A new computational method to estimate adaptation time in Avicennia by using divergence time

Area of Science:

• Evolutionary biology
• Molecular genetics
• Plant speciation

Background:

• The genus Avicennia exhibits wide geographical distribution and diverse ecological conditions, suggesting significant local adaptation.
• Limited molecular phylogenetic studies exist for Avicennia, hindering a comprehensive understanding of its evolutionary history and speciation mechanisms.

Purpose of the Study:

• To estimate species divergence times using distinct nuclear and chloroplast DNA regions.
• To identify potentially adaptive genetic sequences through latent factor mixed models (LFMM) analysis.
• To explore the phylogenetic signal of DNA regions and their role in Avicennia speciation, introducing a novel method for estimating adaptive sequence evolution.

Main Methods:

• Analysis of nuclear ribosomal internal transcribed spacer (ITS) and chloroplast DNA regions for phylogenetic reconstruction.
• Application of latent factor mixed models (LFMM) to detect adaptive single nucleotide polymorphisms (SNPs).
• Development of a computational strategy for estimating adaptive sequence divergence times.

Main Results:

• Different genetic markers yielded varying estimates for species divergence times within the Avicennia genus.
• Both nuclear ITS and chloroplast DNA sequences harbor potentially adaptive SNPs.
• A preliminary timeline for the evolution of these adaptive sequences was established.

Conclusions:

• Local adaptation and independent mutations are key drivers of speciation and evolution in the Avicennia genus.
• The study provides novel insights into the evolutionary processes shaping this ecologically important plant group.
• The developed computational strategy offers a new tool for evolutionary genetic research.