Model adequacy tests for probabilistic models of chromosome-number evolution

Area of Science:

• Evolutionary Biology
• Genomics
• Computational Biology

Background:

• Chromosome number is a fundamental characteristic of eukaryote genomes.
• Understanding chromosome number evolution is crucial for inferring whole genome duplications and ancestral chromosome numbers.
• Existing tools like ChromEvol evaluate models of chromosome number evolution but lack adequacy assessment.

Purpose of the Study:

• To develop and present a model adequacy test for likelihood models of chromosome number evolution.
• To assess whether a model can generate data with characteristics similar to observed data.
• To identify limitations in current modeling approaches for chromosome number evolution.

Main Methods:

• Developed a model adequacy test procedure for likelihood models.
• Evaluated the test's ability to detect inadequate models and their impact on inference.
• Applied the test to 200 angiosperm genera to assess model fit to empirical data.

Main Results:

• Demonstrated that inadequate models can lead to inflated errors in evolutionary inference tasks.
• Found that in many angiosperm genera, the best-fitting model provides a poor fit to the data.
• Observed an increased inadequacy rate in large clades and those involving hybridizations.

Conclusions:

• The developed model adequacy test is essential for identifying phylogenies with evolutionary patterns deviating from modeling assumptions.
• Current models may not adequately capture the complexity of chromosome number evolution in many plant groups.
• The test should guide future development of more robust methods for evolutionary inference.