Anthropogenic nitrogen (N) pollution is a cause of eutrophication globally1. However, recent datasets indicate that some ecosystems may be experiencing widespread oligotrophication—declining N availability—which is suggested to be a response to elevated atmospheric carbon dioxide (CO2)2.
However, recent datasets indicate that some ecosystems may be experiencing widespread oligotrophication—declining N availability—which is suggested to be a response to elevated atmospheric carbon dioxide and carbon cycles.
. Since 1960, humans have increased the rate of reactive nitrogen creation by a factor of 10 owing to the industrial Haber–Bosch process, expansion of leguminous crops and fossil fuel combustion; and Nr creation is expected to increase by a factor of 18 by 2050 concentrations by more than 50% since the start of the industrial age, which has been shown to enhance terrestrial net primary productivity has been proposed to interact with the N cycle, potentially reducing N availability and intensifying N limitation of terrestrial NPP increases the plant C:N ratio that stimulates microbial N immobilization during detrital decomposition, thus also reducing soil N availability. Consequently, although it is well known that Nr deposition causes eutrophication in some regions, it is plausible that rising COmay have an opposing effect, leaving substantial uncertainty in the trajectory of terrestrial N limitation, which has implications for Earth-system model predictions of the future terrestrial C sink Although eutrophication owing to excess N has been an active focus of research over many decades in natural ecosystems, several recent datasets instead indicate that widespread oligotrophication may be occurring. Chronologies of N stable isotope ratios in plant tissues that span decades to centuries have served as key evidence for oligotrophication because they integrate multiple N-cycling processes in ecosystems, yet the interpretation of these chronology datasets has been the subject of ongoing debateN values of plants broadly reflect sources of plant N as well as soil and ecosystem N availability, with higher N availability yielding elevated plant δ. At the ecosystem level, high N availability relative to plant N demand promotes losses of soil inorganic N with low δ. In contrast to the above, only small N-isotope discrimination occurs by direct root N uptake at levels present in natural ecosystems, and N-isotope ratios are generally well correlated among plant tissues N declines for several sample types, namely, foliar, wood, lake sediment and herbarium samples, as well as other N-cycling response variables, for example, direct measurement of watershed N exports and soil N mineralization. Corresponding datasets showing temporal declines of foliar N content provide further support for interpretations of reported δN trends have been proposed. First, it is proposed that increasing N limitation over the past three decades is instead the result of declining Nr deposition rates , which reached peak levels in North America and Europe in the 1980s. Furthermore, it is suggested that the decline in N deposition rates during recent decades has been driven by policy-regulated reductions in the oxidized forms of Nr deposition ; decadal increments from tree cores representing the time span 1950 to 2017 across Sweden’s 23.5-million-hectare forest area. We focused on δN rather than %N, given that %N of wood is not considered a good indicator of plant N limitation owing to its very low concentration and subsequent sensitivity to concentration variability of more abundant wood elements ). Swedish forests span a 1,500-km latitudinal gradient of Nr deposition, with southern Sweden experiencing around fourfold higher Nr deposition during the industrial age, as well as stronger reductions in total and NO= 1609) across 4 Nr deposition regions spanning Sweden’s north–south gradient, representing increasing deposition rates from lowest to highest .L., from the Swedish National Forest Inventory tree-core archive for which sample collection occurred between 1961 and 2018, using a stratified random sampling approach , we selected four separate models, substituting each Nr deposition variable in successive model runs, as well as successive model variants that included either latitude and longitude, and absolute temperature or temperature change . This conclusion persisted among model variants that held space constant by including latitude and longitude as fixed factors . The weak positive relationship may reflect that higher-fertility sampling plots within a given grid cell have higher basal area, after accounting for some degree of broad spatial or temporal co-linearity with temperature and COOur results have multiple implications for understanding the response of boreal forest N availability to environmental change