Deep Phylogeographic Structure and Environmental Differentiation in the CarnivorousPlant Sarracenia alata
AMANDA J. ZELLMER1, MARGARET M. HANES2, SARAH M. HIRD1, AND BRYAN C. CARSTENS1,∗1Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; and 2Department of Biology, Eastern Michigan University,
Ypsilanti, MI 48197, USA;*Correspondence to be sent to: Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
Received 9 September 2011; reviews returned 29 November 2011; accepted 25 April 2012Associate Editor: Emily Lemmon
Abstract.---We collected ∼29 kb of sequence data using Roche 454 pyrosequencing in order to estimate the timing and patternof diversification in the carnivorous pitcher plant Sarracenia alata. Utilizing modified protocols for reduced representationlibrary construction, we generated sequence data from 86 individuals across 10 populations from throughout the range of thespecies. We identified 76 high-quality and high-coverage loci (containing over 500 SNPs) using the bioinformatics pipelinePRGmatic. Results from a Bayesian clustering analysis indicate that populations are highly structured, and are similarin pattern to the topology of a population tree estimated using *BEAST. The pattern of diversification within Sarraceniaalata implies that riverine barriers are the primary factor promoting population diversification, with divergence across theMississippi River occurring more than 60,000 generations before present. Further, significant patterns of niche divergenceand the identification of several outlier loci suggest that selection may contribute to population divergence. Our resultsdemonstrate the feasibility of using next-generation sequencing to investigate intraspecific genetic variation in nonmodelspecies. [Carnivorous plants; local adaptation; next-generation sequencing; phylogeography; Roche 454; Sarracenia alata.]
The carnivorous pitcher plant, Sarracenia alata, has adisjunct distribution, with populations on either sideof the Atchafalaya Basin/Mississippi River (Fig. 1).This wide biogeographic barrier (>100 km) has beenimplicated as a source for phylogeographic breaksin a variety of organisms (Soltis et al. 2006; Jacksonand Austin 2010), and has inspired several previousinvestigations in S. alata (Sheridan 1991; Neyland2008; Koopman and Carstens 2010). These studies didnot identify substantial morphological (Sheridan 1991)or fixed genetic variation (Neyland 2008; Koopmanand Carstens 2010) across the range of this pitcherplant species, presumably because of the paucityof genetic markers available at the time. However,with novel next-generation sequencing methods quicklybecoming available for phylogeography, the questionof diversification within S. alata is worth revisiting,primarily because much of what we know about thelife history of Sarracenia suggests that these physicallyisolated populations should be genetically isolatedas well.
Sarracenia alata are habitat specialists with a patchydistribution (Bayer et al. 1996), confined primarily tolongleaf pine savannahs characterized by abundantsunlight and frequent fires. Seeds in the genus havelimited dispersal and low rates of establishment (Ellisonand Parker 2002). The seeds are small with noadornments for animal-mediated movement and donot float, suggesting that they are not transferred viafloodwater (but see Neyland 2008). Furthermore, whilelittle is known about pollination in this species, thedistance separating the eastern and western ranges
of S. alata is large enough that it is unlikely thatanimal-mediated long distance pollination is currentlycontributing to significant gene flow between thesepopulations. Thus, the life history characteristics ofS. alata strongly suggest that the eastern and westernpopulations should be genetically isolated.
Human-induced habitat modification has led todramatic reductions in longleaf pine savannah habitatover the last two centuries (Noss 1988). As a result,contemporary S. alata populations occupy isolatedpatches of habitat surrounded by land unsuitablefor growth. Although previous results based onmicrosatellite data indicate that S. alata exhibitspopulation genetic structure (Koopman and Carstens2010), it is unclear if this structure is in response torecent habitat fragmentation, biogeographic barriers, orenvironmental variation between the disjunct portionsof its range. Given the low levels of variation identifiedin commonly used markers such as ITS or chloroplastDNA (Neyland 2008; Koopman and Carstens 2010),we instead collected sequence data from anonymousportions of the nuclear genome. Loci were isolated viathe creation of a reduced representation library (RRL),and sequenced using Roche 454 Titanium chemistry.RRLs allow for isolation of a specific, largely unbiased(e.g., including both coding and noncoding regions)subset of the genome (Altshuler et al. 2000; Whitelawet al. 2003; Barbazuk et al. 2005; Williams et al. 2010).Using these data, we estimated the timing and patternof divergence among populations of S. alata and exploredthe potential for adaptive differentiation using bothgenetic and environmental modeling approaches.
FIGURE 1. Distribution map of Sarracenia alata in the southern US. Dashed lines show the approximate range of the species. Western samplinglocalities from this study are marked with filled circles and include the following populations: Sundew (S), Pitcher Trail (P), Bouton Lake (B),Cooter’s Bog (C), and Kisatchie (K). Eastern populations are marked with open circles and include the following populations: Abita Springs (A),Talisheek (T), Lake Ramsey (L), Franklin Creek (F), and DeSoto (D). Minor rivers (light grey) and major rivers and water bodies (dark gray andthick black lines) are shown.
METHODS
Field Collections and DNA ExtractionTissue from 89 S. alata leaves was collected from
10 populations across the range of the species (Fig. 1;online Table S1, available from Dryad data repository;doi:10.5061/dryad.hk25q4d6). An entire leaf wasremoved from the plant, all fluid from the pitcher wasimmediately drained, and the leaf was immediatelyplaced in silica gel in the field. To avoid sequencinga large proportion of repetitive, plastid DNA, weremoved plastid DNA by following a nuclear DNAextraction protocol, modified to allow for small-scaleextractions from dried leaf material (Rabinowicz et al.1999). Between 0.3 and 0.8 g of dried tissue was groundin liquid N2 and homogenized with a handheld tissuehomogenizer. Cetyl trimethylammonium bromideincubation was performed for 45 min on an orbitalshaker. Isopropanol precipitation was conductedovernight. DNA was quantified on an ND 1000Spectrophotometer (NanoDrop, Wilmington, DE, USA).
