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4,243 results for “seasonality”
Fig. 3 in Transportation of microplastic during high-flow and low-flow seasons in southeastern Black Sea: A modelling approach
Fig. 3 — Snapshots of microplastic distribution on southeastern Black Sea in high-flow (S1, S2, and S3) and low-flow (S4, S5, and S6)
Figure 5 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 5. Distribution and mean monthly trap captures of Dacus longicornis, in relation with abiotic factors and host fruit availability.
Figure 4 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 4. Distribution and mean monthly trap captures of Zeugodacus cucurbitae (A) and Z. tau (B), in relation with abiotic factors and host fruit availability.
Figure 3 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 3. Distribution and mean monthly trap captures of Bactrocera rubigina (A) and B. correcta (B), in relation with abiotic factors.
Figure 2 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 2. Distribution and mean monthly trap captures of Bactrocera dorsalis (A) and B. zonata (B), in relation with abiotic factors and host fruit availability.
Figure 1. A in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 1. A: Fruit fly trapping sites maintained at the Atomic Energy Research Establishment compound in Bangladesh in 2016–2017 (sites 1 to 10) and 2017–2018 (sites 1, 8, 9). B: Mean monthly rainfall and minimum and maximum temperature recorded in Dhaka, Bangladesh, during the study period.
Fig. 1 in Seasonal residency of loggerhead turtles Caretta caretta tracked from the Gulf of Manfredonia, South Adriatic Abstract
Fig. 1: Positions and paths of four loggerhead turtles (A, B, D, E) which remained in the Gulf of Manfredonia during the monitored period. The grey areas represent KDE 50%. Isobaths are shown (10, 20, 50 m).
Fig. 2 in Seasonal residency of loggerhead turtles Caretta caretta tracked from the Gulf of Manfredonia, South Adriatic Abstract
Fig. 2: Turtle C. (a) entire path.(b) coastal subarea of the periods 4 Jul-11 Nov 2012 and 3 May-27 Jun 2013, where the grey area represents KDE 50% for aggregated data. (c)the same subarea with separate KDE 50% for the period 2012 (grey area) and 2013 (ellipse). AL: Albania; BA: Bosnia and Herzegovina; HR: Croatia; ME: Montenegro. Isobaths 200m (a) and 20 m (b and c) are shown.
Ten-a-day: bumblebee pollen loads reveal high consistency in foraging breadth among species, sites, and seasons
<p>Pollen and nectar are crucial resources for bees, but vary greatly amongst plant species in their quantity, nutritional quality, and timing of availability. This makes it challenging to identify an appropriate range of plants to meet the nutritional needs of pollinators through the year, though this information is important in the design of pollinator conservation schemes.</p> <p>Using DNA metabarcoding of pollen loads, we record the floral resource use of UK farmland bumblebees at different stages of their colony lifecycle, and compare this with null models of 'expected' resource use based on landscape-scale resource availability (pollen and nectar), to identify foraging priorities and preferences. We use this approach to ask three main questions: i) what is the foraging breadth of individual bumblebees?; ii) do bumblebees utilise a greater or lesser diversity of plant species than expected if they foraged in proportion to resource availability?; iii) which plant species do bumblebees preferentially utilise?</p> <p>Individual bumblebees foraged from a highly consistent number of different plant taxa (mean: 10 ±0.37 SE per bee), regardless of their species, sampling site, or time of year. This high consistency in foraging breadth, despite large changes in the quantity, identity, and diversity of resource availability, implies a strong behavioural tendency towards a fixed range of foraging resources. This effect was most striking in April when foraging diversity was maintained despite very low landscape-level resource diversity.</p> <p>Bumblebees used some plant taxa significantly more than predicted from their landscape-level floral abundance, nectar, or pollen supply, implying certain desirable characteristics beyond the mere quantity of resource. These included <em>Allium</em> spp. and <em>Vicia</em> spp. in April; <em>Trifolium repens</em> and <em>Lotus corniculatus</em> in July; and <em>Cynareae</em> spp. (thistles) and <em>Taraxacum officinale</em> in September.</p> <p>Our results strongly indicate that resource quantity is not the only factor driving bumblebee foraging patterns, and that resource diversity and quality are also important factors. Thus, in addition to providing large quantities of floral resources, we recommend that pollinator conservation schemes also focus on providing a sufficient diversity of preferred floral resources, enabling pollinators to self-select a diverse and nutritious diet.</p>
Figure 1 in Circadian and seasonal variations in the metabolism of carbohydrates in Aegla ligulata (Crustacea: Anomura: Aeglidae)
Figure 1. Circadian and seasonal variations of haemolymphatic glucose levels in Aegla ligulata Bond-Buckup and Buckup, 1994, males and females. Data are given as mean ± SEM. The number of animals at each point varied between 15 and 20. The same letter denotes significantly different means (P<0.05). * denotes significantly different means of the spring (Sep, Oct and Nov), winter (Jun, Jul and Aug), summer (Dec, Jan and Feb) and autumn (Mar, Apr and May). Numbers 1, 2 and 3 stand for the collection times: 0600, 1200 and 1800 h, respectively.
