# Convergence of soil nitrogen isotopes across global climate gradients

Joseph M. Craine, Andrew J. Elmore, Lixin Wang, Laurent Augusto, W. Troy Baisden, E. N. Jack Brookshire, Michael D. Cramer, Niles J. Hasselquist, Erik A. Hobbie, Ansgar Kahmen, et al.

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► To cite this version:

Joseph M. Craine, Andrew J. Elmore, Lixin Wang, Laurent Augusto, W. Troy Baisden, et al. Convergence of soil nitrogen isotopes across global climate gradients. Scientific Reports, 2015, 5, 8 p. 10.1038/srep08280. This version is licensed under a Creative Commons Attribution 4.0 International License.

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STABLE ISOTOPE
ANALYSIS

ECOSYSTEM ECOLOGY

Received
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6 January 2015

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6 February 2015

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# Convergence of soil nitrogen isotopes across global climate gradients

Joseph M. Craine¹, Andrew J. Elmore², Lixin Wang³, Laurent Augusto⁴, W. Troy Baisden⁵,
E. N. J. Brookshire⁶, Michael D. Cramer⁷, Niles J. Hasselquist⁸, Erik A. Hobbie⁹, Ansgar Kahmen¹⁰,
Keisuke Koba¹¹, J. Marty Kranabetter¹², Michelle C. Mack¹³, Erika Marin-Spiotta¹⁴, Jordan R. Mayor¹⁵,
Kendra K. McLauchlan¹⁶, Anders Michelsen¹⁷, Gabriela B. Nardoto¹⁸, Rafael S. Oliveira¹⁹,
Steven S. Perakis²⁰, Pablo L. Peri²¹, Carlos A. Quesada²², Andreas Richter²³, Louis A. Schipper²⁴,
Bryan A. Stevenson²⁵, Benjamin L. Turner²⁶, Ricardo A. G. Viani²⁷, Wolfgang Wanek²³ & Bernd Zeller²⁸

¹Division of Biology, Kansas State University, Manhattan, KS, 66506, USA, ²Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, 21532, USA, ³Department of Earth Sciences, Indiana University-Purdue University, Indianapolis, IN, 46202, USA, ⁴UMR 1220 TCEM, INRA, Bordeaux Sciences Agro, Villenave d'Ornon, 33883, France, ⁵National Isotope Centre, GNS Science, Lower Hutt, 5040, New Zealand, ⁶Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, 59717, USA, ⁷Department of Biological Sciences, University of Cape Town, Rondebosch, 7701, South Africa, ⁸Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Umeå, 90183, Sweden, ⁹Earth Systems Research Center, Morse Hall, University of New Hampshire, Durham, NH, 03824, USA, ¹⁰Departement of Environmental Sciences - Botany, Schönbeinstrasse 6, 4056 Basel Switzerland, ¹¹Institute of Agriculture, Tokyo University of Agriculture and Technology, Fuchū, Tokyo, 1838509, JAPAN, ¹²British Columbia Ministry of Forests, Lands and Natural Resource Operations, Victoria, British Columbia, V8Z 5J3, Canada, ¹³Department of Biology, University of Florida, Gainesville, FL, 32607, USA, ¹⁴Department of Geography, University of Wisconsin, Madison, WI, 53706, USA, ¹⁵Department of Forest Ecology & Management, Swedish University of Agricultural Sciences, Umeå 90183, Sweden, ¹⁶Department of Geography, Kansas State University, Manhattan, KS, 66506, USA, ¹⁷Department of Biology, University of Copenhagen, Copenhagen Ø, 2100, Denmark, ¹⁸Faculdade UnB Planaltina, Universidade de Brasília, Brasília, 73345-010, Brazil, ¹⁹Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, 13083-862, Brazil, ²⁰Forest and Rangeland Ecosystem Science Center, US Geological Survey, Corvallis, OR, 97331, USA, ²¹Universidade Nacional de la Patagonia Austral-INTA-CONICET, Río Gallegos, Santa Cruz, 9400, Argentina, ²²Coordenação de Dinâmica Ambiental, Instituto Nacional de Pesquisas da Amazonia, Manaus, 69011, Brazil, ²³Department of Terrestrial Ecosystem Research, University of Vienna, Vienna, 1090, Austria, ²⁴Environmental Research Institute, University of Waikato, Hamilton, 3240, New Zealand, ²⁵Landcare Research, Hamilton, 3240, New Zealand, ²⁶Smithsonian Tropical Research Institute, Balboa, Ancón, Republic of Panama, ²⁷DBPVA, Centro de Ciências Agrárias, Universidade Federal de São Carlos, Araras, SP, 13600-970, Brazil, ²⁸Biogéochimie des Ecosystèmes Forestiers, INRA Nancy, Champenoux, 54280, France.

