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284 results for “Future climate”
Data for "Realistic representation of mixed-phase clouds increases future climate warming
<p>Data to reproduce the figures from Hofer et al. (2023) <a href="https://www.researchsquare.com/article/rs-2981113/v1">Realistic representation of mixed-phase clouds increases future climate warming</a></p>
Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)
<p>This is the outcome data from our study titled "<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.170481" target="_blank" rel="noopener"><em><u>Prediction of global wheat cultivation distribution under climate change and socioeconomic development</u></em></a>" which was published in The Science of The Total Environment. The present study represents a significant extension of our previous research on "<em><a href="http://dx.doi.org/10.1016/j.scitotenv.2019.06.153" target="_blank" rel="noopener">The Potential Distribution and Dynamics of Global Wheat under Multiple Climate Change Scenarios"</a></em>.</p> <p>Socioeconomic and climate change are both critical factors influencing the global distribution of crop cultivation. However, there has been limited exploration of the role of socioeconomic factors in predicting future crop cultivation distribution under climate change.</p> <p>We have proposed the MaxEnt-SPAM approach under the assumption that environmental conditions are the primary determinants of land suitability for cultivating wheat, while socioeconomic factors play a crucial role in influencing farmers' crop choices. In essence, the distribution of wheat cultivation is contingent upon maximizing potential revenue and ensuring suitability for wheat planting.</p> <p>The proposed MaxEnt-SPAM approach was utilized to estimate the distribution of wheat cultivation in three combined Representative Concentration Pathway (RCP) - Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. The methodology involved estimating wheat planting suitability under future RCP scenarios using the MaxEnt model, predicting farmers' crop choices under future SSP scenarios through Time series-Backpropagation (TS-BP) models, and ultimately estimating global wheat cultivation distribution based on the SPAM model. Validation of this approach against major known datasets on the distribution of wheat cultivation demonstrated satisfactory accuracy, with a predictive accuracy exceeding 85% and a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets.</p> <p>Based on the aforementioned concept and methodology, a global wheat cultivation distribution grid (0.5 degree × 0.5 degree) was projected under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios.</p> <p>The findings suggest that RCP8.5-SSP3 may offer the most favorable conditions for wheat cultivation. Additionally, socioeconomic development significantly constrains the potential distribution of wheat cultivation, with estimated areas accounting for an average of 77% of the potential distribution determined by climatic factors under the selected RCP-SSP scenarios. Socioeconomic development appears to have a positive impact on wheat cultivation in Africa.</p> <p>Our results illustrate the influence of socioeconomic factors on crop distribution within a market economy framework, underscoring the importance of integrating socioeconomic factors and climate change for accurate predictions of crop cultivation distribution.</p> <p>We contend that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable addition to existing products. This prediction data is among the few products to consider both climate change and socio-economic development, providing a more comprehensive understanding of crop cultivation distribution dynamics.</p> <p>The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to enhance our comprehension of the dynamics and distribution of global wheat cultivation under different climate change and socio-economic development paths in the future, potentially supporting research in earth system simulation and agricultural sciences.</p> <p>The dataset for the Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) and the Maxent-SPAM approach code is stored in a zip package named SPAM_MaxEnt.zip, which contains two folders (code and data).</p> <p><strong>code: </strong></p> <p>This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information.</p> <p>The exampleData contains</p> <p>1. h_pri.tif: prior data</p> <p>2. h_res.tif: global C3 crop cultivation proportion</p> <p>Run the main programme: cross_entroy.m</p> <p><strong>data: </strong></p> <p>This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format.</p> <p><strong>1 Global distribution of the long-term wheat-</strong><strong>c</strong><strong>ultivation area fraction: </strong></p> <p>This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated.</p> <p><strong>r2s1f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario</p> <p><strong>r4s2f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario</p> <p><strong>r8s3f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario</p> <p><strong>2 </strong><strong>S</strong><strong>patial overlap between the long-term period of land suitability for wheat </strong><strong>planting </strong><strong>and wheat cultivation distribution: </strong></p> <p>This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:<strong>{1, 2, 3}</strong>, <strong>1</strong> wheat cultivation existed but was predicted to be unsuitable to plant wheat; <strong>2 </strong>presented a reduction in the wheat cultivation area compared to the land's suitability; <strong>3</strong> presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat.</p> <p><strong>com_suit_fra126.tif: </strong>the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6</p> <p><strong>com_suit_fra245.tif: </strong>the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5</p> <p><strong>com_suit_fra385.tif:</strong> the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5</p> <p><strong>3 Differences in the proportion of long-term wheat </strong><strong>c</strong><strong>ultivation: </strong></p> <p>This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability.</p> <p><strong>r2s1_f.tif:</strong> Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario</p> <p><strong>r4s2_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario</p> <p><strong>r8s3_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario </p> <p><strong>References:</strong></p> <p>Yaojie Yue, Puying Zhang, Yanrui Shang. The Potential Distribution and Dynamic of Global Wheat under Multiple Climate Change Scenarios. Science of the Total Environment, 2019, 688: 1308-1318.</p> <p>Xi Guo, Puying Zhang, Yaojie Yue. Prediction of global wheat cultivation distribution under climate change and socio-economic development. Science of the Total Environment, 2024, 919: 170481.</p> <p>For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014).</p> <p>Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x.</p> <p>Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.002</p>
Data for 'Future Transboundary Water Stress and Its Drivers Under Climate Change: A Global Study'
<p><strong>This dataset is a supplement to the following publication (please cite that when using the data):</strong></p> <p>Munia et al. 2020. Future transboundary water stress and its drivers under climate change: a global study. Earth’s future. <a href="https://doi.org/10.1029/2019EF001321">https://doi.org/10.1029/2019EF001321</a></p> <p> </p> <p><strong>Water stress category data</strong></p> <p>Dataset presents the water stress category in transboundary basins at sub-basin level for different scenarios (see article for details):</p> <ul> <li> <p>stress_category_Historical.gpkg: stress for years 1980 and 2010</p> </li> <li> <p>stress_category_SSP1‐RCP26.gpkg: stress for year 2050, SSP1‐RCP2.6 scenario</p> </li> <li> <p>stress_category_SSP1‐RCP45.gpkg: stress for year 2050, SSP1‐RCP4.5 scenario</p> </li> <li> <p>stress_category_SSP2‐RCP60.gpkg: stress for year 2050, SSP2‐RCP6.0 scenario</p> </li> <li> <p>stress_category_SSP3‐RCP60.gpkg: stress for year 2050, SSP3‐RCP6.0 scenario</p> </li> </ul> <p> </p> <p><strong>Dataset specifications:</strong></p> <p>Type: geopackage (gpkg)</p> <p>Spatial extent: -165, 141.5, -54.5, 70.5 (xmin, xmax, ymin, ymax)</p> <p>Temporal extent: see above</p> <p>Projection: long/lat WGS84 (EPSG:4326)</p> <p>Information: sub-basin name, country, stress level, stress category</p> <p>Unit: -</p> <p> </p>
Reanalysis and future wave climate projections of the wave climate of the Gulf of Riga 1993-2100
<h4><strong>Data sets</strong></h4><p>There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100).</p><p>The dataset provides gridded monthly mean values of the parameters of the wind waves in the Gulf of Riga, Baltic Sea. The variables of the dataset of the wave field state of the Gulf of Riga are as follows (Long name: <i>acronym</i>, <i>units</i>) </p><ul><li>Mean wave direction: <i>VMDR_WW, </i>°</li><li>Spectral significant wave height: <i>VHM0_WW, m</i></li><li>Spectral moment (0,1) of wave period or mean wave period: <i>VTM01_WW, s</i></li><li>Eastward wave energy flux: <i>WWEFu, W/m</i></li><li>Northward wave energy flux: <i>WWEFv, W/m</i></li></ul><p> </p><p>The grid size of the dataset is 101 (latitude) x 93 (longitude). The horizontal grid spacing is 1 nm. The time resolution of the dataset is monthly – the monthly mean value is provided in the 1st day of the month in the time dimension.</p><p>The original climatic calculations are based on the University of Latvia (UL) set-up of the SWAN model for the Gulf of Riga. The original output of the model run is hourly data series. </p><h4><strong>Reanalysis</strong></h4><p>Time period: 1993-2021, 29 years.</p><p>The main characteristics of the input data and approach for the reanalysis run are as follows: </p><ul><li>EMODNET2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) – ERA5 meteorology.</li><li>Ice conditions – LU HBM, see Frishfelds et. al. 2023.</li><li>Boundary conditions – Baltic Sea Wave Hindcast.