RRL ConstructionA modified AFLP protocol (Vos et al. 1995) was
performed in four steps following the protocol ofGompert et al. (2010; all enzymes from New EnglandBiolabs unless otherwise noted): (i) digestion andligation: 250 ng DNA was digested and adaptors wereligated to the digested DNA in an 11 μL reactioncontaining T4 Ligase buffer, NaCl, 5U EcoRI, 5U
MseI, 10 μM EcoR1 adaptor, 10 μM MseI adaptor,1 mg/mL BSA, and 0.5U T4 ligase for 2 h at37◦C (see Vos et al. 1995 for adaptor sequence). (ii)Preamplification: a 20 μL PCR was performed with10 μL of a 10-fold dilution of ligation product, 2 μLof 25 mM MgCl2, 2 μL of 5X Phusion buffer, 0.4 μLof 10 μM dNTPs, 0.06 μL of 100 μM each adaptor-specific primer (EcoR1: 5’ GACTGCGTACCAATTC;MseI: 5’ GATGAGTCCTGAGTAA), and 0.08 μL of5 U/μL Phusion High Fidelity DNA Polymerase(Phusion products: Finnzymes Woburn, MA, USA).The conditions of the PCR program were: 98◦C for2 min, 20 cycles of: 98◦C for 15 s, 56◦C for 30 s, and72◦C for 30 s, with a final extension cycle at 72◦C for 10min. (iii) Size selection: all PCR product was separatedby size on a 1.5% SeaKem gel (Lonza Rockland, ME,USA). A band was cut at 450–600 bp, purified usingthe Qiagen gel extraction kit (Valencia, CA, USA)and eluted with a final volume of 50 μL H2O. (iv)Selective amplification: a selective amplification stepwas completed to add the 454 A and B primingsequences and the 10-bp MID barcodes (Roche) to eachsample and to reduce the portion of the genome to beamplified and sequenced. Barcodes identify individualsand prevent the opportunity that substitution errorwould cause incorrect sample assignment (Hamadyet al. 2008). All barcodes differed by at least two basesfrom any other barcode. We evaluated the effectivenessof one vs. two selective base pairs, where additionalselective base pairs are expected to further reducethe portion of the genome to be sequenced and thusresult in higher coverage per locus. The primers
2012 ZELLMER ET AL.—DEEP PHYLOGEOGRAPHIC STRUCTURE WITHIN S. ALATA 765
were as follows—forward (A Fusion/MID-tag/EcoRISequence): 5′(CGTATCGCCTCCCTCGCGCCATCAG)(XXXXXXXXXX) (GACTGCGTACCAATTC)3′, where Xrefers to the MID-tag location; reverse (B Fusion/MseISequence/Selective Base(s)): 5′(CTATGCGCCTTGCCAGCCCGCTCAG) (CTATGCGCCTTGCCAGCCCGCTCAG)(C(G))3′. Test 454 sequencing runs wereconducted by Engencore (Columbia, SC, USA). Finalselective PCRs and 454 sequencing were performedand purified by gel as above by Research and TestingLaboratories (Lubbock, TX, USA).
Bioinformatic ProcessingThe sequences generated from 454 sequencing were
quality controlled using the RDP Pipeline InitialProcessor (Cole et al. 2007, 2009). We removed allsequence reads <100 bp, those with a quality score<20, and any containing an ambiguous base or lackinga forward primer, reverse primer, or individual tag.The remaining sequences were then sorted by MIDbarcodes and separated by individual. We then usedthe program PRGmatic (Hird et al. 2011) to constructa provisional reference genome (PRG) from our data.PRGmatic clusters all the reads within an individual thenuses the high-coverage clusters as provisional alleles; itthen reduces the provisional alleles into provisional lociby clustering at a lower percent identity to gather allalleles for a given locus together. These loci are thenconcatenated and used as a reference. The program canthen quickly align all the original data to the referenceand call diploid loci for all individuals that surpasscoverage thresholds set by the user. PRGmatic aims tocreate a high confidence set of loci from species thatlack a reference genome so that genotyping and/or SNPcalling tools developed for application in model systems(where phase is known with high confidence in at leastone individual) can be applied to nonmodel systemssuch as S. alata.
We were conservative throughout our bioinformaticsprocessing in order to minimize errors in genotypingthat might subsequently lead to errors in inference. Forexample, we required that an allele be sequenced ata minimum of 5X coverage before including it in thePRG so that we could be confident in the quality of thereference genome even given the relatively high errorrates of 454 sequencing. This parameter has no effect onwhich loci were called in the individuals, but insteaddictates the coverage required within a single individualfor an allele to be used in our PRG. We chose 5X based onpreliminary runs and the desire to minimize discardinggood data while not including spurious or unsupportedalleles. This represents a relatively conservative choice,since the same sequence needs to be almost identicalin one individual at least five times. By using a highcoverage threshold here, we can manipulate the coveragerequired to call the genotype in all individuals, since thePRG was constructed from a minimum of 5X coveragewithin a single individual. Note that any sequence reads
included in the PRG were subject to SNP calling asdescribed below.
We experimented with a variety of settings forclustering, alignment, and SNP calling. We exploredfour different clustering percentages (90%, 92%, 95%,and 98% similarity) to cluster reads into loci that willform the PRG. If the clustering level is set too high,the putative alleles at any given locus will be separatedinto different loci (oversplitting) and as a result fewindividuals will appear heterozygous. However, whenreads are clustered at too low a value of percentsimilarity, separate loci will be falsely combined anda majority of individuals will appear heterozygous(overlumping). For each of the clustering levels, weemployed a BLASTn search against all sequencesin GenBank (accepting alignments with an E-value<1.0×10−4). At a 90% clustering level, the resultingdata aligned to the fewest loci in GenBank, whereas ata 98% clustering level, some loci that were called asindependent by PRGmatic aligned to the same loci inGenBank. Results from the 92% and 95% levels weresimilar to one another, and we chose to analyze thosesequences from the 95% clustering level because thislevel produced the fewest loci that had evidence of beingoversplit or overlumped (based on BLASTn results andvisual evaluation of alignments per the recommendationof Hird et al. 2011). Preliminary population structureanalyses were run using each of the four clustering levelsand the results were similar regardless of clustering levelused. We also explored both liberal and conservativesettings for the number of sequence reads requiredfor calling SNPs within an individual (three versus.six sequences, respectively). Because the liberal settingsoften resulted in high levels of ambiguous bases, weused the conservative settings (i.e., a minimum of 6)for all analyses. Thus, every base in all of the genotypesproduced by PRGmatic was represented by a minimumof six sequence reads in any particular individual.
After determining the individual genotypes for eachlocus, we performed additional quality control steps toremove paralogs and loci with low coverage. Due to thepotential for paralogy, loci were not analyzed if they metone of the following criteria: (i) loci that had alleles ofvarious lengths, which can be evidence of paralogy sincethis is indicative of mutations in one of the restriction cutsites; and (ii) loci that contained any nucleotide positionwith more than two nucleotide bases in an individual.Because the latter can occur from sequencing error aswell, we set a very conservative threshold of 0.0005sites with >2 bp per number of individuals sequencedper locus size. Only loci falling below this conservativethreshold were retained for the analyses. Additionally,this quality control step provided an opportunity toaddress the issue of homopolymer errors. AlthoughPRGmatic does not specifically isolate homopolymererrors, the sequences including homopolymer errorsoften resulted in individuals having >2 bp at anucleotide site. As a result, many of the loci withhomopolymer errors were discarded from the analysesduring this step.
