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Molecular Phylogenetics and Evolution 93 (2015) 234–248

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Molecular Phylogenetics and Evolution

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Comparison of five methods for delimitating species in Ophion Fabricius,a diverse genus of parasitoid wasps (Hymenoptera, Ichneumonidae)q

http://dx.doi.org/10.1016/j.ympev.2015.08.0031055-7903/� 2015 Elsevier Inc. All rights reserved.

q This paper was edited by the Associate Editor S.L. Cameron.⇑ Corresponding author at: Canadian National Collection of Insects, Arachnids

and Nematodes, 960 Carling Ave., Ottawa, Ontario K1A 0C6, Canada.E-mail address: [email protected] (M.D. Schwarzfeld).

Marla D. Schwarzfeld ⇑, Felix A.H. SperlingDepartment of Biological Sciences, CW 405 Biological Sciences Building, University of Alberta, Edmonton, Alberta T6G 2E9, Canada

a r t i c l e i n f o

Article history:Received 6 January 2015Revised 31 July 2015Accepted 4 August 2015Available online 8 August 2015

Keywords:ABGDCompensatory base changeDNA barcodingGMYCITS2Poisson tree processesSpecies delimitation

a b s t r a c t

DNA taxonomy has been proposed as a method to quickly assess diversity and species limits in highlydiverse, understudied taxa. Here we use five methods for species delimitation and two genetic markers(COI and ITS2) to assess species diversity within the parasitoid genus, Ophion. We searched for compen-satory base changes (CBC’s) in ITS2, and determined that they are too rare to be of practical use in delim-iting species in this genus. The other four methods used both COI and ITS2, and included distance-based(threshold analysis and ABGD) and tree-based (GMYC and PTP) models. We compared the results of theseanalyses to each other under various parameters and tested their performance with respect to 11Nearctic species/morphospecies and 15 described Palearctic species. We also computed barcode accumu-lation curves of COI sequences to assess the completeness of sampling. The species count was highly vari-able depending on the method and parameters used, ranging from 47 to 168 species, with moreconservative estimates of 89–121 species. Despite this range, many of the Nearctic test species were fairlyrobust with respect to method. We concluded that while there was often good congruence betweenmethods, GMYC and PTP were less reliant on arbitrary parameters than the other two methods and moreeasily applied to genetic markers other than COI. However, PTP was less successful at delimiting test spe-cies than was GMYC. All methods, as well as the barcode accumulation curves, indicate that severalPalearctic species remain undescribed and that we have scarcely begun to appreciate the Nearctic diver-sity within this genus.

� 2015 Elsevier Inc. All rights reserved.

1. Introduction

Species are a basic unit in studies of ecology, biogeography, andevolution, and are essential to applications in conservation or bio-logical control (Claridge et al., 1997; Sites and Marshall, 2004). Butmost of Earth’s species remain undescribed, while taxonomists aretoo few and traditional taxonomy is too slow to deal fully with thisunknown diversity (Brooks and Hoberg, 2000; Godfray, 2002;Wheeler, 2004). With the advent of rapid DNA sequencing, molec-ular taxonomy has been proposed for quickly assessing speciesdiversity in diverse, little-known taxa (Blaxter, 2004; Pons et al.,2006; Tautz et al., 2003). In particular, the Barcode of Life projecthas popularized use of half of the cytochrome oxidase I (COI) geneof mitochondrial DNA as a standardized ‘‘barcode” for speciesidentification and discovery (Hebert et al., 2003a,b; Miller, 2007).

Many studies using DNA taxonomy, particularly of the COI gene,have delimited species with distance-based clustering methods.For example, species have been defined as terminal monophyleticclusters on neighbor-joining trees (Hajibabaei et al., 2006), by astandard interspecific threshold of 1–3% (Hebert et al., 2003a,b,2004; Smith et al., 2009, 2013; Stalhut et al., 2013;Strutzenberger et al., 2011; Tang et al., 2012), or by an interspecificdistance of 10X the intraspecific distance (Hebert et al., 2004).While it is widely acknowledged that a single threshold divergencewill not apply to all taxa (Cognato, 2006; Hendrich et al., 2010;Monaghan et al., 2009), pre-defined thresholds have nonethelessbeen implicitly or explicitly used as criteria for assessing speciesdiversity in a number of studies (Barrett and Hebert, 2005;Hebert et al., 2003a; Smith et al., 2009, 2013; Strutzenbergeret al., 2011). As well, the universality of barcoding gaps is contro-versial, with several studies finding significant overlap betweenintra- and interspecific divergences (Meyer and Paulay, 2005;Wiemers and Fiedler, 2007). The challenge of objectively determin-ing this gap led to the development of the automated barcode gapdiscovery (ABGD) algorithm, which infers confidence intervals forintraspecific divergences, and recursively partitions the sequences

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M.D. Schwarzfeld, F.A.H. Sperling /Molecular Phylogenetics and Evolution 93 (2015) 234–248 235

according to the empirically observed divergences (Puillandreet al., 2012a).

Distance-based methods have been criticized for being purelyphenetic, rather than incorporating evolutionary theory (DeSalle,2007; Rubinoff et al., 2006; Will and Rubinoff, 2004; Will et al.,2005). Some explicitly evolutionary approaches have been devel-oped to address this concern, such as coalescence or tree-basedmethods, and these may be used to delimit large numbers of spe-cies (Carstens and Dewey, 2010; Fujita et al., 2012; Leaché andFujita, 2010; O’Meara, 2010; Yang and Rannala, 2010). One of thesemethods, the generalized mixed Yule coalescent (GMYC) model,determines the point at which coalescent branching patterns(within species) transition to Yule patterns (between species)(Fontaneto et al., 2007; Pons et al., 2006). This method has shownpromise for identifying species boundaries in diverse, little knowntaxa (Astrin et al., 2012; Ceccarelli et al., 2012; Monaghan et al.,2009); however, the results depend on the choice of parametersused to construct the ultrametric tree (Ceccarelli et al., 2012).The Poisson Tree Processes model (PTP) is another tree-basedmethod that uses a non-ultrametric phylogenetic tree as input,thus avoiding the challenges of constructing an accurate ultramet-ric tree (Zhang et al., 2013). The method delimits species under theassumption that the number of substitutions within a species issignificantly lower than the number of substitutions betweenspecies.

Regardless of the delimitation method used, reliance on a singlegenetic marker may give misleading results (Dupuis et al., 2012).While COI has many advantages for studies of species delimitation,such as its ease of amplification and relatively rapid rate of muta-tion (Li et al., 2010; Monti et al., 2005; Williams et al., 2012), it maynot accurately delimit species due to factors such as introgression,retained ancestral polymorphisms, or coamplification of nuclearpseudogenes (numts) (Cognato, 2006; Dupuis et al., 2012; Funkand Omland, 2003; Meier et al., 2006; Schmidt and Sperling,2008). It is therefore important to use additional data, such as fromother genes or morphology, in an integrative taxonomic analysis tomore accurately delimit species (Dayrat, 2005; Roe and Sperling,2007; Schlick-Steiner et al., 2010; Schwarzfeld and Sperling, 2014).

The internal transcribed spacer 2 (ITS2) of ribosomal RNA isanother rapidly evolving genetic marker that has proved usefulfor phylogenetic analyses at the species level (Alvarez and Hoy,2002; Campbell et al., 1993; Gomez-Zurita et al., 2000; Hunget al., 2004; Li et al., 2010; Wagener et al., 2006). Ribosomal RNAis found in multiple tandem repeats within the genome, whichundergo concerted evolution so that (in most taxa) the copies areidentical (Hillis and Davis, 1988; Ji et al., 2003; but see Kelleret al., 2006). Because of these multiple copies, ITS2 is relativelyeasy to amplify. However, the high rate of mutation and the pres-ence of numerous insertion-deletion events (indels) can makeaccurate alignment of this gene challenging, particularly amongmore distantly related species (Coleman and Vacquier, 2002).ITS2 has a highly conserved secondary structure, with characteris-tic paired domains and unpaired regions (Coleman, 2007).Incorporating this secondary structure can improve alignment ofITS2, thus facilitating phylogenetic reconstruction, and also per-mits the discovery of compensatory base changes (CBC’s)(Coleman and Vacquier, 2002; Wiemers et al., 2009; Wolf et al.,2013). These are substitutions where two paired nucleotides (oftenquite far apart in the linear sequence) both mutate such that theoriginal pairing is maintained (Coleman, 2009; Coleman andVacquier, 2002; Gutell et al., 1994). Müller et al. (2007) determinedthat even a single CBC between two individuals indicates thatenough evolutionary time has passed for reproductive isolationto arise, resulting in separate species status. Such CBC’s do notcause reproductive isolation directly, instead simply indicating acorrelation (Wolf et al., 2013). As well, a lack of CBC’s is not proof

of conspecificity, as closely related species may not have any CBC’s(Müller et al., 2007; Wiemers et al., 2009). CBC’s therefore onlyindicate a minimum number of species among the specimensexamined.

