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135 results for “Sample size”
Data from: Monitoring the effective population size of a brown bear (Ursus arctos) population using new single-sample approaches
The effective population size (Ne) could be the ideal parameter for monitoring populations of conservation concern as it conveniently summarizes both the evolutionary potential of the population and its sensitivity to genetic stochasticity. However, tracing its change through time is difficult in natural populations. We applied four new methods for estimating Ne from a single sample of genotypes to trace temporal change in Ne for bears in the Northern Dinaric Mountains. We genotyped 510 bears using 20 microsatellite loci, and determined their age. The samples were organized into cohorts with regard to the year when the animals were born and yearly samples with age categories for every year when they were alive. We used the Estimator by Parentage Assignment (EPA) to directly estimate both Ne and generation interval for each yearly sample. For cohorts, we estimated the effective number of breeders (Nb) using Linkage Disequilibrium, Sibship Assignment and Approximate Bayesian Computation methods, and extrapolated these estimates to Ne using the generation interval. The Ne estimate by EPA is 276 (183-350 95% CI), meeting the inbreeding-avoidance criterion of Ne > 50 but short of the long-term minimum viable population goal of Ne > 500. The results obtained by the other methods are highly consistent with this result, and all indicate a rapid increase in Ne probably in the late 1990s and early 2000s. The new single-sample approaches to estimation of Ne provide efficient means for including Ne in monitoring frameworks, and will be of great importance for future management and conservation.
Data from: Effects of sample size and full sibs on genetic diversity characterization: a case study of three syntopic Iberian pond-breeding amphibians
Accurate characterization of genetic diversity is essential for understanding population demography, predicting future trends and implementing efficient conservation policies. For that purpose, molecular markers are routinely developed for nonmodel species, but key questions regarding sampling design, such as calculation of minimum sample sizes or the effect of relatives in the sample, are often neglected. We used accumulation curves and sibship analyses to explore how these 2 factors affect marker performance in the characterization of genetic diversity. We illustrate this approach with the analysis of an empirical dataset including newly optimized microsatellite sets for 3 Iberian amphibian species: Hyla molleri, Bufo calamita, and Pelophylax perezi. We studied 17–21 populations per species (total n = 547, 652, and 516 individuals, respectively), including a reference locality in which the effect of sample size was explored using larger samples (77–96 individuals). As expected, FIS and tests for Hardy–Weinberg equilibrium and linkage disequilibrium were affected by the presence of full sibs, and most initially inferred disequilibria were no longer statistically significant when full siblings were removed from the sample. We estimated that to obtain reliable estimates, the minimum sample size (potentially including full sibs) was close to 20 for expected heterozygosity, and between 50 and 80 for allelic richness. Our pilot study based on a reference population provided a rigorous assessment of marker properties and the effects of sample size and presence of full sibs in the sample. These examples illustrate the advantages of this approach to produce robust and reliable results for downstream analyses.
Data from: Breaking down the lithification bias: the effect of preferential sampling of larger specimens on the estimate of species richness, evenness, and average specimen size
Lithification, the transition of unconsolidated sediments to fully indurated rocks, can potentially bias estimates of species richness, evenness, and body size distribution derived from fossil assemblages. Fossil collections made from well-indurated rocks consistently exhibit lower species richness, lower evenness, and a specimen size distribution skewed towards larger specimens relative to collections made from unconsolidated sediments, even when collections are drawn from the same assemblage. This phenomenon is known as the lithification bias. While the bias itself has been demonstrated empirically, much less attention has been paid to its causes. Proposed causes include taphonomic processes (e.g., destruction of small specimens during early diagenesis) or methodological differences (e.g., sieving vs. counting specimens on outcrops, bedding surfaces, or mechanically split surfaces). Here we investigate the potential effects of preferential intersection that could also result in a methodologically related bias: the preferential sampling of larger specimens relative to smaller ones when fossils are counted on rock surfaces. We used an analog model to simulate preferential intersection (fossil collection via splitting fossiliferous rock) and compare the results to a random draw model that approximates the effects of sieving. The model was parameterized using nine different combinations of species abundance and species size distributions. The results show that, with rare exceptions, species richness is 5–23% lower, evenness 5-25% lower, and average specimen size 24–150% higher in preferential intersection than in random draw simulations. We conclude that preferential intersection can impose a significant bias independent of other mechanisms (e.g., preferential destruction of smaller specimens during diagenetic or sampling processes), that the magnitude of this bias is partially dependent on the species abundance and size distributions, and that this bias alone does not fully account for empirically observed lithification bias on species richness (i.e., other sources of bias are also at work).
