the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Sep 2024
Optimal set of leaf and whole-tree elements for predicting forest functioning
Abstract. The role played by environmental factors in the functioning of forest ecosystems is relatively well known. However, the potential of the elemental composition of trees (i.e., elementomes) as a predictor of forest functioning remains elusive. We assessed the predictive power of elemental composition from different perspectives: testing whether whole-plant element stocks or concentrations explain forest production and productivity (i.e., production per unit of standing biomass) better than leaf elements or environmental factors; identifying the optimal set (combination and quantity) of elements that best predicts forest functioning. To do so, we used a forest inventory of 2000 plots in the northeast of the Iberian Peninsula, containing in-site information about the elementomes (C, Ca, K, Mg, N, Na, P, and S) of leaves, branches, stems and barks, in addition to annual biomass production per organ. We found that models using leaf element stocks as predictors achieve the highest explained variation in forest production. The optimal dimensionality was achieved by combining the foliar stocks of C, Ca, K, Mg, N, P, and interactions (C×N, C×P, and N×P). Forest biomass productivity was best predicted by forest age. Hence, our results indicate that leaf element stocks are better predictors of forest biomass production than element concentrations or stocks of the whole trees, suggesting that analyzing leaves alone is a good enough approach to study ecosystem functioning.
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RC1: 'Comment on egusphere-2024-2572', Helena Vallicrosa, 03 Oct 2024
In this study, Diniz et al. are using elemental composition and biomass information from the Ecological and Forest Inventory of Catalonia (trees) to determine the best variables to determine forest functioning. The study is relevant and interesting, and the methodology seems sound. Nonetheless, the manuscript requires attention in some aspects before being suitable for publication:
General comments:
My main concern is the use of “whole-tree” terminology to describe only the aboveground section of the tree. I acknowledge the value of including information coming from leaves but also branches, stems and bark, which is rare to find in a dataset that combines biomass and elemental composition information. Nonetheless, disregarding roots in the conception of “whole-tree” is misleading. Belowground biomass (coarse and fine roots) represents at least 22% of the total forest biomass in forests (Ma et al., 2021), and play key roles in tree growth, stand development and nutrient uptake (Gao et al., 2021). Therefore, including the role of roots in a similar analysis would be extremely valuable and interesting. For instance, a recent study emphasized the importance of accounting for both leaves and fine roots in the calculation of plant nutrient uptakes, where stems could be inferred or gap-filled from inventories (Dybzinski et al., 2024). In conclusion, we all seem to agree that based on the evidence provided by Diniz et al. and other studies, stem-related tissues are not as relevant for forest functioning determination, but below-ground information is desired. Thus, I recommend changing the terminology used in this study to above-ground elements instead of whole-tree.
Related to the above, it would be desirable to include a paragraph or section in the discussion disclosing whether the non-inclusion of below-ground tissues can affect the conclusions of the study and encourage the use of below-ground information in future studies. Also, other “disclaimers” regarding the methodology could be fleshed out. For instance, could the low predictive power of “whole-plant” related analysis be attributed to the aggregation of error while sampling production and elements of branches, stems and bark?
Using the methodology and data followed in this paper, I believe it would be interesting also to calculate the predictive power of single elements and relative stocks. What is the element describing ecosystem functioning the most? Does it correspond with the most sampled (normally N)? Could this be a reason to start sampling other elements more? Even though it is not the same information, could maybe this information be somehow inferred from the information displayed in Figure 3a?
Specific comments:
Line 11: What is the difference between forest production and productivity? It would be desired to describe the difference between productivity and production somewhere in the introduction. Both are concepts used throughout the paper that can be easily confused.
Line 65: Any other gradient other than forest formations? Altitude, climates…? You mention this in the methods section and including it here would help emphasize the value of the database.
Line 70-72: I would rewrite it as such (or something similar): element stocks better explain functioning than elementomes, as the former incorporates the effect of growth, encompassing factors such as age and hidden limitations in forest functioning;
Methods: What is the time scale of the sampling? When were the samples collected?
