Climate and traits drive bark decomposition patterns at global scale

Domain: chemistry
Published: 2026-01-13
Editorial outcome: major_revision

Abstract
Tree bark represents a large global carbon stock, comprising 2-20% of woody biomass, and plays a distinct role in carbon and nutrient cycling. It is poorly understood how different abiotic and biotic drivers contribute to bark decomposition globally, or how these relationships play out in a changing climate. Here, we compile a global dataset to estimate the spatial variability of current bark decomposition rates (k values) and employ three machine learning approaches to project the response of k in future climate scenarios. We find that, globally, bark decomposes 2.9 times faster than the rate previously estimated for tree trunks (including bark). Mean k values increase latitudinally from 0.093 year-1 in the boreal region to 1.339 year-1 in the tropical region. Angiosperm bark decomposes faster (k = 0.450 year-1) than gymnosperm bark (0.198 year-1). Climate, especially mean annual temperature and mean annual precipitation, and bark phosphorus concentration, are the predominant factors driving bark decomposition. Four leading future climate scenarios predict bark decomposition to slow down in warm and wet regions and accelerate in cold and dry regions. Our results highlight the importance of considering bark decomposition in carbon cycling models.

Introduction
Tree bark, the outermost layer of the tree trunk in woody plants, constitutes a critical component of woody plant biomass, accounting for a non-trivial 2–20% of stem biomass1,2. Bark is also vital to the ecosystem nutrient cycles, contributing a third of nitrogen (N) and phosphorus (P) concentrations in stems3,4; and probably releasing these nutrients faster after death because of (on average) faster decomposition rate compared to xylem5–7. While the role of bark in carbon (C) and nutrient cycling during the life of trees is well-established8, its ecological importance extends further, particularly after tree or woody stem death9. Decomposing bark not only promotes nutrient availability but also enhances biodiversity, increases habitat heterogeneity, and diversifies trophic niches, thus fulfilling multiple functions in the ecosystem10–12. However, the response of bark decomposition to abiotic and biotic drivers, especially in the context of climate change, remains poorly understood compared to insight into decomposition of other plant organs such as leaves and shoots13. It is crucial to comprehend the contributions of these drivers of bark decomposition to accurately predict how future climate will impact forest ecosystems and their carbon budgets.

In most biomes, decomposition is a biologically driven process, mainly carried out by microbes and soil fauna, which is jointly regulated by substrate quality and climate14–16. The type of dominant decomposers involved and the assembly history may have significant and long-lasting impacts on the decomposition process of bark and even the whole deadwood17,18. Bark persistence can range from several years to decades7,19, which is tree species- and region-specific. For example, deadwood in tropical forests tends to decompose faster than that in temperate or high-altitude regions, partly due to high activity of termites, which have a strong and positive temperature sensitivity20. However, most research has primarily focused on how the substrate quality-related factors, such as tree species, diameter, and decomposition stage7,21, affect decomposer activity, while little attention has been paid to the effects of climate and their interactions with the aforementioned factors. This is likely due to the climate-driven functional convergence of tree species (and their substrate quality) within the same region, where the effects of climate and substrate quality are often closely linked, making it difficult to differentiate their relative importance. At a high taxonomic level, for example, gymnosperms, which predominate in cool and cold biomes, had generally poorly decomposable bark compared to angiosperms in a temperate study1, probably partly due to their high contents of extracts (e.g., resins) in bark. Another reason lies in the limitations of experimental design: bark decomposition study of multiple tree species is usually carried out at a single site, making it difficult to compare decomposition rates and disentangle their drivers at a broader scale. Disentangling the drivers of bark decomposition from regional to global scale through a comprehensive analysis would allow us to better estimate bark decomposition in regions with limited observations, improve the accuracy of predictions about bark’s decomposition response to future climate change, and help anticipate the consequences for carbon dynamics and ecosystem functioning. However, no research has systematically investigated the separate and combined contributions of different drivers of decomposition related to both bark substrate quality and climate at the global scale.

Temperature and precipitation are among the most important climate factors driving tree growth and tree decomposition22,23. Global warming and the spatial redistribution of precipitation may affect bark decomposition rates or patterns by altering tree species composition, substrate quality, decomposer community composition, and decomposer activity24,25. For example, deadwood in dryland decomposed faster with increased precipitation due to the higher decomposer activity and different nutrient status26. However, such responses have not been studied for bark. Although temperature projections show general agreement in the global warming trends, precipitation projections exhibit significant regional uncertainties27, leading to regionally distinct wetting or drying conditions. This spatial heterogeneity in climate projections suggests that the response of bark decomposition to climate change will vary across regions, depending on local changes in temperature and precipitation13. For instance, decomposer activity would be greatly stimulated in high-latitude or high-altitude regions, where warmer and wetter conditions are expected globally under climate change28. Meanwhile, decomposers in arid or semi-arid regions would be challenged by the increased high temperature extremes and decreased precipitation29,30. Identifying the key factors driving bark decomposition in different climate regions is essential for accurately predicting potential responses of forest ecosystems to future climate scenarios. This is not only a significant scientific issue but also a prerequisite for developing forest management and policy.

Using 350 records across biomes, including tropical, subtropical, temperate, and boreal, where woody plants occur (Fig. 1a), we investigate the spatial variation of bark decomposition and identify key drivers across and within climate regions. We subsequently project how bark decomposition will respond to the changing climate (Fig. S1). First, we use three climate variables and four substrate quality related variables as input factors in three widely used machine learning models, i.e., random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machines (SVM) (see Methods, Machine learning), and rank the models’ performance by comparing their R2 and Mean Squared Error (MSE) scores. Next, we assess the relative importance of climate factors and substrate quality in driving bark decomposition. The machine learning results show that climate factors have stronger effects than substrate quality on bark decomposition at the global scale. Thus, we project how bark decomposition rates would change in response to global climate change. We hypothesize that (1) bark decomposition rates and their drivers will vary significantly across regions due to differences in both climate and substrate quality of different tree taxa and functional groups, and their interactions; (2) the responses of bark decomposition to climate change will vary spatially as the response pattern of temperature and precipitation to climate change will itself depend on the current local hydrothermal conditions.

