Dear Laurent Augusto,

Thank you very much for your feedback and helpful comment. Please note that the results of our study are based on a novel extensive database which allows investigating the controls of N concentrations in stems, branches and coarse roots for the first time across the entire northern hemisphere boreal and temperate forests. It is correct that we partly confirm previous results which were based on selected species and forest stands. However, as discussed in Lines 76-81 of the preprint manuscript, the observed relationships between tissue N concentrations and tree age/size were not always consistent in different previous studies. These issues motivated our study which covers large gradients in tree age/size and environmental controls.

We agree that decreases in tissue N concentration with increasing tree age are, in addition to the possible reasons we already mention in Lines 81-85 and Lines 406-408, also caused by the increasing proportion of tissues with low N concentration and by internal N retranslocation. Note that the decreasing share of living cells in older trees due to the conversion of sapwood to heartwood is already mentioned there. We will modify these paragraphs in the Introduction and Discussion sections and refer to Augusto et al. (2008) as follows:

“Possible underlying mechanisms are a) a decline in photosynthetic capacity with increasing tree age/size and associated decline in required N to support photosynthesis (Yoder et al. 1994; Steppe et al. 2011), b) a decreasing share of tissues with high N concentrations in older trees due to the conversion of living cells in the sapwood to heartwood and due to N retranslocation (Augusto et al., 2008; Thurner et al., 2019), and c) a depletion of soil N during early growth stages or a stabilisation of N in organic matter (especially in boreal forests), which limits growth in mature forests (Norby et al. 2010).”

“This finding is in line with different mechanisms that can explain the decline in tissue N concentration with tree age/size, including a decline in photosynthetic capacity (Yoder et al. 1994; Steppe et al. 2011), a decreasing share of tissues with high N concentrations (Augusto et al., 2008; Thurner et al., 2019), and a depletion of soil N (Norby et al. 2010).”

Augusto L, Meredieu C, Bert D et al. 2008. Improving models of forest nutrient export with equations that predict the nutrient concentration of tree compartments. Annals of Forest Science 65: 808.

Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE. 2010. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc Natl Acad Sci U S A 107(45): 19368-19373.

Steppe K, Niinemets Ü, Teskey RO 2011. Tree Size- and Age-Related Changes in Leaf Physiology and Their Influence on Carbon Gain. Size- and Age-Related Changes in Tree Structure and Function, 235-253.

Thurner M, Beer C, Crowther T, Falster D, Manzoni S, Prokushkin A, Schulze ED, Gillespie T. 2019. Sapwood biomass carbon in northern boreal and temperate forests. Global Ecology and Biogeography 28(5): 640-660.

Yoder BJ, Ryan MG, Waring RH, Schoettle AW, Kaufmann MR. 1994. Evidence of Reduced Photosynthetic Rates in Old Trees. Forest Science 40(3): 513-527.

Sincerely,

Martin Thurner

On behalf of all Authors

Please find the Reviewer's comments in italic font and our replies in regular font directly below each point.

The paper presents a new dataset of nitrogen concentrations observed in four major plant compartments: leaves, branches, stems and roots of boreal and temperate trees in the northern hemisphere. Some statistics are applied to characterise the correlations of these concentrations to some biotic and abiotic conditions.

I have the impression that the dataset has been developed with the explicit purpose of supporting global vegetation models of the coupled carbon and nitrogen cycles with a relatively low complexity. Those models are highly relevant and useful for analysing long-term global vegetation developments in a changing environment and climate system. The dataset is also highly valuable for supporting these models, providing observational evidence for either calibration or evaluation.

I suggest being upfront about this purpose. Otherwise, the dataset and the manuscript could be criticised for their low-complexity approach. Addressing this purpose could not only give a reason for low complexity but also open opportunities to motivate and compare to existing models.

We are grateful to the Reviewer for emphasizing the importance of this study and the constructive feedback.

Indeed, one purpose of the development of the tissue N concentration database has been the support of global vegetation models (GVMs). Please note that there have been also other purposes, especially an improved understanding of the coupling of C and N cycles in the vegetation and progress towards the spatially extensive mapping of tissue N concentrations and contents and spatial estimates of plant respiration in boreal and temperate forests in future studies. These motivations are already described in the preprint manuscript, for instance at the end of the Discussion section in Lines 515-537. We further clarify our motivations accordingly (see our responses below).

