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124 results for “Genetic distance”
Even short‐distance dispersal over a barrier can affect genetic differentiation in Gyraulus, an island freshwater snail
<p>Supplementary dataset for a published paper, "Saito T., Sasaki T., Tsunamoto Y., Uchida S., Satake K., Suyama Y., <em>et al.</em> (2022). Even short‐distance dispersal over a barrier can affect genetic differentiation in <em>Gyraulus</em> , an island freshwater snail. <em>Freshwater Biology</em> <strong>67</strong>, 1971–1983. <a href="https://doi.org/10.1111/fwb.13990">https://doi.org/10.1111/fwb.13990</a>"</p>
Influence of Paleolithic Range Contraction, Admixture and Long-Distance Dispersal on Genetic Gradients of Modern Humans in Asia
<p>Each folder is identified according to the scenario, and contains another folder with the input files (files *.txt, *.par, *.sam, *.asc) to simulate it, the corresponding simulated genetic data (files *.arp) and the derived PC maps (files *.png). A file with the locations of the samples is also included (coord.txt).</p> <p>* Pure Paleolithic expansion * <br> The folder “Paleo” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion, ignoring the range contraction induced by the LGM and LDD events.</p> <p>* Pure Paleolithic expansion considering the range contraction induced by the LGM * <br> The folder “Paleo_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion suffering the range contraction induced by the LGM.</p> <p> * Pure Paleolithic expansion considering long-distance dispersal (LDD) events * <br> The folder “Paleo_LDD” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion considering LDD events. </p> <p>* Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and East Asia considering the range contraction induced by the LGM * <br> The folder “Paleo2NeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and later from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and East Asia considering LDD events * <br> The folder “Paleo2NeoIR0_LDD” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by two Neolithic expansions from Middle East and later from East Asia considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from Middle East considering the range contraction induced by the LGM * <br> The folder “Paleo_MiddleEastNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0) from Middle East suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering the range contraction induced by the LGM * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES) and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from Middle East considering LDD events * <br> The folder “Paleo_MiddleEastNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0) from Middle East considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering LDD events * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES) the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from Middle East considering the range contraction induced by the LGM * <br> The folder “Paleo_MiddleEastNeoIR004_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering the range contraction induced by the LGM * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0.04) from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from Middle East considering LDD events * <br> The folder “Paleo_MiddleEastNeoIR004_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering LDD events * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0.04) from East Asia considering LDD events.</p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Rare, long-distance dispersal underpins genetic connectivity in the pink sea fan, Eunicella verrucosa
<p>Characterising patterns of genetic connectivity in marine species is of critical importance given the anthropogenic pressures placed on the marine environment. For sessile species, population connectivity can be shaped by many processes, such as pelagic larval duration, oceanographic boundaries, and currents. This study combines restriction-site associated DNA sequencing (RADseq) and passive particle dispersal modelling to delineate patterns of population connectivity in the pink sea fan, <em>Eunicella verrucosa, </em>a temperate octocoral. Individuals were sampled from 20 sites covering most of the species' northeast Atlantic range, and a site in the northwest Mediterranean Sea to inform on connectivity across the Atlantic-Mediterranean transition. Using 7,510 neutral SNPs, a geographic cline of genetic clusters was detected, partitioning into: Ireland, Britain, France, Spain (Atlantic), and Portugal and Spain (Mediterranean). Evidence of significant inbreeding was detected at all sites, a finding not detected in a previous study of this species based on microsatellite loci. Genetic connectivity was characterised by an isolation by distance pattern (IBD) (<em>r<sup>2</sup></em> = 0.78, <em>p</em><0.001), which persisted across the Mediterranean-Atlantic boundary. In contrast, exploration of ancestral population assignment using the program ADMIXTURE indicated genetic partitioning across the Bay of Biscay, which we suggest represents a natural break in the species' range, possibly linked to a lack of suitable habitat. As the pelagic larval duration (PLD) is unknown, passive particle dispersal simulations were run for 14 and 21 days. For both modelled PLDs, inter-annual variations in particle trajectories suggested that in a long-lived, sessile species, range-wide IBD is driven by rare, longer dispersal events which act to maintain gene flow. These results suggest that oceanographic patterns may facilitate range-wide stepping-stone genetic connectivity in <em>E. verrucosa</em>, and highlight that both oceanography and natural breaks in a species' range should be considered in the designation of ecologically coherent MPA networks.</p>
Data and codes from "Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA"
<p><span>Data and codes used for </span><span>“Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA”</span></p>
Fig. 3. The genetic distances among populations. A in Phylogeography and Genetic Structure of the Bush Cricket (Orthoptera, Tettigoniidae) in Southern China.
Fig. 3. The genetic distances among populations. A: based on Kimura's 2-parameter; B: based on Tamura 3-parameter.
