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481 results for “freezing”
Thermal adaptations to extreme freeze-thaw cycles in the high tropical Andes
<p>Temperature plays a key role in the biology of ectotherms, including anurans, which are found at higher elevations in the tropics than anywhere in the temperate zone. High-elevation tropical environments are characterized by extreme daily thermal fluctuation including high daily maxima and nightly freezing. Our study investigated the contrasting operative temperatures of the anurans <i>Telmatobius marmoratus </i>and<i> Pleurodema marmoratum</i> in different environmental contexts at the same elevation and biome above 5200 meters. <i>Telmatobius marmoratus</i> avoids extremes of daily temperature fluctuation by utilizing thermally buffered aquatic habitat at all life stages, with minimal operative temperature variation (range: 4.6–8.0°C).<i> Pleurodema marmoratum</i>, in contrast, experienced operative temperatures from -3.5 to 44°C and has one of the widest thermal breadths reported for any tropical frog, from >32°C (critical thermal maximum) to surviving freezing periods of 1 hr and 6 hr down to -3.0°C. Our findings expand experimental evidence of frost tolerance in amphibians to the widespread Neotropical family Leptodactylidae, the first such evidence of frost tolerance in a tropical amphibian. Our study identifies three strategies (wide thermal tolerance breadth, use of buffered microhabitats, and behavioral thermoregulation), which allow these tropical frogs to withstand the current wide daily thermal fluctuation above 5000 masl and which may help them adapt to future climatic changes.</p>
Metabolic cost of freeze-thaw and source of CO2 production in the freeze-tolerant cricket Gryllus veletis
<p>Freeze-tolerant insects can survive the conversion of a substantial portion of their body water to ice. While the process of freezing induces active responses from some organisms, these responses appear absent from freeze-tolerant insects. Recovery from freezing likely requires energy expenditure to repair tissues and re-establish homeostasis, which should be evident as elevations in metabolic rate after thaw. We measured carbon dioxide (CO<sub>2</sub>) production in the spring field cricket (<i>Gryllus veletis</i>) as a proxy for metabolic rate during cooling, freezing and thawing and compared the metabolic costs associated with recovery from freezing and chilling. We hypothesized that freezing does not induce active responses, but that recovery from freeze-thaw is metabolically costly. We observed a burst of CO<sub>2</sub>release at the onset of freezing in all crickets that froze, including those killed by either cyanide or an insecticide (thiacloprid), implying that the source of this CO<sub>2</sub>was neither aerobic metabolism or a coordinated nervous system response. These results suggest that freezing does not induce active responses from <i>G. veletis</i>, but may liberate buffered CO<sub>2 </sub>from hemolymph. There was a transient 'overshoot' in CO<sub>2</sub>release during the first hour of recovery, and elevated metabolic rates at 24, 48 and 72 hours, in crickets that had been frozen compared to crickets that had been chilled (but not frozen). Thus, recovery from freeze-thaw and the repair of freeze-induced damage appears metabolically costly in <i>G. veletis</i>, and this cost persists for several days after thawing. </p>
Data from: How to survive a glaciation: the challenge of estimating biologically realistic potential distributions under freezing conditions
Correlative ecological niche models are increasingly used to estimate potential distributions during the Last Glacial Maximum (LGM) for biogeographical research. In the case of presence-background/pseudoabsences techniques, cold environments that are poorly represented in existing geography can complicate the process of model calibration and transfer into more extreme cold environments that were very common during the LGM (non-analog conditions). This may lead to biologically unrealistic estimations. Using one cold-adapted North American mammal, we explore a real scenario to better understand the effect of restricting the range of environmental conditions over which niche models are calibrated and then transferred to LGM conditions. We performed two sets of experiments in MAXENT: (1) we calibrated models in the context of only present‐day climate conditions, which is the most common practice, and compared predictions under LGM conditions based on two extrapolation methods (clamping vs unconstrained); (2) we calibrated single models using both present‐day and LGM conditions as part of the same background in order to include more extreme environments in the model calibration. Our experiments led to dramatically different estimates of species' potential distributions, showing notable differences with respect to latitudinal and elevational shifts during the LGM. Models calibrated using present‐day climates yielded biologically unrealistic estimations, suggesting that species survived in the glaciers during the LGM. Even more unrealistic estimations were achieved when clamping was enforced as the method to extrapolate. Models calibrated in the context of both modern and past climates reduced the required degree of extrapolation and allowed more realistic potential distributions, suggesting that the species avoided extremely cold conditions during the LGM. This study alerts to the possibility of obtaining implausible potential distributions during the LGM due to restricted background datasets and offers recommendations that should promote better strategies to estimate distributional changes during glaciations.
