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617 results for “Climate models”
Figure 1 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 1. Current potential geographic distribution of Costalimaita ferruginea determined by the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Figure 2 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 2. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the recent climate scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Climate matching models for the yellow starthistle rosette weevil (Coleoptera: Apionidae) for the western U.S.
<p>The CSV file contains weekly estimates of daily climate data for the western U.S. at 1 km2 resolution. The file was too large to upload to GitHub so must be downloaded separately and placed in the "/data/weekly/away" directory of hte "CEBA_climMatch" repository at: https://github.com/bbarker505/CEBA_climMatch.</p> <p>Column names are the variable plus week number, except for column 1, which is annual precipitation ("ppt_ann"). Variables include: precipitation ("ppt"), soil moisture ("sm"), maximum tempearture ("tmax"), and minimum temperature ("tmin"). See Barker et al. (2024) pre-print at XXX for more details about the climate data and how they were used.</p>
Intercomparison of Two Model Climates Simulated by a Unified Weather-Climate Model System (GRIST)
<p>Part I and other scripts.</p>
Linked collectors and determiners for: Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data.
Natural history specimen data linked to collectors and determiners held within, "Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa">https://bionomia.net/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa">https://gbif.org/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa</a>. Formatted as a Frictionless Data package.
Demographic inferences and climatic niche modeling shed light on the evolutionary history of the emblematic cold-adapted Apollo butterfly at regional scale
<p>Cold-adapted species escape climate warming by latitudinal and/or altitudinal range shifts, and currently occur in Southern Europe in isolated mountain ranges within 'sky islands.</p> <p>Here we studied the genetic structure of the Apollo butterfly in five such alpine islands (above 1000 m) in France, and infer its demographic history since the last interglacial, using single nucleotide polymorphisms (ddRADseq SNPs). The Auvergne and Alps populations show strong genetic differentiation but not alpine massifs, although separated by deep valleys. Combining three complementary demographic inference methods and species distribution models (SDMs) we show that the LIG period was highly defavorable for Apollo that probably survived in small population in the highest summits of Auvergne. The population shifted downslope and expanded eastward between LIG and LGM throughout the large climatically suitable Rhône valley between the glaciated summits of Auvergne and Alps. The Auvergne and Alps populations started diverging before the LGM but remained largely connected till the mid-Holocene. Population decline in Auvergne was more gradual but started before (~7 kya versus 800 ya), and was much stronger with current population size ten times lower than in the Alps. In the Alps, the low genetic structure and limited evidence for isolation by distance suggest a non-equilibrium metapopulation functioning. The core Apollo population experienced cycles of contraction-expansion with climate fluctuations with largely inter-connected populations over time according to a 'metapopulation-pulsar' functioning. This study demonstrates the power of combining demographic inferences and SDMs to determine past and future evolutionary trajectories of an endangered species at a regional scale.</p>
Projected shifts in deadwood bryophytes in Sweden, data used for species distribution modelling and for climate and forest scenario analysis
<p>Climate change and habitat loss are main threats to forest biodiversity. We fitted ensembles of single species distribution models for 23 deadwood-living bryophyte species in Sweden, based on species records from the Swedish Lifewatch website. This data set comprises the species and environmental data used for species distribution modelling, and coefficients of the fitted single species distribution models (GLM, Poisson point-process, MaxEnt).</p> <p>Based on the fitted species distribution models, we conducted simulations of future species distributions given realistic climate and forest projection scenarios at the national scale of Sweden. The data used for the scenario analysis are stored here.</p>
Interspecific trait variability and local soil conditions modulate grassland model community responses to climate
<p><span><span><span><span><span><span><span><span><span><span><span><b> </b>High elevation grasslands provide critical services in agriculture and ecosystem stabilization. However, these ecosystems face elevated risks of disturbance due to predicted soil and climate changes. We experimentally exposed model grassland communities, comprised of three species grown on either local or reference soil, to varied climatic environments along an elevational gradient in the European Alps, measuring the effects on species and community traits. Although species-specific biomass varied across soil and climate, species' proportional contributions to community-level biomass production remained consistent. Where species experienced low survivorship, species-specific biomass production was maintained through increased production of surviving individuals. Species responded directionally to climatic variation, segregating differentially by plant traits (including height, reproduction, biomass, survival, leaf dry weight, and leaf area) across all sites. Local soil variation drove stochastic trait responses across all species. This soil variability obscured climate-driven responses: we recorded no directional trait responses driven by climate. Our species-based approach contributes to our understanding of grassland community stabilization and suggests that these communities show some stability under climatic variation. </span></span></span></span></span></span></span></span></span></span></span></p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 6
