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While novel approaches that directly use fossil data are certainly preferable [ 35, 78], they require high-quality morphological datasets for both fossil and extant taxa which are currently not available for most groups.
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We followed the procedures of Smith [ 43] to code additional genera of avian Ischnocera (Additional file 1) for a morphological dataset of 138 adult and nymphal characters (Additional file 2).
We show that the combination of well-resolved phylogenies obtained by phylogenomic analyses and well-documented extensive morphological datasets is an appropriate basis for reconstructing complex morphological transformations and for the inference of evolutionary histories.
The congruence between the molecular and morphological datasets provides especially strong support for this result [ 44].
This serves to remind us that the main questions surrounding the phylogeny reconstruction program begin with experimental design, for both molecular and morphological datasets.
Due to the low statistical support for many relationships within Pelecaniformes and the waterbird clade, as well as enduring conflicts between datasets, an emerging consensus of recent studies is that additional data and revision are needed for both molecular and morphological datasets.
Thus, although the explanation of adaptive convergence related to foot-propelled diving remains a tempting explanation for the loon/grebe signal in morphological datasets, more rigorous functional and comparative phylogenetic analyses are needed to support this claim.
Regarding the performed FDG-PET/CT studies, the acquired functional and morphological datasets were read separately.
Furthermore, lower-level relationships inferred within Suloidea exhibit varying degrees of congruence and conflict between molecular and morphological datasets.
The lengths of the branches of the morphological datasets were calculated using a Euclidean distance matrix (SAS Procedure DISTANCE SASS version 9.1.3).
To evaluate the magnitude of difference between the morphological and molecular datasets, we first examined the effect of combining molecular and morphological datasets.
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