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156 results for “genetic sampling”
Soil physical and chemical properties based on genetic horizon from 4 replicate pits placed around the replicate LTER control plots sampled in 1988 and 1989.
Dataset contains the following soil properties for each genetic horizon - site, Soil pit, upper and lower boundary (cm), Mg meq/100gm, Ca meq/100gm, K meq/100gm, CEC meq/100gm, pH, %C, %sand, %silt, %clay, Total %N, Total %P, % organic matter, Mn meq/100gm, Available-P ppm, %CO3, bulk density gm/cm3, Volume wt gm/m2.
FIGURE 2 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 2: (Left) Maximum clade credibility tree depicting the phylogenetic position of Pristimantis penelopus within the P. ridens series. Numbers on nodes indicate posterior probabilities. Numbers below nodes represent nodal support using the ultrafast bootstrap (see methods). Asterisks indicate nodal support above 95% in both Bayesian and ML methods. (Right) Haplotype network based on 460 bp of the COI region. Numbers of mutational steps are shown on the lines connecting haplotypes. Colors refer to geographic locations shown in Figure 1.
FIGURE 4 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 4: Potential distribution of Pristimantis penelopus based on ecological niche modeling (red). Yellow dots represents occurrence localities used to calibrate the model. See main text for details.
FIGURE 1 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 1: Geographic sampling of Pristimantis penelopus. Colored circles indicate sequenced specimens. Different colors represent the populations used in the genetic analysis (see Figure 2 for color codes).
FIGURE 3 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 3: Phenotypic variation of Pristimantis penelopus across its distribution. Localitites are shown in Appendix 1.
Fig. 6 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 6 Map of sampling sit_s of Paramecium biaurelia strains coll_ct_d during fi_ld r_s_arch in th_ Kraków ar_a. a Kraków, "At th_ brickyard" pond, 1 sampling point. b Kraków, Zaczarowana Dorożka Park (pond), 2 sampling points. c Pi_skowa Skała (pond), 1 sampling point. d Kraków,
Fig. 5 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 5 Haplotyp_ n_twork of Paramecium biaurelia construct_d using th_ 123 s_qu_nc_s of ribosomal ITS1- 5.8S-ITS2-5'LSU fragm_nts (a) and 139 of mitochondrial COI fragm_nts (b). Th_ n_twork pr_s_nts a comparison of haplotyp_s obtain_d in th_ Kraków ar_a vs. th_ oth_r localiti_s, wh_r_ mol_cular data for P. biaurelia is availabl_. Black dash_s on particular branch_s r_pr_s_nt nucl_otid_ substitutions b_tw__n particular haplotyp_s. Analys_s w_r_ conduct_d using th_ M_dian Joining m_thod in PopART softwar_ v. 1.7
Fig. 4 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 4 Haplotyp_ n_twork of Paramecium biaurelia construct_d using th_ 123 s_qu_nc_s of ribosomal ITS1- 5.8S-ITS2-5'LSU fragm_nts (a) and 139 of mitochondrial COI fragm_nts (b). Th_ n_twork pr_s_nts r_ciprocal r_lationships b_tw__n, and th_ origin of P. biaurelia haplotyp_s id_ntifi_d in curr_nt study. Black dash_s on particular branch_s r_pr_s_nt nucl_otid_ substitutions b_tw__n particular haplotyp_s. Analys_s w_r_ conduct_d using th_ M_dian Joining m_thod in PopART softwar_ v. 1.7
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 5. Image down sample
<p>Our proposal is to store each column and row bits count in a separate file and used that to reproduce the image using genetic algorithm.If we take 10% of an image size and the row and column image hamming bit count our total size will be approximately below 15% of the actual image size. We proposed a method to reproduce original image from using this 15% information.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 8. Sample Data Extraction for a 20*20 size image
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
Fig. 2 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 2 Phylog_n_tic tr__ construct_d for Paramecium aurelia compl_x, P. jenningsi compl_x and P. schewiakoffi (two sp_ci_s: P. caudatum and P. multimicronucleatum w_r_ us_d as an outgroup). Th_ tr__ was construct_d on th_ basis of a comparison of s_qu_nc_s from th_ ribosomal ITS1-5.8S-ITS2-5'LSU fragm_nt using th_ maximum lik_lihood m_thod. Bootstrap valu_s for n_ighbor joining, maximum parsimony, maximum lik_lihood, and post_rior probabiliti_s for