Ophion is a genus of large-sized (10–20 mm) nocturnal para-sitoid wasps in the subfamily Ophioninae of the familyIchneumonidae. Unlike the majority of ophionine genera, Ophionare most diverse in temperate regions, with few species in tropicalhabitats (Gauld, 1980, 1988). Ophion are large, abundant, and comereadily to lights; however, Nearctic species have received almostno taxonomic attention despite being highly apparent and easilycollected. Seventeen Nearctic species are currently described(Schwarzfeld and Sperling, 2014; Yu et al., 2012), yet it has beenestimated that the fauna consists of approximately 50 species(Gauld, 1985). In the Palearctic region, Ophion are better known,with 79 described species (Yu et al., 2012), but there have beenno species revisions that span the Palearctic. The fauna has beenmost comprehensively examined in the United Kingdom, with anumber of revisions (Gauld, 1973, 1976, 1978) culminating in acomprehensive revision of 16 known species (Brock, 1982).

Ophion are notoriously difficult to identify as they are morpho-logically homogenous among species and yet variable within spe-cies (Brock, 1982; Gauld, 1980). They are thus well-suited to theapplication of molecular taxonomic methods as a first step towardspecies delimitation. Here we estimate Ophion species diversityusing tree-based methods (GMYC, PTP), distance-based methods(ABGD, threshold analysis), and by assessing compensatory basechanges of ITS2. The study is mainly based on Canadian specimens,with the addition of specimens from the United States and repre-sentatives of all species, except one, that have been described fromthe United Kingdom.

2. Methods

2.1. Specimen collection

A total of 674 specimens of Ophion were included in this study,including 572 from Canada, 81 from Europe, 19 from the UnitedStates, and 2 from Costa Rica (Supplementary Table 1). MostNearctic specimens were newly collected and sequenced for thisstudy, while 25 were sequenced from alcohol-preserved materialon loan from the Canadian National Collection of Insects,Arachnids and Nematodes, and 92 COI sequences from BritishColumbia were included from the Barcode of Life database(BOLD), courtesy of J. DeWaard (project code: ICHBC). Specimensselected for sequencing represent the range of morphological vari-ation observed in over 4000 specimens from a variety of habitats.Eighty specimens from 15 species of Palearctic Ophion were alsonewly sequenced, all of which are from the United Kingdom exceptfor one specimen from Spain and one from France. Finally, oneadditional sequence of O. obscuratus Fabricius (also from the UK)and two sequences of O. flavidus Brullé from Costa Rica wereincluded from Genbank and BOLD. With the exception ofsequences obtained from GenBank or BOLD, all Nearctic taxa usedin the current study were identified by M. D. Schwarzfeld.Palearctic taxa were identified by G. R. Broad (Natural HistoryMuseum, London), with the exception of O. forticornis, which wasidentified by M. R. Shaw (National Museums Scotland, Edinburgh).

2.2. Sequencing

DNA was extracted from a single hind leg and amplified follow-ing methods described in Schwarzfeld and Sperling (2014). Wesequenced COI using the primers lco hym (5’–CAA ATC ATA AAGATA TTG G–3’) and hco out (5’–CCA GGT AAA ATT AAA ATA TAA

236 M.D. Schwarzfeld, F.A.H. Sperling /Molecular Phylogenetics and Evolution 93 (2015) 234–248

ACT TC–3’) (Schulmeister, 2003), which produce a 676 base pairfragment equivalent to the ‘‘barcode” region (Hebert et al.,2003a,b). We analyzed ITS2 using the primers ITS2-F (5’-GGGTCG ATG AAG AAC GCA GC-3’) and ITS2-R (5’-ATA TGC TTA AATTCA GCG GG-3’) which anneal to the flanking 28S and 5.8S genes(Navajas et al., 1994). Sequences are deposited in NCBI’sGenBank, and accession numbers are listed in SupplementaryTable 1.

We sequencedCOI for 565Ophion specimens. A fewwerenot suc-cessfully sequenced in both directions, or were otherwise incom-plete; these sequences were included with gaps coded as missingdata. The 95 Ophion sequences obtained from BOLD and Genbankwere sequenced using different primers (see BOLD website forsequencing protocols) and were slightly shorter in length.Alignment was unambiguous, and was confirmed by translatingnucleotides to amino acids in Mesquite (Maddison and Maddison,2011). The data matrix can be found deposited on the Dryad datarepository (available at datadryad.org, doi: 10.5061/dryad.hf155).

Since analysis of ITS2 secondary structure (see Section 2.4 andSupplementary Data 2) was sensitive to missing data, we onlyincluded sequences that were complete and lacked ambiguousregions; the final dataset included 394 ITS2 sequences. Excludingthe flanking 28S and 5.8S regions, the unaligned sequences rangedfrom 717 to 1053 base pairs in length. A subset of these (380 spec-imens) were successfully sequenced for both ITS2 and COI(Table 1).

2.3. Barcode accumulation curves

All species-delimitation algorithms are dependent on sampling(Bergsten et al., 2012; Lohse, 2009; Meyer and Paulay, 2005;Papadopoulou et al., 2009). We assessed the completeness of sam-pling by calculating ‘‘barcode accumulation curves” of the COIsequences, following the procedure outlined by Smith et al.(2009). Similar to species accumulation curves used in ecologicalstudies, barcode accumulation curves should approach an asymp-tote as fewer unique haplotypes are added to the specimen pool,indicating that most of the diversity has been sampled. Since thevast majority of specimens were from Canada, we calculated thecurve for Canadian material alone (n = 561), as well as a separatecurve including all Nearctic specimens (n = 580). We also calcu-lated an accumulation curve for specimens from the province ofAlberta, since this is the most densely sampled region (n = 342).Finally, we calculated a barcode accumulation curve including onlyPalearctic specimens (n = 78). These curves are not based on ran-dom samples and do not represent comparable regions. We usethem to indicate the pattern of sequence accumulation withinthe current study, but do not expect them to represent the truediversity of each region.

To calculate the curves, we first constructed a neighbor-joiningtree for each dataset in MEGA version 5 (Tamura et al., 2011), usingthe K2P model and pairwise deletion of missing data, and imported

Table 1Summary of specimens sequenced from each geographic region represented in thisstudy. Palearctic specimens are all from the western Palearctic region, primarily theUnited Kingdom.

COI only ITS2only

COI & ITS2 COItotal

ITS2total

Total No.specimens

Canada 198 11 363 561 374 572USA 12 0 7 19 7 19Costa

Rica2 0 0 2 0 2

Palearctic 68 3 10 78 13 81

Total 280 14 380 660 394 674

the tree into the program Conserve (Agapow and Crozier, 2008).We excluded all sequences, and then randomly added sequencesin sets of 10 (Nearctic, Canada and Alberta datasets) or sets of 5(UK dataset), and calculated the phylogenetic diversity (PD) ofthe tree at each successive addition. These results were plottedto assess whether the phylogenetic diversity was approaching anasymptote.

2.4. ITS2 secondary structure and CBC’s

The complete protocol used to determine the secondary struc-ture of ITS2 is described in Supplementary Data 2. Briefly, we esti-mated the structure for an arbitrarily chosen sequence using theRNAfold webserver, using the minimum free energy method withdefault settings (Hofacker, 2003). We imported this sequence andstructure into the ITS2 database (Koetschan et al., 2010; Schultzet al., 2006; Selig et al., 2008) and used it as a template for foldingthe remaining sequences. If any sequences had less than 90% sim-ilarity to the existing template, one of the anomalous sequenceswas directly folded in RNAfold and added as an additional templateto the database. We repeated this iterative process until allsequences were folded; the final dataset consisted of eightsequences that were folded directly and 386 sequences that werefolded using homology. Sequences and structures were alignedusing 4Sale, a program designed to perform structural alignmentsof RNA sequences (Seibel et al., 2006), and a matrix of compen-satory base changes (CBC’s) was exported. The final alignment(excluding the 28S and 5.8S flanking regions) was 1750 base pairsin length. This alignment was used for all phylogenetic analysesdescribed below, and can be found on the Dryad data repository(datadryad.org, doi: 10.5061/dryad.hf155). We calculated aneighbor-joining tree based on the CBC distance matrix using theprogram CBCAnalyzer (Wolf et al., 2005).

2.5. Species delimitation

We analyzed sequence data using four quantitative methods ofspecies delimitation: generalized mixed Yule coalescent analysis(GMYC) (Fontaneto et al., 2007; Pons et al., 2006); Poisson tree pro-cesses model (PTP) (Zhang et al., 2013); automatic barcode gap dis-covery (ABGD) (Puillandre et al., 2012a); and threshold analysisusing the program jMOTU (Jones et al., 2011). Each analysisuses different terminology to refer to the delimited taxa(GMYC = ‘‘entities”; PTP = ‘‘phylogenetic species”; ABGD = ‘‘groups”or ‘‘hypothetical species”; jMOTU = ‘‘molecular taxonomic units”or ‘‘MOTU”), thus acknowledging that they may not representbiologically meaningful species. For clarity and consistency, weare using the term ‘‘putative species” in place of each of the aboveterms, while the term ‘‘species” is applied only to taxa that aredelimited more robustly by multiple data sources.

