DOI: 10.5061/dryad.jdfn2z3cf
title: Ecological traits drive genetic structuring in two open-habitat birds from the morphologically cryptic genus Elaenia (Aves: Tyrannidae)

description: Understanding the relative contributions of the many factors that shape
 population genetic structuring is a central theme in evolutionary and
 conservation biology. Historically, abiotic or extrinsic factors (such as
 geographic barriers or climatic shifts) have received greater attention
 than biotic or intrinsic factors (such as dispersal or migration). This
 focus stems in part from the logistical difficulties in taking a
 comparative phylogeographic approach that contrasts species that have
 experienced similar abiotic conditions during their evolution yet differ
 in the intrinsic attributes that might shape their genetic structure. To
 explore the effects of intratropical migration on the genetic structuring
 of Neotropical birds, we chose two congeneric species, the Lesser Elaenia
 (Elaenia chiriquensis) and the Plain-crested Elaenia (E. cristata), that
 are largely sympatric, and which have similar plumage, habitat
 preferences, and breeding phenology. Despite these many commonalities,
 they differ in migratory behavior: E. chiriquensis is an intratropical
 migratory species while E. cristata is sedentary. We used a reduced
 representation genomic approach to test whether migratory behavior is
 associated with increased gene flow and therefore lower genetic population
 structure. As predicted, we found notably stronger genetic structuring in
 the sedentary species than in the migratory ones. E. cristata comprises
 genetic clusters with geographic correspondence throughout its
 distribution, while there are no geographic groups within Brazil for E.
 chiriquensis. This comparison adds to the growing evidence about how
 intrinsic traits like migration can shape the genetic structuring of
 birds, and advances our understanding of the diversification patterns of
 the understudied, open habitat species from South America.
description: <strong>Species distribution and tissue
 sampling</strong> We sampled 218 specimens
 (<em>E. cristata</em>, n=98; <em>E.
 chiriquensis</em>, n=120, but see the Results section for
 information about misidentifications) from 2003 to 2018 across 33 sites in
 South America (Supplementary Table S1, Summarized in Table 1; Fig. 1).
 Specimens were captured in the field using mist-nest and banded to avoid
 duplicate sampling. Approximately 20 µl of blood was obtained from each
 individual using sterile needles and glass capillary tubes and stored in
 absolute ethanol at room temperature. To increase the geographic coverage
 of our sampling, we obtained 108 <em>Elaenia</em> tissue
 samples from ornithological collections for a total of 326 samples (Table
 S1 in Supporting information). <strong>DNA
 extraction and quality control</strong> Total
 genomic DNA was extracted following a phenol, chloroform, isoamyl alcohol
 protocol (as in Friesen et al., 1997), or using the PureLink™ Genomic DNA
 Mini Kit (Invitrogen, Carlsbad, CA, US) (for museum tissue samples),
 following manufacturer instructions. Genomic DNA quality and
 concentrations were verified on a 1% agarose gel stained with ethidium
 bromide and using the Qubit™ dsDNA BR Assay Kit (ThermoFisher, Waltham,
 MA, US), respectively. <strong>ddRADseq
 dataset</strong> We generated ddRADseq loci
 following the approach outlined by Peterson et al. (2012) with
 modifications as described by Thrasher et al. (2018). Briefly, we digested
 each sample with <em>SbfI</em> and <em>MspI</em>
 and ligated adapters that allowed multiplexing. The libraries, each
 containing approximately 20 samples, were size-selected and PCR-enriched,
 incorporating the Illumina HiSeq adapters (Illumina, San Diego,
 California, US). Finally, all groups of samples were combined in equimolar
 proportions and sequenced, single end 100bp, on two lanes of an Illumina
 HiSeq 2500. After assessing read quality with FastQC
 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc), we used
 FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit) to trim sequences
 to 98 bp to discard lower-quality base calls at the 3′ end of the
 sequence. Subsequently, we used FASTX-Toolkit to retain reads without a
 single base below a Phred quality score of 10 and with at least 95% of
 bases with quality above 20. We demultiplexed reads using the
 ‘process_radtags’ program from the STACKS v.2.41 bioinformatics pipeline
 (J. Catchen et al., 2013; J. M. Catchen et al., 2011), discarding reads
 that did not pass the Illumina filter, had barcode contamination, lacked
 an <em>SbfI</em> cut site or one of the unique barcodes used
 for multiplexing at the 5′ end. We obtained an average of 550,757
 (±263,678 reads) quality-filtered reads per individual (Supplementary
 Table S1). We assembled the reads from both species
 into RADseq loci using the <em>de novo</em> pipeline from
 STACKS. We conducted a sensitivity analysis by testing different values
 for coverage (m= 5, 10, 20, and 30) as suggested by Rochette and Catchen
 (2017) yet did not find substantial differences in the number of loci
 recovered. Parameters were therefore set to a minimum coverage of 5 (m),
 up to seven differences between alleles of the same locus (M), and seven
 differences among aligned loci of different individuals (n). This
 combination of parameters produced an average coverage per locus ranging
 from 11.24 – 68.55x, with an overall average of 29.61x (±9.2). We exported
 SNPs using the program ‘populations’ in STACKS for all the samples
 combined (n=326) and again for each species separately, <em>E
 cristata</em> (n=156) and <em>E. chiriquensis</em>
 (n=150). We retained loci that were present in at least 80% of the
 individuals and exported both one SNP per RADSeq locus (to avoid including
 linked loci) and all SNP per RADSeq loci.
 <strong>References</strong> Catchen, J.,
 Hohenlohe, P. A., Bassham, S., Amores, A., & Cresko, W. A. (2013).
 Stacks: an analysis tool set for population genomics. <em>Molecular
 Ecology</em>, <em>22</em>(11), 3124–3140.
 https://doi.org/10.1111/mec.12354 Catchen, J. M.,
 Amores, A., Hohenlohe, P., Cresko, W., & Postlethwait, J. H.
 (2011). Stacks: Building and Genotyping Loci De Novo From Short-Read
 Sequences. <em>Genes|Genomes|Genetics</em>,
 <em>1</em>(3), 171–182.
 https://doi.org/10.1534/g3.111.000240 Friesen, V. L.,
 Congdon, B. C., Walsh, H. E., & Birt, T. P. (1997). Intron
 variation in marbled murrelets detected using analyses of single-stranded
 conformational polymorphisms. <em>Molecular Ecology</em>,
 <em>6</em>(11), 1047–1058.
 https://doi.org/10.1046/j.1365-294X.1997.00277.x Peterson, B.
 K., Weber, J. N., Kay, E. H., Fisher, H. S., & Hoekstra, H. E.
 (2012). Double Digest RADseq: An Inexpensive Method for De Novo SNP
 Discovery and Genotyping in Model and Non-Model Species. <em>PLoS
 ONE</em>, <em>7</em>(5), e37135.
 https://doi.org/10.1371/journal.pone.0037135 Rochette, N.
 C., & Catchen, J. M. (2017). Deriving genotypes from RAD-seq
 short-read data using Stacks. <em>Nature Protocols</em>,
 <em>12</em>(12), 2640–2659.
 https://doi.org/10.1038/nprot.2017.123 Thrasher, D.
 J., Butcher, B. G., Campagna, L., Webster, M. S., & Lovette, I. J.
 (2018). Double-digest RAD sequencing outperforms microsatellite loci at
 assigning paternity and estimating relatedness: A proof of concept in a
 highly promiscuous bird. <em>Molecular Ecology Resources</em>,
 <em>18</em>(5), 953–965.
 https://doi.org/10.1111/1755-0998.12771