Global patterns of nitrogen saturation in forests

doi:10.21203/rs.3.rs-3559857/v1

Download PDF

Physical Sciences - Article

Global patterns of nitrogen saturation in forests

https://doi.org/10.21203/rs.3.rs-3559857/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Since the industrial revolution, accelerated atmospheric nitrogen (N) deposition by human activities have increased N availability in forest ecosystems close to human settlements, potentially causing many nitrogen-limited forests to become nitrogen-saturated, with significant effects on productivity, biodiversity, and biogeochemical cycles. Four decades after recognizing the N saturation problem, however, global patterns of N saturation in forests still remain uncertain. In N-saturated forests, oversupply of N leads to higher N losses including those in form of N₂O as compared to N-limited forests, suggesting that the sensitivity of soil N₂O emission to N deposition (s_N) might be used as an indicator of N saturation. In this study, we modeled the s_N of global forests using data from N addition experiments. Testing with field observations on N saturation status, the global patterns of N-limited and N-saturated forests indicated by s_N show an accuracy above 70% on global and geographic-regional scales. Our results suggest that 43% of global forests are N-saturated, and the proportions of forests being N-saturated are particularly high in East Asia and Western Europe (over 60%). The produced global map of N-saturated forests sheds light on the spatially varying N availability in forests, which founds a basis for predicting the influence of changing N deposition on forest greenhouse gas emissions and productivity, facilitating optimized environmental management practices for different regions.

Earth and environmental sciences/Biogeochemistry/Element cycles

Earth and environmental sciences/Ecology/Forest ecology

Earth and environmental sciences/Climate sciences/Atmospheric science

Earth and environmental sciences/Environmental sciences/Environmental impact

Nitrogen (N), an essential component of amino acids and nucleic acids, is fundamental for the survival and growth of all organisms¹. In a specific ecosystem, the mass of living organisms that can be supported sustainably is constrained by multiple resources². N content in organisms is regulated by stoichiometric homeostasis³, which means that organisms in an ecosystem have a limited demand for N. In contrast, N supply, especially those from atmospheric deposition, can be substantial and drive ecosystems to reach “N saturation”, a status defined as supply of N exceeding plant and microbial N demand⁴, whereafter surplus N may be lost along hydrological and gaseous pathways⁵. Excessive N availability may even be toxic to plants and microbes (termed “hormesis effect”)⁶, thereby negatively affecting productivity and biodiversity at ecosystem scale⁷. N saturation leads to many environmental problems such as ground and surface water eutrophication, harmful algal blooms, soil acidification, and increased soil greenhouse gas (GHG) emissions^8–10 (Fig. 1).

Since the 19th century, the amount of N deposited via precipitation and gaseous aerosols has more than tripled on a global scale¹¹. In contrast to biological N fixation, which is a form of N input constrained by energy cost and rarely leads to N oversupply in ecosystems¹², atmospheric N deposition is mainly driven by anthropogenic reactive N emissions. These include NH_x volatilized from applied fertilizers, and NO_y from combustion processes, and they are not regulated by ecosystems. Forests are severely affected by N deposition due to the filtering effect of canopies, where N deposition rates can be 2–3 times higher than over open land^13,14. Consequently, elevated N deposition has caused many forest ecosystems to shift from N-limited to N-saturated status^15–18, although N limitation is still widespread in other natural ecosystems^19–21. Efforts have been devoted to identifying the N limitation or saturation status (hereafter referred to as “N saturation status”) of global forests since the 1980s, such as to assess the human-activity-induced change in N availability in natural ecosystems²², and to determine the critical load of N deposition in managed forests^23,24.

Understanding the global patterns of N-saturated forests is critical, especially as countries are setting goals to reach net-zero GHG emissions (or carbon neutrality) by the mid-21st century²⁵ for mitigating global warming. Forestland-based practices (e.g., reforestation, forest management) are the most cost-effective and readily applicable climate solutions^26,27. However, the predicted CO₂ uptake by global forests is significantly affected by N availability^26–28. Assessing the spatially varying N availability (N-limited vs. N-saturated) in global forests is fundamental to effective carbon sequestration.

In recent decades, studies have identified various indicators of N saturation (see Supplementary Materials Text S1 for different indicators). N leaching from forests was widely used from the 1990s to the 2000s to indicate whether a forest was N-limited or N-saturated²⁹. Nevertheless, owing to differences in sampling protocols (sampling in deep soils or nearby water bodies) and the complexity of soil hydrological processes³⁰, combining site-level N leaching data to reveal forest N saturation status on a regional scale has been challenging. Other indicators, such as soil properties and plant traits, have limitations regarding applicability or data availability³¹. Therefore, the N saturation patterns of global forests have remained unclear, despite of progresses in N limitation and saturation status studies^19–21,32.

In N-limited forest, plants and microbes utilize N conservatively, aiming at keeping this key nutritional element within the system at minimal environmental losses^33,34. However, when forest ecosystems become N-saturated due to high rates of atmospheric N deposition, these conservative strategies become less effective, leading to significant N losses³⁵ (Supplementary Materials Fig. S1). In this context, a rise in gaseous N emissions like N₂O, NO, and N₂ relative to N deposition could serve as an indicator that a forest is reaching N saturation¹⁸. This relationship suggests a higher sensitivity of these gaseous N emissions to N deposition, making it a potentially useful metric for assessing N saturation. Coincidently, studies have measured nitrous oxide (N₂O) greenhouse gas emissions under different N input levels since the 1980s in global forests, using a controlled experiment design and standard sampling method³⁶. The accumulated experimental data provide an opportunity to quantify the sensitivity of N₂O emissions to N deposition in global forests.