Quantifying global patterns of terrestrial nitrogen (N) cycling is central to predicting future patterns of primary productivity, carbon sequestration, nutrient fluxes to aquatic systems, and climate forcing. With limited direct measures of soil N cycling at the global scale, syntheses of the ¹⁵N:¹⁴N ratio of soil organic matter across climate gradients provide key insights into understanding global patterns of N cycling. In synthesizing data from over 6000 soil samples, we show strong global relationships among soil N isotopes, mean annual temperature (MAT), mean annual precipitation (MAP), and the concentrations of organic carbon and clay in soil. In both hot ecosystems and dry ecosystems, soil organic matter was more enriched in ¹⁵N than in corresponding cold ecosystems or wet ecosystems. Below a MAT of 9.8°C, soil δ¹⁵N was invariant with MAT. At the global scale, soil organic C concentrations also declined with increasing MAT and decreasing MAP. After standardizing for variation among mineral soils in soil C and clay concentrations, soil δ¹⁵N showed no consistent trends across global climate and latitudinal gradients. Our analyses could place new constraints on interpretations of patterns of ecosystem N cycling and global budgets of gaseous N loss.

Quantifying global patterns of terrestrial nitrogen (N) cycling is central to predicting future patterns of primary productivity, carbon sequestration, nutrient fluxes to aquatic systems, and climate forcing¹⁻⁴. With limited direct measurements of soil N cycling at the global scale, past syntheses of the ¹⁵N:¹⁴N ratio of soil organic matter (represented as δ¹⁵N relative to a standard) have inferred that hotter and drier ecosystems
<figure><img src="image_5.png" /><figcaption>Figure 1 | Map of sites used in this study. Map created in JMP 10.0.2.</figcaption></figure>

tend to lose a greater proportion of their N through gaseous pathways<sup>5,6</sup>. Global soil δ<sup>15</sup>N patterns are also among the main evidence used to support the idea that plant productivity in tropical ecosystems is less N limited than in temperate ecosystems<sup>6,7</sup>. These conclusions have assumed that soils with high δ<sup>15</sup>N lose a greater proportion of N through strongly <sup>15</sup>N-discriminating loss processes such as NH<sub>3</sub> volatilization and denitrification rather than through less discriminating loss pathways such as dissolved organic N and NO<sub>3</sub><sup>-</sup> leaching<sup>8-10</sup>.

<figure><img src="image_6.png" /><figcaption>Figure 2 | Map of climate space of sites in this study to global terrestrial climate density. Sites used in this study are red. Background points represent the density of ice-free land surface area at particular combinations of mean annual temperature (MAT) and mean annual precipitation (MAP).</figcaption></figure>

Past analyses of global soil δ¹⁵N patterns have examined relatively simple direct relationships between soil δ¹⁵N and climate or latitude⁵,⁶,¹¹. Yet, other factors that might affect soil δ¹⁵N and co-vary with climate could influence global relationships between soil δ¹⁵N and climate. For example, a large proportion of the global variation in foliar δ¹⁵N is explained by the N concentrations of leaves¹², most likely because plants that have high foliar N concentrations are more likely to be growing in soils where fractionating loss pathways dominate N losses. Like leaves, soil C or N concentrations might be important covariates for soil δ¹⁵N. Although rates of microbial processing of plant litter are known to vary across climate gradients¹³⁻¹⁵, the extent to which soil C or N concentrations might shape geographic patterns of soil δ¹⁵N remains unexplored. Soils with different C or N concentrations might consistently vary in their δ¹⁵N due to differences in the composition of organic matter inputs or rates of microbial processing of organic matter, both of which could affect the N isotopic composition of soil organic matter. In addition, soil texture has the potential to affect the relative importance of different loss pathways¹⁶ and/or the differential retention of ¹⁵N-enriched organic matter¹⁷⁻²¹. With highly weathered tropical sites more likely to have greater clay concentrations than many high-latitude ecosystems²², this may be an additional confounding influence on global patterns of soil δ¹⁵N.

