baseline for blue carbon development, at least for southern Tenerife in the Canary Islands.

Ecosystem Services Assessment

CICES Application

The authors apply the CICES framework to identify five ecosystem services (primary production, nursery habitat, biodiversity maintenance, climate regulation, knowledge). This is appropriate, but the selection of indicators could be more clearly justified. The authors cite multiple sources for indicator selection (lines 197-200) but do not explain why some indicators were prioritised over others. For example, the "nursery habitat" service is identified based on "presence of juveniles" without quantitative assessment of juvenile abundance, survival, or contribution to adult populations.

Based on the consulted bibliography and according to the CICES method, we selected the indicators for this study that we directly measured and observed. Based on the CICES framework we adjusted our results accordingly to the various CICES classes without priorities, we simply adjusted CICES classes to ecosystem services reference definitions already accepted in the literature (e.g., Millenium Ecosystem Assessment, 2005). The matches between CICES classes and our indicators was based on literature validated studies and assessments (lines 200 to 204 in the original version of the manuscript).

The presence of juvenile fishes was used as an indicator of nursery habitat, because the quantification, survival or contribution to adult populations (e.g., follow-up of the nursery power) was not in the scope of this work. In this case, this qualitative indicator was only used to easily identify the provision of this ecosystem service.

Supporting vs. Regulating Services

The authors classify "biodiversity maintenance" as a supporting service (following CICES 5.1). However, biodiversity maintenance could also be considered a "regulating and maintenance" service in CICES 5.1, which may cause confusion. The authors should clarify this classification and justify their interpretation.

In the original version of the manuscript, the classification of biodiversity maintenance as a supporting ecosystem service was used based on current literature usage, however this classification is not used in the CICES method introducing confusion in the manuscript when using the CICES section definitions. Based on the reviewer’s comment and to be consistent along the manuscript, we maintained the CICES section terminology.

Interpretation of Grazing and Carbon Transfer

The calculation of carbon transferred via grazing (section 3.5, lines 337-342, Table 6) is an innovative aspect of the study. However, the methodology for this calculation is not fully described. How exactly were "grazed biomass" and "no-grazed" areas distinguished from the photographs? The classification appears to be based on "dental scratching pattern on the fiberglass surface" (line 337), but this could be subjective. A more systematic approach to quantifying grazed vs. ungrazed areas (e.g., point-intercept analysis, detailed image classification) would strengthen this analysis.

The absence of biomass (white color) combined with the observation of Serranus atricauda grazing over the station surface (curvy shape) made us identify the grazed biomass areas. For the quantification of grazed and non-grazed areas, the systematic method used was the “Supervised classification analysis” in ArcMap 10.8 software and then the Maximum Likelihood classification algorithm was applied to quantify the size of the grazed patches (white colored areas without blue carbon, where the color of the fiber glass sediment trap body is visible) and the non-grazed areas (areas with the color of the algae). The application of this algorithm identified and classified, after an initial training, the white and algae-colored areas in two different categories. Then the number of pixels for every category were calculated. As a result, we finally obtained the percentage of grazed and non-grazed areas. This detail has been introduced in the methodology of the revised version of the manuscript in the lines 175-179, 183 and 341.

General comment

This manuscript addresses an important topic and provides valuable preliminary data on blue carbon development and ecosystem services in an understudied oligotrophic environment. The study is well-intentioned, and the data collection effort (18 months of environmental monitoring, periodic biomass sampling, and biodiversity surveys) represents a significant contribution.

However, the methodological limitations (single installation without replication, sample losses, sensor gaps) and the largely descriptive nature of the analysis mean that the conclusions should be tempered. With careful revision, particularly regarding the quantification of uncertainty, stronger statistical analysis, and better contextualisation of results, this manuscript would be suitable for publication.

We are grateful for the constructive comment of the reviewer, which greatly improved the quality of the manuscript. In the revised version we introduced information on the quantification of uncertainty, new statistical analysis (e.g., on seasonal basis) and rewrote some paragraphs in the Results and Discussion sections to better contextualize the results and tempered the conclusions (e.g., the study site rather than the SW coast of Tenerife).

