A general framework for nitrogen deposition effects on soil respiration in global forests

Domain: earth_science
Published: 2025-12-16
Editorial outcome: major_revision

Abstract
Since the Industrial Revolution, human activities have altered atmospheric nitrogen (N) deposition to global forests, affecting carbon dioxide emissions from soils (soil respiration or SR) – one of the largest land-atmosphere carbon fluxes. However, experimental studies have demonstrated both positive and negative effects of N deposition on SR in global forests, leading to debates on how N deposition increases or decreases SR. We developed a framework for generalizing SR responses to N deposition using synthesized data from 168 N addition experiments worldwide and observed SR across the global natural N deposition gradient. The findings indicate that N deposition decreased SR in 2.9% of global forested areas, particularly in eastern China, western Europe, and the eastern USA. However, the net effect of N deposition increased the global forest SR by ~5% (1.7 ± 0.1 Pg C yr–1). If N pollution could be effectively controlled, global forest SR would decrease, potentially contributing to a reduction in the terrestrial carbon emissions.

Introduction
Globally, carbon dioxide (CO2) emission from soils (soil respiration or SR; Box 1) is one of the largest carbon fluxes between the land and atmosphere, estimated to be 7–8 times greater than current anthropogenic CO2 emissions1–3. Forests cover about one-third of the world’s land area, and their SR rates are higher than those of other ecosystems4, making forest soils an important component of the global carbon cycle. Due to the high carbon sequestration potential of forest ecosystems, afforestation, reforestation and sustainable forest management have become the preferred natural climate solutions in many countries aiming to achieve net zero emissions (or carbon neutrality) by the mid-21st century5–7. Accordingly, evaluating the effects of human-induced disturbances on forest SR is essential for assessing changes in the global carbon cycle and for optimizing climate change mitigation efforts.

Human activities, such as fertilizer use, fossil fuel combustion, vehicle emissions, etc., have released large amounts of reactive nitrogen (N) into the atmosphere. As a result, the annual flux of N deposited to terrestrial ecosystems has more than tripled globally compared to preindustrial levels8,9. Despite recent declines in N deposition in some regions9,10, high anthropogenic N deposition in past decades may have caused many forests to become N-saturated (i.e., N supply exceeds biotic N demand)11, particularly tropical and temperate forests close to human settlements12,13. Meanwhile, N limitation is widespread in forests far from human activities, in regenerating young forests, and in boreal forests14. In N-saturated versus N-limited forests, plant and microbial processes (such as net primary production and soil N mineralization) may respond differently to N deposition11,15, potentially leading to varying effects of N deposition on SR—the sum of plant root respiration and microbial respiration.

Over the past half century, N addition experiments conducted in forests worldwide have reported both positive and negative responses of SR to N inputs16–18. Meta-analyses of N addition experiments have demonstrated the important role of N input rate and forest background N status (Box 1) on SR responses19–22. However, the analyses reached divergent conclusions as to whether N deposition increased or decreased the gross SR budget in global forests19–22. Early meta-analysis using experimental N addition data revealed that N deposition reduced SR in global forests especially in N-rich forests19,23, whereas some recent meta-analyses using updated data sets have suggested that realistically low N deposition levels increased global forest SR20,21.

The N deposition-induced reduction of SR is consistent with the N saturation hypothesis11,24, which predicts the negative responses of SR when forests have high N availability and become N-saturated. On the other hand, there are observations that low N inputs increase SR even in some N-rich forests and in forests receiving long-term N additions16,25. Such discrepancy implies the need to reevaluate the N deposition-induced changes in forest SR, in order to provide a general explanation for the diverse effects of N deposition on SR in N-limited and N-saturated forests.

Previous research has shown that N inputs can both facilitate biomass growth and induce species loss26. N-facilitated, continuous biomass growth could increase SR. Conversely, decrease in SR may result from N-induced species loss, which often occurs abruptly. The different mechanisms that dominate SR changes (see Fig. 1) imply that increases and decreases in SR caused by N inputs must be analyzed separately. Based on biological and ecological theories, we first proposed a hypothesis that the increased SR by N inputs would follow a biphasic response pattern (Fig. 1)11,27–29. Short-term, low N inputs often promote plant and microbial growth30,31 and exoenzyme synthesis32,33. Given the power-law relationship between increased plant and microbial biomass and metabolic respiration27 and the positive relationship between exoenzyme concentration and soil organic matter decomposition rate29, there may be a positive dose-response relationship between low N inputs and increased SR (Supplementary Fig. S1). However, high exogenous N supply, such as from atmospheric deposition, can exceed the ability of plants and microbes to maintain cellular homeostasis and become toxic to organisms34,35. Soil acidification caused by excessive N can be detrimental to certain species. Depletion of labile organic carbon in soils may also limit the N-facilitated biomass growth and the positive response of SR. These factors may contribute to a biphasic dose-response relationship between N inputs and the increased SR (i.e., higher increment under low N, and lower increment under high N)36.

The N toxicity and low soil pH caused by high N inputs could reduce respiration at the organism or population level. But at the community level, biodiversity and functional compensation among species ensure community stability under changes in N availability within a certain range37. When high N availability exceeds a critical threshold, however, species adapted to N-poor environments cannot survive38,39, and community functions—including plant productivity and microbial decomposition—can be suddenly weakened28. Thus, soil respiration, which is controlled by plant and microbial activities, may abruptly decrease under excessively high N availability. Such phased transitions of microbial biomass and plant productivity were observed in an experimental forest receiving long-term N additions, where SR decreased significantly during the transition period40. Based on these theories and observations, we hypothesized that the decreased SR by N inputs would follow an abrupt transition pattern, as outlined in Fig. 1.

This study was designed to (1) reveal the forest SR increase and decrease patterns under changes in N deposition and experimental N addition, (2) provide a framework for generalizing the N deposition effects on SR in N-limited and N-saturated forests, and (3) assess the net effect of N deposition on the annual SR budget in global forests. In this study, we compiled data from 168 experimental N addition forests worldwide (CO2_exp dataset; Fig. 2). At each site, SR response factors (Box 1) were calculated for each N addition level and year, as metrics to assess the effects sizes of N inputs (see Supplementary Text S1 for the response factors, their theoretical basis and calculation). Quantitative models of the response factors were constructed to validate the hypothesized positive and negative response patterns of SR (Fig. 2), and also to estimate the altered SR by current N deposition levels. Meanwhile, we used SR observations in non-experimental forests from a published database (SRDB)41 to check the change in SR along the natural gradient of N deposition, and also to derive the annual SR budget in global forests.

Results
Increased and decreased soil respiration by N inputs
Due to the different dominant mechanisms underlying the positive and negative responses, which are likely dominated by continuous biomass change and abrupt species loss, respectively, different mathematical models were required to quantify the SR responses (see Figs. 1, 2e, f). Thus, we analyzed the increased and decreased SR by N inputs separately.

Using data from experimental forests where N addition increased SR, we found that across forest biomes and N status, the positive response factor (fpos) of SR was negatively related to low N inputs; fpos was barely related to high N inputs (see Supplementary Fig. S2 for regression models and change points; details of piecewise linear regression models were provided in Supplementary Table S1). The negative relationship between fpos and low N inputs could be described using linear regression models (Fig. 3; Supplementary Fig. S3). Such a negative relationship between low N inputs and fpos (which is like the sensitivity of increased SR to N inputs) suggests an inverted-U-shaped quadratic relationship between N inputs and increased SR, supporting the hypothesized biphasic response of increased SR to N inputs (Fig. 2e). In the Methods section, we provided an equational inference of how the relationship between N inputs and fpos translated into the quadratic relationship between N inputs and increased SR (see Eq. 3). Additionally, the quadratic relationship was validated at the site scale using data from four experimental sites with at least five levels of N inputs (Supplementary Fig. S4).

Random forest models built with the experimental dataset (CO2_exp) showed that N input rate was one of the most important factors in explaining the negative SR responses (as measured by the negative response factor fneg; see Supplementary Table S2 for model parameters). However, fneg did not change gradually with increasing N input as fpos did. Statistically, no reduction in SR was observed in N addition experiments when N input rates were not higher than 15 kg N ha–1 yr–1 (Supplementary Table S3). When N input rates were between 15–100 kg N ha–1 yr–1, mean fneg of experimental forests remained nearly constant, regardless of the forest biome or N status (Fig. 4; Supplementary Fig. S5). When the N inputs were further increased to between 100–200 kg N ha–1 yr–1, fneg values suddenly dropped, indicating a pronounced decrease in SR. Under N inputs of 200–400 kg N ha–1 yr–1, fneg stabilized again. The results supported our hypothesis that the decreased SR by N input followed an abrupt transition pattern.

Also, we analyzed the N effects on the two components of SR, root respiration and microbial respiration. The N-induced increases in root respiration and microbial respiration followed inverted-U-shaped patterns. The N input rate corresponding to the peak increase in microbial respiration was lower than that corresponding to the peak increase in root respiration (Fig. 3e, f). Abrupt transition ways were found in the decreased root respiration and microbial respiration by N input. The magnitude of the reduction in root respiration was larger than that in microbial respiration (as indicated by the fneg values). The abrupt transition in the response of microbial respiration occurred at a lower N input rate than the abrupt transition in the response of root respiration (Fig. 4d, e).

An additional analysis of the integrated response of SR to N inputs (i.e., without separating the positive and negative responses) provides qualitative support for the biphasic and abrupt transition patterns derived from our separate analyses of the positive and negative responses. These analyses revealed the same patterns: N inputs first increase, then decrease, and then increase SR in N-limited forests (see Supplementary Text S2 and Fig. S6 for details).

Effect of natural N deposition on soil respiration in forests
Using SR observations from non-experimental forests (CO2_obs), we built random forest models to quantify the partial dependence of SR on natural N deposition rates (see Supplementary Table S4 for the model parameters). This analysis statistically removed the influence of factors other than N deposition (e.g., climatic and edaphic factors). Data from N-limited and N-saturated forests were separated based on the global forest N status map13.

Across all forest biomes and N status types, inverted-U shaped response patterns were detected in the N deposition-induced SR changes (Fig. 5). This finding was consistent with the responses revealed by experimental data (Figs. 3, 4; Supplementary Fig. S4). Under high N deposition, SR decreased. The declining trends were more pronounced in tropical forests and N-saturated forests than in other forests, suggesting that N deposition is more likely to reduce SR where background N availability is high.

Probability of soil respiration being decreased by N input
In the compiled experimental dataset (CO2_exp), both increased and decreased SR were observed even at the same N input rate. Based on the proportions of experimental forests where SR was decreased, we estimated the probability of SR being decreased (Pdec) by N input in forests. Considering the potential influence of background N status and duration of N addition on the responses of SR, we separately analyzed experimental data from initially N-limited forests where N addition experiments were conducted for no more than 3 years (N-limited forests), data from initially N-saturated forests where N addition experiments were conducted for no more than 3 years (N-saturated forests), and data from forests where N addition experiments were conducted for more than 3 years (Long-term experimental forests). Data from long-term experimental forests were treated separately because the accumulated artificial N additions could completely override the background, natural N status of the forests.

Generally, Pdec increased with increasing N availability. For any given N input rate, Pdec was the lowest in N-limited forests, the second lowest in N-saturated forests, and the highest in long-term experimental forests (Fig. 6). Within a given forest type, Pdec was positively correlated with N input rate. Using nonlinear regression models, we estimated the Pdec of global forests using the current level of N deposition as N input data. The area-weighted global mean of Pdec was 2.9%, meaning that the current N deposition decreased SR in 2.9% of global forests (Supplementary Table S5). Pdec was estimated to be high in temperate forests of Eastern China, Western Europe, Eastern United States, and some tropical forests. In short-term experiments (≤3 years) with N addition rates no higher than 100 kg N ha–1 yr–1, sites observing decreased SR were mostly in Eastern China, Western Europe, and Eastern United States, consistent with the spatial pattern of the estimated Pdec (Fig. 6e).

Contribution of N deposition to global forest soil respiration
We built random forest regression models using SR observed under natural conditions (CO2_obs dataset; see Supplementary Table S4 for the model parameters) and estimated the SR in global forests at the grid scale (Fig. 7). The correlation coefficient between observed and predicted SR was 0.71 (p < 2e−16). We found that the annual SR of global forests was 34.4 Pg C yr–1. As expected, annual SR generally decreased from low to high latitude forests.

We modeled the response factors (fpos and fneg) corresponding to the current level of N deposition (Supplementary Table S5), and then calculated the change in SR due to N deposition in global forests. In each forest grid (0.5° × 0.5°, which contains many forests), we estimated the proportion of forests where SR was decreased by N deposition (equal to Pdec based on the Law of Large Numbers) and derived the grid-level net change in SR caused by the current level of N deposition. We found that hotspots of changed SR due to N deposition were mostly located in Eastern China, Western Europe, and the Eastern United States (Fig. 7).

Globally, we found that current levels of N deposition increased SR in most forests, by a total of 1.8 ± 0.1 Pg C yr–1. Meanwhile, N deposition decreased SR in other forests, with a total reduction of 0.1 ± 0.01 Pg C yr–1. The net effect was that N deposition increased annual SR by 4.9% (+1.7 Pg C yr–1; i.e., a net effect size of +84 kg C kg N–1) in global forests (Table 1; Supplementary Table S5).

Methods
Data sources
We systematically compiled annual soil respiration fluxes observed in forests worldwide where N addition experiments were conducted, by searching for relevant literature published before January 1, 2022 in the Web of Science Core Collection (www.webofscience.com) and the China National Knowledge Infrastructure Theses and Dissertations Database (https://oversea.cnki.net/kns?dbcode=CDMD). These databases are often used in global meta-analysis21,65,66. The search terms used were: forest AND (greenhouse gas OR CO2 OR carbon dioxide OR soil respiration) AND (nitrogen OR fertiliz*). Then, we manually refined the retrieved 9417 studies based on the following criteria: (i) N addition experiment was conducted in the forest ecosystem, with records of site location and N addition dose; (ii) soil CO2 flux was observed in the field and measured using gas chromatography, infrared gas analyzer, or alkali absorption techniques; (iii) where available, we also compiled data on root respiration and microbial respiration measured using the root trenching method. The compiled CO2_exp dataset contains 1112 observations from paired control and experimental plots at 168 sites (Fig. 2).

To estimate the global forest soil respiration budget, we used soil respiration data observed under natural conditions (without manipulative experiments) from the global Soil Respiration DataBase (SRDB)41. In line with the CO2_exp dataset, we only used data that met the following criteria: (i) soil CO2 fluxes were observed in forest ecosystems with records of site location; (ii) soil CO2 fluxes were observed in the field and measured using gas chromatography, infrared gas analyzer, or alkali absorption techniques. The derived dataset covers 966 sites worldwide (Fig. 2).

In addition, we collected auxiliary information on environmental factors (climate, N deposition, soil properties) from the same literature. For sites where not all the required information was available, we extracted data from spatial datasets based on site coordinates. Specifically, temperature and precipitation data were obtained from the Climatic Research Unit, University of East Anglia (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/). Soil texture data were from the Harmonized World Soil Database (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). Soil chemistry data were from a published dataset by Shangguan et al.67. Global N deposition data were from a published dataset by Ackerman et al.9. Forest cover data were from the GLASS-GLC project68. Forest biome information was extracted from a spatial dataset of the Global Forest Monitoring project69.

Response of increased soil respiration to N input
Due to the different mechanisms dominating the positive and negative responses of SR to N input, the increased and decreased SR by N input may follow the biphasic and abrupt transition response patterns, respectively (Fig. 1; Supplementary Fig. S1). Therefore, we separately analyzed observations where N input increased or decreased SR. In our analysis, to avoid over-representation of data from the same experiment, we summarized data from the same site-year with the same N addition level by taking their averages. Each unique site-year-N addition level combination was used as a data point when we calculated the response factors of SR to N addition. The potential non-independence of the experimental data points was addressed indirectly by using an alternative effect size metric (i.e., the integrated response factor) and by running the analysis on subgroups of the dataset (See Supplementary Text S3 for details).

For each observation of increased SR (i.e., higher SR in N addition plot than in the corresponding control plot), we calculated a positive response factor (fpos) to represent the effect size of the increase in SR due to N input (see Supplementary Text S1 for explanation and illustration of the response factors). The relationship between fpos and N input rate was analyzed for forests in each biome (i.e., tropical, temperate, and boreal) and in each N status (i.e., N-limited, N-saturated) or long-term experimental forest.