</li></ul><h4><strong>Future climate projection</strong></h4><p>Time period: 2015-2100, 86 years.</p><p>The main characteristics of the input data and approach for the future wave climate projections run are as follows: </p><ul><li>Emodnet2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) from downscaled CMIP6 climate projection model NorESM2-MM_ssp585_r1i1p1f1 (search string – project:'CMIP6', source_id:'NorESM2-MM', experiment_id:'ssp585', variant_label:'r1i1p1f1').</li><li>Ice conditions – LU HBM, see Frishfelds et. al. 2023. </li><li>Boundary conditions – fetch model according to Shore protection manual, 1984.</li></ul><h4><strong>References</strong></h4><p>Frishfelds, V., Cepīte-Frišfelde, D., Timuhins, A., Bethers, U., Sennikovs, J., Reanalysis and future climate projections of the physical state of the Gulf of Riga 1993-2100, Zenodo, <a href="https://zenodo.org/doi/10.5281/zenodo.8248942">10.5281/zenodo.8248942</a>, (2023).</p><p>Baltic Sea Wave Hindcast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). doi: <a href="https://doi.org/10.48670/moi-00014">https://doi.org/10.48670/moi-00014</a>.</p><p>Shore protection manual, Army Corps of Engineers, Coastal Engineering Research Center (CERC), (1984).</p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
Datasets related to the study "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach"
<p>This dataset contains the data of the manuscript "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach" under publication in Atmospheric Chemistry and Physics. <br>It includes CNRM-ALADIN64 simulations of surface solar radiation, cloud fraction, aerosol optical depth and water vapor content. <br>A directory is dedicated to HINDCAST simulations. It includes all datasets involved in the evaluation of CNRM-ALADIN64 simulations, as well as all datasets used for the analysis of the spatial variability of surface solar irradiance over the recent past. <br>Another directory is dedicated to future climate simulations. In this case, several sub directories can be found, representing either the simulations over the historical period (2005-2014, i.e. HIST directory), or simulations at mid (2045-2054, "mid" suffix) and long term (2091-2100, "end" suffix) horizons for SSP1-1.9 and SSP3-7.0. Each set of climate simulations is composed of three members (r1f, r2f, r3f), which were used collectively to increase the statistical significance of our analysis. </p>
Data and literature repository for "Climate Futures are Political Futures: Integrating Political Development Into the Shared Socioeconomic Pathways (SSPs)"
<p>The datasets provided in the repository (listed in Table 1 of the manuscript):</p> <ul> <li>Governance (Andrijevic et al., 2020)*</li> <li>Government effectiveness (Andrijevic et al., 2020)*</li> <li>Violent conflict (Hegre et al., 2016)</li> <li>Rule of law (update to the Soergel et al., 2021)</li> </ul> <p>*Please note that these two variables can be found in the same data file.<br><br></p> <p>The indicators can also be retrieved through the <a href="https://ssp-extensions.apps.ece.iiasa.ac.at/">SSP Extensions Explorer.</a> <br><br><strong><br>For applications of the projections of political indicators in further analyses, please consult the following references: </strong> </p> <p>Brutschin, E., Pianta, S., Tavoni, M., Riahi, K., Bosetti, V., Marangoni, G., & Van Ruijven, B. J. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abf0ce/meta">A multidimensional feasibility evaluation of low-carbon scenarios.</a> <em>Environmental Research Letters </em>2021, <em>16</em>(6), 064069.</p> <p>Gidden MJ, Brutschin E, Ganti G, Unlu G, Zakeri B, Fricko O<em>, et al. </em><a title="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5" href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5">Fairness and feasibility in deep mitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate</a>. <em>Environmental Research Letters </em>2023, <strong>18</strong>(7)<strong>: </strong>074006. </p> <p>Hoch JM, de Bruin SP, Buhaug H, Von Uexkull N, van Beek R, Wanders N. <a title="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2" href="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2">Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach</a>. <em>Environmental Research Letters </em>2021, <strong>16</strong>(12)<strong>: </strong>124068. </p> <p>Joshi DK, Hughes BB, Sisk TD. <a title="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145" href="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145">Improving governance for the Post-2015 Sustainable Development Goals: Scenario forecasting the next 50 years</a>. <em>World Development </em>2015, <strong>70: </strong>286-302. </p> <p>Moyer JD. <a title="https://www.sciencedirect.com/science/article/pii/S0305750X23000062" href="https://www.sciencedirect.com/science/article/pii/S0305750X23000062">Blessed are the peacemakers: The future burden of intrastate conflict on poverty</a>. <em>World Development </em>2023, <strong>165: </strong>106188. </p> <p>Moyer JD, Turner SD, Meisel CJ. <a title="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740" href="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740">What are the drivers of diplomacy? Introducing and testing new annual dyadic data measuring diplomatic exchange</a>. <em>Journal of Peace Research </em>2021, <strong>58</strong>(6)<strong>: </strong>1300-1310. </p> <p>Petrova, K, Olafsdottir, G, Hegre, H, Gilmore, EA (2023). <a title="https://iopscience.iop.org/article/10.1088/1748-9326/acb163" href="https://iopscience.iop.org/article/10.1088/1748-9326/acb163">The ‘conflict trap’ reduces economic growth in the shared socioeconomic pathways</a>. <em>Environmental Research Letters</em>, 2023, <strong>18</strong>(2), 024028. </p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
