Predictive distribution maps of rdent reservoir species of zoonoses in South America

Autores
Porcasi Gomez, Ximena; Calderón, Gladys E.; Lamfri, Mario; Scavuzzo, Marcelo; Sabattini, Marta S.; Polop, Jaime Jose
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We model potential distribution for three species of rodents known to be reservoirs of zoonotic diseases: Calomys musculinus, Oligoryzomys flavescens and O. longicaudatus. These models provide general distribution hypotheses obtained using environmental data from record localities. Satellite remote sensing is then used to extrapolate climatic and ecological features of potentially suitable habitats for these rodents. In the three species mapped, we found high overall correspondence between predicted (based on environmental data) and specimen based distributions. The maps proposed here provide several advantages over dot and shaded outline maps. First, the predictive maps incorporate geographically explicit predictions of potential distribution into the test. Second, the validity of the predictive map can be appreciated when localities of previous records of the studied species, not used as training sites or used as control sites, are overlaid on the map. In this approach, environmental factors, criteria and analytical techniques are explicit and can be easily verified. Hence, we can temporally fit data in more precise distribution maps.
Fil: Porcasi Gomez, Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina
Fil: Calderón, Gladys E.. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud “Dr. C. G. Malbrán”; Argentina
Fil: Lamfri, Mario. Comision Nacional de Actividades Espaciales; Argentina
Fil: Scavuzzo, Marcelo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Sabattini, Marta S.. Universidad Nacional de Córdoba; Argentina
Fil: Polop, Jaime Jose. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Ciencias Naturales; Argentina
Materia
Environmental factors
Geographic distribution
Rodent reservoirs
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/81622
Acceder
id CONICETDig_996e4b02f243febe4ce7fd22481837be
oai_identifier_str oai:ri.conicet.gov.ar:11336/81622
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predictive distribution maps of rdent reservoir species of zoonoses in South AmericaPorcasi Gomez, XimenaCalderón, Gladys E.Lamfri, MarioScavuzzo, MarceloSabattini, Marta S.Polop, Jaime JoseEnvironmental factorsGeographic distributionRodent reservoirshttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1We model potential distribution for three species of rodents known to be reservoirs of zoonotic diseases: Calomys musculinus, Oligoryzomys flavescens and O. longicaudatus. These models provide general distribution hypotheses obtained using environmental data from record localities. Satellite remote sensing is then used to extrapolate climatic and ecological features of potentially suitable habitats for these rodents. In the three species mapped, we found high overall correspondence between predicted (based on environmental data) and specimen based distributions. The maps proposed here provide several advantages over dot and shaded outline maps. First, the predictive maps incorporate geographically explicit predictions of potential distribution into the test. Second, the validity of the predictive map can be appreciated when localities of previous records of the studied species, not used as training sites or used as control sites, are overlaid on the map. In this approach, environmental factors, criteria and analytical techniques are explicit and can be easily verified. Hence, we can temporally fit data in more precise distribution maps.Fil: Porcasi Gomez, Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; ArgentinaFil: Calderón, Gladys E.. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud “Dr. C. G. Malbrán”; ArgentinaFil: Lamfri, Mario. Comision Nacional de Actividades Espaciales; ArgentinaFil: Scavuzzo, Marcelo. Comision Nacional de Actividades Espaciales; ArgentinaFil: Sabattini, Marta S.. Universidad Nacional de Córdoba; ArgentinaFil: Polop, Jaime Jose. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Ciencias Naturales; ArgentinaSociedad Argentina para el Estudio de los Mamíferos2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/81622Porcasi Gomez, Ximena; Calderón, Gladys E.; Lamfri, Mario; Scavuzzo, Marcelo; Sabattini, Marta S.; et al.; Predictive distribution maps of rdent reservoir species of zoonoses in South America; Sociedad Argentina para el Estudio de los Mamíferos; Mastozoologia Neotropical; 12; 2; 12-2005; 199-2160327-93831666-0536CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sarem.org.ar/wp-content/uploads/2012/11/SAREM_MastNeotrop_12-2_07_Porcasi.pdfinfo:eu-repo/semantics/altIdentifier/url/https://www.sarem.org.ar/mastozoologia-neotropical-vol12-no2/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:53:59Zoai:ri.conicet.gov.ar:11336/81622instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:53:59.951CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predictive distribution maps of rdent reservoir species of zoonoses in South America
title Predictive distribution maps of rdent reservoir species of zoonoses in South America
spellingShingle Predictive distribution maps of rdent reservoir species of zoonoses in South America
Porcasi Gomez, Ximena
Environmental factors
Geographic distribution
Rodent reservoirs
title_short Predictive distribution maps of rdent reservoir species of zoonoses in South America
title_full Predictive distribution maps of rdent reservoir species of zoonoses in South America
title_fullStr Predictive distribution maps of rdent reservoir species of zoonoses in South America
title_full_unstemmed Predictive distribution maps of rdent reservoir species of zoonoses in South America
title_sort Predictive distribution maps of rdent reservoir species of zoonoses in South America
dc.creator.none.fl_str_mv Porcasi Gomez, Ximena
Calderón, Gladys E.
