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98 results for “future scenario”
Data from "Projections of leaf turgor loss point shifts under future climate change scenarios" (Tordoni et al. 2022 Global Change Biology)
<p>The dataset includes four sheets representing the average turgor loss point (tlp) values at grid cell level (tlp_data) and the climatic variables and related climate change scenarios derived from the three models used in this study (HadGEM2-ES-RACMO22E, EC-EARTH_RACMO22E, EC-EARTH_CCLM4-8-17, respectively).</p> <p>The sheet "tlp_data" reports the cell ID (OGU) and the average tlp values for each taxonomic group considered in this study (gymnosperms, angiosperms, herbaceous and woody angiosperms). </p> <p>Each of the other three sheets reports the cell ID (OGU), coordinates of the cell centroid (Long, Lat) and a set of six climatic variables: 95<sup>th</sup> percentiles of average temperature (BIO1.95, °C), temperature seasonality (BIO4, °C), annual consecutive frost days where temperature was ≤ 0 °C (CFD.ann, n° days), annual consecutive dry days where precipitation was < 1 mm (CDD.ann, n° days), 5<sup>th</sup> percentiles of cumulate annual precipitation (BIO12.5, mm), and precipitation seasonality (BIO15, %). For each model, "hist" refers to historical data encompassing the period 1970-2005, whereas "RCP2.6" and "RCP8.5" reports the average value of future projections for the period 2080-2100 in two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5).</p> <p> </p>
Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
<p><span><strong>Aim</strong>:</span><span> Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (<em>Ailuropoda melanoleuca</em>). </span></p> <p><span><strong>Location</strong>:</span><span> Sichuan Province, China.</span></p> <p><span><strong>Methods</strong>: </span><span>We used ensemble species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also</span><span> compared the differences in </span><span>distributional changes of giant pandas among the five mountains in the study area. </span></p> <p><span><strong>Results</strong>: </span><span>Our ensemble SDMs exhibited good model performance in terms of both AUC (0.931) and TSS (0.747), and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover and total annual precipitation are the most important factors in shaping the current distribution patterns for the giant pandas. Our projections of future species distribution also suggested a range expansion under an optimistic greenhouse gas emission, while suggesting a range contraction under a pessimistic greenhouse gas emission. Moreover, we found that there is considerable variation in the projected range change patterns among the five mountains in the study area. Especially, the suitable habitat of the giant panda is predicted to increase under all scenarios in Minshan mountains, while is predicted to decrease under all scenarios in Daxiangling and Liangshan mountains, indicating the vulnerability of the giant pandas at low latitudes. </span></p> <p><span><strong>Main conclusions</strong>: </span><span>Our findings highlight the importance of an integrated approach that combines climate and land use change to predict the future species distribution and the need for a spatial explicit consideration of the projected range change patterns of target species for guiding conservation and management strategies. </span></p>
Peak refreezing in the Greenland firn layer under future warming scenarios
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Peak refreezing in the Greenland firn layer under future warming scenarios". The data consist of:</p> <p>1. Time series of annual Greenland ice sheet (GrIS) integrated <strong>SMB components</strong> (Gigatons or Gt per year). These time series are available in ASCII format for all simulations presented in the manuscript.</p> <p><strong>RACMO2.3</strong><strong>p2-ERA: </strong>1 member</p> <ul> <li><strong>SMB-components_RACMO2.3p2-ERA_1958-2020_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the ERA-forced RACMO2.3p2 simulation at 5.5 km, statistically downscaled to 1 km spatial resolution (1958-2020).</li> </ul> <p><strong>RACMO2.3</strong><strong>p2</strong><strong>-CESM2</strong>: 3 members</p> <ul> <li><strong>SMB-components_RACMO2.3p2-CESM2-HIST-12_1950</strong><strong>-</strong><strong>2014_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 historical reconstruction (HIST) at 11 km, statistically downscaled to 1 km spatial resolution (1950-2014). RACMO2.3p2 was forced by member 12 of the CESM2-HIST ensemble (HIST-12, see <strong>CESM2-HIST</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP126-4_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 4 of the CESM2-SSP126 ensemble (SSP126-4, see <strong>CESM2-SSP126</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP</strong><strong>585</strong><strong>-</strong><strong>3</strong><strong>_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 3 of the CESM2-SSP585 ensemble (SSP585-3, see <strong>CESM2-SSP585</strong> below).</li> </ul> <p><strong>CESM2</strong><strong>-IND: </strong>11 members</p> <ul> <li><strong>SMB-components_CESM2-IND-</strong><strong>X</strong><strong>_1850-1949_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 pre-industrial reconstructions (IND) at ~111 km, statistically downscaled to 1 km spatial resolution (1850-1949). The term “X” in the filename above represents the CESM2 member ranging from 1 to 11.