info:eu-repo/semantics/article
Environmental effects on phlebotominae sand flies (Diptera:Phychodidae) and implications for sand fly vector disease transmission in Corrientes city, northern Argentina
Fecha
2021-11Registro en:
Estallo, Elizabet Lilia; Santana, Mirta Sara; Martín, Mía Elisa; Galindo, Liliana María; Willener, Juana Alicia; et al.; Environmental effects on phlebotominae sand flies (Diptera:Phychodidae) and implications for sand fly vector disease transmission in Corrientes city, northern Argentina; Academia Brasileira de Ciencias; Anais da Academia Brasileira de Ciencias; 93; 3; 11-2021; 1-17; e20191278
0001-3765
1678-2690
CONICET Digital
CONICET
Autor
Estallo, Elizabet Lilia
Santana, Mirta Sara
Martín, Mía Elisa
Galindo, Liliana María
Willener, Juana Alicia
Kuruc, Jorge A.
Stein, Marina
Resumen
We evaluated species richness, abundance, alpha diversity, and true diversity of Phlebotominae sand flies temporal changes in domiciles within the northern Argentina city of Corrientes. A total of 16 sampling nights were conducted seasonally throughout the years 2012-2014 through light traps supplemented with CO2. Meteorological and remote sensing environmental factors were used to assessed for vectors implications in disease transmission through Generalized Mixt Models. Lutzomyia longipalpis was the most abundant and common species, followed by Nyssomyia neivai and Migonemyia migonei. Lutzomyia longipalpis was more abundant in urban areas, Ny. neivai was associated with vegetation in periurban areas, both were found all sampling years with higher abundance during the rainy season. Positive association of Lu. longipalpis with precipitation and relative humidity and negative association with temperature were observed. Models showed humidity and vegetation as making effects on Lu. longipalpis abundance. Precipitation was significant for Mg. migonei models, with higher abundance in periurban and periurban-rural environments. For Ny. neivai models, relative humidity was the most important variable, followed by precipitation frequency. Our findings led to identify high risk areas and develop predictive models. These are useful for public health stakeholders giving tolls to optimized resources aim to prevent leshmaniasis transmission on the area.