Ecological Niche Modeling and Land Cover Risk Areas for Rift Valley Fever Vector, Culex tritaeniorhynchus Giles in Jazan, Saudi Arabia

Abstract

BackgroundThe mosquito, Culex tritaeniorhynchus Giles is a prevalent and confirmed Rift Valley Fever virus (RVFV) vector. This vector, in association with Aedimorphus arabiensis (Patton), was responsible for causing the outbreak of 2000 in Jazan Province, Saudi Arabia.Methodology/Principal FindingsLarval occurrence records and a total of 19 bioclimatic and three topographic layers imported from Worldclim Database were used to predict the larval suitable breeding habitats for this vector in Jazan Province using ArcGIS ver.10 and MaxEnt modeling program. Also, a supervised land cover classification from SPOT5 imagery was developed to assess the land cover distribution within the suitable predicted habitats. Eleven bioclimatic and slope attributes were found to be the significant predictors for this larval suitable breeding habitat. Precipitation and temperature were strong predictors of mosquito distribution. Among six land cover classes, the linear regression model (LM) indicated wet muddy substrate is significantly associated with high-very high suitable predicted habitats (R2 = 73.7%, P<0.05). Also, LM indicated that total dissolved salts (TDS) was a significant contributor (R2 = 23.9%, P<0.01) in determining mosquito larval abundance.Conclusion/SignificanceThis model is a first step in understanding the spatial distribution of Cx. tritaeniorhynchus and consequently the risk of RVFV in Saudi Arabia and to assist in planning effective mosquito surveillance and control programs by public health personnel and researchers.

A dynamic, climate-driven model of Rift Valley fever

“…Culex lay their eggs directly on the surface of water bodies and as such increase greatly in abundance during periods of heavy rainfall and flooding and can also therefore amplify any RVF transmission (Bird et al, 2009;Anyamba et al, 2010). A recent statistical modelling study which correlated the spatial distribution of the RVF vector Culex tritaeniorhynchus with bioclimatic variables and land use types highlighted the importance of rainfall during the warmest months of the year and the presence of wet muddy substrate (Sallam et al, 2013). Variation in the viraemia of RVF virus across multiple species of mosquito from region to region is dependent on many underlying factors such as the relative distribution and size of indigenous populations, presence or absence of an epizootic and the timing and method of field measurements.…”

“…Aedes mcintoshi and various Culex species (Culex zombaensis, Culex poicilipes, Culex bitaeniorhynchus, Culex quinquefasciatus, Culex univittatus) were heavily involved in RVF virus transmission in Kenya and Tanzania during the large 1997-1998 outbreak (Himeidan et al, 2014). The 2000 RVF outbreak in Saudi Arabia is believed to have come from East Africa and transmitted by Aedes vexans arabiensis and Culex tritaeniorhynchus although only Culex competence was confirmed (Jupp et al, 2002;Sallam et al, 2013). We conclude that the important distinction between Aedes and Culex is in their respective roles at different points during RVF outbreaks and the potential reservoir behaviour of Aedes rather than relative viraemia of specific species.…”

Mosquito vectors survey reveals new record of Culiseta subochrea in Al-Ahsa Oasis, Saudi Arabia

“…Such results could be correlated with low to moderated aquatic bodies temperatures (Table 1) and the higher rainfall incidence in such periods. Similarly, precipitation and temperature were strong predictors for the distribution of Culex tritaeniorhynchus mosquito in Jazan Province, Southwestern Saudi Arabia [38]. Aedes aegypti larval abundance also showed significant temporal variation, being usually more abundant in wet season compared to dry one [26].…”

Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya

“…Spatial risk maps have been successfully developed for many diseases including Japanese encephalitis (24), leishmaniasis (25) West Nile virus (26), RVF (27, 28), and mosquito vectors in general (29–31). This study contributes to the growing success of using spatial distribution maps in the prediction of disease risk that may assist in prioritization of vector and disease control.…”