factors, particularly rising atmospheric CON that we observed, using a well-constrained sampling methodology that minimizes isotopic effects associated with tree ageing and N translocation across tree rings. As our sampling area spanned a large gradient in latitude, forest biomass, mean annual temperature and Nr deposition, these findings are broadly relevant for understanding the trajectory of N availability in northern forests where declining δmay be tightening the N cycle in many environments, including declining foliar N content in Europe and North American forestsBoreal forests similar to those we studied across Sweden have an important role in the global C cycle by accumulating and storing a considerable amount of terrestrial C can increase plant C:N ratios and the production of high C:N litter, which increases the N demand for detritus decomposition, and stimulates soil microbial N immobilizationIn support that increased forest growth may contribute to reducing N availability, data from the Swedish NFI show that growth of mesic pine and spruce forests, similar to those from which we established δ), which is consistent with the prediction of PNL that increasing plant N demand promotes a negative feedback on N availability . It has alternatively been suggested that photosynthetic downregulation of foliar N is a dominant process that reduces plant N demand in response to rising CO). Although not mutually exclusive, our analysis indicates that increasing forest N demand at the stand level, owing to increasing biomass and growth, appears to outweigh any potential physiological reduction in N demand at the leaf level), which is also consistent with predictions by some Earth-system models that PNL feedbacks eventually constrain the response of terrestrial NPP to rising CO), possibly interacting with other factors . Two previous experiments in northern Sweden that applied experimental COIn addition to increased growth and plant C:N ratios of plant biomass and detritus, mycorrhizal fungi provide the other non-mutually exclusive mechanism that links rising CON chronologies. As forest N demand increases, trees may enhance their C investment in mycorrhizal fungi to enhance soil N acquisition, particularly as soil inorganic N availability declines. Many tree species worldwide, and especially those in high-latitude regions such as boreal forests, associate with ectomycorrhizal fungi that mobilize N from the soilN chronologies we observed may reflect that an increasing proportion of plant N is obtained via ectomycorrhizal fungi, possibly owing to an increase in fungal biomass that sequesters a larger fraction of the mobilized N pool. Furthermore, studies from boreal forest fertility gradients indicate that some specific ectomycorrhizal taxa are especially responsive to increasing N limitation intensity, including some taxa that acquire N by degrading soil organic matter leads to increased soil C in most ecosystem types, ecosystems dominated by ectomycorrhizal tree species showed no such increase, despite positive NPP responses. This suggests that acquisition of soil organic N by ectomycorrhizal decomposers may allow sustained forest growth in response to rising CON values across different boreal forest contexts, and the resulting sensitivity of above- versus belowground C compartments in response to CO-induced PNL may be addressed through a combination of approaches, such as intercomparisons of coupled C–N Earth-system models, and deployment of eCOexperiments, which are poorly represented in boreal ecosystems. Although it is clear that humans have already surpassed several of Earth’s planetary boundaries, in part owing to alterations of both the N and C cycles, our data show that these cycles are not changing independently. It further suggests that declining N availability in response to rising COwill be a more dominant driver of boreal forest C exchange in the future, compared with N enrichment from atmospheric Nr deposition.). The Swedish NFI samples approximately 10,000 plots per year on productive, evergreen-dominated forestlands , from which tree cores are collected and archived. To systematically sample independent tree cores from the archive, we applied a grid of 250 square cells over Sweden. We then identified samples in the archive by filtering their associated database, for cores originating from dominant or co-dominant Norway spruce samples from inventory plots categorized as mesic site types, with slopes of 20% slope or less, and from trees in the 41–60-year-old age class. This age class was selected to minimize the likelihood of N fertilization application; forest fertilization treatment in Sweden is applied to a relatively small area and late in stand rotation. Samples were then randomly selected for each tree species from each grid cell, and for each of 6 sampling decades . Specific sampling