Together, these quality control steps resulted in avery conservative data set with only the highest qualityloci, which we hereafter refer to as the full data set. Wealso calculated a set of basic summary statistics usingthe compute package in libsequence (Thornton 2003),including: Tajima’s D, Fu and Li’s D* and Fu and Li’s F*(Fu and Li 1993). For all phylogenetic analyses (describedbelow), we utilized a reduced data set including onlyloci that were found in all 10 sampled populations.Additionally, since the phylogenetic analyses assumeno recombination within loci, we tested each locus forinternal recombination sites using IMgc (Woerner et al.2007). Loci that showed evidence of recombination werebroken up into their two largest nonrecombining blocks.
Population StructureIndividual and population assignments were
conducted using the program Structure (Pritchard et al.2000). We explored an admixture model to determinethe number of population clusters (k) from 1 through11 using a burn-in of 2.0×106 and 2.0×106 replications.Analyses were repeated five times for each k value. Theaverage and standard deviation (SD) of the likelihoodof each model were used to calculate �k (Evanno et al.2005) using Structure Harvester (Earl and vonHoldt2011), and the partitioning scheme with the highest �kwas selected as the model with the most support.
Outlier LociA Bayesian analysis of molecular variance (BAMOVA;
Gompert and Buerkle 2011) was used to test forpopulation subdivision (�ST) and to identify outlierloci (i.e., loci that fall outside of the expectation forgenome-level �ST). The program Bamova (Gompertand Buerkle 2011) calculates both genome-level andlocus-specific �ST to determine the amount of geneticvariation explained by population substructure takinginto account the presence of loci potentially underselection and also to identify those loci that maybe under selection. The Markov chain was sampledevery 10 steps for 250,000 generations with the first5000 samples discarded as burn-in. The random walkhaplotype frequency vector was used, with additionalparameters set to the default. The posterior distributionsfor the genome- and locus-level �ST (as well astheir associated 95% confidence intervals (CIs)) weresummarized. Loci were considered to be outliers iftheir CIs were completely outside of the genome-level�ST CIs. Loci with �ST values below the genome-levelCIs are thought to be under balancing or purifyingselection, whereas loci above the genome-level CIs areeither under positive selection within populations ordivergent selection among populations (Gompert andBuerkle 2011).
Phylogeographic AnalysesWe conducted two complementary sets of
phylogeographic analyses to estimate the pattern andtiming of diversification of S. alata populations. First,we generated a species tree estimate of the populationphylogeny using *BEAST v 1.6.2 (Drummond andRambaut 2007; Heled and Drummond 2010). Models ofsequence evolution for each locus were chosen usingDT-ModSel (Minin et al. 2003) and set to the closestmatch in *BEAST. We experimented with both strictand relaxed clock (exponential and lognormal) priors(Drummond et al. 2006) for each locus. Although bothsettings produced the same pattern of diversification,the final analyses were conducted with a relaxed clockto allow for heterogeneity in substitution rate acrossloci. *BEAST analyses were conducted using 5.0×108
steps in the Markov chain with the initial 5.0×106 stepsdiscarded as burn-in. TreeAnnotator (Drummond andRambaut 2007) in the BEAST package was used tovisualize the maximum clade credibility phylogeny.
Estimates of phylogeny under the species treeparadigm (Edwards 2009) can be biased by populationdynamics such as gene flow (Eckert and Carstens 2008);consequently, we sought to compare the estimates ofdivergence generated by *BEAST with an estimate ofpopulation divergence that also incorporated gene flow.The program IMa2 (Hey 2010), which jointly estimatesθ (4Neμ), migration, and population divergence, wasutilized to estimate these parameters across the easternand western populations. The run was conducted using aburn-in of 100,000 steps, prior values of θ =30, M=5, τ =10, and a geometric heating scheme using 150 coupledMarkov chains (as described in the user manual).Although the divergence estimates from *BEAST andIMa2 are not exactly equivalent, the comparison allowsat the very least for a qualitative assessment of the degreeto which the phylogenetic estimate of divergence may bemisled by unaccounted-for gene flow. The comparisonof divergence estimates across the Mississippi Riverusing these approaches is further justified by the recentfinding that population substructure does not stronglybias estimates of divergence time using IMa2 (Strasburgand Rieseberg 2011).
Environmental Niche DivergenceWe developed ecological niche models for both eastern
and western populations of S. alata using climate datafor each of the 19 BIOCLIM variables downloadedfrom the WorldClim data set (Hijmans et al. 2004;Hijmans et al. 2005). MaxEnt v 3.3.3e (Phillips et al.2006) was used to produce niche models for the easternand western lineages using 24 known S. alata sites(Fig. 4): the 10 sampling locations plus 14 additionalgeoreferenced S. alata sites either downloaded from theGlobal Biodiversity Information Facility Data Portal(www.gbif.org, last accessed May 25, 2012) or kindlyprovided by other researchers (Horner J., personal
2012 ZELLMER ET AL.—DEEP PHYLOGEOGRAPHIC STRUCTURE WITHIN S. ALATA 767
communication). To prevent pseudo-replication, onlysampling localities that were separated by at least 1 kmwere used in the analyses.
To test for divergence in the niches occupied on eitherside of the Mississippi River, we used a multivariateniche method (McCormack et al. 2010). This methodutilizes a principal components analysis to describeenvironmental variation across multiple niche axes foreach of the georeferenced S. alata localities and forrandomly chosen background locations (including bothsuitable and nonsuitable habitat to illustrate the habitatavailable to either lineage). The average difference in theprincipal components axes are then calculated for theeastern and western populations and evaluated against1000 jackknife comparisons based on the backgrounddata. If the average difference between the eastern andwestern populations for any axis is significantly greaterthan the average difference in the background points,then this suggests that niches in the eastern and westernportions of S. alata’s range are significantly different.Similarly, niche conservatism is supported for any axiswhere eastern and western populations are significantlymore similar to one another than the difference in thebackground points of each.
To conduct the analyses, climate data were extractedfrom the WorldClim data set for both the 24 actualS. alata localities as well as for 850 random backgroundlocalities across the distribution of the species. For thebackground points, a minimum convex polygon wasdrawn between the most distal sampling localities forboth the eastern and western S. alata lineages and 850random background points were chosen within bothof those polygons using Hawth’s Tools (Beyer 2004) inArcGIS V 9.2 (ESRI 2006). Because BIOCLIM variablescan be highly correlated, we evaluated correlationamong each of the variables and removed variablessuch that none of the remaining variables had acorrelation higher than 0.9 or less than -0.9. Our resultingdata set included 10 BIOCLIM variables describingtemperature and precipitation variation across the rangeof S. alata. Statistical analyses were conducted in Statav 11 (StataCorp 2009). Significance in niche differenceswas calculated using t-tests after evaluating for equalityof variances and applying a Bonferroni correction(P=0.007). All principal components axes that explained>1% of the data were retained.
