Differentiation in Aquatic Metabolism between Littoral Habitats with Floating-Leaved and Submerged Macrophyte Growth Forms in a Shallow Eutrophic Lake
Round 1
Reviewer 1 Report
Review of ‘Differentiation in aquatic metabolism between littoral habitats with floating-leaved and submerged macrophyte growth forms in a shallow eutrophic lake’ by Stefanidis and Dimitriou
General comments
In this study, the authors calculate rates of gross primary production (GPP), respiration (R), and the metabolic balance – Net ecosystem production (NEP) – in four littoral habitats with different macrophyte growth forms (floating-leaved vs submerged) over a 28-month period in a shallow lake in Greece. The authors demonstrate that net heterotrophy dominates over the study period. They demonstrate that temporal variations in the metabolic rates were driven predominantly by the seasonal variation in irradiance and water temperature. The peak of metabolic activity occurred in summer and early autumn. Substantial spatial variation among the four habitats were observed and were associated with the different macrophyte growth forms that occurred in the different sites. The paper provides some important insights which should be of great interest to the scientific community. In particular, the importance of habitat specific characteristics for the assessment of metabolic balance is particularly relevant given the recent interest in the contribution of lakes to the global carbon cycle. Also, the author’s findings of potentially high contribution of littoral habitats to whole-lake metabolism is important. Overall, I think the paper is well-written, the data well-chosen, and the manuscript should be considered for publication.
Specific comments
The introduction is very well-written. The authors might also consider discussing the findings of Andersen et al., (2016), which would complement the findings of this paper.
Andersen, M. R., et al. (2016), Profound daily vertical stratification and mixing in a small, shallow, wind-exposed lake with submerged macrophytes, Aquatic Sciences, doi:10.1007/s00027-016-0505-0
The method section was easy to follow and describes in detail what was done in this study.
Line 131 – Can the authors provide further information on the algorithm used for wind speed adjustment. Specifically, one can either use a scaling algorithm that assumes a neutral atmospheric boundary layer while others correct for atmospheric stability. The latter is a much more accurate methods for this conversion. As an example, see:
Wollway, R. I., et al. (2015), Automated calculation of surface energy fluxes with high-frequency lake buoy data, Env. Modell. Soft. 70, 191-198
Even if the authors apply the simple algorithm for this conversion, I think they should make it clear in the methods that they are aware of the more sophisticated method and, if other would like to perform a similar analysis for their lake, they should use the ‘more accurate’ method, if they have sufficient data.
However, given that you have all the meteorological data you should be able to use the more-advanced method. One of the most important results of the Dugan et al paper was that they demonstrated that the gas flux model chosen can influence considerably k600. So, in line 135 it would be useful if the authors would write that they are aware of these inconsistencies between methodologies and have considered these in their interpretation of the results.
Also, please explain what is meant by ‘provides similar estimates of k600 with those surface renewal models…’ – do the authors mean that they calculated k600 using the different methods or are they referring to another study which has shown this? If the authors could provide an ensemble of k600 values and use these in their study, that would be extremely useful.
Table 1 – It seems strange to include a citation for the latitude of the lake. I think this is unnecessary.
Figure 1 – please include latitude and longitude instead of easting and northing. Also, consider increasing the size of the font within the figure.
Table 3 – should be ’Wind speed at 10 m height’ Line 186 – Was criteria was used to select appropriate models with regards to the AIC?
Author Response
Reviewer 1
General comments
In this study, the authors calculate rates of gross primary production (GPP), respiration (R), and the metabolic balance – Net ecosystem production (NEP) – in four littoral habitats with different macrophyte growth forms (floating-leaved vs submerged) over a 28-month period in a shallow lake in Greece. The authors demonstrate that net heterotrophy dominates over the study period. They demonstrate that temporal variations in the metabolic rates were driven predominantly by the seasonal variation in irradiance and water temperature. The peak of metabolic activity occurred in summer and early autumn. Substantial spatial variation among the four habitats were observed and were associated with the different macrophyte growth forms that occurred in the different sites. The paper provides some important insights which should be of great interest to the scientific community. In particular, the importance of habitat specific characteristics for the assessment of metabolic balance is particularly relevant given the recent interest in the contribution of lakes to the global carbon cycle. Also, the author’s findings of potentially high contribution of littoral habitats to whole-lake metabolism is important. Overall, I think the paper is well-written, the data well-chosen, and the manuscript should be considered for publication.
We thank the reviewer for the fruitful comments and suggestions which we carefully considered for revising the manuscript.
Specific comments
The introduction is very well-written. The authors might also consider discussing the findings of Andersen et al., (2016), which would complement the findings of this paper.
Andersen, M. R., et al. (2016), Profound daily vertical stratification and mixing in a small, shallow, wind-exposed lake with submerged macrophytes, Aquatic Sciences, doi:10.1007/s00027-016-0505-0
Following the reviewer’s suggestion we have considered the findings of Andersen et al. (2017) in the discussion as indeed they are very relevant with the scope and main outcomes of our work. Specifically we added the following lines “Aquatic macrophytes are known to engineer their environment in various ways. For instance Andersen et al. [39] showed that extensive coverage by charophytes in a shallow lake can influence the thermal regime and mechanical mixing within the lake.”
