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1,604 results for “Wintering”
A 4.5 year-long record of Svalbard water vapor isotopic composition documents winter air mass origin
<p>Isotopic data from the article in JGR A: A 4.5 year-long record of Svalbard water vapor isotopic composition documents winter air mass origin</p> <p><strong>CLDS_2020_ground_iso_vapor_1h.dat</strong></p> <p><strong>CLDS_2020_zeppelin_iso_vapor_1h.dat </strong></p> <p>first column: date in matlab format</p> <p>second column: humidity in ppmv</p> <p>thrid column: d18O in per mil</p> <p>forth column: dD in per mil</p> <p>fifth column: not to take into account</p> <p><strong>CLDS_2020_iso_precip.dat </strong></p> <p>first column: date in matlab format</p> <p>second column: temperature at noon in degre C</p> <p>thrid column: d18O in per mil</p> <p>forth column: dD in per mil</p> <p>fifth column:type of precip 1: water & 2 : snow & 3 : other (melt, etc.)</p>
Social partners and temperature jointly affect morning foraging activity of small birds in winter
Daily foraging activity of small wintering birds is classically thought to be driven by the need to gather enough energy reserves to survive each night. A separate line of research has shown that sociality is a major driver in winter foraging activities in many species. Here, we use wintering birds as a study system to move towards an integrative understanding of the influence of energy requirements and sociality on foraging ecology. We used RFID-enabled feeders in Lincoln, Nebraska, USA in January-March 2019 to measure foraging activity in two species (downy woodpecker, <i>Picoides pubescens</i>, and white-breasted nuthatches, <i>Sitta carolinensis</i>). We analyzed the relationship between overnight temperature and morning foraging activity and found that lowest overnight temperature was negatively correlated with morning visitation at feeders. We then used a network approach to ask if flock associations explain similarity in birds' foraging activity. In both species, individuals with stronger associations in a social network were more likely to share similar feeder activity, and an index of social partners' activity explained foraging activity better than overnight temperature. This brings forth new questions about the interplay between individual response to temperature and social factors in shaping how small animals cope with harsh winter conditions.
Data from: Nocturnal foraging lifts time-constraints in winter for migratory geese but hardly speeds up fueling
<p>Climate warming advances the optimal timing of breeding for many animals. For migrants to start breeding earlier, a concurrent advancement of migration is required, including pre-migratory fueling of energy reserves. We investigate whether barnacle geese are time-constrained during pre-migratory fueling and whether there is potential to advance or shorten the fueling period to allow an earlier migratory departure. We equipped barnacle geese with GPS-trackers and accelerometers to remotely record birds' behavior, from which we calculated time budgets. We examined how time spent foraging was affected by the available time (during daylight and moonlit nights) and thermoregulation costs. We used an energetic model to assess onset and rates of fueling, and whether geese can further advance fueling by extending foraging time. We show that d<span>uring winter, when facing higher thermoregulation costs, geese consistently foraged at night, especially during moonlit nights, in order to balance their energy budgets. In spring, birds made use of the increasing day length and gained body stores by foraging longer during the day, but birds stopped foraging extensively during the night. Our model indicates that by continuing night-time foraging throughout spring, geese may have some leeway to advance and increase fueling rate, potentially reaching departure body mass 4 days e</span>arlier. In light of rapid climatic changes on the breeding grounds, whether this advancement can be realized and whether it will be sufficient to prevent phenological mismatches remains to be determined.</p>
Meteo and hydrodynamic data in the Mar Grande and Mar Piccolo by the LIC Survey, winter and summer 2015
