  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.