Data from: The role of glycine betaine in range expansions; protecting mangroves against extreme freeze events
1. Due to a warming climate, mangrove populations within the Gulf of Mexico and along the Florida Atlantic coastline are expanding their range poleward. As mangroves expand their range limit, leading edge individuals are more likely to experience an increased incidence of freeze events. However, we still lack a clear understanding of the mechanisms used by mangroves to survive freezing conditions. 2. Here, we conducted common garden experiments at different locations experiencing variable winter freeze conditions to show glycine betaine, an organic osmolyte, increases significantly with freeze exposure, playing an important role in the freeze tolerance of Avicennia germinans, a widespread Neotropical mangrove. 3. We found glycine betaine accumulation was similar across all source populations and freeze exposure locations, suggesting glycine betaine is not a range limit adaptation and is instead used for freeze tolerance by A. germinans irrespective of source population. Plants sourced from populations that experience freezing conditions exhibited greater rates of survival, indicating range edge populations of A. germinans have other heritable adaptations in addition to glycine betaine for freeze tolerance. 4. Synthesis. Continued mangrove expansion poleward will result in a greater incidence of freeze events for individuals at the leading edge. Our findings suggest freeze tolerance in this species may be genetically based and that leading edge A. germinans have the potential to survive extreme freeze events and recover post-freeze, allowing for their continued expansion poleward. This process of selective survival may act to promote adaptation of freeze tolerance in range edge populations.
Data from: Population structure, genetic variation and linkage disequilibrium in perennial ryegrass populations divergently selected for freezing tolerance
Low temperature is one of the abiotic stresses seriously affecting the growth of perennial ryegrass (Lolium perenne L. Understanding the genetic control of freezing tolerance would aid in the development of cultivars of perennial ryegrass with improved adaptation to frost. A total number of 80 individuals (24 of High frost [HF]; 29 of Low frost [LF] and 27 of Unselected [US]) from the second generation of the two divergently selected populations and an unselected control population were genotyped using 278 genome-wide SNPs derived from Lolium perenne L. transcriptome sequence. Our studies showed that the HF and LF populations are very divergent after selection for freezing tolerance, whereas the HF and US populations are more similar. Linkage disequilibrium (LD) decay varied across the seven chromosomes and the conspicuous pattern of LD between the HF and LF population confirmed their divergence in freezing tolerance. Furthermore, two Fst outlier methods; finite island model (fdist) by LOSITAN and hierarchical structure model using ARLEQUIN detected six loci under directional selection. These outlier loci are most probably linked to genes involved in freezing tolerance, cold adaptation and abiotic stress and might be the potential marker resources for breeding perennial ryegrass cultivars with improved freezing tolerance.
Data from: Decoupled evolution of foliar freezing resistance, temperature-niche and morphological leaf traits in Chilean Myrceugenia
1. Phylogenetic conservatism of tolerance to freezing temperatures has been cited to explain the tendency of plant lineages to grow in similar climates. However there is little information about whether or not freezing resistance is conserved across phylogenies, and whether conservatism of physiological traits could explain conservatism of realized climatic niches. Here we compared the phylogenetical lability of realized climatic niche, foliar freezing resistance, and four morphological leaf traits that are generally considered adaptations to frost resistance in Chilean species of Myrceugenia, which grow in a wide range of habitats. 2. We estimated the predicted niche occupancy profiles with respect to minimum temperature (minT) of all species. We measured foliar freezing resistance (using chlorophyll fluorescence), leaf size, leaf mass per area (LMA), stomatal and trichome densities of ten individuals per species. Finally, we estimated phylogenetic signal and we performed independent contrast analyses among all variables. 3. We found that both foliar freezing resistance and minT were subject to a significant phylogenetic signal, but the former had a stronger signal. We also detected a significant but weak correlation between them (r=0.49, pone tail= 0.04). Morphological traits evolved independent of any phylogenetic effect. Synthesis. Our results show that freezing resistance evolved in association with temperature niche, but with some delay that could result from phylogenetic inertia. Our results also show that morphological leaf traits are more labile than realized climatic niche and frost tolerance and the former probably evolved associated to microhabitat preferences.
