Data Types and the Phylogeny of Neoaves

Abstract

The phylogeny of Neoaves, the largest clade of extant birds, has remained unclear despite intense study. The difficulty associated with resolving the early branches in Neoaves is likely driven by the rapid radiation of this group. However, conflicts among studies may be exacerbated by the hypothesis that relationships are sensitive to the data type analyzed. For example, analyses of coding exons typically yield trees that place Strisores (nightjars and allies) sister to the remaining Neoaves, while analyses of non-coding data typically yield trees where Mirandornites (flamingos and grebes) is the sister of the remaining Neoaves. Our understanding of data type effects is hampered by the fact that previous analyses have used different taxa, loci, and types of non-coding data. Herein, we provide strong corroboration of the data type effects hypothesis for Neoaves by comparing trees based on coding and non-coding data derived from the same taxa and gene regions. A simple analytical method known to minimize biases due to base composition (coding nucleotides as purines and pyrimidines) resulted in coding exon data with increased congruence to the non-coding topology using concatenated analyses. These results improve our understanding of the resolution of neoavian phylogeny and point to a challenge - data type effects - that is likely to be an important factor in phylogenetic analyses of birds (and many other taxonomic groups). Using our results, we provide a summary phylogeny that identifies well-corroborated relationships and highlights specific nodes where future efforts should focus.

Categorical edge-based analyses of phylogenomic data reveal conflicting signals for difficult relationships in the avian tree

“…For example, when the ∑Δln L criterion is used, VI + VII as the earliest Neoavian branch received much higher support in analyses of non-coding data, especially introns, than it did in analyses of coding data (10.9% for introns, 7.4% for UCEs, and 2% for exons). These results agree with the data type effects reported by Reddy et al (2017) and by Braun and Kimball (2021), although the differences among data types also appear to be more complex than the results suggested before.…”

“…Fundamentally, there are only two ways to examine conflict using the concatenated approach. First, one can subdivide data based on some criterion, such as coding vs. non-coding data (e.g., Jarvis et al 2014, Reddy et al 2017; Braun and Kimball 2021) or protein structure (e.g., Pandey and Braun 2020; Gordon et al 2021), and conduct concatenated tree searches on the data subsets. Second, one can examine differences in the tree selection criterion (typically the likelihood) for various topologies at the level of the genes (Shen et al 2017) or the individual site level (Kimball et al 2013).…”

“…In contrast, the position of the Hoatzin has been even more variable across studies (e.g., Reddy et al 2017) and its position does not exhibit an obvious correlation with data type. Due to biased base composition and high GC content variation (hereafter GCCV), exons received the most criticism for model violation and impact on phylogenetic reconstruction (Jarvis et al 2014; Reddy et al 2017; Braun and Kimball 2021). Using models that incorporate base compositional heterogeneity (e.g., Galtier and Gouy 1998; Morgan et al 2013) might improve analyses, but dramatic incongruence among gene trees is often observed within data types (e.g., Jarvis et al 2014) so the available models that incorporate base compositional heterogeneity are unlikely to eliminate incongruence among exonic trees.…”

Divergence time estimation of Galliformes based on the best gene shopping scheme of ultraconserved elements

“…However, the use of different data types in that study [ 11 ] still yield limited support for the weakly supported relationships found in this study. Incomplete lineage sorting and data-type effects (the topological differences associated with the use of different types of markers [ 44 , 45 ] may have caused the instability within this clade [ 13 , 46 ].…”

An early Eocene fossil from the British London Clay elucidates the evolutionary history of the enigmatic Archaeotrogonidae (Aves, Strisores)

“…Morphological data, by contrast, suggest a sister group relationship between the Caprimulgidae and the Nyctibiidae and recover both taxa as nested within Strisores (Mayr 2010). Whereas morphological data suggest that the Steatornithidae are the earliest branching taxon of the Strisores, most current molecular analyses support a sister group relationship between the Steatornithidae and Nyctibiidae (this topology is, however, sensitive to the data type analysed and is not recovered in analyses of non‐coding sequences; Braun & Kimball 2021).…”