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127 results for “Temperature sensitivity”
Temperature Sensitivity of Microbial Activity in Three Forest Soils at Harvard Forest 2010-2011
We evaluated possible seasonal variation in the temperature sensitivity of microbially mediated soil fluxes related to decomposition (net N mineralization, net nitrification, proteolysis, the maximum velocity (Vmax) of proteolysis, microbial respiration, and the Vmax of four soil exo-enzymes) across forests dominated by eastern hemlock (Tsuga canadensis), white ash (Fraxinus americana), and red oak (Quercus rubra) in Harvard Forest. We asked two simple questions: (1) do temperature sensitivities vary across forest types or different steps of the decomposition process, and (2) do temperature sensitivities display plasticity on a seasonal time frame?We observed substantial variation in temperature sensitivities (Q10 and R10 values) across the different fluxes and forest types. The ash soils exhibited the strongest temperature sensitivities and the mineral-N fluxes exhibited higher temperature sensitivities relative to the proteolytic fluxes or microbial respiration. The Vmax of soil exo-enzymes varied considerably in an interactive manner across forests and time, and the response of some enzymes was consistent with the thermal plasticity. The enzymatic kinetic properties Vmax and Km (half-saturation constant) were strongly correlated with slopes that differed across enzymes, reflecting an enzyme-specific tradeoff between maximum catalytic rate and substrate-binding efficiency. Generally, Q10 values were largely constant, but R10 values varied in a manner consistent with distinct seasonal plasticity. There was a consistent seasonal shift in R10 values coincident with snowmelt, suggesting that the time following snowmelt is a particularly interesting and dynamic period of microbial activity in these temperate forests.
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>
MITgcm Dataset for paper: Sensitivity analysis of a data-driven model of ocean temperature
<p>MITgcm dataset used in paper, Sensitivity analysis of a data-driven model of ocean temperature, made available here. The dataset comes from running a sector config of the MITgcm model, briefly described in the paper. This dataset is used to train the regression model described in the paper.</p> <p>Updated to include ncra_cat_tave.nc file which was accidentally missed on first version.</p>
CO2-dependence of Longwave Clear-sky Feedback is sensitive to Temperature [Dataset]
<p>These data are simulated from PyRads (https://github.com/danielkoll/PyRAD) and are used to plot figures in our study.</p>
Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes
<p>Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".</p>
Room-Temperature Phosphorescence of Cocrystals of Aromatic Bisimides and Triplet Sensitizer Pt(acac)2
<p>Additional data to report <a href="https://doi.org/10.1002/adfm.202412843">https://doi.org/10.1002/adfm.202412843</a>:</p> <p>Four sterically shielded aromatic bisimides are synthesized and their ability to form cocrystals with the triplet sensitizer platinum(II) acetylacetonate (Pt(acac)<sub>2</sub>) is investigated. Single crystals of the individual electron-deficient bisimides are studied first, two showing bright blue fluorescence with quantum yields up to 28%, while the other two barely emit at all. After successful cocrystallization of all four aromatic bisimides with the electron-rich Pt(II) complex,<sub> </sub>two different stoichiometries, either 1:1 or 1:2, are found in the crystalline solid state, whose absorbance and photoluminescence exhibit distinct bathochromic shifts. While two naphthalene bisimides show strong and long-lived room-temperature phosphorescence (RTP) with quantum yield up to over 50%, the other two aromatic bisimides show only minor photoluminescence at RT, but a bright low-temperature phosphorescence (LTP) at 80 K. Theoretical calculations reveal the importance of changing the magnetic quantum number <em>m</em> of the involved <em>d</em>-orbitals at the platinum center as well as the alignment of the Pt(II) center above an aromatic C-C bond for an efficient intersystem crossing in these systems.</p>
Sensitivities to temperature and evaporative demand in wheat relatives
<p>Data used in the paper "Sensitivities to temperature and evaporative demand in wheat relatives" accepted by Journal of Experimental Botany.<br> There are three tables:</p> <ul> <li>Metadata: genotype information</li> <li>Definition: Definition, symbol and unit of the variables used</li> <li>Genotype variables: set of variables used for analyses, figures and tables in the article. </li> </ul>
Model Output and Figure Scripts for: "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records"
<p>New climate model output and figure scripts for the paper "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records".</p>
