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176 results for “temperature variation”
Data and code for: Rachel A Reeb, J Mason Heberling, & Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. AoB PLANTS, plae058
<p>Data and Analysis Code for: </p> <p>Rachel A Reeb, J Mason Heberling, Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. <em>AoB PLANTS</em>, plae058. <a href="https://doi.org/10.1093/aobpla/plae058">https://doi.org/10.1093/aobpla/plae058</a></p> <p>Includes two R markdown files ("climate_data_extraction_code.rmd" is the script for data extraction and cleaning and "Data_Analysis_V2.rmd" is the analysis script), the associated datasets (in .csv format), and the metadata file ("readme.txt").</p>
Variation in Landsat 8-estimated land surface temperature with elevation from Spartina alterniflora marsh cross sections in the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site and Virginia Coast Reserve (VCR) LTER sites for winter and summer observations spanning 2013-2018
We estimated land surface temperature from top of atmosphere brightness temperature provided by Landsat 8's band 10 (a thermal band). We collected these measurements first for Spartina alterniflora dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected from pixels along three east-west cross sections that spanned a marsh edge to interior gradient. We extracted Landsat 8 data for all available cloud-free low tide dates during August, September, January and February during the years 2013 to 2018 and associated these with marsh elevation information from a 1 m^2 Digital Elevation Model (DEM), created by Haldik et al 2013, also available from the GCE data catalog (http://dx.doi.org/10.6073/pasta/4c5187ef603f70cd0a77ece24ef0fed9). We rescaled the DEM to the coarser spatial resolution of Landsat 8 (30 x 30 m) where the rescaled elevation was the mean of the constituent DEM values. Ultimately, we used generalized additive models to relate land surface temperature to elevation, while accounting for variation from spatial proximity, transect and sample date. These models revealed that land surface temperature was negatively related to marsh elevation on the marsh platform. We then confirmed the generality of this pattern by rederiving these same relationships for three cross sections of Spartina alterniflora marsh at Virginia Coast Reserve (VCR) LTER for winter sampling dates only (data also included here). DEM data for VCR LTER are available at https://www.vcrlter.virginia.edu/gisdata/LIDAR/USGS2015/. We used custom R functions that can convert Landsat 8 top of atmosphere brightness temperature or top of atmosphere radiance from band 10 data to land surface temperature, which are available at https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature. Currently, a provisional land surface temperature product is available on earthexplorer.usgs.gov, w
Replication data for Global variation in the preferred temperature for recreational outdoor activity
<p><strong>Description</strong></p> <p>This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2024): <a href="https://doi.org/10.1016/j.jeem.2024.103032">Global variation in the preferred temperature for recreational outdoor activity</a>, published in the Journal of Environmental Economics and Management.</p> <p>The main data on temperature and rainfall are from ERA5 reanalysis (Hersbach et al. 2018). Data on mobile phone activity are from the Google Mobility Reports. Data on GDP per capita are from the World Bank.</p> <p><strong>Acknowledgements</strong></p> <p>The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Bibliography</strong></p> <ul> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.adbb2d47</li> </ul>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
Biogeographic parallels in thermal tolerance and gene expression variation under temperature stress in a widespread bumble bee
<p>Global temperature changes have emphasized the need to understand how species adapt to thermal stress across their ranges. Genetic mechanisms may contribute to variation in thermal tolerance, providing evidence for how organisms adapt to local environments. We determine physiological thermal limits and characterize genome-wide transcriptional changes at these limits in bumble bees using laboratory-reared <em>Bombus vosnesenskii</em> workers. We analyze bees reared from latitudinal (35.7–45.7°N) and altitudinal (7–2154 m) extremes of the species' range to correlate thermal tolerance and gene expression among populations from different climates. We find that critical thermal minima (CT<sub>MIN</sub>) exhibit strong associations with local minimums at the location of queen origin, while critical thermal maximum (CT<sub>MAX</sub>) was invariant among populations. Concordant patterns are apparent in gene expression data, with regional differentiation following cold exposure, and expression shifts invariant among populations under high temperatures. Furthermore, we identify several modules of co-expressed genes that tightly correlate with critical thermal limits and temperature at the region of origin. Our results reveal that local adaptation in thermal limits and gene expression may facilitate cold tolerance across a species range, whereas high temperature responses are likely constrained, both of which may have implications for climate change responses of bumble bees.</p>
Measurements of diurnal variations of meteorological parameters and subsurface water temperature in Lake Kinneret, Israel, during the period (Sept. 6 – 20, 2015)
<p>The datasets include in-situ 10-minute measurements of subsurface water temperature taken at a depth of 20 cm, at a site A (32.82 <sup>o</sup>N; 35.60 <sup>o</sup>E) located near the center of Lake Kinneret, during the period (Sept. 6 – 20, 2015). Lake Kinneret is located in Israel. The Campbell 107-L temperature probe was used (specifications are available online at <a href="https://www.campbellsci.asia/107-l">https://www.campbellsci.asia/107-l</a> ). The datasets also include meteorological measurements taken at the same site, such as air temperature, relative humidity, wind speed, upwelling and downwelling longwave (4.5 - 42 µm) radiation. The above meteorological measurements were taken at a height of 2 - 3 m above the lake surface. Measurements at the site A are associated with the Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research (<a href="https://www.ocean.org.il/kinneret-limnological-laboratory-center/">https://www.ocean.org.il/kinneret-limnological-laboratory-center/</a> ).</p> <p><em>Data format: xlsx file. The file includes water temperature (WT, <sup>o</sup>C), wind speed (WS, m/s), air temperature (Tair, <sup>o</sup>C), relative humidity (RH, %), upwelling longwave radiation (Upwelling LW, W/m<sup>2</sup>) and downwelling longwave radiation (Downwelling LW, W/m<sup>2</sup>).</em></p> <p>Files (140.40 KB)</p>
