Genome

Molecular diversity and species delimitation in the family (: )

Journal: Genome

Manuscript ID gen-2019-0186.R1

Manuscript Type: Article

Date Submitted by the 20-Apr-2020 Author:

Complete List of Authors: Parslow, Ben; Flinders University of South Australia, College of Science and Engineering; South Australian Museum, Schwarz, Michael; Flinders University Stevens, Mark;Draft South Australia Museum Keyword: ABGD, GMYC, Hymenoptera, COI, DNA barcode

Is the invited manuscript for consideration in a Special Trends in DNA Barcoding and Metabarcoding 2019 Issue? :

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1 Molecular diversity and species delimitation in the family Gasteruptiidae

2 (Hymenoptera: Evanioidea)

3

4 BEN A. PARSLOW1,2,5, MICHAEL P. SCHWARZ1, MARK I. STEVENS2,3

5 6 1Biological Sciences, College of Science and Engineering, Flinders University, Adelaide, SA

7 5001, Australia.

8 2South Australian Museum, North Terrace, GPO Box 234, Adelaide, SA 5001, Australia.

9 3School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA

10 5001, Australia

11 5Corresponding author: [email protected] 12 Draft 13 Abstract

14 Gasteruptiidae Ashmead is an easily recognised family of with circa 589 described

15 species worldwide. Although well characterised by traditional , multiple authors

16 have commented on the extreme morphological uniformity of the group, making species-

17 level identification difficult. This problem is enhanced by the lack of molecular data and

18 molecular phylogenetic research for the group. We used 187 cytochrome c oxidase subunit I

19 (COI) barcodes to explore the efficiency of sequence data to delimitate species in

20 Gasteruptiidae. We undertook a graphical and discussion-based comparison of six methods

21 for species delimitation, with the success of methods judged based on known species

22 boundaries and morphology. Both distance-based (ABGD and jMOTU threshold analysis)

23 and tree-based (GMYC and PTP) methods compared across multiple parameters recovered

24 variable molecular operational taxonomic units (MOTU’s), ranging from 55 to 123 MOTU’s.

25 Tree-based methods tended to split known morphological species less than distance-based

26 methods, with the single-threshold GMYC method the most concordant with known

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27 morphospecies. Our results suggest that the incorporation of molecular species delimitation

28 techniques provides a powerful tool to assist in the interpretation of species and help direct

29 informed decisions with taxonomic uncertainty in the family.

30

31 Key Words: DNA barcode, ABGD, GMYC, COI, Hymenoptera

32 Running Head: Diversity and species delimitation in Gasteruptiidae

33

34

Draft

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35 1. Introduction

36 There are multiple different methods for species delimitation that are used to assess

37 the species richness of understudied and highly diverse invertebrate taxa. The use of the COI

38 barcode gene fragment is a well-established tool used for species identification (Trivedi et al.

39 2018), species discovery (Dorey et al. 2019; Packer and Ruz 2016; Wang et al. 2018),

40 detection of invasive species (Groom et al. 2014; Shell and Rehan 2019), biological control

41 (Peixoto et al. 2018; Petrović et al. 2019), species conservation (Trivedi et al. 2018), and

42 identification of associated hosts (Smit et al. 2018). But different species delimitation

43 methods often recover variable molecular operational taxonomic units (MOTU’s), which

44 suggest different putative species boundaries (Hofmann et al. 2019). This can lead to

45 ambiguity, making the choice of algorithms to analyse DNA barcode data important. For the

46 analysis of single-locus datasets, the mostDraft commonly used methods can be split into two

47 types; distance-based methods and tree-based methods. Distance-based methods use the level

48 of differences between sequences to calculate intraspecific and interspecific thresholds, with

49 tree-based methods using a phylogenetic tree to calculate the variation in branches to

50 delimitate species. The use of COI barcode fragments has been readily used for species

51 delimitation in multiple hymenopteran families, including hyper diverse groups such as

52 (Microgastrinae (Fagan-Jeffries et al. 2018) and Doryctinae (Zaldívar-Riverón et

53 al. 2010)) and Formicidae (Oberprieler et al. 2018).

54 The family Gasteruptiidae Ashmead (Hymenoptera: Evanioidea) comprises two

55 extant monophyletic subfamilies, Gasteruptiinae Ashmead and Hyptiogastrinae Crosskey

56 (Crosskey 1962; Jennings and Austin 2002). The smaller subfamily Hyptiogastrinae consists

57 of two stable monophyletic genera, Hyptiogaster Kieffer (11 species) and Pseudofoenus

58 Kieffer (78 species) (Jennings and Austin 1997, 2002; Parslow and Jennings 2018). In

59 contrast, the larger subfamily Gasteruptiinae comprises four genera, with most of its

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60 members belonging to the large cosmopolitan Latreille (c. 500 species)

61 (Aguiar et al. 2013; Parslow et al. 2018; Tan et al. 2016), with three small genera restricted to

62 the Neotropical region; Plutofoenus Kieffer (3 species), Spinolafoenus Macedo (1 species),

63 and Trilobitofoenus Macedo (3 species) (Macedo 2009). The Gasteruptiidae exhibit

64 interesting biologies and are considered parasitoids, with their larvae being predator-

65 inquilines in the nest of solitary and wasps (Grieve et al. 2018; Jennings and Austin

66 2004; Parslow et al. 2020a). Systematic research on the family using molecular data is scarce,

67 with the current literature restricted to isolated taxa in large-scale phylogenetic datasets (e.g.

68 Carpenter 1999; Deans et al. 2006; Dowton and Austin 2001; Heraty et al. 2011; Klopfstein

69 et al. 2013; Klopfstein et al. 2018; Li et al. 2018; Peters et al. 2017; Tang et al. 2018), and

70 only a single study using COI barcoding for species delimitation and species discovery

71 (Saure et al. 2017). Additionally, publiclyDraft available COI barcode sequences across BOLD

72 systems (Ratnasingham and Hebert 2007) and Genbank (Clark et al. 2016) are restricted to

73 190 public records; of these 87 have species names but only represent 25 individual species.

