* File name: README.md * Authors: Zikun Mao, Xugao Wang * Other contributors: Fons van der Plas, Adriana Corrales, Kristina J. Anderson-Teixeira, Norman A. Bourg, Chengjin Chu, Zhanqing Hao, Guangze Jin, Juyu Lian, Fei Lin, Buhang Li, Wenqi Luo, William J. McShea, Jonathan A. Myers, Guochun Shen, Xihua Wang, En-Rong Yan, Ji Ye, Wanhui Ye, Zuoqiang Yuan * Date created: 2022-11-20 * Date modified: 2024-05-13 ## Dataset Attribution and Usage * Dataset Title: "Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility" * Persistent Identifier: [https://doi.org/10.5061/dryad.612jm646w](https://doi.org/10.5061/dryad.612jm646w) * Dataset Contributors: * Creators: Zikun Mao, Fons van der Plas, Adriana Corrales, Kristina J. Anderson-Teixeira, Norman A. Bourg, Chengjin Chu, Zhanqing Hao, Guangze Jin, Juyu Lian, Fei Lin, Buhang Li, Wenqi Luo, William J. McShea, Jonathan A. Myers, Guochun Shen, Xihua Wang, En-Rong Yan, Ji Ye, Wanhui Ye, Zuoqiang Yuan, Xugao Wang * License: Use of these data is covered by the following license: * Title: CC0 1.0 Universal (CC0 1.0) * Specification: [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/); the authors respectfully request to be contacted by researchers interested in the re-use of these data so that the possibility of collaboration can be discussed. * Suggested Citations: * Dataset citation: > Mao, Z., F. van der Plas, A. Corrales, K. J. Anderson-Teixeira, N. A. Bourg, C. Chu, Z. Hao, G. Jin, J. Lian, F. Lin, et al. 2023. Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility. Dryad, Dataset, [https://doi.org/10.5061/dryad.612jm646w](https://doi.org/10.5061/dryad.612jm646w) * Corresponding publication: > Mao, Z., F. van der Plas, A. Corrales, K. J. Anderson-Teixeira, N. A. Bourg, C. Chu, Z. Hao, G. Jin, J. Lian, F. Lin, et al. 2023. Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility. Ecological Monographs, 93: e1568 ## Contact Information * Name: Zikun Mao * Affiliations: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China * ORCID ID: [https://orcid.org/0000-0002-7035-9129](https://orcid.org/0000-0002-7035-9129) * Email: [maozikun@iae.ac.cn](mailto:maozikun@iae.ac.cn) * Alternate Email: [maozikun15@mails.ucas.ac.cn](mailto:maozikun15@mails.ucas.ac.cn) * Alternate Email 2: [maozikun15@126.com](mailto:maozikun15@126.com) * Alternative Contact Name: Xugao Wang * Affiliations: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China * ORCID ID: [https://orcid.org/0000-0003-1207-8852](https://orcid.org/0000-0003-1207-8852) * Email: [wangxg@iae.ac.cn](mailto:wangxg@iae.ac.cn) --- # Additional Dataset Metadata ## Acknowledgements * Funding sources: This work was financially supported by the National Natural Science Foundation of China (Grant 31961133027), the National Key Research and Development Program of China (2022YFF1300501), the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant ZDBS-LY-DQC019), the K. C. Wong Education Foundation, the General Program of China Postdoctoral Science Foundation (2021M703397), the Special Research Assistant Project of Chinese Academy of Sciences (2022000056), and the Major Program of Institute of Applied Ecology, Chinese Academy of Science (IAEMP202201). Chengjin Chu was funded by the National Natural Science Foundation of China (31925027). Funding for the data collections was provided by many organizations, including the Smithsonian Institution, the National Science Foundation (DEB 1557094), the National Zoological Park, the HSBC Climate Partnership, the International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis and the Tyson Research Center # Methodological Information * Methods of data collection/generation: see manuscript for details --- # Data and File Overview ## Summary Metrics * File count: 6 * Total file size: 42.4 MB * Range of individual file sizes: 12.3 KB - 41.5 MB * File formats: .RData, .R, .xlsx ## Table of Contents * 1\. Data source to run the R code.RData * 2\. Codispersion null model analysis.R * 3\. Generalized least squares model analysis.R * 4\. Structural equation modeling analysis.R * Observed data source.xlsx * Mycorrhizal types.xlsx Note: * These datasets contain the data for seven forest mega-plots, i.e., FL: Fenglin; TRC: Tyson Research Center; CBS: Changbaishan; SCBI: Smithsonian Conservation Biology Institute; TTS: Tiantongshan; DHS: Dinghushan; HSD: Heishiding * The authors respectfully request to be contacted by researchers interested in the datasets of other three scales (i.e., 10-m, 50-m, and 100-m) so that the possibility of collaboration can be discussed ## Setup * Recommended software/tools: R version 3.6.3 ([https://www.r-project.org/](https://www.r-project.org/)) for .RData and .R files; Microsoft Office EXCEL 2013 for .xlsx files --- * Relationship between data files * To run the R codes in the three .R files, you need to first open the R software and then load the R workspace "1. Data source to run the R code.RData" * The .xlsx file "Observed data source.xlsx" contains all the observed datasets in the .RData file "1. Data source to run the R code.RData" --- # File/Folder Details ## Details for: 1. Data source to run the R code.RData * General description: a .RData