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OPEN Towards a global DNA barcode reference library for quarantine identifcations of lepidopteran Received: 28 November 2018 Accepted: 5 April 2019 stemborers, with an emphasis on Published: xx xx xxxx pests Timothy R. C. Lee 1, Stacey J. Anderson2, Lucy T. T. Tran-Nguyen3, Nader Sallam4, Bruno P. Le Ru5,6, Desmond Conlong7,8, Kevin Powell 9, Andrew Ward10 & Andrew Mitchell1

Lepidopteran stemborers are among the most damaging agricultural pests worldwide, able to reduce crop yields by up to 40%. Sugarcane is the world’s most prolifc crop, and several stemborer species from the families , , and attack sugarcane. is currently free of the most damaging stemborers, but biosecurity eforts are hampered by the difculty in morphologically distinguishing stemborer species. Here we assess the utility of DNA barcoding in identifying stemborer pest species. We review the current state of the COI barcode sequence library for sugarcane stemborers, assembling a dataset of 1297 sequences from 64 species. Sequences were from specimens collected and identifed in this study, downloaded from BOLD or requested from other authors. We performed species delimitation analyses to assess species diversity and the efectiveness of barcoding in this group. Seven species exhibited <0.03 K2P interspecifc diversity, indicating that diagnostic barcoding will work well in most of the studied taxa. We identifed 24 instances of identifcation errors in the online database, which has hampered unambiguous stemborer identifcation using barcodes. Instances of very high within-species diversity indicate that nuclear markers (e.g. 18S, 28S) and additional morphological data (genitalia dissection of all lineages) are needed to confrm species boundaries.

Stemborers are a polyphyletic group of from the families Noctuidae, Tortricidae, Crambidae and Pyralidae, the larvae of which bore into the stems of grasses and eat them from the inside. Te grasses () comprise the world’s most economically important plant family1 including cereals and sugarcane. Cereals provide more than 50% of the world’s daily food calories2 and sugarcane is the world’s most prolifc crop, with agricultural pro- duction by weight in 2016 78% higher than the next largest crop, maize3. Globally, annual crop losses due to pests amount to 20–40%4 and lepidopteran stemborers are the most signifcant pests of graminaceous crops; many are polyphagous, feeding on multiple crop species and alternative host plants. Tis makes stemborers among the most signifcant pests in the world and of major quarantine concern.

1Department of , Australian Museum Research Institute, 1 William St, Darlinghurst, NSW, 2010, Australia. 2Biosecurity Operations, NAQS, Department of Agriculture and Water Resources, 1 Pederson Road, Eaton, NT, 0812, Australia. 3Northern Territory Department of Primary Industry and Resources, GPO Box 3000, Darwin, NT, 0801, Australia. 4Department of Agriculture and Water Resources, 114 Catalina Crescent, Airport Business Park, Cairns Airport, Cairns, QLD, 4870, Australia. 5African Insect Science for Food and Health (ICIPE), PO Box 30772- 00100, Nairobi, Kenya. 6IRD/CNRS, UMR IRD 247 EGCE, Laboratoire Evolution Génomes Comportement et Ecologie, Avenue de la terrasse, BP1, 91198, Gif-sur-Yvette, and Université Paris-Sud 11, 91405, Orsay, France. 7Department of Conservation Ecology and Entomology, Faculty of AgriSciences, University of Stellenbosch, Private Bag X1, Matieland, Western Cape, 7602, South . 8South African Sugarcane Research Institute, 170 Flanders Drive, Mount Edgecombe, KwaZulu-Natal, 4300, South Africa. 9Sugar Research Australia, 71378 Bruce Highway, Gordonvale, QLD, 4865, Australia. 10Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia. Correspondence and requests for materials should be addressed to T.R.C.L. (email: [email protected])

