• Energy Research
• 2. Zero hunger close
• NL close
• English close

The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

1. Natural populations deliver a wide range of products that provide income for millions of people and need to be exploited sustainably. Large heterogeneity in individual performance within these exploited populations has the potential to improve population recovery after exploitation and thus help sustaining yields over time. 2. We explored the potential of using individual heterogeneity to design smarter harvest schemes, by sparing individuals that contribute most to future productivity and population growth, using the understorey palm Chamaedorea elegans as a model system. Leaves of this palm are an important non-timber forest product and long-term inter-individual growth variability can be evaluated from internode lengths. 3. We studied a population of 830 individuals, half of which was subjected to a 67 % defoliation treatment for three years. We measured effects of defoliation on vital rates and leaf size – a trait that determines marketability. We constructed integral projection models in which vital rates depended on stem length, past growth rate, and defoliation, and evaluated transient population dynamics to quantify population development and leaf yield. We then simulated scenarios in which we spared individuals that were either most important for population growth or had leaves smaller than marketable size. 4. Individuals varying in size or past growth rate responded similarly to leaf harvesting in terms of growth and reproduction. By contrast, defoliation-induced reduction in survival chance was smaller in large individuals than in small ones. Simulations showed that harvest-induced population decline was much reduced when individuals from size and past growth classes that contributed most to population growth were spared. Under this scenario cumulative leaf harvest over 20 years was somewhat reduced, but long-term leaf production was sustained. A three-fold increase in leaf yield was generated when individuals with small leaves are spared. 5. Synthesis and applications This study demonstrates the potential to create smarter systems of palm leaf harvest by accounting for individual heterogeneity within exploited populations. Sparing individuals that contribute most to population growth ensured sustained leaf production over time. The concepts and methods presented here are generally applicable to exploited plant and animal species which exhibit considerable individual heterogeneity. Vital rate and internode dataThis data file contains annual vital rate data (stem length growth, fruit production, survival and leaf production) of 830 individuals of the understorey palm Chamaedorea elegans, collected in a 0.7 ha plot in Chiapas, Mexico, during the period November 2012 - November 2015. A 2/3 defoliation treatment was repeatedly applied to half of the individuals. The data file also contains measurements of the lengths of all internodes of all individuals.

In this study, we test the effects of abiotic factors (light variation, caused by logging disturbance, and soil fertility) and biotic factors (species richness and functional trait composition) on biomass stocks (aboveground biomass, fine root biomass), SOM and productivity in a relatively monodominant Guyanese tropical rainforest. This forest grows on nutrient-poor soils and has few species that contribute most to total abundance. We therefore expected strong effects of soil fertility and species’ traits that determine resource acquisition and conservation, but not of diversity. We evaluated 6 years of data for 30 0.4-ha plots and tested hypotheses using structural equation models. Our results indicate that light availability (through disturbance) and soil fertility – especially P – strongly limit forest biomass productivity and stocks in this Guyanese forest. Low P availability may cause strong environmental filtering, which in turn results in a small set of dominant species. As a result, community trait composition but not species richness determines productivity and stocks of biomass and SOM in tropical forest on poor soils.

This dataset contains the underlying data for the study:Van Oort, P. A. J., B. G. H. Timmermans, H. Meinke, and M. K. Van Ittersum. "Key weather extremes affecting potato production in The Netherlands." European Journal of Agronomy 37, no. 1 (2012): 11-22.http://dx.doi.org/10.1016/j.eja.2011.09.002The possible impact of climate change on frequency and severity of weather extremes is hotly debated among climate scientists. Weather extremes can have a significant impact on agricultural production, but their effect is often unclear; this due to interaction with other factors that affect yield and due to lack of precise definitions of relevant weather extremes. We show that an empirical analysis of historical yields can help to identifying such rare, high impact climate events.A reconstructed time series of ware potato production in Flevoland (The Netherlands) over the last 60 years (1951–2010) enabled us to identify the two main yield affecting weather extremes. In around 10% of the years yield anomalies were larger than −20%. We found that these anomalies could be explained from two weather extremes (and no other), namely a wet start of the growing season and wet end of the growing season. We derived quantitative, meteorological definitions of these extremes. Climate change scenarios for 2050 show either no change or increased frequency of the two extremes. We demonstrate there is large uncertainty about past and future frequencies of the extremes, caused by a lack of sufficiently long historical weather records and uncertainties in climate change projections on precipitation. The approach to identify weather extremes presented here is generally applicable and shows the importance of long term crop and weather observations for investigating key climatic risks to production.

‘Productive Uses of Energy and gender in the Street Food Sector’, is a title of our four year project which is part of the DFID funded ENERGIA Gender and Energy Research programme. This research focuses on male and female owned micro enterprises preparing and selling food in Rwanda, Senegal and South Africa. This sector provides livelihoods for many women and men in these countries and this project provides the gender and energy nexus analysis. One of the primary goals of this project is to influence energy policy making and implementation in the focus countries.

Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms. van der Meij et al_Biogeosciences2017_dataThe experimental set-up, treatments, data collection and data analyses are thoroughly described in the Biogeoscience manuscript ‘Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensors’ doi:10.5194/bg-2016-452. Therefore we refer to the manuscript for detailed information an here we provide a brief summary to enable readers to follow what the data entail. The data were collected from a 2-year field experiment with plant rotations in a full factorial design. The plant treatments we focused on are legacy effects of the plant treatments (listed below) to the following oat crop. In this oat crop we quantified several plant traits both in situ and via remote sensing by use of UAV and hyperspectral and EGB sensors. The experiment was set-up in five randomized field blocks. We used part of the in situ collected data to parameterize the hyperspectral data based models and we validated these models with the other half of the field plots. Plant treatments Fa= fallow Lp= Lolium perenne Rs= Raphanus sativus Tr= Trifolium repens Vs= Vicia sativa Lp+Tr= 50:50 species mixture (relative to the monoculture seed densities) of the species Lp and Tr Rs+Vs= 50:50 species mixture (relative to the monoculture seed densities) of the species Rs and Vs

* 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.