the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Oct 2023
Environmental controls on the brGDGT and brGMGT distributions across the Seine River basin (NW France): Implications for bacterial tetraethers as a proxy for riverine runoff
Abstract. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are bacterial lipids that have been largely used as environmental proxies in continental paleorecords. Another group of related lipids, branched glycerol monoalkyl glycerol tetraethers (brGMGTs), has recently been proposed as a potential paleotemperature proxy. Nevertheless, the sources and environmental dependencies of both brGDGTs and brGMGTs along the river-sea continuum are still poorly understood, complicating their application as paleoenvironmental proxies in aquatic settings. In this study, the sources of brGDGTs and brGMGTs and the potential factors controlling their distributions are explored across the Seine River basin (NW France), which encompasses the freshwater to seawater continuum. To this aim, brGDGTs and brGMGTs were analyzed in soils, Suspended Particulate Matter (SPM) and sediments (n=237) collected all along this basin, from land to sea. Both types of compounds are shown to be produced in situ, in freshwater as well as saltwater. Redundancy analysis further shows that both salinity and nitrogen loadings dominantly control the brGDGT distributions. Furthermore, the relative abundance of 6-methyl vs. 5-methyl brGDGTs (IR6Me ratio), Total Nitrogen (TN), δ15N and chlorophyll a concentration co-vary in a specific zone with low salinity, suggesting that 6-methyl brGDGTs are preferentially produced under low-salinity and high-productivity conditions. In contrast with brGDGTs, brGMGT distribution appears to be primarily regulated by salinity, with a distinct influence on the individual homologues. Salinity is positively correlated with homologues H1020a and H1020b, and negatively correlated with compounds H1020c and H1034b. This suggests that bacteria thriving in freshwater preferentially produce compounds H1020c and H1034b, whereas bacteria primarily growing in saltwater appear to be predominantly responsible for the production of homologues H1020a and H1020b. Based on the abundance ratio of the freshwater-derived compounds (H1020c and H1034b) vs. saltwater-derived homologues (H1020a and H1020b), a novel proxy, Riverine IndeX (RIX) is proposed to trace riverine organic matter inputs, with high values (>0.5) indicating higher riverine contribution. RIX was then applied to the Godavari River basin (India) and a paleorecord across the upper Paleocene and lower Eocene, showing its potential applicability in both modern samples and in paleorecords.
Status: closed
-
RC1: 'Comment on egusphere-2023-2367', Anonymous Referee #1, 21 Nov 2023
In this manuscript, Zhang et al propose a new proxy to reconstruct fluvial organic matter inputs to coastal marine settings. They suggest that brGMGTs are produced in-situ in rivers and estuaries and that the distribution of brGMGTs is principally controlled by salinity. Based on these facts they generate a new Riverine Index (RIX) using the fractional abundances of H1020c and H1034b versus H1020a and H1020b. To validate the RIX in deep time they compare RIX values to the BIT index and terrestrial pollen/spore deposits deposited during the PETM from IODP Expedition 302 Hole 4A. They report a closer relationship between RIX and terrestrial pollen abundance than BIT and terrestrial pollen abundance, indicating that at least in this site RIX outperforms BIT in accurately reconstructing riverine inputs. In all, this is an interesting study that will likely be of interest to BG readers. I have a number of comments that aim to strengthen the manuscript.
General comments:
In some sections (see specific comments) the use of English in the paper is poor and obfuscates the meaning of the text. I suggest that the authors carefully read through the manuscript to catch all typos and grammatical errors. Likewise figure quality varies considerably. In some cross plots, it is impossible to see the data as the marker size is so small (see specific comments). Characters that should be superscripted/subscripted are left as regular text (see specific comments). Lines of best fit are drawn through data but there is no information as to how these lines were constructed (see specific comments). As such, this manuscript would benefit greatly from more attention to detail from the authors.
Additionally, as the authors are proposing a new GDGT salinity index, I would like them to calculate and report previously formulated salinity indices from their samples. Specifically, the ACE index (Turich and Freeman 2011) and the IR6+7me (Wang et al 2021) both have been calibrated against water salinity in marine saline ponds and hypersaline lakes respectively. I know the author's brief touched on comparing IR6me from this study to values from Wang et al (2021) in the text but a more thorough examination of prior GDGT-derived water salinity reconstructions would strengthen the manuscript. Readers will be interested to see how these indices compare against RIX in reconstructing salinity from an estuarine environment.
Additionally, the evidence for in situ production of brGDGTs and brGMGTs in downstream estuary sites is pretty weak. This is demonstrated by Fig 2 where we see that distributions of d13Corg and d15N in soils and downstream estuary samples are very similar in addition to Fig 5 where your PCA on sample brGDGT and brGMGT distributions cannot separate out soils from downstream estuary samples. Yes you see (on average) higher concentrations of brGDGTs and brGMGTs in downstream estuary samples than in soils but the actual distributions of brGDGT and brGMGT abundance in soils are pretty large, indicating that some soils have pretty substantial quantities of these compounds. A great way to add more clarity to this sourcing issue is to train a random forest model using a similar method to Martinez-Sosa et al (2023) on your brGDGT and brGMGT samples (and isoGDGTs as these should be available to you). If the random forest model can accurately separate out soils from downstream estuary samples then you can be pretty sure that your downstream estuary samples were produced in situ. This won’t require much additional work and can be implemented easily using python (https://scikit-learn.org/) or another language of your choice.
Specific comments
Line 35: This complicates paleoenvironmental interpretations in SOME aquatic settings not ALL aquatic settings
Line 37: “all along this basin, from land to sea” awkward phrasing
Line 40: “Redundancy analysis further shows that both salinity and nitrogen loadings dominantly control the brGDGT distributions.” No, the loadings indicate that SALINITY (not salinity loadings) controls the brGDGT distribution.