Temperature sensitivity of mountain glaciers
<p><strong>Distributed summer air temperatures across mountain glaciers in the south-east Tibetan Plateau: temperature sensitivity and </strong><strong>comparison with existing glacier datasets</strong></p> <p>Thomas E. Shaw<sup>1</sup>, Wei Yang<sup>2,3</sup>, Álvaro Ayala<sup>4</sup>, Claudio Bravo<sup>5</sup>, Chuanxi Zhao<sup>2</sup>, Francesca Pellicciotti<sup>1,6</sup></p> <p> </p> <p><sup>1</sup> Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland</p> <p><sup>2</sup> Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing, China</p> <p><sup>3</sup> CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China</p> <p><sup>4 </sup>Centre for Advanced Studies in Arid Zones (CEAZA), La Serena, Chile</p> <p><sup>5</sup> School of Geography, University of Leeds, Leeds, UK</p> <p><sup>6 </sup>Department of Geography, Northumbria University, Newcastle, UK</p> <p><em>Corresponding author: Thomas E. Shaw (</em><a href="mailto:thomas.shaw@wsl.ch"><em>thomas.shaw@wsl.ch</em></a><em>)</em></p> <p>Keywords: Air Temperature, Glaciers, Tibetan Plateau, Temperature Sensitivity</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>Dataset provided:</strong></p> <p>'<strong>Climatic_Sensitivity_Mountain_Glaciers.mat'</strong> = Matlab file with data structures for each glacier site. The following sites are:</p> <p>% Parameter set calculated from data on Parlung Glaciers (this study)<br> % Parameter set from Shea and Moore (2010) for Rockies- Canada (Published parameters)<br> % Parameter set from Carturan et al. (2015) for Ortles Cevedale, Italy (k1/k2 data from author)<br> % Parameter set from Shaw et al. (2017) on Tsanteleina Glacier, Italy (Reassesed parameters)<br> % Parameter set calculated from data of Bravo et al., (2019) on South Patagonian Icefield (SPI), Chile<br> % Parameter set calculated from data of Bravo et al., (2017) on Universidad Glacier, Chile<br> % Parameter set calculated from data of Ayala et al., (2015) on Arolla Glacier, Switzerland<br> % Parameter set calculated from data of Ayala et al., (2015) on JuncalNorte Glacier, Chile<br> % Parameter set calculated from data of Troxler et al., (2020) on McCall Glacier, Alaska<br> % Parameter set calculated from data of Greuell and Böhm (1998) on Pasterze Glacier, Austria<br> % Parameter set calculated from data of Rets et al., (2019) on Djankuat Glacier, Russia<br> % Parameter set calculated from data of Pradhananga et al., (2020 In prep) on Peyto Glacier, Canada</p> <p><strong>Variables include:</strong></p> <p>'Name' = name of individual observation station (AWS or Temp/RH 'T-logger')<br> 'Elevation' = Elevation (m a.s.l.) of given observation station<br> 'Flowline' = The distance along the glacier flowline from an upslope summit or crest (m)<br> 'k1' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature below the onset of katabatic onset (following Shea and Moore, 2010)<br> 'k2' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature above the onset of katabatic onset (following Shea and Moore, 2010)<br> 'Tst' = The T* parameter that defines the threshold (off-glacier) temperature for katabatic conditions parameterised following Carturan et al. (2015)<br> 'T1' = The equivalent on-glacier threshold temeprature derived from k1 and Tst<br> 'DataSource' = The citation readout</p> <p><br> <strong>Cited literature</strong><br> Ayala, A., Pellicciotti, F., & Shea, J. (2015). Modeling 2m air temperatures over mountain glaciers: Exploring the influence of katabatic cooling and external warming. Journal of Geophysical Research: Atmospheres, 120, 1–19. https://doi.org/10.1002/2015JD023137.</p> <p><br> Bravo, C., Quincey, D. J., Ross, A. N., Rivera, A., Brock, B. W., Miles, E., & Silva, A. (2019). Air Temperature Characteristics , Distribution , and Impact on Modeled Ablation for the South Patagonia Ice field. Journal of Geophysical Research : Atmospheres, 124, 907–925. https://doi.org/10.1029/2018JD028857</p> <p><br> Bravo, C., Lorlaux, T., Rivera, A., & Brock, B. W. (2017). Assessing glacier melt contribution to streamflow at Universidad Glacier, central Andes of Chile. Hydrology and Earth System Sciences, 21, 3249–3266. https://doi.org/10.5194/hess-21-3249-2017</p> <p><br> Carturan, L., Cazorzi, F., De Blasi, F., & Dalla Fontana, G. (2015). Air temperature variability over three glaciers in the Ortles–Cevedale (Italian Alps): effects of glacier fragmentation, comparison of calculation methods, and impacts on mass balance modeling. The Cryosphere, 9(3), 1129–1146. https://doi.org/10.5194/tc-9-1129-2015</p> <p><br> Greuell, W., & Böhm, R. (1998). 2 m temperatures along melting mid-latitude glaciers , and implications for the sensitivity of the mass balance to variations in temperature. Journal of Glaciology, 44(146), 9–20.