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Prediction of temperature tolerance in Lilium based on distribution and climate data

There are plenty publications providing guidance for resistant taxa selection by experimental researches while the number of experimental taxa is often restricted. In this study, we presented a concise method to predict the temperature tolerance of wild Lilium in China based on open access botanical...

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Detalles Bibliográficos
Autores principales: Xu, Jie, Chai, Nan, Zhang, Ting, Zhu, Ting, Cheng, Yulin, Sui, Shunzhao, Li, Mingyang, Liu, Daofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324855/
https://www.ncbi.nlm.nih.gov/pubmed/34355143
http://dx.doi.org/10.1016/j.isci.2021.102794
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author Xu, Jie
Chai, Nan
Zhang, Ting
Zhu, Ting
Cheng, Yulin
Sui, Shunzhao
Li, Mingyang
Liu, Daofeng
author_facet Xu, Jie
Chai, Nan
Zhang, Ting
Zhu, Ting
Cheng, Yulin
Sui, Shunzhao
Li, Mingyang
Liu, Daofeng
author_sort Xu, Jie
collection PubMed
description There are plenty publications providing guidance for resistant taxa selection by experimental researches while the number of experimental taxa is often restricted. In this study, we presented a concise method to predict the temperature tolerance of wild Lilium in China based on open access botanical and associated environmental datasets. We divided all taxa into five groups to present an overview of Lilium's adaptability to temperature stress. Furthermore, according to the environmental conditions, the prediction of heat and cold tolerance in Lilium was made based on the combined multi-sources data at taxon level. Thirteen taxa with potential temperature tolerance were predicted of 42 taxa. The results showed that not only is tolerance prediction created by large-scale data analysis possible, but that it may supplement traditional laboratory researches with a comprehensive list of taxa.
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spelling pubmed-83248552021-08-04 Prediction of temperature tolerance in Lilium based on distribution and climate data Xu, Jie Chai, Nan Zhang, Ting Zhu, Ting Cheng, Yulin Sui, Shunzhao Li, Mingyang Liu, Daofeng iScience Article There are plenty publications providing guidance for resistant taxa selection by experimental researches while the number of experimental taxa is often restricted. In this study, we presented a concise method to predict the temperature tolerance of wild Lilium in China based on open access botanical and associated environmental datasets. We divided all taxa into five groups to present an overview of Lilium's adaptability to temperature stress. Furthermore, according to the environmental conditions, the prediction of heat and cold tolerance in Lilium was made based on the combined multi-sources data at taxon level. Thirteen taxa with potential temperature tolerance were predicted of 42 taxa. The results showed that not only is tolerance prediction created by large-scale data analysis possible, but that it may supplement traditional laboratory researches with a comprehensive list of taxa. Elsevier 2021-06-29 /pmc/articles/PMC8324855/ /pubmed/34355143 http://dx.doi.org/10.1016/j.isci.2021.102794 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Xu, Jie
Chai, Nan
Zhang, Ting
Zhu, Ting
Cheng, Yulin
Sui, Shunzhao
Li, Mingyang
Liu, Daofeng
Prediction of temperature tolerance in Lilium based on distribution and climate data
title Prediction of temperature tolerance in Lilium based on distribution and climate data
title_full Prediction of temperature tolerance in Lilium based on distribution and climate data
title_fullStr Prediction of temperature tolerance in Lilium based on distribution and climate data
title_full_unstemmed Prediction of temperature tolerance in Lilium based on distribution and climate data
title_short Prediction of temperature tolerance in Lilium based on distribution and climate data
title_sort prediction of temperature tolerance in lilium based on distribution and climate data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324855/
https://www.ncbi.nlm.nih.gov/pubmed/34355143
http://dx.doi.org/10.1016/j.isci.2021.102794
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