Model for Predicting the Hydraulic Conductivity of Frozen Soils Using the Soil Freezing Characteristic Curve
<p>This is the data used in this manuscript.</p>
Sea ice bulk density estimates during the MOSAiC freezing season from October 2019 to April 2020
<h2><strong><em>MOSAiC: the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.</em></strong></h2> <p>Assuming hydrostatic equilibrium, we integrated and adjusted sea ice thickness and snow depth data from an IMB array (comprising 15 buoys), high-resolution along-track freeboard data from airborne laser scanning (ALS) and the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) measurements, as well as snow bulk density data from snow pits, to estimate the ice bulk density (IBD) within the MOSAiC DN (defined as the area within 50 km of Polarstern) during the MOSAiC freezing season from late October 2019 to late April 2020 (<strong>Regional buoy sites</strong>). Additionally, we also provided core-based IBD data from the MOSAiC Main Coring Sites (MCS), derived using the weighing method (<strong>MCS-FYI </strong>and <strong>MCS-SYI</strong>). Due to the challenges in reconciling spatially non-overlapping observations, we implemented two alternative approaches—<strong>Regional Case 1</strong> and <strong>Regional Case 2</strong>—to further upscale the hydrostatic equilibrium-based IBD retrievals within the MOSAiC DN. </p> <h2><em><strong>Variable Details:</strong></em></h2> <p><em><strong>Note that all density results are in kilograms per cubic metre.</strong></em></p> <blockquote> <p><strong>[Time]: </strong>covering the period from 5 October 2019 to 30 April 2020 in the format yyyyMMdd.</p> <p><strong>[Longitude] & [Latitude]</strong>: characterizing the daily position for the MOSAiC Central Observatory (CO).</p> </blockquote> <p> </p> <blockquote> <p><strong>[Reg_buoy_DN]: </strong>Estimated mean sea ice bulk density at the buoy siteswithin a radius of ~50 km from CO (DN scale).</p> <p><strong>[Reg_buoy_DN_Unc1]: </strong>Uncertainty of <strong>[Reg_buoy_DN]</strong> resulting from the input parameters, calculated using the Gaussian error propagation method.</p> <p><strong>[Reg_buoy_DN_Unc2]: </strong>Uncertainty of <strong>[Reg_buoy_DN]</strong> resulting from the introduced empirical adjustment coefficient, characterised using the mean absolute difference of multiple freeboard estimates.</p> </blockquote> <p> </p> <blockquote> <p><strong>[Reg_buoy_Lsite]: </strong>Estimated mean sea ice bulk density at the buoy sites within a radius of ~25 km from CO (Lsite scale).</p> <p><strong>[Reg_buoy_Lsite_Unc1]: </strong>Uncertainty of <strong>[Reg_buoy_Lsite]</strong> resulting from the input parameters, calculated using the Gaussian error propagation method.</p> <p><strong>[Reg_buoy_Lsite_Unc2]: </strong>Uncertainty of <strong>[Reg_buoy_Lsite]</strong> resulting from the introduced empirical adjustment coefficient, characterized using the mean absolute difference of multiple freeboard estimates.</p> </blockquote> <p> </p> <blockquote> <p><strong>[Reg_buoy_DL]: </strong>Mean value of <strong>[Reg_buoy_DN]</strong> and <strong>[Reg_buoy_Lsite]</strong>, representing regional buoy estimates of IBD within the MOSAiC DN.</p> <p><strong>[Reg_buoy_DL_Unc1]: </strong>Uncertainty of <strong>[Reg_buoy_DL]</strong> resulting from the input parameters, calculated using the Gaussian error propagation method.</p> <p><strong>[Reg_buoy_DL_Unc2]: </strong>Uncertainty of <strong>[Reg_buoy_DL] </strong>resulting from the introduced empirical adjustment coefficient, characterized using the mean absolute difference of multiple freeboard estimates.