Beyond improving models of the controls on soil δ¹⁵N, it is imperative to continue to expand databases of soil δ¹⁵N because non-linear relationships may become apparent as data on soil ¹⁵N accumulate, which may affect interpolation of N cycling rates in poorly characterized ecosystems. For example, at the global scale, foliar δ¹⁵N increases with increasing foliar N concentrations, but only above a mean annual temperature (MAT) of -0.5°C¹². Such observations are important, because they prompt mechanistic hypotheses that test critical understanding how climate influences ecosystem N cycling. For soil δ¹⁵N, past syntheses of soil δ¹⁵N included too few samples, especially for cold ecosystems, to adequately determine whether non-linear relationships exist between soil δ¹⁵N and climate.

To better understand global patterns of soil δ¹⁵N, we assembled a global dataset of published and original surface soil δ¹⁵N values and
<figure><img src="image_7.png" /><figcaption>**Figure 3** | Relationships among climate and soil parameters. Relationships between mean annual temperature (MAT) and mean annual precipitation (MAP) and (A,B) soil δ¹⁵N (n = 910) and (C,D) soil [C] of surface soils (n = 828). Each point represents values for all samples averaged per 0.1° latitude and longitude. The relationship between soil [C] and soil δ¹⁵N is shown in panel (E) (n = 828). After accounting for the variation in soil δ¹⁵N explained by soil [C] (n = 828), the residual variation in soil δ¹⁵N is shown vs. (F) MAT and (G) MAP (n = 828). Gray regression line is not significant at *P* > 0.05. Values displayed in relationships with MAT and MAP were corrected for variation that could be explained by the other climate variable and soil depth.</figcaption></figure>
<table><caption>Table 1 | Regression results for mineral soil δ¹⁵N vs. climate. Inflection point for MAT in the breakpoint model was 9.83 ± 1.83 °C. r² = 0.26 for the breakpoint model and 0.24 for the linear model. Units for MAP were mm before log-transformation</caption><thead><tr><th></th><th>Estimate</th><th>P</th></tr></thead><tbody><tr><td><strong>Breakpoint</strong></td><td></td><td></td></tr><tr><td>Intercept (‰)</td><td>7.71 ± 0.65</td><td>&lt;0.001</td></tr><tr><td>MAT<sub>Cold</sub> (‰ °C<sup>-1</sup>)</td><td>0.03 ± 0.02</td><td>&gt;0.1</td></tr><tr><td>MAT<sub>Hot</sub> (‰ °C<sup>-1</sup>)</td><td>0.18 ± 0.02</td><td>&lt;0.001</td></tr><tr><td>log<sub>10</sub> MAP (‰)</td><td>-1.78 ± 0.24</td><td>&lt;0.001</td></tr><tr><td>Average Depth (‰ cm<sup>-1</sup>)</td><td>0.08 ± 0.02</td><td>&lt;0.001</td></tr><tr><td><strong>Linear</strong></td><td></td><td></td></tr><tr><td>Intercept (‰)</td><td>8.31 ± 0.64</td><td>&lt;0.001</td></tr><tr><td>MAT (‰ °C<sup>-1</sup>)</td><td>0.12 ± 0.01</td><td>&lt;0.001</td></tr><tr><td>log<sub>10</sub> MAP (‰)</td><td>-2.10 ± 0.23</td><td>&lt;0.001</td></tr><tr><td>Average Depth (‰ cm<sup>-1</sup>)</td><td>0.09 ± 0.02</td><td>&lt;0.001</td></tr></tbody></table>

examined relationships of soil δ¹⁵N with climate, soil organic C and N concentrations ([C], [N]), and soil clay concentrations. The dataset comprised 5824 measurements of δ¹⁵N of soil organic matter from surface (<30 cm) mineral soils. Data for an additional 973 organic soils were compiled, but are only analyzed secondarily here as δ¹⁵N signatures of organic soils are more likely to represent the signatures of plants than the ultimate decomposition products of plant biomass. When averaged at 0.1° latitude and longitude, soils represented 910 locations (Fig. 1) that spanned 44°C (MAT) (–14°C to 30°C) and over 9000 mm mean annual precipitation (MAP) (84–9510 mm) (Fig. 2).