74 Although the genera are well characterised by traditional taxonomy, multiple authors have

75 commented on the extreme morphological uniformity, making species-level identification

76 difficult (Crosskey 1962; Jennings and Austin 2002; Saure et al. 2017). When we consider

77 the current number of described species, the difficulty in identification and the paucity of

78 material in molecular studies suggests there is a need to expand molecular databases and

79 explore molecular species delimitation techniques. The objective of this study was to explore

80 the utility of a large-scale DNA barcode analysis of the Gasteruptiidae, including all publicly

81 available sequences. We compare commonly used molecular delimitation techniques using a

82 graphical and discussion based comparison. Successful species boundary delimitation was

83 based on the recognition of morphologically known species in the group. This approach was

84 used to suggest putative MOTU’s that are likely to represent species under the general

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85 lineage species concept (de Querioz 1998). Our study examining the diversity within

86 Gasteruptiidae adds to those highlighting DNA barcodes to better inform future taxonomic

87 and phylogenetic research.

88

89 2. Material and methods

90 2.1 Specimen collection

91 A total of 187 sequences were included in the study, with 77 sequences newly

92 generated in this study and 109 sequences mined from publicly available databases

93 (Supplementary Table S1.). We attempted to sample across all biogeographical regions, with

94 100 sequences from the Australasian region, 21 from the Palearctic, 12 from the Afrotropical, 95 46 from the Nearctic, seven from the NeotropicalDraft and one from the Indomalayan region. 96 Australasian Gasteruption material was identified where possible to species level using

97 Pasteels’s (1957) key to Australian Gasteruption. All newly sequenced Palearctic species

98 were determined by C.A. van Achterberg (Naturalis Centre, Leiden), with South

99 African material not identified to species as type material is unavailable for examination, and

100 the only available key is currently out of date (Pasteels 1962). Newly sequenced

101 Hyptiogastrinae specimens where identified where possible using Jennings & Austin (2002)

102 identification key to Pseudofoenus. All publicly available Gasteruptiidae sequences were

103 initially examined (190 sequences), with sequences duplicated across public databases;

104 sequences with over 2% ambiguous nucleotides and contaminated sequences that were

105 misidentified and not in Gasteruptiidae (checked against the NCBI BLAST database) were

106 removed from further analysis. In total 104 sequences were obtained from BOLD systems

107 (Ratnasingham and Hebert 2007) (BOLD search for “Gasteruptiidae” in the public data

108 portal, using the API search method, conducted on 21 July 2018) with six from Genbank

109 (Clark et al. 2016) (search for “Gasteruptiidae” in the nucleotide database, using the API

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110 search method, conducted on 21 July 2018) (accession numbers and metadata for samples

111 included in Supplementary Table S1). Sequences were coarsely identified where possible to

112 genus using photos associated with the sequences provided by BOLD systems. The final

113 publicly available dataset contained 73 Gasteruption sequences and 37 Hyptiogastrinae

114 sequences (32 Pseudofoenus and 5 Hyptiogaster sequences).

115

116 2.2 Sequencing and data alignment

117 77 new sequences were generated, covering a 654 bp fragment of the cytochrome c

118 oxidase subunit I (COI) gene were generated using universal primers (LCOI490 (Fwd) –

119 GGTCAACAAATCATAAAGATATTGG and HCO2198 (Rev) –

120 TAAACTTCAGGGTGACCAAAAAATCA) (Folmer et al. 1994). In addition to these

121 sequences obtained through Sanger sequencing,Draft we mined the same COI barcode fragment

122 from non-targeted regions in a preliminary ultraconserved element dataset set based on

123 Evanioidea (15 species from Parslow et al. unpublished data). DNA was extracted either non-

124 destructively from full specimens or destructively from the right mid leg of specimens using

125 the Qiagen Gentra Puregene kit, following the manufacturer’s protocol with the following

126 changes; samples were incubated overnight at 55°C and centrifuged for 15 minutes after

127 protein precipitation. Final elution volume was 50.0 µl. Extraction of DNA was conducted at

128 the South Australian Regional Facility for Molecular Ecology and Evolution (SARFMEE).

129 For sequences generated using the Sanger sequencing method the polymerase chain

130 reaction (PCR) amplification was carried out in an Eppendorf thermal sequencer, 25 µl

131 volume reactions of 16.5 µl of DNAase/RNAase-free water, 5.0 µl of 5x Immolase buffer,

132 1.2 µl of both forward and reverse primers (5.0 µM), 0.1 µl of Immolase enzyme and 1.0 µl

133 of neat DNA. PCR conditions were as follows, initial denatured at 95°C (9 min), thirty-five

134 cycles of 94°C (30 sec), 47°C (30 sec), 72°C (1 min), single cycle of 72°C (6 min) and 24°C

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135 (3 min). Purification and bidirectional Sanger sequencing was carried out at Macrogen

136 (Seoul, South Korea).

137 To mine the C0I Barcode sequences from the preliminary ultraconserved element

138 dataset, extracted DNA was sheared to a length of ~600 bp, with generation of DNA libraries

139 using a specialized Tru-seq style dual indexing adaptor system allowing for multiplexing and

140 hybridization to enrich libraries. Quantification of adapter-ligated fragments post enrichment

141 was performed via quantitative polymerase chain reaction (qPCR) to capture UCE loci. A

142 library of all individuals was combined into one pool and submitted to one lane on an

143 Illumina sequencer at the University of Utah, USA. To process and align the sequence data

144 we used the PHYLUCE v1.6.6 pipeline (Faircloth 2015) with the following programs to

145 process the raw target capture data and extract the targeted loci. We used the program

146 ILLUMIPROCESSOR (Faircloth 2013),Draft a wrapper around TRIMMOMATIC, to remove

147 adaptor contamination and low-quality reads. We assembled the read using the wrapper

148 (phyluce_assembly_assemblo_spades) around SPADES genome assembler v3.13.0 on a

149 combination of computational resources at the University of Utah, USA and Flinders

150 University, Adelaide, Australia. After assembly we used the program

151 (phyluce_assembly_match_contigs_to_barcodes) to extract loci from the completed pool of

152 contigs. Extracted contigs from the exon capture dataset were checked against the NCBI

153 BLAST database to screen for contamination. Sequences were examined in Geneious v10.2.2

154 (https://www.geneious.com) for stop codons before being pooled with our newly sequenced

155 Sanger sequences and publicly available sequence data. The final alignment was

156 concatenated with the overall length of the sequences trimmed to 654 bp to exclude missing

157 characters in the final matrix. All new sequences are deposited in NCBI’s GenBank with

158 accession numbers listed in Supplementary Table S1.

