Global leaf sulphur stoichiometry and the relationships with nitrogen and phosphorus: phylogeny, growth form and environmental controls
收藏Mendeley Data2024-05-31 更新2024-06-28 收录
下载链接:
https://datadryad.org/stash/dataset/doi:10.5061/dryad.fj6q5741c
下载链接
链接失效反馈官方服务:
资源简介:
# Global leaf sulphur stoichiometry and the relationships with nitrogen and phosphorus: phylogeny, growth form and environmental controls In total, we collected 31939 records of data , and 2600 plant species belonging to 1186 genera, 210 families, 69 orders, 7 classes and 4 divisions. The overall plants were divided into two growth forms: woody and herb, five growth forms within woody plants: conifer tree, deciduous broad-leaf tree (DB), evergreen broad-leaf tree (EB), deciduous (D) shrub and evergreen (E) shrub. Herbs were subdivided into aquatic and terrestrial herbs by habitats, and into graminoids and forbs by leaf types. The collected environment variables (MAT, MAP, BIO2, BIO5, BIO6, BIO13, BIO14 and BIO15) were used to analyze the response of the leaf elements (leaf S, leaf N and leaf P) to them. ## Description of the Data and file structure We have thirteen sheets in this dataset file. Sheet 1, named "Overall_data", we have a 31940*36 matrix. Sheet 2, named "Metadata" contains a detailed description of the abbreviations that appear in the dataset. Sheet 3, we recorded the source of the dataset. Sheet 4, named "Fig5_data", contains SiteID, OrderID, FamilyID, SpeciesID and elemental concentrations (i.e., CON). All data in "Fig5_data" is derived from "Overall_data" (After remove all null values for each column). Sheet 5 contains the variance of each component (Site, Species, Family, Order and Residual, %) to leaf S, N and P at different levels. Sheet 6, named "Fig6_data", contains environmental variables (BIO15, BIO14, BIO13, BIO6, BIO5, BIO2, MAT and MAP ) and element variables (leaf S, N and P) at different levels. "Fig6_data" is obtained by extracting from "Overall_data" and then deleting the null value. Sheet 7, Sheet 8 and Sheet 9 named "pearson", "lm_result" and "env_importance" respectively, are all results obtained in the course of calculating. Sheet 10, named "BIO", contains 19 environment variables (BIO1-BIO19) obtained from the WorldClim ([https://worldclim.org/).](https://worldclim.org/\).) Sheet 11, Sheet 12 and Sheet 13 named "LeafS_species", "LeafN_species" and "LeafP_species" respectively, includes value of all S, N and P in the database and family information on the corresponding species, respectively. Since some samples did not have exact species information, their taxonomic information was incomplete and we filled by "NA". Some samples did not provide latitude and longitude information on the raw data, and we filled in with "NA". On samples that lacked latitude and longitude, environmental information could not be obtained based on latitude and longitude, and this part of the environmental data were filled with "NA". **The abbreviations for variables**: Variable Description AQ Aquatic plant C Conifer tree DS Deciduous shrub DB Deciduous broad-leaf tree EB Evergreen broad-leaf tree shrub Evergreen shrub TH Terrestrial herb MAT Mean average temperature (°C) MAP Mean average precipitation (mm) BIO2 Mean diurnal range (°C) BIO5 Max temperature of warmest month (°C) BIO6 Min temperature of coldest month (°C) BIO13 Precipitation of wettest month (mm) BIO14 Precipitation of driest month (mm) BIO15 Precipitation seasonality (coefficient of variation) (mm) Leaf S Total sulphur concentrations in leaves (mg g-1) Leaf N Total nitrogen concentrations in leaves (mg g-1) Leaf P Total phosphorus concentrations in leaves (mg g-1) NS Ratio of leaf sulfur concentrations to leaf nitrogen concentrations PS Ratio of leaf phosphorus concentrations to leaf sulfur concentrations NP Ratio of leaf nitrogen concentrations to leaf phosphorus concentrations ## Sharing/Access information Links to other publicly accessible locations of the data: N/A Was data derived from another source? Yes Data was derived from the following sources: \[1] Cui P, Shen Z, Fu P, Bai K, Jiang Y, Cao K. 2020 Comparison of foliar element contents of plants from natural forests with different substrates in southern China. *Acta Ecol. Sin.* **40**, 9148-9163. \[2] Fernández‐Martíne M, Preece C, Corbera J, Cano O, Garcia‐Porta J, Sardans J, Janssens IA, Sabater F, Peñuelas J. 2021 Bryophyte C:N:P stoichiometry, biogeochemical niches and elementome plasticity driven by environment and coexistence. *Ecol. Lett.* **24**, 1375-1386. (doi:10.1111/ele.13752). \[3] Dalle Fratte M, Pierce S, Zanzottera M, Cerabolini BEL. 2021 The association of leaf sulfur content with the leaf economics spectrum and plant adaptive strategies. Funct. Plant Biol. 48, 924-935. (doi:10.1071/FP20396) \[4] Han WX, Fang JY, Reich PB, Woodward FI, Wang ZH. 2011 Biogeography and variability of eleven mineral elements in plant leaves across gradients of climate, soil and plant functional type in China. *Ecol. Lett.* **14**, 788–796. (doi:10.1111/j.1461-0248.2011.01641.x). \[5] Hou X. 1982 *Vegetation geography and chemical composition of dominant plants in China*. Beijing, Science Press. \[6] Kattge J, Bonisch G, Diaz S, Lavorel S, Prentice IC, Leadley P, Tautenhahn S, Werner GDA, Aakala T, Abedi M*, et al.* 2020 TRY plant trait database - enhanced coverage and open access. *Global Change Biol.* **26**, 119-188. (doi:10.1111/gcb.14904). \[7] Sardans J, Vallicrosa H, Zuccarini P, Farré-Armengol G, Fernández-Martínez M, Peguero G, Gargallo-Garriga A, Ciais P, Janssens IA, Obersteiner M*, et al.* 2021 Empirical support for the biogeochemical niche hypothesis in forest trees. *Nat. Ecol. Evol.* **5**, 184-194. (doi:10.1038/s41559-020-01348-1). \[8] Zhang J, Wang Y, Cai C. 2020 Multielemental Stoichiometry in Plant Organs: A Case Study With the Alpine Herb Gentiana rigescens Across Southwest China. *Front. Plant Sci.* **11**, 441. (doi:10.3389/fpls.2020.00441). \[9] Zhang SB, Zhang JL, Slik JWF, Cao KF. 2012 Leaf element concentrations of terrestrial plants across China are influenced by taxonomy and the environment. *Global Ecol. Biogeogr.* **21**, 809-818. (doi:10.1111/j.1466-8238.2011.00729.x). \[10] Zuo Z, Zhao H, Yang L, Lv T, Li X, Ma F, Wang Z, Yu D. 2022 Salinity induces allometric accumulation of sulfur in plants and decouples plant nitrogen‐sulfur correlation in alpine and arid wetlands. *Global Biogeochem. Cy.***36**, e2022GB007372. (doi:10.1029/2022GB007372).
创建时间:
2024-03-07



