Exploring systematic biases, rooting methods and morphological evidence to unravel the evolutionary history of the genus Ficus (Moraceae)

摘要

Despite many attempts in the Sanger sequencing era, the phylogeny of fig trees remains unresolved, which limits our ability to analyze the evolution of key traits that may have contributed to their evolutionary and ecological success. We used restriction-site-associated DNA sequencing (c. 420 kb) and 102 morphological characters to elucidate the relationships between 70 species of Ficus. To increase phylogenetic information for higher-level relationships, we targeted conserved regions and assembled paired reads into long loci to enable the retrieval of homologous loci in outgroup genomes. We compared morphological and molecular results to highlight discrepancies and reveal possible inference bias. For the first time, we recovered a monophyletic subgenus Urostigma (stranglers) and a clade with all gynodioecious Ficus. However, we show, with a new approach based on iterative principal component analysis, that it is not (and will probably never be) possible to homogenize evolutionary rates and GC content for all taxa before phylogenetic inference. Four competing positions for the root of the molecular tree are possible. The placement of section Pharmacosycea as sister to other fig trees is not supported by morphological data and considered a result of a long-branch attraction artefact to the outgroups. Regarding morphological features and indirect evidence from the pollinator tree of life, the topology that divides Ficus into monoecious versus gynodioecious species appears most plausible. It seems most likely that the ancestor of fig trees was a freestanding tree and active pollination is inferred as the ancestral state, contrary to previous hypotheses. However, ambiguity remains on the ancestral breeding system. Despite morphological plasticity, we advocate restoring a central role to morphology in our understanding of the evolution of Ficus, as it can help detect systematic errors that appear more pronounced with larger molecular datasets.