Temporal stability of forest productivity declines over stand age at multiple spatial scales

Domain: earth_science
Published: 2025-03-20
Editorial outcome: major_revision

Abstract
There is compelling experimental evidence and theoretical predictions that temporal stability of productivity, i.e., the summation of aboveground biomass growth of surviving and recruitment trees, increases with succession. However, the temporal change in productivity stability in natural forests, which may undergo functional diversity loss during canopy transition, remains unclear. Here, we use the forest inventory dataset across the eastern United States to explore how the temporal stability of forest productivity at multi-spatial scales changes with stand age during canopy transition. We find that productivity stability decreases with stand age at the local and metacommunity scales. Specifically, consistent declines in local diversity result in less asynchronous productivity dynamics among species over succession, consequently weakening local stability. Meanwhile, increasing mortality and the transition from conservative to acquisitive species with succession weaken species and local stability. Successional increases in species composition dissimilarity among local communities cause more asynchronous productivity dynamics among local communities. However, the decline in local stability surpasses the rise in asynchronous productivity dynamics among local communities, resulting in lower metacommunity stability in old forests. Our results suggest lower productivity stability in old-growth forests and highlight the urgency of protecting diversity at multiple spatial scales to maintain productivity stability.

Introduction
Temporal stability of forest aboveground productivity (i.e., the ratio of mean productivity, which is the summation of aboveground biomass growth of surviving trees and in-growth by new recruitment trees, to its variation in time) is a widely accepted measure of forests’ ability to maintain productivity over time1,2. Traditional succession studies have focused on the community states, such as biomass and cover, with recent field experiments showing that their temporal stability increases with succession at multiple spatial scales3,4. However, how the temporal stability of productivity changes during late-successional stages in natural forests remains unclear at multiple spatial scales. Elucidating these dynamics will help managers ensure the stable provisioning of ecosystem services and efficient restoration practices under future conditions5–7.

Recently, a multiscale stability framework has shown that temporal stability at the larger scale (i.e., metacommunity stability) can be decomposed into temporal stability at the smaller scale (i.e., local stability) and spatial asynchrony, i.e., asynchronous productivity dynamics among local communities under heterogenous spatial environments8–10. Similarly, local stability can be decomposed into species stability and species asynchrony, i.e., temporal stability of productivity at the population level and asynchronous population dynamics in local communities. The multiscale stability framework can capture the scale dependence of ecosystem temporal stability over succession, i.e., higher stability at larger scales because deterministic processes (e.g., environmental filtering) dominate community assembly at the metacommunity scale whereas stochastic processes (e.g., random dispersal) dominate at the local scale3,11–13. However, change in temporal stability over stand age at multiple spatial scales has received little attention, particularly in late successional stages dominated by conservative species, due to the lack of long-term and nested permanent plot measurements of forest dynamics.

Increasing local species diversity and species composition dissimilarity among local communities with stand age contributes to the successional increase in metacommunity stability during early field succession experiments3,4. On the one hand, increasing canopy complexity with succession strengthens competition for light resources14,15, which promotes the coexistence of conservative species with high shade tolerance and acquisitive species with an efficient ability to utilize canopy light resources, resulting in higher local diversity16–19. Local stability tends to increase with local diversity because differentiated species functional strategies enable local communities to exhibit higher species asynchrony, i.e., the productivity gain of suitable species compensates for the productivity loss of unsuitable species (compensatory effects)1,18,20,21. On the other hand, as stands develop, stochastic processes (e.g., dispersal limitation and colonization) prompt divergent trajectories over succession among local communities22–25. Meanwhile, initial habitat heterogeneity among local communities could facilitate or inhibit the colonization of different species, resulting in higher species composition dissimilarity among local communities24,26. Dissimilarity among local communities stabilizes forest productivity against spatial environmental heterogeneity from the neighborhood scale (tens of meters) to the landscape scale (tens of kilometers), resulting in higher spatial asynchrony12,27,28. However, increasing local diversity and dissimilarity among local communities during forest succession is not always expected, especially not until late successional stages, which leads to uncertainty about the temporal stability of old forests. Decreasing local diversity and increasing dissimilarity among local communities make predicting changes in metacommunity stability during later forest succession difficult. As stands enter the canopy transition stage, overstory acquisitive species approach their lifespan and release canopy space after mortality, while understory conservative species with asymmetric competitive advantages occupy the forest canopy17,29,30. Although acquisitive species may experience increased recruitment in the understory due to their abundant seeds, their shade intolerance makes it difficult for them to occupy the canopy under the asymmetric advantage of conservative species when stand-replacing disturbances are infrequent17,31,32. This successional trajectory results in the transition from the coexistence of conservative species and acquisitive species to the dominance of conservative species, resulting in lower local diversity32–36. This diversity loss will lead to lower species asynchrony and temporal stability in local communities1,20. Meanwhile, stochastic mortality of overstory acquisitive species will allow conservative species to dominate the newly opened gap when stand-replacing disturbances are infrequent, resulting in higher species composition dissimilarity among local communities which leads to spatial asynchrony12,29,37. Previous studies have shown that increasing local stability or spatial asynchrony can both independently cause successional increases in temporal stability3,4. However, whether reduced local stability or increased spatial asynchrony contributes more to metacommunity stability is unclear.

In this work, we used the national forest survey dataset from the eastern United States to explore how stand age changes temporal stability in natural forests at multiple spatial scales (Supplementary Fig. 1). This dataset is well-suited for studying multiscale stability because each plot in the FIA program contains four circular subplots: one in the center and the other three arranged in a triangular pattern around it (Supplementary Fig. 2). We considered each subplot as a local community and each plot, comprising four subplots, as a metacommunity. Given that functional strategies can effectively capture plant responses to climate change, we used functional diversity to explore how changing diversity alters temporal stability with stand age3,38. Specifically, we examined 1) whether older forests exhibit higher temporal stability at the metacommunity and local scales (Fig. 1); and 2) whether this pattern is attributed to changes in local functional diversity and functional dissimilarity among local communities over stand age. We show decreasing diversity with forest age reduces temporal stability at multiple spatial scales, suggesting the urgency of protecting old-growth forests to ensure forest ecosystem services provisioning under climate change threats.

Results
Stand age weakens temporal stability at multiple scales
We found that temporal stability of productivity at both the metacommunity scale and the local community scale decreased with stand age, but spatial asynchrony did not show a significant relationship with stand age (Fig. 2, estimate [±SE] = −0.370 [0.020], t = −15.538, P < 0.001 for local stability, estimate [±SE] = −0.582 [0.037], t = −15.538, P < 0.001 for metacommunity stability and estimate [±SE] = 0.004 [0.007], t = 0.508, P = 0.612 for spatial asynchrony). After decomposing local stability into species stability and species asynchrony, our results showed that both species stability and species asynchrony decreased with stand age (Fig. 1, estimate [±SE] = −0.258 [0.015], t = −17.655, P < 0.001 for species stability and estimate [±SE] = −0.022 [0.004], t = −5.822, P < 0.001 for species asynchrony). Besides spatial asynchrony, stand age was the major driver for stability at multiple scales (Supplementary Fig. 3A and Supplementary Table 1). Similar results were found in our sensitivity analysis which considered detrended stability (Supplementary Fig. 4), different ecoregions (Supplementary Fig. 5), other confounding variables (Supplementary Fig. 6A and Supplementary Fig. 7; observational frequency, survey interval, initial survey year, and final survey year), stand structure (Supplementary Fig. 8), and biomass loss due to mortality (Supplementary Fig. 8). Decreasing mean productivity and increasing standard deviation of productivity with stand age jointly contributed to successional declines in temporal stability (Supplementary Fig. 9 and Supplementary Fig. 10).

