the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Comparison of paleobotanical and biomarker records of mountain peatland and forest ecosystem dynamics over the last 2600 years in Central Germany
Carrie L. Thomas
Boris Jansen
Sambor Czerwiński
Mariusz Gałka
Klaus-Holger Knorr
E. Emiel van Loon
Markus Egli
Guido L. B. Wiesenberg
Abstract. As peatlands are a major terrestrial sink in the global carbon cycle, gaining understanding of their development and changes throughout time is essential to predict their future carbon budget and potentially mitigate negative influences of climate change. With this aim to understand peat development, many studies have investigated the paleoecological dynamics through the analysis of various proxies, including pollen, macrofossil, elemental, and biomarker analyses. However, as each of these proxies are known to have their own benefits and limitations, examining them in parallel potentially allows for a deeper understanding of these paleoecological dynamics at the peatland and for a systematic comparison of the power of these individual proxies. In this study, we therefore analyzed soil cores from a peatland in Germany (Beerberg, Thuringia) to a) characterize the vegetation dynamics over the course of the peatland development during the late Holocene and b) evaluate to what extent the inclusion of multiple proxies, specifically pollen, macrofossil, and biomarkers, contributes to a deeper understanding of those dynamics and interaction among factors. We found that, despite a major shift in regional forest composition from primarily beech to spruce as well as many indicators of human impact in the region, the local plant population in the Beerberg area remained stable over time following the initial phase of peatland development up until the last couple of centuries. Therefore, little variation could be derived from the paleobotanical data alone. The combination of pollen and macrofossil analyses with the elemental and biomarker analyses enabled further understanding of the site development as these proxies added valuable additional information including the occurrence of climatic variations, such as the Little Ice Age, and more recent disturbances such as drainage and dust deposition.
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Carrie L. Thomas et al.
Status: final response (author comments only)
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RC1: 'Comment on bg-2023-57', Anonymous Referee #1, 30 Apr 2023
This manuscript presents an interesting dataset comprising pollen, spores, microcharcoal, plant macrofossils and geochemistry data, as well as sedimentary biomarkers from a mountain peatland in Beerberg (Germany). The time interval of the peat core goes back to ca. 2600 cal yr BP. The main goal of the study is to reconstruct vegetation dynamics over the last 2600 years and assess how the biomarker record adds to the understanding derived from the combined classical palaeoecological proxies in the reconstruction of past environmental conditions and peatland development.
I really liked reading this manuscript. The text is clear and nicely structured, methods are presented in sufficient detail, while the idea behind this study is definitely worth exploring. I have two main observations though. The first is that, in my opinion, the manuscript needs a stronger justification for its novelty. The second observation concerns the biomarker dataset which, despite being one of the fewest of such kind in the temperate Europe, is discussed somewhat superficially. I would have liked to see an extension of the discussion towards exploring potential sources of these biomarker compounds in the study area, backed by some statistics, as pollen and macrofossil data are already available in sufficient resolution.
Some specific comments below:
Abstract
L. 14-16: I didn’t see any evidence in the proxy record for dust deposition. Perhaps consider rephrasing?
Methods
2.1. Study area
It would have been nice to see a map with the study area, given that this is a mountainous region and local topography is important in the interpretation of the proxy signals.
Sampling: It was not explained how the overlapping peat sequences were correlated. Based on stratigraphy? Furthermore, what is the reason behind reporting the averages of geochemistry and biomarker data for the overlapping sequences?
2.2 Elemental analysis: please add the sample volume for the samples used for elemental geochemistry.
L. 99-100. You mentioned that there are some lithological transitions. I would suggest to add a lithological column to one of the figures (e.g., the geochemistry figure, or the agedepth model).
L. 102. Consider highlighting more in the introduction the use/purpose of analysing the stable isotopes of N and C. The same suggestion for n-alkanols and n-fatty acids.
2.7.3 Radiocarbon dating: What is the reason for exclusion of the uppermost radiocarbon date?
Results
Suggestion: When describing the results, focus on time, not on zones. In my opinion, it is more interesting for the reader to know when something happened, rather than the depth intervals.
Figure 2. d15N is missing from the figure, although it was mentioned as a performed analysis in the Methods.
Figure 3. It would be useful to see the phases in the macrofossil record drawn on the figure.
Discussion
L. 325-329. ‘The disappearance of Neurospora and Gelasinospora together with the rapid decline in CHAR-micro […] This may also indicate drier conditions on peatlands.’ This interpretation is confusing.