Line 40-43: “Furthermore, the relative abundance of 6- methyl vs. 5-methyl brGDGTs (IR6Me ratio), Total Nitrogen (TN), δ 15N and chlorophyll a concentration co-vary in a specific zone with low salinity” Is this zone geographical, in your redundancy analysis, or something else?
Line 44-45: “Salinity is positively correlated with homologs H1020a and H1020b, 45 and negatively correlated with compounds H1020c and H1034b.” Is this in soils, sediments or SPM?
Line 45: “This suggests that bacteria thriving…” thriving is not the correct word (carries implications of a value judgment) replace with “living”.
Line 45-47: It seems like you aren’t mentioning results from soils and sediments, only from SPM? Or maybe all your sediment samples are exclusively from rivers? The reader is unclear on this.
Line 51-52: “a paleorecord across the upper Paleocene and lower Eocene,” You should name this record and say where it is.
Lines 51: “showing its potential applicability in both modern samples and in paleorecords.” Perhaps you could evaluate its usage in both these cases e.g. - we successfully/unsuccessfully applied RIX in …
Line 55: “, although some of them were attributed to the phylum Acidobacteria” Imprecise language.
Line 57-58: “The distribution of brGDGTs (number of cyclopentane moieties and methyl groups; cf. structures in Fig. S1) was empirically linked with pH and Mean Annual Air Temperature” Again, imprecise language. The phrase “empirically linked” doesn’t convey much useful information.
Line 60: Should really cite some earlier lake GDGT papers in addition to Martinez-Sosa et al., 2021.
Lines 60-61: “The brGDGT-based proxies (i.e. MBT’5ME and CBT’) have been largely applied to reconstruct MAAT and pH from sedimentary archives (Coffinet et al., 2018; Harning et al., 2020; Wang et al., 2020).” Not quite true - Martinez-Sosa et al (2021) and Dearing Crampton-Flood et al (2020) generated Bayesian linear regressions between the Mean temperature of months above freezing and MBT’5me. These BayMBT models have been used widely in the community since their publication.
Line 62-63: “In aquatic settings, brGDGTs were initially suggested to be predominantly derived from watershed soils and transported by erosion in the sediments (Hopmans et al., 2004).” Maybe you mean “transported by erosion to the sediments”?
Lines 63-78: The use of English throughout this paragraph is poor and hard to follow. Needs copyediting.
Lines 63-78: You should read and cite Martinez-Sosa et al (2023) here for their work on a Random Forest approach to classify GDGT sources (i.e. Marine, Soil, Lake etc).
Line 80-83: “The improvement of analytical methods allowed the separation and quantification of 5-, 6- and 7-methyl brGDGTs (methyl groups at the fifth, sixth, and seventh positions; Fig. S1), that in previous chromatographic protocols co-eluted (De Jonge et al., 2013, 2014; Ding et al., 2016).” No real link between the previous paragraph and this one. Also, which methods? How did they improve?
Lines 86-87: “In addition to temperature and pH, other environmental factors may influence brGDGT distributions in terrestrial and aquatic settings and hence the application and interpretation of brGDGT-derived proxies” This is a repetition from earlier in the introduction.
Lines 91-99: You should mention that brGMGTs have previously been called H-brGDGTs in the literature.
Lines 91-111: This paragraph was very well written and is an example of the standard the entire manuscript should meet.
Lines 117-123: You go from talking about the hypothesis you aim to test in the paper to talking about the aims of the paper. Surely your aim is to test the hypothesis you have just laid out - why do we need to talk about more aims here?
Line 125-126: “by high population density”. High population density of what?
Line 127: “macrotidal”. Please define this term.
Figure 1: I really like this figure - it nicely summarizes your water sampling campaign.
Line 167: “Both decarbonated and non-decarbonated samples (~6 mg for SPM and ~20 mg for soils) were enclosed in a tin capsule” You should mention that you will split your samples and decarbonate one aliquot. Otherwise, the reader is confused as to where your non-decarbonated samples are coming from.
Line 172-174: “The isotopic composition (δ 13C or δ 15N) was expressed as the relative difference between isotopic ratios in samples and in standards (Vienna Pee Dee Belemnite for carbon or atmospheric N2 for nitrogen)” Should be “and atmospheric N2…”.
Line 176: What were these “additional…analyses”? Do you mean the same analyses aforementioned but on different samples, or different analyses on different samples?
Line 177: “(4-20g, n=51)” Looks to me like you’ve used the minus sign, not the en dash here. If so use the en dash.
Line 180-183: “The total lipid extracts were then separated into fractions of increasing polarity on an activated silica gel column, using (i) 30 mL of heptane, (ii) 30 mL of heptane:DCM (1/4, v/v), and (iii) 30 mL of DCM/MeOH (1/1, v/v) as eluents.” That seems like a nonstandard amount of solvent. Are you using very large columns here? If so state how many g of silica gel were used.
Line 233: Vegan should be vegan. No capital V.
Lines 240-243: I don’t think you effectively explain how your hierarchical partitioning method actually works here. As some readers won’t be familiar with this method, more details are needed.
Figure 2. I really don’t like how the axis of these plots has been extended to include chart labels. The top left panel scale is completely distorted by the addition of these labels. You should also define the features of your “boxes” in your box plot. These comments apply to all boxplots in the manuscript.
Line 268: “The different brGDGTs were detected in all studied samples” Which brGDGTs?
Line 275: “The relative abundances of the brGDGTs were determined all along the Seine River basin” I feel like this sentence should be at the start of this section not in the second paragraph.
Line 290: “which explained 40.9% of the variance in two dimensions” Which two dimensions are these?
Line 291: “Samples from the downstream estuary clustered well” Colloquial language, you should describe the data using words that don’t convey a value judgment.