This study synthesizes soil N₂O emission data from 102 nitrogen addition experiment sites worldwide (Fig. 2). To avoid the potential skewing of ecosystem properties by exceedingly high simulated N inputs, we focused our analysis on studies that employed low simulated N inputs (≤ 150 kgN ha^–1 yr^–1; see Supplementary Materials Text S2 for determination of low N input). Our objective was to quantify the sensitivity of soil N₂O emission to N deposition (s_N = ∆N₂O emission rate / ∆N deposition rate) at a given experimental site. We also compiled data on field-observed N₂O emissions under natural conditions as well as N cycling parameters derived from N addition experiments. These two independent datasets were used to validate the modeled s_N. We then compiled data from the literature and a published database by Du et al.²¹ to form a dataset of field-observed N-limited and N-saturated forests. Using this dataset, we tested whether s_N was significantly different between N-limited and N-saturated forests, and determined the global patterns of N saturation in forests using s_N.

Sensitivity of soil N₂O emissions to N deposition in global forests

We quantified and modeled the sensitivity of soil N₂O emissions to N deposition (s_N) using data from experimental sites (N₂O_exp dataset; Supplementary Materials Data S1) where low rates of N input were applied to mimic the effect of atmospheric N deposition (Supplementary Materials Table S1, Table S2). Using modeled s_N, we estimated the global forest soil N₂O emission rate. The agreement of estimated and observed soil N₂O emission rates (N₂O_obs dataset; Supplementary Materials Data S2) indirectly supported the validity of the intermediate parameter s_N (Spearman’s ρ = 0.50; Supplementary Materials Fig. S2). Additionally, s_N is mechanistically related to N cycling parameters including the sensitivity of total N loss to N deposition (c₁), the sensitivity of N leaching to N deposition (c₂), and the nitrification and denitrification end-product ratio (c₃). Using c₁ and c₂ quantified from a different set of experimental data (Ncycle_exp dataset; Supplementary Materials Data S3), and c₃ modeled by Bai, et al. ³⁷, we calculated s_N on the biome scale (Supplementary Materials Text S3). Calculated s_N was compared with the biome-mean values of the modeled s_N. The good agreement further confirmed the validity of modeled s_N values (Pearson’s r = 0.998).

The global mean value of s_N was 0.013 kg N₂O-N per kg N deposited (kgN₂O-N kgN^–1). s_N generally decreased from low- to high-latitude forests (Fig. 3). The mean s_N was significantly different across the biomes (p < 0.01; Fig. 3). Global hotspots of s_N include tropical rainforests, forests in East Asia, Western Europe, eastern United States, eastern Siberia, and northwestern Canada. A relatively open N cycle in N-rich tropical rainforests contributes to higher s_N³⁸. Also, plants and microbes may not be able to immediately adapt to changed N input, causing increased N loss³⁹; thus, a positive correlation was observed between interannual variation of N deposition and s_N (Supplementary Materials Fig. S3). This may be a reason why in temperate forests experiencing relatively high changes in atmospheric N deposition due to human activities (mostly in East Asia, Western Europe, and eastern United States), s_N values were also high. Furthermore, the s_N of forests in northwestern Canada and eastern Siberia were predicted to be high (Fig. 3), possibly resulting from enhanced N deposition by forest fires and biomass burning⁴⁰, and increased leakage of N₂O from thawing permafrost N pools under a warming climate^41,42.

In general, N deposition and soil texture factors dominated the spatial variation in s_N. Specifically, N deposition and its annual variation (N_depo.cv) explained more than 50% of the variation in s_N in temperate forests, supporting that atmospheric N deposition played a more important role in this biome than in tropical and boreal biomes^43,44. In addition to environmental factors used for constructing the regression model on s_N (temperature, precipitation, N deposition, and soil texture), other factors may as well influence the response of soil N₂O emission to N deposition, such as soil chemical variables and forest type ⁴⁵. Soil chemical variables (e.g., total N, C:N) were not used because they could be changed by N deposition over time, and the temporal coverage of existing spatial datasets could hardly match with the response variable (s_N). Although forest type was not used in this study to model s_N, the interactions of the considered factors may have accounted for the variation it represents, thus the modeled s_N could indicate the difference in N loss pathways in deciduous broadleaf and needleleaf forests (Supplementary Materials Text S4), which was consistent with field observation ⁴⁶.

Studies have determined N limitation or N saturation of forests worldwide based on indicators such as N leaching rate (Nleach dataset) and plant growth response to N input (NuLi dataset)²¹. For each site, s_N was estimated using our constructed model which had been validated with two independent datasets. Comparing sites with different N saturation status, s_N was significantly higher in forests identified as N-saturated in previous studies than in N-limited forests, on both global and geographic-regional scales (p < 0.001; Fig. 4). This was also consistent with the field study⁴⁷. A possible explanation is that in N-limited forests, plants and microbes utilize N conservatively, so that microbial processes creating reactive N (e.g., mineralization, ammonification, and nitrification) are closely linked to microbial, plant and physicochemical N immobilization processes^33,34. On the contrary, in N-saturated forests, excessive N supply leads to more open N cycling processes⁴⁸, causing a higher fraction of deposited N to be lost in forms such as N₂O as compared to N-limited forests.

Additionally, the robustness of the relationship between s_N and the N saturation status across geographic regions (Fig. 4) shows that s_N is a suitable indicator of forest N saturation, unparalleled in its performance as compared to various other potential indicators (Supplementary Materials Fig. S4).

