Find research datasets worth reusing

opencc-by-4.0Aug 2021View details →

The genetic population structure of Lake Tanganyika's Lates species flock, an endemic radiation of pelagic top predators

Data associated with the manuscript "The genetic population structure of Lake Tanganyika’s Lates species flock, an endemic radiation of pelagic top predators," where we investigate the genetic population structure of the four endemic Lates species in Lake Tanganyika. Abstract: Life history traits are important in shaping gene flow within species and can thus determine whether a species exhibits genetic homogeneity or population structure across its range. Understanding genetic connectivity plays a crucial role in species conservation decisions, and genetic connectivity is an important component of modern fisheries management in fishes exploited for human consumption. In this study, we investigated the population genetics of four endemic Lates species of Lake Tanganyika (Lates stappersii, L. microlepis, L. mariae and L. angustifrons), using reduced-representation genomic sequencing methods. We find the four species to be strongly differentiated from one another, with no evidence for contemporary admixture. We also find evidence for high levels of genetic structure within L. mariae, with the majority of individuals from the most southern sampling site forming a genetic group distinct from the individuals at other sampling sites. We find evidence for much weaker structure within the other three species, L. stappersii, L. microlepis, and L. angustifrons, although small and unbalanced sample sizes and imprecise geographic sampling locations may hinder our ability to detect weak population structure. We call for further research into the origins of the genetic differentiation that we observe in these four species, particularly that of L. mariae, which may be important for the conservation and management of this species. Code associated with the analysis of these data can be found on GitHub at <a href="https://github.com/jessicarick/lates-popgen">https://github.com/jessicarick/lates-popgen</a>.

opencc-by-4.0Oct 2022View details →

The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high-levels of lineage-specific diversity driven by recombination

Aspergillus fumigatus is a deadly agent of human fungal disease, where virulence heterogeneity is thought to be at least partially structured by genetic variation between strains. While population genomic analyses based on reference genome alignments offer valuable insights into how gene variants are distributed across populations, these approaches fail to capture intraspecific variation in genes absent from the reference genome. Pan-genomic analyses based on de novo assemblies offer a promising alternative to reference-based genomics, with the potential to address the full genetic repertoire of a species. Here, we use a combination of population genomics, phylogenomics, and pan-genomics to assess population structure and recombination frequency, phylogenetically structured gene presence-absence variation, evidence for metabolic specificity, and the distribution of putative antifungal resistance genes in A. fumigatus.  We provide evidence for three distinct populations of A. fumigatus, structured by both gene variation (SNPs and indels) and distinct gene presence-absence variation with unique suites of accessory genes present exclusively in each clade. Accessory genes displayed functional enrichment for nitrogen and carbohydrate metabolism, hinting that populations may be stratified by environmental niche specialization. Similarly, the distribution of antifungal resistance genes and resistance alleles were often structured by phylogeny. Despite low levels of outcrossing, A. fumigatus demonstrated a large pan-genome including many genes unrepresented in the Af293 reference genome. These results highlight the inadequacy of relying on a single-reference based approach for evaluating intraspecific variation, and the power of combined genomic approaches to elucidate population structure, genetic diversity, and the putative ecological drivers of clinically relevant fungi. Accompanying manuscript is available as preprint at <a href="https://dx.doi.org/10.1101/2021.12.12.472145">https://dx.doi.org/10.1101/2021.12.12.472145</a>  Lotus A. Lofgren, Brandon S. Ross, Robert A. Cramer, Jason E. Stajich. Combined Pan-, Population-, and Phylo-Genomic Analysis of Aspergillus fumigatus Reveals Population Structure and Lineage-Specific Diversity bioRxiv 2021.12.12.472145; doi: https://doi.org/10.1101/2021.12.12.472145

opencc-by-4.0Jul 2024View details →

Data from Neutral genetic structuring of pathogen populations during rapid adaptation

Datasets and temporary dataframes relating to the article "Neutral genetic structuring of pathogen populations during rapid adaptation". These datasets and temporary dataframes are necessary to run the scripts from the public GitLab repository: <a href="https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation">https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation</a>. Please refer to this public GitLab repository for the latest version of the codes and to perform all analyses presented in the article. Original datasets from the demogenetic model: <ul> <li>Output_RandomDesign.txt</li> <li>Output_RegularDesign_With_host_alternation.txt</li> <li>Output_RegularDesign_Without_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_With_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_Without_host_alternation.txt</li> </ul> All remaining files correspond to temporary dataframes generated by the scripts in the GitLab repository, provided here for reproducibility of the results and to save time at certain time-consuming scripts.

opencc-by-4.0Aug 2024View details →

Two decades of body length measurements in size-structured larval and juvenile fish populations in English rivers.

