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2,171 results for “;migration;”
Data from: Early-life variation in migration is subject to strong fluctuating survival selection in a partially migratory bird
<p>Population dynamic and eco-evolutionary responses to environmental variation and change fundamentally depend on combinations of within- and among-cohort variation in phenotypic expression of key life-history traits, and on corresponding variation in selection on those traits. Specifically, in partially migratory populations, spatio-seasonal dynamics depend on the degree of adaptive phenotypic expression of seasonal migration versus residence, where more individuals migrate when selection favours migration.</p> <p>Opportunity for adaptive (or, conversely, maladaptive) expression could be particularly substantial in early life, through initial development of migration versus residence. However, within- and among-cohort dynamics of early-life migration, and of associated survival selection, have not been quantified in any system, preventing any inference on adaptive early-life expression. Such analyses have been precluded because data on seasonal movements and survival of sufficient young individuals, across multiple cohorts, have not been collected.</p> <p>We undertook extensive year-round field resightings of 9,359 colour-ringed juvenile European Shags (<em>Gulosus aristotelis</em>) from 11 successive cohorts in a partially-migratory population. We fitted advanced Bayesian multi-state capture-mark-recapture models to quantify early-life variation in migration versus residence and associated survival across short temporal occasions through each cohort's first year from fledging, thereby quantifying the degree of adaptive phenotypic expression of migration within and across years.</p> <p>All cohorts were highly partially migratory, but the degree and timing of migration varied considerably within and among cohorts. Episodes of strong survival selection on migration versus residence occurred both on short timeframes within years, and cumulatively across whole years, generating instances of instantaneous and cumulative net selection that would be obscured at coarser temporal resolutions. Further, the magnitude and direction of selection varied among years, generating strong fluctuating survival selection on early-life migration across cohorts, as rarely evidenced in nature. Yet, the degree of migration did not strongly covary with the direction of selection, indicating limited early-life adaptive phenotypic expression.</p> <p>These results reveal how dynamic early-life expression and selection on a key life-history trait, seasonal migration, can emerge across seasonal, annual, and multi-year timeframes, yet be substantially decoupled. This restricts the potential for adaptive phenotypic, micro-evolutionary, and population dynamic responses to changing seasonal environments.</p>
Data for: Dysregulation of mTOR signaling mediates common neurite and migration defects in both idiopathic and 16p11.2 deletion autism neural precursor cells
<p>Autism spectrum disorder (ASD) is defined by common behavioral characteristics, raising the possibility of shared pathogenic mechanisms. Yet, vast clinical and etiological heterogeneity suggests personalized phenotypes. Surprisingly, our iPSC studies find that six individuals from two distinct ASD subtypes, idiopathic and 16p11.2 deletion, have common reductions in neural precursor cell (NPC) neurite outgrowth and migration even though whole genome sequencing demonstrates no genetic overlap between the datasets. To identify signaling differences that may contribute to these developmental defects, an unbiased phospho-(p)-proteome screen was performed. Surprisingly, despite the genetic heterogeneity, hundreds of shared p-peptides were identified between autism subtypes including the mTOR pathway. mTOR signaling alterations were confirmed in all NPCs across both ASD subtypes and mTOR modulation rescued ASD phenotypes and reproduced autism NPC-associated phenotypes in control NPCs. Thus, our studies demonstrate that genetically distinct ASD subtypes have common defects in neurite outgrowth and migration which are driven by the shared pathogenic mechanism of mTOR signaling dysregulation.</p>