years were chosen for each decade based on the available number of archived samples: 1961, 1977, 1986–1988, 1996–1998, 2006–2008 and 2016–2018. Three-year periods were sampled for the 1980s, 1990s, 2000s and 2010s because the NFI’s sampling intensity was reduced from this period onwards, requiring an expanded selection period to ensure a comparable number of samples to those available in 1961 and 1977. Some grid cells did not contain trees, and occasionally samples were not available for a given species, grid cell and decade combination, resulting in a total of= 1,609 samples collected from the archive for analysis. Data for two counties in Sweden were previously reportedand were combined with new data for all other counties in Sweden. For each sample, collected at breast height , we removed the outer bark and cambial layers, and the most recent annual ring to exclude incomplete ring growth during the collection year. Then, to obtain sufficient material for chemical analysis, the subsequent 10-year annual growth segment was separated from the remainder of the core. Dissections were performed using a No. 11 stainless steel surgical blade under a stereo microscope with ×20 magnification, with an accuracy of 0.01 mm. As the samples represent a 10-year growth segment, we designated each sample by its corresponding intermediate 10-year growth increment year .N) were analysed at the Central Appalachians Stable Isotope Facility , University of Maryland Center for Environmental Science Appalachian Laboratory with a Carlo Erba NC2500 elemental analyser interfaced with a Thermo Finnigan Delta V+ isotope-ratio mass spectrometer . The Carlo Erba NC2500 Elemental Analyser with Costech zero-blank autosampler modifications permits for analysis of N isotopes in solid organic samples with content less than 0.5% N, such as wood. From each tree core, a radial slice was precisely sectioned to represent each 10-year segment and chopped with a steel razor. Approximately 10 mg of wood from the radial core slice was then weighed, placed in a tin and analysed for the δN data were normalized to the Ambient Inhalable Reservoir scale using a two-point normalization curve with internal standards, including ground corn, cocoa and caffeine powder calibrated against international standards, USGS40 and USGS41. Analytical precision isotopes of samples is expressed in standard delta notation with reference to a standard of known isotopic ratio.are the ratios of the heavy to light isotopes in the sample and standard, respectively, and are expressed in units of parts per thousand or per mil . The chosen standard for N is AIR. Corresponding wood %N data are reported in Extended Data Fig.We leveraged external databases for use in the linear mixed-effects model, including the climate parameters mean annual temperature and atmospheric CO); and the forest stand parameters total basal area and stand age. Monthly temperature data were extracted from CRU TS v4.07 from which we calculated a 10-year mean annual temperature specific to each wood core sample, using its unique geographic coordinates. In addition to absolute temperature, we also calculated a relative temperature change variable, using 1961 as the reference temperature value for each grid cell, to minimize the large latitudinal gradient in temperature across our study area. We acquired COdry air mole fraction data for the period 2005–2017 from the National Oceanic and Atmospheric Administration Global Monitoring Laboratory recorded at PAL: Pallas-Sammaltunturi, GAW Station, Finland and the 10-year average total N deposition for each sample based on specific geographic location. The forest stand parameter total basal area was used as a proxy for forest biomass variation, collected at the time of each tree-core collection in the field and archived in the Swedish NFI database. The observed increase in total basal area over time can be attributed to the combined influence of forest management and environmental change factorsData from the NFI are acquired through annual systematic sampling of plots covering Swedish forests. The NFI sampling protocol is based on probability sampling, using a stratified systematic cluster design with a combination of permanent and temporary circular plots. Temporary plots, from where cores are extracted from sample trees, have a 7-m radius, whereas permanent plots have a 10-m radius. Permanent plots are resampled every 5 years and temporary plots are measured once. The survey includes over 100,000 individual tree measurements each year . The growth rate is estimated using different approaches for both permanent and temporary plots. For permanent plots, growth is estimated as the difference in volume estimates between two consecutive inventories. Growth from temporary plots is estimated via the collection of tree cores, from which radial increment