Lamfri, Mario
Scavuzzo, Marcelo
Sabattini, Marta S.
Polop, Jaime Jose
author Porcasi Gomez, Ximena
author_facet Porcasi Gomez, Ximena
Calderón, Gladys E.
Lamfri, Mario
Scavuzzo, Marcelo
Sabattini, Marta S.
Polop, Jaime Jose
author_role author
author2 Calderón, Gladys E.
Lamfri, Mario
Scavuzzo, Marcelo
Sabattini, Marta S.
Polop, Jaime Jose
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Environmental factors
Geographic distribution
Rodent reservoirs
topic Environmental factors
Geographic distribution
Rodent reservoirs
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We model potential distribution for three species of rodents known to be reservoirs of zoonotic diseases: Calomys musculinus, Oligoryzomys flavescens and O. longicaudatus. These models provide general distribution hypotheses obtained using environmental data from record localities. Satellite remote sensing is then used to extrapolate climatic and ecological features of potentially suitable habitats for these rodents. In the three species mapped, we found high overall correspondence between predicted (based on environmental data) and specimen based distributions. The maps proposed here provide several advantages over dot and shaded outline maps. First, the predictive maps incorporate geographically explicit predictions of potential distribution into the test. Second, the validity of the predictive map can be appreciated when localities of previous records of the studied species, not used as training sites or used as control sites, are overlaid on the map. In this approach, environmental factors, criteria and analytical techniques are explicit and can be easily verified. Hence, we can temporally fit data in more precise distribution maps.
Fil: Porcasi Gomez, Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina
Fil: Calderón, Gladys E.. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud “Dr. C. G. Malbrán”; Argentina
Fil: Lamfri, Mario. Comision Nacional de Actividades Espaciales; Argentina
Fil: Scavuzzo, Marcelo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Sabattini, Marta S.. Universidad Nacional de Córdoba; Argentina
Fil: Polop, Jaime Jose. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Ciencias Naturales; Argentina
description We model potential distribution for three species of rodents known to be reservoirs of zoonotic diseases: Calomys musculinus, Oligoryzomys flavescens and O. longicaudatus. These models provide general distribution hypotheses obtained using environmental data from record localities. Satellite remote sensing is then used to extrapolate climatic and ecological features of potentially suitable habitats for these rodents. In the three species mapped, we found high overall correspondence between predicted (based on environmental data) and specimen based distributions. The maps proposed here provide several advantages over dot and shaded outline maps. First, the predictive maps incorporate geographically explicit predictions of potential distribution into the test. Second, the validity of the predictive map can be appreciated when localities of previous records of the studied species, not used as training sites or used as control sites, are overlaid on the map. In this approach, environmental factors, criteria and analytical techniques are explicit and can be easily verified. Hence, we can temporally fit data in more precise distribution maps.
publishDate 2005
dc.date.none.fl_str_mv 2005-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/81622
Porcasi Gomez, Ximena; Calderón, Gladys E.; Lamfri, Mario; Scavuzzo, Marcelo; Sabattini, Marta S.; et al.; Predictive distribution maps of rdent reservoir species of zoonoses in South America; Sociedad Argentina para el Estudio de los Mamíferos; Mastozoologia Neotropical; 12; 2; 12-2005; 199-216
0327-9383
1666-0536
CONICET Digital
CONICET
url http://hdl.handle.net/11336/81622
identifier_str_mv Porcasi Gomez, Ximena; Calderón, Gladys E.; Lamfri, Mario; Scavuzzo, Marcelo; Sabattini, Marta S.; et al.; Predictive distribution maps of rdent reservoir species of zoonoses in South America; Sociedad Argentina para el Estudio de los Mamíferos; Mastozoologia Neotropical; 12; 2; 12-2005; 199-216
0327-9383
1666-0536
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sarem.org.ar/wp-content/uploads/2012/11/SAREM_MastNeotrop_12-2_07_Porcasi.pdf
info:eu-repo/semantics/altIdentifier/url/https://www.sarem.org.ar/mastozoologia-neotropical-vol12-no2/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedad Argentina para el Estudio de los Mamíferos
publisher.none.fl_str_mv Sociedad Argentina para el Estudio de los Mamíferos
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846782236996665344
score 12.982451

Publicaciones similares