Throughout the analyses, the N-limited forest dataset (CO2_exp_NL) consisted of experimental data from initially N-limited forests where N addition experiments had been conducted for no more than 3 years; the N-saturated forest dataset (CO2_exp_NS) consisted of experimental data from initially N-saturated forests where N addition experiments had been conducted for no more than 3 years; the long-term experimental forest dataset (CO2_exp_LT) contained data from forests where N addition experiments had been conducted for more than 3 years, regardless of the initial N status. This is because long-term N addition could have resulted in accumulated and exceptionally high N availability, overriding the initial N status difference of the sites. Previous studies have also suggested different biotic responses in N addition experiments lasting for less than or more than 3 years70–72. The N limitation or saturation status of global forests was determined using the sensitivity of soil N2O emission to N deposition as an indicator13—N-saturated forests have higher sensitivity of soil N2O emission to N deposition than N-limited forests because of the relatively open N cycle.

Positive response factor (fpos). The positive response factor of soil respiration to N input (fpos; kg CO2 kg N–1) was designed to reveal the increased SR per unit of artificial N addition (Fig. 2e). The observed fpos was calculated using experimental data:

fpos,obs = ΔSRN-inc / Nadd = (SR+N - SRCL) / Nadd (1)

In the equation, SRCL (kg CO2 ha–1 yr–1) was the soil respiration rate in the control plot without artificial N addition, and SR+N (kg CO2 ha–1 yr–1) was the soil respiration rate observed at the same site under an artificial N addition rate of Nadd (kg N ha–1 yr–1). ΔSRN-inc was the N-induced increase in soil respiration.

Increased soil respiration by N input. It is difficult to directly quantify the biphasic (quadratic) relationship between the increased SR and N input, especially when the change point(s) in the relationship are uncertain. In addition, the differences between sites could further complicate the analysis (Supplementary Fig. S10). Therefore, we used a derivative method and analyzed the potentially linear relationship between the positive response factor (fpos; which is like the sensitivity of increased SR to N input) and N input rate. Specifically, we built linear models to predict fpos of forests (one for each biome and each N status):

fpos,pred = c1 × Ninput + c2 (2)

Here, c1 and c2 were characteristic parameters of the linear models estimated using the lm function in R73. Ninput represented any N input rate (unit: kg N ha–1 yr–1), which was the sum of atmospheric N deposition (Ndep) and artificial N addition (Nadd).

Because fpos was similar to the sensitivity of increased SR to N input, it was then inferred from Eq. 1 and Eq. 2 that the increased SR by N input could be quantified as

ΔSRN-inc = SR+N - SRCL = fpos,pred × Nadd = (c1 × Ninput + c2) × Nadd = c1 × Ninput × Nadd + c2 × Nadd = c1 × (Ndep + Nadd) × Nadd + c2 × Nadd = c1 × Ndep × Nadd + c1 × Nadd^2 + c2 × Nadd (3)

For each site, c1, c2, and Ndep were nearly constant, and Nadd was the variable in Eq. 3. Therefore, the linear relationship between fpos and N input rate could be translated to the quadratic (and inverted U-shaped when c1 was negative) relationship between the increased SR and N input rate. The inverted U-shaped curve is a typical response curve indicating a biphasic effect.

If there was no N deposition or artificial addition (i.e., N input rate was 0), fpos,pred = c2. It was inferred that the increased soil respiration by N deposition in a forest could be quantified as

ΔSRN-inc = SRCL - SR0 = fpos,pred × (0 - Ndep) = c2 × Ndep (4)

where SR0 (kg CO2 ha–1 yr–1) was the “background” soil respiration rate when there was no external N input (natural N deposition or artificial addition), SRCL was the soil respiration rate observed in the control plot receiving natural N deposition (kg CO2 ha–1 yr–1).

Response of decreased soil respiration to N input
Decreased soil respiration by N input may result from an abrupt transition in the community structure and function, the magnitude of which may be independent of the N input rate that triggers the transition. Therefore, we quantified a negative response factor (fneg; see Supplementary Text S1 for explanation and illustration of the response factors) to represent the magnitude of the reduction.

Negative response factor (fneg). The negative response factor of soil respiration to N input (fneg) was calculated for each N addition plot that observed a decreased SR as compared to the control plot. fneg represents the effect size of the decrease in SR due to N input. Observed fneg was calculated for forests in each biome and each N status using experimental data:

fneg,obs = ΔSRN-dec / SRCL = (SR+N - SRCL) / SRCL (5)

where SR+N was the soil respiration rate observed under each level of N addition (unit: kg CO2 ha–1 yr–1), SRCL was the soil respiration rate observed in the control plot (unit: kg CO2 ha–1 yr–1). ΔSRN-dec was the N-induced decrease in soil respiration (unit: kg CO2 ha–1 yr–1).

Decreased soil respiration by N input. If fneg does not change gradually with N input, but instead changes abruptly at a certain point, then it can be inferred that the decrease in soil respiration is not a dose-dependent, gradual process, but rather an abrupt transition triggered by a certain level of N input.

If fneg remains relatively constant within a certain level of low N input, the decreased soil respiration by N deposition in a forest could be quantified as

ΔSRN-dec = SR × fneg,obs (6)

where SR was the soil respiration rate under natural N deposition and climatic conditions (the estimation of the SR of global forests is detailed in a later section), fneg was the negative response factor corresponding to low N inputs (Eq. 5).

Integrated response of soil respiration to N input
To support our separate analysis on the increased and decreased SR by N inputs, we also did an integrated analysis without distinguishing the positive and negative responses of SR. The algorithm for calculating the integrated response factor (fint) has been used in our previous research15. This method, however, can only qualitatively reveal the trend of how SR changes in response to increasing N inputs.

Integrated response factor (fint). The integrated response factor (fint; kg CO2 kg N–1) was calculated to reveal the change in SR (positive or negative) within a given range of N input. fint was calculated as

fint = (SR2 - SR1) / (N2 - N1) (7)

where SR1 and SR2 (unit: kg CO2 ha–1 yr–1) were the soil respiration rates observed under two different N input rates of N1 and N2 (kg N ha–1 yr–1), respectively. To make the fint comparable across forest sites, fint was calculated within four defined N input ranges, ≤ 50 kg N ha–1 yr–1, 50–100 kg N ha–1 yr–1, 100–150 kg N ha–1 yr–1, and ≥ 150 kg N ha–1 yr–1.

We calculated the integrated response factors using SR observations from experimental N addition forests that were initially N-limited (CO2_exp_NL dataset in the main text). Initially N-saturated forests were not used in this analysis because only the N-limited forests would go through the entire process (including the community collapse and reconstruction phase) and show a bimodal response pattern under increasing N inputs. We then compared the integrated response patterns of SR to N inputs with the patterns revealed in our previous separate analysis of positive and negative responses (see Supplementary Text S2 for details).

Random forest regression model for estimating global forest soil respiration rate
Using soil respiration (SR) data observed under natural conditions (CO2_obs dataset), we predicted the SR of each forest-covered grid (0.5° × 0.5°) using the random forest regression method74. In practice, multiple SR observations from the same site-year were aggregated by taking the mean value. After excluding outliers that were at least three interquartile ranges away from the global median, we randomly sampled 20% of the data to form a test dataset. 75% of the remaining data (i.e., 60% of the total data) were randomly selected for training the random forest models, and the remaining data were used to allow the variation in the training dataset (to reduce the influence of random sampling on the derived models). Climate, N deposition, soil texture, soil organic carbon, soil nitrogen content, soil pH, and N status variables were used as predictors (Supplementary Table S4). These variables were selected based on data availability and mechanistic relevance50,75–79.

Because the constructed models can vary depending on the data used to train the models, the random sampling of the training data was repeated 1000 times, deriving 1000 models. When estimating SR at the grid level, each grid had 1000 predicted SR values from the 1000 models. The mean SR of the 1000 values was used as the estimated SR of the grid. Estimated SR for grids in the test dataset, which were never used in model construction, were then compared with observed values to measure the accuracy of prediction.

Meanwhile, we estimated the partial dependence of SR on N deposition using each of the 1000 random forest models. The derived partial dependence curves depict the change in SR across the natural gradient of N deposition, which could potentially validate the responses of SR to N input that we estimated using experimental data.

In the partial dependence analysis, N deposition-induced SR change (ΔSRN) was estimated as the difference in SR between forests with N deposition rates below 1 kg N ha–1 yr–1 and forests with N deposition rates above the level, thus removing the influence of basal SR differences across biomes and N status.

Similarly, we built random forest models for the negative response factor (fneg) using the experimental dataset (CO2_exp) with 1000 repetitions. This analysis was not for prediction, but to assess the relative importance of environmental factors on fneg, and to estimate the partial dependence of fneg on N input rate.

We used the randomForest package80 in R73 for the random forest regression analysis, the relative importance analysis, and the partial dependence analysis. Random forest model parameters were optimized using tuneRF function in the same package. The segmented package81 was used to detect the change points in the partial dependence curves.

Probability of soil respiration being decreased by N input (Pdec)
The proportions of forests where experimental N inputs decreased SR were used to model the probability of SR being decreased (Pdec) to quantify the proportion of forests where SR was decreased by natural N deposition.

The same N input level may decrease SR in one forest while increasing SR in another. Based on the CO2_exp_NL dataset, we summarized the frequencies of each N input rate decreasing SR in N-limited forests. A nonlinear regression model was then constructed to predict the probability of soil respiration being decreased by N inputs (Pdec) in N-limited forests. The same processes were carried out using the CO2_exp_NS and CO2_exp_LT datasets, deriving models for predicting Pdec in N-saturated and long-term experimental forests, respectively. On a regional scale, because there are plenty of forests, the proportion of the forests where a N input level decreases SR approximates Pdec (Law of Large Numbers).

The frequency of SR being decreased by a given N input rate in N addition experiments was the observed Pdec. That is,

Pdec,obs = n(ΔSRN < 0) / n(ΔSRN) = n(SR+N - SRCL < 0) / n(SR+N - SRCL) (8)

In the equation, n(ΔSRN < 0) was the number of observations that SR was decreased by a given level of N input. n(ΔSRN) was the total number of N-induced SR changes (ΔSRN) corresponding to the N input level. However, the ΔSRN values corresponding to a given N input rate may be few in number. To reduce the potential bias due to insufficient observations, we counted all ΔSRN values corresponding to N input rates that were no more than 10 kg N ha–1 yr–1 different from the analyzed N input level. For example, Pdec,obs corresponding to 20 kg N ha–1 yr–1 was calculated using ΔSRN corresponding to the N input rates ranging from 10 to 30 kg N ha–1 yr–1.

By fitting models to Pdec,obs, we could predict Pdec for any N input rates. Pdec as a probability should be between 0 and 1, so we only tried to fit regression models with lower and upper bounds. After comparing the performance of several different types of regression models (including log-logistic model, Weibull model, Michaelis–Menten model) based on Akaike information criterion (AIC; Supplementary Table S6), we used the Michaelis–Menten-type model, shown in Eq. 9, to predict Pdec.

Pdec = Ninput / (c3 + Ninput) (9)

where c3 was the characteristic parameter of this nonlinear regression model, estimated using the drc package in R82; Ninput represented any N input rate (unit: kg N ha–1 yr–1).

Quantifying the contribution of N deposition to global forest soil respiration
Using Eq. 4 and Eq. 6, we could estimate the increased or decreased SR by N deposition under any N deposition level. Using Eq. 9, we could estimate the proportion of forests where SR was decreased by a N deposition rate in a grid (0.5° × 0.5°, which contains many forests). On the basis, we quantified the effect of N deposition on forest SR at the grid level (Eqs. 10–12). Summarizing the grid-level data, we obtained the annual SR in global forests, the N deposition-induced change of SR, and the contribution of N deposition to the global forest SR budget (Eq. 13):

ΔSRN-inc,i = c2,i × Ndep,i × (1 - Pdec,i) = c2,i × Ndep,i × (1 - Ndep,i / (c3,i + Ndep,i)) (10)

ΔSRN-dec,i = SRi × fneg,i × Pdec,i = SRi × fneg,i × (Ndep,i / (c3,i + Ndep,i)) (11)

ΔSRN,i = ΔSRN-inc,i + ΔSRN-dec,i (12)

Contribution rate = (Σ (ΔSRN,i × Areai)) / (Σ (SRi × Areai)) × 100% (13)

where ΔSRN,i represented the N deposition-induced change in forest soil respiration in grid i (kg CO2 ha–1 yr–1); ΔSRN-inc,i represented the increased soil respiration by N deposition in grid i (kg CO2 ha–1 yr–1); ΔSRN-dec,i represented the decreased soil respiration by N deposition in grid i (kg CO2 ha–1 yr–1), SRi was the soil respiration rate in grid i (kg CO2 ha–1 yr–1), Areai was the forest area in grid i (ha).

