bioRxivGeneticsPreprint
High genetic diversity and presence of genetic structure characterise the Corso-Sardinian endemics Ruta corsica and Ruta lamarmorae (Rutaceae)
Marilena Meloni, Caterina Angela Dettori, Andrea Reid, Gianluigi BacchettaiD, Laëtitia Hugot, Elena ContiiD
Abstract
Corsica and Sardinia form one of the ten areas with highest biodiversity in the Mediterranean and are considered one of the priority regions for conservation in Europe. In order to preserve the high levels of endemism and biological diversity at different hierarchical levels, knowledge of the evolutionary history and current genetic structure of Corso-Sardinian endemics is instrumental. Microsatellite markers were newly developed and used to study the genetic structure and taxonomic status of Ruta corsica and Ruta lamarmorae, rare endemics of Corsica and Sardinia, respectively, and previously considered a single species. Our analyses identified high levels of genetic variation within each species (P=0.883, He=0.543 for R. corsica; P=0.972, He=0.627 for R. lamarmorae). Intrinsic traits of the species (hermaphroditism, proterandry and polyploidy) and island-dependent factors (i.e. age, origin and history of the islands) might explain the detected high levels of genetic variation. We discovered differentiation between R. corsica and R. lamarmorae, and genetic structure within each species, which are consistent with the observation of low dispersal ability for both species. Our genetic results support the recent taxonomic classification of R. corsica and R. lamarmorae as separate species and suggest that they diverge at only few loci. One R. corsica population (SA) strongly differed from all other studied populations and appeared to be the product of hybridization between the two species in STRUCTURE analyses. Our results provide important insights for the conservation of the two rare endemics. Further genetic analyses are recommended for R. lamarmorae and for population SA (R. corsica).
- organism: plant (other)
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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