Results
Global pattern and drivers of bark decomposition
The global averaged bark k value was 0.362 year−1 (ranging from 0.001 year−1 to 5.495 year−1) under the current climate, which varied significantly (p < 0.05) among biomes and functional groups (Fig. 1c, d). Bark decomposition showed a latitudinal gradient, being faster in tropical regions (k = 1.339 ± 0.148 year−1) than that in other regions, with the lowest bark k values in boreal regions (k = 0.093 ± 0.013 year−1). Bark k at the major clade level contributed most to the differences among functional groups and was much higher for angiosperms (0.450 ± 0.050 year−1) than for gymnosperms (0.198 ± 0.033 year−1). Decomposition rates were notably higher in Angiosperm-Deciduous (AD, k = 0.614 ± 0.084 year−1) bark as compared with Angiosperm-Evergreen (AE, k = 0.236 ± 0.017 year−1) bark, and higher in Gymnosperm-Evergreen (GE, k = 0.214 ± 0.038 year−1) bark than in Gymnosperm-Deciduous (GD, k = 0.086 ± 0.034 year−1) bark.

By comparing the R2 and MSE scores of the RF, XGBoost, and SVM models (Table 1), we found that all three models showed a generally good and broadly similar performance. The RF model had the best fit to the data, and we therefore employed it for the subsequent analyses. The RF model yielded significant relationships between bark k values and all seven predictor variables (Fig. 2a, Table S2) across all tree species. Mean annual temperature (MAT) and mean annual precipitation (MAP) showed the highest relative importance values (25.3% and 17.7%, respectively) and the strongest positive Spearman correlation coefficients (r = 0.551 and r = 0.328, respectively, p < 0.001, Fig. 2c) in explaining bark k while bark phosphorus (P) also had a rather strong positive correlation (r = 0.289, p < 0.001).

Climate and trait effects on bark decomposition across climate regions
Since climate had the strongest effect on bark k, we further quantified the influence of the variables across different climate regions. The averaged bark k values in warm and wet regions were 0.586 (± 0.061) year−1 and 0.580 (± 0.064) year−1, which were 5.1 and 4.1 times higher than the values in cold (0.116 ± 0.013 year−1) and dry regions (0.142 ± 0.013 year−1), respectively (Fig. 3). In both warm (Fig. 4a) and wet regions (Fig. 4c), the RF model indicated that bark P concentration had the strongest effect on bark k, followed by MAT (Table S4). Spearman correlation coefficients also revealed a significant (p < 0.05) positive relationship between bark k and bark nitrogen (N) concentration (r = 0.284 and r = 0.256 for warm and wet regions, respectively), as well as bark carbon (C) concentration (r = 0.333 and r = 0.173, respectively). A significant (p < 0.05) negative relationship was found between bark C/N ratio (r = −0.252 and r = −0.220, respectively) and bark k. In both cold (Fig. 4b) and dry regions (Fig. 4d), the RF model indicated that MAT had the strongest effect on bark k (Table S4). Spearman correlation coefficients also revealed a significant (p < 0.05) positive relationship between bark k and MAP (r = 0.198 and r = 0.487 in the cold and dry regions, respectively), whereas a significant (p < 0.05) negative relationship was observed between bark k and bark C concentration (r = −0.207 and r = −0.255, respectively).

Bark k predictions in different climate regions
The projected responses of bark k in the future climate scenarios also vary across climate regions (Figs. S2, S3, S4). The global averaged bark k by the end of this century is projected at 0.386, 0.376, 0.380, and 0.374 year−1 under the scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP 5-8.5, respectively. Based on RF model prediction, the bark k averages in the warm region (Fig. 5a) and wet region (Fig. 5c) are projected to decrease under all four SSP scenarios. By the end of this century (Fig. 5, S2), bark k is expected to decline from 0.586 ± 0.061 year−1 to 0.494 ± 0.012 year−1 (Δk% = −15.7%) in the warm region, and to decline from 0.580 ± 0.064 year−1 to 0.484 ± 0.013 year−1 (Δk% = −16.6%) in the wet region. In contrast, the bark k in cold and dry regions is projected to increase in all four SSP scenarios. By the end of this century (Figs. 5b, d, S2), bark k in the cold region is expected to rise from 0.116 (± 0.013) year−1 to 0.260 ± 0.006 year−1 (Δk% = 124.1%), and to rise from 0.142 ± 0.013 year−1 to 0.273 ± 0.006 year−1 (Δk% = 92.3%) in the dry region.

Methods
Data assembly
We compiled a global database of bark decomposition rate for woody plants from published literature using Web of Science (http://apps.webofknowledge.com), Google Scholar (http://scholar.google.com), and the National Knowledge Infrastructure Database (http://cnki.net). We used the combination of keywords (bark) AND (decomposition rate OR decay rate OR mass loss OR density loss OR decay constant) AND (twig OR branch OR stem OR woody debris OR deadwood OR dead wood OR dead tree). We further filtered the literature by reviewing the abstracts and experimental designs to exclude studies that (1) were conducted in the laboratory; (2) did not provide latitude, longitude and elevation information; (3) involved bark or woody plants that were attacked by pest or pathogen before death; (4) involved woody plants died unnaturally such as due to drought, fire, or other disturbances; (5) only included control records of bark decomposition rates if bark was decomposed with treatments such as fertilization, invertebrate exclusion (control: mesh size > 2 mm), or heating. Based on these criteria, we compiled a dataset that consists of 350 observations from 48 sites and 80 tree species (Fig. 1, see Table S1 for the full list).

There are two types of methodology (Table S1, methodology) of decomposition study in the literature: (1) For the studies using “chronosequence” method57, multiple decomposition rates were reported for different decomposition durations. As decomposition rates of different durations were measured for different logs, we extracted a unique record for each duration. (2) The studies using the ‘direct’ method7 determined decomposition rates by “direct” measurement of bark initial dry weight before decomposition and dry mass remaining after decomposition. Two types of bark material were recorded in the literature: (1) bark naturally attached to the tree trunk during decomposition and (2) bark detached from the tree trunk and decomposed as litter (Table S1, “bark status”). Data were extracted from the main text, tables, and digitized graphs of primary references. We used GetData Graph Digitizer (version 2.26) to extract raw data presented only in figures. Three linked databases were incorporated in this study: “decomposition rate”, “site” and “trait”. The detailed information of each database is below.

Decomposition rate. Bark decomposition is the transformative process by which complex organic compounds, such as cellulose and lignin, are catabolized into smaller units and ultimately mineralized58. Bark decomposition rate refers to the speed at which the bark and its residuals are broken down by respiration, fire, photodegradation, and physical degradation14,59. Consequently, the decomposition rate of bark could be expressed in the form of mass loss (mass loss/area, mass loss/length), volumetric loss, coverage loss, and thickness loss, etc. In our study, the “decomposition rate” database contains various measures of decomposition rates, including the decomposition rate constant (k values)60, mass loss, density loss, or thickness loss, from which k values could be calculated. All metrics of decomposition rates were converted to k values (Eq. 1) using the single negative exponential model because this model has been documented as the most useful and realistic biologically in decomposition study22. A larger k value indicates a greater proportional mass loss, volumetric loss, coverage loss, or thickness loss per unit time.

k = -ln(Yt/Y0)/t (1)
Y0 is the initial mass/density/thickness before decomposition started. Yt is the mass/density/thickness remaining after decomposition for a certain time duration t.

