Fig 1.
Sketch courtesy of Cambridge University Library [21].
Fig 2.
Capture and layout of networks.
(A) PhyloSketch capture of a hybridization network representing lizard evolution, based on [Fig 2, 22], which was produced using ggnetworx [17]. (B) PhyloSketch layout of the network in a combining view. (C) PhyloSketch capture of a transfer network representing cat evolution, based on the image shown in [Fig S12E, 25], which was produced using PhyloNetworks [16]). (D) PhyloSketch layout of the network in a transfer view.
Fig 3.
(A) PhyloSketch circular combining view, network from [Fig 2, 22]. (B) PhyloSektch circular transfer view, network from [Fig S12E, 25].
Fig 4.
Cladogram, phylogram and IcyTree visualization.
A rooted phylogenetic network obtained by applying PhyloFusion [9] to 11 NADH dehydrogenase-associated gene trees of water lilies [27], is shown here as (A) a combining cladogram and (B) a combining phylogram, computed using the described algorithms. Note that the vertical spacing of leaves is uniform. In contrast, (C) an “ancestral recombination graph” visualization computed by IcyTree [15] exhibits gaps in the vertical spacing.
Fig 5.
Using randomly generated rooted phylogenetic networks with taxa and h = 0.2 × n reticulations (10 replicates each), we compared the performance of the new displacement optimization (DO) algorithm with that implemented in Dendroscope. (A) Wall-clock time (in seconds) on a MacBook Pro (M4 processor) to compute and optimize a combined rectangular cladogram. (B) Total reticulate displacement for both methods, normalized by the total height of the drawing.
Fig 6.
This shows the main window of the app, with several nodes and edges that have been interactively sketched and labeled. A late-branching circular layout has been applied to the bottom nework.