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379 results for “population history”
Inferring whole-genome histories in large population datasets: inferred tree sequences for 1000 Genomes
<p>Tree sequences inferred for the 1000 Genomes phase 3 autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip 1kg_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("1kg_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("1kg_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Unique demographic history and population substructure among the Coorgs of Southern India
<p>Quality filtered GSA data of the individuals analysed in Mukhopadhyay et al., 2024 from Coorg, Karnataka, India.</p> <p> </p>
Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse
<p>The main population of ∼5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require ∼45-60 fire-scar sites, making only ∼30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years. </p>
Genome data and resources on the recombination landscape and population history of the harlequin fly
<p>This dataset contains phased vcf files of <em>Chironomus riparius, </em>ouput files of RepeatMasker, MELT, RepeatOBserver, MSMC2, iSMC and bedtools, such as supporting files. </p> <p>For further details also check the GitHub page: <a href="https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024" target="_blank" rel="noopener">https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024</a></p> <ul> <li><strong>phased-vcfs: </strong>Artificially phased vcf-files of five populations with four individuals each. Needed to generate multihetsep files. Input files for iSMC.<br> <ul> <li>Hesse in Germany = MG</li> <li>Rhône-Alpes in France = MF</li> <li>Lorraine in France = NMF</li> <li>Piemont in Italy = SI</li> <li>Andalusia in Spain = SS</li> </ul> </li> <li><strong>multihetsep-files: </strong>Created with msmc-tools. Input files for MSMC2. </li> <li><strong>RepeatMasker: </strong>Raw output of RepeatMasker run. Summary file and file with filtered <em>Cla</em>-element (a transposable element) included.<strong><br></strong></li> <li><strong>MELT: </strong>MELT ouput with added info on population and numbered insertions reflecting all 441 detected <em>Cla </em>insertions.<strong><br></strong></li> <li><strong>RepeatOBserver: </strong>Summary files on centromere predictions based on histograms and Shannon Diversity from RepeatOBserver. Genome-wise Shannon Diversity per chromosome included. <strong><br></strong></li> <li><strong>MSMC2: </strong>Raw ouput of combined cross-coalescence and mean values if MSMC2 per populations. <strong><br></strong></li> <li><strong>iSMC: </strong>Recombination rate rho in 10 kb windows and 100 kb windows along the genome. <strong><br></strong></li> <li><strong>bedtools closest ismc 10 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 10 kb windows.<strong><br></strong></li> <li><strong>bedtools closest ismc 100 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 100 kb windows.</li> <li><strong>input-files figures: </strong>Supporting files needed to create figures.<strong><br></strong></li> </ul>
Thermal performance of Aedes sierrensis life history traits for populations collected across the species range
<p>How mosquitoes may respond to rapid climate warming remains unknown for most species, but will have major consequences for their future distributions, with cascading impacts on human well-being, biodiversity, and ecosystem function. We investigated the adaptive potential of a wide-ranging mosquito species, <em>Aedes sierrensis</em>, across a large climatic gradient by conducting a common garden experiment measuring the thermal limits of mosquito life history traits. Although field-collected populations originated from vastly different thermal environments that spanned over 1,200 km, we found limited variation in upper thermal tolerance between populations. In particular, the upper thermal limits of all life history traits varied by <3°C across the species range and, for most traits, did not differ significantly between populations. For one life history trait—pupal development rate—we did detect significant variation in upper thermal limits between populations, and this variation was strongly correlated with source temperatures, providing evidence of local thermal adaptation for pupal development. However, we found that maximum environmental temperatures across most of the species' range already regularly exceed the highest upper thermal limits estimated under constant temperatures. This result suggests that strategies for coping with and/or avoiding thermal extremes are likely key components of current and future mosquito thermal tolerance.</p>
Whole-genome analysis of multiple wood ant population pairs supports similar speciation histories, but different degrees of gene flow, across their European ranges
<p>The application of demographic history modelling and inference to the study of divergence between species has become a cornerstone of speciation genomics. Speciation histories are usually reconstructed by analysing single populations from each species, assuming that the inferred population history represents the actual speciation history. However, this assumption may not be met when species diverge with gene flow, e.g., when secondary contact may be confined to specific geographic regions. Here, we tested whether divergence histories inferred from heterospecific populations may vary depending on their geographic locations, using the two wood ant species <em>Formica polyctena</em> and <em>F. aquilonia</em>. We performed whole-genome resequencing of 20 individuals sampled in multiple locations across the European ranges of both species. Then, we reconstructed the histories of distinct heterospecific population pairs using a coalescent-based approach. Our analyses always supported a scenario of divergence with gene flow, suggesting that divergence started in the Pleistocene (ca. 500 kya) and occurred with continuous asymmetrical gene flow from <em>F. aquilonia</em> to <em>F. polyctena</em> until a recent time, when migration became negligible (2-19 kya). However, we found support for contemporary gene flow in a sympatric pair from Finland, where the species hybridise, but no signature of recent bidirectional gene flow elsewhere. Overall, our results suggest that divergence histories reconstructed from a few individuals may be applicable at the species level. Nonetheless, the geographical context of populations chosen to represent their species should be taken into account, as it may affect estimates of migration rates between species when gene flow is spatially heterogeneous.</p>
Joint analysis of microsatellites and flanking sequences enlightens complex demographic history of interspecific gene flow and vicariance in rear-edge oak populations