2.5.1. GMYCModels for the construction of the ultrametric trees required

for the GMYC analysis were selected using the programPartitionFinder version 1.0.1 (Lanfear et al., 2012), with each geneanalyzed as a single partition. For the ITS2 dataset, the selectedmodel was TVMef + G, and COI used HKY + I + G. We obtained theultrametric trees using BEAST version 1.7.5 (Drummond et al.,2012) on the CIPRES Science Gateway computing cluster (Milleret al., 2010), with ITS2 and COI analyzed separately. Identical hap-lotypes were removed using the program Alter (Glez-Peña et al.,2010), and outgroups were excluded. The choice of tree prior canimpact the results of the phylogenetic analysis (Ceccarelli et al.,2012). For example, the coalescent tree prior is often more appro-priate for population-level data and recently diverged taxa,whereas the Yule prior is recommended for species-level data

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(Drummond et al., 2006). We therefore calculated trees using arandom starting tree, the uncorrelated lognormal relaxed clockmodel, and two different tree models (coalescent and Yule-process) for a total of four analyses for each marker (ITS2 and COI).

Each analysis was run with either four (COI coalescent, COIYule, ITS2 coalescent) or five (ITS2 Yule) independent threads,and log files and tree files were combined using LogCombiner.The two COI analyses were run for 40 million generations, withtrees sampled every 4000 generations and a burnin of 25%.Combined logfiles were examined in Tracer version 1.5 (Rambautand Drummond, 2009) to assess convergence; all combined esti-mated sample sizes (ESS) were >200 with the exception of a singleparameter (mean.Rate, ESS = 179) using the Yule prior. The ITS2analysis with the coalescent prior was run for 80 million genera-tions, sampling every 4000 generations, with a burnin of 25%; allESS were >200 except for two parameters (mean.Rate, treemodel.Root) that were between 100 and 200. Obtaining convergence inthe ITS2 analysis with the Yule prior was more challenging. Thefinal analysis included three threads of 160 million generationsand two threads of 80 million generations, sampled every 4000generations. The first 25% of the longer runs, and the first 50% ofthe shorter runs were removed as burnin. Two parameters failedto show convergence (coeffofVariation and covariance, ESS <100);however, the remaining parameters were either P200 or verynearly so. All analyses were resampled in LogCombiner (part ofthe BEAST package) to give a combined total of approximately15,000 trees. Maximum clade credibility trees (the tree in eachanalysis with the highest product of posterior clade probabilities)were produced from the combined tree files in TreeAnnotator (partof the BEAST package), using the mean heights option, and used asinput for GMYC analyses.

We conducted GMYC analyses with the SPLITS package (avail-able from http://r-forge.r-project.org/projects/splits/) using theprogram R (RStudio, 2012; R Core Development Team, 2013).Each tree was analyzed using both single and multiple thresholdsettings, including all haplotypes (COIall and ITS2all). To facilitatecomparison between genetic markers, we also ran the GMYC anal-yses only including taxa that were represented in both datasets(COIcommon and ITS2common). Since in several instances a singleCOI haplotype was represented by multiple ITS2 haplotypes, orvice versa, the two analyses did not necessarily have the samenumber of individuals. We obtained the reduced trees by pruningnon-overlapping taxa from ultrametric trees in R, prior to runningGMYC analyses.

2.5.2. PTPPoisson tree processes analyses used maximum-likelihood trees

as input. Models for the trees were determined usingPartitionFinder version 1.0.1 (Lanfear et al., 2012). For COI, eachcodon position was included as a potential partition, while ITS2was not partitioned. Only models that could be implemented inthe GARLI web service were included. For COI, the first and thirdcodon position used GTR + I + G, while the second position usedF81 + G. ITS2 was analyzed using SYM + G.

Maximum likelihood trees were calculated using the web ser-vice for GARLI 2.1 (Bazinet et al., 2014; available at www. molecu-larevolution.org), using a neighbor-joining starting tree (calculatedin Mega 5.0; Tamura et al., 2011). The GARLI web service uses anadaptive approach wherein additional search replicates are basedon the statistical probability that the best tree has been obtainedwith 95% confidence. For ITS2 a total of 352 replicates were con-ducted, while the COI analysis reached the maximum number of1000 replicates. In both cases, there was essentially no differencein topology between the many best-scoring trees. COI trees wererooted with Enicospilus, another genus in the subfamilyOphioninae, whereas ITS2 trees were rooted with O. minutus, since

molecular and morphological both suggest that this species is basalwithin Ophion (Schwarzfeld, unpublished data).

PTP was conducted using the online web service available athttp://species.h-its.org/ptp (Zhang et al., 2013). This server con-ducts both the original maximum likelihood PTP, as well as a newlydeveloped Bayesian version of PTP (bPTP) that adds Bayesian sup-port values to the delimited species. However, our analyses failedto show convergence even with the maximum number of genera-tions (500,000) available on the web server; we have thereforeonly included the maximum likelihood results in this paper. Weconducted PTP on trees including all sequences (COIall, ITS2all),and on trees including only those specimens with sequences avail-able for both genes (COIcommon, ITS2common). Non-overlappingsequences were pruned from the ML trees in R, prior to runningPTP.

2.5.3. ABGDAutomatic barcode gap discovery (ABGD) infers clusters of

sequences by recursively partitioning the data using a range ofprior intraspecific sequence divergences and by calculating amodel-based confidence limit for intraspecific divergence at eachiteration (Puillandre et al., 2012a). We first calculated distancematrices for COI and ITS2 in MEGA 5.1 using both K2P correcteddistances and uncorrected p-distances. Separate matrices were cal-culated for all specimens (COIall and ITS2all), or only including spec-imens that were successfully sequenced for both genes (COIcommon,ITS2common). We calculated the number of clusters using the ABGDonline tool (Puillandre et al., 2012a; available at http://wwwabi.snv.jussieu.fr/public/abgd/abgdweb.html). Analyses of COI usedthe default priors (prior intraspecific divergence = 0.001–0.1,divided into 10 partitions), since the program is optimized foruse with the COI barcoding gene, based on numerous studies. ForITS2, intraspecific sequence divergence has been less commonlyassessed and is highly taxon-dependent. Within Ichneumonidae,Wagener et al. (2006) found the intraspecific sequence divergenceof Diadegma (subfamily Campopleginae) to be 0.2–0.6%. Within theOphion scutellaris species group (Schwarzfeld and Sperling, 2014),intraspecific variation was similarly very low, ranging from a singlehaplotype per species to a maximum of 0.1%. We therefore ran theABGD analysis using prior intraspecific divergences of 0.0001–0.1(divided into 20 partitions). For each analysis we used both thedefault barcode relative gap width of 1.5 and a gap width of halfthe default (0.75) to increase the sensitivity of the analysis(Puillandre et al., 2012a)

2.5.4. Threshold analysisCOI and ITS2 sequences were clustered into molecular taxo-

nomic units (MOTU) using the program jMOTU (Jones et al.,2011). Commonly used thresholds for species delimitation basedon COI are 1–3% (Bribiesca-Contreras et al., 2013; Hebert et al.,2003a, b, 2004; Smith et al., 2009, 2013; Stalhut et al., 2013;Strutzenberger et al., 2011; Tang et al., 2012). Since computationalexpense increases very little with more thresholds in the jMOTUanalysis, we calculated the number of species using a wider rangeof thresholds, from a single base pair difference (approximately0.15%) to a 50 base pair threshold (7.5% of the average sequencelength). For the analysis of ITS2, we used the same range of thresh-olds (1–50 base pairs) as in the COI dataset, which is equivalent to0.1–5.7% of the average sequence length. Since the ITS2 sequencescontain numerous large indels, we used a range of low identityBLAST filters, from 80 to 97, to attempt to obtain the best align-ment (Jones et al., 2010); however, the results were identical inall cases. We therefore ran analyses on both the complete datasets(COIall and ITS2all) and on the common datasets (COIcommon andITS2common), using the default settings of a minimum 60% overlapand low identity BLAST filter set to 97.

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Fig. 1. Barcode accumulation curves of all COI sequences from this study.