</p> <p><br> Rets, E. P., Popovnin, V. V, Toropov, P. A., Smirnov, A. M., Tokarev, I. V, Chizhova, J. N., … Kireeva, M. B. (2019). Djankuat glacier station in the North Caucasus , Russia : a database of glaciological , hydrological , and meteorological observations and stable isotope sampling results during 2007 – 2017. Earth System Science Data, ||, 1463–1481. https://doi.org/https://doi.org/10.5194/essd-11-1463-2019</p> <p><br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65</p> <p><br> Shea, J. M., & Moore, R. D. (2010). Prediction of spatially distributed regional-scale fields of air temperature and vapor pressure over mountain glaciers. Journal of Geophysical Research, 115(D23), D23107. https://doi.org/10.1029/2010JD014351</p> <p><br> Troxler, P., Ayala, Á., Shaw, T. E., Nolan, M., Brock, B. W., & Pellicciotti, F. (2020). Modelling spatial patterns of near-surface air temperature over a decade of melt seasons on McCall Glacier , Alaska. Journal of Glaciology, 1–15. https://doi.org/https://doi.org/10.1017/jog.2020.12</p>
Data from: Soil incubation methods lead to large differences in inferred methane production temperature sensitivity
<p>Quantifying the temperature sensitivity of methane (CH4) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q10 values, with a large portion of this variation remaining unexplained. Here we applied observations in Stordalen Mire, a thawing permafrost peatland, and a well-tested process-rich model, ecosys, to interpret incubation observations and investigate controls on inferred CH4 production temperature sensitivity. We developed a Field-Storage-Incubation (FSI) modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season,refrigerated storage, and the laboratory incubation process, followed by model evaluation. We found that CH4 production rates during incubation are regulated by seasonally-dependent substrate availability and active microbial biomass of key microbial functional groups. Applying a model sensitivity analysis, we found that storage duration, storage temperature, and field sampling time significantly affect CH4 production during incubation. Shorter storage duration and lower storage temperature led to larger CH4 production during incubation. Our findings revealed a wide range of inferred Q10 values (1.2 to 3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. Q10 of CH4 production is controlled by many interacting biological, biochemical, and physical processes, which cause the aggregated Q10 values to differ from those of the component processes. Terrestrial ecosystem models that use a constant Q10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.</p> <p>This dataset includes all the data used to plot figures in the manuscript, including Fig.2-6 and Fig.S2-S11. Each sheet in the aggregated spreadsheet corresponds to one figure in the manuscript. The simulation experiment setup and analyses are thoroughly described in the manuscript. Here we provide a brief summary. The data includes field greenhouse gas observations and laboratory incubation measurements of CH4 production in Stordalen Mire. These datasets were already published and references were provided in the manuscript and spreadsheet. The data also includes simulation data, including modeled cumulative CH4 production, CH4 production rates, substrate concentrations, and active microbial biomass under different incubation temperature, sampling time and storage conditions. This data also includes inferred temperature sensitivity of CH4 production as Q10 values under different scenarios. Please refer to the manuscript for more detailed information.</p> <p>Please see "Related works" at the bottom of this page and the "References" tab in the spreadsheet for a full list of source datasets and associated publications.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). Incubation and field observation data were collected under the IsoGenie Project, which was funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p>
Thermal adaptation in Aedes aegypti does not constrain temperature-sensitive growth of bacteria or dengue virus
<p>Data set and R script used for the following manuscript : </p> <p><strong>Thermal adaptation in <em>Aedes aegypti</em> does not constrain temperature-sensitive growth of bacteria or dengue virus</strong></p> <p><span lang="EN-US">Alida Kropf<sup>1*#</sup>, Stéphanie Dabo<sup>2</sup>, Marine Amann<sup>3</sup>, Louis Lambrechts<sup>2</sup>, Jacob C Koella<sup>1</sup></span></p> <p><span lang="EN-US">PROCEEDINGS OF THE ROYAL SOCIETY B THE ROYAL SOCIETY B BIOLOGICAL SCIENCES</span></p> <p><strong><em><span lang="IT-CH">DOI: 10.1098/rspb.2025-0832.R1 </span></em></strong></p>
Developmental temperature alters the thermal sensitivity of courtship activity and signal-preference relationships, but not mating rates
