Acquisitive plants exhibit stronger phenological shifts in response to warming: insights from meta-analysis and long-term monitoring

Domain: biology
Published: 2026-03-26
Editorial outcome: major_revision

Abstract
As climate warming accelerates, shifts in plant phenology are reshaping the functioning and stability of terrestrial ecosystems. While the roles of climatic drivers in shaping phenological responses to warming are well established, the influence of intrinsic plant functional traits remains poorly understood. Here, we combine two complementary approaches through a meta-analysis of 124 field warming experiments and an analysis of long-term phenological monitoring networks (CPON and USA-NPN) to evaluate phenological responses to warming across a spectrum of resource-use strategies in seasonally cold biomes. Our meta-analysis demonstrates that resource-acquisitive plants, characterized by higher nutrient concentrations and thinner leaves, show significantly stronger phenological responses to experimental warming. This pattern is observed consistently across both leaf-out in spring and senescence in autumn. These results from meta-analysis are further supported by two long-term observational datasets, which also show more pronounced phenological shifts in acquisitive species under long-term warming. Our findings present a trait-climate integration framework that extends beyond conventional environmental drivers, providing a mechanistic foundation to enhance the accuracy of forecasts for plant responses to climate change.

Introduction
Terrestrial ecosystems are rapidly changing in response to climatic warming1–3. Plant phenology is one of the most sensitive indicators of climatic warming4,5 and plays important roles in regulating ecosystem carbon, nutrient and water cycling6,7, biodiversity maintenance8,9 and ecological stability10,11. Extensive research has documented the environmental cues driving plant phenological shifts, including temperature, photoperiod, and precipitation12–15. Yet, phenological responses to climate warming, especially the frequently observed species-specific patterns, may arise from a complex interplay between these external drivers and internal plant traits16–19.

Plant functional traits reflect fundamental ecological strategies for resource acquisition and growth20. Reich et al.21 and Wright et al.22 proposed the concept of the “leaf economic spectrum”, which characterizes species along a continuum from acquisitive (e.g., high specific leaf area and photosynthetic rates, but short lifespan) to conservative leaf strategies (e.g., low specific leaf area and photosynthetic rates, but long lifespan). Trait-based approaches have significantly advanced understanding of plant responses to environmental change, by uncovering mechanisms behind carbon sequestration, ecosystem resistance, and temporal stability23–25. However, how plant functional traits mediate phenological responses to climate warming remains poorly understood.

Trait-based processes are likely to underpin plant phenological responses to climate warming16,26,27, with divergent possibilities underpinned by functional differences. In spring, warming often advances leaf-out by accelerating the fulfillment of thermal accumulation requirements4,28. Acquisitive species may show greater advancement, as their high foliar nitrogen concentrations support rapid macromolecule synthesis, including Rubisco, thereby enabling earlier initiation of leaf growth in response to warming29,30 (see Fig.1b: Hypothesis I). In contrast, conservative species, characterized by thicker leaves and low specific leaf area, may respond by advancing leaf-out even more markedly than acquisitive species due to their stronger resistance to early spring frost31. In autumn, warming typically delays leaf senescence by slowing chlorophyll degradation32. Acquisitive species may exhibit greater delays in autumnal phenology under warming, due to their higher photosynthetic rates and foliar nitrogen concentrations and sometimes younger leaves (for species with indeterminate growth), which sustain net carbon gain despite shortening photoperiods25. However, when warming induces water stress, the delay may become more pronounced in conservative species, which tend to have thicker leaves and lower water requirements, conferring greater drought tolerance24 (see Fig.1c: Hypothesis II). Elucidating whether and how plant resource acquisition strategies, as defined by the leaf economics spectrum, regulate phenological responses to warming is crucial for improving predictions of future phenological shifts.

We combine two complementary approaches through a meta-analysis of 124 global field warming experiments and an analysis with data from two long-term phenological monitoring networks, China Phenological Observation Network (CPON) and USA National Phenology Network (USA-NPN), to position species along a resource-use continuum from acquisitive to conservative. These datasets enable us to explicitly link plant functional traits to phenological responses to climate warming in seasonally cold ecosystems. We test two competing hypotheses; Hypothesis I is that acquisitive species respond more to warming (Fig.1b), while Hypothesis II is that conservative species respond more to warming (Fig.1c). Our findings, as described below, provide compelling evidence that acquisitive species show greater phenological responses to warming than conservative species, underscoring the critical role of the leaf economic spectrum in shaping phenological changes under future climate change.

Results
The links between leaf traits and phenological responses: meta-analysis
Our meta-analysis showed that warming-induced advances in spring phenology were modestly greater in species with higher foliar nitrogen and phosphorus concentrations, larger leaf area, and lower carbon-to-nitrogen ratios (Supplementary Fig. 1). Shifts in autumnal foliar coloring were also associated with multiple foliar traits. For instance, delays in autumnal foliar coloring were more significant in species with higher foliar nitrogen and phosphorus concentrations, larger leaf areas, lower carbon-to-nitrogen ratios, and shorter leaf lifespans (Supplementary Fig. 1). Most of the results remained robust regardless of whether we controlled for confounding climatic factors and experimental variables (see Methods) (Supplementary Fig. 2). These trends were consistent across subgroups based on mean annual temperature, mean annual precipitation, ecosystem type, warming method, warming magnitude, and experimental duration, although the response levels varied (Supplementary Figs. 3-8). Principal component analysis (PCA) reduced the dimensionality of five foliar functional traits. The first principal component (PC1), which explained 54.3% of the total variance, was positively correlated with foliar nitrogen concentrations and specific leaf area, and negatively correlated with foliar dry-matter concentrations and leaf thickness (Fig. 2a and Supplementary Table 1). Species with low PC1 scores (thicker leaves and lower foliar nitrogen concentrations) tended to be resource-conservative, and those with high PC1 scores (thinner leaves and higher foliar nitrogen concentrations) were resource-acquisitive. Warming-induced shifts in spring phenology were negatively correlated with PC1, and shifts in autumnal phenology were positively correlated with PC1 (Fig. 2b,c). The conclusion remained consistent when we used all ten foliar traits collected in our study (Supplementary Fig. 9). Hierarchical clustering also confirmed that spring and autumnal phenology changed more for acquisitive species but minimally for conservative species (Fig. 2d-f and Supplementary Table 2). The phylogenetic analysis confirmed these patterns, even after controlling for phylogeny (Supplementary Fig. 10 and Supplementary Tables 3,4).

When analyzing the data separately for woody and herbaceous plants, we found that although the impact of functional traits on phenological responses to warming was more pronounced and statistically significant in woody plants, herbaceous plants exhibited a similar directional trend. Specifically, for herbaceous plants, the warming-induced delays in autumn senescence were more evident in species with larger leaf areas (Supplementary Fig. 11n). This overall pattern, where acquisitive species (e.g., those with larger leaf areas) showed greater phenological responses, was further supported by PCA for herbaceous plants, even though the results did not reach statistical significance (Supplementary Fig. 11u-w).

