Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America
1 Department of Biomedical Sciences, School of Public Health, State University of New York at Albany, Albany, New York 12222, USA
2 Molecular Genomics Core Facility, Wadsworth Center, New York State Department of Health, Slingerlands, New York 12159, USA
3 Florida Medical Entomology Lab, Vero Beach, Florida 32962, USA
4 Coordenação de Pesquisas em Entomologia, Instituto Nacional de Pesquisas da Amazônia, Av. André Araújo 2936, Manaus, 69011-970, AM, Brasil
5 Programa de Pesquisas em Malaria, Instituto Evandro Chagas, Br. 316, km 7, s/n, 67.030-000, Ananindeu, Pará, Brasil
6 Medical Entomology Research and Training Unit Guatemala (MERTU/G), c/o US Embassy, APO Miami, FL 43024, USA
7 Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
8 Griffin Laboratory, Wadsworth Center, New York State Department of Health, Slingerlands, New York 12159, USA
Citation and License
BMC Ecology 2008, 8:3 doi:10.1186/1472-6785-8-3Published: 26 March 2008
Anopheles darlingi is the most important malaria vector in the Neotropics. An understanding of A. darlingi's population structure and contemporary gene flow patterns is necessary if vector populations are to be successfully controlled. We assessed population genetic structure and levels of differentiation based on 1,376 samples from 31 localities throughout the Peruvian and Brazilian Amazon and Central America using 5–8 microsatellite loci.
We found high levels of polymorphism for all of the Amazonian populations (mean RS = 7.62, mean HO = 0.742), and low levels for the Belize and Guatemalan populations (mean RS = 4.3, mean HO = 0.457). The Bayesian clustering analysis revealed five population clusters: northeastern Amazonian Brazil, southeastern and central Amazonian Brazil, western and central Amazonian Brazil, Peruvian Amazon, and the Central American populations. Within Central America there was low non-significant differentiation, except for between the populations separated by the Maya Mountains. Within Amazonia there was a moderate level of significant differentiation attributed to isolation by distance. Within Peru there was no significant population structure and low differentiation, and some evidence of a population expansion. The pairwise estimates of genetic differentiation between Central America and Amazonian populations were all very high and highly significant (FST = 0.1859 – 0.3901, P < 0.05). Both the DA and FST distance-based trees illustrated the main division to be between Central America and Amazonia.
We detected a large amount of population structure in Amazonia, with three population clusters within Brazil and one including the Peru populations. The considerable differences in Ne among the populations may have contributed to the observed genetic differentiation. All of the data suggest that the primary division within A. darlingi corresponds to two white gene genotypes between Amazonia (genotype 1) and Central America, parts of Colombia and Venezuela (genotype 2), and are in agreement with previously published mitochondrial COI gene sequences interpreted as incipient species. Overall, it appears that two main factors have contributed to the genetic differentiation between the population clusters: physical distance between the populations and the differences in effective population sizes among the subpopulations.