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MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to pa...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477563/ https://www.ncbi.nlm.nih.gov/pubmed/28632236 http://dx.doi.org/10.1038/sdata.2017.78 |
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author | C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel |
author_facet | C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel |
author_sort | C. Vega, Greta |
collection | PubMed |
description | Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe. |
format | Online Article Text |
id | pubmed-5477563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-54775632017-06-23 MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel Sci Data Data Descriptor Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe. Nature Publishing Group 2017-06-20 /pmc/articles/PMC5477563/ /pubmed/28632236 http://dx.doi.org/10.1038/sdata.2017.78 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0 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 made available in this article. |
spellingShingle | Data Descriptor C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title | MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_full | MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_fullStr | MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_full_unstemmed | MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_short | MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_sort | merraclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477563/ https://www.ncbi.nlm.nih.gov/pubmed/28632236 http://dx.doi.org/10.1038/sdata.2017.78 |
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