Plastid phylogenomics and molecular evolution of Alismatales

A survey of 659 papers mostly published since 1987 was conducted to compile a checklist of mycorrhizal occurrence among 3,617 species (263 families) of land plants. A plant phylogeny was then used to map the mycorrhizal information to examine evolutionary patterns. Several findings from this survey enhance our understanding of the roles of mycorrhizas in the origin and subsequent diversification of land plants. First, 80 and 92% of surveyed land plant species and families are mycorrhizal. Second, arbuscular mycorrhiza (AM) is the predominant and ancestral type of mycorrhiza in land plants. Its occurrence in a vast majority of land plants and early-diverging lineages of liverworts suggests that the origin of AM probably coincided with the origin of land plants. Third, ectomycorrhiza (ECM) and its derived types independently evolved from AM many times through parallel evolution. Coevolution between plant and fungal partners in ECM and its derived types has probably contributed to diversification of both plant hosts and fungal symbionts. Fourth, mycoheterotrophy and loss of the mycorrhizal condition also evolved many times independently in land plants through parallel evolution.

Background Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics. Methods We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere. Results We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores. Conclusions These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.