file containing the observed datasets and null model datasets at the 20-m scale to run the three analyses, i.e., codispersion null model analysis (codes in "2. Codispersion null model analysis.R"), generalized least squares model analysis ("3. Generalized least squares model analysis.R"), and structural equation modeling analysis ("4. Structural equation modeling analysis.R") * Format(s): .RData * Size(s): 41.5 MB * Contains: 14 datasets * Description for the 14 datasets: * Running "ls()" in the R software to see the names of these 14 datasets * The names of these 14 datasets are: "FL", "FL_Null_20", "TRC", "TRC_Null_20", "CBS", "CBS_Null_20", "SCBI", "SCBI_Null_20", "DHS", "DHS_Null_20", "TTS", "TTS_Null_20", "HSD", "HSD_Null_20" * FL: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for FL plot * FL_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for FL plot * TRC: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for TRC plot * TRC_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for TRC plot * CBS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for CBS plot * CBS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for CBS plot * SCBI: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for SCBI plot * SCBI_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for SCBI plot * DHS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for DHS plot * DHS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for DHS plot * TTS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for TTS plot * TTS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for TTS plot * HSD: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for HSD plot * HSD_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for HSD plot * Variables in these datasets: * Quad.num: The serial number of 20m * 20m quadrats * gx, gy: The coordinate of each 20m × 20m quadrat (m) * AGB.all: Aboveground biomass (AGB) of all trees in one quadrat (Mg/ha) * AGB.AM: AGB of AM (i.e., arbuscular mycorrhizal) trees in one quadrat (Mg/ha) * AGB.EM: AGB of EM (i.e., ectomycorrhizal) trees in one quadrat (Mg/ha) * SpNum.all: Tree species richness or number of tree species with > 1 individuals in one quadrat * SpNum.AM: AM tree species richness or number of AM tree species with > 1 individuals in one quadrat * SpNum.EM: EM tree species richness or number of EM tree species with > 1 individuals in one quadrat * Num.all: The number of tree individuals in one quadrat * Num.AM: The number of AM tree individuals in one quadrat * Num.EM: The number of EM tree individuals in one quadrat * AMdomi: AM tree dominance in one quadrat quantified using the proportion of AM tree individuals * EMdomi: EM tree dominance in one quadrat quantified using the proportion of EM tree AGB * Soil.PC1: Soil fertility index from the first principal component of the principal component analysis (only for observed datasets) * Soil.PC2: Soil fertility index from the second principal component of the principal component analysis (only for observed datasets) * Soil: Soil fertility index from the first principal component (for FL, TRC, CBS, SCBI, DHS plots) or the second principal component (for TTS and HSD plots) of the principal component analysis (only for null model datasets) ## Details for: 2. Codispersion null model analysis.R * Description: a .R file containing all codes to conduct our codispersion null model analyses (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 80 KB * Note: * Please open this file using R software * All necessary explanations for the "codispersion null model analysis" code can be found in the text after the "#" label in this .R file * Very important note: anyone who want to use this code to run the codispersion analysis, please cite the Buckley's paper in 2016 ([https://doi.org/10.1111/nph.13934](https://doi.org/10.1111/nph.13934)). ## Details for: 3. Generalized least squares model analysis.R * Description: a .R file containing all codes to conduct our generalized least squares model analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 12.3 KB * Note: * Please open this file using R software * All necessary explanations for the "generalized least squares model analysis" code can be found in the text after the "#" label in this .R file ## Details for: 4. Structural equation modeling analysis.R * Description: a .R file containing all codes to conduct our structural equation modeling analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 41.0 KB * Note: * Please open this file using R software * All necessary explanations for the "structural equation modeling analysis" code can be found in the text after the "#" label in this .R file ## Details for: Observed data source.xlsx * Description: a .xlsx file containing all the observed datasets of each 20m * 20m quadrats for the seven forests * Format(s): .xlsx * Size(s): 657 KB * Contents: 9 sheets * 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 * Fenglin: the observed dataset containing 16 columns for FL plot * TRC: the observed dataset containing 16 columns for TRC plot * Changbaishan: the observed dataset containing 16 columns for CBS plot * SCBI: the observed dataset