Figure 3 in Effects of genetic relatedness, spatial distance, and context on intraspecific aggression in the red wood ant Formica pratensis (Hymenoptera: Formicidae)
Figure 3. Correlation between spatial distance and aggression levels in the field. Open circles correspond to monodomous colonies and filled circles correspond to the polydomous one.
Figure 1. Map showing the localities where F in Effects of genetic relatedness, spatial distance, and context on intraspecific aggression in the red wood ant Formica pratensis (Hymenoptera: Formicidae)
Figure 1. Map showing the localities where F. pratensis colonies were sampled for the analysis of genetic relatedness and tested for their aggressive behavior towards each other. The numbers denote the localities. 1: Balaban village (N 41°49ʹ18ʺ, E 27°40ʹ44ʺ) containing three nests; B1, B2, and B3, 2: Asilbeyli village (N 41°39ʹ32ʺ, E 27°13ʹ50ʺ), one nest (As), 3: Ulukonak village (N 41°39ʹ35ʺ, E 27°01ʹ52ʺ) one nest (U), 4: Doğanköy village (N 41°56ʹ12ʺ, E 26°41ʹ20ʺ) one nest (D), and 5: Ahmetler village (N 42°00ʹ37ʺ, E 27°11ʹ12ʺ), three nests; Ah1, Ah2, and Ah3.
Rare, long-distance dispersal underpins genetic connectivity in the pink sea fan, Eunicella verrucosa
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Data from: Influence of paleolithic range contraction, admixture and long-distance dispersal on genetic gradients of modern humans in Asia
<p>Cavalli-Sforza and coauthors originally explored the genetic variation of modern humans throughout the world and observed an overall east-west genetic gradient in Asia. However, the specific environmental and population genetics processes causing this gradient were not formally investigated and promoted discussion in recent studies. Here we studied the influence of diverse environmental and population genetics processes on Asian genetic gradients and identified which could have produced the observed gradient. To do so, we performed extensive spatially-explicit computer simulations of genetic data under the following scenarios: (<i>i</i>) variable levels of admixture between Paleolithic and Neolithic populations, (<i>ii</i>) migration through long-distance dispersal (LDD), (<i>iii</i>) Paleolithic range contraction induced by the last glacial maximum (LGM) and, (<i>iv</i>) Neolithic range expansions from one or two geographic origins (the Fertile Crescent and the Yangzi and Yellow River Basins). Next, we estimated genetic gradients from the simulated data and we found that they were sensible to the analyzed processes, especially to the range contraction induced by LGM and to the number of Neolithic expansions. Some scenarios were compatible with the observed east-west genetic gradient, such as the Paleolithic expansion with a range contraction induced by the LGM or two Neolithic range expansions from both the east and the west. In general, LDD increased the variance of genetic gradients among simulations. We interpreted the obtained gradients as a consequence of both <i>allele surfing</i> caused by range expansions and isolation by distance along the vast east-west geographic axis of this continent.</p>
Data from: Understanding the social dynamics of breeding phenology: indirect genetic effects and assortative mating in a long distance migrant
Phenological traits, such as the timing of reproduction, are often influenced by social interactions between paired individuals. Such partner effects may occur when pair members affect each other's pre-breeding environment. Partner effects can be environmentally and/or genetically determined, and quantifying direct and indirect genetic effects is important for understanding the evolutionary dynamics of phenological traits. Here, using 26 years of data from a pedigreed population of a migratory seabird, the common tern ( Sterna hirundo ), we investigate male and female effects on female laying date. We find that female laying date harbors both genetic and environmental variation, and is additionally influenced by the environmental, and, to a lower extent, genetic, component of her mate. We demonstrate this partner effect to be largely explained by male arrival date. Interestingly, analyses of mating patterns with respect to arrival date show mating to be strongly assortative and, using simulations, we show that this assortative mating for arrival date leads to overestimation of partner effects. Our study provides evidence for partner effects on breeding phenology in a long distance migrant, while uncovering the potential causal pathways underlying the observed effects and raising awareness for confounding effects due to assortative mating or other common environmental effects.
Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
1.Dispersal distances are commonly inferred from occupancy data but have rarely been validated. Estimating dispersal from occupancy data is further complicated by imperfect detection and the presence of unsurveyed patches. 2.We compared dispersal distances inferred from seven years of occupancy data for 212 wetlands in a metapopulation of the secretive and threatened California black rail (Laterallus jamaicensis coturniculus) to distances between parent-offspring dyads identified with 16 microsatellites. 3.We used a novel autoregressive multi-season occupancy model that accounted for both unsurveyed patches and imperfect detection to quantify patch isolation using buffer radius (BRM) and incidence function (IFM) connectivity measures at 15 scales (1–10, 15, 20, 25, and 30 km). Connectivity measures were then fit as colonization covariates in occupancy models to estimate a model-averaged dispersal distance. 4.As predicted, colonization was more strongly related to connectivity at small spatial scales (< 10 km). AIC weights were greatest at 7 km for BRM and at 4 km for IFM. 5.Model-averaged dispersal distances (BRM = 7.46 km; IFM = 5.48 km) showed good agreement with the mean (± SE) dispersal distance from 23 parent-offspring dyads (5.58 ± 1.92 km), indicating reasonably accurate mean dispersal distances can be inferred from occupancy data when isolation strongly affects colonization.
