Potential distribution of mosquito vector species in a primary malaria endemic region of Colombia

• Published in: PloS one 2017-06, Vol.12 (6), p.e0179093-e0179093
• Main Authors: Altamiranda-Saavedra, Mariano, Arboleda, Sair, Parra, Juan L, Peterson, A Townsend, Correa, Margarita M
• Format: Article
• Language: English
• Subjects: Adaptation; Algorithms; Analysis; Animals; Anopheles; Aquatic ecosystems; Aquatic insects; Biology and Life Sciences; Causes of; Collection; Colombia - epidemiology; Design; Design analysis; Design optimization; Diagnosis; Disease; Dispersal; Dispersion; Earth Sciences; Ecological conditions; Ecology; Ecology and Environmental Sciences; Ecosystem; Ecosystems; Endemic animals; Endemic Diseases - statistics & numerical data; Endemic species; Entropy; Forecasts and trends; Genetic transformation; Geographical distribution; Geography; Hypotheses; Malaria; Malaria - epidemiology; Malaria - parasitology; Mathematical models; Maximum entropy; Medicine and Health Sciences; Mitigation; Models, Theoretical; Morphology; Mosquito Vectors - physiology; Mosquitoes; Niche breadth; Niches (Ecology); People and places; Population; Populations; Principal Component Analysis; Principal components analysis; Remote sensing; Riparian environments; Risk factors; Satellite imagery; Satellites; Seasonal variations; Similarity; Spatial analysis; Spatial distribution; Species Specificity; Studies; Sympatry; Transformation; Vector-borne diseases; Vectors (Biology); Vegetation
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0179093

Rapid transformation of natural ecosystems changes ecological conditions for important human disease vector species; therefore, an essential task is to identify and understand the variables that shape distributions of these species to optimize efforts toward control and mitigation. Ecological niche modeling was used to estimate the potential distribution and to assess hypotheses of niche similarity among the three main malaria vector species in northern Colombia: Anopheles nuneztovari, An. albimanus, and An. darlingi. Georeferenced point collection data and remotely sensed, fine-resolution satellite imagery were integrated across the Urabá -Bajo Cauca-Alto Sinú malaria endemic area using a maximum entropy algorithm. Results showed that An. nuneztovari has the widest geographic distribution, occupying almost the entire study region; this niche breadth is probably related to the ability of this species to colonize both, natural and disturbed environments. The model for An. darlingi showed that most suitable localities for this species in Bajo Cauca were along the Cauca and Nechí river. The riparian ecosystems in this region and the potential for rapid adaptation by this species to novel environments, may favor the establishment of populations of this species. Apparently, the three main Colombian Anopheles vector species in this endemic area do not occupy environments either with high seasonality, or with low seasonality and high NDVI values. Estimated overlap in geographic space between An. nuneztovari and An. albimanus indicated broad spatial and environmental similarity between these species. An. nuneztovari has a broader niche and potential distribution. Dispersal ability of these species and their ability to occupy diverse environmental situations may facilitate sympatry across many environmental and geographic contexts. These model results may be useful for the design and implementation of malaria species-specific vector control interventions optimized for this important malaria region.