2.5.5. Comparison with morphologyComparisons of species-delimitation methods provide useful

information into the performance of algorithms. However, even ifmultiple methods converge on the same results, there is no guar-antee that the clusters represent species. Morphological analysisof Ophion is ongoing, with many species thus far lacking clear mor-phological characters. Nevertheless, some species are morphologi-cally distinguishable. We selected 11 morphospecies to use as testspecies to determine whether or at what threshold they wererecovered by various analyses. Seven of these species are theNearctic members of the O. scutellaris species group (O. idoneus,O. importunus, O. keala, O. clave, O. aureus, O. brevipunctatus, andO. dombroskii) that were resolved using a combination of morphol-ogy, wing morphometrics and classical morphometrics(Schwarzfeld and Sperling, 2014). The other species include O.nigrovarius Provancher, O. slossonae Davis, and two undescribed,morphologically distinct species, sp. A and sp. B. Descriptions andphotographs of the diagnostic features of each species are providedin Supplementary Data 3, or in Schwarzfeld and Sperling (2014).

We calculated the average and maximum sequence divergencewithin each species (excluding those represented by singletons).Distances are uncorrected p-distances, calculated in Mega 5.1(Tamura et al., 2011), using the pairwise deletion option for gapsand missing data. For the species delimitation analyses, we exam-ined results from the COIall and ITS2all datasets. We considered ananalysis to have successfully recovered a species if a single clusterincluded all of the sequenced individuals of a morphologicallydefined species and no additional sequences. For the GMYC andPTP analyses, we reported if a species was successfully recovered(+), split between more than one cluster (Sp), or lumped with othernon-conspecific sequences (L). For the ABGD and jMOTU analyses,we listed the range of prior intraspecific divergences or thresholds,respectively, that successfully recovered the species, or recorded ifthe species was never recovered (�).

2.5.6. Species delimitation of Palearctic OphionSince the Nearctic fauna is almost entirely undescribed, few

well-characterized species are available for assessing the perfor-mance of species-delimitation methods. Ophion from the UK havebeen much more thoroughly studied, with a comprehensive mor-phological revision and workable key to species (Brock, 1982).However, these species have not been assessed with moleculardata, and several species present potential taxonomic difficulties(Brock, 1982, G. Broad, pers. comm.). We have therefore analyzedthe species limits of 15 of the 16 described British species, basedon COI sequence data. We restricted the analysis to COI as wehad limited success sequencing these specimens for ITS2. Basedon the results of the morphological comparisons described above,we selected a subset of analyses that were most effective for distin-guishing Nearctic species. We assessed each Palearctic speciesusing GMYC (coalescent prior, single threshold method), PTP,ABGD (relative gap width: 0.75, prior intraspecific divergence0.0046), and jMOTU (threshold: 7, 14, and 20 base pairs, or approx-imately 1%, 2%, and 3% sequence divergence). While the 3% thresh-old had only limited success in delimiting the Nearctic test species,we included it because this threshold is commonly used in the bar-coding literature (e.g. Hebert et al., 2003a; Tang et al., 2012).

3. Results

3.1. Barcode accumulation curves

The three Nearctic barcode accumulation curves for COI (allNearctic specimens, Canadian only, and Alberta only) showed asimilar shape and, as expected, diversity increased with geographic

scale of sampling (Fig. 1). No dataset reached an asymptote,although the Alberta sequences may be approaching one.Sequences from the UK were still in the steep initial part of thecurve, precluding comparison with the other three datasets. TheNearctic closely follows the Canadian curve, since most specimenswere included in both datasets. However, each specimensequenced from the southern United States (a total of ten speci-mens from Arizona, Florida, Georgia, Kentucky, and New Mexico)almost certainly represents an additional species, based on bothgenetic divergence and morphology (Schwarzfeld, unpublisheddata). This curve therefore greatly underestimates the true diver-sity for the Nearctic region.

3.2. CBC analysis

There was a maximum of 5 compensatory base changes (CBC’s)between any two specimens. The NJ tree derived from the CBCmatrix supported the overall topology of species groups withinthe genus, as determined through a molecular phylogenetic analy-sis of Ophion (Schwarzfeld, unpublished data). However, therewere no CBC’s within any species group except for two specimensthat differed from the rest of their group by one (O. obscuratus fromthe U.K., DNA6917) or two (undescribed species from Arizona,DNA5569) CBC’s. Within species groups, CBC’s were thus generallyuninformative for distinguishing species.

3.3. GMYC

The results of the GMYC analyses are found in Table 2. Three ofthe single threshold analyses of the Yule prior trees (ITS2all,ITS2common, and COIcommon) failed to significantly reject the nullhypothesis that the sequences cannot be differentiated from a sin-gle cluster. Nonetheless, the estimated numbers of clusters(excluding singletons) and putative species (=clusters plus single-tons, or ‘‘entities”) from these analyses were within the range ofall other analyses. Across all analyses, the proportion of singletonsranged from 29% to 57% of the total number of GMYC species, withan average of 40%.

In general, the number of clusters was fairly robust with respectto both tree prior (Yule (Y) or coalescent (C)) and GMYC method(single (S) or multiple (M)). For the COIall data, the number of clus-ters varied from 72 to 81, while in the smaller ITS2all dataset threeof the four analyses resulted in 52 or 53 clusters. The YM analysisof the ITS2all data, however, only recovered 35 clusters. When onlycommon taxa were included, the number of clusters was also quiterobust between the two genes, with all analyses recovering 45–50clusters.

Table 2Results of generalized mixed Yule coalescent (GMYC) analysis. Method = number of thresholds; Clu = number of clusters; Spp = number of species (clusters + singletons);CI = confidence interval for the number of species; L-Null = likelihood of the null model (=all sequences form a single cluster); L-GMYC = likelihood of GMYC model; LRT = p-valueof likelihood ratio test; COIall and ITS2all = all haplotypes included in analysis; COIcommon and ITS2common = only haplotypes represented by both markers were included.

Dataset Tree prior Method Clu Spp CI L-Null L-GMYC LRT

COIall Coalescent Single 72 114 100–137 3119.398 3135.499 <0.000Multiple 83 150 129–167 3119.398 3145.07 <0.000

Yule Single 81 121 117–134 3057.645 3064.79 0.003Multiple 79 124 88–125 3057.645 3073.966 0.000

ITS2all Coalescent Single 53 85 85–104 2326.429 2334.213 0.001Multiple 52 95 93–96 2326.429 2339.508 0.000

Yule Single 53 75 62–81 2162.679 2166.034 0.0818a

Multiple 35 51 47–104 2162.679 2173.709 0.001

COIcommon Coalescent Single 45 80 67–93 1728.258 1739.615 <0.000Multiple 49 107 103–107 1728.258 1744.155 <0.000

Yule Single 50 86 2–106 1695.617 1698.06 0.180a

Multiple 44 103 72–116 1695.617 1704.424 0.024

ITS2common Coalescent Single 45 76 53–94 2135.929 2143.237 0.002Multiple 49 78 78–92 2135.929 2147.648 0.001

Yule Single 48 69 59–77 1990.495 1994.305 0.0545a

Multiple 48 73 39–99 1990.495 2001.224 0.0181

a Not significant.


The total number of putative species was more variable. For theCOIall dataset, the number was fairly similar between three of theanalyses (114–124); however, the CM analysis recovered 150 puta-tive species. For the ITS2all analyses, the number of putative speciesvaried from 51 (YM) to 95 (CM). For the ‘‘common” datasets, therewas a minimum of 69 putative species (ITS2 YS) and a maximum of107 (COI CM). All of the COI analyses recovered more putative spe-cies than did the corresponding ITS2 analyses. Within COIcommon,the YM and CM analyses recovered far more putative species(103 and 107, respectively) than did the corresponding singlethreshold analyses (86 and 80).

3.4. PTP

Analysis of COIall gave a total of 89 putative species, while ITS2allanalysis resulted in 54 putative species. It was fairly congruentwith GMYC where lineages were relatively deep (Fig. 2A), but moreconservative with respect to splitting taxa when divergences wereshallow (Fig. 2B). When only ‘‘common” taxa were included, sev-eral more putative species were delimited in the COI analysis(COIcommon: 84 putative species) compared to ITS2 (61 putativespecies). Interestingly, the ‘‘common” datasets tended to split taxain comparison to the ‘‘all” datasets, to the point that more putativespecies were delimited in the ITS2common analysis compared toITS2all.

3.5. ABGD

For the COI datasets, ABGD delimited the same number of puta-tive species regardless of which distance matrix was used as theinput data (K2P or uncorrected p-distance). In the ITS2 analyses,a few intermediate partitions had a maximum of three additionalputative species with the uncorrected distance matrix, comparedwith the K2P distance matrix. Since the results were so similar,we are only reporting the results of the uncorrected analysis.

The relative gap width had a strong influence on the intermedi-ate partitions of both the COI and ITS2 analyses (Fig. 3). For exam-ple, at a prior intraspecific divergence of 0.0017 (COI) or 0.0013(ITS2), the more sensitive analysis (gap width = 0.75) recoveredover twice as many putative species as the default gap width(=1.5).