The links between leaf traits and phenological responses: long-term observations
Analyses of long-term phenological data from CPON and USA-NPN indicated that plant traits were key to explain the phenological responses to warming. In the CPON dataset, the advancement of spring phenology was greater in species with thinner leaves and the delay of autumnal phenology was greater in species with larger leaf area (Supplementary Fig. 12). The PCA of the CPON dataset explained 46.4% of the variance, with positive loadings on specific leaf area and negative loadings on leaf thickness (Fig. 3a and Supplementary Table 1). Higher PC1 values indicated stronger acquisitive strategies, and phenological sensitivity in spring was negatively correlated with PC1, indicating that the acquisitive species in China had more pronounced advancement in spring phenology with long-term climate change (Fig. 3b). In the USA-NPN dataset, the advancement of spring phenology was greater in species with thinner leaves and the delay of autumnal phenology was greater in species with higher foliar phosphorus concentrations (Supplementary Fig. 12). The PCA of USA-NPN dataset indicated that PC1 explained 41.9% of the variance, with positive loadings on foliar nitrogen concentrations and specific leaf area and negative loadings on leaf thickness and foliar dry-matter concentrations (Fig. 3d and Supplementary Table 1). Higher PC1 values indicated acquisitive strategies, and lower values indicated conservative strategies. We found that phenological sensitivity in autumn was positively correlated with PC1, suggesting that acquisitive species also experienced greater delays in autumnal phenology under climate change (Fig. 3f). Plant functional traits generally played a key role in the phenological responses to warming, second only to environmental factors (Supplementary Fig. 13).

Trait-based forecasting of future plant phenological change
Instead of using a fixed temperature sensitivity obtained from the meta-analysis, we calculated the dynamic phenological sensitivity by parameterizing it with different foliar nitrogen concentrations (Supplementary Fig. 14). Compared to the climate-only projection, our trait-integrated projections forecast smaller phenological shifts in both spring and autumn. Across the two climate scenarios (SSP1-2.6 and SSP5-8.5), incorporating plant traits reduces the projected shifts by 4.00-7.90 days in spring and 1.73-3.45 days in autumn, suggesting systematic overestimation bias in traditional climatically driven projections (Fig. 4, Supplementary Fig. 15 and Supplementary Table 5).

Methods
Meta-analysis data compilation
We compiled a data set on the effects of experimental warming on spring (i.e. leaf-out) and autumnal (i.e. leaf coloring) phenology before January 2024. Specifically, relevant peer-reviewed literature was identified through systematic searches of Web of Science, Google Scholar, and the China National Knowledge Infrastructure, using the following key words: (climate change OR warming OR temperature rise OR elevated temperature OR increased temperature) AND (leaf out OR leaf unfold* OR leaf emergence OR bud burst OR burst break OR green-up OR leaf color OR leaf senescence) AND (experiment* OR treatment* OR control*).

We then included studies that met the following criteria: (i) warming experiments were conducted in terrestrial ecosystems, (ii) initial environmental conditions were comparable between control and warming plots, (iii) the method, duration, and magnitude of warming were clearly described, (iv) experimental species were known and indicated, and (v) the timing of phenological events (measured as day of year) under both warming and control treatments, or the phenological shifts induced by warming (in days), along with their sample sizes, were reported. Based on the included studies, phenological data were extracted either directly from tables and appendices or extracted from figures using WebPlotDigitizer version 4.7 (https://apps.automeris.io/wpd/). In total, our screening process (Supplementary Fig. 16) produced 3079 observations (including herbaceous and woody plants) from 124 peer-reviewed articles published, comprising 1941 observations for spring phenology and 1138 observations for autumnal phenology (Fig. 1a).

We gathered information on environmental and experimental conditions from each study, including latitude, longitude, ecosystem type, mean annual temperature (MAT), mean annual precipitation (MAP), warming method, experimental duration, and warming magnitude. In cases where MAT and MAP were not provided in the articles, we obtained them from the WorldClim database (https://www.worldclim.org/). We provide a comprehensive overview and descriptions of all predictor variables in Supplementary Table 6.

Long-term ground phenological observations
We constructed the database for the long-term ground monitoring of spring and autumnal phenology using data from the China Phenology Observation Network61 (CPON, 1982-2018) and the USA National Phenology Network62 (USA-NPN, 1949-2020). We also considered the dataset from the Pan European Phenological database (PEP725; www.pep725.eu) but did not conduct a detailed analysis due to the small number of species in this dataset, which prevented the formation of a standard economic spectrum. In CPON, we extracted the data (only for woody plants) of leaf-out and leaf senescence with records of no less than 10 years. In USA-NPN, we downloaded all “individual phenometrics” data (including herbaceous and woody plants) with at least 5 years of observations and defined the data as spring or autumnal phenology based on the description of phenophase (Supplementary Table 7). In both datasets, robust statistical methods were used for detecting outliers, with records more than 2.5 × the median absolute deviation excluded63. We elaborated on the processing flow of USA-NPN data in detail in Supplementary Fig. 17. The final database encompassed 395 observation sites (30-55°N) and 705 taxa of plants (spanning 114 families and 339 genera), comprising 40098 observations of foliar phenological sequences. Data for mean monthly temperature and mean monthly precipitation for each location were obtained from the CRU TS v.4.08 data set at a spatial resolution of 0.5° (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.08/).

Plant trait data collection
We quantified trait-mediated phenological responses to warming by systematically compiling a suite of 10 foliar functional traits from the TRY Plant Trait Database (version 5.0, www.try-db.org)64. The selected traits, critical for characterizing variation in the leaf economic spectrum were: foliar nitrogen concentration (FN, mg g⁻¹), foliar phosphorus concentration (FP, mg g⁻¹), foliar nitrogen/phosphorus ratio (FN:FP), leaf area (LA, cm²), specific leaf area (SLA, mm² mg⁻¹), foliar carbon concentration (FC, mg g⁻¹), foliar carbon/nitrogen ratio (FC:FN), foliar dry-matter concentration (FDMC, %), leaf thickness (LT, mm), and leaf lifespan (LLS, month). We calculated the mean for each species where the database contained multiple entries for these traits.

Statistical analysis
Meta-analysis
We quantified the effects of warming on spring and autumnal phenology for each observation using phenological sensitivity (d °C⁻¹)65,66, calculated as in equation (1):

Phenological sensitivity = (Xw - Xc) / ΔT (1)

where Xw and Xc are the phenological timings in the warming and control plots, respectively, and ΔT is the temperature difference induced by experimental warming.

Each observation was weighted by its reported sample sizes67 as follows (equation (2)):

Wr = (Nw × Nc) / (Nw + Nc) (2)

where Wr is the weight, and Nw and Nc are the sample sizes for the warming and control plots, respectively. Larger values of Wr, derived from larger sample sizes, typically provided more precise effect size estimates and were thus given greater weight in the analysis.

