A maximum likelihood approach to generate hypotheses on the evolution and historical biogeography in the Lower Volga Valley regions (southwest Russia)


  • Funded in part by a National Science Foundation grant (NSF-DEB 0953677) to Nico Cellinese and the Russian Foundation for Basic Research (RFBR) to Alexy Laktionov (Grant 12-04-01680-a).

Nico Cellinese or Evgeny Mavrodiev, Florida Museum of Natural History, University of Florida, Dickinson Hall, Gainesville, Florida 32611–7800, USA. Tel: +1-352-273-1979; Fax: +1-352-846-1861; E-mail: ncellinese@flmnh.ufl.edu or evgeny@ufl.edu


The evolution of the diverse flora in the Lower Volga Valley (LVV) (southwest Russia) is complex due to the composite geomorphology and tectonic history of the Caspian Sea and adjacent areas. In the absence of phylogenetic studies and temporal information, we implemented a maximum likelihood (ML) approach and stochastic character mapping reconstruction aiming at recovering historical signals from species occurrence data. A taxon-area matrix of 13 floristic areas and 1018 extant species was constructed and analyzed with RAxML and Mesquite. Additionally, we simulated scenarios with numbers of hypothetical extinct taxa from an unknown palaeoflora that occupied the areas before the dramatic transgression and regression events that have occurred from the Pleistocene to the present day. The flora occurring strictly along the river valley and delta appear to be younger than that of adjacent steppes and desert-like regions, regardless of the chronology of transgression and regression events that led to the geomorphological formation of the LVV. This result is also supported when hypothetical extinct taxa are included in the analyses. The history of each species was inferred by using a stochastic character mapping reconstruction method as implemented in Mesquite. Individual histories appear to be independent from one another and have been shaped by repeated dispersal and extinction events. These reconstructions provide testable hypotheses for more in-depth investigations of their population structure and dynamics.