Site. In addition to bark k, we also compiled the “site” database, which includes geographic information (longitude, latitude, and elevation) and climate conditions for each observation. Temperature and water availability are the key climatic drivers in the classic decomposition model22. Considering the data availability at a global scale and the collinearity of climate variables, we chose the MAT and MAP to represent climate conditions for this study. The climate conditions for each observation (observational data) are extracted from the articles. For those articles in which the MAT and/or MAP are not provided, we filled in the missing data from WorldClim (https://worldclim.org/) using longitude and latitude information with a resolution of 2.5 arc-minutes. The added climate data are sourced from the observed monthly average data for the current period (1970–2000, WorldClim version 2.1).

Trait. Bark substrate quality is a key predictor for decomposition rate22. Bark elemental chemistry is likely more phylogenetically conserved than other commonly measured morphological and physiological traits61, and macro-elements tend to be more strongly linked to phylogeny than micro-elements62. Carbon (C), nitrogen (N), and phosphorus (P) are considered as the most basic elements in the ecological stoichiometry, which are also essential for tree growth and also for the metabolism of decomposer organisms4,63. Furthermore, empirical studies1,31 have revealed that bark decomposition rates varied with phylogeny. Thus, the third database, “trait” includes initial (pre-decomposition) bark traits (Fig. S6), including bark C, N, carbon to nitrogen ratio (C/N ratio), and P, and these traits were extracted and recorded when available in the literature or related publications from the same experimental setup. Authors were contacted if it was clear that the desired data were collected but not reported. Otherwise, we obtained the data of the same tree species from the TRY Plant Trait Database (try-db.org) because these macro-elements exhibited a stronger taxonomic conservatism than environmental control, exhibiting a narrow range of internal concentrations64.

Data analysis
Division of climate regions. To better understand the drivers of bark decomposition at different climate regions, we further divided the 48 sites into warm region and cold region based on the median MAT value (13 °C), or divided the sites into wet region and dry region based on the median MAP value (1100 mm)15, hereafter named as the “dichotomous approach”. Alternatively, we divided the sites into four groups (Fig. S7) based on (1) whether they were warmer or colder than the median MAT value, and (2) whether they were wetter or drier than the median MAP value. We found significant differences in bark k among the four groups (Fig. S7). However, we encountered difficulties in the in-depth analysis of the warm-dry region and cold-wet region because of the limited sample size. To improve the robustness of our analysis, given the generally linear relationship (r = 0.600, p < 0.001, Pearson’s correlation) between MAT and MAP, we selected the dichotomous approach for subsequent analysis.

General analysis. The missing observational MAT and MAP values of each plot were extracted using ArcGis (version 10.7) after downloading the raster data from WorldClim. All the other analysis were performed in R (version 4.4.1, https://www.r-project.org/). Before performing the analyses, we tested the collinearity of the climate and trait variables using the “vif” function from the R package “car”. The “GLM” function from the “stats” package was used to test the effects of biomes and functional types on bark k. The Shapiro–Wilk test indicated that the data were non-normally distributed, so we calculated Spearman’s rank correlation coefficient between each variable and bark k using the “cor.test” function from the “psych” package. To test the differences in bark k and bark traits among biomes, functional groups, and climate regions, the nonparametric Kruskal–Wallis Test applying “kruskal.test” in “coin” package was used. If there is significant difference at p < 0.05 level, we further applied the “dunn.test” in “dunn.test” package for post-hoc analysis.

Machine learning. To identify the best fit model for predicting bark k and projecting the potential response of bark k in different climate regions, we applied three machine learning approaches: RF, eXtreme Gradient Boosting (XGBoost), and SVM. Each approach represents distinct modeling strategies with different strengths in handling non-linearity, variable interactions, and generalization. RF is an ensemble learning method based on the bagging principle, where multiple decision trees are constructed using bootstrapped samples and random feature subsets65. XGBoost is a powerful and scalable machine learning algorithm based on gradient boosting decision trees66. SVMs are margin-based models that aim to find an optimal hyperplane for classification67. Climate factors (MAT, MAP), elevation, bark traits (C, N, P, and C/N ratio), and bark status (see Table S1) were assigned as explanatory variables in the three models. For each model, we used 10-fold cross-validation in the training data set to test the model performance and to rank the models’ performance by comparing their R2 and MSE. We found that all three models showed a generally good and broadly similar performance, with R2 values ranging from 63% to 70% (Table 1). The lower MSE and the higher R2 scores indicated that the RF model had the best fit to the model (Table 1). The lower MSE and the higher R2 scores indicated that the RF model had the best fit to the model (Table 1). We also included bark status as a variable in the training model, which indicated a better model performance (Fig. S5). Thus, we decided to use climate factors and bark traits as explanatory variables in the processes of machine learning.

The relative importance of the variables in the RF model was calculated to evaluate the effects of climate factors and bark traits on the bark decomposition process. The functions and packages used for each machine learning model are listed in Table S3.

Second, we projected bark k under different climate conditions using the model with the lowest MSE and the highest R2 scores. To better represent plausible trajectories of future conditions of temperature and precipitation, we used four illustrative Shared Socio-economic Pathways scenarios (i.e., SSP scenarios) beyond the current scenarios. SSP scenarios ranged from sustainability-focused pathways (SSP1-2.6) to very high greenhouse gas emissions scenarios (SSP5-8.5), with SSP2-4.5 and SSP3-7.0 in between, instructed by Coupled Model Intercomparison Projects (CIMP6). Bark traits and climate factors were assigned as explanatory variables in the predicting models of machine learning. We assumed that bark traits and elevation would not change during the research period, whereas MAT and MAP would change due to the different climate conditions of the four SSP scenarios. The future climate data was categorized by four different SSPs scenarios for the following time periods: 2021–2040 (near-term), 2041–2060 (mid-term I), 2061–2080 (mid-term II), and 2081–2100 (long-term).

Third, we calculated the changes in bark k (Δk) and the relative changes in bark k (Δk%) by the end of this century (Figs. 5, S3, S4) based on Eq. 2 and Eq. 3, respectively.
Δk = kpredicted - kcurrent (2)
Δk% = Δk / kcurrent × 100% (3)
kpredicted is the model projected bark decomposition constant by the end of this century under different scenarios. kcurrent is the observational bark decomposition constant sourced from the literature (Table 1).

