Phlebotominae of epidemiological importance in cutaneous leishmaniasis in northwestern Argentina: risk maps and ecological niche models

Abstract

In Argentina, 58.2% out of the 8126 Cutaneous Leishmaniasis (CL) incident cases accumulated from 1954 to 2006 were reported in the provinces of Salta and Jujuy. The aim of this study was to develop an exploratory risk map and a potential distribution map of the vector, in order to offer recommendations for CL prevention. A total of 12 079 Phlebotominae (Diptera: Psychodidae) belonging to the species Lutzomyia neivai (Pinto), Lu. migonei (França), Lu. cortelezzii (Brèthes), Lu. shannoni (Dyar), Lu. quinquefer (Dyar) and Brumptomyia spp. (França & Parrot) were captured. Potential distribution models were created for two species, Lu. neivai (incriminated vector of Leishmania braziliensis) and Lu. migonei, associated with domestic animals in Argentina and that in turn could be involved as a link between zoonotic transmission cycles and anthropozoonotic. The Maximum Entropy Modeling System (MaxEnt) was used. The Jackknife test was performed, and the 'rainfall of the driest month' was the variable that best generalized the models. Accuracy was evaluated by the area under the curve (AUC) and validated by the Cohen's kappa index. This approximation provides a new analytical resource of high potential for the prevention of the disease, in order to allocate resources properly and to develop the most suitable strategies for action.

Importance of individual analysis of environmental and climatic factors affecting the density of Leishmania vectors living in the same geographical area: the example of Phlebotomus ariasi and P. perniciosus in northeast Spain

“…The influence of temperature has been considered not only on the biology and ecology of sand flies (rate of egg production, development of the juvenile stages, number of annual generations, feeding activity, period of activity and survival of adults), but also on the development of the parasite inside the vector (Rioux et al, 1969;Elnaiem et al, 1998;Ready, 2008;Martín Sánchez et al, 2009;Hartemink et al, 2011). Rainfall has been associated with the activity period of sand flies, particularly in some South American foci (Queiroz et al, 2012;Quintana et al, 2012). In the present study, both species were found in a similar range of climatic conditions.…”

Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America

“…[16] because of the earlier paucity of verified presence records for L. intermedia and L. neivai . A published niche model of L. neivai is restricted to north-west Argentina because it is based on field-collected data [18]. However, datasets and methods are frequently being improved [19, 23–25], and the present report is the first to investigate how differences in the fundamental ecological niches of L. intermedia and L. neivai could affect their future distributions throughout South America.…”

Ecological Niche Modeling of Seventeen Sandflies Species (Diptera, Psychodidae, Phlebotominae) from Venezuela

“…Finally, they reported high abundance of L. longipalpis in the rainy season, and in L. cruzi followed moderate to regular precipitation and minimum temperature elevation. Quintana et al [29] showed that precipitation seasonality and precipitation in the warmest quarter were the variables that most contributed to the ENM of Lutzomyia neivai and Lutzomyia migonei in Argentina. Our results suggest that precipitation of driest quarter and precipitation of driest month were the most important variables in L. migonei model prediction.…”

“…Because of that, we included the sandflies species within small records sizes, in order to evaluate the accuracy of obtained models in such taxa. We compiled a country database (from 21 (29). Lutzomyia pseudolongipalpis was not included in this study because its known distribution is only restricted to three close localities in Lara state.…”

“…This technique has been used in modeling distribution of diseases, such as dengue, malaria, and West Nile and yellow fever vectors, such as Aedes Meigen 1818 [19], Anopheles Meigen 1818 [20][21][22], Culex Linnaeus 1758 [23], and Haemagogus Williston 1896 [24], respectively. In addition, ENM has been used to examine the potential distribution of Lutzomyia França 1924 in America [25][26][27][28][29][30] and Phlebotomus Rondani 1840 in western Asia [31]. Recently, Foley et al [32] proposed a new map service that allows free public online access to global sandfly collection records and habitat suitability models.…”