DOI: 10.5061/dryad.3r2280gv3
title: Data from: Diversification in Amazonian <em>Hypocnemis</em> antbirds (Aves: Thamnophilidae) inferred from Ultraconserved Elements (UCEs)

description: This dataset contains sequences of 2,222 Ultra-conserved Elements (UCEs)
 and 30 exons loci from 58 specimens belonging to all currently
 recognized Hypocnemis species and all but one subspecies. This
 dataset was used to infer phylogenies, population structure, interspecific
 limits, and the genus’ biogeographic history.
description: A total of 58 specimens belonging to all currently recognized
 <em>Hypocnemis</em> species and all but one subspecies (i.e.,
 <em>H. s.</em> <em>subflava</em>) from 49
 different localities were sampled, 46 belonging to the
 <em>Hypocnemis cantator</em> complex and 12 to the
 <em>H. hypoxantha</em> complex (see Table S1 in Cronemberger
 et al. 2025). Published UCE sequences of the sister genus
 <em>Drymophila</em> (<em>D. genei</em>) were used
 as outgroup (Harvey et al., 2020). The full description of laboratory and
 bioinformatics steps associated with each exon and UCEs sequence datasets
 used in different analyses are provided below and in the Supplementary
 materials of Cronemberger et al. 2025. All samples had
 their DNA extracted from pectoral muscle using DNAeasy (Qiagen) using the
 manufacturer’s protocol. DNA was quantified in a QuBit^®^ 2.0 Fluorometer
 (Thermofisher), using Qubit^®^ hsDNA Assay (Life Technologies). Libraries
 were prepared and sequenced by RAPiD Genomics (Gainesville, FL, EUA).
 Libraries followed the protocol described in Faircloth et al., (2012) with
 some modifications, as reported in Toews et al., (2016) and Thom et al.,
 (2018). Sequencing was carried out in a multiplexed batch of 96 samples
 (including non <em>Hypocnemis</em> individuals) in an Illumina
 HiSeq 2500 sequencer. We used Illumina’s Casava 1.8
 software to isolate each sample from the raw read files, according to
 individual barcodes. The quality of reads was assessed with the FastQC
 software (Andrews, 2014). Each sample was trimmed to remove the sequence
 adaptors and low-quality reads using Trimmomatic as part of the
 Illumiprocessor tool (Bolger et al., 2014). Next, we performed
 <em>de novo</em> assemblies of the genomic library using
 Trinnity v.2.4 (Grabherr et al., 2011) that resulted in different contigs
 (i.e., consensus sequences generated from reads overlap). For this and
 next steps we used programs available in the Phyluce 1.5 package
 (Faircloth et al., 2012; https://github.com/faircloth-lab/phyluce). Probe reference sequences containing UCE and exons were aligned to contigs using the “phyluce_match_contigs_to_probes.py” script. Contigs aligned to multiple loci or that did not match with probe references were removed with the LASTZ software. From that, we summarized the coverage of probes in each sample with <em>probes.match.sqlite</em> and generated a file with extension “.conf” by “phyluce_assembly_get_match_counts”, setting an incomplete matrix database to extract the sequences in the fasta format. After getting the fasta sequences, we aligned them using software MAFFT (Katoh & Standley, 2013), generating a concatenated matrix file with a maximum of 15% of missing data. We used the program BWA (Li & Durbin, 2009) to individually align UCEs and exons raw reads to a generated <em>Hypocnemis</em> reference containing the longest sequences of each locus. Next, we extracted SNPs with the Genome Analyses Tool Kit (GATK; McKenna et al., 2010), keeping only high-quality bases (&gt;Q30). The resulted file was filtered using VCFTools v.0.1.12 software (Danecek et al., 2011), which randomly selected only one SNP per locus from the matrix, to avoid linkage disequilibrium biases. Finally, we removed uninformative sites or those containing low allele frequencies (≤ 1/46 for the database with all individuals of the <em>H</em>. <em>cantator</em> complex, ≤ 1/20 for the database encompassing only individuals of polytypic <em>H</em>. <em>striata</em> and <em>H. rondoni</em> lineages and ≤ 1/12 for database that encompassed <em>H. hypoxantha</em> individuals). We performed a Maximum Likelihood (ML) analysis with software RAxML v8.2.12 (Stamatakis, 2014). Using the concatenated Dataset1 (see Abstract above), we searched for the best fitting ML tree, under the GTRGAMMA evolutionary model. Bootstrap support values were generated with 1000 nonparametric <em>bootstrap</em> pseudoreplicates under <em>rapidbootstrap.</em> We run the analysis in CIPRES Science Gateway Portal v3.1 (San Diego Supercomputer Center; Miller et al., 2010). We rooted the tree on the branch leading to the chosen outgroup (<em>Drymophila genei</em>). We adopted a threshold for robust bootstrap values of ≥75 (Hillis & Bull, 1993; Erixon et al., 2003). We used Principal Coordinate Analysis (PCoA) and sNMF to visualize Euclidean distances and to estimate genetic structure and levels of genetic admixture within the two main clades recovered in the ML analysis, corresponding to the <em>H. cantator</em> complex (datasets 6 and 8), and the <em>H. hypoxantha</em> complex (dataset 7; see Abstract above). PCoA was performed in PAST v.3.23 (Hammer et al., 2001). We excluded MPEG 56241 from these analyses due to a higher degree of missing data. In sNMF (Frichot et al., 2014), we set multiple K values according to the number of lineages recovered by the ML tree for each <em>Hypocnemis</em> species complex, as follows: <em>H. cantator</em> complex, dataset 6 (K = 1 - 10) and <em>H. hypoxantha</em>, dataset 7 (K = 1 - 5). A separate sNMF with K = 1 - 5 was conducted to evaluate the genetic structure in <em>H. rondoni</em> and <em>H. striata</em> in more detail (dataset 8). All sNMF analyses were performed with 500 replicate runs for each K value, and by testing four different alpha regularization parameters (10, 100, 500, 1000) to ensure robustness of the results. We also represented the relatedness among lineages recovered by the ML analysis in a phylogenetic network using the SNPs dataset, which included all sampled taxa within the <em>H. cantator</em> and <em>H. hypoxantha</em> complexes (datasets 6 and 7 – see Abstract above). This analysis was implemented in Splitstree v.4 (Huson & Bryant, 2006) and assumed an uncorrected p-distance and the NeighborNet method with equal angle splits. The softwares STACEY (Jones, 2016) and SNAPP (Bryant et al., 2012) were used to generate multilocus coalescent species trees and define species delimitation in the <em>H. cantator</em> and <em>H. hypoxantha</em> complexes. We also estimated divergence times under a coalescent approach using software *BEAST. Analyses were implemented in BEAST v.2.6.3 (Bouckaert et al., 2014) using filtered datasets with highly informative loci (STACEY and *BEAST) and SNPs (SNAPP) (datasets 2, 3, 4, 5, and 7 in Table 1). For the sake of standardization, the same randomly chosen specimens were used across species-tree analyses, (see Supplementary material – <em>Dataset Assemblage</em> and Table 1). For species delimitation in SNAPP and *BEAST, lineages were defined <em>a priori</em> according to groups recovered by the ML, PCoA and Splitstree analyses. For detailed priors and settings employed in our multilocus coalescent species tree analyses see Supplementary material of Cronemberger et al (2025). Different models of ancestral area reconstructions were evaluated to infer the biogeographic history of the genus <em>Hypocnemis</em> using the time-calibrated coalescent species tree based on dataset 2. We employed dataset 2 in this analysis to base our biogeographic reconstructions on a robust coalescent time-tree with minimum linkage disequilibrium effects (maximizing the use of independent loci), while at the same time incorporating the most variable parts found in each UCEs.