159

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160 2.3 Phylogenetic tree construction

161 Ultrametric trees required for tree-based species delimitation methods were estimated

162 using BEAUTi and BEAST 1.10.4 (Drummond et al. 2002). A single model of evolution,

163 GTR + I + Γ, was applied across all codon partitions as suggested by PartitionFinder v2.1.1

164 (Lanfear et al. 2016). The choice of tree prior has been shown to affect tree-based species

165 delimitation results (Ceccarelli et al. 2012), with the Yule tree prior recommended for

166 species-level data and the Coalescent tree prior for population-level data (Drummond et al.

167 2006). We used a strict molecular clock to calculate trees with empirical frequency-based

168 priors starting from a random tree and two different tree priors: speciation: Yule process tree

169 prior and the Coalescent: constant size tree prior. Analyses were completed on the CIPRESS

170 science Gateway (Miller et al. 2010) with each analysis run for 107 generations, sampling

171 every 10,000 trees. Convergence and stationarityDraft of model parameters was assessed with

172 Tracer v1.7.1 (Rambaut et al. 2018) with 10% of sampled trees discarded as burn-in, and the

173 maximum credibility tree was generated using Tree Annotator v1.10.4.

174

175 2.4 Species delimitation

176 2.4.1 Distanced-based methods

177 Two different distance-based species delimitation methods were tested, the automatic

178 barcode gap discovery method (ABGD) (Puillandre et al. 2012a) and jMOTU (Jones et al.

179 2011). ABGD is a distanced-based method that detects clusters of sequences using the

180 distribution of pairwise distances. This computationally efficient technique recursively

181 partitions the data and compares the difference between sequences to identify a “barcode

182 gap” that may indicate species boundaries. The method requires an input in the form of an

183 alignment to generate a distance matrix. We calculated the number of clusters for all genera

184 combined (Gasteruption, Hyptiogaster and Pseudofoenus) using the ABGD web server

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185 (Puillandre et al. 2012a: available at https://bioinfo.mnhn.fr/abi/public/abgd/abgdweb.html),

186 using the default priors, Pmin = 0.001, Pmax = 0.1, Steps 10, and with barcode relative gap

187 width = 1.00. To check if distances between genera influenced the clustering of species, we

188 calculated the number of clusters for each genus separately, which recovered no variation

189 compared to the combined analysis.

190 The program jMOTU uses several predefined thresholds to calculate the genetic

191 differences within the average sequence length. This is a common method with thresholds

192 ranging from 1–3% (Hebert et al. 2003). We ran the analysis on the full dataset with all

193 genera combined using threshold values initially from 1–20 bp with a low BLAST identity

194 filter of 97% and percentage of minimum sequence length of 60% as per the manual’s

195 suggestions (Jones et al. 2011). From the program’s outputs we compared sequence

196 differences for three threshold values, Draft1% = 7 bp, 2% = 14 bp and 3% = 20 bp differences.

197

198 2.4.2 Tree-based methods

199 We analysed the sequence data using four tree-based methods for species

200 delimitation, two thresholds using the generalized mixed Yule coalescent (GMYC) analysis

201 (Pons et al. 2006) and two versions of the Poisson tree processes model (PTP) (Zhang et al.

202 2013). The generalized mixed Yule coalescent (GMYC) analysis is a coalescent based

203 phylogenetic method that sets thresholds between coalescent and species-level processes to

204 delimit species (Fujisawa and Barraclough 2013; Fontaneto et al. 2015). Being a tree-based

205 method, the only input needed is an ultrametric phylogenetic tree. For our analysis the

206 maximum credibility tree obtained from BEAST was used as the input for the GMYC

207 analysis using the SPLITS package in the R platform (Team (2019): available from http://r-

208 forge.r-project.org/projects/splits/). The tree was analysed separately using the default single-

209 threshold (sGMYC) (Pons et al. 2006) and the multiple-threshold (mGMYC) (Monaghan et

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210 al. 2009), which was developed to take into account the different branching patterns and rates

211 across an ultrametric input tree.

212 The PTP analysis models speciation events relative to the number of substitutions in

213 a given branch, which equates to a higher expected number of substitutions between species

214 than within species (Zhang et al. 2013). The advantage of the original PTP method is it

215 doesn’t need an ultrametric tree, which can be computationally intensive to create. For our

216 analysis we used the BEAST tree created for the GMYC analysis so we could compare the

217 outputs generated. We used the newly developed Bayesian version of the PTP model (bPTP)

218 which was run using the online web servers (Zhang et al. 2013): available at https://species.h-

219 its.org/ptp/) with default parameters. We also tested the Multi-rate Poisson tree process 220 (mPTP) with default parameters (availableDraft at https://mptp.h-its.org/#/tree), which 221 incorporates different values of intraspecific divergence caused by differences in the

222 evolutionary history or sampling of the species (Kapli et al. 2017). To visualise species

223 delimitation outputs on the phylogenetic tree in figure 1, we used FigTree (ver. 1.4.4,

224 http;www.beast.community/figtree) before adding the graphical representation of species

225 delimitation methods in Adobe Illustrator (Adobe Systems, Inc., San Jose, CA).

226

227 2.4.3 Comparing concordance between MOTU’s

228 To assess the outputs between species delimitation methods we graphically compared

229 the clustering of sequences to morphologically known species boundaries. We used this to

230 inform our comparisons in terms of the degree that each method “split” or “lumped” the

231 sequences. We were unable to further explore the clustering results through a quantitative

232 metric of concordance patterns (e.g. Young et al. 2018) due to the large number of singleton

233 sequences included in our analysis.

