An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

Detalhes bibliográficos
Autor(a) principal: Rosa-Freitas,Maria Goreti
Data de Publicação: 2007
Outros Autores: 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
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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spelling 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
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reponame_str Memórias do Instituto Oswaldo Cruz
collection Memórias do Instituto Oswaldo Cruz
instname_str Fundação Oswaldo Cruz
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repository.name.fl_str_mv Memórias do Instituto Oswaldo Cruz - Fundação Oswaldo Cruz
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