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We inferred patterns of biogeographic diversification and trait evolution in Vitaceae based on a robust phylogeny with dense sampling. We inferred patterns of biogeographic diversification and trait evolution in Vitaceae based on a robust phylogeny with dense sampling.

# Why we cannot always expect life history strategies to directly inform on sensitivity to environmental change --- The dataset provides the code to run DEB-IPM models used in the study in MATLAB and analyze PCA-results. ## Global description of the data and file structure The PCA zip.file contains a data table and R code to run the PCA analysis presented in the manuscript. The MATLAB zip.file contains all matlab files required to run the DEB-IPMS and perform perturbation analysis presented in the manuscript. **Detail PCA zip.file description** * PCA_table.xlsx contains the trait data and sensitivity values for all model species used as input for the PCA analysis. * PhyloPCA_Fish.R contains the code to run a PCA analysis using PCA_table.xlsx as input and correcting for phylogeny. * PhyloPCA_Fish_No_Phyl.R contains the code to run a PCA analysis using PCA_table.xlsx as input without correcting for phylogeny. * PhyloPCA_Fish_Body_size_correction.R contains the code to run a PCA analysis using PCA_table.xlsx as input and correcting for phylogeny and body size. * **Detail MATLAB zip.file description** * The subfolder Model_and_Parameter_files contains fifteen matlab files: * M1_parameters.m contains code describing the trait values for all iteroparous obligate breeders * M2_parameters.m contains code describing the trait values for all iteroparous skip breeders * M3_parameters.m contains code describing the trait values for all semelparous skip breeders * M1_iteroparous_obligate.m contains the code describing the life history functions for iteroparous obligate breeders * M2_iteroparous_skip.m contains the code describing the life history functions for iteroparous skip breeders * M3_semelparous_skip.m contains the code describing the life history functions for semelparous skip breeders * M1_stochseq_DEBIPM.m contains code describing the functions to estimate population growth rates for all iteroparous obligate breeders * M2_stochseq_DEBIPM.m contains code describing the functions to estimate population growth rates for all iteroparous skip breeders * M3_stochseq_DEBIPM.m contains code describing the functions to estimate population growth rates for all semelparous skip breeders * M1_stochseq_DEBIPMsens.m contains code describing the functions to estimate population sensitivity for all iteroparous obligate breeders * M2_stochseq_DEBIPMsens.m contains code describing the functions to estimate population sensitivity for all iteroparous skip breeders * M3_stochseq_DEBIPMsens.m contains code describing the functions to estimate population sensitivity for all semelparous skip breeders * * habit_matrix.m contains the code to initialize the habitat transition matrix * linspencer.m contains the code ensuring different colors per species in output figures * MeanVar.m contains the code to calculate the mean and variance of trait values in perturbation analysis * The subfolder Model_Run.m contains five matlab files to run model analysis using the files in the Model_and_Parameter_files folder as input * M1_stoch_run.m computes and generates figures of the population growth of all iteroparous obligate breeders over environmental autocorrelation. * M2_stoch_run.m computes and generates figures of the population growth of all iteroparous skip breeders over environmental autocorrelation. * M3_stoch_run.m computes and generates figures of the population growth of all semelparous skip breeders over environmental autocorrelation. * Perturb.m runs a perturbation analysis on trait importance for all species in all three DEB-IPM models. * MR_Functional_trait_response.m visualizes the outputs of Perturb.m ## Sharing/Access information This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. Links to other publicly accessible locations of the data: *NONE **Data collection** Functional trait data was derived from scientific literature and a full reference set is provided in table 1 of the manuscript. ## Code/Software This is an optional, freeform section for describing any code in your submission and the software used to run it. -MATLAB 2021A was used to run the DEB-IPMS -RStudio with R 4.2.1 was used to run R. Speed of life and reproductive strategy form the two major axes that organize variation in life history strategies across plant and animal species. The position of a species along these axes can inform on their sensitivity to environmental change. This provides a tantalizing link between sets of traits and population responses to change, contained in a highly generalizable theoretical framework. The underlying mechanisms are assumed to be governed by life history tradeoffs at the individual level. Examples include the tradeoff between current and future reproductive success, and investing energy into growth versus reproduction. But the importance of such tradeoffs in structuring population-level responses to environmental change remains understudied. We aim to increase our understanding of the link between individual-level life history tradeoffs and the structuring of life history strategies across species, and if they link to population responses to environmental change. We find that the classical association between life history strategies and population responses to environmental change breaks down when accounting for individual-level tradeoffs and reproductive decisions. Projecting population responses to environmental change can therefore not always be inferred based on a limited set of species traits alone. We summarize our perspective and a way forward in a conceptual framework.