Stand age reduce diversity at multiple scales
Functional diversity at both the metacommunity scale and the local community scale decreased with stand age, but functional dissimilarity among local communities increased with stand age (Fig. 3, estimate [±SE] = −0.025 [0.007], t = −3.423, P < 0.001 for local functional diversity, estimate [±SE] = 0.039 [0.015], t = 2.613, P = 0.009 for functional dissimilarity among local communities, and estimate [±SE] = −0.020 [0.009], t = −2.210, P = 0.027 for metacommunity functional diversity). After considering confounding factors (observational frequency, survey interval, initial survey year, and final survey year), the trends of functional diversity with stand age at multiple spatial scales remained unchanged (Supplementary Figs. 3B, Supplementary Fig. 6B, and Supplementary Table S2). Similar results were found using the Shannon diversity index, but there was a non-significant relationship between metacommunity diversity and stand age (Fig. 3, estimate [±SE] = −0.018 [0.005], t = −3.364, P < 0.001 for local Shannon diversity, estimate [±SE] = 0.063 [0.002], t = 3.656, P < 0.001 for composition dissimilarity among local communities, and estimate [±SE] = −0.008 [0.008], t = −1.042, P = 0.297 for metacommunity Shannon diversity).

Methods

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper investigates how the temporal stability of forest productivity at multiple spatial scales changes with stand age and how this change is related to diversity during succession. Using a space-for-time substitution method, the authors found that the temporal stability of forest productivity decreases with stand age at both local and metacommunity scales. The findings of this study, such as older forests being less stable and the dominance of conservative species reducing species stability, differ from classic ecological theories. If accurate, these results would challenge established succession theories and our understanding of forest development in the United States. However, I recommend that the authors provide more detailed evidence supporting the reliability of their data analysis, to ensure the robustness and validity of their results and conclusions.\n<General concerns>\nSpace-for-time substitutions, while useful, must be carefully employed. This approach has often been misused, leading to inaccurate conclusions about ecological patterns and processes (Walker et al. 2010; Damgaard 2019). Unfortunately, this paper appears to fall into this category, as it examines plots across the United States with varying ecoregions, climates, vegetation types, and soil conditions. It is challenging to believe that all these plots with different stand ages are comparable, and would follow the same successional trajectory. There are many confounding factors beyond stand age that could influence changes in diversity and stability. These factors should be considered and controlled during the analysis. For example, the authors could compare the temporal stability of plots with the same vegetation form, ecoregion, and soil type to ensure more accurate comparison. Additionally, it would be beneficial to assess whether climate and soil variables play more significant roles in driving stability changes than stand ages. The authors should perform more sensitivity analyses to validate and explain why their space-for-time substitution is valid. Without careful consideration of these factors, it is difficult to have confidence in the study’s conclusions.\nThe conceptual figure (Fig. 1) aligns better with the common understanding of forest succession, where community stability increases from young to mature forests and may decrease in old forests due to natural tree mortality. It is harder to believe that community stability consistently and linearly decreases over succession than to consider that the space-for-time substitution might be invalid. Given that many plots have a stand age of less than 50 years and are still in the early stages of succession, it is crucial to clarify the model selection process. Did you compare the performance of nonlinear versus linear fitting models? This comparison would help determine the most appropriate model for analyzing the relationship between stand age and stability.\nFurther, the detailed methodology needs better clarification. For example, stand age is a key variable in the analysis, but its definition and measurement are unclear. Was it calculated as the mean stand age of all observed years for each plot, or based on the stand age at the first or last survey? For the 2925 selected plots, how many observations were used to calculate the stability of each community, and what was the time span of these observations? How did the authors control for differences in the year of initial and last surveys, the time span, and the number of observations during the analysis? These factors could significantly influence the stability calculation and should be clearly explained to ensure robust results.\nAdditionally, how were α and γ diversity calculated? Were they based on the diversity of the initial year, the total, or the average of all observation years? How were the shifts in diversity during the surveys were considered?\nThe authors propose that the observed decrease in diversity and stability is due to a shift from co-dominance of acquisitive and conservative species to dominance by conservative species as succession progresses. This result is surprising, as it is difficult to believe that conservative trees are less stable than acquisitive trees or a mix of both. With existing trait data and methods (e.g., Díaz et al. 2016), it is crucial to directly quantify the conservative versus acquisitive strategies of each species and plot. Visualizing species composition shifts towards conservative species with increasing stand age, comparing the species stability of conservative versus acquisitive trees, and directly quantifying the effect of resource-use strategies on species and community stabilities are important steps. Additionally, the authors should provide more convincing results and explanations on why conservative trees are less stable, as this challenges the common understanding of the 'fast–slow' plant economics spectrum.\nOverall, the research has the potential to make a significant impact, but the authors need to strengthen their methodological framework and provide more convincing evidence that their findings are not merely artifacts of methodological choices.\n<Specific comments>\nLine 17: The statement may be too absolute. Previous studies often provide evidence that functional diversity also increases during forest succession.\nLine 19: Given that many plots have a stand age of less than 50 years, can they be accurately classified as late successional stages?\nLine 22: Did functional alpha diversity increase and then decrease with stand age, or did it consistently decrease throughout succession?\nLine 101: Do you mean “spatial asynchrony did not show a significant relationship with stand age”?\nLine 102: It should be “P = 0.621 for spatial asynchrony”\nLines 180-195: The authors suggest that changes in environmental heterogeneity with succession might be an important factor driving spatial asynchrony. The paper already considers environmental data such as mean annual temperature and climate moisture. Calculating the variation of these environmental factors with succession would be helpful to support these arguments.\nWalker LR, Wardle DA, Bardgett RD, Clarkson BD. The use of chronosequences in studies of ecological succession and soil development. Journal of ecology. 2010, 98:725-36.\nDamgaard C. A critique of the space-for-time substitution practice in community ecology. Trends in ecology & evolution. 2019, 34:416-21.\nDíaz, S., Kattge, J., Cornelissen, J.H., Wright, I.J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Colin Prentice, I. and Garnier, E., 2016. The global spectrum of plant form and function. Nature, 529:167-171.", "reviewer_2": "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.", "reviewer_3": "The manuscript „Forest temporal stability declines over stand age at multiple spatial scales“ by Shan et al. presents an analysis on growth variation in forest inventory and analysis (FIA) data collected across the United States. The authors present an analysis partitioning stability in three components of scale (alpha, beta, gamma), and distinguishing between components of stability and asynchrony. The question is of high relevance: how does the stability (here: in terms of tree growth, which is closely correlated to carbon uptake) of forest ecosystems change with age, and which components contribute to this stability? To do this the authors build on a framework for analyzing stability that was previously presented. Contradictory to many other works, the authors find that the stability of forest ecosystems is decreasing as they develop towards old-growth conditions. This would be an interesting finding, yet I see a number of problems with the design and analysis, essentially calling into question the strong claims of “unexpected findings” made by the authors. My main concerns are as follows:\n(1) The study focuses on temporal stability of growth, which is key variable also used in seminal previous works on stability. The insurance hypothesis, for instance, was developed for a similar variable (Yachi and Loreau 1999, PNAS). However, assessing temporal stability requires data with reasonable (= moderate to high) temporal resolution. FIA data, however, have an approximate temporal resolution of one observation in 10 years. So rather than temporal variability, the study more likely assesses mid-term forest development. This, very likely, contributes to the unexpected results – see #2 below. In general, I think FIA data are not well suited for assessing the temporal stability of growth, because of their low temporal resolution.