DOI: 10.5061/dryad.bk3j9kdhw
title: Data from: Interspecific differences in nitrogen form acquisition strategies contribute to species dominance

description: Plant’s ability to use prevalent or less prevalent soil nitrogen (N) forms
 may affect their dominance within vegetation types, and these
 partitioning-driven changes in dominance may facilitate species
 co-existence. However, the mechanisms underlying these processes remain
 unclear, particularly given the strong influence of altitude on soil N
 forms, which in turn affects plant N acquisition strategy. In this study,
 we first determined the effects of preference and plasticity in N form
 uptake on partitioning of soil N forms and species dominance, and then
 assessed the relative importance of these two N form use strategies for 19
 dominant and non-dominant species in three vegetation types along an
 altitudinal gradient on Changbai Mountain, northeast China. To achieve
 this, we measured dominance, the contents of different N forms in
 rhizosphere soils, their proportional contributions to leaf N, and N form
 uptake preference and plasticity for these 19 species. Our results show
 significant interspecific differences in the proportional contributions of
 different soil N forms to leaf N within all three vegetation types,
 providing a novel mechanism underlying niche differentiation among plants.
 Species dominance was positively associated with the proportional
 contributions of soil dissolved organic N (the most prevalent N form) and
 the main inorganic N form to leaf N, while negatively with that of the
 subordinate inorganic N. These associations were not altered by the
 altitude-driven changes in the absolute and proportional contents of
 different soil N forms, suggesting a potentially widespread phenomenon.
 Both preference and plasticity in N form uptake contributed to the
 proportional contributions of different N forms to leaf N, and therefore
 to species dominance and co-existence within vegetation types.
 Furthermore, N form preference was more critical for non-dominant relative
 to dominant species and at high relative to low altitude, while N form
 uptake plasticity was more important for dominant species and at low
 altitude. Our study provides robust evidence for the interspecific niche
 differentiation in N form uptake, contributing to species dominance and
 co-existence within vegetation types, and reveals the mechanisms
 (plasticity and preference) underlying the association between species
 dominance and the uptakes of different N forms.
description: <strong>Study sites</strong> This
 study was conducted on the north slope of Changbai Mountain, Jilin
 Province, northeast China (41°23′ – 42°36′ N; 126°55′ – 129°00′ E). This
 region has a typical continental temperate monsoon climate, with long cold
 winters and short warm summers. From the lower part of the mountain at 740
 m to the summit at 2691 m, the mean annual temperature decreases from 2.8
 to –7.3<sup>o</sup>C and the mean annual precipitation
 increases from 750 to 1340 mm (He et al., 2005). Korean pine
 and broadleaved mixed forest (KPBL), Erman’s birch forest (EB), and alpine
 tundra (AT) were selected as study plant communities based on their
 prevalent forms of soil N. In KPBL, dissolved inorganic N (DIN) accounts
 for a large proportion of total dissolved soil N (TDN), and
 NO<sub>3</sub><sup>- </sup>is the prevailing
 inorganic N form; in EB, DIN also accounts for a large proportion of soil
 TDN, but NH<sub>4</sub><sup>+ </sup>is the
 predominant inorganic N form; in AT, however, DON is the principal soil N
 form (CERN; http://www.cerndata.ac.cn/). Similar altitudinal changes in N
 forms were also found in our study (Appendix S1: Figure S1). The main
 properties of the three vegetation types are summarized in Appendix S1:
 Table S1. The sample plots are located in a national nature reserve, and
 thus human interference is negligible.
 <strong>Study species</strong> In
 mid-July of 2018, three 30 × 30 m plots (&gt; 20 m apart from one
 another) were randomly established in EB and AT, respectively. First, we
 investigated the background information such as altitude, slope degree and
 vegetation cover in each plot. Second, all woody plants [diameter at
 breast height (DBH) ≥ 1 cm] were identified. Third, we measured the
 number, DBH, basal area, crown width, and height of each individual of
 each tree species, and the number, height and cover of each individual of
 each shrub species in each plot. Finally, we calculated relative density
 (RD<sub>y</sub>) and relative frequency (RF) for the tree and
 shrub species, relative dominance (RD<sub>e</sub>) for the
 tree species, relative cover (RC) for the shrub species, and then the
 importance values (IV) for the tree and shrub species in each plot based
 on Zhang et al. (1995) and
 Asigbaase et al. (2019).