Two examples of low complexity:

The dataset is simple in terms of the selection of the four compartments. However, there needs to be more than one compartment for roots to be justified against current ecological knowledge. Addressing this with appropriate detail indicates that coarse roots with a cross-sectional diameter >2mm are in form and function as different from fine roots (<2mm) as stems are from leaves. More recent research indicates that fine roots should even be distinguished from fine roots with the primary function of absorption (aka leaf-blade) and fine roots with the primary function of transport (aka leaf-petiole, maybe twigs). This distinction of form and function is directly mirrored in the N concentrations of the different root parts. This has been shown by research but is also evident in the data in the FRED (Fine Root Ecology database) and TRY databases. However, such a fine granular distinction might not need to be realised or appropriate in low-complexity vegetation models.

We absolutely agree that N concentrations of fine roots (or even distinguishing between absorptive and transport fine roots) are of great importance and very different from coarse root N concentrations. One important reason for not including analyses of the controls of fine root N concentrations in this manuscript is that we intend to facilitate future studies on large-scale estimates of tree tissue N contents and plant maintenance respiration in boreal and temperate forests. Such studies are dependent on remote sensing biomass data and measurements of biomass allometry, which more frequently include measurements of total root biomass but rarely fine root biomass separately (Thurner et al., 2014; 2019; Schepaschenko et al., 2017). Estimates of root N concentrations, root N contents and root respiration are important, for instance, for improving estimates of the land C sink in C budgets (Friedlingstein et al., 2023). Another reason why we do not include analyses of the controls of fine root N concentrations here is because they will be presented in a separate manuscript currently prepared by a co-author of this study (Kailiang Yu), which will focus specifically on fine roots.

We will clarify our motivation to concentrate on total root N concentration in the Introduction of the manuscript as follows:

“While numerous N concentration measurements are available for fine roots (Iversen et al., 2017; Wang et al., 2019; 2021), N concentration data representative for the entire root system including coarse roots are comparatively sparse due to the complexity of such measurements. To address this knowledge gap, and since our study aims to facilitate large-scale estimates of tissue N contents and R_m in boreal and temperate forests in future studies, here we focus on total root N concentrations. Such estimates of tissue N contents and R_m are dependent on remote sensing biomass data and measurements of biomass allometry, which (in contrast to measurements of N concentrations) more frequently include total root biomass but rarely fine root biomass separately (Thurner et al., 2014; 2019; Schepaschenko et al., 2017). Estimates of root N concentrations, root N contents and root respiration are important, for instance, for improving estimates of the land C sink in C budgets (Friedlingstein et al., 2023).”

Friedlingstein P, O'Sullivan M, Jones MW et. al. 2023. Global Carbon Budget 2023, Earth Syst. Sci. Data 15: 5301–5369.

Iversen CM, McCormack ML, Powell AS, Blackwood CB, Freschet GT, Kattge J, Roumet C, Stover DB, Soudzilovskaia NA, Valverde-Barrantes OJ, et al. 2017. A global Fine-Root Ecology Database to address below-ground challenges in plant ecology. New Phytologist 215(1): 15-26.

Schepaschenko D, Shvidenko A, Usoltsev V et al. 2017. A dataset of forest biomass structure for Eurasia. Scientific Data 4: 170070.

Thurner M, Beer C, Santoro M, Carvalhais N, Wutzler T, Schepaschenko D, Shvidenko A, Kompter E, Ahrens B, Levick SR, et al. 2014. Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography 23(3): 297-310.

Thurner M, Beer C, Crowther T, Falster D, Manzoni S, Prokushkin A, Schulze ED, Gillespie T. 2019. Sapwood biomass carbon in northern boreal and temperate forests. Global Ecology and Biogeography 28(5): 640-660.

Wang Z, Yu K, Lv S, Niklas KJ, Mipam TD, Crowther TW, Umaña MN, Zhao Q, Huang H, Reich PB, et al. 2019. The scaling of fine root nitrogen versus phosphorus in terrestrial plants: A global synthesis. Functional Ecology 33(11): 2081-2094.

Wang Z, Huang H, Yao B, Deng J, Ma Z, Niklas KJ. 2021. Divergent scaling of fine-root nitrogen and phosphorus in different root diameters, orders and functional categories: A meta-analysis. Forest Ecology and Management 495.