## Results

Congruent with previous research, soil organic matter was more enriched in ¹⁵N in both hot ecosystems and dry ecosystems than corresponding cold ecosystems or wet ecosystems (Fig. 3; Table 1). Soil δ¹⁵N increased with increasing MAT at a rate of 0.18 ± 0.02‰ °C⁻¹ for soils from ecosystems with MAT > 9.8°C. Yet, below 9.8°C, soil δ¹⁵N did not change with increasing MAT (0.035 ± 0.024‰ °C⁻¹; *P* > 0.1). High-precipitation ecosystems had lower soil δ¹⁵N with soil δ¹⁵N decreasing at a rate of 1.78 ± 0.24‰ per order of magnitude increase in MAP, after accounting for any co-variation in MAT.

<figure><img src="image_8.png" /><figcaption>Figure 4 | Patterns of soil δ¹⁵N with soil C and N concentrations. Relationships between (A,D) soil carbon concentrations, (B,E) soil nitrogen concentrations, and (C,F) soil C:N with soil δ¹⁵N for (A–C) mineral soils and (D–F) mineral as well as organic soil horizons. For (A–C), each point represents soils averaged for 0.1° latitude and longitude. All relationships significant at *P* < 0.001.</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 5** | Relationships between soil carbon concentrations and texture. Shown are the percentages of (A,D) sand, (B,E) silt, and (C,F) clay vs. (A–C) soil carbon (mg g⁻¹) as well as (D–F) residual log₁₀-transformed soil carbon (mg g⁻¹) after accounting for MAT, log₁₀MAP, and average depth. All points represent soil values averaged to 0.1° latitude and longitude.</figcaption></figure>

At the global scale, soil [C] also declined with increasing MAT and decreasing MAP (*P* < 0.001; r² = 0.42; Fig. 3). Soil δ¹⁵N was highest for low-C soils and decreased with increasing soil [C] (r² = 0.16, *P* < 0.001, n = 828; Fig. 3, Fig. 4). After accounting for variation among mineral soils in soil [C], the global range in MAT observed here was associated with just 2.7‰ variation in soil δ¹⁵N (Fig. 3), with MAT explaining much less variation in residual soil δ¹⁵N (*P* < 0.001, r² =

0.14, n = 828). Likewise, after calculating the residuals between soil δ¹⁵N and [C], MAP no longer predicted variation in soil δ¹⁵N (*P* = 0.1). Soil [N] and C:N were weaker predictors of soil δ¹⁵N than soil [C], but generate similar patterns as soil [C] (Fig. 4).

The variation in soil δ¹⁵N explained by MAT after taking into account relationships with soil [C] could be caused by the high concentrations of clay found in many tropical soils. 30% of the residual

<figure><img src="image_11.png" /><figcaption>**Figure 6** | Patterns of soil clay concentrations with climate. Relationships between (A) mean annual temperature (MAT) and (B) mean annual precipitation (MAP) vs. clay concentrations of surface mineral soils. log (%Clay) = -0.84 + 0.016 × MAT + 0.55 × log(MAP); r² = 0.47, *P* < 0.001, n = 359. All points represent soil values averaged to 0.1° latitude and longitude.</figcaption></figure>
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 7** | Lack of relationship between climate and soil δ¹⁵N after accounting for variation in clay concentrations. Relationship between (A) clay concentrations in soil and residual soil δ¹⁵N after accounting for the relationship between soil δ¹⁵N and soil [C] (y = 1.71 + 2.01 × log(x), r² = 0.22, P < 0.001; n = 355). Also shown are relationships between (B) mean annual temperature (MAT), (C) mean annual precipitation (MAP) and residual soil δ¹⁵N after accounting for variation in soil [C], clay concentrations, and soil depth. Non-significant relationships are shown in gray. All points represent soil values averaged to 0.1° latitude and longitude.</figcaption></figure>

variation in soil [C] after accounting for variation in climate could be explained by soil texture (Fig. 5). Soils with greater silt and clay concentrations had greater [C] than sandy soils (Fig. 5). Within our data, hot sites also had greater clay concentrations than cold sites (Fig. 6). After taking into account the positive relationship between clay concentrations and soil δ¹⁵N (Fig. 7), MAT had no remaining influence on soil δ¹⁵N (Fig. 7). Interpretations of these sequential regression results were further supported by a structural equation model that simultaneously evaluated both direct effects of climate on soil δ¹⁵N as well as indirect effects via effects on soil [C] and clay concentrations (Fig. 8). Mean annual temperature and precipitation only influenced soil δ¹⁵N indirectly through their effects on soil [C] and clay. Direct relationships between climate and soil δ¹⁵N were not significant (Fig. 8).

