Global distribution and drivers of relative contributions among soil nitrogen sources to terrestrial plants
土壤氮源对陆生植物相对贡献的全球分布和驱动因素

Nitrogen is a vital nutrient element for life on Earth. Vascular plants dominate biomass and carbon (C) capture on land where N limitation is widespread¹. Accordingly, a better understanding of plant N-use mechanisms is critical for assessing and predicting primary productivity of terrestrial ecosystems^2,3. Global changes such as climate warming and increasing atmospheric N deposition have significantly impacted the soil N cycle and plant N utilization and consequently terrestrial primary productivity^4,5. Nevertheless, the exact contributions of soil N sources to terrestrial plants (i.e., how plants utilize the available soil N sources) remain unquantified and their variations among global terrestrial environments remain unclear⁶. This knowledge gap is preventing an accurate evaluation of N-cycle effects on biodiversity and the C cycle, as well as their responses to projected environmental changes^3,7.
氮是地球上生命的重要营养元素。维管植物在 N 限制普遍存在的陆地上主导生物量和碳 （C） 捕获¹。因此，更好地了解植物氮利用机制对于评估和预测陆地生态系统的初级生产力至关重要^2,3。气候变暖和大气氮沉降增加等全球变化显著影响了土壤氮循环和植物氮利用，从而影响了陆地初级生产力^4,5。然而，土壤氮源对陆生植物的确切贡献（即植物如何利用可用的土壤氮源）仍未量化，它们在全球陆地环境中的变化仍不清楚⁶。这种知识差距阻碍了对 N 循环对生物多样性和 C 循环的影响，以及它们对预计环境变化的反应的准确评估^3,7。

Non-N₂-fixing plants are assumed to primarily acquire bioavailable N from soils via roots¹. The total extractable N (TEN) pool accessible to microbes and plants includes nitrate (NO₃^-), ammonium (NH₄⁺), and organic N (EON) (Fig. 1)^3,6,8. Plant roots acquire soil extractable N directly or via mycorrhizal associaton⁹. Acquired N is allocated and assimilated among leaves, stems, and roots, which combined constitutes the whole-plant N pool (Fig. 1)^10,11. Before the 1980s, soil inorganic N was recognized as the exclusive plant N source, and thus soil N mineralization has long been used to evaluate the plant-used N in soils (PUN; Fig. 1)¹². Nevertheless, subsequent findings of root absorption of EON molecules (such as amino acids, peptides, proteins, and even microbes) in ecosystems under different climate contexts indicate that soil EON is a non-negligible contributor of PUN and soil N mineralization could not fully account for PUN^13,14,15. Since then, the concentrations and pool sizes of NO₃^-, NH₄⁺, and EON have been measured and used in combination with plant C/N ratios to model the plant-soil N and C cycle^6,7,16. However, parallel evidence on the complex N competition between plants and microbes and intrinsic N preference of plants among NO₃^-, NH₄⁺, and EON indicates that TEN cannot be simply taken as PUN^8,12. For example, specific chemical fractions of the TEN pool that are immobilized and transformed by soil microbes can differ substantially from those observed in soil TEN¹⁷. Also, plant acquisition of soil NO₃^-, NH₄⁺, and EON do not follow their proportions in soil TEN because of the verified plant N preference^18,19,20. These two factors result in different estimates of pool sizes and chemical proportions between TEN and PUN in soils. Among the existing studies, the ¹⁵N isotope dilution method is effective at partitioning contributions of NO₃^-, NH₄⁺, and EON to PUN, respectively⁸. However, the high cost of ¹⁵N tracers and artificial injection makes it most applicable for incubating plants and small-scale experiments. Further, asynchrony in N species, time, and space among short-term ¹⁵N additions, microbial turnover, and root absorption caused substantial uncertainties in evaluating the integrative long-term mechanisms of plant N utilization (Table s1)^8,12. For natural ecosystems, the ¹⁵N-tracer application with water can strongly alter soil chemistry and disturb microbe-soil-plant N relationships^13,14. For EON, the ¹⁵N tracer is restricted to few molecules (e.g., free amino acids, which only account for <5% of EON in soil²¹) and thus cannot accurately elucidate plant EON utilization. Therefore, an integrative and non-invasive approach is strongly needed for deciphering ‘in-situ’ N-use mechanisms of terrestrial plants.
假设非 N₂ 固定植物主要通过根¹ 从土壤中获取生物可利用的 N。微生物和植物可利用的总可提取氮 （TEN） 库包括硝酸盐 （NO₃^-）、铵态氮 （NH₄⁺） 和有机氮 （EON）（图 D）。1）^3,6,8.植物根系直接或通过菌根关联⁹ 获取土壤可提取的 N。获得的氮在叶、茎和根之间分配和同化，它们共同构成了全株氮库（图 D）。1）^10,11.在 1980 年代之前，土壤无机氮被认为是唯一的植物氮来源，因此土壤氮矿化长期以来一直被用于评估土壤中植物使用的氮 （PUN;无花果。1）¹². 然而，在不同气候背景下生态系统中根系对 EON 分子（如氨基酸、肽、蛋白质甚至微生物）的吸收结果表明，土壤 EON 是 PUN 不可忽视的贡献者，土壤氮矿化不能完全解释 PUN^13,14,15。从那时起，已经测量了 NO₃^-、NH₄⁺ 和 EON 的浓度和池大小，并与植物 C/N 比率结合使用，以模拟植物-土壤 N 和 C 循环^6,7,16。然而，关于植物和微生物之间的复杂氮竞争以及植物在 NO₃^-、NH₄⁺ 和 EON 中的内在氮偏好的平行证据表明，TEN 不能简单地被视为 PUN^8,12。 例如，被土壤微生物固定和转化的 TEN 池的特定化学组分可能与在土壤 TEN¹⁷ 中观察到的化学组分大不相同。此外，由于经过验证的植物 N 偏好^18,19,20，土壤 NO₃^-、NH₄⁺ 和 EON 的植物采集不遵循它们在土壤 TEN 中的比例。这两个因素导致土壤中 TEN 和 PUN 之间的水池大小和化学比例估计不同。在现有研究中，¹⁵N 同位素稀释法可有效将 NO₃^-、NH₄⁺ 和 EON 的贡献分别分配给 PUN⁸。然而，¹⁵N 示踪剂和人工注射的高成本使其最适用于孵化植物和小规模实验。此外，短期 ¹⁵N 添加、微生物周转和根系吸收之间 N 物种、时间和空间的异步性在评估植物 N 利用的综合长期机制时造成了很大的不确定性（表 s1）^8,12。对于自然生态系统，与水一起施用 ¹⁵N-示踪剂会强烈改变土壤化学并扰乱微生物-土壤-植物 N 关系^13,14。对于 ^EON，15N 示踪剂仅限于少数分子（例如，游离氨基酸，其仅占土壤²¹ 中 EON 的 <5%），因此无法准确阐明植物 EON 的利用率。因此，迫切需要一种综合和非侵入性方法来破译陆生植物的“原位”氮利用机制。

PUN plant-used N, SON soil organic N, TEN total extractable N, EON extractable organic N, NH₄⁺ ammonium, NO₃^- nitrate, Dep depolymerization, Min mineralization, Nitri nitrification.
PUN 植物用氮、SON 土壤有机氮、TEN 总可萃取氮、EON 可萃取有机氮、NH₄⁺ 铵、NO₃ 硝酸盐、Dep 解聚、Min 矿化、硝酸硝化。

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The ratio of natural N isotopes (i.e., ¹⁵N/¹⁴N, denoted as δ¹⁵N values and expressed in per mille units; δ¹⁵N = [(¹⁵N/¹⁴N)_sample / (¹⁵N/¹⁴N)_standard) – 1] × 1000, (where the standard is atmospheric N₂) has been known as a non-invasive measure to decipher N-cycle processes^{22,23,24,25,26,27}. Hitherto, the combination of leaf δ¹⁵N (denoted as δ¹⁵N_leaf) with the δ¹⁵N of soil NO₃^-, NH₄⁺, and EON (denoted as δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON, respectively) and Bayesian isotope mixing models has been recognized as a feasible method to quantify respective contributions of soil NO₃^-, NH₄⁺, and EON to PUN^18,19,20. However, two fundamental questions concerning the quantification of soil N sources to PUN remain unresolved at the global scale. First, the intra-plant ¹⁵N heterogeneity causes differences between the whole-plant δ¹⁵N (denoted as δ¹⁵N_plant) and δ¹⁵N_leaf²⁸. This difference caused errors in interpreting plant N sources by using δ¹⁵N_leaf but has not been constrained to characterize δ¹⁵N_plant signatures¹⁸. Additionally, the preferential uptake and transformation of ¹⁴N from mycorrhiza to plants cause lower δ¹⁵N_plant than the corresponding δ¹⁵N of PUN (denoted as δ¹⁵N_PUN)^24,26. The discrepancies between δ¹⁵N_plant and δ¹⁵N_PUN (∆_m) not only differ among mycorrhiza types, but also vary with environmental conditions influencing the dependence of plant N uptake on mycorrhiza⁹. However, no study has constrained ∆_m values for specific mycorrhizal symbioses across contrasting environments, thus hampering understanding of variation in δ¹⁵N_PUN of global terrestrial plants.
天然氮同位素的比值（即 ¹⁵N/¹⁴N，表示为 δ¹⁵N 值，以每千分单位表示;δ¹⁵N = [（¹⁵N/¹⁴N）_样品/（¹⁵N/¹⁴N）_标准品）– 1] × 1000，（其中标准品为大气 N₂）被认为是破译 N 循环过程的非侵入性措施^{22，23,24,25,26,27}。迄今为止，叶片δ¹⁵N（表示为 δ¹⁵N_叶片）与δ土壤 NO₃^-、NH₄⁺ 和 EON（分别表示δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON）和贝叶斯同位素混合模型的组合已被公认为量化土壤 NO₃^-、 NH₄⁺ 和 EON 到 PUN^18,19,20。然而，在全球范围内，关于土壤氮源量化为 PUN 的两个基本问题仍未解决。首先，植物内 ¹⁵N 异质性导致全株 δ¹⁵N （表示为^{δ 15}N_植物）和^{δ 15}N_叶片²⁸ 之间的差异。这种差异导致使用^{δ 15}N_叶片解释植物 N 来源时出错，但并不仅限于表征^{δ 15}N_植物特征¹⁸。 此外，¹⁴N 从菌根优先吸收和转化到植物δ导致¹⁵N_植物低于相应的 δ¹⁵N PUN（表示为 δ¹⁵N_PUN）^24,26。δ¹⁵N_植株和^{δ 15}N_PUN （∆_m） 之间的差异不仅在菌根类型之间有所不同，而且也因影响植物 N 吸收对菌根⁹ 的依赖性的环境条件而异。然而，没有研究限制了对比环境中特定菌根共生的 ∆_m 值，从而阻碍了对全球陆生植物^{δ 15}N_PUN 变化的理解。

