Regarding the performed FDG-PET/CT studies, the acquired functional and morphological datasets were read separately.
The lengths of the branches of the morphological datasets were calculated using a Euclidean distance matrix (SAS Procedure DISTANCE SASS version 9.1.3).
However, the estimated K-scores indicated that the three distance-optimized trees based on the different morphological datasets were almost congruent in topology and branch lengths, implying similar changes on nodes between the trees.
Despite their different and largely non-overlapping taxon sampling schemes, the molecular and morphological datasets were congruent in their placement of T. pseudonana as sister to Cyclotella.
Phenotypic variation in our morphological dataset is affected by both genetics and environment, whereas fish from the kinematic dataset would have been less influenced by environment.
The morphological dataset is based on 41 characters, of these, 25 are floral and important traits previously used for subgeneric classification within Petrocosmea (see Additional file 1: Appendix S1).
Several patterns regarding higher-level relationships inferred from the present morphological dataset are also noteworthy.
The patristic distances calculated for each morphological dataset were significantly correlated with those of the ML tree estimated from the molecular dataset based on Pearson correlation or Mantel tests (Table 4).
The morphological dataset was combined with the 20.1kb molecular supermatrix of Beck [10]-which comprises DNA sequence data from 7 nuclear genes (APOB, BRCA1, IRBP, PGK1, P1, RAG1, and VWF) and 15 mitochondrial loci (12S rRNA, 16S rRNA, tRNA valine, and 12 H-strand protein-coding genes -and analysed usingenes -and3.1.2 [37].
However, our morphological dataset was not constructed to solve this issue, since it relies on larval characters only.
