Ecological Characterization of Mosquitoes (Diptera: Culicidae) at the Southern Coast of Mar Chiquita Lake, Argentina.

In the southern coast of Mar Chiquita Lake, central Argentina, mosquitoes affect public health and community livelihood, since they transmit pathogens to human beings causing diseases such as malaria, filariasis, encephalitis, yellow fever, and dengue, among others, and have a negative effect on cattle farming as well. To characterize the structure of the mosquito assemblage of the region, we determined the species composition and diversity, the temporal distribution of different species, and the patterns of species richness, abundance, and diversity across seasons. We collected adult mosquitoes over a two-year period (October 2004-September 2006) by means of CDC light traps baited with CO2 from 18:00 to 08:00 h during the warm season (October-April) and from 12:00 h to 18:00 h in the cold season (May-September). A total of 71,501 individuals from 30 species were collected, with Culex Linnaeus and Aedes Meigen genera representing more than 98% of collected specimens (61.5% and 37.3%, respectively). The higher values of richness and abundance of Culicidae were registered in warm seasons compared to cold seasons. Chao1 estimates suggested that more than 90% of the species were detected in all seasons. Mosquito abundance distribution fit the logarithmic series and log-normal models. Aedes albifasciatus (Macquart), Ae. scapularis (Rondani), Culex interfor Dyar, Cx. saltanensis Dyar, and Cx. dolosus (Lynch Arribálzaga), vectors incriminated in arbovirus transmission, were abundant year-round, with Cx. saltanensis and Cx. dolosus most prevalent in cold seasons. Further studies are needed to assess the role of these species in arbovirus transmission in this region of central Argentina.

Models for predicting Aedes aegypti larval indices based on satellite images and climatic variables.

Forecasting models were developed for predicting Aedes aegypti larval indices in an endemic area for dengue (cities of Tartagal and Orán, northwestern Argentina), based on the Breteau and House indices and environmental variables considered with and without time lags. Descriptive models were first developed for each city and each index by multiple linear regressions, followed by a regional model including both cities together. Finally, two forecasting regional models (FRM) were developed and evaluated. FRM2 for the Breteau index and House index fit the data significantly better than FRMI. An evaluation of these models showed a higher correlation FRM1 than for FRM2 for the Breteau index (r = 0.83 and 0.62 for 3 months; r = 0.86 and 0.67 for 45 days) and the House index (r = 0.85 and 0.79 for 3 months; r = 0.79 and 0.74 for 45 days). Early warning based on these forecasting models can assist health authorities to improve vector control.