Michelle G Colacicco-Mayhugh1, Penny M Masuoka, John P Grieco. 1. Department of Sand Fly Biology, Division of Entomology, Walter Reed Army Institute of Research, 503 Robert Grant Avenue, Silver Spring, MD 20910, USA. michelle.colacicco@us.army.mil
Abstract
BACKGROUND: The purpose of this study is to create distribution models of two sand fly species, Phlebotomus papatasi (Scopoli) and P. alexandri (Sinton), across the Middle East. Phlebotomus alexandri is a vector of visceral leishmaniasis, while P. papatasi is a vector of cutaneous leishmaniasis and sand fly fever. Collection records were obtained from literature reports from 1950 through 2007 and unpublished field collection records. Environmental layers considered in the model were elevation, precipitation, land cover, and WorldClim bioclimatic variables. Models were evaluated using the threshold-independent area under the curve (AUC) receiver operating characteristic analysis and the threshold-dependent minimum training presence. RESULTS: For both species, land cover was the most influential environmental layer in model development. The bioclimatic and elevation variables all contributed to model development; however, none influenced the model as strongly as land cover. CONCLUSION: While not perfect representations of the absolute distribution of P. papatasi and P. alexandri, these models indicate areas with a higher probability of presence of these species. This information could be used to help guide future research efforts into the ecology of these species and epidemiology of the pathogens that they transmit.
BACKGROUND: The purpose of this study is to create distribution models of two sand fly species, Phlebotomus papatasi (Scopoli) and P. alexandri (Sinton), across the Middle East. Phlebotomus alexandri is a vector of visceral leishmaniasis, while P. papatasi is a vector of cutaneous leishmaniasis and sand fly fever. Collection records were obtained from literature reports from 1950 through 2007 and unpublished field collection records. Environmental layers considered in the model were elevation, precipitation, land cover, and WorldClim bioclimatic variables. Models were evaluated using the threshold-independent area under the curve (AUC) receiver operating characteristic analysis and the threshold-dependent minimum training presence. RESULTS: For both species, land cover was the most influential environmental layer in model development. The bioclimatic and elevation variables all contributed to model development; however, none influenced the model as strongly as land cover. CONCLUSION: While not perfect representations of the absolute distribution of P. papatasi and P. alexandri, these models indicate areas with a higher probability of presence of these species. This information could be used to help guide future research efforts into the ecology of these species and epidemiology of the pathogens that they transmit.
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