<p><span>Inference of recent population divergence requires fast evolving markers and necessitates to differentiate shared genetic variation caused by ancestral polymorphism and gene flow. Theoretical research shows that the use of compound marker systems integrating linked polymorphisms with different mutational dynamics, such as a microsatellite and its flanking sequences, can improve estimation of population structure and inference of demographic history, especially in the case of complex population dynamics. However, empirical application in natural populations has so far been limited by lack of suitable methods for data collection. A solution comes from the development of sequence-based microsatellite genotyping which we used to study molecular variation at 36 sequenced nuclear microsatellites in seven <em>Quercus canariensis</em> and four <em>Q. faginea</em> rear-edge populations across Algeria. We aim to decipher their taxonomic relationship, past evolutionary history and recent demographic trajectory. First, we compare the estimation of population genetics parameters and simulation-based inference of demographic history from microsatellite sequence alone, flanking sequence alone or the combination of linked microsatellite and flanking sequence variation. Second, we apply random forest approximate Bayesian computation to identify which of these sequence types is most informative. Whereas analysing microsatellite variation alone indicates recent interspecific gene flow, additional information gained by integrating nucleotide variation in flanking sequences, by reducing homoplasy, suggests ancient interspecific gene flow followed by drift in isolation instead. The weight of each polymorphism in the inference also demonstrates the value of linked variations with contrasted mutation dynamic to improve estimation of both demographic and mutational parameters.</span></p>
Two-sex integrated population model reveals intersexual differences in life history strategies in Cooper's Hawks
<p>This site contains data files and model code for a dynamic nesting territory occupance model and 2-sex integrated population model for Cooper's hawks in Albuquerque, New Mexico, USA, 2011 - 2020.</p>
Data for Contrasting life-history responses to climate variability in eastern and western North Pacific sardine populations
<p><span>Massive populations of sardines inhabit both the western and eastern boundaries of the world's subtropical ocean basins, supporting both commercial fisheries and populations of marine predators. Sardine populations in western and eastern boundary current systems have responded oppositely to decadal scale anomalies in ocean temperature, but the mechanism for differing variability has remained unclear. Here, based on otolith microstructure and high-resolution stable isotope analyses, we show that habitat temperature, early life growth rates, energy expenditure, metabolically optimal temperature and, most importantly, the relationship between growth rate and temperature were remarkably different between the two subpopulations in the western and eastern North Pacific. Varying metabolic response to environmental changes partly explain the contrasting growth responses. Consistent differences in the life-history traits are observed between subpopulations in the western and eastern boundary current systems around South Africa. These growth and survival characteristics can facilitate the contrasting responses of sardine populations to climate change.</span></p>
Fitness of evolving bacterial populations is contingent on deep and shallow history but only shallow history creates predictable patterns
<p><span>Long term evolution experiments have tested the importance of genetic and environmental factors in influencing evolutionary outcomes. Differences in phylogenetic history, recent adaptation to distinct environments, and chance events all influence the fitness of a population. However, the interplay of these factors on a population's evolutionary potential remains relatively unexplored. We tracked the outcome of 2,000 generations of evolution of four natural isolates of Escherichia coli bacteria that were engineered to also create differences in shallow history by adding previously identified mutations selected in a separate long-term experiment. Replicate populations started from each progenitor were evolved in four environments. We found that deep and shallow phylogenetic histories both contributed significantly to differences in evolved fitness, though by different amounts in different selection environments. With one exception, chance effects were not significant. Whereas the effect of deep history did not follow any detectable pattern, effects of shallow history followed a pattern of diminishing-returns whereby fitter ancestors had smaller fitness increases. These results are consistent with adaptive evolution being contingent on the interaction of several evolutionary forces but demonstrate that the nature of these interactions is not fixed and may not be predictable even when the role of chance is small.</span></p>
Fig. 2 in What drives population-level effects of parasites? Meta-analysis meets life-history
Fig. 2. Funnel plot of effect size (Hedges' g) by standard error (SE). The white circles represent studies included in the meta-analysis. The black circles represent missing imputed studies. The white diamond represents overall effect size as calculated in the meta-analysis, and the black diamond represents the corrected effect size.
Fig. 1 in What drives population-level effects of parasites? Meta-analysis meets life-history
Fig. 1. Forest plot of effect sizes (rectangles) and confidence intervals (bars) for each study and the effect averaged across all studies (diamond).
Fig. 3. A in What drives population-level effects of parasites? Meta-analysis meets life-history
Fig. 3. A meta-regression (random effects model) of effect size against host average lifespan using the complete dataset (n = 60). The slope of this regression is significant (p = 0.05).
The Accretion History of AGN I: Supermassive Black Hole Population Synthesis Model
<p>The X-ray Luminosity Function attached to this paper: https://ui.adsabs.harvard.edu/#abs/arXiv:1810.02298</p> <p>The python script contains instructions on how to calculate space densities using the numpy array.</p>
Fig. 6 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 6. Nymphargus balionotus breeding behavior. (A) Calling male; (B) Male and female in amplexus; (C) Egg mass deposited on the bottom surface on a leaf. Photos by Ross J. Maynard (A) and Jaime Culebras (B–C).
Fig 5 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig 5. Iris variation in Nymphargus balionotus. Photos by Jaime Culebras (A–C) and Scott Trageser (D).
Fig. 4 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 4. Metamorphic life stage of Nymphargus balionotus; dorsal (A) and ventral (B) views (ZSFQ 3895). Photos by Ross J. Maynard.
Fig. 3 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 3. Pattern and color variation in adult Nymphargus balionotus. (A) Male, uncollected; (B) Male, ZSFQ 0531; (C) Male, uncollected; (D) Male, uncollected; (E–F) Male, ZSFQ 0532; (G–H) Gravid female, uncollected. Photos by Ross J. Maynard.
Fig. 2 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 2. Audio spectrogram (top), oscillogram (middle), and power spectrum (bottom) graphs of a single male Nymphargus balionotus advertisement call.
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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)
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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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