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This paper aims at reviewing the life cycle assessment (LCA) literature on second generation bioethanol based on lignocellulosic biomass and at identifying issues to be resolved for good LCA practice. Reviews are carried out on respective LCA studies published over the last six years. We use the classification of lignocellulosic biomass to define system boundaries, so that the comparison among LCA results can be thoroughly assessed based on identified system components. A basis for attributing environmental burden for different biomass feedstocks is also suggested. Despite the non-homogeneous systems, we conclude that second generation bioethanol performs better than fossil fuel at least for the two most studied impact categories, net energy output and global warming. For the latter category, carbon sequestration at the biomass generation stage can even consistently offset the GHG emissions from all parts of the life cycle chains at high ethanol percentage (≥85%). The aspect of biogenic carbon and agrochemical input for energy crops and biomass residues, and the effect of removal of the latter from soil have not been treated consistently. In contrast, the exclusion of upstream chain of biomass waste feedstocks is observed in practice. The bioethanol conversion process is mostly based on simultaneous saccharification and co-fermentation, characterized by high yield and low energy input. In this regard, the LCA results tend to under estimate the real impacts of the current technology. The choice of allocation methods strongly influences the final results, particularly when economic value is used as a reference. Substitution of avoided burden seems to be the most popular allocation method in practice, followed by partition based on mass, energy, and economic values.

Data collated from data provided by original data collectors or from data provided within published articles. The MetaData.csv provides information on each of the original data sources, including bibliographic information about the original article and information on how many sites were sampled. The SiteData.csv gives site-level variables, such as geographic coordinates, the environmental parameters as well as site-level community metrics (species richness, total abundance and total biomass). The SppOccData.csv provides the observation level data - the occurrence, abundance and/or biomass of individual species/morpho-species/life-stage at a particular site. Not every data source contained such observation level data. Metadata information about the variables in each file are provided in the files MetaData_info.csv, SiteData_info.csv and SppOccData_info.csv, respectively. All files provided use the character encoding UTF-8, and missing values are represented by "NA". This dataset contains key characteristics about the data described in the Data Descriptor Global data on earthworm abundance, biomass, diversity and corresponding environmental properties. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format ---------------------------------------------------------------- Please remove before publishing. manuscript number:SDATA-20-00920 edit url: https://scientificdata.metadata-creator.com/?id=ag5maWdtZXRhLTIzMDExMXIXCxIKU3VibWlzc2lvbhiAgICgzKucCgw Related publications: https://doi.org/10.1126/science.aax4851 Please remove before publishing. ----------------------------------------------------------------

* 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.

Fuel ethanol production from plant biomass hydrolysates by Saccharomyces cerevisiae is of great economic and environmental significance. This paper reviews the current status with respect to alcoholic fermentation of the main plant biomass-derived monosaccharides by this yeast. Wild-type S. cerevisiae strains readily ferment glucose, mannose and fructose via the Embden-Meyerhof pathway of glycolysis, while galactose is fermented via the Leloir pathway. Construction of yeast strains that efficiently convert other potentially fermentable substrates in plant biomass hydrolysates into ethanol is a major challenge in metabolic engineering. The most abundant of these compounds is xylose. Recent metabolic and evolutionary engineering studies on S. cerevisiae strains that express a fungal xylose isomerase have enabled the rapid and efficient anaerobic fermentation of this pentose. L: -Arabinose fermentation, based on the expression of a prokaryotic pathway in S. cerevisiae, has also been established, but needs further optimization before it can be considered for industrial implementation. In addition to these already investigated strategies, possible approaches for metabolic engineering of galacturonic acid and rhamnose fermentation by S. cerevisiae are discussed. An emerging and major challenge is to achieve the rapid transition from proof-of-principle experiments under 'academic' conditions (synthetic media, single substrates or simple substrate mixtures, absence of toxic inhibitors) towards efficient conversion of complex industrial substrate mixtures that contain synergistically acting inhibitors.

The tendency of Saccharomyces cerevisiae to favor alcoholic fermentation over respiration is a complication in aerobic, biomass-directed applications of this yeast. Overproduction of Hap4p, a positive transcriptional regulator of genes involved in respiratory metabolism, has been reported to positively affect the balance between respiration and fermentation in aerobic glucose-grown batch cultures. In this study, the effects of HAP4 overexpression have been quantified in the prototrophic S. cerevisiae strain CEN.PK 113-7D under a variety of growth conditions. In aerobic glucose-limited chemostat cultures, overexpression of HAP4 increased the specific growth rate at which aerobic fermentation set in by about 10% relative to the isogenic wild-type. Upon relief of glucose-limited conditions, the HAP4-overexpressing strain produced slightly less ethanol than the wild-type strain. The effect of Hap4p overproduction was most drastic in aerobic, glucose-grown chemostat cultures in which ammonium was limiting. In such cultures, the biomass yield on glucose was double that of the wild-type.

The present research demonstrates the biological treatment of refinery sulfidic spent caustics in a continuously fed system under halo-alkaline conditions (i.e. pH 9.5; Na(+)= 0.8M). Experiments were performed in identical gas-lift bioreactors operated under aerobic conditions (80-90% saturation) at 35°C. Sulfide loading rates up to 27 mmol L(-1)day(-1) were successfully applied at a HRT of 3.5 days. Sulfide was completely converted into sulfate by the haloalkaliphilic sulfide-oxidizing bacteria belonging to the genus Thioalkalivibrio. Influent benzene concentrations ranged from 100 to 600 μM. At steady state, benzene was removed by 93% due to high stripping efficiencies and biodegradation. Microbial community analysis revealed the presence of haloalkaliphilic heterotrophic bacteria belonging to the genera Marinobacter, Halomonas and Idiomarina which might have been involved in the observed benzene removal. The work shows the potential of halo-alkaliphilic bacteria in mitigating environmental problems caused by alkaline waste.

Over half of the world's forests are disturbed, and the rate at which ecosystem processes recover after disturbance is important for the services these forests can provide. We analyze the drivers' underlying changes in rates of key ecosystem processes (biomass productivity, litter productivity, actual litter decomposition, and potential litter decomposition) during secondary succession after shifting cultivation in wet tropical forest of Mexico.We test the importance of three alternative drivers of ecosystem processes: vegetation biomass (vegetation quantity hypothesis), community‐weighted trait mean (mass ratio hypothesis), and functional diversity (niche complementarity hypothesis) using structural equation modeling. This allows us to infer the relative importance of different mechanisms underlying ecosystem process recovery.Ecosystem process rates changed during succession, and the strongest driver was aboveground biomass for each of the processes. Productivity of aboveground stem biomass and leaf litter as well as actual litter decomposition increased with initial standing vegetation biomass, whereas potential litter decomposition decreased with standing biomass. Additionally, biomass productivity was positively affected by community‐weighted mean of specific leaf area, and potential decomposition was positively affected by functional divergence, and negatively by community‐weighted mean of leaf dry matter content.Our empirical results show that functional diversity and community‐weighted means are of secondary importance for explaining changes in ecosystem process rates during tropical forest succession. Instead, simply, the amount of vegetation in a site is the major driver of changes, perhaps because there is a steep biomass buildup during succession that overrides more subtle effects of community functional properties on ecosystem processes. We recommend future studies in the field of biodiversity and ecosystem functioning to separate the effects of vegetation quality (community‐weighted mean trait values and functional diversity) from those of vegetation quantity (biomass) on ecosystem processes and services.