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Recent population differentiation in the habitat specialist Glossy Antshrike (Aves: Thamnophilidae) across Amazonian seasonally flooded forests: Final SNPs dataset

Citation

Silva, Sofia Marques; Ribas, Camila C.; Aleixo, Alexandre (2022), Recent population differentiation in the habitat specialist Glossy Antshrike (Aves: Thamnophilidae) across Amazonian seasonally flooded forests: Final SNPs dataset, Dryad, Dataset, https://doi.org/10.5061/dryad.w9ghx3fp6

Abstract

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.

Methods

Genomic DNA was extracted from tissues of 32 specimens of S. luctuosus 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 (Taeniopygia guttata 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 Fst 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.

Usage Notes

Tissue / Voucher #

Origin

Institution

Locality

Lat

Long

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

Funding

Brazilian Research Council , Award: 150657/2017-0; 311732/2020-8

USAID, Award: AID-OAA-A-11-00012

Fundação de Amparo à Pesquisa do Estado de São Paulo

National Science Foundation

United States Agency for International Development

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Brazilian Research Council, Award: 150657/2017-0; 311732/2020-8

USAID, Award: AID-OAA-A-11-00012