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284 results for “Future climate”
Data for: Forecasting shifts in habitat suitability of three marine predators suggests a rapid decline in inter-specific overlap under future climate change
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Data from: Estimating potential global sources and secondary spread of freshwater invasions under historical and future climates
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Data from: Current distributions and future climate‐driven changes in diatoms, insects and fish in U.S. streams
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Downscaled climate projections of future mesopelagic habitat in the California Current Ecosystem
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Data from: Local adaptation and future climate vulnerability in a wild rodent
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Climate change threatens the future of rainforest ringtail possums by 2050
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Data from: Warm-loving species perform well under limiting resources: Trait combinations for future climate
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Data from: More future synergies and less trade‐offs between forest ecosystem services with natural climate solutions instead of bioeconomy solutions
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Changes in species composition mediate direct effects of climate change on future fire regimes of boreal forests in northeastern China
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Data from: Prediction of Platycodon grandiflorus distribution in China using MaxEnt model concerning current and future climate change
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How useful is genomic data for predicting maladaptation to future climate?
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Data from: Conservation of Chinese Theaceae species under future climate and land use changes
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Data from: Future climatically suitable areas for bats in South Asia
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Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
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Present status, future trends, and control strategies of invasive alien plants in China affected by human activities and climate change
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Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
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High-resolution future climate data for species distribution models in Europe
<p><strong>Description</strong></p> <p>This dataset contains a set of 13 climatological variables (<code>Variable</code>, <code>VariableName</code>) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (<code>ClimatePeriod</code>) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. <a href="https://worldclim.org/data/bioclim.html">WorldClim</a>, <a href="http://chelsa-climate.org/bioclim/">CHELSA</a>) that have been specifically developed for species distribution modelling and ecological applications.</p> <p>The climatological data correspond to 35-year (<code>Startyear_Endyear</code> = <code>1971_2005</code>) and 30-year (<code>Startyear_Endyear</code> = <code>2041_2070</code>) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the <a href="https://www.ipcc.ch/">Intergovernmental Panel on Climate Change (IPCC)</a> are used (<code>ClimatePeriod</code> = <code>rcp26</code>, <code>rcp45</code>, <code>rcp85</code>).</p> <p>The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below.</p> <p>The source data for the climate layers were assembled from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a> (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied:</p> <ol> <li><strong>Processing</strong> (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (<code>ClimatePeriod</code> = evaluation, historical, rcp) for the climatological variables.</li> <li><strong>Interpolation</strong> of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial <a href="http://chelsa-climate.org/">CHELSA</a> (Karger et al., 2017) reference grid (see files <code>lat_1km.csv</code> and <code>lon_1km.csv</code>).</li> <li><strong>Calculate differences</strong> between the 1x1km-interpolated variables (<code>Variable</code> = only for var[1-9]) from the evaluation model experiments (or <code>ClimatePeriod</code>) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (<code>ClimatePeriod</code>).</li> </ol> <p>The dimensions of the 1x1km grid (excl. the first row and column):</p> <ul> <li>y-dimension = number of columns = 6071</li> <li>x-dimension = number of rows = 13147</li> </ul> <p>The longitudes and latitudes of respectively the southwest and northeast corner of the grid are:</p> <ul> <li>longitude -44.592; latitude 21.991 (southwest corner)</li> <li>longitude 64.967; latitude 72.583 (northeast corner)</li> </ul> <p>The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the <a href="https://osf.io/7dpgr/">Tracking Invasive Alien Species (TrIAS)</a> project.</p> <p><strong>Variables</strong></p> <ul> <li><strong>Variable</strong> (VariableName): Unit</li> <li><strong>var1</strong> (AnnualMeanTemperature): °C</li> <li><strong>var2</strong> (AnnualAmountPrecipitation): mm year<sup>-1</sup></li> <li><strong>var3</strong> (AnnualVariationPrecipitation): coefficient of variation</li> <li><strong>var4</strong> (AnnualVariationTemperature): stdev</li> <li><strong>var5</strong> (MaximumTemperatureWarmestMonth): °C</li> <li><strong>var6</strong> (MinimumTemperatureColdestMonth): °C</li> <li><strong>var7</strong> (TemperatureAnnualRange): °C</li> <li><strong>var8</strong> (PrecipitationWettestMonth): mm</li> <li><strong>var9</strong> (PrecipitationDriestMonth): mm</li> <li><strong>var10</strong> (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days</li> <li><strong>var11</strong> (AnnualMeanPotentialEvapotranspiration): mm day<sup>-1</sup></li> <li><strong>var12</strong> (AnnualMeanSolarRadiation): W m<sup>-2</sup></li> <li><strong>var13</strong> (AnnualVariationSolarRadiation): stdev</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>varX_VariableName_ClimatePeriod_Startyear_Endyear.csv</strong>: climatological data layers for the 13 variables listed above</li> <li><strong>lon_1km.csv</strong>: longitudes for the 1x1km grid</li> <li><strong>lat_1km.csv</strong>: latitudes for the 1x1km grid</li> </ul>
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>
Effects of future climate on coral-coral competition
As carbon dioxide (CO 2 ) levels increase, coral reefs and other marine systems will be affected by the joint stressors of ocean acidification (OA) and warming. The effects of these two stressors on coral physiology are relatively well studied, but their impact on biotic interactions between corals are poorly understood. While coral-coral interactions are less common on modern reefs, it is important to document the nature of these interactions to better inform restoration strategies in the face of climate change. Using a mesocosm study, we evaluated whether the combined effects of ocean acidification and warming alter the competitive interactions between the common coral Porites astreoides and two other mounding corals ( Montastraea cavernosa or Orbicella faveolata ) common in the Caribbean. After 7 days of direct contact, P. astreoides suppressed the photosynthetic potential of M. cavernosa by 100% in areas of contact under both present (~28.5°C and ~400 μatm p CO 2 ) and predicted future (~30.0°C and ~1000 μatm p CO 2 ) conditions. In contrast, under present conditions M. cavernosa reduced the photosynthetic potential of P. astreoides by only 38% in areas of contact, while under future conditions reduction was 100%. A similar pattern occurred between P. astreoides and O. faveolata at day 7 post contact, but by day 14, each coral had reduced the photosynthetic potential of the other by 100% at the point of contact, and O. faveolata was generating larger lesions on P. astreoides than the reverse. In the absence of competition, OA and warming did not affect the photosynthetic potential of any coral. These results suggest that OA and warming can alter the severity of initial coral-coral interactions, with potential cascading effects due to corals serving as foundation species on coral reefs.
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>
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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.