Genome
Molecular diversity and species delimitation in the family Gasteruptiidae (Hymenoptera: Evanioidea)
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 wasps with circa 589 described
15 species worldwide. Although well characterised by traditional taxonomy, 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 Braconidae (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 genus Gasteruption 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 bees 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 Biodiversity 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 (Ichneumonidae) 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 Gasteruption assectator 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 Gasteruption jaculator 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 Insect 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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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