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511 results for “climate effects”
Habitat structure mediates vulnerability to climate change through its effects on thermoregulatory behavior
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Data from: Behavior and nutritional condition buffer a large-bodied endotherm against direct and indirect effects of climate
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Effects of biochar soil amendments on soil properties and plant recruitment in coastal climate change adaptation projects
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Urbanization mediates the effects of water quality and climate on a model aerial insectivorous bird
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Combined effects of global climate change and nutrient enrichment on the physiology of three temperate maerl species
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Biome-BGC: Modeling Effects of Disturbance and Climate (Thornton et al. 2002)
This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.1, to recreate the results of: Thornton, P.E., Law, B.E., Gholz, H.L., Clark, K.L., Falge, E., Ellsworth, D.S., Goldstein, A.H., Monson, R.K., Hollinger, D., Falk, M., Chen, J. and Sparks, J.P. 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113:185-222.Thornton et al., 2002 excerpt: AbstractThe effects of disturbance history, climate, and changes in atmospheric carbon dioxide (CO2) concentration and nitrogen deposition (Ndep) on carbon and water fluxes in seven North American evergreen forests are assessed using a coupled water, carbon, nitrogen model, canopy-scale flux observations, and descriptions of the vegetation type, management practices, and disturbance histories at each site. The effects of interannual climate variability, disturbance history, and vegetation ecophysiology on carbon and water fluxes and storage are integrated by the ecosystem process model Biome-BGC, with results compared to site biometric analyses and eddy covariance observations aggregated by month and year. The model produced good estimates of between-site variation in leaf area index, with mixed performance for between- and within-site variation in evapotranspiration. There is a model bias toward smaller annual carbon sinks at five sites, with a seasonal model bias toward smaller warm-season sink strength at all sites.
A comprehensive study to understand the effects of climate warming, simulated by soil transplant, on soil microbial community and its feedback responses
GEO Series GSE51592. Bacteria; uncultured bacterium. 54 samples. Type: Genome variation profiling by array.
RB920160The gas-phase oxidation of mixed organic films at the air-water interface testing for cloud climate effects in more representative proxies
<p>Binned neutron reflectivity data for titled experiment.</p>
Data from: Effects of livestock grazing on soil, plant functional diversity and ecological traits vary between regions with different climate in northeastern Iran
Understanding the responses of vegetation characteristics and soil properties to grazing in different precipitation regimes is useful for the management of rangelands, especially in the arid regions. In northeastern Iran, we studied the responses of vegetation to livestock grazing in three regions with different climate: arid, semi-arid and sub-humid. In each region, we selected 6-7 pairwise sampling areas of high versus low grazing intensity and six traits of the present species were recorded on 1 m2 plots - 5 grazed and 5 ungrazed in each area. The overall fertility was compared using the dissimilarity analysis, linear mixed-effect models were used to compare the individual fertility parameters, functional diversity indices and species traits between the plots with high and low grazing intensity and between the climatic regions. Both climate and grazing, as well as their interaction, affected fertility parameters, functional diversity indices and the representation of species traits. Grazing reduced functional evenness, height of the community, the representation of annuals, but increased the community leaf area. In the sub-humid region, grazing also reduced functional richness. Further, grazing decreased the share of annual species in the semi-arid region and seed mass in the arid region. Larger leaf area and seed mass, smaller height and lower share of annuals were associated with intensive grazing. Species with large LA and seed mass, lower height and perennials can be therefore presumed to tolerate trampling and benefit from high nutrient levels, associated with intensive grazing. By providing a detailed view on the impacts of overgrazing, this study highlights the importance of protection from grazing as an effective management tool for maintaining the pastoral ecosystems. In general, the composition of plant traits across the pastures of northeastern Iran was more affected by intensive grazing compared to climate.
