DOI: 10.5061/dryad.w9ghx3fp6
title: Recent population differentiation in the habitat specialist Glossy Antshrike (Aves: Thamnophilidae) across Amazonian seasonally flooded forests: Final SNPs dataset

description: We assessed population structure and the spatio-temporal pattern of
 diversification in the Glossy Antshrike Sakesphorus luctuosus (Aves,
 Thamnophilidae) to understand the processes shaping the evolutionary
 history of Amazonian floodplains and address unresolved taxonomic
 controversies surrounding its species limits. By targeting ultraconserved
 elements (UCEs) from 32 specimens of S. luctuosus, we identified
 independent lineages and estimated their differentiation, divergence times
 and migration rates. We also estimated current and past demographic
 histories for each recovered lineage. We found evidence confirming that S.
 luctuosus consists of a single species, comprising at least four
 populations, with some highly admixed individuals and overall similar
 levels of migration between populations. We confirmed the differentiation
 of the Araguaia River basin population (S. l. araguayae), and gathered
 circumstantial evidence indicating that the taxon S. hagmanni may
 represent a highly introgressed population between 3 distinct phylogroups
 of S. luctuosus. Divergence time estimates between populations seem to be
 recent, occurring during the last 183 kya. Signs of population expansions
 were detected for populations attributed to subspecies S. l. luctuosus,
 but the S. l. araguayae population had probably maintained its effective
 size through time. Our results support that S. luctuosus has had a complex
 population history, resulting from a high dependence on southeastern
 “clear-water” habitats and their availability through time. Spatial and
 demographic expansions towards the western “white water” flooded forests
 might still be ongoing. Our study reinforces the view that isolation due
 to absence of suitable habitat has been an important driver of population
 differentiation within Amazonian flooded forests, but also that
 differences between várzeas (“white water” floodplains, mostly in
 southwestern Amazonia) and igapós (“clear- water” floodplains, especially
 located in the east) should be further explored as powerful drivers of
 micro-evolution.
description: Genomic DNA was extracted
 from tissues of 32 specimens of <i>S. luctuosus </i>from
 across its known range. We used DNeasy Blood & Tissue kit (Qiagen)
 for DNA extraction, and Qubit® 2.0 Fluorometer (Life Technologies) to
 assess quantity and quality of the extracted DNA. Sequence capture and
 sequencing of Ultra Conserved Elements (hereafter UCEs) were performed
 according to Faircloth et al. (2012) by RAPiD Genomics (Gainesville, FL,
 USA). More than 2,300 UCEs and 97 exons were targeted (Harvey, Aleixo,
 Ribas, & Brumfield, 2017; Zucker et al., 2016). We followed the PHYLUCE pipeline
 (Faircloth, 2016, 2017) to first remove adapters, barcodes and low quality
 sequence regions using Illumiprocessor 2.0.7 (Faircloth, 2013), with the
 trimming tool Trimmomatic 0.32.1 (Bolger, Lohse, & Usadel, 2014);
 and to assemble trimmed reads using Trinity (Grabherr et al., 2011). These
 analyses were performed using default parameters. Contigs were blasted
 against the probe set of exons and UCEs using
 phyluce_match_contigs_to_probes. The annotated set was divided by locus,
 and each was aligned with MAFFT using the scripts
 phyluce_assembly_get_match_counts,
 phyluce_assembly_get_fastas_from_match_counts, and
 phyluce_align_seqcap_align. We used seqcap_pop pipeline to extract one
 biallelic SNP from each locus (Harvey, Smith, Glenn, Faircloth, &
 Brumfield, 2016). Briefly, the longest contigs were retrieved from an
 incomplete matrix, which included all loci identified in all 32 Glossy
 Antshrike specimens sampled using BWA (Li & Durbin, 2009). These
 contigs were blasted against the zebra finch genome (<i>Taeniopygia
 guttata</i> v. 3.2.4, NCBI code: GCF_000151805.1), to annotate loci
 linked to the Z chromosome. These loci were removed. Then, one biallelic
 SNP, present in all samples, was randomly chosen from each of the
 alignments, using Genome Analyses Tool Kit (McKenna et al., 2010) and
 VCFTools (Danecek et al., 2011). To identify <i>Fst</i>
 outliers (i.e. loci likely under selection), we first performed a
 principal component analysis in the SNPs dataset to set a preliminary
 population structure using PAST3 (Hammer, Harper, & Ryan, 2001),
 and then we run BayeScan (Foll & Gaggiotti, 2008). Loci putatively
 under selection were also excluded. This dataset includes 1008
 SNPs.