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234

235 3. Results

236 3.1 Phylogeny and sequence analysis

237 The 187 aligned sequences of Gasteruptiidae had a maximum length of 654 bp with

238 two taxa, Gasteruption_albicuspis_G50 and Pseudofoenus_sp.BP342 with a sequence length

239 of 428 and 491 bp respectively. In total there were 322 variable sites (49.31%), with 285

240 (43.82%) parsimoniously informative sites (Table 1). The sequences were AT-biased at

241 72.7%, and with an extreme AT bias (92.5%) at 3rd codon positions. The mean pairwise

242 distance (Kimura 2-parameter model) between genera is 0.17 (0.16–0.18), while the mean

243 distance among different species within genera was lower for Gasteruption (0.13),

244 Hyptiogaster (0.10) and Pseudofoenus (0.07) (Table S3). The recovered COI phylogram

245 provides high support for the monophylyDraft of the Gasteruptiinae (Gasteruption) and

246 Hyptiogastrinae (Hyptiogaster and Pseudofoenus) (Fig. 1, Nodes A and B both with PP = 1).

247 Support for relationships at apical nodes in the tree were generally high (>98 PP); however,

248 basal nodes had varying levels of low support (0.01–0.95 PP).

249

250 3.2 Species delimitation analyses

251 In general, most of the six tested methods recovered similar grouping of MOTU’s

252 (Fig. 1), with the mPTP method being the most conservative, lumping the sequences into

253 fewer MOTU’s and the jMOTU 1% method the most relaxed, lumping the sequences into

254 several MOTU’s.

255

256 3.2.1 Distance-based methods

257 The two distance-based methods recovered vastly different numbers of MOTU’s, with

258 ABGD being more conservative in its delimitation compared to jMOTU. The ABGD analysis

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259 returned a total of 87 MOTU’s (Gasteruption = 75, Hyptiogastrinae = 12) at a prior

260 intraspecific divergence of 0.021544, whereas a total of 123, 109 and 105 MOTU’s were

261 delimited by the jMOTU analysis at thresholds of 1%, 2% and 3% respectively. There was a

262 large difference in clustering of MOTU’s compared to the topology of the Bayesian

263 phylogram estimated with BEAST and the Yule process. These disagreements are marked as

264 arrows representing modification of taxa placement in the rings jMOTU 1% rings, jMOTU

265 2% and jMOTU 3%.

266

267 Tree-based methods

268 In general, a similar number of MOTU’s was recovered between the two different

269 tree-building methods (Yule and Coalescent), with only four disagreements in topology

270 between the two tree priors. The two GMYCDraft methods, single (sGMYC) and multiple

271 (mGMYC), recovered similar MOTU’s across both tree priors, with the results for sGMYC

272 for both tree priors both recovering 96 MOTU’s compared to mGMYC with 108 using the

273 Yule prior and 111 for the coalescent prior. A single sequence placement disagreement for

274 the taxa Gasteruption_sp.OPPEE3126_17 in the Coalescent mGMYC result is represented in

275 Fig.1. Ring CmGMYC.

276 There was a vast difference in the number of MOTU’s delimited between the two

277 different PTP methods (bPTP and mPTP) but only a small difference in the MOTU’s

278 between Yule and Coalescent tree models. bPTP delimited 104 and 105 MOTU’s for the

279 Yule and Coalescent priors, respectively, compared to 58 and 55 for the mPTP method using

280 the Yule and Coalescent tree priors. There were three disagreements in topology between the

281 Coalescent tree mPTP results and the Yule tree (Fig. 1, arrows in ring CmBPTP), with two

282 singleton taxa and a pair being moved to create MOTU’s not recovered by other methods.

283

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284 4. Discussion

285 The objective of this study was to explore the utility of a large-scale DNA barcode

286 analysis of the Gasteruptiidae to examine the molecular diversity of the family and to

287 compare the suitability of molecular delimitation techniques. Our results found that tree-

288 based methods were more concordant with MOTU’s that are likely to represent species under

289 the general lineage species concept (de querioz 1998). This success was judged based on the

290 known species boundaries and morphology in the family and how the techniques either

291 lumped or split sequences according to these.

292

293 4.1 Sampling

294 Quantitative species delimitation methods require species to be adequately sampled

295 (Dopheide et al. 2019) with sufficient Draftsampling across generic and geographical ranges to

296 improve delimitation methods. In practice this is difficult for Gasteruptiidae with the known

297 worldwide diversity of ~589 described species (Jennings and Austin 2002; Aguiar and

298 Lohrmann 2013; Tan et al. 2016; Parslow and Jennings 2018; Parslow et al. 2018) and the

299 expected number including undescribed species closer to three times that (Jennings and

300 Austin 2002). Recent taxonomic treatments of regional fauna have been undertaken, for

301 example Western Asia (van Achterberg and Talebi 2014; Saure et al. 2017), Eastern Asia

302 (Zhao et al. 2012; Tan et al. 2016), Eastern Europe (van Achterberg 2013; Zikic et al. 2014;

303 Johansson and van Achterberg 2016) and South America (Macedo 2011). Despite these, there

304 are large regions that are in need of modern taxonomic treatments, for example Australia

305 (Pasteels 1958a), Africa (Pasteels 1958b) and Papua New Guinea (Pasteels 1956). Because of

306 the potentially large number of undescribed species, it is difficult to estimate the level of

307 sampling completeness across the genus.

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308 Our study included all publicly available COI sequences for Gasteruptiidae but was

309 biased towards Australasian (53.5 %) and Nearctic (24.6 %) taxa, with a limited

310 representation of Palearctic (11.2 %), Afrotropical (6.4%), Neotropical (3.7%) and a single

311 species from Indomalaya (0.5%). All species delimitation methods detected a high number of

312 species represented by only a single sequence (CmGMYC – 77 singletons, YmGMYC – 76,

313 CsGMYC - 64, YsGMYC – 64, CmPTP – 19, YmPTP – 20, CbPTP – 76, YbPTP – 74,

314 jMOTU 1% - 96, jMOTU 2% - 77, jMOTU 3% - 80, ABGD – 59), which is a typical result

315 for understudied taxa (Velasco-Castrillón et al. 2014; Zhang et al. 2018). COI gene trees can

316 be good at informing species boundaries and relationships among closely related species but

317 have limited reliability for deeper phylogenetic relationships. Given this, and the reduced

318 support medially within the tree, the deeper structure of the recovered tree is not robust.

319 However, our phylogeny did support theDraft monophyly of both subfamilies, Hyptiogastrinae

320 (Fig. 1, Node A) and Gasteruptiinae (Fig. 1, Node B), which has been recovered by previous

321 studies (Jennings and Austin 2002; Macedo 2009; Parslow et al. 2020b).