Seismic profiles, migration velocities and tomographic inversion results of the deep reflection profiles in the central South China
<h1><strong>Overview</strong></h1> <p>The following set of data and scripts are meant to accompany the paper:</p> <p>Jiang, W. B., Wang, Q., Zhang, Y.Q., Dong, S.W., Ruan, Y.Q., Cui, J.J., Kuang, Z.Y., Paleoproterozoic collision to Mesozoic crustal reworking in central South China: evidence from borehole data and seismic crustal structure, Submitted to JGR: Solid Earth</p> <p>The data and scripts are intended to reproduce seismic profiles, migration velocities, and tomographic inversion results shown in the paper.</p> <p>The repository contains five directories:</p> <p><strong>./01_Seismic_Profiles/</strong> -> Four seismic profiles shown in the manuscript. (1) psdm_line01.sgy (Figure 7a); (2) psdm_line02.sgy (Figure 7b); (3) SCB_PSDM.segy (Figure 9); (4) SCB_PSTM.segy (Figure S5a).</p> <p><strong>./02_Tomographic_Data_Velocity/</strong> -> picked_traveltimes.tt, picked traveltime for the normal shots (Figure 6a). The file contains location of shots and receivers, picked traveltimes; Tomo_inv.mdl, P-wave velocity model derived from first-arrival traveltime tomography (Figure 8a); Tomo_Fx1_Fz1_ray.mdl, Ray density distribution calculated using the Vp model (Figure 8b); traveltime_data_FILE_FORMAT.pdf, this pdf file describes the format of *.tt file; mdl_data_FILE_FORMAT.pdf, this pdf file describes the format of *.mdl file.</p> <p><strong>./03_Migration_Velocity_Models/ </strong>-> Migration velocity models to produce prestack time migration profile and prestack depth migration profile. VEL_RMS.mdl, RMS velocity field used in prestack time migration (Figure S4a); VEL_INTERVAL.mdl, Interval velocity field used in prestack depth migration (Figure S4b).</p> <p><strong>./04_Bouguer_Gravity_Anomaly/ </strong>-> gravity_data.grv, the Bouguer gravity anomaly data used in 2-D gravity modelling; gravity_data_FILE_FORMAT.pdf, this pdf file describes the format of *.grv file.</p> <p><strong>./05_Scripts/</strong> -> Matlab scripts to reproduce seismic profiles, migration velocities, and tomography inversion results shown in the paper. </p>
F I G U R E 5 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea
F I G U R E 5 Rose diagrams depicting (a) the hour of the day and (b) the direction of currents () when Atlantic salmon (Salmo salar) post-smolts were initially detected at a unique acoustic receiver on monitoring line B. The green and blue arrows show the mean hour (a) and mean current direction (b) when post-smolts were initially detected respectively (Lilly et al., 2022). The orange and yellow bands (a) show the variation in sunrise and sunset times for the total period over which any post-smolts were detected on monitoring line B (ie. April 21st–June 20th).
F I G U R E 4 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea
F I G U R E 4 Heatmaps displaying the number of Atlantic salmon (Salmo salar) post-smolts detected at each acoustic receiver on monitoring lines A and B (Figure 1) during the period of this study. The black stars show the location of each river (n = 11) where Atlantic salmon post-smolts originated. Rivers are grouped by coastal region where they entered the Irish Sea (Figure 1, (a) Region 1: Rivers Derwent, Nith, Bladnoch; (b) Region 2: Rivers Endrick, Gryffe; (c) Region 3: Rivers Bann, Bush, Carey, Glendun; (d) Region 4: Rivers Roe, Faughan).
F I G U R E 2 A in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea
F I G U R E 2 A boxplot plot displaying the dates (mm-dd) when Atlantic salmon (Salmo salar) post-smolts (n = 582) were last detected in their natal river/estuary (Rivers Endrick, Gryffe, Roe, Faughan) or coastal embayment (River Burrishoole) and entered the coastal zones of the Irish Sea or the west coast of Ireland (River Burrishoole; Figure 1: Clew Bay) and were detected on monitoring lines A and B (excluding the River Burrishoole Figure 1). In the boxplots, the centre line represents the median, the box encompasses the 25 to 75% quartiles, the bars are the values within 1.5 interquartile units and the dots represent outliers. It should be noted that the dates when smolts were tagged (represented by the dashed black line) differed in each river system. The thick black lines divide rivers into their coastal regions (see methods).
