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19,493 results for “Populism”
Data from: When the "selfish herd" becomes the "frozen herd": spatial dynamics and population persistence in a colonial seabird
Aggregations are common in ecological systems at a range of scales and may be driven by exogenous constraints such as environmental heterogeneity and resource availability or by 'self-organizing' interactions among individuals. One mechanism leading to self-organized animal aggregations is captured by Hamilton's 'selfish herd' hypothesis, which suggests that aggregations may be driven by an individual's effort to minimize their risk of predation by surrounding themselves with conspecifics. We demonstrate that aggregations observed in Adélie penguin (Pygoscelis adeliae) colonies are a convolution of both self-organized dynamics and external forcing arising from landscape terrain. In fluid, highly mobile aggregations, individuals are constantly moving in response to changing environmental conditions, the locations of predators, or the movements of conspecifics. However, when the ability to rearrange is limited and spatial reconfiguration occurs on slower time scales than changes in population size, systems may become trapped in sub-optimal arrangements. We use simulated annealing to demonstrate that Adélie penguin colonies are frozen in sub-optimal spatial arrangements, and employ an individual-based modelling approach to demonstrate that this sub-optimal spatial configuration is driven by a convolution of nest site fidelity and stochastic events at the level of individual nests. The resulting spatial dynamics are responsible for a hysteretic response to long-term changes in abundance. We find that declining abundance leads to fragmentation even in a homogeneous environment, which has population-level consequences for reproductive success because predation is biased towards colony edges. Strong edge effects from heterogeneous predation coupled with fragmentation in response to population declines creates a positive feedback cycle that can accelerate population decline. This work provides a mechanistic understanding of complex spatial structuring in penguin colonies, provides a link between current spatial patterning and past dynamics, and suggests the possibility of critical collapse in seabird populations.
Figure 1 in 'Mainland-island' population structure of a terrestrial salamander in a forest-bocage landscape with little evidence for in situ ecological speciation
Figure 1. ContinentalFrancewiththedepartmentMayennehighlighted (A) andhabitatmodelforthe Fire salamander indepartment Mayenne (B). Themap representsthe habitat suitability model Ps = (1/ (1 + exp(−0.0303*percent_forest_cover-0.00562*altitude-0.0299*percent_hedgerow_cover + 1.769))) and was visualized with ILWIS 3.6 software58, available at https://52north.org/software/software-projects/ilwis/. Habitat suitability increases from deep blue with a probability of occurrence of zero to deep red with a probability of occurrence at unity (see colour bar). Prime fire salamander habitats are found at higher altitudes and are forested (in black) or with a dense hedgerow cover. Populations genetically investigated are located in and around the largely deciduous forests Forêt de Bourgon (FB) and Bois de Hermet (BH) and listed in Table 1.The outer geographicalcoordinates of the department are 1.239–0.049W and 47.733–48.568N.
Figure 3 in 'Mainland-island' population structure of a terrestrial salamander in a forest-bocage landscape with little evidence for in situ ecological speciation
Figure 3. (A) Clustering of pairwise Fst-values of Kottenforstfire salamanderpopulations (localities K01-K47) with the UPGMA-method. Numbers K01-K27 represent populations in the western section of the forest and K28-K47 represent populationsin the eastern section of the forest. The basal cluster at Fst <0.04 is composed of two groups (shaded) composed of mostly eastern (14/16 = 88%) or mostlywestern localities (14/15 = 93%). Populations breeding in streams are shown by the letter S. Note that populations that join the dendrogram at higher Fst-values are characterized by mostlysmall effectivepopulation sizes (Ňe ≤ 10, indicated by small open dots; X – Ňe not determined). B top panel - Populationsplotted along the firstand second axis of a principal component analysis. Middle panel - Ellipses represent means ± standarddeviation for sevenstream populations (left ellipse) and 40 non-streampopulations (right ellipse). Lower panel - Ellipsesrepresent means ± standard deviation forthe western (left) and eastern (right) sectionof the Kottenforst, forsmall populations (Ňe ≤ 10) shown by interruptedlines andfor larger populations (Ňe> 10) shown by uninterrupted lines. Notethat for the larger populations the ellipses for western and eastern localities do not overlap.