<p>The Coastal Engineering Laboratory (LIC) of the DICATECh of the Polytechnic University of Bari (Italy) maintains a place-based research program in the Mar Grande and Mar Piccolo of Taranto (a coastal system in southern Italy), providing records of hydrodynamic and water-quality measurements. This site is one of the most complex marine ecosystem models in terms of ecological, social, and economic activities. It is considered highly vulnerable for the presence of the naval base, the biggest still mill of Europe and an oil refinery. Two fixed stations have been installed, one in the Mar Grande (MG station) and another in Mar Piccolo (MP station). In the MG station constituents include wind speed and direction, air temperature and humidity, barometric pressure, net solar radiation, water salinity, water temperature, water pressure, dissolved oxygen, fluorescence, turbidity, CDOM, crude oil and refined fuels, sea currents and waves. In the MP station constituents include water temperature, sea currents and waves. We provide a summary of how these data have been collected by the research group and how they can be used to deepen understanding of the hydrodynamic structures and characteristics of the basin.</p> <p>The dataset is made by a total number of 14 files (Data citation 1).</p> <p>For MG station 4 files are available for the month of January 2015 (from 01.01.2015 to 31.01.2015) and other 4 files for the month of July 2015 (from 01.07.2015 to 31.07.2015).</p> <p>They are for both months: current datafile; wave datafile; meteo datafile; water quality parameters datafile.</p> <p>For MP station 3 files are available for the month of January 2015 (from 01.01.2015 to 31.01.2015) and other 3 files for the month of July 2015 (from 01.07.2015 to 31.07.2015).</p> <p>They are for both months: current datafile; wave datafile; temperature datafile.</p> <p>The dataset supplied in tab-delimited text format ASCII, contains timeseries of relevant meteocean variables marked up with the SeaDataNet common vocabularies from Library P01, P02 and P03 (https://vocab.seadatanet.org/search, last access: 19 February 2021, P01, P02, P03) and divided as follows:</p> <p> </p> <p><strong>Current datafile format:</strong></p> <p>Progressive data number</p> <p>Date (year/month/day/hour/minute)</p> <p>Position (Lat, lon)</p> <p>- SDN: P01:: MBANZZZZ: Cell of measurement with indication of its depth from surface (z=0);</p> <p>- SDN: P01:: LCSAAP01: cell current intensity (m/s);</p> <p>- SDN: P01:: LCDAAP01: cell current direction (in degree, referenced to North)</p> <p> </p> <p><strong>Wave datafile format:</strong></p> <p>Progressive data number</p> <p>Date (year/month/day/hour/minute)</p> <p>Position (Lat, lon)</p> <p>- SDN: P01:: GTHDAP01: Significant wave height H<sub>s </sub>(m)</p> <p>- SDN: P01:: GTZHAW01: Significant wave period T<sub>s </sub>(s);</p> <p>- SDN: P01:: GWMDAD01: Significant wave incoming direction (in degree, referenced to North);</p> <p>- SDN: P01:: MBANZZZZ: Local depth (mm);</p> <p>- SDN: P01:: GTDHAP01: H<sub>1/10</sub>- Average of the 1/10 highest waves;</p> <p> - SDN: P01:: GTAMZD01: Average wave period T<sub>mean</sub> (s)</p> <p> </p> <p><strong>Meteo datafile format:</strong></p> <p>Progressive data number</p> <p>Date (year/month/day/hour/minute)</p> <p>Position (Lat, lon)</p> <p>- SDN: P01:: EGTSSS01: Average wind velocity (m/s) ;</p> <p>- SDN: P01:: ESSAMX01: Max wind velocity (m/s);</p> <p>- SDN: P01:: EGTDSS01: Wind incoming direction N (deg);</p> <p>- SDN: P01d:: CDTAZZ01: Air temperature (°C);</p> <p>- SDN: P01:: CDEWZZ01:Dew point (°C);</p> <p>- SDN: P01:: CAPHZZ01:Atmospheric pressure (mbar);</p> <p>- SDN: P01:: CHUMZZ01: Relative humidity (%).</p> <p> </p> <p> </p> <p><strong>Water quality parameters datafile</strong></p> <p>Progressive data number</p> <p>Date (year/month/day/hour/minute)</p> <p>Position (Lat, lon)</p> <p>-SDN: P01:: TEMPS901: Water Potential Temperature measured in ITS-90 degrees Celsius (°C); </p> <p>- SDN: P01:: CNDCST01: Conductivity (S/m); </p> <p>- SDN: P01:: <a href="http://vocab.nerc.ac.uk/collection/P01/current/PRESPR01/">PRESPR01</a>: Absolute Pressure (dbar);</p> <p>- SDN: P01:: PSLTZZ01: Practical Salinity (PSU) using PSS78 algorithm;</p> <p>- SDN: P01:: SIGTPR01: Density (kg/m<sup>3</sup>);</p> <p>- SDN: P01:: DOXYOP01:Dissolved oxygen (ml/l);</p> <p>- SDN: P01:: CLSDPM01: Chlorophyll (µg/l); </p> <p>- SDN: P01:: CLSDPM01: Turbidity (NTU);</p> <p>- SDN: P01:: GP001:CDOM (RFU);</p> <p>- SDN: P01:: GP001: Crude oil (RFU);</p> <p>- SDN: P01:: GP001: Refined oil (RF</p> <p> </p> <p><strong>Temperature datafile format:</strong></p> <p>Progressive data number</p> <p>Date (year/month/day/hour/minute)</p> <p>Position (Lat, lon)</p> <p>- SDN: P01:: MBANZZZZ: Sensor depth (m);</p> <p>- SDN: P01:: TEMPS901: Water Potential Temperature measured in ITS-90 degrees Celsius (°C)</p>