The number of putative species recovered by each COI analysisdropped precipitously between the first and second partition (COI

priors = 0.0010 and 0.0017, respectively; Fig. 3a). Over the remain-ing partitions, the pattern was analysis- and dataset-dependent,though by a prior of 0.036 all analyses were recovering a singlecluster. The COIall analysis using the default gap width had a clearplateau in the number of species recovered, ranging from 66 to 82.However, the pattern was less clear in the other analyses. The moresensitive analyses (gap width = 0.75) had a range of 52–168 puta-tive species (COIall) or 23–120 species (COIcommon) over the inter-mediate priors, with the second and third partition recoveringthe same number of putative species. The COIcommon analysis withthe default gap width (1.5) had only a single intermediate parti-tion, with 48 putative species.

The ITS2all and ITS2common ABGD analyses gave identical results,except that 0–3 fewer putative species were delimited in each par-tition for ITS2common. For clarity, we have therefore only shown theITS2all data in Fig. 3b. Using any prior intraspecific divergences ator below 0.001 resulted in the same number of putative species(COIall: 120; COIcommon: 117). With priors above 0.001, the numberof putative species dropped rapidly. With a prior of 0.0013, 27 and59 putative species were recovered by the two ITS2all analyses (gapwidth 1.5 and 0.75, respectively) and with a prior of 0.0078 orgreater, fewer than 20 putative species were recovered by allanalyses.

3.6. Threshold analysis

The number of putative species delimited for both COI and ITS2initially dropped steeply as the threshold increased, eventuallyflattening out (Fig. 4). The curve was more gradual for COI, notreaching a clear plateau until over 5% sequence divergence,whereas for ITS2, the curve flattened out by approximately 1%divergence. A total of 128, 90, and 47 putative species were delim-ited by the analysis of COIall at thresholds of approximately 1%, 2%,and 3%, respectively (7, 13, and 20 base pairs). At these thresholds,the COIcommon dataset delimited 74, 43, and 20 putative species.Comparing this to the ITS2common dataset, the most similar num-bers of putative species were found at thresholds of 0.3%, 0.8%,and 2.6–3% (3, 7, and 23–26 base pairs).

3.7. Test species

The mean percent sequence divergence within each test speciesranged from 0.04% to 0.6% divergence for COI. The highest

Fig. 2. Demonstration of putative species limits as obtained through different quantitative delimitation methods. The phylogenetic tree is the coalescent tree of COI obtainedthrough BEAST analysis; it is being used for representation, but is not intended to be a definitive phylogenetic tree for the genus. Section A of the tree demonstrates relativelygood congruence between methods, while section B demonstrates wide-spread incongruence. Red = GMYC: c–s = coalescent prior, single threshold; c–m = coalescent prior,multiple threshold; y–s = Yule prior, single threshold; y–m = Yule prior, multiple threshold; Green = PTP; Orange = jMOTU: 1 = 1%, 2 = 2%, 3 = 3%; Blue = ABGD: a = 1.5 gapwidth, 2nd partition; b = 0.75 gap width, 2nd partition; c = 1.5 gap width, 4th partition; d = 0.75 gap width, 4th partition. Each bar represents a putative species. Black barsindicate the associated specimen(s) were not included within the putative species (i.e. taxa were not monophyletic with respect to this tree). Numbers are used to indicatewhen two separate bars belong to the same species (non-monophyletic on this tree). Identical haplotypes were removed for this phylogenetic analysis; therefore, someapparent singletons may be represented by multiple specimens in the complete dataset.


Fig. 3. Results of ABGD analyses over a range of prior intraspecific divergences with two different gap widths (1.5 and 0.75). (A) COI: all = all sequences; common = onlyincluding sequences in common with ITS2. (B) ITS2: all sequences (ITS2common not shown, since results are equivalent).

Fig. 4. Number of species delimited by jMOTU at thresholds of 0–50 base pairs(equivalent to 7.5% (COI) or 5.7% (ITS2) sequence divergence); ITS2common notshown as results are equivalent to ITS2all.

Table 3Mean and maximum percent sequence divergence of COI and ITS2 within eachNearctic test species that was represented by more than one individual. Divergencesare uncorrected p-distances (%), with gaps and missing data treated by pairwisedeletion.

Species COI mean COI max ITS2 mean ITS2max

aureus 0.4 0.4 0.0 0.0clave 0.2 0.3 0.0 0.0idoneus 0.04 0.3 0.0 0.0keala 0.4 0.7 0.0 0.0nigrovarius 0.2 0.3 0.0 0.0slossonae 0.3 0.4 – –Sp. A 0.3 0.8 0.0 0.0Sp. B 0.6 0.9 0.1 0.3


maximum divergence within any species was found in Sp. B, with0.9% divergence. ITS2 intraspecific divergence was almost non-existent, with only sp. B showing any divergence (mean 0.1%, max-imum 0.3%) (Table 3).

GMYC analysis of COI was generally more successful at correctlydelimiting species than was that of ITS2 (Table 4). Within the COIanalysis, the coalescent or Yule tree priors were equally successfulat recovering the test species. The single threshold method wasmost successful, correctly delimiting all species except O. bre-vipunctatus and O. dombroskii, which were recovered as a singlecluster. The multiple-threshold method also lumped these twospecies, and in addition failed to recover O. keala and Sp. A. The

CS, CM, and YS analyses of the ITS2 data each delimited 5 of the10 species but which species were successfully delimited variedbetween the analyses. The YM analysis of ITS2 was the least suc-cessful GMYC analysis, recovering only two species. O. nigrovariuswas the only species to be recovered by all GMYC analyses andboth markers.

Similarly, species were more successfully delimited in the PTPanalysis of COI, compared to ITS2. However PTP in general was lesssuccessful than GMYC, with the COI analysis correctly delimitingfour species out of eleven, while in the ITS2 analysis only a singlespecies was successfully delimited (Table 4).

Sp. B was never recovered by ABGD, being either split orlumped, depending on the parameters used (Table 5). All otherspecies were recovered by at least one ABGD analysis. Again, theanalyses of COI were generally more successful than the ITS2 anal-yses, with most species being successfully delimited over a rangeof partitions. The more sensitive analysis of COI (gap width = 0.75)was most successful, with all species except for Sp. B being cor-rectly delimited in at least one, and usually several, partitions.Within this analysis, the prior intraspecific divergence of 0.0046was the only partition that correctly delimited all species (exclud-ing Sp. B). ABGD of the ITS2 dataset recovered 7 of the 10 species inall partitions with prior intraspecific divergences 60.001); three ofthese species were also recovered in additional partitions. Theremaining three species were never successfully delimited.

All of the test species were successfully delimited by jMOTUwith the exception of Sp. B in the analysis of COI and O. clave inthe analysis of ITS2 (Table 5). In both cases, these species were splitat low thresholds, and lumped with other sequences at higherthresholds, but never delimited successfully. In the COI analysis,all remaining species were delimited at a threshold of 1% sequencedivergence, and all except O. brevipunctatus and O. dombroskii at 2%divergence. Only three species (O. nigrovarius, O. slossonae, and O.keala) were delimited at 3% divergence or higher. For the ITS2 anal-ysis, most species were delimited at very low divergences, withonly O. nigrovarius and Sp. A being successfully delimited at thresh-olds of more than 1% divergence (9 base pairs).

3.8. Species delimitation of Palearctic Ophion

Compared to the Nearctic test species, sequence divergencewithin several of the Palearctic ‘‘species” was high (Table 6). Ninespecies had maximum intraspecific divergences of over 2%. Twospecies (O. pteridis and O. perkinsi) were represented by a singlehaplotype, and sequence divergence within O. ventricosus and O.ocellaris were also very low (Table 6).

Table 4Summary of congruence between generalized mixed-Yule coalescence analyses (GMYC) and Poisson tree processes (PTP) with eleven species/morphospecies of Nearctic Ophion.C = coalescent tree prior; Y = Yule tree prior; S = single threshold model; M = multiple threshold model; + = species successfully delimited; L = species lumped with othersequences; Sp = species split into >1 groups.

Species COI ITS2 COI ITS2

(n) (n) CS CM YS YM PTP CS CM YS YM PTP

clave 5 2 + + + + + + + + L Sp/Laureus 2 4 + + + + L + L L L Lbrevipunctatus 1 1 L L L L L L L L L Ldombroskii 1 1 L L L L L L L L L Lkeala 7 7 + Sp + Sp Sp L Sp L L Limportunus 1 1 + + + + L + + + L Lidoneus 13 13 + + + + L L + L L Lnigrovarius 5 2 + + + + + + + + + Spslossonae 3 0 + + + + + n/a n/a n/a n/a n/asp. A 11 7 + L + L + + + + + +sp. B 9 9 + + + + L Sp Sp + L L

Table 5Summary of congruence between automatic barcode gap discovery (ABGD) andthreshold analyses with eleven species/morphospecies of Nearctic Ophion. ABGD usedtwo gap widths: 1.5 and 0.75; numbers represent the range of prior intraspecificpercent divergence that successfully delimited each species. jMOTU = thresholdanalysis using the program jMOTU; numbers indicate the range of thresholds, as apercentage of the mean sequence length, that successfully delimited each species; forboth analyses, – indicates the species was never delimited.

