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      Ecological niche model of Phlebotomus alexandri and P. papatasi (Diptera: Psychodidae) in the Middle East

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          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 Collection records were obtained from literature reports from 1950 through 2007 and unpublished field collection records.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.

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          Most cited references21

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          ORIGINAL ARTICLE: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar

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            Lutzomyia vectors for cutaneous leishmaniasis in Southern Brazil: ecological niche models, predicted geographic distributions, and climate change effects.

            Geographic and ecological distributions of three Lutzomyia sand flies that are cutaneous leishmaniasis vectors in South America were analysed using ecological niche modelling. This new tool provides a large-scale perspective on species' geographic distributions, ecological and historical factors determining them, and their potential for change with expected environmental changes. As a first step, the ability of this technique to predict geographic distributions of the three species was tested statistically using two subsampling techniques: a random-selection technique that simulates 50% data density, and a quadrant-based technique that challenges the method to predict into broad unsampled regions. Predictivity under both test schemes was highly statistically significant. Visualisation of ecological niches provided insights into the ecological basis for distributional differences among species. Projections of potential geographic distributions across scenarios of global climate change suggested that only Lutzomyia whitmani is likely to be experiencing dramatic improvements in conditions in south-eastern Brazil, where cutaneous leishmaniasis appears to be re-emerging; Lutzomyia intermedia and Lutzomyia migonei may be seeing more subtle improvements in climatic conditions, but the implications are not straightforward. More generally, this technique offers the possibility of new views into the distributional ecology of disease, vector, and reservoir species.
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              Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

              Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.
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                Author and article information

                Journal
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central
                1476-072X
                2010
                21 January 2010
                : 9
                : 2
                Affiliations
                [1 ]Department of Sand Fly Biology, Division of Entomology, Walter Reed Army Institute of Research, 503 Robert Grant Avenue, Silver Spring, MD 20910, USA
                [2 ]Uniformed Services University of the Health Sciences, Department of Preventive Medicine and Biometrics, 4301 Jones Bridge Road, Bethesda, MD 20814, USA
                Article
                1476-072X-9-2
                10.1186/1476-072X-9-2
                2823717
                20089198
                41756c0e-1be2-4595-ae02-5e6644cbe9af
                Copyright ©2010 Colacicco-Mayhugh et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 October 2009
                : 21 January 2010
                Categories
                Research

                Public health
                Public health

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