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The aim of this work was to explore the relationship between population density of Akodon azarae (Muridae: Sigmodontinae) and climatic and environmental variables, and determine which of them are associated to within and among-year changes in rodent abundance in agro-ecosystems from south Córdoba, Argentina. The study was carried out in a rural area of central Argentina, from 1983 to 2003. Density was estimated as a relative density index (RDI). Temperature, precipitation and humidity were obtained from records of the National University of Rio Cuarto. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature were recorded from National Oceanic and Atmospheric Administration (1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998) and Landsat (1998Landsat ( -2003 imagery data sets. We performed simple correlations, multiple regressions and distributed lag analysis. Direct association of climatic and environmental variables with RDI was in general, low. The amount of variability in seasonal changes in density explained by climatic and environmental variables altogether varied from 10% to 70%. Seasonal population fluctuations were influenced by NDVI and rainfall with one and two seasons of delay. Autumn maximum density of the species was also associated with vegetation and rainfall of previous seasons. There also seemed to be an indirect influence of rainfall through vegetation given that we found a positive correlation between them. Results were consistent with basic aspects of the ecology of the species, such as its strong preference for highly covered areas, which provide food and protection from predators, likely increasing its reproductive success. Therefore, in the rural area central Argentina, A. azarae showed seasonal fluctuations with delayed influence of rainfall and vegetation and indirect effects of rainfall.

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Distribution and abundance of disease vectors are directly related to climatic conditions and environmental changes. Remote sensing data have been used for monitoring environmental conditions influencing spatial patterns of vector‐borne diseases. The aim of this study was to analyze the effect of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic factors (temperature, humidity, wind velocity, and accumulated rainfall) on the distribution and abundance of Anopheles species in northwestern Argentina using Poisson regression analyses. Samples were collected from December, 2001 to December, 2005 at three localities, Aguas Blancas, El Oculto and San Ramón de la Nueva Orán. We collected 11,206 adult Anopheles species, with the major abundance observed at El Oculto (59.11%), followed by Aguas Blancas (22.10%) and San Ramón de la Nueva Orán (18.79%). Anopheles pseudopunctipennis was the most abundant species at El Oculto, Anopheles argyritarsis predominated in Aguas Blancas, and Anopheles strodei in San Ramón de la Nueva Orán. Samples were collected throughout the sampling period, with the highest peaks during the spring seasons. LST and mean temperature appear to be the most important variables determining the distribution patterns and major abundance of An. pseudopunctipennis and An. argyritarsis within malarious areas.

The aim of this work was to establish the relationship between different Argentine hemorrhagic fever (AHF) epidemiological situations found at different sites and the related large-scale environmental conditions. Large-scale environmental records (vegetation index, temperature, precipitation and elevation) were obtained from a series of monthly NOAA satellite images and global databases considered suitable for modeling climatic and other environmental determinants of large-scale biogeographical regions. The temporal variation in vegetation for cycles of winter-summer showed a greater variation in the nonendemic region than in the other two regions. On the other hand, the average of the temporal variation in precipitation in cycles of spring-autumn was more different in the historic region than in the other two regions, and land surface temperatures in cycles of spring-autumn showed differences between the epidemic region and the other two regions. We found good separation among the epidemic, historic and nonendemic sites, with the greatest difference found between epidemic and nonendemic sites. The classification of sites showed a tendency for grouping according to the epidemiological situation, but there was some variation. It seems possible to establish a close relationship between the state of AHF incidence and the environmental history of sites suggesting the possibility of predicting epidemiological behavior using environmental conditions derived from satellite data.

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