Many questions in comparative biology
require that new data be collected, either to build a comparative
database for the first time or to augment existing data. Given
resource limitations in collecting data, which species should be
studied to increase the size of comparative datasets? By taking the
hypotheses, other comparative data relevant to the hypotheses, and
an estimate of phylogeny, we show that a method of
“phylogenetic targeting” can systematically identify
the species to study. Phylogenetic targeting selects potential
candidates for future data collection based on a flexible scoring
system that maximizes the differences in pairwise comparisons while
taking potentially confounding variables into account. The method
can control for confounding variables, or it can maximize the power
to test competing hypotheses. We used simulations to assess the
performance of phylogenetic targeting, as compared to a less
systematic approach of randomly selecting species (as might occur
when data have been collected on species without regard to
variation in the traits of interest). The simulations revealed that
selection of species using phylogenetic targeting increased the
statistical power to detect correlations and that power increased
with the number of species in the clade even when the number of
samples collected was not increased. We also developed a web-based,
freely available and publicly accessible computer program called
PhyloTargeting to implement the approach. It provides a
user-friendly interface, a variety of options to analyze the
dataset, and graphical visualizations of the results.
For more information about phylogenetic targeting, see
Arnold and Nunn 2010 or the
help page.
web (dot) de & clnunn