To determine the N saturation pattern of global forests, we firstly calculated a threshold of s_N using bootstrap method⁴⁹, and distinguished between N-saturated and N-limited forests (Supplementary Materials Fig. S5). Specifically, forests with s_N values greater than 0.0143 kgN₂O-N kgN^–1 were classified to be N-saturated, whereas the others were classified to be N-limited. 76% of the 154 forest sites with field-observed N saturation status information were accurately classified (Fig. 5). In three geographic regions with abundant field observations, Western Europe, East Asia, and North America, the classification accuracies were as well above 70%, showing that the used classification was reliable on global and geographic-regional scales. Therefore, we produced a rasterized global map of N-limited and N-saturated forests that could be applied to research requiring N saturation status information, such as prediction of forest productivity.

Influences of human activities on the N saturation status of forests

The global patterns of forest N saturation using s_N as an indicator show that 42.6% of global forests are N-saturated. Exploration of the inter-regional variation of forest N saturation reveals higher N saturation ratios (ratio of N-saturated forest area to total forest area) in tropical and temperate forests (53.2% and 51.1%, respectively) than in boreal forests (23.1%). Geographic regions with high N saturation ratios (South Asia, East Asia, and Western Europe) coincided with those that were highly industrialized and/or intensively used for agriculture (Fig. 6). This implies that socioeconomic development may have played a role in the formation of the N saturation patterns in global forests.

We studied the relationship between economic development and forest N saturation for countries with significant forest areas (Supplementary Materials Fig. S6). In the tropics, economically more prosperous countries, such as Malaysia and Cuba, had lower N saturation ratios. However, the relationship was reversed in the boreal biome, where economically more prosperous countries (e.g., Norway, Finland) had higher forest N saturation ratios. A possible explanation is that deforestation accompanying economic development in the tropics has replaced primary tropical forests (which are naturally in or close to N saturation^38,50) with N-limited secondary forests⁵¹, thereby lowering the N saturation ratios. Most temperate and boreal forests may naturally be N-limited; increased reactive N emissions from combustion processes and intensive agriculture alongside economic development, however, can enhance atmospheric N deposition and drive forests to reach N saturation⁵². The resulted positive relationship between economic development and N saturation ratio was more prominent in boreal than in the temperate forests, probably because the opposite effects of deforestation and N deposition on N saturation ratio counterbalance each other in temperate biome.

In addition to land cover change, other anthropogenic global change components, such as CO₂ emissions and global warming, can influence N saturation status of global forests. Previous research has shown that increasing atmospheric CO₂ concentration from anthropogenic emissions and extended growing season by global warming can enhance the biotic demand for N, lowering the relative N availability in natural ecosystems⁵³ and potentially induce N limitation. But in ecosystems close to densely-populated and more industrialized regions, high N deposition may overwhelm this “background” trend (Supplementary Materials Fig. S7), causing ecosystems to become N-saturated¹⁶ at least transiently. To fully capture the spatial and temporal variation of N saturation status of global forests, it will be necessary to comprehensively study the combined effects of multiple global change components and periodically assess the global patterns of N saturation, such as to provide realistic and explicit implications for environmental management.

Implications and potential applications of the global patterns of N-saturated forests

Similar to the sensitivity of soil respiration to temperature⁵⁴, the sensitivity of soil N₂O emissions to N deposition (s_N) is also a relatively stable parameter due to resource constraints⁵⁵ and the functional stability of microbial communities⁵⁶. The s_N of global forests, estimated using data from N addition experiments from the 1980s to the 2010s, indicates forest N saturation status during the time.

The derived patterns of global forest N saturation indicate high spatial variation in N availability. We also estimated the temporal change of N availability using s_N as an indicator (Supplementary Materials Fig. S8), which revealed lowering N availability in the two geographic regions, Asia Pacific and Europe, where N addition experiment data were abundant. This is consistent with recent findings about the oligotrophication (declining N availability) trend in natural ecosystems caused by increasing atmospheric CO₂ concentration and warming ^57–59. Considering the spatial variation and temporal change of N availability, different environmental management practices should be adopted in different regions ^57,58,60,61. For those where N availability is expected to decrease (e.g., Western Europe and the United States)⁵⁷ whilst the N saturation ratio is currently high (probably a legacy effect of high N deposition in the past few decades), sustained N pollution regulation and environmental assessment may be helpful⁶². For those where N saturation ratio is high and N availability may further increase (e.g., China and India)⁵⁷, effective N pollution regulation and treatment may be needed.

Additionally, we speculate that the derived pattern of N-limited and N-saturated forests will be helpful for quantifying biogeochemical processes and revealing underlying mechanisms across scales. Studies have observed the stimulating or suppressing effect of N deposition on soil methane (CH₄) flux at different forest sites, probably resulting from differences in the N saturation status; soil CH₄ uptake flux tend to be stimulated by N deposition in N-limited forests, while N deposition may suppress soil CH₄ uptake in N-saturated forests ^63,64. The N saturation map we produced may allow researchers to quantify the effect of N deposition on soil CH₄ flux in forests under different N status, thus improving model structures and reducing uncertainties in global CH₄ budgets. Similarly, the global map of N-saturated forests may be applied to reduce uncertainties in predicting forest productivity and soil respiration. Moreover, the map can be used as a reference for forest management, so that conservation efforts could be focused, and N-deposition-induced biodiversity loss could be reduced⁶⁵.

In this study, we validated s_N indirectly with soil N₂O emission rates estimated from s_N. The N cycling parameters were also used to calculate and validate s_N on a biome scale (Supplementary Materials Text S3). Although these two approaches can ensure the reliability of the model for s_N, more N addition experiments are required to derive s_N and improve the model, especially in boreal forests where the uncertainty of modeled s_N is high (Supplementary Materials Fig. S9). In the future when more long-term experimental data become available, researchers can also model the temporal change of s_N, predicting the N availability change under future climate and N deposition regime. Besides, more attention should be paid to the N transformation processes (mineralization, nitrification, and denitrification) governing s_N^66,67. Connecting s_N with underlying biochemical processes can help to consolidate the intrinsic correlation between s_N and N saturation status from a microbiological perspective.