Supplementary Data - Haplotype-based inference of recent effective population size in modern and ancient DNA samples
<p>This repository contains the simulated data analyzed in our manuscript titled "Haplotype-based Inference of Recent Effective Population Size in Modern and Ancient DNA Samples".The data is split into 7 datasets. </p><p><strong>- demographies.tar.gz</strong>: the simulated demographic models.<br><strong>- modern_data.tar.gz:</strong> simulated SNP-araray data. The genotypes were simulated under 4 demographic histories (see demographies.tar.gz) for 256 samples and under 10 different random seeds (Replicate 1-10). Additionally, Replicates 11 and 12 include simulations at larger sample sizes. Note that the results at lower sample sizes can be obtained by keeping the first N samples of the simulated files.<br><strong>- true_ibd.tar.gz :</strong> This dataset contains the IBD segments from the simulated modern_data (ground truth from ARGON simulator).<br><strong>- ancient_data.tar.gz</strong> dataset contains simulated aDNA data. The dataset contains data simulated at different coverages (MISSING_$M, where $M = exp(-coverage)), sample sizes, demographic models, and random seeds. Split into three parts. See below for instructions on how to reconstruct the dataset. <br><strong>- structure.tar.gz</strong> and<strong> admixture.tar.gz :</strong> contain the data simulated under more complex demographic histories involving 2 isolated populations (structure) or a single population undergoing a recent admixture event (admixtuer.tar.gz). The manuscript provides more details about the demographic histories.<br>- <strong>imputed.tar.gz</strong> dataset contains simulated imputed aDNA data. Each region was simulated independently. Each folder corresponds to a chromosome arm and contains:<br> - data.anc_array.npy: list of the SNPs included in the analysis (simulating a 1240k array)<br> - data.glimpse.vcf.gz: phased ancient population data as phased by GLIMPSE v1<br> - data.glimpse.vcf.gz.csi: index for the above VCF file<br> - data.imputed.vcf.gz: imputed ancient population data (unphased, including dosages and genotype posteriors)<br> - data.imputed.vcf.gz.csi: index file for the above VCF<br> - data.map.gz: genetic map for GLIMPSE (tab-separated format: pos chr cM)<br> - data.ref.tsv.gz: file used for calculating genotype likelihoods with BCFtools mpileup command to use as input for GLIMPSE (format: chromosome position ref_allele,alt_allele)<br> - data.ref.tsv.gz.tbi: index for the above file<br> - data.ref.vcf.gz: simulated sequencing data from the reference panel<br> - data.ref.vcf.gz.csi: index file for the above VCF<br> - data.target_ground_truth.vcf.gz: simulated sequencing data for the ancient population (ground truth)<br> - datalist.txt: list of genotype likelihood files for each target individual (to be used for merging into a single file)<br> - data.temp.map.gz: genetic map for data creation with msprime simulator (format: chr position rate(cM/Mb) cM)<br> - data.tree -> msprime simulator output containing all samples, both reference panel samples and ancient population samples<br> - data.vcf.gz: VCF containing ground truth sequencing data, phased genotypes for reference panel samples and for ancient population samples<br> - dataref.fa.fai: index for reference fasta file used during reads creation</p><p> </p><p>Note that some of the datasets have been split into multiple parts, for example admixed.tar.gz has been split into three different parts admixed.tar.gz-part-aa, admixed.tar.gz-part-ab, and admixed.tar.gz-part-ac<br>You can get the data by typing:</p><p>cat admixed.tar.gz.part-* > admixed.tar.gz</p><p><br> </p>
Figure 3. Example relative fluorescence histograms for samples stained with propidium iodide. The 2C in Peaceful revolution in genome size: polyploidy in the Nabidae (Heteroptera); autosomes and nuclear DNA content doubling
Figure 3. Example relative fluorescence histograms for samples stained with propidium iodide. The 2C peaks represent diploid cells, and 4C peaks represent cells in the G2 phase of the cell cycle, with replicated DNA. Standard used: Solanum pseudocapsicum 2C = 2.61 pg. A, Himacerus apterus female with 2n = 36 + XX and 2C = 9.71 pg. B, Nabis maoricus female with 2n = 16 + XX and 2C = 4.21 pg.
