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High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020
Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10023705/ https://www.ncbi.nlm.nih.gov/pubmed/36932126 http://dx.doi.org/10.1038/s41597-023-02050-0 |
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author | Zhan, Ning Liu, Weihang Ye, Tao Li, Hongda Chen, Shuo Ma, Heng |
author_facet | Zhan, Ning Liu, Weihang Ye, Tao Li, Hongda Chen, Shuo Ma, Heng |
author_sort | Zhan, Ning |
collection | PubMed |
description | Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region. |
format | Online Article Text |
id | pubmed-10023705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100237052023-03-19 High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 Zhan, Ning Liu, Weihang Ye, Tao Li, Hongda Chen, Shuo Ma, Heng Sci Data Data Descriptor Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region. Nature Publishing Group UK 2023-03-18 /pmc/articles/PMC10023705/ /pubmed/36932126 http://dx.doi.org/10.1038/s41597-023-02050-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhan, Ning Liu, Weihang Ye, Tao Li, Hongda Chen, Shuo Ma, Heng High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
title | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
title_full | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
title_fullStr | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
title_full_unstemmed | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
title_short | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
title_sort | high-resolution livestock seasonal distribution data on the qinghai-tibet plateau in 2020 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10023705/ https://www.ncbi.nlm.nih.gov/pubmed/36932126 http://dx.doi.org/10.1038/s41597-023-02050-0 |
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