<p><span>Mating behaviours are susceptible to novel or stressful thermal conditions, particularly for ectothermic organisms. One way to deal with changes in thermal conditions is to exhibit developmental plasticity, whereby the thermal sensitivity of mating behaviours depend on developmental conditions. </span></p> <p><span>We test how developmental temperature affects the thermal sensitivity of courtship behaviour and mating rates, as well as mating signal and preference coupling. </span></p> <p><span>We rear treehoppers under two temperature regimes and then test how a range of ambient temperature affects behaviours involved in the coordination of mating. We also test for sex-specific thermal sensitivity and developmental plasticity.</span></p> <p><span>We find developmental plasticity in the thermal sensitivity of courtship behaviour and mating signals for males. However, we found no developmental plasticity in females, and no change in the thermal sensitivity of mating rates.</span></p> <p><span>We discuss the implications of signal-preference decoupling for sexual selection, how reversible acclimation may drive sex-specific results, and the potential for mismatches between developmental and mating thermal environments under future climate change predictions. </span><span><br></span></p>
Universal temperature sensitivity of denitrification nitrogen losses in forest soils
<p><span>Soil nitrous oxide (N<sub>2</sub>O) and dinitrogen (N<sub>2</sub>) emissions from denitrification are crucial to the global nitrogen (N) cycle. </span><span>However, the temperature sensitivities of gaseous N losses in forest soils are poorly understood, limiting our ability to predict N cycling responses to global warming. We quantified temperature sensitivities (Q10) of denitrification-derived potential N<sub>2</sub>O and N2 production ex-situ for 18 forest soils across China.</span><span> N<sub>2</sub>O</span><span> and N<sub>2</sub> production rates increased exponentially with temperature, showing large variation among soils. By contrast, the Q10 values for N<sub>2</sub>O (</span><span>2.1±0.5</span><span>) and N<sub>2 </sub>(</span><span>2.6±0.6</span><span>) were surprisingly similar across soils. N<sub>2</sub> was more sensitive to temperature than N<sub>2</sub>O, suggesting warming could promote complete denitrification. The Q10 values for denitrification (</span><span>2.3±0.5)</span><span> were similar to those reported for aquatic sediments. Collectively, our results indicate a universal temperature sensitivity of gaseous N losses from denitrification, which will facilitate modelling N losses in response to warming on the global scale<a>.</a></span><span> </span></p>
Dataset for Smaller_Sensitivity_of_Precipitation_to_Surface_Temperature_under_Massive_Atmosphere
<p>This file is the dataset for "Smaller Sensitivity of Precipitation to Surface Temperature under Massive Atmosphere".</p> <p>Uploaded as 5 groups (1-D radiative transfer model, GCM fixsst simulations, GCM aqua planet simulations, GCM present continent simulations, and cloud-resolving simulations), The data is time average of balanced state.</p> <p>For our article, GCM fixsst simulations are designed for group A, H and sensitivity test 1; GCM aqua planet simulations are designed for group B, C, D, and sensitivity test 2; GCM present continent simulations for group E, F, G; and RCE simulations for sensitivity test 4.</p>
Dataset and codes for: Partitioning the apparent temperature sensitivity between autotrophic and heterotrophic protists
<p>Conventional <span>analyses suggest the metabolism of heterotrophs is thermally more sensitive than that of autotrophs, implying that warming leads to pronounced trophodynamic imbalances. However, these analyses inappropriately combine within- and across-taxa trends. We present a novel mathematic framework to separate these, revealing that the higher temperature sensitivity of heterotrophs is mainly caused by within-taxa responses which account for 92% of the difference between autotrophic and heterotrophic protists. This dataset contains both the datasets and R codes of per capita growth rates of autotrophic and heterotrophic protists as well as heterotrophic bacteria and insects.</span></p>
The effect of a temperature‐sensitive prophage on the evolution of virulence in an opportunistic bacterial pathogen
<p>Viruses are key actors of ecosystems and have major impacts on global biogeochemical cycles. Prophages deserve particular attention as they are ubiquitous in bacterial genomes and can enter a lytic cycle when triggered by environmental conditions. We explored how temperature affects the interactions between prophages and other biological levels by using an opportunistic pathogen, the bacterium <em>Serratia marcescens</em>, that harbours several prophages and that had undergone an evolution experiment under several temperature regimes. We found that the release of one of the prophages was temperature-sensitive and malleable to evolutionary changes. We further discovered that the virulence of the bacterium in an insect model also evolved and was positively correlated with phage release rates. We determined through analysis of genetic and epigenetic data that changes in the bacterial outer cell wall structure possibly explain this phenomenon. We hypothezise that the temperature-dependent phage release rate acted as a selection pressure on <em>S. marcescens</em> and that it resulted in modified bacterial virulence in the insect host. Our study system illustrates how viruses can mediate the influence of abiotic environmental changes to other biological levels and thus be involved in ecosystem feedback loops.</p>