containing 16 columns for SCBI plot * Dinghushan: the observed dataset containing 16 columns for DHS plot * Tiantongshan: the observed dataset containing 16 columns for TTS plot * Heishiding: the observed dataset containing 16 columns for HSD plot * Note: please see the sheet "Column name" in this .xlsx file for the explanation of each column ## Details for: Mycorrhizal types.xlsx * Description: a .xlsx file showing the mycorrhizal type and the referred literature of each tree species * Format(s): .xlsx * Size(s): 70.9 KB * Contents: 10 sheets * Description for each sheet: * Article information: listing the the article title, authors, journal name, and abbreviation of mycorrhizal association * References: listing all the references (in total 49 items) used to classify the mycorrhizal type of studied species * Mycorrhizal associations: listing the basic information (including Family, Genera, and Species name), mycorrhizal classification, and the referred literatures for each tree species Column "Family": The Family name of each species Column "Genera": The Genera name of each species Column "Species": The Species name of each species Column "Mycorrhizal_type": Mycorrhizal types of each species to conduct our primary analyses, but for the species in red font, their mycorrhizal type was reassigned in the robustness test (see the note in the brackets for details) Column "Mycorrhizal_type_detailed": more detailed mycorrhizal types for each tree species Column "Reference and Note": referred literature and the detailed notes for each tree species * Fenglin: the mycorrhizal type and the referred literature of each tree species in FL plot * TRC: the mycorrhizal type and the referred literature of each tree species in TRC plot * Changbaishan: the mycorrhizal type and the referred literature of each tree species in CBS plot * SCBI: the mycorrhizal type and the referred literature of each tree species in SCBI plot * Dinghushan: the mycorrhizal type and the referred literature of each tree species in DHS plot * Tiantongshan: the mycorrhizal type and the referred literature of each tree species in TTS plot * Heishiding: the mycorrhizal type and the referred literature of each tree species in HSD plot * Access Information --- * To generate these datasets, we used the raw census and soil data of the ForestGEO network that can only be shared on request because most PIs have not made them publicly available. Forest census data from the ForestGEO data portal can be obtained by filling out the online Data RequestForm ([http://ctfs.si.edu/datarequest/index.php/main/plotdata](http://ctfs.si.edu/datarequest/index.php/main/plotdata)). Soil data are available to qualified researchers from ForestGEO network by contacting the mega-plot PIs ([https://forestgeo.si.edu/meet-team/principal-investigators](https://forestgeo.si.edu/meet-team/principal-investigators)). --- END OF README

Diversity–biomass relationships (DBRs) often vary with spatial scale in terrestrial ecosystems, but the mechanisms driving these scale-dependent patterns remain unclear, especially for highly heterogeneous forest ecosystems. This study explores how mutualistic associations between trees and different mycorrhizal fungi (i.e., arbuscular mycorrhizal (AM) vs. ectomycorrhizal (EM) association) modulate scale-dependent DBRs. We hypothesized that in soil-heterogeneous forests with a mixture of AM and EM tree species, (i) AM and EM tree species respond in contrasting ways (i.e., positively vs. negatively respectively) to increasing soil fertility, (ii) AM tree dominance contributes to higher tree diversity and EM tree dominance contributes to greater standing biomass and that as a result, (iii) mycorrhizal associations exert an overall negative effect on DBRs across spatial scales. To empirically test these hypotheses, we collected detailed tree distribution and soil information (nitrogen, phosphorus, organic matter, pH, etc.) from seven temperate and subtropical AM-EM mixed forest mega-plots (16–50 ha). Using spatial codispersion null model and structural equation modeling, we identified the relationships among AM or EM tree dominance, soil fertility, tree species diversity and biomass, and thus DBRs across 0.01–1 ha scales. We found first evidence overall supporting the above three hypotheses in these AM-EM mixed forests: (i) In most forests, with increasing soil fertility tree communities changed from EM-dominated to AM-dominated. (ii) Increasing AM tree dominance had an overall positive effect on tree diversity and a negative effect on biomass, even after controlling for soil fertility and number of trees. Together, (iii) the changes in mycorrhizal dominance along soil fertility gradients weakened the positive DBR observed at 0.01–0.04 ha scales in nearly all forests and drove negative DBRs at 0.25–1 ha scales in four out of seven forests. Hence, this study highlights a soil-related mycorrhizal dominance mechanism that could partly explain why in many natural forests, biodiversity-ecosystem functioning (BEF) relationships shift from positive to negative with increasing spatial scale.

See the "Materials and Methods" section in the manuscript for details.