title: Habitat suitability mapping of Anopheles darlingi in the surroundings of the Manso hydropower plant reservoir, Mato Grosso, Central Brazil.
abstract: BACKGROUND
  Hydropower plants provide more than 78 % of Brazil's electricity generation, but the country's reservoirs are potential new habitats for main vectors of malaria. In a case study in the surroundings of the Manso hydropower plant in Mato Grosso state, Central Brazil, habitat suitability of Anopheles darlingi was studied. Habitat profile was characterized by collecting environmental data. Remote sensing and GIS techniques were applied to extract additional spatial layers of land use, distance maps, and relief characteristics for spatial model building.
  
  
  RESULTS
  Logistic regression analysis and ROC curves indicate significant relationships between the environment and presence of An. darlingi. Probabilities of presence strongly vary as a function of land cover and distance from the lake shoreline. Vector presence was associated with spatial proximity to reservoir and semi-deciduous forests followed by Cerrado woodland. Vector absence was associated with open vegetation formations such as grasslands and agricultural areas. We suppose that non-significant differences of vector incidences between rainy and dry seasons are associated with the availability of anthropogenic breeding habitat of the reservoir throughout the year.
  
  
  CONCLUSIONS
  Satellite image classification and multitemporal shoreline simulations through DEM-based GIS-analyses consist in a valuable tool for spatial modeling of A. darlingi habitats in the studied hydropower reservoir area. Vector presence is significantly increased in forested areas near reservoirs in bays protected from wind and wave action. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics.
authors.affiliation: Department of Geography, Federal University of Mato Grosso, Cuiabá, Brazil. pitalike@terra.com.br
authors.forename: Peter
authors.initials: P
authors.lastname: Zeilhofer
authors.forename: Emerson Soares
authors.initials: ES
authors.lastname: dos Santos
authors.forename: Ana L M
authors.initials: AL
authors.lastname: Ribeiro
authors.forename: Rosina D
authors.initials: RD
authors.lastname: Miyazaki
authors.forename: Marina Atanaka
authors.initials: MA
authors.lastname: dos Santos
mesh_terms: D000818:Animals; D000852:Anopheles; D001733:Bites and Stings / Q000453:epidemiology*; D001938:Brazil / Q000453:epidemiology; D002980:Climate; D017753:Ecosystem; D004777:Environment; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D040362:Geographic Information Systems; D006801:Humans; D007303:Insect Vectors; D016015:Logistic Models; D008288:Malaria / Q000453:epidemiology / Q000635:transmission; D011210:Power Plants; D012372:ROC Curve; D012307:Risk Factors; D018531:Spacecraft