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9,936 results for “endemic”
The genetic population structure of Lake Tanganyika's Lates species flock, an endemic radiation of pelagic top predators
<p>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 <em>Lates </em>species in Lake Tanganyika.</p> <p><strong>Abstract</strong>: 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 <em>Lates</em> species of Lake Tanganyika (<em>Lates stappersii</em>, <em>L. microlepis</em>, <em>L. mariae</em> and <em>L. angustifrons</em>), 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 <em>L. mariae</em>, with the majority of individuals from the most southern sampling site forming a genetic group distinct from the individuals at other sampling sites<em>.</em> We find evidence for much weaker structure within the other three species, <em>L. stappersii,</em> <em>L. microlepis</em>, and <em>L. angustifrons</em>, 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 <em>L. mariae</em>, which may be important for the conservation and management of this species.</p> <p>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>.</p>
Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat
<p>SNPs obtained by UNEAK pipeline for <em>Habromys schmidlyi </em>and <em>Reithrodontomys microdon</em>. </p> <p>Pleae cite as: </p> <p>Colunga-Salas P., T Marines-Macías, G Hernández-Canchola, S Barbosa, C Ramírez, JB Searle, L León-Paniagua. 2022. <strong>Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat</strong>. Mammalian Reasearch. Doi: 10.1007/s13364-022-00667-x</p>
Molecular and Taxonomic Reevaluation of the Digitaria filiformis Complex (Poaceae) including a Globally Extinct, Single Site Endemic from New Hampshire, USA, and a New Species from Mexico
<p>We examine the <em>Digitaria filiformis </em>complex, to determine the proper taxonomic rank and rarity of each taxon. The taxonomy of the <em>D. filiformis </em>complex is highly debated and includes two widespread species, <em>D. filiformis </em>and <em>D. villosa</em>; a possibly extinct species endemic to a single-site in New Hampshire, <em>D. laeviglumis</em>; and a rare species of southern Florida and the West Indies, <em>D.</em><em> dolichophylla. </em>We conducted morphologic comparisons and molecular analysis of the four members of the <em>D. filiformis</em> complex, together with specimens from Mexico and Venezuela purportedly identified as <em>D. laeviglumis</em> (morphology only). Based on results of phylogenetic analyses of plastid and nuclear ITS sequences and morphologic comparisons, we recognize five species in the <em>D. filiformis </em>complex, including a newly described Mexican endemic <em>D. glabrifloris. </em>After field investigation we have moved the global rank of <em>D. laeviglumis </em>from globally historical (GH) to extinct (GX), as there is virtually no likelihood of rediscovery. <em>Digitaria</em><em> dolichophylla </em>is much rarer than previously recognized, moving from secure (T5) to imperiled with extinction (G2).</p>
Porto Santo landscape features and endemic lichens occurrence data
<p>Landscape features of Porto Santo island of and observation data of endemic lichens belonging to Sparrius et al. 2017, Bryologist.</p>
Mammals under pressure: presence data for assessing extinction of endemic, threatened, and mammals subject to use, in Colombia
<p>This is the first dataset that provides a complete compilation of mammal records based on camera traps, human observations, and specimens deposited in biological collections in Colombia. We compiled a dataset with unpublished information, including 97,943 records corresponding to 136 species, of which 38 are endemic, 92 are identified as species subject to use by humans in the literature, and 33 are categorized either as Data Deficient or threatened according to international or unofficial national assessments. The information comes from 31 out of 32 departments of Colombia and constitutes relevant input for future distribution and conservation assessments. Most records (n=96,417, 98.44%) come from non-invasive sampling methods such as camera traps. However, we highlight the contribution of museum specimens (n= 1,332), especially for small and medium-sized species, many of them with restricted distributions in the country. This dataset constitutes a joint collaborative and interinstitutional effort that serves as the basis for cooperative work to comprehensively assess the current conservation status of all mammal species in Colombia.</p>
Supplementary Material - The conservation status of the Cretan Endemic Arthropods under Natura 2000 network
