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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Assessing model adequacy for Bayesian Skyline plots using posterior predictive simulation.

Emanuel M Fonseca^1,2, Drew J Duckett^1,2, Filipe G Almeida³

¹Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, United States of America.

|July 25, 2022

Summary

This summary is machine-generated.

Bayesian skyline plots (BSPs) help infer demographic history but have assumptions. The new P2C2M.Skyline R package assesses BSP model adequacy using posterior predictive simulation, preventing spurious results from violated assumptions.

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Area of Science:

Population genetics
Phylogenetics
Computational biology

Background:

Bayesian skyline plots (BSPs) are widely used to infer demographic history and test hypotheses about factors like climate change influencing genetic diversity.
BSP analyses rely on assumptions, such as the absence of population genetic structure, which can be violated in empirical data, leading to inaccurate conclusions.

Purpose of the Study:

To introduce P2C2M.Skyline, an R package designed to rigorously assess the model adequacy of Bayesian skyline plots.
To provide a user-friendly and computationally efficient tool for researchers to validate BSP assumptions and ensure reliable demographic inferences.

Main Methods:

P2C2M.Skyline utilizes phylogenetic trees and Bayesian Skyline analysis log files to generate posterior predictive datasets.
The package compares statistics from simulated datasets under the null model to those from empirical data to detect model violations.
Model adequacy is assessed using posterior predictive simulation to evaluate the fit of the BSP model to the data.

Main Results:

P2C2M.Skyline successfully identified model violations when simulated data violated BSP assumptions, such as the presence of genetic structure.
The package demonstrated a low rate of false positives when data were simulated under the BSP model's assumptions.
Empirical analyses using P2C2M.Skyline detected model violations when DNA sequences from multiple populations were combined.

Conclusions:

P2C2M.Skyline is a valuable tool for ensuring the reliability of demographic inferences made using Bayesian skyline plots.
The package enhances the accuracy of population genetic studies by providing a robust method for checking BSP model assumptions.
Researchers can confidently apply BSPs with P2C2M.Skyline to investigate demographic history and the impact of environmental factors on genetic diversity.