L.402-403. Paq and Pwax basically mirror each other. Is this situation site-specific? If not, why are both indices necessary? Also, there is no discussion around the n-alkanols and n-fatty acids.
Citation: https://doi.org/10.5194/bg-2023-57-RC1 -
AC1: 'Reply on RC1', Carrie Thomas, 09 Jul 2023
"I really liked reading this manuscript. The text is clear and nicely structured, methods are presented in sufficient detail, while the idea behind this study is definitely worth exploring. I have two main observations though. The first is that, in my opinion, the manuscript needs a stronger justification for its novelty. The second observation concerns the biomarker dataset which, despite being one of the fewest of such kind in the temperate Europe, is discussed somewhat superficially. I would have liked to see an extension of the discussion towards exploring potential sources of these biomarker compounds in the study area, backed by some statistics, as pollen and macrofossil data are already available in sufficient resolution."
Author Response: Thank you for your kind feedback! We agree that the novelty of the study should be more explicitly addressed, including the rarity of the biomarker dataset including not only n-alkanes but n-alkanols and n-fatty acids as well. Due to the lack of previous paleoenvironmental studies within the region, the study already provides novel insights into the paleoenvironmental history of the Thuringian Forest. We also agree that the discussion of the biomarker dataset in the current version of the manuscript should be expanded to discuss the n-alkanol and n-fatty acid data more and investigate other potential diagnostic ratios for identifying vegetation sources. If offered the opportunity to revise, we will change and expand the introduction and discussion sections accordingly.
Some specific comments below:
Abstract
L. 14-16: I didn’t see any evidence in the proxy record for dust deposition. Perhaps consider rephrasing?
AR: The evidence for dust deposition is the transition in Sphagnum species from S. fuscum to S. medium/divinum in the plant macrofossil data as found by Galka et al., 2019, 2022. As there is not direct evidence, we will remove this point from the abstract but leave it in the discussion where we explain this more in detail.
Methods
2.1. Study area
It would have been nice to see a map with the study area, given that this is a mountainous region and local topography is important in the interpretation of the proxy signals.
AR: We agree and will add this.
Sampling: It was not explained how the overlapping peat sequences were correlated. Based on stratigraphy?
AR: The overlapping sequences were correlated based on sampling depth as they were collected at a distance less than 20 cm from each other. We will add this detail to the methods section.
Furthermore, what is the reason behind reporting the averages of geochemistry and biomarker data for the overlapping sequences?
AR: We averaged the overlapping sequences as there were no clear outliers and to make it easier to treat the data as one core. Because the cores were so close together, we viewed the individual samples from the overlapping sections as replicates.
2.2 Elemental analysis: please add the sample volume for the samples used for elemental geochemistry.
AR: We will add the sample masses used for the EA-IRMS analysis.
L. 99-100. You mentioned that there are some lithological transitions. I would suggest to add a lithological column to one of the figures (e.g., the geochemistry figure, or the agedepth model).
AR: Will be added.
L. 102. Consider highlighting more in the introduction the use/purpose of analysing the stable isotopes of N and C. The same suggestion for n-alkanols and n-fatty acids.
AR: While d15N was measured simultaneously with d13C and the C and N concentrations, the sample weights measured were too small to provide accurate d15N readings and we decided not to include these in the manuscript but neglected to remove d15N from the methods section. We will adjust this. d13C was measured as it can provide information about the hydrological conditions during peat development (Loisel et al., 2010). The n-alkanols and n-fatty acids are less commonly used vegetation biomarkers and were measured and included in this study partially to further investigate their potential contribution to a biomarker based reconstruction. In spite of the addition of two extra categories of straight-chain lipids to the n-alkanes potentially tripling the amount of information obtained, only a very limited number of studies have included these compound classes so far. Therefore, an important aspect of novelty of our present study was to assess the potential of improving the biomarker based reconstruction by addition of these compound classes. We will expand on this in the introduction (see also our answer to the general comments above)
2.7.3 Radiocarbon dating: What is the reason for exclusion of the uppermost radiocarbon date?
AR: The reason for excluding the uppermost date was that it was from a sample within 10 cm of the surface and yielded a date of -552 +/- 23 cal yr BP, which is not only stratigraphically inconsistent with the rest of the core but is also way too young. The measure is due to the organic matter being formed and incorporated following nuclear weapons testing in the 1950s and 1960s which created a peak of atmospheric 14C.