Figure 4. MicrosatellitepopulationgeneticdataforthefiresalamanderintheKottenforst, Germany21,24 in 'Mainland-island' population structure of a terrestrial salamander in a forest-bocage landscape with little evidence for in situ ecological speciation
Figure 4. MicrosatellitepopulationgeneticdataforthefiresalamanderintheKottenforst, Germany21,24 analyzed in the framework of allopatric speciation, i.e. a secondary spatial contact of a western pond-breeding lineage and an eastern stream-breeding lineage. The 95% credible cline regions are shown by grey shading. Solid and open round symbolsrepresent larger (Ňe> 10) andsmall populations (Ňe ≤ 10), respectively. Note that the stream-breeding populations that gave the composite genotype its name are all located in the eastern section of the Kottenforst (six data points indicated with a forward slash (/). One 'intermittent stream' in the western section is indicated by a backward slash. Also note the paucity of data at and around the steepest part of the clines. A – loadings on the first PC axis versus geographical distance. The clinecentre is at km 365.3 of the Universal Transverse Mercator (UTM) grid. Cline width is 3952 m. B – frequency of the stream-breeding genotype versus distance (after21). Thecline centre is at UTM km 365.1 and the cline widthis 1108 m. For model details see Supplementary Information VI.
Figure 2 in 'Mainland-island' population structure of a terrestrial salamander in a forest-bocage landscape with little evidence for in situ ecological speciation
Figure 2. (A) Clustering of pairwise Fst-values of firesalamander populations (Mayenne localities 1–41) with the UPGMA-method. The basal cluster at Fst <0.010 is mostly composed of forest populations (F, 17/21 = 81%) whereas populationsthat jointhe dendrogramat higher Fst-values are mostly fromthe bocage (B, 14/20 = 70%). At Fst> 0.025 the contribution of the bocage populations is eightout of eight. Notethat populations thatjoin the dendrogram at the highest Fst-values are characterizedby mostly small effective population sizes (Ňe ≤ 10, indicated by small open dots). (B) Populations plotted along the first and second axis of a principal component analysis. The 23 forest populations are shown by small solid round symbols and the solid ellipse represents the mean ± standard deviation. Eighteenpopulations from the bocageare shown by large open round symbols, with the mean ± standard deviation shown bythe widerellipse with the interrupted line.
Data from: Intraspecific correlations between growth and defense vary with resource availability and differ within- and among-populations
<p>A paradigm in the plant defense literature is that defending against herbivores comes at a cost to growth, resulting in a growth-defense tradeoff. However, while there is strong evidence for growth-defense tradeoffs across species, evidence is mixed within species. Several mechanisms can account for this equivocal support within species, but teasing them apart requires examining growth-defense relationships both within and among populations, an approach seldom employed. We examined correlations between plant biomass (growth) and terpene production (defense) within and among populations of Monarda fistulosa, a perennial herb. We sampled populations from Montana and Wisconsin, regions that differ in resource availability characterized by different summer precipitation and associated abiotic conditions that influence plant productivity. We found negative, neutral, and positive growth-defense correlations, depending on the scale examined. Negative correlations occurred across populations originating from divergent regions, positive correlations occurred across populations originating from within the high-resource region, and neutral correlations were found within single populations. Collectively, these results challenge the general expectation of ubiquitous tradeoffs and support emerging views that resource availability (as it affects productivity) shapes the evolution of defense at different scales.</p>
Code and data associated with Christiansen et al. 2021 "Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing"
<p>All code and data input and output files (except reference genome and raw sequencing data) needed to reproduce the results of Christiansen et al. 2021 as released on <a href="https://github.com/notothen/radpilot">https://github.com/notothen/radpilot</a> alongside journal publication. See published paper:</p> <p>Christiansen, H., Heindler, F.M., Hellemans, B. <em>et al.</em> Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing. <em>BMC Genomics</em> <strong>22, </strong>625 (2021). <a href="https://doi.org/10.1186/s12864-021-07917-3">https://doi.org/10.1186/s12864-021-07917-3</a></p>
Pairwise FST values for Aedes aegypti populations in Florida and southern California