Fig. 3 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 3 Phylog_n_tic tr__ construct_d for Paramecium aurelia compl_x, P. jenningsi compl_x and P. schewiakoffi (two sp_ci_s: P. caudatum and P. multimicronucleatum w_r_ us_d as an outgroup). Th_ tr__ was construct_d on th_ basis of a comparison of s_qu_nc_s from th_ mitochondrial COI fragm_nt using th_ maximum lik_lihood m_thod. Bootstrap valu_s for n_ighbor joining, maximum parsimony, maximum lik_lihood, and post_rior probabiliti_s for Bay_sian inf_r_nc_ ar_
Fig. 2 in Genetic variability in four samples of Neoplecostomus yapo (Teleostei: Loricariidae) from the rio Paranapanema basin, Brazil
Fig. 2. Lateral view of four specimens of Neoplecostomus yapo from the following localities: A, ribeirão Atlântico, 85.9 mm SL; B) ribeirão Uraí, 93.3 mm SL; C) rio Verde, 96.0 mm SL; and D) rio Fortaleza, 97.4 mm SL (type locality of N. yapo).
Fig. 1 in Genetic variability in four samples of Neoplecostomus yapo (Teleostei: Loricariidae) from the rio Paranapanema basin, Brazil
Fig. 1. Partial map of South America showing the collecting sites of Neoplecostomus yapo. Circle = ribeirão Atlântico, município de Mandaguaçu; triangle = ribeirão Uraí, mun. Londrina; lozenge = rio Fortaleza, mun. Tibagi; and star = rio Verde, mun. Ponta Grossa, Paraná State.
Testing the effectiveness of genetic monitoring using genetic non-invasive sampling
<p>1. Effective conservation requires accurate data on population genetic diversity, inbreeding, and genetic structure. Increasingly, scientists are adopting genetic non-invasive sampling as a cost-effective population-wide genetic monitoring approach. Genetic non-invasive sampling has, however, known limitations which may impact the accuracy of downstream genetic analyses.</p> <p>2. Here, using high quality SNP data from blood/tissue sampling of a free-ranging koala population (n = 430), we investigated how the reduced SNP panel size and call rate typical of genetic non-invasive samples (derived from experimental and field trials) impacts the accuracy of genetic measures, and also the effect of sampling intensity on these measures.</p> <p>3. We found that genetic non-invasive sampling at small sample sizes (14% of population) can provide accurate population diversity measures, but slightly underestimated population inbreeding coefficients. Accurate measures of internal relatedness required at least 33% of the population to be sampled. Accurate geographic and genetic spatial autocorrelation analysis requires between 28% and 51% of the population to be sampled.</p> <p>4. We show that genetic non-invasive sampling at low sample sizes can provide a powerful tool to aid conservation decision-making and provide recommendations for researchers looking to apply these techniques to free-ranging systems.</p>
Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate
Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).
Data from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'
<p>Data sets from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'. These include the test statistics from analyses of real and simulated data, and the data used to generate the figures relating to the goodness-of-fit of the generalised extreme value distribution to the GPS test statistics under the null. Please see the enclosed README for more details.</p>
Testing the effectiveness of genetic monitoring using genetic non-invasive sampling
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Data for: Sampling affects population genetic inference: a case study of the Allen's (Selasphorus sasin) and rufous hummingbird (Selasphorus rufus) (Part 1/2)
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Data for: Sampling affects population genetic inference: a case study of the Allen's (Selasphorus sasin) and rufous hummingbird (Selasphorus rufus) (Part 2/2)
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