F I G U R E 1 Map displaying the 14 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea
F I G U R E 1 Map displaying the 14 capture sites in 12 rivers where Atlantic salmon smolts (n = 1008) were captured for tagging in England, Scotland, Northern Ireland and the Republic of Ireland for this study. In addition, 60 hatchery origin smolts were tagged and released in the River Burrishoole. The coastal region each river belongs to is referenced in brackets next to the river name. Where Region one (1) refers to the Solway Firth (Rivers Derwent, Nith, Bladnoch); Region two (2) refers to the Clyde Estuary (Rivers Endrick and Gryffe); Region three (3) refers to the Bush Coastal region (rivers Bann, Bush, Carey and Glendun); Region four (4), refers to Lough Foyle (rivers Roe and Faughan); Region five (5), refers to Clew Bay (River Burrishoole). Tagged fish release sites are represented by stars, and acoustic receivers (n = 183) are represented by gray dots. Marine monitoring lines (A and B) in the Irish Sea are labeled in alphabetical order from south to north. Twenty-two acoustic receivers were initially deployed at monitoring line A. One hundred and eight acoustic receivers were deployed at monitoring line B and are labeled in numerical order from the furthest west receiver (R1) on the monitoring line to the furthest east (R108). Refer to Figure S2 for the locations of acoustic receivers that were not retrieved from marine monitoring line A (n = 2) and B (n = 9).
F I G U R E 3 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea
F I G U R E 3 The binomial General Linear Model (GLM) model showing the effect of minimum migration distance (Distance [km]) from the exit of smolts natal river/estuary to monitoring line B on the probability of migration success (measured as minimum migration success) of Atlantic salmon (Salmo salar) post-smolt through the Irish Sea. The shaded region is the 95% confidence interval of the final model.
Fig. 1 in Infestation With Ixodes Ricinus Ticks On Migrating Passerine Birds In Lithuania And Norway
Fig. 1 Molecular taxonomical identification of the I. ricinus by PCR assay. Lines 1 and 15 – 50 bp marker; Line 2 –negative control; Lines 2-13 –positive results: amplified 150 bp specific fragment for I. ricinus; Line 14 – positive control of I. ricinus (150 bp)
Panel of self-reported environmental and non-environmental shocks and migration in Tanzania
<p>This clean dataset was produced by Julia Blocher and Roman Hoffmann using the National Panel Survey - Tanzania: Data on Living Standards conducted by the World Bank and National Bureau of Statistics (https://www.worldbank.org/en/programs/lsms/brief/national-panel-survey-data-on-the-living-standards-of-tanzania). Our data set contains three survey waves (2008/9, 2010/11, 2012/13) and is different from the TZNPS data because it is grouped by a harmonized household and individual identifiers that can be tracked across the three waves. It was used for the paper "The effects of environmental and non-environmental shocks on livelihoods and migration in Tanzania", Population & Environment 46(1). Later waves are not included as the sample differs.</p>
Supplementary material for "In-flight reactions of nocturnally migrating birds to winds"
<p><strong>Abstract</strong></p> <p>Available knowledge on in-flight reactions of nocturnal bird migrants to winds is reviewed, with emphasis on the challenging topographical and meteorological conditions in Western Europe, and differences from the situation in North America discussed. Conclusions drawn are used for a new approach: using individual radar tracks of nocturnal migrants (mainly passerines) as well as winds measured at their flight altitudes, we defined the basic direction (BD=average flight direction of all migrants tracked under negligible wind influence) as a reference. For two altitudinal zones above a radar site near Nuremberg, we modelled the deviations of tracks and headings from BD for increasing wind from six 60° sectors. A comparison of birds’ air speeds Va with winds from four 90°-sectors confirmed that Va increased with opposing winds from ~11 to 13 (14) m/s; a similar increase occurred with side winds. An expected, slight decrease of Va with increasing following winds was only indicated for high-flying, not for low-flying birds. A predicted increase in average Va due to decreasing air density with increasing height was not observed; possible explanations (birds climbing to high altitudes in following, but not in strong opposing winds) are discussed. Over the whole autumn migration season, headings were concentrated in a sector of ±30° around 230° in both altitudinal zones. Prevailing winds from 230 to 320° (SW–NW, i.e. opposing from right) led to widely scattered tracks primarily between 190° and 270°, but additional ones in the SE sector (mainly 100°–170°). The analysis of tracks and