## Discussion

The global-scale relationships between soil δ¹⁵N and both soil [C] and clay suggest that the relationship between soil δ¹⁵N and climate is indirect, and mediated through climatic effects on soil [C] and clay. Our analyses indicate that known dependencies of microbial processing of soil C and N on temperature and moisture that have been observed experimentally, within soil profiles, and at local to regional scales²³⁻²⁶ largely converge in their effects on soil δ¹⁵N at the global

scale. Further understanding of global soil δ¹⁵N patterns will require direct quantification of the relative importance of different loss pathways as well as the δ¹⁵N values and relative quantities of different soil fractions across broad gradients. Among ecosystems, SOM δ¹⁵N has the potential to be influenced by variation in the ¹⁵N signature of atmospheric N deposition. Yet, with the signatures of deposited N often near 0‰⁹,²⁷, the lack of pattern in SOM δ¹⁵N along climate gradients once the degree of decomposition and clay are taken into account is unlikely to be driven by specific regions of the world receiving greater amounts of deposited N.

In all, the global patterns of soil δ¹⁵N revealed here support the hypothesis that hot and/or dry ecosystems might not lose a greater proportion of N to the atmosphere relative to other ecosystems, as has been previously concluded from soil N isotope data⁵,⁶. Although the relationships between clay and soil δ¹⁵N could be driven by greater proportions of fractionating gaseous N loss in soils with high clay concentrations, an alternative explanation is the relative proportion of fractionating N loss is not directly influenced by clays, but instead is due to clays stabilizing more ¹⁵N-enriched soil organic matter¹⁷⁻²¹. As a result of having greater decomposition of organic matter and/or greater concentrations of clay, it is possible that soils in hotter and/or drier ecosystems have soil organic matter with high δ¹⁵N as a result of having a greater proportion of their N contained in

<figure><img src="image_14.png" /><figcaption>**Figure 8** | Structural equation model of direct and indirect effects of climate on soil δ¹⁵N. Path-diagram of final structural equation model containing only significant relationships. Positive coefficients overlaid on solid paths and negative coefficients on dashed paths. Arrow thickness proportional to path coefficient. Direct effects of climate on soil δ¹⁵N were not significant.</figcaption></figure>
<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Figure 9** | Conceptual model of a hypothesis to explain the higher δ¹⁵N of soils from hot and/or dry ecosystems. Hot and/or dry ecosystems might not have a greater proportion N lost to fractionating (gradient arrow) vs. non-fractionating (solid arrow) losses. Note, arrows not to scale. Instead, as a result of having organic matter that has been decomposed to a greater degree and/or greater clay concentrations, soils from hot and/ or dry ecosystems might simply have a greater proportion of their N in mineral-associated organic matter, which is enriched in ¹⁵N relative to non-mineral-associated organic matter.</figcaption></figure>

mineral-associated organic matter as opposed to having a greater proportion of N being lost to fractionating pathways (Fig. 9).

In support of the idea that the proportion of N lost via fraction-
ating pathways might not vary predictably across global climate
gradients, recent research has revealed underappreciated amounts
of N loss to the atmosphere and aquatic ecosystems suggestive of
similar proportions of fractionating N losses across climatic gradi-
ents. For example, although tundra ecosystems are typically consid-
ered dominated by organic N cycling with little net N
mineralization²⁸, gross N mineralization rates and N₂O fluxes can
contribute to a high proportion of losses, especially given seasonal
asynchronies between mineralization and uptake²⁹⁻³¹. Tropical eco-
systems may lose a larger quantity of N to the atmosphere than many
temperate ecosystems. Yet, NO₃⁻ fluxes in tropical streams can also
be more than an order of magnitude greater than temperate
streams³²,³³. These examples from geographically disparate ecosys-
tems may reflect similar, proportional fractionating losses of N to the
atmosphere across latitudinal gradients. Higher N availability that is
characteristic of many low-latitude ecosystems may in fact reduce the
proportion of N lost in gaseous forms relative to leaching³⁴.