ABGD (%) jMOTU (%)

Species COI ITS2 COI ITS2

1.5 0.75 1.5 0.75

clave 0.2–0.8 0.2–1.3 60.01 60.01 0.3–2.9 –aureus – 0.5 60.01 60.01 1.1–2.0 0.1–0.3brevipunctatus 0.1 0.1–0.5 – – 61.1 60.2dombroskii 0.1 0.1–0.5 – – 61.1 60.1keala 0.2–0.8 0.5–1.3 60.01 60.01 0.6–4.1 0.1importunus 0.1–0.8 0.1–1.3 <0.01–0.06 <0.01–0.06 62.3 60.8idoneus 0.2–0.8 0.2–1.3 60.01 60.01 0.3–2.3 0.1nigrovarius 0.5–0.8 0.5–1.3 <0.01–1.7 <0.01–1.7 0.5–5.2 0.1–4.3slossonae 0.2–0.8 0.2–1.3 n/a n/a 0.5–4.5 n/asp. A 0.2–0.8 0.2–1.3 <0.01–0.4 <0.01–0.4 0.6–2.3 0.1–2.6sp. B – – – – – 0.5–0.8

Table 6Mean and maximum percent sequence divergence of COI within each Palearcticspecies that was represented by more than one individual. Divergences areuncorrected p-distances (%), with gaps and missing data treated by pairwise deletion.

Species Mean Max

brevicornis 1.0 2.1costatus 2.4 6.0crassicornis 1.5 2.2luteus 0.6 2.7minutus 1.4 3.7mocsaryi 2.2 4.2obscuratus 4.9 7.9ocellaris 0.1 0.2parvulus 9.5 9.5perkinsi 0.0 0.0pteridis 0.0 0.0scutellaris 1.4 3.5ventricosus 0.1 0.3

Table 7Number of putative species delimited for each morphologically identified species ofOphion, based on the COI barcode region. All specimens are from the United Kingdom,with the exception of a single specimen of O. obscuratus from Spain and one specimenof O. forticornis from France. Analyses that successfully delimited a species areidentified with +. The GMYC analysis used the single threshold method on anultrametric tree produced in BEAST, with a relaxed lognormal clock and coalescenttree prior. PTP was conducted on a maximum likelihood tree obtained with GARLI.The ABGD analysis used a relative gap width of 0.75 and a prior intraspecificdivergence of 0.0046. The jMOTU analysis used thresholds of 7, 13 and 20 base pairs,or approximately 1%, 2%, and 3% sequence divergence.

Number of putative species

Species n GMYC PTP ABGD jMOTU

1% 2% 3%

brevicornis 3 2 2 + 3b 3b 2b

costatus 13 3a,c 4a,c 4a,c 5c 5c 4b,c

crassicornis 4 2 2 2 2 2 +forticornis 1 + + + + + +longigena 1 + + + + + +luteus 14 2 + 2 2 1b 1b

minutus 8 2 + 3 4 2 +mocsaryi 9 3a,c 2a,c 4a,c 5c 4c 2b,c

obscuratus 7 3 4b 3 5b 5b 3b

ocellaris 3 + + + + + +parvulus 3 2 2 2 2 2 2b

perkinsi 2 + 2 + + + 1b

pteridis 3 + 1b + + 1b 1b

scutellaris 5 2 2 2 2 2 +ventricosus 4 + + + 2 + +

a Two sequences identified as O. mocsaryi and two identified as O. costatus wererecovered as a single species by ABGD and PTP, and as a monophyletic group splitinto two species by GMYC.

b At least one of the delimited species lumped with other, presumably non-conspecific sequences.

c One sequence identified as O. costatus was consistently found within one of theO. mocsaryi species – likely a misidentification of this specimen.


ABGD, GMYC, and PTP were broadly congruent in their delimi-tation of Palearctic species, with only slight differences in the num-ber of species within a few clades (Table 7). In comparison, whilethe jMOTU analyses often split species into a similar number ofgroups, in several instances it lumped these putative species withother sequences. In some cases jMOTU analyses combined highly

divergent taxa. For example, at thresholds of 2% and 3% divergence,it combined O. pteridis and O. luteus, along with several Nearcticspecies, into one large group, even though they differ by approxi-mately 6% sequence divergence.

Of the 15 Palearctic Ophion species that were analyzed, three (O.forticornis, O. longigena, O. ocellaris) were delimited by all analyses,although two of these species (O. forticornis and O. longigena) wererepresented by singletons. O. ventricosus was recovered as a singlespecies by all except the 1% jMOTU analysis, and O. perkinsi by allexcept PTP (oddly, since it was represented by a single haplotype).Also represented by a single haplotype, O. pteridis were never splitinto multiple species; however, according to the 2% and 3% jMOTUanalyses, it was lumped with other sequences. All other specieswere split to various extents by the different analyses.


4. Discussion

4.1. Sampling

All quantitative species-delimitation methods can be misled ifspecies are not adequately sampled, a common issue in diverse,understudied taxa (Lim et al., 2012; Lohse, 2009; Papadopoulouet al., 2009). In particular, undersampling can lead to either under-estimation of species numbers (since some species are not sam-pled) or overestimation (since some of the diversity withinspecies may not be sampled, resulting in apparent genetic structur-ing) (Papadopoulou et al., 2009). However, determining what levelof sampling is considered ‘‘adequate” is not a trivial task. Speciesranges, geographical structuring, effective population sizes,intraspecific genetic variation, habitat niches (possibly includinghost-races in the case of parasitoids) all play a role in the observeddata (Bergsten et al., 2012; Hamilton et al., 2014). In theory, evensampling across the genetic and geographic ranges of each specieswould improve delimitation methods; however in practice this islikely impossible to achieve. For example, Bergsten et al. (2012)found up to 70 individuals per species of diving beetle wererequired to sample 95% of intraspecific variation.

In our study, none of the barcode accumulation curves reachedan asymptote, a common finding among diverse taxa such as par-asitoids (Fernández-Triana et al., 2011; Smith et al., 2009; Zaldívar-Riverón et al., 2010). This study is almost entirely restricted tospecimens from Canada and the northern United States. Whilethe southern Nearctic region is almost entirely unexamined, themore extensively studied Canadian fauna is also insufficiently sam-pled. The most densely sampled region is the province of Alberta,which provided 52% of the sequences for this study; the curve rep-resenting these sequences appears to be approaching an asymp-tote, but even here, more sampling is needed to fully assess thegenetic diversity of these species. Included samples were collectedhaphazardly, in many cases as bycatch from moth collectors.Future studies should therefore place a high priority on samplingthroughout a wider range of habitats and geographic regions.

A high proportion of singleton species were recovered in allanalyses, another typical result for diverse, understudied taxa(Ceccarelli et al., 2012; Lim et al., 2012; Monaghan et al., 2009).The robustness of species-delimitation algorithms with regard toundersampled species is an important consideration as singletonspecies are abundant in taxonomic and biodiversity studies (Limet al., 2012). The GMYCmethod has been found to be accurate evenwith a high proportion of singleton species (Ceccarelli et al., 2012;Monaghan et al., 2009; Talavera et al., 2013). In contrast, for accu-rate results with ABGD, it is recommended that 3–5 sequencesshould be included per species (Puillandre et al., 2012a).However, without a priori knowledge of the group being studied,this may be difficult to achieve, and at least in our study, the sin-gletons did not appear to negatively affect the results. PTP has beenfound to overestimate diversity when sampling is uneven betweenspecies (Zhang et al., 2013), yet despite the uneven sampling in thisstudy, PTP was more prone to underestimating the number of spe-cies, compared to GMYC. However, this pattern may be indicatedby the increased splitting of putative species when sequences wereremoved in the ‘‘common” analyses. Further studies of the effect oftaxon sampling with regards to PTP are needed.

Increased sampling does not always lead to more accurate spe-cies delimitation, particularly in threshold-based analyses. Anapparently distinct barcoding gap often disappears with increasedsampling across the range of genetic variation of a given taxon(Bergsten et al., 2012; Meyer and Paulay, 2005; Moritz andCicero, 2004; Wiemers and Fiedler, 2007). In this study, at thecommonly used 2% threshold, the single-linkage clustering algo-rithm used by jMOTU clustered several distantly-related taxa. For

example, O. luteus, O. pteridis, and a number of Nearctic species,were grouped into a single MOTU, as there were apparentlyenough intermediate sequences to act as a bridge between them.However, a trial analysis with the dataset restricted to O. luteusand O. pteridis retained them as separate MOTUs at thresholds ofup to 6% divergence.

Hamilton et al. (2014) recommend incorporating prior knowl-edge of natural history, genetic variation, and morphology in orderto design an effective sampling strategy for accurate species delim-itation. This may be difficult to achieve with the first attempt atdelimiting species in an almost entirely unexplored genus, suchas Ophion. However, as knowledge of the taxon accumulates, itshould be possible to gradually ‘‘fill in the holes”, and thus refineand improve confidence in the delimitation results.

