Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling.
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.3/6836 |
Resumo: | ABSTRACT: Drosophila suzukii is one of the main pests that attack soft-skinned fruits and cause significant economic damage worldwide. Madeira Island (Portugal) is already affected by this pest. The present work aimed to investigate the potential distribution of D. suzukii on Madeira Island to better understand the limits of its geographical distribution on the island using the Maximum Entropy modeling (MaxEnt). The resultant model provided by MaxEnt was rated as regular discrimination with the area under the curve (AUC, 0.7–0.8). Upon scrutinizing the environmental variables with the greatest impact on the distribution of D. suzukii, altitude emerged as the dominant contributor, with the highest percentage (71.2%). Additionally, elevations ranging from 0 to 500 m were identified as appropriate for the species distribution. With the results of the model, it becomes possible to understand/predict which locations will be most suitable for the establishment of the analyzed pest and could be further applied not only for D. suzukii but also for other species that hold the potential for substantial economic losses in this insular region. |
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Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling.HabitatMaximum EntropyEcological Niche ModelingModelingMachine LearningABSTRACT: Drosophila suzukii is one of the main pests that attack soft-skinned fruits and cause significant economic damage worldwide. Madeira Island (Portugal) is already affected by this pest. The present work aimed to investigate the potential distribution of D. suzukii on Madeira Island to better understand the limits of its geographical distribution on the island using the Maximum Entropy modeling (MaxEnt). The resultant model provided by MaxEnt was rated as regular discrimination with the area under the curve (AUC, 0.7–0.8). Upon scrutinizing the environmental variables with the greatest impact on the distribution of D. suzukii, altitude emerged as the dominant contributor, with the highest percentage (71.2%). Additionally, elevations ranging from 0 to 500 m were identified as appropriate for the species distribution. With the results of the model, it becomes possible to understand/predict which locations will be most suitable for the establishment of the analyzed pest and could be further applied not only for D. suzukii but also for other species that hold the potential for substantial economic losses in this insular region.MDPIRepositório da Universidade dos AçoresMacedo, Fabrício Lopes DeRagonezi, CarlaReis, FábioFreitas, José G.R. deLopes, David João HortaAGUIAR, ANTÓNIOCravo, DéliaCarvalho, Miguel A. A. Pinheiro2024-01-12T14:34:30Z2023-09-062023-09-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.3/6836engMacedo, F. L., Ragonezi, C., Reis, F., Freitas, J. G. R., Lopes, D. H., Aguiar, A. M. F., Cravo, D., & Carvalho, M. A. A. P. (2023). Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. “Agriculture”, 13, 1764. DOI:10.3390/ agriculture13091764 (IF2021 3,408; Q1 Agronomy)10.3390/ agriculture130917642077-0472info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-13T02:00:32Zoai:repositorio.uac.pt:10400.3/6836Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:36:17.067363Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. |
title |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. |
spellingShingle |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. Macedo, Fabrício Lopes De Habitat Maximum Entropy Ecological Niche Modeling Modeling Machine Learning |
title_short |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. |
title_full |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. |
title_fullStr |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. |
title_full_unstemmed |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. |
title_sort |
Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. |
author |
Macedo, Fabrício Lopes De |
author_facet |
Macedo, Fabrício Lopes De Ragonezi, Carla Reis, Fábio Freitas, José G.R. de Lopes, David João Horta AGUIAR, ANTÓNIO Cravo, Délia Carvalho, Miguel A. A. Pinheiro |
author_role |
author |
author2 |
Ragonezi, Carla Reis, Fábio Freitas, José G.R. de Lopes, David João Horta AGUIAR, ANTÓNIO Cravo, Délia Carvalho, Miguel A. A. Pinheiro |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade dos Açores |
dc.contributor.author.fl_str_mv |
Macedo, Fabrício Lopes De Ragonezi, Carla Reis, Fábio Freitas, José G.R. de Lopes, David João Horta AGUIAR, ANTÓNIO Cravo, Délia Carvalho, Miguel A. A. Pinheiro |
dc.subject.por.fl_str_mv |
Habitat Maximum Entropy Ecological Niche Modeling Modeling Machine Learning |
topic |
Habitat Maximum Entropy Ecological Niche Modeling Modeling Machine Learning |
description |
ABSTRACT: Drosophila suzukii is one of the main pests that attack soft-skinned fruits and cause significant economic damage worldwide. Madeira Island (Portugal) is already affected by this pest. The present work aimed to investigate the potential distribution of D. suzukii on Madeira Island to better understand the limits of its geographical distribution on the island using the Maximum Entropy modeling (MaxEnt). The resultant model provided by MaxEnt was rated as regular discrimination with the area under the curve (AUC, 0.7–0.8). Upon scrutinizing the environmental variables with the greatest impact on the distribution of D. suzukii, altitude emerged as the dominant contributor, with the highest percentage (71.2%). Additionally, elevations ranging from 0 to 500 m were identified as appropriate for the species distribution. With the results of the model, it becomes possible to understand/predict which locations will be most suitable for the establishment of the analyzed pest and could be further applied not only for D. suzukii but also for other species that hold the potential for substantial economic losses in this insular region. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-06 2023-09-06T00:00:00Z 2024-01-12T14:34:30Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.3/6836 |
url |
http://hdl.handle.net/10400.3/6836 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Macedo, F. L., Ragonezi, C., Reis, F., Freitas, J. G. R., Lopes, D. H., Aguiar, A. M. F., Cravo, D., & Carvalho, M. A. A. P. (2023). Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling. “Agriculture”, 13, 1764. DOI:10.3390/ agriculture13091764 (IF2021 3,408; Q1 Agronomy) 10.3390/ agriculture13091764 2077-0472 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136838049333248 |