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A thorough understanding of the diversity of pest species, and robust and associated diagnostic tools, underpin biosecurity and the global quarantine measures protecting agriculture5. Te last two decades have seen immense progress towards documenting the diversity of stemborers, particularly for the Apameini, Sesamiina (Noctuidae) of Africa6–10. However, stemborers of the Asian and Australasian regions and pyraloid stemborers globally, despite recent work on Diatraea11, remain poorly characterised and reliable resources for identifying the species remain few and narrow in scope. A major difculty confronting early warning detection lies in distinguishing the minutia of species. Australia has no signifcant exotic stemborer pest species present, likely due to its geographical isolation and employment of stringent quarantine12. Te native Australian sugarcane stemborer, Bathytricha truncata13 (: Noctuidae: Acronictinae), does not cause signifcant damage, which could be due to control by natural enemies14. Australia is the seventh largest sugarcane producer globally; the Australian industry generated 1.75 billion AUD in revenue in 2017, with ~83% of sugar produced for export3,15. Exotic stemborers could arrive in Australia due to Australia’s close proximity to Papua and , where signifcant stemborer pest species are present14. Introductions are made more likely by possible changes in stemborer range due to climate change16 and increased trade. Australian biosecurity agencies therefore require the capacity for rapid identifcation of exotic stemborers. Te establishment of these pests in Australia could have a devastating efect on industry by reducing sugarcane yields up to 40%17–19. Sallam14 listed the 36 most signifcant sugarcane stemborer species ranked in terms of the threat posed to Australia, with seven species from two families (Crambidae and Noctuidae) regarded as ‘high threat’: terrenellus20, Chilo infuscatellus21, Chilo sacchariphagus22, Chilo auricilius23, excerptalis24 (Crambidae), grisescens25 and Sesamia inferens13 (Noctuidae). To improve the ability of biosecurity agencies to detect stemborer incursions, and to circumvent the difcul- ties of morphological identifcation, (including the need for rearing juveniles to adulthood, which relies on live material and greatly slows the identifcation process), DNA barcoding could be used to establish species-level identity. DNA barcoding is the practice of sequencing a fragment of one mitochondrial gene from a large num- ber of accurately identifed specimens to form a database, and comparing sequences of that gene from uniden- tifed specimens to this database26,27. DNA barcoding is an increasingly useful tool for identifying plant pests28 and, in particular, moths of quarantine concern29. Few studies of stemborers to date have used the barcode-standard region of the cytochrome c oxidase I (COI) gene, e.g. Lange et al.30 sequenced the COII and 16S genes of 24 species, while Barrera et al.31 analysed the COII gene in the . Assefa et al.32 was the frst study to use DNA barcoding to identify stemborers, specifcally Busseola spp. larvae in Ethiopia. Tat study and subsequent barcoding studies of stemborers have been limited in scope or have not conformed to community standards for vouchering of specimens and the deposition of sequences and associated data on the Barcode of Life Data System website, (BOLD)33. In some cases, the species identifcations associated with such sequences are demonstrably incorrect. Tese factors formed part of the motivation for the current study. An assessment of species-level diversity in a sequence dataset where some individuals are unidentifed can be performed using molecular species delimitation techniques. Some methods are based on genetic distances, such as Automated Barcode Gap Discovery (ABGD)34 and Refned Single Linkage Analysis (RESL)35. Other methods are tree-based, including the Generalized Mixed Yule Coalescent method (GMYC)36,37, the Bayesian Poisson Tree Process (bPTP)38, and the multi-rate Poisson Tree Process (mPTP)39. Applying multiple methods to the same dataset can provide a more reliable picture of species-level clustering40. Tis can assist in the identifcation of spe- cies which may be in need of taxonomic revision, and also instances where the COI barcode does not align with species boundaries, which can be due to introgression, incomplete lineage sorting or selective sweeps41. Species delimitation methods have been applied previously in stemborers, with the bPTP method having been shown to successfully delimit species in the genus Acrapex42. Examination of mean and extreme intra- and inter-specifc genetic distance is also useful in investigating species boundaries43–46. Due to the threat that stemborer incursions pose to agricultural crops, particularly for sugarcane in Australia, there is a need for both a comprehensive and well-curated database of barcode sequences and a reliable species delimitation method to identify intercepted specimens. In this study, we extend the work of Lange et al.30 by applying the universal COI barcode to this group. Tis paper has four aims:

1) Assemble a new dataset of stemborer COI sequences to serve as the core of a verifed reference DNA bar- code dataset for biosecurity identifcations, including all species listed by Sallam14 as posing a high risk to Australia, and as many of the medium and low risk species as possible. 2) Evaluate the accuracy of existing DNA barcode resources (BOLD) for stemborer species identifcation. 3) Survey the diversity of stemborer species afecting sugarcane and cereal crops, particularly those species of biosecurity concern, largely through matching barcodes from larval specimens reared from crops to those of adults identifed robustly using morphological techniques. 4) Apply and evaluate diferent species delimitation methods (GMYC, mPTP, bPTP, ABGD, RESL), to determine accuracy in delimiting in accordance with current taxonomy, and also in accordance with one another. Results Dataset. Te initial COI barcode dataset contained 508 sequences generated by us in this study, 73 sequences from the study of Chinese stemborers by Wang et al.47, including only those of their sequences without gaps and excluding their outgroup sequence, and 716 sequences downloaded from BOLD, for a total of 1297 sequences. Te most sampled species was Chilo orichalcociliellus, with 142 sequences; nine species were represented by one sequence. We found 24 individuals in our dataset which, based on their position in the FastTree tree, were highly likely to have been misidentifed (Supplementary Table 1). Tis reduced the total number of specimens correctly

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Key: Numbers at nodes: FastTree support / BEAST posterior probability/ RAxML Bootstrap support/Mr Bayes posterior probability * = maximum support/posterior probability - = no support/posterior probability, or node does not exist */0.8/*/* Tetramoera in tree 0.99/0.92/*/* Acrapex 0.98/0.83/76/0.99 0.91/0.93/72/0.99 Acrapex 1/0.94/93/* Sesamia 0.83/-/-/- 0.12/-/-/- 0.99/0.89/0.93/0.98 Bathytricha 0.94/0.89/96/* 0.73/0.61/-/- Busseola 0.86/-/27/- */0.93/*/* Acrapex 0.96/0.93/94/* 0.89/0.63/-/- Acrapex

0.1/0.31/-/0.73 0.98/0.59/41/- Sesamia & Acrapex

Rivula */0.92/*/* 0.81/-/-/- Eldana 0.85/0.23/73/* 0.99/0.84/83/* Emmalocera

0.26/-/-/- 0.98/0.8/80/* Scirpophaga

0.91/0.86/51/0.7 0.81/-/-/- 0.88/0.81/55/* 0.89/0.37/-/0,67 Chilo

0.49/-/-/-

*/0.88/48/0.95 Diatraea

0.98/0.56/7/*

0.96/0.57/46/0.81 Chilo

0.03

Figure 1. Phylogenetic tree estimated in FastTree from an alignment of 667 bp and 1297 sequences. Numbers at nodes indicate support values (in order), FastTree SH-like support, BEAST posterior probabilities, RAxML bootstrap values and MrBayes posterior probabilities. Asterisks represent maximum support; dashes represent no support. Shading on triangles indicates families: Black = Pyralidae, White = Tortricidae, Light grey = Noctuidae, Dark grey = Crambidae. Rivula is in the family Erebidae (not shaded on the diagram).

identifed to species under current taxonomy to 1064, across 64 species. In the haplotypes dataset, there were 18 such misidentifed sequences.

Phylogenetic analysis for gene tree reconstruction. We estimated relationships among the haplotypes using FastTree, MrBayes, RAxML and BEAST. Trees were rooted using the Tortricidae as the outgroup, as the remaining families belong to the monophyletic Obtectomera48. Although the relationships among families and at deeper nodes within families were ofen poorly supported in all analyses, support values towards the tips were generally higher. Some genera were found to be paraphyletic in the analyses with high support, most notably Acrapex and Sesamia, which were divided into multiple clades in all haplotype dataset analyses (Fig. 1). Te majority of species were recovered as monophyletic. Bathytricha truncata was rendered paraphyletic through the insertion of a clade containing B. monticola, B. leonina, B. phaeosticha and B. aethalion. Seasamia inferens consisted of two separate clades within the Sesamia part of the tree. was found to be paraphyletic in all but the BEAST analysis, through the insertion of .