4.2. Comparison of ITS2 and COI as markers for species delimitation ofOphion

ITS2 in Ophion has a very different pattern of divergence com-pared to COI, with high divergence between species groups, butvery low intra- and interspecific divergence among closely relatedspecies (Schwarzfeld and Sperling, 2014; Schwarzfeld, unpublisheddata). Nevertheless, the branching pattern was largely congruentwith COI, and at least according to GMYC, a similar number of spe-cies were delimited.

Due to its length, the presence of microsatellite regions, and thehigh number of indels, ITS2 was more logistically challenging tosequence and align for Ophion. However, both ITS2 and COI haveadvantages and disadvantages with regards to species delimita-tion. In this study, COI successfully identified more test species inGMYC and PTP, and the choice of priors or thresholds for ABGDand jMOTU is more straightforward, due to the large number ofstudies focusing on this gene. In some studies, however, ITS2 hasbeen found to be more effective than COI at distinguishing closelyrelated species (e.g. Wagener et al., 2006). As well, certain indels ofITS2 can be a species-specific character, and have been used to suc-cessfully distinguish species of parasitic wasps in several studies(Klopfstein, 2014; Quicke et al., 2006; Van Veen et al., 2003).While beyond the scope of the current study, more detailed exam-ination of indels may resolve some of the ambiguously delimitedspecies of Ophion,

Despite the apparent advantages of COI in this study, relying onthis single gene may provide misleading results due to incompletelineage sorting, introgression, bacterial endosymbionts, or nuclearmitochondrial pseudogenes (numts) (Dupuis et al., 2012; Funk andOmland, 2003; Raychoudhury et al., 2010; Schmidt and Sperling,2008; Song et al., 2008). In at least one species in this study, anapparent numt (Gellissen et al., 1983; Lopez et al., 1994) wasamplified, as there is a homologous 2 base pair deletion in eachsequence which would result in a shift in the codon reading frameof the COI gene. Since the GMYC analysis of ITS2 also recovered thesame species, it appears this numt may be taxonomically informa-tive. However not all numts are as readily recognizable, and can beamplified by chance in taxa that are not closely related. It is there-fore critical to include an independent molecular marker to verifythe results obtained with COI. ITS2 is an important tool for delim-iting species in this genus, though the investigation of other rela-tively rapidly evolving nuclear genes (e.g. CAD) (Sharanowskiet al., 2011; Wild and Maddison, 2008) may provide additionaland potentially more tractable species-level genetic markers.

4.3. CBC analysis

The potential for a single compensatory base change (CBC) tounambiguously identify species as being reproductively isolatedhas been a promising ‘‘magic bullet” for species delimitation


(Coleman, 2009; Müller et al., 2007; Wolf et al., 2013). The initialstudies focused on plants and fungi, but did not assess theutility of CBC’s in animal taxa. The first test of CBC’s for speciesdelimitation of animals was provided by Ruhl et al. (2010),where they found it to effectively delimit two morphologicallyindistinguishable species of flea beetle (Chrysomelidae: Altica).However, studies of blue butterflies (Lycaenidae; Wiemers et al.,2009), mosquitoes (Alquezar et al., 2010), and budwormparasitoids (Smith et al., 2011) found CBC’s were too rare todistinguish closely-related species. The almost complete lack ofCBC’s between closely related Ophion species provides furtherevidence that for many insects, CBC’s are apparently tooscarce to be of practical use in distinguishing closely-relatedspecies. They are, however, potentially useful in defining highertaxonomic groups.

4.4. Parameter selection and species delimitation

Two main parameters that must be selected for GMYC analysesare the priors used to construct the phylogenetic tree, and thechoice of single- or multiple-threshold methods. While themultiple-threshold method of GMYC was developed to take intoaccount different branching patterns and rates of evolution acrossa tree (Monaghan et al., 2009; Papadopoulou et al., 2009), evidencesuggests it is consistently less accurate than the single-thresholdmethod (Esselstyn et al., 2012; Fujisawa and Barraclough, 2013).This study supports this finding with fewer test species being cor-rectly delimited with this method in both the analyses of COI (bothtree priors) and ITS2 (Yule prior). While the multiple-thresholdmethod did not always recover higher numbers of species thandid the corresponding single analysis, all of the highest estimateswere obtained using this method, further supporting the conclu-sion of Esselstyn et al. (2012) that the multiple-threshold methodoverestimates the number of species.

Yule priors are generally considered more appropriate for anal-yses of speciation, whereas coalescent priors are more applicableto intraspecific population level data (Drummond et al., 2006). Ina GMYC analysis of a diverse genus of Braconidae, Ceccarelliet al. (2012) found the accuracy of each prior depended on the genebeing analyzed, with the coalescent prior being more consistentwith morphology for COI data. However, this result may betaxon-specific; in the current study, both the estimated numberof species and the accuracy of the analyses were largely unaffectedby which tree prior was used to construct the phylogenetic tree.The birth–death prior is another alternative that may be appropri-ate for species diversification (Gernhard, 2008; Nee et al., 1994),though it was not tested in the current study.

While fewer parameters must be selected for PTP, the analysisdoes depend on the accuracy of the input phylogenetic tree. Weonly tested a single ML tree, and therefore cannot determine ifthe use of different models or tree-building algorithms wouldaffect the results. Tang et al. (2014) found that PTP was quiterobust with respect to the phylogenetic methods used; however,the tendency of taxa to be more split when fewer specimens wereincluded again indicates the importance of taxon sampling for phy-logenetic reconstruction.

The results of the ABGD analyses were highly dependent on thegap width selected. In most recent studies that have employed thismethod, the default gap width of 1.5 is either explicitly used(Hendrixson et al., 2013; Kekkonen and Hebert, 2014; Tang et al.,2012), or implicitly used (Crawford et al., 2012; Hamilton et al.,2014; Puckridge et al., 2013). However, Puillandre et al. (2012b)increased this setting to 10 in order to decrease the sensitivity oftheir analysis to small gaps, while Weigand et al. (2013) used arange of more sensitive gap widths (0.01–0.9). In this study, reduc-ing the gap width doubled the number of delimited species in

several partitions for both COI and ITS2. As well, there was no clearplateau in the estimated number of species with the more sensitivegap width, making the choice of prior a subjective but essentialtask if this analytical method is to be used to assess species diver-sity. While both gap widths recovered most test species, in the COIanalysis, more species were recovered over a wider range of priorsusing the more sensitive gap width. The sensitivity of the ABGDanalysis to different gap widths is most apparent in one species-group of apparently weakly diverged species (Schwarzfeld, unpub-lished data). For example, in the analysis of COI using anintraspecific prior of 0.0017, the default analysis estimated 15 spe-cies within this group, whereas the more sensitive analysis recov-ered 81 species (Fig. 2b shows part of this group). The one testspecies within this species group (Sp. B) was not accurately delim-ited in either case, being split with the more sensitive analysis andlumped with the default analysis. The sensitivity of the analysis tothe gap width may only be an issue within species complexes orrecent radiations, which can confound any species-delimitationmethod (Monaghan et al., 2006; Reid and Carstens, 2012).Nonetheless, the impact of gap width should be considered whenusing this method, since the choice of this parameter is largelyarbitrary.

While gap width also affected the intermediate partitions of theITS2 analysis this had little impact on the overall results, as onlythree test species were recovered with any intraspecific divergenceabove 0.001. This supports the results seen in the O. scutellarisgroup (Schwarzfeld and Sperling, 2014) where all species had amaximum of 0.1% intraspecific divergence.

Based on thresholds of 1–3%, there was almost a threefold dif-ference in the number of species estimated by jMOTU, from 47 spe-cies at 3% divergence to 128 species at 1%. While 2% is one of themost commonly used thresholds in DNA barcoding studies (e.g.Bribiesca-Contreras et al., 2013; Fernandez-Flores et al., 2013;Smith et al., 2013; Strutzenberger et al., 2011), other thresholdsare also used, such as 1.6% (Smith et al., 2009), 2.3% (Younget al., 2012), 3% (Hendrich et al., 2010; Tang et al., 2012), or 3.2%(Weigand et al., 2013). Although the choice of threshold is some-times based on empirical studies (e.g. Hebert et al., 2003a;Weigand et al., 2011), there is no reason to believe the samethresholds will apply in different taxa; even within a taxon, therecan be highly variable patterns of intra- and interspecific diver-gence (e.g. Bergmann et al., 2013).

Despite this range, with a standard 2% threshold the estimatednumber of species (90) was within the range of the GMYC and theintermediate ABGD analyses, and most test species were recov-ered. However, due to the ‘‘greedy algorithm” used in the single-linkage clustering employed by this method (Jones et al., 2011),the delimitation of species boundaries sometimes differed consid-erably from the other two methods (Fig. 2).