We conducted a hierarchical random-effects meta-analysis to estimate the overall effect sizes of warming on phenology and their 95% confidence intervals. The meta-analytical model was implemented using the ‘ram.mv’ function from the ‘metafor’ R package (version 4.4-0), with the variable ‘Article ID’ included as a random factor to account for potential non-independence among multiple observations reported within the same study68. We considered the effects of warming to be significant if the 95% confidence intervals did not overlap with 0. Egger’s regression and a fail-safe analysis were used to test the publication bias (Supplementary Table 8).

We also conducted a phylogenetic meta-analysis that incorporated evolutionary information among species to account for the potential influence of phylogenetic relatedness on phenological sensitivity to warming. To do so, we used the following steps. First, we constructed phylogenetic trees for species using the ‘V.phyloMaker’ R package69 (version 0.1.0). Second, the phylogenetic trees were transformed into ultrametric trees based on Grafen’s methods in the ‘ape’ R package70 (version 5.8-1). Third, we then transformed the ultrametric trees into variance-covariance matrices that represented the phylogenetic relatedness among plant species. Finally, we calculated the sensitivity of species to warming using the phylogenetic meta-analysis by including the corresponding matrix as an additional random factor. We also assessed whether the phenological sensitivity to warming was influenced by phylogenetic relatedness using the “phylosignal” function in the “picante” R package (version 1.8.2) to calculate Blomberg’s K metric71.

We classified species into the three functional types (acquisitive, intermediate, and conservative) based on 10 foliar traits mentioned above using hierarchical clustering in the ‘stats’ R package (version 4.3.2). Subsequently, we used the hierarchical random-effects meta-analysis and phylogenetic meta-analysis to calculate spring and autumnal phenological sensitivities to temperature for the three functional types species respectively. We also categorized plants into deciduous, evergreen, angiosperms, and gymnosperms and calculated their phenological sensitivities to temperature using the hierarchical random-effects meta-analysis (Supplementary Fig. 10 and Supplementary Table 2).

To analyze the regulatory role of plant functional traits in phenological sensitivity to warming, we first used the ‘rma.mv’ function to investigate the relationships between the foliar traits and the sensitivity of phenology to temperature, incorporating the 10 foliar traits as moderating variables. Then we performed a principal component analysis (PCA) on 5 foliar traits (FN, FP, SLA, LT and FDMC), standardized using Z-scores, using the ‘FactoMineR’ package (version 2.9)72 to capture the underlying plant economic spectrum. We used the ‘rcorr’ function from the ‘Hmisc’ package (version 5.2-2) to calculate the correlations between the first principal component from the PCA and 5 foliar traits. Consistent with the methodology applied in the first step, we investigated the relationships between the first principal component from the PCA (PC1) and the sensitivity of phenology to temperature. We also used ecosystem type, MAT, MAP, latitude, warming method, experimental duration, and magnitude of warming as random factors in the hierarchical model to test the robustness of the results. Besides, we categorized environmental and experimental manipulation factors into subsets to identify differences in the relationships between the plant functional traits and the sensitivity of phenology to temperature across various subsets, and we also examined the interaction effects of foliar functional traits and experimental and environmental factors on foliar phenology temperature sensitivity of warming (Supplementary Table 9).

Long-term ground phenological data analysis
To calculate the sensitivity of spring and autumnal phenology to long-term temperature change, we first used a partial correlation analysis to calculate the optimal preseason length for each species at each location. The optimal preseason length is the period (with 30-d steps) before the mean leaf-out or foliar coloring date for which the partial correlation coefficient between leaf-out or foliar coloring and air temperature was highest. We then calculated the phenological sensitivity to temperature as the slope of the least-squares regression between the date of leaf-out or foliar coloring and mean air temperature over the preseason for each species at each location. For the long-term data for ground phenological observations, we first used the ‘lme’ function in the ‘nlme’ package (version 3.1-163) to analyzed the relationship between the sensitivity of spring and autumnal phenology to temperature and 10 foliar traits, with ‘Site ID’ as a random factor. Then, we used a PCA to analyze the relationship between the 5 foliar traits (FN, FP, SLA, LT and FDMC) for all species and examine the relationship between the sensitivity of spring and autumnal phenology to temperature and PC1. We also calculated the correlations between PC1 and 5 foliar traits. Since USA-NPN is a citizen-science dataset, we screened and reanalyzed the data for at least three observed individuals of the same species in a single site to reduce potential bias in the results (Supplementary Fig. 17).

Importance of predictors influencing the sensitivity of phenology to temperature
The importance of factors affecting the sensitivity of foliar phenology to temperature was ranked using the ‘randomForest’ function from the ‘randomForest’ package (version 4.7-1.2). A regression model with 1000 decision trees was constructed based on the random-forest algorithm. The relative importance of environmental and experimental and environmental and experimental variables was quantified using two metrics: the permutation importance score and the Gini decrease index. We then used the ‘rcorr’ function from the ‘Hmisc’ package to calculate the correlations between the sensitivity of phenology to temperature and the experimental and environmental factors (Supplementary Table 10).