Bias correction for future climate data. The projected temperature and precipitation data were sourced from the Scenarios Model Intercomparison Project (ScenarioMIP) of CMIP6. We adopted the average values of the available outputs from 28 distinct Earth System Models (ESMs, Table S5, Fig. S8) at the monthly resolution. Since the outputs of ESMs models are generally biased compared to observations68 and these outputs are at coarser resolutions, we bias-corrected and downscaled the temperature and precipitation outputs before we added the values to the predicting model of machine learning. Briefly, we utilized a trend-preserving bias correction method68,69 to the monthly mean temperature and precipitation data using the observation data in TerraClimate70 as baseline climate (period 1985–2014, T0). We applied an additive correction approach (Eq. 4) to correct the monthly mean temperature values. Given the positive constraint on precipitation data, we applied a multiplicative correction approach (Eq. 5) to correct the monthly mean precipitation values.
tems_t,m = tems_t,m + (temo_T0,m - tems_T0,m) (4)
tems_t,m and tems_t,m are the corrected and uncorrected monthly mean temperature for the period t at the 1/24° (~4-km) resolution. C m is an additive factor for each month m {m ∈ Z+, m ≤ 12}, which represents the differences between the simulated temo_T0,m and tems_T0,m monthly mean temperature during the reference period T0.
pres_t,m = pres_t,m × (preo_T0,m / pres_T0,m) (5)
pres_t,m and pres_t,m are the corrected and uncorrected monthly mean precipitation for period t at the 1/24° (~4-km) resolution. C m is an additive factor for each month m {m ∈ Z+, m ≤ 12}, which represents the long-term differences between the simulated preo_T0,m and pres_T0,m monthly mean temperature during the reference period T0.
For detailed information about the bias correction and downscaling procedure see Xu et al.69.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Dear Editor Pavithra,\nThank you for giving me the opportunity to review the article “Spatial variability of tree bark decomposition at global scale” in\nNature Communication journal. I appreciate the chance to contribute to the evaluation of this important study.\nThis study presents a global analysis of bark decomposition, using data from 43 experiments to identify key climate and\nsubstrate drivers across different regions. The authors applied a Random Forest model and found that bark phosphorus\nconcentration is a major driver in warm, wet areas, while mean annual temperature i.e. (MAT) is more important in cold, wet\nregions.\nOverall, this study provides a significant impact because understanding the regional difference in bark decomposition can\ninfluence and improve the future projection of carbon emissions predictions. While the study offers a valuable framework, it\nis limited by the use of only one random forest analysis (i.e. without comparison to other ML classifiers/statistical\napproaches. Another limitation of this study is that it does not incorporate historical data, which could enhance the\nrobustness of the predictions; however, the use of data from 2021 onward to project future scenarios up to 2100 (IV-long\nterm) is quite impressive. Future research should incorporate advanced AI techniques, compare multiple models, and\ninclude more diverse and long-term datasets to improve predictive power and understanding of decomposition under\nchanging climate scenarios.\nThe overall intention of this submission is a good one. I have outlined my comments, suggestions, and a few questions that\ncould help improve the manuscript:\nI. The author should elaborate both (Equation 1 and Equation 2) properly in the manuscript.\nII. The author should include the framework or flowchart for their work methodology to enhance understanding, as the\nsections on data collection and data analysis are very crucial for this subject.\nIII. The authors selected four specific future periods for analysis, based on different SSP scenarios. Could you please\nexplain the reason for not including earlier historical data, for example, from 2015 onwards? Would incorporating past\nclimate data not provide additional insights or help improve the accuracy of future projections?\nIV. The author can answer how your study and findings can further help: (incorporate spatial variability in bark\ndecomposition into global carbon turnover rates and change the contribution of bark decomposition to the global carbon\nbudget).\nV. In this study, the authors used an RF model to evaluate the relative importance of climate and substrate quality factors in\ndriving bark decomposition globally. RF is known for its strength in handling non-linear relationships and ranking feature\nimportance. However, could you elaborate on the specific reasons behind choosing RF over other machine learning\nclassifiers or regression models? Additionally, why did you not compare your results with other modeling approaches (e.g.,\ngradient boosting, support vector machines, or neural networks)? Including such comparisons might help validate the\nrobustness of your findings.", "reviewer_2": "General comments.\nThe topic of the study is hot. The share of tree bark in tree volume and biomass may reach tens of percents. However, its\ndecomposition patterns remain understudied. Presence of bark and its quality may influence the ecological role of\ndeadwood in carbon and nutrient cycles, as well is in safeguarding forest biodiversity.\nThe manuscript has a good potential, but it needs major revision.\nThe study has a potential to contribute to our understanding of decomposition processes in tree bark. However, many\nessential details are not elucidated. The essence and pathways of decomposition process in tree bark are not seen well\nenough. Differences in the decomposition of bark attached to tree trunk and as a part of litter are not considered.\nThe findings are discussed in too ‘mechanistic way’, without explanations of physical and biological mechanisms and\norganisms involved.\nIn the text, there are many terms and concepts that are not defined.\nWhat is bark? How are the presence and quality of different bark tissues considered in the decomposition process? It is\ntouched a bit in the discussion, but probably should be brought already to the introduction.\nMoreover, maybe better to write about the role of bark in shaping deadwood quality, which, in turn, affect carbon and nutrient\ncycling etc.? In a dead tree trunk, bark itself, without wood, is not a ‘biodiversity nursery’.\nWhat is ‘decomposition’ in this study? Does it include fragmentation and foraging by insects? If yes, how? Different factors\nwould affect e.g. 1) fungal decay, 2) mechanical bark sloughing from a tree trunk, and 3) consumption of phloem by bark\nbeetles.\nWhat is ‘ecosystem stability’?\nWhat is main research question of this study?\nSpecific comments.\nAbstract. The abstract is written in too general way. The objectives, methods and results of the study should be\ncomprehended from the abstract. In the manuscript, this is not the case.\nIt is not explained, what is the ‘decomposition rate’? It is not mentioned that the constant is calculated based on the\nexponential model. Does ‘decomposition’ include fragmentation and foraging by insects? If yes, how?\nIn general, study methodology is not described. What does the ‘synthesis’ mean?\nWhere does the wood decomposition constant come from?\n‘Cold’, ‘warm’, ‘dry’ and ‘wet’ are relative concepts, do not tell much without explanations.\nAnd finally, it is unclear, how the ‘findings will help to … assess the consequences of forest habitat change for the\ncontribution of bark decomposition to the global carbon budget.’