FIG. 3 in The canopy-forming alga Ericaria brachycarpa (J.Agardh) Molinari-Novoa & Guiry (Fucales, Phaeophyceae) shows seasonal and depth adaptation to the incoming light levels
FIG. 3. — Lineal fitting of the photosynthesis/PFD data at the lineal part of the P/PFD curve for the algal specimens collected at different depths.
FIG. 4 in The canopy-forming alga Ericaria brachycarpa (J.Agardh) Molinari-Novoa & Guiry (Fucales, Phaeophyceae) shows seasonal and depth adaptation to the incoming light levels
FIG. 4. — Photosynthesis at saturation (Psat), photosynthesis at low light levels (Pb) and dark respiration (Rd) for specimens thriving at 3 and 20 m (not transplanted: nt3 and nt20) and for those transplanted at the same depth (3to3 and 20to20) and at different depths (3to20 and 20to3) after 11 and 90 days after transplantation.
FIG. 1 in The canopy-forming alga Ericaria brachycarpa (J.Agardh) Molinari-Novoa & Guiry (Fucales, Phaeophyceae) shows seasonal and depth adaptation to the incoming light levels
FIG. 1. — Percentage abundance of Ericaria brachycarpa (J. Agardh) MolinariNovoa & Guiry at the sampling station estimated from 50 reticulated quadrats of 625 cm2 per depth.
Fig.3 in Ancyrophora gracilis L , 1892 and Actinocephalus permagnus Wellmer, 1910 (Eugregarinorida: Apicomplexa) in natural populations of ground beetles (Coleoptera, Carabidae) - hosts preferences, intensity and seasonal dynamic
Fig.3. The mean density ± SE of Actinocephalus permagnus (circle) and Ancyrophora gracilis (square) in consecutive seasons
Fig. 1 in Ancyrophora gracilis L , 1892 and Actinocephalus permagnus Wellmer, 1910 (Eugregarinorida: Apicomplexa) in natural populations of ground beetles (Coleoptera, Carabidae) - hosts preferences, intensity and seasonal dynamic
Fig. 1. Mean density of Actinocephalus permagnus (AT) and Ancyrophora gracilis (AC) in relation to host size classes. circle – medium sized, squares – broad sized hosts
Data from: Evidence for seasonal compensation of hunting mortalities in a long-lived migratory bird
<p>Understanding whether hunting mortality is additive to or compensated by other mortality sources is at the heart of managing harvested populations. Long-lived species are expected to exhibit hunting mortality additive to other sources of mortality, making them ideal candidates for population management through sport harvest. Previous studies on these processes have focussed on density-dependent natural mortality compensating for hunting mortality, but when harvest occurs in distinct periods of the year, heterogeneity in hunting vulnerability between individuals could also lead to compensatory mortality between these periods. We explore this new idea using the case of the greater snow goose (<em>Anser caerulescens atlantica</em>), a harvested species whose population became overabundant in the late 20<sup>th</sup> century. To control this population, wildlife agencies liberalized hunting regulations with unprecedented actions such as special hunting seasons implemented in spring 1999 in Canada and in winter 2009 in the USA. To determine the relative impact of each measure on survival, we estimated survival of adult geese on a seasonal basis using 30 years of capture-mark-reencounter data in a joint live-and-dead-encounter multievent model. We also used this quasi-experimental set-up to evaluate possible compensation in hunting mortality between seasons. We found that both special hunting seasons decreased goose survival in the seasons and periods in which they were implemented. However, survival increased during the spring hunting season after the establishment of the special winter hunting season in the USA in 2009. There was a negative relationship between annual spring and winter mortalities, suggesting that the increase in hunting mortality in winter was compensated by a reduction in spring mortality after 2009.</p> <p><em>Synthesis and applications:</em> To our knowledge, we report the first documented instance of hunting mortality in one season being compensated by a reduction in hunting mortality in a subsequent season. We suggest that heterogeneity in hunting vulnerability among individuals, possibly linked to the presence of juveniles, may explain this phenomenon. A better knowledge of seasonal patterns and relationships between mortality components is needed to improve our understanding of population dynamics and management of harvested populations.</p>