Projections of future phenological changes
To assess how plant traits would affect the prediction of future changes in foliar phenology, we calculated the shifts of foliar phenology with and without the consideration of plant traits under different climatic scenarios (Supplementary Fig. 14). Initially, when considering plant traits, we refined our model by incorporating foliar nitrogen concentrations. First, we sourced the global distribution of foliar nitrogen concentrations from publicly available data provided by Butler et al.73. With this data, we obtained plant phenological sensitivities to foliar nitrogen concentration from this study (Supplementary Fig. 2a,k), which allowed us to calculated how nitrogen levels could influence phenological responses. Next, we collected historical temperature data (1970-2000) and future projections (2081-2100) under two climate scenarios (SSP1-2.6, and SSP5-8.5) from the WorldClim database. Using these temperature data, we predicted future shifts in foliar phenology by integrating the projected temperature changes with both the sensitivity of phenology to temperature and the effects of foliar nitrogen concentrations. However, when plant traits were not considered, we estimated shifts in foliar phenology by multiplying temperature changes by a fixed sensitivity value derived from our meta-analysis. All statistical analyses were conducted in R 4.3.274,75.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall, this is a strong, well-written paper that brings novel insight into how plant traits play a substantial role in shaping temperature-driven shifts in plant phenology. These insights have strong implications for forecasting, especially in light of dynamic global vegetation models; the authors’ findings suggest that to-date, forecasted changes in phenology have been much greater for some plant groups than what may actually play out.\n\nThe authors take advantage of both published studies and in situ phenology observations from two continents to arrive at their conclusions. I appreciate this comprehensive and creative approach. My only hesitation is that it feels the authors have not provided enough detail regarding which USA-NPN data they downloaded and in which format, and whether they undertook any post-processing of these data before analyzing. I feel this may impact the reproducibility of the study.\n\nSpecific comments:\n- I wonder if you might consider a conceptual diagram to support the hypotheses presented in the 2nd paragraph of the Introduction. I found myself wanting to bring reference to Figure 1b,c up to this section of the paper, but realize it encapsulates more scenarios than are described in this part of the Introduction. Keeping track of all of the potential changes, and the underlying mechanisms for them, is a lot; a figure might help here. OR, perhaps you could move reference to Figure 1 up here (reordering panels b & to a & b so that panels are referenced in sequence). Figure 1 really is great - I just crave having it aligned with the mechanisms somehow.\n- L99: missing the word “will” before “advance”\n- L130: for this reference to PC1 – shouldn’t the correlations between PCs and traits be reported in a table? As well, it seems like you could reference Extended Data Fig 1a\n- L158: drop “s” from “Figs” here\n- L240: “study has” should be “studies have”; consider adding a comma after “respiration”\n- L247: add “a” before “study”; perhaps changed “shows” to “showed”\n- L249: I don’t think you need to say here (again) that conservative species are slower-growing, this has been established multiple times already in the paper\n- L257: a readability suggestion: consider changing “the aboveground ones” to “aboveground”\n- Please cite the USA-NPN dataset: https://www.usanpn.org/about/terms#DataUse, http://doi.org/10.5066/F78S4N1V\n- Looking at the manuscript text, code, data files on Figshare, and even the reference provided in the Methods for how the USA-NPN dataset was accessed and processed (Wang et al. 2025), I can’t tell whether the authors used “status and intensity” or “individual phenometrics” data. I also can’t tell if they filtered or processed the data in any other way other than to remove outliers. Given that these data are largely volunteer-contributed, there is the potential that some records could be incorrect, there may be reason to consider the data more carefully. If the authors did implement any sort of filters, it would be very helpful to know.\n- L607, Figure 1 legend: “hypothesis” should be “hypotheses”\n- I am intrigued by the difference in findings in autumn phenology between CPON and NPN and desired a bit of treatment in the Discussion. Do the authors think these differences may be the result of differences in the duration of the temporal record between the two datasets, in environmental conditions between the continents, in how the data were collected in the two networks, or any other reason?\n- Overall, the Introduction and Discussion are very clear and tell a compelling narrative\nSupp Info:\n- L133, Fig 11: which observational datasets (CPON or NPN) are included in panels c and d? Are these findings from both datsets combined?", "reviewer_2": "In this manuscript, the authors combine the results of a meta-analysis of warming experiments with long-term ground-based observations to investigate the influence of plant strategies along the acquisitive and conservative continuum on the phenological sensitivity of leaf unfolding and senescence. They found that species with thinner leaves and higher nitrogen concentrations (i.e. acquisitive species) respond stronger to warming including earlier leaf unfolding and later senescence. This has been demonstrated both in experiments and in long-term observations. The authors conclude, and also demonstrate with modelling, that species-specific phenological patterns influenced by traits should be considered in modelling approaches.\n\nMajor comment:\n1. In many parts in the manuscript, it remains unclear which life forms were considered (trees vs. herbaceous species) to perform the analyses. The phylogenetic analyses indicate that herbaceous species are included, but if I understand correctly, only trees were considered in the analyses with the data from CPON and USA-NPN. The introduction and discussion largely refer to literature on trees, and the pictograms of trees in some graphs also suggest that only trees were analysed in this study. If you want to consciously analyse both life forms, then in my opinion greater emphasis should be placed on highlighting the differences. For me, for example, environmental drivers do not necessarily have the same effect on the phenology of trees and herbaceous species (e.g. winter chilling to break bud dormancy). Furthermore, there is already literature that examines the relationship between phenological sensitivity and traits in herbaceous species (e.g. Bucher and Römermann, 2021; Rauschkolb et al. 2024). Herbaceous species from CPON and USA-NPN should also be considered, or it should be clearly stated that these species are missing from the analyses. In this context, I wonder whether the found patterns are strongly influenced by the fact that herbaceous species, which tend to be acquisitive, and trees, which tend to be conservative, were analysed together. This question arises for me in particular when analysing PC scores. This does not, of course, call the results into question, but I think it is very important that greater attention should be paid to different life forms.\nBucher and Römermann 2021: The timing of leaf senescence relates to flowering phenology and functional traits in 17 herbaceous species along elevational gradients\nRauschkolb et al. 2024: Spatial variability in herbaceous plant phenology is mostly explained by variability in temperature but also by photoperiod and functional traits\n2. Methodologically, it remains somewhat unclear to me which studies laid the foundations for your study. I think it would be immensely important to include a list of the original studies in the appendix so that readers can research individual aspects themselves. For example, I wonder how warming experiments with trees were carried out. Were they conducted on plots in forests or on pruned branches or young trees in climate chambers and greenhouses? If these were not field experiments, how does it make sense to take MAT and MAP into account? These questions about methods are certainly also influenced by the lack of clarity about life forms.\nMinor comments:\nL116: For me, the results of the individual traits and the analyses with the PC axes are somewhat duplicated. This is particularly relevant if one assumes that many of the traits are autocorrelated. You should consider whether you want to show only the analyses with the PC axes in the main document and move the other graphs to the appendix. This would also give you more space to include graphs from the appendix in the main text.\nL122: I would write that most of the results remained robust.\nL166/Figure 4: I really like the analyses. However, I would like to see the differences of modelling already in the graph. You could only consider two climatic projections but show the graphs with and without the inclusion of foliar nitrogen concentration.