Historical and future climate change fosters expansion of Australian harvester termites, Drepanotermes
<p>Past evolutionary adaptations to Australia's aridification can help us to understand potential responses of species in the face of global climate change. Here, we focus on the Australian-endemic termite genus <em>Drepanotermes</em>, which is widespread in semi-arid and arid regions of Australia. We used species delineation, phylogenetic inference, and ancestral state reconstruction to investigate the evolution of mound-building and in relation to reconstructed past climatic conditions. Our results suggest that mound-building evolved several times independently, apparently facilitating expansion into tropical and mesic regions of Australia. Strong phylogenetic signal of bioclimatic variables, especially of limiting environmental factors (e.g. precipitation of warmest quarter), indicates that climate exerts a strong selective pressure. Finally, we used environmental niche modeling to predict present and future habitat suitability for eight <em>Drepanotermes</em> species. Abiotic factors such as annual temperature contributed disproportionately to calibrations, while the inclusion of biotic factors like vegetation cover improved ecological niche models in some species. A comparison between present and future habitat suitability under two different emission scenarios revealed continued suitability of current ranges as well as substantial habitat gains for most studied species, irrespective of nesting habit, yet extensive range expansions in the near future are likely precluded by low dispersal abilities.</p>
Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia (The genome assembly and annotion fiile)
<p>The genome assembly and annotion fiile used in the manuscript: <strong>Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia</strong></p>
WRF-Chem output supporting manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates"
<p>Selected output from WRF-Chem v3.6.1 on black carbon deposition, rainfall, and snowfall over Southeast Asia. These files were used to prepare the figures and tables in the manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates" by these authors. Files are in NetCDF format and contain metadata describing their contents. The naming convention is:</p> <p>YYYY_MM_EXT_daily_12km_dustfix.nc</p> <p>where YYYY_MM is the simulated year and month and the extensions are:</p> <p>NFC - No Further Control emission scenario</p> <p>MIT - Mitigation emission scenario</p> <p>EN - Simulated El Nino year</p> <p>LN - Simulated La Nina year</p> <p> </p>
FIGURE 2 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 2 Distributions (left), maximum likelihood (ML) phylogenetic trees (middle), principal component analysis (PCA) ordination plots from cranial measurements, photographs or drawings of the baculum and sonograms of echolocation calls (right) of selected groups of paramontane southern African bats having ranges categorized as arid (red symbols), Mediterranean (turquoise symbols), temperate-montane (blue), savanna-montane (orange), and tropical rain forest (green; see Table S1 for classification): horseshoe bats (Rhinolophus) of the R. capensis (a), R. darlingi (b), R. ferrumequinum (c), R. fumigatus (d) groups, wing-gland bats (Family Cistugidae, genus Cistugo (e), and long-eared serotine bats of the genus Laephotis (f)). Distribution maps were based on IUCN Redlist maps (open polygons), correctly identified vouchers from molecular studies (colored symbols; this study; GenBank; Curran et al., 2022; Demos et al., 2019; Dool et al., 2016; Taylor et al., 2018) and skulls measured in this study (crosses). In a few cases (see legends), GBIF records were indicated for the Angolan range of species. Gray shading indicates elevations over 1200 m a.s.l. Phylogenetic trees are shown for sub-clades (i.e., excluding outgroups) of three separate ML analyses undertaken with IQTREE of Rhinolophus, Cistugo, and Laephotis (Figures S2–S4). Values above nodes (in bold) represent median dates obtained for corresponding nodes from separate BEAST analyses in Figures S5–S7 (see text for details). Node support values for ML trees, obtained by the IQTREE program, are given below the nodes for SH-like approximate likelihood ratio tests (SH-aLRT), aBayes posterior probabilities, and ultra-fast