headings relative to BD revealed the following features. (1) Overcompensation was frequently observed at low wind speeds (<3 m/s); (2) under all wind conditions, but particularly with opposing winds and at low flight levels, tracks were widely scattered, including birds deviating more than 90° from BD. (3) Under opposing and side winds from the right compensatory efforts led to partial drift compensation up to wind speeds of ~8–10 m/s. Because efforts to compensate drift dwindled with increasing wind speeds, birds were fully drifted. Many even shifted their heading to due south and, hence, overdrifted. (4) Opposing and side winds from the left induced partial compensation at low flight levels and full drift above 1500 m asl. (5) The lateral components of the rare and weak following winds led to tracks close to expected minimal drift (without important compensation needed). In general, migrants compensated less for deviations by wind force than expected. The tendency of birds to maintain headings close to BD under opposing winds was so strong that many individuals continued migration with minimal progress over ground or even with retrograde migration as an extreme. On the other hand, there was an omnipresent fraction of birds with tracks far from seasonally favourable directions, including reverse migration.</p>
Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration
<p>Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration https://zenodo.org/records/14997527</p>
Data from: Reversal of the adipostat control of torpor during migration in hummingbirds
<p>Many small endotherms use torpor to reduce metabolic rate and manage daily energy balance. However, the physiological "rules" that govern torpor use are unclear. We tracked torpor use and body composition in ruby-throated hummingbirds (<i>Archilochus colubris</i>), a long-distance migrant, throughout the summer using respirometry and quantitative magnetic resonance. During the mid-summer, birds entered torpor at consistently low fat stores (~5% of body mass), and torpor duration was negatively related to evening fat load. Remarkably, this energy-emergency strategy was abandoned in the late summer when birds accumulated fat for migration. Migrating birds were more likely to enter torpor on nights when they had higher fat stores, and fat gain was positively correlated with the amount of torpor used. These findings demonstrate the versatility of torpor throughout the annual cycle and suggest a fundamental change in physiological feedback between adiposity and torpor during migration. Moreover, this study highlights the underappreciated importance of facultative heterothermy in migratory ecology.</p>
Fig. 2 in Flock Size Measures Of Migrating Lesser White-Fronted Geese Anser Erythropus
Fig. 2. The distribution of Lesser White-fronted Geese flocks (above) and individuals (below) among flock size categories in the autumn at Hortobágy, 1994–2006
Surveys to understand asylum-related migration
<p>In May-June 2021, <a href="http://cost.eu/actions/CA16111/">ETHMIGSURVEYDATA</a>—a COST Action and research network dedicated to quantitative surveys on ethnic and migrant minorities' (EMMs') integration and inclusion—organized a <em>Policy Dialogue Webinar Series </em>to share ETHMIGSURVEYDATA’s main results, as well as to showcase EMM projects that are ongoing in policy-relevant institutions. These webinars have included presentations by the European Asylum Support Office (EASO), the European Commission Joint Research Centre (JRC), the International Organization for Migration (IOM), and the Organisation for Economic Co-operation and Development (OECD).</p> <p>This is the video recording for the 4th webinar of the <em>Policy Dialogue Webinar Series, </em>which was held on 29 June 2021: "Surveys to understand asylum-related migration." This video recording has also been made available on the <a href="https://www.youtube.com/watch?v=mfQxhA1c7lA&t=19s">ETHMIGSURVEYDATA Youtube channel</a>.</p> <p>The full program for this webinar can be found below:</p> <p>Speakers</p> <ul> <li>Sarah Henriques (European Asylum Support Office-EASO) - "SAM Project – Surveys to understand asylum-related migration"</li> <li>Maria Narayani Lasala-Blanco (Arizona State University, ASU) - "The Syrian Refugee Panel Study 2017-2021. Preliminary Findings"</li> <li>Yuliya Kosyakova (Institute for Employment Research- IAB) - “Integration of refugees in Germany: (some) results based on the IAB-BAMF-SOEP Survey of Refugees”</li> </ul> <p>Chair</p> <ul> <li>Laura Morales (Chair of COST Action 16111 -ETHMIGSURVEYDATA and Sciences Po, CEE)</li> </ul>
Surveys for mapping migration paths