Technical corrections:
Line 60: Please, join the two parenthesis sections in one
Line 61: I don’t think ODs has been defined before. Please define.
Line 64: OES was previously defined. No need to define it again. Same in line 160.
Line 65-73: It would help clarity to number the questions and make them match with the hypothesis.
Ma, H., Mo, L., Crowther, T.W. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat Ecol Evol 5, 1110–1122 (2021). https://doi.org/10.1038/s41559-021-01485-1
Guoqiang Gao, Zhi Liu, Yan Wang, Siyuan Wang, Cunyong Ju, Jiacun Gu. Tamm Review: Fine root biomass in the organic (O) horizon in forest ecosystems: Global patterns and controlling factors, Forest Ecology and Management, 491,2021, https://doi.org/10.1016/j.foreco.2021.119208.
Dybzinski, R., Segal, E., McCormack, M.L. et al. Calculating Nitrogen Uptake Rates in Forests: Which Components Can Be Omitted, Simplified, or Taken from Trait Databases and Which Must Be Measured In Situ?. Ecosystems 27, 739–763 (2024). https://doi.org/10.1007/s10021-024-00919-8
Citation: https://doi.org/10.5194/egusphere-2024-2572-RC1 -
AC1: 'Reply on RC1', Mr Souza Diniz, 14 Oct 2024
In this study, Diniz et al. are using elemental composition and biomass information from the Ecological and Forest Inventory of Catalonia (trees) to determine the best variables to determine forest functioning. The study is relevant and interesting, and the methodology seems sound. Nonetheless, the manuscript requires attention in some aspects before being suitable for publication:
Authors’ answer: Dear referee, thank you for your feedback on our manuscript and the contributions for your comments and suggestions. Thank you very much also for the references you shared. We really appreciate it and acknowledge for the time you devoted to conduct this review. Below, we provide answers to each of your comments.
General comments:
My main concern is the use of “whole-tree” terminology to describe only the aboveground section of the tree. I acknowledge the value of including information coming from leaves but also branches, stems and bark, which is rare to find in a dataset that combines biomass and elemental composition information. Nonetheless, disregarding roots in the conception of “whole-tree” is misleading. Belowground biomass (coarse and fine roots) represents at least 22% of the total forest biomass in forests (Ma et al., 2021), and play key roles in tree growth, stand development and nutrient uptake (Gao et al., 2021). Therefore, including the role of roots in a similar analysis would be extremely valuable and interesting. For instance, a recent study emphasized the importance of accounting for both leaves and fine roots in the calculation of plant nutrient uptakes, where stems could be inferred or gap-filled from inventories (Dybzinski et al., 2024). In conclusion, we all seem to agree that based on the evidence provided by Diniz et al. and other studies, stem-related tissues are not as relevant for forest functioning determination, but below-ground information is desired. Thus, I recommend changing the terminology used in this study to above-ground elements instead of whole-tree.
Authors’ answer: Thank you for pointing this out. Indeed, the terminology aboveground is more appropriate to our context since our analysis did not include roots data. We replaced ‘whole-plant’ by ‘aboveground’ throughout the text and in the figures.
Related to the above, it would be desirable to include a paragraph or section in the discussion disclosing whether the non-inclusion of below-ground tissues can affect the conclusions of the study and encourage the use of below-ground information in future studies. Also, other “disclaimers” regarding the methodology could be fleshed out. For instance, could the low predictive power of “whole-plant” related analysis be attributed to the aggregation of error while sampling production and elements of branches, stems and bark?