Genetic distances and the barcoding gap. As genetic distance underpins some species delimitation analyses such as ABGD34 and RESL35, we investigated diversity in our haplotypes dataset by computed mean and

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SpeciesIdentifer MEGA7 Minimum K2P Mean K2P Standard Species Pair distance Distance Error Bathytricha leonina and Bathytricha monticola 0.0015 0.003 0.0017 Bathytricha leonina and Bathytricha phaeosticha 0.018 0.019 0.0056 Bathytricha monticola and Bathytricha phaeosticha 0.020 0.022 0.0059 Chilo orichalcociliellus and Chilo thyrsis 0.021 0.030 0.0064 Scirpophaga innotata and Scirpophaga nivella 0.022 0.042 0.0066 Acrapex albivena and Acrapex minima 0.028 0.029 0.0061

Table 1. Species pairs with minimum interspecifc K2P distances between them of less than 0.03. Number in italics indicates an instance where SpeciesIdentifer calculated no value, as Bathytricha monticola and B. phaeosticha are not nearest one another, as both are closer to Bathytricha leonina. Tis value was instead calculated in MEGA7.

maximal Kimura-2-Parameter genetic distances both within and between clades. Six comparisons between spe- cies had a minimum interspecifc K2P distance of less than 0.03 (Table 1). Of the 49 non-singleton species, there were 27 with maximum within-species diversity above 0.02 (Table 2).

Species delimitation. Species delimitation was performed on the haplotypes dataset and the genus-specifc datasets using the GMYC, mPTP, bPTP, RESL and ABGD methods. Varying the relative gap width (X) or prior maximal intraspecific distance (PMID) values had a marked effect on the number of taxa estimated in the ABGD delimitations, ranging from 1 taxon (X = 1.5, PMID 0.0215 or X = 1, PMID = 0.0359) to 188 taxa (X = 1, PMID = 0.0017). Te ABGD estimates most in line with the other delimitation methods’ estimates ranged from 94 taxa (X = 1, PMID = 0.0215) to 188 taxa (Fig. 2). Te bPTP MrBayes method delimited the highest number of taxa, at 197. Te GMYC single threshold method estimated 145 taxa, while the GMYC method with multi- ple thresholds delimited 192 species. Te mPTP method delimited 107 and 122 species using the RAxML and MrBayes trees respectively, and the RESL method delimited 170 taxa. The different delimitation methods applied had varying rates of success in matching current taxonomy (Fig. 3). Species were categorised as ‘matching’ (all individuals in one delimited group and no individuals identi- fed as other species included), ‘merged’, (grouped with one or more other species), ‘split’ (two or more groupings containing the one identifed species), or ‘complex’, (the species is split and at least one partition is merged with at least one other species), following Kekkonen et al.49, and we add a further category ‘single’, for taxa which are in the ‘match’ category but are represented by a single identifed specimen in our dataset. Te multiple threshold GMYC method had the highest number of split taxa (28) of all methods (Fig 4–8). Te GMYC single threshold method had the highest number of matches of the non-ABGD methods (30), while the ABGD methods exceeded this: PMID = 0.0129X = 1 (34) and X = 1.5 (33), and ABGD PMID = 0.0215X = 1 (35). Te ABGD analyses were sensitive to changes in the PMID and X values, producing a range of delimitations rang- ing from entirely merged (ABGD PMID = 0.0359X = 1 and PMID = 0.0215X = 1.5, both delimiting one taxon across all specimens) to highly split (188 taxa). Congruence among methods was high in many species. Based on the haplotypes dataset, in 29 out of the 64 species, at least 11 out of 12 methods agreed on whether the taxon was matching, single, split, merged or a complex (Table 3). Tree of the seven high priority species were in this category: was split in all 12 delimitations, Chilo terrenellus matched in all 12 and was split in 11 out of 12 methods. Of the remaining four high priority species, matched in 10 out of 12 meth- ods, and lent towards split (9 split/3 match), and was about even (7 split/5 match). Overall, the delimitations highlighted that diversity is likely to be underestimated among these high priority species, as most species either matched with current taxonomy or were split into multiple species. In the delimitations based on the Chilo, Sesamia and Scirpophaga subtrees, results were sim- ilar (Supplementary Table 1). To test whether sampling bias infuenced the number of species delimited in each taxon, we performed regres- sion analyses on fve of the whole haplotypes tree delimitations: GMYC single threshold, mPTP MrBayes, bPTP RAxML, RESL and ABGD 0.00774 TN X = 1. Tese fve methods encompass the narrowest possible range of total number of species delimited (121–183, Fig. 2), while still including one delimitation from each method. In each case, a regression analysis was performed between the number of matching and split taxa delimited, and the num- ber of specimens present of that species in the dataset. Singleton taxa were excluded to prevent biasing towards ‘matched’ taxa (as singletons cannot be split) and merged and complexed taxa were also excluded. Analyses were conducted using the Data Analysis package in Microsof Excel. In all cases, there was a signifcant correlation between the number of taxa delimited and the number of samples included in the database (Table 4). However, r2 values in all cases were relatively low; although the value for RESL was high (0.75), this was due primarily to one outlier value (the large number of species delimited in Scirpophaga excerptalis), and without this species the r2 value was 0.46.