</p> </blockquote> <p> </p> <blockquote> <p><strong>[Reg_case1_DN]: </strong>Mean IBD value for typical ice conditions at the DN scale, calculated using the integration of multi-source observations.</p> <p><strong>[Reg_case2_DN]: </strong>Mean IBD value for typical ice conditions at the DN scale, calculated using initial values from large-scale observations and trend extrapolation.</p> </blockquote> <p> </p> <blockquote> <p><strong>[Loc_MCS_FYI]: </strong>Local-scale core-based bulk density derived from the main first-year coring sites (MCS-FYI) during MOSAiC.</p> <p><strong>[Loc_MCS_SYI]: </strong>Local-scale core-based bulk density derived from the main second-year coring sites (MCS-SYI) during MOSAiC.</p> <p>The density of these ice cores was measured in a freezing laboratory at an ambient temperature of –15 °C using the hydrostatic weighing method, achieving a relatively low uncertainty of <strong>only 0.2%</strong> (Pustogvar and Kulyakhtin, 2016).</p> <p>Pustogvar, A. and Kulyakhtin, A.: Sea ice density measurements. Methods and uncertainties, Cold Regions Science and Technology, 131, 46-52, https://doi.org/10.1016/j.coldregions.2016.09.001, 2016.</p> </blockquote> <p> </p> <h2><em><strong>Relevant datasets:</strong></em></h2> <p><em><strong>Sea ice mass balance buoys (IMBs): sea ice thickness and snow depth</strong></em></p> <blockquote> <p>SIMBA buoy measurements are available from PANGAEA: https://doi.org/10.1594/PANGAEA.938244.</p> <p>SIMB buoy measurements are available from the Arctic Data Center: https://doi.org/10.18739/A20Z70Z01.</p> <p>Lei, R., Cheng, B., Hoppmann, M., and Zuo, G.: Snow depth and sea ice thickness derived from the measurements of SIMBA buoys deployed in the Arctic Ocean during the Legs 1a, 1, and 3 of the MOSAiC campaign in 2019-2020, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.938244, 2021.</p> <p>Perovich, D., Raphael, I., Moore, R., Clemens-Sewall, D., Polashenski, C., and Planck, C.: Measurements of ice mass balance and temperature from autonomous Seasonal Ice Mass Balance buoys in the Arctic Ocean, 2019-2020, Arctic Data Center [data set], https://doi.org/10.18739/A20Z70Z01, 2022.</p> </blockquote> <p> </p> <p><em><strong>Airborne laser scanning (ALS): sea ice total freeboard (sea ice freeboard + snow depth)</strong></em></p> <blockquote> <p>Airborne laser scanning measurements during MOSAiC are available from PANGAEA: https://doi.org/10.1594/PANGAEA.950896.</p> <p>Hutter, N., Hendricks, S., Jutila, A., Birnbaum, G., von Albedyll, L., Ricker, R., and Haas, C.: Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950339, 2023.</p> </blockquote> <p> </p> <p><em><strong>Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): sea ice total freeboard (sea ice freeboard + snow depth)</strong></em></p> <blockquote> <p>ICESat-2 ATL10 total freeboard data (version 6, latest version) are available from NSIDC: https://doi.org/10.5067/ATLAS/ATL10.006.</p> <p>Kwok, R., Petty, A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A., Wimert, J., Bagnardi, M., and Kurtz, N.: ATLAS/ICESat-2 L3A Sea Ice Freeboard, Version 6, National Snow and Ice Data Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/ATLAS/ATL10.006, 2023. </p> </blockquote> <p> </p> <p><em><strong>Snow pits: snow density</strong></em></p> <blockquote> <p>Snow pit data collected during the MOSAiC expedition are available from PANGAEA: https://doi.org/10.1594/PANGAEA.940214.</p> <p>Macfarlane, A. R., Schneebeli, M., Dadic, R., Wagner, D. N., Arndt, S., Clemens-Sewall, D., Hämmerle, S., Hannula, H.