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1,604 results for “Wintering”
Fig. 14 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 14. Sex ratio in M. daubentonii and P. auritus in winter in three Liptsy mines (L1, L2 and 3–4): n — number of bats; Mdau — M. daubentonii; Paur — P. auritus.
Fig. 16. Sex ratioin M. daubentonii and P in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 16. Sex ratioin M. daubentonii and P. auritus during autumn swarming and spring departure periods — an example from Liptsy 1 mine: VIII–V — number of months, August to May; n — number of bats; Mdau — M. daubentonii; Paur — P. auritus.
Fig. 10 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 10. Bat distribution in galleries of different height. An example from Liptsy 1: Mdau — M. daubentonii; Paur — P. auritus.
Fig. 11 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 11. Bat distribution in crevices or in the open; and on walls or ceilings. An example from Liptsy 1 mine: n — bat number included in each category; Mdau — M. daubentonii; Paur — P. auritus.
Fig. 7 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 7. Fluctuations in the number of hibernating bats in Liptsy 3–4: n — number of counts; Mdau — M. daubentonii; Mdas — M. dasycneme; Paur — P. auritus. In winter 2004–2005 censuses were not conducted.
Fig. 9 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 9. Annual fluctuations of bat numbers (shown by lines) and relative abundance (shown by bars) in Liptsy 1: VIII–VI — number of months, August to June; XIa — autumn period in November; XIw — winter period in November; IIIw — winter period in March; IIIs — spring period in March; N — number of counts with bats in each period; n — total number of bats in each period; Mdau — M. daubentoni; Mdas — M. dasycneme; Paur — P. auritus.
Fig. 4 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 4. Cumulative number of M. daubentonii (MDA) and P. auritus (PAU) in three Liptsy mines (1, 2 and 3–4), for 10 winter seasons (1999–2009) in period of phenological winter: MDA1 (23 counts); PAU1 (17 counts); MDA2 (16 counts); PAU2 (11 counts); MDA3–4 (19 counts); PAU3–4 (7 counts).
Fig. 6 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 6. Fluctuations in the number of hibernating bats in Liptsy 2: n — number of counts; Mdau — M. daubentonii; Mdas — M. dasycneme; Paur — P. auritus. In winters 2002–2003 and 2004–2005 censuses were not conducted.
Fig. 3 in Ornithological Fauna Of The Waste Water Treatment Plants In The Northern Left Bank Ukraine (Chernihiv And Kyiv Regions): Winter Populations And Ecological Structure
Fig. 3. Similarity clusters of bird populations' species composition in winter according to the water treatment facilities' biotopic zones: 1 — zone of water bodies; 2 — dam zone; 3 — technological zone 4 — meadows agricultural zone.
Fig. 3 in Contribution To Ecology Of Brandt'S Bat, Myotis Brandtii (Chiroptera, Vespertilionidae) In The North-Eastern Ukraine: Comparison Of Local Summer And Winter Bat Assemblages
Fig. 3. Body mass (g) characteristic of females (F) and (M) of M. brandtii in periods of spring departure April (S_dep) and swarming August (Swarm) from the Tetlega mines (dot — mean value, line — median value, whiskers — min and max values, not filling circles — outliers).
Fig. 2 in Contribution To Ecology Of Brandt'S Bat, Myotis Brandtii (Chiroptera, Vespertilionidae) In The North-Eastern Ukraine: Comparison Of Local Summer And Winter Bat Assemblages
Fig. 2. Forearm length (mm) of females (F) and males (M) of M. brandtii from Tetlega mines (dot — mean value, line — median value, whiskers — min and max values).
Fig. 1 in Contribution To Ecology Of Brandt'S Bat, Myotis Brandtii (Chiroptera, Vespertilionidae) In The North-Eastern Ukraine: Comparison Of Local Summer And Winter Bat Assemblages
Fig. 1. Allocation of M. brandtii (Mbra) and M. daubentonii (Mdau) inside the Tetlega mines from November to April (n — number of counted bats); A — in crevices or open, B — on walls or ceiling.