Line 314-315: “It allowed to explain 39.79% of the variability through two dimensions.” Doesn’t make sense - please proofread your manuscript.
I feel like you have just randomly placed the figures in the text. You should line up the first in-text citation of a figure with the location of the figure in the manuscript. Currently, the text and the figures are out of sequence which makes reading this document a challenge.
Figure 5: Visually this figure is quite busy. I don’t think having the brGDGT names in blue (the same colour used for the downstream bubble) helps. I would use black for these names and also the arrows.
Line 336: “The brGMGTs identified in previous studies” Which brGMGTs and which studies? This lack of precise usage of language is present throughout the text.
Line 343-345: “In SPM and river channel sediments, the total brGMGT concentration was observed to be slightly higher in the riverine part (0.26 ± 0.24 μg/g Corg) than in downstream (0.20 ± 0.13 μg/g Corg) and upstream estuary samples (0.17 ± 0.18 μg/g Corg; Fig. S4b).” Slightly higher but not significantly higher. If it’s not significant you should say so.
Line 346: “The PCA analysis based on the brGMGT relative abundances (Fig. 5b) explained 70 % of the variance”. I’m unsure what the authors are trying to say here but I think they mean that the first two PCs sum to 70%. The second half of the sentence “which allows to observe that samples from the different parts of the basin clustered well apart from each other.” doesn’t make sense and I’m unsure what the authors are trying to say.
Line 357: “allows to explain” This phrase doesn’t make sense in this context - please remove all uses of it from the manuscript.
Lines 406-408: “The similarity in distributions between soils and downstream samples may be due to the overrepresentation of downstream soil samples, as 82% of the soils were collected downstream (Fig. 1a and Table 1).” I don’t understand your point here. Are you saying that the similarity between downstream estuary brGDGT distributions and soil brGDGTs is because the downstream estuary predominantly receives brGDGTs from downstream soils?
Lines 409-412: “Nevertheless, the soil-derived brGDGT contribution to the downstream samples is expected to be much lower than the autochthonous one, as the average brGDGT concentration in soils was ca. 3 times lower than the one in downstream (i.e. SPM and river channel sediment) samples (Fig. S4a).” Right, but it’s curious that the distributions are so similar between brGDGTs in soils and downstream estuaries. To bring more clarity to this point it would be interesting to see you attempt a machine learning approach (see Martinez-Sosa et al 2023, PP) to investigate whether (or not) a random forest model can distinguish soil samples from downstream estuary samples.
Lines 426-429: It would be great to see you calculate and report IR6+7me following Wang et al (2021) to determine if these indices correlate to salinity in an estuarine location.
433-436: “The distinct behavior of 6-methyl brGDGTs between lakes and the Seine river-sea continuum might be due to the lower salinity range in the Seine River basin (0-32 psu) vs. the lakes (0-376 psu) 435 investigated by Wang et al. (2021). This suggests that the limited range of salinity variation in the Seine River basin might be insufficient to trigger significant 6-methyl brGDGT production, as observed in hypersaline lakes.” This is actually incorrect. Wang et al 2021 report that IR6me is sensitive to salinity in the range of 5-1000 (mg/L) but relatively insensitive beyond this range.
458-460: “As the nutrient concentration is higher in the upstream part of the Seine estuary (Wei et al., 2022), the substantial 6-methyl brGDGT production observed in the aforementioned zone (260 460 < KP < 340, Fig. 8)” Right but why would the nutrient runoff be higher for this specific section of the basin? Do we see more agricultural activity here or something? It would be good to try and flesh out this point.
Figure 8 and throughout: Make sure to superscript 15 in d15N and subscript 6 in IR6me.
509-510: “The current knowledge on the parameters controlling the brGMGT distributions in the terrestrial and marine realm is still limited.” Why is it limited? Be specific.
Fig 9: Almost impossible to see the data points on some of the figure panels (e.g. panel e). Make the points bigger. Also, keep a consistent label text size to make the figure look neater. Also, you should say in the caption how you constructed the straight lines drawn through the data in some panels (e for instance). I’m assuming this is a linear regression but you have to inform the reader of your methods.
557-558: “ However, the average concentrations of brGMGTs are an order of magnitude lower in the soils than in the river channel sediments and SPM samples of the Seine basin (Fig. S4b).” Maybe it is, but visually it doesn’t look like that, so include the numbers in this sentence. You can also argue that the brGMGT abundance within soils varies by an order of magnitude. Do you know what is driving such a large variance in the soil brGMGT abundance?
589: Missing the word “index” after BIT
You need a map showing the location of IODP 302 Hole 4A
Lines 605-607: “This core is considered proximal to the coast and has considerable changes in terrestrial inputs (i.e. continental spores and pollen) over time (Sluijs et al., 2009, 2006), making it a suitable paleorecord for testing runoff proxies.” Again would be great to have some specifics. The readers will be interested in how close this core site was to the coast around the PETM. You should also say why there was a considerable change in terrestrial inputs (I’m assuming large changes in sea level are responsible).
Lines 616-617: “Such decreased runoff during the PETM body was previously attributed to a local sea level rise” Ah here is the explanation - this should have been in the previous paragraph. Also, be specific, are you saying there was decreased runoff during the PETM, OR did your sediment core record decreased runoff due to a change in sea level? These are two different things.
References:
Martínez‐Sosa, P., Tierney, J. E., Pérez‐Angel, L. C., Stefanescu, I. C., Guo, J., Kirkels, F., ... & Reyes, A. V. (2023). Development and application of the Branched and Isoprenoid GDGT Machine learning Classification algorithm (BIGMaC) for paleoenvironmental reconstruction. Paleoceanography and Paleoclimatology, 38(7), e2023PA004611.