-R., Jaggi, M., Kolabutin, N., Krampe, D., Lehning, M., Matero, I., Nicolaus, M., Oggier, M., Pirazzini, R., Polashenski, C., Raphael, I., Regnery, J., Shimanchuck, E., Smith, M. M., and Tavri, A.: Snowpit snow density cutter profiles measured during the MOSAiC expedition, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940214, 2022.</p> </blockquote> <p> </p> <p><em><strong>Transects: sea ice thickness and snow depth</strong></em></p> <blockquote> <p>Transect data collected during MOSAiC are available from PANGAEA: https://doi.org/10.1594/PANGAEA.937781.</p> <p>Itkin, P., Webster, M., Hendricks, S., Oggier, M., Jaggi, M., Ricker, R., Arndt, S., Divine, D. V., von Albedyll, L., Raphael, I., Rohde, J., and Liston, G. E.: Magnaprobe snow and melt pond depth measurements from the 2019-2020 MOSAiC expedition. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.937781, 2021.</p> </blockquote> <p> </p> <p><em><strong>Ice cores: sea ice density</strong></em></p> <blockquote> <p>Ice core data collected from the MOSAiC Main Coring Sites are available from PANGAEA: https://doi.org/10.1594/PANGAEA.956732 (MCS-FYI) and https://doi.org/10.1594/PANGAEA.959830 (MCS-SYI).</p> <p>Oggier, M., Salganik, E., Whitmore, L., Fong, A. A., Hoppe, C. J. M., Rember, R., Høyland, K. V., Divine, D. V., Gradinger, R., Fons, S. W., Abrahamsson, K., Aguilar-Islas, A. M., Angelopoulos, M., Arndt, S., Balmonte, J. P., Bozzato, D., Bowman, J. S., Castellani, G., Chamberlain, E., Creamean, J., D'Angelo, A., Damm, E., Dumitrascu, A., Eggers, S. L., Gardner, J., Grosfeld, L., Haapala, J., Immerz, A., Kolabutin, N., Lange, B. A., Lei, R., Marsay, C. M., Maus, S., Müller, O., Olsen, L. M., Nuibom, A., Ren, J., Rinke, A., Sheikin, I., Shimanchuk, E., Snoeijs-Leijonmalm, P., Spahic, S., Stefels, J., Torres-Valdés, S., Torstensson, A., Ulfsbo, A., Verdugo, J., Vortkamp, M., Wang, L., Webster, M., Wischnewski, L., and Granskog, M. A.: First-year sea-ice salinity, temperature, density, oxygen and hydrogen isotope composition from the main coring site (MCS-FYI) during MOSAiC legs 1 to 4 in 2019/2020. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.956732, 2023.</p> <p>Oggier, M., Salganik, E., Whitmore, L., Fong, A. A., Hoppe, C. J. M., Rember, R., Høyland, K. V., Gradinger, R., Divine, D. V., Fons, S. W., Abrahamsson, K., Aguilar-Islas, A. M., Angelopoulos, M., Arndt, S., Balmonte, J. P., Bozzato, D., Bowman, J. S., Castellani, G., Chamberlain, E., Creamean, J., D'Angelo, A., Damm, E., Dumitrascu, A., Eggers, L., Gardner, J., Grosfeld, L., Haapala, J., Immerz, A., Kolabutin, N., Lange, B. A., Lei, R., Marsay, C. M., Maus, S., Olsen, L. M., Müller, O., Nuibom, A., Ren, J., Rinke, A., Sheikin, I., Shimanchuk, E., Snoeijs-Leijonmalm, P., Spahic, S., Stefels, J., Torres-Valdés, S., Torstensson, A., Ulfsbo, A., Verdugo, J., Vortkamp, M., Wang, L., Webster, M., Wischnewski, L., and Granskog, M. A.: Second-year sea-ice salinity, temperature, density, oxygen and hydrogen isotope composition from the main coring site (MCS-SYI) during MOSAiC legs 1 to 4 in 2019/2020. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.959830, 2023.</p> </blockquote> <p> </p>
Supplementary Data and Videos for "Problems of classifying predator-induced prey immobility: an unexpected case of post-contact freezing"
