Abstract
This study aims to identify events that modeled the historical biogeography of Phymaturus, using three methodologies: Spatial Analysis of Vicariance (VIP), Statistical Dispersal-Vicariance Analysis (S-DIVA), and Bayesian Binary Method MCMC (BBM). In order to assign areas for the Dispersal-Vicariance and the BBM analyses, we preferred not to use predefined areas, but to identify areas defined via an endemism analysis of Phymaturus species. The analyses were conducted using the same basic topology, which we obtained by constructing a metatree with two recent phylogenies, both morphology and molecular-based. This topology was also used to obtain time divergence estimates in BEAST, using more outgroups than for the metatree in order to get more accurate estimates. The S-DIVA analysis based on the metatree found 25 vicariance events, 20 dispersals and two extinctions; the S-DIVA analysis based on the BEAST tree yielded 30 vicariance events, 42 dispersal events and five extinctions, and the BBM analysis yielded 63 dispersal events, 28 vicariance events and 1 extinction event. According to the metatree analysis, the ancestral area for Phymaturus covers northern Payunia and southern Central Monte. A vicariant event fragmented the ancestral distribution of the genus, resulting in northern Payunia and southern Central Monte as ancestral area for the P. palluma group, and southern Payunia for the P. patagonicus group. The analysis based on the BEAST tree showed a more complex reconstruction, with several dispersal and extinction events in the ancestral node. The Spatial Analysis of Vicariance identified 41 disjunct sister nodes and removed 10 nodes. The barrier that separates the P. palluma group from the P. patagonicus group is roughly congruent with the southern limit of the P. palluma group. The ancestral range for the genus occupies a central position relative to the distribution of the group, which implies that the species must have migrated to the north (P. palluma group) and to the south (P. patagonicus group). To answer questions related to the specific timing of the events, a molecular clock for Phymaturus was obtained, using a Liolaemus fossil for calibration. The present contribution provides a hypothetical framework for the events that modeled the distribution of Phymaturus.
Highlights
Vicariance is crucial in the speciation process [1,2]
Statistical Dispersal-Vicariance Analysis (S-DIVA) (Statistical DIVA) and Bayesian Binary Method MCMC (BBM) analyses are methods implemented in RASP (Reconstruct Ancestral State in Phylogenies) [6,11] based on the algorithm used by DIVA; unlike DIVA, they calculate probabilities of different ancestral areas at each node
The ancestral area for Phymaturus is BC, which approximately corresponds to Payunia and southern Central Monte, and a vicariant event fragmented this area into the ancestral areas for the P. palluma and for P. patagonicus groups (Fig 3)
Summary
Vicariance is crucial in the speciation process [1,2]. Even though several other biogeographic processes (duplication, extinction, and dispersal) are important, vicariance has most largely contributed to the generation of the current distribution patterns. Molecular phylogenetic studies date the time at which different speciation events occurred [8,9] Despite all these advances, few studies have attempted to provide the specific location of events through a spatial analysis of distribution. Few studies have attempted to provide the specific location of events through a spatial analysis of distribution The lack of these types of studies promoted the development of a new method for reconstructing biogeographic history, which focuses on finding vicariant events rather than just searching for an ancestral area [10]. The Spatial analysis of Vicariance, implemented in Vicariance Inference Program (VIP), uses distributional data in a phylogenetic context This program identifies sister nodes with disjunct distributions (allopatric/vicariant) and detects possible barriers [10]. S-DIVA (Statistical DIVA) and BBM analyses are methods implemented in RASP (Reconstruct Ancestral State in Phylogenies) [6,11] based on the algorithm used by DIVA; unlike DIVA, they calculate probabilities of different ancestral areas at each node
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