Second, it is a long-standing question what proportions of soil N sources contribute to PUN under different environmental backgrounds, so that the environmental control mechanisms of the fractional variations remain unclear. Regarding the contributing proportions, some studies showed that plants under lower MAT (<5 °C) or at higher latitudes (> 63 °N) mainly utilized EON (43–66% (c.a. >59%) for tundra plants) (data compiled in Table s1). In contrast, the other studies in these regions estimated much lower contributions of organic N (c.a. <22%), with c.a. 14–61% and 24–63% from NH₄⁺ and NO₃^-, respectively (Table s1). Similarly, some studies showed that plants under higher MAT (>12 °C) or at lower latitudes (<38 °N) mainly used inorganic N (c.a. 86–95%), but other studies in these regions argued that the contribution of organic N to PUN reached 20-39% (Table s1). These contrasting findings demonstrate that the proportional contributions of soil N sources to PUN remain an open question. Regarding the environmental drivers, global change studies have confirmed that increasing atmospheric N deposition, temperature, and precipitation are three main factors affecting ecosystem N cycling⁶. However, existing studies have been mostly based on simulations of single or two factors and mainly concerned plant inorganic N utilization²⁹. In the ‘real’ world of global terrestrial ecosystems, it remains uncertain whether and how these factors influence the geographic distribution of the relative contributions of soil NH₄⁺, NO₃^-, EON to PUN. Based on higher soil inorganic N concentrations, mineralization and nitrification rates under simulated warming and N additions^17,30, plant inorganic N and NO₃^- utilization were assumed to increase with increasing temperature and N deposition³¹. However, based on a data compilation of sparse observations (data compiled in Table s2), only a temperature effect was observed on plant inorganic N uptake. For precipitation, an observed phenomenon is that the contribution of NH₄⁺ relative to NO₃^- to PUN increased with mean annual precipitation (MAP) because of inhibition of nitrification and enhanced denitrification^18,29. However, due to variation in experimental conditions it remains uncertain which environmental factors are regulating soil N source contributions to PUN and how these contributions change across terrestrial biomes.
其次，在不同环境背景下，土壤氮源对 PUN 的贡献比例是多少是一个长期存在的问题，因此分数变化的环境控制机制尚不清楚。关于贡献比例，一些研究表明，较低 MAT （<5 °C） 或较高纬度 （> 63 °N） 的植物主要利用 EON （43–66% （c.a. >59%） 为苔原植物）（数据汇编在表 s1 中）。相比之下，这些地区的其他研究估计有机氮的贡献要低得多（约 <22%），NH₄⁺ 和 NO₃^- 的贡献分别为 14-61% 和 24-63%（表 s1）。同样，一些研究表明，在较高 MAT （>12 °C） 或低纬度 （<38 °N） 下的植物主要使用无机氮 （约占 86-95%），但这些地区的其他研究认为，有机氮对 PUN 的贡献达到 20-39%（表 s1）。这些对比鲜明的发现表明，土壤氮源对 PUN 的比例贡献仍然是一个悬而未决的问题。关于环境驱动因素，全球变化研究证实，大气氮沉降、温度和降水增加是影响生态系统氮循环的三个主要因素⁶。然而，现有的研究大多基于单一或两个因素的模拟，主要涉及植物无机氮的利用²⁹。在全球陆地生态系统的“真实”世界中，这些因素是否以及如何影响土壤 NH₄⁺、NO₃^-、EON 对 PUN 的相对贡献的地理分布仍然不确定。 基于模拟变暖和氮添加下较高的土壤无机氮浓度、矿化和硝化速率^17,30，假设植物无机氮和 NO₃^- 利用率随着温度和氮沉积的增加而增加³¹。然而，根据稀疏观测的数据汇编（数据汇编在表 s2 中），仅观察到温度对植物无机氮吸收的影响。对于降水，观察到的一个现象是 NH₄⁺ 相对于 NO₃^- 对 PUN 的贡献随着平均年降水量 （MAP） 的增加而增加，因为抑制硝化作用和增强反氮化^{作用 18,29}。然而，由于实验条件的变化，仍不确定哪些环境因素在调节土壤氮源对 PUN 的贡献，以及这些贡献在陆地生物群落中如何变化。

Here, this study resolves the long-standing question of natural N abundance isotope methods for constraining δ¹⁵N_PUN signatures and accomplishes the quantification of soil NO₃^-, NH₄⁺, and EON contributions to PUN and their global patterns, respectively. First, we update the global δ¹⁵N_leaf dataset based on that in Craine et al.²⁶ and the literature published since January 10^th, 2018 (Figs. s1a, s2a; Supplementary Text 1). By compiling the global data of δ¹⁵N_leaf, stem δ¹⁵N (δ¹⁵N_stem), and root δ¹⁵N (δ¹⁵N_root) measured for the same plant individuals (Figs. s1b, s2; Supplementary Text 2), we establish the relationship between δ¹⁵N_leaf and δ¹⁵N_plant to constrain the corresponding δ¹⁵N_plant values of global δ¹⁵N_leaf observations (Fig. s3). Then, we analyze the effects of MAT, MAP, and plant life form on δ¹⁵N_plant of different mycorrhizal plants (Table s3, Fig. s4) and establish the relationships of δ¹⁵N_plant with MAT for plants with the same mycorrhizal type and life form (Fig. s5). Using these relationships, we constrain ∆_m values for global δ¹⁵N_plant values and finally obtain the corresponding δ¹⁵N_PUN signatures (Table s4). Further, we compile a global dataset of δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON (Figs. s1c, s6; Supplementary Text 3), analyze the effects of climate and soil parameters on these source δ¹⁵N values of PUN (Table s5), establish their relationships with MAT (Fig. 2) to constrain site-based mean δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON values for the corresponding site-based δ¹⁵N_PUN values (Fig. 2). Based on site-based δ¹⁵N_PUN and source δ¹⁵N values in each grid cell (0.1° (latitude) × 0.1° (longitude)) and using the Stable Isotope Analysis in R (i.e., the SIAR model) (detailed in Methods), we accomplish the calculations of fractional contributions of soil NO₃^-, NH₄⁺, and EON to PUN for global 1610 grid cells (Fig. s7). Finally, we examine relationships between the PUN source contributions and major environmental factors to evaluate the environmental controls of plant N utilization across terrestrial ecosystems.
在这里，本研究解决了长期存在的限制^{δ 15}N_PUN 特征的天然 N 丰度同位素方法的问题，并分别完成了土壤 NO₃^-、NH₄⁺ 和 EON 对 PUN 的贡献及其全局模式的量化。首先，我们根据 Craine 等人 ²⁶ 和自 2018 年 1 月 10 日以来发表的文献更新了全球 δ¹⁵N_叶片数据集（图 .s1a、s2a;补充文本 1）。通过汇编相同植物个体测量的δ¹⁵N_叶片、茎δ¹⁵N （δ¹⁵N_茎） 和根 δ¹⁵N （δ¹⁵N_根） 的全球数据（图 .s1b、s2;补充文本 2），我们建立了^{δ 15}N_叶片和^{δ 15}N_植物之间的关系，以约束全球 δ¹⁵N_叶片观测的相应^{δ 15}N_植物值（图 D）。s3） 的然后，我们分析了 MAT、MAP 和植物生命形式对不同菌根植物的¹⁵N_植物δ影响（表 s3，图 3）。s4）并建立δ¹⁵N_植物与具有相同菌根类型和生命形式的植物的 MAT 的关系（图 D）。s5）。使用这些关系，我们约束全局 δ¹⁵N_植物值的 ∆_m 值，并最终获得相应的^{δ 15}N_PUN 特征（表 s4）。此外，我们编制了一个全球数据集，包括 δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON（图 D）。s1c、s6;补充文本 3），分析气候和土壤参数对这些来源的影响δ¹⁵N 的 PUN 值（表 s5），建立它们与 MAT 的关系（图 S5）。2） 将基于站点的平均值δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON值约束为相应的基于站点δ¹⁵N_PUN 值（图 D）。基于基于站点的^{δ 15}N_PUN 和源δ每个网格单元（0.1°（纬度）× 0.1°（经度））中的¹⁵N 值，并使用 R 中的稳定同位素分析（即 SIAR 模型）（详见方法），我们完成了全球 1610 个网格单元的土壤 NO₃^-、NH₄⁺ 和 EON 对 PUN 的分数贡献的计算（图 D）。s7）。最后，我们研究了 PUN 来源贡献与主要环境因子之间的关系，以评估陆地生态系统中植物氮利用的环境控制。