Data for the preprint "Intra and inter-annual climatic conditions have stronger effect than grazing intensity on root growth of permanent grasslands.", by Catherine Picon-Cochard, Nathalie Vassal, Raphaël Martin, Damien Herfurth, Priscilla Note, Frédérique Louault
<p>VariableDetailyearyear of measurementdatedate of measurementsblockName of the blocktreatmentName of treatments: Ab: abandomnent; Cattle-: low stocking rate; Cattle+: high stocking rateIDPlot replicateRootGrowthRoot growth (g m-2 day-1)SoilTSoil temperature (°C)RSWCRelative soil water contentP-PETAridity index (mm)BNPPAnnual root production (g m-2 y-1)ANPPAnnual above-ground production (g m-2 y-1) RootShootRoot to shoot ratioNNINitrogen Nutrition Index (%)DiamRoot diameter (mm)SRLSpecific Root Length (m g-1)RTDRoot Tissue Density (g cm-3)SRASpecific Root Area (m2 g-1)% 0-0.1 mmPercentage of length in the class diameter 0-0.1mm; ; % 0.1-0.2 mmPercentage of length in the class diameter 0.1-0.2mm; ; % 0.2-0.3 mmPercentage of length in the class diameter 0.2-0.3 mm% > 0.3 mmPercentage of length in the class diameter > 0.3 mmCWM_HeightCommunity-weighted mean (CWM) of plant height (cm)CWM_SLACommunity-weighted mean (CWM) of Specific Leaf Area (cm2 g-1)CWM_LDMCCommunity-weighted mean (CWM) of leaf dry matter (g g-1)RootMassStock of root dry matter (g m-2)</p>
Data from: Hot tops, cold bottoms: synergistic climate warming and shielding effects increase carbon burial in lakes
In this article, we challenge the notion that global warming stimulates organic matter min-eralization and increases greenhouse gas emissions in lakes via direct temperature effects. We show that the interactive effects of warming and transparency loss due to eutrophica-tion or browning override effects of atmospheric warming alone. Thermal shielding ena-bles a longer and more stable stratification that results in bottom-water cooling, prolonged anoxia and enhanced carbon preservation in a large proportion of global lakes. These ef-fects are strongest in shallow lakes where an additional burial of 4.5 Tg C y-1 increases current global estimates by 9%. Despite more burial, the net global warming potential of lakes will increase via enhanced methane production, related to prolonged periods of an-oxia, rather than warming. Our understanding of how whole-lake carbon cycling responds to climate change needs revision, as the synergistic influence of warming and transparency loss has much broader ecosystem level functional consequences.
Climate model (CM2.6) and regional model (ACM) outputs used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key variables from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README.txt</em> for more details.</p>
Data supporting the publication of "Interactive effects of climate change and land-use change on mammal range retraction in Great Britain"
<p><strong>Table S1 (Species records) provided as a separate .xlsx file in Supporting Information. </strong>List of species included in the sample with corresponding attributes, number of records and rates of change over time.</p> <p>Column A (Scientific name): species’ accepted scientific name (n = 43 species).</p> <p>Column B (Common name): species’ common name in Great Britain (n = 43 names).</p> <p>Column C (Order): species’ taxonomical Order (n = 6 Orders).</p> <p>Column D (Family): species’ taxonomical Family (n = 14 Families).</p> <p>Column E (Guild): species’ sampling guild (n = 3 Guilds, either Bats, Midlarge, or Small</p> <p>Column F (Distribution): species’ distribution status in Great Britain (n = 3 Statuses, either Native, Naturalised, or Non-Native).</p> <p>Column G (Habitat): species’ habitat preference (n = 2 Habitats, either Terrestrial or Freshwater).</p> <p>Column H (Records): total number of records per species from 1960 to 2016 (average = 10,931).</p> <p>Column I (1960s): total number of records per species from 1960 to 1969 (average = 420).</p> <p>Column J (1970s): total number of records per species from 1970 to 1979 (average = 457).</p> <p>Column K (1980s): total number of records per species from 1980 to 1989 (average = 423).</p> <p>Column L (1990s): total number of records per species from 1990 to 1999 (average = 641).</p> <p>Column M (2000s): total number of records per species from 2000 to 2010 (average = 943).</p> <p>Column N (2010s): total number of records per species from 2011 to 2016 (average = 870).</p> <p>Column O (Hectads TP1): number of hectads where the species has been recorded in Time Period 1, from 1960 to 1992 (average = 892).</p> <p>Column P (Hectads TP2): number of hectads where the species has been recorded in Time Period 2, from 2000 to 2016 (average = 1,117).</p> <p>Column Q (Hectads Total): number of hectads where the species has been recorder from 1960 to 2016 (average = 1,315).