</li> </ul> <p><strong>CESM2</strong><strong>-HIST: </strong>12 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>HIST</strong><strong>-</strong><strong>X</strong><strong>_1</strong><strong>9</strong><strong>50-</strong><strong>2014</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 historical reconstructions (HIST) at ~111 km, statistically downscaled to 1 km spatial resolution (1950-2014). The term “X” in the filename above represents the CESM2 member ranging from 1 to 12.</li> </ul> <p><strong>CESM2</strong><strong>-SSP126: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP1-2.6 projections (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>6</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP1-2.6 projection (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299).</li> </ul> <p><strong>CESM2</strong><strong>-SSP245: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP245</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP2-4.5 projections (SSP245) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> </ul> <p><strong>CESM2</strong><strong>-SSP370: </strong>5 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP370</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP3-7.0 projections (SSP370) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> </ul> <p><strong>CESM2</strong><strong>-SSP585: </strong>8 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP5-8.5 projections (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>7</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 7) does not include ice dynamics.</li> <li><strong>SMB-components_CESM2-CISM2-SSP585-8_2100-2299_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2-CISM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 8) includes ice dynamics.</li> </ul> <p>2. Time series of annual GrIS-wide <strong>runoff line altitude</strong> in meters above sea-level (m a.s.l.). These time series are available in ASCII format for the RACMO2.3p2-ERA simulation (ERA), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) including both native CESM2 and RACMO2.3p2-CESM2, statistically downscaled to 1 km.</p> <ul> <li><strong>Runoff-line-altitude_ERA_1958-2020_GrIS_1km.txt</strong>: time series of GrIS-wide runoff line altitude (m a.s.l.) derived from the ERA-forced RACMO2.3p2 simulation at 1 km (1958-2020).</li> <li><strong>Runoff-line-altitude_IND-EnsembleMean_1850-1949_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all pre-industrial reconstructions at 1 km (1850-1949, 11 IND members).</li> <li><strong>Runoff-line-altitude_HIST-EnsembleMean_1950-2014_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all historical reconstructions at 1 km (1950-2014, 13 HIST members).</li> <li><strong>Runoff-line-altitude_SSP126-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP1-2.6 projections at 1 km (2015-2299, 7 SSP126 members).</li> <li><strong>Runoff-line-altitude_SSP245-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP2-4.5 projections at 1 km (2015-2099, 6 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP370-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP3-7.0 projections at 1 km (2015-2099, 5 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP585-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP5-8.5 projections at 1 km (2015-2299, 9 SSP585 members).</li> </ul> <p>3. Time series of annual <strong>500hPa global temperature anomalies</strong> (ºC). These time series are available in ASCII format for the ERA5 reanalysis (ERA5-reanalysis), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) from the native CESM2 model at ~111 km spatial resolution.</p> <ul> <li><strong>Tglobal-500hPa-anomaly_ERA5-reanalysis_1950-2020.txt</strong>: time series of annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from the ERA5 climate reanalysis (1950-2020). Anomalies are estimated relative to the reference period 1950-1990.</li> <li><strong>Tglobal-500hPa-anomaly_IND-EnsembleMean_1850-1949.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 pre-industrial reconstructions (1850-1949, 11 IND members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_HIST-EnsembleMean_1950-2014.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 historical reconstructions (1950-2014, 12 HIST members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP126-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP1-2.6 projections (2015-2299, 6 SSP126 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP245-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP2-4.5 projections (2015-2099, 6 SSP245 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP370-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP3-7.0 projections (2015-2099, 5 SSP370 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP585-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP5-8.5 projections (2015-2299, 8 SSP585 members). Anomalies are estimated relative to the reference period 1850-1949.</li> </ul> <p>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6 and high-end SSP5-8.5 warming scenario, as well as gridded, monthly downscaled SMB data sets from native CESM2 under pre-industrial (IND), historical (HIST), and short/long term climate projections (SSPs) are freely available from the authors upon request and without conditions (contact: b.p.y.noel@uu.nl). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention and total sublimation (surface and drifting snow) at 1 km horizontal resolution. </p> <p><strong>Abstract</strong>: Firn (compressed snow) covers approximately 90% of the Greenland ice sheet (GrIS) and currently retains about half of rain and meltwater through refreezing, reducing runoff and subsequent mass loss. The loss of firn could mark a tipping point for sustained GrIS mass loss, since decades to centuries of cold summers would be required to rebuild the firn buffer. Here we estimate the warming required for GrIS firn to reach peak refreezing, using 51 climate simulations statistically downscaled to 1 km resolution, that project the long-term firn layer evolution under multiple emission scenarios (1850–2300). We predict that refreezing stabilises under low warming scenarios, whereas under extreme warming, refreezing could peak and permanently decline starting in southwest Greenland by 2100, and further expanding GrIS-wide in the early 22<sup>nd</sup> century. After passing this peak, the GrIS contribution to global sea level rise would increase over twenty-fold compared to the last three decades.</p> <p> </p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 5. Future scenarios of sustainable use of wild species
<p>Schematic and adapted figures from Chapter 5 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Daily streamflow at 0.5 deg resolution generated using 0.22 deg runoff from the historical (1986-2005) and two future scenarios' (RCP 4.5 and 8.5, 2081-2100) simulations of CanRCM4 for its North American domain
<p>These data are provided in support of the following manuscript which is currently (July 2024) under revision for the <strong>Hydrology and Earth System Science</strong> journal. The details about how these data were generated can be found in this manuscript (or its final accepted version, hoping our manuscript will be accepted). <br><br><a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/</a><br><br>The effect of climate change on the simulated streamflow of six Canadian rivers based on the CanRCM4 regional climate model<br>Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha <br><br>The netcdf files provide here contain two variables.<br><br>1) streamflow, variable name is fout_land, units are m3/s<br>2) flow velocity, variale name is velocity, units are m/s<br><br><br></p>
Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés
<p>The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.</p>
The Future of Compulsory Schooling: Participant developed scenarios from a Modified Delphi Survey
<p>This paper describes results from a modified Delphi process that has been informed by research into anticipatory systems to design an approach that is responsive to a future world which is uncertain and in rapid change. We present five scenarios that describe preferred futures for the design of compulsory schooling and addresses the overall, counterfactual, research question, “What if compulsory schooling was a 21st century invention?” The scenarios have been developed by participants as the last round in the modified Delphi process, utilising a set of statements created in the earlier rounds. This scenario development round describes a novel use of the modified Delphi process in addition to the previous rounds that were used to determine more traditional Delphi results in terms of consensus and dissensus. </p> <p>The attachments provide an annexure with additional information about the Expert panel as well as a complete list of statements from the modified Delphi process.</p>
Predicting geographic distribution and habitat suitability of Opuntia streptacantha in paleoclimatic, current, and future scenarios in Mexico
<p>Geographical records. A total of 825 records (Figure 1), representing the natural distribution historically recognized for <em>O</em>. <em>streptacantha.</em></p> <p>Maps for past, current, and future models in QGIS format</p>
Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region
<p>This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.</p>
Prediction of potential suitable areas for Phoebe zhennan in future different climate scenarios
<p>This dataset includes sample collection data of existing <em>Phoebe zhennan</em> in China, as well as historical climate data and future climate data (with a resolution of 2.5 minutes and using the BCC-CSM2-MR GCM model) collected by Worldclim, along with geographical elevation data. These data are used to predict the potential distribution range of <em>Phoebe zhennan</em> in the future.</p>
Future land cover and land cover dynamic trajectories in China under anthropogenic and climate forcing in 8 SSP-RCP scenarios
<p>The projection of future land cover in 21st century in China and land cover dynamic trajectories in 8 SSP-RCP scenarios</p>
A predictive approach to assess urban biodiversity and plan for future development scenarios
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Data From: Conservation planning in an uncertain climate: identifying projects that remain valuable and feasible across future scenarios
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Plant invasion in Mediterranean Europe: current hotspots and future scenarios
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Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
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Scenario Input files for "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM"
<p>The GCAM scenario input files needed for the experiments described in the paper "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM".</p>