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SpeciesIdentifer MEGA7 Monophyletic Maximum K2P Mean K2P Standard on the FastTree Species distance Distance Error tree? Sesamia inferens 0.110 0.0523 0.0059 No Scirpophaga excerptalis 0.107 0.0516 0.0054 Yes Bathytricha truncata 0.098 0.0530 0.0061 No Chilo sacchariphagus 0.081 0.0269 0.0037 Yes Emmalocera callirrhoda 0.063 0.0394 0.0059 Yes Chilo crypsimetalla 0.063 0.0158 0.0024 Yes Chilo infuscatellus 0.062 0.0322 0.0041 Yes Eldana saccharina 0.055 0.0235 0.0035 Yes Tetramoera gracilistra 0.053 0.0319 0.0055 Yes Scirpophaga innotata 0.053 0.0387 0.0069 Yes Chilo auricilius 0.048 0.0224 0.0039 Yes 0.044 0.0201 0.0032 Yes Emmalocera latilimbella 0.044 0.012 0.0022 Yes Eoreuma densella 0.039 0.0203 0.0036 Yes Chilo orichalcociliellus 0.038 0.0125 0.0024 Yes 0.034 0.0187 0.0036 Yes 0.034 0.0166 0.0030 Yes Sesamia cretica 0.033 0.0223 0.0052 Yes Eoreuma lofini 0.031 0.0181 0.0037 Yes Sesamia grisescens 0.030 0.0134 0.0029 Yes Diatraea grandiosella 0.029 0.0287 0.0067 Yes Acrapex albicostata 0.028 0.0157 0.0031 Yes Chilo quirimbellus 0.027 0.0148 0.0037 Yes 0.026 0.0107 0.0024 Yes Scirpophaga nivella 0.025 0.014 0.0029 No 0.025 0.0098 0.0021 Yes Acrapex exsanguis 0.021 0.0101 0.0020 Yes

Table 2. Species with maximum intraspecifc K2P distances higher than 0.02.

250

200

150

100

50

0

Figure 2. Number of taxa resulting from the species delimitation methods used in this study, based on the haplotypes dataset. Black = ABDG; Grey = RESL; White = mPTP; Spotted = GMYC; Striped = bPTP.