322

323 4.2 Comparison of parameters

324 Because the number of MOTU’s in a dataset will vary depending on the method used

325 and threshold value used, we tested six different methods with variation in parameters to

326 explore what might be an appropriate technique for species delimitation in Gasteruptiidae.

327 The parameters for distance-based methods are priors that effect the sensitivity of the

328 analysis. ABGD results are sensitive to the variation in the gap width used, with recent

329 studies often using the default value of 1.5 (Tang et al. 2012; Kekkonen and Hebert 2014).

330 However, for our analyses we were forced to use a finer relative barcode gap value of 1.00 as

331 coarser values above 1.05 group all sequences into a single partition. This might be indicative

332 of a rapid speciation event within the group, with recent research by Parslow et al. (2020b)

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333 suggesting a crown age for the family during the Paleocene at 60.23 MYA, which correlates

334 with diversification of their hosts. Schwarzfeld et al. (2015) compared a 1.5 and 0.75 barcode

335 gap when delimitating species of Ophion Fabricus () and found that by

336 reducing the barcode gap the number of putative species increased. We tested three

337 thresholds for the jMOTU analysis, which recovered a similar delimitation of MOTU’s for

338 the 2% and 3% threshold with 109 and 105, respectively. The 1% threshold recovered 123

339 MOTU’s, which was the highest number for all methods. The lowest threshold also divided

340 sequences identified as species (based on morphology) into separate units; for example, the

341 Pseudofoenus extraneus clade consisting of four sequences (two sequences from specimens

342 collected at higher elevations, and two from lower elevations in Fiji) were separated into four

343 individual MOTU’s. Although the separation in the clustering of the sequences could be due

344 to cryptic species diversity, which is oftenDraft found in Hymenoptera (e.g Csősz et al. 2014; Li et

345 al. 2010), more extensive examination of these clusters is required. The 2% threshold is

346 commonly used as a threshold for species delimitation (Hebert et al. 2003), but there is little

347 consistency with this value as other taxonomic groups use both higher and lower thresholds

348 (e.g. 1.6% Smith et al. 2009, 2% Smith et al. 2012, and 3% Tang et al. 2012).

349 Tree-based methods were tested using two different tree priors, and two types of

350 analysis for each technique. The selection of the tree prior and analysis type affected the

351 number of returned MOTU’s in all methods except the sGMYC analysis. It is generally

352 considered that Yule priors are appropriate for speciation-level data, whereas coalescent

353 priors are often used for intraspecific population-level data (Drummond et al. 2006). Based

354 on these assumptions we would expect the trees constructed with the Yule prior to be more

355 accurate with these data given large number of singletons sampled (Ceccarelli et al. 2012).

356 Schwarzfeld et al. (2015) found in their study of palearctic Ophilion (Ichneumonidae:

357 Ophioninae) that the ability to delimit species and the number of estimated species were

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358 unaffected by the change in tree priors between Yule and Coalescent. We recovered only a

359 small difference in MOTU’s between tree priors, with the Yule prior delimitation a lower

360 number of MOTU in the mGMYC (yule prior = 108, coalescent prior = 111) and bPTP (yule

361 = 104, coalescent = 105) analyses, but recovering the opposite for the mPTP analysis which

362 recovered more MOTU’s using the coalescent prior (yule prior = 58, coalescent prior = 55).

363 The mGMYC method was developed to take into account the different branching

364 patterns and rates across an ultrametric input tree, although it has been found it to be less

365 accurate when compared to the sGMYC method (Fujisawa and Barraclough 2013) with it

366 tending to overestimate putative species (Esselstyn et al. 2012; Schwarzfeld and Sperling

367 2015). Our dataset found a similar pattern with it over splitting groups; for example the New

368 Zealand Pseudofoenus clade (Node A) was split into 4 MOTU’s and the Gasteruption

369 assectator clade (Node B) into eight MOTU’s.Draft

370 For these data there was a large difference between recovered MOTU’s between

371 bPTP methods. There was a 46 MOTU difference between the mPTP and bPTP methods

372 using the Yule tree prior and a 50 MOTU difference for the coalescent tree prior. mPTP was

373 the most conservative and regularly underestimated species by grouping singleton species

374 (represented in the tree by long-isolated branches) into MOTU’s. Similar to our results, other

375 studies found these methods lead to a lower number of recovered species when compared

376 with other methods (e.g. da Silva et al. 2018).

377

378 4.3 Comparison of species delimitation methods

379 We suggest that tree-based methods are more reliable than distanced-based because

380 they are able to incorporate evolutionary theory and therefore don’t need arbitrary thresholds

381 (Schwarzfeld et al. 2015). The trade off in increased reliability is the computationally

382 demanding task of constructing a phylogenetic tree and incorporating more tree-building

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383 assumptions. The GMYC method has been suggested to overestimate species but is suitable

384 for large numbers of singleton taxa (Talavera et al. 2013). In contrast, PTP methods are often

385 favoured when analysing large datasets, as this method does not require an ultrametric tree

386 for input, so is less computationally intensive. In addition, previous studies have found only

387 small differences between the results of a maximum likelihood tree and Bayesian inference

388 tree when using PTP (Dumas et al. 2015) and that this method generally performs better

389 when compared to GMYC, except when using a BEAST tree (Tang et al. 2014). In our

390 analysis the bPTP methods tended to be less accurate compared to our GMYC results; this

391 can be probably be attributed to both methods using an ultrametric tree created in BEAST.

392 The accuracy of PTP relies on the quality of the phylogenetic tree input; taxon sampling is

393 important to help with the accuracy of the tree reconstruction (Tang et al. 2014). The sGMYC

394 method was considered to be most accurateDraft to our species concept with a total 96 putative

395 species, and the correct delimitation of 68 putative species where morphology was known.

396 The ABGD method was more reliable compared to the jMOTU method we tested as

397 the program determines the sequence divergence threshold given the dataset instead of using

398 arbitrary values. It gave reliable results, correctly delimitating species in most cases where

399 morphology was known, however was the most conservative when grouping sequences in the

400 New Zealand Pseudofoenus clade (Node A) and clade (Node B).

401 ABGD tended to group all members in these clades into a single MOTU; in contrast all other

402 methods separated these clades into multiple clusters. Although the ABGD method is

403 computationally efficient and for our dataset shows good utility at delimiting MOTU’s, there

404 are some limitations with the method. If the data lack gaps between species (i.e. taxa which

405 have recently speciated and have minimal variation between sequences), then the method

406 doesn’t work well for species delimitation (Reid and Carstens 2012). It is also recommended

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407 to include three to five sequences per species, providing enough information within and

408 between species to delimit species accurately (Puillandre et al. 2012b).