Variations of site-based mean δ¹⁵N of leaf N (δ¹⁵N_leaf), whole-plant N (δ¹⁵N_plant), PUN (δ¹⁵N_PUN), soil NO₃^- (δ¹⁵N_soil-NO3-), soil NH₄⁺ (δ¹⁵N_soil-NH4+), and soil EON (δ¹⁵N_soil-EON) with MAT are shown in panels (a)–(f), respectively. Mean and SD values were based on sample replicates at each site (n = 1–2118 for plants, and n = 2–21 for soils). Site distribution is shown in Fig. s1. The regression was analyzed by fitting effects with 95% confidence intervals.
图 （a）-（f） 分别显示了叶 N （δ¹⁵N_叶片）、全株 N （δ¹⁵N_植物）、PUN （δ¹⁵N_PUN）、土壤 NO₃ （δ¹⁵N_土壤-NO3-）、土壤 NH₄⁺ （δ¹⁵N_土壤-NH4+） 和土壤 EON （δ¹⁵N_土壤-EON） 的基于点δ地的平均变化。平均值和 SD 值基于每个地点的样品重复（植物 n = 1-2118，土壤 n = 2-21）。站点分布如图 2 所示。s1 的通过拟合效应和 95% 置信区间来分析回归。

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δ¹⁵N values of leaf, stem, root, and the whole plant
δ 叶、茎、根和全株的¹⁵N 值

The δ¹⁵N_leaf, δ¹⁵N_stem, and δ¹⁵N_root of terrestrial plants showed substantial differences from each other (p < 0.05, Fig. s2a–c). Based on the same plant individuals, δ¹⁵N_leaf was higher by 0.5 ± 2.5‰ than δ¹⁵N_root and by 0.4 ± 1.2‰ than δ¹⁵N_stem (Fig. s2d, e), positive correlations between δ¹⁵N_leaf and δ¹⁵N_root or δ¹⁵N_stem showed slope values of 0.57 and 0.90, respectively (Fig. s3). There was a linear correlation between δ¹⁵N_leaf and the corresponding δ¹⁵N_plant for herbs, shrubs, and trees (Fig. s3c), which were used to calibrate global δ¹⁵N_leaf observations to the corresponding δ¹⁵N_plant values. Globally, δ¹⁵N_plant values differed distinctly among herb, shrub, and tree (Table s3). Moreover, δ¹⁵N_plant values of the same life form differed among mycorrhizal types, showing a general order of nonmycorrhizal (NM) > arbuscular mycorrhizal (AM) > ectomycorrhizal (ECM) > ericoid mycorrhizal (ERM) (Fig. s4). For the same life form and mycorrhizal type, δ¹⁵N_plant values varied linearly with the MAT (Fig. s5), which was used to calculate the corresponding δ¹⁵N_PUN values (Table s4).
陆生植物的^{δ 15}N_叶、δ¹⁵N_茎和 δ¹⁵N_根彼此之间存在很大差异（p < 0.05，图 s2a-c）。基于相同的植物个体，δ 15 N_叶比¹⁵N 根高 0.5 ± 2.5‰δ比¹⁵N_茎高 0.4 ± 1.2δ‰（图¹⁵N_）。s2d， e），δ¹⁵N_叶片与δ¹⁵N_根或^{δ 15}N_茎之间的正相关分别显示 0.57 和 0.90 的斜率值（图 D）。s3） 的δ¹⁵N_叶子与相应的 δ¹⁵N_植物之间存在线性相关性（图 D）。s3c），用于将全球 δ¹⁵N_叶片观测值校准为相应的 δ¹⁵N_植物值。在全球范围内，草本、灌木和乔木之间δ¹⁵氮_植物的价值存在明显差异（表 s3）。此外δ，同一生命形式的^{15 种 N}_植物值在菌根类型之间有所不同，表现出非菌根 （NM） >丛枝菌根 （AM） >外生菌根 （ECM） >类菌根 （ERM） 的一般顺序（图 D）。s4）。对于相同的生命形式和菌根类型，δ¹⁵N_植物值随 MAT 线性变化（图 D）。s5），用于计算相应的 δ¹⁵N_PUN 值（表 s4）。

δ¹⁵N values of PUN and soil N sources
δ PUN 和土壤 N 源的¹⁵N 值

The δ¹⁵N_PUN values averaged 3.4 ± 3.3‰ (−15.0‰–21.7‰) (Fig. s6a) and increased linearly with MAT (δ¹⁵N_PUN = 0.09 × MAT + 1.98; Fig. 2). Similarly, soil δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_EON values were also mainly influenced by MAT (Table s5) and increased linearly with MAT (Fig. 2), showing different slope and intercept values (δ¹⁵N_soil-NO3- = 0.18 × MAT – 4.51, δ¹⁵N_soil-NH4+ = 0.37 × MAT + 0.01, and δ¹⁵N_EON = 0.28 × MAT + 1.63; Fig. 2) from that of δ¹⁵N_PUN. For sites with simultaneous N concentration and isotope observations in both plant and soil, the site-based mean δ¹⁵N values of TEN (δ¹⁵N_TEN) (5.4 ± 2.7‰) were higher by 2.3 ± 2.7‰ on average than the corresponding δ¹⁵N_PUN (3.1 ± 2.6‰) and the differences generally increased with MAT (Fig. s8).
δ¹⁵N_PUN 值平均为 3.4 ± 3.3‰ （-15.0‰–21.7‰）（图 D）。s6a）并随 MAT 线性增加 （δ¹⁵N_PUN = 0.09 × MAT + 1.98;无花果。同样，土壤δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和^{δ 15}N_EON 值也主要受 MAT 影响（表 s5），并随 MAT 线性增加（图 D）。2），显示不同的坡度和截距值（δ¹⁵N_土壤-NO3- = 0.18 × MAT – 4.51，δ¹⁵N_土壤-NH4+ = 0.37 × MAT + 0.01，δ¹⁵N_EON = 0.28 × MAT + 1.63;无花果。2） 从 δ¹⁵N_PUN。对于植物和土壤中同时进行氮浓度和同位素观测的站点，TEN （δ¹⁵N_TEN） （5.4 ± 2.7‰） 的基于站点δ的¹⁵N 值平均比相应的^{δ 15}N_PUN （3.1 ± 2.6‰） 高 2.3 ± 2.7‰，并且差异通常随 MAT 的增加而增加（图 D）。s8）。

Fractional contributions of soil NO₃ ^-, NH₄ ⁺, and EON to PUN
土壤 NO₃^-、NH₄⁺ 和 EON 对 PUN 的贡献分数

The plant f_NO3-, f_NH4+, and f_EON values averaged 29 ± 19%, 42 ± 18%, and 29 ± 19%, respectively (Fig. s7). Generally, the f_NO3- and f_NH4+ increased while the f_EON decreased with the latitude (Figs. 3, s9). Neither plant f_NO3-, f_NH4+, nor f_EON values showed a clear relationship with MAP and the flux of atmospheric N deposition, instead they varied with the MAT nonlinearly (Figs. 4, s10). Across the observed temperature spectrum, the f_NO3- increases with MAT and reaches a peak of 46% at 28.5 °C (f_NO3- = 0.01 × MAT² + 0.65 × MAT + 20.8; Fig. 4a). The f_NH4+ also increased with MAT, achieving a maximum of 46% at 14.4 °C, followed by a decline as temperatures further increased (f_NH4+ = −0.05 × MAT² + 1.52 × MAT + 35.6; Fig. 4b). Meanwhile, the f_EON gradually decreased with MAT, stabilizing at about 20% when the MAT exceeded 15 °C (f_EON = 0.04 × MAT² – 2.20 × MAT + 48.6; Fig. 4c).
植物 f_NO3-、f_NH4+ 和 f_EON 值分别为 29 ± 19%、42 ± 18% 和 29 ± 19%（图s7）。通常，f_NO3- 和 f_NH4+ 随纬度的增加而增加，而 f_EON 随纬度的增加而降低（图 D）。3， s9）。植物 f_NO3-、f_NH4+ 和 f_EON 值均未显示出与 MAP 和大气氮沉降通量的明显关系，而是它们随 MAT 非线性变化（图4、s10）。在整个观察到的温度光谱中，f_NO3- 随 MAT 增加，并在 28.5 °C 时达到 46% 的峰值 （f_NO3- = 0.01 × MAT² + 0.65 × MAT + 20.8;无花果。4a）. f_NH4+ 也随 MAT 增加，在 14.4 °C 时达到最大值 46%，随后随着温度进一步升高而下降 （f_NH4+ = -0.05 × MAT² + 1.52 × MAT + 35.6;无花果。同时，f_EON随MAT逐渐降低，当MAT超过15 °C时稳定在20%左右（f_EON = 0.04 × MAT² – 2.20 × MAT + 48.6;无花果。4c）。