Contains all raw data measured in the Schwingbach Earth Observatory (SEO) from Spring, Summer and Autumn 2020. Data was measured with an on-site LGR laser from the GHG emissions, and with 100cm³ soil cores for the soil characteristics. Details can be found in the corresponding manuscript "Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type"

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CAMS.CAMS-CSM1-0.ssp370' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

This site hosts our environmental NGO rates data, and all data needed for replicating our analysis in this paper. For replication code for the NGO rates data, which were created for a prior article in Climatic Change (2021), please see the following url: https://doi.org/10.7910/DVN/OINQQY Abstract: Which communities host the most grassroots resources for activism on energy, environment, and climate policy, per capita? This article introduces a recently developed data resource measuring rates of environmental organizations in every Japanese city over the last two decades. These data were first validated in 2021 against prior measures of environmental organizing and linked to changes in emissions levels (Fraser and Temocin 2021). This paper clears new ground by demonstrating best-use cases for scholars and activists seeking to understand the state of environmental organizing in their city. By detailing in-depth ways to analyze these indices and focusing on case studies based on Kitakyushu and an average city in post-Fukushima Japan, this study aims to encourage a wave of new research testing the relationship between levels of grassroots and professionalized environmental organizing in cities and urban environmental governance outcomes, including solar, wind, emissions, and pollution governance.

Original data and code for the study: Wei, J., Xu, F., Cole, E. F., Sheldon, B. C., de Boer, W. F., Wielstra, B., Fu, H., Gong, P., & Si, Y. (2024, Accepted). Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network. Global Change Biology. The dataset mainly contains data showing the climate change-induced heterogeneous shifts in vegetation phenology and the migration integrity change from 2000 to 2020 for 16 Asian herbivorous waterfowl species. These data were derived from the following resources available in the public domain. The Global Lakes and Wetlands Database is available from “https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database”. The global land cover datasets are available from European Space Agency (ESA) Climate Change Initiative (CCI) products, “https://maps.elie.ucl.ac.be/CCI/viewer/download.php”. The Global Multi-resolution Terrain Elevation Data are available from “https://www.usgs.gov/centers/eros/science/terrain-monitoring-and-modeling”. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance product is available from “https://modis.gsfc.nasa.gov/data/dataprod/mod09.php”. The bird distribution maps are available from Birdlife International, “https://www.birdlife.org/”. The bird foraging attribute data are available from EltonTraits 1.0, “https://figshare.com”. The bird occurrence data are available from eBird Basic Dataset (EBD), “https://science.ebird.org/en/use-ebird-data/download-ebird-data-products”. The Hackett backbone phylogenetic trees are available from “https://birdtree.org/”. The code contains the R scripts and MATLAB scripts that we used for this study. For details please see the file “Readme.txt”, and the research paper. Original data and code for the study: Wei, J., Xu, F., Cole, E. F., Sheldon, B. C., de Boer, W. F., Wielstra, B., Fu, H., Gong, P., & Si, Y. (2024, Accepted). Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network. Global Change Biology. The dataset mainly contains data showing the climate change-induced heterogeneous shifts in vegetation phenology and the migration integrity change from 2000 to 2020 for 16 Asian herbivorous waterfowl species. These data were derived from the following resources available in the public domain. The Global Lakes and Wetlands Database is available from “https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database”. The global land cover datasets are available from European Space Agency (ESA) Climate Change Initiative (CCI) products, “https://maps.elie.ucl.ac.be/CCI/viewer/download.php”. The Global Multi-resolution Terrain Elevation Data are available from “https://www.usgs.gov/centers/eros/science/terrain-monitoring-and-modeling”. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance product is available from “https://modis.gsfc.nasa.gov/data/dataprod/mod09.php”. The bird distribution maps are available from Birdlife International, “https://www.birdlife.org/”. The bird foraging attribute data are available from EltonTraits 1.0, “https://figshare.com”. The bird occurrence data are available from eBird Basic Dataset (EBD), “https://science.ebird.org/en/use-ebird-data/download-ebird-data-products”. The Hackett backbone phylogenetic trees are available from “https://birdtree.org/”. The code contains the R scripts and MATLAB scripts that we used for this study. For details please see the file “Readme.txt”, and the research paper.