Data from: Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery
The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly-unbalanced datasets common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA-ARS in 1931 to characterize performance and quality of near-release wheat varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be reentered only once. The trial is grown at more than 30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-by-sequencing for 939 SPRN entries dating back to 1992. We measured the diversity within the nursery and have explored its potential use as a GS training population. GS prediction models across years (average r= 0.33) outperformed year-to-year phenotypic correlation for yield (r=0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set training populations. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks.
Data from: Mid-winter temperatures, not spring temperatures, predict breeding phenology in the European starling Sturnus vulgaris
In many species, empirical data suggest that temperatures less than 1 month before breeding strongly influence laying date, consistent with predictions that short lag times between cue and response are more reliable, decreasing the chance of mismatch with prey. Here we show in European starlings (Sturnus vulgaris) that mid-winter temperature ca 50–90 days before laying (8 January–22 February) strongly (r2 = 0.89) predicts annual variation in laying date. Mid-winter temperature also correlated highly with relative clutch size: birds laid later, but laid larger clutches, in years when mid-winter temperatures were lower. Despite a high degree of breeding synchrony (mean laying date 5–13 April = ±4 days; 80% of nests laid within 4.8 days within year), European starlings show strong date-dependent variation in clutch size and productivity, but this appears to be mediated by a different temporal mechanism for integration of supplemental cue (temperature) information. We suggest the relationship between mid-winter temperature and breeding phenology might be indirect with both components correlating with a third factor: temperature-dependent development of the starling's insect (tipulid) prey. Mid-winter temperatures might set the trajectory of growth and final biomass of tipulid larvae, with this temperature cue providing starlings with information on breeding season prey availability (though exactly how remains unknown).
Data from: Validation of grain yield QTL from soft winter wheat using a CIMMYT spring wheat panel
Validation of quantitative trait loci (QTLs) is an essential step in marker-assisted breeding. The objectives of this study were to validate grain yield (GY) QTLs previously identified in soft red winter wheat (Triticum aestivum L.) through biparental and association mapping using the spring wheat association mapping initiative (WAMI) panel from CIMMYT, Mexico, and to identify allele combinations of the validated QTLs that resulted to the highest GY. Linked single-nucleotide polymorphisms for IWA3560 (3A), IWA1818 (4B), and IWA755 (6B) were significantly associated (P < 0.001) with GY, grain number, and thousand-grain weight in the WAMI. Lines possessing the favorable allele for the QTL at the 3A, 4B, and 6B loci (ACG allele combination) validated on the WAMI had the highest mean GY at 4.55 t ha−1, but three other haplotypes (ACA, GCA, and GCG) differing by one or two alleles in the validated QTL regions were not significantly different. These results validate GY QTLs across winter and spring wheat through genome-wide association analysis and further demonstrate the potential for pyramiding favorable alleles for the genetic improvement of wheat breeding populations.
