Anopheles species associations in Southeast Asia: indicator species and environmental influences

• Published in: Parasites & vectors Vol. 6; no. 1; p. 136
• Main Authors: Obsomer, Valérie, Dufrene, Marc, Defourny, Pierre, Coosemans, Marc
• Format: Journal Article Web Resource
• Language: English
• Published: England Springer-Verlag 04.05.2013; BioMed Central Ltd; BioMed Central
• Subjects: Animals; Anopheles; Anopheles - growth & development; Anopheles - physiology; Asia, Southeastern; Biota; Care and treatment; Climate; data collection; discriminant analysis; Environmental sciences & ecology; Geography; growth & development; Health aspects; Humans; indicator species; land cover; Life sciences; Malaria; monitoring; physiology; rain; Remote sensing; Sciences de l’environnement & écologie; Sciences du vivant; South East Asia; Southeast Asia; temperature; variance; vegetation; villages; South East Asia; Asia, Southeastern; Southeast Asia; Vietnam
• ISSN: 1756-3305; 1756-3305
• DOI: 10.1186/1756-3305-6-136

BACKGROUND: Southeast Asia presents a high diversity of Anopheles. Environmental requirements differ for each species and should be clarified because of their influence on malaria transmission potential. Monitoring projects collect vast quantities of entomological data over the whole region and could bring valuable information to malaria control staff but collections are not always standardized and are thus difficult to analyze. In this context studying species associations and their relation to the environment offer some opportunities as they are less subject to sampling error than individual species. METHODS: Using asymmetrical similarity coefficients, indirect clustering and the search of indicator species, this paper identified species associations. Environmental influences were then analysed through canonical and discriminant analysis using climatic and topographic data, land cover in a 3 km buffer around villages and vegetation indices. RESULTS: Six groups of sites characterized the structure of the species assemblage. Temperature, rainfall and vegetation factors all play a role. Four out of the six groups of sites based on species similarities could be discriminated using environmental information only. CONCLUSIONS: Vegetation indices derived from satellite imagery proved very valuable with one variable explaining more variance of the species dataset than any other variable. The analysis could be improved by integrating seasonality in the sampling and collecting at least 4 consecutive days.