Discussion High-threat species identifed by Sallam14 were generally found to have high levels of intraspecifc diversity. Sesamia inferens occurs in South, South-East and East , New Guinea and the , and is a pest of sugarcane and several cereals50. Although some studies have investigated its genetic diversity within parts of this range, particularly in China47,51, its overall genetic diversity is not well characterised. We found the species to be paraphyletic in all of the haplotypes dataset analyses, being split into two clades (Fig. 5). Tis species has the highest maximum intraspecifc genetic distance in our dataset, at 11% K2P, and all delimitation methods

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70

60

50

40

30

20

10

0

MATCH SINGLE MERGE SPLIT COMPLEX

Figure 3. Composite bar graph showing the congruence of morphological identifcation with species delimitation method, for the haplotypes dataset.

applied split the taxon into at least two species, (e.g., RESL analysis split it into 6). Tis strongly indicate that our S. inferens specimens are actually from two diferent species. We include no sequences from the type locality (Sri Lanka13), but the clade from and is geographically closer to the type locality than the clade from . It should be noted, however, that the India/Pakistan clade consisted only of sequences downloaded from BOLD, so we are unable to assess morphologically whether they might be a diferent species. More broadly, the Asian Sesamia includes 15 described species52, however its systematics is confused, and a revision combining morphological, ecological and molecular data is needed. Scirpophaga excerptalis occurs throughout East, South-East and South Asia53. S. excerptalis formed a mono- phyletic clade in our haplotypes dataset analyses. S. excerptalis had a high maximum intraspecifc divergence of 10.7% K2P, and was split into multiple species in all but one delimitation analysis. We identifed ten of the S. excerptalis specimens in this study using genitalia dissections, including representatives from the three major clades. Tese results suggest either that S. excerptalis is a species complex, or that the mitochondrial gene tree does not match the species tree. Further work is required to test these possibilities. Chilo infuscatellus occurs throughout Asia and parts of the Oceanian region54 and is the main pest of sug- arcane in China55. In our dataset this species exhibited high intraspecifc diversity, (maximum 6.2%). Species delimitation methods either matched current taxonomy (9 analyses) or split the taxon into at least six species (15 analyses). As we did not perform any genitalia dissections on our material for this species, we cannot discount the possibility that some of these specimens are misidentifed. Chilo sacchariphagus occurs in southern and south-eastern Asia, south-eastern Africa, , Reunion and Madagascar56. Species delimitation analyses favoured splitting (18 analyses) over matching current taxonomy (6 analyses). C. sacchariphagus was divided into three groups in ten delimitation analyses, which our genitalia dissections indicate correspond to the three subspecies C. sacchariphagus sacchariphagus22, C. sacchariphagus indicus57 and C. sacchariphagus stramineellus58. Tese results have been confrmed by genitalia dissections for the frst two subspecies. However, while C. sacchariphagus stramineellus can be diferentiated by male genitalia, none of the dissected specimens of this subspecies have yielded COI sequences to date. Chilo auricilius was recovered as monophyletic in all phylogenetic analyses. The distance to its closest non-conspecifc neighbour, Chilo orichalcociliellus, is 6.93% K2P, which is sufciently high to distinguish them when DNA barcoding. Species delimitation analysis favoured splitting (18 analyses) over matching current tax- onomy (6 analyses). Eight defnitively identifed S. grisescens sequences were included in the haplotypes dataset, all from . Te closest distance from S. grisescens to its nearest congeneric, S. inferens, was 5.22% K2P distance. Twenty species delimitations matched current taxonomy, with one delimitation merging the species with S. infe- rens and three splitting it into multiple species. Two additional specimens (am12397 and am12399) clustered with S. grisescens in the tree, but as they were larval, without morphological identifcation and 2.94% divergent from the other specimens we considered them to be Sesamia af. grisescens. Adult specimens would be useful in exploring whether these specimens are conspecifc or not. Four defnitively identifed C. terrenellus individuals occurred in our haplotypes dataset, all from Papua New Guinea. Te closest distance from C. terrenellus to its nearest congeneric, C. partellus, was 7.91% K2P distance. All 24 species delimitation analyses matched the current taxonomy of C. terrenellus. Eleven sequences from Indonesian and Papua New Guinean specimens cluster very close to C. terrenellus and either represent this spe- cies or the morphologically similar species C. louisiadalis. Dissection of a larger series will be needed to confrm the identity of this clade. Of the species of lesser biosecurity concern, 20 were also found to have maximum within-species divergences of more than 2% (Supplementary Material 1).

