Login / Signup

A holocenic and dynamic hybrid zone between two cactophilic Drosophila species in a coastal lowland plain of the Brazilian Atlantic Forest.

Dora Yovana Barrios-LealRodolpho Santos Telles MenezesJoão Victor RibeiroLuiz BizzoFabio Melo de SeneJoão Neves-da-RochaMaura Helena Manfrin
Published in: Journal of evolutionary biology (2021)
Hybridization and introgression are processes that contribute to shaping biological diversity. The factors promoting the formation of these processes are multiples but poorly explored in a biogeographical and ecological context. In the southeast coastal plain of the Brazilian Atlantic Forest, a hybrid zone was described between two closely related cactophilic species, Drosophila antonietae and D. serido. Here, we revisited and analysed specimens from this hybrid zone to evaluate its temporal and spatial dynamic. We examined allopatric and sympatric populations of the flies using independent sources of data such as mitochondrial and nuclear sequences, microsatellite loci, morphometrics of wings and male genitalia, and climatic niche models. We also verified the emergence of the flies from necrotic tissues of collected cacti to verify the role of host association for the population dynamics. Our results support the existence of a hybrid zone due to secondary contact and limited to the localities where the two species are currently in contact. Furthermore, we detected asymmetric bidirectional introgression and the maintenance of the species integrity, ecological association and morphological characters, suggesting selection and limited introgression. Considering our paleomodels, probably this hybrid zone is recent and the contact occurred during the Holocene to the present day, favoured by range expansion of their populations due to expansion of open and dry areas in eastern South America during palaeoclimatic and geomorphological events.
Keyphrases
  • climate change
  • genetic diversity
  • human health
  • gene expression
  • minimally invasive
  • drinking water
  • machine learning
  • drug induced