Controlled experiments and field observations from global sites have provided the foundation for quantifying the sensitivity of ecosystems to various environmental variables, such as temperature, precipitation, N deposition rate, and CO₂ concentration. The spatial variation and temporal change in the sensitivities are of high theoretical and realistic importance. For example, N addition experiments revealed that only approximately 1% of the deposited N would leave the ecosystem as N₂O. However, the spatial variation in the percentage, equivalent to s_N, indicates the N saturation status of global forests. This implies, reasonably coordinated experimental networks and long-term experiments are of enormous value^68,69. In addition, standard experimental design and sampling protocols are very important for integrating observations from different sites and revealing ecosystem properties on large scale. The quantified sensitivities of ecosystems may provide new perspectives and parameters for ecosystem models, thus improving the predictions of productivity, biodiversity, and GHG emissions under global change.

Although the N-saturation status of global forests has been a concern over the past four decades, previously used indicators have not clarified the global pattern of N-saturated forests. Therefore, we modeled and validated the sensitivity of soil N₂O emissions to N deposition (s_N) of global forests and determined the N saturation status of global forests using five independent datasets (Supplementary Materials S1–S5).

Data sources

To quantify the sensitivity of soil N₂O emissions to N deposition (s_N), we compiled soil N₂O emission data observed in N addition experiments conducted in global forests. We searched for papers and theses published before 01/01/2022 from the Web of Science Core Collection database (www.webofscience.com) and China National Knowledge Infrastructure Theses and Dissertations Database (https://oversea.cnki.net/kns?dbcode=CDMD), using the following keywords: “forest” AND “greenhouse gas” OR “N₂O” OR “nitrous oxide”. The retrieved 7422 papers and 718 theses were then refined manually based on the following criteria: (i) experimental N addition was conducted in forest ecosystem; (ii) literature recorded the location, time, and dose of the experiment(s); (iii) soil N₂O flux was observed in experimental sites using chamber method³⁶. As a result, the compiled “N₂O_exp” dataset (Supplementary Materials Data S1) contained 553 observations from 102 sites worldwide (Fig. 2).

Similarly, we compiled data on the soil N₂O emission rates of global forests observed under natural conditions. We refined from the same papers and theses as above, using a different set of criteria: (i) no nutrients, including N, were artificially added to the forest site so the site only received naturally deposited N; (ii) literature recorded the location, and time of flux measurement; (iii) soil N₂O flux was observed in the field using chamber method³⁶. The compiled “N₂O_obs” dataset (Supplementary Materials Data S2) contained 246 observations from 140 sites worldwide (Fig. 2).

In addition, we compiled data on total N loss (N leaching and gaseous N emission combined), N leaching, and change in soil N pool, from N addition experiments in global forests. We searched in the aforementioned databases using the following keywords: “forest” AND “nitrogen addition” OR “fert*” AND “nitrogen loss” OR “nitrogen leaching” OR “nitrogen budget”. Retrieved 2693 papers and theses were then refined based on the following criteria: (i) literature recorded the location, time, and dose of experimental N addition in forests; (ii) total N loss rate, N leaching rate, or change rate of soil N pool was observed or estimated in the experiments. The compiled “Ncycle_exp” dataset (Supplementary Materials Data S3) contained 169 observations from 37 sites (Fig. 2).

To analyze the relationship between s_N and N saturation status, we compiled data on field-observed N-limited and N-saturated forests indicated by N leaching. On 10/31/2022, we searched for literature in the aforementioned databases using the following keywords: “forest” AND “leaching” AND “nitrogen limit*” OR “nitrogen saturat*”. Retrieved 823 papers and theses were then refined based on the following criteria: (i) literature recorded whether the forest was N-limited or N-saturated, and its location; (ii) literature used nitrogen leaching as an indicator of N limitation or saturation status. The compiled “Nleach” dataset (Supplementary Materials Data S4) contains 136 observations from 92 sites worldwide (Fig. 5). We also used data on field-observed N-limited and N-saturated ecosystems indicated by plant growth response to N input (“NuLi” dataset; Supplementary Materials Data S5) from a published database by Du, et al. ²¹. It covers 106 sites worldwide, 65 of which are forest sites (Fig. 5).

Moreover, we extracted auxiliary information from the literature on environmental factors (including mean annual temperature, MAT; mean annual precipitation, MAP; mean annual N deposition rate, N_depo; coefficients of temporal variation, MAT.cv, MAP.cv, and N_depo.cv; soil sand content, soil clay content, and other soil properties) for the forest sites in the datasets. However, the literature did not provide the necessary auxiliary information for all sites; therefore, spatial datasets were used to fill in the missing data based on the location of the sites, and datasets of the same year as missing data were used if possible. Global temperature and precipitation datasets were obtained from the Climatic Research Unit, University of East Anglia (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/). The soil C:N ratio was obtained from a published database by Shangguan, et al. ⁷⁰. Other soil properties were obtained from the HWSD dataset (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). N deposition rate was from a published database by Ackerman, et al. ⁴⁰. Forest cover data were from a published database by Liu, et al. ⁷¹. The forest biome map was derived from the Global Forest Monitoring project⁷². We also acquired the GDP per capita of global countries from the World Bank Open Data portal (https://data.worldbank.org/).

Quantifying the sensitivity of soil N₂O emissions to N deposition

Studies in forest or agricultural systems has reported that, under low rates of simulated N deposition, soil N₂O emission rate responded almost linearly to N input, whereas non-linear responses were observed under high rates of N addition^18,73. This is probably because high N input changes ecosystem properties, leading to a deviation from the natural response of ecosystems to environmental change. Therefore, we used a linear model (Eq. 1) to define and quantify the sensitivity (s_N) of soil N₂O emissions to N deposition (or low N input), for s_N to reflect intrinsic property of an ecosystem.

