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      The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis

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          Abstract

          Background

          This is the second in a series of three articles documenting the geographical distribution of 41 dominant vector species (DVS) of human malaria. The first paper addressed the DVS of the Americas and the third will consider those of the Asian Pacific Region. Here, the DVS of Africa, Europe and the Middle East are discussed. The continent of Africa experiences the bulk of the global malaria burden due in part to the presence of the An. gambiae complex. Anopheles gambiae is one of four DVS within the An. gambiae complex, the others being An. arabiensis and the coastal An. merus and An. melas. There are a further three, highly anthropophilic DVS in Africa, An. funestus, An. moucheti and An. nili. Conversely, across Europe and the Middle East, malaria transmission is low and frequently absent, despite the presence of six DVS. To help control malaria in Africa and the Middle East, or to identify the risk of its re-emergence in Europe, the contemporary distribution and bionomics of the relevant DVS are needed.

          Results

          A contemporary database of occurrence data, compiled from the formal literature and other relevant resources, resulted in the collation of information for seven DVS from 44 countries in Africa containing 4234 geo-referenced, independent sites. In Europe and the Middle East, six DVS were identified from 2784 geo-referenced sites across 49 countries. These occurrence data were combined with expert opinion ranges and a suite of environmental and climatic variables of relevance to anopheline ecology to produce predictive distribution maps using the Boosted Regression Tree (BRT) method.

          Conclusions

          The predicted geographic extent for the following DVS (or species/suspected species complex*) is provided for Africa: Anopheles ( Cellia) arabiensis, An. ( Cel.) funestus*, An. ( Cel.) gambiae, An. ( Cel.) melas, An. ( Cel.) merus, An. ( Cel.) moucheti and An. ( Cel.) nili*, and in the European and Middle Eastern Region: An. ( Anopheles) atroparvus, An. ( Ano.) labranchiae, An. ( Ano.) messeae, An. ( Ano.) sacharovi, An. ( Cel.) sergentii and An. ( Cel.) superpictus*. These maps are presented alongside a bionomics summary for each species relevant to its control.

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          Most cited references 297

          • Record: found
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          Very high resolution interpolated climate surfaces for global land areas

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            Novel methods improve prediction of species’ distributions from occurrence data

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              A working guide to boosted regression trees.

              1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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                Author and article information

                Journal
                Parasit Vectors
                Parasites & Vectors
                BioMed Central
                1756-3305
                2010
                3 December 2010
                : 3
                : 117
                Affiliations
                [1 ]Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
                [2 ]Public Health and Malaria Control Department, PT Freeport Indonesia, Kuala Kencana, Papua, Indonesia
                [3 ]Institut de Recherche pour le Développement, Lab. d'Immuno-Physiopathologie Moléculaire Comparée, UMR-MD3/Univ. Montpellier I, Faculté de Pharmacie, 15, Ave Charles Flahault, 34093 Montpellier, France
                [4 ]Malaria Entomology Research Unit, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [5 ]Vector Control Reference Unit, National Institute for Communicable Diseases of the National Health Laboratory Service, Private Bag X4, Sandringham 2131, Johannesburg, South Africa
                [6 ]KEMRI/Wellcome Trust Programme, Centre for Geographic Medicine Research - Coast, Kilifi, Kenya
                [7 ]Liverpool School of Tropical Medicine, Liverpool, UK
                [8 ]Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI - Univ. Oxford - Wellcome Trust Collaborative Programme, Kenyatta National Hospital Grounds, P.O. Box 43640-00100 Nairobi, Kenya
                [9 ]Biological Control and Spatial Ecology, Université Libre de Bruxelles CP160/12, Av FD Roosevelt 50, B1050, Brussels, Belgium
                [10 ]Department of Entomology, The Natural History Museum, Cromwell Road, London, SW7 5BD, UK
                1756-3305-3-117
                10.1186/1756-3305-3-117
                3016360
                21129198
                Copyright ©2010 Sinka et al; licensee BioMed Central Ltd.

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

                Categories
                Research

                Parasitology

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