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* 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.

Aim: Climate change is transforming mountain summit plant communities worldwide, but we know little about such changes in the High Andes. Understanding large-scale patterns of vegetation changes across the Andes, and the factors driving these changes, is fundamental to predicting the effects of global warming. We assessed trends in vegetation cover, species richness (SR) and community-level thermal niches (CTN) and tested whether they are explained by summits’ climatic conditions and soil temperature trends. Location: High Andes Time period: Between 2011/2012 and 2017/2019 Major taxa studied: Vascular plants Methods: Using permanent vegetation plots placed on 45 mountain summits and soil temperature loggers situated along a ~6,800 km N-S gradient, we measured species and their percentage cover and estimated CTN in two surveys (intervals between 5-8 years). We then estimated the annual rate of changes for the three variables and used generalized linear models to assess their relationship with rates of change in the locally recorded soil temperatures, annual precipitation, and the minimum air temperatures of each summit. Results: Over time, there was an average loss of vegetation cover (mean = -0.26 %/yr), and a gain in SR across summits (mean = 0.38 species m2/yr), but most summits had significant increases in SR and vegetation cover. Changes in SR were positively related to minimum air temperature and soil temperature rate of change. Most plant communities experienced shifts in their composition by including greater abundances of species with broader thermal niches and higher optima. However, the measured changes in soil temperature did not explain the observed changes in CTN. Main conclusions: High-Andean vegetation is changing in cover and SR and is shifting towards species with wider thermal niche breadths. The weak relationship with soil temperature trends could have resulted from the short study period that only marginally captures changes in vegetation through time. (1) R-studio; (2) QGis For further information, users are advised to refer to the README document ("README_Dataset-compositional-changes_Andes.md") and the accompanying published article: Cuesta, F., Carilla, J., LLambí, L.D., Muriel, P., Lencinas, M. V., Meneses R.I., Feeley, K., Pauli, H., Aguirre, N., Beck, S., Bernardi, A., Cuello, Duchicela, S. A., Eguiguren, P., Gamez, L.E., Halloy, S., Hudson, L., Jaramillo, R., Peri, P.L., Ramírez, L. A., Rosero-Añazco, P., Thompson N., Yager, K., Tovar, C. Compositional shifts of alpine plant communities across the high Andes. Global Ecology and Biogeography. Accepted. DOI: 10.1111/geb.13721 The information reported here comes from two main sources: (1) Data collected on the field during vegetation surveys (between 2011/2012 and 2017/2019) on permanent vegetation plots plus soil temperature collected in dataloggers installed on each research site across the Andes. (2) Species records obtained from GBIF, TROPICOS, La Paz herbarium (LPB) and ULA -Merida herbarium. These records represent species in the permanent plots during the first and second surveys.