Data analysis in this study was performed using R73. The level of significance was set at p < 0.05 unless otherwise noted. Maps were generated using ArcGIS software83.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study examines the effects of N deposition on forest soil respiration using both data from N addition experiments and observations under real atmospheric N deposition. The approach of integrating these two datasets to compare soil respiration responses is certainly a valuable idea. However, at this stage, I have several major and fundamental concerns regarding the hypotheses and statistical analyses that need to be addressed before I can fully assess the details of the manuscript or recommend it for publication.\n1. The unclear Conceptual Framework: The authors dedicate considerable discussion and three figures to presenting a hypothesized framework for forest soil respiration responses to N inputs. While this conceptual framework aimed at helping understanding the SR response patterns and mechanisms, I did not find sufficient theoretical support or empirical evidence in the introduction or relevant literature to substantiate this framework. For instance, in Lines 330–332, the authors mention a \"transition from an oligotroph-dominated to a copiotroph-dominated community,\" but this concept is not clearly introduced or explained in the introduction. Furthermore, the study’s results do not appear to provide strong support for this framework—most of the quadratic relationships shown in Figure 3 are not statistically significant, making it difficult to justify a biphasic response pattern. Overall, I find it difficult to be convinced by this new framework. Besides, even though the framework is illustrated in three figures, this level of redundancy does not compensate for the lack of mechanistic support. Additionally, Figure 1 does not effectively aid in understanding why a biphasic and abrupt response would be expected.\n2. It is unclear why the authors chose to analyze positive and negative SR responses separately. SR response to N deposition is a continuous variable that can range from positive to negative within the same experiment. While I agree that different pathways can drive either positive or negative responses, this does not necessarily mean that these responses should be analyzed in isolation. It is more likely that both positive and negative mechanisms operate simultaneously, with the net response depending on which mechanisms dominate under a given N deposition rate and background conditions. Without a clear theoretical justification for this separation, this analytical approach seems somewhat arbitrary.\n3. Lines 526–535:The distinction between “N-limited,” “N-saturated,” and “long-term” experimental forests is not clearly justified. In particular, the rationale for defining three years as the threshold for high N stress is unclear—why not two, five, or ten years? Moreover, N stress is not solely determined by duration; the N addition rate also plays a critical role. For example, a low N addition rate over a longer period (e.g., 10 kg ha⁻¹ yr⁻¹ for four years) may impose less stress than a high N addition rate over a shorter period (e.g., 100 kg ha⁻¹ yr⁻¹ for three years), given differences in total N input. This issue requires further clarification and justification.\n4. For the data derived from N addition experiments, it is essential to clarify how potential non-independence among data points within each original study was accounted for in the statistical analyses. Addressing this concern would help ensure the robustness of the findings.\n5. Use of Abbreviations: The manuscript introduces and relies on numerous abbreviations, many of which are not clearly defined, making the text difficult to follow. In several cases, I only fully understood their meaning after referring to the Methods section. I recommend using abbreviations only when necessary and ensuring that all abbreviations are explicitly defined in the introduction to improve readability.", "reviewer_2": "Cen et al. systematically compiled and analyzed a dataset of annual soil respiration fluxes observed in forests worldwide where nitrogen addition experiments were conducted. Meanwhile, they applied a natural gradient approach to analyze the SRDB database and the global nitrogen deposition dataset. Based on these analyses, they proposed a general framework for the effects of nitrogen deposition on soil respiration in global forests. Subsequently, they assessed the net effect of nitrogen deposition on the global forest soil respiration budget. The proposed general framework for the response of soil respiration to nitrogen inputs is innovative. In addition, they have also made several new estimations of large-scale indicators. I have a few suggestions for your consideration.\nFirst, it is important to note that N addition typically promotes soil respiration under conditions where nitrogen saturation has not yet been reached. If a forest is already in a nitrogen-saturated state, the more plausible outcome would be nitrogen addition primarily leading to inhibition of soil respiration. In most ecosystems, nitrogen addition initially stimulates soil respiration, followed by a suppression phase, and eventually leads to a collapse, resulting in a new equilibrium. Once this equilibrium is reached, the ecosystem becomes nitrogen-unsaturated, and this cycle may repeat. I recommend that the authors clarify this point in the context of nitrogen saturation and its role in the nitrogen addition experiments discussed in the manuscript.\nSecond, in multi-level N addition single-point experiments, if the data can be fitted to a unimodal curve, it would be helpful to assess whether the majority of experimental fits exhibit a unimodal pattern. Such evidence could help reduce the noise caused by heterogeneity between experiments, thereby strengthening the reliability of the conclusions drawn in the manuscript. Although the dataset is relatively small, I recommend that the authors attempt to provide more substantial evidence to support their findings, which would enhance the robustness of the study and its conclusions.\nFinally, this study involves numerous estimations and predictions, making the methods for model construction and the reliability of predictions crucial. I recommend that the authors provide more detailed and reliable explanations, as outlined in the Specific Comments section below.\nSpecific Comments:\nLine 53-55: \"High exogenous N inputs (deposition or fertilization) \"and\" long-term N additions\" are somewhat repetitive.\"\nYou should clarify what you want to express.\nLine 56-57: In my opinion, nitrogen availability refers only to the amount of nitrogen in an ecosystem that is available for biological use.\nLine 60-61: \"background N stress\" It should be the availability of nitrogen, not every forest is under N stress.\nLine 66: \"Soil respiration (SR)\" Rs is more commonly used as the abbreviation for soil respiration.\nLine 69: \"mixed effects\" diverse effects\nLine 112-114: The studies by Janssens, I. A. et al. and Zhou, L. Y. et al. are both based on meta-analysis results, revealing the overall effects of numerous studies, many of which show that nitrogen addition promotes soil respiration. However, the entry point you mentioned, that low nitrogen promotes soil respiration, seems inconsistent with their findings, and this point comes across as somewhat confusing.\nLine 134-136: The current phrasing may give the impression that the biphasic dose-response relationship is solely due to soil acidification. It is recommended to separate the explanation and the summary for better clarity.\nLine 187-189: Rs, Ra, and Rh should be the commonly used abbreviations for soil respiration and its components.\nIn addition, I have a question: The effect size of such an abrupt transition is often disproportionate to the N addition level triggering the change. How does the algorithm used in this paper differ from the response ratio commonly used in meta-analysis? This is just a curiosity and not a challenge to your calculation method.\nLine 240, 248: The change in soil respiration (∆SRN) should be explained in more detail, as I am unclear about how the change is calculated up to this point.\nLine 265-266: The classification of \"N-limited forests,\" \"N-saturated forests,\" and \"long-term experimental forests\" is confusing, as it is unclear why \"long-term experimental forests\" should be considered separately.\nLine 326-327: I have been unable to understand the meaning of Pinh in Fig. 7. According to this framework, the results from the N-limited and N-saturated forest datasets are the most important evidence and should be highlighted in the main text.\nLine 353: In N-limited forests, the old communities transition into new communities. Does this mean that the new communities represent N-saturated forests? Will their nitrogen demand increase?\nIn Appendix Text S2, with increasing N input, the local response factor (fb) of SR to N input shifted from positive to negative and then back to positive in N-limited forests across all biomes (Fig. S5).\nLine 390: The two explanations for nitrogen addition inhibiting soil respiration should not be seen as an incremental relationship like \"even\".\nLine 417-419: The theme of this paragraph is to discuss the similarities and differences across different ecosystems and nitrogen status categories. The mention of response range here seems somewhat out of place.\nLine 422: The explanation, i.e., from N-limited to N-saturated to long-term experimental forests, might be more helpful to the reader if mentioned earlier in the text.\nLine 451-455: This content should be placed at the end in the conclusion and significance section. It might serve as a transition to the next section, but it feels somewhat abrupt when read.\nLine 451-482: It is recommended to organize and categorize these insights and limitations. As it stands, the reading feels somewhat disorganized and chaotic.\nLines 499-505: Did you use only the SRDB data, or did you also include the CK data from your own CO₂_exp dataset?\nLine 608-609: What was the basis for selecting the predictor variables? Why were these specific variables ultimately chosen?\nLine 600: The selection and determination of random forest model parameters (e.g., Ntree, Mtry, and nodesize) have a significant impact on model performance. The authors should clarify how this issue was addressed in the Methods section.\nThe soil respiration data were likely collected over different time periods, leading to potential inconsistencies. How did the authors account for the influence of study duration in their analysis?\nThe study by Ni Huang, Spatial and temporal variations in global soil respiration and their relationships with climate and land cover, has also conducted global-scale soil respiration predictions, achieving an R² of 0.6–0.8. In contrast, the R² in this study is only 0.25. How reliable are the predictions? Have the authors considered alternative machine learning approaches, such as support vector machines (SVM), to improve model performance?\nLines 651-658: What was the basis for selecting this nonlinear regression model? Is it the optimal model? The authors should provide a more detailed explanation, as the current description remains unclear.\nUsing this empirical equation for prediction does not seem to be theoretically superior to machine learning. Why not use machine learning to predict Pdec for any N input rates?", "reviewer_3": "Overall, this is a timely manuscript in which the authors sought to determine the net effect of atmospheric nitrogen deposition on rates of soil respiration in forests around the globe. They conducted a meta-analysis with data compiled through published papers cited in Web of Science and from the China National Knowledge Infrastructure Theses and Dissertations Database.\nHere are my main constructive critiques of the paper:\nTheir dataset is biased toward Chinese collections given they looked at papers cited in Web of Science, but only dissertations from the “China National Knowledge Infrastructure Theses and Dissertations database”. It’s unclear why they used student papers from China and not elsewhere.\nThere are many places where the writing needs to be improved. For example, in the Highlights it says “Experimental and observational data agree on a new framework…” As written, it sounds like the data themselves are thinking and agreeing on something; the authors need to make it clearer that researchers can agree on a new framework, data cannot. This and other text throughout the paper should be revised in this way.\nIn general I suggest using fewer abbreviations. Many are not necessary. For example, why is “Pdec” used throughout and not just “N inputs”? If the authors want this paper to be read and understand by a broad audience, it would be much easier to read and follow if fewer abbreviations are used.\nThere are several places where the authors make a comparison, but don’t finish it. See below for details.\nThe following detailed comments are meant to improve the manuscript:\nHighlights\nLine 42: Change first bullet to be less anthropomorphic\nLine 44: Change to “N deposition reduces…” and finish the statement “more frequently in N-saturated than non-N saturated”? Or “more frequently than “N-limited forests”? Or something else?\nLine 46: “Responses of plant root and microbial respirations to N input differ in magnitude and pace”\n- I suggest replacing this with the actual differences.\nLine 47: “Current level of N deposition enhanced global forest soil respiration by ~5%” relative to what? Pre-industrial levels of atmospheric deposition? Non-forest ecosystems?\nAbstract\nLines 73-76: This text is unclear “Using a novel probabilistic approach, we estimated that N deposition decreased SR in 2.9% of global forests, mostly N-saturated forests in eastern China, western Europe, and eastern USA. But the net effect of N deposition increased the global forest SR budget by 5.1% (1.7 PgC yr–1).” The title of the paper includes these three regions only (eastern China, western China, and eastern USA); what regions are the authors referring to about the “net effect of N deposition”?\nLines 73-75: Also related to this text: “Using a novel probabilistic approach, we estimated that N deposition decreased SR in 2.9% of global forests, mostly N-saturated forests in eastern China, western Europe, and eastern USA.”\n-Are the authors saying that N deposition increased SR in the non N-saturated forests of eastern China, Western Europe, and eastern USA?\n-Or, are they saying that N deposition increased SR in other regions of the world?\nHere and elsewhere the authors often state the first half of a comparison, but don’t complete it so it’s difficult to assess the assertion they are making\nLines 76-77: “If N pollution could be effectively controlled, global forest SR and its variability would decrease, thereby reducing the uncertainty in the projected terrestrial carbon dynamics.”\n-It’s unclear what evidence the authors are using to draw this final conclusion.\n-I suggest they share results about variability and uncertainty before jumping from trends in N deposition effects on SR (Lines 73-76) to how reductions in N deposition would impact variability in SR and our predictions of this important process (Lines 76-77)\nLine 70: Include the total number of experiments included in the data synthesis\nIntroduction\nLine 80: Spell out “carbon dioxide” first time CO2 is used\nLines 148-149: The authors need to make clear what the experimental unit is here. Why 1112 observations, but only 168 experiments? Did they keep data separate by tree species? Form of N addition? How did they handle time series data? Take averages over time before including in the data synthesis?\nLines 156-159: Move this text to start the paragraph so the main goals are stated first, then followed by the methods (including # of observations and experiments included in the study)\nFigure 3:\nHow were biomes defined? In other words, did the authors utilize type of biome noted by authors of published data? Did they compile latitude and longitude and categorize biomes themselves? Did they utilize mean annual precipitation and temperature?\nIt appears from Figure 3A-C that rates of total soil, root, and microbial respiration all decline with greater rates of experimental N inputs AND that the steepest declines occur at the lowest rates of N inputs (which is contrary to their hypotheses and conclusions). It is unclear why the authors describe these relationships as “increased SR by N input”\nFigures 3A-C and G-I: I suggest showing the statistical results for both slopes (the red and gray lines in each)\nI understand the detailed Methods come below, but the authors need to explain in the legend for Figure 3 what the experimental unit is (each dot). Is it site? Tree species kept separate? How are time series from individual sites handled?\nLine 241: The authors state “Under high N deposition, ∆SRN decreased. The declining trends were more pronounced in tropical forests and N-saturated forests,…\"\n-However, it’s unclear how they are designating sites as “N-saturated” or not. It should be briefly defined here\nLines 263-265: The authors state “Generally, Pdec increased with increasing N stress, which can result from high N inputs, forest N saturation, or long-term artificial N additions.”\n-It’s unclear how the authors have defined “N stress” based on their results. It is clear conceptually that high N input sites that are N saturated can experience “stress”, but it’s unclear how the authors are utilizing their data to come to this conclusion.\n-Is “stress” here defined as the “probably of forest soil respiration being decreased by N input”?\nLine 303-304: The authors state “Globally, we found that current levels of N deposition increased SR in most forests, by a total of 1.8±0.1 PgC yr–1.”\n-It would be stronger for the authors to report this in terms of kg N added through atmospheric inputs.\nLines 303-304 and Table 1: The results would be more intuitive and powerful if the authors expressed the reduction or increase in soil respiration caused by N inputs as something like: “PgC kg N inputs-1 yr-1”\nDiscussion (no line numbers provided by authors)\nMethods Section (no line numbers provided by authors)\nSection 4.1: The authors need to explain how they handled multiple samplings from the same study (1112 observations).\nDid they take the average across time so only one soil respiration rate for control and N-addition plots were included? Did they keep growing season from winter measurements separate or did they combine them?"}, "author_response": "Response to reviewers’ comments:\nReviewer #1 (Remarks to the Author):\n#2. This study examines the effects of N deposition on forest soil respiration using both\ndata from N addition experiments and observations under real atmospheric N\ndeposition. The approach of integrating these two datasets to compare soil respiration\nresponses is certainly a valuable idea. However, at this stage, I have several major and\nfundamental concerns regarding the hypotheses and statistical analyses that need to be\naddressed before I can fully assess the details of the manuscript or recommend it for\npublication.\n1. The unclear Conceptual Framework: The authors dedicate considerable discussion\nand three figures to presenting a hypothesized framework for forest soil respiration\nresponses to N inputs. While this conceptual framework aimed at helping understanding\nthe SR response patterns and mechanisms, I did not find sufficient theoretical support\nor empirical evidence in the introduction or relevant literature to substantiate this\nframework. For instance, in Lines 330–332, the authors mention a \"transition from an\noligotroph-dominated to a copiotroph-dominated community,\" but this concept is not\nclearly introduced or explained in the introduction.