Long term ecological datasets are valuable in providing context and understanding to complex ecological processes that occur over broad temporal scales, and provide a baseline for analysing change. Monitoring of fish populations in UK waterbodies and elsewhere is typically through measuring the length of individual fish caught in surveys. Through this method, the age structure of fish populations can be determined, as well as over winer survival rates and future recruitment success and cohort sizes can be predicted. The larval and juvenile period are when fish are considered most vulnerable to predation, competition, disease and environmental perturbations.  This study presents the first long-term larval and juvenile fish lengths dataset for 67 survey sites over two decades (1999-2018) from the rivers Ancholme, Warwickshire Avon, Don, Trent, and Yorkshire Ouse (including the Swale, Ure, Nidd and Wharfe) in the United Kingdom. These rivers represent a range of topographical and biotopical characteristics. For the majority of this study, surveys were conducted on a monthly or fortnightly basis making both annual and seasonal analyses of size structure, growth and body length possible. Although there is some variation in the sampling frequency and some locations varied throughout the study according to requirements. In total, more than 380,000 larval or juvenile fish of 30 species were measured, likely representing one of the most comprehensive datasets of its type. Surveys were conducted in river margins, where the velocity was slowest and larval and juvenile fish tend to aggregate. Fish were captured using a 25 x 3 m micromesh (3 mm mesh size) seine net that was set in a rectangle parallel to the bank. This net capture fish as small as 5 mm and is the most appropriate method of catching larvae and juvenile fish, although occasionally some larger adult fish may have also been captured and measured as part of this dataset for completeness. All fish were identified to species and measured to standard length (mm) and released at the point of capture. The exception was the smallest larvae, which were euthanised with an overdose of methanesulphonate (MS-222) and preserved in 4% formalin solution for microscopic examination. The dataset contains 384,090 rows and 13 columns. Each row corresponds to a single fish that was measured at each site and date. Associated site information (site name, location, area fished (m2) and survey date) is reported for each row. When only a fraction of the catch was processed, the sub-sample size was reflected in the Count column (e.g. when half the sample was processed, the numbers of fish measured or only counted were multiplied by two). This enables accurate densities to be calculated as the total number of both measured and unmeasured fish is recorded. Description of columns found in the dataset:   <table> <tbody> <tr> <td> Column heading </td> <td> Column description </td> <td> Data type </td> <td> Units </td> </tr> <tr> <td> Fish _Catchment </td> <td> The river catchment/basin location of each fish site </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Fish_River </td> <td> The river/watercourse location of each fish site. </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Fish_SiteName </td> <td> The name of each fish site </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Fish_Latitude </td> <td> The latitude of each fish site (WGS 1984) </td> <td> Integer </td> <td> Decimal degrees </td> </tr> <tr> <td> Fish_Longitude </td> <td> The longitude of each fish site (WGS 1984) </td> <td> Integer </td> <td> Decimal degrees </td> </tr> <tr> <td> Fish_Area </td> <td> Area of fish site surveyed </td> <td> Integer </td> <td> m-2 </td> </tr> <tr> <td> Fish_SurveyDate </td> <td> Date fish survey was carried out </td> <td> Integer </td> <td> dd/mm/yyyy </td> </tr> <tr> <td> Fish_Year </td> <td> Year fish survey was carried out </td> <td> Integer </td> <td> yyyy </td> </tr> <tr> <td> Common_Name </td> <td> The common/vernacular name of each fish taxon recorded in the dataset. </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Latin_Name </td> <td> The scientific name of each fish taxon recorded in the dataset </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Net_Number </td> <td> The net number the fish in a given survey were caught on </td> <td> Integer </td> <td> n/a </td> </tr> <tr> <td> Length_mm </td> <td> Length of individual fish caught </td> <td> Integer </td> <td> mm </td> </tr> <tr> <td> Count </td> <td> Count of fish caught accounting for sub- sampling </td> <td> Integer </td> <td> Number of fish </td> </tr> </tbody> </table>

opencc-by-4.0Oct 2021View details →

Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones

This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint. Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software. Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.