<p>In the affiliated paper we compare likely the oldest populations of <i>Aedes aegypti</i> in continental North America with some of the newest to illuminate the range of genetic diversity and structure that can be found within the invasive range of this important disease vector. <i>Aedes aegypti</i> populations in Florida have likely persisted since the 1600-1700s, while populations in southern California derive from new invasions that occurred in the last ten years. For this comparison, we genotyped 1,193 individuals from 29 sites at 12 highly variable microsatellites and a subset of these individuals at 23,961 single nucleotide polymorphisms (SNPs). This dataset contains the pairwise FST values generated with both genetic datasets.</p>
SNP Data for Aedes aegypti populations in Florida and southern California
<p>In the affiliated paper we compare likely the oldest populations of <i>Aedes aegypti</i> in continental North America with some of the newest to illuminate the range of genetic diversity and structure that can be found within the invasive range of this important disease vector. <i>Aedes aegypti</i> populations in Florida have likely persisted since the 1600-1700s, while populations in southern California derive from new invasions that occurred in the last ten years. For this comparison, we genotyped 1,193 individuals from 29 sites at 12 highly variable microsatellites and a subset of these individuals at 23,961 single nucleotide polymorphisms (SNPs). This dataset contains the SNP genetic information.</p>
Microsatellite data for Aedes aegypti populations in Florida and southern California
<p>In the affiliated paper we compare likely the oldest populations of <i>Aedes aegypti</i> in continental North America with some of the newest to illuminate the range of genetic diversity and structure that can be found within the invasive range of this important disease vector. <i>Aedes aegypti</i> populations in Florida have likely persisted since the 1600-1700s, while populations in southern California derive from new invasions that occurred in the last ten years. For this comparison, we genotyped 1,193 individuals from 29 sites at 12 highly variable microsatellites and a subset of these individuals at 23,961 single nucleotide polymorphisms (SNPs).</p>
WORK-LIFE BALANCE OF THE EMPLOYED POPULATION DURING THE EMERGENCY SITUATION OF COVID-19 IN LATVIA
<p>All the employees face the challenge of finding the right work-life balance. The ability of employees to deal with the successful combining of work, family responsibilities, and personal life is crucial for both employers and family members of employees. During the COVID-19 emergency situation, many people around the world were forced to work remotely. Initially, there were observed some certain expectations about the possibility of working from home as a positive factor that will promote work-life balance. However, over time, negative tendencies were also revealed, as employees were only one call or message away from the employer, and uncertainty and leisure time with family often created more stress. As many organizations and individuals were not ready for this sudden change, many mistakes were made, which further raised the issue of work-life balance. The aim of the research was to evaluate the flexibility of reconciling work and private life of Latvian employees in various socio-demographic groups during the COVID-19 emergency situation in spring 2020, to investigate how family life influenced employees’ ability to perform work duties, to find out if employees had any additional housework responsibilities and how their workload changed concerning housework amount during the COVID-19 emergency situation. The research is based on the data obtained in the survey of the Latvian employed population, which was conducted within the framework of the Latvian National Research Programme Project “CoLife” in the second half of 2020. As a result, the hypothesis of the research that all groups of employees experienced work-life balance difficulties during the COVID-19 emergency situation has been partially confirmed, i.e. women in the 18-44 age group and respondents with minor children in the household more likely faced difficulties of work-life balance. The scientific research methods that were used in the research are the monographic method, content analysis, survey, data processing with SPSS to determine the mutual independence of the data from the questionnaires. </p>
Image 1 in Ecology and population structure of a terrestrial mycoheterotrophic orchid, Aphyllorchis montana Rchb.f. (Orchidaceae) in Soppinabetta forests of the Western Ghats, India
Image 1. Morphology of Aphyllorchis montana. © P.A. Sinu A - a clump of the orchid; B - a single flower; C - terminal part of an inflorescence with mature capsules.