\nResponse: We have revised the relevant text in the Introduction section (L105–116) and\nFig. 1 to clearly introduce the N-induced change in community composition. Previous\nstudies, such as Gilliam (2006) and Bobbink et al. (2010), reported that excessive N\ndeposition could lead to a transition from a community dominated by species adapted\nto N-poor environments to one dominated by nitrophilic copiotrophs. We have included\nthese references in the Introduction to better support the proposed conceptual\nframework.\nAdditionally, we clarified the theoretical basis of the proposed conceptual framework\nusing text (L92–116 in the main text, Supp. Text S1) and figure (Supp. Fig. S1). There\nhave been several studies to explain the effects of N deposition on forest soil respiration,\nsuch as Janssens et al. (2010) and de Vries et al. (2014). By proposing a new conceptual\nframework, we don’t mean to undermine the previous theories, but to complement them\n– the inclusion of the community collapse-reconstruction mechanism into the\ntraditional N saturation theory may help to better explain the N-induced increase in soil\nrespiration in some N-rich forests and long-term experimental N addition forests, as we\ndiscussed in more details in the Discussion section of the revised manuscript.\n#3. Furthermore, the study’s results do not appear to provide strong support for this\nframework—most of the quadratic relationships shown in Figure 3 are not statistically\nsignificant, making it difficult to justify a biphasic response pattern. Overall, I find it\ndifficult to be convinced by this new framework. Besides, even though the framework\nis illustrated in three figures, this level of redundancy does not compensate for the lack\nof mechanistic support. Additionally, Figure 1 does not effectively aid in understanding\nwhy a biphasic and abrupt response would be expected.\nResponse: The quadratic relationship between N input and soil respiration has been\nmentioned or suggested in several previous studies, including Li et al. (2019), Wang et\nal. (2020), and Xing et al. (2022a). In this synthesis analysis, the lack of statistical\nsignificance of the quadratic relationships (in Fig. 3) may result from the different initial\nN availability and biotic properties in the experimental sites. Even though there is a\nquadratic relationship between N input and the increased soil respiration in each site,\nthe differences in the peaks of the quadratic curves (which may depend on the nitrogen\nuse efficiency of the biotic communities), the differences in the y-intercepts (which may\ndepend on the initial N availability in the ecosystem), and the differences in the x-\nintercepts (which may depend on the biotic tolerance to high N availability before the\ncommunity collapse) can lead to a relatively weak quadratic relationship when data\nfrom all sites were analyzed together. We added a Supp. Fig. S10 to graphically explain\nthe potential causes that may weaken the quadratic relationships.\nFig. S10 Potential causes that may weaken the quadratic relationship between N inputs\nand increased soil respirations in a data synthesis analysis. In panels A–C, points of the\nsame color show experimental data from the same site, and dashed curves of the\ncorresponding color show the site-specific quadratic curve. The black solid curve shows\nthe fitted quadratic curve using data from all sites. As can be seen, the goodness of fit\nof the black curves is not as good as that of the site-specific colored curves, due to the\ndifferent peaks and intercepts of the site-specific curves. However, with increasing N\ninput, the overall response factor of increased soil respiration to N input (f ; see Supp.\na\nFig. S1 for detailed explanation and illustration) decreases regardless of whether the\nmodel was fitted for each site or for all sites combined (see the change from f to f ’ in\na a\nthe panels). Therefore, a significant negative relationship between f and N input was\na\nfound in all forests (Fig. 3A–C, G–I in the main text), despite the differences between\nsites.\nIn practice, we cannot test the quadratic relationship for every site in the compiled\nexperimental dataset (CO _exp) because of the limited number of N addition levels\n2\nused at most experimental sites – at least five data points may be needed to determine\na quadratic curve with an acceptable level of confidence. For this sake, we conducted\nan additional analysis using data from experimental sites with at least five N input levels\n(including the control). For sites where multiple N addition experiments were\nconducted, data from experiments of the same duration were considered together, while\ndata from experiments of different durations were considered separately. Four sites met\nthe criteria (i.e., having more than 4 levels of N addition), and significant (or nearly\nsignificant) quadratic relationships were found at all sites (Supp. Fig. S4; attached\nbelow).\nFig. S4 Relationship between N input rate and the N-induced change in soil respiration\nrate (∆SR ) in four experimental forests with at least five N addition levels (including\nN\nthe control). In each panel, a gray point represents one N addition level and the\ncorresponding ∆SR . Here, ∆SR was calculated as the difference in SR in paired\nN N\ncontrol and experimental N addition plots. The blue curves show the quadratic models\nfitted to the experimental data of each forest. Blue fonts show the goodness of fit of the\nquadratic models. Black fonts show the basic information for each experimental forest.\nThe information was retrieved from the original sources of data: Forest A (Forsmark et\nal. 2020), Forest B (Yu 2019, Zhao 2019), Forest C (Yu et al. 2019), Forest D (Geng et\nal. 2017, Peng et al. 2021).\nIn addition to Fig. 1, we provided a Supp. Text S1 and Fig. S1 (attached below) which\ncontained information about the theoretical basis for the hypothesized biphasic and\nabrupt responses. We also revised the introduction section to provide additional content\nabout mechanisms underlying the biphasic and abrupt collapse hypotheses (see L92–\n104).\nFig. S1 Theoretical basis and graphical illustration of the overall response factor (f )\na\nand relative response factor (f ) of soil respiration to N input. N is the atmospheric N\nr dep\ndeposition rate. SR is the soil respiration rate in the control plot receiving background\nCL\nN deposition only. SR is the soil respiration rate observed in the experimental plot\n+N\nreceiving artificial N addition. N is the N addition rate.\nadd\n#4. 2. It is unclear why the authors chose to analyze positive and negative SR responses\nseparately. SR response to N deposition is a continuous variable that can range from\npositive to negative within the same experiment. While I agree that different pathways\ncan drive either positive or negative responses, this does not necessarily mean that these\nresponses should be analyzed in isolation. It is more likely that both positive and\nnegative mechanisms operate simultaneously, with the net response depending on\nwhich mechanisms dominate under a given N deposition rate and background\nconditions. Without a clear theoretical justification for this separation, this analytical\napproach seems somewhat arbitrary.\nResponse: This is a very good question. In fact, we did not separate the positive and\nnegative SR responses in our initial analysis, hypothesizing that low and short-term N\nenrichment would increase SR, whereas high or long-term N enrichment would\ndecrease SR (which is essentially what N saturation theory would predict)(Aber et al.\n1998). However, the patterns revealed (see fig. 1 below) suggest that low N additions\ncould increase SR (and its components, RR and MR) in both N-limited and N-saturated\nforests. This implies that the initial hypothesis about the response of SR to N input was\nincomplete.\nfig. 1 Local polynomial regression (LOESS) models of N input rate and the N-induced\nchanges in soil respiration (∆SR ), root respiration (∆RR ), and microbial respiration\nN N\n(∆MR ). Different panels show experimental N addition data from N-limited forests,\nN\nN-saturated forests, and long-term experimental forests. In each panel, gray points show\nobservations of ∆SR (or ∆MR or ∆RR ) derived from the CO _exp dataset; each point\nN N N 2\nrepresents a site-year-N addition level combination. The red curve shows the LOESS\nmodel, and the pink shading shows the range of uncertainty of the model.\nTherefore, we revised our initial hypothesis based on biochemical theories and\nmechanisms underlying soil respiration, including the metabolism theory (Brown et al.\n2004) and stress-induced community shifts (Egidi et al. 2023). Considering the\ntheoretical and observational connections between belowground living biomass and\nsoil respiration changes (Brown et al. 2004, Treseder 2008, Xing et al. 2022b), the\nincreased and decreased SR by N addition may arise from different mechanisms\nfunctioning at different scales – the increased SR results from the biphasic effect of N\non organisms (i.e., the low N-facilitated biomass growth and high N becomes toxic),\nwhereas the decreased SR results from the community-level collapse (i.e., N-induced\nbiodiversity loss and the mortality of living biomass).\nWhen analyzing the increased and decreased SR by N input separately, the biphasic\n(quadratic) responses of increased SR by N input could be seen in almost all forests\n(see red curves in fig. 2 below). The abrupt collapse of the N-induced decrease in SR\n(blue curves) is not as pronounced as in Fig. 3, probably because of the different initial\nN availability and basal soil respiration rate across the sites. It should be noted that the\nincreased and decreased SR change divergently with increasing N input, suggesting that\nthe two mechanisms operate in parallel. This is exactly the reason why we need to\nanalyze the positive and negative SR responses separately, only by which we could\nquantitatively reveal the SR response curves and predict the N deposition effects on\nforest SR. We added information in the main text (L132–135), Supp. Text S1, and Fig.\nS1 to justify the separation of the increased and decreased SR.\nfig. 2 Local polynomial regression (LOESS) models of N input rate and the N-induced\nincrease and decrease in soil respiration (∆SR ), root respiration (∆RR ), and microbial\nN N\nrespiration (∆MR ). Different panels show experimental N addition data from N-limited\nN\nforests, N-saturated forests, and long-term experimental forests. In each panel, gray\npoints show observations of ∆SR (or ∆MR or ∆RR ) derived from the CO _exp\nN N N 2\ndataset; each point represents a site-year-N addition level combination. The red curve\nshows the LOESS model for the N-induced increase of SR (or RR or MR), and the blue\ncurve shows the LOESS model for the N-induced decrease of SR (or RR or MR). The\ncolored shadings show the ranges of uncertainty of the models.\nOn a regional scale, both positive and negative responses are likely to exist at the same\ntime (but in different forests). In a given forest ecosystem, the biotic community would\neither be in a collapse-rebuild phase (with decreased SR) or still benefiting from N\nsupply (with increased SR). In fact, Zheng et al. (2022) provided a nice analysis of such\nphased changes using data from a long-term experimental N addition site. However,\nsuch long-term experimental data capturing the different phases of SR change are scarce.\nTherefore, it has been challenging to analyze the positive and negative responses\ncombinedly, because the same N addition level could lead to different SR responses\ndepending on which phase the forest is in - this may be an important reason why\nprevious research that did not separate the different responses was unable to reach a\nuniversal framework for explaining N-induced SR changes.\n#5. 3. Lines 526–535:The distinction between “N-limited,” “N-saturated,” and “long-\nterm” experimental forests is not clearly justified. In particular, the rationale for\ndefining three years as the threshold for high N stress is unclear—why not two, five, or\nten years? Moreover, N stress is not solely determined by duration; the N addition rate\nalso plays a critical role. For example, a low N addition rate over a longer period (e.g.,\n10 kg ha⁻¹ yr⁻¹ for four years) may impose less stress than a high N addition rate over a\nshorter period (e.g., 100 kg ha⁻¹ yr⁻¹ for three years), given differences in total N input.\nThis issue requires further clarification and justification.\nResponse: Considering the potential influence of background N status (i.e., N-limited\nvs. N-saturated) and duration of N addition on the SR responses, we separately analyzed\nexperimental data from N-limited forests, N-saturated forests, and long-term\nexperimental forests. Please refer to the response to Comment #8 for a detailed\nexplanation of the distinction between N-limited and N-saturated forests in this study.\nFollowing previous studies (Wang and Tang 2019, Xu et al. 2021, Wang et al. 2023),\nwe distinguished between forests where experiments were conducted for less than or\nmore than 3 years. Experiments of two years were barely considered as long-term\nexperiments in any study.\nA realistic reason for the adopted experimental duration threshold (i.e., 3 years) is that\nmany of the experimental data were collected through master’s or Ph.D. projects, and\nthe length of these experiments is typically no more than 3 years. Experiments longer\nthan 3 years are few, which means that not many observations would be left in the long-\nterm experimental data set and a high uncertainty in the derived response factor of SR\nto N addition would occur, if we set the threshold of long-term experiments to a higher\nlevel, such as 5 years.\nIn order to fully address the reviewer’s concern, we tried reanalyzing the data using\ndifferent thresholds (5 yrs, 10 yrs). Other thresholds could be tried using the\nsupplementary data and R code (downloadable from\nhttps://data.mendeley.com/preview/wfdfjds6by?a=c296db55-569b-4c5f-8b65-\nd85e9e51dd3a). As can be seen in fig. 3 below, the response patterns remain unchanged,\ndespite high uncertainty in the responses of SR in long-term experimental forests which\nis likely due to the limited number of observations classified as long-term experiments.\nTherefore, we prefer to keep using 3 years as the threshold. We added some text in\nL436–437 to justify the used threshold.\nfig. 3 Using different thresholds for analyzing increased soil respiration (SR) by N input\nin N-limited, N-saturated, and long-term experimental forests (supplemental to Supp.\nFig. S3). (A–C) Piecewise linear regression models relating N input rate to the response\nfactor of increased SR by N input (f ) in experimental forests. Gray points show the\na\nobservations of f derived from experimental dataset (CO _exp). Red (gray) lines and\na 2\nfont show the fitted linear models for fa and low (high) N input rate. The linear models\nfor f and low N input rate (red font) were then used to infer the quadratic models of\na\nincreased SR (∆SR ) and N input. (D–F) Quadratic models on the increased soil\nN-inc\nrespiration (∆SR ) and N input rate in forests. Gray points show the observed ∆SR\nN-inc N-\ninc N-inc\nin experimental forests. Black points show the moving average of ∆SR for every\nN-inc N-inc\n10 kgN ha–1 yr–1; this aggregation of data was to mitigate the influence of extreme\nvalues. The red lines show the quadratic models calculated from the linear models.\nPink\ndashed lines show the range of uncertainties of the quadratic models. Red font shows\nthe correlation coefficient (R) between the observed and predicted mean ∆SR .\nN-inc\nBlue\nlines and font show the turning points of the quadratic models. “N-limited” forests refer\nto experimental forests which were initially N-limited and where N addition\nexperiments had been conducted for no more than 3/5/10 years; “N-saturated” forests\nrefer to experimental forests which were initially N-saturated and where N addition\nexperiments had been conducted for no more than 3/5/10 years; “long-term” forests\nrefer to those where N addition experiments had been conducted for more than 3/5/10\nyears, regardless of the initial N status.\nAlso, we considered following the practice of some studies (such as Chen and Chen\n(2023)) and analyzing the response of SR to total N addition over the entire\nexperimental period. But still, a N addition of 100 kg ha⁻¹ yr⁻¹ for one year may have\ndifferent ecological effects than a N addition of 25 kg ha⁻¹ yr⁻¹ for four years – the total\nN addition may be the same but the N losses through leaching and gaseous emission\ncould be very different, leading to different availability of retained N. Therefore, we\ntook extra efforts to divide the original data into short-term (which was further divided\ninto N-limited and N-saturated forest subsets) and long-term experimental datasets, and\nthen analyzed the relationship between N addition rate and SR for each subset,\nintending to separate the effects of N addition rate, duration, and background N status\non forest SR.\n#6. 4. For the data derived from N addition experiments, it is essential to clarify how\npotential non-independence among data points within each original study was\naccounted for in the statistical analyses. Addressing this concern would help ensure the\nrobustness of the findings.\nResponse: In our analysis, to avoid the over-representation of data from the same\nexperiment (i.e., data from the same plot-year may have been analyzed and reported in\ndifferent papers), we summarized data from the same year and N addition level by\ntaking their averages. Each unique site-year-N addition level combination was used as\na data point when we calculated the response factors of SR to N addition.\nNonetheless, non-independence of the calculated response factors (as effect size metrics)\ncould result from: (1) The shared control was used to calculate the overall response\nfactors of SR to different N additions applied at the same site, so that the derived overall\nresponse factors could be correlated (Cause 1). (2) The effect of N addition was\nestimated for each experimental year so that, for a given site, the response factors\ncorresponding to different years could be temporally correlated (Cause 2).\nIn this study, we used response factors as metrics of effect size rather than the\ncommonly used response ratio (see response to Comment #19 for details). The response\nfactors were used to derive a biphasic/abrupt-transition relationship between SR and N\ninputs, instead of deriving an overall effect size metric showing whether N inputs\nincrease or decrease SR. Although these differences between our study and common\nmeta-analysis were critical for revealing the complex, phased relationships between SR\nand N inputs, they prevented us from directly testing the influence of non-independence\nof data points on the revealed relationships using a meta-regression method as\nsuggested by Gleser and Olkin (2009).