Landscape Genetics AnalysesIn order to determine whether population divergence
should be attributed strictly to the distance separatingpopulations or to barriers to dispersal, we comparedgenetic divergence among populations with bothgeographic distance (isolation by distance) andlandscape-weighted geographic distance (isolation byresistance) among populations (where “landscape-weighted geographic distance” refers to a distancethat accounts for the permeability of the landscapeseparating two populations). Genetic divergence was
quantified using both FST (calculated in GENEPOPwith the full data set; Raymond and Rousset 1995) andpatristic distance (PD; calculated using the phylogenyestimate from *BEAST with the reduced data set).
Both geographic (GEO) and landscape-weightedresistance distance (RD) were calculated usingCircuitscape v 3.5, which uses circuit theory tocalculate the total resistance of the landscape separatingpairs of populations (McRae 2006). To calculateresistance distance, GIS layers are used to create frictionmatrices describing the permeability of the landscapeto dispersal, and each cell on the matrix is given aseparate cost based on the permeability of the landscapefeatures occupying that cell. The major landscapefeature hypothesized to impact landscape permeabilityin this system is water. At a large scale, major rivers andwater bodies (e.g., lakes, Mississippi River) are expectedto impede gene flow and dispersal, whereas at a smallscale, minor rivers and waterways may have little impactor may instead facilitate gene flow among populations(Fig. 1). In this way, we evaluated the effects of bothmajor and minor rivers on genetic divergence amongpopulations. Spatial landscape data were acquired frominland water shapefiles available through DIVA-GIS(www.diva-gis.org, last accessed May 25, 2012) andwere converted into friction matrices using ArcGISv 9.2 (ESRI 2006) by assigning a cost to the differentlandscape features. Because the choice of cost values canimpact the correlation between genetic and landscapedistance, we evaluated a range of costs (1–5000) for boththe major and minor landscape features, starting withthe lowest possible cost (1) and then up until increasesin cost had little to no effect on the results (5000).
Landscape distances were considered to have asignificant effect on genetic divergence if they weresignificantly correlated with genetic distance (Manteltest; Mantel 1967) and remained significant aftercontrolling for geographic distance (partial Mantel test;Smouse et al. 1986). Both Mantel and partial Mantel testswere done in IBDWS (Jensen et al. 2005) with 30,000randomizations. Sequential Bonferroni corrections wereused to evaluate significance (Rice 1989). Partial Manteltests were only performed if the initial Mantel test wassignificant or marginally nonsignificant, since partialMantel tests on nonsignificant Mantel correlations areuninterpretable.
In addition, we evaluated whether environmentaldifferences among the sampling localities havecontributed to divergence of the sampled populations(isolation by environment) by assessing the correlationbetween genetic divergence and pairwise environmentaldifferences between each of the 10 sampled populations.Environmental difference was calculated as the absolutepairwise difference among populations in principalcomponents scores for each of the axes describingvariation in climate data (see ‘Environmental nichedivergence’ section above). Mantel tests were usedto evaluate the correlation between genetic andenvironmental distances, and partial Mantel tests wereused to evaluate whether this correlation remained
significant after controlling for geographic distanceamong populations. We used the results from thelandscape genetics analyses to inform the isolationby environment analyses, using only the genetic andgeographic distance measures with the highest R-valuein the aforementioned landscape genetics tests for theMantel and partial Mantel tests.
RESULTS
Next-Generation SequencingOver all runs (2½ 454 plates; online Table S2,
available from Dryad data repository; doi:10.5061/dryad.hk25q4d6), we obtained 1,044,772 useablesequences (defined as high quality, tagged, and withboth primer regions sequenced) across 86 individualswith an average length of 282.1 bp per sequence. Atthe 95% clustering level with 6X coverage for SNPcalling, PRGmatic identified 1821 loci. Of these, 125were aligned to existing GenBank sequences (E-value<1.0×10−4) using BLAST (Altschul et al. 1990). Afterthe quality control steps, removing loci with no variation(3 loci), with too few individuals (1576 loci), or withpotential paralogs (166 loci), and removing individualswith low coverage (8 individuals), we ultimatelyanalyzed 76 loci across 82 individuals. In total, thesedata represent ∼29 kb of data, averaging 381 bp inlength, and containing an average of 10 SNPs perlocus (online Table S3, available from Dryad datarepository; doi:10.5061/dryad.hk25q4d6). Of the lociused in the 76 locus data set, 5 loci had significantBLASTn matches, and 100% of those were to plantsequences (online Table S4, available from Dryaddata repository; doi:10.5061/dryad.hk25q4d6). These76 loci were utilized for population genetic analysesand for initial screening of the loci. In our finalindividual-by-locus matrix (86 individuals by 76 loci,online Table S5 and Fig. S1, available from Dryad datarepository; doi:10.5061/dryad.hk25q4d6), 1545 cells(23.6%) had coverages >6 sequences; 2472 cells (37.8%)had coverages greater than zero but <6 sequences (thus,were excluded from further analysis); 2519 cells (38.5%)had no sequencing reads recovered. Alignments of the76 locus data set were deposited in GenBank underaccession numbers JN665096–JN667881. Of those 76 loci,there were 10 loci that were sampled in all 10 samplinglocalities. These loci were all visually inspected. Threeof the 10 loci showed significant evidence of containingrecombination blocks using IMgc (Woerner et al. 2007),and were subsequently broken up into two loci each.A total of 13 loci were thus utilized for the phylogeneticanalyses.
Population Genetic StructureLikelihood values from the structure analyses
increased as sample partitioning increased (Fig. 2a) andleveled off at approximately k =7, with the highest
mean likelihood at k =9 and the highest likelihoodfor a single run at k = 8. The highest �k value wasfound at k =3 (Fig. 2b), with the three identified clustersbeing: (i) all populations east of the Mississippi River;(ii) the Kasatchi sampling location (the most northernpopulation in the west); and (iii) the remaining fourwestern populations (online Fig. S2, available fromDryad data repository; doi:10.5061/dryad.hk25q4d6).Additional �k peaks were found at k =7 and k =9,suggesting there is hierarchical substructure (Fig. 2b).
