Evolutionary shift detection with ensemble variable selection

TL;DR

This paper introduces an ensemble variable selection method for detecting evolutionary shifts in traits, compares it with existing methods, and discusses how different criteria and errors affect performance and robustness.

Contribution

The paper presents a new ensemble method (ELPASO) for evolutionary shift detection and evaluates its performance against existing methods under various scenarios.

Findings

Performance depends on the selection criterion used.

BIC performs better with small signals, pBIC with large signals.

Ensemble + pBIC is less conservative, Ensemble + BIC more conservative.

Abstract

1. Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of phenotypes. 2. We propose an ensemble variable selection method (R package ELPASO) for the evolutionary shift detection task and compare it with existing methods (R packages l1ou and PhylogeneticEM) under several scenarios. 3. The performances of methods are highly dependent on the selection criterion. When the signal sizes are small, the methods using the Bayesian information criterion (BIC) have better performances. And when the signal sizes are large enough, the methods using the phylogenetic Bayesian information criterion (pBIC) (Khabbazian et al., 2016) have better performance. Moreover, the performance is heavily impacted by measurement error and tree reconstruction error. 4. Ensemble method + pBIC tends to perform less conservatively than l1ou + pBIC, and Ensemble method + BIC is more conservatively than l1ou + BIC. PhylogeneticEM is even more conservative with small signal sizes and falls between l1ou + pBIC and Ensemble method + BIC with large signal sizes. The results can differ between the methods, but none clearly outperforms the others. By applying multiple methods to a single dataset, we can access the robustness of each detected shift, based on the agreement among methods.