**** We used the BioGeoBears R package (Matzke, 2013), and tested six alternative models: Dispersal-Extinction Cladogenesis (DEC), Dispersal-Vicariance Analysis (DIVALIKE), Bayesian inference of historical biogeography for discrete areas (BAYAREALIKE), plus all the aforementioned models with a founder-event speciation (Jump–J) parameter added to each of them (i.e., DEC+J, DIVALIKE+J and BAYAREA+J). We interpreted models with the J parameter cautiously, given that they may overestimate dispersal probabilities, particularly among non-insular lineages such as <em>Hypocnemis</em> (Ree & Sanmartin, 2018). Ancestral areas were defined according to two alternative criteria, varying in spatial scale. First, three areas were considered, each corresponding to distinct major Amazonian geological provinces (as in Silva et al., 2019): (FB) Amazonian Foreland / Western Sedimentary Basins; (GU) Guiana Shield; and (BS) Brazilian Shield. Second, six major Amazonian interfluves inhabited by distinct lineages of <em>Hypocnemis</em> were considered: Amazonian Northwest (NW), encompassing the Imeri and Napo areas of endemism (see Silva et al., 2019); Guiana (GU); Xingu (XI); Rondonia (RO): reaching south and east to the Xingu area - see also Thom & Aleixo 2015; Azuaje-Rodriguez et al., 2018; Quaresma et al. 2022); Tapajos (TA); and Inambari (IN). For each scenario, we set the maximum number of range areas as three (all major Amazonian geological provinces) and six (all major Amazonian interfluves / areas of endemism), according to the geographical ranges of <em>Hypocnemis</em> taxa. Lastly, we considered the best-fit model according to values of Log-likelihood (Ln L), Akaike Information Criterion (AIC), ΔAIC and Akaike weight (ωi). All references cited herein can be found in the paper associated with this dataset (Cronemnberger et al. 2025).
description: # Data from: Diversification in Amazonian *Hypocnemis* antbirds (Aves:
 Thamnophilidae) inferred from Ultraconserved Elements (UCEs) Dataset DOI:
 [10.5061/dryad.3r2280gv3](10.5061/dryad.3r2280gv3) ## Description of the
 data and file structure We used this dataset to estimate taxonomically
 densely sampled multilocus phylogenies for the genus *Hypocnemis* using
 ultraconserved elements (UCEs) and exon sequences with the goal of (1)
 assessing levels of phyletic diversity and interspecific limits in the *H.
 cantator* and *H. hypoxantha* species complexes, and (2) building a
 taxonomically complete and well-supported phylogeny for the genus. Based
 on these phylogenies, we reconstructed the spatio-temporal context of
 *Hypocnemis* diversification in the Amazon through coalescent-based
 ancestral-area analyses. ### Files and variables The whole dataset was
 divided into 8 different subsets, as described below. Dataset 1
 encompassed all sampled *Hypocnemis* individuals plus the outgroup
 belonging to the genus *Drymophila*, whereas dataset 2 contained only all
 *Hypocnemis* individuals sequenced (Table 1 in the companion paper, i.e.,
 Cronemberger et al. 2025). Datasets 3+5 and datasets 4+7 included,
 respectively, the same randomly selected individuals of the *H. cantator*
 and *H. hypoxantha* complexes, i.e., up to three specimens per lineage
 recovered by the ML phylogeny (Table 1 in Cronemberger et al. 2025).
 Finally, dataset 6 contained all specimens sequenced of the *H. cantator*
 complex, whereas dataset 8 included only specimens belonging to *H.
 striata* and *H. rondoni* (Table 1 in Cronemberger et al. 2025).  Datasets
 2, 3 and 4 were obtained through the selection of loci with highly
 informative sites. For this, we used the script
 “phyluce_allign_get_informative_sites” and retained only loci with more
 than 16 informative sites, which accounted for approximately 25% of all
 variable sites, an acceptable threshold according to Hosner et al. (2015).
