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.

Species distribution and tissue sampling

We sampled 218 specimens (E. cristata, n=98; E. chiriquensis, 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 Elaenia tissue samples from ornithological collections for a total of 326 samples (Table S1 in Supporting information).

DNA extraction and quality control

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.

ddRADseq dataset

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 SbfI and MspI 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 SbfI 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 de novo 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, E cristata (n=156) and E. chiriquensis (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.

References

Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A., & Cresko, W. A. (2013). Stacks: an analysis tool set for population genomics. Molecular Ecology, 22(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. Genes|Genomes|Genetics, 1(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. Molecular Ecology, 6(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. PLoS ONE, 7(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. Nature Protocols, 12(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. Molecular Ecology Resources, 18(5), 953–965. https://doi.org/10.1111/1755-0998.12771