\nKeywords should not overlap with the title.\nIntroduction. The introduction can be improved a lot. The start is nice, but then the text becomes less focused with many\nredundant parts and missing details. Not even a study objective is clearly formulated.\nLines 75-77. The sentence is awkward.\nLine 79. Considering ‘… the role of bark in the multifunctionality of forests’ is the same as considering e.g. ‘the role of tree\nleaves in the landscapes’.\nLines 83 – 86. References are needed.\nLines 101-102. In its current form, this sentence belongs to the ‘Methods’ section, and requires further explanations that are\nactually given later. I would like to see the objective here.\nLine 103. What is the ‘random forest analysis’?!!!\nLines 104-105. Again, this belongs to methods. The sentence cannot stay in the introduction without even description of\nprinciples of classification.\nLines 107-108. The statement about projection of the change in the bark decomposition rates under different future climate\nscenarios without any explanations does not work.\nLines 110 - 112. I would recommend the authors to organise a text in a logical way, providing grounds and references for\neach statement instead of praising their results, especially in the introduction.\nResults.\nThe results are difficult to interpret without understanding the methods…\nDiscussion.\nThe discussion is written in too general way. The first paragraph repeats the points from introduction, the study design and\nresults. Organising the manuscript by formulating study objectives and hypotheses, reporting the results and discussing\nthem accordingly would improve the logical framework.\nMethods.\nAll terms, concepts and the details of the data processing should be opened.\nSome grounds and references would help in understanding, how the climatic variables were chosen.\nThe same comment for choosing the traits.\nModelling of climate scenarios should be better explained. How were the models constructed?\nThe random forest model should be described for those not familiar with it.\nFig. 1. a) The triangle with arrows is not understandable. In its current form, the figure does not work. b) I don’t see any logics\nin the cryptic row of words: ‘Seasonality, geology and climate change’ … Substrate quality definitely affects the\ndecomposition rates of bark, but two circles with something inside do not explain, which substrate attributes are meaningful. I\ndoubt that the organisms drawn play primary role in the community of decomposers…", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We appreciate your comments and suggestions. Following your suggestion,\nwe have incorporated RF, XGBoost, and SVM analyses to strengthen the robustness of\nour results. We have also included the historical climate data of the period 1983-2014,\nand corrected the future climate data using 28 Earth System Models. More details are\nprovided in the point-by-point response below.\nSpecific comments.\n1. The author should elaborate both (Equation 1 and Equation 2) properly in the\nmanuscript.\nResponse: We elaborated more on these equations and the newly added equations in\nthe manuscript. Please see the revised manuscript, L373-377 (Eq.1), L465-471 (Eq.2,\n3), L482-496 (Eq.4, 5).\n2. The author should include the framework or flowchart for their work methodology\nto enhance understanding, as the sections on data collection and data analysis are very\ncrucial for this subject.\nResponse: We now provide a workflow chart, please see the Figure S1.\n3. The authors selected four specific future periods for analysis, based on different\nSSP scenarios. Could you please explain the reason for not including earlier historical\ndata, for example, from 2015 onwards? Would incorporating past climate data not\nprovide additional insights or help improve the accuracy of future projections?\nResponse: Based on your suggestion, we included climate data between 1985 and 2014\nas the baseline climate for correction. Further, we downloaded the observational climate\ndata from TerraClimate and downloaded the simulated climate data (28 distinct Earth\nSystem Models, Table S2) from the Scenarios Model Intercomparison Project\n(ScenarioMIP) of CMIP6. We used trend-preserving bias correction analysis to correct\nthe monthly mean temperature and precipitation values. We projected the future bark\ndecay (k) using the corrected climate data. For more details, please see ‘Bias correction\nfor future climate data’(L472-496).\n4. The author can answer how your study and findings can further help: (incorporate\nspatial variability in bark decomposition into global carbon turnover rates and change\nthe contribution of bark decomposition to the global carbon budget).\nResponse: Thanks for your advice. Our study and findings highlight the importance of\nconsidering the fraction (non-trivial) of bark biomass and bark decomposition rates in\nthe Earth System Models of climate and carbon cycle. We have added this information\nin the Abstract, Introduction and Discussion sections.\n5. In this study, the authors used an RF model to evaluate the relative importance of\nclimate and substrate quality factors in driving bark decomposition globally. RF is\nknown for its strength in handling non-linear relationships and ranking feature\nimportance. However, could you elaborate on the specific reasons behind choosing RF\nover other machine learning classifiers or regression models? Additionally, why did\nyou not compare your results with other modeling approaches (e.g., gradient boosting,\nsupport vector machines, or neural networks)? Including such comparisons might help\nvalidate the robustness of your findings.\nResponse: We included XGBoost and Support Vectors Machines (SVM) to evaluate\nthe relative importance of climate and substrate quality factors. First, we ran the three\nmodels and calculated the R2 and MSE value. The results indicated that the three models\ngenerally produced good and broadly similar output (Table 1), with RF model showing\nthe best performance (the highest R2 and the highest MSE value). All the three models\nrevealed that climate has stronger effects than substrate quality at global scale (Figure\n2), although bark phosphorus is an important driver as well. The functions and packages\nfor each model applied in this study are listed in Table S2. The structure of each model\nand selection process are described in the subsection entitled ‘Machine learning’.\nFor more details, please see L429-471 in the revised manuscript.\nResponse: We are grateful to your valuable time and comments. Thank you also for\nrecognizing the importance of this topic. Following your suggestions, we have added\ninformation regarding the decomposition mechanisms, definition and research question\nin the manuscript. We also explained how bark status (see below) and bark with\ndifferent substrate qualities could affect decomposition. You can find the details\nregarding our specific response to your comments.