Wang, H., Liu, W., He, Y., Zhou, A., Zhao, H., Liu, H., Cao, Y., Hu, J., Meng, B., Jiang, J., Kolpakova, M., Krivonogov, S., and Liu, Z.: Salinity-controlled isomerization of lacustrine brGDGTs impacts the associated MBT5ME’ terrestrial temperature index, Geochimica et Cosmochimica Acta, 305, 33–48, https://doi.org/10.1016/j.gca.2021.05.004, 2021.
Citation: https://doi.org/10.5194/egusphere-2023-2367-RC1 - AC1: 'Reply on RC1', Zhe-Xuan Zhang, 22 Dec 2023
-
RC2: 'Comment on egusphere-2023-2367', Anonymous Referee #2, 25 Nov 2023
The authors analyzed brGDGTs and brGMGTs in soils, suspended particulate matter, and river sediments in the Seine River basin to evaluate the environmental controls on and sources of these lipids. The basin ranges from freshwater to estuarine, allowing the authors to evaluate the effects of salinity on the GDGT compositions. The major motivation seems to be development of a new GMGT index, called “RIX”, to detect terrestrial inputs of GMGTs to marine environments. The authors test this index through application of Cenozoic sections of an IODP site.
There is now a relatively large literature on the environmental controls on GDGTs, though there is less on GMGTs, and combining these across a riverine salinity gradient is a strength of the paper. Overall I think the paper does provide some novel contributions and findings that merit publication. That said, there are a number of technical problems that will require major revision before the paper can be published.
First, the Seine basin is complicated by the presence of a dam that separates sections of the river influenced by tides (salinity) from sections upstream. The dam also presumably traps upstream sediment and likely presents a barrier for transport of GDGTs (other than SPM). The authors also have relatively few soil sampling sites – there are only 5 sites and the soil samples are dominated by downstream estuarine soils. I don’t think these challenges are adequately discussed in the paper. The dam may be a good thing for the study, since it establishes clear environmental boundaries, but it could be tricky to apply a GDGT index from this environment to other sites/time periods.
Second, there are a lot of data / statistical difficulties with this paper, the details of which are discussed below. At times the authors compare concentrations of GDGTs to evaluate in situ production, which is generally not a good way to do this due to the effects of sediment transport from soils to river to estuaries – concentrations may be higher in SPM than soils, for instance, as SPM contains less coarse-grained particles. Although the writing is a bit unclear, the authors appear to compare results of two PCAs, one on soils and one on aquatic samples, to differentiate these two sample types, which is not possible given how PCA works.
Third, Section 4.4 compares the application of the RIX to IODP site 302 to results from other measurements, such as the BIT and % terrestrial palynomorphs. The comparison is largely qualitative, and it’s hard to tell from Figure 11 how well these compare in a statistical sense. Could the authors provide correlation coefficients to show that the RIX captures terrestrial inputs?
Detailed comments:
Section 2.2. It is a bit hard to tell from this description and the table exactly what samples were collected and analyzed. I take it from the description that 1) subsurface SPM was collected from every green dot (correct?). 2) deeper water SPM was filtered from 5 sites (perhaps these could be indicated in the table), 3) Sediment samples from 8 cores were collected. I cannot tell from the description what depth in the core these samples were taken from (10 cm?), nor how 8 cores yielded n = 68.
Perhaps the dots could be color coded to indicate what types of samples exist (surface SPM, subsurface SPM, these + sediment). It might also be helpful to designate the environment type (river, upstream estuary, downstream estuary) on the map. It would be particularly helpful to indicate the city of Poses/location of the dam on this map.
What differentiates “upstream estuary” and “downstream estuary”? Is this salinity? Or judgement?
Line 237: “correlations” here should be “relationships”. These are not correlations in the statistical sense.
Line 271? Should this be “decreased in the downstream estuary” samples (not just “downstream”)? Having defined upstream estuary and downstream estuary it is good to stay with these terms.
Line 290. “Negative loadings” is confusing. On which axis? Both? I suggest describing the results by axis – first axis 1, then 2.
Figure 3 is not particularly helpful to the reader. If the authors wish to retain it, I suggest moving it to supplemental text.
Results:
The results of the “bulk parameters” describes the elemental and bulk stable isotopic composition of the solid samples. Nowhere does the paper describe results of other environmental parameters – temperature, etc. It would be helpful to have at least a table indicating the mean and range of these. I expect, for instance, that there is a large range of salinities associated with these samples and a very narrow range of temperatures (they are all close to each other).
The treatment of the soils samples in the analysis and results is difficult to understand. It appears that a large number of soils (up to 34) was taken from some sampling sites, whereas at others 1 sample was taken. These data were then analyzed via PCA separately from the aquatic samples, and the PCA was overlayed onto the PCA of the aquatic samples. The authors conclude that the PCAs show that the GDGT distribution of soils overlap with the SPM and channel sediments. It the PCAs were done separately, one cannot simply overlay the biplots and conclude that they overlap – the PCAs may capture different variance structures such that the PCA axes are not the same. If the authors wish to compare the soils and aquatic samples, do a PCA on all the data together. It’s always possible to do a second PCA excluding the soils to evaluate the variance structure of the aquatic samples alone.
Line 290: “explained 40.9% of the variance in two dimensions”. What is meant by this? Based on the plot, axis 1 captures 40.9% of the variance and axis 2 13.2%.
Line 346: Similar problem. I think the authors mean that axes 1 and 2 capture 71%. The PCA will capture more than this on axes 3 - ???
Similar problems exist in the description of the RDA, Section 3.3
4.1.1. Why do the authors focus on the 6-methyl brGDGTs here?
Line 390: The authors suggest that the higher abundances of 6-methyl brGDGTs in upstream vs. downstream samples may reflect degradation:
“It may reflect the fact that riverine 6-methyl brGDGTs are more easily degraded than soil-derived homologues and only partially transferred downstream.”