Results
Suggestion: When describing the results, focus on time, not on zones. In my opinion, it is more interesting for the reader to know when something happened, rather than the depth intervals.
AR: We agree and will adjust this.
Figure 2. d15N is missing from the figure, although it was mentioned as a performed analysis in the Methods.
AR: See above comment in reference to L 102.
Figure 3. It would be useful to see the phases in the macrofossil record drawn on the figure.
AR: We agree and will add this.
Discussion
L. 325-329. ‘The disappearance of Neurosporaand Gelasinospora together with the rapid decline in CHAR-micro […] This may also indicate drier conditions on peatlands.’ This interpretation is confusing.
AR: Thank you for pointing this out, it should be wetter and not drier.
L.402-403. Paq and Pwax basically mirror each other. Is this situation site-specific? If not, why are both indices necessary?
AR: This is not site-specific, but they are conventionally both reported in similar studies using biomarkers (e.g., Andersson et al., 2011; Ronkainen et al., 2015; Baker et al., 2016).
Also, there is no discussion around the n-alkanols and n-fatty acids.
AR: As mentioned above, we plan to further investigate potential diagnostic ratios from the n-alkanols and n-fatty acids and will add this to the discussion.
References:
Andersson, R. A., & Meyers, P. A. (2012). Effect of climate change on delivery and degradation of lipid biomarkers in a Holocene peat sequence in the Eastern European Russian Arctic. Organic Geochemistry, 53, 63–72.
Baker, A., Routh, J., & Roychoudhury, A. N. (2016). Biomarker records of palaeoenvironmental variations in subtropical Southern Africa since the late Pleistocene: Evidences from a coastal peatland. Palaeogeography, Palaeoclimatology, Palaeoecology, 451, 1–12.
Gałka, M., Szal, M., Broder, T., Loisel, J., and Knorr, K.-H. (2019). Peatbog resilience to pollution and climate change over the past 2700 years in the Harz Mountains, Germany, Ecological Indicators, 97, 183–193.
Gałka, M., Diaconu, A.-C., Feurdean, A., Loisel, J., Teickner, H., Broder, T., and Knorr, K.-H. (2022). Relations of fire, palaeohydrology, vegetation succession, and carbon accumulation, as reconstructed from a mountain bog in the Harz Mountains (Germany) during the last 6200 years, Geoderma, 424.
Loisel, J., Garneau, M., & Hélie, J.-F. (2010). Sphagnum δ13C values as indicators of palaeohydrological changes in a peat bog. The Holocene, 20(2), 285–291.
Citation: https://doi.org/10.5194/bg-2023-57-AC1
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AC1: 'Reply on RC1', Carrie Thomas, 09 Jul 2023
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RC2: 'Comment on bg-2023-57', Anonymous Referee #2, 02 Jun 2023
The manuscript reports a multiproxy caracterisation of a peat core from central Germany. Carbon, nitrogen, macrofossil, pollen and biomarker contents along the 320 cm core are interpreted in term of palaeovegetation and palaeoenvironment. Data undoubtly deserve publication and their palaeoecology implications are relevant toward a better understanding of recent vegetation changes under global change. However, the manuscript requires some rewriting before being publishable in Biogeosciences.
While the multiproxy approach is certainly more powerful than a single method approach, it is not the first time it is adopted and claiming it as a main objective and discussion point of the paper appears slightly over the top. I suggest to directly integrate biomarkers in the paleoreconstruction of each phase.
The methodology of data processing and depth clustering should be homogenized between proxies, or at least better argued/justified and discussed. Why pollen and biomarker data are clustered through a CONISS approach while depth grouping based on macrofossil is achieved visually and EA-IRMS were submitted to no clustering approach ? Why is the main phase determination based on macrofossils? The cluster-differences between proxies should be discussed at the beginning of the discussion so as to further directly integrate all proxies in the paleoreconstructions.
In addition to the palaeoecological reconstruction phase by phase, peat and vegetation dynamics should be further discussed in relation to other local peats and regional changes. Can general trends be drawn from the studied core (and other previously studied peats) in term of peat evolution under climate change?
Provide sketch palaeoenvironmental reconstruction of each phase
Replace “arboreal” by “tree” or “arborescent”
Briefly mention biomarker absolute amount before describing their distribution in 3.5.