The other example I would like to mention could be criticised for its low complexity is the first sentence of the Abstract: ‘Nitrogen (N) concentration in tree tissues controls photosynthesis, growth and plant maintenance respiration.’ At least concerning photosynthesis, there seems to be recent evidence that average environmental conditions of, e.g. light, atmospheric CO2 concentration, temperature, or water availability, determine optimal photosynthetic capacity, which determines required N concentrations due to involved enzymes. Instead, Nitrogen availability seems to be related to the amount of leaf area (LAI) developed. In addition, leaf N concentration is also determined by other structural and functional aspects. In summary, it seems like structure and function determine leaf N concentration. However, to get away with a simple approach, one could rephrase to the following: ‘photosynthesis, growth, and plant maintenance respiration are closely related to tissue N concentrations’. In low-complexity models, this relation may be formulated as described in the first sentence. Nevertheless, these are two different ways to think about this relationship.

As suggested, we will rephrase the first sentence of the abstract:

“Photosynthesis, growth and plant maintenance respiration are closely related to tree tissue nitrogen (N) concentrations.”

Accordingly, we will rephrase a sentence in the Conclusions section (Lines 547-549 in the preprint manuscript):

“These relationships have considerable implications for the coupling of the C and N cycles in vegetation, since photosynthesis, growth and plant respiration as well as litter decomposition are closely related to tissue N concentrations.”

These are just two examples, but I suggest using this motivation throughout the manuscript.

With respect to statistical analyses, I am missing an analysis of the phylogenetic control of data distributions. I also ask myself what the ecological meaning of correlating stem traits to leaf type is. However, both make sense in the context of vegetation modelling: phylogeny is no aspect, and the leaf types determine different Plant Functional Types (PFTs) widely used in such models and beyond.

It is correct that we did not implement an explicit analysis of the phylogenetic controls of tissue N concentrations, but we instead present differences between tree species in tissue N concentrations in Fig. S1 and Tables S10 – S13 of the Supporting Information S7. We refer to them in Lines 246-251 of the preprint manuscript.

The relation between tissue (including stem) N concentration and leaf types (leaf lifespan) is discussed in Lines 89-100 and Lines 409-418. Differences in stem N concentrations between broadleaf deciduous and needleleaf trees are highly significant (Table 1).

Simple statistics could be added to show the different N concentrations of the different compartments in the different PFTs.

The N concentrations of the different tissues for different plant functional types (PFTs) are already presented in Tables S5 – S8 in Supporting Information S5. There we list summary statistics (mean, median, quartiles) for all growth / leaf type classes.

Apart from this, I find the presentation results of the partial regression analyses and the generalized additive models overwhelming and confusing: many results, no clear main primary focus. Therefore, I suggest considering other statistical opportunities, such as random forest models. For this, the data could be systematically linked to global maps of environmental data for soil and climate – as has been done partly for some climate data. A result of these models could be the environmental drivers relevant to the variation of N concentrations of different compartments within the different PFTs.

We agree that random forest models are a very useful machine learning approach, mainly because they have high predictive power and multiple independent variables can be included. However, generalized additive models (GAMs) also have their strengths, especially that a selection of meaningful interaction effects between explanatory variables based on ecological process knowledge can be explicitly included (and others excluded). This facilitates a high interpretability of GAMs. For this reason, we prefer to apply GAMs in this study, and we do not expect that random forest models would substantially improve the results for our purpose.

As noted by the reviewer, we already linked the tissue N concentration database to spatial maps of climate data and included these climate data as explanatory variables in the GAMs to explain the variation in tissue N concentrations. This method is independent of the statistical method used. Since our main focus is not fitting a model with the highest predictive power based on numerous explanatory variables, we decided not to use random forest models in this study. Instead, we opt for partial regression analysis and GAMs for the following reasons:

Like every statistical analysis, partial regression analysis and GAMs both have benefits and limitations. Partial regression analysis accounts for interaction effects between explanatory variables and is at the same time little susceptible to overfitting and overinterpreting relationships between variables, but restricted to detect linear relations. In contrast, GAMs allow to account for non-linear relationships and interaction effects between explanatory variables and to include numerical as well as factorial variables, but are more prone to overfitting and overinterpreting the detected relations between variables. Consequently, we apply both, but for different applications. Partial regression analysis is used to draw conclusions on the positive or negative correlation between tissue N concentrations and explanatory variables. GAMs are applied to investigate how much of the variation in tree tissue N concentration can be explained by the selected explanatory variables when considered together and to gain additional insights into the relative importance of plant traits versus environmental conditions. In this way, we have a high confidence in the existence of the relationships as detected by partial regression analysis and can also show that the investigated explanatory variables together can explain a large fraction of the variation in tissue N concentrations.