\n(2) One important element of the study is the analysis across scales. I generally commend the authors for an attempt at this, as I think these kinds of multiscale studies are important. However, I again have severe doubts, whether the US FIA data are indeed well suited for this kind of analysis. Essentially, the plots that make up beta diversity in the current analysis are very close together – 36.6m from the central plot. Considering the processes that create diversity/ asynchrony at the beta level – disturbances by wildfire, insects, harvesting – and their spatial footprint, at 36.6m apart these plots are highly unlikely to capture the true beta diversity of the system, and are likely highly spatially autocorrelated. And there is a second scale consideration, this one concerning the alpha level: The individual FIA-subplots are rather small, a radius of 7.32m, so they will only have a handful of trees on them in old stands. As a consequence, it is logical that in older forests variability goes up, or stability goes down at such a small scale, because if a single tree dies in older stands, this will have a strong effect on growth (while the effect of a smaller tree dying in a younger stand is going to be much less strong, as the stem density in these stands is much higher). So I assume that your unexpected finding is – to a large degree – also an artifact of the rather small scale of analysis, relative to the size of old trees.\n(3) One aspect that the authors do not talk about is how “stand age” was determined, and how certain they are in this variable. It is notoriously difficult to determine tree age, and determining stand age is even more difficult. Was a single (dominant?) tree used to determine stand age? Was age averaged across trees? Were also small trees cored or only the biggest/ most dominant trees per site? Was this averaged across species per plot? These things are relevant because age is a central variable of the current analysis, but is difficult to determine particularly in highly complex stands (i.e., late stages of stand development) that have a component of older and younger trees. Without knowing more how age was determined and how the high structural complexity of older stands was considered here it is difficult to interpret the findings presented here.\n(4) The authors analyze FIA data across the US along a gradient of stand development. They do, however, not control for the fact of the particular land-use history of the US that affects the dataset they analyze. Particularly, the share of old and complex forests is higher in the Western US (which was colonized by Western people only in the 19th century), while in the Eastern US many forests are on a trajectory of recovery from past land use (as a result of the much longer western land use history in the east). Now, without controlling for this, comparing US FIA plots across stand development means comparing east (mostly young and middle-aged) to west (often old, with some young forests from fires). As the species diversity is much higher in the east, this could be another explanation for the “unexpected” pattern found by the authors.\n(5) The concept that the authors used (presented previously) is interesting. I was not intimately familiar with it, and I’m afraid based on reading the authors description, I am not able to understand it completely. I perused some of the original literature, but still, I think the description (and also the equations!) need to be more clear so that the reader can understand what is going on. If we accept the concept and its indicators as is, the analyses are rather basic, mainly correlating one indicator value with another. The SEM is an exception from that, but here it is not well explained how the structure of the SEM was generated, whether other structures were tested, etc.\n(6) Lastly, the wording is confusing in parts of the text, and some terms are rather uncommon (what are “conservative trees”, for instance)? Another example is in l154-155 – a lot of big words here, but what does this sentence really mean?", "reviewer_4": "Contents of the paper\nThe manuscript explores how the stability of net primary productivity (NPP) changes as forest become older in the Eastern portion of the United States. The authors used the US National Forest Inventory (NFI) for this purpose, using plots with repeated measurements to estimate their NPP values for the period comprised between 1995 and 2022. They convincingly show that NPP stability decreases with stand age. The authors then tested the hypothesis that stability decline can be attributed to decreasing species- and functional-diversity, and they did so at multiple spatial scales (local and metacommunity levels). The study concludes that decreasing diversity is the main driver behind decreasing stability as forests get older.\nPositive remarks\nI really liked the conceptual framing of this paper. The hypothesis is convincing and challenges the traditional view that forests get more stable as they become older. The argumentation behind their hypothesis is almost flawless in my opinion. The analyses seem well executed, and appropriate to answer their research questions. The narrative of the manuscript is also clear and engaging. The topic of this paper is absolutely relevant, and I think it constitutes frontier science when it comes to forest dynamics.\nPoints of improvement\nYet, the manuscript sacrifices scientific precision to make an engaging story. Specially when it comes to ecosystem stability, which has been a problematic concept since long ago (together with its resilience “relative”), definitions matter a lot. I don’t really disagree with their definition of stability (or what I assume they mean by this concept), but I think it’s important to clearly state what do they mean by it, since is a complex and abstract concept. Also, a number of operative definitions are missing throughout the paper: their response variable (stability of what?), their study area (which can only be really inferred from the map presented in the supplementary material), their stability metrics, the US NFI sampling design (which I had to search in internet), what do they mean by alfa, beta and gamma scales within the US NFI design, etc. Also, the study isn’t reproductible with the code provided: only the graphs and the regressions can be reproduced from it. But the difficult part is missing in the code, which is estimating the stability metrics and the functional diversity metrics. Since some parts of the methods are underexplained, I was relying on seeing this in the code, but unfortunately I still have questions about how some procedure were executed. I also missed the limitations of your study, which are many, as in any other scientific study.\nGeneral assessment of the paper\nAll in all, my conclusion is that this is a good paper and that it’s absolutely worth publication, here in Nature Communications or elsewhere. Is addresses a very relevant, novel and challenging question, has a great argumentation and narrative, and the analyses seem appropriate and well executed. Yet, I believe it also requires a thorough revision to ensure that all operative definitions are included, ideally on the main text. Also, the code to replicate the study should be provided in full, implying that anyone can reuse the code to extract stability measures from the US NFI data. Below, I’m including some specific remarks (major and minor) that the authors can use to improve their study, which I hope they find useful.\nCongratulations on your study, and best of lucks with the revision!\nBest regards,\nDr. Rodrigo Muñoz\nMajor remarks\n1. The abstract is too technical. Try to simplify it, so anyone with a general ecological background can understand it. Also, replace the term “ecosystem stability” for “productivity stability” (or even better: NPP stability).\n2. Line 153: Here you state that diversity loss produces a decrease in alfa stability, without nuances. However, what I see in your graphs is that stability decreases sharply and clearly, both for alfa and gamma metrics. Yet, functional diversity only decreases slightly, and the data look rather as a dot cloud. I know that the “FD ~ stand age” regression is significant, but that doesn’t imply that the effect is large (and here’s where you need to use common sense, not statistical metrics). I concur with you that that the decrease in Shannon and functional diversity has a role in the observed low stability of old-forests, but I disagree that this is THE cause. I think you need to expand the discussion in this sense, and think of alternative mechanisms that can affect stability, beyond biodiversity.\n3. Alfa, beta and gamma spatial scales need to be defined in the body of the paper. These concepts are defined in the methods, but in the Nature Communications format, the methods are a sort of appendix. Therefore, it’s essential that these core concepts get defined in the introduction or the results.\n4. Stability and their response variable, productivity, need also to be defined in the introduction. I think that the study is about above-ground primary productivity, but even after reviewing the whole study, I’m not completely sure.\n5. Lines 208: As mentioned above, the study is impossible to reproduce from the code provided. It should be possible for whoever to get FIA’s data, and using your code, get to the final graphs. I know this is cumbersome, but I believe it’s a requirement for publishing in NatComm.\nMinor remarks\n6. Lines 67-69: Have you ever came across the paper of multiple successional pathways from John Vandermeer and colleagues? A bit old, but it reminded me a lot about your study while reading your manuscript: Vandermeer et al. 2004. Multiple basins of attraction in a tropical forest: Evidence for nonequilibrium community structure. Ecology.\n7. Lines 83-86: Is this statement true? I think that, in canopy gap dynamics, the trees that get recruited when a gap is formed are rather acquisitive, especially when the gap size is large. I would say that this would increase beta and gamma diversity. Not saying is objectively true, but this is what I’d infer from my field experience.\n8. Also define what do you mean by species stability and asynchrony. It can be inferred from the formulas (or so I think), but it’d be good if you provide an explanation in simple words. This can go in the methods.\n9. Lines 176-178: Also dispersal limitations and environmental filtering can play a role in stand divergence.\n10. Lines 186-188: Your study also uses unimodal models (i.e., regressions fitting a straight line).\n11. Lines 211-216: Can you please include the sampling design diagram from the FIA’s sampling? You may include it in the Supplementary Material. I think this is important to understand what do you mean by alfa, beta and gamma “scales”.\n12. Lines 219-228: Why only the plots from the Eastern portion of the US got included? Why these conditions weren’t met for the Western side? Mention this in the methods, please.\n13. Lines 237-239: Please, briefly mention the properties of the indicators of alfa stability, spatial asynchrony and gamma stability in your methods. What does it mean if the metric = 1, or 0, or 5? How do I interpret the Y axis of Figures 2 and 4?