<p>Arthropod decline has been globally and locally documented, yet they are still not sufficiently protected. Crete (Greece), a Mediterranean biodiversity hotspot, is a continental island renowned for its diverse geology, ecosystems and endemicity of flora and fauna, with continuous research on its Arthropod fauna dating back to the 19th century. Here we investigate the conservation status of the Cretan Arthropods using Preliminary Automated Conservation Assessments (PACA) and the overlap of Cretan Arthropod distributions with the Natura 2000 protected areas. Moreover we investigate their endemicity hotspots and propose candidate Key Biodiversity Areas. In order to perform these analyses, we assembled occurrences of the endemic Arthropods in Crete located in the collections of the Natural History Museum of Crete together with literature data. These assessments resulted in 75% of endemic Arthropods as potentially threatened. The hotspots of endemic taxa and the candidate Key Biodiversity Areas are distributed mostly on the mountainous areas where the Natura 2000 protected areas have great coverage. Yet human activities have significant impact even in those areas, while some taxa are not sufficiently covered by Natura 2000. These findings call for countermeasures and conservation actions.</p> <p><strong></strong><br>The code is available here <a href="https://github.com/savvas-paragkamian/arthropods_assessment_crete">https://github.com/savvas-paragkamian/arthropods_assessment_crete</a> and a zipped version of the repository is included here (arthropods_assessment_crete-1.2.0.tar). </p> <p>Supplementary Material 1 is the Supplementary-material-1.xlsx file which contains the Arthropod occurrences as compiled from the literature and the specimens of the NHMC. In addition all references of the literature are included in a separate sheet.</p> <p>Supplementary Material 2 1 is the Supplementary-material-2.docx which contains two (2) supplementary figures and two (2) supplementary tables.</p> <p> </p>
Data from an investigation into colibactin-producing Escherichia coli endemicity globally
<p>This upload is a part of the study "Geographical variation in the incidence of colorectal cancer and urinary tract cancer is associated with population exposure to colibactin-producing <em>Escherichia coli</em>" published in <em>Lancet Microbe</em> on 5 December 2024, doi: <a href="https://doi.org/10.1016/j.lanmic.2024.101015">10.1016/j.lanmic.2024.101015</a>.</p> <p><strong>Contents</strong></p> <p>Data and scripts from the "<em>Geographical variation in colorectal and urinary tract linked cancer incidence is associated with population exposure to colibactin-producing Escherichia coli</em>" study.</p> <p>For the <em>E. coli</em> assembly collection presented in the study, see the separate upload at <a href="../records/13374348">https://zenodo.org/records/13374348</a>.</p>
Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>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.</p> <p>The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod)</p> <p>1. <strong>Skeleto_dat_ready_Jan2021.csv</strong> Data from frog surveys conducted by Anna Turner</p> <p>2. <strong>Geoffs_data.csv</strong> Data from frog surveys conducted by Geoff Heard</p> <p>3. <strong>Environmental_variables_skeleto.csv</strong> Environmental data collected during surveys </p> <p>4. <strong>sk.dat_July21.csv</strong> Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis</p> <p>5. <strong>Variables_that_are_highly_correlated_with_each_other_season_wide.csv</strong> Testing for correlation</p> <p>6. <strong>Model_structure_skeleto_2.csv </strong>creates model structure for analysis</p> <p>7. <strong>Model_selection_statistics_June_21.csv </strong>Output from model</p> <p>R code is provided seperately for each of the following components:</p> <p>1. <strong>Data_collation_for_analysis_2.R</strong> Collating data from Anna and Geoffs datasets</p> <p>2. <strong>Skeleto_analysis_5.R - </strong>First uses regression modelling to explore factors correlated with variation in age</p> <p> - Following Scheele et al. (2016) regression models with a poisson distribution</p> <p> - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data</p> <p> - Plots male and female growth curves</p> <p> - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie (2012) but with bayesian implementation</p>
First genetic data for the Critically Endangered Cuban endemic Zapata Rail Cyanolimnas cerverai, and the taxonomic implications