Systematic review for optimizing sample size in dairy cow methane emission studies: a comprehensive methodological approach
<p>Collection of research data focusing on methane (CH4) emissions from dairy cows across various breeds and conditions. The dataset encompasses a range of studies published from 2012 to 2023, each documented with specific parameters including the study title, authors, country of research, cow breed, lactation status, methods used for CH4 measurement, experimental designs, CH4 yield and its variability, dry matter intake, and diet composition.</p>
Seed color and size analysis using sample quinoa images
<p>The dataset comprises of sample quinoa images acquired using a scanner. In addition, Matlab code is provided that can be used to extract the color and size features. The project was funded by competitive USDA-NIFA Agriculture and Food Research Initiative award WNP08532 (accession no. 1008828).</p>
Fig. 1 Sampling locations. A in Does size matter? Comparative population genetics of two butterflies with different wingspans
Fig. 1 Sampling locations. A map of the Western Ghats showing sampling locations for both species. Inset: map of India showing the location of the Western Ghats in blue
One size does not fit all: Tuning eDNA protocols for high and low turbidity water sampling
<p class="abstract_para">Findings from eDNA metabarcoding are strongly influenced by experimental approach, yet the effect of pre‐PCR sample processing on taxon detection and estimates of biodiversity across different water types is still poorly resolved. To fill this data gap, we investigated the impact of sampling effort, extraction method, and filter pore size on DNA yield, PCR inhibition, and 16S rDNA metabarcoding results for fishes in water samples collected from inshore turbid‐ and offshore clear‐water environments. The turbid‐water samples had high concentrations of suspended organic and/or inorganic material and yielded ~3.2× more DNA and exhibited high levels of PCR inhibition compared with the low‐turbidity, clear‐water samples. Importantly, there were no striking differences in the results of our metabarcoding experiments based on extraction method or filter pore size. While a small number of unique species of relatively low read count were detected in all turbid‐water treatments, most species were consistently detected across samples. Results for the clear‐water samples were strikingly different, with low DNA yield, high levels of variation across replicates, and a high number of non‐overlapping species across treatments. These findings indicate a patchy distribution of eDNA in offshore environments, which means higher volumes of water (≥ 2 L per replicate) must be filtered in habitats where target DNA is likely to be sparse. In semi‐closed systems such as estuaries, higher concentrations of target DNA are expected, and we found that either a 1.0 or 3.0 µm filter pore size was sufficient to capture standing diversity, while decreasing the risk of clogging. For economical DNA extraction and inhibitor removal, we recommend a combination of Omega Bio‐tek E.Z.N.A Tissue DNA kit followed by a PCR inhibitor removal step using the Zymo Kit. Finally, we emphasize that pilot studies should be undertaken whenever sampling in a new environment to identify which protocol is most appropriate.</p>
Figure 4. Regression lines between average length and sampling depth for the 19 in Bathymetric trends in distribution and size of demersal fish species in the north Aegean Sea
Figure 4. Regression lines between average length and sampling depth for the 19 species with significant size-depth relationships.
Figure 3. Example relative fluorescence histograms for samples stained with propidium iodide. The 2C in Peaceful revolution in genome size: polyploidy in the Nabidae (Heteroptera); autosomes and nuclear DNA content doubling
Figure 3. Example relative fluorescence histograms for samples stained with propidium iodide. The 2C peaks represent diploid cells, and 4C peaks represent cells in the G2 phase of the cell cycle, with replicated DNA. Standard used: Solanum pseudocapsicum 2C = 2.61 pg. A, Himacerus apterus female with 2n = 36 + XX and 2C = 9.71 pg. B, Nabis maoricus female with 2n = 16 + XX and 2C = 4.21 pg.
Figure 2 in Estimating optimal sample size for tardigrade morphometry
Figure 2. The relationship between estimation error and sample size has an asymptotic character both for means (A) and for ranges (B), meaning that the larger the sample size, the smaller the estimation error, but the reduction in error diminishes with the increasing sampling effort. Graphs show means ƚSD calculated from six species means (see Table 3). Dashed horizontal lines represent acceptable error rates: A, for means, errors under the dashed line are acceptable (i.e. sample sizes n ≥ 20; B, for ranges, errors above the dashed line are acceptable (i.e. none of the shown sample sizes returns an acceptable range estimation).