\n14. Lines 249: Detrend for what? I didn’t follow why the detrending was necessary in the first place.\n15. Lines 277-282: What was this information used for? It isn’t completely clear from your text.\n16. Lines 293: How did you defined the ecoregions? It’s the first, and probably the last, time that the term appeared in the manuscript."}, "author_response": "RESPONSE: Thank you very much for your positive evaluation and constructive comments. The concerns you raised are very helpful for improving our manuscript. We have provided more detailed evidence and performed additional analyses to ensure the robustness and validity of our results and conclusion. Please see our responses to each of your comment below.\n<General concerns>\nSpace-for-time substitutions, while useful, must be carefully employed. This approach has often been misused, leading to inaccurate conclusions about ecological patterns and processes (Walker et al. 2010; Damgaard 2019). Unfortunately, this paper appears to fall into this category, as it examines plots across the United States with varying ecoregions, climates, vegetation types, and soil conditions. It is challenging to believe that all these plots with different stand ages are comparable, and would follow the same successional trajectory. There are many confounding factors beyond stand age that could influence changes in diversity and stability. These factors should be considered and controlled during the analysis. For example, the authors could compare the temporal stability of plots with the same vegetation form, ecoregion, and soil type to ensure more accurate comparison. Additionally, it would be beneficial to assess whether climate and soil variables play more significant roles in driving stability changes than stand ages. The authors should perform more sensitivity analyses to validate and explain why their space-for-time substitution is valid. Without careful consideration of these factors, it is difficult to have confidence in the study’s conclusions.\nRESPONSE: Thank you very much for this insightful comment. We fully agree that space-for-time substitutions must be employed carefully. To validate our space-for-time substitution is valid, additional analysis were performed.\nFirstly, following your suggestion, we considered and controlled soil conditions besides climate factors in our analysis. Specifically, we obtained key soil properties [total nitrogen (g kg-1, TOTN), organic carbon content (g kg-1, SOC), cation exchange capacity (cmol kg-1, CEC), pH in H2O (pH) of the 0-30 cm soil horizon] of studying plot and performed a principal component analysis (PCA) to assess overall soil conditions (lines 359-365 and Fig. S21B). Then, the first two component of PCA were added to our linear mixed effects model to control for soil conditions (equation 1 in lines 371-372).\nSecondly, following your suggestion, we separately fitted the relationship between multi-scale stability and forest age in each ecoregion and obtained similar conclusions, i.e., ecosystem stability at multiple scales decreases with forest age (Fig. S5 and lines 403-405). The ecoregions were delineated primarily by evaluation and integration of the physical and biological components including land use, climate, physiography, lithology, soils, and potential natural communities in U.S. Department of Agriculture (USDA) Forest Service National Hierarchical Framework.\nThirdly, as suggested, we evaluated the importance of forest age and environmental variables (climate and soil variables) in explaining model variance, and found that stand ages played more significant roles in determining multiscale stability than environmental variables (Fig. S3; lines 114-115).\nFinally, in the discussion section, we also noted the potential limitation of space-for-time approach: “First, the space-for-time approach we employed is often criticized for potentially overlooking different successional trajectories across varied environments. To address this limitation, we conducted separate analyses for ecoregions with similar environmental conditions and potential communities, leading to consistent conclusions (Fig. S5). Further research extending single time series into late successional stages would be valuable for generalizing the observed patterns” (lines 225-229).\nThe conceptual figure (Fig. 1) aligns better with the common understanding of forest succession, where community stability increases from young to mature forests and may decrease in old forests due to natural tree mortality. It is harder to believe that community stability consistently and linearly decreases over succession than to consider that the space-for-time substitution might be invalid. Given that many plots have a stand age of less than 50 years and are still in the early stages of succession, it is crucial to clarify the model selection process. Did you compare the performance of nonlinear versus linear fitting models? This comparison would help determine the most appropriate model for analyzing the relationship between stand age and stability.\nRESPONSE: Thank you for the comment. In our initial analysis, we compared the performance of nonlinear (logarithmic and quadratic transformed stand age) versus linear functions describing the temporal trend of stability at multiple spatial scales, based on Akaike information criterion (AIC; lines 380-384; Table S1). We found that all three models performed similarly (with similar AIC; Table S1) and showed a consistent decrease in stability with forest age (Fig. S6). The continuous decrease in stability with forest age may be because our plots were generally concentrated in the canopy transition stage (Fig. S19 and S20, mainly 30-100 years), where the early acquisitive trees changed to the late conservative species (Fig. S19) and species diversity decreases (Fig. 3). Considering the simplicity and interpretability, we choose to use the untransformed stand age in the main text, and presented the logarithmic and quadratic transformed results in the appendix (Fig. S22). As suggested, we have clarified the model selection process in our method section (lines 380-384).\nFurther, the detailed methodology needs better clarification. For example, stand age is a key variable in the analysis, but its definition and measurement are unclear. Was it calculated as the mean stand age of all observed years for each plot, or based on the stand age at the first or last survey? For the 2925 selected plots, how many observations were used to calculate the stability of each community, and what was the time span of these observations? How did the authors control for differences in the year of initial and last surveys, the time span, and the number of observations during the analysis? These factors could significantly influence the stability calculation and should be clearly explained to ensure robust results. Additionally, how were α and γ diversity calculated? Were they based on the diversity of the initial year, the total, or the average of all observation years? How were the shifts in diversity during the surveys were considered?\nRESPONSE: As suggested, we have added more detailed methodology clarification including the definition/measurement of stand age, observation spans, and the calculation of stability and diversity index.\nFor stand age: “The stand age at plot level was estimated by counting growth rings on increment cores taken from two or three dominant or codominant trees in stand-size class (three stand-size classes with minimum DBH values of 0 cm, 12.7 cm, and 22.9 cm) with highest canopy cover. If the overstory covers a wide range of tree species, field crews tried to select the trees accordingly and calculated the weighted average stand age based on canopy cover (lines 261-266).……SA are mean stand age of all observed years in ith plot” (lines 375).\nFor observational details: “Among the 2886 plots included in the study, 1893 plots were measured 4 times, 940 plots were measured 5 times, and 53 plots were measured 6 times. The observational period ranged from 11 to 24 years (17.0 ± 2.6 years). The observational interval was 5.05 ± 1.36 years” (lines 277-280).\nFor α and γ diversity: “Specifically, functional α diversity was measured as mean functional dispersion based on the above five functional traits in local subplots across all observation years. Functional γ diversity was measured as mean functional dispersion at the metacommunity across all observation years” (lines 344-347).\nFollowing your suggestions regarding the observation span, observational frequency, diversity changes during the survey, and so on, we conducted a series of sensitivity analyses (lines 405-411): (1) When selecting plots, we retained plots with 4 or more observations to calculate productivity stability. Finally, there were 2886 plots retained (4 measurements: 1893 plots; 5 measurements: 940 plots; 6 measurements: 53 plots). We fitted the temporal trend of productivity stability for plots with different observation frequencies and results are qualitatively similar (Fig. S6); (2) The observation span was 11-24 years (17.0 ± 2.6 years, mean ± s.d.). We fitted the temporal trend of productivity stability for plots with different observation spans which also generated similar results (Fig. S8); (3) We also fitted the temporal trend of productivity stability for plots with different initial (Fig. S9) and final years (Fig. S10). These sensitivity analyses all produced qualitatively similar results, i.e., forest temporal stability declines over stand age at multiple spatial scales; (4) Considering the change of tree diversity during the survey, we also fitted the stability component with the average diversity of all observation, the diversity in the initial year, the diversity in the middle year and the diversity in the final year. These alternative models produced similar results, i.e., diversity strengthened temporal stability at multiple scales (Fig. S13). Together, these additional analyses have strengthened the robustness of our findings and conclusion.