Nyssomyia whitmani (Diptera: Psychodidae) Activity From Sunset to Sunrise: A Two-Year Study in a Rural Area in Northeast Argentina

Abstract

We model the nocturnal patterns of the main vector of Leishmania braziliensis (Vianna) in the Paranaense region of South America, Nyssomyia whitmani (Antunes & Coutinho). Occurrence and abundance variation were analyzed by hourly periods, and in relation to sunrise and sunset to avoid differences in luminosity at fixed times throughout the year. The possible role of climatic and environmental variables on nocturnal activity curves was explored. A rotating light trap was operated biweekly in the peridomestic henhouse of a farm in Puerto Iguazú, from May 2016 to May 2018. Ny. whitmani, is the predominant Phlebotominae species (6,720 specimens captured), and is present throughout the night and seasons. Generalized additive models of nocturnal abundance of Ny. whitmani show a bell shape with an acrophase in the early evening. Models of abundance distribution as a function of total abundance showed significant nonlinear variations, only for the tertile of highest vector abundance, in the curves by hourly periods, and related to dawn, while female abundance relative to total abundance showed an asymptotic increasing curve, suggesting density-dependent events with abundance thresholds. Finally, temperature was the variable that best explained the pattern of abundance distribution of Ny. whitmani during the night, although triggers for scotophase activity due to internal clocks or luminosity could not be ruled out. The relationship of activity and abundance with climatic variables would also explain the nights of extraordinary abundance, and therefore of greater probability of vector–human and vector–reservoir contacts.

Population dynamics of Nyssomyia whitmani (Diptera: Psychodidae) in domestic and peridomestic environments in Northeast Argentina, a tegumentary leishmaniasis outbreak area

“…Describing the variables potentially associated with this fragmented distribution requires a regional macro-scale modeling, which is currently being carried out, and could effectively be extrapolated to micro-scale data such as those analyzed in the presented work. Moreover, these micro-scale variables have been described in time and space in the same study region in several studies (Fernández et al 2020, 2023, Quintana et al 2020, Manteca-Acosta et al 2021, Santini et al 2022).…”