ƒ_NO3- (a, b), ƒ_NH4+ (c, d), and ƒ_EON (e, f) are fractional contributions of soil NO₃^-, NH₄⁺, and EON to PUN, respectively. In panels (a), (c), and (e), gray areas are deserts, glaciers, perennial snow-covered areas, bare ground, and agricultural land etc. The mapping was conducted by ArcGIS version 10.8 (Esri Inc., USA) using the Kriging spatial interpolations based on the 0.1° (latitude) × 0.1° (longitude) grid-based data. The base map was downloaded from https://hub.arcgis.com/datasets/esri::world-countries-generalized. In panels (b), (d), and (f), red lines and gray areas show mean and SD values.
ƒ_NO3- （a， b）、ƒ_NH4+ （c， d） 和 ƒ_EON （e， f） 分别是土壤 NO₃、NH₄⁺ 和 EON 对 PUN 的分数贡献。在面板 （a）、（c） 和 （e） 中，灰色区域是沙漠、冰川、常年积雪区域、裸露地面和农业用地等。制图由 ArcGIS 10.8 版（Esri Inc.，美国）使用基于 0.1°（纬度）× 0.1°（经度）格网数据的克里金空间插值法进行。底图是从 https://hub.arcgis.com/datasets/esri::world-countries-generalized 下载的。在面板 （b）、（d） 和 （f） 中，红线和灰色区域显示平均值和 SD 值。

Full size image

ƒ_NO3- (a), ƒ_NH4+ (b), and ƒ_EON (c) are fractional contributions of soil NO₃^-, NH₄⁺, and EON to PUN, respectively. The 0.1° (latitude) × 0.1° (longitude) grid-based mean ± SD values are shown. The regression was analyzed by fitting effects with 95% confidence intervals.
ƒ_NO3- （a）、ƒ_NH4+ （b） 和 ƒ_EON （c） 分别是土壤 NO₃、NH₄⁺ 和 EON 对 PUN 的分数贡献。将显示 0.1°（纬度）× 0.1°（经度）基于网格的平均值± SD 值。通过拟合效应和 95% 置信区间来分析回归。

Full size image

Differing δ¹⁵N_leaf, δ¹⁵N_stem, and δ¹⁵N_root of terrestrial plants at both site (Fig. s2a–c) and individual levels (Fig. s2d–f) demonstrated substantial N isotope effects of intra-plant N assimilation/allocation^28,32,33 and none of them can exactly represent the corresponding δ¹⁵N_plant signatures^28,34. The generally higher δ¹⁵N_leaf than the corresponding δ¹⁵N_plant (Figs. 2a, b, s3c) points to a potential risk of overestimating the contribution of ¹⁵N-enriched soil N sources to terrestrial plants when neglecting the intra-plant N isotope effects on δ¹⁵N_leaf values. Accordingly, the δ¹⁵N_leaf vs δ¹⁵N_plant correlation based on the same plant individuals (Fig. s3c) provides a transformation of global δ¹⁵N_leaf measurements (Fig. 2a) to the corresponding δ¹⁵N_plant values (Fig. 2b).
在两个地点，陆生植物的¹⁵δ N_叶、δ¹⁵N_茎和 δ¹⁵N_根不同（图S2a-c）和单个级别（图s2d-f）证明了植物内氮同化/分配的重要氮同位素效应^{28,32,33，并且}它们都不能准确代表相应的 δ¹⁵氮植物特征^28,34。通常δ¹⁵N_的叶子高于相应的 δ¹⁵N_植物（图 D）。2a、b、s3c）指出，当忽视植物内氮同位素对¹⁵N_叶片值的影响时，存在高估 ¹⁵种富含氮的土壤氮源对陆生植物贡献δ潜在风险。因此，基于相同植物个体的 δ¹⁵N_叶片与 δ¹⁵N_植物的相关性（图 D）。s3c）提供了全局 δ¹⁵N_叶片测量值的变换（图 D）。2a） 与相应的¹⁵N_植物值δ （图 D）2b）。

The generally increasing δ¹⁵N_plant of the same life form and mycorrhizal type with MAT (Fig. s5) revealed temperature as an effective predictor of mycorrhizal N isotope effects on δ¹⁵N_plant on the global scale (Table s4). The decreasing mycorrhizal N isotope effects with increasing MAT for most plants (Table s4) confirmed a weaker mycorrhizal mediation of the whole-plant N acquisition under warmer climate conditions^9,24. This tendency might be related to the generally increasing soil bioavailable N (particularly inorganic N sources) with MAT for the direct N absorption via plant roots^9,24. The correlation between mycorrhizal N isotope effects and MAT for the same life form and mycorrhizal type identified in our study (Table s4) allowed a transformation of global δ¹⁵N_plant measurements (Fig. 2b) to the corresponding δ¹⁵N_PUN values in soils (Fig. 2c).
通常增加的^{δ 15}N_植物具有相同的生命形式和菌根类型，与 MAT （图 D）。S5）揭示了温度是全球范围内菌根 N 同位素对¹⁵N_植物影响的有效预测因子δ（表 s4）。大多数植物的菌根氮同位素效应随着 MAT 的增加而降低（表 s4）证实了在温暖的气候条件下，全植物氮获取的菌根介导较弱 ^9,24。这种趋势可能与土壤生物可利用氮（特别是无机氮源）普遍增加有关，MAT 通过植物根系直接吸收氮 ^9,24。在我们的研究中确定的相同生命形式和菌根类型的菌根 N 同位素效应与 MAT 之间的相关性（表 s4）允许转换全球 δ¹⁵N_植物测量值（图 s4）。2b） 与土壤中相应的^{δ 15}N_PUN 值（图2c）。

Globally, δ¹⁵N_soil-TEN (Fig. s8b), δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON all increased linearly with MAT (Fig. 2d–f). Increasing temperature generally enhances rates of microbial N mineralization, nitrification, and denitrification in soils³⁰. The openness of the soil N cycle would further increase the production and losses of ¹⁵N-depleted N species, such as the N-containing gases (e.g., NO, N₂O, N₂, NH₃)^22,25,35. This mechanism was well supported by increasing ¹⁵N abundances of soil bulk N pools with MAT^25,36. Because the N nutrition of terrestrial plants depends on acquiring NO₃^-, NH₄⁺, and EON from soils, the δ¹⁵N_PUN naturally followed soil N sources to increase with MAT (Fig. 2c). These results demonstrate a temperature-induced openness of the soil N cycle²⁵ and offer δ¹⁵N_PUN as an indicator of the plant-soil N cycle.
在全球范围内，δ¹⁵N_{土壤 TEN} （图 .s8b）、δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON 均随 MAT 线性增加（图 1）。2d-f）。温度升高通常会提高土壤中微生物氮矿化、硝化和反硝化的速率³⁰.土壤氮循环的开放性将进一步增加 ¹⁵种贫氮物质的产生和损失，例如含氮气体（例如 NO、N₂O、N₂、NH₃）^22,25,35。MAT^25,36 增加 ¹⁵N 丰度的土壤散装氮池很好地支持了这一机制。由于陆生植物的氮营养取决于从土壤中获取 NO₃^-、NH₄⁺ 和 EON，因此 δ 15 N_PUN 自然跟随土壤氮来源随着 MAT 的增加而增加（图¹⁵N）。这些结果表明温度诱导的土壤 N 循环开放性²⁵ 并提供^{δ 15}N_PUN 作为植物-土壤 N 循环的指标。

