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

• Autores: Porcasi, X; Calderón, Gladys E.; Lamfri, Mario; Scavuzzo, Marcelo; Sabattini, Marta S.; Polop, Jaime J
• Año de publicación: 2005
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Mastozoología Neotropical 2005;12(2):199-216
• Materia: Roedores; Zoonosis; América del Sur
• Institución: Administración Nacional de Laboratorios e Institutos de Salud "Dr. Carlos G. Malbrán"

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. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Virales Humanas; Argentina.
Fil: Lamfri, Mario. Instituto Gulich.Comisión Nacional de Actividades Espaciales, Centro Espacial Teófilo Tabanera, Córdoba; Argentina.
Fil: Scavuzzo, Marcelo. Instituto Gulich. Comisión Nacional de Actividades Espaciales, Centro Espacial Teófilo Tabanera, Córdoba; Argentina.
Fil: Sabattini, Marta S. Instituto Gulich. Comisión Nacional de Actividades Espaciales, Centro Espacial Teófilo Tabanera, Córdoba; Argentina.
Fil: Polop, Jaime Jose. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Ciencias Naturales; Argentina.
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.