409 The jMOTU results were consistent when delimiting singletons but tended to over

410 split species groups at all thresholds when compared to other methods; in addition this

411 method produced the largest number of disagreements in cluster number and membership.

412 The 2% and 3% thresholds recovered MOTU’s similar to the mGMYC and bPTP results with

413 the 1% threshold delineating the largest number of MOTU’s at 123.

414 All the tested methods used in this study rely on a single locus to delimit species. It is

415 generally considered that the analysis of additional loci increases average delimitation

416 accuracy (Dupuis et al. 2012), for example nuclear and ribosomal RNA fragments such as

417 28s (Nugnes et al. 2017) and ITS2 (Schwarzfeld and Sperling 2015; Fagan-Jeffries et al.

418 2018). We did not explore the delimitationDraft results with an additional fragment as a large

419 portion of sequences included in these data were mined from publicly available sequences,

420 which are often restricted to a single fragment. We suggest to increase the robustness of

421 species delimitation it would be a beneficial to explore additional fragments or with the

422 increasing accessibility of high-throughput sequencing methods, multilocus species

423 delimitation methods (Waichert et al. 2019), such as Bayesian Phylogenetics and

424 Phylogeography (Lin et al. 2018; Yang 2015).

425 Our study assessed the outputs between species delimitation methods by graphically

426 comparing the clustering of sequences to known species boundaries and morphology where

427 available. The lack of confident identification for a large portion of the included sequences

428 made it difficult to quantitively validate the clustering results against known species

429 boundaries. Young et al. (2018) suggested a more thorough method to further explore the

430 clustering results by calculating a quantitative metric of concordance patterns. They

431 compared the concordance of two independent datasets using an adjusted Wallace coefficient

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432 (Wall 1983). To avoid singleton sequences from biasing the results towards concordance, any

433 species represented by a single sequence was removed from the analysis. Because our

434 sequences with known morphology consist almost entirely of singletons (47 singletons out of

435 68 morphospecies) we were unable to uses this technique, but recommend future studies use

436 internal methods for cluster validation to provide greater support for the interpretation of

437 species boundaries (Young et al. 2018).

438

439 4.5 Diversity of Gasteruptiidae

440 The inclusion of publicly available sequences is a common method to group known

441 morphospecies with unidentified specimens for coarse identification purposes or to highlight

442 taxa for further detailed analysis (Song et al. 2018). Our dataset includes 106 sequences

443 mined from BOLD + Genbank and 77Draft newly sequenced specimens, with the known

444 morphologies suggesting 68 morphospecies. There was some success with associating

445 individual morphospecies with unidentified sequences, but overall there are minimal

446 sequences available for Gasteruptiidae that represent the true diversity. In addition, regions

447 with high biodiversity (e.g. Australasian and Afrotropical region) are often underrepresented

448 in publicly accessible data. Although our data represent a limited sample of the overall

449 Australian fauna, which is currently at 114 described species (Pasteels 1957; Jennings and

450 Parslow 2014), our study suggests that we have been able to sequence many previously

451 unknown morphospecies.

452 The species delimitation methods correctly grouped known species into clusters in

453 most cases, for example, Gasteruption platycephala Pasteels where three known BOLD

454 sequences and one unknown sequence were grouped, Gasteruption angusticeps Kieffer with

455 three known sequences and Gasteruption primotarsale Pasteels with two sequences. But the

456 techniques were not always successful in some cases, with identified species being recovered

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457 separately, Gasteruption tournieri Schletterer, a GenBank COI fragment mined from a partial

458 mitochondrial genome was recovered separately from our sequenced individual; in addition

459 the species delimitation techniques found our sequence for Gasteruption tournieri grouped

460 with a specimen. These failures to associate the determined species is

461 probably due to misidentification of specimens and is a limitation of incorporating sequence

462 data when specimens are not available for morphological examination (Collins and

463 Cruickshank 2013).

464 The species Gasteruption assectator (Linnaeus) is considered a very common species

465 with a Holarctic distribution and wide intraspecific variation. A recent review of the complex

466 divided it into three distinct species based on morphology, G. assectator, G. boreale

467 (Thomson) and G. nigritarse (Thomson) (Johansson and van Achterberg 2016). Although the

468 authors found morphological and distributionalDraft differences in the three species, our analyses

469 consistently recovered the three species as a single MOTU. When we look at the distribution

470 of sampled sequences, the three sequences are from the Palearctic region, with all other

471 sequences within node B from the Nearctic region. The high support separating these clades

472 could suggest further geographic structure based on genetic divergence, but further

473 examination of material is necessary.

474 The Hyptiogastrinae was underrepresented in the study with only a single identified

475 species of Hyptogaster and four Pseudofoenus morphospecies. The current known diversity

476 for the group is 89 described species (11 Hyptiogaster and 78 Pseudofoenus species) but with

477 approximately 50 undescribed species from Australia (J. Jennings 2019, pers. comm.). There

478 were inconsistences recovered in the clustering of some sequences from BOLD (Fig. 1, Node

479 A), with different identified sequences being grouped into the same cluster. Among the

480 unidentified species in this clade there are two sequences determined as P. uniculatus and one

481 as P. pedunculatus. The structure suggested from the species delimitation analyses also

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482 separates these species, suggesting there is either more complexity in the species or that the

483 specimens has been misidentified. These examples highlight the need to increase both species

484 sampling effort but also duplication of individuals to obtain representation of groups with

485 variability across populations.

486 Our results suggest that there is a high diversity of Gasteruptiidae with barcode-based

487 MOTU’s likely to represent evolutionarily distinct species, for future research additional

488 information in the form of molecular, morphological and ecological information should be

489 considered before formal taxonomic revisions are supported (Collins and Cruickshank 2013).

