the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jul 2024
Nitrogen concentrations in boreal and temperate tree tissues vary with tree age/size, growth rate and climate
Abstract. Nitrogen (N) concentration in tree tissues controls photosynthesis, growth and plant maintenance respiration. While earlier studies of its variation and underlying controls have mostly focused on leaves, here we identify the large-scale controls of N concentration in other tree compartments for the first time. This is achieved by constructing and analysing an unprecedented database of N concentrations in stems, roots and branches covering all common Northern hemisphere boreal and temperate tree genera, combined with data for leaves mostly from existing databases. This database allows us to explore the large-scale abiotic (climate, soil N concentration) and biotic controls (tree age/size, leaf type, growth rate) of tree tissue N concentration. We find that N concentrations decrease with increasing tree age (or size) and are significantly higher in deciduous compared to evergreen trees in all tissues. Low growth rates or unfavorable climate conditions (very cold or dry climate) significantly decrease leaf (the latter only for needleleaf deciduous and needleleaf evergreen trees), but not stem N concentration, indicating their effects on N allocation. Plant traits and environmental conditions together explain very large parts of the variation in tissue N concentrations. These results suggest that changes in the distribution of tree age/size, species, and extreme climate, induced by climate change, forest management or disturbances, will have substantial consequences for the carbon (C) sequestration potential of boreal and temperate forests by altering tissue N concentrations. We expect that the expansion of tree species better adapted to dry conditions in European temperate forests will result in a higher N concentration in all tree tissues and elevated N allocation fractions to stems, which might lead to higher productivity, but also higher maintenance respiration. The identified relationships need to be represented in dynamic global vegetation models (DGVMs) to estimate future effects of N limitation on the C cycle.
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CC1: 'Comment on egusphere-2024-1794', Laurent Augusto, 08 Aug 2024
Dear colleagues,
This is a very interesting study.
It confirms older results, with the same patterns. More specifically, when trees are getting older, the proportion of low-nutrients tissues (e.g. heartwood in stems) increases, resulting in decreasing values of nutrient content in the whole compartment (e.g. "stem", composed of heartwood, sapwood, phloem and bark).
Within a given tissue (for instance sapwood), the nutrient content decreases because of internal retranslocation.
These two phenomena explain why the nutrient content values decrease with the tree ageing. I suggest to read the following references:
Augusto et al. (2008). Improving models of forest nutrient export with equations that predict the nutrient concentration of tree compartments. Annals of Forest Science. DOI: 10.1051/forest:2008059
Wernsdörfer et al. (2014). Modelling of nutrient concentrations in roundwood based on diameter and tissue proportion: Evidence for an additional site-age effect in the case of Fagus sylvatica. Forest Ecology and Management, 330: 192-204.Augusto et al. (2015). Biomass and nutrients in tree root systems–sustainable harvesting of an intensively managed Pinus pinaster (Ait.) planted forest. Global Change Biology Bioenergy, 7(2), 231-243.Best regardsLaurentCitation: https://doi.org/10.5194/egusphere-2024-1794-CC1 -
RC1: 'Comment on egusphere-2024-1794', Anonymous Referee #1, 02 Sep 2024
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.
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.
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.
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.
Simple statistics could be added to show the different N concentrations of the different compartments in the different PFTs.
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.
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.
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).
Citation: https://doi.org/10.5194/egusphere-2024-1794-RC1 -
RC2: 'Comment on egusphere-2024-1794', Anonymous Referee #2, 07 Oct 2024
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.
Major comments
- 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.
- 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.
- 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.
Minor comments
- 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.
- Please avoid using the phrase "unprecedented dataset" in the title of section 2.1, and instead opt for a more neutral, less sensationalistic tone.
Citation: https://doi.org/10.5194/egusphere-2024-1794-RC2
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