Outlier LociThe BAMOVA estimated a genome-level �ST value
of 0.63, suggesting that 63% of the genetic variationwas partitioned among populations (95% CI: 0.59–0.67).Consistent with summary statistics calculated usingcompute, the BAMOVA suggested that some of theloci were under either positive or purifying selection(online Table S3, available from Dryad data repository;doi:10.5061/dryad.hk25q4d6). However, only two lociwere identified by at least one of the summarystatistics and the BAMOVA, suggesting that many of thesignificant results from summary statistics may be falsepositives.
that eastern and western populations are eachmonophyletic, with very high support (posteriorprobability = 1.0). Effective sample sizes (ESSs) werehigh (posterior ESS > 4217) suggesting that the Markovchains were convergent across parameter values.Population divergence mirrored the structure resultsin that the deepest nodes of the population phylogenycorresponded to the partitioning of samples at thelowest k values (online Fig. S2, available from Dryaddata repository; doi:10.5061/dryad.hk25q4d6). Thediversification of S. alata populations also mirroredthe physical division of the landscape by major rivers(Fig. 3). If one is willing to assume the genome-wideestimate of mutation rate from Arabidopsis thalianaof 7.0×10−9 substitutions per site per generation(Ossowski et al. 2010) for divergence dating in S. alata,divergence among the eastern and western lineages isestimated to be ∼60,000 generations before present.
The two population (east and west) IMa2 analysiswas conducted for ∼1.1 × 106 generations following theburn-in. Effective samples sizes were moderate (Log[P]=57, τ =59, remainder > 150), but plots of the sampledparameter values did not exhibit trends, suggestingthat the Markov chain sampled from a stationaryposterior distribution of parameter values. If mutationrates of Arabidopsis thaliana are assumed (Ossowskiet al. 2010), population divergence is estimated to be∼279,567 (95% highest posterior density = 113,107–428,954) generations before present. Results also suggest
2012 ZELLMER ET AL.—DEEP PHYLOGEOGRAPHIC STRUCTURE WITHIN S. ALATA 769
a)
b)
FIGURE 2. Population clustering analyses suggest significant population structure among S. alata populations. A) Likelihood scores for eachvalue of k genetic clusters from Structure (Pritchard et al. 2000). B) �k scores for each value of k genetic clusters following Evanno et al. (2005).Figures were generated using Structure Harvester (Earl et al. 2011).
FIGURE 3. Maximum Clade Credibility tree for the 10 sampled populations generated using *BEAST (Drummond and Rambaut 2007; Heledand Drummond 2010). The population phylogeny is shown at top, with posterior probabilities of each node shown. Scale bar to left of phylogenycorresponds to 5.0 × 10−5 substitutions / site / generation. The bottom of the figure shows histograms generated by Structure (Pritchard et al.2000) for the sampling localities (i.e., k =10).
TABLE 1. Results from IMa2 analysis, showing estimates and95% highest posterior density (HPD) values of divergence time (τ = tμ,where t is the number of generations of divergence) among eastern (E)and western (W) lineages, ancestral (θA = 4Neμ) and current (θE andθW ) population sizes, as well as migration rates between populations
that effective population sizes in the eastern populationsare larger than either the western or the ancestralpopulations, with low estimates of gene flow from westto east and slightly higher gene flow from east to west(Table 1).
Environmental Niche DivergenceClimate varied significantly between S. alata
populations to the east and west of the MississippiRiver. Niche models for the western populationspredicted distribution in the west with small overlapinto the eastern range, whereas the eastern distributionwas predicted almost exclusively to the east of theMississippi River (Fig. 4a). The principal componentsanalysis also showed significant variation among theenvironmental conditions of the populations east andwest of the Mississippi River. The first seven principalcomponents accounted for ∼99% of the variation in the10 BIOCLIM variables (Table 2).
Using the multivariate niche method, we foundevidence for both niche divergence and conservatismacross the Mississippi River (Figs. 4b,c; Table 2).The differences among the eastern and westernpopulations in both PC1 (mean divergence =3.25) andPC6 (mean divergence =0.70) were significant (t-testswith Bonferroni correction: P<0.007) and significantlygreater than expected based on the difference in thebackground data (background mean and 95% CIs: PC1= 2.52 (2.39–2.63), PC6 = 0.09 (0.05–0.13); P<0.01),suggesting divergence. In comparison, there was nosignificant difference between the eastern and westernpopulations for either PC2 (0.631) or PC3 (0.338), andthese differences were significantly less than expectedbased on the background data (PC2 = 2.13 (2.03–2.22),PC3 = 0.78 (0.71–0.85); P < 0.01), suggesting nicheconservatism.
Landscape GeneticsThere was a significant positive correlation between
genetic divergence and geographic distance, regardlessof whether FST or PD was used as the measure ofgenetic divergence (FST: R=0.36, P=0.019; PD: R=0.86,P<0.0001; online Table S6, available from Dryad data
repository; doi:10.5061/dryad.hk25q4d6), resulting in asignificant pattern of isolation by distance. Although thetrends were similar between FST and PD, PD showeda stronger correlation (higher R-value) than FST witheach of the alternative models of geographic distance(online Table S6, available from Dryad data repository;doi:10.5061/dryad.hk25q4d6). We, therefore, focus onlyon the results of the analyses using PD as the measureof genetic divergence.
There was also significant evidence of isolation byresistance. Although there was a significant correlationbetween PD and resistance distance (RD) for eachof the alternative geographic distance models (onlineTable S6, available from Dryad data repository;doi:10.5061/dryad.hk25q4d6), the results suggest thatonly major rivers and water bodies have a significantimpact on genetic divergence. First, the R-values for allRD models including major rivers were greater thanthe R-value for geographic distance (GEO), whereas theR-values for RD models including minor rivers were allless than the R-value for geographic distance. Second,increasing cost values resulted in increasing R-values forRD models with major rivers, whereas for RD modelsincluding minor rivers increasing cost values led todecreasing R-values (online Table S6, available fromDryad data repository; doi:10.5061/dryad.hk25q4d6).Lastly, partial Mantel tests confirm that RDs based onmajor rivers are a better predictor of genetic divergence.For RDs based on major rivers (except in the caseof the lowest cost model), the correlation betweengenetic distance and RD remained significant aftercontrolling for GEO, whereas the correlation betweengenetic distance and GEO was not significant aftercontrolling for RD (online Table S6, available fromDryad data repository; doi:10.5061/dryad.hk25q4d6).The opposite pattern was observed for partial Manteltests of RDs based on minor rivers: the correlationbetween genetic distance and RD was not significantafter controlling for GEO, whereas the correlationbetween genetic distance and GEO was significant aftercontrolling for RD (online Table S6, available fromDryad data repository; doi:10.5061/dryad.hk25q4d6).Overall, the model with the best support was theRD model including major rivers at a cost of5000 (R=0.89, P<0.0001). This RD model remainedsignificantly correlated with genetic divergence aftercontrolling for geographic distance (R=0.51, P=0.008),whereas geographic distance was not significantlycorrelated with genetic divergence after controllingfor this RD model (online Table S6, available fromDryad data repository; doi:10.5061/dryad.hk25q4d6).Consequently, this RD model was used for subsequentanalyses.