\nL259: It would certainly be worth mentioning here that you only considered the influence of temperature on phenology for the projections and no other environmental parameters.\nL422: Figs. 15 is missing.\nL500: I guess Fig. 12 is not the right figure here.\nFigure 1: The colours chosen are difficult for red-green colour-blind people like me to distinguish.", "reviewer_3": "This manuscript combined meta-analysis and two long-term monitoring datasets to show that plant phenological responses to warming depending upon plant functional traits. The results revealed that acquisitive species, with thinner leaves and higher nitrogen concentrations, showed stronger phenological responses to warming, including earlier leaf-out in spring and delayed senescence in autumn. The findings present a trait-climate integration framework that extends beyond conventional environmental drivers, providing a mechanistic foundation to enhance the accuracy of forecasts for plant responses to climate warming.\n\nIt is good to see that two long-term monitoring datasets were used to support their meta-analysis, which confirmed the findings in the study. However, the manuscript needs major revision on the hypotheses and discussion sections.\n\nThe major flaw of the study is that the expression of hypotheses are too complicated in the introduction and figure 1. I suggest the author to combine the hypotheses into one: For spring phenology: acquisitive species, characterized by high nitrogen concentrations, advance leaf-out more under warming. For autumnal phenology, acquisitive species show more pronounced advancement of senescence in response to warming, while conservative species exhibit opposite response due to thicker leaves and greater carbohydrate reserves. You do not need to break the hypotheses into hypothesis I and II, scenario A scenario B and so on. You can discuss why the hypothesis is testified or not in the discussion in details. In this case Figure 1 need to be changed, only one hypothesis should be shown. The legends of figure 1 needs to be adjusted accordingly. It is annoying to see too complicated hypotheses and confusing to discern whether figure 1 is the hypotheses or the results.\n\nThe discussion can be shorthand and more focused after the hypothesis is adjusted."}, "author_response": "We appreciate the three anonymous reviewers’ valuable comments and constructive suggestions on the previous version of the manuscript. We have thoroughly addressed all the suggestions made by the reviewers. Below, we list point by point response in the regular text next to the all comments/suggestions (in italics).\nReviewers’ comments to authors\n*The line number in this response letter refers to that in the clean version of the revised manuscript.\nReviewer #1 (Remarks to the Author):\n[R1_1] Overall, this is a strong, well-written paper that brings novel insight into how plant traits play a substantial role in shaping temperature-driven shifts in plant phenology. These insights have strong implications for forecasting, especially in light of dynamic global vegetation models; the authors’findings suggest that to-date, forecasted changes in phenology have been much greater for some plant groups than what may actually play out.\n[Response] We thank you very much for your overall positive evaluation and encouraging comments.\n[R1_2] The authors take advantage of both published studies and in situ phenology observations from two continents to arrive at their conclusions. I appreciate this comprehensive and creative approach. My only hesitation is that it feels the authors have not provided enough detail regarding which USA-NPN data they downloaded and in which format, and whether they undertook any post-processing of these data before analyzing. I feel this may impact the reproducibility of the study.\n[Response] We thank the reviewer for the positive assessment of our study and sincerely apologize for the insufficient details previously provided on the USA-NPN data processing. To address this crucial point regarding reproducibility and data robustness, we have made the following comprehensive changes:\n1) We have added a new Supplementary Figure 12 that visually outlines the complete USA-NPN data processing workflow, from initial download to outlier exclusion.\n2) We have revised the Methods section (Lines 463-470) to provide explicit details on which data types were used (‘individual phenometrics’), the criteria for inclusion (e.g., woody plants for CPON, both herbaceous and woody plants for USA-NPN with at least 5 years of observations), and the robust statistical methods employed for outlier detection (2.5 × median absolute deviation).\n3) Recognizing the volunteer-contributed nature of USA-NPN data, we have performed an additional sensitivity analysis to further ensure the reliability of our conclusions. This involved restricting the USA-NPN dataset to sites with at least three observed individuals per species (as depicted in Supplementary Fig. 12, Part II) to minimize potential observational variability and bias. Crucially, this sensitivity analysis reaffirmed our main findings: acquisitive species continued to show significantly greater advances in spring phenology (P < 0.001) and marginally greater delays in autumnal phenology (P = 0.05) even under these more stringent filtering criteria. These additions significantly enhance the transparency, reproducibility, and robustness of our findings derived from the USA-NPN dataset.\n[R1_3] I wonder if you might consider a conceptual diagram to support the hypotheses presented in the 2nd paragraph of the Introduction. I found myself wanting to bring reference to Figure 1b,c up to this section of the paper, but realize it encapsulates more scenarios than are described in this part of the Introduction. Keeping track of all of the potential changes, and the underlying mechanisms for them, is a lot; a figure might help here. OR, perhaps you could move reference to Figure 1 up here (reordering panels b & to a & b so that panels are referenced in sequence). Figure 1 really is great - I just crave having it aligned with the mechanisms somehow.\n[Response] We thank the reviewer for these suggestions. To enhance the alignment between the text and the figure, we have revised the second paragraph of the introduction and simplified Figure 1 to ensure they present a consistent set of scenarios. The corresponding changes are highlighted in Introduction (Lines 69-81): “In spring, warming often advances leaf-out by accelerating the fulfillment of thermal accumulation requirements4,28. Acquisitive species may show greater advancement, as their high foliar nitrogen concentrations support rapid macromolecule synthesis, including Rubisco, thereby enabling earlier initiation of leaf growth in response to warming29,30 (see Fig.1b: Hypothesis I). In contrast, conservative species, characterized by thicker leaves and low specific leaf area, may respond by advancing leaf-out even more markedly than acquisitive species due to their stronger resistance to early spring frost31. In autumn, warming typically delays leaf senescence by slowing chlorophyll degradation32. Acquisitive species may exhibit greater delays in autumnal phenology under warming, due to their higher photosynthetic rates and foliar nitrogen concentrations, which sustain net carbon gain despite shortening photoperiods25. However, when warming induces water stress, the delay may become more pronounced in conservative species, which tend to have thicker leaves and lower water requirements, conferring greater drought tolerance24 (see Fig.1c: Hypothesis II).”\n[R1_4] L99: missing the word “will” before “advance”\n[Response] We thank for your suggestion, and have revised it as suggested (Lines 91-93), which is now quoted as follows: “Hypothesis I predicts acquisitive species, characterized by high nitrogen concentrations, will advance leaf-out more under warming.”\n[R1_5] L130: for this reference to PC1 – shouldn’t the correlations between PCs and traits be reported in a table? As well, it seems like you could reference Extended Data Fig 1a\n[Response] Thanks for mentioning this issue. We supplemented an additional analysis and presented the results in a table (Supplementary Table 1) to provide the correlations between PCs and traits. We further added the description of this analysis in Methods as (Lines 530-532): “We used the ‘rcorr’ function from the ‘Hmisc’ package (version 5.2-2) to calculate the correlations between the first principal component from the PCA and 5 foliar traits.”\n[R1_6] L158: drop “s” from “Figs” here\n[Response] We have corrected it as suggested (Line 159).\n[R1_7] L240: “study has” should be “studies have”; consider adding a comma after “respiration”\n[Response] We have revised them as suggested (Lines 230-231).\n[R1_8] L247: add “a” before “study”; perhaps changed “shows” to “showed”\n[Response] Done as suggested (Lines 267-268): “a recent study has shown that under field conditions, acquisitive tree species generally grow more slowly24.”