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Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data
The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes impera...
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/PMC10444803/ https://www.ncbi.nlm.nih.gov/pubmed/37608210 http://dx.doi.org/10.1038/s41598-023-40929-5 |
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author | Adeogun, Adedapo Babalola, Ayodele Samuel Okoko, Okefu Oyale Oyeniyi, Tolulope Omotayo, Ahmed Izekor, Romoke Tawakalitu Adetunji, Oluwakemi Olakiigbe, Abiodun Olagundoye, Olalekan Adeleke, Monsuru Ojianwuna, Cynthia Adamu, Dagona Daskum, Abdullahi Musa, Jibrin Sambo, Obadiah Adedayo, Oduola Inyama, Petrus Uchenna Samdi, Lazarus Obembe, Abiodun Dogara, Musa Kennedy, Poloma Mohammed, Suleiman Samuel, Rebecca Amajoh, Chioma Adesola, Musa Bala, Mohammed Esema, Mary Omo-Eboh, Mamudu Sinka, Marianne Idowu, Olufunmilayo Ajoke Ande, Adeolu Olayemi, Israel Yayo, Abdulsalami Uhomoibhi, Perpetua Awolola, Samson Salako, Babatunde |
author_facet | Adeogun, Adedapo Babalola, Ayodele Samuel Okoko, Okefu Oyale Oyeniyi, Tolulope Omotayo, Ahmed Izekor, Romoke Tawakalitu Adetunji, Oluwakemi Olakiigbe, Abiodun Olagundoye, Olalekan Adeleke, Monsuru Ojianwuna, Cynthia Adamu, Dagona Daskum, Abdullahi Musa, Jibrin Sambo, Obadiah Adedayo, Oduola Inyama, Petrus Uchenna Samdi, Lazarus Obembe, Abiodun Dogara, Musa Kennedy, Poloma Mohammed, Suleiman Samuel, Rebecca Amajoh, Chioma Adesola, Musa Bala, Mohammed Esema, Mary Omo-Eboh, Mamudu Sinka, Marianne Idowu, Olufunmilayo Ajoke Ande, Adeolu Olayemi, Israel Yayo, Abdulsalami Uhomoibhi, Perpetua Awolola, Samson Salako, Babatunde |
author_sort | Adeogun, Adedapo |
collection | PubMed |
description | The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model resulting in maps of the potential geographical distribution of three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria. |
format | Online Article Text |
id | pubmed-10444803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104448032023-08-24 Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data Adeogun, Adedapo Babalola, Ayodele Samuel Okoko, Okefu Oyale Oyeniyi, Tolulope Omotayo, Ahmed Izekor, Romoke Tawakalitu Adetunji, Oluwakemi Olakiigbe, Abiodun Olagundoye, Olalekan Adeleke, Monsuru Ojianwuna, Cynthia Adamu, Dagona Daskum, Abdullahi Musa, Jibrin Sambo, Obadiah Adedayo, Oduola Inyama, Petrus Uchenna Samdi, Lazarus Obembe, Abiodun Dogara, Musa Kennedy, Poloma Mohammed, Suleiman Samuel, Rebecca Amajoh, Chioma Adesola, Musa Bala, Mohammed Esema, Mary Omo-Eboh, Mamudu Sinka, Marianne Idowu, Olufunmilayo Ajoke Ande, Adeolu Olayemi, Israel Yayo, Abdulsalami Uhomoibhi, Perpetua Awolola, Samson Salako, Babatunde Sci Rep Article The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model resulting in maps of the potential geographical distribution of three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria. Nature Publishing Group UK 2023-08-22 /pmc/articles/PMC10444803/ /pubmed/37608210 http://dx.doi.org/10.1038/s41598-023-40929-5 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Adeogun, Adedapo Babalola, Ayodele Samuel Okoko, Okefu Oyale Oyeniyi, Tolulope Omotayo, Ahmed Izekor, Romoke Tawakalitu Adetunji, Oluwakemi Olakiigbe, Abiodun Olagundoye, Olalekan Adeleke, Monsuru Ojianwuna, Cynthia Adamu, Dagona Daskum, Abdullahi Musa, Jibrin Sambo, Obadiah Adedayo, Oduola Inyama, Petrus Uchenna Samdi, Lazarus Obembe, Abiodun Dogara, Musa Kennedy, Poloma Mohammed, Suleiman Samuel, Rebecca Amajoh, Chioma Adesola, Musa Bala, Mohammed Esema, Mary Omo-Eboh, Mamudu Sinka, Marianne Idowu, Olufunmilayo Ajoke Ande, Adeolu Olayemi, Israel Yayo, Abdulsalami Uhomoibhi, Perpetua Awolola, Samson Salako, Babatunde Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data |
title | Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data |
title_full | Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data |
title_fullStr | Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data |
title_full_unstemmed | Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data |
title_short | Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data |
title_sort | spatial distribution and ecological niche modeling of geographical spread of anopheles gambiae complex in nigeria using real time data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444803/ https://www.ncbi.nlm.nih.gov/pubmed/37608210 http://dx.doi.org/10.1038/s41598-023-40929-5 |
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