These motivations for applying partial regression analysis (Lines 215-218) and GAMs (Lines 226-230) are already described in the preprint manuscript.

The dataset is, to a large extent, derived from a few existing major compilations, with some data added from the literature. Therefore, I would not tend to call it “unprecedented.” Nevertheless, it could be of utmost usability.

Only in case of leaf N concentration, the database is mainly derived from existing compilations. For the other tissues (branch, stem and root N concentration), the database consists almost entirely of data compiled by the authors from the literature. This is already described in Lines 178-181 of the preprint manuscript:

“While almost all of the stem (911 collected from literature, 1 own, 52 from TRY, 84 from BAAD), root (266 collected from literature, 1 own) and branch (all collected from literature) N concentration measurements have been collected from in total 192 studies from the literature, leaf N concentration measurements are to a large extent available from existing databases (188 collected from literature, 5 own, 5522 from TRY, 229 from BAAD).”

Nevertheless, we will use the word “novel” instead of “unprecedented” to describe the database.

Please cite references of data used via the TRY database in the main paper or an appendix (not in supplementary materials, see, e.g., https://onlinelibrary.wiley.com/page/journal/14668238/homepage/forauthors.html Citation to data sources).

We agree and would also prefer to cite references of the data contained in the compiled tissue N concentration database in the main paper or an appendix as suggested, but it is not practical. Note that the list of data sources does not only contain references contributing to the TRY database, but far more references. Unfortunately, this very extensive list would substantially increase the Article Processing Charges. We have already consulted the Editorial Support about this issue. The Editorial Support suggested to put the list of data sources (previously in the Appendix) in the Supplement. Please note that the list of data sources will also be cited in the associated dataset that we provide in any case.

Sincerely,

Martin Thurner

On behalf of all Authors

Please find the Reviewer’s comments in italic font and our replies in regular font directly below each point.

The manuscript presents a valuable attempt to establish a global database of nitrogen (N) concentrations in various tissues of trees growing in boreal and temperate forests of the Northern Hemisphere, along with an assessment of the drivers influencing its variability. While I agree with the authors on the significance of their findings for improving the representation of carbon (C) and nitrogen (N) dynamics in global vegetation models, there are some points that need to be addressed to enhance the quality, clarity, and readability of the manuscript.

We are grateful to the Reviewer for acknowledging the significance of our study and for the constructive feedback.

Major comments

1. Relevance to C cycle modelling:

  This paper holds significant potential for improving our understanding and modelling of the C cycle in global vegetation models. Given the conflicting effects that higher N concentrations can have on C dynamics (i.e., increasing respiration costs and enhancing photosynthesis), I suggest that the authors dedicate more attention to this topic. Specifically, they should clearly outline the expected effects on photosynthesis and respiration for different N concentration in each organ (leaf, branch, stem, roots), and, where possible, hypothesize an overall impact on the C balance. In other words, I recommend expanding the sections discussing C/N dynamics in both the Introduction (lines 39-49) and the Discussion (from line 515 onwards), to provide a clearer explanation of the effects of N concentrations in different tissues on the C cycle.

Thank you for this suggestion. We will include a short explanation of the possible effects of changes in tissue N concentrations on photosynthesis and plant respiration in the Introduction of the preprint manuscript. Please note that these effects are already described to some extent in Lines 515-523 in the Discussion section.

We will add in the Introduction section:

“Since leaf N concentration is strongly related to carboxylation capacity (Dong et al., 2022), increases in leaf N concentration are associated to higher photosynthetic rates, especially in N-limited ecosystems (Wright et al., 2004). At the same time, increased N concentrations in leaves, but also in other tissues (branches, stems, roots), directly translate into higher maintenance respiration rates (Ryan, 1991; Reich et al., 2006b).”

Dong N, Prentice IC, Wright I et al. 2022. Leaf nitrogen from the perspective of optimal plant function. Journal of Ecology 110: 2585–2602.