\({R}_{N2O}={s}_{N}\times {N}_{depo}+{R}_{0}\) (Eq. 1)

where R_N2O is the soil N₂O emission rate (kgN₂O-N ha^–1 yr^–1), N_depo is the atmospheric N deposition rate (kg N ha^–1 yr^–1), s_N is the sensitivity of soil N₂O emission to N deposition, quantified as soil N₂O emission per unit of low N input (kgN₂O-N kgN^–1), and R₀ is the background soil N₂O emission rate when there is no N deposition or artificial N addition (kgN₂O-N ha^–1 yr^–1).

According to the results of a segmented regression analysis and previous studies (Supplementary Materials Text S2), experimental data with N addition rates not exceeding 150 kg N ha^–1 yr^–1 were used as “low N input” data in this study. The N deposition rates in global forests were lower than the level⁴⁰. For all the low-N input data in the N₂O_exp dataset, we aggregated them to 0.5° × 0.5° grids based on their coordinates to match the spatial resolution with environmental factors and reduce random errors in sampling. A linear model (Model: R_N2O ~ N input rate) was built for each grid with low-N input data. The slope and intercept of the linear model were, respectively, the estimated s_N and R₀ of the grid (Supplementary Materials Table S1). Based on the estimated s_N and R₀ of all grids and the corresponding environmental variables (MAT, MAT.cv, MAP, MAP.cv, N_depo, N_depo.cv, Sand, Clay), we built two generalized linear models to simulate s_N and R₀, respectively (Supplementary Materials Table S2).

To validate s_N, we firstly used the modeled s_N, together with the modeled R₀ and N_depo datasets, to estimate R_N2O (Eq. 1). The estimated R_N2O values were compared with R_N2O observations (N₂O_obs dataset) and indirectly validated the intermediate variable s_N (Fig. 2). In addition, s_N was validated using a second approach. The sensitivity of total N loss to N input (c₁), the sensitivity of N leaching to N input (c₂), and the end-product ratio of nitrification and denitrification processes (c₃) were either derived from the Ncycle_exp dataset or extracted from the literature³⁷; s_N was then calculated from these parameters (Supplementary Materials Text S3). The limited observations allowed us to calculate s_N on a biome scale, which was then compared with the biome-mean value of the modeled s_N to validate it.

Determining N saturation status of global forests using s_N

We tested whether s_N can distinguish between N-limited and N-saturated forests using data from forests having field-observed N saturation status data. First, we separately analyzed the Nleach and NuLi datasets, in which different variables indicated the forest N saturation status. As the two datasets functioned similarly in classifying N-limited and N-saturated forests (Supplementary Materials Text S5), we combined them to enlarge the sample size and derive a universal classification. Excluding three duplicate sites in both datasets, the combined dataset had 154 sites with field-observed N saturation status (86 N-limited and 68 N-saturated sites).

We modeled the s_N of the 154 sites using our constructed generalized linear model (Supplementary Materials Table S2). We then analyzed the s_N of N-limited and N-saturated forests and verified if there were significant differences on the global and biome scales. In Western Europe, North America, and East Asia, where there were abundant sites, we also compared the s_N of forests with N-limited or N-saturated status on geographic-regional scale.

After proving that s_N can indicate N limitation or saturation status in forests, we calculated an optimal threshold for s_N using data from 154 sites with field-observed N saturation status and s_N information. When calculating threshold, the bootstrap method was used, to accounted for the different sample sizes of N-limited and N-saturated sites⁴⁹. Specifically, from the 154 sites, we randomly sampled 10 N-limited and 10 N-saturated sites and selected a cutoff value for their s_N at a precision of 0.0001 kgN₂O-N kgN^–1. Sites in which s_N were above the cutoff value were classified as “N-saturated”, and the rest were classified as “N-limited”. The classified N saturation status of the sites was compared with field observations to determine the accuracy of the classification, which was calculated as the proportion of sites accurately classified into the same category as that observed. All possible cutoff values were tested, and the one with the highest classification accuracy was the “optimal” cutoff value. Random sampling and detection of optimal cutoff values were repeated 5000 times, during which some optimal cutoff values were detected more frequently than others. The most frequently detected optimal cutoff value, 0.0143 kgN₂O-N kgN^–1, was then used as the optimal threshold for s_N.

The N saturation status of global forests was determined based on the optimal threshold. Forests with s_N above the threshold were classified as N-saturated, and the rest were classified as N-limited. The accuracy of the classification was checked on global and geographic-regional scales. Based on the classification, we produced a rasterized map of N-limited and N-saturated forests (0.5° × 0.5° resolution) in ArcGIS software⁷⁴.

Data analysis

To analyze the relative importance of environmental factors for s_N, we used the “relaimpo” package⁷⁵ in R⁷⁶. The N saturation ratio of forests was calculated as the ratio of the N-saturated forest area to the total forest area. To explore the relationship between forest N saturation status and economic development, we built a linear regression model for countries in each biome on the N saturation ratio of forests and GDP per capita (averaged over 1985–2015). Four of the 160 forest-covered countries did not have GDP per capita data (Supplementary Materials Data S6); therefore, the models were built using data from the remaining 156 countries.

In this study, data analysis processes were conducted using R software⁷⁶, and the significance level was set at p < 0.05. Statistical results and figures in this paper (and supplementary materials) are reproducible using the supplementary datasets and code (Supplementary Materials Data S1–S7, Code S1). Maps were produced using ArcGIS software⁷⁴.

Data availability

The authors declare that the data supporting the findings of this study are available as its supplementary files (Supplementary Materials Data S1–S7).

