DOI: 10.5061/dryad.cnp5hqcdt
title: Phylogenomic analysis of the dwarfgobies (Teleostei: Gobiidae: <em>Eviota</em> and <em>Sueviota</em>)

description: The goby genera Eviota and Sueviota (family Gobiidae) are commonly known
 as dwarfgobies, and collectively the two genera are among the most
 abundant and diverse groups of fishes on coral reefs. Despite the
 diversity, abundance, and ecological importance of this group, and the
 large and growing number of species described to date (132 Eviota, 9
 Sueviota), there is a lack of understanding of the phylogenetic
 relationships among the major clades of Eviota, poor knowledge of the
 relationships between dwarfgobies and other Gobiidae species, and no
 information on the placement of Sueviota. In addition, as is the case with
 most small reef fishes, a clear understanding of taxonomically informative
 phenotypic characters is also lacking. To resolve the evolutionary history
 of dwarfgobies, we inferred a time-calibrated phylogeny of the group using
 genome-wide data from 440 ddRADseq loci captured across 98 Eviota and
 Sueviota specimens, plus 66 specimens of other related gobies. We also
 assessed the distribution of 14 external and osteological morphological
 characters across the tree to assess which may be useful for diagnosing
 clades. Our results robustly established the non-monophyly of the genus
 Eviota, which was resolved into two separate clades, both of which were
 resolved within a lineage of other coral-associated genera (Gobiodon,
 Paragobiodon, Pleurosicya, Minisicya, and Bryaninops). One of the two
 clades is herein elevated to its own genus, Eviotops, a name which was
 previously considered synonymous with Eviota.  Additionally, we
 established that the genus Sueviota is deeply nested within one of the
 Eviota clades and is herein synonymized with Eviota. We also found strong
 phylogenetic signal for 12 out of the 14 phenotypic traits examined,
 providing strong complementary support for the two recovered clades, and
 establishing the validity of phenotypic traits that strongly correspond
 with genetic groupings that should aid in future taxonomic studies for
 this group.
description: To capture genome-wide SNP data, we used ddRADseq (Peterson et
 al. 2012). DNA extractions were sent to the NGS labs at the University of
 Wisconsin for sequencing and library preparation. In summary, the ddRADseq
 libraries were prepared following the methodology outlined in Elshire et
 al. (2011), which had been used successfully for a prior
 <em>Eviota</em> phylogeny using SNP data (Gómez-Buckley et al.
 2025). To assemble the ddRADseq raw data, we used the Stacks 2.4.1
 pipeline (Catchen et al. 2013) on the University of Washington’s High
 Performance Computer Cluster. We used the R packages RADstackshelpR 0.1.0
 (https://github.com/DevonDeRaad/RADstackshelpR ) to calculate summary statistics from Stacks output vcf files, and to determine the optimal parameters for assembling putative RAD loci de novo (Mastretta et al. 2015, Paris et al. 2017) following the same protocols as in Gómez-Buckley et al. 2025.
description: # Phylogenomic analysis of the dwarfgobies (Teleostei: Gobiidae: *Eviota*
 and *Sueviota*) Dataset overview To examine the effects of missing data on
 the support of phylogenies, we tested three values of the parameter ‘-R’,
 the minimum percentage of individuals across the entire alignment required
 to include a locus, using the ‘populations’ module in Stacks. The values
 chosen were R=0.2, 0.3, and 0.4, corresponding to thresholds of 80%, 70%,
 and 60% missing data allowable, respectively.   We generated maximum
 likelihood trees under the GTR+G for the three -R filtered datasets using
 RAxML-NG v1.1 (Kozlov et al. 2019) on the CIPRES (Miller et al. 2010)
 supercomputer portal. Support for nodes was assessed with 100 bootstrap
 replicates and trees were visualized using FigTree v1.4.4 (Rambaut 2012).
 The entire alignment, including invariant sites were used for phylogenetic
 inference. Heterozygote site,s were coded using IUPAC ambiguity codes. For
 the R=0.4 dataset, we also inferred a tree using Bayesian Inference via
 the software MrBayes 3.2 (Ronquist et al. 2012) using the same
 substitution model. The MCMC analysis was run for 10 million generations,
 sampling every 1000 generations, discarding the first 20 percent of trees
 as burn-in. Convergence and mixing of MCMC chains were assessed in Tracer
 1.5 (Rambaut and Drummond 2018). A 50% majority-rule consensus tree was
 generated from the resulting posterior distribution of trees.
 Descriptionof the data and file structure Files Files for the ddRADseq
 phylogenetic maximum likelihood trees (1-3) obtained from the
 Stacks/populations datasets using the 0.2-0.4 -R filtering parameters, and
 Bayesian inference tree (4) using the 0.4 -R filtering: 1. -R = 0.2
 (FigS1_Eviota_160x2441.R0.2_MLtree.tre) 2. -R = 0.3
 (FigS2_Eviota_164x824_R0.3_MLtree.tre) 3. -R = 0.4
 (Fig6_Eviota_164x440_R0.4_MLtree.tre) 4. -R = 0.4
 (FigS3_Eviota_159x381_R0.4_MrBayestree.tree)