Authors’ answer: We added a “Caveats, limitations, and implications” section at the end of the discussion and included sentences recognizing the importance of belowground organs like roots for biomass and elements storage and recommending their inclusion in further studies using our modelling approach, current lines 346-362, reading:
“Caveats, limitations, and implications
In this study, we bring new insights into the effects of the optimal elemental sets, compared to climate and stand age, on both forest biomass production and productivity. As practical implications for future research, our results suggest that using only data on leaf elements, especially stocks, allows us to achieve robust predictions of variations in forest biomass. Such information contributes to decision-making by researchers and forest managers about the types of data (aboveground elements or just leaves’ elements) they should prioritize collecting when assessing forest growth. Nevertheless, our presented results should also be interpreted cautiously since they might be influenced by sampling limitations and analyses conducted only on aboveground organs (barks, branches, leaves, and stems). In the data used in this study, measurements of element concentrations in different above-ground organs of trees were obtained for different numbers of individuals per species. This difference in the number of individuals may have influenced, even if subtly, the results. The biomass of belowground organs (e.g., fine roots) may account for at least 22% of the total forest biomass (Ma et al., 2021) and display important roles in nutrient uptake and storage (Gao et al., 2021; Dybzinski et al., 2024). The importance of roots for element stocks is also underscored by the fact that around 24% of total plant carbon is stored belowground (Ma et al., 2021). Furthermore, climatic factors such as temperature are influential in roots biomass and future changes driven by warmer and drier climates are expected to affect the balance between aboveground and belowground biomass allocations and element stocks (Pornon et al. 2019; Ma et al., 2021). Thus, including element concentrations and stocks of roots in the models like the ones we used may enhance the predictability of forest functioning, while also forest functioning controlling for effects of environment (e.g., climate) in whole-tree biomass. For future research, we recommend the addition of belowground elements, so one can also compare predictive performances using whole-plant elements (above and belowground) and only aboveground elements”.
Using the methodology and data followed in this paper, I believe it would be interesting also to calculate the predictive power of single elements and relative stocks. What is the element describing ecosystem functioning the most? Does it correspond with the most sampled (normally N)? Could this be a reason to start sampling other elements more? Even though it is not the same information, could maybe this information be somehow inferred from the information displayed in Figure 3a?
Authors’ answer: Thank you for your comment. The information on the importance of predictors based on individual elements (concentrations and stocks) in contributing to the performance of models in predicting forest functioning can be inferred from the outputs of the model averages presented in Figures 3, 4 and A3. In the methods section (lines 185-191) we explain that, reading:
“Finally, to obtain a reliable overview of which were the most important variables (e.g., elements concentration and stocks) for explaining forest functioning, we performed model averaging for models with ΔAIC < 4 using the function “model.avg” of the “MuMIn” package (Bartoń, 2023) in R 4.3.3. We used the argument “beta=TRUE” to standardize the coefficients, allowing for a comparison of the relative importance of each predictor variable in the average models. Model averaging computes an average model output from the estimates of a set of models and weights their relative importance by their AIC (Burnham and Anderson, 2002). Therefore, this approach allowed us to obtain information on the importance of predictor variables extracted from the best model subsets (i.e., ΔAIC < 4).”
Thus, for example, the results in figure A3 show the elements (Ca, N, P) that most affect forest functioning, their coefficients having been based on the average of several well-ranked models (delta < 4).
Further, we added a sentence in the results section (lines 235-236) to summarize it more explicitly. Reading:
“The information contained in the figures 3, 4 and A3 outline the importance of individual elements (concentrations and stocks) in contributing to the performance of models in predicting forest functioning.”
Specific comments:
Line 11: What is the difference between forest production and productivity? It would be desired to describe the difference between productivity and production somewhere in the introduction. Both are concepts used throughout the paper that can be easily confused.
Authors’ answer: We added new sentences in the fourth paragraph (current lines 48-53), reading:
“Considering the entire aboveground elements (concentration and stocks) and at the leaf level may contribute to enhancing the understanding of ecosystem processes (Luo et al., 2020; Rocha et al., 2011). For instance, forest biomass production (i.e., the overall total amount of biomass accumulated) is influenced by the concentrations of elements the plants store (Dar and Parthasarathy, 2022; Ullah et al., 2024). Further, tree element concentration significantly impacts ecosystem productivity (Bitomský et al., 2023; Elser et al., 2010). Therefore, the elements concentration also contributes to forest biomass productivity – the biomass generation by plants per unit area over time that reflects ecosystem efficiency (Vargas-Larreta et al. 2021; Bitomský et al., 2023).”