\nHowever, the influence of data point non-independence could generally (and somewhat\nindirectly) be addressed by using an alternative effect size metric or by performing the\nanalysis on subgroups of the dataset (Noble et al. 2017). To account for the non-\nindependence arising from Cause 1, we have defined and calculated an alternative\neffect size metric, local response factor (see Supp. Text S2 for details), to verify the\nrevealed phased relationship between SR and N inputs. The local response factor was\ncalculated using data from any two N addition treatments within a given range of N\ninput rates. That is, the local response factors do not have a “shared control” problem\nlike the overall response factors. Because the local response factors showed the same\npattern as the overall response factors in how SR responded to increasing N inputs (Fig.\n7, Supp. Fig. S6), Cause 1 of non-independence is unlikely to challenge our finding\nabout the response of SR to N inputs.\nRegarding non-independence due to Cause 2, we have tested and validated that the\nrevealed patterns persist regardless of using subsets of the experimental data from short-\nterm or long-term experiments (i.e., data from experiments within/later than three years;\nSupp. Figs. S3, S5). The fig. 3 in the response to Comment #5 may further demonstrate\nthat the patterns revealed are independent of the temporal range of the experimental\ndata used for analysis, meaning that the potential temporal correlation of data points is\nunlikely to challenge our finding about the response of SR to N inputs.\nAdditionally, the same patterns revealed in our global analysis (Fig. 3) and site-level\nanalysis (Supp. Fig. S4) may indirectly support that the revealed the response of SR to\nN inputs is unlikely to be challenged by the potential spatial correlation of the\nexperimental sites. Our analysis on the observational dataset (Fig. 4), which is\nindependent of the analysis on the experimental dataset, also revealed the same patterns.\nWe believe that all these evidences can support the robustness of our findings. We\nrevised the main text (L421–423) and added a Supp. Text S3 to clarify.\n#7. 5. Use of Abbreviations: The manuscript introduces and relies on numerous\nabbreviations, many of which are not clearly defined, making the text difficult to follow.\nIn several cases, I only fully understood their meaning after referring to the Methods\nsection. I recommend using abbreviations only when necessary and ensuring that all\nabbreviations are explicitly defined in the introduction to improve readability.\nResponse: We have added a glossary of terms and abbreviations at the beginning of the\npaper (Box 1). Unnecessary abbreviations have been avoided wherever possible.\nHopefully the revised manuscript is more readable now.\nReviewer #2 (Remarks to the Author):\nReviewer Comments to the Author:\n#8. Cen et al. systematically compiled and analyzed a dataset of annual soil respiration\nfluxes observed in forests worldwide where nitrogen addition experiments were\nconducted. Meanwhile, they applied a natural gradient approach to analyze the SRDB\ndatabase and the global nitrogen deposition dataset. Based on these analyses, they\nproposed a general framework for the effects of nitrogen deposition on soil respiration\nin global forests. Subsequently, they assessed the net effect of nitrogen deposition on\nthe global forest soil respiration budget. The proposed general framework for the\nresponse of soil respiration to nitrogen inputs is innovative. In addition, they have also\nmade several new estimations of large-scale indicators. I have a few suggestions for\nyour consideration.\nFirst, it is important to note that N addition typically promotes soil respiration under\nconditions where nitrogen saturation has not yet been reached. If a forest is already in\na nitrogen-saturated state, the more plausible outcome would be nitrogen addition\nprimarily leading to inhibition of soil respiration. In most ecosystems, nitrogen addition\ninitially stimulates soil respiration, followed by a suppression phase, and eventually\nleads to a collapse, resulting in a new equilibrium. Once this equilibrium is reached, the\necosystem becomes nitrogen-unsaturated, and this cycle may repeat. I recommend that\nthe authors clarify this point in the context of nitrogen saturation and its role in the\nnitrogen addition experiments discussed in the manuscript.\nResponse: This manuscript was intended to clarify the effects of N addition on SR in\nN-limited and N-saturated forests. As the traditional N saturation theory would predict,\nN addition increases SR in N-limited forests and decreases SR in N-saturated forests\n(Hypothesis 1). However, if we consider the adaptation of biotic community to\nenvironmental stress, such as that caused by high anthropogenic N deposition, it is\nunlikely that excessive N could persistently suppress biotic activities and respiration at\nthe community level – the community would adapt to high N availability through trait\nplasticity or species composition change, thereby further making use of the additional\nN supply (Hypothesis 2).\nIn a previous study (Cen et al. 2025), we determined the N limitation or saturation status\nof global forests using the sensitivity of soil N O emission to N deposition (s ) as an\nindicator. In previous papers, researchers reported the N limitation or saturation status\nof forest sites based on either the forest growth response to N addition, or the N leaching\nlevel. Those observations were used to determine the s threshold between N-limited\nN\nand N-saturated forests, based on which we classified global forests to be N-limited or\nN-saturated.\nUsing the derived N status map of global forests (Fig. 2)(Cen et al. 2025), we were able\nto separately analyze the SR responses to N addition in N-limited and N-saturated\nforests. It was found that low N addition increased SR in both N-limited and N-saturated\nforests (see figs. 1 and 2 in the response to Comment #4). This finding is partly different\nfrom what the traditional N saturation theory would predict (i.e., Hypothesis 1), but\nexplainable if we incorporate the community structure change mechanism (Hypothesis\n2) – at least the microbial communities could quickly change and make use of the high\nN availability for biotic activities, and hence SR could further increase even when\nplants are barely responsive to N addition (which is a sign of N saturation).\nThere are some ongoing controversies about the definitions of N limitation and N\nsaturation, and we agree it is perhaps reasonable to think that the community becomes\nN-limited again after community reconstruction, considering that the newly recruited\ncopiotrophs could keep growing and respond positively to further N addition. However,\nthe background N availability and N tolerance level of organisms in the reconstructed\ncommunity is for sure different from the initial, N-limited community. For better\ndistinguishing between the old and new communities, we hesitate to call the\nreconstructed community “N-limited”. For this sake, the community change was\nintentionally not drawn as a closed cycle in Fig. 1 – it would likely be a spiral rather\nthan a cycle if going to further stages after community reconstruction, although we\ndidn’t go that far to validate the entire spiral model.\nIt is perhaps also controversial to call the rebuilt community N-saturated, but we haven’t\nthought of other terms that could provide readers with a straightforward impression\nabout the different N status of communities. In the context of this study and previous\nstudies of ours, the N-limited and N-saturated forests are intended to refer to two types\nof forests with different N availability that exhibit disparate responses to future N\ndeposition and climate change (see Box 1 below from Cen et al. (2025)).\nWe have clarified the definition of N-limited/N-saturated forests in the glossary of terms\nat the beginning of the paper. We have also added some text in the Discussion section\nto clarify the similarities and differences in SR responses in N-limited and N-saturated\nforests (L251–264).\n#9. Second, in multi-level N addition single-point experiments, if the data can be fitted\nto a unimodal curve, it would be helpful to assess whether the majority of experimental\nfits exhibit a unimodal pattern. Such evidence could help reduce the noise caused by\nheterogeneity between experiments, thereby strengthening the reliability of the\nconclusions drawn in the manuscript. Although the dataset is relatively small, I\nrecommend that the authors attempt to provide more substantial evidence to support\ntheir findings, which would enhance the robustness of the study and its conclusions.\nResponse: Thank you for this great suggestion. We used data from four experimental\nsites having more than four N addition levels and revealed significant quadratic\nrelationships (Supp. Fig. S4; attached below), supporting the overall biphasic response\nof SR to N input. The supplementary R code file has been updated to reflect how we\nfiltered out the sites having more than four N addition levels and how Fig. S4 was\nproduced.\nFig. S4 Relationship between N input rate and the N-induced change in soil respiration\nrate (∆SR ) in four experimental forests with at least five N input levels (including the\nN\ncontrol). In each panel, a gray point represents one N addition level and the\ncorresponding ∆SR . Here, ∆SR was calculated as the difference in SR in paired\nN N\ncontrol and experimental N addition plots. The blue curves show the quadratic models\nfitted to the experimental data of each forest. Blue fonts show the goodness of fit of the\nquadratic models. Black fonts show the basic information for each experimental forest.\nThe information was retrieved from the original sources of data: Forest A (Forsmark et\nal. 2020), Forest B (Yu 2019, Zhao 2019), Forest C (Yu et al. 2019), Forest D (Geng et\nal. 2017, Peng et al. 2021).\n#10. Finally, this study involves numerous estimations and predictions, making the\nmethods for model construction and the reliability of predictions crucial. I recommend\nthat the authors provide more detailed and reliable explanations, as outlined in the\nSpecific Comments section below.\nResponse: We revised the Methods section accordingly to provide more details about\nthe models and predictions. Please refer to the responses to Comments #29–34.\nSpecific Comments:\n#11. Line 53-55: \"High exogenous N inputs (deposition or fertilization) \"and\" long-\nterm N additions\" are somewhat repetitive.\" You should clarify what you want to\nexpress.\nResponse: Because there are multiple comments about the use of “N stress”, the term\nhas been deprecated in the revised manuscript. The explanation to the term (originally\nin L53–55) was therefore removed.\n#12. Line 56-57: In my opinion, nitrogen availability refers only to the amount of\nnitrogen in an ecosystem that is available for biological use.\nResponse: We agree that traditionally, N availability refers to the amount of biologically\navailable N. This definition of N availability (i.e., N supply in relative to biotic N\ndemand) follows that of Mason et al. (2022). We added the reference here to clarify\n(Box 1 in the main text).\nIn this paper, the term N availability was used in parallel with N status (N\nlimitation/saturation) to indicate that the continuous change of N availability caused by\nenvironmental changes can lead to alteration of ecosystem N status, thereby influencing\nthe SR response to N input. Please also refer to Box 1 in the response to Comment #8\nfor details.\n#13. Line 60-61: \"background N stress\" It should be the availability of nitrogen, not\nevery forest is under N stress.\nResponse: This sentence has been removed, but we have deprecated the use of “N stress”\nthroughout the manuscript.\n#14. Line 66: \"Soil respiration (SR)\" Rs is more commonly used as the abbreviation for\nsoil respiration.\nResponse: Yes, we initially used Rs as the abbreviation for soil respiration. However,\nsince we also need an abbreviation for the N-induced change of soil respiration, we\nrealized that Rs looks somewhat confusing (the lowercase “s” and subscript “N” may\nN\nlook like a connected “SN”). Therefore, we ended up using SR and SR as\nN\nabbreviations of soil respiration and the N-induced change of soil respiration.\n#15. Line 69: \"mixed effects\" diverse effects\nResponse: Revised accordingly (L44).\n#16. Line 112-114: The studies by Janssens, I. A. et al. and Zhou, L. Y. et al. are both\nbased on meta-analysis results, revealing the overall effects of numerous studies, many\nof which show that nitrogen addition promotes soil respiration. However, the\nentry point you mentioned, that low nitrogen promotes soil respiration, seems inconsistent\nwith their findings, and this point comes across as somewhat confusing.\nResponse: Previous meta-analyses of experimental N addition data revealed that N\ndeposition reduced global forest SR budget. Some recent meta-analyses, however,\nsuggested that realistically low N deposition increased global forest SR budget. The\ninconsistency of their conclusions may result from the potential bias in the available\nexperimental N addition data including: (1) the N addition level used in most N addition\nexperiments are higher than the real-world N deposition level; (2) the high\nrepresentation of N-saturated forests (e.g., temperate forests close to human settlements)\nin global N addition experiments; (3) the mixed use of short-term and long-term\nexperimental data. Also, the meta-analysis method itself may have limited capability to\nquantitatively reveal the complex, phased relationship between N input and SR – please\nsee the response to Comment #19 for details.\nFor these reasons, we conducted this research, separately and quantitatively accounted\nfor the effects of N input rate, background N status, and N addition duration on SR.\nWe briefly mentioned the different effects of N deposition on SR found in different meta-\nanalyses (L78–89) – we were afraid that too much information about methodology here\nin the Introduction section would distract readers. Instead, we added some discussion\n(L345–364) to clarify the reasons behind the different arguments about how low N\ninputs influence SR.\n#17. Line 134-136: The current phrasing may give the impression that the biphasic\ndose-response relationship is solely due to soil acidification. It is recommended to\nseparate the explanation and the summary for better clarity.\nResponse: Revised accordingly. Please see L100–104.\n#18. Line 187-189: Rs, Ra, and Rh should be the commonly used abbreviations for soil\nrespiration and its components.\nResponse: Yes. For the same reason as specified in the response to Comment #14,\nwe\nended up using SR, RR, and MR to stand for soil respiration and its components.\n#19. In addition, I have a question: The effect size of such an abrupt transition is often\ndisproportionate to the N addition level triggering the change. How does the algorithm\nused in this paper differ from the response ratio commonly used in meta-analysis?\nThis is just a curiosity and not a challenge to your calculation method.\nResponse: This is a good question. In meta-analysis, the response ratio (RR) is\ncommonly quantified as the target variable measured in the experimental plot (X )\nEXP\nrelative to the background value (measured in the control plot; X ). That is, RR = X\nCL EXP\n/ X . Response ratio can measure the qualitative effect of the experimentally changed\nCL\nenvironmental condition (i.e., whether a target variable is increased or decreased by N\ndeposition, temperature, precipitation, or atmospheric CO level, etc.). However, this\n2\nmetric can hardly be applied to reveal a complex, phased relationship such as that\nbetween N input and soil respiration. This might be a reason why previous meta-\nanalyses (which used response ratio for measuring the N input effects on SR) led to\ndifferent conclusions whether atmospheric N deposition increases or decreases forest\nsoil respiration on a global level.\nIn this study, based on biochemical and ecological theories, we used different metrics\nfor quantifying the N deposition effects on soil respiration. Due to the positive\nrelationship between biomass and respiration (“power law”)(Reich et al. 2006), and\nbetween enzyme concentration and decomposition (“Michaelis-Menten\nkinetics”)(Michaelis and Menten 1913), it is inferred that there is an intrinsic\nrelationship between the amount of N added and the increased respiration (Supp. Fig\nS1, attached below). Therefore, we defined an “overall response factor” (f ; calculated\na\nas the increased soil respiration per unit of artificial N addition) to reveal the dose-\nresponse relationship between N input and the increased soil respiration.\nNiche complementarity allowed the increasing stress from high N availability to be\nshared among species, ensuring the stability of the community and preventing a gradual\ndecline of biodiversity and living biomass. However, excessively high N availability\ncould at a point exceed the N tolerance limit of the entire community and kill the N-\nsensitive oligotrophs, leading to abrupt reductions in the living biomass and respiration\nof the community (Bobbink et al. 2010, Zheng et al. 2022, Egidi et al. 2023). The effect\nsize of such an abrupt transition is often disproportionate to the N addition level\ntriggering the change. Therefore, we defined another “relative response factor” (f ) to\nr\nquantify the magnitude of the reduction, calculated as the decreased soil respiration\nrelative to the initial soil respiration (Supp. Fig. S1).\nDetails of the response factors have been provided in Supp. Text S1.\nFig. S1 Theoretical basis and graphical illustration of the overall response factor (f )\na\nand relative response factor (f ) of soil respiration to N input. N is the atmospheric N\nr dep\ndeposition rate. SR is the soil respiration rate in the control plot receiving background\nCL\nN deposition only. SR is the soil respiration rate observed in the experimental plot\n+N\nreceiving artificial N addition. N is the N addition rate.\nadd\n#20. Line 240, 248: The change in soil respiration (∆SRN) should be explained in more\ndetail, as I am unclear about how the change is calculated up to this point.\nResponse: Revised accordingly. Please see L845–846 and L865–866 for the revision.\n#21. Line 265-266: The classification of \"N-limited forests,\" \"N-saturated forests,\" and\n\"long-term experimental forests\" is confusing, as it is unclear why \"long-term\nexperimental forests\" should be considered separately.\nResponse: Previous research shows that the SR response to N inputs may depend on\nthe initial N status (i.e., N-limited or N-saturated), the rate of N addition, and the\nduration of N addition (Janssens et al. 2010, Chen and Chen 2023, Liu et al. 2023). To\ndistinguish between the influence of different factors, we divided the original data into\nshort-term (which was further divided into N-limited and N-saturated forest subsets)\nand long-term experimental data (which was not further divided because of the\nrelatively few observations from long-term experiments; see response to Comment #5),\nand then analyzed the relationship between N addition rate and SR for each subset. Data\nfrom N-limited, N-saturated, and long-term experimental forests showed clear\ndifferences in their SR responses (see Fig. 5 in the main text), which may support the\nvalidity of separately analyzing the datasets. We have added some text here to clarify\nthe reason for the classification (L189–196).\nFrom another perspective, previous research that did not distinguish between short-term\nand long-term experimental data may overestimate the negative effect of N deposition\non SR, because in long-term N addition experiments, plant and microbial properties\ncould be altered by the accumulated high N availability, causing forest SR to deviate\nfrom the natural response to N deposition. We therefore advocated that the short-term\nand long-term experimental data should be used to reveal the short-term response and\nlong-term adaptation of SR to changes in N deposition, respectively (L361–364).