Image 1 in Subspecific status of the southern Indian population of Nyctemera coleta (Lepidoptera: Arctiidae)
Image 1. Specimens of southern Indian Nyctemera coleta examined in the present study. Female is in centre of right row.
BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations
<p><span><span>The demand for agent-based models to explore the effects of environmental change on pollinator population dynamics is growing. However, models need a simple yet flexible interface to enable adoption by a wide range of stakeholders. </span></span><span><span>We introduce BEE-STEWARD: a research and decision-support software tool, enabling researchers, policy-makers, land management advisors, and practitioners to predict and compare the effects of bee-friendly management interventions on bumblebee populations over several years. </span></span><span><span>BEE-STEWARD integrates the BEESCOUT and <i>Bumble</i>-BEEHAVE agent-based models of bumblebee behaviour, colony growth and landscape exploration into a user-friendly interface, with reconstructed code, and expanded functionality. Bespoke automatic reports can be created to illustrate how different land management interventions can affect the densities of bumblebees and their colonies over time. </span></span><span><span>BEE-STEWARD could be an important virtual test-bed for scientists exploring the impacts of different stressors on bumblebees and used by those with little or no modelling experience, enabling a shared methodology between research, policy, and practice.</span></span></p>
The role of neutral and adaptive genomic variation in population diversification and speciation in two ground squirrel species of conservation concern
<p>Understanding the neutral (demographic) and adaptive processes leading to the differentiation of species and populations is a critical component of evolutionary and conservation biology. In this context, recently diverged taxa represent a unique opportunity to study the process of genetic differentiation. Northern and southern Idaho ground squirrels (Urocitellus brunneus – NIDGS, and U. endemicus - SIDGS, respectively) are a recently diverged pair of sister species that have undergone dramatic declines in the last 50 years and are currently found in metapopulations across restricted spatial areas with distinct environmental pressures. Here we genotyped single-nucleotide polymorphisms (SNPs) from buccal swabs with restriction site-associated DNA sequencing (RADseq). With these data we evaluated neutral genetic structure at both the inter- and intraspecific level, and identified putatively adaptive SNPs using population structure outlier detection and genotype-environment association (GEA) analyses. At the interspecific level, we detected a clear separation between NIDGS and SIDGS, and evidence for adaptive differentiation putatively linked to torpor patterns. At the intraspecific level, we found evidence of both neutral and adaptive differentiation. For NIDGS, elevation appears to be the main driver of adaptive differentiation, while neutral variation patterns match and expand information on the low connectivity between some populations identified in previous studies using microsatellite markers. For SIDGS, neutral substructure generally reflected natural geographic barriers, while adaptive variation reflected differences in land cover and temperature, as well as elevation. These results clearly highlight the roles of neutral and adaptive processes for understanding the complexity of the processes leading to species and population differentiation, which can have important conservation implications in susceptible and threatened species.</p>
REPIN population analysis in 4 Dokdonia genomes