Line 65: Any other gradient other than forest formations? Altitude, climates…? You mention this in the methods section and including it here would help emphasize the value of the database.
Authors’ answer: We added in the sentence (current line 68-70) the climate and altitude as part of a notable environmental gradient that results also in distinct forest formations, reading:
“This region is a suitable model to investigate topics related to OES, as it is composed of a notable environmental gradient (e.g., wide variations in climate and altitude) that influences the formation of distinct forest types.”
Line 70-72: I would rewrite it as such (or something similar): element stocks better explain functioning than elementomes, as the former incorporates the effect of growth, encompassing factors such as age and hidden limitations in forest functioning;
Authors’ answer: Thank you. We incorporated your suggestion in the sentence (current lines 76-78), reading:
“H2: element stocks better explain functioning than elementomes, as the former incorporates the effect of growth, while also encompasses effects of factors such as age and hidden limitations (e.g., carbon saturation, nutrient limitation), in forest functioning age”
Methods: What is the time scale of the sampling? When were the samples collected?
Authors’ answer: We added the period (years) of the sampling (current lineS 99-100), reading:
“We used the Ecological and Forest Inventory of Catalonia (IEFC) database, originally sampled in the period 1989-1996 (Gracia et al., 2004) (http://www.creaf.uab.es/iefc).”
Technical corrections:
Line 60: Please, join the two parenthesis sections in one
Authors’ answer: We corrected it (current line 64).
Line 61: I don’t think ODs has been defined before. Please define.
Authors’ answer: It was a typo. We wanted mean “OES”. So, we replaced ODs for OES (current line 65).
Line 64: OES was previously defined. No need to define it again. Same in line 160.
Authors’ answer: We deleted the repetition of definition (current lines 168 and 167).
Line 65-73: It would help clarity to number the questions and make them match with the hypothesis.
Authors’ answer: We added numbering to the questions and related them to the hypotheses (current lines 70-79), reading:
“In this study, we used a database including forest element composition and biomass growth in the northeast of the Iberian Peninsula. This region is a suitable model for investigating topics related to OES, as it is composed of a notable environmental gradient (e.g., wide variations in climate and altitude) that influences the formation of distinct forest types. We aimed to answer four questions: Q1-Are the aboveground elements (elementomes and stocks) better predictors of forest functioning (production and productivity) than only leaf elements? Q2-Do element stocks better explain forest functioning than elementomes? Q3-Do element stocks and elementomes (leaf and aboveground) explain better forest functioning than environmental factors and stand age? Q4-What is the OES that best predicts forest functioning? We departed from three central hypotheses: H1: aboveground elements (elementomes and stocks) are better predictors of forest functioning (biomass production and productivity) than only leaf elements (Q1); H2: element stocks better explain functioning than elementomes, as the former incorporates the effect of growth, while also encompasses effects of factors such as age and hidden limitations (e.g., carbon saturation, nutrient limitation), in forest functioning (Q2, Q3); H3: OES effects in forest biomass production and productivity models are greater in models using whole organisms than leaf elementomes (Q4).”
Ma, H., Mo, L., Crowther, T.W. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat Ecol Evol 5, 1110–1122 (2021). https://doi.org/10.1038/s41559-021-01485-1
Guoqiang Gao, Zhi Liu, Yan Wang, Siyuan Wang, Cunyong Ju, Jiacun Gu. Tamm Review: Fine root biomass in the organic (O) horizon in forest ecosystems: Global patterns and controlling factors, Forest Ecology and Management, 491,2021, https://doi.org/10.1016/j.foreco.2021.119208.
