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

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    "name": "The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis methods",
    "description": "Evidence-backed execution summary for The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis methods from 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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        "position": 1,
        "name": "Methods",
        "text": "The exclusive (Exc.) column counts those species identified in all four reviews. The inclusive (Inc.) column counts those species identified by any of the four authors and are the candidate DVS considered for mapping. The numbers given in each of the review author columns record in which Macdonald's malaria epidemiology zones the species can be found: 4 - North Eurasian; 5 - Mediterranean; 6 - Afro-Arabian 320. The final DVS species listed were defined during two separate Technical Advisory Group (TAG) meetings. EO = Expert Opinion."
      },
      {
        "@type": "HowToStep",
        "position": 2,
        "name": "Boosted Regression Trees, climatic/environmental variables and model protocol",
        "text": "The BRT model was provided with a suite of open access, environmental and climatic variable 5 × 5 km resolution grids, relevant to the ecology and bionomics of the DVS in the African, European and Middle Eastern regions. Each grid has undergone a series of processing steps to ensure all land and sea pixels exactly correspond, and, using nearest neighbour interpolation, to fill in any small gaps in the data due to, for example, cloud cover (see Sinka et al. [ ]). Where the remotely sensed imagery was available as multi-temporal data, temporal Fourier analysis (TFA) was applied to ordinate the data, generating seven products for each temporal variable: the overall mean, maximum and minimum of the data cycles; the amplitude (maximum variation of the cycle around the mean) and the phase (the timing of the cycle) of the annual and bi-annual cycles [ ]. The environmental/climatic varia..."
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      {
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        "position": 3,
        "name": "Predictive maps",
        "text": "Precipitation, in one form or another, is identified repeatedly in previous models (where these data are presented, Table ) as an influential variable in predicting the range of An. gambiae. Within the top five contributing covariates from the suite applied to the BRT model, precipitation was identified three times, with mean precipitation as the highest contributor with a relative influence of over 37%. Maximum precipitation was placed second (19.42%) with the amplitude of the bi-annual cycle of precipitation ranked forth (8.85%). In common with the Maxent niche model presented by Moffett et al. [ ], elevation (altitude) and minimum land surface temperature were also identified by the BRT model within the top five influencing climatic/environmental variables (relative influence of 12.36% and 5.68%, respectively)."
      },
      {
        "@type": "HowToStep",
        "position": 4,
        "name": "Anopheles melas",
        "text": "Anopheles melas is commonly associated with brackish water and can utilise saline environments that other species, for example, An. gambiae, cannot tolerate [, ], yet does not appear to require brackish water for larval stage development [ - ]. It is generally restricted to coastal areas [ - ] but has been found up to 150 km inland along the Gambia River, where salt water can intrude great distances (up to 180 km) upriver [,, ]. Unlike other African DVS, the density fluctuations of An. melas are closely associated with tidal changes rather than seasons, for example, Gelfand [ ] identified a peak in adult numbers 11 days after spring tides. The larvae of this species are associated with salt marsh grass ( Paspalum spp.) and mangroves, but only trees of the genus Avicenna, which include white, grey and black mangrove, and not those from the genus Rhizophora ('true' or red mangrove..."
      },
      {
        "@type": "HowToStep",
        "position": 5,
        "name": "Anopheles labranchiae",
        "text": "Despite similarity in larval site characteristics, An. labranchiae and An. atroparvus do not, or only have limited, overlap in their distributions [ ]. This lack of sympatry may be simply a factor of temperature, with An. labranchiae making use of warmer waters than typical of An. atroparvus [, ]. However, when Capinha et al. [ ] modelled the habitat suitability of An. atroparvus across Portugal, they concluded that the most suitable locations include drier areas with higher temperatures (i.e. conditions where An. labranchiae typically dominate), whereas wetter areas with milder temperatures, where An. atroparvus are mostly found, were unsuitable. They concluded that An. atroparvus is not found in many other 'suitable' Mediterranean areas due to competitive exclusion. Conversely, de Zulueta [ ] suggested that the absence of An. atroparvus in Sardinia allowed the wide distribution of..."
      },
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        "position": 6,
        "name": "List of abbreviations",
        "text": "AUC: Area Under the operating characteristic Curve; AVHRR: Advanced Very High Resolution Radiometer; BRT: Boosted Regression Trees; COI: (mitochondrial) Cytochrome Oxidase 1; DEM: Digital Elevation Model; DVS: Dominant Vector Species; EO: Expert Opinion; EVI: Enhanced Vegetation Index; GIS: Geographic Information System; IRS: Insecticide Residual Spraying; ITNs: Insecticide Treated Bednets; ITS2: Internal Transcribed Spacer 2; IVCC: Innovative Vector Control Consortium; LST: Land Surface Temperature; MAP: Malaria Atlas Project; MIR: Middle Infrared Radiation; MODIS: MODerate Resolution Imaging Spectroradiometer; NDVI: Normalized Difference Vegetation Index; PCR: Polymerase Chain Reaction; TAG: Technical Advisory Group; TFA: Temporal Fourier Analysis; WRBU: Walter Reed Biosystematics Unit."
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        "position": 7,
        "name": "Acknowledgements",
        "text": "We wish to thank Rosalind Howes, Edward Haynes, Philip Mbithi, Owen Yang, Carolynn Tago, and Elisabeth Thiveyrat for primary data abstraction. We also thank the Technical Advisory Group for extended support over the duration of the project (in addition to co-authors Michael Bangs, Sylvie Manguin, Maureen Coetzee, Ralph Harbach, Janet Hemingway and Charles M. Mbogo, these include, Theeraphap Chareonviriyaphap and Yasmin Rubio-Palis). MES is funded by a project grant from the Wellcome Trust (#083534) to SIH. SIH is funded by a Senior Research Fellowship from the Wellcome Trust (#079091) which also supports CWK and PWG. APP and WHT are funded by a Wellcome Trust Principal Research Fellowship (#079080) to Professor Robert Snow. This work forms part of the output of the Malaria Atlas Project (MAP, http://www.map.ox.ac.uk ), principally funded by the Wellcome Trust, U.K."
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    "tool": [
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        "@type": "HowToTool",
        "name": "Anopheles messeae"
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        "name": "List of abbreviations"
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        "name": "Boosted Regression Trees, climatic/environmental variables and model protocol"
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        "name": "Mapping trials"
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        "name": "Predictive maps"
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        "name": "Anopheles labranchiae"
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      "headline": "The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis",
      "datePublished": "2010",
      "author": [
        {
          "@type": "Person",
          "name": "Marianne E Sinka"
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        {
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          "name": "Michael J Bangs"
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          "name": "Sylvie Manguin"
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          "name": "Maureen Coetzee"
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          "name": "Charles M Mbogo"
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      "identifier": "10.1186/1756-3305-3-117"
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