Appendix S4 Additional laboratory notes and results
Additional Methods
DNA extraction
Our precipitation protocol involved placing 5–20 mg of tissue (for ethanol or frozen
samples) or 20 μL of tissue lysate (for samples originally stored in lysis buffer) in 300 μL
of cell lysis buffer (100 mM NaCl, 100 mM Tris-Cl pH 8.0, 25 mM EDTA pH 8.0, 0.5%
SDS) and incubating overnight at 55 C. Following this digestion phase, we used a protein
precipitation solution (4 M Guanidine Thiocyanate, 0.1 M Tris-Cl pH 7.5) to remove
proteins via centrifugation. We then washed and centrifuged each sample twice using
ethanol (100% and 75%, respectively). After the 75% ethanol wash, we dried and
rehydrated the purified DNA samples with 50–200 μL of Tris pH 8.0. This protocol is
similar to that used by Rovito et al. (2012) except that we used ethanol instead of
isopropanol to precipitate DNA.
Double digest RADseq
In general, we followed the protocol for library construction described by Peterson et al.
(2012). Digestions were performed in 50 μL volumes with each containing 5 μL of
NEBuffer 4 (NEB#B7004S), 1 μL of each enzyme, and 43 μL of dilute DNA (>200 ng).
We digested samples for 8 hrs at 37 C. These digestions were subsequently cleaned using
a Sera-Mag Speedbeads solution. In order to maintain the chain of custody on Craugastor
samples once they were pooled for sequencing, we made adaptor oligonucleotides that
leveraged the two-index combinatorial tagging approach described by Peterson et al.
(2012). We made adaptors by annealing primers from the ddRAD oligo table provided by
Peterson et al. (2012) to one another in 100 μL reactions (each containing 20 μL of each
primer [200 μM], 10 μL of 10X annealing buffer [100 mM Tris HCL, pH 8.0; 500 mM
NaCL; 10 mM EDTA], and 50 μL of nuclease-free water. We incubated this solution in a
water bath at 97.5 C for 2.5 min. We then turned off the water bath and allowed the
solutions to slowly return to room temperature.
Adaptor ligations were performed on double digested samples in 40 μL volumes
that each contained 30 μL of digested sample, 2 μL of each adaptor, 4 μL of ligase
reaction buffer (NEB#B0202S), 1 μL of T4 DNA ligase (NEB#M0202L), and 1 μL of
water. Ligations were incubated at 23 C for 30 min and then brought to 65 C for 10 min.
Following this step, ligations were slowly cooled to room temperature at 2 C per 90 sec
until they reached 23 C. These ligations were cleaned using a Sera-Mag Speedbeads
solution.
After size selection on the Blue Pippen electrophoresis platform, we performed 11
separate PCRs on each Illumina® index group in 25 μL reactions (each containing 11 μL
of water, 5 μL of 5X HF Buffer, 2.5 μL dNTPs [2 mM each], 1.25 μL of each primer, and
0.25 μL of Phusion® polymerase). We used the following thermal cycling profile: initial
denaturation at 98 C for 30 sec followed by 12 cycles of 98 C for 10 sec, 65 C for 30 sec,
and 72 C for 110 sec. A final elongation stage at 72 C for 10 min completed the PCR.
Following PCR we pooled the 11 reactions from each Illumina® index group and cleaned
the five samples with a Sera-Mag Speedbeads solution (Rohland and Reich 2012).
Analysis
Sequence reads were de-multiplexed in two steps. First, samples were sorted by
Illumina® index at the UT Southwestern Medical Center Genomics Core Facility. Prior
to the second stage of de-multiplexing we used fastx_trimmer command in the FastQ
toolkit (available at: http://hannonlab.cshl.edu/fastx_toolkit/index.html) to remove the
first 8 bp of each sequence read which corresponded to random nucleotides included in
the adaptors we used (see Appendix S3). We then used the process_radtags module in
STACKS for a final de-multiplexing of the five Illumina® indices into sequence datasets
that corresponded to the 46 individuals in our analysis. Prior to aligning RADseq data we
removed the 6 bp remnants of the SbfI recognition site using the fastx_trimmer.
Additional Results
Amount of missing data does not produce detectable pattern variation
Overall, the six different SNP datasets we constructed produced nearly identical
topologies and clustering groups. There were, however, some exceptions to this trend. In
the 20% missing bi-allelic dataset (Dryad document 4) the Central Texas (ESU 1) and
West Texas/New Mexico (ESU 2) lineages were not distinguished from one another by a
STRUCTURE analysis. In other STRUCTURE analyses, there were often variable
patterns of relatedness for six individuals from western and southern Mexico (TJD 834,
JAC 23544, JAC 23564, JAC 30517, JAC 30108, and JAC 30105; Appendix S2), though
we feel that this variability may be produced by shared alleles resulting from an episode
of introgression that we identify below.