Sensitivity analysis of ice wedge temperature to polygonal microtopography
<p>This repository contains input files, model output, and postprocessing scripts from a sensitivity analysis of ice wedge temperature to polygonal rim height and trough depth. Simulations are constructed in Amanzi-ATS (https://github.com/amanzi/ats), v. 0.86. See the included README file for instructions using this content.</p> <p>This analysis is presented in:</p> <ul> <li>Abolt CJ, Young MH, Atchley AL, Harp DR. 2018. Microtopographic control on the ground thermal regime in ice wedge polygons. <em>The Cryosphere</em>, 12, 1957-1968. DOI:10.5194/tc-12-1957-2018.<br> </li> </ul>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Supplemental Data to PhD Dissertation entitled "Non-Coding and Epigenetic Regulators of Ambient Temperature Sensitive Flowering"
<p>Supplemental Data to the PhD dissertation entitled "Non-Coding and Epigenetic Regulators of Ambient Temperature Sensitive Flowering" from Suze Blom, Wageningen University & Research (doi:10.18174/670822). Supplementary data to the chapter entitled "Ambient temperature dependent changes in DNA methylation in Arabidopsis thaliana Col-0 and the decrease in DNA methylation 1 mutant" [manuscript in preparation]</p> <p>Includes Supplemental Data:</p> <p>Supplementary Data 1: Significantly differently expressed (DE) genes in Col-0 after the 24 hour ambient temperature switch from 16°C to 25°C. padj value of 0.05 was set as the cutoff value for statistical significance.</p> <p>Supplementary Data 2: Significantly differently expressed (DE) genes in ddm1-10 after the 24 hour ambient temperature switch from 16°C to 25°C. padj value of 0.05 was set as the cutoff value for statistical significance.</p> <p>Supplementary Data 3: Alternative splicing (AS) of transcripts after an ambient temperature change from 16°C to 25°C in Col-0. Gene ID, splicing event, genomic location, AS frequency and padj values are indicated.</p> <p>Supplementary Data 4: Alternative splicing (AS) of transcripts after an ambient temperature change from 16°C to 25°C in ddm1-10. Gene ID, splicing event, genomic location, AS frequency and padj values are indicated.</p> <p>Supplementary Data 5: List of genes in Col-0 (sheet 1) and ddm1-10 (sheet 2) with both significant AS and DMC associated with them. Gene ID and number of DMCs associated with the gene are indicated.</p>
Timing of increased temperature sensitivity coincides with nervous system development in winter moth embryos
<p>Climate change is rapidly altering the environment and many species will need to genetically adapt their seasonal timing to keep up with these changes. Insect development rate is largely influenced by temperature, but we know little about the mechanisms underlying temperature sensitivity of development. Here we investigate seasonal timing of egg hatching in the winter moth, one of the few species which has been found to genetically adapt to climate change, likely through selection on temperature sensitivity of egg development rate. To study when during development winter moth embryos are most sensitive to changes in ambient temperature, we gave eggs an increase or decrease in temperature at different moments during their development. We measured their developmental progression and timing of egg hatching, and used fluorescence microscopy to construct a timeline of embryonic development for the winter moth. We found that egg development rate responded more strongly to temperature once embryos were in the fully extended germband stage. This is the phylotypic stage at which all insect embryos have developed a rudimentary nervous system. Furthermore, at this stage timing of ecdysone signaling determines developmental progression, which could act as an environment dependent gateway. Intriguingly, this may suggest that, from the phylotypic stage onward, insect embryos can start to integrate internal and environmental stimuli to actively regulate important developmental processes. As we found evidence that there is genetic variation for temperature sensitivity of egg development rate in our study population, such regulation could be a target of selection imposed by climate change.</p>
ScienceDex guides
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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.