\nThe authors propose that the observed decrease in diversity and stability is due to a shift from co-dominance of acquisitive and conservative species to dominance by conservative species as succession progresses. This result is surprising, as it is difficult to believe that conservative trees are less stable than acquisitive trees or a mix of both. With existing trait data and methods (e.g., Díaz et al. 2016), it is crucial to directly quantify the conservative versus acquisitive strategies of each species and plot. Visualizing species composition shifts towards conservative species with increasing stand age, comparing the species stability of conservative versus acquisitive trees, and directly quantifying the effect of resource-use strategies on species and community stabilities are important steps. Additionally, the authors should provide more convincing results and explanations on why conservative trees are less stable, as this challenges the common understanding of the 'fast–slow' plant economics spectrum.\nRESPONSE: Thank you for this insightful comment. Following your suggestions, we calculated the community-weighted trait means (CWM) and conducted principal component analysis (PCA) to measure functional strategies at the plot level (lines 333-339). The PCA showed that CWM represents coniferous to broad-leaved, and PC1 CWM represents conservative to acquisitive growth strategies (Fig. S21A). As the PC2 main characteristic of canopy transition, we found that the community composition shifted from coniferous acquisitive species to broad-leaved conservative species (Fig. S17; lines 146-148 in Results). However, we found that conservative strategies did not promote species stability which agree with previous study (Schnabel et al., 2021). It may be because while conservative strategies reduce productivity variability in fluctuating environments (i.e., decrease the standard deviation of productivity, SD), their higher construction costs also result in lower growth rates (i.e., lower average productivity, u), ultimately leading to neglectable stability improvement (u/SD) (Schnabel et al., 2021). On the contrary, the transition from coniferous trees to broad-leaved trees may reduce species stability, as broad-leaved species typically exhibit weaker adaptability to environmental stress and fluctuations compared to conifers species (McCulloh et al., 2023; Díaz et al., 2016; Münchinger et al., 2023). We have added more explanations in the discussion (lines 178-187).\nSchnabel, F. et al. Species richness stabilizes productivity via asynchrony and drought-tolerance diversity in a large-scale tree biodiversity experiment. Science Advances 7, eabk1643, doi:10.1126/sciadv.abk1643 (2021).\nMcCulloh, K. A. et al. At least it is a dry cold: the global distribution of freeze-thaw and drought stress and the traits that may impart poly-tolerance in conifers. Tree Physiol 43, 1-15, doi:10.1093/treephys/tpac102 (2023).\nDíaz, S. et al. The global spectrum of plant form and function. Nature 529, 167-171, doi:10.1038/nature16489 (2016).\nMünchinger, I. K., Hajek, P., Akdogan, B., Caicoya, A. T. & Kunert, N. Leaf thermal tolerance and sensitivity of temperate tree species are correlated with leaf physiological and functional drought resistance traits. J. For. Res. 34, 63-76, doi:10.1007/s11676-022-01594-y (2023).\nOverall, the research has the potential to make a significant impact, but the authors need to strengthen their methodological framework and provide more convincing evidence that their findings are not merely artifacts of methodological choices.\nRESPONSE: Again, we appreciate your valuable feedbacks and insightful comments. We believe the improved clarification of methodological framework and additional analyses have strengthened the robustness of our findings, demonstrating that the patterns we reported are not merely artifacts of methodological choices.\n<Specific comments>\nLine 17: The statement may be too absolute. Previous studies often provide evidence that functional diversity also increases during forest succession.\nRESPONSE: We have reworded the statement to “may experience functional diversity loss during canopy transition”\nLine 19: Given that many plots have a stand age of less than 50 years, can they be accurately classified as late successional stages?\nRESPONSE: Thanks for the comment. The succession of temperate forests in North America can usually be divided into four stages: stand establishment, stem exclusion, canopy transition, and gap dynamics (Fig. 1). From the perspective of species composition, our plot reflects the transition from coniferous trees to broad-leaved conservative trees, known as the canopy transition stages, representing a relatively late successional stage (Fig. S19). Following your suggestion and for accuracy, we replaced late successional stage to “during canopy transition” (lines 18-19).\nLine 22: Did functional alpha diversity increase and then decrease with stand age, or did it consistently decrease throughout succession?\nRESPONSE: Consistently decrease. We have revised the sentence for clarity: “Therefore, consistent declines in functional α diversity during canopy transition cause lower α stability in old-growth forests” (lines 22-23). The continuous decrease in stability and diversity with forest age may be due to the fact that our plots were generally concentrated in the canopy transition stage, and we thus added the qualifier “canopy transition stage” here.\nLine 101: Do you mean “spatial asynchrony did not show a significant relationship with stand age”?\nRESPONSE: Yes, and we have revised it accordingly (lines 109-110).\nLine 102: It should be “P = 0.621 for spatial asynchrony”\nRESPONSE: Thanks. We have corrected it (lines 110-111).\nLines 180-195: The authors suggest that changes in environmental heterogeneity with succession might be an important factor driving spatial asynchrony. The paper already considers environmental data such as mean annual temperature and climate moisture. Calculating the variation of these environmental factors with succession would be helpful to support these arguments.\nRESPONSE: Thank you for this important comment. We added the coefficient of variation of annual mean temperature (CVT) and climate moisture index (CVM) in the analysis to account for the interannual fluctuations of climate (lines 356-358). However, since FIA does not contain environmental variables such as soil moisture and soil properties for each subplot, we find it difficult to quantify the environmental heterogeneity between subplots and added some descriptions in the Discussion to illustrate this potential limitation: “Second, due to the limited investigation, our analysis did not account for environmental heterogeneity between subplots, potentially influencing the role of environmental filtering in spatial asynchrony 12” (lines 229-232)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I would like to thank the authors for their detailed responses and the additional analyses they provided. The manuscript has improved significantly in clarity and data analysis rigor. I appreciate the authors' efforts, particularly in incorporating sensitivity analyses to address the limitations of the space-for-time substitution method. However, I have a few additional comments for further consideration.\n1. Sensitivity Analyses: The authors have made commendable efforts to conduct additional sensitivity analyses, including the integration of soil and climate variables and analyses across different ecoregions. However, these analyses come across as largely descriptive and complex. The current approach, which examines confounding variables individually, does not capture their combined effects, resulting in a total of 23 supplementary figures that may overwhelm readers. I strongly recommend that the authors adopt a unified linear mixed effects model to evaluate the impacts of stand age and all confounding variables simultaneously. This approach would facilitate a clearer comparison of their relative importance (see Figure 4 in https://doi.org/10.1038/s41559-017-0132 for reference). Instead of analyzing confounding variables one by one, the authors could add survey interval, observational span, initial and final survey years, and observational frequency as fixed or random effects in the same model (Line 371). A model selection procedure could then identify the best predictors of stability and diversity and assess the relative importance of all fixed effects considered as drivers of these components. This would provide clearer evidence regarding the significance of stand age when accounting for confounding variables and highlight which other variables are important in determining stability and diversity relative to stand age.\n2. Definition of Concepts. I concur with the other reviewers that the concepts utilized in the study require clearer definitions, particularly regarding the term \"temporal stability.\" This distinction is crucial, as classic succession theory and empirical studies focus on the status of communities (e.g., temporal stability of composition, biomass, and cover), whereas this study emphasizes the change rate of the community (i.e., invariability of annual biomass growth), which may diverge from the classical definition. According to classic succession theory, late successional communities are expected to reach equilibrium, potentially leading to minimal biomass growth. In this context, the temporal stability of productivity, measured by the mean to standard deviation ratio, would also be expected to decrease in late successional communities. While I do not dispute the authors' approach, it is essential to clarify how their definition of stability differs from traditional theories. Additionally, some fundamental results and data are missing, making it challenging to evaluate consistency with classic succession theory. I recommend the authors include basic information, such as how biomass and annual productivity (along with their standard deviation) change with stand age, and whether decreased stability is driven by reduced productivity or increased standard deviation. Providing this context will enhance readers' understanding of the observed patterns. If decreased stability is primarily due to lower annual biomass growth, this study may support rather than challenge traditional succession theories.\n3. Functional Trait Analysis. I appreciate the authors' attempt to use PCA based on functional traits to capture species' adaptive strategies and to conduct structural equation models (SEMs) to explore how these strategies change with stand age and influence stability. However, In the PCA of S21A, the authors suggest that \"The first axis (CWMPC1) represents traits associated with broad-leaved strategies (high SLA, Nmass, Pmass, and low LDMC), while the second axis (CWMPC2) refers to traits associated with acquisitive strategies (low WD).