Figure 4. - Intralineage and interlineage uncorrected genetic distance values for the "ivonicus/yuna" and "carteri" lineages.
Figure 4. - Intralineage and interlineage uncorrected genetic distance values for the "ivonicus/yuna" and "carteri" lineages.
Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
<p>Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci.</p> <p>We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization.</p> <p>The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples.</p>
Data from: Range-wide genetic analysis of an endangered bumble bee (Bombus affinis) reveals population structure, isolation by distance, and low colony abundance
<p>Declines in bumblebee species ranges and abundances are documented across multiple continents and have prompted the need for research to aid species recovery and conservation. The rusty patched bumblebee (<em>Bombus affinis</em>) is the first federally-listed bumblebee species in North America. We conducted a range-wide population genetics study of <em>B. affinis</em> from across all extant conservation units to inform conservation efforts. To understand the species' vulnerability and help establish recovery targets, we examined population structure, patterns of genetic diversity, and population differentiation. Additionally, we conducted site-level analysis of colony abundance to inform prioritizing areas for conservation, translocation, and other recovery actions. We find substantial evidence of population structuring along an east-to-west gradient. Putative populations show evidence of isolation by distance, high inbreeding coefficients, and a range wide male diploidy rate of ~15%. Our results suggest the Appalachians represents a genetically distinct cluster with high levels of private alleles and substantial differentiation from the rest of the extant range. Site-level analyses suggest low colony abundance estimates for <em>B. affinis</em> compared to similar datasets of stable, co-occurring species. These results lend genetic support to trends from observational studies suggesting B. affinis has undergone a recent decline and exhibits substantial spatial structure. The low colony abundances observed here suggest caution in overinterpreting the stability of populations even where <em>B. affinis</em> is reliably detected interannually. These results help delineate informed management units, provide context for the potential risks of translocation programs, and can help set clear recovery targets for this and other threatened bumblebee species.</p>
Isolation-by-distance and genetic parentage analysis provide similar larval dispersal estimates
<p>An R studio project that includes original SNP data files used to quantify dispersal in <em>Elacatinus lori</em> via the isolation-by-distance (IBD) method. Associated R-code used to generate IBD regression slopes, calculate sigma, and construct dispersal kernels. Includes output from NeEstimator, estimating effective population size. </p> <p>Folders 1-3 contain the code/data needed to obtain the slope of the IBD relationship, effective population size, and the standard deviation (sigma) of the dispersal distribution, respectively. Folder 4 contains the R code needed to construct Laplacian dispersal kernels. </p>
Data from: Local prey community composition and genetic distance predict venom divergence among populations of the northern Pacific rattlesnake (Crotalus oreganus)
Identifying the environmental correlates of divergence in functional traits between populations can provide insights into the evolutionary mechanisms that generate local adaptation. Here, we assess patterns of population differentiation in expressed venom proteins in Northern Pacific rattlesnakes (Crotalus oreganus) from 13 locations across California. We evaluate the relative importance of major biotic (prey species community composition), abiotic (temperature, precipitation, and elevation) and genetic factors (genetic distance based on RADseq loci) as correlates of population divergence in venom phenotypes. We found that over half of the variation in venom composition is associated with among-population differentiation for genetic and environmental variables, and that this variation occurred along axes defining previously observed functional trade-offs between venom proteins that have neurotoxic, myotoxic and hemorrhagic effects. Surprisingly, genetic differentiation among populations was the best predictor of venom divergence, accounting for 46% of overall variation, whereas differences in prey community composition and abiotic factors explained smaller amounts of variation (23% and 19%, respectively). The association between genetic differentiation and venom composition could be due to an isolation by distance effect or, more likely it may reflect an isolation-by-environment effect where selection against recent migrants is strong, producing a correlation between neutral genetic differentiation and venom differentiation. Our findings suggest that even coarse estimates of prey community composition can be useful in understanding the selection pressures acting on patterns of venom protein expression. Additionally, our results suggest that factors other than adaptation to spatial variation in prey need to be considered when explaining population divergence in venom.
Long-distance dispersal drives the genetic variation and historical demography of Quercus magnoliifolia and Quercus resinosa (Fagaceae) in the Mexican highlands
<p>Genotypes of chloroplast microsatellites used in Albarrán-Lara et al. Data comprises six loci from 61 localities sampling in Mexico</p>
Data from: Inferring the timing of long-distance dispersal between Rail metapopulations using genetic and isotopic assignments
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Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
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International Brain Laboratory public data
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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.