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A long-term dataset on wild bee abundance in Mid-Atlantic United States
With documented global declines in insects, including wild bees, there has been increasing interest in developing and expanding insect monitoring programs. Our objective here was to organize, validate, and share an analysis-ready version of one of the few existing long-term monitoring datasets for w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371858/ https://www.ncbi.nlm.nih.gov/pubmed/32686678 http://dx.doi.org/10.1038/s41597-020-00577-0 |
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author | Kammerer, Melanie Tooker, John F. Grozinger, Christina M. |
author_facet | Kammerer, Melanie Tooker, John F. Grozinger, Christina M. |
author_sort | Kammerer, Melanie |
collection | PubMed |
description | With documented global declines in insects, including wild bees, there has been increasing interest in developing and expanding insect monitoring programs. Our objective here was to organize, validate, and share an analysis-ready version of one of the few existing long-term monitoring datasets for wild bees in the United States. Since 1999, the Native Bee Inventory and Monitoring Lab (BIML) of the United States Geological Survey has sampled wild-bee communities in the Mid-Atlantic U.S., but samples were collected in multiple studies and the datasets are not fully integrated. Furthermore, critical information about sampling methodology was often lacking, though these factors can significantly influence collection outcomes and must be considered in analyses. We cleaned and verified BIML data from Maryland, Delaware, and Washington DC, USA, and generated sampling methodology for over 84% of the 99,053 pan-trapped occurrences in this region. We enthusiastically invite creative analyses of this rich dataset to advance understanding of the biology and ecology of wild bees, inform conservation efforts, and perhaps help design a nationwide bee monitoring program. |
format | Online Article Text |
id | pubmed-7371858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73718582020-07-22 A long-term dataset on wild bee abundance in Mid-Atlantic United States Kammerer, Melanie Tooker, John F. Grozinger, Christina M. Sci Data Data Descriptor With documented global declines in insects, including wild bees, there has been increasing interest in developing and expanding insect monitoring programs. Our objective here was to organize, validate, and share an analysis-ready version of one of the few existing long-term monitoring datasets for wild bees in the United States. Since 1999, the Native Bee Inventory and Monitoring Lab (BIML) of the United States Geological Survey has sampled wild-bee communities in the Mid-Atlantic U.S., but samples were collected in multiple studies and the datasets are not fully integrated. Furthermore, critical information about sampling methodology was often lacking, though these factors can significantly influence collection outcomes and must be considered in analyses. We cleaned and verified BIML data from Maryland, Delaware, and Washington DC, USA, and generated sampling methodology for over 84% of the 99,053 pan-trapped occurrences in this region. We enthusiastically invite creative analyses of this rich dataset to advance understanding of the biology and ecology of wild bees, inform conservation efforts, and perhaps help design a nationwide bee monitoring program. Nature Publishing Group UK 2020-07-20 /pmc/articles/PMC7371858/ /pubmed/32686678 http://dx.doi.org/10.1038/s41597-020-00577-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Kammerer, Melanie Tooker, John F. Grozinger, Christina M. A long-term dataset on wild bee abundance in Mid-Atlantic United States |
title | A long-term dataset on wild bee abundance in Mid-Atlantic United States |
title_full | A long-term dataset on wild bee abundance in Mid-Atlantic United States |
title_fullStr | A long-term dataset on wild bee abundance in Mid-Atlantic United States |
title_full_unstemmed | A long-term dataset on wild bee abundance in Mid-Atlantic United States |
title_short | A long-term dataset on wild bee abundance in Mid-Atlantic United States |
title_sort | long-term dataset on wild bee abundance in mid-atlantic united states |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371858/ https://www.ncbi.nlm.nih.gov/pubmed/32686678 http://dx.doi.org/10.1038/s41597-020-00577-0 |
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