Why would 6-methyl brGDGTs produced in a river degrade faster than those produced elsewhere? The authors argue that this could reflect attachment to particles – but how do these particles differ in upstream vs. downstream river environments.
It seems likely that production of the 6-methyl compounds is suppressed in downstream environments and the dam traps the upstream sediments (and lipids). Can the authors show that this is not the case?
Line 405. Here the authors suggest that the brGDGT distributions in estuarine soils is similar to that of the downstream samples, based on the PCA (see comment above about the PCA). In the next section (4.1.2), this is not discussed and instead production of the brGDGTs in saline environments is the primary factor accounting for compositional differences in upstream vs. downstream samples. Please coordinate these ideas.
Line 487, 559: One cannot conclude from concentrations alone that the GMGTs are produced in aquatic environments. Soils contain abundant coarse clastic material that may be lost in the fine SPM and river sediment. The distributions (relative abundances) of GMGTs are key to identifying in situ production.
Citation: https://doi.org/10.5194/egusphere-2023-2367-RC2 - AC2: 'Reply on RC2', Zhe-Xuan Zhang, 22 Dec 2023
Status: closed
-
RC1: 'Comment on egusphere-2023-2367', Anonymous Referee #1, 21 Nov 2023
In this manuscript, Zhang et al propose a new proxy to reconstruct fluvial organic matter inputs to coastal marine settings. They suggest that brGMGTs are produced in-situ in rivers and estuaries and that the distribution of brGMGTs is principally controlled by salinity. Based on these facts they generate a new Riverine Index (RIX) using the fractional abundances of H1020c and H1034b versus H1020a and H1020b. To validate the RIX in deep time they compare RIX values to the BIT index and terrestrial pollen/spore deposits deposited during the PETM from IODP Expedition 302 Hole 4A. They report a closer relationship between RIX and terrestrial pollen abundance than BIT and terrestrial pollen abundance, indicating that at least in this site RIX outperforms BIT in accurately reconstructing riverine inputs. In all, this is an interesting study that will likely be of interest to BG readers. I have a number of comments that aim to strengthen the manuscript.
General comments:
In some sections (see specific comments) the use of English in the paper is poor and obfuscates the meaning of the text. I suggest that the authors carefully read through the manuscript to catch all typos and grammatical errors. Likewise figure quality varies considerably. In some cross plots, it is impossible to see the data as the marker size is so small (see specific comments). Characters that should be superscripted/subscripted are left as regular text (see specific comments). Lines of best fit are drawn through data but there is no information as to how these lines were constructed (see specific comments). As such, this manuscript would benefit greatly from more attention to detail from the authors.
Additionally, as the authors are proposing a new GDGT salinity index, I would like them to calculate and report previously formulated salinity indices from their samples. Specifically, the ACE index (Turich and Freeman 2011) and the IR6+7me (Wang et al 2021) both have been calibrated against water salinity in marine saline ponds and hypersaline lakes respectively. I know the author's brief touched on comparing IR6me from this study to values from Wang et al (2021) in the text but a more thorough examination of prior GDGT-derived water salinity reconstructions would strengthen the manuscript. Readers will be interested to see how these indices compare against RIX in reconstructing salinity from an estuarine environment.
Additionally, the evidence for in situ production of brGDGTs and brGMGTs in downstream estuary sites is pretty weak. This is demonstrated by Fig 2 where we see that distributions of d13Corg and d15N in soils and downstream estuary samples are very similar in addition to Fig 5 where your PCA on sample brGDGT and brGMGT distributions cannot separate out soils from downstream estuary samples. Yes you see (on average) higher concentrations of brGDGTs and brGMGTs in downstream estuary samples than in soils but the actual distributions of brGDGT and brGMGT abundance in soils are pretty large, indicating that some soils have pretty substantial quantities of these compounds. A great way to add more clarity to this sourcing issue is to train a random forest model using a similar method to Martinez-Sosa et al (2023) on your brGDGT and brGMGT samples (and isoGDGTs as these should be available to you). If the random forest model can accurately separate out soils from downstream estuary samples then you can be pretty sure that your downstream estuary samples were produced in situ. This won’t require much additional work and can be implemented easily using python (https://scikit-learn.org/) or another language of your choice.
Specific comments
Line 35: This complicates paleoenvironmental interpretations in SOME aquatic settings not ALL aquatic settings
Line 37: “all along this basin, from land to sea” awkward phrasing
Line 40: “Redundancy analysis further shows that both salinity and nitrogen loadings dominantly control the brGDGT distributions.” No, the loadings indicate that SALINITY (not salinity loadings) controls the brGDGT distribution.
Line 40-43: “Furthermore, the relative abundance of 6- methyl vs. 5-methyl brGDGTs (IR6Me ratio), Total Nitrogen (TN), δ 15N and chlorophyll a concentration co-vary in a specific zone with low salinity” Is this zone geographical, in your redundancy analysis, or something else?
Line 44-45: “Salinity is positively correlated with homologs H1020a and H1020b, 45 and negatively correlated with compounds H1020c and H1034b.” Is this in soils, sediments or SPM?
Line 45: “This suggests that bacteria thriving…” thriving is not the correct word (carries implications of a value judgment) replace with “living”.
Line 45-47: It seems like you aren’t mentioning results from soils and sediments, only from SPM? Or maybe all your sediment samples are exclusively from rivers? The reader is unclear on this.
Line 51-52: “a paleorecord across the upper Paleocene and lower Eocene,” You should name this record and say where it is.
Lines 51: “showing its potential applicability in both modern samples and in paleorecords.” Perhaps you could evaluate its usage in both these cases e.g. - we successfully/unsuccessfully applied RIX in …
Line 55: “, although some of them were attributed to the phylum Acidobacteria” Imprecise language.