is measured on a microscope, upon which regression models are used to estimate volume growth. The estimated growth rate for permanent and temporary plots is then combined into a weighted mean, with the uncertainty of the estimate ≤1% for each of the two tree species across the geographical regions: north, central, southeast and southwest . We checked for normal distribution and homoscedasticity of all model residuals. Next, we constructed a linear mixed-effects model using stepwise forwards and backwards selection combined with Akaike information criteria using maximum likelihood to choose the model with the best combination of predictors to explain the observed patterns on the response variable, δ, mean annual temperature, total Nr deposition, total basal area, stand age and tree species, and the interactions of tree species with CO. Numerical fixed effects were centred and scaled using the scale function in R. Grid cell was included as a random effect to account for spatial autocorrelation among plots within the same grid. The variance inflation factor , a check for multi-collinearity of continuous numerical main predictors in the model, was calculated and a VIF limit of five was used to indicate an acceptable level of collinearity . The final model with the lowest Akaike information criterion retained all variables with the exception of stand age and the interaction of COwith total Nr deposition. Finally, we repeated the linear mixed-effects model and replaced total Nr deposition with each of the remaining N deposition variables: NH. The final model was run using restricted maximum likelihood . We calculated the marginal , conditional and partialpackage in R. We also ran the final model with two variations: all fixed and random effects of the final model with the addition of latitude and longitude ; and all fixed and random effects from the final model with mean annual temperature replaced by temperature change and estimated the slopes of significant model variables using the emmeans packageN values, we calculated an additional forest growth variable describing ‘relative forest volume growth change’ in each grid cell. This was done by calculating a mean forest volume growth value for all sampling times for each species in each grid cell for which we had δN values and then subtracting this mean value from each of the absolute forest volume growth values for each individual sample plot. We note that forest volume growth change and δN values were based on 5-year and 10-year increments before the sampling date, respectively. We then constructed two linear mixed-effects models where relative forest volume growth change and either absolute forest volume growth or basal area were included as an additional fixed factor, and grid cell was included as a random factor as additional fixed effects, while also accounting for the random effect of grid. Slopes of the significant fixed effects were estimated from the models. We conducted a principal component analysis with normalized climate, N, and forest stand parameters that were significant in the final model, as well as the remaining three Nr deposition parameters to provide a visual representation of the impact of the specific parameters . All variables were scaled using the scale function in R. We conducted linear regression of all predictor variables in the final model: atmospheric CO, mean annual temperature, total N deposition and total basal area, and temperature difference as a function of predictor variables time and %N as a function of time to determine whether a change through time occurred for each of the two species . Atmospheric reactive nitrogen deposition data from the Inter-Sectoral Impact Model Intercomparison Project phase 3a datasets are available atHiltbrunner, E., Körner, C., Meier, R., Braun, S. & Kahmen, A. Data do not support large-scale oligotrophication of terrestrial ecosystems.Galloway, J. N., Bleeker, A. & Erisman, J. W. The human creation and use of reactive nitrogen: a global and regional perspective.Hungate, B. A., Dukes, J. S., Shaw, M. R., Luo, Y. Q. & Field, C. B. Nitrogen and climate change.Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability.Vitousek, P. M., Cen, X. Y. & Groffman, P. M. Has nitrogen availability decreased over much of the land surface in the past century? A model-based analysis.Doucet, A., Savard, M. M., Bégin, C. & Smirnoff, A. Is wood pre-treatment essential for tree-ring nitrogen concentration and isotope analysis?Bukata, A. R. & Kyser, T. K. Response of the nitrogen isotopic composition of tree-rings following tree-clearing and land-use change.Poulson, S. R., Chamberlain, C. P. & Friedland, A. J. Nitrogen isotope variation of tree rings as a potential indicator of environmental change.Thurner, M. et al. Nitrogen concentrations in boreal and temperate tree tissues vary with tree age/size, growth rate and climate. 