490

491 5. Conclusion

492 Molecular species delimitation techniques are powerful tools, but multiple techniques should

493 be used in conjunction with traditionalDraft morphology for the best results. We evaluated several

494 methods for species delimitation in the Gasteruptiidae using a single locus and found that

495 sGMYC methods split known morphological species less than other tested methods. We

496 suggest increased taxon sampling and the use of additional molecular data for greater

497 resolution when using molecular species delimitation techniques for the Gasteruptiidae. Our

498 results highlight the already large sequenced diversity for the Gasteruptiidae, and with more

499 regional sampling the incorporation of species delimitation techniques will provide a

500 powerful tool to assist in the discovery of new species and help direct informed decisions

501 with taxonomic uncertainty in the family.

502

503 Acknowledgements

504 We thank the following institutions and curators for their contribution of material:

505 Naturalis Biodiversity Centre (Cornelis van Achterberg), Iziko South African Museum

506 (Simon van Noort), Queensland Museum (Susan Wright), South Australian Museum (Peter

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507 Hudson), Waite and Nematode Collection (John Jennings and Andrew Austin);

508 Australian National Insect Collection (Juanita Rodriguez), Australian Museum (Derek

509 Smith), Tasmanian Museum & Art Gallery (Simon Grove) and the West Australian Museum

510 (Nikolai Tatarnic). We would also like to thank Emily Sadler for library preparation and

511 sequencing of the Target capture dataset and for bioinformatics assistance and Alejandro

512 Velasco Castrillón for assistance in the wet lab. The research was funded by an ABRS

513 National Taxonomy Research Grant Programme grant RF217-14 and a Holsworth Wildlife

514 Research Endowment awarded to BAP.

515

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842 Zhao, K.–x., van Achterberg, C., and Xu, Z.–f. 2012. A revision of the Chinese 843 Gasteruptiidae (Hymenoptera, Evanioidea). Zookeys, 237: 1–123. 844 doi:10.3897/zookeys.237.3956. 845 Zikic, V., van Achterberg, C., Stankovic, S.S., Dubaic, J.B., and Cetkovic, A. 2014. Review 846 of the Gasteruptiidae (Hymenoptera: Evanioidea) from the territory of the former 847 Yugoslavia, with three newly reported species. Zootaxa, 3793(5): 573–586. 848 doi:10.11646/zootaxa.3793.5.5. 849 850

851

852 Figure 1. Phylogram of 187 Gasteruptiidae COI sequences showing different results of

853 species delimitation methods. Colours rings represent each method with bars representing

854 molecular operational taxonomic units (MOTUs), ABGD 1.0 gap = 87 MOTUs, jMOTU 3%

855 = 105 MOTUs, jMOTU 2% = 109 MOTUs, jMOTU 1% = 123 MOTUs, YbPTP = 104

856 MOTUs, = CbPTP = 105 MOTUs, = YmPTP = 58 MOTUs, CmPTP = 55 MOTUs,

857 YsGMYC = 96 MOTUs, CsGMYC = Draft96 MOTUs, YmGMYC = 108 MOTUs and CmGMYC

858 = 111 MOTUs. Black arrows represent variation in the membership of individual clusters

859 compared to the presented tree. Black dots on nodes represent posterior probability >0.98.

860 letters identify nodes discussed in the text.

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1 Table 1. Variable and informative sites, and average nucleotide composition (in %) for each

2 codon position in the aligned COI gene sequences.

variable Informative Nucleotide position A C G T AT GC sites (%) sites (%) 1st 38.5 32.6 33.0 10.9 23.7 32.5 65.5 34.6 2nd 15.6 9.6 15.2 22.4 17.2 45.2 60.4 39.6 3rd 93.6 88.5 42.5 1.1 6.5 49.8 92.3 7.7 All 49.2 43.6 30.2 11.5 15.8 42.5 72.7 27.3 3

Draft

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Pseudofoenus_sp.NZHYM1806_12 Pseudofoenus_sp.NZHYM1531_12 Pseudofoenus_sp.NZHYM1803_12 Pseudofoenus_sp.NZHYM1964_13 Pseudofoenus_unguiculatus_NZHYM517_10 Pseudofoenus_sp.NZHYM1807_12 Pseudofoenus_sp.NZHYM1532_12 Pseudofoenus_pedunculatus_NZHYM514_10 Pseudofoenus_sp.NZHYM1965_13 Pseudofoenus_sp.NZHYM1805_12 Pseudofoenus_sp.NZHYM1804_12

Pseudofoenus_sp.NZHYM513_10 Pseudofoenus_unguiculatus_NZHYM097_10Pseudofoenus_sp.NZHYM1512_12Pseudofoenus_sp.NZHYM515_10

Pseudofoenus_sp.NZHYM1831_12 Gasteruption_sp.G94

Pseudofoenus_sp.NZHYM096_10

Pseudofoenus_sp.NZHYM1527_12 Pseudofoenus_sp.NZHYM1525_12 Pseudofoenus_sp.NZHYM1524_12 Pseudofoenus_sp.NZHYM1526_12 Pseudofoenus_sp.NZHYM1523_12

Pseudofoenus_sp.NZHYM516_10 Pseudofoenus_sp.DERV060_AY800159.1Pseudofoenus_sp.NZHYM512_10