4.5. Comparison of methods

We concluded that GMYC and PTP offer some distinct advan-tages over the other two methods of species delimitation. The lar-gest advantage is that these methods incorporate evolutionarytheory and are therefore much less reliant on the application ofarbitrary thresholds. Because of this, they are also more tractablewith genetic markers such as ITS2 that have less well-characterized intra- and interspecific patterns of diversity. Withappropriate tree-building methodologies, these methods can easilybe applied to any genetic marker. In comparison, choosing anappropriate threshold or range of thresholds for a marker otherthan COI is particularly arbitrary, since no ‘‘standard” divergencehas been accepted for species delimitation.

Nonetheless, caution must be employed when using either ofthese methods. One disadvantage of single-locus tree-based


methods is that all species, by definition, must be monophyletic,despite evidence that a high proportion of species are not mono-phyletic with respect to their gene trees (Funk and Omland,2003). GMYC relies on the priors and parameters used to con-struct the ultrametric tree (Ceccarelli et al., 2012), and the con-struction of the trees can be computationally demanding. GMYCmay also overestimate species diversity compared to other meth-ods (Kekkonen and Hebert, 2014; Hamilton et al., 2014; Mirallesand Vences, 2013; Paz and Crawford, 2012; Talavera et al., 2013;Weigand et al., 2013). PTP is far less computationally demandingthan the GMYC and since an ultrametric tree is not required,relies on fewer tree-building assumptions. It has been found tobe as accurate, if not more so, than GMYC when tested againstwell-defined species (Dumas et al., 2015; Modica et al., 2014;Tang et al., 2014; Zhang et al., 2013). In our study, it delimitedconsiderably fewer putative species than the GMYC analyses, pos-sibly an advantage due to the hypothesized oversplitting byGMYC. However, since it lumped several of the test species thatwere correctly delimited by GMYC, its success likely depends toa large extent on the phylogenetic patterns within the taxon inquestion.

Both jMOTU and ABGD provide the flexibility of examining arange of thresholds or priors, respectively, which can be informa-tive in understanding the effects of the algorithms and the diver-sity within the group. However, the majority of barcoding studiesuse a single threshold to estimate species diversity (e.g.Bribiesca-Contreras et al., 2013; Fernandez-Flores et al., 2013;Smith et al., 2013; Strutzenberger et al., 2011; but see Laetschet al., 2012). A range of priors is more often examined withABGD (Paz and Crawford, 2012; Puckridge et al., 2013); however,the impact of the relative gap width is rarely considered, eventhough it can have a significant impact on the results obtained. Afurther disadvantage of threshold analysis as implemented injMOTU is the grouping of distantly related species among taxa thatlack a clear barcoding gap. Despite these issues, distance-basedspecies-delimitation methods do not rest on the assumption ofreciprocal monophyly between species. It is therefore recom-mended that at least one distance-based and one tree-basedmethod be used in any species-delimitation study (Fontanetoet al., 2015).

All of the above methods are single-locus methods. While usingsingle-locus methods on multiple loci independently allows quali-tative comparisons and reduces the potential biases from relyingon a single molecular marker, several multi-locus methods havebeen developed that can better account for some of the issuesdescribed above (e.g. gene-tree discordance, non-monophyleticspecies, recently-diverged species with gene flow, etc.) (Dupuiset al., 2012; Fontaneto et al., 2015). Examples of multi-locus meth-ods include: Bayesian phylogenetics and phylogeography (BP&P;Yang and Rannala, 2010; Rannala and Yang, 2013); SpedeSTEM(Ence and Carstens, 2011); O’Meara’s heuristic model (O’Meara,2010); and Bayes Factor Delimitation (BFD; Grummer et al.,2014). BP&P is one of the most widely-used methods; however,until recently it required a user-supplied guide tree, thus being lesseffective for unguided species-delimitation in entirely unknowntaxa. Recently, methods have been developed that do not requireprevious knowledge of the included species, such as an unguidedversion of BP&P (Yang and Rannala, 2014) and DISSECT (Joneset al., 2015). Most multi-locus methods are computationally-expensive, and, depending on the study system, are arguably‘‘overkill” (Collins and Cruickshank, 2014). Nevertheless, assequencing becomes increasingly affordable, multi-locus methodswill no doubt become the norm. Future studies of Ophion, and ofspecies-delimitation studies in general, should include multi-locus methods.

4.6. How many Ophion species are there?

Based on COI (since many species were not successfullysequenced for ITS2), there was a wide range of estimates for thetotal number of species present in this study. Considering all plau-sible results of the quantitative species-delimitation analyses (i.e.including all GMYC analyses; PTP; ABGD with gap widths of 1.5and 0.75 and priors of 0.0017–0.0077 and 0.0017–0.0129intraspecific divergence, respectively; and jMOTU thresholds of1–3% divergence), estimates ranged from 47 to 168 species ofOphion represented in this study. Excluding the more extreme esti-mates (i.e. including only single threshold GMYC analyses; PTP;ABGD analyses with a prior of 0.0046; and jMOTU results with athreshold of 2%) resulted in estimates of 89–121 species (64–97of which are from the Nearctic region).

The wide range of estimates emphasizes that all methods ofmolecular species delimitation provide only ‘‘primary specieshypotheses” (Puillandre et al., 2012b), which need to be assessedusing additional methods. As a first step, these methods can be ahelpful tool to delimit hypothetical species; however, for compar-ative studies relying on species numbers (e.g. for conservationapplications or for analysis of patterns of biodiversity), it is vitalthat researchers also consider the parameters and potential short-comings of the delimitation method(s) used to obtain theestimates.

Our finding that most described British species were split maybe due to the failure of delimitation methods for successfully cir-cumscribing true biological species, or alternatively may indicatethe presence of cryptic or previously unrecognized species. Inseveral instances, putative species were not recovered as a mono-phyletic clade by phylogenetic analyses, indicating that multiple,previously unrecognized species are almost certainly present. Aswell, the steep slope of the barcode accumulation curve forthese specimens suggests that genetic sampling is far fromcomplete.

The use of a variety of analytical methods and parameters canaid in planning subsequent research to assess the hypothetical spe-cies obtained with these methods (Carstens et al., 2013). For exam-ple, groups of sequences that are consistently delimited with avariety of quantitative methods are good candidate species toexamine in more detail for confirmatory morphology, ecology orother sources of data that either support or refute themolecularly-defined species (Kekkonen and Hebert, 2014). In com-parison, species that are inconsistently delimited, while alsorequiring an integrative taxonomy approach, are good candidatesfor more fine-scaled analyses, such as quantitative morphometrics(Lumley and Sperling, 2010; Schwarzfeld and Sperling, 2014) orpopulation genetics (Shaffer and Thomson, 2007; Lumley andSperling, 2011).

5. Conclusion

Based on morphology, Gauld (1985) estimated a fauna ofapproximately 50 Nearctic species of Ophion. This study indicatesthat the true number of Nearctic species is considerably higherand with more complete sampling will easily double that estimate.The 17 described species of Ophion therefore represent just a min-ute fraction of the total fauna. Similarly, even for the relativelywell-studied British fauna, there appear to be a surprising numberof previously unrecognized species. It is widely acknowledged thatthere are vast numbers of undescribed species among tropical,small-bodied taxa (e.g. Quicke, 2012). However, this study showsthat even in a large-bodied, primarily temperate genus that is


readily attracted to lights and easily collected in large numbers,there is a wealth of diversity waiting to be discovered.

Acknowledgments

We are very grateful to Gavin Broad for loaning and donatingBritish Ophion, for sending Ophion legs for sequencing, and forfreely sharing his knowledge of British Ophion. Thanks also toMark Shaw for sending a leg from O. forticornis. We wish to thankJeremy deWaard for providing many specimens as well as nearly100 COI sequences through BOLD. This project would not havebeen possible without the many collectors who provided speci-mens, especially: John Acorn, John Adams, Gary Anweiler,Charley Bird, Heather Bird, Brett Bodeux, Bryan Brunet, JasonDombroskie, Julian Dupuis, Jessica Edwards, Dave Holden, WesHunting, Dave Langor, Nina Lany, Dave Lawrie, Lisa Lumley, DougMacaulay, Boyd Mori, Ted Pike, Greg Pohl, and Chris Schmidt. Wewould also like to thank Martin Jones for assistance with jMOTUand Matthias Wolf for help regarding 4Sale and ProfDistS. Thankyou to Seraina Klopfstein and two anonymous reviewers for theirconstructive comments on an earlier version of this manuscript.This work was supported by: the Sustainable Forest ManagementNetwork; Alberta Conservation Association Grants inBiodiversity; Natural Sciences and Engineering Research Council(NSERC) and Alberta Ingenuity (Alberta Innovates) student scholar-ships to MDS; and an NSERC Discovery grant to FAHS.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.ympev.2015.08.003.

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