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Ecological niche modeling and land cover risk areas for rift valley fever vector, culex tritaeniorhynchus giles in Jazan, Saudi Arabia.

Sallam MF, Al Ahmed AM, Abdel-Dayem MS, Abdullah MA - PLoS ONE (2013)

Bottom Line: Eleven bioclimatic and slope attributes were found to be the significant predictors for this larval suitable breeding habitat.Among six land cover classes, the linear regression model (LM) indicated wet muddy substrate is significantly associated with high-very high suitable predicted habitats (R(2) = 73.7%, P<0.05).Also, LM indicated that total dissolved salts (TDS) was a significant contributor (R(2) = 23.9%, P<0.01) in determining mosquito larval abundance.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Protection, College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia.

ABSTRACT

Background: The 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 findings: Larval 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 (R(2) = 73.7%, P<0.05). Also, LM indicated that total dissolved salts (TDS) was a significant contributor (R(2) = 23.9%, P<0.01) in determining mosquito larval abundance.

Conclusion/significance: This 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.

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Related in: MedlinePlus

Map of Jazan Province representing distribution of human population.
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pone-0065786-g003: Map of Jazan Province representing distribution of human population.

Mentions: Jazan is divided into nine administrative districts (Fig. 1): Al Shaquiq (69,134 people/3632 km2), Baysh (774,421 people/827 km2) Sabya (22,8375 epeopl/1,983 km2), Al Eidaby (60,799 people/1,290 km2), Abu Arish (197,112people/927 km2), Al Ardah (76,705people/852 km2), Jazan (157,536 people/887 km2), Ahd Al Msarhah (110,710 people/1,348 km2), and Farasan (17,999 people/686 km2) (Fig. 3).


Ecological niche modeling and land cover risk areas for rift valley fever vector, culex tritaeniorhynchus giles in Jazan, Saudi Arabia.

Sallam MF, Al Ahmed AM, Abdel-Dayem MS, Abdullah MA - PLoS ONE (2013)

Map of Jazan Province representing distribution of human population.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3675080&req=5

pone-0065786-g003: Map of Jazan Province representing distribution of human population.
Mentions: Jazan is divided into nine administrative districts (Fig. 1): Al Shaquiq (69,134 people/3632 km2), Baysh (774,421 people/827 km2) Sabya (22,8375 epeopl/1,983 km2), Al Eidaby (60,799 people/1,290 km2), Abu Arish (197,112people/927 km2), Al Ardah (76,705people/852 km2), Jazan (157,536 people/887 km2), Ahd Al Msarhah (110,710 people/1,348 km2), and Farasan (17,999 people/686 km2) (Fig. 3).

Bottom Line: Eleven bioclimatic and slope attributes were found to be the significant predictors for this larval suitable breeding habitat.Among six land cover classes, the linear regression model (LM) indicated wet muddy substrate is significantly associated with high-very high suitable predicted habitats (R(2) = 73.7%, P<0.05).Also, LM indicated that total dissolved salts (TDS) was a significant contributor (R(2) = 23.9%, P<0.01) in determining mosquito larval abundance.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Protection, College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia.

ABSTRACT

Background: The 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 findings: Larval 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 (R(2) = 73.7%, P<0.05). Also, LM indicated that total dissolved salts (TDS) was a significant contributor (R(2) = 23.9%, P<0.01) in determining mosquito larval abundance.

Conclusion/significance: This 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.

Show MeSH
Related in: MedlinePlus