Preliminary Breakdown Process of Winter Positive Cloud-to-Ground Lightning Flash and Its relation to the Following First Return Stroke
<p>The file <em>+CG statistics.xlsx</em> contains various statistical parameters for 60 +CG events.</p> <p>The files <em>3D_UPB.dat</em>, <em>3D_DPB.dat</em>, <em>3D_IRPB1.dat</em>, and <em>3D_IRPB2.dat</em> provide the 3D location results for the four example events discussed in the main text. Each file includes data organized in four lines, representing time (ms), x (m), y (m), and z (m), respectively.</p> <p><strong> </strong></p>
QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations
<p><span>The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (<em>QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, </em>and<em> QYR-APR-4D.2</em>) for YR APR and four QTLs (<em>QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, </em>and<em> </em></span><em>QLR-APR-5A.2</em>) <span>for LR APR were identified. </span><span>In silico analysis revealed that the key putative candidate genes such as <em>Cytochrome P450</em></span><em><span>, Protein kinase-like domain superfamily</span><span>, Zinc-binding ribosomal protein</span><span>, SANT/Myb domain</span><span>, WRKY transcription factor</span><span>, Nucleotide-sugar transporter,</span></em><span> and <em>NAC</em> </span><em><span>domain superfamily</span></em><span> were in the QTL regions and involved in the regulation of host response towards the pathogen infection. </span><span>The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).</span></p>
Carbon isotope discrimination and yield of winter wheat in an agrivoltaic system (Heggelbach, Herdwangen-Schönach, Germany) from 2016/17 - 2019/20
Open the record for dataset details and reuse information.
The winter subset of the Saildrone 2021-2022 Mission to the Gulf Stream used for the publication "The importance of contemporaneous measurements for regional air-sea CO2 flux estimates"
<p>Data from the Saildrone 2021-2022 observational mission to the Gulf Stream. These data are published to accompany the publication "The importance of contemporaneous measurements for regional air-sea CO<sub>2</sub> flux estimates." Included in this dataset are the primary and processed variables used throughout the paper. The data associated with each saildrone is named by the drone number. Additionally, included in the structure for each drone are the gas transfer velocities for each scenario, MBL atmospheric CO2 interpolated to the time and location of the drone, and ERA-5 wind speed, sea level pressure, significant wave height, and drag coefficient interpolated to the time and location of the drone. These variables are used to calculate CO<sub>2</sub> fluxes for each scenario and are named as follows: "F" + gas transfer velocity equation used (DM18 or W14) + drone ID + scenario. Scenario A-D correspond to those outlined in the paper. Scenarios E and F correspond to the calculation of air-sea fluxes using all saildrone observed variables except for atmospheric CO<sub>2</sub> (from MBL product) and significant wave height (from ERA-5), respectively. </p>
Air phyto-cleaning by an urban meadow – Filling the winter gap - DATA
<p>Database of article: Air phyto-cleaning by an urban meadow – Filling the winter gap.</p>
Hunting constrains wintering mallard response to habitat and environmental conditions
<p>The spatiotemporal allocation of activity is fundamental to how organisms balance energetic intake and predation risk. Activity patterns fluctuate daily and seasonally, and they are proximately affected by exogenous and endogenous conditions. For birds, flight activity is often necessary for relocating between foraging patches but is energetically expensive and can increase mortality risk. Hunted species may have to adjust their behavior and activity patterns to minimize anthropogenic mortality risk. We used hourly locations from 336 GPS-marked mallards (<em>Anas platyrhynchos</em>) to examine how hunting pressure affected flight activity in response to