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s s

MrBayeRAxML MrBaye RAxML

GMYCGMYC singlemPT multiplePmPT PbPTP bPTPRESLABGDABGD X=1ABGD p=0.00774X=1ABGD p=0.0129X=1ABGD p=0.0215X=1.5 X=1.5 p=0.00774 p=0.0129 Wang2017 Dehong1 Tetramoera schistaceana CHINA (+1) Wang2017 Dehong4 Tetramoera schistaceana CHINA (+1) Wang2017 Dehong2 Tetramoera schistaceana CHINA Key to am12475 Tetramoera sp. L INDONESIA (+1) Paraphyletic Taxa am12466 Tetramoera sp. L INDONESIA (+4) BOLD LSAFR918-12 Tetramoera sp. SOUTH_AFRICA BOLD LOQTC158-07 Tetramoera gracilistria AUSTRALIA (+1) A BOLD LOQTB386-07 Tetramoera gracilistria AUSTRALIA BOLD ANICW322-11 Tetramoera gracilistria AUSTRALIA B BOLD ANICW321-11 Tetramoera gracilistria AUSTRALIA A BOLD ANICC223-09 Tetramoera gracilistria AUSTRALIA (+1) ukzn0531 Rivula atimeta B am12389 Eldana saccharina KENYA BOLD PMANL3279-14 Eldana saccharina SEYCHELLES (+1) A BOLD PMANL4009-15 Eldana saccharina SEYCHELLES ww06188 Eldana saccharina SOUTH AFRICA B BOLD GBGL3692-06 Eldana saccharina A ww06182 Eldana saccharina SOUTH AFRICA (+8) BOLD GBGL3697-06 Eldana saccharina ETHIOPIA C BOLD GBGL3696-06 Eldana saccharina ETHIOPIA A BOLD GBGL3701-06 Eldana saccharina SENEGAL BOLD GBGL3700-06 Eldana saccharina SENEGAL ww06173 Eldana saccharina CAMEROON BOLD NSWHM1083-11 Emmalocera sp. AUSTRALIA BOLD LOLI260-08 Emmalocera sp. 1 AUSTRALIA BOLD LOQ128-04 Emmalocera sp. AUSTRALIA BOLD IMLS315-12 Emmalocera sp. AUSTRALIA BOLD IMLR237-08 Emmalocera sp. AUSTRALIA BOLD IMLS200-12 Emmalocera sp. 2 AUSTRALIA (+1) BOLD IMLS329-12 Emmalocera sp. 2 AUSTRALIA (+2) BOLD IMLS343-12 Emmalocera sp. 2 AUSTRALIA BOLD IMLS215-12 Emmalocera sp. 2 AUSTRALIA BOLD IMLS342-12 Emmalocera sp. 2 AUSTRALIA BOLD NSWHN553-11 Emmalocera sp. AUSTRALIA (+4) BOLD NSWHN602-11 Emmalocera sp. AUSTRALIA (+1) BOLD LOQTC752-08 Emmalocera sp. AUSTRALIA BOLD LOQT855-07 Emmalocera callirrhoda AUSTRALIA BOLD LOQT854-07 Emmalocera callirrhoda AUSTRALIA BOLD LOQTB118-07 Emmalocera sp. AUSTRALIA (+1) BOLD LOQTD063-08 Emmalocera sp. AUSTRALIA BOLD ANICR1850-11 Emmalocera callirrhoda AUSTRALIA BOLD LOQTB119-07 Emmalocera callirrhoda AUSTRALIA BOLD ANICR1848-11 Emmalocera callirrhoda AUSTRALIA BOLD MAIMB232-09 Emmalocera sp. PAKISTAN* BOLD MAMOT648-10 Emmalocera sp. PAKISTAN* BOLD MAMOT655-10 Emmalocera sp. PAKISTAN* BOLD MAIMB231-09 Emmalocera sp. PAKISTAN* BOLD MAMOT576-10 Emmalocera sp. PAKISTAN* am11080 Polyocha depressellus L PAKISTAN BOLD IMLS598-12 Emmalocera latilimbella AUSTRALIA BOLD NSWLP767-13 Emmalocera sp. AUSTRALIA BOLD NSWLP349-13 Emmalocera sp. AUSTRALIA (+3) BOLD NSWHM1470-11 Emmalocera sp. AUSTRALIA (+1) BOLD NSWLP316-13 Emmalocera sp. AUSTRALIA BOLD NSWHH752-09 Emmalocera sp. AUSTRALIA (+2) BOLD NSWBB627-08 Emmalocera latilimbella AUSTRALIA BOLD NSWHN542-11 Emmalocera latilimbella AUSTRALIA BOLD NSWHN486-11 Emmalocera latilimbella AUSTRALIA BOLD IMLR216-08 Emmalocera latilimbella AUSTRALIA BOLD NSWBB073-08 Emmalocera latilimbella AUSTRALIA BOLD ANICR1859-11 Emmalocera latilimbella AUSTRALIA BOLD NSWLP604-13 Emmalocera latilimbella AUSTRALIA (+1) BOLD NSWHM1334-11 Emmalocera latilimbella AUSTRALIA 0.2 BOLD NSWLP408-13 Emmalocera latilimbella AUSTRALIA (+8) BOLD NSWHM1508-11 Emmalocera latilimbella AUSTRALIA