Code availability

R codes used for analyzing the data and producing the figures are available as supplementary file (Supplementary Materials Code S1).

Acknowledgments

We thank P. Vitousek for helpful comments and suggestions. We are grateful to all the researchers who spent time and efforts on manipulative experiments and observations, and made the data publicly accessible. This work was financially supported by the National Natural Science Foundation of China (42141004, 31988102, 32171544), the National Key R&D Program of China (2022YFF080210102), and CAS Project for Young Scientists in Basic Research, YSBR-037. KVS acknowledges support from the Belgian American Educational Foundation, the Fulbright Commission in Belgium and Luxemburg, and the Fund for Scientific Research – Flanders (FWO award number 1222323N). KBB received funding by the Danish National Research Foundation (DNRF), Grant Number P2 “Pioneer Center for Research in Sustainable Agricultural Futures (Land-CRAFT).

Author contributions

X.C. and N.H. conceived the study. X.C. carried out the analysis with feedback from N.H. and K.B.B. X.C. drafted the manuscript, and all authors edited the manuscript.

Competing interests

The authors declare no competing interests.

Liebig, J. F. v. Organic chemistry in its applications to agriculture and physiology. (printed for Taylor and Walton, 1840).
Odum, E. P. Fundamentals of ecology. (W. B. Saunders, 1953).
Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere. (Princeton University Press, 2002).
Aber, J. D., Nadelhoffer, K. J., Steudler, P. & Melillo, J. M. Nitrogen saturation in northern forest ecosystems: Excess nitrogen from fossil fuel combustion may stress the biosphere. BioScience 39, 378-386, doi:10.2307/1311067 (1989).
Ågren, G. I. & Bosatta, E. Nitrogen saturation of terrestrial ecosystems. Environ Pollut 54, 185-197, doi:10.1016/0269-7491(88)90111-X (1988).
Agathokleous, E., Kitao, M. & Calabrese, E. J. Hormesis: Highly generalizable and beyond laboratory. Trends in Plant Science 25, 1076-1086, doi:10.1016/j.tplants.2020.05.006 (2020).
de Vries, W. & Schulte-Uebbing, L. in Atlas of Ecosystem Services: Drivers, Risks, and Societal Responses (eds Matthias Schröter et al.) 183-189 (Springer International Publishing, 2019).
Aber, J. D. Nitrogen cycling and nitrogen saturation in temperate forest ecosystems. Trends in Ecology & Evolution 7, 220-224, doi:10.1016/0169-5347(92)90048-G (1992).
Lu, X. K., Mao, Q. G., Gilliam, F. S., Luo, Y. Q. & Mo, J. M. Nitrogen deposition contributes to soil acidification in tropical ecosystems. Global Change Biol 20, 3790-3801, doi:10.1111/gcb.12665 (2014).
Vitousek, P. M. et al. Human alteration of the global nitrogen cycle: Sources and consequences. Ecol Appl 7, 737-750, doi:10.2307/2269431 (1997).
Galloway, J. N. et al. Nitrogen cycles: Past, present, and future. Biogeochemistry 70, 153-226, doi:10.1007/s10533-004-0370-0 (2004).
Vitousek, P. & Field, C. B. in Global Biogeochemical Cycles in the Climate System (eds Ernst-Detlef Schulze et al.) 217-225 (Academic Press, 2001).
Goulding, K. W. T. et al. Nitrogen deposition and its contribution to nitrogen cycling and associated soil processes. The New Phytologist 139, 49-58, doi:10.1046/j.1469-8137.1998.00182.x (1998).
Fowler, D. et al. The global exposure of forests to air pollutants. Water Air Soil Poll 116, 5-32, doi:10.1023/a:1005249231882 (1999).
Gundersen, P., Emmett, B. A., Kjonaas, O. J., Koopmans, C. J. & Tietema, A. Impact of nitrogen deposition on nitrogen cycling in forests: a synthesis of NITREX data. Forest Ecol Manag 101, 37-55, doi:10.1016/s0378-1127(97)00124-2 (1998).
De Schrijver, A. et al. Nitrogen saturation and net ecosystem production. Nature 451, E1-E1, doi:10.1038/nature06578 (2008).
Peterjohn, W. T., Adams, M. B. & Gilliam, F. S. Symptoms of nitrogen saturation in two central Appalachian hardwood forest ecosystems. Biogeochemistry 35, 507-522, doi:10.1007/BF02183038 (1996).
Aber, J. D. et al. Nitrogen saturation in temperate forest ecosystems: hypotheses revisited. BioScience 48, 921-934, doi:10.2307/1313296 (1998).
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371-379, doi:10.1890/06-2057.1 (2008).
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol Lett 10, 1135-1142, doi:10.1111/j.1461-0248.2007.01113.x (2007).
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat Geosci 13, 221-226, doi:10.1038/s41561-019-0530-4 (2020).
Galloway, J. N. et al. The nitrogen cascade. BioScience 53, 341-356, doi:10.1641/0006-3568(2003)053[0341:TNC]2.0.CO;2 (2003).
Reinds, G. J., Posch, M., De Vries, W., Slootweg, J. & Hettelingh, J. P. Critical loads of sulphur and nitrogen for terrestrial ecosystems in Europe and Northern Asia using different soil chemical criteria. Water Air Soil Poll 193, 269-287, doi:10.1007/s11270-008-9688-x (2008).
Reynolds, B., Wilson, E. J. & Emmett, B. A. Evaluating critical loads of nutrient nitrogen and acidity for terrestrial systems using ecosystem-scale experiments (NITREX). Forest Ecol Manag 101, 81-94, doi:10.1016/s0378-1127(97)00127-8 (1998).
van Soest, H. L., den Elzen, M. G. J. & van Vuuren, D. P. Net-zero emission targets for major emitting countries consistent with the Paris Agreement. Nat Commun 12, 2140, doi:10.1038/s41467-021-22294-x (2021).