weather conditions and habitat availability during winter in western Tennessee, USA. Mallards were more likely to fly during crepuscular times, particularly dusk, across winter months. Mallards conducted more flights after shooting hours when habitat availability increased during open hunting season; conversely, mallard flights decreased with increasing habitat availability when hunters were present on the landscape. Mallards were least active during periods open to hunting. However, indicators of approaching inclement weather (i.e., increased wind speed, precipitation, and decreasing barometric pressure) increased flights during periods open to hunting. Mallard flights decreased at lower temperatures except when hunting season was closed, wherein mallards increased nighttime flights. Flight activity was directly influenced by hunting disturbance which constrained when and how mallards reacted to environmental and habitat conditions. An understanding of the temporal shifts in waterfowl flight patterns can be used by natural resource managers to better manage stakeholder satisfaction and expectations.</p>
Migratory and winter movements of Arctic Alaska breeding Sabine's Gulls (Xema sabini)
<p>The Sabine's Gull (<em>Xema sabini</em>) is a pelagic, Arctic-breeding species with a circumpolar breeding distribution. Little is known about migration routes for Sabine's Gulls breeding in the Alaskan Arctic. We tagged Sabine's Gulls on their northern Alaska breeding grounds to identify migration routes and wintering areas and compare geolocators and GPS pinpoint tags for use on small-bodied gulls. Twelve geolocators were deployed in northern Alaska in 2011 (Colville River Delta) of which four were recovered, and five GPS pinpoint tags in 2021 (Qupaluk). Although the GPS pinpoint tags provided more accurate locations allowing for finer-scale habitat evaluation, and did not require recapture of birds, the overall coverage provided by geolocators was superior in this study given the constraints of the number of locations GPS pinpoint tags can record. Broadly, the four (one tag failed) tracked Sabine's Gulls migrated away from the breeding grounds as expected, passing along the west coast of Alaska and south along the west coast of the Americas to winter in the Humboldt Current off the coast of Peru. Our tracked gulls used the same migratory staging and wintering areas as did Sabine's Gulls breeding in the Canadian Arctic (Davis <em>et al.,</em> 2016). Such reliance on specific marine areas presents risks from climate-related changes or ecological damage to those areas.</p>
Data from: Consistent seasonal flexibility of the gut and its regions across wild populations of a winter-quiescent fish
<p>Here, we investigated phenotypic flexibility in the size of the gastrointestinal (GI) tract across three northern populations of a winter-dormant warm-water fish, pumpkinseed sunfish (<em>Lepomis gibbosus</em>). The dried masses of all gut regions (stomach, pyloric caeca, intestine) were measured approximately biweekly between January and August 2021. In all populations, pumpkinseed exhibited pronounced structural flexibility in the GI tract, aligned with winter and the timing of reproduction. The dry mass of the GI increased by 1.3- to nearly 2.5-fold in the early spring. The pyloric caeca demonstrated the greatest capacity for flexibility, increasing by up to 3.7-fold prior to reproduction. In all populations, minimum dry GI mass was consistently achieved during winter and mid-summer. This capacity for gut flexibility may represent a novel mechanism for facilitating rapid adaptive responses (e.g., metabolic plasticity) to future environmental change.</p>
Fig. 4 in Seasonal Variation (Winter Vs. Summer) Crustacean Fauna Of The Oualidia Lagoon, Morocco
Fig. 4. Canonical correspondence analysis plots: A — winter; B — summer.
Fig. 1 in Seasonal Variation (Winter Vs. Summer) Crustacean Fauna Of The Oualidia Lagoon, Morocco
Fig. 1. Location map of the study area and sampling sites in the Oualidia lagoon.