\n[R1_9] L249: I don’t think you need to say here (again) that conservative species are slower-growing, this has been established multiple times already in the paper\n[Response] Thanks for mentioning this issue and we revised this part as (Lines 268270): “In contrast, conservative tree species often exhibit superior growth performance due to their enhanced ability to tolerate adverse environmental conditions24.”\n[R1_10] L257: a readability suggestion: consider changing “the aboveground ones” to “aboveground”\n[Response] Thanks for this suggestion and we have replaced “the aboveground ones” with “aboveground” in the revised manuscript (Line 277).\n[R1_11] Please cite the USA-NPN dataset: https://www.usanpn.org/about/terms#DataUse, http://doi.org/10.5066/F78S4N1V\n[Response] Thank you for pointing this out. We have now properly cited the USA-NPN dataset as suggested, both in the main text (Line 460) and in the reference list. “We constructed the database for the long-term ground monitoring of spring and autumnal phenology using data from the China Phenology Observation Network (CPON, 1982-2018) and the USA National Phenology Network63 (USA-NPN, 19492020).”\n[R1_12] Looking at the manuscript text, code, data files on Figshare, and even the reference provided in the Methods for how the USA-NPN dataset was accessed and processed (Wang et al. 2025), I cant’ tell whether the authors used “status and intensity” or “individual phenometrics” data. I also can’t tell if they filtered or processed the data in any other way other than to remove outliers. Given that these data are largely volunteer-contributed, there is the potential that some records could be incorrect, there may be reason to consider the data more carefully. If the authors did implement any sort of filters, it would be very helpful to know.\n[Response] We sincerely apologize for the insufficient elaboration on the USA-NPN data processing workflow in our initial submission. To address this, we have now included a detailed schematic (Supplementary Fig. 12) outlining the complete data processing steps. Specifically, we used the “individual phenometrics” dataset and applied a robust outlier exclusion criterion (records exceeding 2.5 times the median absolute deviation). This robust outlier exclusion criterion was specifically chosen, following established methods (Wang et al., 2025 and Gu et al., 2022), to effectively mitigate the inherent noise and potential inaccuracies often associated with citizen-science datasets. We have uploaded our code for processing long-term ground phenological data (please refer to the Code Availability section).\nWe are also grateful to the reviewer for highlighting the potential data quality concerns inherent in citizen-science datasets. We fully acknowledge that volunteer-based observations may introduce biases, and we have therefore conducted an additional sensitivity analysis to enhance the reliability of our conclusions. This analysis restricted the USA-NPN data to sites with at least three observed individuals per species to reduce observational variability (Supplementary Fig. 12). The results reaffirm our main findings which is that acquisitive species show significantly greater advances in spring phenology (P < 0.001) and marginally greater delays in autumn phenology (P = 0.05). We further supplemented the description of this analysis in Methods as (Lines 557559): “Since USA-NPN is a citizen-science dataset, we screened and reanalyzed the data for at least three observed individuals of the same species in a single site to reduce potential bias in the results (Supplementary Fig. 12).”\n[R1_13] L607, Figure 1 legend: “hypothesis” should be “hypotheses”\n[Response] We have corrected it as suggested (Line 648).\n[R1_14] I am intrigued by the difference in findings in autumn phenology between CPON and NPN and desired a bit of treatment in the Discussion. Do the authors think these differences may be the result of differences in the duration of the temporal record between the two datasets, in environmental conditions between the continents, in how the data were collected in the two networks, or any other reason?\n[Response] We thank the reviewer for this insightful question regarding the differences in autumn phenology between the CPON and USA-NPN datasets. We agree that this discrepancy is an important aspect to explore. Our study found that functional traits showed no significant regulatory effect on autumnal phenology in the CPON dataset, whereas a more pronounced effect was observed in the USA-NPN dataset. This discrepancy likely stems from distinct phenological response strategies shaped by regional climatic backgrounds. In the East Asian monsoon region, synchronized water-heat conditions in spring drive plants to significantly advance spring phenology to maximize resource use, resulting in a strong spring response to warming. However, intrinsic constraints on growing season length may limit the plasticity of autumn phenological adjustments, thereby weakening the observable regulatory role of traits on autumn phenology in the CPON dataset. In contrast, under the continental climate of North America, the spring phenological response to warming is comparatively weaker, allowing plants greater flexibility in adjusting autumn phenology. This expanded window for autumn adjustment may lead to a more detectable influence of functional traits, as evidenced in the USA-NPN dataset.\nWe have accordingly added a detailed explanation in the Discussion section (Lines 252-264): “Beyond plant growth forms, our study reveals that climate background conditions also modulate how functional traits regulate phenological responses. For instance, functional traits showed minimal effect on autumnal phenology in CPON dataset but a stronger effect in the USA-NPN dataset. In East Asia’s monsoon region, synchronized water-heat conditions in spring drive plants to significantly advance spring phenology, possibly maximizing resource use and resulting in a strong spring response to warming55,56. However, intrinsic constraints on growing season length might limit autumnal phenological plasticity, potentially weakening the observable regulatory role of traits in the CPON dataset. In contrast, in North America’s continental climate, the spring phenological response to warming is comparatively weaker, which could allow plants greater flexibility in adjusting autumnal phenology, and might make trait regulation more detectable55. These findings underscore that investigating the regulatory role of traits on phenological responses requires integrating additional factors such as plant growth forms and climatic context.”\n[R1_15] Overall, the Introduction and Discussion are very clear and tell a compelling narrative\n[Response] We thank you very much for your overall positive evaluation and many constructive comments.\n[R1_16] L133, Fig 11: which observational datasets (CPON or NPN) are included in panels c and d? Are these findings from both datsets combined?\n[Response] Sorry for the confusion. These findings are the result of the combination of two datasets (CPON and NPN). We have revised the corresponding figure legend and added data labels to the figure (Supplementary Fig. 10).\nReviewer #2 (Remarks to the Author):\n[R2_1] In this manuscript, the authors combine the results of a meta-analysis of warming experiments with long-term ground-based observations to investigate the influence of plant strategies along the acquisitive and conservative continuum on the phenological sensitivity of leaf unfolding and senescence. They found that species with thinner leaves and higher nitrogen concentrations (i.e. acquisitive species) respond stronger to warming including earlier leaf unfolding and later senescence. This has been demonstrated both in experiments and in long-term observations. The authors conclude, and also demonstrate with modelling, that species-specific phenological patterns influenced by traits should be considered in modelling approaches.\n[Response] Thank you very much for this concise and accurate summary of our work, and for acknowledging the methods we used. We have carefully gone through your comments, and addressed each of them accordingly. For more details, please see our point-to-point response to your comments below.\nMajor comment:\n[R2_2] In many parts in the manuscript, it remains unclear which life forms were considered (trees vs. herbaceous species) to perform the analyses. The phylogenetic analyses indicate that herbaceous species are included, but if I understand correctly, only trees were considered in the analyses with the data from CPON and USA-NPN. The introduction and discussion largely refer to literature on trees, and the pictograms of trees in some graphs also suggest that only trees were analysed in this study. If you want to consciously analyse both life forms, then in my opinion greater emphasis should be placed on highlighting the differences. For me, for example, environmental drivers do not necessarily have the same effect on the phenology of trees and herbaceous species (e.g. winter chilling to break bud dormancy). Furthermore, there is already literature that examines the relationship between phenological sensitivity and traits in herbaceous species (e.g. Bucher and Römermann, 2021; Rauschkolb et al. 2024). Herbaceous species from CPON and USA-NPN should also be considered, or it should be clearly stated that these species are missing from the analyses. In this context, I wonder whether the found patterns are strongly influenced by the fact that herbaceous species, which tend to be acquisitive, and trees, which tend to be conservative, were analysed together. This question arises for me in particular when analysing PC scores. This does not, of course, call the results into question, but I think it is very important that greater attention should be paid to different life forms.