opencc-by-4.0Jan 2023View details →

Data from: Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide

Summary This dataset accompanies the publication "Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide" published in Zoo Biology. It contains anonymised data from 540 captive flamingo populations, and includes the four species: Phoeniconaias minor, Phoenicopterus chilensis, Phoenicopterus roseus and Phoenicopterus ruber. Data were sourced from the Zoological Information Management System (ZIMS), operated by Species360 (https://www.species360.org/). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each flamingo species and both their current and historic population sizes. These data were used to investigate the relationship between reproductive success and both flock size, and structure, on a global scale. This dataset also contains climatic data provided by WorldClim, which were used to assess the influence of climatic variables on captive flamingo reproductive success globally. The WorldClim database averages 19 different climatic variables derived from monthly temperature and rainfall values at a 1 km spatial resolution for the period 1970-2000. Using geographic coordinates (latitude and longitude) we calculated several climatic metrics for each institution.    Description of the Dataset One file is provided for each species (P. minor, P. chilensis, P. roseus and P. ruber) as a csv file. Each file contains the following 15 columns: <ul> <li>Institution Code: An anonymous code used to identify individual zoological institutions.       </li> <li>Country: The country where the institution is located.</li> <li>Year: Current year (t).</li> <li>Flock Size: Flock size in year t.</li> <li>Males: The number of males in the flock in year t.           </li> <li>Females: The number of females in the flock in year t.</li> <li>Unsexed: The number of unsexed individuals in the flock in year t.</li> <li>Proportion of Females: The proportion of the flock made up of female individuals in year t.        </li> <li>Proportion of Unsexed: The proportion of the flock made up of unsexed individuals in year t.</li> <li>Hatches: Number of birds hatched in year t.</li> <li>Proportion of Additions: The proportion of the flock in year t made up of additions from year t-1 (not including new birds hatched into the flock).</li> <li>MAP: Mean annual precipitation (mm).</li> <li>MAT: Mean annual temperature (°C).</li> <li>MAP Var: Mean annual variation in precipitation (MAP coefficient of variation).</li> <li>MAT Var: Mean annual variation in temperature (MAT standard deviation).</li> </ul> Note: Mean Annual Temperature (MAT) is provided by WorldClim as °C multiplied by 10, and similarly mean annual variation in temperature as MAT standard deviation multiplied by 100. In the corresponding publication, both were divided (by 10 and 100 respectively) prior to modelling to avoid confusion in the units used.   Acknowledgements We acknowledge and thank all Species360 member institutions for their continued support and data input. The research which data refers to was funded by the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. Additionally, S.Q.S. received funding from the International Max Planck Research School for Organismal Biology. The Species360 Conservation Science Alliance would like to thank their sponsors: the World Association of Zoos and Aquariums, Wildlife Reserves of Singapore, and Copenhagen Zoo.    Disclaimer Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data contained within ZIMS are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA).    Credit If you use this dataset, please cite the corresponding publication: Mooney, A., Teare, J. A., Staerk, J.,Smeele, S. Q., Rose, P., Edell, R. H., King, C. E., Conrad, L., & Buckley, Y. M. (2023). Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide. Zoo Biology, 1–14. <a href="https://doi.org/10.1002/zoo.21753">https://doi.org/10.1002/zoo.21753</a>

opencc-by-4.0Jun 2022View details →

Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat

SNPs obtained by UNEAK pipeline for Habromys schmidlyi and Reithrodontomys microdon.  Pleae cite as:  Colunga-Salas P., T Marines-Macías, G Hernández-Canchola, S Barbosa, C Ramírez, JB Searle, L León-Paniagua. 2022. Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat. Mammalian Reasearch. Doi: 10.1007/s13364-022-00667-x

opencc-by-4.0Jan 2022View details →

Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'

This repository provides all data and R code from the analysis presented in the following paper: Turner, A., Heard, G., Hall, A., Wassens, S. (in review). Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk. The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod) 1. Skeleto_dat_ready_Jan2021.csv Data from frog surveys conducted by Anna Turner 2. Geoffs_data.csv Data from frog surveys conducted by Geoff Heard 3. Environmental_variables_skeleto.csv Environmental data collected during surveys  4. sk.dat_July21.csv Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis 5. Variables_that_are_highly_correlated_with_each_other_season_wide.csv Testing for correlation 6. Model_structure_skeleto_2.csv creates model structure for analysis 7. Model_selection_statistics_June_21.csv Output from model R code is provided seperately for each of the following components: 1. Data_collation_for_analysis_2.R Collating data from Anna and Geoffs datasets 2. Skeleto_analysis_5.R - First uses regression modelling to explore factors correlated with variation in age                                          - Following Scheele et al. (2016) regression models with a poisson distribution                                          - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data                                          - Plots male and female growth curves                                          - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie                                                   (2012) but with bayesian implementation

opencc-by-4.0Oct 2021View details →

Datasets for 'Mandrake: visualising microbial population structure by embedding millions of genomes into a low-dimensional representation'

Datasets for the paper 'Mandrake: visualising microbial population structure by embedding millions of genomes into a low-dimensional representation' Files: <ul> <li>616k* - Files for the analysis of 661k bacterial genomes from the SRA (note typo 616-661k). Includes mandrake output and input files (.npz)</li> <li>gps_acc - Files for the analysis of 20k S. pneumoniae accessory genomes from the GPS project. Original accessory matrix is gps_gene_presence_absence.Rtab</li> <li>sc2million_v1* - Files for the analysis of ~1M SARS-CoV-2 genomes. sc2million_v3.npz are the input distances.</li> <li>sce<commit hash>.qdrep - Nvidia systems profile of code at that commit hash</li> <li>sce<commit hash>.ncu-rep - Nvidia kernel profile of code at that commit hash</li> </ul>

opencc-by-4.0Nov 2022View details →

Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves

We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format).  The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions. Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.