<p>Future projections of precipitation by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 10
<p>Extra data of the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia" that did not fit in their respective deposits:</p> <p>Future projections of:</p> <p>Precipitation by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the UKESM1-0-LL GCM.</p> <p>2-meter maximum and minimum temperatures by the BM1 model forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p> <p>2-meter mean temperature by the BMlinear and BM1 models forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 4
<p>Future projections of 2-meter mean temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
Predictors for "Integrating High-Temporal-Resolution Climate Projections into Species Distribution Models"
<p>Predictors to train and predict SDMs</p>
A dataset to model Levantine landcover and land-use change triggered by climate change, the Arab Spring and COVID-19
<p>The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war, which has strained neighbouring countries like Jordan due to the influx of Syrian refugees. Jordan, in particular, has seen rapid population growth and significant changes in land use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This article uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.</p>
Data supporting "Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization"
<p>This data supports the analysis of the manuscript Asmus et al. 2023 "Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization".</p><p><strong>Simulation data</strong></p><p>The simulation data is created with REMO2020-iMOVE using the new irrigation parameterization. The results are saved as NetCDF files with monthly mean values and/or time series (hourly) of single variables for the analysis period. A list of the simulations can be found below.</p><p><strong>Observation data </strong></p><p>The observation data is published with the kind permission of ISPRA which hosts the SCIA database (www.scia.isprambiente.it). If you use this data, please make sure to include the following data source:<br>SCIA by ISPRA - Area Climatologia operativa - Via V. Brancati 48 00144 Roma. <br>We downloaded monthly mean values for the variables T2Max, T2Min and T2Mean from <a href="http://193.206.192.214/servertsutm/serietemporali100.php">http://193.206.192.214/servertsutm/serietemporali100.php </a>(last accessed on 14/10/2022) to verify the model results with and without irrigation parameterization. </p><p>By untarring the tarballs, the data structure is created that is necessary to execute the analysis scripts.</p><p>For more information or additional data please contact the author.</p><p> </p><p><strong>tarball | exp_number | description </strong></p><p>067015.tar.gz | 067015 | not irrigated</p><p>067016.tar.gz | 067016 | irrigated with "adaptive water application scheme"</p><p>067017.tar.gz | 067017 | irrigated with "adaptive water application scheme"</p><p>067019.tar.gz | 067019 | irrigated with "flexible time water application scheme"</p><p>067020.tar.gz | 067020 | irrigated with "prescribed water application scheme"</p><p>observation_scia.tar | - | observation data from SCIA </p><p> </p><p><strong>Data structure for simulation data</strong></p><p>\<exp_number><br> \monthly<br> \hourly<br> \var_series<br> \<variable><br><br>Note:<br>\067015 includes static variables<br> \irrifrac (irrigated fraction)<br> \bla (land-sea-mask)</p>
Data from: Testing models of speciation from genome sequences: divergence and asymmetric admixture in Island Southeast Asian Sus species during the Plio-Pleistocene climatic fluctuations
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Data for: Modeling climate-driven range shifts in populations of two bird species limited by habitat independent of climate
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Demographic inferences and climatic niche modeling shed light on the evolutionary history of the emblematic cold-adapted Apollo butterfly at regional scale
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Interspecific trait variability and local soil conditions modulate grassland model community responses to climate
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Data from: Molecular data and ecological niche modeling reveal population dynamics of widespread shrub Forsythia suspensa (Oleaceae) in China’s warm-temperate zone in response to climate change during the Pleistocene
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Projected shifts in deadwood bryophytes in Sweden, data used for species distribution modelling and for climate and forest scenario analysis
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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.