\n[Response] We sincerely thank the reviewer for raising these important points regarding the clarification of plant life forms in our analyses. In the revised version, we have explicitly stated the life-form composition of each dataset in the Methods section (Line 448 and Lines 464-466): the meta-analysis included both herbaceous and woody plants, the CPON dataset contained only woody plants, and the USA-NPN dataset encompassed both herbaceous and woody species, with 295 herbaceous species and 371 woody species.\nIn addition, we acknowledge that herbaceous and woody plants may exhibit differential responses to environmental drivers. Accordingly, we conducted separate analyses for these two life forms (Supplementary Fig. 9). The results demonstrate that functional traits influence phenological responses in both herbaceous and woody plants, but the strength of this relationship differs markedly between them. For instance, although single-trait analysis indicated that herbaceous species with larger leaf areas showed more pronounced delays in autumn senescence under warming, the overall regulatory effect of traits on phenological responses to warming was stronger in woody plants. We propose that this divergence arises from fundamental physiological and ecological distinctions. Herbaceous plants, particularly perennials, often rely on belowground nutrient reserves, which may buffer their phenology from direct influences of aboveground traits and atmospheric conditions. In contrast, woody plants possess aerial buds that respond more directly to temperature changes. Moreover, woody species typically follow fixed annual growth cycles, whereas herbaceous plants, especially annuals, may display more flexible, multi-cyclical growth patterns. These characteristics complicate trait-based predictions of phenological changes in herbaceous species.\nWe have added a description of the analysis results in the Results section as (Lines 131-138): “When analyzing the data separately for woody and herbaceous plants, we found that although the impact of functional traits on phenological responses to warming was more pronounced and statistically significant in woody plants, herbaceous plants exhibited a similar directional trend. Specifically, for herbaceous plants, the warming-induced delay in autumn senescence was more evident in species with larger leaf areas (Supplementary Fig. 9n). This overall pattern, where acquisitive species (e.g., those with larger leaf areas) showed greater phenological responses, was further supported by PCA for herbaceous plants, even though the results did not reach statistical significance (Supplementary Fig. 9u-w).”\nCorrespondingly, in the Discussion section, we have added the possible reasons for the differences between the phenological responses of woody and herbaceous plants as (Lines 236-250): “Notably, we found that acquisitive plant species exhibited a stronger phenological response to climate warming, a pattern that was more pronounced in woody plants than in herbaceous plants. Herbaceous plants, particularly perennial grass, tend to store nutrients in underground organs during unfavorable seasons52. Consequently, their phenology may be more dependent on the state of these underground storage organs, with weaker relationships to the traits of the above-ground leaves52. In contrast, the buds of woody plants are usually exposed to the air and can directly sense temperature changes, whereas herbaceous plant buds are often close to the ground or buried in the soil, where they are buffered by the soil environment. As a result, their responses may be more delayed, and the regulatory effect of traits may be slower. Finally, woody plants generally exhibit stable resource allocation patterns and fixed reproductive cycles (e.g., flowering once a year)53, while herbaceous plants, especially annual species, have more flexible growth strategies, quickly shifting from vegetative to reproductive growth under favorable conditions, sometimes completing multiple reproductive cycles within a year54. This high variability might make it more challenging to capture the role of traits in regulating phenological responses.”\nAdditionally, we found the literature you recommended to be highly insightful for understanding how functional traits shape the phenological responses of herbaceous plants to climate change. Accordingly, we have incorporated these references into the Introduction section (Line 58 and Line 69).\n[R2_3] Methodologically, it remains somewhat unclear to me which studies laid the foundations for your study. I think it would be immensely important to include a list of the original studies in the appendix so that readers can research individual aspects themselves. For example, I wonder how warming experiments with trees were carried out. Were they conducted on plots in forests or on pruned branches or young trees in climate chambers and greenhouses? If these were not field experiments, how does it make sense to take MAT and MAP into account? These questions about methods are certainly also influenced by the lack of clarity about life forms.\n[Response] We thank the reviewer for these suggestions. Based on your suggestions, we have supplemented a list (please see Supplementary Table 6) of all the original studies included in the meta-analysis in the Supplementary file. Meanwhile, we have also supplemented the specific research subjects and warming methods adopted in each study (please refer to the Data availability section). We agree with the reviewer’s point that if it is not a field experiment, taking MAT and MAP into account may not make sense. Therefore, we classified the experimental types (field/controlled) for all studies based on the experimental methods. Correspondingly, we modified all analyses considering MAT, MAP and ecosystem types (Supplementary Tables 10 and 11, Supplementary Figs.1, 5, 6, 7 and 10). In these analyses, only field experiments data is included and the overall results remained the same.\nMinor comments:\n[R2_4] L116: For me, the results of the individual traits and the analyses with the PC axes are somewhat duplicated. This is particularly relevant if one assumes that many of the traits are autocorrelated. You should consider whether you want to show only the analyses with the PC axes in the main document and move the other graphs to the appendix. This would also give you more space to include graphs from the appendix in the main text.\n[Response] We thank the reviewer for these constructive suggestions. We have moved the results of the individual traits in the meta-analysis and the long-term ground phenological data analysis to the Extended Data Fig section, and rearranged Figure 2 and Figure 3.\n[R2_5] L122: I would write that most of the results remained robust.\n[Response] We thank for your suggestion, and have revised it as suggested (Lines 111-113), which is now quoted as follows: “Most of the results remained robust regardless of whether we controlled for confounding climatic factors and experimental variables (see Methods) (Supplementary Fig. 1).”\n[R2_6] L166/Figure 4: I really like the analyses. However, I would like to see the differences of modelling already in the graph. You could only consider two climatic projections but show the graphs with and without the inclusion of foliar nitrogen concentration.\n[Response] Again, thank for your great suggestions. We have revised Figure 4 as you suggested. We simultaneously presented the results of projections of future foliar phenological changes in terrestrial ecosystems based on climate-only and traits integration projections under SSP1-2.6 and SSP5-8.5 (Fig.4). We also revised the result description of Figure 4 in the Results section as (Lines 161-168): “Instead of using a fixed temperature sensitivity obtained from the meta-analysis, we dynamically calculated the phenological sensitivity by parameterizing it with different foliar nitrogen concentrations (Extended Data Fig. 4). Compared to the climate-only projection, our trait-integrated projections significantly attenuated forecasted shifts in both spring and autumn. In two different climate change scenarios (SSP1-2.6 and SSP5-8.5), trait integration attenuated forecasted shifts by 4.00-7.90 d (spring) and 1.73-3.45 d (autumn), identifying systematic overestimation bias in traditional climatically driven projections (Fig. 4, Extended Data Fig. 5 and Supplementary Table 5).”\n[R2_7] L259: It would certainly be worth mentioning here that you only considered the influence of temperature on phenology for the projections and no other environmental parameters.