Login / Signup

Museum genomics approach to study the taxonomy and evolution of Woolly-necked storks using historic specimens.

Prashant GhimireCatalina PalaciosJeremiah TrimbleSangeet Lamichhaney
Published in: G3 (Bethesda, Md.) (2024)
The accessibility of genomic tools in evolutionary biology has allowed for a thorough exploration of various evolutionary processes associated with adaptation and speciation. However, genomic studies in natural systems present numerous challenges, reflecting the inherent complexities of studying organisms in their native habitats. The utilization of museum specimens for genomics research has received increased attention in recent times, facilitated by advancements in ancient DNA techniques. In this study, we have utilized a museum genomics approach to analyze historic specimens of Woolly-necked storks (Ciconia spp.) and examine their genetic composition and taxonomic status and explore the evolutionary and adaptive trajectories of populations over the years. The Woolly-necked storks are distributed in Asia and Africa with a taxonomic classification that has been a matter of ambiguity. Asian and African Woollynecks were recently recognized as different species based on their morphological differences; however, their genomic validation was lacking. In this study, we have used ∼70-year-old museum samples for whole-genome population-scale sequencing. Our study has revealed that Asian and African Woollynecks are genetically distinct, consistent with the current taxonomic classification based on morphological features. However, we also found a high genetic divergence between the Asian subspecies Ciconia episcopus neglecta and Ciconia episcopus episcopus, suggesting this classification requires a detailed examination to explore processes of ongoing speciation. Because taxonomic classification directly impacts conservation efforts, and there is evidence of declining populations of Asian Woollynecks in Southeast Asia, our results highlight that population-scale studies are urgent to determine the genetic, ecological, and phylogenetic diversity of these birds.
Keyphrases
  • genome wide
  • machine learning
  • single cell
  • deep learning
  • copy number
  • gene expression
  • depressive symptoms
  • cell free
  • human health
  • fine needle aspiration
  • quality improvement