Figure 2 in Non-breeding season records of the Alpine Leaf Warbler Phylloscopus occisinensis
Figure 2. Alpine Leaf Warbler Phylloscopus occisinensis, before release, Hang Dong District, Chiang Mai Province, Thailand, 24 January 2020 (Sontaya Manawattana)
Seasonal and local time variation in the observed peak of the meteor altitude distributions by meteor radars
<p>These uploaded datasets support and appear in the the paper entitled "<strong>Seasonal and local time variation in the observed peak of the meteor altitude distributions by meteor radars</strong>" prepared by: </p> <p>E.C.M. Dawkins<sup>1,2</sup>, D. Janches<sup>1</sup>, G. Stober<sup>3</sup>, J.D. Carrillo-Sánchez<sup>1,2</sup>, R.S. Lieberman<sup>1</sup>, C. Jacobi<sup>4</sup>, T. Moffat-Griffin<sup>5</sup>, N.J Mitchell<sup>5,6</sup>, N. Cobbett<sup>5</sup>, P.P.Batista<sup>7</sup>, V.F. Andrioli<sup>7,8</sup>, R.A. Buriti<sup>9</sup>, D.J. Murphy<sup>10</sup>, J. Kero<sup>11</sup>, N. Gulbrandsen<sup>12</sup>, M. Tsutsumi<sup>13,14</sup>, A. Kozlovsky<sup>15</sup>, M. Lester<sup>16</sup>, J.-H. Kim<sup>17</sup>, C. Lee<sup>17</sup>, A. Liu<sup>18</sup>, B. Fuller<sup>19</sup>, D. O’Connor<sup>19</sup>, S.E. Palo<sup>20</sup>, M.J. Taylor<sup>21</sup>, J.Marino<sup>22</sup>, and N. Rainville<sup>20</sup>.</p> <p> </p> <p>1 ITM Physics Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, U.S.A.</p> <p>2 Department of Physics, Catholic University of America, DC, U.S.A.</p> <p>3 University Bern, Institute of Applied Physics, Microwave Physics, Bern, Switzerland</p> <p>4 Institute for Meteorology, Leipzig University, Germany</p> <p>5 British Antarctic Survey, Cambridge, U.K.</p> <p>6 University of Bath, Bath, U.K.</p> <p>7 National Institute for Space Research (INPE), São José dos Campos, SP, Brazil</p> <p>8 China-Brazil Joint Laboratory for Space Weather, NSSC/INPE, São José dos Campos, SP, Brazil</p> <p>9 Department of Physics, Federal University of Campina Grande, Campina Grande, PB, Brazil</p> <p>10 Australian Antarctic Division, Kingston, TAS, Australia</p> <p>11 Swedish Institute of Space Physics (IRF), Kiruna, Sweden</p> <p>12 Tromsø Geophysical Observatory, UiT - The Arctic University of Norway, Tromsø, Norway</p> <p>13 National Institute of Polar Research, Tachikawa, Japan</p> <p>14 The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan</p> <p>15 Sodankylä Geophysical Observatory, University of Oulu, Finland</p> <p>16 Department of Physics and Astronomy, University of Leicester, Leicester, U.K.</p> <p>17 Division of Atmospheric Sciences, Korea Polar Research Institute, Incheon, S. Korea</p> <p>18 Center for Space and Atmospheric Research and Department of Physical Sciences, Embry-Riddle Aeronautical University, Daytona Beach, Florida, U.S.A.</p> <p>19 Genesis Software, Pty Ltd., Adelaide, SA, Australia</p> <p>20 Colorado Center for Astrodynamics Research (CCAR), Ann and H.J. Smead Aerospace Engineering Sciences, College of Engineering and Applied Sciences, University of Colorado Boulder, Boulder, CO, U.S.A.</p> <p>21 Department of Physics, Utah State University, Logan, UT, U.S.A</p> <p>22 University of Colorado at Boulder, Boulder, CO, U.S.A</p> <p> </p> <p> </p> <p>The datasets below are titled according to the figure in which they are used (e.g. "Fig3" for Figure 3, "Fig4" for Figure 4).