<p> </p> <p>The data file and five videos below accompany our paper "Problems of classifying predator-induced prey immobility: an unexpected case of post-contact freezing", published in <em>Web Ecology</em> <strong>24,</strong> 35-40 (2024), https://doi.org/10.5194/we-24-35-2024.</p> <p> </p> <p><strong>Video 1.</strong> <em>Pachyoliva semistriata</em> freezes after making contact with <em>Agaronia propatula</em> (I).</p> <p><strong>Video 2.</strong> <em>Pachyoliva semistriata</em> freezes after making contact with <em>Agaronia propatula</em> (II). Surface water flows against the direction of movement of <em>Agaronia</em>.</p> <p><strong>Video 3.</strong> <em>Pachyoliva semistriata</em> freezes after making contact with <em>Agaronia propatula</em> (III). Surface water flows in the direction of movement of <em>Agaronia</em>.</p> <p><strong>Video 4.</strong> Responses of <em>Pachyoliva semistriata</em> when making contact with an obstacle; control experiments.</p> <p><strong>Video 5.</strong> <em>Pachyoliva semistriata</em> freezes when making contact with an obstacle and sensing <em>Agaronia</em> odours.</p> <p><strong>Data File 1.</strong> Numerical data used in creating Figure 2.</p>
Data for: How does a hyperuniform fluid freeze
<p>This is the repository of the original data for our research article "How does a hyperuniform fluid freeze"</p>
Simulation outputs for the study on the transition from freezing rain to ice pellets during WINTRE-MIX IOP4
<p>Simulation outputs used in the study that investigates the transition from freezing rain to ice pellets during WINTRE-MIX IOP4. The name of each zip file represents the corresponding simulation described in the manuscript (i.e., CTL, MYJ, and MYJ_HM simulations). Due to the size limit, only variables used in the study are uploaded.</p>
Key roles for the freezing line and disturbance in driving the low plant species richness of temperate regions
<p><b>Aim</b>: At the macroscale, climate strongly correlates with species richness gradients, resulting from differences in <i>in-situ</i> diversification and dispersal. One historical explanation for the pattern is that regions spanning temperate climates contain few species because past disturbances have generated high extinction rates, and species from tropical regions are unable to easily colonize temperate regions. We test these postulates for Himalayan plants, which span subtropical to temperate climates over steep elevational gradients.</p> <p><b>Location: </b>Himalaya</p> <p><b>Time period:</b> Present day</p> <p><b>Major taxa studied:</b> Angiosperms</p> <p><b>Methods: </b>We use<b> </b>a comprehensive survey of 31 floras to document the elevational and geographical distributions of native Himalayan plants, augmented by field studies of trees in both the east and west Himalaya. We use grade of membership models to cluster species according to locations shared and phylogenetic analysis to evaluate diversification rates.</p> <p><b>Results: </b>Species fall into four cohesive biotas, organized by climate. Points of turnover between biotas occur where the mean minimum temperature of the coldest month is approximately 0<sup>o</sup>C (2,000 m - 2,500 m), and at the point of occasional annual freezing (1,000 m - 1,500 m); these boundaries run the length of the Himalaya. The patterns are retained when we consider whole clades rather than species. All plants (and the subsets trees, herbs and shrubs) belonging to the biota above the 2,000 m - 2,500 m line have higher recent speciation rates than those lower down.</p> <p><b>Main conclusions:</b> We attribute the high rate of recent speciation in temperate climates to high rates of turnover, creating ecological and geographical opportunity. The high elevation biota has few species, but spans the largest area, implying species numbers are far from any carrying capacity, at least with respect to accumulation of allopatric forms. This study thus links climatic restrictions of clades to differences in diversification rates, and by inference species numbers.</p>
Data sets for "Bridgmanite freezing in shocked meteorites due to amorphization-induced stress" by Nishi et al.