<p>Data associated with the publication First genetic data for the Critically Endangered Cuban endemic Zapata Rail <em>Cyanolimnas cerverai</em>, and the taxonomic implications.</p>
Global patterns in endemicity and vulnerability of soil fungi
<p>This repository contains the data associated with the paper Tedersoo et al. (2022) <em>Global patterns in endemicity and vulnerability of soil fungi</em> // <strong>Global Change Biology</strong>. DOI:10.1111/gcb.16398</p> <p>Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general.</p> <p>This repository contains the following data associated with the publication:</p> <ul> <li>Supplementary tables S1 - S6 (`<strong>Tables_S1-S6.xlsx</strong>`):</li> </ul> <p>- Table S1. Definition of ecoregions and assignment of samples to ecoregions<br> - Table S2. GSMc dataset used for endemicity analyses<br> - Table S3. Dataset used for modeling endemicity values<br> - Table S4. Dataset used for calculating and mapping vulnerability scores<br> - Table S5. Dataset used for calculating and mapping conservation value<br> - Table S6. Additional funding sources by authors</p> <ul> <li>OTU distribution by samples and ecoregions (`<strong>Data_taxon_assignment_to ecoregions.xlsx</strong>`)</li> </ul> <p>Gridded maps:</p> <ul> <li>Conservation priorities for all fungi and fungal groups</li> </ul> <p>- ConservationPriority_AllFungi.tif<br> - ConservationPriority_AM.tif<br> - ConservationPriority_EcM.tif<br> - ConservationPriority_Moulds.tif<br> - ConservationPriority_NonEcMAgaricomycetes.tif<br> - ConservationPriority_OHPs.tif<br> - ConservationPriority_Pathogens.tif<br> - ConservationPriority_Unicellular.tif<br> - ConservationPriority_Yeasts.tif</p> <ul> <li>The average vulnerability of all fungi and fungal groups and the model uncertainty estimates</li> </ul> <p>- AverageVulnerability_AllFungi.tif<br> - AverageVulnerability_AM.tif<br> - AverageVulnerability_EcM.tif<br> - AverageVulnerability_Moulds.tif<br> - AverageVulnerability_NonEcMAgaricomycetes.tif<br> - AverageVulnerability_OHPs.tif<br> - AverageVulnerability_Pathogens.tif<br> - AverageVulnerabilityUncertainty_AllFungi.tif<br> - AverageVulnerabilityUncertainty_AM.tif<br> - AverageVulnerabilityUncertainty_EcM.tif<br> - AverageVulnerabilityUncertainty_Moulds.tif<br> - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif<br> - AverageVulnerabilityUncertainty_OHPs.tif<br> - AverageVulnerabilityUncertainty_Pathogens.tif<br> - AverageVulnerabilityUncertainty_Unicellular.tif<br> - AverageVulnerabilityUncertainty_Yeasts.tif<br> - AverageVulnerability_Unicellular.tif<br> - AverageVulnerability_Yeasts.tif</p> <ul> <li>The relative importance of predicted vulnerability of all fungi</li> </ul> <p>- RelativeImportanceOfVulnerability_AllFungi.tif</p> <ul> <li>Vulnerability to drought, heat, and land cover change for all fungi</li> </ul> <p>- Vulnerability_AllFungi_Heat-Drought-LandCoverChange.tif<br> - VulnerabilityUncertainty_AllFungi_Heat-Drought-LandCoverChange.tif</p> <ul> <li> Human footprint index based on the Land-Use Harmonisation (LUH2; Hurtt et al., 2020, doi:10.5194/gmd-13-5425-2020) - `<strong>LandCoverChange_1960-2015.tif</strong>`</li> <li> MD5 checksums for all files (`<strong>MD5.md5</strong>`)</li> </ul> <p>Fungal groups:<br> - <strong>AM</strong>, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes)<br> - <strong>EcM</strong>, ectomycorrhizal fungi (excluding dubious lineages)<br> - <strong>NonEcMAgaricomycetes</strong>, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies)<br> - <strong>Moulds</strong> (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales)<br> - Putative <strong>pathogens</strong> (including plant, animal and fungal pathogens as primary or secondary lifestyles)<br> - <strong>OHPs</strong>, opportunistic human parasites (excluding Mortierellales)<br> - <strong>Yeasts</strong> (excluding dimorphic yeasts)<br> - <strong>Unicellular</strong>, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages)</p> <p>Detailed processing steps can be found here:<br> <a href="https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability">https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability</a></p>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
Figure 4 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 4. Parastacus pilicarpus sp. nov. A, habitus dorsal view (holotype); B, cephalon dorsal view (holotype); C, cephalon lateral view (holotype); D, female pleon, dorsal view (paratype 1); E, male first to third pleonal pleura (holotype); F, female first to third pleonal pleura (paratype 1); G, telson and uropods dorsal view (holotype). Scale bars: A, D, F – 1 cm; E - 5 mm; C, G – 3.33 mm; B – 2.5 mm.
