Bayesian phylogenetic inference involves sampling from posterior distributions of trees, which sometimes exhibit local optima, or peaks, separated by regions of low posterior density. Markov chain Monte Carlo (MCMC) algorithms are the most widely used numerical method for generating samples from these posterior distributions, but they are susceptible to entrapment on individual optima in rugged distributions when they are unable to easily cross through or jump across regions of low posterior density. Ruggedness of posterior distributions can result from a variety of factors, including unmodeled variation in evolutionary processes and unrecognized variation in the true topology across sites or genes. Ruggedness can also become exaggerated when constraints are placed on topologies that require the presence or absence of particular bipartitions (often referred to as positive or negative constraints, respectively). These types of constraints are frequently employed when conducting tests of topological hypotheses (Bergsten et al. 2013; Brown and Thomson 2017). Negative constraints can lead to particularly rugged distributions when the data strongly support a forbidden clade, because monophyly of the clade can be disrupted by inserting outgroup taxa in many different ways. However, topological moves between the alternative disruptions are very difficult, because they require swaps between the inserted outgroup taxa while the data constrain taxa from the forbidden clade to remain close together on the tree. While this precise form of ruggedness is particular to negative constraints, trees with high posterior density can be separated by similarly complicated topological rearrangements, even in the absence of constraints.