 The importance value was used to quantified species dominance in our
 study.         IV of tree = (RD<sub>y</sub>
 + RF + RD<sub>e</sub>) / 300…….
 ………………………………….……………(1)         IV of shrub =
 (RD<sub>y</sub> + RF + R<sub>C</sub>) /
 300………………………………………………..……(2)        
 RD<sub>y</sub> = number of individuals of the species / total
 number of individuals of all species ×
 100……………………………..………………………………………………………………(3)         RF
 = frequency of the species / sum frequency of all species ×
 100……………………..(4)         RD<sub>e</sub> =
 basal area of the tree species / total basal area of all tree species ×
 100…………..(5)         RC = cover of the shrub species /
 total cover of all shrub species × 100……………..…..(6) For
 KPBL, three 30 × 30 m plots were randomly established in a 25-ha KPBL
 permanent plot. In the permanent plot, only 17 woody plant species had
 more than 10 individuals per hectare, which accounted for more than 95% of
 the individuals of all woody plants (Hao et al., 2008). Our studied species were
 selected from these 17 species using the following criteria: individuals
 &gt; 3 in each plot; and only one species was randomly selected when
 more than one from the same family had similar numbers of individuals in
 each plot. Ultimately, 10 species were selected, and their importance
 values were calculated using the methods described above. These species
 together accounted for more than 80% of the total aboveground biomass (Li
 et al., 1981).
 In total, 19 woody plant species were studied, and only one
 occurred in more than one vegetation types, i.e., <em>Rhododendron
 aureum</em> in both EB and AT (Appendix S1: Table S2). The species
 were divided into dominant (≥ 3) and non-dominant (&lt; 3) according
 to their importance values (Avolio et al., 2019). The numbers of the dominant
 and non-dominant species are 6 and 4, 3 and 3, and 2 and 1 in KPBL, EB,
 and AT, respectively (Appendix S1: Table S2).
 <strong>Leaf and soil sampling, measurements and
 calculations</strong> In August of 2018, 10 fully
 developed, intact and healthy leaves or bunches of current-year needles on
 the south side of the upper canopy were randomly collected from each of
 the three sampling individuals for each studied species in each plot. The
 leaves or needles were collected using tree pruners when the sampling
 individuals were short. For high trees, we climbed up to collect the
 leaves and needles. The leaves or needles from the same species in each of
 the three plots were mixed as one sample. This sampling procedure ensured
 a representative and homogeneous sample, reducing potential effects of
 intraspecific differences within-plot. Rhizosphere
 soils were also collected from the sample individuals mentioned above. We
 first carefully removed the litter and humus layers from the soil surface
 at four sides (east, south, west and north) around each sample tree (≈ 1 m
 from the trunk). Then, fine roots (diameter &lt; 2 mm) were dug out at
 each side, and rhizosphere soils were collected using a hand-shaking
 method (Guan et al., 2023).