Based on simultaneous N concentration and isotope observations in both plants and soils (Figs. s8, s11–12), we confirmed distinctly lower δ¹⁵N values in PUN than in TEN (Fig. s8). Clearly, neither δ¹⁵N_plant nor δ¹⁵N_PUN can exactly represent soil δ¹⁵N_TEN to elucidate the soil or ecosystem bioavailable N, also soil δ¹⁵N_TEN cannot be simply taken as δ¹⁵N_PUN. These results reflected differing sizes and compositions between the total bioavailable N and PUN pools in soils (Fig. 1). The generally lower δ¹⁵N_PUN than δ¹⁵N_TEN in soils found in our study (Fig. s8c) demonstrated that plants utilized relatively more ¹⁵N-depleted NO₃^- than ¹⁵N-enriched NH₄⁺ and EON in soils (Fig. s12). Interestingly, we found that δ¹⁵N_PUN increased by a lower magnitude than the δ¹⁵N_soil-TEN with increase MAT (Fig. s8a, b), which caused an increasing δ¹⁵N_TEN-PUN value with MAT (Fig. s8c). In principle, δ¹⁵N_PUN values were controlled by both δ¹⁵N values and fractional contributions of the corresponding soil NO₃^-, NH₄⁺, and EON^18,20,23. Because δ¹⁵N values of soil N sources are mainly regulated by microbial N cycles^17,37 and they increase with MAT (Fig. 2d–f), the lower sensitivity of δ¹⁵N_PUN than δ¹⁵N_TEN to MAT (Fig. s8) indicates changing fractional contributions of soil NO₃^-, NH₄⁺, and EON to PUN, i.e., changing plant N-use strategies with MAT.
基于植物和土壤中 N 浓度和同位素的同步观察（图 .s8、s11-12），我们证实 PUN δ¹⁵N 值明显低于 TEN（图 D）。s8）。显然，δ¹⁵N_植物和^{δ 15}N_PUN 都不能准确代表土壤δ¹⁵N_TEN 来阐明土壤或生态系统生物可利用的氮，也不能简单地将土壤δ¹⁵N_TEN 视为^{δ 15}N_PUN。这些结果反映了土壤中生物可利用的 N 和 PUN 总池之间的不同大小和组成（图 D）。1）. 在我们研究中发现的土壤中，δ¹⁵N_PUN 通常低于 δ¹⁵N_TEN（图 D）。s8c）表明植物在土壤中利用的 ¹⁵种贫 NO₃^- 比 ¹⁵种富含氮的 NH₄⁺ 和 EON 相对多（图 D）。s12）。有趣的是，我们发现δ¹⁵N_PUN 的增加幅度低于 δ¹⁵N_{土壤 TEN}（图 D）。s8a、b），这导致 MAT δ¹⁵N_TEN-PUN值增加（图 D）。s8c）。原则上，δ¹⁵N_PUN 值受 δ¹⁵N 值和相应土壤 NO₃^-、NH₄⁺ 和 EON^18,20,23 的分数贡献控制。因为土壤的¹⁵δ氮源的氮值主要受微生物氮循环^17,37 的调节，并且它们随着 MAT 的增加而增加（图 D）。2d-f），δ¹⁵N_PUN 的灵敏度低于 δ¹⁵N_TEN 对 MAT 的灵敏度（图 D）。s8）表示改变土壤 NO₃^-、NH₄⁺ 和 EON 对 PUN 的分数贡献，即用 MAT 改变植物 N 利用策略。

Globally, plant f_NO3-, f_NH4+, and f_EON values averaged 29%, 42%, and 29%, respectively (Fig. s7). Clearly, plants mainly utilized inorganic N, though organic N is an important N source for plants^6,13,14. Moreover, significant changes in f_NO3-, f_NH4+, and f_EON with MAT, rather than with MAP or atmospheric N deposition flux (Figs. 4, s10), indicate a global temperature-dependent plant N-use pattern. This suggests that N deposition and MAP do not significantly affect plant N utilization, whereas MAT plays a critical role in driving the geographical distribution of plant N-use patterns at the global scale. From polar to tropical regions, both f_NO3- and f_NH4+ increased, and f_EON decreased, with decreasing latitude (Figs. 3, s9) and the increase in MAT (Fig. 4). These results provide quantitative evidence confirming that plants in relatively colder climate conditions (<0 °C) utilized soil EON in higher proportions (>50%) than plants in relatively warmer climate (Fig. 4c). Fractional contributions of three N forms to PUN changed with MAT nonlinearly (Fig. 4), which revealed that plant utilization of soil NO₃^-, NH₄⁺, and EON differed in responsiveness and sensitivity between colder and warmer climates. In colder climates (MAT of −18.7 to −2.0 °C), plant utilization of soil NO₃^- was proportionally low (below 20%), attributed to significantly reduced nitrification rates^6,19. In contrast, in warmer climates at sites with MAT ranging from 11.4 to 28.5 °C, soil NO₃^- contribution to PUN exceeded the global average (29%), reaching up to 46% (Fig. 4a). Additionally, the contribution of soil NH₄⁺ to PUN increased with rising MAT, peaking at 46% at 14.4 °C (Fig. 4b) due to increased N mineralization rates^12,30. However, f_NH4+ declined with MAT when exceeding 14.4 °C (Fig. 4b), likely due to enhanced nitrification rates³⁸ and the toxic effects of excessive NH₄⁺, causing lower intracellular pH and ionic imbalance in plants³.
在全球范围内，植物 f_NO3-、f_NH4+ 和 f_EON 值平均分别为 29%、42% 和 29%（图 D）。s7）。显然，植物主要利用无机氮，尽管有机氮是植物的重要氮来源 ^6,13,14。此外，使用 MAT 而不是 MAP 或大气 N 沉积通量时，f_NO3-、f_NH4+ 和 f_EON 的显著变化（图 D）。4， s10） 表示全局温度依赖性植物 N 使用模式。这表明氮沉积和 MAP 不会显著影响植物氮利用，而 MAT 在推动全球植物氮利用模式的地理分布方面起着关键作用。从极地到热带地区，f_NO3- 和 f_NH4+ 都增加了，而 f_EON 随着纬度的降低而降低（图 1）。3，s9）和 MAT 的增加（图 D）。4）. 这些结果提供了定量证据，证实植物在相对寒冷的气候条件下 （50%） 比在相对温暖的气候中的植物 （Fig.3 种 N 形式对 PUN 的小数贡献随 MAT 非线性变化（图 D）。4），揭示了植物对土壤 NO₃^-、NH₄⁺ 和 EON 的利用在寒冷和温暖气候之间的响应性和敏感性上存在差异。在较冷的气候下（MAT 为 -18.7 至 -2.0 °C），植物对土壤 NO₃^- 的利用率成比例较低（低于 20%），这归因于硝化速率显着降低^6,19。 相比之下，在 MAT 范围为 11.4 至 28.5 °C 的温暖气候中，土壤 NO₃^- 对 PUN 的贡献超过了全球平均水平 （29%），高达 46%（图 D）。此外，土壤 NH₄⁺ 对 PUN 的贡献随着 MAT 的增加而增加，在 14.4 °C 时达到 46% 的峰值（图 D）。4b） 由于氮矿化速率增加^12.30.然而，当超过 14.4 °C 时，f _NH4+ 随 MAT 下降（图 D）。4b），可能是由于硝化速率增加³⁸ 和过量 NH₄⁺ 的毒性作用，导致植物细胞内 pH 值降低和离子失衡³。

Based on simultaneous N concentration and isotope observations in both plant and soil (Figs. s8, s11-12), we found that f_NO3-, f_NH4+, and f_EON did not differ substantially from those based on non-synchronous observations, showing consistently low Δf_NO3-, Δf_NH4+, and Δf_EON values of −1.1 ± 11.1%, 2.0 ± 9.3%, and −0.9 ± 9.1%, respectively (Fig. s11). Also, calculating results based on simultaneous observations confirmed a globally temperature-dependent plant N-use pattern, i.e., both f_NO3- and f_NH4+ increased and f_EON decreased with MAT (although not significantly, Fig. s11). Moreover, we found that both β_NO3- and β_NH4+ increased while β_EON decreased with the increasing MAT (β represents the preference degree of plant NO₃^-, NH₄⁺, or EON utilization, detailed in Methods, Fig. s12). This pointed to an important plant N-use strategy that plants in relatively colder climate conditions preferred soil organic N sources over inorganic N sources^13,15. Conversely, plants in relatively warmer climates preferred soil inorganic N sources over organic N sources^18,20. In general, plants display a clear plasticity of relative N preference in response to increased MAT.
基于植物和土壤中 N 浓度和同位素的同步观测（图 D）。s8、s11-12），我们发现 f_NO3-、f_NH4+ 和 f_EON 与基于非同步观测的数值没有太大差异，显示始终较低的 Δf_NO3-、Δf_NH4+ 和 Δf_EON 值分别为 -1.1 ± 11.1%、2.0 ± 9.3% 和 -0.9 ± 9.1%（图 D）。s11）。此外，基于同时观测的计算结果证实了全球温度依赖性的植物氮使用模式，即 f_NO3- 和 f_NH4+ 都增加了，而 f_{EON 随着 MAT} 的减少（尽管不显着，图 1）。s11）。此外，我们发现β_NO3- 和 β_NH4+ 均增加，而 β_EON 随着 MAT 的增加而降低（β 代表植物 NO₃^-、NH₄⁺ 或 EON 利用的偏好程度，详见方法，图 3。s12）。这指出了一个重要的植物氮利用策略，即气候相对较冷的植物更喜欢土壤有机氮源而不是无机氮源^13,15。相反，气候相对温暖的植物更喜欢土壤无机氮源而不是有机氮源^18,20。一般来说，植物对 MAT 的增加表现出相对 N 偏好的明显可塑性。

