Data from: Random Tanglegram Partitions (Random TaPas): an Alexandrian approach to the Cophylogenetic Gordian Knot

Symbiosis is a key driver of evolutionary novelty and ecological
diversity, but our understanding of how macroevolutionary processes
originate extant symbiotic associations is still very incomplete.
Cophylogenetic tools are used to assess the congruence between the
phylogenies of two groups of organisms related by extant associations. If
phylogenetic congruence is higher than expected by chance, we conclude
that there is cophylogenetic signal in the system under study. However,
how to quantify cophylogenetic signal is still an open issue. We present a
novel approach, Random Tanglegram Partitions (Random TaPas) that applies a
given global-fit method to random partial tanglegrams of a fixed size to
identify the associations, terminals and nodes that maximize phylogenetic
congruence. By means of simulations, we show that the output value
produced is inversely proportional to the number and proportion of
cospeciation events employed to build simulated tanglegrams. In addition,
with time-calibrated trees, Random TaPas is also efficient at
distinguishing cospeciation from pseudocospeciation. Random TaPas can
handle large tanglegrams in affordable computational time and incorporates
phylogenetic uncertainty in the analyses. We demonstrate its application
with two real examples: Passerine birds and their feather mites, and
orchids and bee pollinators. In both systems, Random TaPas revealed low
cophylogenetic signal, but mapping its variation onto the tanglegram
pointed to two different coevolutionary processes. We suggest that the
recursive partitioning of the tanglegram buffers the effect of
phylogenetic nonindependence occurring in current global-fit methods and
therefore Random TaPas is more reliable than regular global-fit methods to
identify host-symbiont associations that contribute most to cophylogenetic
signal. Random TaPas can be implemented in the public-domain statistical
software R with scripts provided herein. A User’s Guide is also available
at GitHub.