An ecoregional classification for the state of Roraima, Brazil. The importance of landscape in malaria biology|
Rosa-Freitas, Maria Goreti; Tsouris, Pantelis; Peterson, A Townsend; Honório, Nildimar Alves; de Barros, Fábio Saito Monteiro; de Aguiar, Ducinéia Barros; da Costa Gurgel, Helen; de Arruda, Mércia Eliane; Vasconcelos, Simão Dias & Luitgards-Moura, José Francisco
Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.
malaria - ecoregions - Amazon - Roraima - Brazil - Anopheles - Genetic Algorithm for Rule-set Prediction (GARP)