Revue d’Elevage et de Médecine Vétérinaire des Pays Tropicaux (Mar 2020)
Composition and seasonality of Culicoides in three host environments in Rabat region (Morocco)
Abstract
Morocco has suffered several outbreaks of Culicoides-borne viruses in recent decades and most studies have focused on Culicoides imicola, considered for a long time as the only important vector. The change in bluetongue (BT) epidemiology in the Mediterranean Basin and Europe over the past two decades has highlighted the role of other Culicoides species in BT virus transmission. The objective of this study was to provide new insights on the Culicoides species composition and seasonality in three different host environments (a horse-riding center, a goat farm and a cattle farm) around Rabat, the capital of Morocco, where BT has been endemic since 2004. Light / suction trap collections were carried out on two consecutive nights at fortnight intervals from May 2016 to May 2017. Culicoides were identified morphologically at the species level when possible. Multivariate analyses were used to compare the impact of the site / vertebrate species, and the collection month on the species communities. In addition, statistical modeling was used to identify environmental drivers of the Culicoides seasonality. A total of 12,460 Culicoides individuals belonging to at least 15 different species were collected during the survey. Culicoides imicola was by far the most abundant species (71.4% of total catches). The site location, and thus the vertebrate species, did not influence the species composition, which was mainly impacted by the month of collection. Surprisingly, the atmospheric pressure was the environmental parameter the most frequently selected in seasonal models. The potential impact of this meteorological parameter along with the other selected variables is discussed. Identifying the environmental parameters driving Culicoides seasonal abundance is the first step to implementing robust Culicoides dynamic models that could later be used in transmission risk modeling
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