\n#22. Line 326-327: I have been unable to understand the meaning of Pinh in Fig. 7.\nAccording to this framework, the results from the N-limited and N-saturated forest\ndatasets are the most important evidence and should be highlighted in the main text.\nResponse: Sorry for the mistake here. Pinh was the previously used abbreviation for the\nprobability of SR being inhibited by N addition. Pdec was used instead of Pinh in a\nrevision, but I forgot to update Fig. 7.\nAccording to the results (Supp. Figs. S3, S5, and Fig. 4), SR in N-limited and N-\nsaturated forests showed similar patterns in response to increasing N inputs. The major\ndifference between them was that for the same level of N input, SR is more likely to\ndecrease in N-saturated than in N-limited forests (Fig. 5). Both the inter-biome\ndifferences in SR responses and the inter-N-status differences may be of interest to\nreaders. To avoid making Fig. 3 look too busy, we have provided some results from N-\nlimited/N-saturated forests as Supp. Figs. S3 and S5. Nevertheless, we have highlighted\nthe inter-N-status differences in Fig. 4, Table 1, and also in the Discussion section\n(L251–264).\n#23. Line 353: In N-limited forests, the old communities transition into new\ncommunities. Does this mean that the new communities represent N-saturated forests?\nWill their nitrogen demand increase?\nIn Appendix Text S2, with increasing N input, the local response factor (fb) of SR to N\ninput shifted from positive to negative and then back to positive in N-limited forests\nacross all biomes (Fig. S5).\nResponse: Both experimental and observational data (Fig. S6 and Fig. 4) suggest that\nthe newly recruited copiotrophs in the reconstructed new communities could further\nutilize the additional N for biological activities, thereby further increasing soil\nrespiration. In a sense that the new communities have a higher N-tolerance level and\nhigher N-availability than the original N-limited communities, we considered it to be\nN-saturated. On the other hand, we can see the reasonableness of considering the\nreconstructed communities to be N-limited, as the newly recruited copiotrophs could\nutilize the additional N (although we don’t think the biological N demand increase, but\nthat oligotroph species were replaced by copiotrophs).\nThis is more a question of how we define N limitation and N saturation. In the context\nof this study, the N-limited and N-saturated forests are intended to refer to two types\nof forests with different N availability that exhibit disparate responses to future N\ndeposition and climate change. Please refer to the response to Comment #8 for more\ndetails.\n#24. Line 390: The two explanations for nitrogen addition inhibiting soil respiration\nshould not be seen as an incremental relationship like \"even\".\nResponse: Revised accordingly. Please see L282–284 for the revision.\n#25. Line 417-419: The theme of this paragraph is to discuss the similarities and\ndifferences across different ecosystems and nitrogen status categories. The mention of\nresponse range here seems somewhat out of place.\nResponse: Revised accordingly. Please see L305–319 for the revision.\n#26. Line 422: The explanation, i.e., from N-limited to N-saturated to long-term\nexperimental forests, might be more helpful to the reader if mentioned earlier in the\ntext.\nResponse: Revised accordingly. We added explanation in the Results section about the\nclassification of experimental forests (L189–196).\n#27. Line 451-455: This content should be placed at the end in the conclusion and\nsignificance section. It might serve as a transition to the next section, but it feels\nsomewhat abrupt when read.\nResponse: Revised accordingly. Please see L345–348 for the revision.\n#28. Line 451-482: It is recommended to organize and categorize these insights and\nlimitations. As it stands, the reading feels somewhat disorganized and chaotic.\nResponse: Revised accordingly (L349–364). Hopefully it reads more organized now.\nLines 499-505: Did you use only the SRDB data, or did you also include the CK data\nfrom your own CO₂_exp dataset?\nResponse: The SRDB data (from non-experimental forests) were used to build the\nrandom forest model and derive the soil respiration budget of global forests. CO _exp\n2\ndataset was only used for quantifying the N input effects on SR.\nAlthough the CK data from CO _exp may as well be used in combination with SRDB\n2\ndata for building the random forest regression model, we don’t think the model would\nbe significantly changed considering the relatively small size of CO _exp (with 390\n2\nobs. from CK plots) as compared to the SRDB data (n = 3689).\n#29. Lines 608-609: What was the basis for selecting the predictor variables? Why were\nthese specific variables ultimately chosen?\nResponse: The predictors were selected based on (1) the availability of data for each\nexperimental site and for global forests; (2) the mechanistic connection between SR\nand climate (Bond-Lamberty and Thomson 2010, Huang et al. 2020), N deposition\n(Reay et al. 2008), and soil texture (Wang et al. 2003).\nTo improve the performance of the regression model for prediction, we further\nincorporated soil pH, soil organic carbon, and soil nitrogen content variables. These\nvariables were also found to be related to soil respiration in previous research (Chen et\nal. 2014, Bae et al. 2015).\n#30. Lines 600: The selection and determination of random forest model parameters\n(e.g., Ntree, Mtry, and nodesize) have a significant impact on model performance. The\nauthors should clarify how this issue was addressed in the Methods section.\nResponse: Thank you for pointing this out. Mtry parameter (i.e., number of variables\ntried at each split) was optimized using “tuneRF” function in “randomForest” package.\nIn many cases, the out-of-bag error rate was the lowest when Mtry was set to 4 (fig. 4).\nThis also agrees with the rule of thumb (i.e., optimal Mtry being approximately the\nsquare root of the number of predictors). We have clarified the parameter selection in\nthe Methods section (L537–539) and in Supp. Tables S2, S4.\nThe error rate barely changed when Ntree (i.e., number of trees) was above 200. To\nkeep a balance between model error rate and computational cost, we set the Ntree\nparameter to be 500.\nfig. 4 Out-of-bag error of random forest model when the Mtry (left panel) and Ntree\n(right panel) parameters were set to different values.\nThe model performance did not change significantly when we set the nodesize to be 1,\n5, or 10 (see below for the model output). Therefore, we kept using the default setting\nof the randomForest function (i.e., nodesize equals 5 when the response variable is a\ncontinuous variable).\nCall:\nrandomForest(formula = Rs ~ MAT + MAP + MAT.cv + MAP.cv + MAN + MAN.cv + Clay + Sa\nnd + SOC + TN + pH + sN, data = rf.data, nodesize = 1, mtry = 4, ntree = 500, importance = TRU\nE, proximity = TRUE)\nType of random forest: regression\nNumber of trees: 500\nNo. of variables tried at each split: 4\nMean of squared residuals: 111496131\n% Var explained: 51.77\nCall:\nrandomForest(formula = Rs ~ MAT + MAP + MAT.cv + MAP.cv + MAN + MAN.cv + Clay + Sa\nnd + SOC + TN + pH + sN, data = rf.data, nodesize = 5, mtry = 4, ntree = 500, importance = TRU\nE, proximity = TRUE)\nType of random forest: regression\nNumber of trees: 500\nNo. of variables tried at each split: 4\nMean of squared residuals: 112353284\n% Var explained: 51.39\nCall:\nrandomForest(formula = Rs ~ MAT + MAP + MAT.cv + MAP.cv + MAN + MAN.cv + Clay + Sa\nnd + SOC + TN + pH + sN, data = rf.data, nodesize = 10, mtry = 4, ntree = 500, importance = TR\nUE, proximity = TRUE)\nType of random forest: regression\nNumber of trees: 500\nNo. of variables tried at each split: 4\nMean of squared residuals: 113407470\n% Var explained: 50.94\n#31. The soil respiration data were likely collected over different time periods, leading\nto potential inconsistencies. How did the authors account for the influence of study\nduration in their analysis?\nResponse: The duration of experimental N additions influences soil respiration\nresponses. Therefore, we separately analyzed soil respiration data from experiments\nthat lasted less than or more than three years (Xu et al. 2021, Wang et al. 2023). The\ninterannual variation of soil respiration was less of a problem because the analysis was\nperformed using data from paired control and N addition plots. The difference in SR\nbetween the plots was therefore attributable to N input rather than the temporally\nvarying climatic factors.\nOn the other hand, the interannual variation of soil respiration may add to the\nuncertainty in the estimated global forest SR budget, because we did not distinguish\nbetween data collected from different years. The derived budget should thus be\ninterpreted as the mean SR budget of the past few decades (when soil respiration rates\nwere measured in the field). However, because the interannual variation of SR is\nrelatively small as compared to the basal value (Huang et al. 2020), the estimated SR\nbudget and spatial pattern are comparable to those reported in previous studies (\nRaich\nand Schlesinger 1992, Wang et al. 2010, Zhao et al. 2024)(Supp. Fig. S7).\n#32. The study by Ni Huang, Spatial and temporal variations in global soil respiration\nand their relationships with climate and land cover, has also conducted global-scale soil\nrespiration predictions, achieving an R² of 0.6–0.8. In contrast, the R² in this study is\nonly 0.25. How reliable are the predictions? Have the authors considered alternative\nmachine learning approaches, such as support vector machines (SVM), to improve\nmodel performance?\nResponse: Thank you for the suggestion. Our original model for predicting SR had an\nR2 of 0.45. To improve the performance of the model, we incorporated soil chemical\nvariables (i.e., soil pH, organic carbon content, and soil nitrogen content) which were\nmechanistically related to SR (Chen et al. 2014, Bae et al. 2015). The new model has\nan R2 of 0.51 (Supp. Table S4). Although the model performance may be further\nimproved if we build plant-functional-type-specific models as Huang et al. (2020) did,\nwe hesitate to make the model such complicated because the major focus of this study\nis to reveal the N deposition effects on SR. The random forest model for predicting SR\nserves to derive the basal SR rates, on which basis we could quantify the contribution\nof N deposition to the current SR budget – the model barely influenced our results about\nthe N deposition effects. Also, the global forest SR budget we estimated is comparable\nto those reported in previous studies (see Supp. Fig. S7, attached below).\nFig. S7 Comparing the estimated global forest soil respiration budgets from previous\nstudies (Raich and Schlesinger 1992, Wang et al. 2010, Zhao et al. 2024) with the results\nof this study.\nThe random forest model for the relative response rate of decreased soil respiration (f)\nr\nhad an R2 of 0.25 (original Supp. Table S1). It should be noted, however, that this model\nwas only used to analyze the partial dependence of f on N input rate, not to predict f.\nr r\nWe also included soil chemical variables to improve model performance (current R2 is\n0.34; Supp. Table S2). The derived partial dependence plots barely changed before and\nafter the model revision, because the relationship depends mostly on f and N input rate,\nr\nand little on other variables used in the model (Fig. 3).\n#33. Lines 651-658: What was the basis for selecting this nonlinear regression model?\nIs it the optimal model? The authors should provide a more detailed explanation, as the\ncurrent description remains unclear.\nResponse: Pdec is a probability with lower and upper bounds of 0 and 1. We tried using\nseveral different types of models that fit for such a variable, including the Weibull\nmodels (W1.2 and W2.2), the log-logistic model (LL.2), and the Michaelis-Menten\nmodel (MM.3). Since the MM.3 model has the lowest AIC value (see model\ncomparison below), it was considered the optimal model and was used for the non-\nlinear regression analysis.\nModel fitting and selection were performed in R using the “drc” package (Ritz et al.\n2016). The supplementary R code has been updated to reflect the model selection\nprocess. We also added some text in the Methods section (L565–570) and a Supp. Table\nS6 to clarify.\n> ar.all <- drm(P_inh ~ N_in, data = subset(output, N_in <= 400), Nstat, fct = MM.3(fixed = c(0, 1, N\nA)))\n> mselect(ar.all, list(LL.2(), W1.2(), W2.2()), icfct = AIC)\nlogLik IC Lack of fit Res var\nW1.2 33.79337 -53.58674 NA 0.04859006\nLL.2 33.39288 -52.78577 NA 0.04870814\nW2.2 32.34107 -50.68213 NA 0.04901963\nMM.3 19.23693 -30.47386 NA 0.05258460\n#34. Using this empirical equation for prediction does not seem to be theoretically\nsuperior to machine learning. Why not use machine learning to predict Pdec for any N\ninput rates?\nResponse: Machine learning algorithms often work better with large datasets (e.g., with\nthousands of obs.). Because of the relatively few Pdec data available (n = 330), machine\nlearning algorithms may not fit for predicting Pdec.\nAlso, an important purpose here is to explore how Pdec changes with N input rates and\nacross forests in different N status. The “black box” nature of machine learning\nalgorithms may lower the model interpretability (Pichler and Hartig 2023), making it\ndifficult to convince readers of a solid, consistent relationship between Pdec and N input\n(or N status) like what was shown in Fig. 5. Therefore, we prefer to keep using empirical\nequations here.\nReviewer #2 (Remarks on code availability):\n#35. The code execution is reliable.\nResponse: Thank you for helping check the code!\nReviewer #3 (Remarks to the Author):\n#36. Overall, this is a timely manuscript in which the authors sought to determine the\nnet effect of atmospheric nitrogen deposition on rates of soil respiration in forests\naround the globe. They conducted a meta-analysis with data compiled through\npublished papers cited in Web of Science and from the China National Knowledge\nInfrastructure Theses and Dissertations Database.\nHere are my main constructive critiques of the paper:\nTheir dataset is biased toward Chinese collections given they looked at papers cited in\nWeb of Science, but only dissertations from the “China National Knowledge\nInfrastructure Theses and Dissertations database”. It’s unclear why they used student\npapers from China and not elsewhere.\nResponse: We collected data from the Web of Science database because it covers the\nmain-stream English-language journals. We also collected data from the China National\nKnowledge Infrastructure Theses and Dissertations Database because some Chinese\nstudents did not publish their data in English-language journals but only in their theses\nor dissertations. The first author of this study (who compiled the data) as a native\nChinese speaker can read the masters’ theses and doctoral dissertations written in\nChinese. Therefore, we were able to take advantage of our bilingual skills and expand\nthe size of our dataset. We believe that retrieving papers from the Web of Science and\nChina National Knowledge Infrastructure Theses and Dissertations databases is a\ncommon practice in many global meta-analysis studies, such as Yan et al. (2022), Chen\nand Chen (2023), Liu et al. (2023). We added some text in L385–386 to clarify.\nIn the future, if we could have the help of collaborators with different language skills,\nwe would also like to extend the data search to include published literature written in\nother languages, thereby further expanding our datasets.\n#37. There are many places where the writing needs to be improved. For example, in\nthe Highlights it says “Experimental and observational data agree on a new\nframework…” As written, it sounds like the data themselves are thinking and agreeing\non something; the authors need to make it clearer that researchers can agree on a new\nframework, data cannot. This and other text throughout the paper should be revised in\nthis way.\nResponse: Thank you for pointing this out. The entire manuscript has been revised to\navoid using anthropomorphic statements.\n#38. In general I suggest using fewer abbreviations. Many are not necessary. For\nexample, why is “Pdec” used throughout and not just “N inputs”? If the authors want\nthis paper to be read and understand by a broad audience, it would be much easier to\nread and follow if fewer abbreviations are used.\nResponse: Sorry for the inconvenience. We have reduced the use of abbreviations\nwherever possible. We also added a glossary of terms and abbreviations at the beginning\nof the paper (Box 1) to increase its readability.\n#39. There are several places where the authors make a comparison, but don’t finish it.\nSee below for details.\nResponse: Thank you for pointing this out. We have revised accordingly.\nThe following detailed comments are meant to improve the manuscript:\nHighlights\n#40. Line 42: Change first bullet to be less anthropomorphic\nResponse: This sentence has been removed to comply with the formatting requirements.\nBut we have revised the entire manuscript to avoid being anthropomorphic.\n#41. Line 44: Change to “N deposition reduces…” and finish the statement “more\nfrequently in N-saturated than non-N saturated”? Or “more frequently than “N-limited\nforests”? Or something else?\nResponse: This sentence has been removed to comply with the formatting requirements,\nbut we have revised the entire manuscript to clearly state the comparisons.\n#42. Line 46: “Responses of plant root and microbial respirations to N input differ in\nmagnitude and pace”\n- I suggest replacing this with the actual differences.\nResponse: This sentence has been removed to comply with the formatting requirements.\n#43. Line 47: “Current level of N deposition enhanced global forest soil respiration by\n~5%” relative to what? Pre-industrial levels of atmospheric deposition? Non-forest\necosystems?\nResponse: We intended to mean that 5% of the global forest soil respiration budget is\nattributable to atmospheric N deposition (i.e., global forest soil respiration budget\nwould decrease by ~5% if there were no atmospheric N deposition). This sentence has\nbeen removed.\nAbstract\n#44. Lines 73-76: This text is unclear “Using a novel probabilistic approach, we\nestimated that N deposition decreased SR in 2.9% of global forests, mostly N-saturated\nforests in eastern China, western Europe, and eastern USA. But the net effect of N\ndeposition increased the global forest SR budget by 5.1% (1.7 PgC yr–1).” The title of\nthe paper includes these three regions only (eastern China, western China, and eastern\nUSA); what regions are the authors referring to about the “net effect of N deposition”?\nResponse: Here we intended to say that N deposition decreased SR in 2.9% of global\nforests. Those forests are mostly N-saturated forests, which are mainly located in\neastern China, western Europe, and eastern USA.\nEven in the same region, N deposition can increase and decrease SR depending on the\nbackground N availability in a forest. Therefore, in this study, we separately quantified\nthe increased and decreased SR by N deposition (see Fig. 6 in the main text) and\nreported here the net effect of N deposition (i.e., increased SR minus decreased SR).