 Soils from the same species in each plot were thoroughly mixed as one
 sample. The soils were immediately put into an ice box, transported to the
 laboratory of the Research Station of Changbai Mountain Forest Ecosystems,
 Chinese Academy of Sciences, passed through a 2 mm sieve, and stored at
 4<sup>o</sup>C until measurements. Leaf
 total N concentration and δ<sup>15</sup>N were measured using
 an element analyzer-stable isotopic mass spectrometer (Flash EA 1112
 HT-Delta V Advantages, Thermo Fisher Scientific, USA). The contents of DIN
 (NH<sub>4</sub><sup>+</sup> and
 NO<sub>3</sub><sup>-</sup>), DON, and TDN in
 rhizosphere soil of each sample species were measured using the standard
 method commonly used in literature (Guan et al., 2023; Zhang et al., 2018). Soil water content was
 measured using the oven-drying method. The δ<sup>15</sup>N
 values of soil NH<sub>4</sub><sup>+</sup>,
 NO<sub>3</sub><sup>-</sup>, and TDN
 (δ<sup>15</sup>N<sub>NH4</sub><sup>+</sup>, δ<sup>15</sup>N<sub>NO3</sub><sup>-</sup>, and δ<sup>15</sup>N<sub>TDN</sub>) were measured using the chemical method (Liu et al., 2014), azide method (Tu et al., 2016), and high-temperature catalytic oxidation techniques performed with a total organic carbon analyzer-stable isotope ratio mass spectrometer (Iso TOC cube-isoprime100, Elementar, Germany), respectively. The δ<sup>15</sup>N value of DON (δ<sup>15</sup>N<sub>DON</sub>) was calculated as the mass-weighted difference between δ<sup>15</sup>N<sub>TDN </sub>versus δ<sup>15</sup>N<sub>NH4</sub><sup>+</sup> and δ<sup>15</sup>N<sub>NO3</sub><sup>-</sup>. The measurement data mentioned above were used to estimate the proportional contributions of soil NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+, </sup>and DON to leaf N (<em>f</em><sub>NO3</sub><sup>-</sup>, <em>f</em><sub>NH4</sub><sup>+, </sup>and <em>f</em><sub>DON</sub>; %) (Houlton et al., 2007; Stock et al., 2018). The effects of possible isotope fractionation during within-plant N allocation and/or N transport from mycorrhizal fungi to host plants were taken into account (Houlton et al., 2007; Zhu et al., 2019). Plant preferential uptake for soil NO<sub>3</sub><sup>-</sup>, NH<sub>4,</sub><sup>+ </sup>and DON (<em>β</em><sub>NO3</sub><sup>-</sup>, <em>β</em><sub>NH4,</sub><sup>+</sup> and <em>β</em><sub>DON</sub>) were estimated by comparing their proportional contribution to leaf N and their relative contents in soil (Guan et al., 2023; Zhang et al., 2018). To estimate plasticity in N form uptake, percentage similarity between plant uptake pattern of different N forms and their availability pattern in rhizosphere soil (percentage similarity) was determined for each species in each plot based on McKane et al. (2002) and Guan et al. (2023). For detailed measurement and calculation methods please see Appendix S1: Section S1, and mean value of three plots was reported for each variable in the results section.
description: The data
 files are .csv files, so they can be opened in many programs. For example,
 Excel. The data files are analyzed using code
 files. The code files are .R files, so they can be
 opened in R.