Ultimately, future interpretations of global patterns of N cycling will depend strongly on understanding the mechanisms that drive global relationships between clay content, soil C concentrations and soil organic matter δ¹⁵N. From these relationships, we hypothesized that global patterns of soil δ¹⁵N may reflect the degree to which soil organic matter has been enriched in ¹⁵N by microbial processing and protected by mineral association. This would parallel the δ¹⁵N of soil organic matter consistently increasing with depth across soils globally²⁵, which can be attributed to greater degrees of processing of soil organic matter at depth³⁵⁻³⁷. Recognizing the influence of the degree of microbial processing and protection on soil δ¹⁵N may potentially alter assumptions that soil δ¹⁵N scales positively with the proportion of N lost via gaseous processes, which has been central to estimates of global denitrification and N₂-fixation⁹,³⁸. Clarifying whether such assumptions are valid is critical. In addition, global patterns of soil δ¹⁵N need to be rectified with global patterns of plant δ¹⁵N. For example, it is poorly understood how the relatively high tissue δ¹⁵N of plants in hot, dry places¹² relates to our finding that high soil

δ¹⁵N in these locations is best explained by a high degree of decom-
position and protection of organic matter. More research is also
needed to understand the mechanisms that explain the different
inflection points in plant and soil δ¹⁵N relationships with mean
annual temperature, as well as why plants are generally depleted in
¹⁵N relative to soils¹². With geographic gradients able to provide a
surrogate for future climates³⁹⁻⁴¹, interpretations of global patterns of
soil δ¹⁵N will factor prominently in predicting changes in patterns of
gaseous N flux to the atmosphere in response to changes in the Earth-
climate system.

**Methods**

Data on soil δ¹⁵N were acquired from the literature and by contacting individual researchers known to have collected soil nitrogen isotope data in the past. For each soil, we collected data on geographic coordinates with climate data taken from the original source or 50-year climatic means (1950–2000) were acquired from WorldClim⁴². For each soil sample, we recorded soil depth, soil δ¹⁵N, and where possible [C], [N], ratio of C to N on a mass basis, and soil texture (percentages of sand, silt, and clay). Due to correlations among soil texture categories, only clay concentrations are included in models here.

Only soils considered by the authors as mineral soils (no litter or O horizons) with depths that averaged <30 cm were included in the main synthesis. Select permafrost samples from below this zone were also included, in order to improve the representation of cold sites as there should be little further processing of soil organic matter once frozen. Subsequent analyses also examined patterns across mineral and organic soil horizons. Soils that were under crops were not included in the synthesis.

To reduce over-representation of individual sites, average soil δ¹⁵N were derived for each 0.1° latitude and longitude. Average soil δ¹⁵N was derived by calculating the average δ¹⁵N for each soil depth and then weighting the average soil δ¹⁵N by N content if multiple depths were measured. To examine relationships between soil δ¹⁵N and climate, a non-linear model was used to predict soil δ¹⁵N with MAT, log-transformed MAP, and average soil depth, which included an independently-fit breakpoint for the relationship between MAT and soil δ¹⁵N¹². Subsequent models were used to predict the residuals of the relationship between soil δ¹⁵N and [C]. A structural equation model (SEM)⁴³ was used to test the relative importance of direct vs. indirect linkages between climate and soil δ¹⁵N. A non-hierarchical model was first developed and then downward stepwise selection was used to generate a hierarchical model that included only significant paths. SEM statistics were calculated in IBM SPSS AMOS version 20.0.0.1 (AMOS Development Corp. Meadville, Pennsylvania, USA). All other statistics were computed in JMP 10.0.2 (SAS Institute, Cary, North Carolina, USA).

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## Acknowledgments

The authors express their sincere appreciation to the numerous individuals who provided data for this synthesis, especially Stephen Porder, Georg Guggenberger, Robert Mikutta, Valery Terwilliger and Yuanhe Yang. Mac Post, Ben Houlton, Phil Taylor, Sharon Hall, Sharon Billings, and Matt Wallenstein provided helpful advice on earlier versions of this manuscript. Any use of trade names is for descriptive purposes only and does not imply endorsement by the US Government.

## Author contributions

J.M.C. designed research and created all figures. J.M.C. and L.W. analysed the data, J.M.C. led the writing of the paper with substantial input from A.J.E., L.A., W.T.B., E.N.J.B., M.D.C., N.J.H., E.A.H., A.K., K.K., J.M.K., M.C.M., E.M.S., J.R.M., K.K.M., A.M., G.B.N., R.S.O., S.S.P., P.L.P., C.A.Q., A.R., L.A.S., B.A.S., B.L.T., R.A.G.V., W.W., L.W. and B.Z.

## Additional information

Competing financial interests: The authors declare no competing financial interests.

How to cite this article: Craine, J.M. et al. Convergence of soil nitrogen isotopes across global climate gradients. *Sci. Rep.* **5**, 8280; DOI:10.1038/srep08280 (2015).

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