 To assemble datasets 5, 6, 7 and 8, we followed procedures of SNPs
 extraction detailed in McCormack et al., (2016). See the table below for a
 short description of the different dataset subsets and the analyses
 employed with each of them. | **Dataset** | 
 
 | 
 
 | 
 
 | 
 
 | | :---------- | :-------------- |
 :----------------------------------------------------------------------------------------------- | :---------------------- | :------------------------------------------------------------------------------------------------------------------------------------- | | 
 
 | **(n) samples** | **Taxa** | **(n) genetic markers** | **Analyses
 (softwares)** | | 1 | 59 | *Hypocnemis cantator* complex, *Hypocnemis
 hypoxantha* complex and *Drymophila genei* (outgroup) | 2,222 UCEs + 30
 exons | Likelihood phylogenetic tree (RaxML) | | 2 | 58 | *Hypocnemis
 cantator* complex and *Hypocnemis hypoxantha* complex | 93 UCEs + 7 exons
 | Coalescent species tree (\*Beast) and Biogeographic history (BioGeoBEARS
 – Rpackage) | | 3 | 28 | *Hypocnemis cantator* complex | 93 UCEs + 7 exons
 | Species delimitation tree (STACEY–Beast2) | | 4 | 12 | *Hypocnemis
 hypoxantha* complex | 120 UCEs + 8 exons | Species delimitation tree
 (STACEY–Beast2) | | 5 | 28 | *Hypocnemis cantator* complex | 1,069 SNPs |
  Species delimitation tree (SNAPP–Beast2) | | 6 | 46 | *Hypocnemis
 cantator* complex | 1,069 SNPs | Phylogenetic network (Splitstree),
 PcoA\^-1\^ (Past) and Populational structure\^-1\^ (sNMF) | | 7 | 12 |
 *Hypocnemis hypoxantha* complex | 1,242 SNPs | Phylogenetic network
 (Splitstree), PcoA\^-1\^ (Past), Species delimitation tree (SNAPP–Beast2)
 and Populational structure\^-1\^ (sNMF) | | 8 | 20 | Polytypic *H.
 striata* + *H. rondoni* | 1,029 SNPs | PcoA\^-1\^ (Past) and Populational
 structure\^-1\^ (sNMF) | #### File: DATASET1-20250811T093919Z-1-001.zip
 **Description:**  2,222 UCEs + 30 exons sequences belonging to 59
 specimens of the *Hypocnemis cantator* complex, *Hypocnemis hypoxantha*
 complex and *Drymophila genei* (outgroup) #### File:
 DATASET2-20250811T094232Z-1-001.zip **Description:**  93 UCEs + 7 exons
 sequences belonging to 59 specimens of the *Hypocnemis cantator* complex
 and *Hypocnemis hypoxantha* complex #### File:
 DATASET3-20250811T095537Z-1-001.zip **Description:**  93 UCEs + 7 exons
 sequences belonging to 58 specimens of the *Hypocnemis cantator* complex
 #### File: DATASET4-20250811T095656Z-1-001.zip **Description:**  120 UCEs
 + 8 exons sequences belonging to 28 specimens of the *Hypocnemis
 hypoxantha* complex #### File: DATASET5-20250811T095855Z-1-001.zip
 **Description:**  1,069 SNPs from 12 specimens of the *Hypocnemis
 cantator* complex #### File: DATASET6-20250811T100423Z-1-001.zip
 **Description:**  1,069 SNPs from 46 specimens of the *Hypocnemis
 cantator* complex #### File: DATASET7-20250811T105400Z-1-001.zip
 **Description:**  1,242 SNPs from 12 specimens of the *Hypocnemis
 hypoxantha* complex #### File: DATASET8-20250811T105808Z-1-001.zip
 **Description:**  1,029 SNPs from 20 specimens of polytypic *H. striata* +
 *H. rondoni* ## Code/software The data can be viewed with any text editor
 software. The UCE sequences were processed with the following softwares:
 Illumina’s Casava 1.8; FastQC software (Andrews, 2014); Trinnity v.2.4
 (Grabherr et al., 2011); the Phyluce 1.5 package (Faircloth et al., 2012;
 [https://github.com/faircloth-lab/phyluce](https://github.com/faircloth-lab/phyluce)); LASTZ ([https://lastz.github.io/lastz/](https://lastz.github.io/lastz/)); and MAFFT (Katoh & Standley, 2013).