Inferring species divergence times using pairwise sequential Markovian coalescent modelling and low-coverage genomic...

Area of Science:

• Evolutionary biology
• Population genetics
• Genomics

Background:

• Determining species divergence times is crucial for understanding speciation drivers.
• The cessation of gene flow between populations is often challenging to pinpoint using genetic data.

Purpose of the Study:

• To evaluate the effectiveness of pairwise sequential Markovian coalescent (PSMC) modeling in inferring species and population divergence times.
• To demonstrate PSMC's capability in detecting the end of gene flow, even with limited genetic data.

Main Methods:

• Utilizing artificial hybrid genomes to generate PSMC plots and identify divergence signals.
• Comparing PSMC outputs from artificial hybrids with simulations of known demographic histories.
• Applying PSMC analysis to genetic data from great apes and bears (genus Ursus).

Main Results:

• PSMC modeling accurately identified divergence times in simulated and real datasets.
• The study confirmed established divergence times for great ape and bear lineages.
• Evidence suggests significant post-divergence gene flow, with up to one million years between chimpanzee and bonobo lineages.

Conclusions:

• PSMC is a robust method for inferring species divergence and detecting the end of gene flow.
• Gene flow may have been more common in recently diverged lineages of great apes and bears than previously thought.
• The findings provide new insights into the complex evolutionary histories of primates and ursids.