<p>This is the datasets for the article "Bridgmanite freezing in shocked meteorites due to amorphization-induced stress" by Nishi et al. Tables S1 and S2 contain Experimental conditions and results. </p>
Ground Freezing Index (GFI) and the relative change of the minimum seasonal velocity compared to the mean velocity between 2005 and 2017 at Becs-de-Bosson rock glacier in the Swiss Alps
<p>This dataset contains the Ground Freezing Index (GFI) and the relative change of the minimum seasonal velocity compared to the mean velocity between 2005 and 2017 at Becs-de-Bosson rock glacier in the Swiss Alps. Velocity data is measured by GNSS surveys. GFI is the sum of the daily negative GSTs during the entire freezing season, indicating the coldness of the winter temperature.</p>
Data supporting "Freeze-thaw migration behavior of scree deposits in the cold regions"
<p>All data used in the study to support this research is available on repository via 10.5281/zenodo.12542438</p>
Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data
<p>This is the electronic supplemental data for the publication entitled </p> <p>"<strong>Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data</strong>"</p> <p>submitted to <strong>Seismological Research Letters (SRL)</strong>. </p> <p>The names of the compressed files represent the experiment number and station number. For example, "1_2" indicates data collected from the second station during the first experiment. Each compressed file contains seismic raw data in the ".SAC" format. The filenames include the UTC end time of data collection. For instance, "453003616.00000001.2021.10.20.06.40.22.000.z.sac" indicates that data collection ended at 06:40:22 on October 20, 2021. Each complete .sac file contains 4 days of data with a sampling interval of 0.002 seconds.</p>
Raw data of the manuscript: Heterogeneous Freezing of Liquid Suspensions Including Juices and Extracts from Berries and Leaves from perennial Plants
<p>Freezing data</p>
The source code for a new capillary and adsorption‒force model predicting hydraulic conductivity of soil during freeze‒thaw processes
<p>The source code is related to "A New Capillary and Adsorption‒Force Model Predicting Hydraulic Conductivity of Soil during Freeze‒thaw Processes" (Shufeng Qiao, Rui Ma, Yunquan Wang, Ziyong Sun, Helen Kristine French, Yanxin Wang)</p>
A 6-hourly 0.1° resolution freezing rain dataset of China during 2000-2019 based on deep kernel learning
<p> We provided FR products with a 0.1° spatial resolution in China from January 1, 2000, to December 31, 2019. This is the dataset generated by the study '</p> <p>A 6-hourly 0.1° resolution freezing rain dataset of China during 2000-2019 based on deep kernel learning' from the journal 'Scientific Data'. Please cite both the Zenodo dataset and the paper: https://www.nature.com/articles/s41597-025-04582-z.</p>
Freeze-tolerance of poleward-spreading mangrove species weakened by soil properties of resident salt marsh competitor
<p class="MsoCommentText"><b>1. Background</b>: Increasing temperatures associated with climate change are shifting plant species to higher latitudes. Soil communities could aid the plants' shift into novel areas by harbouring fewer soil-borne antagonists or more mutualists that influence the fitness and stress tolerance of the shifting species. Alternatively, they could contain novel antagonists or fewer mutualists. Thus, soil communities could positively or negatively affect plant range expansion, particularly if they influence plants' responses to climate, such as freeze tolerance, that feedback to affect expansion.</p> <p class="CxSpFirst"><b>2. Methods: </b>We used the northward range expansion of the black mangrove<i>,</i> <i>Avicennia germinans</i>, into a system dominated by marsh cordgrass, <i>Spartina alterniflora</i><i>, </i>in northern Florida, USA to study how the novel soil environment (i.e., <i>S. alterniflora</i> soil) affects mangrove fitness, susceptibility to cold stress, and the colonization of mutualist fungi. We quantified abundance of root mutualistic fungi in mixed marsh-mangrove habitat and conducted a laboratory experiment to test effects of steam-sterilized and live soils from <i>A. germinans </i>and <i>S. alterniflora</i> on the growth, condition, fungal colonization, and freeze tolerance of <i>A. germinans</i> seedlings.</p> <p class="CxSpMiddle"><b>3. Results and Conclusions:</b> In the field, we found two times higher dark septate endophyte (DSE) colonization of <i>A. germinans</i> roots and three times higher fungal spore density in <i>A. germinans</i> soil compared to <i>S. alterniflora </i>roots and soil. In the laboratory experiment, seedlings in steamed <i>S. alterniflora</i> soil treatments had 50-65% survival after freezing, compared to 0% survival in treatments with live <i>S. alterniflora</i> soil. <i>A. germinans</i> live soil mixed with <i>S. alterniflora</i> steamed soil yielded <i>A. germinans</i> roots with the highest DSE colonization and seedlings with greater shoot biomass and lower root:shoot ratios. <i>S. alterniflora</i> live soil lowered the freeze tolerance of <i>A. germinans</i>, decreased mangrove survival, and depressed DSE colonization.</p> <p class="CxSpMiddle"><b>4. Synthesis:</b> <i>S. alterniflora </i>soil could impede <i>A. germinans</i> establishment in salt marsh communities. As climate warming gradually allows <i>A. germinans</i> to displace <i>S. alterniflora</i>, the rhizosphere could become increasingly hospitable to <i>A. germinans</i>. Our work suggests the soil community associated with resident species mediates climatic stressors to affect expansion success. </p> <p class="CxSpFirst"> </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.