opencc-by-4.0Feb 2023View details →

A lack of population structure characterizes the invasive Lonicera japonica in West Virginia and across eastern North America

Figure S1. Mig-seq primers used in the current study. Dataset S1. SNP data in .vcf format for Lonicera japonica. Figure S2: STRUCTURE analyses. Left: Delta K plot showing the optional number of ancestral population clusters (based on Evanno et al. 2015 method). Right: Ancestry plots from analysis with ParallelStructure for k = 3 (above) and k = 5 (below). Colors correspond to each ancestral cluster.

opencc-by-4.0Sep 2023View details →

Genetic diversity, population structure, and linkage disequilibrium among tropical quality protein maize (QPM) lines assessed with high-density SNP markers

The study of genetic diversity (GD), population structure, and linkage disequilibrium (LD) provides a better understanding of the genetic relationships between individuals in a population which can be utilized in crop research and improvement. Genotyping-by-sequencing (GBS) was used to detect and genotype single nucleotide polymorphisms (SNPs) in a collection of 74 quality protein maize (QPM) lines and further to characterize their genetic diversity, population structure, and linkage disequilibrium. A total of 235,214 high-quality SNPs were used for different genetic analyses except for structure analysis where 11,950 SNPs were used. Analysis of molecular variance (AMOVA) based on these SNPs revealed high genetic heterozygosity among the five populations with 1% of the total genetic variation present among the subpopulations and 99% of the variation among individuals within the populations.  Population structure analysis using Bayesian-based clustering revealed that the 74 lines could be clustered into four groups. However, neighbor-joining trees indicate the lines are grouped into three major clusters.  Further analysis using principal component analyses (PCA) clustered the genotypes into five groups which are concordant with the groups based on pedigree information. Higher genetic diversity was detected in population 1 with a GD value of 0.484 and the lowest in population 5 (0.396) and overall, with a mean of 0.434. The LD pattern in the quality protein maize was investigated and we observed a relatively rapid LD decay of 3.53kb and 10.66kb at r2 =0.2 and r2= 0.1, respectively. Our findings provide important information for future Linkage mapping studies, genome-wide association analyses, and marker-assisted selective breeding of maize as well as genomic prediction-based selection in tropical germplasm.

opencc-by-4.0Apr 2019View details →

Fig. 3 in Genetic diversity and population structure of Brycon nattereri (Characiformes: Bryconidae): a Neotropical fish under threat of extinction

Fig. 3. Haplotype network based on partial sequencing of the D-loop region (mtDNA) of 92 individuals of Brycon nattereri from the Laranjinha River. Circle sizes are pro- portional to haplotype frequency.

opencc-by-4.0Jun 2014View details →

Figure 3 in Population structure of a native and an alien species of snail in an urban area of the Atlantic Rainforest

Figure 3. Detectability probability (A), abundance (B) and recruitment (C) estimated for Achatina fulica during the study. The error bars show 90% confidence intervals.

Figure 3 in Seasonal changes in the population structure of dominant planktonic copepods collected using a sediment trap moored in the western Arctic Ocean

Figure 3. Seasonal changes in sea ice concentration, surface chl. a (from satellite) and total mass flux (a), and daylight hours (b) at St. NAPt from October 2010 to September 2012.

Figure 8 in Seasonal changes in the population structure of dominant planktonic copepods collected using a sediment trap moored in the western Arctic Ocean

Figure 8. Seasonal changes in sea ice concentration, daylight hours, chl. a, and total mass flux from January to December (upper panel). The ecological characteristics of the five dominant copepods (lower panel). The open and solid bars indicate the high abundance and reproductive periods for each species, respectively.

Figure 4 in Inter-oceanic comparison of planktonic copepod ecology (vertical distribution, abundance, community structure, population structure and body size) between the Okhotsk Sea and Oyashio region in autumn

Figure 4. Copepod species composition (centre) and copepodid stage structures of the dominant species (left: Oyashio region, right: Okhotsk Sea). All data are integrated means of a 0– 500 m water column based on the IONESS samples in the Oyashio region (St. 19) and Okhotsk Sea (St. OK24) from October to November 1996. Error bars for the copepodid stage indicate standard deviations of each daily duplicate.

Figure 3 in Inter-oceanic comparison of planktonic copepod ecology (vertical distribution, abundance, community structure, population structure and body size) between the Okhotsk Sea and Oyashio region in autumn

Figure 3. Vertical distribution of zooplankton biovolume in the Oyashio region (upper panels) and Okhotsk Sea (lower panels) from September to December in 1996–1998. Note that the biovolume axes are not the same between panels. Tc: thermocline.