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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...

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Autores principales: C. Vega, Greta, Pertierra, Luis R., Olalla-Tárraga, Miguel Ángel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
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.
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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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