<p>In May-June 2021, <a href="http://cost.eu/actions/CA16111/">ETHMIGSURVEYDATA</a>—a COST Action and research network dedicated to quantitative surveys on ethnic and migrant minorities' (EMMs') integration and inclusion—organized a <em>Policy Dialogue Webinar Series </em>to share ETHMIGSURVEYDATA’s main results, as well as to showcase EMM projects that are ongoing in policy-relevant institutions. These webinars have included presentations by the European Asylum Support Office (EASO), the European Commission Joint Research Centre (JRC), the International Organization for Migration (IOM), and the Organisation for Economic Co-operation and Development (OECD).</p> <p>This is the video recording for the 2nd webinar of the <em>Policy Dialogue Webinar Series, </em>which was held on 25 May 2021: "Surveys for mapping migration paths." This video recording has also been made available on the <a href="https://www.youtube.com/watch?v=tec5qKcE81E">ETHMIGSURVEYDATA Youtube channel</a>.</p> <p>The full program for this webinar can be found below:</p> <p>Speakers</p> <ul> <li>Laura Bartolini, IOM-Italy - IOM’s Displacement Tracking Matrix and mixed migration flows in the Mediterranean</li> <li>Philippe Wanner, University of Geneva - Measuring Migrants’ Integration in Switzerland. The Migration-Mobility Survey</li> <li>Cris Beauchemin, Institut national d'études démographiques (Ined) - Trajectories and Origins Surveys (France): Beyond the “immigration bias”</li> </ul> <p>Chair</p> <ul> <li>Marcello Carammia, University of Catania</li> </ul>
Multilingual MigrationsKB: A Mulitlingual Knowledge Base of Migration related annotated Tweets
<p><strong>Multilingual MigrationskB (MGKB) </strong>is a mulitlingual extended version of English <a href="https://zenodo.org/record/5206820#.YRqF1nUza0o">MGKB</a>. The tweets geotagged with Geo location from 32 European Countries (<em><strong>Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Iceland, Liechtenstein, Norway, Switzerland, the United Kingdom</strong></em>) are extracted and filtered by 11 languages (<em><strong>English, French, Finnish, German, Greek, Dutch, Hungarian, Italian, Polish, Spain, Swedish</strong></em>). Metadata information about the tweets, such as <strong>Geo information (place name, coordinates, country code)</strong> are included. <strong>MGKB</strong> contains <strong>sentiments, offensive and hate speeches, topics, hashtags, user mentions</strong> in RDF format. The schema of <strong>MGKB</strong> is an extension of TweetsKB for migration related information. Moreover, to associate and represent the potential economic and social factors driving the migration flows, the data from <a href="https://ec.europa.eu/eurostat/web/main/home">Eurostat</a> and <a href="https://spec.edmcouncil.org/fibo/ontology/">FIBO</a> ontology was used. To represent multilinguality, the<a href="https://www.cidoc-crm.org/"> CIDOC Conceptual Reference Model (CIDOC-CRM)</a> is used. The extracted economic indicators, i.e., GDP Growth Rate, Total Unemployment Rate, Youth Unemployment Rate, Long-term Unemployment Rate and Income per househould, are connected with each tweet in RDF using geographical and temporal dimensions. </p> <p>For this version, the Multilingual MGKB is delivered separated by year. The extracted topic words are also published.</p> <p>Code: <a href="https://github.com/migrationsKB/MRL">https://github.com/migrationsKB/MRL</a></p> <p>Please contact Yiyi Chen (yiyi.chen@partner.kit.edu) for pretrained models (Sentiment analysis/hate speech detection/ETM) if necessary.</p> <p> </p> <p> </p>
Fig. 4 in Autumn Migration Of Birds Over Polonyna Borzhava (Ukrainian Carpathians)
Fig. 4. Distribution of the passage flow of most numerous species of birds (%) migrating over Polonyna Borzhava and their main migration directions.
Fig. 3 in Autumn Migration Of Birds Over Polonyna Borzhava (Ukrainian Carpathians)
Fig. 3. Dynamics of passage intensity in some common bird species across Polonyna Borzhava in autumn 2018.
Migrating birds real flight V-formation spatial configuration.
<p>Bird real flight V-formation dataset: Arbitrary (pixel) coordinates of migrating birds, probably Geese, flying in V-formation. Photo is taken in an angle so their formation data is only a cross-section in 3-D perspective but with entire pack. However, despite this limitation this V-formation configuration provides a quantitative data for the understanding for the spatial properties, i.e., V-shape characteristics. There are 95 birds in total including the lead bird. Lower V-arm is denoted with tags dXX has 51 birds and upper V-arm is denoted by tags uXX has 43 birds. Lead bird has two entries d00 and u00 for consistency. Annotated image provides boxes and labels. The data is given under bird_arbitrary_coordinates as pixel location on the plane with tags. In coordinate annotation head of the bird is taken as a refrence point.</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
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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.
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