‹ › ×

    FRED
    • Contact
    • GDPR policy
    • Imprint
    • About
    • Sign Up
    • Login
    • SEARCH
    • Search and find
    • Packages
    • Map
    • By Category ...
      • Study sites
      • Sampling sites
      • Parameters
      • Sampling types
      • Species groups
      • Current DOIs

    795 Mayflies of South America

    DOI Info:

    • DOI: 10.18728/igb-fred-795.2
    • Citation suggestion: Carolina Nieto, Carlos Molineri, Eduardo Dominguez (2022-11-15 ) Mayflies of South America. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries. dataset. https://doi.org/10.18728/igb-fred-795.2
    • Previous DOI version :10.18728/igb-fred-753.1
    • DOI history

      Date DOI PackageId Note
      2022-05-0310.18728/igb-fred-718.0718
      2022-07-0410.18728/igb-fred-753.1753
      2022-11-1510.18728/igb-fred-795.2795this package latest
    Title
    Mayflies of South America
    Sampling interval
    Irregular Interval
    Description

    Mayflies of South America

     

    Study site: South America. Authors: Carolina Nieto, Carlos Molineri & Eduardo Domínguez. Species names available upon communication with the authors.

     

    Contact details:

    Carolina Nieto. E-mail: carolinanieto@gmail.com. ORCID: https:/orcid.org/0000-0003-1909-1750. Affiliation: IBN-Conicet. Facultad de Ciencias Naturales e IML. Tucumán. Argentina.

    Carlos Molineri. E-mail: carlosmolineri@gmail.com. ORCID: https:/orcid.org/0000-0003-2662-624X. Affiliation: IBN-Conicet. Facultad de Ciencias Naturales e IML. Tucumán. Argentina.

    Eduardo Domínguez. E-mail: eduardo.mayfly@gmail.com. ORCID: https:/orcid.org/0000-0002-4201-7869. Affiliation: IBN-Conicet. Facultad de Ciencias Naturales e IML. Tucumán. Argentina.

     

    The column 'year' of the records includes a range '1970-2019' and not the actual year of the sample.

     

    Coordinates of the bounding box (ulx, uly, lrx, lry): -110.9, 29.1, -34.85104, -54.78806

    Centroid coordinates (x,y): -61.724826,-17.099112

    Number of records: 7776

     

    The dataset includes records in areas: 

    Not protected on the observation date (0)

    Protected on the observation date (1)

    Protected on a date following the observation date (2)

    using the November 2022 version of the Protected Planet WDPA WD-OECM dataset 

        (Protected Planet: World Database on Protected Areas (WDPA) 

        and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UNEP-WCMC and IUCN, 2022). 

        Please see publication for further information.

     

    Freshwater Ecoregions of the World (FEOW, Abell et al., 2008) covered: 0,134,160,162,164,166,167,169-171,173,174,201-207,209,210,301-308,310-337,339-349,352

     

     

    Taxonomic focus of the study: Ephemeroptera

     

    Survey methods: Light traps and hand net

     

    Study site
    _global
    GeoNode references

    GeoNode layers

    • Mayflies_of_South_America
    Contact
    Afroditi Grigoropoulou
    Licence for data
    All rights reserved. Please send a request to Afroditi Grigoropoulou if you like to use this data. Mind our data policy: IGB Data Policy

    Metadata files

    TitleUpload dateFiletypeLicenceActions
    Column_explanation.pdf15. Nov. 2022 14:39.pdfODC-By Download

    Data files (e.g. excel)

    TitlecreatedFiletypeActions
    Mayflies_of_South_America.csv 15. Nov. 2022 14:39 datatable: .csv Download

    Machine Readable Metadata Files

    FRED provides all metadata of this package in a maschine readable format. There is a pure XML file and one EML file in Ecological Metadata Language. Both files are published under the freeODC-ByLicence.

    • Mayflies_of_South_America.xml
    • Mayflies_of_South_America.eml

    You are about to leaving FRED and visting a third party website. We are not responsible for the content or availability of linked sites.

    To remain on our site, click Cancel.

    Parsing data File

    Estimated Time:

    Why does it take so much time?

    While parsing a file, the database has to perform various tasks, some of them needs a lot of CPU and memory for larger files.

    • preprocessing: means automatic detection of headlines, table body, format values or csv-separators
    • copying: means read the file cell by cell and copy all elements to the database. During this format settings can be calculated (for example iso-time)
    • analyzing: check out for different data types (can be time, numeric or text)