\nSpecifically, we want to respond to your query about the effects of bark attached to\nwoody stems versus bark as detached litter on decomposition. We fully agree with you\nthat bark status should have a strong effect on bark decomposition, and the effect could\nbe positive, neutral or even negative based on the existing empirical data. We checked\nthrough the articles and marked the bark status of each study in Table 2 (see ‘bark\nstatus’). Then we added bark status as explanatory variable in the random forest model\n(Bark k ~ MAT + MAP + Elevation + C + N + P + C/N ratio + bark status) to\ncurrent current\ncompare the relative importance of each factor. The random forest model indicated that\nbark status scored the lowest importance value (Fig. S5). We assume that the overall\nweak effects of bark status are because the strength and direction\n(positive/negative/neutral) of bark status are controlled by multiple factors, including\ntree species, diameter and decay stages, whereas few studies have provided that\ninformation. Moreover, we found a decrease in R2 and an increase in MSE if the bark\nstatus was added as an explanatory variable in the random forest model. This means the\noverall performance reduced. After comprehensive consideration, we decided to\nremove bark status as a factor from the model in the follow-up analysis. We also\ndiscussed the bark status effects and suggested further studies to consider the effects of\nbark status and recorded the factors that could affect bark decomposition process in the\nDiscussion section. Please see the detailed description about these steps in L215-224,\nL352-356, and L429-439 of the revised manuscript.\nSpecific comments.\nAbstract.\n6. The abstract is written in too general way. The objectives, methods and results of\nthe study should be comprehended from the abstract. In the manuscript, this is not\nthe case.\nResponse: We rewrote the ‘Abstract’ section and believe the key information is\nincorporated while keeping the Abstract brief. Please see the major revision in the\nmanuscript.\n7. It is not explained, what is the ‘decomposition rate’? It is not mentioned that the\nconstant is calculated based on the exponential model. Does ‘decomposition’\ninclude fragmentation and foraging by insects? If yes, how?\nResponse: These are valid points although we have to keep the Abstract concise.\nWe now define ‘decomposition rate’ and introduced the procedures of calculating decay\nconstant in the Methods section. Please see the point-by-point response of comment 24.\nIn our study, bark decomposition includes fragmentation and foraging by insects,\nbecause the selected studies were all carried out naturally in the field. For the studies\nwith treatment of invertebrate exclusion, usually this exclusion was controlled by mesh\nsize, and we only collected the data of the control treatment, i.e., mesh size larger than\n2 mm which allowed the key decomposers access. We added this description in the\nrevised manuscript in L345 in the Methods section.\n8. In general, study methodology is not described. What does the ‘synthesis’ mean?\nResponse: We apologize for any unclear description. We added the description of the\nmethodology in the revised Abstract.\n9. Where does the wood decomposition constant come from?\nResponse: We rewrote the sentence and gave a detailed description of decay constant\nin the Methods section. Please see the point-by-point response of comment 24.\n10. ‘Cold’, ‘warm’, ‘dry’ and ‘wet’ are relative concepts, do not tell much without\nexplanations.\nResponse: We deleted these terms in relation to the drivers, while we think they are\nimportant in relation to the future climate scenarios.\n11. And finally, it is unclear, how the ‘findings will help to … assess the consequences\nof forest habitat change for the contribution of bark decomposition to the global\ncarbon budget.’\nResponse: We revised the sentence as follows: “Our results highlight the importance of\nconsidering bark decomposition in carbon cycling models”.\nKeywords.\n12. Keywords should not overlap with the title.\nResponse: Corrected.\nIntroduction.\n13. The introduction can be improved a lot. The start is nice, but then the text becomes\nless focused with many redundant parts and missing details. Not even a study\nobjective is clearly formulated.\nResponse: We revised the Introduction section and rewrote the last paragraph of this\nsection. First, we added the definition of bark at the beginning of the revised\nIntroduction. Then, we emphasized the roles of bark in controlling deadwood\ndecomposition process. Next, we added the description about the mechanism, such as\nthe ways that decomposers function and the substrate quality effects on decomposition.\nIn the last paragraph, we focused on the objective and hypotheses. Please also see the\npoint-by-point response to comment 21.\n14. Lines 75-77. The sentence is awkward.\nResponse: We rewrote the sentence, and it now reads as follows: ‘Another reason lies\nin the limitations of experimental design: bark decomposition study of multiple tree\nspecies is usually carried out at a single site, making it difficult to compare\ndecomposition rates and disentangle their drivers at a broader scale’. Please check L74-\n76 in the revised manuscript.\n15. Line 79. Considering ‘… the role of bark in the multifunctionality of forests’ is the\nsame as considering e.g. ‘the role of tree leaves in the landscapes’.\nResponse: We are sorry for any misleading description. We rewrote the sentence:\n‘Disentangling the drivers of bark decomposition from regional to global scale through\na comprehensive analysis would allow us to better estimate bark decomposition in\nregions with limited observations, improve the accuracy of predictions about bark’s\ndecomposition response to future climate change, and help anticipate the consequences\nfor carbon dynamics and ecosystem functioning’. Please check L77-81 in the revised\nmanuscript.\n16. Lines 83 – 86. References are needed.\nResponse: Refences added. Please check L88 and L90.\n17. Lines 101-102. In its current form, this sentence belongs to the ‘Methods’ section,\nand requires further explanations that are actually given later. I would like to see\nthe objective here.\nResponse: We reorganized the paragraph after integrating your comments 18-21. Please\nsee the response to comments 18-21.\n18. Line 103. What is the ‘random forest analysis’?!!!\nResponse: It should have been ‘random forest model analysis’. The word “model” was\nmissing in previous version. We corrected this in the revised manuscript. We now also\nadded that RF modeling is based on machine learning, see L434-436.\n19. Lines 104-105. Again, this belongs to methods. The sentence cannot stay in the\nintroduction without even description of principles of classification.\nResponse: Sentence removed from the Introduction section. We added the description\nof principles of classification in ‘Division of climate regions’. Please see the detailed\nrevision in L409-412.\n20. Lines 107-108. The statement about projection of the change in the bark\ndecomposition rates under different future climate scenarios without any\nexplanations does not work.\nResponse: We moved the highlighted sentences to the ‘Machine learning’ section.\nPlease see the detailed revision in L454-459, L462-465.\n21. Lines 110 - 112. I would recommend the authors to organise a text in a logical way,\nproviding grounds and references for each statement instead of praising their\nresults, especially in the introduction.\nResponse: Combining your and the other reviewers’ comments, we rewrote the whole\nparagraph. We first described the overall objective of this study, then we gave a brief\nintroduction to the workflow of the study, and finally presented the hypothesis of the\nstudy. We think the current text is more logical and objective. Please see the detailed\nrevision in L105-121.\nResults.\n22. The results are difficult to interpret without understanding the methods…\nResponse: We revised the Methods and added one figure presenting the workflow of\nour study (Fig. S1). We hope the revised Methods are now clear. We also revised the\nResults accordingly.\nDiscussion.\n23. The discussion is written in too general way. The first paragraph repeats the points\nfrom introduction, the study design and results. Organising the manuscript by\nformulating study objectives and hypotheses, reporting the results and discussing\nthem accordingly would improve the logical framework.