\n[Response] Thanks for mentioning this issue, and have revised it as suggested (Lines 285-287), which is now quoted as follows: “It is worth emphasizing that our current predictions, which are based solely on temperature effects on phenology, may oversimplify reality and therefore urgently require validation using process-based models.”\n[R2_8] L422: Figs. 15 is missing.\n[Response] Sorry for the mistake. We have added the correct diagram here (Line 447), and we have now thoroughly revised the original manuscript to ensure that the manuscript matches all the figures.\n[R2_9] L500: I guess Fig. 12 is not the right figure here.\n[Response] We apologize again for this mistake. We have corrected the corresponding Figure (Line 524).\n[R2_10] Figure 1: The colours chosen are difficult for red-green colour-blind people like me to distinguish.\n[Response] We thank the reviewer for mentioning this issue, and we have revised the color in Figure 1 and used different symbols to distinguish data from different sources.\nReviewer #3 (Remarks to the Author):\n[R3_1] This manuscript combined meta-analysis and two long-term monitoring datasets to show that plant phenological responses to warming depending upon plant functional traits. The results revealed that acquisitive species, with thinner leaves and higher nitrogen concentrations, showed stronger phenological responses to warming, including earlier leaf-out in spring and delayed senescence in autumn. The findings present a trait-climate integration framework that extends beyond conventional environmental drivers, providing a mechanistic foundation to enhance the accuracy of forecasts for plant responses to climate warming.\n[Response] Thank you very much for this encouraging summary of our work.\n[R3_2] It is good to see that two long-term monitoring datasets were used to support their meta-analysis, which confirmed the findings in the study. However, the manuscript needs major revision on the hypotheses and discussion sections.\n[Response] Thank you again for your constructive feedback. We have carefully gone through your comments, and addressed each of them accordingly. For more details, please see our point-to-point response to your comments below.\n[R3_3] The major flaw of the study is that the expression of hypotheses are too complicated in the introduction and figure 1. I suggest the author to combine the hypotheses into one: For spring phenology: acquisitive species, characterized by high nitrogen concentrations, advance leaf-out more under warming. For autumnal phenology, acquisitive species show more pronounced advancement of senescence in response to warming, while conservative species exhibit opposite response due to thicker leaves and greater carbohydrate reserves. You do not need to break the hypotheses into hypothesis I and II, scenario A scenario B and so on. You can discuss why the hypothesis is testified or not in the discussion in details. In this case Figure 1 need to be changed, only one hypothesis should be shown. The legends of figure 1 needs to be adjusted accordingly. It is annoying to see too complicated hypotheses and confusing to discern whether figure 1 is the hypotheses or the results.\n[Response] Thank you very much for pinpointing the core issue of over-complex hypotheses and the confusing structure of Figure 1. We fully agree that our previous hypotheses may be too complicated.\nFollowing your suggestion, we have: 1) Simplified the hypotheses. We removed Scenario A and Scenario B of the autumnal phenology. The revised hypotheses in the Introduction section are as follows (Lines 91-99): “We test two competing hypotheses (Fig. 1): Hypothesis I predicts that acquisitive species, characterized by high nitrogen concentrations, will advance leaf-out more rapidly under warming. These species are also predicted to delay more their autumnal phenology more, likely benefiting from a warming-induced extension of the photosynthetic window that allows nitrogen-rich plants to fix carbon more efficiently. In contrast, Hypothesis II proposes that conservative species, with thicker leaves and greater carbohydrate reserves, will exhibit a stronger spring advancement. These species are expected to delay autumnal phenology more, likely due to their superior drought tolerance under warming-induced drought.” 2) Modified the Figure 1 and the corresponding legends. Based on the simplified hypotheses, we accordingly modified the Figure 1 and simplified the legend.\n[R3_4] The discussion can be shorthand and more focused after the hypothesis is adjusted.\n[Response] Thank you for your valuable feedback. Following the revision of our hypotheses, we have streamlined the Discussion section to improve clarity and focus. The updated discussion now more explicitly addresses how the findings support the specific hypotheses, with a concise explanation of the underlying mechanisms. Additionally, we have minimized repetition of established concepts, such as the widely recognized effect of climate warming on advancing spring phenology, to keep the emphasis on the core conclusions of our study."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I feel the authors have sufficiently addressed the concerns I raised in my previous review. I appreciate the pains they took to revise text and figures based on my feedback. I have no further concerns; I feel this paper merits publication.", "reviewer_2": "Thank you for the very comprehensive and successful revision of the paper. My comments have been corrected to my satisfaction. Below are just a few minor suggestions for improvement.\nL140: I would delete “also” here.\nL197-199: Is this true for all plants or only for trees? Please be more precise here.\nFigure 1: I would not change the order of “Acquisitive species” and “Conservative species” in the panels b and c.\nFigure 4: Thanks for revising this figure. However, I still see potential for improvements. You are actually interested in the comparison of models with and without considering traits, right? If so, I would create a panel a (spring phenology) and b (autumnal phenology). Left colum of panels could be climate-only projections and the right the projections with the traits. Using this layout it will be easy to compare the two different projections.", "reviewer_3": "The authors have well responded to reviewers questions, I think the manuscript is ready for publication."}, "author_response": "Response letter\nWe appreciate the reviewers’ valuable comments and constructive suggestions on the previous version of the manuscript. We have thoroughly addressed all the suggestions made by the reviewers. Below, we list point by point response in the regular text next to the all comments/suggestions (in italics).\nReviewers’ comments to authors\n*The line number in this response letter refers to that in the clean version of the revised manuscript.\nReviewer #1 (Remarks to the Author):\n[R1_1] I feel the authors have sufficiently addressed the concerns I raised in my previous review. I appreciate the pains they took to revise text and figures based on my feedback. I have no further concerns; I feel this paper merits publication.\n[Response] We thank you very much for your overall positive evaluation and encouraging comments.\nReviewer #2 (Remarks to the Author):\n[R2_1] Thank you for the very comprehensive and successful revision of the paper. My comments have been corrected to my satisfaction. Below are just a few minor suggestions for improvement.\n[Response] We thank the reviewer for the positive assessment of our study. We have carefully gone through your suggestions, and addressed each of them accordingly. For more details, please see our point-to-point response to your comments below.\n[R2_2] L140: I would delete “also” here.\n[Response] We have revised them as suggested (Line 139).\n[R2_3] L197-199: Is this true for all plants or only for trees? Please be more precise here.\n[Response] Thanks for mentioning this issue and we revised this part as (Lines 198-200): “Third, plants like temperate deciduous woody species need to fulfill both chilling and heat requirements to break endodormancy and ecodormancy, thereby initiating spring leaf-out4,12.”\n[R2_4] Figure 1: I would not change the order of “Acquisitive species” and “Conservative species” in the panels b and c.\n[Response] Thanks for this suggestion and we have revised it as suggested.\n[R2_5] Figure 4: Thanks for revising this figure. However, I still see potential for improvements. You are actually interested in the comparison of models with and without considering traits, right? If so, I would create a panel a (spring phenology) and b (autumnal phenology). Left colum of panels could be climate-only projections and the right the projections with the traits. Using this layout it will be easy to compare the two different projections.\n[Response] We thank the reviewer for this constructive suggestion. We have revised Figure 4 as suggested.\nReviewer #3 (Remarks to the Author):\n[R3_1] The authors have well responded to reviewers questions, I think the manuscript is ready for publication.\n[Response] Thank you very much for this encouraging summary of our work."}]

Decision letter