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Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map

SIMPLE SUMMARY: The self-organizing map (SOM), an unsupervised artificial neural network model, has emerged as a tool for analyzing insect species assemblages associated with geographic regions. By comparing regions that share similar pest assemblages, SOM provides available information to rank pote...

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Autores principales: Deng, Jun, Li, Junjie, Zhang, Xinrui, Zeng, Lingda, Guo, Yanqing, Wang, Xu, Chen, Zijing, Zhou, Jiali, Huang, Xiaolei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380675/
https://www.ncbi.nlm.nih.gov/pubmed/37504579
http://dx.doi.org/10.3390/insects14070572
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author Deng, Jun
Li, Junjie
Zhang, Xinrui
Zeng, Lingda
Guo, Yanqing
Wang, Xu
Chen, Zijing
Zhou, Jiali
Huang, Xiaolei
author_facet Deng, Jun
Li, Junjie
Zhang, Xinrui
Zeng, Lingda
Guo, Yanqing
Wang, Xu
Chen, Zijing
Zhou, Jiali
Huang, Xiaolei
author_sort Deng, Jun
collection PubMed
description SIMPLE SUMMARY: The self-organizing map (SOM), an unsupervised artificial neural network model, has emerged as a tool for analyzing insect species assemblages associated with geographic regions. By comparing regions that share similar pest assemblages, SOM provides available information to rank potential invasive species. Scale insects, an important group of Hemiptera, attract a great deal of attention from researchers and quarantine authorities due to their significant threats to crops and ornamental plants. There are few studies on distribution pattern and predicting the invasion of scale insects at a global scale. In the present study, a global presence⁄absence dataset, including 2486 scale insect species in 157 countries, was extracted to assess the establishment risk of potential invasive species based on a self-organizing map (SOM). We provide preliminary establishment risk assessments of numerous scale insects at a global scale and confirm that SOM can be a reliable tool for analyzing a large number of species simultaneously. ABSTRACT: In the present study, a global presence/absence dataset including 2486 scale insect species in 157 countries was extracted to assess the establishment risk of potential invasive species based on a self-organizing map (SOM). According to the similarities in species assemblages, a risk list of scale insects for each country was generated. Meanwhile, all countries in the dataset were divided into five clusters, each of which has high similarities of species assemblages. For those countries in the same neuron of the SOM output, they may pose the greatest threats to each other as the sources of potential invasive scale insect species, and therefore, require more attention from quarantine departments. In addition, normalized ζ(i) values were used to measure the uncertainty of the SOM output. In total, 9 out of 63 neurons obtained high uncertainty with very low species counts, indicating that more investigation of scale insects should be undertaken in some parts of Africa, Asia and Northern Europe.
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spelling pubmed-103806752023-07-29 Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map Deng, Jun Li, Junjie Zhang, Xinrui Zeng, Lingda Guo, Yanqing Wang, Xu Chen, Zijing Zhou, Jiali Huang, Xiaolei Insects Article SIMPLE SUMMARY: The self-organizing map (SOM), an unsupervised artificial neural network model, has emerged as a tool for analyzing insect species assemblages associated with geographic regions. By comparing regions that share similar pest assemblages, SOM provides available information to rank potential invasive species. Scale insects, an important group of Hemiptera, attract a great deal of attention from researchers and quarantine authorities due to their significant threats to crops and ornamental plants. There are few studies on distribution pattern and predicting the invasion of scale insects at a global scale. In the present study, a global presence⁄absence dataset, including 2486 scale insect species in 157 countries, was extracted to assess the establishment risk of potential invasive species based on a self-organizing map (SOM). We provide preliminary establishment risk assessments of numerous scale insects at a global scale and confirm that SOM can be a reliable tool for analyzing a large number of species simultaneously. ABSTRACT: In the present study, a global presence/absence dataset including 2486 scale insect species in 157 countries was extracted to assess the establishment risk of potential invasive species based on a self-organizing map (SOM). According to the similarities in species assemblages, a risk list of scale insects for each country was generated. Meanwhile, all countries in the dataset were divided into five clusters, each of which has high similarities of species assemblages. For those countries in the same neuron of the SOM output, they may pose the greatest threats to each other as the sources of potential invasive scale insect species, and therefore, require more attention from quarantine departments. In addition, normalized ζ(i) values were used to measure the uncertainty of the SOM output. In total, 9 out of 63 neurons obtained high uncertainty with very low species counts, indicating that more investigation of scale insects should be undertaken in some parts of Africa, Asia and Northern Europe. MDPI 2023-06-21 /pmc/articles/PMC10380675/ /pubmed/37504579 http://dx.doi.org/10.3390/insects14070572 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Deng, Jun
Li, Junjie
Zhang, Xinrui
Zeng, Lingda
Guo, Yanqing
Wang, Xu
Chen, Zijing
Zhou, Jiali
Huang, Xiaolei
Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map
title Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map
title_full Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map
title_fullStr Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map
title_full_unstemmed Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map
title_short Potential Global Invasion Risk of Scale Insect Pests Based on a Self-Organizing Map
title_sort potential global invasion risk of scale insect pests based on a self-organizing map
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380675/
https://www.ncbi.nlm.nih.gov/pubmed/37504579
http://dx.doi.org/10.3390/insects14070572
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