Replication data for a study quantifying and regressing the social equity and distribution of financial burden of introducing renewable energy technologies in 47 Japanese communities. Includes data for calculating these values (see Centroid Calculator), population and sample level data for proving representativeness, and R database and syntax data for identifying covariates and causal factors.

Although many studies have tested the direct effects of drought on alien plant invasion, less is known about whether drought affects alien plant invasion indirectly via interactions of plants with other groups of organisms such as soil mesofauna. To test for such indirect effects, we grew single plants of nine naturalized alien target species in pot-mesocosms with a community of five native grassland species under four combinations of two drought (well-watered vs drought) and two soil-mesofauna-inoculation (with vs without) treatments. We found that drought decreased the absolute and the relative biomass production of the alien plants, and thus reduced their competitive strength in the native community. Drought also decreased the abundance of soil mesofauna, particularly soil mites, but did not affect the abundance and richness of soil herbivores. Soil-fauna inoculation did not affect biomass of the alien plants but increased biomass of the native plant community, and thereby decreased the relative biomass production of the alien plants. This increased invasion resistance due to soil fauna, however, tended (p = 0.09) to be stronger for plants growing under well-watered conditions than under drought. Synthesis. Our multispecies experiment thus shows that soil fauna might help native communities to resist alien plant invasions, but that this effect might be weakened under drought. In other words, soil mesofauna may buffer the negative effects of drought on alien plant invasions. The file archives 'SoilFauna_Drought_PlantInvasion_Date_YJL.tar' include three dataset, one named 'SoilFauna_Drought_PlantInvasion.csv' (Biomass data), one named 'SoilFaunaData.csv' (Soil Fauna data), and one named 'SoilNitrogenData.csv' (soil nitrogen data). The file 'SoilFauna_Drought_PlantInvasion.Rmd' is the R script, and its output is 'SoilFauna_Drought_PlantInvasion.html'. All data were collected from a greenhouse expeirment at the Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences.

This dataset contains source data for all figures in the Hanssen et al. study on the 'The climate change mitigation potential of bioenergy with carbon capture and storage'. Specifically: • NetCDF files of global emission factor maps for BECCS electricity and liquid fuels over 30 and 80 year evaluation periods (0.5 degree x 0.5 degree resolution) [Figure 1 +2] • CSV files of the emission-supply curves of BECCS electricity and liquid fuels over a 30 and 80 year evaluation period [Figure 1 +2] • CSV files of the emission-supply curves of BECCS electricity over a 30 and 80 year evaluation period for scenarios with alternative initial biomass uses [Figure 3] • CSV files of time series of the net annual and net cumulative carbon sequestration through BECCS in climate change mitigation pathways S2 and S5 [Figure 4] • CSV files of the emission-supply curves of BECCS electricity over a 30 and 80 year evaluation period for the sensitivity analysis scenarios [Figure 5] BECCS fuels included are: lignocellulosic FT-diesel, lignocellulosic ethanol, and sugarcane ethanol.