The results for the test of isolation by environmentwere inconclusive. Genetic divergence (PD) andenvironmental distance (pairwise absolute differencein principal components scores) among populationswere significantly positively correlated for one principalcomponent axis, PC1 (Mantel test: R=0.32, P<0.002), marginally nonsignificant for two axes, PC5
2012 ZELLMER ET AL.—DEEP PHYLOGEOGRAPHIC STRUCTURE WITHIN S. ALATA 771
a)
b) c)
FIGURE 4. Environmental niche models and environmental variation for eastern (blue) and western (red) populations of S. alata. a) Divergencein niches across the Mississippi River. Predictions were calculated using MAXENT v 3.3.3e (Phillips et al. 2006), with darker colors showing greaterprediction scores. The thick black line denotes the Mississippi River. b) Principal components axes 1 and 6 show significant niche divergence, c)whereas axes 2 and 3 show significant niche conservatism. WS: western sampled points; WB: western background points; ES: eastern sampledpoints; EB: eastern background points.
(R=0.21,P<0.053) and PC7 (R=0.26, P<0.055; Table 3),and nonsignificant for the other four PC axes. However,partialMantel correlations show that these correlationsare mostly due to the distance separating populationsrather than the environmental differences amongpopulations. The correlation between genetic distanceand landscape distance (RD) remained significantafter controlling for environmental differences (e.g.,PC1, PC5, and PC7), whereas the correlation betweengenetic distance and environmental distance waseither nonsignificant (e.g., PC1) or was marginallynonsignificant (e.g., PC5 and PC7; Table 3) aftercontrolling for RD.
DISCUSSION
Similar to other Gulf Coast organisms (e.g., Soltiset al. 2006), the range of Sarracenia alata is dividedby the Atchafalaya Swamp and Mississippi River. Incontrast to previous investigations, which did notidentify diagnosable differences of either morphologicalcharacters (Sheridan 1991) or ITS sequences (Neyland2008), here we identified substantial divergence (at least
60,000 generations before present; Fig. 3) between easternand western populations of S. alata and determinedthat these populations have been isolated by majorrivers. Our results were consistent with previouswork that identified population genetic structure usingmicrosatellite markers (Koopman and Carstens 2010);however, it is still prudent to explore our approach insome detail given that the application of next-generationsequencing to phylogeographic investigations is notas well understood as genotyping microsatellites orsequencing genes such as ITS or chloroplast DNA.
Phylogeography of S. alataEarly phylogeographic investigations applied ‘tree
thinking’ to population level variation (e.g., Avise 2000)by estimating genealogies from organellar genes andassuming that the pattern of coalescence reflected thepattern of population diversification (e.g., Avise et al.1987). Over time, phylogeographers became convincedthat single locus data were unlikely to adequately trackthe history of population divergence (Edwards andBeerli 2000; Hudson and Coyne 2002; Knowles 2004). It is
Notes: Loadings for each variable on the seven retained principal components axes for the multivariate niche assessment. Niche differences amongthe eastern and western lineages as well as among the background environments are listed at the bottom of the table. The proportion of varianceexplained by each axis is listed in parentheses. The test for significance in niche differences between the eastern and western populations basedon locality data alone is indicated by an asterisk. The test for niche divergence versus niche conservatism based on the background environmentaldata is indicated by either a D (axes with significant divergence relative to the background) or a C (axes with significant niche conservatismrelative to the background).aNiches differ significantly (t-test, Sequential Bonferroni adjustment (Rice et al. 1989)).
TABLE 3. Correlation between environmental distance (pairwise absolute difference in principal components scores, PC1-PC7) and geneticdistance (Patristic Distance)
Envt variable Mantel test Partial Mantel tests
EnvtDis vs. Genetics RD vs. Genetics (controlling for EnvtDist) EnvtDist vs. Genetics (controlling for RD)
R P R P R PPC1 0.315 0.002* 0.879 0.000* 0.097 0.211PC2 –0.018 0.487 — — — —PC3 0.053 0.316 — — — —PC4 0.125 0.163 — — — —PC5 0.207 0.053 0.893 0.000* 0.241 0.028PC6 0.090 0.248 — — — —PC7 0.263 0.055 0.894 0.000* 0.310 0.041
Partial Mantel results show the correlation between landscape distance (RD) and genetic distance controlling for environmental distance(EnvtDist) as well as for EnvtDist and genetic distance controlling for RD. The RD model used included major rivers with a cost of 5000, which wasthe best-fit model from the landscape genetics analyses. Significance at P< 0.05 is indicated by italics. Significance after sequential Bonferroniadjustment is indicated with an asterisk. Partial Mantel tests were only performed on axes that had significant or marginally nonsignificantMantel correlations.
time to revisit this question. Methodological advances inspecies tree phylogeny estimation (e.g., Edwards 2009),particularly when coupled with the larger data setsmade possible by new sequencing technologies, allowus to directly estimate population divergence to a degreepreviously impossible. Although the general utility of
tree thinking (e.g., Smouse 1998) for any given systemshould still be explored, it seems particularly applicableto our focal taxa. In S. alata, rivers appear to divide thespecies into several isolated populations, as evidencedby the structure analyses and the correlation betweengenetic isolation and resistance distance, and it seems
2012 ZELLMER ET AL.—DEEP PHYLOGEOGRAPHIC STRUCTURE WITHIN S. ALATA 773
reasonable to represent the divergence among thesepopulations as a phylogenetic tree. Further, our resultsare also consistent with aspects of S. alata’s life history(e.g., low rates of seed dispersal and establishment)as well as previous investigations using microsatellites(Koopman and Carstens 2010).
The deep divergence among populations using*BEAST implies that a phylogenetic model is appropriatefor these data. Divergence between eastern and westernpopulations is estimated to be at least 60,000 generationsbefore present (Fig. 3) on the basis of the phylogenyestimate from *BEAST. In addition, analysis using acoalescent model suggests that population divergenceacross the Mississippi River is substantial (Table 1). It isworth emphasizing that for our system these modelsare each inadequate in their own manner. *BEAST doesnot parameterize gene flow, and while estimates of thisparameter from IMa2 are low, it is possible that allelesharing across lineages due to gene flow could leadto estimation error in species tree methods. Althoughsimulations indicate that topology can be accuratelyestimated when gene flow occurs among sister lineages(Eckert and Carstens 2008), increased polymorphismthat results from unaccounted for gene flow may leadto errors in estimation of branch lengths (and thusthe estimates of divergence). IMa2, on the other hand,estimates divergence and gene flow simultaneouslyand can accommodate more than two populations;however, we were unable to achieve good resultswhen attempting to estimate parameters using thephylogeny of the 10 populations investigated here. Giventhese shortcomings, we are not satisfied with absoluteestimates of divergence generated by either method,but still hope to make some broad inferences regardingdiversification within S. alata.