<br>All uploaded datasets comprised of ASCII files.<br><br>Dataset descriptions:</p> <ul> <li>Figure 3 datasets (<strong>18 files in total</strong>): Each of the 18 different files corresponds to a different meteor radar station (SVA, TRO, KIR, SOD, COL, BLO, CAR, ASI, LEA, CPa, SMa, CON, TdF, KEP, KSS, ROT, DAV, MCM). Within each file, the data comprise of peak meteor altitudes (km) as a function of local time (24) and day-of-year (DOY). </li> <li>Figure 4 datasets (<strong>18 files in total</strong>): As above, but the data now represent the weighted elevation angle in degrees.</li> <li>Figure 5 datasets (<strong>24 files in total</strong>): These data can be used to plot the residual seasonal variation in peak altitude for each of the 18 locations, organized by geographic clusters. There are 24 different Figure 5 datasets, with each including the normalized residual seasonal variation in peak altitude (km) for stations within one of six different geographic clusters (Nordic high-latitude, Northern mid-latitude, Near-equatorial, Southern low/mid-latitude, Southern Andes, Mainland Antarctica) for each local time (00:00 LT, 06:00 LT, 12:00 LT, or 18:00 LT). Each file includes the data for all stations within that given cluster (i.e., "Fig5__Mainland_Antarctica__06LT__Dawkins_et_al_2024.tex" includes data for the Mainland Antarctica cluster (both DAV and MCM) for 06:00 LT), as a function of day-of-year (365) and normalized altitue (km).</li> <li>Figure 6 datasets (<strong>4 files in total</strong>): These data represent the mean absolute deviation (MAD, km) of each of the different geographic clusters as function of DOY (365) for four different local times (00:00 LT, 06:00 LT, 12:00 LT, and<br>18:00 LT).</li> <li>Figure 7 datasets (<strong>14 files in total</strong>): These files present the kinetic gravity wave energy (KGWE) as a function of day-of-year and altitude (km). 12 of the files correspond to one of the following locations: SVA, TRO, KIR, SOD, COL, BLO, CON (ALO only), TdF, KEP, KSS, ROT or DAV. There are two additional files ("Fig7__KGWE__time__Dawkins_et_al_2024.txt" and "Fig7__KGWE__altitude__Dawkins_et_al_2024.txt") which include the time (day-of-year) and altitudes (km) used.</li> <li>Figure 8 datasets (<strong>2 files in total</strong>): These two files ("Fig8__CABMOD_profiles__data__Dawkins_et_al_2024.txt" and "Fig8__CABMOD_profiles__altitude__Dawkins_et_al_2024.txt") include the data necessary to reproduce all panels in Figure 8 which shows the vertical mass profiles from CABMOD for a meteoric particle with a fixed initial mass (178 μg) and velocity (31 kms−1), at a latitude of 60 deg S. The dataset (mass, μg) corresponds to 8 different month and entry angles (in order: March, June, September, December for particle entry angles of 5 deg and 25 deg, respectively) and 201 altitudes (km).</li> <li>Figure 9 datasets (<strong>8 files in total</strong>): These data represent the simulated and observed peak altitudes (km) as a function of day-of-year and LT for each of the four Southern Andes meteor radar station locations (TdF, KEP, KSS, ROT).</li> </ul>
Fig. 6 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall
Fig. 6: Rank-frequency diagram for the Ser(a) and Res(b) stations using the cumulative abundance (as relative frequency) for y-axe and the decreasing rank order for x-axe of each species for each sample. Both axes are on logarithmic scale.
Fig. 4 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall
Fig. 4: Foraminiferal data for Ser and Res stations using the complete living assemblage of the 0-2 cm level: a) Foraminiferal density (FD) as the number of specimens normalised to 50 cc of sediments and species richness; b) Dominance index; c) bias corrected Shannon (H' bc) and the exponential function Exp(H' ) indexes.
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.