Gasteruption_sp.BP52 Gasteruption_BP357_UCE Gasteruption_sp.BP359 Gasteruption_sp.BP358 Gasteruption_bulbosum_G112_UCE Gasteruption_sp.G64 Gasteruption_sp.AUSCL1679_12 Gasteruption_sp.G74 Gasteruption_flaviarse_G05 Gasteruption_albicuspis_G50_UCE Gasteruption_sp.NZ2_NZHYM523_10 Pseudofoenus_BP342_UCE Gasteruption_sp.NZ1_NZHYM518_10 Gasteruption_sp.LKMSC190_15 Pseudofoenus_sp.GBAH1551_06 Gasteruption_cornutum_G08_UCE Gasteruption_sp.BP364 Gasteruption_sp.BP365 Gasteruption_sp.BP363 Gasteruption_sp_NT_BP331_UCE Pseudofoenus_sp.TTHYB136_09Pseudofoenus_BP335_UCE Gasteruption_primotarsale_G04 Gasteruption_primotarsale_G79 Pseudofoenus_extraneus_PL02 Gasteruption_sereiceps_EU02 Gasteruption_smitorum_EU01 Pseudofoenus_extraneus_PL01 Gasteruption_sp.CNWBG3038_13 Gasteruption_jaculator_EU11 Pseudofoenus_extraneus_PH01 Gasteruption_sp.HPPPC1292_13 Gasteruption_sp.TTHYW565_08 Pseudofoenus_extraneus_PH02 Gasteruption_sp.NGNAL134_13 Pseudofoenus_sp.HYQT166_08 Gasteruption_sp.CNNHB2567_14 Gastruption_sp.CNKLC2492_14 Pseudofoenus_sp.HYQTB115_12 Gasteruption_sp.SAF16 Gasteruption_bispinosum_NT01 Pseudofoenus_sp.HYAS1214_12 Gasteruption_sp.SAF03 Pseudofoenus_sp.HYAS1122_12 Gasterup_jaculator_france_FR01_UCE Gasteruption_tournieri_EU15 Pseudofoenus_sp.HYAS1106_12 Gasteruption_sp.BP355 Gasteruption_SAF07_UCE Pseudofoenus_BP319_UCE Gasteruption_sp.SAF06 Hyptiogaster_sp.HYAS1327_12 A Gasteruption_sp.SAF07 Gasteruption_sp.ASBZI102_10 Hyptiogaster_sp.HYAS1343_12 Gasteruption_sp.ASBZI132_10 Hyptiogaster_sp.HYAS1336_12 Gasteruption_novaehollandia_G47 Hyptiogaster_sp.HYAS1328_12 Gasteruption_sp.GBAH11242_15 Gasteruption_sp.G29 Hyptiogaster_sp.HYAS1345_12 Gasteruption_sp.TTHYB095_09_F Hyptiogaster_arenicola_BP337_UCE Gasteruption_sp.TTHYB071_09_M Gasteruption_sp.BBHYA1491_12 Gasteruption_sp.JSAUG1449_11

0.02 Draft Gasteruption_sp.ASGLE1616_10 Gasteruption_sp.AGAKQ117_17 YmGMYC CmGMYC YbPTP CbPTP YmPTP CmPTP ABGD YsGMYC CsGMYC

jMOTU 3% jMOTU 2% jMOTU 1% jMOTU Gasteruption_phragmiticola_EU14 Gasteruption erythrostomum EU09 Gasteruption_dilutum_EU03 Gasteruption_sp.BBHYJ836_10 Gasteruption_sp.SAF10 Gasteruption oshimense EU05_UCE Gasteruption_sp.OPPQM2444_17 Gasteruption_tournieri_MG923496.1 B Gasteruption_japonicum_EU04 Gasteruption_assectator_CNRMF1690_12 Gasteruption_sp.GMGMN872_14 Gasteruption_sp.JSHYN643_11 Gasteruption_sp.RO01 Gasteruption_sp.CNRME2324_12 Gasteruption_caucasicum_EU08 Gasteruption_sp.BBHYA2248_12 Gasteruption_sp.CNPPD1957_12 Gasteruption_sp.CNTIG121_15 Gasteruption_sp.JSHYN195_11 Gasteruption_sp.CNPPD007_12 Gasteruption_assectator_OPPEE3379_17Gasteruption_sp.OPPEE3140_17 Gasteruption_sp.JSHYM852_11 Gasteruption_sp.CNPPE1220_12 Gasteruption_sp.JSHYN787_11 Gasteruption_assectator_JSHYN163_11 Gasteruption_sp.OPPOE231_17 Gasteruption_sp.OPPOE438_17 Gasteruption_assectator_OPPOC848_17 Gasteruption_sp.OPPOC833_17 Gasteruption_cylindricum_G52_UCE Gasteruption_assectator_POBGA897_15Gasteruption_sp.BBHEC790_10 Gasteruption_sp.BP281 Gasteruption_sp.HPPPH999_13 Gasteruption_youngi_BP334 Gasteruption_tomanivi_BP245_UCE Gasteruption_sp.BP297 Gasteruption_sp.BP323 Gasteruption_sp.JSHYM518_11 Gasteruption_luteidens_G18 Gasteruption_sp.BP360 Gasteruption_assectator_OPPQM2505_17 Gasteruption_sp.G66 Gasteruption_sp.HPPPH1017_13 Gasteruption_sp.G30 Gasteruption_sp.BP356 Gasteruption_sp.TTHYB040_09 Gasteruption_spinigerum_G58_BP338 Gasteruption_sp.HYAS1333_12 Gasteruption_sp.NZHYM1509_12 Gasteruption_sp.HYQT188_08 Gasteruption_sp.BP100 Gasteruption_assectator_JSHYM281_11 Gasteruption_sp.GBAH9094_14 Gasteruption_platycephala_HYAS139_11 Gasteruption_platycephala_HYAS141_11 Gasteruption_platycephala_HYAS140_11 Gasteruption_sp.BP286 Gasteruption_sp.JSAUG432_11 Gasteruption_sp.GBMIN77143_17

Gasteruption_sp.BP340 Gasteruption_angusticeps_VAQT391_09 Gasteruption_sp.BP316 Gasteruption_assectator_JSHYO506_11 Gasteruption_angusticeps_HYQT158_08 Gasteruption_angusticeps_HYQTB020_11 Gasteruption_assectator_JSHYN651_11 Gasteruption_sp.GMOTE3789_15

Gasteruption_sp.OPPEE3126_17 Gasteruption_assectator_JSHYN921_11Gasteruption_sp.CNEIC3014_12 Gasteruption_sp.TTHYW540_08Gasteruption_boreale_EU07 Gasteruption_sp.HPPPC165_13

Gasteruption_assectator_EU06 Gasteruption_nigritarse_EU13

Gasteruption_merceti_EU12Gasteruption_sp.SAF05

Gasteruption_sp.GMMCJ077_14 Gasteruption_sp.SAF15 Gasteruption_sp.SAF02 Gasteruption_sp.TH01 Gasteruption_sp.GMMCK164_14Gasteruption_hastator_EU10 Gasteruption_sp.SAF04Gasteruption_sp.SAF11

Gasteruption_sp.SAF08

Gasteruption_nobile_G111

Gasteruption_merceti_GMGMK443_14

Gasteruption_bicarinatum_G09

Gasteruption_parvicollarium_KR270643.1

https://mc06.manuscriptcentral.com/genome-pubs