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19,493 results for “Populism”
Fig. 5 in Population dynamics and predatory impact of the alien jellyfish Aurelia solida (Cnidaria, Scyphozoa) in the Bizerte Lagoon (southwestern Mediterranean Sea) Abstract
Fig. 5: Relation between Aurelia solida bell diameter (cm) and (A) the prey in the gut contents and (B) the prey diversity.
Fig. 4 in Population dynamics and predatory impact of the alien jellyfish Aurelia solida (Cnidaria, Scyphozoa) in the Bizerte Lagoon (southwestern Mediterranean Sea) Abstract
Fig. 4: Variation of the diet composition of Aurelia solida in Bizerte Lagoon in (A) 2013 and (B) 2014; (n) number of analyzed specimens.
Fig. 7 in Population dynamics and predatory impact of the alien jellyfish Aurelia solida (Cnidaria, Scyphozoa) in the Bizerte Lagoon (southwestern Mediterranean Sea) Abstract
Fig. 7: Seasonal variation of Aurelia solida (A-B) feeding rate (prey consumed medusae-1) and (C-D) predation impact (% prey standing stock consumed day-1) in Bizerte Lagoon in 2013-2014.
Fig. 3 in Population dynamics and predatory impact of the alien jellyfish Aurelia solida (Cnidaria, Scyphozoa) in the Bizerte Lagoon (southwestern Mediterranean Sea) Abstract
Fig. 3: Seasonal variation of Aurelia solida (A) abundance and (B) bell diameter in Bizerte lagoon between November 2012 and August 2014; white spots: 0 ind.m-3.
Fig. 1 in Population dynamics and predatory impact of the alien jellyfish Aurelia solida (Cnidaria, Scyphozoa) in the Bizerte Lagoon (southwestern Mediterranean Sea) Abstract
Fig. 1: Map of the Mediterranean Sea showing with locations of the sampling station and the transect in the Bizerte Lagoon between November 2012 and August 2014.
Fig. 5 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract
Fig. 5: Common trends (uniteless) and factor loadings of anchovy and sardine landings and recruits at age 0. Trends were calculated with DFA using three common trends and two explanatory variables (T100, S100). Factor loadings are plotted for: 1 - sardine recruits age 0, 2 - sardine landings, 3 - anchovy recruits, 4 - anchovy landings.
Fig. 4 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract
Fig. 4: Annual time series of temperature, salinity, apparent oxygen utilisation (AOU), total inorganic nitrogen (TIN) and orthophosphates (HPO 2-), all collected at station S between 1975 and 2010.
Fig. 3 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract
Fig. 3: From top to bottom: annual time series of total (sardine+anchovy) recruits and landings; sardine recruits and landings; and anchovy recruits and landings, all between 1975 and 2010.
Fig. 2 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract
Fig. 2: Schematic diagram of the fisheries and environmental data, methods and models used in analyses.
Fig. 6 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract
Fig. 6: Observed values (diamonds) and fitted values (black lines) of: sardine recruits age 0, sardine landings, anchovy recruits age 0, anchovy landings. Fitted values were calculated with DFA using three common trends and two explanatory variables (T100,S100).
Fig. 1 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract
Fig. 1: The geography of the Adriatic-Ionian basin, with major dynamical features and oceanographic processes illustrated. WAC stands for the surface Western Adriatic Current driven by freshwater inputs, ADW denotes the bottom density current which originates from wintertime dense water generation at the shallow northern (D1) and deep southern (D2) Adriatic regions, while EAC stands for the Eastern Adriatic Current flowing at surface and intermediate layers. Ionian Sea circulation includes cyclonic (C) or anticyclonic (A) circular current regime in its northern parts, resulting in warm and saline Levantine Intermediate Water (LIW) and colder and less saline upper-layer Western Mediterranean Waters (WMW) flowing at different pathways depending on the BIOS (Bimodal Adriatic-Ionian Oscillation) phase (full line denotes pathways during cyclonic BIOS phase and dashed line during anticyclonic BIOS phase). Finally, EMT denotes the deep massive outflow of dense waters from the Aegean Sea to the Eastern Mediterranean during the Eastern Mediterranean Transient in early 1990s. Sardine and anchovy data were collected northwest from the GSA 17 line (pink, dashed). Station at which environmental data were collected is marked with S.
Figure 2 in Indotyphlops braminus (Daudin, 1803): distribution and oldest record of collection dates in Oceania, with report of a newly established population in French Polynesia (Tahiti Island, Society Archipelago)
Figure 2. Anterior ventral area of Indotyphlops braminus MNHN-RA 2015.0057 showing the whitish gular coloration. Picture: I. Ineich.
Figure 1 in Indotyphlops braminus (Daudin, 1803): distribution and oldest record of collection dates in Oceania, with report of a newly established population in French Polynesia (Tahiti Island, Society Archipelago)
Figure 1. Two introduced specimens of Indotyphlops braminus from Tahiti Island in French Polynesia. MNHN-RA 2015.0058 above and MNHN-RA 2015.0057 below. Scale bar: 1cm. Picture: I. Ineich.
Figure 5 in The First Cases of Evolving Glyphosate Resistance in UK Poverty Brome (Bromus sterilis) Populations
Figure 5. Calculated glyphosate GR50 values from log-logistic dose–response model of 11 field-collected B. sterilis populations from the United Kingdom. Error bars are standard error of GR50 parameter estimates.
Figure 2 in The First Cases of Evolving Glyphosate Resistance in UK Poverty Brome (Bromus sterilis) Populations
Figure 2. Percentage reduction in foliage dry weight relative to untreated controls for 35 UK B. sterilis populations treated with 270 g glyphosate ha − 1. Shaded bars represent sensitive (dark gray) and suspected resistant (gray) populations based upon the initial glyphosate screen. Error bars are standard error of the mean.