Meyerholt, J., Sickel, K. & Zaehle, S. Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake. Global Change Biol 26, 3978-3996, doi:10.1111/gcb.15114 (2020).
Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nature Climate Change 4, 471-476, doi:10.1038/nclimate2177 (2014).
Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nature Climate Change 9, 684-689, doi:10.1038/s41558-019-0545-2 (2019).
Fenn, M. E. et al. Nitrogen excess in North American ecosystems: Predisposing factors, ecosystem responses, and management strategies. Ecol Appl 8, 706-733, doi:10.2307/2641261 (1998).
Braakhekke, M. C. et al. Nitrogen leaching from natural ecosystems under global change: a modelling study. Earth System Dynamics 8, 1121-1139, doi:10.5194/esd-8-1121-2017 (2017).
Van Sundert, K. et al. Towards comparable assessment of the soil nutrient status across scales-Review and development of nutrient metrics. Global Change Biol 26, 392-409, doi:10.1111/gcb.14802 (2020).
Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: How can it occur? Biogeochemistry 13, 87-115, doi:10.1007/BF00002772 (1991).
Chapman, S. K., Langley, J. A., Hart, S. C. & Koch, G. W. Plants actively control nitrogen cycling: uncorking the microbial bottleneck. New Phytol 169, 27-34, doi:10.1111/j.1469-8137.2005.01571.x (2006).
Van Der Heijden, M. G. A., Bardgett, R. D. & Van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol Lett 11, 296-310, doi:10.1111/j.1461-0248.2007.01139.x (2008).
Hietz, P. et al. Long-term change in the nitrogen cycle of tropical forests. Science 334, 664-666, doi:10.1126/science.1211979 (2011).
Holland, E. et al. in Standard Soil Methods for Long-Term Ecological Research (eds G. P. Robertson, D. C. Coleman, C. S. Bledsoe, & P. Sollins) 185-201 (Oxford University Press, 1999).
Bai, E., Houlton, B. Z. & Wang, Y. P. Isotopic identification of nitrogen hotspots across natural terrestrial ecosystems. Biogeosciences 9, 3287-3304, doi:10.5194/bg-9-3287-2012 (2012).
Brookshire, E. N. J., Gerber, S., Menge, D. N. L. & Hedin, L. O. Large losses of inorganic nitrogen from tropical rainforests suggest a lack of nitrogen limitation. Ecol Lett 15, 9-16, doi:10.1111/j.1461-0248.2011.01701.x (2012).
Ochoa-Hueso, R. et al. Nitrogen deposition effects on Mediterranean-type ecosystems: an ecological assessment. Environ Pollut 159, 2265-2279, doi:10.1016/j.envpol.2010.12.019 (2011).
Ackerman, D., Millet, D. B. & Chen, X. Global estimates of inorganic nitrogen deposition across four decades. Global Biogeochem Cy 33, 100-107, doi:10.1029/2018GB005990 (2019).
Strauss, J. et al. A globally relevant stock of soil nitrogen in the Yedoma permafrost domain. Nat Commun 13, 6074, doi:10.1038/s41467-022-33794-9 (2022).
Ramm, E. et al. A review of the importance of mineral nitrogen cycling in the plant-soil-microbe system of permafrost-affected soils-changing the paradigm. Environ Res Lett 17, doi:10.1088/1748-9326/ac417e (2022).
Matson, P. A., Lohse, K. A. & Hall, S. J. The globalization of nitrogen deposition: consequences for terrestrial ecosystems. Ambio, 113-119, doi:10.1579/0044-7447-31.2.113 (2002).
Gilliam, F. S. Forest ecosystems of temperate climatic regions: from ancient use to climate change. New Phytol 212, 871-887, doi:10.1111/nph.14255 (2016).
Pilegaard, K. et al. Factors controlling regional differences in forest soil emission of nitrogen oxides (NO and N2O). Biogeosciences 3, 651-661, doi:10.5194/bg-3-651-2006 (2006).
Butterbach-Bahl, K., Gasche, R., Breuer, L. & Papen, H. Fluxes of NO and N2O from temperate forest soils: impact of forest type, N deposition and of liming on the NO and N2O emissions. Nutr Cycl Agroecosys 48, 79-90, doi:10.1023/a:1009785521107 (1997).
Butterbach-Bahl, K., Breuer, L., Gasche, R., Willibald, G. & Papen, H. Exchange of trace gases between soils and the atmosphere in Scots pine forest ecosystems of the northeastern German lowlands 1. Fluxes of N2O, NO/NO2 and CH4 at forest sites with different N-deposition. Forest Ecol Manag 167, 123-134, doi:10.1016/s0378-1127(01)00725-3 (2002).
Gao, W. et al. Ammonium fertilization causes a decoupling of ammonium cycling in a boreal forest. Soil Biology and Biochemistry 101, 114-123 (2016).
Davison, A. C. & Hinkley, D. V. in Cambridge Series in Statistical and Probabilistic Mathematics Ch. Chapter 4, (Cambridge University Press, 1997).
Hall, S. J. & Matson, P. A. Nitrogen oxide emissions after nitrogen additions in tropical forests. Nature 400, 152-155 (1999).
Figueiredo, V., Enrich-Prast, A. & Rütting, T. Evolution of nitrogen cycling in regrowing Amazonian rainforest. Sci Rep-Uk 9, 8538, doi:10.1038/s41598-019-43963-4 (2019).
Aber, J. D. et al. Is nitrogen deposition altering the nitrogen status of northeastern forests? BioScience 53, 375-389, doi:10.1641/0006-3568(2003)053[0375:INDATN]2.0.CO;2 (2003).
Díaz, S., Grime, J. P., Harris, J. & McPherson, E. Evidence of a feedback mechanism limiting plant response to elevated carbon dioxide. Nature 364, 616-617, doi:10.1038/364616a0 (1993).
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165-173, doi:10.1038/nature04514 (2006).