description: * File name: README.md * Authors: Ming Guan, Yu-Long Feng * Other
 contributors: Xiao-Cui Pan, Jian-kun Sun, Ji-Xin Chen, Xiao-Lin Wei,
 Bernhard Schmid, Michel Loreau * Date created: 2024-08-10 ## Dataset
 Attribution and Usage * Dataset Title: Data from: Interspecific
 differences in nitrogen form acquisition strategies contribute to species
 dominance * Persistent Identifier:
 [https://doi.org/10.5061/dryad.bk3j9kdhw](https://doi.org/10.5061/dryad.bk3j9kdhw) * Dataset Contributors: * Creators: Ming Guan, Xiao-Cui Pan, Jian-kun Sun, Ji-Xin Chen, Xiao-Lin Wei, Bernhard Schmid, Michel Loreau, Yu-Long Feng * Suggested Citations: * Dataset citation: &gt; Guan M, Pan XC, Sun JK, Chen JX, Wei XL, Schmid B, Loreau M, Feng YL. 2025.Data from: Interspecific differences in nitrogen form acquisition strategies contribute to species dominance. Dryad, Dataset, [https://doi.org/10.5061/dryad.bk3j9kdhw](https://doi.org/10.5061/dryad.bk3j9kdhw) * Corresponding publication: &gt; Guan M, Pan XC, Sun JK, Chen JX, Wei XL, Schmid B, Loreau M, Feng YL. 2025. Interspecific differences in nitrogen form acquisition strategies contribute to species dominance. Ecology. ## Contact Information * Name: Ming Guan * Affiliations: Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, School of Life Sciences, Taizhou University, Taizhou, Zhejiang Province 318000, China * ORCID ID: [https://orcid.org/0009-0006-9734-2219](https://orcid.org/0009-0006-9734-2219) * Email: [guanmingtzc@163.com](mailto:guanmingtzc@163.com) * Alternate Email: [guanming8812345@sina.com](mailto:guanming8812345@sina.com) * Alternative Contact Name: Yu-Long Feng * Affiliations:Liaoning Key Laboratory for Biological Invasions and Global Changes, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning Province 110866, China * ORCID ID: [https://orcid.org/0000-0003-0243-2280](https://orcid.org/0000-0003-0243-2280) * Email: [fyl@syau.edu.cn](mailto:fyl@syau.edu.cn) ## Funding sources ``` National Key R&D Program of China (2023YFC2604500) Natural Sciences Foundation of China (32001238, 32171666 and 32271741) Zhejiang Provincial Natural Science Foundation of China (LQ20C030004) ``` # Methodological Information * Methods of data collection/generation: see manuscript for details --- # Data and File Overview ## Summary Metrics * File count: 29 * Total file size: 732KB * File formats: .xlsx, .csv, .R ## Table of Contents * 1\. "ANOVAs" named folder, including 1 data files (.csv) * 2\. "HPM" named folder, including 21 data files (.csv) * 3\. "LMMs" named folder, including 1 data file (.csv) * 4\. "LRA" named folder, including 1 data file (.csv) * 5\. data.xlsx * 6\. Hierarchical partitioning analysis.R * 7\. Linear mixed-effects model analysis.R * 8\. ANOVAs and independent sample t-tests.R * 9\. Linear regression analyses.R ## Setup * Recommended software/tools: R version 4.2.2 ([https://www.r-project.org/](https://www.r-project.org/)) for .R files; Microsoft Office EXCEL 2013 for .xlsx and .csv files --- * Relationship between data files * To run the R code in "ANOVAs and independent sample t-tests.R" file, you need to first open the R software and then load the .csv files in the "ANOVAs" folder. * To run the R code in "Hierarchical partitioning analysis.R" file, you need to first open the R software and then load the .csv files in the "HPM" folder. * To run the R code in "Linear mixed-effects model analysis.R" file, you need to first open the R software and then load the .csv files in the "LMMs" folder. * To run the R code in "Linear regression analyses.R" file, you need to first open the R software and then load the .csv files in the "LRA" folder. * The .xlsx file "data.xlsx" contains all the observed datasets in the "HPM", "ANOVAs", "LMMs" and "LRA" folders. --- # File/Folder Details ## Details for: ANOVAs folder * General description: a folder containing the data to run ANOVAs and independent sample t-tests * Format(s): .csv * Size(s): 10.6 KB * Contains: 1 data files (.csv) * Variable correspondence and its explanation can be found in the text and after the "#" label in "ANOVAs and independent sample t-tests.R" file ## Details for: HPM folder * File naming convention and contents of the HPM folder: Each file in the HPM folder is named based on the vegetation type (KPBL, EB, AT), species type (dominant or non-dominant), and whether it includes or excludes certain explanatory variables (e.g., "variables-EB-dominant.csv", "variables-EB-dominant1.csv"). These files contain input matrices used in the hierarchical partitioning analysis (Figure 6). The corresponding response matrices (e.g., "IV-EB-dominant.csv") contain the importance values of plant species in each vegetation type. These