description: <b>Tissue / Voucher
 #</b> <b>Origin</b> <b>Institution</b> <b>Locality</b> <b>Lat</b> <b>Long</b> A23918 1. Paraná dos Mundurucus; ca. 45km SW Manacupurú; Brazil -3.63867 -60.87583 INPA T16394 2. Autazes; Uricurituba; Ilha; Brazil -3.5919 -58.9431 MPEG A23789 3. Boca do Purus; Rio Purus right margin/ Rio Solimões right margin; Brazil -3.70366 -61.4585 INPA T22892 4. Parintins; Rio Amazonas left margin; Brazil -2.5797 -56.6792 MPEG T22832 5. Itacoatiara; Rio Amazonas; Ilha do Risco; Brazil -3.1586 -58.3703 MPEG T22748 6. Borba; Rio Madeira; Ilha do Mandi; Brazil -4.4803 -59.8617 MPEG T16263 7. Novo Aripuanã; Prainha; W Bank Rio Madeira; Brazil -4.8897 -60.1597 MPEG 80362 8. Rosarinho island in Rio Madeira in front of town of Rosarinho; Amazonas; Brazil -3.6837 -59.0937 LSUMNS A16536 9. Rebio Trombetas; Rio Trombetas left margin; Brazil -1.4167 -56.7500 INPA T9529 10. Oriximiná; Lake Sapucuá; Brazil -1.7658 -56.2267 MPEG 80628 11. Island in Rio Acari Mouth; Amazonas; Brazil -5.2911 -59.6758 LSUMNS T15890 12. Humaitá; Mirari; W Bank Rio Madeira; Brazil -7.7758 -62.9414 MPEG 77904 13. Barra de Sao Manuel; Amazonas; Brazil -7.3600 -58.1386 LSUMNS T19378 14. Itaituba;Rio Tapajós right margin; Rio Rato; Brazil -5.4306 -56.9044 MPEG 78435 15. Rio Juruena; Amazonas; Brazil -7.6853 -58.2528 LSUMNS T11511 16. Paranaíta; Rio Teles Pires; Brazil -9.5028 -56.7592 MPEG T10849 17. Jacareacanga; FLONA do Crepori; Rio das Tropas; Cotovelo; Brazil -6.5189 -57.4444 MPEG A23542 18. Rio Xingu; Jericoá, Barra do Vento ca. 65 km SE Altamira; Brazil -3.4927 -51.6982 INPA 562276 19. Altamira; 52 km SSW; E Bank Rio Xingu; Brazil -3.6500 -52.3700 USNM T1666 20. Rio Xingu; Altamira; Ilha da Taboca (UHE Belo Monte); Brazil -3.2975 -52.1883 MPEG A23515 21. Rio Iriri; Rio Xingu left margin, fluvial island; Brazil -3.8175 -52.6351 INPA T16723 22. PARNA da Serra do Pardo; Sede do ICMBio; Brazil -5.7972 -52.7200 MPEG T21987 23. Uruará; Fazenda Alcatéia; Brazil -3.6969 -53.5621 MPEG A23595 24. Rio Xingu; Vitória do Xingu; fluvial island; Brazil -2.9293 -51.8873 INPA T22906 25. Monte Alegre; Ilha Cacoal Grande; Brazil -2.3881 -54.3614 MPEG T8627 26. Almeirim; FLOTA do Paru; Brazil -0.9333 -53.2333 MPEG T22958 27. Almeirim; Rio Amazonas; Ilha do Camaleão; Brazil -1.5375 -52.5236 MPEG T25494 28. Pium; C. Pesquisa Canguçu; Furo do Sambaíba; Brazil -9.9788 -50.0359 MPEG T25522 29. Pium; Rio Javaés; Furo do Sambaíba; Brazil -9.9788 -50.0359 MPEG T15379 30. Fazenda Três Batistella; Rio Claro; Montes Claros de Goiás; Brazil -15.9031 -51.4106 MPEG T15535 31. Fazenda Lagos; Araguaiana; Brazil -15.4447 -51.7322 MPEG T15638 32. Rio do Coco right margin; Caseara; Brazil -9.3139 -49.9600 MPEG