Morgan Ernest, S. K. & Brown, J. H. Homeostasis and compensation: The role of species and resources in ecosystem stability. Ecology 82, 2118-2132, doi:10.1890/0012-9658(2001)082[2118:HACTRO]2.0.CO;2 (2001).
Briones, A. & Raskin, L. Diversity and dynamics of microbial communities in engineered environments and their implications for process stability. Current Opinion in Biotechnology 14, 270-276, doi:10.1016/S0958-1669(03)00065-X (2003).
Mason, R. E. et al. Evidence, causes, and consequences of declining nitrogen availability in terrestrial ecosystems. Science 376, eabh3767, doi:10.1126/science.abh3767 (2022).
Craine, J. M. et al. Isotopic evidence for oligotrophication of terrestrial ecosystems. Nature Ecology & Evolution 2, 1735-1744, doi:10.1038/s41559-018-0694-0 (2018).
Groffman, P. M. et al. Nitrogen oligotrophication in northern hardwood forests. Biogeochemistry 141, 523-539, doi:10.1007/s10533-018-0445-y (2018).
Hiltbrunner, E., Körner, C., Meier, R., Braun, S. & Kahmen, A. Data do not support large-scale oligotrophication of terrestrial ecosystems. Nature Ecology & Evolution 3, 1285-1286, doi:10.1038/s41559-019-0948-5 (2019).
Olff, H. et al. Explanations for nitrogen decline. Science 376, 1169-1170, doi:10.1126/science.abq7575 (2022).
Yu, G. et al. Stabilization of atmospheric nitrogen deposition in China over the past decade. Nat Geosci 12, 424-429, doi:10.1038/s41561-019-0352-4 (2019).
Aronson, E. L. & Helliker, B. R. Methane flux in non-wetland soils in response to nitrogen addition: a meta-analysis. Ecology 91, 3242-3251, doi:10.1890/09-2185.1 (2010).
Steinkamp, R., Butterbach-Bahl, K. & Papen, H. Methane oxidation by soils of an N limited and N fertilized spruce forest in the Black Forest, Germany. Soil Biol Biochem 33, 145-153, doi:10.1016/s0038-0717(00)00124-3 (2001).
Bleeker, A., Hicks, W. K., Dentener, F., Galloway, J. & Erisman, J. W. N deposition as a threat to the World’s protected areas under the Convention on Biological Diversity. Environ Pollut 159, 2280-2288, doi:https://doi.org/10.1016/j.envpol.2010.10.036 (2011).
Elrys, A. S. et al. Global gross nitrification rates are dominantly driven by soil carbon-to-nitrogen stoichiometry and total nitrogen. Global Change Biol 27, 6512-6524, doi:10.1111/gcb.15883 (2021).
Elrys, A. S. et al. Patterns and drivers of global gross nitrogen mineralization in soils. Global Change Biol 27, 5950-5962, doi:10.1111/gcb.15851 (2021).
Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Methods in Ecology and Evolution 5, 65-73, doi:10.1111/2041-210X.12125 (2014).
Luo, Y. Q. et al. Coordinated approaches to quantify long-term ecosystem dynamics in response to global change. Global Change Biol 17, 843-854, doi:10.1111/j.1365-2486.2010.02265.x (2011).
Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems 6, 249-263, doi:10.1002/2013MS000293 (2014).
Liu, H. et al. Annual dynamics of global land cover and its long-term changes from 1982 to 2015. Earth Syst. Sci. Data 12, 1217-1243, doi:10.5194/essd-12-1217-2020 (2020).
Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proceedings of the National Academy of Sciences 107, 8650-8655, doi:10.1073/pnas.0912668107 (2010).
Kim, D.-G., Hernandez-Ramirez, G. & Giltrap, D. Linear and nonlinear dependency of direct nitrous oxide emissions on fertilizer nitrogen input: A meta-analysis. Agriculture, Ecosystems & Environment 168, 53-65, doi:10.1016/j.agee.2012.02.021 (2013).
ArcGIS Desktop: Release 10 (Redlands, CA, 2011).
Grömping, U. Relative importance for linear regression in R: the package relaimpo. Journal of statistical software 17, 1-27, doi:10.18637/jss.v017.i01 (2006).
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).

There is NO Competing Interest.

CodeS1.txt
Code S1
SuppMaterials231030.pdf
Supplementary texts, figures, tables

Download PDF

Version 1

posted

You are reading this latest preprint version

Global patterns of nitrogen saturation in forests

Status:

Version 1

Abstract

Figures

Introduction

Results and Discussion

Sensitivity of soil N₂O emissions to N deposition in global forests

Influences of human activities on the N saturation status of forests

Implications and potential applications of the global patterns of N-saturated forests

Materials and Methods

Data sources

Quantifying the sensitivity of soil N₂O emissions to N deposition

Determining N saturation status of global forests using s_N

Data analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Global patterns of nitrogen saturation in forests

Status:

Version 1

Abstract

Figures

Introduction

Results and Discussion

Sensitivity of soil N2O emissions to N deposition in global forests

Influences of human activities on the N saturation status of forests

Implications and potential applications of the global patterns of N-saturated forests

Materials and Methods

Data sources

Quantifying the sensitivity of soil N2O emissions to N deposition

Determining N saturation status of global forests using sN

Data analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Sensitivity of soil N₂O emissions to N deposition in global forests

Quantifying the sensitivity of soil N₂O emissions to N deposition

Determining N saturation status of global forests using s_N