Line 57-58: “The distribution of brGDGTs (number of cyclopentane moieties and methyl groups; cf. structures in Fig. S1) was empirically linked with pH and Mean Annual Air Temperature” Again, imprecise language. The phrase “empirically linked” doesn’t convey much useful information.
Line 60: Should really cite some earlier lake GDGT papers in addition to Martinez-Sosa et al., 2021.
Lines 60-61: “The brGDGT-based proxies (i.e. MBT’5ME and CBT’) have been largely applied to reconstruct MAAT and pH from sedimentary archives (Coffinet et al., 2018; Harning et al., 2020; Wang et al., 2020).” Not quite true - Martinez-Sosa et al (2021) and Dearing Crampton-Flood et al (2020) generated Bayesian linear regressions between the Mean temperature of months above freezing and MBT’5me. These BayMBT models have been used widely in the community since their publication.
Line 62-63: “In aquatic settings, brGDGTs were initially suggested to be predominantly derived from watershed soils and transported by erosion in the sediments (Hopmans et al., 2004).” Maybe you mean “transported by erosion to the sediments”?
Lines 63-78: The use of English throughout this paragraph is poor and hard to follow. Needs copyediting.
Lines 63-78: You should read and cite Martinez-Sosa et al (2023) here for their work on a Random Forest approach to classify GDGT sources (i.e. Marine, Soil, Lake etc).
Line 80-83: “The improvement of analytical methods allowed the separation and quantification of 5-, 6- and 7-methyl brGDGTs (methyl groups at the fifth, sixth, and seventh positions; Fig. S1), that in previous chromatographic protocols co-eluted (De Jonge et al., 2013, 2014; Ding et al., 2016).” No real link between the previous paragraph and this one. Also, which methods? How did they improve?
Lines 86-87: “In addition to temperature and pH, other environmental factors may influence brGDGT distributions in terrestrial and aquatic settings and hence the application and interpretation of brGDGT-derived proxies” This is a repetition from earlier in the introduction.
Lines 91-99: You should mention that brGMGTs have previously been called H-brGDGTs in the literature.
Lines 91-111: This paragraph was very well written and is an example of the standard the entire manuscript should meet.
Lines 117-123: You go from talking about the hypothesis you aim to test in the paper to talking about the aims of the paper. Surely your aim is to test the hypothesis you have just laid out - why do we need to talk about more aims here?
Line 125-126: “by high population density”. High population density of what?
Line 127: “macrotidal”. Please define this term.
Figure 1: I really like this figure - it nicely summarizes your water sampling campaign.
Line 167: “Both decarbonated and non-decarbonated samples (~6 mg for SPM and ~20 mg for soils) were enclosed in a tin capsule” You should mention that you will split your samples and decarbonate one aliquot. Otherwise, the reader is confused as to where your non-decarbonated samples are coming from.
Line 172-174: “The isotopic composition (δ 13C or δ 15N) was expressed as the relative difference between isotopic ratios in samples and in standards (Vienna Pee Dee Belemnite for carbon or atmospheric N2 for nitrogen)” Should be “and atmospheric N2…”.
Line 176: What were these “additional…analyses”? Do you mean the same analyses aforementioned but on different samples, or different analyses on different samples?
Line 177: “(4-20g, n=51)” Looks to me like you’ve used the minus sign, not the en dash here. If so use the en dash.
Line 180-183: “The total lipid extracts were then separated into fractions of increasing polarity on an activated silica gel column, using (i) 30 mL of heptane, (ii) 30 mL of heptane:DCM (1/4, v/v), and (iii) 30 mL of DCM/MeOH (1/1, v/v) as eluents.” That seems like a nonstandard amount of solvent. Are you using very large columns here? If so state how many g of silica gel were used.
Line 233: Vegan should be vegan. No capital V.
Lines 240-243: I don’t think you effectively explain how your hierarchical partitioning method actually works here. As some readers won’t be familiar with this method, more details are needed.
Figure 2. I really don’t like how the axis of these plots has been extended to include chart labels. The top left panel scale is completely distorted by the addition of these labels. You should also define the features of your “boxes” in your box plot. These comments apply to all boxplots in the manuscript.
Line 268: “The different brGDGTs were detected in all studied samples” Which brGDGTs?
Line 275: “The relative abundances of the brGDGTs were determined all along the Seine River basin” I feel like this sentence should be at the start of this section not in the second paragraph.
Line 290: “which explained 40.9% of the variance in two dimensions” Which two dimensions are these?
Line 291: “Samples from the downstream estuary clustered well” Colloquial language, you should describe the data using words that don’t convey a value judgment.
Line 314-315: “It allowed to explain 39.79% of the variability through two dimensions.” Doesn’t make sense - please proofread your manuscript.
I feel like you have just randomly placed the figures in the text. You should line up the first in-text citation of a figure with the location of the figure in the manuscript. Currently, the text and the figures are out of sequence which makes reading this document a challenge.
Figure 5: Visually this figure is quite busy. I don’t think having the brGDGT names in blue (the same colour used for the downstream bubble) helps. I would use black for these names and also the arrows.
Line 336: “The brGMGTs identified in previous studies” Which brGMGTs and which studies? This lack of precise usage of language is present throughout the text.
Line 343-345: “In SPM and river channel sediments, the total brGMGT concentration was observed to be slightly higher in the riverine part (0.26 ± 0.24 μg/g Corg) than in downstream (0.20 ± 0.13 μg/g Corg) and upstream estuary samples (0.17 ± 0.18 μg/g Corg; Fig. S4b).” Slightly higher but not significantly higher. If it’s not significant you should say so.
Line 346: “The PCA analysis based on the brGMGT relative abundances (Fig. 5b) explained 70 % of the variance”. I’m unsure what the authors are trying to say here but I think they mean that the first two PCs sum to 70%. The second half of the sentence “which allows to observe that samples from the different parts of the basin clustered well apart from each other.” doesn’t make sense and I’m unsure what the authors are trying to say.