Enlarge captions of figure 3 and 4
L230: write “C/N” instead of “N” ?
L358: delete “and”
L369: insert “1” before “657”
Citation: https://doi.org/10.5194/bg-2023-57-RC2 -
AC2: 'Reply on RC2', Carrie Thomas, 09 Jul 2023
"The manuscript reports a multiproxy caracterisation of a peat core from central Germany. Carbon, nitrogen, macrofossil, pollen and biomarker contents along the 320 cm core are interpreted in term of palaeovegetation and palaeoenvironment. Data undoubtly deserve publication and their palaeoecology implications are relevant toward a better understanding of recent vegetation changes under global change. However, the manuscript requires some rewriting before being publishable in Biogeosciences.
While the multiproxy approach is certainly more powerful than a single method approach, it is not the first time it is adopted and claiming it as a main objective and discussion point of the paper appears slightly over the top "
AR: We agree as this indeed is not a novel approach and will shift the objective and discussion to focus more on exploring the potential of n-alkanols and n-fatty acids as studies including these compounds are limited. Additionally, we will further highlight that there are very few paleoenvironmental records of the Thuringian Forest, therefore this research is filling a current knowledge gap.
I suggest to directly integrate biomarkers in the paleoreconstruction of each phase.
AR: We agree and will rework the discussion to make it more coherent.
The methodology of data processing and depth clustering should be homogenized between proxies, or at least better argued/justified and discussed. Why pollen and biomarker data are clustered through a CONISS approach while depth grouping based on macrofossil is achieved visually and EA-IRMS were submitted to no clustering approach ?
AR: Because the macrofossil data is quite sparse, it is not possible to perform a CONISS analysis. However, there are other statistical methods that we can apply to the macrofossil data (constrained ordination such as Redundancy Analysis (RDA) and canonical correspondence analysis (CCA) (Birks, 2014)) to determine phases in a more quantitatively robust way and we will revise this. The EA-IRMS data was not submitted to a clustering approach because it is not being used as a vegetation proxy. However, we will also apply the same ordination techniques that we use for the macrofossil data to the EA-IRMS data to better identify any patterns or trends.
Why is the main phase determination based on macrofossils?
AR: We based the main phases on the macrofossils because we believed they would be the most reliable proxy reflecting in situ vegetation and peat development as is conventional in paleoecology (e.g. Birks & Birks, 2000).
The cluster-differences between proxies should be discussed at the beginning of the discussion so as to further directly integrate all proxies in the paleoreconstructions.
AR: We agree and will adjust the discussion accordingly.
In addition to the palaeoecological reconstruction phase by phase, peat and vegetation dynamics should be further discussed in relation to other local peats and regional changes. Can general trends be drawn from the studied core (and other previously studied peats) in term of peat evolution under climate change?
AR: We agree that this should be further discussed and will investigate this further for the next version of the manuscript. However, we are limited by the fact that only a few local and regional archives available. In fact, this lack of archives is an important justification for performing the present study. We will now more clearly indicate this in the manuscript.
Provide sketch palaeoenvironmental reconstruction of each phase
AR: Will add this. We will add a short sketch to a revised and improved version of Figure A1.
Replace “arboreal” by “tree” or “arborescent”
AR: We respectfully disagree about this as “arboreal” is the conventionally used term in similar paleovegetation reconstructions. Therefore, we will keep the respective terminology.
Briefly mention biomarker absolute amount before describing their distribution in 3.5.
AR: We will add this to the manuscript.
Enlarge captions of figure 3 and 4
AR: We agree and will adjust the captions to make the figures more legible.
L230: write “C/N” instead of “N” ?
AR: Thank you! Will adjust.
L358: delete “and”
AR: Thank you! Will adjust.
L369: insert “1” before “657”
AR: Thank you! Will adjust.
References
Birks, H. H., & Birks, H. J. B. (2000). Future Uses of Pollen Analysis Must Include Plant Macrofossils. Journal of Biogeography, 27(1), 31–35.
Birks, H. J. B. (2014). Challenges in the presentation and analysis of plant-macrofossil stratigraphical data. Vegetation History and Archaeobotany, 23, 309-330.
Citation: https://doi.org/10.5194/bg-2023-57-AC2
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AC2: 'Reply on RC2', Carrie Thomas, 09 Jul 2023
Carrie L. Thomas et al.
Carrie L. Thomas et al.
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