Figure 8 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 8. Distribution of Parastacus buckupi sp. nov. (star) and P. pilicarpus sp. nov. (triangle) in the states of Rio Grande do Sul and Santa Catarina, southern Brazil.
Figure 7 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 7. Comparative board of the chelipeds of selected species of genus Parastacus Huxley, 1879 with pilous cutting edge of fingers. A – P. buckupi sp. nov. (holotype); B – P. pilicarpus sp. nov. (holotype); C – P. fluviatilis Ribeiro & Buckup in Ribeiro et al. (2016) (UFRGS 2704); D – P. pilimanus (von Martens, 1869) (UFRGS 2413, CL 38.74). Scale bars: 1 cm.
Figure 6 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 6. Parastacus pilicarpus sp. nov., habitat and living specimens. A, B, Typical habitat, a first order stream in the municipality of Morro Grande, state of Santa Catarina; C, living specimen. Photographs by Caio R. M. Feltrin. No available information of scale in photograph C.
Figure 1 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 1. Parastacus buckupi sp. nov. A, habitus dorsal view (holotype); B, cephalon dorsal view (holotype); C, cephalon lateral view (holotype); D, female pleon (paratype 4); E, male first to third pleonal pleura (holotype); F, female first to third pleonal pleura (paratype 4); G, tailfan dorsal view (holotype). Scale bars: A – 1 cm; B–D, G – 5 mm; E, F – 3.33mm.
High MHC gene copy number maintains diversity despite homozygosity in a Critically Endangered single-island endemic bird, but no evidence of MHC-based mate choice
<p>Raw sequence data from two amplicon libraries of MHC class I exon 3 of Raso Lark <em>Alauda razae</em>, sequenced on an Illumina Miseq. The two different libraries (two different Illumina runs) are collected in separat tar archive (.tar). Within each of those are individual sequence reads as gzipped fastq files (.fastq.gz). Each sample has two files, one for read 1 (R1) and one for read 2 (R2), with file names structured as follows. Delimited by underscore (_) are:</p> <ol> <li>sample name as referred to in the data and paper (“RingNo” in the Supporting data table);</li> <li>formal ID (also referred to in data table, often corresponding to full ring number);</li> <li>Illumina sample number (i.e. based on the order that samples are listed in the sample sheet);</li> <li>Illumina lane number (static as L001, as Miseq instruments have a single lane on their flow cells);</li> <li>read number (R1 [forward] or R2 [reverse]);</li> <li>static identifier from Illumina (001).</li> </ol> <p>Thus, the file 83304_TJ83304_S163_L001_R2_001.fastq.gz is the reverse (read 2) MHC class I exon 3 sequence of individual 83304 (ring number TJ83304).</p>
Fig. 2 in Larval morphology of Yateberosus, a New Caledonian endemic subgenus of Laccobius (Coleoptera: Hydrophilidae), with notes on 'Berosus - like' larvae in Hydrophiloidea
Fig. 2. Head morphology of the third instar larva of Laccobius (Yateberosus) sp. A – head in dorsal view; B – head in ventral view; C – detail of clypeolabrum in dorsal view. Chaetotaxy omitted in A–B.
Fig. 6 in Reproductive biology of the eyespot skate Atlantoraja cyclophora (Elasmobranchii: Arhynchobatidae) an endemic species of the Southwestern Atlantic Ocean (34ºS - 42ºS)
Fig. 6. Seasonal variation in gonadosomatic (GSI) and hepatosomatic (HSI) indexes for a.-b. males and c.-d. females of Atlantoraja cyclophora. The number of samples analyzed is between parentheses. The boxes represent the interquartile range between Q1 and Q3 with the 50% of data, the central line represents the median value and whiskers extend to the maximum and minimum values
Figure 4 in Two new endemic species of Chrysopodes (Neosuarius) (Neuroptera, Chrysopidae) from the Galapagos Islands
Figure 4. Forewing. Į Chrysopodes (Neosuarius) nigripilosus 2 C. (N.) nigricubitus 3 C. (N.) pecki. icux2, second intracubital crossvein; i.g., inner gradate veins; ma, median arculus; scx, subcostal crossveins (between subcosta and radius). The scale applies to all images.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.
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.