\" This interpretation may be incorrect. According to the well-accepted leaf economic spectrum, the LDMC-Nmass dimension reflects the acquisitive-conservative continuum (e.g., Diaz et al. 2016). Therefore, A more accurate interpretation is needed to enhance the study's reliability.\nMinor comments:\nLine 1: The authors should clarify in the title that this study focuses specifically on the temporal stability of productivity (i.e., annual biomass growth rate) rather than other components of temporal stability.\nLines 178-187: This section requires substantial revision, as PC1 (the LDMC-Nmass dimension) reflects the acquisitive-conservative continuum, not PC2 (the WD dimension).\nLine 258: This claim may not hold true; even within the same ecoregion, plots can differ significantly in terms of land use, climate, physiography, lithology, soils, and potential natural communities.\nLine 260: How is aboveground biomass measured in this study?\nLines 336-339: The definition provided differs from the commonly accepted understanding of the leaf economic spectrum. Why not consider the positive correlations with CWMSLA, CWMNmass, and CWMPmass, alongside the negative correlation with CWMLDMC, as indicators of acquisitive strategies, as is typically done?\nLine 347: Since functional dispersion is a distance-based index of functional diversity that operates largely independently of species richness, please verify whether it allows for a multiplicative partitioning of diversity into gamma, alpha, and beta components, and clarify how this can be achieved (see Chao et al. 2019, Ecol. Monogr. 89: e01343).\nLine 371: I recommend that the authors further enhance the model by incorporating survey interval, observational span, initial and final survey years, and observational frequency as either fixed or random effects. A model selection procedure using the ‘dredge’ function from the R package MuMIn could then identify the best predictors. This would allow for a clear comparison of the relative importance of predictors (such as stand age, temperature, and soil properties) to determine whether stand age is a key driver of the temporal stability of productivity in US forests.\nOverall, the authors have made commendable strides in addressing the study's limitations, enhancing its robustness. I remain optimistic about the manuscript, though the strong claim regarding the challenge to the traditional view of forest succession requires further substantiation. A more thorough analysis and additional details will clarify the dynamics at play in these plots and their implications for traditional succession theory.", "reviewer_2": "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.", "reviewer_3": "I have reviewed a previous version of this manuscript, and was rather critical about many of the assumptions and conclusions drawn. In their revision, the authors did a good job in addressing some of my major concerns. These were specifically (numbering following my initial comments):\n(3) Stand age: The authors now clarify how stand age was determined, which is important for contextualizing the results of the paper. Also, the authors conducted an additional sensitivity analysis looking into the effect of stand structural complexity (which is often associated with age), which is great.\n(4) US FIA data: The authors stated (and still state) in the abstract and in the objectives section at the end of the introduction that they are analyzing US FIA data, which led me to believe that we are talking the entirety of the US. In their response they clarify that they only focused on the Eastern US, which makes some of my comments on the original version moot. This is now clearer for me, but the revised version still talks about “US” in many places, e.g. line 17 (“forest inventory dataset across the United States”) or line 95 (“the national forest survey dataset from the United States”), which is highly misleading to the reader. Please don’t oversell, but state clearly what your study area was throughout the text!\n(5) Explanation of the concept: This is now better explained and better to follow, thanks!\n(6) Wording: Some of the wording choices that were confusing to me are now rectified, thank you very much! Nonetheless, parts of the text still have grammatical errors and the manuscript would overall benefit a close read of a native speaker, if available.\nHowever, there are also elements where my initial concerns were not really addressed in the revision. This is mostly because they are fundamental concerns that are related to the data, and how the data is used in the context of the concept applied here. I will reiterate here why I think there are inherent problems with these issues.\n(1) Time scale: Thank you for clarifying the – on average – five-year remeasurement interval in your data. I still maintain, that 5-10 year averages are not able to represent temporal stability of growth, particularly since you frame the analyses strongly in the context of environmental fluctuations (e.g., in lines 38, 53, 70, 101 etc.). These fluctuations that you aim to study stability against happen at a much finer temporal grain, and will not be visible at the grain of your analysis, where they will be strongly masked by other trends (such as those of succession). So I do think that framing the results in the context of stability is misleading. In your rebuttal you use the argument that somebody else has done the same (mostly one author, Qiao et al.) – well, this doesn’t make it better, to be honest. Furthermore, the sensitivity analysis you’ve added is not helpful here, as it goes up in survey interval (from 5 to ten years), while the actual question is about higher frequency data, i.e. shorter survey intervals. So fundamentally I think the framing around temporal stability and environmental fluctuations is misleading.\n(2) Spatial scale: Although I do not agree I can accept the point on the different spatial levels. This negates large parts of ecological research and will confuse many readers (that think about much larger scales when they read beta or gamma diversity), but the scales are well defined in the text, so there’s that. My fundamental criticism, however, is even stronger than for #1 above: I maintain that your “unexpected” results are simply the result of the small scale of your analysis. Treating the small subplots of FIA as a single analysis entity and considering the average size of a fully grown tree, variability at the sub-plot level have to go up with age, simply because a single tree dying (or declining in growth) will have a much larger share on the plot area then in younger stand development stages. Please note that the initial design of the FIA was not to analyze subplots individually, but to jointly analyze all four plots together as a single analysis entity. So I inherently think that the design of the inventory (which was not done by the authors) is driving the results, and what we see is mainly an effect of the plot design. This, to me, is underscored by the fact that in all the sensitivity analyses that the authors have added, there is hardly any variation, across all the variables tested. Again, the fact that another author (the same one mentioned already in the other point above) did something like this before does not make it better.\nTo conclude I truly appreciate the authors for their efforts in the revision. I nonetheless think that there are fundamental flaws in the analysis, particularly regarding the assumptions of scale, that drive the results. I cannot help but feel that this is a classical case of the fallacy of “have pet concept, will apply to any data”, i.e., not considering the limitations of the data for the sake of implementing a certain concept.", "reviewer_4": "I am reviewing this study for the second time. I went through the rebuttal letter in response to the review provided by the four reviewers of this manuscript, and through the manuscript once again to assess the changes implemented. I believe the authors did a great job specially in their rebuttal letter, giving detailed responses to most of our comments. I am satisfied in general with their responses and I believe that most of my concerns where either addressed in the manuscript or argued against in an convincing manner.\nYet, after reading the four reviews and their answers, I have to agree with Reviewer # 3 on an important point: there are problems with the concepts of beta and gamma diversity (Rev 3, point 2). I envision the concepts of alpha, beta, and gamma diversity from a landscape perspective, which is not in line with how the authors use the concepts (please refer to the abstract on Sepkoski 1988 for a very straightforward definition of these). Actually, I think that each FIA sampling unit (i.e., the four sub-plots in a triangular arrange) is intended to capture the variability within a forest patch. However, the concept of beta diversity is intended to capture the variability across different patches within a landscape, while gamma diversity reflects the diversity in a defined landscape. In this study, we can’t really speak about beta and gamma diversities in my opinion, since the four-subplot design intends to provide a more robust estimate of properties in a single forest patch. This doesn’t imply that the study results are not valid, but rather that the concepts used in the manuscript need to be clarified and probably modified in accordance to how these concepts are defined in ecological theory.\nIn their response to Reviewer 3, the authors refer repeatedly to studies by Qiao et al. While it’s good that the authors anchor their operative definitions to the work of others, it’s important they go beyond just a single group of researchers. Quite possibly, they would find that most other authors assess diversity scales from a landscape perspective.\nFor the rest, I would only suggest the authors to carefully check the writing of the new text segments, as I found some writing mistakes, in contrast to the original version which was very well polished.\nI hope this new assessment helps the authors reach a decision about the manuscript, and the authors to continue improving their manuscript. I was already very positive about this study, and I maintain this assessment in this new version."}, "author_response": "RESPONSE: Thank you very much for your positive evaluation and constructive comments. The additional comments are very helpful for improving our manuscript. We have performed additional analyses and made corresponding modifications (all changes were marked in red). Please see our responses to each of your comments below.