In summary, this study presents a quantitative analysis on levels and spatial variation of soil N source contributions to global terrestrial plants, providing methods and evidence for evaluating plant N-use mechanisms and N cycling of terrestrial ecosystems. By explicitly constraining isotope effects of intra-plant N assimilation/allocation and mycorrhizal N acquisition of different plant life forms under different climates, we transferred the δ¹⁵N_leaf to a parameter of δ¹⁵N_PUN for elucidating plant N utilization. Substantially differing signatures between δ¹⁵N_PUN and δ¹⁵N_TEN informed that neither δ¹⁵N_leaf nor δ¹⁵N_TEN can be directly taken as an indicator of soil N availability or the relative availability of PUN to soil N supply. Globally, we found that variations of δ¹⁵N_PUN and therefore soil N source contributions to terrestrial plants were temperature-dependent and nonlinear, showing increasing plant inorganic N utilization and relative preference while decreasing organic N utilization and preference with increasing MAT. Our finding revealed the important role of plant N-use strategies in regulating plant δ¹⁵N records and their responses to warming climate. Besides, due to differing C costs among plant NH₄⁺, NO₃^-, and EON assimilation, our finding aids further evaluation of the effects of plant N utilization on C cycles.
综上所述，本研究对土壤氮源对全球陆生植物的贡献水平和空间变化进行了定量分析，为评价植物氮素利用机制和陆地生态系统氮循环提供了方法和证据。通过明确限制不同气候下不同植物生命形式的植物内部氮同化/分配和菌根氮获取的同位素效应，我们将^{δ 15}N_叶片转移到 δ¹⁵N_PUN 的参数，以阐明植物氮的利用。δ¹⁵N_PUN 和^{δ 15}N_TEN 之间的特征存在很大差异，表明^{δ 15}N_叶片和^{δ 15}N_TEN 都不能直接作为土壤氮可用性或 PUN 与土壤氮供应的相对可用性的指标。在全球范围内，我们发现^{δ 15}N_PUN 的变化以及土壤氮源对陆生植物的贡献是与温度相关的非线性的，表现出植物无机氮利用率和相对偏好的增加，而有机氮利用率和偏好随着 MAT 的增加而降低。我们的研究结果揭示了植物氮利用策略在调控植物 δ¹⁵次氮记录及其对气候变暖的响应中的重要作用。此外，由于植物 NH₄⁺、NO₃^- 和 EON 同化的 C 成本不同，我们的发现有助于进一步评估植物 N 利用对 C 循环的影响。

However, extant δ¹⁵N observations on roots and stems were still sparse among terrestrial plants, and simultaneous observations on δ¹⁵N, N concentrations, and biomass for leaves, stems, and roots were even less. Particularly, δ¹⁵N values of soil extractable N sources have seldom been observed together with those of plants, particularly for sites and ecosystems with relatively lower MAT. Further, δ¹⁵N values of soil EON sources that were actually used by plants remain difficult to determine. These are substantial uncertainties in the established relationships and calculated results of this study. Although it might not influence the general patterns of soil N source contributions to global plants found in the present study, to widely conduct simultaneous and point-to-point observations on plant-soil δ¹⁵N parameters would provide more precise estimates on plant N sources and availability for biogeochemical and earth system modeling efforts.
然而，现存的δ¹⁵N 对根和茎的观测在陆生植物中仍然很少，同时对叶、茎和根的^{δ 15}N 、 N 浓度和生物量的观测甚至更少。特别是δ，很少观察到土壤可提取氮源的 15 N 值与植物的¹⁵N 值，特别是对于 MAT 相对较低的地点和生态系统。此外，植物实际使用的土壤 EON 源的¹⁵N 值仍然难以确定δ。这些是本研究的既定关系和计算结果中的重大不确定性。虽然它可能影响本研究中发现的土壤氮源对全球植物贡献的一般模式，但对植物-土壤进行广泛同步和点对点的观测δ¹⁵N 参数将提供更精确的植物氮源估计以及生物地球化学和地球系统建模工作的可用性。

Data compilation of δ¹⁵N_leaf, N_leaf, and N deposition
δ¹⁵N_叶、N_叶和 N 沉积的数据汇编

We collected the δ¹⁵N_leaf data published after January 10^th, 2018, and combined them with the existing δ¹⁵N_leaf dataset collected by Craine et al.²⁶. Briefly, we searched literature published since 2018 on the Web of Science and Google Scholar with the terms “nitrogen isotope or ¹⁵N” and “leaf or foliar”. The δ¹⁵N_leaf data of (1) urban areas, (2) agricultural ecosystems, (3) non-control samples of manipulative experiments, (4) non-vascular plants, (5) fertilized plants, (6) semi-aquatic or aquatic plants, and (7) N₂-fixing plants were excluded. Data in figures of publications were extracted using the software of Web Plot Digitizer (Version 4.2, San Francisco, California, USA).
我们收集了 2018 年 1 月 10 日之后发布的 δ¹⁵N_叶片数据，并将它们与 Craine 等人收集的现有^{δ 15}N_叶片数据集相结合²⁶。简而言之，我们检索了自 2018 年以来在 Web of Science 和 Google Scholar 上发表的带有术语 “氮同位素或 ¹⁵N” 和 “叶或叶面 ”的文献。排除了 （1） 城市地区、（2） 农业生态系统、（3） 操纵实验的非对照样本、（4） 非维管束植物、（5） 受精植物、（6） 半水生或水生植物和 （7） N₂ 固定植物的 δ¹⁵N_叶数据。使用 Web Plot Digitizer 软件（版本 4.2，美国加利福尼亚州旧金山）提取出版物中的数据。

By August 1^st of 2022, 37 publications (listed in Supplementary Text 1) with the required δ¹⁵N_leaf data were available, and a total of 16494 observations at 1218 sites were newly added to the dataset collected by Craine et al.²⁶ (41669 observations at 5296 sites before January 10^th of 2018). Sites of all δ¹⁵N_leaf observations (58163 observations at 6514 sites) distribute between 54.5°S and 71.1°N (Figs. s1a, s2a), with the MAT spanning from −18.7 °C to 28.6 °C and MAP ranging from 50.1 mm to 6576.0 mm. The data of MAT and MAP were collected either from the original literature or cited from the climatic database at http://www.worldclim.org using the coordinate information. All δ¹⁵N_leaf observations were conducted for 5632 plant species of 2172 herbs, 1156 shrubs, and 2304 trees. The sampling years of δ¹⁵N_leaf observations range from 1876 to 2022, with 90% sampled after 1995.
截至 2022 年 8 月 1 日，已有 37 篇出版物（列于补充文本 1）和δ所需的¹⁵N_叶数据，并且在 Craine 等人收集的数据集中新增了 1218 个地点的 16494 次观测（²⁰¹⁸ 年 1 月 10 日之前在 5296 个地点进行了 41669 次观测）。所有^{δ 个 15}N_叶片观测点（58163 个点的 6514 个点）分布在南纬 54.5°和 71.1°N 之间（图 D）。s1a、s2a），MAT 范围为 −18.7 °C 至 28.6 °C，MAP 范围为 50.1 mm 至 6576.0 mm。MAT 和 MAP 的数据要么来自原始文献，要么使用坐标信息从 http://www.worldclim.org 气候数据库中引用。对 2172 种草本植物、1156 种灌木和 2304 棵乔木的 5632 种植物进行了所有^{δ 15}N_叶观察。δ¹⁵N_叶片观测的采样年份从 1876 年到 2022 年不等，其中 90% 的采样是在 1995 年之后进行的。

The N_leaf data were also collected from the corresponding literature if available. Besides, to examine atmospheric N deposition effects on plant and soil N variables, we collected the data of deposition fluxes of inorganic and organic N in wet and dry deposition based on the observations in 12 individual years during 1984 − 2016³⁹. We used the coordinate information of plant or soil δ¹⁵N observation sites in our study to match and extract the corresponding data and then calculated mean annual fluxes of total N deposition.
如果可用，也从相应的文献中收集 N_叶数据。此外，为了研究大气氮沉降对植物和土壤氮变量的影响，我们根据 1984 - 2016 年 12 个单独年份的观测收集了湿法和干法氮的沉积通量数据³⁹。本研究利用¹⁵个氮观测点δ植物或土壤的坐标信息进行匹配和提取，计算出总氮沉降的年平均通量。