Given that population divergence between the easternand western S. alata populations is estimated to beat least 60,000 generations, what can we infer aboutthe absolute timing of diversification in this group?Although there are no direct estimates of generationlength in Sarracenia, the plants are long-lived (Brewer2001), so even a conservative estimate of generationlength (e.g., 2 years) would indicate that eastern andwestern S. alata began to diverge well into the Pleistocene.Although the deepest subdivision within S. alata doesnot predate the formation of the Mississippi River,which is thought to have originated well before thePleistocene (Mann and Thomas 1968; Cox and VanArsdale 1997), it may correspond to dramatic shifts inthe river’s course that occurred during the Pleistocene.Finally, our finding that the Mississippi River representsa major phylogeographic break in S. alata is consistentwith findings in co-distributed species (Soltis et al. 2006;Jackson and Austin 2010).
At a smaller scale, sampled populations also showsignificant divergence that likewise corresponds to majorrivers (online Table S6, available from Dryad datarepository; doi:10.5061/dryad.hk25q4d6), particularlyfor the more basal splits, and to habitat boundaries, forthe shallower divergences (online Fig. S2, available from
Dryad data repository; doi:10.5061/dryad.hk25q4d6).Additionally, the BAMOVA estimates indicate thatover 63% of the genetic variation can be attributedto among-population variation for the 10 sampledpopulations. This amount of genetic variation is veryhigh compared with previous studies using BAMOVA(e.g., Gompert et al. 2010), further suggesting thatS. alata populations are highly structured. The generalcongruence among these results supports the previouslyproposed hypothesis (Koopman and Carstens 2010) thatriverine barriers are the major factor that promotespopulation divergence in S. alata.
These results also have implications for evolutionthroughout the genus Sarracenia. Historical biogeo-graphic hypotheses for this genus suggest potentialexpansion along the east coast of the United Statesfollowing the retreat of the Pleistocene glaciers (Oard1997). Many Sarracenia species are distributed along theeastern Gulf and Atlantic coasts of the United States;in this region, there are many broad rivers, and eachof these rivers has the potential to bisect a Sarraceniaspecies. Species such as S. psittacina, S. oreophila, S. flavaand S. minor are all bisected by major rivers; it is thereforereasonable to hypothesize that each may contain a largeamount of cryptic genetic subdivision.
Environmental Differentiation within S. alataAlthough genetic structure among S. alata populations
appears to be largely due to neutral divergence acrossmajor rivers and with increasing geographic distance,there are significant environmental differences amongpopulations, which may contribute to populationdivergence. At a broad scale, we found significantniche differences among the eastern and westernlineages for two axes (PC1, PC6) of environmentalvariation (Fig. 4b; Table 2), and these differencesare significantly greater than differences in randomlysampled background localities (Table 2). Despite thefact that this species usually inhabits longleaf pinesavannahs, the climatic conditions of the habitats oneither side of the Mississippi River are significantlydifferent. Surprisingly, however, the strongest axis ofenvironmental difference does not appear to be alonga longitudinal axis across the Mississippi River. Thedivergence across PC1 (tradeoff between temperatureannual/diurnal range and annual mean temperature),which accounts for most of the variation amongpopulations, is primarily due to differences between thecoastal populations and the interior populations. This isfurther reflected in the niche models, where the westernniche model encompasses the western populations plusall interior eastern populations, whereas the easternniche model is centered on the coastal populations, withthe interior eastern populations showing low predictionscores (Fig. 4). Thus, it appears that if selection isoccurring in this system due to abiotic factors, it is likelydue to differences between coastal and inland climaticconditions as opposed to arbitrarily bisecting into east
and west habitats. Future studies focusing on the inlandversus coastal populations will be necessary to furtherinvestigate the effects of these environmental differenceson genetic divergence.
When considering genetic variation among the10 sampled populations, rather than the morebroadly defined eastern and western habitats, there islimited evidence for the presence of local adaptation.Populations with greater environmental differences(absolute difference in PC5 and PC7) show only amarginally nonsignificant trend toward increasedgenetic divergence (PD) after controlling for landscapedistance (Table 3). At this smaller scale, PC5 and PC7(differences in the mean temperature of the wettestquarter and mean temperature of the driest quarter,respectively; Table 2), rather than PC1 or PC6, may bemore important as drivers of diversification among localpopulations. The lack of strong evidence for a correlationbetween local environmental and genetic divergenceafter controlling for landscape distance may indicate thatthere either has not been sufficient time for accumulationof genome-wide population subdivision due toenvironmental differences or else that 10 populationsmay not be sufficient to evaluate correlations in localenvironmental variation and genetic divergence.
Lastly, the presence of significant values for thevarious molecular tests of selection (D, D*, and F* and�ST outliers; online Table S3, available from Dryad datarepository; doi:10.5061/dryad.hk25q4d6) also indicatesthat natural selection may be influencing the patternof genetic variation across these populations. Althoughthe results of these analyses are subject to type I errorsand can be confounded by demographic forces suchas population size change (e.g., Hammer et al. 2003)or population substructure (Excoffier et al. 2009), theconsistency among the various tests of selection suggestthat our results are robust. Future research will focuson screening these loci in order to identify the nature ofselection operating within S. alata, with a particular focuson responses to environmental differences between thecoastal and inland populations.
Roche 454 Sequencing of an RRLNext-generation sequencing has been quickly
transforming many areas of evolutionary biology, yetthese new technologies have been slower to come tothe field of phylogeography. This delay may be due todifficulties bringing these novel sequencing methodsto nonmodel organisms, obtaining orthologous lociamong individuals, affordably sequencing multipleindividuals, and the need for long sequence readsfor gene-tree analyses (McCormack et al. 2011). Next-generation sequencing of RRLs (e.g., Gompert et al. 2010)using individual barcodes added to sequences via PCR(Williams et al. 2010), in conjunction with bioinformaticstools that reduce the need for reference genomes (e.g.,Hird et al. 2011), provides an efficient and inexpensivemethod to generate a large amount of sequence
data for nonmodel organisms. However, severalchallenges must be met before this approach can bewidely utilized in phylogeographic analyses, includingboth preventing and identifying paralogous loci,distinguishing sequencing error from rare variants,and reducing the presence of and understanding theconsequences of missing data in these large data sets.