Figure 1 in The First Cases of Evolving Glyphosate Resistance in UK Poverty Brome (Bromus sterilis) Populations
Figure 1. Mean foliage fresh weight per plant (g) for suspected glyphosate-resistant (OXON-R and SEL-R) and glyphosate-sensitive (ADAS and SEL-S) populations of B. sterilis following glyphosate treatment: untreated control (gray), 360 g ha − 1 (white), and 540 g ha − 1 (dark gray). Error bars are standard error of the mean.
Figure 4 in The First Cases of Evolving Glyphosate Resistance in UK Poverty Brome (Bromus sterilis) Populations
Figure 4. Glyphosate dose–response curves for survival of three suspected glyphosate-resistant B. sterilis populations (SEL-R, OXON-R, and 09D118) and three glyphosatesensitive populations (SEL-S, OXON-S, and ADAS). Symbols represent mean observed survival data, and lines are fitted regression models. (A) SEL-R (continuous line) and SEL-S (dashed line); (B) OXON-R (continuous line) and OXON-S (dashed line); and (C) 09D118 (continuous line) and ADAS (dashed line).
Interview Data: Nativism, Islamophobism and Islamism in the Age of Populism dataset
<h1><strong>Interview Data</strong></h1> <h2><strong>Nativism, Islamophobism and Islamism in the Age of Populism dataset</strong></h2> <p>Transcripts of 302 interviews conducted with self-identified Muslim youth of migrant origin and native youth with affiliation or sympathy with movements labeled far-right in Belgium, France, the Netherlands, and Germany.<a title="" href="#_ftn1" name="_ftnref1">[1]</a> Local researchers conducted the interviews using the same interview guide, including 17 questions. Interviews were not audio-recorded. The researchers took notes, verifying their transcriptions with the research participant during and after the interview. Originally, the research consisted 307 interviews but five participants (1 French native; 2 German Muslims; 2 German natives) did not want their interview transcriptions to be made public on the ERC data repository site. In accordance with our ethical guidelines, we anonymized the transcripts. The dataset also includes the summary output (as an excel file) of the values analysis of the 307 interviews. Please visit the attachment named Data Summary to learn more about our method of analysis and how to use the attached documents. As a complementary file, we hope that the additional excel sheet detailing the demographic characteristics of our research participants (sex, age, educational background, etc.) helps advance researchers’ understanding of our participant profile.</p> <p> </p> <p><strong>Additional information: </strong></p> <p>302 transcription files (zipped), 2 data summary files, 2 documentation files</p> <p> </p> <p><strong>Cadmus permanent link: </strong><a href="https://hdl.handle.net/1814/76159">https://hdl.handle.net/1814/76159</a></p> <p><strong>Series/Number: </strong>EUI; RSC; Research Data; 2023</p> <p><strong>Publisher: </strong>European University Institute</p> <p><strong>Keyword(s): </strong><a href="https://cadmus.eui.eu/discover?order=desc&rpp=10&sort_by=dc.date.issued_dt&query=%22Deprivation%22">Deprivation</a>, <a href="https://cadmus.eui.eu/discover?order=desc&rpp=10&sort_by=dc.date.issued_dt&query=%22Radicalism%22">Radicalism</a>, <a href="https://cadmus.eui.eu/discover?order=desc&rpp=10&sort_by=dc.date.issued_dt&query=%22Nativism%22">Nativism</a>, <a href="https://cadmus.eui.eu/discover?order=desc&rpp=10&sort_by=dc.date.issued_dt&query=%22Populism%22">Populism</a>, <a href="https://cadmus.eui.eu/discover?order=desc&rpp=10&sort_by=dc.date.issued_dt&query=%22Islamism%22">Islamism</a></p> <p><strong>Sponsorship and Funder information: </strong></p> <p>ERC Advanced Grant research project "Nativism, Islamophobism and Islamism in the Age of Populism: Culturalisation and Religionisation of what is Social, Economic and Political in Europe" (No: 785934). The project’s details and output can also be accessed at the project website, <strong><u> https://bpy.bilgi.edu.tr/en/</u></strong></p> <div><br> <div> <p><a title="" href="#_ftnref1" name="_ftn1">[1]</a> ERC Advanced Grant research project "Nativism, Islamophobism and Islamism in the Age of Populism: Culturalisation and Religionisation of what is Social, Economic and Political in Europe" (No: 785934). The project’s details and output can also be accessed at the project website, <a href="https://bpy.bilgi.edu.tr/en/">https://bpy.bilgi.edu.tr/en/</a> .</p> </div> </div>
IuliaMargineanGitHub/Projecting-Heat-Stress-Vulnerable-Populations-at-Intra-Urban-Scales: Projecting heat stress vulnerable populations at intra-urban scales
<p>This repository provides the data and scripts necessary for full reproduction of results, as well as example datasets that were used for data generation and analysis in the manuscript Marginean et al., "High-resolution Modelling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress".</p> <p>The folders in this repository contain the following:</p> <ol> <li>The input datasets: <ul> <li>shares of educational attainment in 2012 and 2020, by sex, age group and census tract</li> <li>internal migration by sex, age group and census tracts</li> <li>decadal mortality and fertility, by sex, age group, scenario, and census tract</li> </ul> </li> <li>Projected decadal data (2012 to 2050) by sex, age group, scenario and census tract</li> <li>Scripts for generating high resolution projections for three Shared Socioeconomic Pathways (SSPs): one script for SSP 2 projections and one scripts for SSP 1 and SSP 3 projections</li> <li>Example input data to reconstruct the projections in SSP 1, 2 and 3 for females ages 25 to 64</li> </ol>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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