MEASUREMENTS AND CONTROLS ON MID-WINTER ALPINE GROUND THERMAL REGIME IN THE PURCELL MOUNTAINS, BRITISH COLUMBIA [Dataset]
<p>Datasets and coding from my MSc Thesis titled MEASUREMENTS AND CONTROLS ON MID-WINTER ALPINE GROUND THERMAL REGIME IN THE PURCELL MOUNTAINS, BRITISH COLUMBIA. Data includes shallow ground, surface, and basal snow temperatures from 29 alpine ground thermal regime monitoring sites and meteorological data from one station located at Conrad Glacier basin in the Purcell Mountains, BC. Data were collected from August 2020 to August 2021.</p>
PRIMAVERA European winter windstorm event set
<p>PRIMAVERA was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets, for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data.</p> <p>The uploaded dataset contains the model and re-analysis windstorm footprints in netcdf format and documentation of the data. Further information is given in Lockwood et al., Using high-resolution global climate models from the PRIMAVERA project to create a European winter windstorm event set, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-12, in review, 2022.</p> <p>Any products or applications which use this dataset must state the following attribution “<em>Acknowledgment to the PRIMAVERA partners. The information/material contained has been produced with funding from the European Union’s Horizon 2020 Research & Innovation Programme under grant agreement no. 641727.”</em></p>
Data and code for Winter conditions structure extratropical patterns of species richness of amphibians, birds, and mammals globally
<p>This repository contains the dataset analyzed in 'Winter conditions structure extratropical patterns of species richness of amphibians, birds, and mammals globally' - published in the journal Global Ecology and Biogeography - and the R code used to generate the correlations, generalized additive models, and related figures presented in the manuscript. Column descriptions for the data can be found in the associated README.txt file. Please refer to the manuscript for further detail on the variables and how they were derived.</p> <p>The Winter Indices (WIs) were derived using satellite remote sensing data from optical (MODIS, snow cover) and microwave (MEaSUREs freeze/thaw, frozen ground) sensors. The species richness maps were derived using IUCN range maps for individual species of amphibians, birds, and mammals (data requests can be made here: <a href="https://www.iucnredlist.org/resources/spatial-data-download">https://www.iucnredlist.org/resources/spatial-data-download</a>). Climatic varibales were derived from WorldClim v2.0 data, elevation from USGS GMTED2010 data, and primary productivity from the cumulative dynamic habitat index available here: <a href="http://silvis.forest.wisc.edu/maps-data/">http://silvis.forest.wisc.edu/maps-data/</a>.</p>
Data from: Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat
<div> <div> <p>Barley yellow dwarf is one of the major viral diseases of cereals. Phenotyping barley yellow dwarf in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment; (2) identify genomic regions associated with barley yellow dwarf resistance, and (3) evaluate the ability of genomic selection models to predict barley yellow dwarf resistance. Up to 107 wheat lines were phenotyped during each of 5 field seasons under both insecticide treated and untreated plots. Across all seasons, barley yellow dwarf severity was lower within the insecticide treatment along with increased plant height and grain yield compared with untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying the resis- tance gene Bdv2. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a poten- tially novel genomic region for barley yellow dwarf resistance on chromosome 5AS. Given the variable heritability of the trait (0.211–0.806), we obtained a predictive ability for barley yellow dwarf severity ranging between 0.06 and 0.26. Including the presence or absence of Bdv2 as a covariate in the genomic selection models had a large effect for predicting barley yellow dwarf but almost no effect for other ob- served traits. This study was the first attempt to characterize barley yellow dwarf using field-high-throughput phenotyping and apply geno- mic selection to predict disease severity. These methods have the potential to improve barley yellow dwarf characterization, additionally identifying new sources of resistance will be crucial for delivering barley yellow dwarf resistant germplasm.</p> </div> </div>
Ice Core Measurements - Northern Norwegian Fjord Ice - Winter 2018/2019
<p>Dataset from the 2018-2019 field season in six northern Norwegian fjords including ice bulk salinity and d18O, seawater salinity and d18O, and river water d18O. The fjords included are Beisfjord (Nordland), Lavangen (Nordland), Nordkjosbotn (Tromsø), Storfjord (Tromsø), Storfjord (Tromsø), Ramfjord (Tromsø), and Kattfjord (Tromsø).</p>
Full-factorial breeding experiment with lake char (Lake Geneva, winter 2017/2018)