Regarding nodal support for phylogenetic reconstructions, with the exception of a
single node in the 28,152 SNP dataset (the node subtending the ‘southern’ group; Fig. 3)
we found that bootstrap support was higher in the larger SNP datasets (see Fig. 3). This
may indicate that despite large amounts of missing data, our larger SNP datasets
produced more robust phylogenetic hypotheses. However, Rubin et al. (2012)
demonstrated via simulation with Drosophila genomes that in RADseq phylogeny
reconstruction incorrect nodes can have high bootstrap support. As such, these authors
advocated examining consistency across input-order replicates to determine the
robustness of bootstrap support measures. To further investigate the distribution of nodal
support in our barking frog datasets, we ad libitum re-ordered the population maps used
to generate our dataset and reran some of the ML and NJ phylogenetic analyses. We
found that the input order, at least at the later end of our analytical pipeline (following
catalog construction in cSTACKS), did not change the topology, number of RAD loci, or
bootstrap support values obtained from phylogenetic reconstruction.
Rationale and experimental design for D-statistic tests
The first of these regions is in the southwestern United States. Specifically, we
were interested in testing for gene flow between the New Mexico (ESU 2) and Arizona
(ESU 3) structural units. We were interested in this comparison because the westernmost
population of ESU 2 occurs in the Organ Mountains of New Mexico, less than 370 km
away from the easternmost population of ESU 3 in the Huachuca Mountains of Arizona
(Murray & Painter 2003; Goldberg et al. 2004). Despite this proximity, these ESUs are
not monophyletic in our SNP phylogenetic reconstruction (Figs. 3, 4). Furthermore, these
ESUs occur on either side of a notable gap in the geographic distribution of C. augusti:
the arid Central Mexican Plateau (CMP; Fig. 1). Recognizing this gap, Goldberg et al.
(2004) speculated that the “U” shaped geographic distribution of the complex around the
CMP could indicate that ESUs 2 and 3 are the respective termini of an incipient ring
species complex. To test for the presence of gene flow between ESUs 2 and 3, we used
individuals from Texas (JWS 277; ESU 1) and New Mexico (MVZ 226838; ESU 2) as
P1 and P2 (the monophyletic ingroup) and an Arizona specimen (BF 86; ESU 3) as P3
(the potential source of introgression). The D-statistic was not significant (Table 4; test
T1) and we therefore observed no evidence for gene flow between the northernmost
ESUs of C. augusti. We also tested the assumption that ESUs 1 and 2 are sister taxa
relative to the other eastern clade lineage, ESU 4 (Table 4; test T2), and found no
evidence for introgression or paraphyly.
The second region we examined for the signature of introgression was in southern
Mexico where two ESUs (4 and 5) occur in adjacent regions of the Sierra Madre del Sur.
Interestingly, while these ESUs have a short ‘straight line’ distance separating their
distributions (10 km between closest samples analyzed), the elevational range of sampled
individuals differs (762–1030 m [ESU 5] vs. 1357–2524 m [ESU 4]). Because montane
habitats are known to promote unusually strong genetic isolation among frog populations
(Monson & Blouin 2004; Gonzales-Voyer et al. 2011), we were interested in examining
these ESUs for patterns consistent with recent gene flow. To test for gene flow across
elevational strata, we used individuals from Texas (JWS 277; ESU 1) and eastern Mexico
(TJD 830; ESU 4) as P1 and P2 and a Guerrero specimen (TJD 847; ESU 5) as P3. The
D-statistic was not significant (Table 4; test T3) and we therefore observed no evidence
of introgression or paraphyly.
The third region we examined for introgressive interactions among ESUs was
western Mexico. This region — which included samples from the Mexican states of
Sonora, Jalisco, Colima, Nayarit, and Michoacán — is home to six ESUs of C. augusti
(3, 5, 6, 7, 8, and 9). In addition to parapatric distributions, these ESUs include genetic
units derived from the earliest branching ancestral stock of C. augusti and in general
possess more within-ESU DNA sequence variation than their northern counterparts (Fig.
3). This may suggest that barking frogs have long been present in this region and thus
would have had more time to experience overlap in geographic distribution. To test for
the signature of introgression among these ESUs, we conducted a series of D-statistic
tests (Table 4; tests T4–T13). We used the following individuals to represent each ESU:
BF 36 (ESU 3), TJD 877 (ESU 7), JAC 23564 (ESU 8), JAC 23345 (ESU 9), JAC 30105
(ESU 6), and TJD 847 (ESU 5). In total we used 10 four-taxon combinations of these
individuals to test for the signature of introgression (Table 4; tests T5–T13). All of these
tests resulted in significant D-statistics, so we further examined these taxa using
partitioned D-statistics (Eaton & Ree 2013). We designed our five-taxon datasets so that
we could identify the directionality of introgression among our samples (Table 5; tests
T14–T25).
Appendix S4 References
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