</p> <p>Column R (Extirpation rate): species’ extirpation rate, calculated as the ratio of extirpations over the sum of extirpations and persistences (average = 0.24). The sum of extirpation and persistence rates is always equal to 1.</p> <p>Column S (Persistence rate): species’ persistence rate, calculated as the ratio of persistences over the sum of extirpations and persistences (average = 0.76). The sum of persistence and extirpation rates is always equal to 1.</p> <p>Column T (Occupancy TP1): species’ occupancy estimate in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.395).</p> <p>Column U (Occupancy TP2): species’ occupancy estimate in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.403).</p> <p>Column V (Occupancy change): change in the species’ occupancy estimates between Time Periods 1 and 2, as calculated in Frescalo (average = 0.076).</p> <p>Column W (Occupancy change slope): average yearly change in the species’ occupancy estimates from 1960 to 2016, as calculated in Frescalo (average = -0.001).</p> <p>Column X (Frequency TP1): adjusted frequency of occurrence in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.527).</p> <p>Column Y (Frequency TP2): adjusted frequency of occurrence in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.461).</p> <p>Column Z (Frequency change): change in the adjusted frequency of occurrence between Time Periods 1 and 2, as calculated in Frescalo (average = -0.066).</p>
Storyline data used in the paper "Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes"
<p>We provide the storyline data (in NetCDF format) used in the paper:”<strong>Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes”. </strong>The data is structured five .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climates. The data includes the five ensemble members (E1 to E5) and ensemble mean variables at winter season (DJF) in 2020/2021.</p> <p>Files of simulation ensemble member data are named as:</p> <p><span> </span>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_E{ensemble member}_DJF-{years}_dailymean.nc”</p> <p>Files of simulation ensemble-mean data are names as:</p> <p>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_DJF-{years}_ensmean.nc”</p> <p>Files of free-run (CMIP6) data are names as:</p> <p>“freerun_{variable name}_DJF-{year}_ensmean_31days-runmean_11years-ydaymean.nc”</p> <p>Variables includes:<span> </span></p> <ul> <li>Mean 2m Temperature (t2m)</li> </ul> <ul> <li>Downward net surface solar radiation (srads)</li> </ul> <ul> <li>Total cloud cover (aclcov)</li> <li>Downward solar radiation at clear sky (rsdscs)</li> <li>sea ice concentration (friac)</li> </ul> <p>Only for present climate:</p> <ul> <li>Zonal/meridional wind at 850hPa (u850,v850)</li> <li>500 hPa Geopotential Height (z500)</li> </ul> <p><span> </span></p>
Occurrence dataset for Sales & Parrott (2023): "The owls are coming: positive effects of climate change in Northern ecosystems depend on grassland protection"
<p>Records from virtual databases were downloaded using the function occ() from the R package spocc. All occurrences were thoroughly assessed for their completeness and reliability. Occurrence records located exactly over centroids of municipalities and political polygons were removed from the dataset, in addition to duplicates, incomplete coordinates, and those from museums, using the suite of clean_coordinates functions from R package CoordinateCleaner. Occurrences with spatial autocorrelation structures widely divergent from the rest of the dataset coupled with coordinates outside the known extent of occurrence of the species, taken from the International Union for the Conservation of Nature, were also removed. Maintaining the contemporaneity with the climatic dataset, we also removed records dated before the year 1970.<br> </p>
Psoriasis and Climate Therapy- Effect of Motivational Follow- up Calls on Clinical and Health Economic Parameters
ClinicalTrials.gov study NCT01352780. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Nursing Interventions to Mitigate Climate Change-related Effects on Symptom Severity and Physical Capacity
ClinicalTrials.gov study NCT07111143. IPD Sharing: NO. Countries: 1. Publications: 0.
Visual and Acoustic Effects of Human Thermal Comfort and Perception in a Micro-Climatically Steady Environment
ClinicalTrials.gov study NCT06985940. IPD Sharing: YES. Countries: 1. Publications: 0.
Effect Of Reusing the Operative Supplies On Cataract Surgery and Climate Change
ClinicalTrials.gov study NCT06102265. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effect of Climatic Factors on the Seasonal Fluctuation of Pulmonary Embolism
ClinicalTrials.gov study NCT04419194. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.