Spatial population dynamics and temporal analysis of the distribution of Lutzomyia longipalpis (Lutz & Neiva, 1912) (Diptera: Psychodidae: Phlebotominae) in the city of Clorinda, Formosa, Argentina

BACKGROUND: Lutzomyia longipalpis, the vector for the causal agent of visceral leishmaniasis (VL), has extended its distribution in the southern cone in the Americas. The first urban record of Lu. longipalpis in Argentina was from the City of Clorinda in 2004. The aim of this study was to analyse the monthly distribution and abundance of Lu. longipalpis and to evaluate its association with environmental and climatic variables in Clorinda City, Province of Formosa. METHODS: Phlebotominae sampling was performed using CDC light mini-traps that were placed in different sites of the city between January 2012 and December 2013. Environmental variables including the normalised difference vegetation index, normalized difference water index, land surface temperature and precipitation were evaluated using a spatiotemporal model. RESULTS: A total of 4996 phlebotomine sandflies were captured during the study period, and eight species were reported: Lu. longipalpis, Migonemyia migonei, Nyssomyia whitmani, Ny. neivai, Brumptomyia guimaraesi, Evandromyia cortelezzii/sallesi, Psathyromyia bigeniculata and Expapillata firmatoi. This is the first urban record of Ex. firmatoi in Argentina. Lutzomyia longipalpis was the most abundant species between 2012 and 2013, and it appeared in all the sampled sites. Moreover, the model applied showed that ground humidity and temperature were significantly associated with the abundance of Lu. longipalpis. CONCLUSIONS: This longitudinal approach at city scale allows for modelling that explains more than 60% of the temporal variability of the abundance of Lu. longipalpis based exclusively on satellite obtained data. The results support the hypothesis of steady ‘hot spots’ of abundance with time, while other sites could change its abundance due to eventual microenvironment changes. The Lu. longipalpis abundance driving factors are breeding site-related variables, highlighting the importance both for modelling and surveillance to use lag data.