Reich PB, Tjoelker MG, Machado JL, Oleksyn J. 2006b. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439(7075): 457-461.

Ryan MG. 1991. Effects of Climate Change on Plant Respiration. Ecological Applications 1(2): 157-167.

Wright I, Reich P, Westoby M et al. 2004. The worldwide leaf economics spectrum. Nature 428: 821–827.

We will expand the paragraph in the Discussion section:

“Our findings have important implications for the coupling of the C and N cycles in vegetation. For instance, changes in climate are expected to lead to the expansion of tree species better adapted to dry conditions in large parts of European temperate forests (e.g., Quercus species; Hanewinkel et al., 2013), which replace (amongst others) needleleaf evergreen with broadleaf deciduous trees, exhibit relatively low growth rates, initially are of younger age, and meet soil conditions affected by increased N deposition (Schwede et al., 2018). In this example, as a result of these species shifts, we would expect a higher N concentration in all tree tissues and elevated N allocation fractions to stems. An increased leaf N concentration will, in turn, support higher photosynthesis (especially in N-limited ecosystems), but higher tissue N concentrations would result also in higher R_m and the elevated N allocation fraction to stems might lead to a reduced C use efficiency (CUE; Manzoni et al., 2018) due to elevated stem sapwood R_m (Thurner et al., 2019). However, depending on the interplay of changes in the controls of tree tissue N concentration and other processes, the resulting net effects on N and C cycles remain largely unknown and require further investigation. In particular, our analyses do not cover the effects of increasing atmospheric CO₂.”

Hanewinkel M, Cullmann DA, Schelhaas M-J, Nabuurs G-J, Zimmermann NE. 2013. Climate change may cause severe loss in the economic value of European forest land. Nature Climate Change 3(3): 203-207.

Manzoni S, Čapek P, Porada P et al. 2018. Reviews and syntheses: Carbon use efficiency from organisms to ecosystems – definitions, theories, and empirical evidence, Biogeosciences 15: 5929–5949.

Schwede DB, Simpson D, Tan J, Fu JS, Dentener F, Du E, deVries W. 2018. Spatial variation of modelled total, dry and wet nitrogen deposition to forests at global scale. Environ Pollut 243(Pt B): 1287-1301.

Thurner M, Beer C, Crowther T, Falster D, Manzoni S, Prokushkin A, Schulze ED, Gillespie T. 2019. Sapwood biomass carbon in northern boreal and temperate forests. Global Ecology and Biogeography 28(5): 640-660.

1. Timing of analysis and phenological stages:

The authors examine the variation of N as a function of several tree characteristics, such as leaf type, growth rate, and age. Considering the relatively rapid internal cycling of N, which can lead to significant differences in the tissue N concentration depending on the phenological stage, I believe the timing of the analysis should be taken into account. I wonder if this information can be extracted from the existing datasets. While this issue is briefly mentioned in the list of recommendations for further studies (point d under potential confounding factors), given the relatively high variability in N% observed within trees of the same leaf type regarding age or height, I suggest addressing this point earlier in the manuscript. A more careful consideration of the phenological stages during which the analyses were conducted is important, and data normalization by this factor could be beneficial. In particular, without this information, hypothesis 2 and the related results (lines 273-280) as well as the discussion (lines 409-419) may be misinterpreted. A similar observation is also valid for the needle age, with the older ones likely showing lower N concentration due to resorption. Indicating the average needle age among the collected data is thus relevant information.

We agree that seasonal variations in tissue N concentrations could potentially affect our results. We did not exclude measurements from any phenological stage, but please note that the vast majority of measurements in the database that we compiled have been collected during summer months. Our results should thus not be strongly influenced by seasonal variations in tissue N concentrations.

To further investigate this potential issue, we have now collected information on the measurement month-of-the-year (MOY) as far as this information is available from the compiled studies and the studies contained in the TRY and BAAD databases. In this additional analysis, we focused on leaf N concentration measurements, because leaf N concentrations should be more strongly affected by seasonal variations compared to the other investigated tissues.

Please find attached in Fig. R1 (figure on page 1 of the attached pdf document) the relation between leaf N concentrations and measurement MOY for broadleaf deciduous (BD), needleleaf deciduous (ND) and needleleaf evergreen (NE) trees (number of observations for each leaf type in brackets). We find that

1. the vast majority of measurements has been taken during the summer season (June – September), and
2. there is no clear pattern of lower leaf N concentrations outside the summer season evident in this data.