Dybzinski, R., Segal, E., McCormack, M.L. et al. Calculating Nitrogen Uptake Rates in Forests: Which Components Can Be Omitted, Simplified, or Taken from Trait Databases and Which Must Be Measured In Situ?. Ecosystems 27, 739–763 (2024). https://doi.org/10.1007/s10021-024-00919-8
Citation: https://doi.org/10.5194/egusphere-2024-2572-AC1
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AC1: 'Reply on RC1', Mr Souza Diniz, 14 Oct 2024
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RC2: 'Comment on egusphere-2024-2572', Emma Hauser, 12 Nov 2024
In the article Optimal set of leave and whole-tree elements for predicting forest functioning the authors analyze data from over 2000 trees in the Forest Inventory of Catalonia to examine the role of tree leaf and whole tree (comprised of leaf, bark, stem, and branches) elemental composition in explaining forest biomass production and productivity rates. The authors find that, while forest age best predicts forest biomass productivity, variations in forest production are better explained by leaf nutrient stocks than whole tree nutrients or nutrient concentrations. The authors also identify which foliar elements and interactions of elements can best model forest productivity and production, highlighting the importance of N and P in forest production variables.
This study presents an important advance in our understanding of forest ecosystems. First, as the authors suggest, these results could guide forest sampling, as they indicate that foliage may be sufficient to estimate forest productivity metrics. Such information could streamline forest sampling as well as model developments reliant on subsequent data. The degree to which forest ecosystem level information can be represented from foliar data is needed and useful information, especially in efforts to estimate large scale forest nutrient demands and model ecological processes. Further, this analysis pairs data from bark, stems, branches, and leaves from many individual trees, a rare number of forest data components to have all in one place. These data could give us a better sense of how different parts of a tree contribute to whole tree nutrition, as well as C and nutrient allocation patterns. The importance of these results for understanding tree resource allocation could be described more strongly in the introduction and/or discussion, but overall this work represents an important next step in our understanding of forest ecosystems.
However, there are a few crucial details that are missing from this paper that challenge whether leaves alone can be sufficient to estimate ecosystem function as the authors suggest. My primary concern is that there is no representation of belowground drivers of productivity, namely root and soil processes. Roots especially are an important component of tree biomass production due to their role in nutrient uptake, as well as the relatively quick growth and turnover of fine roots. Given that root data are not a part of the database, including roots may be beyond the scope of this work, but then the authors should be more explicit throughout the manuscript that their work pertains specifically to aboveground production.
Further, there is no mention of the role of soil nutrients even though soil nutrients are key drivers of forest productivity. There is discussion of productivity drivers other than tree nutrients in the manuscript such as climate and stand age, but soil nutrients are not part of the analysis or in any of the introductory or conclusion text. Soil nutrients are the source of tree nutrients so it seems a little odd that these are overlooked. I don’t think they need to be a central focus of the manuscript, but soil nutrients are at least worth mentioning and possibly bringing into the analyses if they are available. It would be nice to also have some details about the types of soils found in these forests and any known differences between the soils in the site description.
Finally, it would be helpful to clarify and expand the carbon paragraph that is brought in at the end of the discussion. In most of the manuscript, carbon is referred to in the same way as nutrients are in the analyses. Given that C and nutrients serve different roles in the plant and that biomass is approximately 50% carbon, would the authors expect a direct relationship between biomass production and C, and would they expect this relationship to be different than that between biomass and nutrients? The difference between C and nutrients in these analyses is touched on in lines 324 -329, but I’m wondering if the authors could expand this discussion and maybe bring it into the manuscript earlier, as I wondered about this as I was reading the introduction, results and discussion.
Overall I think this is an important and useful contribution to ecosystem science, but requires a bit more specificity in the text given the data presented. In the results and conclusions it often seems like the authors are making claims that are too broad and generalized for the results (for example saying that leaf nutrients are sufficient to understand ecosystem function, when leaf nutrient stocks are specific to forest production and age matters more for productivity). It would also be great if the authors could add some information about the belowground contributions to ecosystem production, especially in the introduction and conclusions to the manuscript. In making the claims more nuanced, the paper would highlight a specific ecological relationship that is important for guiding next steps in ecosystems research in both empirical and modeling applications. I have included more specific ideas in my line by line comments below.