To rise to temperature: Variation in temperature effects within and among populations
<p>Temperature drives physiological changes on three timescales: acute, acclimatory and evolutionary. Acutely, passive temperature effects often dictate an expected two-fold increase in metabolic processes for every 10°C increase (Q<sub>10</sub>). Yet for acclimatory or evolutionary time scales, selection often mitigates these acute effects. This selection also should concomitantly reduce interindividual variation. However, the individual variation in physiological trait thermal sensitivity is not well characterized. Here we quantified physiological responses to a 16°C temperature difference across nine thermally distinct <em>Fundulus heteroclitus </em>populations<em>.</em> Traits included whole animal metabolism (WAM), critical thermal maximum (CTmax), and substrate-specific cardiac metabolism measured in approximately 350 individuals. These traits exhibit high variation among both individuals and populations that depends on acclimation temperature. Thermal sensitivity or Q<sub>10</sub> variation is unexpected and ranges from 0.6 to 5.4 for WAM. Thus, with a 16°C temperature increase, some individuals have the same or lower metabolic rates while others have metabolic rates almost seven-fold higher (Q<sub>10</sub> = 5.4). Furthermore, a significant portion of this variation is related to habitat temperature, such that warmer populations have a significantly lower Q<sub>10</sub> for WAM and CTmax than colder populations. These data support a novel hypothesis: individual variation in thermal sensitivity reflects different physiological strategies to respond to environmental temperature variation and provides the potential for many different adaptive responses to temperature.</p>
Additive genetic variation, but not temperature, influences warning signal expression in Amata nigriceps moths (Lepidoptera: Arctiinae)
<p>Many aposematic species show variation in their colour patterns even though selection by predators is expected to stabilise warning signals towards a common phenotype. Warning signal variability can be explained by trade-offs with other functions of colouration, such as thermoregulation, that may constrain warning signal expression by favouring darker individuals. Here, we investigated the effect of temperature on warning signal expression in aposematic <em>Amata nigriceps</em> moths that vary in their black and orange wing patterns. We sampled moths from two flight seasons that differed in the environmental temperatures and also reared different families under controlled conditions at three different temperatures. Against our prediction that lower developmental temperatures would reduce the warning signal size of the adult moths, we found no effect of temperature on warning signal expression in either wild or laboratory-reared moths. Instead, we found sex- and population-level differences in wing patterns. Our rearing experiment indicated that ~70% of the variability in the trait is genetic but understanding what signalling and non-signalling functions of wing colouration maintain the genetic variation requires further work. Our results emphasise the importance of considering both genetic and plastic components of warning signal expression when studying intraspecific variation in aposematic species.</p>
Survival, wet weight and muscle cellular stress responses of Palaemon varians shrimps exposed to combined temperature and salinity variations
<p>Shrimps were exposed to a full factorial experiment combining different temperatures (20, 23 and 26 ºC) and salinities (20, 40). Cellular stress response biomarkers were assessed in the shrimps muscle at several time-points, namely the 7th, 14th, 21st and 28th days of exposure. Wet weight (as proxy for growth) and survival were also assessed during the experiment. These datasets refer to the publication of an article in STOTEN (<a href="https://doi.org/10.1016/j.scitotenv.2022.158732">https://doi.org/10.1016/j.scitotenv.2022.158732</a>).</p> <p>Note: the biomarker dataset contained 3.2% of missing values, which were replaced by group averages for the purpose of the statistical analyses in the article.</p>
Figure 1 in Temperature variation in nests of Paleosuchus palpebrosus (Crocodylia: Alligatoridae) near the southern edge of the species´ range, Brazil
Figure 1. Egg-chamber temperature (upper filled circles), air temperature in the shade near the nest (triangle), and rainfall (lower filled circles). Nest 1 was from the ESSA in 2008; Nest 2 from the Serra do Urucum in 2010; Nest 3 from the Serra do Urucum in 2010; and Nest 4 from the Serra do Urucum in 2011.
Figure 3. Daily temperature variation for a in On the oviposition of Homonota aff. darwinii in the Puna region of the Central Andes of Argentina
Figure 3. Daily temperature variation for a potential Homonota aff. darwinii nesting site. Average and standard errors are indicated.
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part06
<p>This dataset is part six of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part05
<p>This dataset is part five of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part02
<p>This dataset is part two of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part03
<p>This dataset is part three of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part04
<p>This dataset is part one of the? FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-Part01
<p>This dataset is part one of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
Figure 2 in Vocalizations of the Brazilian torrent frog Hylodes heyeri (Anura: Hylodidae): Repertoire and influence of air temperature on advertisement call variation
Figure 2. Territorial call of Hylodes heyeri from the Municipality of Morretes, Parana´, Brazil. Recorded on 11 January 2002, at 22.4°C. (A) Power spectrum; (B) spectrogram; (C) oscillogram.
Figure 3 in Vocalizations of the Brazilian torrent frog Hylodes heyeri (Anura: Hylodidae): Repertoire and influence of air temperature on advertisement call variation
Figure 3. Mean number of advertisement calls (bars) emitted by males of Hylodes heyeri during 5 min of monitoring each hour, and air temperature (line).
Fig. 7. Daily mean temperature variation recorded from 28 December 2004 in A New Genus of Microteiid Lizard from the Caparaó Mountains, Southeastern Brazil, with a Discussion of Relationships among Gymnophthalminae (Squamata)
Fig. 7. Daily mean temperature variation recorded from 28 December 2004 to 17 November 2005 by a data logger left at 2400 m elevation at Parque Nacional do Caparaó, near the trail leading to the Pico da Bandeira.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.