Line 357: “allows to explain” This phrase doesn’t make sense in this context - please remove all uses of it from the manuscript.
Lines 406-408: “The similarity in distributions between soils and downstream samples may be due to the overrepresentation of downstream soil samples, as 82% of the soils were collected downstream (Fig. 1a and Table 1).” I don’t understand your point here. Are you saying that the similarity between downstream estuary brGDGT distributions and soil brGDGTs is because the downstream estuary predominantly receives brGDGTs from downstream soils?
Lines 409-412: “Nevertheless, the soil-derived brGDGT contribution to the downstream samples is expected to be much lower than the autochthonous one, as the average brGDGT concentration in soils was ca. 3 times lower than the one in downstream (i.e. SPM and river channel sediment) samples (Fig. S4a).” Right, but it’s curious that the distributions are so similar between brGDGTs in soils and downstream estuaries. To bring more clarity to this point it would be interesting to see you attempt a machine learning approach (see Martinez-Sosa et al 2023, PP) to investigate whether (or not) a random forest model can distinguish soil samples from downstream estuary samples.
Lines 426-429: It would be great to see you calculate and report IR6+7me following Wang et al (2021) to determine if these indices correlate to salinity in an estuarine location.
433-436: “The distinct behavior of 6-methyl brGDGTs between lakes and the Seine river-sea continuum might be due to the lower salinity range in the Seine River basin (0-32 psu) vs. the lakes (0-376 psu) 435 investigated by Wang et al. (2021). This suggests that the limited range of salinity variation in the Seine River basin might be insufficient to trigger significant 6-methyl brGDGT production, as observed in hypersaline lakes.” This is actually incorrect. Wang et al 2021 report that IR6me is sensitive to salinity in the range of 5-1000 (mg/L) but relatively insensitive beyond this range.
458-460: “As the nutrient concentration is higher in the upstream part of the Seine estuary (Wei et al., 2022), the substantial 6-methyl brGDGT production observed in the aforementioned zone (260 460 < KP < 340, Fig. 8)” Right but why would the nutrient runoff be higher for this specific section of the basin? Do we see more agricultural activity here or something? It would be good to try and flesh out this point.
Figure 8 and throughout: Make sure to superscript 15 in d15N and subscript 6 in IR6me.
509-510: “The current knowledge on the parameters controlling the brGMGT distributions in the terrestrial and marine realm is still limited.” Why is it limited? Be specific.
Fig 9: Almost impossible to see the data points on some of the figure panels (e.g. panel e). Make the points bigger. Also, keep a consistent label text size to make the figure look neater. Also, you should say in the caption how you constructed the straight lines drawn through the data in some panels (e for instance). I’m assuming this is a linear regression but you have to inform the reader of your methods.
557-558: “ However, the average concentrations of brGMGTs are an order of magnitude lower in the soils than in the river channel sediments and SPM samples of the Seine basin (Fig. S4b).” Maybe it is, but visually it doesn’t look like that, so include the numbers in this sentence. You can also argue that the brGMGT abundance within soils varies by an order of magnitude. Do you know what is driving such a large variance in the soil brGMGT abundance?
589: Missing the word “index” after BIT
You need a map showing the location of IODP 302 Hole 4A
Lines 605-607: “This core is considered proximal to the coast and has considerable changes in terrestrial inputs (i.e. continental spores and pollen) over time (Sluijs et al., 2009, 2006), making it a suitable paleorecord for testing runoff proxies.” Again would be great to have some specifics. The readers will be interested in how close this core site was to the coast around the PETM. You should also say why there was a considerable change in terrestrial inputs (I’m assuming large changes in sea level are responsible).
Lines 616-617: “Such decreased runoff during the PETM body was previously attributed to a local sea level rise” Ah here is the explanation - this should have been in the previous paragraph. Also, be specific, are you saying there was decreased runoff during the PETM, OR did your sediment core record decreased runoff due to a change in sea level? These are two different things.
References:
Martínez‐Sosa, P., Tierney, J. E., Pérez‐Angel, L. C., Stefanescu, I. C., Guo, J., Kirkels, F., ... & Reyes, A. V. (2023). Development and application of the Branched and Isoprenoid GDGT Machine learning Classification algorithm (BIGMaC) for paleoenvironmental reconstruction. Paleoceanography and Paleoclimatology, 38(7), e2023PA004611.
Wang, H., Liu, W., He, Y., Zhou, A., Zhao, H., Liu, H., Cao, Y., Hu, J., Meng, B., Jiang, J., Kolpakova, M., Krivonogov, S., and Liu, Z.: Salinity-controlled isomerization of lacustrine brGDGTs impacts the associated MBT5ME’ terrestrial temperature index, Geochimica et Cosmochimica Acta, 305, 33–48, https://doi.org/10.1016/j.gca.2021.05.004, 2021.
Citation: https://doi.org/10.5194/egusphere-2023-2367-RC1 - AC1: 'Reply on RC1', Zhe-Xuan Zhang, 22 Dec 2023
-
RC2: 'Comment on egusphere-2023-2367', Anonymous Referee #2, 25 Nov 2023
The authors analyzed brGDGTs and brGMGTs in soils, suspended particulate matter, and river sediments in the Seine River basin to evaluate the environmental controls on and sources of these lipids. The basin ranges from freshwater to estuarine, allowing the authors to evaluate the effects of salinity on the GDGT compositions. The major motivation seems to be development of a new GMGT index, called “RIX”, to detect terrestrial inputs of GMGTs to marine environments. The authors test this index through application of Cenozoic sections of an IODP site.
There is now a relatively large literature on the environmental controls on GDGTs, though there is less on GMGTs, and combining these across a riverine salinity gradient is a strength of the paper. Overall I think the paper does provide some novel contributions and findings that merit publication. That said, there are a number of technical problems that will require major revision before the paper can be published.