Abstract:
Lines 19-20: The statement that analyzing only leaves is a good enough approach to study ecosystem functioning seems a little too general. Ecosystem functioning can include a lot of processes besides just productivity. In addition roots and soil nutrients were not analyzed here, which are also likely important to productivity. I’d make this sentence a little more specific to the study, possibly “our results indicate that leaf element stocks…hinting toward leaf measurements as a critical for predicting forest productivity” (or something along those lines).
Introduction
Line 38: In these other studies, are there different measures of ecosystem function? Is elementome in these studies specifically correlated with productivity? It seems like ecosystem function and productivity are sometimes used interchangeably in the manuscript but there are numerous functions other than productivity.
Line 42: I would add roots to this list.
Lines 47-49: The first two sentences of this paragraph feel repetitive with the beginning of the last paragraph. Maybe these two paragraphs could be trimmed and condensed.
Line 61: Should ODs be OES?
Line 65: Could the authors describe more explicitly in this sentence why the environmental gradient and different forest forms are important to testing OES topics?
Line 69: What do the authors mean by ‘departed from’ here? A rewording might make the intent clearer.
Line 73: a concluding sentence that wraps up the introduction stating why these findings will be important would be nice here.
Materials and Methods
Study area—it would be great to be a little more specific about why this study area is useful/chosen. Does climate diversity suggest there will also be elementome diversity? Will this allow the authors to test different effects of climate vs. elementome? The authors do have some text relevant to this in lines 35-36, but it might be nice to add that here as well (or maybe move some of that here) to make it clearer why these sites were chosen.
Line 113: might rephrase “5 to 5” as “each 5 cm increment” to make clearer what is meant here.
Line 115: Since root data are not available, it would be more accurate to say “aboveground productivity” throughout the manuscript to make it clear that that’s what is being examined here.
Line 118: It might be worth it to set the equations out in a separate line rather than having them embedded within the paragraph. That can make some of the math a little easier to follow.
Lines 119-120: here and throughout I found the use of production vs. productivity somewhat confusing. I understand the difference, and these may be the established conventions in which case this can be disregarded, but if it would be possible to rename one of the terms so they are more distinct, it would make it easier to follow which is being discussed later in the paper.
Line 141: It would be a little clearer to put ‘generalized additive mixed models’ first and then put (GAMMs) in parentheses.
Results
Line 191: Here ‘forest functioning’ is used a little too broadly. I’d suggest using productivity or production since that is what was measured.
Line 191-193: I found these sentences hard to follow. It seems like there are 2 models presented, one in each sentence, but they are each described as the ‘best model.’ Is the second sentence for productivity and that word has been omitted from the sentence?
Line 200: Nothing is mentioned about climate or age in the first paragraph but figure 1 seems to suggest that climate/age explains production best so might be worth mentioning that in this first paragraph.
Discussion
Line 289-294: This may just be a wording issue, but it seems like these sentences are contradictory. The sentence starting “We found a possible effect…” suggests that less moisture caused plants to retain more nutrients in leaves to cope with drought. The sentence starting in line 293, “Therefore, our observed…” suggests that high precipitation coincides with high foliar nutrient storage, so it is unclear whether more nutrients are stored with more or less water.
Line 325: It would be nice to restate more specifically here what the decrease in forest biomass is that the authors refer to.
Conclusion
Lines 343-345: The authors point out that productivity was not driven by nutrients but stand age, then suggest that focusing on leaf elements is sufficient for understanding variations in forest biomass. This seems a little misleading–more weight should be given to the fact that other variables besides leaf nutrients were primary drivers and that root and soil nutrients were not considered as a part of this analysis.
Citation: https://doi.org/10.5194/egusphere-2024-2572-RC2
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