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A new GIS model for ecologically suitable distributions of medicinal plants

BACKGROUND: The endangered rate of medicinal plant exceeds that of endangered plant species. However, blindly introducing medicinal plants in regions without comprehensively considering the involved environmental factors results in diseases and insect pests and the consequent overproof pesticide res...

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Autores principales: Wu, Jie, Li, Xiwen, Huang, Linfang, Meng, Xiangxiao, Hu, Haoyu, Luo, Lu, Chen, Shilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383245/
https://www.ncbi.nlm.nih.gov/pubmed/30828359
http://dx.doi.org/10.1186/s13020-019-0226-0
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author Wu, Jie
Li, Xiwen
Huang, Linfang
Meng, Xiangxiao
Hu, Haoyu
Luo, Lu
Chen, Shilin
author_facet Wu, Jie
Li, Xiwen
Huang, Linfang
Meng, Xiangxiao
Hu, Haoyu
Luo, Lu
Chen, Shilin
author_sort Wu, Jie
collection PubMed
description BACKGROUND: The endangered rate of medicinal plant exceeds that of endangered plant species. However, blindly introducing medicinal plants in regions without comprehensively considering the involved environmental factors results in diseases and insect pests and the consequent overproof pesticide residue as well as reduces the quality of herbal medicine produced. METHODS: Global Medicinal Plant Geographic Information System (GMPGIS) was developed to analyze environmental information of ecologically suitable regions, thus guiding the conservation and introduction of medicinal plants. This system is based on theories and methods from multiple disciplines, including computer science, geoinformatics, ecology, and traditional herbal medicine. Using a range-based method, the previously established ecologically suitable regions were evaluated. This new method effectively resolved the problem of outlier points, and its functions were implemented in Python. The system automatically calculates the Euclidean distance of climatic factors and intersection of soil factors, thus identifying regions with high ecological similarity and those are climatically and edaphically suitable for the cultivation of medicinal plants. RESULTS: These results, validated using real-world regions, revealed that GMPGIS is highly accurate in screening ecologically suitable regions for the cultivation of medicinal plants worldwide. CONCLUSIONS: Overall, because of these features, the GMPGIS is considered as a suitable distribution analysis system for global medicinal plant cultivation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13020-019-0226-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-63832452019-03-01 A new GIS model for ecologically suitable distributions of medicinal plants Wu, Jie Li, Xiwen Huang, Linfang Meng, Xiangxiao Hu, Haoyu Luo, Lu Chen, Shilin Chin Med Research BACKGROUND: The endangered rate of medicinal plant exceeds that of endangered plant species. However, blindly introducing medicinal plants in regions without comprehensively considering the involved environmental factors results in diseases and insect pests and the consequent overproof pesticide residue as well as reduces the quality of herbal medicine produced. METHODS: Global Medicinal Plant Geographic Information System (GMPGIS) was developed to analyze environmental information of ecologically suitable regions, thus guiding the conservation and introduction of medicinal plants. This system is based on theories and methods from multiple disciplines, including computer science, geoinformatics, ecology, and traditional herbal medicine. Using a range-based method, the previously established ecologically suitable regions were evaluated. This new method effectively resolved the problem of outlier points, and its functions were implemented in Python. The system automatically calculates the Euclidean distance of climatic factors and intersection of soil factors, thus identifying regions with high ecological similarity and those are climatically and edaphically suitable for the cultivation of medicinal plants. RESULTS: These results, validated using real-world regions, revealed that GMPGIS is highly accurate in screening ecologically suitable regions for the cultivation of medicinal plants worldwide. CONCLUSIONS: Overall, because of these features, the GMPGIS is considered as a suitable distribution analysis system for global medicinal plant cultivation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13020-019-0226-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-20 /pmc/articles/PMC6383245/ /pubmed/30828359 http://dx.doi.org/10.1186/s13020-019-0226-0 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Wu, Jie
Li, Xiwen
Huang, Linfang
Meng, Xiangxiao
Hu, Haoyu
Luo, Lu
Chen, Shilin
A new GIS model for ecologically suitable distributions of medicinal plants
title A new GIS model for ecologically suitable distributions of medicinal plants
title_full A new GIS model for ecologically suitable distributions of medicinal plants
title_fullStr A new GIS model for ecologically suitable distributions of medicinal plants
title_full_unstemmed A new GIS model for ecologically suitable distributions of medicinal plants
title_short A new GIS model for ecologically suitable distributions of medicinal plants
title_sort new gis model for ecologically suitable distributions of medicinal plants
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383245/
https://www.ncbi.nlm.nih.gov/pubmed/30828359
http://dx.doi.org/10.1186/s13020-019-0226-0
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