This repository contains the data associated with the paper Tedersoo et al. (2022) Global patterns in endemicity and vulnerability of soil fungi // Global Change Biology. DOI:10.1111/gcb.16398 Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general. This repository contains the following data associated with the publication: Supplementary tables S1 - S6 (`Tables_S1-S6.xlsx`): - Table S1. Definition of ecoregions and assignment of samples to ecoregions - Table S2. GSMc dataset used for endemicity analyses - Table S3. Dataset used for modeling endemicity values - Table S4. Dataset used for calculating and mapping vulnerability scores - Table S5. Dataset used for calculating and mapping conservation value - Table S6. Additional funding sources by authors OTU distribution by samples and ecoregions (`Data_taxon_assignment_to ecoregions.xlsx`) Gridded maps: Conservation priorities for all fungi and fungal groups - ConservationPriority_AllFungi.tif - ConservationPriority_AM.tif - ConservationPriority_EcM.tif - ConservationPriority_Moulds.tif - ConservationPriority_NonEcMAgaricomycetes.tif - ConservationPriority_OHPs.tif - ConservationPriority_Pathogens.tif - ConservationPriority_Unicellular.tif - ConservationPriority_Yeasts.tif The average vulnerability of all fungi and fungal groups and the model uncertainty estimates - AverageVulnerability_AllFungi.tif - AverageVulnerability_AM.tif - AverageVulnerability_EcM.tif - AverageVulnerability_Moulds.tif - AverageVulnerability_NonEcMAgaricomycetes.tif - AverageVulnerability_OHPs.tif - AverageVulnerability_Pathogens.tif - AverageVulnerabilityUncertainty_AllFungi.tif - AverageVulnerabilityUncertainty_AM.tif - AverageVulnerabilityUncertainty_EcM.tif - AverageVulnerabilityUncertainty_Moulds.tif - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif - AverageVulnerabilityUncertainty_OHPs.tif - AverageVulnerabilityUncertainty_Pathogens.tif - AverageVulnerabilityUncertainty_Unicellular.tif - AverageVulnerabilityUncertainty_Yeasts.tif - AverageVulnerability_Unicellular.tif - AverageVulnerability_Yeasts.tif The relative importance of predicted vulnerability of all fungi - RelativeImportanceOfVulnerability_AllFungi.tif Vulnerability to drought, heat, and land cover change for all fungi - Vulnerability_AllFungi_Heat-Drought-LandCoverChange.tif - VulnerabilityUncertainty_AllFungi_Heat-Drought-LandCoverChange.tif Human footprint index based on the Land-Use Harmonisation (LUH2; Hurtt et al., 2020, doi:10.5194/gmd-13-5425-2020) - `LandCoverChange_1960-2015.tif` MD5 checksums for all files (`MD5.md5`) Fungal groups: - AM, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes) - EcM, ectomycorrhizal fungi (excluding dubious lineages) - NonEcMAgaricomycetes, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies) - Moulds (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales) - Putative pathogens (including plant, animal and fungal pathogens as primary or secondary lifestyles) - OHPs, opportunistic human parasites (excluding Mortierellales) - Yeasts (excluding dimorphic yeasts) - Unicellular, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages) Detailed processing steps can be found here: https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability This repository contains the data associated with the paper Tedersoo et al. (2022) Global patterns in endemicity and vulnerability of soil fungi // Global Change Biology. DOI:10.1111/gcb.16398 Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general.

Colombia requires to decarbonize the power sector in order to achieve the National Determined Contribution (NDC 2020). Variable renewable sources along with energy storage options are critical to accomplish the carbon neutrality options. Using OSeMOSYS framework, the study develops a power sector model for Colombia, and three scenarios are analyzed. The presentation summarizes the project developed as part of the EMP-LAC 2023.

The thermally stimulated processes in a pyrochlore-type ceramic ionic conductor were examined by the thermally stimulated depolarization current (TSDC) technique. Three polarization processes have been found in the thermogram. Thefirst one, revealed to result from the convolution of three simple processes with approximately similar activation energies value and can be basically attributed to the reorientation of cation-anion dipoles by means of nearest-neighbor (NN) to nearest-neighbor jumps (that is to say, a NN→NN relaxation type). The second process originated most likely also from a dipolar mechanism now involving nearest-neighbor to next-nearest-neighbor relaxation processes (NN→NNN relaxation type). On the other hand, the third process has been related to a space-charge relaxation, arising from the migration of K+ free-charge carriers. The activation energies and the pre-exponential factors for all these mechanisms were also reported

This material has been produced under the Climate Compatible Growth (CCG) programme, which brings together leading research organizations and is led out of the STEER centre, Loughborough University. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. This work was developed with the Energy Transition Council (ETC), which assisted in identifying needed research areas and connected to relevant stakeholders for a discussion on essential aspects of research.  Supplementary material about the research article titled Open Energy System Modelling for Low-Emission Hydrogen Roadmap Planning: The Case of Colombia

This assessment report establishes the value of the Hindu Kush Himalaya (HKH) for the 240 million hill and mountain people across the eight countries sharing the region, for the 1.65 billion people in the river basins downstream, and ultimately for the world. Yet, the region and its people face a range of old and new challenges moving forward, with climate change, globalization, movement of people, conflict and environmental degradation. At the same time, we also see incredible potential to meet these challenges in a sustainable manner

Around 90 percent of greenhouse gas (GHG) emissions come from land use and energy in Colombia. The increasing GHG emissions affects the climate and water systems; therefore, the representation of the Climate-Land-Energy-Water (CLEWs) nexus is paramount. Using OSeMOSYS framework, the study develops an integrated CLEWs model for Colombia, and three scenarios are analyzed. Key takeaways are: fossil fuel resources are needed for a secure transition; electricicity and hydrogen will play a significant role in the decarbonization process; and reforestation plus efficient livestock are critical to reach carbon neutrality by mid-century. The poster presents a summary of the project as part of the EMP-LAC 2022.