An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Memórias do Instituto Oswaldo Cruz |
Texto Completo: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762007000300015 |
Resumo: | Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns. |
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Memórias do Instituto Oswaldo Cruz |
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An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biologymalariaecoregionsAmazonRoraimaBrazilAnophelesGenetic Algorithm for Rule-set Prediction (GARP)Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.Instituto Oswaldo Cruz, Ministério da Saúde2007-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762007000300015Memórias do Instituto Oswaldo Cruz v.102 n.3 2007reponame:Memórias do Instituto Oswaldo Cruzinstname:Fundação Oswaldo Cruzinstacron:FIOCRUZ10.1590/S0074-02762007005000052info:eu-repo/semantics/openAccessRosa-Freitas,Maria GoretiTsouris,PantelisPeterson,A TownsendHonório,Nildimar AlvesBarros,Fábio Saito Monteiro deAguiar,Ducinéia Barros deGurgel,Helen da CostaArruda,Mércia Eliane deVasconcelos,Simão DiasLuitgards-Moura,José Franciscoeng2020-04-25T17:50:09Zhttp://www.scielo.br/oai/scielo-oai.php0074-02761678-8060opendoar:null2020-04-26 02:15:11.493Memórias do Instituto Oswaldo Cruz - Fundação Oswaldo Cruztrue |
dc.title.none.fl_str_mv |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology |
title |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology |
spellingShingle |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology Rosa-Freitas,Maria Goreti malaria ecoregions Amazon Roraima Brazil Anopheles Genetic Algorithm for Rule-set Prediction (GARP) |
title_short |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology |
title_full |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology |
title_fullStr |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology |
title_full_unstemmed |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology |
title_sort |
An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology |
author |
Rosa-Freitas,Maria Goreti |
author_facet |
Rosa-Freitas,Maria Goreti Tsouris,Pantelis Peterson,A Townsend Honório,Nildimar Alves Barros,Fábio Saito Monteiro de Aguiar,Ducinéia Barros de Gurgel,Helen da Costa Arruda,Mércia Eliane de Vasconcelos,Simão Dias Luitgards-Moura,José Francisco |
author_role |
author |
author2 |
Tsouris,Pantelis Peterson,A Townsend Honório,Nildimar Alves Barros,Fábio Saito Monteiro de Aguiar,Ducinéia Barros de Gurgel,Helen da Costa Arruda,Mércia Eliane de Vasconcelos,Simão Dias Luitgards-Moura,José Francisco |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Rosa-Freitas,Maria Goreti Tsouris,Pantelis Peterson,A Townsend Honório,Nildimar Alves Barros,Fábio Saito Monteiro de Aguiar,Ducinéia Barros de Gurgel,Helen da Costa Arruda,Mércia Eliane de Vasconcelos,Simão Dias Luitgards-Moura,José Francisco |
dc.subject.por.fl_str_mv |
malaria ecoregions Amazon Roraima Brazil Anopheles Genetic Algorithm for Rule-set Prediction (GARP) |
topic |
malaria ecoregions Amazon Roraima Brazil Anopheles Genetic Algorithm for Rule-set Prediction (GARP) |
dc.description.none.fl_txt_mv |
Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns. |
description |
Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762007000300015 |
url |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762007000300015 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0074-02762007005000052 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto Oswaldo Cruz, Ministério da Saúde |
publisher.none.fl_str_mv |
Instituto Oswaldo Cruz, Ministério da Saúde |
dc.source.none.fl_str_mv |
Memórias do Instituto Oswaldo Cruz v.102 n.3 2007 reponame:Memórias do Instituto Oswaldo Cruz instname:Fundação Oswaldo Cruz instacron:FIOCRUZ |
reponame_str |
Memórias do Instituto Oswaldo Cruz |
collection |
Memórias do Instituto Oswaldo Cruz |
instname_str |
Fundação Oswaldo Cruz |
instacron_str |
FIOCRUZ |
institution |
FIOCRUZ |
repository.name.fl_str_mv |
Memórias do Instituto Oswaldo Cruz - Fundação Oswaldo Cruz |
repository.mail.fl_str_mv |
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1669937700714577920 |