\nResponse: Thanks for your feedback and suggestions. We revised the discussion\naccordingly. Please see the changes in the revised manuscript.\nMethods.\n24. All terms, concepts and the details of the data processing should be opened.\nSome grounds and references would help in understanding, how the climatic variables\nwere chosen.\nResponse: We now explained the terms, concepts, and details of the data processing\nwhere necessary. Briefly, (1) We explained the bark decomposition related process\n(Ulyshen, 2016) and gave the definition of decomposition rate (Cornwell et al. 2009;\nErdenebileg et al. 2020) in L362-364. (2) We provided the reason why we opted for the\nk value to represent decomposition rate (Harmon et al., 1986) and highlighted its\necological significance in L370-373. (3) We provided a new workflow chart (Fig. S1)\nto better present our research.\nBased on the review of ‘Ecology of coarse woody debris in temperate ecosystems’\nwritten by Harmon et al. (1986), climate factors (dominant thermal conditions),\ntogether with substrate quality and decomposers are the most important drivers of plant\nmatter decomposition. Considering the collinearity of climate variables and the data\navailability of these variables at global scale, we finally chose mean annual temperature\nand mean annual precipitation to represent the climate conditions. The detailed\nexplanation is provided in L381-384 in the revised manuscript.\n25. The same comment for choosing the traits.\nResponse: Thank you again for your constructive suggestions which are really helpful\nfor improving the quality of manuscript. We chose carbon, nitrogen and phosphorus as\nrepresentative indicators for bark traits because these three elements are the most\nessential ones for the biochemical stoichiometry and metabolism of decomposer\norganisms (Gong et al. 2024; Wijas et al. 2024), which has been widely acknowledged\nwith a strong potential for predicting decomposition. Moreover, many studies have\nshown they exhibit a rather strong taxonomic conservation, exhibiting a narrow range\nof internal concentrations. For more details, please see L391-399 in the revised\nmanuscript.\n26. Modelling of climate scenarios should be better explained. How were the models\nconstructed?\nResponse: We explained the SSP scenarios at L455-459. The structure of the models,\nand the criteria and the process for selecting best-fit model were described in L440-452\n(Machine learning). We also recorded these steps in Fig.S1.\n27. The random forest model should be described for those not familiar with it.\nResponse: We have now described the random forest model in the ‘Machine learning’\nsub-section. Please check L434-436 in the revised manuscript.\n28. Fig. 1. a) The triangle with arrows is not understandable. In its current form, the\nfigure does not work. b) I don’t see any logics in the cryptic row of words:\n‘Seasonality, geology and climate change’ … Substrate quality definitely affects\nthe decomposition rates of bark, but two circles with something inside do not\nexplain, which substrate attributes are meaningful. I doubt that the organisms\ndrawn play primary role in the community of decomposers…\nResponse: We deleted this figure. Considering comment 26, we created a new\nworkflow chart to make the understanding of this study easier. Please check Fig. S1 for\nmore details.\nResponse:\nThanks for your precious time and comments. Please check above our specific response\nto Reviewer 2 comments/suggestions. We hope that we have correctly addressed your\ncomments and hope our revision would satisfy your concerns. We look forward to\nreceiving any further comments/suggestions."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thanks a lot for the revision. It was a pleasure to notice, how the manuscript has been improved.\nFirst of all, I need to apologize that I probably did not read the instructions carefully. I did not realize that I need to include the\nearly-career’s researcher comments in my reviewer report. In most cases, the comments now come too late, since the points\nhave been addressed already. But I still decided to give all the comments here to acknowledge the valuable work. So, here it\nis.\n“Overall I found that the text was well written, and the subject matter of import and interest. This synthesis reveals good\nbaseline information for a topic that has not been looked at much, eg sa the author says, most studies treating deadwood as\none homogenous substrate. Particularly, demonstrating what ecotypes and conditions hold certain patterns should help\ndeadwood researchers in the future consider best study practices. The final dataset used seems to be appropriately large\nand represent a valid selection of studies.\nComments:\nFig 1: assuming that the triangle with arrows in (a) represents exactly what is shown in (b), I would suggest changing the\norder of these two images as the triangle in (a) doesn’t make sense without the context then provided next, so at least for my\nmind, the logical order is reversed. However also the large “Decomposition” arrow in (b) is also unclear to me. It makes\nsense in (a) as a directional increase, but I’m not sure the directionality fits together with the three factors? If it does, perhaps\nelaborate this in the caption.\nOther figures all very good, well-made, clear, and useful.\nline 49: “such as regions in China” seems either an overly or underly specific comment. Would the impacts not likely affect\nall global arid and semi-arid regions similarly (Africa, Australia, western US), and if not, if there is reason to draw attention to\ncertain regions in China, why?\nLine 165: “decomposes”\nLine 178: “was closely related”\nLine 267: I can also recommend another citation on the matter:\nWojciech, P., Ewa, B., & Jarosław, L. (2019). Soil biochemical properties and stabilisation of soil organic matter in relation to\ndeadwood of different species. FEMS Microbiology Ecology, 95(3), fiz011. https://doi.org/10.1093/femsec/fiz011\nLines 366-375: They didn’t cite R or any of the packages. I’ve always been told to do so, should in-text citations be added\nhere?\nLine 373: “non-normally distributed” right? “non-normality” being the noun form, but here it should be adjective form\ndescribing “distributed”. Based only on my English knowledge, not knowledge of most common use in these contexts.”\nMy own comments on the revised version of the manuscript.\nThinking of a title. Isn’t a ‘spatial’ word redundant? Isn’t ‘global scale’ enough to show the context? Actually, no spatial\nanalysis was made…\nAbstract.\nLine 23. Maybe a carbon reservoir, not stock? To my understanding, carbon stock refers to the amount of carbon stored in a\nparticular ecosystem. Probably you can start a sentence with ‘globally’?\nThe words ‘global’ and ‘globally’ are repeated too many times.\nLine 29. ‘…than the ‘tree trunk’. Bark is a part of a tree trunk. Maybe ‘woody substance’, or simply ‘wood’ or ‘xylem’?\nLine 33. What is the meaning of a word ‘leading’ here? Main?\nConclusion(s?)\nLine 316. ‘This study represents the first… ‘ Is something missing?\nLine 319. I am doubting that the word ‘spatial’ works well here. Regional context is crucial? or something like that.\nLine 322. ‘… supressed by … condition’ does not sound good.\nLine 324. It is better to specify, that ‘deadwood decomposition’ means ‘wood + bark’, does not it?\nLines 327-328. ‘we further revealed that bark plays a pivotal role (role in what?) in the early stages, followed by xylem.\nFigure 1. I suggest explaining the regions’ classification here.\nTable 2. Shouldn’t the list be in an alphabetical order?