<p>This dataset is the output of RAREFAN (http://rarefan.evolbio.mpg.de/) a webserver to identify REPIN populations across an entire bacterial species. The data was created using the following command "java -jar -Xmx10g rarefan.jar dokdonia/in/ dokdonia/out/ 4h-3-7-5.fas 55 21 in/yafM_Ecoli.faa dokdonia.nwk 1e-10 true 1"</p> <p>All input files are located in the folder dokdonia/in/, all output data is located in dokdonia/out/.</p> <p>The input files include the 4 fasta formatted <em>Dokdonia</em> genome files (*.fas) and a RAYT protein sequence called yafM_Ecoli.faa.</p> <p>The output files include the following:</p> <p>A phylogenetic tree "dokdonia.nwk" of all genomes generated with andi (<a href="http://github.com/evolbioinf/andi/">http://github.com/evolbioinf/andi/</a>) and clustDist (http://guanine.evolbio.mpg.de/problemsBook/node1.html).</p> <p>A file containing the frequencies of all 21bp long sequences found in the 4h-3-7-5 genome: 4h-3-7-5.wfr</p> <p>A file containing all 21bp long sequences that occur more frequently than 55 times in the 4h-3-7-5 genome: 4h-3-7-5.overrep</p> <p>A file containing information on the RAYTs and their cooccurrence with different REPIN populations: prox.stats</p> <p>A file containing the nucleotide sequences of all yafM_Ecoli.faa relatives identified with BLAST+ in the <em>Dokdonia </em>species: yafM_relatives.fna</p> <p>maxREPIN_0.txt Contains the most frequent REPIN identified for each sequence type in each <em>Dokdonia</em> strain.</p> <p> presAbs_0.txt Contains for each strain information on the number of RAYTs, the number of REPINs, the master sequence, the number of master sequences, the entire REP/REPIN population size, the number of REPIN clusters that contain more than 10 sequences, all REPINs in the population as well as all REPINs that differ to the master sequences in at most three nucleotides.</p> <p>rayt_[strain name].tab contains location information for each identified RAYT relative for each strain. The files can be viewed with artemis.</p> <p>results.txt contains for each strain the frequency of the six identified 21bp long seeds.</p> <p>There is one folder called groupSeedSequences, which includes the data for identifying the most common 21 bp long sequences in <em>D. </em>sp. 4h-3-7-5. All 21bp long sequences in the genome that occur more frequently than 55 times are sorted into 6 sequence groups. These sequence groups are stored in the files Group_4h-3-7-5_*.out and .out.fas. There is also a 4h-3-7-5_words.tab file, which contains the locations of all overrepresented 21bp long sequences in the 4h-3-7-5 genome. This file can be viewed in artemis (https://www.sanger.ac.uk/tool/artemis/) together with the 4h-3-7-5 genome file. The most common sequence in each group is used as a seed sequence to determine REPIN populations across all 4 genomes.</p> <p> </p> <p>For each genome there is one output folder (ending in _0), for each sequence group one.</p> <p>Each folder contains the following files:</p> <p>*.dd: Degree distribution of the REPIN network, where each REPIN is a node. A REPIN is connected to another REPIN if they differ in exactly one position. The degree distribution is a histogram of the number of connections of all the nodes. </p> <p>*.hist For the largest sequence cluster determined by mcl that consists of REPINs (two REPs in inverted orientation) this file contains the number of REPINs in each sequence class. Sequence class 0 is the master sequence. By definition the most common REPIN in the sequence population. Sequence class 1 contains all REPINs differing in exactly one position to the master sequence. Sequence class 2 contains REPINs differing in 2 positions etc.</p> <p>*.mcl Contains the clustering output by mcl. Each line contains the member of a cluster. Lines are sorted by cluster size.</p> <p>*.mw Contains the most common 21bp long sequence and its frequency in the genome, which is the basis for identifying first all related REP sequences and from those the REPINs formed by these REP sequences.</p> <p>*.nodes The identity and frequency of all REPINs and REP sequences for either all sequences or only for the largest sequence cluster.</p> <p>*.ss Contains REPINs and REP sequences as well as their positions in fasta format. Position information starts with the location in genome fasta file (first sequence is 0...) followed by the start and end position of the entire REPIN/REP sequence. </p> <p>*.ss.REP REP sequence information in fasta format.</p> <p>*.tab Location in tab format. Can be used to display locations of REPs and REPINs in the genome via artemis.</p> <p>*_[0-9].ss Contains REPIN/REP sequence information for each subcluster separately.</p> <p>*_[0-9].tab Contains the location of REP/REPINs for each subcluster separately for viewing in artemis.