Open AccessGenomic insights into adaptive divergence and speciation among malaria vectors of the Anopheles nili group
Author(s) -
Fouet Caroline,
Kamdem Colince,
Gamez Stephanie,
White Bradley J.
Publication year - 2017
Publication title -
evolutionary applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 68
ISSN - 1752-4571
DOI - 10.1111/eva.12492
Subject(s) - biology , species complex , evolutionary biology , population , gene flow , malaria , anopheles , incipient speciation , genetics , ecology , genetic variation , phylogenetic tree , gene , demography , sociology , immunology
Abstract Ongoing speciation in the most important African malaria vectors gives rise to cryptic populations, which differ remarkably in their behavior, ecology, and capacity to vector malaria parasites. Understanding the population structure and the drivers of genetic differentiation among mosquitoes is crucial for effective disease control because heterogeneity within vector species contributes to variability in malaria cases and allow fractions of populations to escape control efforts. To examine population structure and the potential impacts of recent large‐scale control interventions, we have investigated the genomic patterns of differentiation in mosquitoes belonging to the Anopheles nili group—a large taxonomic group that diverged ~3 Myr ago. Using 4,343 single nucleotide polymorphisms (SNPs), we detected strong population structure characterized by high‐ F ST values between multiple divergent populations adapted to different habitats within the Central African rainforest. Delineating the cryptic species within the Anopheles nili group is challenging due to incongruence between morphology, ribosomal DNA, and SNP markers consistent with incomplete lineage sorting and/or interspecific gene flow. A very high proportion of loci are fixed ( F ST = 1) within the genome of putative species, which suggests that ecological and/or reproductive barriers are maintained by strong selection on a substantial number of genes.