Analysis of the occurrence of the fall armyworm (Spodoptera frugiperda) in the winter season on the southwestern islands of Japan using the insect's strontium radiogenic isotope ratio (87Sr/86Sr)
<p><em>Spodoptera frugiperda</em>, an invasive pest insect that targets maize and other crops, first arrived in Japan in the summer of 2019. This species occurs year-round in East Asian subtropical regions such as southern mainland China and the island of Taiwan, where the mean air temperature in the coldest month is above 10°C. Adults are similarly found throughout the year on the southwestern islands of Japan. Trap monitoring there showed continuous or intermittent <em>S. frugiperda</em> catches in the 3 winter seasons since 2019. However, it was difficult to distinguish between immigrants arriving from these neighboring areas and local individuals occurring on each Japanese island. In this study, the possible natal origin of captured insects on 5 small islands (Yonagunijima, Taramajima, Okinawajima, Amamioshima, and Tanegashima) was determined by investigating the strontium radiogenic isotope ratios (<sup>87</sup>Sr/<sup>86</sup>Sr) and comparing them with those of reference hosts and insects. Since trapping data and the <sup>87</sup>Sr/<sup>86</sup>Sr values of trapped insects didn't support <em>S. frugiperda</em>'s winter breeding on the northernmost island, Tanegashima, further analysis was limited to the 4 southern islands. The <sup>87</sup>Sr/<sup>86</sup>Sr values of reference host plants and reared insects on the 4 islands ranged from 0.70929 to 0.71009, while those of catch insects ranged from 0.70885 to 0.71090. The <sup>87</sup>Sr/<sup>86</sup>Sr values of the catch insects and the reference on the 4 islands did not differ significantly. In addition, the monthly averages of daily mean air temperature in January and February 2020–2022 were above 10°C, and the wind direction at the surface was mostly from the northeast or northwest. These pieces of evidence, together with winter host availability, suggested that <em>S. frugiperda</em> occurs year-round on the islands. In other words, the year-round occurrence area of <em>S. frugiperda</em> in East Asia extends to the Japanese southwestern islands below Amamioshima Island.</p>
Avian diversity study (water birds) of Kamarganti Bheries, Haroa, North 24 parganas, West Bengal, India during winter season
<p>The Nature Mates Nature Club has released a dataset entitled "Avian diversity study (water birds) of Kamarganti Bheries, Haroa, North 24 parganas, West Bengal, India during winter season" </p> <p>Bheries are the type of fisheries generally practiced in low land impounded with earthen embankments all round.</p> <p>The age-old bheri-culture has emerged in North 24 Parganas District in its south and eastern skirts centering the lower course of the Bidyadhari River. Salinity in bheri-water is sourced from brackish sea-water entered through rivers and canals..</p> <p>The district has 22 community development (CD) blocks, though brackish water bheri fishery is usually found to exist in its 13 blocks in the south and eastern skirts.</p> <p>This dataset is concerned with one such area, Kamarganti,Haroa,North 24 Parganas.</p> <p>A list of all the birds identified during a biodiversity survey carried out in the year 2023 is included in the dataset. During the survey Red Necked Phalarope (Phalaropus lobatus) was recorded which is a very rare bird in West Bengal.</p> <p>Species or genus-level identifications have been made for every species. There are 42 bird species total, with records of them in 16 different families and 10 different orders.</p> <p>This study can give a future scope in understanding how bheries affect the biodiversity of an area.