\nThis sentence has been revised to avoid being misleading (L49–51).\n#45. Lines 73-75: Also related to this text: “Using a novel probabilistic approach, we\nestimated that N deposition decreased SR in 2.9% of global forests, mostly N-saturated\nforests in eastern China, western Europe, and eastern USA.”\n-Are the authors saying that N deposition increased SR in the non N-saturated forests\nof eastern China, Western Europe, and eastern USA?\n-Or, are they saying that N deposition increased SR in other regions of the world?\nHere and elsewhere the authors often state the first half of a comparison, but don’t\ncomplete it so it’s difficult to assess the assertion they are making\nResponse: Sorry for being misleading. We intended to say that N deposition decreased\nSR in 2.9% of global forests. Those forests are mostly N-saturated forests, which are\nmainly located in eastern China, western Europe, and eastern USA. This sentence has\nbeen revised to clarify (L49–51). We also revised the entire manuscript to avoid using\nonly the first half of a comparison.\n#46. Lines 76-77: “If N pollution could be effectively controlled, global forest SR and\nits variability would decrease, thereby reducing the uncertainty in the projected\nterrestrial carbon dynamics.”\n-It’s unclear what evidence the authors are using to draw this final conclusion.\n-I suggest they share results about variability and uncertainty before jumping from\ntrends in N deposition effects on SR (Lines 73-76) to how reductions in N deposition\nwould impact variability in SR and our predictions of this important process (Lines 76-\n77)\nResponse: Thanks for pointing it out. The result on SR variability was moved to the\nsupplementary materials (Supp. Fig. S9) in a previous revision. As such, it may not be\nappropriate to appear in the abstract. This sentence has been revised to mention only\nSR but not its variability (L52–53).\n#47. Line 70: Include the total number of experiments included in the data synthesis\nResponse: Revised accordingly (L46).\nIntroduction\n#48. Line 80: Spell out “carbon dioxide” first time CO2 is used\nResponse: Revised accordingly (L57).\n#49. Lines 148-149: The authors need to make clear what the experimental unit is here.\nWhy 1112 observations, but only 168 experiments? Did they keep data separate by tree\nspecies? Form of N addition? How did they handle time series data? Take averages\nover time before including in the data synthesis?\nResponse: In each experiment, observations from different plots (e.g., control, low N\naddition, high N addition plots) and different years are usually reported separately.\nThese data were kept separate in our compiled dataset (CO _exp; downloadable from\n2\nhttps://data.mendeley.com/preview/wfdfjds6by?a=c296db55-569b-4c5f-8b65-\nd85e9e51dd3a). In our analysis, to avoid the over-representation of data from the same\nexperiment (i.e., data from the same plot-year may have been analyzed and reported in\ndifferent papers), we summarized data from the same year and N addition level by\ntaking their averages. Each site-year-N addition level was used as a data point when we\ncalculated the response factors of SR to N addition. We have revised the Methods\nsection to clarify (L416–423). This sentence was also revised (L121).\n#50. Lines 156-159: Move this text to start the paragraph so the main goals are stated\nfirst, then followed by the methods (including # of observations and experiments\nincluded in the study)\nResponse: Thank you for your suggestion. We have revised accordingly (L117–120).\nFigure 3:\n#51. How were biomes defined? In other words, did the authors utilize type of biome\nnoted by authors of published data? Did they compile latitude and longitude and\ncategorize biomes themselves? Did they utilize mean annual precipitation and\ntemperature?\nResponse: Different papers may use different forest biome classifications. For\nconsistency, we used coordinates of each forest to extract the biome information from\na global spatial dataset (Hansen et al. 2010). We have revised the Methods section to\nclarify (L410–411).\n#52. It appears from Figure 3A-C that rates of total soil, root, and microbial respiration\nall decline with greater rates of experimental N inputs AND that the steepest declines\noccur at the lowest rates of N inputs (which is contrary to their hypotheses and\nconclusions). It is unclear why the authors describe these relationships as “increased\nSR by N input”\nResponse: Sorry to be misleading here. Fig. 3A–C and G–I panels show the response\nfactor of increased SR to N input (f ). For reasons specified in the response to Comment\na\n#3, it is very difficult to directly analyze the quantitative relationship between N input\nand the increased SR. In contrast, the relationship between f and N input rate is more\na\nstraightforward and quantifiable.\nThe response factor (f ) is similar to the first-order derivative of the increased SR by N\na\ninput. The negative, linear relationship between the response factor and N input rate\nprovided us with a basis for quantifying the quadratic relationship between increased\nSR and N input (Fig. 3D–F, J–L). The quadratic curves shown in Fig. 3D–F, J–L\nsupported our hypothesized biphasic relationship between N input and the increased\nSR.\nWe have revised Fig. 3 and legend to better show the logistic relationship between the\npanels.\n#53. Figures 3A-C and G-I: I suggest showing the statistical results for both slopes (the\nred and gray lines in each)\nResponse: We hesitate to add additional text to the panels, because Fig. 3 may look too\nbusy. Instead, we provided a supplemental table (Supp. Table S1) to clearly show the\nstatistical results for all the piecewise models in Fig. 3 and Supp. Fig. S3.\n#54. I understand the detailed Methods come below, but the authors need to explain in\nthe legend for Figure 3 what the experimental unit is (each dot). Is it site? Tree species\nkept separate? How are time series from individual sites handled?\nResponse: In Fig. 3, each gray point represents a response factor (f , f ) or soil\na r\nrespiration change (∆SR ) calculated using data from a pair of control and N addition\nN\nplots from the same site-year. At the same site and year, observations from plots with\nthe same N addition level were summarized by taking their averages. We have revised\nthe legend for Fig. 3 (L839–840) and the Methods section (L415–422) to clarify.\nData from different years were treated separately to account for the potential influence\nof the duration of experimental N addition on SR responses. The tree species factor was\nnot considered in this study. In most cases, N addition experiment was only conducted\nin one community (with one forest type) at each site. Thus, the current dataset can\nhardly support species-specific analyses.\n#55. Line 241: The authors state “Under high N deposition, ∆SRN decreased. The\ndeclining trends were more pronounced in tropical forests and N-saturated forests,…\"\n-However, it’s unclear how they are designating sites as “N-saturated” or not. It should\nbe briefly defined here\nResponse: In a previous study (Cen et al. 2025), we determined the N limitation or\nsaturation status of global forests using the sensitivity of soil N O emission to N\n2\ndeposition (s ) as an indicator. In published papers, researchers reported the N\nN\nlimitation or saturation status of forest sites based on either the forest growth response\nto N addition, or the N leaching level. Those observations were used to determine the\ns threshold between N-limited and N-saturated forests, based on which we further\nN\nclassified global forests to be N-limited or N-saturated.\nIn this study, using the derived N status map of global forests, we were able to separately\nanalyze the SR responses to N addition in N-limited and N-saturated forests. We added\nsome text here to clarify (L174–175).\n#56. Lines 263-265: The authors state “Generally, Pdec increased with increasing N\nstress, which can result from high N inputs, forest N saturation, or long-term artificial\nN additions.”\n-It’s unclear how the authors have defined “N stress” based on their results. It is clear\nconceptually that high N input sites that are N saturated can experience “stress”, but it’s\nunclear how the authors are utilizing their data to come to this conclusion.\n-Is “stress” here defined as the “probably of forest soil respiration being decreased by\nN input”?\nResponse: Sorry for the unclarity here. “N stress” was intended to refer to a\nbiochemically unfavorable condition for the fitness of organisms, which may result\nfrom high background N availability (or N saturation), high exogenous N inputs\n(deposition or fertilization), or long-term N additions. But because we have seen\nmultiple comments on the use of “N stress”, this term has been deprecated in the revised\nmanuscript.\nThis sentence has been revised as “Generally, Pdec increased with increasing N\navailability”.\n#57. Line 303-304: The authors state “Globally, we found that current levels of N\ndeposition increased SR in most forests, by a total of 1.8±0.1 PgC yr–1.”\n-It would be stronger for the authors to report this in terms of kg N added through\natmospheric inputs.\nResponse: This is a good suggestion. We have added some text here to report “a net\neffect size of +84 kgC kgN–1”.\nAdditionally, we provided a Supp. Table S5 to show the different SR response factors\nof forests in different biomes (tropical/temperate/boreal) and different N status (N-\nlimited/N-saturated).\n#58. Lines 303-304 and Table 1: The results would be more intuitive and powerful if\nthe authors expressed the reduction or increase in soil respiration caused by N inputs as\nsomething like: “PgC kg N inputs-1 yr-1”\nResponse: This is a good suggestion. Although we hesitate to add columns to Table 1\nfor aesthetic reasons (the revised table would be too wide to display on one page),\nwe\nhave provided a Supp. Table S5 to supplement Table 1 and show the different SR\nresponse factors (in units of kgCO kgN–1) for forests in different biomes and different\nDiscussion (no line numbers provided by authors)\nMethods Section (no line numbers provided by authors)\n#59. Section 4.1: The authors need to explain how they handled multiple samplings\nfrom the same study (1112 observations). Did they take the average across time so only\none soil respiration rate for control and N-addition plots were included? Did they keep\ngrowing season from winter measurements separate or did they combine them?\nResponse: In our analysis, to avoid the over-representation of data from the same\nexperiment (i.e., data from the same plot-year may have been analyzed and reported in\ndifferent papers), we summarized data from the same year and N addition level by\ntaking their averages. Each unique site-year-N addition level combination was used as\na data point when we calculated the response factors of SR to N addition.\nFor instance, if one experimental site has nine plots receiving three different N addition\ntreatments (including the control; each treatment has three replications) and the\nexperiment was conducted and reported for three years, we will have 27 annual-scale\nobservations from that site. Observations from the same year and N addition level will\nbe summarized by taking the averages, meaning that data from the 9 unique year-N\naddition level combinations will be used for further analyses such as the calculation of\nresponse factors. We have revised the Methods section to clarify (L415–422).\nOnly annual soil respiration rates were collected from published papers. We did not\nseparately collect data for the growing and non-growing seasons. This study focused\nonly on the effects of N deposition on annual soil respiration in forests. Interested\nresearchers could investigate the effects of N deposition on the seasonality of soil\nrespiration using a dataset with high temporal resolution.\nReviewer #3 (Remarks on code availability):\nBibw\nThanks again.\nYours sincerely,\nNianpeng He\nReferences\nAber, J. D., W. McDowell, K. Nadelhoffer, A. Magill, G. Berntson, M. Kamakea, S. McNulty,\nW. Currie, L. Rustad, and I. Fernandez. 1998. Nitrogen saturation in temperate forest\necosystems: hypotheses revisited. Bioscience 48:921-934.\nBae, K., T. J. Fahey, R. D. Yanai, and M. Fisk. 2015. Soil Nitrogen Availability Affects\nBelowground Carbon Allocation and Soil Respiration in Northern Hardwood Forests\nof New Hampshire. Ecosystems 18:1179-1191.\nBobbink, R., K. Hicks, J. Galloway, T. Spranger, R. Alkemade, M. Ashmore, M. Bustamante,\nS. Cinderby, E. Davidson, F. Dentener, B. Emmett, J. W. Erisman, M. Fenn, F. Gilliam,\nA. Nordin, L. Pardo, and W. De Vries. 2010. Global assessment of nitrogen deposition\neffects on terrestrial plant diversity: a synthesis. Ecological Applications 20:30-59.\nBond-Lamberty, B., and A. Thomson. 2010. Temperature-associated increases in the global\nsoil respiration record. Nature 464:579-U132.\nBrown, J. H., J. F. Gillooly, A. P. Allen, V. M. Savage, and G. B. West. 2004. TOWARD A\nMETABOLIC THEORY OF ECOLOGY. Ecology 85:1771-1789.\nCen, X., N. He, K. Van Sundert, C. Terrer, K. Yu, M. Li, L. Xu, L. He, and K. Butterbach-Bahl.\n2025. Global patterns of nitrogen saturation in forests. One Earth 8:101132.\nChen, C., and H. Y. H. Chen. 2023. Mapping global nitrogen deposition impacts on soil\nrespiration. Science of the Total Environment 871:161986.\nChen, S., J. Zou, Z. Hu, H. Chen, and Y. Lu. 2014. Global annual soil respiration in relation to\nclimate, soil properties and vegetation characteristics: Summary of available data.\nAgricultural and Forest Meteorology 198-199:335-346.\nde Vries, W., E. Z. Du, and K. Butterbach-Bahl. 2014. Short and long-term impacts of nitrogen\ndeposition on carbon sequestration by forest ecosystems. Current Opinion in\nEnvironmental Sustainability 9-10:90-104.\nEgidi, E., C. Coleine, M. Delgado-Baquerizo, and B. K. Singh. 2023. Assessing critical\nthresholds in terrestrial microbiomes. Nature Microbiology 8:2230-2233.\nForsmark, B., A. Nordin, N. I. Maaroufi, T. Lundmark, and M. J. Gundale. 2020. Low and High\nNitrogen Deposition Rates in Northern Coniferous Forests Have Different Impacts on\nAboveground Litter Production, Soil Respiration, and Soil Carbon Stocks. Ecosystems\n23:1423-1436.\nGeng, J., S. L. Cheng, H. J. Fang, G. R. Yu, X. Y. Li, G. Y. Si, S. He, and G. X. Yu. 2017. Soil\nnitrate accumulation explains the nonlinear responses of soil CO2 and CH4 fluxes to\nnitrogen addition in a temperate needle-broadleaved mixed forest. Ecological\nIndicators 79:28-36.\nGilliam, F. S. 2006. Response of the herbaceous layer of forest ecosystems to excess nitrogen\ndeposition. Journal of Ecology 94:1176-1191.\nGleser, L. J., and I. Olkin. 2009. Stochastically dependent effect sizes. The Handbook of\nResearch Synthesis and Meta-analysis:357-376.\nHansen, M. C., S. V. Stehman, and P. V. Potapov. 2010. Quantification of global gross forest\ncover loss. Proceedings of the National Academy of Sciences 107:8650-8655.\nHuang, N., L. Wang, X. P. Song, T. A. Black, R. S. Jassal, R. B. Myneni, C. Y. Wu, L. Wang,\nW. J. Song, D. B. Ji, S. S. Yu, and Z. Niu. 2020. Spatial and temporal variations in\nglobal soil respiration and their relationships with climate and land cover. Science\nAdvances 6:11.\nJanssens, I. A., W. Dieleman, S. Luyssaert, J. A. Subke, M. Reichstein, R. Ceulemans, P. Ciais,\nA. J. Dolman, J. Grace, G. Matteucci, D. Papale, S. L. Piao, E. D. Schulze, J. Tang, and\nB. E. Law. 2010. Reduction of forest soil respiration in response to nitrogen deposition.\nNature Geoscience 3:315-322.\nLi, Q., X. Z. Song, S. X. Chang, C. H. Peng, W. F. Xiao, J. B. Zhang, W. H. Xiang, Y. Li, and\nW. F. Wang. 2019. Nitrogen depositions increase soil respiration and decrease\ntemperature sensitivity in a Moso bamboo forest. Agricultural and Forest Meteorology\n268:48-54.\nLiu, Y., M. Men, Z. P. Peng, H. Y. H. Chen, Y. H. Yang, and Y. F. Peng. 2023. Spatially\nexplicit estimate of nitrogen effects on soil respiration across the globe. Global Change\nBiology 29:3591-3600.\nMason, R. E., J. M. Craine, N. K. Lany, M. Jonard, S. V. Ollinger, P. M. Groffman, R. W.\nFulweiler, J. Angerer, Q. D. Read, P. B. Reich, P. H. Templer, and A. J. Elmore. 2022.\nEvidence, causes, and consequences of declining nitrogen availability in terrestrial\necosystems. Science 376:261-+.\nMichaelis, L., and M. L. Menten. 1913. Die kinetik der invertinwirkung. Biochem. z 49:352.\nNoble, D. W. A., M. Lagisz, R. E. O'Dea, and S. Nakagawa. 2017. Nonindependence and\nsensitivity analyses in ecological and evolutionary meta-analyses. Molecular Ecology\n26:2410-2425.\nPeng, B., J. F. Sun, J. Liu, Z. W. Xia, and W. W. Dai. 2021. Relative contributions of different\nsubstrates to soil N2O emission and their responses to N addition in a temperate forest.\nScience of the Total Environment 767:8.\nPichler, M., and F. Hartig. 2023. Machine learning and deep learning—A review for ecologists.\nMethods in Ecology and Evolution 14:994-1016.\nRaich, J. W., and W. H. Schlesinger. 1992. The global carbon dioxide flux in soil respiration\nand its relationship to vegetation and climate. Tellus B 44:81-99.\nReay, D. S., F. Dentener, P. Smith, J. Grace, and R. A. Feely. 2008. Global nitrogen deposition\nand carbon sinks. Nature Geoscience 1:430-437.\nReich, P. B., M. G. Tjoelker, J.-L. Machado, and J. Oleksyn. 2006. Universal scaling of\nrespiratory metabolism, size and nitrogen in plants. Nature 439:457-461.\nRitz, C., F. Baty, J. C. Streibig, and D. Gerhard. 2016. Dose-response analysis using R. Plos\nOne 10:e0146021.\nTreseder, K. K. 2008. Nitrogen additions and microbial biomass: a meta-analysis of ecosystem\nstudies. Ecology Letters 11:1111-1120.\nWang, C., F. Ren, X. Zhou, W. Ma, C. Liang, J. Wang, J. Cheng, H. Zhou, and J.-S. He. 2020.\nVariations in the nitrogen saturation threshold of soil respiration in grassland\necosystems. Biogeochemistry 148:311-324.\nWang, C., and Y. Tang. 2019. Responses of plant phenology to nitrogen addition: a meta-\nanalysis. Oikos 128:1243-1253.\nWang, W., W. Chen, and S. Wang. 2010. Forest soil respiration and its heterotrophic and\nautotrophic components: Global patterns and responses to temperature and\nprecipitation. Soil Biology and Biochemistry 42:1236-1244.\nWang, W. J., R. C. Dalal, P. W. Moody, and C. J. Smith. 2003. Relationships of soil respiration\nto microbial biomass, substrate availability and clay content. Soil Biology and\nBiochemistry 35:273-284.\nWang, Z., A. Xing, and H. Shen. 2023. Effects of nitrogen addition on the combined global\nwarming potential of three major soil greenhouse gases: A global meta-analysis.\nEnvironmental Pollution 334:121848.\nXing, A. J., E. Z. Du, H. H. Shen, L. C. Xu, W. de Vries, M. Y. Zhao, X. Y. Liu, and J. Y. Fang.\n2022a. Nonlinear responses of ecosystem carbon fluxes to nitrogen deposition in an\nold-growth boreal forest. Ecology Letters 25:77-88.\nXing, A. J., E. Z. Du, H. H. Shen, L. C. Xu, M. Y. Zhao, X. Y. Liu, and J. Y. Fang. 2022b.\nHigh-level nitrogen additions accelerate soil respiration reduction over time in a boreal\nforest. Ecology Letters 25:1869-1878.\nXu, C., X. Xu, C. Ju, H. Y. H. Chen, B. J. Wilsey, Y. Luo, and W. Fan. 2021. Long-term,\namplified responses of soil organic carbon to nitrogen addition worldwide. Global\nChange Biology 27:1170-1180.\nYan, W., Y. Zhong, J. Yang, Z. Shangguan, and M. S. Torn. 2022. Response of soil greenhouse\ngas fluxes to warming: A global meta‐analysis of field studies. Geoderma 419:115865.\nYu, H. 2019. Effects of changing soil acidity/alkalinity on soil nitrogen and greenhouse gas\nfluxes in a Pinus tabulaeformis forest in Taiyue. Master's thesis. Beijing Forestry\nUniversity.