References

Casanova-Masjoan, M., Pérez-Hernández, M. D., Vélez-Belchí, P., Cana, L., & Hernández-Guerra, A. (2020). Variability of the Canary Current Diagnosed by Inverse Box Models. Journal of Geophysical Research: Oceans, 125(8). https://doi.org/10.1029/2020JC016199

Castellan, G., Taviani, M., Montagna, P., Foglini, F., Paladini de Mendoza, F., Langone, L., Giordano, P., & Miserocchi, S. (2026). In situ growth rates of cold-water corals fouling oceanographic moorings in the Central Mediterranean Sea. Scientific Reports. https://doi.org/10.1038/s41598-025-34582-3

Millenium Ecosystem Assessment. (2005). Ecosystem and human well-being: Opportunities and Challenges for Business and Industry. World Resources Institute.

Pastor, M. V., Pelegrí, J. L., Hernández-Guerra, A., Font, J., Salat, J., & Emelianov, M. (2008). Water and nutrient fluxes off Northwest Africa. Continental Shelf Research, 28(7), 915–936. https://doi.org/10.1016/j.csr.2008.01.011

Villafranca-Sánchez, P., Guijarro-Garcia, E., & Giménez-Casalduero, F. (2025). Population structure of the deep coral Desmophyllum dianthus associated with a lost fishing gear/ line. Regional Studies in Marine Science, 85. https://doi.org/10.1016/j.rsma.2025.104173

Dear reviewer,

We appreciate and are grateful for the time dedicated to review and the constructive and useful comments provided to improve our manuscript.

Please find below these lines our responses to each comment.

The study presents a suite of variables in a long time series along the coast of Tenerife. The combination of physico-chemical variables and biological species monitoring increases the value of this study. Because of the large amount of data, parts of the manuscript sometimes read as a report, although the discussion is generally fine. Maybe the authors could try to modify to some extent. The results are definitely useful as a background for further studies in this larger region.

We appreciate the reviewer’s comment. We have retained the comprehensive dataset because the combination of physical-chemical and biological variables provides an essential characterization of the environmental conditions in which the blue carbon dynamics were investigated. This dataset establishes an environmental baseline for the study area and provides the necessary context for interpreting the observed patterns in blue carbon-related variables. We agree that the large amount of data may give some sections of the manuscript a report-like character; however, and in agreement with the present comment, we consider that the comprehensive dataset is valuable as a baseline for future studies in the region.

What I am missing is a description of the methods, but in particular the precision or accuracy of the different measurements. The reader must be able to assess the significance of the results.

The accuracy and/or precision of the different measurements has now been included in the Methods sections. Information on the accuracy of the sensors used in this study (all the instruments were purchased for the study) was obtained from the technical specifications provided in the manufacturers’ instrument manuals. For the satellite-derived observations, information on accuracy and uncertainty was obtained from the scientific literature, as the satellite data providers did not provide this information directly with the datasets used in this study. We have therefore included references to studies that evaluated and validated the satellite products with in situ measurements. The corresponding information and references have been incorporated into the revised manuscript.

Subsection 3.1.6 The concentration of dissolved oxygen is usually given in µmol/kg or µmol/L

The dissolved oxygen results in Section 3.1.6 are reported in mg/L because the instrument used in this study directly provided the measurements in this unit. The literature used to contextualize our results also reports dissolved oxygen concentrations in mg/L, allowing for direct comparison with the published data. Therefore, we have retained mg/L throughout this section for consistency with both the original measurements and the reference data.

We are grateful to the reviewer for the constructive comments, which have greatly contributed to improving the quality of the manuscript. In the revised version, we have included information on the accuracy and/or precision of the different measurements. Section 1.1 has also been moved to Section 2 (Methods), and all the minor changes suggested by the reviewer have been addressed. In particular, Figures 4 and 5 have been revised to improve the readability of the axis and the overall clarity of the figures. We believe that these revisions have improved the clarity and quality of the manuscript and have addressed the reviewer’s concerns.