</p> <p>Resource Contacts</p> <p>Name: Arjan Basu Roy</p> <p>Position: Secretary</p> <p>Organization: Nature Mates-Nature Club</p> <p>Address: 6/7 Bijoygarh Kolkata-700032</p> <p>Email: basuroyarjan@gmail.com</p> <p>Orcid :<a href="https://orcid.org/0000-0001-9872-3562"> https://orcid.org/0000-0001-9872-3562</a></p> <p>Home page:<a href="http://www.naturematesindia.org/"> http://www.naturematesindia.org/</a></p> <p><strong> </strong></p> <p>Name: Lina Chatterjee</p> <p>Position: Research Associate</p> <p>Organization: Nature Mates-Nature Club</p> <p>Address: 6/7 Bijoygarh Kolkata-700032</p> <p>Email: lina.linachatterjee@gmail.com</p> <p>Orcid :<a href="https://orcid.org/0000-0002-5626-5046"> https://orcid.org/0000-0002-5626-5046</a></p> <p>Home page:<a href="http://www.naturematesindia.org/"> http://www.naturematesindia.org/</a></p> <p>Name: Tarak Samanta</p> <p>Position: Research Associate</p> <p>Organization: Nature Mates-Nature Club</p> <p>Address: 6/7 Bijoygarh Kolkata-700032</p> <p>Email: taraksamanta995@gmail.com</p> <p>Orcid :<a href="https://orcid.org/0000-0001-6809-0549"> https://orcid.org/0000-0001-6809-0549</a></p> <p>Home page:<a href="http://www.naturematesindia.org/"> http://www.naturematesindia.org/</a></p> <p><strong> </strong></p> <p>Name: Nivedita Sengupta</p> <p>Position: Intern</p> <p>Organization: Nature Mates-Nature Club</p> <p>Address: 6/7 Bijoygarh Kolkata-700032</p> <p>Email: niveditasngpta.ns@gmail.com</p> <p>Orcid :<a href="https://orcid.org/0000-0003-1085-7385"> https://orcid.org/0000-0003-1085-7385</a></p> <p>Home page:<a href="http://www.naturematesindia.org/"> http://www.naturematesindia.org/</a></p> <p> </p> <p>Name: Vijay Barve</p> <p>Position: Research Advisor</p> <p>Organization: Nature Mates-Nature Club</p> <p>Address: 6/7 Bijoygarh Kolkata-700032</p> <p>Email: vijay.barve@gmail.com</p> <p>Orcid :<a href="https://orcid.org/0000-0002-4852-2567"> https://orcid.org/0000-0002-4852-2567</a></p> <p>Home page:<a href="http://www.naturematesindia.org/"> http://www.naturematesindia.org/</a></p>
Data from: Variation in prevalence and intensity of macroparasites in moose and their interactions with winter tick load in eastern Canada
<p>Wild animals are infected with a large diversity and abundance of parasites that can affect their behavior, growth, body condition, and ultimately their survival. Although the adverse effects of parasites and the mechanisms involved in the interactions between a host and its parasites are generally well studied, much less is known about the additive or synergistic effects of multiple parasite species on a host. Moose populations in eastern Canada are infected by several species of endoparasites. In the last decades, the intensity of infestations by winter ticks, an ectoparasite, on moose have increased as a result of increased moose densities and favorable weather conditions that benefit winter tick survival. We aimed to document the diversity, intensity, prevalence, and distribution of different parasite species of moose in southern Quebec, Canada. We then evaluated the potential interaction between winter tick and endoparasites of moose, and we evaluated the effect of the simultaneous presence of ticks and endoparasites on moose body condition. To do so, we collected organs to identify and count endoparasite species, estimate winter tick abundance, and measure subcutaneous fat thickness from 174 hunted moose in fall 2019 in 8 regions of Quebec. Our results showed that the prevalence and intensity of winter tick and gastrointestinal parasites differed among regions, as well as the prevalence of the heart parasite <em>Taenia krabbei</em> and the intensity of lung parasite <em>Echinoccocus granulosus</em>. Moose body condition, however, was not influenced by the simultaneous presence of winter tick and endoparasites. The documentation of the interactive effects of multiple parasite species on a host is fundamental given that future environmental conditions in temperate climate will favor the reproduction, development, and survival of several parasite species, which could affect parasite diversity and abundance in the environment and modify host-parasite dynamics.</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.