Prediction of the potential distribution of Drosophila suzukii on Madeira Island using the maximum entropy modeling.

Detalhes bibliográficos
Autor(a) principal: Macedo, Fabrício Lopes De
Data de Publicação: 2023
Outros Autores: Ragonezi, Carla, Reis, Fábio, Freitas, José G.R. de, Lopes, David João Horta, AGUIAR, ANTÓNIO, Cravo, Délia, Carvalho, Miguel A. A. Pinheiro
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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spelling 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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str 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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