First, the Seine basin is complicated by the presence of a dam that separates sections of the river influenced by tides (salinity) from sections upstream. The dam also presumably traps upstream sediment and likely presents a barrier for transport of GDGTs (other than SPM). The authors also have relatively few soil sampling sites – there are only 5 sites and the soil samples are dominated by downstream estuarine soils. I don’t think these challenges are adequately discussed in the paper. The dam may be a good thing for the study, since it establishes clear environmental boundaries, but it could be tricky to apply a GDGT index from this environment to other sites/time periods.
Second, there are a lot of data / statistical difficulties with this paper, the details of which are discussed below. At times the authors compare concentrations of GDGTs to evaluate in situ production, which is generally not a good way to do this due to the effects of sediment transport from soils to river to estuaries – concentrations may be higher in SPM than soils, for instance, as SPM contains less coarse-grained particles. Although the writing is a bit unclear, the authors appear to compare results of two PCAs, one on soils and one on aquatic samples, to differentiate these two sample types, which is not possible given how PCA works.
Third, Section 4.4 compares the application of the RIX to IODP site 302 to results from other measurements, such as the BIT and % terrestrial palynomorphs. The comparison is largely qualitative, and it’s hard to tell from Figure 11 how well these compare in a statistical sense. Could the authors provide correlation coefficients to show that the RIX captures terrestrial inputs?
Detailed comments:
Section 2.2. It is a bit hard to tell from this description and the table exactly what samples were collected and analyzed. I take it from the description that 1) subsurface SPM was collected from every green dot (correct?). 2) deeper water SPM was filtered from 5 sites (perhaps these could be indicated in the table), 3) Sediment samples from 8 cores were collected. I cannot tell from the description what depth in the core these samples were taken from (10 cm?), nor how 8 cores yielded n = 68.
Perhaps the dots could be color coded to indicate what types of samples exist (surface SPM, subsurface SPM, these + sediment). It might also be helpful to designate the environment type (river, upstream estuary, downstream estuary) on the map. It would be particularly helpful to indicate the city of Poses/location of the dam on this map.
What differentiates “upstream estuary” and “downstream estuary”? Is this salinity? Or judgement?
Line 237: “correlations” here should be “relationships”. These are not correlations in the statistical sense.
Line 271? Should this be “decreased in the downstream estuary” samples (not just “downstream”)? Having defined upstream estuary and downstream estuary it is good to stay with these terms.
Line 290. “Negative loadings” is confusing. On which axis? Both? I suggest describing the results by axis – first axis 1, then 2.
Figure 3 is not particularly helpful to the reader. If the authors wish to retain it, I suggest moving it to supplemental text.
Results:
The results of the “bulk parameters” describes the elemental and bulk stable isotopic composition of the solid samples. Nowhere does the paper describe results of other environmental parameters – temperature, etc. It would be helpful to have at least a table indicating the mean and range of these. I expect, for instance, that there is a large range of salinities associated with these samples and a very narrow range of temperatures (they are all close to each other).
The treatment of the soils samples in the analysis and results is difficult to understand. It appears that a large number of soils (up to 34) was taken from some sampling sites, whereas at others 1 sample was taken. These data were then analyzed via PCA separately from the aquatic samples, and the PCA was overlayed onto the PCA of the aquatic samples. The authors conclude that the PCAs show that the GDGT distribution of soils overlap with the SPM and channel sediments. It the PCAs were done separately, one cannot simply overlay the biplots and conclude that they overlap – the PCAs may capture different variance structures such that the PCA axes are not the same. If the authors wish to compare the soils and aquatic samples, do a PCA on all the data together. It’s always possible to do a second PCA excluding the soils to evaluate the variance structure of the aquatic samples alone.
Line 290: “explained 40.9% of the variance in two dimensions”. What is meant by this? Based on the plot, axis 1 captures 40.9% of the variance and axis 2 13.2%.
Line 346: Similar problem. I think the authors mean that axes 1 and 2 capture 71%. The PCA will capture more than this on axes 3 - ???
Similar problems exist in the description of the RDA, Section 3.3
4.1.1. Why do the authors focus on the 6-methyl brGDGTs here?
Line 390: The authors suggest that the higher abundances of 6-methyl brGDGTs in upstream vs. downstream samples may reflect degradation:
“It may reflect the fact that riverine 6-methyl brGDGTs are more easily degraded than soil-derived homologues and only partially transferred downstream.”
Why would 6-methyl brGDGTs produced in a river degrade faster than those produced elsewhere? The authors argue that this could reflect attachment to particles – but how do these particles differ in upstream vs. downstream river environments.
It seems likely that production of the 6-methyl compounds is suppressed in downstream environments and the dam traps the upstream sediments (and lipids). Can the authors show that this is not the case?
Line 405. Here the authors suggest that the brGDGT distributions in estuarine soils is similar to that of the downstream samples, based on the PCA (see comment above about the PCA). In the next section (4.1.2), this is not discussed and instead production of the brGDGTs in saline environments is the primary factor accounting for compositional differences in upstream vs. downstream samples. Please coordinate these ideas.
Line 487, 559: One cannot conclude from concentrations alone that the GMGTs are produced in aquatic environments. Soils contain abundant coarse clastic material that may be lost in the fine SPM and river sediment. The distributions (relative abundances) of GMGTs are key to identifying in situ production.
Citation: https://doi.org/10.5194/egusphere-2023-2367-RC2 - AC2: 'Reply on RC2', Zhe-Xuan Zhang, 22 Dec 2023
Viewed
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 266 | 0 | 2 | 268 | 0 | 0 |
- HTML: 266
- PDF: 0
- XML: 2
- Total: 268
- BibTeX: 0
- EndNote: 0
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1