Constraining terrestrial δ¹⁵N_plant signatures
限制陆生δ¹⁵N_植物特征

The whole-plant N level (N_plant) is mainly determined by leaf, stem, and root N levels, so we have the following mass-balance Eq. (1).
全株 N 水平 （N_plant） 主要由叶、茎和根 N 水平决定，因此我们有以下质量平衡方程 （1）。

where N_leaf, N_stem, and N_root are the N concentrations in the leaf, stem, and root, respectively; F_leaf, F_stem, and F_root are their respective biomass percentages in the whole plant. Then, the δ¹⁵N_plant can be expressed by the following δ¹⁵N mass-balance Eq. (2).
其中 N_叶、N_茎和 N_根分别是叶、茎和根中的 N 浓度;F_叶、F_茎和 F_根是它们各自在整个植物中的生物量百分比。然后，δ¹⁵N_被控对象可以用以下^{δ 15}N 质量平衡方程 （2） 来表示。

where we assume that F_leaf + F_stem + F_root = 1.
其中我们假设 F_叶 + F_茎 + F_根 = 1。

The N concentration and δ¹⁵N data of stems and roots were collected from the publications of δ¹⁵N_leaf observations. In our calculations (Eqs. (1) and (2)), we only used the N concentration and δ¹⁵N data simultaneously measured for the same plant individuals (Fig. s3). The biomass data were collected either from the original literature or cited from the global database of Reich et al.¹⁰. In total, 103 publications (listed in Supplementary Text 2) with 382 and 1752 observations of δ¹⁵N_stem and δ¹⁵N_rootwere available, respectively (Fig. s2b, c). Sites of δ¹⁵N_stem and δ¹⁵N_root observations distribute between 45.8°S and 74.5°N (Fig. s1b), with the MAT spanning from −16.3 °C to 28.0 °C. The available δ¹⁵N_stem and δ¹⁵N_root observations were conducted for a total of 246 plant species, including 120 herbs, 57 shrubs, and 69 trees.
茎和根的 N 浓度和 δ¹⁵N 数据是从^{δ 15}_{N 叶片}观测的出版物中收集的。在我们的计算中（方程。（1） 和 （2）），我们只使用了相同植物个体同时测量的 N 浓度和^{δ 15}N 数据（图 D）。s3） 的生物量数据要么来自原始文献，要么引用自 Reich 等人的全球数据库 ¹⁰。总共有 103 篇出版物（列于补充文本 2 中），分别对^{δ 15}N_茎和^{δ 15}N_根进行了 382 次和 1752 次观察（图 D）。s2b、c）。δ¹⁵N_茎和^{δ 15}N_根的观察位置分布在 45.8°S 和 74.5°N 之间（图 D）。s1b），MAT 范围为 −16.3 °C 至 28.0 °C。 对 246 种植物进行了可用的^{δ 15}N_茎和 δ¹⁵N_根的观测，包括 120 种草本植物、57 种灌木和 69 种乔木。

The δ¹⁵N values showed difference between leaf and stem or root of the same individuals among the observed plants (Fig. s2d-f). The δ¹⁵N_leaf and δ¹⁵N_plant were positively correlated for herbs δ¹⁵N_plant = 0.77( ± 0.08) × δ¹⁵N_leaf – 0.13( ± 0.19) (R = 0.93, p < 0.01), shrubs δ¹⁵N_plant = 0.80( ± 0.06) × δ¹⁵N_leaf – 0.98( ± 0.10) (R = 0.94, p < 0.01), and trees δ¹⁵N_plant = 0.93( ± 0.00) × δ¹⁵N_leaf – 0.13( ± 0.00) (R = 0.98, p < 0.01) (Fig. s3c). These relationships were used to calculate δ¹⁵N_plant values for each δ¹⁵N_leaf observation of each life form in the global dataset (Fig. 2b).
δ¹⁵N 值显示观察到的植物中叶与茎或相同个体的根之间的差异（图 D）。s2d-f）。δ¹⁵N_叶δ¹⁵N_植株δ草本植物呈正相关¹⁵N_植株 = 0.77（ ± 0.08） × δ¹⁵N_植株 – 0.13（ ± 0.19） （R = 0.93，p < 0.01），灌木δ¹⁵N _植株 = 0.80（ ± 0.06） × δ¹⁵N_植株 – 0.98（ ± 0.10） （R = 0.94，p < 0.01），乔木δ¹⁵N_植株 = 0.93（ ± 0.00） × δ¹⁵N_植株 – 0.13（ ± 0.00） （R = 0.98，p < 0.01）（图 s3c）。 这些关系用于δ计算全球数据集中每种生命形式的每 δ¹⁵_{N 叶片}观测的¹⁵N_植物值（图 D）。2b）。

Constraining terrestrial δ¹⁵N_PUN signatures
限制地面δ¹⁵N_PUN 签名

The differences between δ¹⁵N_plant and δ¹⁵N_PUN exist (Δ_m) for plants associated with mycorrhiza due to substantial N isotope effects caused by plant N acquisition via mycorrhizal associations^22,24. Thus, we have Eq. (3) to calculate the δ¹⁵N_PUN.
δ¹⁵N_植物和 δ¹⁵N_PUN 之间存在与菌根相关的植物之间的差异 （Δ_m），因为植物 N 通过菌根关联获得引起了巨大的 N 同位素效应^22,24。因此，我们有方程 （3） 来计算^{δ 15}N_PUN。

To constrain the Δ_m values, we collected the records of the mycorrhizal types of plants either from the original literature or referring to records in other publications⁴⁰. In this study, a total of 240, 4849, 391, and 106 among 5586 plant species are associated with NM, AM, ECM, and ERM, respectively. The Δ_m value for NM plant species was assumed as 0‰ because the direct entering processes of soil N sources into plant roots have no substantial ¹⁵N discrimination^18,23,24. The Δ_m values for plant species associated with mycorrhiza can be estimated as the δ¹⁵N_plant differences between mycorrhizal plants and NM plants, respectively, which have been revealed varying in a general order of AM < ECM < ERM²⁴.
为了限制 Δ_m 值，我们从原始文献或参考其他出版物中的记录⁴⁰ 中收集了植物菌根类型的记录。本研究中，5586 种植物中共有 240 种、 4849 种、 391 种和 106 种与 NM 、 AM 、 ECM 和 ERM 相关。假设 NM 植物物种的 Δ_m 值为 0‰，因为土壤 N 源直接进入植物根系的过程没有实质性的 ¹⁵N 鉴别^18,23,24。与菌根相关的植物物种的 Δ_m 值可以分别估计为菌根植物和 NM 植物之间^{δ 15}N_植物差异，这已经揭示出在 AM < ECM < ERM²⁴ 的一般顺序中有所不同。

In this study, we newly found that the δ¹⁵N_plant variations were influenced by life forms and MAT (Table s3, Fig. s4–5), thus we constructed the δ¹⁵N_plant variations of each mycorrhizal type of each life form with MAT (Fig. s5). Then, we calculated the MAT-specific Δ_m values by using the fitting formula of NM minus the fitting formula of the corresponding each mycorrhizal type plants of the same life form (Table s4, Fig. s5). The negative correlation between MAT and Δ_m (Table s4) indicates that as MAT increases, soil N availability also increases, reducing plant reliance on mycorrhizae and thereby diminishing their N isotopic effects^9,24. Therefore, we calibrated δ¹⁵N_plant to δ¹⁵N_PUN using this relationship to minimize the impact of mycorrhizae. Finally, the MAT-specific δ¹⁵N_PUN values were calculated (Figs. s6a, 2c).
在这项研究中，我们新发现^{δ 15}N_植物变异受生命形式和 MAT 的影响（表 s3，图 1）。s4-5），因此我们用 MAT 构建了每种生命形式的每种菌根类型的^{δ 15}N_植物变体（图 D）。s5）。然后，我们使用 NM 的拟合公式减去相同生命形式的相应每个菌根型植物的拟合公式来计算 MAT 特异性 Δ_m 值（表 s4，图 4）。s5）。MAT 和 Δ_m 之间的负相关（表 s4）表明，随着 MAT 的增加，土壤氮的可用性也会增加，从而减少植物对菌根的依赖，从而减少它们的氮同位素效应^9,24。因此，我们利用这种关系将^{δ 15}N_植物校准为 δ¹⁵N_PUN，以最大限度地减少菌根的影响。最后，计算 MAT 特异性 δ¹⁵N_PUN 值（图 s6a、2c）。

Calculating contributions of soil NO₃ ^-, NH₄ ⁺, and EON to PUN
计算土壤 NO₃^-、NH₄⁺ 和 EON 对 PUN 的贡献

The δ¹⁵N_PUN value is determined by δ¹⁵N values of soil NO₃^-, NH₄⁺, and EON (δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON, respectively) and fractional contributions of soil NO₃^-, NH₄⁺, and EON to PUN (ƒ_NO3-, ƒ_NH4+, and ƒ_EON, respectively)^18,19,23, which can be expressed by Eq. (4).
δ¹⁵N_PUN 值由土壤 NO₃^-、NH₄⁺ 和 EON 的¹⁵N 值确定δ δ分别为¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON）和土壤 NO₃^-、NH₄⁺ 和 EON 对 PUN 的贡献分数（分别为 ƒ_NO3-、ƒ_NH4+ 和 ƒ_EON）^18，19,23，可以用方程 （4） 表示。

where we assume that ƒ_EON + ƒ_NH4+ + ƒ_NO3- = 1.
其中我们假设 ƒ_EON + ƒ_NH4+ + ƒ_NO3- = 1。