\n1. Sensitivity Analyses: The authors have made commendable efforts to conduct additional sensitivity analyses, including the integration of soil and climate variables and analyses across different ecoregions. However, these analyses come across as largely descriptive and complex. The current approach, which examines confounding variables individually, does not capture their combined effects, resulting in a total of 23 supplementary figures that may overwhelm readers. I strongly recommend that the authors adopt a unified linear mixed effects model to evaluate the impacts of stand age and all confounding variables simultaneously. This approach would facilitate a clearer comparison of their relative importance (see Figure 4 in https://doi.org/10.1038/s41559-017-0132 for reference). Instead of analyzing confounding variables one by one, the authors could add survey interval, observational span, initial and final survey years, and observational frequency as fixed or random effects in the same model (Line 371). A model selection procedure could then identify the best predictors of stability and diversity and assess the relative importance of all fixed effects considered as drivers of these components. This would provide clearer evidence regarding the significance of stand age when accounting for confounding variables and highlight which other variables are important in determining stability and diversity relative to stand age.\nRESPONSE: Thank you very much for this insightful comment. We agree that current sensitivity analysis is scattered and difficult to provide unified information for readers. Following your suggestion, we have added survey interval, observational frequency, initial and final survey years as fixed effects in the model 1 and evaluated the relative importance of the variables on stability and diversity (lines 118-122 and 418-422). Our results show that after considering mixed variables, stand age is still the most important driver for temporal stability at multiple scales (Fig. S3 and S6). We did not include observational span in the models because observational span is equal to the final survey year minus the initial year, which indicates that the influence of observational span can be replaced by initial and final survey years in the models. Furthermore, we conduct the model selection procedure using the ‘dredge’ function to identify the best predictors (lines 418-422, Table S1 and S2). Similarly, stand age was included in the best models for the multiscale stability and functional α diversity (lines 118-122, Table S1 and S2).\n2. Definition of Concepts. I concur with the other reviewers that the concepts utilized in the study require clearer definitions, particularly regarding the term \"temporal stability.\" This distinction is crucial, as classic succession theory and empirical studies focus on the status of communities (e.g., temporal stability of composition, biomass, and cover), whereas this study emphasizes the change rate of the community (i.e., invariability of annual biomass growth), which may diverge from the classical definition. According to classic succession theory, late successional communities are expected to reach equilibrium, potentially leading to minimal biomass growth. In this context, the temporal stability of productivity, measured by the mean to standard deviation ratio, would also be expected to decrease in late successional communities. While I do not dispute the authors' approach, it is essential to clarify how their definition of stability differs from traditional theories. Additionally, some fundamental results and data are missing, making it challenging to evaluate consistency with classic succession theory. I recommend the authors include basic information, such as how biomass and annual productivity (along with their standard deviation) change with stand age, and whether decreased stability is driven by reduced productivity or increased standard deviation. Providing this context will enhance readers' understanding of the observed patterns. If decreased stability is primarily due to lower annual biomass growth, this study may support rather than challenge traditional succession theories.\nRESPONSE: Thank you very much for your constructive comments about the definition of successional concepts. We added detailed descriptions to indicate our differences from traditional succession research (lines 39-40). Following your suggestion, we have added basic succession information (how biomass, productivity, and their standard deviations changes with stand age; Fig. S9) and the structural equation model exploring how mean value and standard deviation of species productivity contribute to decreasing stability with stand age (Fig. S10). Our results showed that the increased standard deviation and decreased mean contribute approximately equally to the decreasing temporal stability of productivity with stand age (Fig. S10 and lines 123-124, lines 166-168). This indicates that there is still high fluctuation degree of productivity in older forests, which is different from the traditional succession theory that assumes the equilibrium state in the late-successional forests. We added some descriptions to clarify our conclusions (lines 27-28, and lines 165-168).\n3. Functional Trait Analysis. I appreciate the authors' attempt to use PCA based on functional traits to capture species' adaptive strategies and to conduct structural equation models (SEMs) to explore how these strategies change with stand age and influence stability. However, In the PCA of S21A, the authors suggest that \"The first axis (CWMPC1) represents traits associated with broad-leaved strategies (high SLA, Nmass, Pmass, and low LDMC), while the second axis (CWMPC2) refers to traits associated with acquisitive strategies (low WD).\" This interpretation may be incorrect. According to the well-accepted leaf economic spectrum, the LDMC-Nmass dimension reflects the acquisitive-conservative continuum (e.g., Diaz et al. 2016). Therefore, A more accurate interpretation is needed to enhance the study's reliability.\nRESPONSE: Thank you very much for the comments. We have clarified the methods and discussions related to trait analysis and interpretation to enhance the reliability of our findings. Specifically, according to Diaz et al. 2016, we define CWM as the PC1 acquisitive-conservative continuum, and CWM is defined as the stress tolerance axis PC2 (lines 347-351), as species with high wood density typically exhibit tolerance to environmental stress such as drought and shade (Chave et al., 2009). Based on the well-accepted leaf and wood economic spectrum, we have revised the corresponding text in the discussion to more accurately explain how functional strategies across successional stages impact ecosystem stability (lines 24, and 192-197)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I would like to commend the authors on the substantial improvements made to the manuscript. Most of my previous concerns have been effectively addressed, and I appreciate the thorough revisions. Below are some remaining suggestions, primarily focused on clarity and precision in the writing, which I believe will further strengthen the manuscript.\nLine 18: Specify that the study focuses on “temporal stability of forest productivity” to avoid ambiguity.\nLine 19: Remove “a relatively late successional stage” for brevity.\nLine 20: Currently, this sentence discusses how different stability components change with functional α diversity, which may mislead readers who expect this section to address changes with successional stages\nLine 26: The phrase regarding the stabilizing effects of α stability could be confusing, as stronger α stability would conventionally lead to increased rather than decreased γ stability.\nLine 27: Avoid overstatement; the study does not directly challenge classical succession theory, as it does not directly assess whether community dynamics shift toward an equilibrium state. It would be more accurate to avoid making broad claims about challenging traditional successional perspectives.\nLine 35: Remove the opening sentence about climate change, as it is not directly relevant to the study.\nLine 42: The organization of sentences here could be improved for logical flow, as these cited studies still focus on community status (e.g., biomass and cover), not temporal stability of productivity.\nLine 166: This statement may overstate the study’s findings. As the study does not directly address the stability of forest composition and biomass but rather examines the invariability of annual biomass growth, it would be best to avoid suggesting that the study challenges classical succession theory.\nLine 192: The introduction emphasizes (Lines 61, 90) that acquisitive species are replaced by conservative species over succession, aligning with common forest succession theory. However, the results indicate a shift from conservative to acquisitive species with succession, which seems inconsistent with this expectation. This apparent contradiction should be clearly explained in the revised manuscript to clarify how communities could shift toward more acquisitive strategies over succession.", "reviewer_2": "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.", "reviewer_4": "Dear Authors,\nThis is my third time reviewing your manuscript, and I appreciate your detailed and respectful responses to my earlier comments. My main concern in the last review was your use of the terms alpha, beta, and gamma diversity, an issue also raised by Reviewer #3. While you explained the reasoning behind your multi-scale approach to stability, you didn’t explain why you chose to keep these specific terms. This concern remains unresolved.\nIn my earlier reviews, I focused only on major issues that could prevent publication, leaving out smaller comments. However, I feel strongly about the misuse of these diversity terms. These concepts are well-defined and widely taught in ecology. Introducing confusion, especially in a journal like Nature Communications, could mislead other researchers and cause ongoing misuse of terms that are otherwise clear.\nTo support my point, I carried out a simple check using your FigShare data and the FIA dataset’s sampling design. I randomly picked five plots and checked their locations. Even though the coordinates are shifted for privacy, all five fell within the same forest community. This makes sense given the FIA’s design, which focuses on single forest patches, not landscapes. This further supports my concern that the terms beta and gamma diversity are being used incorrectly. I’ve attached a PowerPoint summary of this check for your review.