Hotspots of species loss do not vary across future climate scenarios in a drought-prone river basin
<p><b>Aim</b>: Climate change is expected to alter the distributions of species around the world, but estimates of species' outcomes vary widely among competing climate scenarios. Where should conservation resources be directed to maximize expected conservation benefits given future climate uncertainty? Here, we explore this question by quantifying variation in fish species' distributions across future climate scenarios.</p> <p><b>Location</b>: Red River basin, south-central United States.</p> <p><b>Methods</b>: We modeled historical and future stream fish distributions using a suite of environmental covariates derived from high-resolution hydrologic and climatic modeling of the basin. We quantified variation in outcomes for individual species across climate scenarios and across space, and identified hotspots of species loss by summing changes in probability of occurrence across species.</p> <p><b>Results</b>: Under all climate scenarios, we find that the distribution of most fish species in the Red River Basin will contract by 2050. However, the variability across climate scenarios was more than 10 times higher for some species than for others. Despite this uncertainty in outcomes for individual species, hotspots of species loss tended to occur in the same portions of the basin across all climate scenarios. We also find that the most common species are projected to experience the greatest range contractions, underscoring the need for directing conservation resources towards both common and rare species</p> <p><b>Main conclusions</b>: Our results suggest that while it may be difficult to predict which species will be most impacted by climate change, it may nevertheless be possible to identify spatial priorities for climate mitigation actions that are robust to future climate uncertainty. These findings are likely to be generalizable to other ecosystems around the world where future climate conditions follow prevailing historical patterns of key environmental covariates.</p>
Integrating stakeholders' perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania
<p>Rapid rates of land use and land cover change (LULCC) in eastern Africa and limited instances of genuinely equal partnerships involving scientists, communities and decision makers challenge the development of robust pathways toward future environmental and socioeconomic sustainability. We use a participatory modelling tool, Kesho, to assess the biophysical, socioeconomic, cultural and governance factors that influenced past (1959-1999) and present (2000-2018) LULCC in northern Tanzania and to simulate four scenarios of land cover change to the year 2030. Simulations of the scenarios used spatial modelling to integrate stakeholders' perceptions of future environmental change with social and environmental data on recent trends in LULCC. From stakeholders' perspectives, between 1959 and 2018, LULCC was influenced by climate variability, availability of natural resources, agriculture expansion, urbanization, tourism growth, and legislation governing land access and natural resource management. Among other socio-environmental-political LULCC drivers, the stakeholders envisioned that from 2018 to 2030 LULCC will largely be influenced by land health, natural and economic capital, and political will in implementing land use plans and policies. The projected scenarios suggest that by 2030 agricultural land will have expanded by 8-20% under different scenarios and herbaceous vegetation and forest land cover will be reduced by 2.5-5% and 10-19% respectively. Stakeholder discussions further identified desirable futures in 2030 as those with improved infrastructure, restored degraded landscapes, effective wildlife conservation, and better farming techniques. The undesirable futures in 2030 were those characterized by land degradation, poverty, and cultural loss. Insights from our work identify the implications of future LULCC scenarios on wildlife and cultural conservation and in meeting the Sustainable Development Goals (SDGs) and targets by 2030. The Kesho approach capitalizes on knowledge exchanges among diverse stakeholders, and in the process promotes social learning, provides a sense of ownership of outputs generated, democratizes scientific understanding, and improves the quality and relevance of the outputs.</p>
Data from: Comparing the impact of future cropland expansion on global biodiversity and carbon storage across models and scenarios
<p>Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land-use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socio-economic scenarios (SSP1-3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritise future protection of biodiverse and carbon-rich areas.</p>
FIGURE 1 in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina
FIGURE 1. General Niche-Environment System Factor Analysis (GNESFA) and Factor Analysis of the Niche, Taking the Environment as the Reference (FANTER) for Pristidactylus species. Left column: grey points show the distribution of the RUs (here the pixels) on the axes found by the analysis and black points correspond to the RUs used by the species. Right column: correlations between the environmental variables and the axes. References: P. achalensis A–B; P. nigroiugulus C–D.
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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.