1999 Protocol to Abate Acidification, Eutrophication and Ground-level Ozone to the Convention on Long-range Transboundary Air Pollution. Protocol to Abate Acidification, Eutrophication and Ground-level OzonePihl Karlsson, G., Akselsson, C., Hellsten, S. & Karlsson, P. E. Atmospheric deposition and soil water chemistry in Swedish forests since 1985—effects of reduced emissions of sulphur and nitrogen..Bassett, K. R., Östlund, L., Gundale, M. J., Fridman, J. & Jämtgård, S. Forest inventory tree core archive reveals changes in boreal wood traits over seven decades.Michaud, T. J., Cline, L. C., Hobbie, E. A., Gutknecht, J. L. M. & Kennedy, P. G. Herbarium specimens reveal that mycorrhizal type does not mediate declining temperate tree nitrogen status over a century of environmental change.Kranabetter, J. M., Saunders, S., MacKinnon, J. A., Klassen, H. & Spittlehouse, D. L. An assessment of contemporary and historic nitrogen availability in contrasting coastal Douglas-Fir forests through δBalderas Torres, A. & Lovett, J. C. Using basal area to estimate aboveground carbon stocks in forests: La Primavera Biosphere’s Reserve, Mexico.Bergh, J., Linder, S., Lundmark, T. & Elfving, B. The effect of water and nutrient availability on the productivity of Norway spruce in northern and southern Sweden.Elmore, A. J., Nelson, D. M. & Craine, J. M. Earlier springs are causing reduced nitrogen availability in North American eastern deciduous forests.McLauchlan, K. K., Craine, J. M., Oswald, W. W., Leavitt, P. R. & Likens, G. E. Changes in nitrogen cycling during the past century in a northern hardwood forest.Goedkoop, W., Adler, S., Huser, B., Gardfjell, H. & Lau, D. C. P. Climate change-induced landscape alterations increase nutrient sequestration and cause severe oligotrophication of subarctic lakes.Bassiouni, M., Smith, N. G., Reu, J. C., Peñuelas, J. & Keenan, T. F. Observed declines in leaf nitrogen explained by photosynthetic acclimation to COThomas, R. Q., Brookshire, E. N. J. & Gerber, S. Nitrogen limitation on land: how can it occur in Earth system models?Gerber, S., Hedin, L. O., Oppenheimer, M., Pacala, S. W. & Shevliakova, E. Nitrogen cycling and feedbacks in a global dynamic land model.Sigurdsson, B. D., Medhurst, J. L., Wallin, G., Eggertsson, O. & Linder, S. Growth of mature boreal Norway spruce was not affected by elevated [COGundale, M. J. et al. The biological controls of soil carbon accumulation following wildfire and harvest in boreal forests: a review.Fridman, J. et al. Adapting National Forest Inventories to changing requirements—the case of the Swedish National Forest Inventory at the turn of the 20th century.Hedwall, P.-O., Gong, P., Ingerslev, M. & Bergh, J. Fertilization in northern forests—biological, economic and environmental constraints and possibilities.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Lan, X., Tans, P. & Thonin, K. W. Atmospheric carbon dioxide dry air mole fractions from the NOAA GML Carbon Cycle Cooperative Global Air Sampling Network, 1968–2023, version: 2024-07-30. Elfving, B. & Tegnhammar, L. Trends of tree growth in Swedish forests 1953–1992: an analysis based on sample trees from the National Forest Inventory.Östlund, L., Zackrisson, O. & Axelsson, A. L. The history and transformation of a Scandinavian boreal forest landscape since the 19th century.Pinheiro, J. et al. nlme: linear and nonlinear mixed effects models. R package v.3.1-166 .We thank the Swedish NFI field crews for sample and data collection; and F. Johansson, M. Karlsson, T. Sundvall, P. Edlund and K. Spies for archive assistance. We thank the Central Appalachians Stable Isotope Facility, Maryland Center for Environmental Science Appalachian Laboratory, for laboratory analysis. We also thank F. Maillard for manuscript review. The research was funded by Stiftelsen Gunnar och Birgitta Nordins fond to K.R.B.; Brattås Stiftelsen, Troëdssons Stiftelsen, VR to M.J.G.; T4F, and the Wallenberg Foundation . Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, SwedenJonas Fridman M.J.G. formulated the original concept presented in the paper. K.R.B., M.J.G., S.S.P., S.J., J.F. and L.Ö. contributed to discussions regarding the study design. K.R.B. managed all archival procedures and prepared samples for analysis. K.R.B. conducted all statistical analyses with assistance from S.F.H. in data management and R programming. K.R.B., S.F.H., M.J.G., S.J., L.Ö. and S.S.P. analysed and interpreted the results. K.R.B. authored the initial draft of the paper. All authors engaged in the discussion and revision processes of the paper.= 1609) are plotted based on their scores along the first two principal components, which explain 45.5% and 19.5% of the variance, respectively and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
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