To constrain the δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON in terrestrial ecosystems, we searched literature published on the Web of Science and Google Scholar with the terms “nitrogen isotope”, “¹⁵N”, “soil ammonium”, “soil NH₄⁺”, “soil nitrate”, “soil NO₃^-”, “soil DON”, “soil dissolved organic nitrogen”, “soil EON”, “soil extractable organic nitrogen”. Observations for fertilized soils (including ¹⁵N-labeling experiments) and soils at depths over 30 cm were excluded from the searched literature. By August 1st, 2022, we obtained 46 publications (listed in Supplementary Text 3) with a total of 321, 356, and 262 observations of δ¹⁵N_soil-NO3- at 56 sites, δ¹⁵N_soil-NH4+ at 62 sites, and δ¹⁵N_soil-EON at 28 sites, respectively (Figs. s1c, s6b-d). Sites of soil δ¹⁵N observations distribute between 30.3°S and 68.4°N, with the MAT spanning from −8.4 °C to 28.0 °C (Figs. s1c, 2d–f). To determine the regulating factor of soil δ¹⁵N variations, we collected the data on soil density, clay, pH, and organic C from the Harmonized World Soil Database v1.2 (https://www.fao.org/home/en/) using the coordinate information. Besides, soil NO₃^-, NH₄⁺, EON, and TEN concentrations were also collected for sites with simultaneous observations on δ¹⁵N_leaf, δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON (Figs. s8, s11–12).
为了限制陆地生态系统中¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON 的δ，我们检索了 Web of Science 和 Google Scholar 上发表的文献，其中包含术语 “氮同位素”、“¹⁵N”、“土壤铵”、“土壤 NH₄⁺”、“土壤硝酸盐”、“土壤 NO₃^-“， ”土壤 DON“， ”土壤溶解有机氮“， ”土壤 EON“， ”土壤可萃取有机氮”。从检索文献中排除了对施肥土壤（包括 ¹⁵个 N 标记实验）和深度超过 30 cm 的土壤的观察结果。截至 2022 年 8 月 1 日，我们获得了 46 篇出版物（列于补充文本 3），分别在 56 个地点对¹⁵N_土壤-NO3-、62 个地点δ¹⁵N_土壤-NH4+ 和 28 个地点δ¹⁵N_土壤-EON 进行了 321、356 和 262 次δ观察（图 D）。S1C、S6B-D）。土壤δ¹⁵N 观测点分布在 30.3°S 和 68.4°N 之间，MAT 范围为 -8.4 °C 至 28.0 °C（图 D）。S1 C，2D-F）。为了确定土壤δ¹⁵N 变化的调节因子，我们使用坐标信息从世界土壤协调数据库 v1.2 （https://www.fao.org/home/en/） 中收集了土壤密度、粘土、pH 值和有机碳的数据。此外，还收集了对^{δ 15}N_叶片、δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON 进行观察的土壤 NO₃^-、NH₄⁺、EON 和 TEN 浓度（图 1）。S8， S11-12）。

The δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+ and δ¹⁵N_soil-EON values are influenced by various production and consumption processes of the corresponding N forms¹⁷. These processes, and thus the above soil δ¹⁵N parameters, are influenced by climatic and environmental factors³⁷. This study found that variations of δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON were all influenced significantly by MAT (Table s5), increasing linearly with MAT (Fig. 2d–f). This finding is supported by evidence that increasing temperature enhances microbial N processes and the ‘openness’ of soil N cycles, leading to more ¹⁵N enrichment in soil N sources^22,25,35. Accordingly, we calculated the mean δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON values for each δ¹⁵N_PUN observation site by using the corresponding MAT and the fitting formula in Fig. 2d–f.
δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和^{δ 15}N_土壤-EON 值受相应 N 形式的各种生产和消费过程的影响¹⁷。这些过程，以及上述土壤δ¹⁵N 参数，都受到气候和环境因素的影响³⁷。本研究发现，δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和^{δ 15}N_土壤-EON 的变化都受到 MAT 的显著影响（表 s5），随 MAT 线性增加（图 s5）。2d-f）。这一发现得到了证据的支持，即温度升高会增强微生物氮过程和土壤氮循环的“开放性”，从而导致土壤氮源中更多的 ¹⁵N 富集^22,25,35。因此，我们使用相应的 MAT 和图中的拟合公式计算了每个^{δ 15}N_PUN 观测点δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+ 和 δ¹⁵N_土壤-EON 值的平均值。2d-f.

Then, we calculated ƒ_NO3-, ƒ_NH4+, and ƒ_EON values using an isotope-mixing model (named Stable Isotope Analysis in R, SIAR). The SIAR model, which is designed around a Bayesian framework, effectively utilizes a Dirichlet distribution to establish a logical prior for estimating source contributions^41,42. This framework not only focuses on managing sample size variability but also prioritizes the data distribution within each sample over the mere number of observations, enhancing the model’s accuracy and robustness in source estimation^41,42. In each run of the SIAR model, the δ¹⁵N_PUN data in a grid cell of 0.1° (latitude) × 0.1° (longitude) in the same sampling year (with 3–1028 observations for each grid cell in each year, referring to Craine et al.²⁶), the mean ± SD of site-based δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, and δ¹⁵N_soil-EON values in the corresponding grid cell were input into the model. Then, the percentage data of each source (n = 10,000) output from each run of the SIAR model was used to calculate the mean ± SD of grid-based ƒ_NO3-, ƒ_NH4+, and ƒ_EON values (Fig. s7) and to map their spatial distribution (Fig. 3).
然后，我们使用同位素混合模型（在 R 中称为稳定同位素分析，SIAR）计算了 ƒ_NO3-、ƒ_NH4+ 和 ƒ_EON 值。SIAR 模型是围绕贝叶斯框架设计的，有效地利用狄利克雷分布来建立估计源贡献的逻辑先验^41,42。该框架不仅侧重于管理样本量的可变性，而且还优先考虑每个样本中的数据分布，而不是仅仅观察的数量，从而提高了模型在来源估计中的准确性和稳健性^41,42。在 SIAR 模型的每次运行中δ，同一采样年（每年每个网格单元有 3-1028 次观测，参考 Craine 等人 ²⁶），基于站点的 δ 15 N_土壤-NO3-× 15 N 土壤-NH4+ 的平均 ± SD¹⁵N 土壤-NO3-， δ¹⁵N_土壤-NH4+， δ相应的网格单元中的¹⁵N_土壤-EON 值被输入到模型中。然后，使用 SIAR 模型每次运行的每个源 （n = 10,000） 输出的百分比数据来计算基于电网的 ƒ_NO3-、ƒ_NH4+ 和 ƒ_EON 值的均值 ± SD（图 D）。s7）并绘制它们的空间分布（图 D）。3）.

For study sites with simultaneous observations on δ¹⁵N_leaf, δ¹⁵N_soil-NO3-, δ¹⁵N_soil-NH4+, δ¹⁵N_soil-EON, and δ¹⁵N_soil-TEN, we separately calculated the site-based δ¹⁵N_plant, δ¹⁵N_PUN, f_NO3-, f_NH4+, and f_EON values based on the above methods and examined their variations with MAT (Figs. s8, s11–12). In combination with simultaneous observations on soil NO₃^-, NH₄⁺, EON, and TEN concentrations at each of the above study sites, we further calculated the relative preference degree (β) of plant N utilization among NO₃^-, NH₄⁺, and EON by using Eq. (5).
对于同时观察δ¹⁵N_叶片、δ¹⁵N_土壤-NO3-、δ¹⁵N_土壤-NH4+、δ¹⁵N_土壤-EON 和^{δ 15}N_土壤-TEN的研究地点，我们根据上述方法分别计算了基于δ¹⁵N_植物、δ¹⁵N_PUN、f_NO3-、f_NH4+ 和 f_EON 值，并用 MAT 检查了它们的变化（图 D）。S8， S11-12）。结合对上述每个研究地点土壤 NO₃^-、NH₄⁺、EON 和 TEN 浓度的同时观察，我们使用方程 （5） 进一步计算了 NO₃^-、NH₄⁺ 和 EON 之间植物氮利用的相对偏好度 （β）。

where i represents NO₃^-, NH₄⁺, or EON, [i] and [TEN] are N concentrations of i and TEN, respectively. Positive, zero, and negative values of β parameters indicated the preference of a given N form over other N forms, no preference, and the preference of the other N forms over a given N form, respectively^20,43.
其中 i 代表 NO₃^-、NH₄⁺ 或 EON，[i] 和 [TEN] 分别是 i 和 TEN 的 N 浓度。β 参数的正值、零值和负值分别表示给定 N 形式优于其他 N 形式、无偏好和其他 N 形式优于给定 N 形式^20,43。

Statistical analyses 统计分析

General Linear Models were used to examine the effects of major environmental and plant variables on δ¹⁵N_plant variations of NM, AM, ECM and ERM plants. Multiple linear regression analyses were used to examine the effects of the major soil and environmental variables on soil δ¹⁵N variations and plant N sources contribution. Regression analyses were used to examine variations of plant and soil δ¹⁵N parameters and fractional contributions of soil N sources to PUN and TEN with MAT and latitude, respectively. The ArcGIS 10.8 software (Esri Inc., USA) was used to perform spatial interpolations, layers overlay and property sheet processing of data points. Regression analyses were conducted using SPSS 16.0 for Windows (SPSS Inc., Chicago, IL, USA). Statistical significance was set at p < 0.05.
一般线性模型用于检查主要环境和植物变量对 NM、AM、ECM 和 ERM 植物δ¹⁵N_植物变化的影响。采用多元线性回归分析研究土壤和环境变量对土壤¹⁵N 变化δ和植物 N 来源贡献的影响。采用回归分析分别δ¹⁵N 参数检查植物和土壤的变化以及土壤 N 源对 PUN 和 TEN 的分数贡献，分别具有 MAT 和纬度。ArcGIS 10.8 软件（Esri Inc.，美国）用于执行数据点的空间插值、图层叠加和属性表处理。使用 SPSS 16.0 for Windows （SPSS Inc.， Chicago， IL， USA） 进行回归分析。统计学显着性设定为 p < 0.05。