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Disentangling the contemporary and historical effects of landscape on the population genomic variation of two bird species restricted to the highland forest enclaves of northeastern Brazil.

Henrique Batalha-FilhoSilvia Britto BarretoMario Henrique Barros SilveiraCristina Yumi MiyakiSandra AfonsoNuno FerrandMiguel CarneiroFernando Sequeira
Published in: Heredity (2023)
Investigating the impact of landscape features on patterns of genetic variation is crucial to understand spatially dependent evolutionary processes. Here, we assess the population genomic variation of two bird species (Conopophaga cearae and Sclerurus cearensis) through the Caatinga moist forest enclaves in northeastern Brazil. To infer the evolutionary dynamics of bird populations through the Late Quaternary, we used genome-wide polymorphism data obtained from double-digestion restriction-site-associated DNA sequencing (ddRADseq), and integrated population structure analyses, historical demography models, paleodistribution modeling, and landscape genetics analyses. We found the population differentiation among enclaves to be significantly related to the geographic distance and historical resistance across the rugged landscape. The climate changes at the end of the Pleistocene to the Holocene likely triggered synchronic population decline in all enclaves for both species. Our findings revealed that both geographic distance and historical connectivity through highlands are important factors that can explain the current patterns of genetic variation. Our results further suggest that levels of population differentiation and connectivity cannot be explained purely on the basis of contemporary environmental conditions. By combining historical demographic analyses and niche modeling predictions in a historical framework, we provide strong evidence that climate fluctuations of the Quaternary promoted population differentiation and a high degree of temporal synchrony among population size changes in both species.
Keyphrases
  • genome wide
  • single cell
  • climate change
  • copy number
  • big data
  • white matter
  • artificial intelligence
  • circulating tumor
  • deep learning
  • data analysis