Because the restriction sites used to reducethe representation of the genome are inheritedfrom common ancestors, the preparation of RRLsusing restriction enzymes is susceptible to themisidentification of loci that are actually paralogousas single copy. This misidentification would havesubstantial effects on various analyses, as levels ofheterozygosity would be inflated, which in turn mayinflate estimates of important parameters such asdivergence time. The use of a double-digest method,as done here, aids in the avoidance of paralogous loci.Regardless, we were extremely conservative when weexamined the initial set of loci and removed a largenumber of loci from the data set, including all locithat showed any evidence of being paralogous (see‘Methods’ section). Although we reduced the size ofour data set and ultimately analyzed data derived fromfewer than 18% of the sequence reads, we are alsoconfident that those loci that were analyzed representhigh-quality data that are not likely to mislead ourinferences regarding the phylogeography of S. alata.For study systems with lower levels of repetitive DNA,this method has much potential for identifying a largenumber of loci across multiple individuals. However,for systems where high levels of paralogy are expected,other methods of library construction may be preferable,such as PCR amplicon sequencing (e.g., Binladen et al.2007; Meyer et al. 2008; Tewhey et al. 2009) or targetenrichment (e.g., Albert et al. 2007; Okou et al. 2007;Gnirke et al. 2009).
Another major issue with anonymous loci generatedvia next-generation sequencing methods is that it isdifficult to identify high-quality, high-coverage errorsas errors or low-quality, low-coverage polymorphismas real variants. Currently, both probability and qualityscores are used to construct a working data set from next-generation sequencing data, in an attempt to minimizeboth the number of errors retained and good readsdiscarded. Unfortunately, setting thresholds (e.g., forcoverage and quality scores) may bias analyses andinferences, since using values too low will includeerrors, but setting cutoffs too high will exclude realvariation. Thresholds are admittedly imperfect, but thegenome is heterogeneous and different values apply todifferent loci; it is currently impossible to determineindividual parameters for each locus. Here, we setseveral different thresholds and analyzed the resultsto determine which set of parameters led to the mostconservative and robust data set (see ‘Methods’ section).For an enhanced picture of the genome and increasedconfidence in the data, SNPs could be validatedthrough resequencing or alternate restriction enzymescould be used to evaluate a second set of loci from
2012 ZELLMER ET AL.—DEEP PHYLOGEOGRAPHIC STRUCTURE WITHIN S. ALATA 775
some individuals. However, these methods are costly,and we therefore encourage researchers to carefullyconsider the distribution of their resources with theseissues in mind.
Although the RRL method utilized here allowedus to recover sequences from 76 high confidenceloci in some individuals, including 13 loci from allsampling localities, there was incomplete coverageacross individuals for the much larger set of ∼1800 lociidentified by PRGmatic. The low coverage is an issuefor two reasons: because of the possible impact on thegenetic analyses and because it represents an inefficientuse of the sequence reads. The presence of missing datawithin a data matrix can have significant consequenceson the results obtained from some analyses, such asestimates of summary statistics (e.g., π or Tajima’s D)as well as estimates of both branch lengths andtree topology (Lemmon et al. 2009). However, sincecoalescent theory assumes that alleles are sampled atrandom from a population, estimates generated usingcoalescent models (e.g., *BEAST, IMa2) should not beaffected by missing data per se, although the quality ofsuch estimates may be correlated with the number ofsampled alleles. Similarly, because BAMOVA accountsfor stochastic sampling in the likelihood function,estimates of �ST should not be biased by missingdata, but may result in lower confidence (althoughsimulations verifying this have, to our knowledge, notbeen published). A bigger concern for researchers withlimited budgets is the inefficiency that results fromthese missing data. Regardless, our results were largelyconsistent with both biological predictions for thisorganism as well as previous genetic work with completedata sets (e.g., Koopman and Carstens 2010), suggestingthat incomplete data sets due to variation in next-generation sequencing efforts among individuals andloci may not prohibit the recovery of phylogeographicpatterns in other studies using similar methods.
With roughly 1.0×106 sequence reads resulting fromour sequencing effort divided among 80 individuals,we would expect ∼1.25×104 reads per individual and∼160X coverage over 76 loci, assuming equal molarconcentrations of input samples and sequencing of onlythese loci. Although we do not yet know the genomesize of S. alata, the closely related diploid S. flava has beenreported to have an unexpectedly large genome (Hansonet al. 2005). If S. alata has a similarly large genome, itcould explain our difficulty in obtaining a more completedata set for all loci. For species with smaller genomesizes, this method may provide much more completedata sets. In addition, we note there are some aspectsof library construction that could be improved to thisend. One possibility is to select a smaller base pair sizerange to excise from the agarose gel in order to increasethe coverage across loci. However, a smaller size rangerequires high precision in order to insure overlap in thefragments excised for each sample. Such precision canbe difficult to achieve. Second, methods that reduce thenumber of PCR steps needed prior to next-generationsequencing (e.g., Kozarewa et al. 2009) will have fewer
issues with PCR bias and as a result should providemuch more complete data sets. Finally, for samples thatrequire amplification via PCR, emulsion methods (e.g.,Williams et al. 2006) produce libraries that are far lessbiased and therefore improve representation across loci.
CONCLUSIONS
As in many organisms that occupy the Gulf Coastof North America, the distribution of S. alata isdivided by the Mississippi River and Atchafalayaswamp. Analysis of sequence data collected using next-generation sequencing suggests that populations inthe eastern and western portions of the range havebeen isolated for tens of thousands of generations.Furthermore, it appears that there is deep divergencewithin the eastern and western populations and thatthis divergence is associated with major rivers. Althoughrecent human-induced habitat fragmentation has likelyreduced the census size of S. alata populations andcontributed to the formation of population geneticstructure at a local scale, landscape processes thatpredate human settlement of the region appear to bethe dominant factor responsible for structuring geneticdiversity in this species.
SUPPLEMENTARY MATERIAL
Data and other supplementary material havebeen deposited in the Dryad data repository underdoi:10.5061/dryad.hk25q4d6 (www.datadryad.org).
FUNDING
This work was supported by grants from the LouisianaBoard of Regents and from the National ScienceFoundation [DEB - 0956069 to B.C.C].
ACKNOWLEDGMENTS
We thank V. Smith and M. Olinde for assistancewith collection permits, J. Horner for providing spatialdata describing S. alata populations, S. Dowd and J.McCormack for assistance with sequencing, and D.Fuselier for assistance with field collection of S. alata. Wethank the Nature Conservancy for access to samplinglocalities, and members of the Carstens lab for theirassistance and discussions regarding various aspects ofthis investigation. We thank E. Moriarty Lemmon andtwo anonymous reviewers for helpful comments on themanuscript.
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