<p>We sampled 16 wild lake char (<em>Salvelinus umbla</em>) and used their gametes to investigate the genetic consequences of different mating scenarios. A full-factorial breeding was used to separate additive genetic from maternal environmental effects, and embryos were raised singly after sublethal exposures to a pathogen, a common pollutant, or water only. In all treatment groups, embryo development was strongly reduced with increased genetic relatedness between the parents. Contrary to predictions of 'good genes' sexual selection, pathogen tolerance of offspring declined with increasing coloration of their fathers.</p> <p>Determination of inbreeding and kinship coefficients: All 10 males and 4 of the 6 females were used in a parallel study that included sperm competition experiments (Nusbaumer et a. 2021). These 14 parents had been prepared in one library for ddRAD sequencing on 2 lanes on an Illumina HIseq 2500 (see de Guttry et al. 2022 for the corresponding 14 gzipped .fastq of these individuals). The remaining 2 females were later genotyped in a different library but again on an Illumina HIseq 2500. After demultiplexing, the 16 individuals were processed together using stacks 2.53, and with the resulting filtered VCF file estimations of kinship and inbreeding have been done. For each individual, the fastq files of the 2 sequencings lanes (2018/2019), or of the replicates within the same library (2021), were merged after demultiplexing using process_radtags (Stacks 2.53). The bash and R scripts are provided. There are no legal or ethical considerations regarding the above-mentioned data.</p>
Remembering Winter Was Coming - Figures
<p><strong>Description. </strong>Figures used in the following article:</p> <ul> <li>X. Bost, S. Gueye, V. Labatut, M. Larson, G. Linarès, D. Malinas & R. Roth, Remembering winter was coming: Character-oriented video summaries of TV series,” <em>Multimedia Tools and Applications </em>78(24):35373–35399, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02278188">hal-02278188</a>⟩ DOI: <a href="https://doi.org/10.1007/s11042-019-07969-4">10.1007/s11042-019-07969-4</a></li> </ul> <p><strong>Citation.</strong> If you use these data, please cite the above article.</p> <p><br><code>@Article{Bost2019,</code><br><code> author = {Bost, Xavier and Gueye, Serigne and Labatut, Vincent and Larson, Martha and Linarès, Georges and Malinas, Damien and Roth, Raphaël},</code><br><code> title = {Remembering Winter Was Coming: Character-oriented Video Summaries of {TV} Series},</code><br><code> journal = {Multimedia Tools and Applications},</code><br><code> year = {2019},</code><br><code> volume = {78},</code><br><code> number = {24},</code><br><code> pages = {35373-35399},</code><br><code> doi = {10.1007/s11042-019-07969-4},</code><br><code>}</code></p>
Low winter temperatures and divergent freezing resistance set the cold range limit of widespread alpine graminoids
<p><span>Aim:</span><span> "Where and why does a species exist" is a fundamental question in ecology. However, the actual range limits of alpine plant species are largely unexplored and unexplained. We aim at identifying the low temperature range limits of the two most abundant alpine graminoid species on acidic soils that intermingle in mosaics of high-elevation habitats across the European Alps.</span></p> <p><span>Location:</span><span> Alpine grasslands in the Swiss Alps.</span></p> <p><span>Taxon:</span><span> Carex curvula (Cyperaceae) and Nardus stricta (Poaceae), named by the genus name hereafter.</span></p> <p><span>Results:</span><span> Carex </span><span>and Nardus clearly segregated across different microsites. Season length, growing degree hours and soil chemistry (pH, C/N-ratio, phosphorus) did not demarcate the two species' ranges, while their distribution was strongly affected by soil minimum temperature in winter. Carex occurred at sites with and without protecting snow cover and resisted low soil temperatures (-13 °C). Nardus was absent at microsites with snow cover duration less than 5 months and soil minimum temperatures below -5 °C. During the growing season, leaves of Carex had a higher freezing resistance with LT50 of -16.1 °C than those of Nardus with LT50 of -13.3 °C (LT50: lethal temperature for 50% of the tissue). Tetrazolium staining in shoots also revealed a higher freezing resistance in Carex compared to Nardus, and shoot apices tolerated lowest temperatures: Carex -30 °C, Nardus -24 °C. Though, a vital shoot apex alone did not ensure regrowth after winter. Regrowth after severe frost events requires intact vessels and roots, all less freezing tolerant than apical meristems and young leaves.</span></p> <p><span>Main conclusions:</span><span> The cold range limits of these widespread alpine graminoid species are evidently set by thermal extremes in winter. Microtopography, thus snow distribution pattern, in concert with the species' freezing resistance explains the cold edge of the fundamental niche of these two species.</span></p>
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Allen Brain Atlas
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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.