CSV files are subsets extracted from "data.xlsx". During hierarchical partitioning analysis, predictors with negative independent percentage contributions (as calculated by "rdacca.hp") were excluded from the explanatory variable set to ensure interpretable and non-redundant models (see Lai et al., 2022). This is documented in the code. Full variable definitions can be found in the “Column name” sheet of "data.xlsx". * General description: a folder containing the datasets to run hierarchical partitioning analysis * Format(s): .csv * Size(s): 7.8 KB * Contains: 21 data files (.csv) * Data correspondence and its explanation can be found in the text and after the "#" label in "Hierarchical partitioning analysis.R" file ## Details for: LMMs folder * General description: a folder containing the data to run linear mixed-effects model analysis * Format(s): .csv * Size(s): 10.6 KB * Contains: 1 data files (.csv) * Variable correspondence and its explanation can be found in the text and after the "#" label in "Linear mixed-effects model analysis" file ## Details for: LRA folder * General description: a folder containing the data to run Linear regression analyses * Format(s): .csv * Size(s): 7.04 KB * Contains: 1 data files (.csv) * Variable correspondence and its explanation can be found in the text and after the "#" label in "Linear regression analyses.R" file ## Details for: data.xlsx * Description: a .xlsx file containing all the observed datasets of each studied species in the three vertical vegetations of the Changbai Mountain * Format(s): .xlsx * Size(s): 624 KB * Contents: 9 sheets * Variables in this dataset: * IV: Important_value * DON: The content of dissolved organic nitrogen (DON) * NH4: The content of ammonium (NH4+) * NO3: The content of nitrate (NO3-) * SDON: The proportional contribution of DON in total dissolved nitrogen (TDN) * SNH4: The proportional contribution of NH4+ in TDN * SNO3: The proportional contribution of NO3- in TDN * fDON: The proportional contributions (uptake) of DON to leaf N * fNH4: The proportional contributions (uptake) of NH4+ to leaf N * fNO3: The proportional contributions (uptake) of NO3- to leaf N * PS: Percentage similarity between plant uptake pattern of different N forms and their availability pattern in rhizosphere soil * pDON: The preference for DON * pNH4: The preference for NH4+ * pNO3: The preference for NO3- * QT_pDON: pDON after quantile transformation * QT_pNH4: pNH4 after quantile transformation * QT_pNO3: pNO3 after quantile transformation * Description for each sheet: * Article information: listing the the article title, authors, and journal name * Column name: listing and explaining each column name in this dataset * data: the observed dataset containing 26 columns * Fig.2: the data of figure 2 * Fig.3: the data of figure 3 * Fig.4: the data of figure 4 * Fig.5: the data of figure 5 * Fig.6: the data of figure 6 * Fig.S1: the data of figure S1 * Fig.S2: the data of figure S2 * Fig.S3: the data of figure S3 * Note: please see the sheet "Column name" in this .xlsx file for the explanation of each column ## Details for: ANOVAs and independent sample t-tests.R * Description: a .R file containing all codes to conduct our one-way analyses of variance and independent sample t-tests (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 45.1 KB * Note: * Please open this file using R software * All necessary explanations for the "ANOVAs and independent sample t-tests" code can be found in the text after the "#" label in this .R file ## Details for: Hierarchical partitioning analysis.R * Description: a .R file containing all codes to conduct our hierarchical partitioning analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 7 KB * Note: * Please open this file using R software * All necessary explanations for the "hierarchical partitioning analysis" code can be found in the text after the "#" label in this .R file ## Details for: Linear mixed-effects model analysis.R * Description: a .R file containing all codes to conduct our linear mixed-effects model analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 3 KB * Note: * Please open this file using R software * All necessary explanations for the "linear mixed-effects model analysis" code can be found in the text after the "#" label in this .R file ## Details for: Linear regression analyses.R * Description: a .R file containing all codes to conduct our linear regression analyses (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 9 KB * Note: * Please open this file using R software * All necessary explanations for the "linear regression analyses" code can be found in the text after the "#" label in this .R file --- END OF README