bootstrap values (UFBS) respectively (see text for details). Tip labels marked in bold represent new sequences from this study. Underlined tip labels represent two instances of mtDNA introgression where morphologically distinct taxa from different biomes have near-identical cyt-b sequences. Species ranges of echolocation call peak frequencies were obtained from the literature for Rhinolophidae (Adams & Kwiecinski, 2018; Curran et al., 2022; Jacobs et al., 2013; Jacobs et al., 2017; Laverty & Berger, 2020; Monadjem et al., 2020; Mutumi et al., 2016; Odendaal & Jacobs, 2011; Odendaal et al., 2014; Schoeman & Jacobs, 2008), Cistugo (Monadjem et al., 2020; Schoeman & Jacobs, 2003, 2008), and long-eared Laephotis (Adams & Kwiecinski, 2018; Jacobs et al., 2005; Monadjem et al., 2020; Pierce et al., 2011). Bacula photographs and drawings were obtained from this study as well as Benda and Vallo (2012), Taylor et al. (2018), Curran et al. (2022). Abbreviation of South African province names: EC, Eastern Cape; FS, Free State; GP, Gauteng; KZN, KwaZulu-Natal; LP, Limpopo; MP, Mpumalanga; NC, Northern Cape; WC, Western Cape. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 1 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 1 Maps of southern, central, and eastern Africa showing (a) topographical features referred to in this study (see text for details), and (b) the extent of minimum monthly temperatures (bioclim6) <0°C from present and past (last glacial maximum [LGM]) models (from Worldclim; https://www.worldclim.com/; see Methods for more details). Gray or darker shading in both maps indicates mountains>1200 m in elevation. In (a), the acronym HEAN stands for the Highlands and Escarpments of Angola and Namibia (Mendelsohn et al., 2023); SEAMA stands for the South-East African Montane Archipelago (Bayliss et al., 2024); LMEE stands for the Limpopo–Mpumalanga– Eswatini Escarpment (Clark et al., 2022). The map in (b) shows distribution points of horseshoe bats, Rhinolophus (crosses), wing-gland bats, Cistugo (open triangles) and long-eared bats, Laephotis (open squares) based on morphological and molecular results from this study and from published a GenBank cyt-b sequences. In (b), minimum monthly temperatures <0°C indicated for the present (blue) and LGM (red), approximating the extent of frost (and hence temperate grasslands) currently and during the LGM (idea from Brain, 1985). Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 3 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 3 Map of southern, central, and eastern Africa showing major geographic features (as in Figure 1a) but with biogeographical barriers elucidated by this study indicated as red dashed lines, labelled as (i) to (vii) (see Discussion), and taxa specific to different ranges indicated according to the predominant biomes (green = tropical; red = arid, turquoise = Mediterranean, blue = temperate, orange = savanna). Note that only one savanna lineage is here indicated for ease of visualization. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
FIGURE 4 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 4 Maps of south-central Africa showing the distribution of Köppen–Geiger climate zones for the present (a) and projected future (2070) (b), as well as past (last glacial maximum: left panel), present (right panel), and projected future (2070; right panel) Maxent distribution models for five species groups of bats; Rhinolophus capensis group (c–e: green = R. swinnyi; blue = R. rhodesiae; orange = R. simulator; turquoise = R. capensis; red = R. denti); R. darlingi group (f–h: blue = R. cervenyi; orange = R. darlingi; red = R. damarensis), R. ferruquinum group, in part (i–k: blue = R. acrotis), Laephotis spp (l–n: blue = L. cf. botswanae; orange = L. angolensis), Cistugo spp (o–q: blue = C. lesueuri; red = C. seabrae). Details of Maxent models given in text. Ranges of species above indicated by colors corresponding to biomes recognized in this study (Tables S1 and S2) as follows: blue or green = temperate; orange = savanna; turquoise = Mediterranean; red = arid. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
Future-ready Nordic homes: Responding to the Changing Climate with Free-running Buildings; Digital Repository of Obtained Results
<p>The figures provided here show complete results of the parametric building performance simulation for each studied location, which was a part of the Master thesis in Energy-efficient and Environmental Building Design (Faculty of Engineering, Lund University, Sweden) by Marko Ljubas.</p> <p>The simulations were performed using software IDA ICE 4.8 by EQUA.</p> <p>The weather files were generated using software Meteonorm 8.2 by Meteotest.</p>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Data for: Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use
<p>This dataset contains the code and the data files needed to create the figures shown in the paper titled "Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use".</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.