Figure 1 in Estimating optimal sample size for tardigrade morphometry
Figure 1. Exemplary graphs illustrating the results of simulations for mean (A) and range estimations (B) of the buccal tube length in the experimental population of Diphascon higginsi (N = 404; global mean = 22.2 µm; global range = 15.1– 25.6 µm = 10.5 µm). A, mean estimation graph: the solid horizontal line represents the global mean whereas the two dashed lines delimit the acceptable error zone, i.e. ƚ10% of the global range (21.2–23.3 µm); dots below and above the global mean delimit the zone within which 95% of computed means fell; the arrow points to the sample size at which 95% of computed means fell within the acceptable error around the global mean (n = 15). B, range estimation graph: the solid horizontal line represents the global range whereas the dashed line delimits the acceptable error zone, i.e. –20% of the global range (8.4 µm); dots at each sample size delimit the zone within which 95% of computed ranges fell; the arrow points to the sample size at which 95% of computed ranges fell within the acceptable error below the global range (n = 130). Graphs for all traits and species are available in the Supplementary Materials.
Text-fig. 16. Length-width scatter diagram of isolated molars of Sayimys hintoni n. sp. and S. cf. minor from sites H.-SGP 81.14a (lower Manchar Formation, Sind, Pakistan) with the size-cloud outlines of molars of the large sample of S. chinjiensis from H.-GSP.24 shown in Text-fig. 17. in An Exceptional Large Sample Of The Early Miocene Ctenodactyline Rodent Sayimys Giganteus, Specific Variation And Taxonomic Implications
Text-fig. 16. Length-width scatter diagram of isolated molars of Sayimys hintoni n. sp. and S. cf. minor from sites H.-SGP 81.14a (lower Manchar Formation, Sind, Pakistan) with the size-cloud outlines of molars of the large sample of S. chinjiensis from H.-GSP.24 shown in Text-fig. 17.
Text-fig. 12. Ranges of the length of the dp4 based on data from the literature. Vertical scale in Ma, the samples are from the Siwaliks (black) and Turkey, Greece and Arabia (red). Type localities of species are indicated with an asterisk. The Y and Z sites are from Baskin (1996), its size ranges are composed of data from several localities and the vertical arrowed broken line indicates the age range of these sites. in An Exceptional Large Sample Of The Early Miocene Ctenodactyline Rodent Sayimys Giganteus, Specific Variation And Taxonomic Implications
Text-fig. 12. Ranges of the length of the dp4 based on data from the literature. Vertical scale in Ma, the samples are from the Siwaliks (black) and Turkey, Greece and Arabia (red). Type localities of species are indicated with an asterisk. The Y and Z sites are from Baskin (1996), its size ranges are composed of data from several localities and the vertical arrowed broken line indicates the age range of these sites.
, phillipsi holotype. the M of of Measurements Measurements. . n purposes size sample comparative and , range for , SD shown ± 0 are as presented Lanka Sri and Measurements India in authors . occurring Lanka different Sri, Idulgashinna Miniopterus of of assortment from species an . by nov other. sp and specimens phillipsi species other . M new the of ) the while mm of (, TK measurements individuals by taken other were Cranial and types . 4. ABLE paratypes nov. T sp in DNA barcoding and morphological analyses reveal a cryptic species of Miniopterus from India and Sri Lanka
, phillipsi holotype. the M of of Measurements Measurements. . n purposes size sample comparative and , range for , SD shown ± 0 are as presented Lanka Sri and Measurements India in authors . occurring Lanka different Sri, Idulgashinna Miniopterus of of assortment from species an . by nov other. sp and specimens phillipsi species other . M new the of ) the while mm of (, TK measurements individuals by taken other were Cranial and types . 4. ABLE paratypes nov. T sp
SEER: Estimating the sampling size in performance models considering errors
<p>Generating a performance model to optimize or to reason about variability-intensive systems is costly and the user never knows (unless she or he is a seer) when to stop sampling. The performance measurement can take from several seconds to several days, depending on the measurement and the noise, so it is very important to know if new measurements are profit. Here is where SEER can support users, by estimating the sampling size to generate performance models below a relative prediction error.</p> <p>There are three folders their files that can help you to start using SEER. The folder CODES include all the source code needed to use it. SEER has been validated with ten configurable systems. Folders Ground Truth and Results include the measurements done to validate SEER.</p>
Figure 1: Global distribution of ESKAPE pathogens sample size
<p><strong>Figure 1: Global distribution of ESKAPE pathogens sample size</strong></p>
Figure 1: Global distribution of ESKAPE pathogens sample size
<p><strong>Figure 1: Global distribution of ESKAPE pathogens sample size </strong></p>
Sample Size for Multivariate Time-to-event Data
ClinicalTrials.gov study NCT03964402. IPD Sharing: NO. Countries: 1. Publications: 3.
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OpenNeuro
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