</p> <p>*allSeed.nw Contains network connections between nodes of all sequences. Can be used to view network in for example R or cytoscape together with the nodes file.</p> <p>*largestCluster.nodes Information on nodes only from the largest REPIN cluster.</p> <p>*largestCluster.ss *.ss file for the largest REPIN cluster.</p> <p>*largestCluster.tab *.tab file for the largest REPIN cluster.</p> <p>*_rayt_repin_prox.txt shows which REPIN/REP cluster is in proximity to any of the RAYT genes identified in the genome (within 200bp).</p> <p>And a subfolder that contains the complete sequences (including the variable region) for all identified REPs and REPINs.</p> <p><strong>The dataset was generated using the following external tools:</strong></p> <p>andi for tree building:</p> <p>B Haubold, F Klötzl, and P Pfaffelhuber. <strong>andi: fast and accurate estimation of evolutionary distances between closely related genomes.</strong> Bioinformatics, 2015 vol. 31 (8) pp. 1169-1175.</p> <p>MCL for REPIN population clustering:</p> <p>A J Enright, S Van Dongen, and C A Ouzounis. <strong>An efficient algorithm for large-scale detection of protein families.</strong> Nucleic Acids Research, 2002 vol. 30 (7) pp. 1575-1584.</p> <p>BLAST+ for identifying RAYT relatives in the different genomes:</p> <p>C Camacho, G Coulouris, V Avagyan, N Ma, J Papadopoulos, K Bealer, and T L Madden. <strong>BLAST+: architecture and applications.</strong> BMC Bioinformatics, 2009 vol. 10 (1) pp. 421-9.</p>
Disk population synthesis data for the article: "Large gaps and high accretion rates in photoevaporative transition disks with a dead zone"
<p>We make available here the results of our gas evolution simulations for the article "Large gaps and high accretion rates in photoevaporative transition disks with a dead zone".</p> <p>We include the result of our population synthesis study, consisting in 10 tables corresponding to 9 models including both dead zone and photoevaporation, and 1 control model including photoevaporation only. The results can be plotted using the "Plotting-PopulationSynthesisModels.ipynb" jupyter notebook.</p> <p>The tables including a dead zone are named as: Macc_Rhole_Pop_AD(X)_RD(Y).txt, where (X)*1.e-4 corresponds to the turbulence in the dead zone, and (Y)*AU corresponds to the dead zone radial extent.</p> <p>The tables include the following fields: Simulation ID, Time [Myr] at which the snapshot was taken, Gap size [AU] measured at the outer edge, Accretion rate log[Msun/yr], and Gas disk mass. We also include a table called "LxRc_Distribution.txt", that indicates the X-ray luminosity log[erg/s] and initial disk characteristic radius [AU] used for the corresponding Simulation ID.</p> <p> </p> <p>We also include the gas surface density evolution of a Control simulation, with photoevaporation only (with Lx = 1.e30 erg/s), and a Dead Zone + Photoevaporation simulation (with dead zone turbulence 1.e-4, and dead zone radial extend 10 AU). These can be plotted using the "Plotting-SingleGasEvolution.ipynb" jupyter notebook.</p>
Population genetics characteristics of a 90 locus panel of microhaplotypes
<p>Microhaplotype genotype data for 556 individuals (with anonymized identifiers) from 16 population samples.</p> <p> </p>
Dataset from: Binary population synthesis with probabilistic remnant mass and kick prescriptions
<p>This contains the data behind the paper "<a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.1380M">Binary population synthesis with probabilistic remnant mass and kick prescriptions</a>"<br> <br> See<a href="https://github.com/TeamCOMPAS/COMPAS"> https://github.com/TeamCOMPAS/COMPAS</a> for code information and instructions on parsing the data.</p>
Figure 18 in Geographic variation in U. S. populations of the tiger beetle Cicindela obsoleta Say (Coleoptera: Cicindelidae)
Figure 18. Collecting localities for the tiger beetle Cicindela obsoleta in the United States, mapped using ArcView GIS software. Open circles represent collecting localities for Cicindela obsoleta obsoleta; open squares represent Cicindela obsoleta santaclarae; closed circles represent Cicindela obsoleta vulturina; and closed stars represent Cicindela obsoleta neojuvenilis.
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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.