\nYu, H., Y. Chen, H. Zhang, and Z. Zhou. 2019. The effect of inorganic nitrogen addition on\nsoil nitrogen and greenhouse gas flux for the Pinus tabulaeformis forest in Taiyue\nMountain, Shanxi Province. JOURNAL OF NANJING FORESTRY UNIVERSITY\n62:85.\nZhao, B. 2019. Soil Respiration in Response to Thinning and Simulated Nitrogen Deposition\nin Pinus Tabuliformis Forest in Taiyue Mountain. Doctoral dissertation. Beijing\nForestry University.\nZhao, Z., X. Ding, G. Wang, and Y. Li. 2024. 30m Resolution Global Maps of Forest Soil\nRespiration and Its Changes From 2000 to 2020. Earth's Future 12:e2023EF004007.\nZheng, M., T. Zhang, Y. Luo, J. Liu, X. Lu, Q. Ye, S. Wang, J. Huang, Q. Mao, J. Mo, and W.\nZhang. 2022. Temporal patterns of soil carbon emission in tropical forests under long-\nterm nitrogen deposition. Nature Geoscience 15:1002-1010.\nPoint-to-point responses to the comments of reviewers\n\"A general framework for nitrogen deposition effects on soil respiration in global\nforests\" [NCOMMS-25-10077A]\nDear reviewer,\nThank you for your comments. To address the remaining major concern of the reviewer,\nwe have substantially revised the relevant sections of the manuscript, including the\nIntroduction, Results, Discussion, and Methods sections of the manuscript, as well as\nthe main figures (Figs. 1–3). As you suggested, the revised manuscript now contains an\nintegrated analysis of the positive and negative responses of soil respiration to nitrogen\ndeposition (see L175–179, 529–543; Supplementary Text S2, Fig. S6). This analysis\nrevealed the same pattern as our previous separate analyses of positive and negative\nresponses (Fig. 7). The data and R code for the analysis are available at a public\nrepository (https://data.mendeley.com/preview/wfdfjds6by?a=c296db55-569b-4c5f-\n8b65-d85e9e51dd3a).\nHere, we are providing three files to show how we revised the manuscript：1) revised\nmanuscript with tracked changes; 2) revised supplementary materials; 3) point-by-point\nresponse to the reviewers’ comments (attached below).\nThank you again for your time and suggestions, which helped us improve our\nmanuscript. We would appreciate it if you could consider our manuscript again for\npublication.\nYours sincerely,\nNianpeng He\nInstitute of Geographic Sciences and Natural Resources Research, CAS\nDatun Road A 11, Chaoyang District, Beijing 100101, P.R. China\nE-mail: henp@igsnrr.ac.cn\nTel: 86-10-64889263\nFax: 86-10-64889432\nResponse to reviewer’s comments:\nReviewers' comments:\nReviewer #1 (Remarks to the Author):\n#1. I appreciate the authors' detailed responses and their efforts to clarify the rationale\nbehind their analytical approach. However, I remain unconvinced by the decision to\nseparate the analysis of positive and negative effects on SR, as no compelling\ntheoretical or empirical justification has been provided to support this separation. Since\nthis constitutes a fundamental step in their analytical framework, it casts doubt on the\nvalidity of the subsequent results and conclusions.\nIn their response, the authors suggest that increased SR results from \"N-facilitated\nbiomass growth,\" while decreased SR arises from \"N-induced biodiversity loss.\"\nHowever, numerous grassland experiments have demonstrated that both biomass\nincrease and biodiversity loss can occur simultaneously under the same nitrogen\ntreatments (e.g., Bai et al., 2009, Global Change Biology,\nhttps://doi.org/10.1111/j.1365-2486.2009.01950.x). This evidence supports the point I\nraised earlier: both positive and negative mechanisms may operate concurrently within\nthe same plot, making it problematic to analyze them in isolation.\nResponse: Thank you. Indeed, N could facilitate biomass growth and increase\nmetabolic respiration (Fog 1988, Brown et al. 2004). Also, excessive N (and the\ndisruption of biotic stoichiometry and soil pH) could cause the N-sensitive species to\nbe replaced by N-tolerant species (Huang et al. 2015, Hedwall et al. 2017), leading to a\nloss of species and a decrease in respiration, at least temporarily, during the transition\nphase (Bobbink et al. 2010, Zheng et al. 2022). We agree that these two mechanisms\ncould both occur under the same N treatments and at the same sites. However, in a\nspecific plot, it is more likely that the two mechanisms function sequentially (i.e., N\nadditions within a favorable range facilitate biomass growth, but accumulated\nexogenous N over time becomes detrimental and leads to the loss of N-sensitive species\n[H1]) than interactively (i.e., N addition facilitates biomass growth, which leads to the\ngradual, competitive exclusion of N-sensitive species in the meantime [H2]). Band et\nal. conducted a global meta-analysis using experimental N addition data from\ngrasslands. They tested both hypotheses and found that the detrimental effect of N (i.e.,\nH1) is the major driver of species loss in N addition experiments conducted in\ngrasslands, rather than the interactive effect of biomass growth (i.e., H2)(Band et al.\n2022). The temporal isolation of the two mechanisms suggests the possibility of\nseparately analyzing the increased SR (likely dominated by N-facilitated biomass\ngrowth) and the decreased SR (likely dominated by N-induced species loss).\nNevertheless, many studies have observed both increased biomass and decreased\nspecies richness in the same grassland N addition site-years (e.g., Bai et al. (2010)).\nOne possible explanation is that, annual changes in biomass (and productivity estimated\nby the harvesting method) reflect the average production over the year, which do not\nsufficiently demonstrate the temporal changes of community composition and function.\nTemporary species loss and decreased respiration may be obscured by intra-annual\nspecies gain (Zhao et al. 2022) and biomass increase following community rebuilding.\nThe rapid replacement of N-sensitive herbaceous plants with N-tolerant ones may\nexplain why plant biomass in grasslands usually responds positively to N enrichment,\nwhile other ecosystems with more complex plant forms (e.g., forests) may witness both\npositive and negative responses (De Schrijver et al. 2011).\nIn addition to the mechanistic validity of analyzing the isolated increases and\ndecreases of SR over time, a realistic reason why we had to analyze the increased and\ndecreased SR separately is that the different characteristics of the mechanisms require\ndifferent mathematical models (fig. 1; Fig. 1 in the main text). The N-induced biomass\ngrowth is a continuous process. In contrast, the N-induced species loss is often abrupt\ndue to the resilience of the community to environmental (nitrogen) stress (Zheng et al.\n2022, Egidi et al. 2023). Considering the different characteristics of the two\nmechanisms underlying the effect of N input on soil respiration (i.e., continuous vs.\ndiscrete), we had to separate the positive response (i.e., a net increase of SR in the N\naddition plot as compared to the control plot, which may be attributable to the dominant\neffect of continuous biomass growth; fig. 1) and the negative response (i.e., a net\ndecrease of SR in the N addition plot as compared to the control plot, which may be\nattributable to the dominant effect of abrupt species loss), so that we could apply\ndifferent mathematical models to quantify the SR responses.\nfig. 1 Potential mechanisms underlying changes in soil respiration due to increasing\nnitrogen availability. This figure has been incorporated into the revised Fig. 1 in the\nmain text.\nOn the other hand, the results of our analysis agrees with that of Bai et al. (2010)\nin that the same level of N input could result in positive or negative responses (although\nin different forests). This is precisely why we had to define and quantify the “probability\nof SR being decreased by N input” (Pdec; Fig. 5) before integrating the two models for\npositive/negative SR responses and evaluating the overall effect of N deposition on\nglobal forest SR.\nWe have revised the entire manuscript to clarify the dominant mechanisms\nunderlying the observed increases/decreases in SR (L93–121; Fig. 1 in the main text).\nWe cited Bai et al. (2010) to support the effects of N on biomass and species richness\n(see L93–94). Analyzing the increase or decrease of SR separately allowed us to apply\ndifferent mathematical models to quantify SR changes (see L138–141 for the revisions).\nWe also incorporated fig. 1 to produce a new Fig. 1 in the main text, which further\nclarifies the mechanistic basis for distinguishing between positive and negative\nresponses.\nTo address your concerns and provide additional empirical support for our\nconclusions, this revision highlights an integrated analysis that does not distinguish\nbetween positive and negative SR responses (see L175–179, 529–543; Supplementary\nText S2, Fig. S6). We have previously used this method and integrated response factor\nto assess soil CH flux responses to N input (Cen et al. 2024). Specifically, we\n4\ncalculated the integrated response factors using SR observations from N limited forests\nin which N was added experimentally (CO _exp_NL dataset in the main text). Forest\n2\nthat were initially N-saturated forests were not included in this analysis because only\nthe N-limited forests would undergo the entire process (including the community\ncollapse and reconstruction phase) and exhibit a bimodal SR pattern under increasing\nN inputs according to our proposed framework (Fig. 7). Integrated response factor (f )\nint\nwas calculated as\n(𝑆𝑆𝑆𝑆2−𝑆𝑆𝑆𝑆1)\nwhere SR and SR (unit: kgCO ha𝑓𝑓– 𝑖𝑖 1 𝑖𝑖 y 𝑖𝑖 r=–1) w(𝑁𝑁e2r−e𝑁𝑁 t1h)e soil respiration rates observed under\n1 2 2\ntwo different N input rates of N and N (kgN ha–1 yr–1), respectively. To make the f\n1 2 int\ncomparable across forest sites, f was calculated within four defined N input ranges, ≤\nint\n50 kgN ha–1 yr–1, 50–100 kgN ha–1 yr–1, 100–150 kgN ha–1 yr–1, and ≥ 150 kgN ha–1 yr–\n1. \nWe found that with increasing N input, the integrated response factor (f ) of SR\nint\nto N input changed from positive to negative and then to positive across all biomes\n(Supplementary Fig. S6). This result suggests that in N-limited forests, SR first\nincreases, then decreases, and then increases in response to increased N input. These\nfindings align with those from our separate analysis of positive and negative responses\nand support our proposed framework (i.e., Fig. 7 in the main text).\nFig. S6 Integrated response factors of soil respiration rate to N input (f ) were\nint\ncalculated for four different N input levels in N-limited experimental forests. Columns\nindicate the mean f corresponding to each N addition level in each biome; error bars\nint\nindicate the standard errors of the mean values. Numbers on the top of the panel are the\ntotal number of calculated f values corresponding to each N addition level in each\nint\nbiome.\n#2. Also, the authors mentioned that the positive N effect on SR is biphasic because of\n“high N becomes toxic”. I would argue that “high N becomes toxic” is also one of the\nreasons for negative responses of SR to N addition. This again challenges the validity\nof separating these effects analytically.\nResponse: We agree that excessive N could lead to inhibited biomass growth and also\nspecies loss (Fig. 1 in the main text; L111–112). We conducted separate analyses of the\npositive and negative responses to apply different mathematical models and quantify\nthe effects of N deposition on SR. A positive response is determined by a net increase\nin SR in the N addition plot compared to the control plot, suggesting that the N-induced,\ncontinuous biomass growth is dominant. The negative response is determined by the\nnet decrease of SR in the N addition plot compared to the control plot, suggesting that\nN-induced, abrupt species loss is dominant. We revised the manuscript to clarify the\npotential mechanisms underlying these responses (L93–121, 138–141).\n#3. Overall, I find the separation of positive and negative SR responses to nitrogen\naddition conceptually confusing and methodologically unsubstantiated. Without\nstronger evidence or theoretical support for this approach, I cannot endorse the results\nor conclusions that depend on it.\nResponse: We have thoroughly revised the manuscript to clarify that the separate\nanalyses of the positive and negative responses were due to the different dominant\nmechanisms underlying the positive response (likely dominated by continuous biomass\nchange) and the negative response (likely dominated by abrupt species loss). This\nseparation enabled us to develop a continuous model (i.e., biphasic model shown in Fig.\n3 in the main text) for quantifying the positive response of SR to any N input level, and\nan abrupt transition model (see Fig. 3 in the main text) for quantifying the negative\nresponse of SR. By quantifying another key variable, probability of SR being decreased\nby N input (Pdec; Fig. 5), we were able to integrate the two models for positive/negative\nSR responses and evaluate the overall effect of any N deposition level on global forest\nSR (fig. 2).\nfig. 2 Illustration and explanation of the workflow how we handled the experimental N\naddition data. Experimental N addition data were separated to build mathematical\nmodels for positive and negative responses of soil respiration (SR) to N inputs. On the\nbasis, we quantified the N deposition-induced increase and decrease of SR. Because the\nsame N input rate can increase or decrease SR, we quantified a “probability of soil\nrespiration being decreased by N input” (P ) for integrating the positive and negative\ndec\neffects and reveal the overall effect of N deposition on SR. SR : soil respiration rate\nCL\nmeasured in the control plot receiving natural nitrogen deposition (N ). SR : soil\ndep +N\nrespiration rate measured in the experimental plot receiving an additional N input (N ).\nadd\n∆SR : increased SR by N input. ∆SR : decreased SR by N input. f : positive\nN-inc N-dec pos\nresponse factor of increased SR to N input. f : negative response factor of decreased\nneg\nSR to N input (see Supplementary Text S1 for the response factors derived from\nbiochemical and ecological theories). P : Probability of SR being decreased by N\ndec\ninput, estimated as the proportion of experimental N addition forests where a N input\nrate caused a decrease in soil respiration. This figure has been incorporated into the\nrevised Fig. 2 in the main text.\nReviewer #2 (Remarks to the Author):\n#4. The authors have made thorough and appropriate revisions in response to the\nEditor's and reviewers’ previous comments. I am satisfied with the current version of\nthe manuscript and have no further comments.\nResponse: Thank you!\nReviewer #2 (Remarks on code availability):\n#5. The code execution is reliable.\nResponse: Thanks for helping check the code execution.\nThanks again.\nYours sincerely,\nNianpeng He\nReferences\nBai, Y., J. Wu, C. M. Clark, S. Naeem, Q. Pan, J. Huang, L. Zhang, and X. Han. 2010.\nTradeoffs and thresholds in the effects of nitrogen addition on biodiversity and\necosystem functioning: evidence from inner Mongolia Grasslands. Global Change\nBiology 16:358-372.\nBand, N., R. Kadmon, M. Mandel, and N. DeMalach. 2022. Assessing the roles of nitrogen,\nbiomass, and niche dimensionality as drivers of species loss in grassland\ncommunities. Proceedings of the National Academy of Sciences\n119:e2112010119.\nBobbink, R., K. Hicks, J. Galloway, T. Spranger, R. Alkemade, M. Ashmore, M. Bustamante,\nS. Cinderby, E. Davidson, F. Dentener, B. Emmett, J. W. Erisman, M. Fenn, F. Gilliam,\nA. Nordin, L. Pardo, and W. De Vries. 2010. Global assessment of nitrogen deposition\neffects on terrestrial plant diversity: a synthesis. Ecological Applications\n20:30-59.\nBrown, J. H., J. F. Gillooly, A. P. Allen, V. M. Savage, and G. B. West. 2004. TOWARD A\nMETABOLIC THEORY OF ECOLOGY. Ecology 85:1771-1789.\nCen, X., N. He, M. Li, L. Xu, X. Yu, W. Cai, X. Li, and K. Butterbach-Bahl. 2024. Suppression\nof Nitrogen Deposition on Global Forest Soil CH4 Uptake Depends on Nitrogen\nStatus. Global Biogeochemical Cycles 38:e2024GB008098.\nDe Schrijver, A., P. De Frenne, E. Ampoorter, L. Van Nevel, A. Demey, K. Wuyts, and K.\nVerheyen. 2011. Cumulative nitrogen input drives species loss in terrestrial\necosystems. Global Ecology and Biogeography 20:803-816.\nEgidi, E., C. Coleine, M. Delgado-Baquerizo, and B. K. Singh. 2023. Assessing critical\nthresholds in terrestrial microbiomes. Nature Microbiology 8:2230-2233.\nFog, K. 1988. THE EFFECT OF ADDED NITROGEN ON THE RATE OF DECOMPOSITION OF\nORGANIC MATTER. Biological Reviews 63:433-462.\nHedwall, P. O., J. Bergh, and J. Brunet. 2017. Phosphorus and nitrogen co-limitation of\nforest ground vegetation under elevated anthropogenic nitrogen deposition.\nOecologia 185:317-326.\nHuang, Y. M., R. H. Kang, J. Mulder, T. Zhang, and L. Duan. 2015. Nitrogen saturation, soil\nacidification, and ecological effects in a subtropical pine forest on acid soil in\nsouthwest China. Journal of Geophysical Research-Biogeosciences 120:2457-\n2472.\nZhao, M., H. Zhang, C. C. Baskin, C. Wei, J. Yang, Y. Zhang, Y. Jiang, L. Jiang, and X. Han.\n2022. Intra-annual species gain overrides species loss in determining species\nrichness in a typical steppe ecosystem after a decade of nitrogen enrichment.\nJournal of Ecology 110:1942-1956.\nZheng, M., T. Zhang, Y. Luo, J. Liu, X. Lu, Q. Ye, S. Wang, J. Huang, Q. Mao, J. Mo, and W.\nZhang. 2022. Temporal patterns of soil carbon emission in tropical forests under long-\nterm nitrogen deposition. Nature Geoscience 15:1002-1010."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the authors' detailed responses and their efforts to clarify the rationale behind their analytical approach. However, I remain unconvinced by the decision to separate the analysis of positive and negative effects on SR, as no compelling theoretical or empirical justification has been provided to support this separation. Since this constitutes a fundamental step in their analytical framework, it casts doubt on the validity of the subsequent results and conclusions.\nIn their response, the authors suggest that increased SR results from \"N-facilitated biomass growth,\" while decreased SR arises from \"N-induced biodiversity loss.\" However, numerous grassland experiments have demonstrated that both biomass increase and biodiversity loss can occur simultaneously under the same nitrogen treatments (e.g., Bai et al., 2009, Global Change Biology, https://doi.org/10.1111/j.1365-2486.2009.01950.x). This evidence supports the point I raised earlier: both positive and negative mechanisms may operate concurrently within the same plot, making it problematic to analyze them in isolation.\nAlso, the authors mentioned that the positive N effect on SR is biphasic because of “high N becomes toxic”. I would argue that “high N becomes toxic” is also one of the reasons for negative responses of SR to N addition. This again challenges the validity of separating these effects analytically.\nOverall, I find the separation of positive and negative SR responses to nitrogen addition conceptually confusing and methodologically unsubstantiated. Without stronger evidence or theoretical support for this approach, I cannot endorse the results or conclusions that depend on it.", "reviewer_2": "The authors have made thorough and appropriate revisions in response to the Editor's and reviewers’ previous comments. I am satisfied with the current version of the manuscript and have no further comments."}, "author_response": ""}]

Decision letter