We also quantified the significance of differences in leaf N concentrations between different measurement MOY by the p-values of pairwise t-tests. For BD trees, in no case we find significant differences between consecutive months at the 5% level. For NE trees, leaf N concentrations are, for instance, significantly different at the 5% level between July and August and between August and September. However, these differences show contradictory trends and are based on a limited number of measurements, and thus do not show a clear relation between leaf N concentrations and the phenological season. We conclude that our results are not strongly affected by seasonal variations in tissue N concentrations.

The average needle age can unfortunately be extracted only from few studies compiled in the database. In general, variations in needle age should be sufficiently covered by the sampling methods of the studies included in the database.

Nevertheless, we have now collected also information on needle age as far as available. Please find attached in Fig. R2 (figure on page 2 of the attached pdf document) the relation between leaf N concentrations and needle age for needleleaf evergreen (NE) trees (number of observations in brackets). Although the median of the leaf N concentration of 1-year old needles is lower than that of current year needles, again we find no significant difference at the 5% level related to needle age. Based on the few measurements where we have information on needle age, we cannot detect that needle age would strongly affect our results.

We will include the results of our additional analyses on the relationships between leaf N concentrations and MOY and leaf age in the Supporting Information of the manuscript. We will refer to them in the Results section of the manuscript as follows:

“Among other things, variations in tissue N concentrations with season and needle age could potentially affect our results. However, the vast majority of measurements included in the compiled database have been taken during the summer season (June – September). In addition, we do not find significantly (at the 5% level) lower leaf N concentrations outside the summer season or with increasing needle age in additional analyses, which are however based on limited data for which information on measurement time and needle age are available (see Supporting Information).”

In addition, we have included season and leaf age as potential confounding factors already in the Discussion section (Lines 507-512) of the preprint manuscript:

“d) coverage of other potential confounding factors (e.g. season (e.g. Vose & Ryan, 2002; Damesin, 2003), including differences between green and senesced plant material, for instance due to N resorption and translocation from senescing leaves (e.g. Vergutz et al., 2012); variation […] with leaf age (e.g. Oren et al. 1988) […])”

Damesin C. 2003. Respiration and photosynthesis characteristics of current-year stems of Fagus sylvatica: from the seasonal pattern to an annual balance. New Phytol 158(3): 465-475.

Oren R, Werk KS, Schulze ED, Meyer J, Schneider BU, Schramel P. 1988. Performance of Two Picea abies (L.) Karst. Stands at Different Stages of Decline. VI. Nutrient Concentration. Oecologia 77(2): 151-162.

Vergutz L, Manzoni S, Porporato A, Novais RF, Jackson RB. 2012. Global resorption efficiencies and concentrations of carbon and nutrients in leaves of terrestrial plants. Ecological Monographs 82(2): 205-220.

Vose JM, Ryan MG. 2002. Seasonal respiration of foliage, fine roots, and woody tissues in relation to growth, tissue N, and photosynthesis. Global Change Biology 8(2): 182-193.

1. Use of acronyms for tree leaf categories:

 The frequent repetition of the three leaf categories (broadleaf deciduous, needleleaf deciduous, needleleaf evergreen) throughout the manuscript disrupts the flow of the text. I recommend introducing acronyms for these three leaf types (such as BLD, NLD, and NLE, respectively). This would improve the manuscript's readability and could also be applied in figure legends.

We will use the acronyms BD, ND and NE for broadleaf deciduous, needleleaf deciduous and needleleaf evergreen trees throughout the manuscript and figure legends.

Minor comments

1. I am not sure if this is related to the preprint stage, but overall, the clarity of the figures needs improvement. Specifically, the font size of the legends and axes should be enlarged in Figures 1, 2, 4, and 6 for the legends, and in Figures 1, 2, 3, 4, and 6 for the axes. Please make sure all the details in all the panels are clear enough when uploading the final figures.

We will increase the font size of the figure legends and axes as requested.

1. Please avoid using the phrase "unprecedented dataset" in the title of section 2.1, and instead opt for a more neutral, less sensationalistic tone.

We will use the word “novel” instead of “unprecedented” to describe the database.

Sincerely,

Martin Thurner

On behalf of all Authors