The method section was easy to follow and describes in detail what was done in this study.
Line 131 – Can the authors provide further information on the algorithm used for wind speed adjustment. Specifically, one can either use a scaling algorithm that assumes a neutral atmospheric boundary layer while others correct for atmospheric stability. The latter is a much more accurate methods for this conversion. As an example, see:
Wollway, R. I., et al. (2015), Automated calculation of surface energy fluxes with high-frequency lake buoy data, Env. Modell. Soft. 70, 191-198
Even if the authors apply the simple algorithm for this conversion, I think they should make it clear in the methods that they are aware of the more sophisticated method and, if other would like to perform a similar analysis for their lake, they should use the ‘more accurate’ method, if they have sufficient data.
This is a very good point made by the reviewer. Indeed we used the simple algorithm that assumes a neutral atmospheric boundary layer as described in the publication of Winslow et al. (2016). However we agree that it should be mentioned in our article that there are other more sophisticated ways to calculate the adjusted wind speed. Specifically we added lines 132-135 where we explicitly state that “This simple algorithm, which is based on a neutrally stable boundary layer assumption, is widely used for estimating standard wind speed at 10 m height from lower height measurements and requires less data than other more sophisticated methods that correct for atmospheric stability”
However, given that you have all the meteorological data you should be able to use the more-advanced method. One of the most important results of the Dugan et al paper was that they demonstrated that the gas flux model chosen can influence considerably k600. So, in line 135 it would be useful if the authors would write that they are aware of these inconsistencies between methodologies and have considered these in their interpretation of the results.
We agree with the reviewer. We have already mentioned in the discussion the many uncertainties that accompany the metabolism studies but we also added the following text in lines 136-138: “Recently, it was shown by Dugan et al. [31] that the choice of gas flux model is of critical importance for metabolism studies as there is more uncertainty in model choice than in the parameterization of the metabolism model.” Thus, we hope that we highlight the high importance of the k600 parameter in calculating metabolic estimates.
Also, please explain what is meant by ‘provides similar estimates of k600 with those surface renewal models…’ – do the authors mean that they calculated k600 using the different methods or are they referring to another study which has shown this? If the authors could provide an ensemble of k600 values and use these in their study, that would be extremely useful.
Here we refer to the study of Dugan et al. that showed that the k600 calculated by the simple wind-based Vachon and Prairie model provided similar estimates with those of more sophisticated surface renewal models that take into account more complex processes. Unfortunately these models require additional data that we lacked.
Table 1 – It seems strange to include a citation for the latitude of the lake. I think this is unnecessary.
The citation is for the first four rows in the table, including altitude and retention time. We corrected the alignment of the citation in the table to show that refers to these information as well and not only to latitude and longitude.
Figure 1 – please include latitude and longitude instead of easting and northing. Also, consider increasing the size of the font within the figure.
We have made the suggested changes
Table 3 – should be ’Wind speed at 10 m height’
Yes. We corrected it.
Line 186 – Was criteria was used to select appropriate models with regards to the AIC?
The model with the minimum AIC value was selected as the most appropriate model, meaning a strong and parsimonious model. ΔAIC was also considered and in cases where ΔAIC was lower than 2, meaning that both models are equally good, the model with the highest R2 was selected.
Reviewer 2 Report
The metabolic balance between GPP and ecosystem respiration is one of essential aspects in understanding the process and functionality of lake ecosystems. This paper adopted a routine monitoring approach, providing important dataset for shallow lakes in warm region. The dataset was presented in a neat way and fully analyzed with multiple environmental parameters. I think this manuscripts can be published at its current format in the journal of Water. One comment for the authors. Is it possible to measure the coverage of aquatic plants (e.g. PVI, percentage of vegetation index) in each site? Furthermore, a PVI matrix with most common species can be achived for each site then can be input as one driving factors for metabolism features.Author Response
Reviewer 2
The metabolic balance between GPP and ecosystem respiration is one of essential aspects in understanding the process and functionality of lake ecosystems. This paper adopted a routine monitoring approach, providing important dataset for shallow lakes in warm region. The dataset was presented in a neat way and fully analyzed with multiple environmental parameters. I think this manuscripts can be published at its current format in the journal of Water. One comment for the authors. Is it possible to measure the coverage of aquatic plants (e.g. PVI, percentage of vegetation index) in each site? Furthermore, a PVI matrix with most common species can be achived for each site then can be input as one driving factors for metabolism features.
We thank the reviewer for the positive comments. Regarding the comment, unfortunately we did not have measurements of plant coverage that we could include in the statistical analysis. A modeling approach for estimating aquatic metabolism taking into consideration temporal PVI changes would require very frequent measurements on aquatic vegetation that we lack. Undoubtedly, it would be very interesting to quantify the effect of vegetation coverage changes on aquatic metabolism within future research, preferably through the conduction of carefully designed experiments.