\nReferences. Many papers that are referred to in the Table 2 are not in the reference list.\nFig. 1S. Biomes and ‘functional groups’ are mixed up.\nEnglish is not my first language, but I have a feeling that probably the paper would benefit from the language check.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: Thank you again for your valuable time and professional suggestions during\nthe previous two rounds of review. We are truly encouraged reading your positive\nfeedback. We now carefully revised our manuscript based on both your comments and\nthe early-career reviewer’s suggestions. All changes are highlighted in red text. For\nyour convenience, we also provided a detailed point-by-point response below\nexplaining how we addressed each specific point raised.\nSpecific comments.\n1. Fig 1: assuming that the triangle with arrows in (a) represents exactly what is shown\nin (b), I would suggest changing the order of these two images as the triangle in (a)\ndoesn’t make sense without the context then provided next, so at least for my mind,\nthe logical order is reversed. However also the large “Decomposition” arrow in (b)\nis also unclear to me. It makes sense in (a) as a directional increase, but I’m not sure\nthe directionality fits together with the three factors? If it does, perhaps elaborate\nthis in the caption.\nResponse: This figure was deleted at the first round of revision because of same\ncomments were given by the other reviewer.\n2. Other figures all very good, well-made, clear, and useful.\nResponse: We sincerely appreciate your comments and suggestions.\n3. line 49: “such as regions in China” seems either an overly or underly specific\ncomment. Would the impacts not likely affect all global arid and semi-arid regions\nsimilarly (Africa, Australia, western US), and if not, if there is reason to draw\nattention to certain regions in China, why?\nResponse: This is a general trend for all global arid and semi-arid regions. We deleted\nthe sentence ‘such as regions in China’.\n4. Line 165: “decomposes”\nResponse: Corrected.\n5. Line 178: “was closely related”\nResponse: Corrected.\n6. Line 267: I can also recommend another citation on the matter:\nWojciech, P., Ewa, B., & Jarosław, L. (2019). Soil biochemical properties and\nstabilisation of soil organic matter in relation to deadwood of different species.\nFEMS Microbiology Ecology, 95(3), fiz011. https://doi.org/10.1093/femsec/fiz011\nResponse: Recommendation added.\n7. Lines 366-375: They didn’t cite R or any of the packages. I’ve always been told to\ndo so, should in-text citations be added here?\nResponse: We cite the R website https://www.r-project.org/ the first time we introduce\nthe R program. We also added the citation of the R package where necessary.\n8. Line 373: “non-normally distributed” right? “non-normality” being the noun form,\nbut here it should be adjective form describing “distributed”. Based only on my\nEnglish knowledge, not knowledge of most common use in these contexts.”\nResponse: Corrected.\n9. My own comments on the revised version of the manuscript. Thinking of a title.\nIsn’t a ‘spatial’ word redundant? Isn’t ‘global scale’ enough to show the context?\nActually, no spatial analysis was made…\nResponse: We appreciate your suggestion. Here we provide an alternative: ‘Climate\nand traits drive bark decomposition patterns at global scale' which we believe takes care\nof any concern. The phrase \"global scale\" indeed is sufficient to make our point ”.\n10. Line 23. Maybe a carbon reservoir, not stock? To my understanding, carbon stock\nrefers to the amount of carbon stored in a particular ecosystem. Probably you can\nstart a sentence with ‘globally’? The words ‘global’ and ‘globally’ are repeated too\nmany times.\nResponse: Thank you for your queries. We checked the articles published on Science\nand Nature about the same topic. In the paper entitled ‘A large and persistent carbon\nsink in the world’s forests’ (Pan et al., 2011, Science), they said ‘Deadwood, litter,\nsoil, and harvested wood products together accounted for 35% of the global sink and\n60% of the global forest C stock, showing the importance of including these\ncomponents.’\nIn the paper entitled ‘The contribution of insects to global forest deadwood\ndecomposition’ (Seibold et al., 2021, Nature), they wrote ‘Deadwood currently stores\n73±6 petagram (Pg; 1015 g) of carbon (C) globally, which is about 8% of the global\nforest carbon stock and 8.5% of atmospheric carbon’. So, we prefer to keep the word\n‘stock’ for consistency among these global studies. We have revised the sentences using\n‘global’ and ‘globally’ according to your suggestion.\n11. Line 29. ‘…than the ‘tree trunk’. Bark is a part of a tree trunk. Maybe ‘woody\nsubstance’, or simply ‘wood’ or ‘xylem’?\nResponse: Sorry for the misunderstanding. Tree trunk here indicates bark plus xylem,\nbecause the previous study estimated the decomposition rate of the whole tree trunk.\nWe have revised the sentence as follows: ‘bark decomposed 2.9 times faster than the\ntree trunk including its bark’. We hope the current sentence is clear to you and the\nreaders; see Line 29 for revisions.\n12. Line 33. What is the meaning of a word ‘leading’ here? Main?\nResponse: Yes.\n13. Conclusion(s?)\nResponse: This heading has been removed due to the guidance of the editorial office.\n14. Line 316. ‘This study represents the first… ‘ Is something missing?\nResponse: This sentence has been deleted.\n15. Line 319. I am doubting that the word ‘spatial’ works well here. Regional context\nis crucial? or something like that.\nResponse: We revised the sentence as follows: ‘regional climate is crucial’.\n16. Line 322. ‘… supressed by … condition’ does not sound good.\nResponse: We revised the sentence as follows: ‘while in the regions with poor\nhydrothermal condition, temperature is the dominant factor driving decomposer\nactivity’.\n17. Line 324. It is better to specify, that ‘deadwood decomposition’ means ‘wood +\nbark’, does not it?\nResponse: Yes, it is. We indicated it in the revised manuscript; see Line 325.\n18. Lines 327-328. ‘we further revealed that bark plays a pivotal role (role in what?) in\nthe early stages, followed by xylem.\nResponse: We revised the sentence as follows: ‘we further revealed that bark plays a\npivotal role in controlling the whole deadwood decomposition rate’; see Lines 329-330.\n19. Figure 1. I suggest explaining the regions’ classification here.\nResponse: Thank you for your suggestion. We aimed to show the geographic\ndistribution of study sites and the global pattern of bark decomposition rate in Figure 1.\nWe are afraid that adding the explaining of classification here would make this figure\ntoo complex to the readers who are not familiar with our manuscript. We prefer to\nintroduce this classification after we show the decomposition pattern and its key drivers\nat global scale.\n20. Table 2. Shouldn’t the list be in an alphabetical order?\nResponse: We have resorted the list by alphabetical order. The new Table has been\nmoved to the Table S1 as suggested by the editorial office.\n21. References. Many papers that are referred to in the Table 2 are not in the reference\nlist.\nResponse: We listed all the related papers with the full information such as author, title,\nand journal name in the supplementary. Please see the Reference section in the\nsupplementary material.\n22. Fig. 1S. Biomes and ‘functional groups’ are mixed up.\nResponse: Corrected.\n23. English is not my first language, but I have a feeling that probably the paper would\nbenefit from the language check.\nResponse: Thank you for your suggestions. We have carried out a thorough language\ncheck."}]

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