\nThat said, I want to emphasize that I still value your work. The idea of multiscale stability is strong, and your finding that productivity becomes less stable as forests age is important. It’s reassuring to see that this pattern holds true even when sampling larger areas of the same forest patch. This finding is useful for those of us working with small plots, as it shows that patterns of instability are not just a result of scale but reflect broader trends. Your study also highlights the role of size inequality (e.g., DBH, height) in driving instability. As older forests tend to have more size inequality, it’s not surprising that they are less stable, but it’s still important to document this clearly.\nIn summary, I think your analysis is solid, and your results are valuable. However, the misuse of the diversity scale terms is a significant problem. Please rename them to better match your sampling design, which focuses on single forest patches, not larger landscapes. This issue has been raised by all four reviewers, and addressing it would make your paper suitable for publication (at least in my perspective).\nLastly, I agree with Reviewer #1’s earlier point about clearly defining stability. As Grimm and Wissel (1997) pointed out, stability is a term so ambiguous that it’s useless. While I don’t think the term is useless, it’s important to define it carefully so readers understand exactly what you are studying. I suggest also looking at Grimm et al. (1992) and Hansson and Helgesson (2003) for helpful discussions on this topic."}, "author_response": "RESPONSE: Thank you for your positive feedback. We have carefully addressed all your additional comments to further enhance the clarity and precision of the manuscript. All changes have been marked in red for your convenience. Please find our detailed responses to each of your comments below.\nLine 18: Specify that the study focuses on “temporal stability of forest productivity” to avoid ambiguity.\nRESPONSE: Thanks, and we have changed it to “temporal stability of forest productivity” (line 18).\nLine 19: Remove “a relatively late successional stage” for brevity.\nRESPONSE: Thanks, and we have removed it from the abstract (line 19).\nLine 20: Currently, this sentence discusses how different stability components change with functional α diversity, which may mislead readers who expect this section to address changes with successional stages\nRESPONSE: Thanks, and we have reworded the sentence for clarity (lines 20-22).\nLine 26: The phrase regarding the stabilizing effects of α stability could be confusing, as stronger α stability would conventionally lead to increased rather than decreased γ stability.\nRESPONSE: Thanks, and we have reworded the sentence for clarity (lines 25-27).\nLine 27: Avoid overstatement; the study does not directly challenge classical succession theory, as it does not directly assess whether community dynamics shift toward an equilibrium state. It would be more accurate to avoid making broad claims about challenging traditional successional perspectives.\nRESPONSE: As suggested, and we have deleted “Unlike the classical succession theory assuming the equilibrium state in the late-successional forests” to avoid overstatement (line 27).\nLine 35: Remove the opening sentence about climate change, as it is not directly relevant to the study.\nRESPONSE: We have removed the opening sentence about climate change as suggested (line 34).\nLine 42: The organization of sentences here could be improved for logical flow, as these cited studies still focus on community status (e.g., biomass and cover), not temporal stability of productivity.\nRESPONSE: Thanks, we have reorganized those sentences to improve the logical flow (lines 37-41).\nLine 166: This statement may overstate the study’s findings. As the study does not directly address the stability of forest composition and biomass but rather examines the invariability of annual biomass growth, it would be best to avoid suggesting that the study challenges classical succession theory.\nRESPONSE: As suggested, we have deleted the corresponding sentence to avoid overstatement of our conclusion (lines 166).\nLine 192: The introduction emphasizes (Lines 61, 90) that acquisitive species are replaced by conservative species over succession, aligning with common forest succession theory. However, the results indicate a shift from conservative to acquisitive species with succession, which seems inconsistent with this expectation. This apparent contradiction should be clearly explained in the revised manuscript to clarify how communities could shift toward more acquisitive strategies over succession.\nRESPONSE: Thanks. The transition from conservative to acquisitive species mainly result from the substitution of coniferous species (e.g., Pinus) by broad-leaved species (e.g., Quercus and Acer) in the forest succession sequence in the United States (Fig. S20), and broad-leaved species typically exhibit higher specific leaf area and leaf nitrogen content than coniferous species. We have added discussion about this transition to clarify how communities could shift toward more acquisitive strategies over succession (lines 190-195).\nRESPONSE: Thank you very much for your positive evaluation and additional comments regarding the use of diversity terms. We fully agree with the concerns (as also indicated by your PowerPoint) that the use of terms such as beta and gamma diversity may cause potential misleading. To address this, we have revised the terminology throughout the manuscript (all figures, legend, and main text; e.g., lines 20-27, 45-47, 59, 63 and so on), replacing α with \"local community,\" γ with \"metacommunity,\" and β with \"dissimilarity among local communities.\" This adjustment was made for the following reasons:\n“Metacommunity” is defined as “a set of local communities that are linked by dispersed of multiple potentially interacting species” (Leibold et al., 2004). The core of this definition is that local communities can be connected through migration. Given the relatively limited dispersal ability of plants, we treated each subplot as a local community and each plot (containing four subplots with a distance of 36.6 meters between them) as a metacommunity (please see lines 301–303 and Fig. S2).\nThis approach preserves the hierarchical structure of the sampling design and the rationale behind our multi-scale approach to stability, while also preventing any potential misleading arising from the use of diversity scale terms."}, {"round": 4, "reviewer_comments": {"reviewer_4": "Dear Authors,\nThis is my fourth time reviewing your manuscript. As always, I appreciate your detailed and respectful responses. I also appreciate your willingness to rename the alpha-, beta- and gamma-stability terms given the concerns I raised in my previous review.\nI would like clear with this response. I believe that each FIA sampling unit represents a community, and not a set of communities (meta-community). I disagree that the dispersal capacity of trees is as limited as you indicate in your response. For instance, a lot of pine and maple trees have wind-dispersed seeds, which can easily travel the ~80 m in diameter that each FIA unit has. The same goes for animal-dispersed tree species. For gravity-dispersed tree species, this would largely depend on the height of the tree and the topography of the terrain. However, if we think about pollination, which in many instances is wind-driven, then it’s very unlikely that subplots within a FIA unit are different communities.\nHowever, as learned in my Community Ecology lectures, setting the (spatial) boundaries of a community is a complicated business. Hence, there is no point in extending a lot this discussion, since we would never agree on where the boundaries of the communities of your study are. This point is well illustrated by Keddy and Laughlin in their book “A Framework for Community Ecology”.\nThinking in a holistic way about your paper and weighing the novel contribution it makes against the flaws in fundamental concepts that we’ve found throughout the review process, I still believe that it’s worth publication. However, I would like to invite you to reflect on the importance on understanding and having a lot of clarity about the fundamental concepts on top of which we build our research. Otherwise, we may run into the problem of “having a good concept, but trying to fit it in contexts where it can’t be appropriately implemented”; or as Reviewer 3 mentioned earlier, adopting a “pet concept”. At least, that’s the lesson I take away from the interesting review process of this manuscript.\nLastly, despite the critical viewpoint above, I want to commend you for the willingness you’ve shown to improve your study, which has resulted in a significant improvement of its scientific soundness."}, "author_response": "RESPONSE: Thank you once again for your thorough review and constructive feedback on our manuscript. We deeply appreciate the time and effort you have dedicated to improving our work, as well as your thoughtful and respectful engagement throughout the review process.\nWe fully agree with your observation that defining the spatial boundaries of a community is a complex and often subjective task, as highlighted by Keddy and Laughlin in A Framework for Community Ecology. Given this inherent complexity, we respect your viewpoint and agree that further debate on this matter may not be productive, as it is unlikely to yield a definitive resolution.\nYour emphasis on the importance of clarity and understanding of fundamental concepts in research is well-taken. We appreciate your caution against applying concepts in contexts where they may not be fully appropriate, as well as your reference to Reviewer 3’s earlier comment about avoiding “pet concepts.” This is a valuable lesson for us, and we will strive to ensure that our future work is built on a solid and well-justified conceptual foundation.\nFinally, we are grateful for your recognition of the improvements made to the manuscript and your overall support for its publication. Your feedback has significantly enhanced the scientific rigor and clarity of our study. Thank you again for your insightful comments and for the constructive dialogue throughout this review process."}]

Decision letter