Figure 4. RAxML tree based on the haplotypes dataset (Part 1 of 5). Bars to the right of the tree indicate species delimitation groupings according to the 12 diferent species delimitation methods. Te group of bars on the lef are delimitations based on the whole haplotypes sequence dataset, bars on the right are based on the genus- specifc analyses. Numbers in brackets indicate how many additional copies of this haplotype were present in the 1297 sequence dataset. Asterisks indicate sequences that we found to be misidentifed, and which have now been corrected on BOLD (new species names appear in this fgure). Names (or numbers in brackets) in bold indicate specimens where we performed genitalia dissections to confrm identity.

Some species were found to have low levels of inter-specifc diversity. Bathytricha species are not well studied, with no taxonomic publications on the genus (other than a species checklist) since the species were described in the late 19th and early 20th century. Although a COI-only phylogeny is not defnitive, high intraspecifc divergence and paraphyly within B. truncata indicates it may represent at least two diferent species, while the high degree of similarity between B. leonina and B. monticola suggests that further investigation of the diferentiation of these species is needed. Chilo thyrsis is known only from Tanzania, while Chilo orichalcociliellus has a much broader distribution across south-east and central Africa59. In its original description, C. thyrsis was described as “Externally very similar to Chilo argyrolepia”60, and C. argyrolepia has been subsequently synonymized with C. orichalcociliellus59. C. thyrsis is merged with C. orichalcociliellus in 13 delimitations and is separate in 11, which does not indicate strong support for either the separation or merging of the taxa. As we only include two specimens of C. thyrsis in our dataset, we can only draw limited conclusions here, but the acknowledged close relationship between these two species may indicate they are recently diverged. Scirpophaga nivella has a broad distribution across South and South-East Asia, southern and eastern China, Australia and the Western Pacifc53. Scirpophaga innotata is known from Indonesia and the Philippines61, and also and northern Australia62. In all but three of our phylogenetic analyses, S. innotata formed a clade inserted into S. nivella, rendering the latter paraphyletic. In the haplotypes BEAST tree, they formed sister clades, and in the FastTree and BEAST Scirpophaga-only analyses, S. nivella formed a clade inserted into S. innotata, ren- dering it paraphyletic. Fourteen of 24 delimitations grouped S. nivella and S. innotata as one species. A minimal K2P distance of 2.19% between the two species is lower than the level of intraspecifc diversity we found in other species of the genus, like S. excerptalis. Acrapex minima and Acrapex albivena are dealt with in Le Ru et al.63. In that study, a phylogenetic tree recon- structed based on four mitochondrial genes and two nuclear genes strongly diferentiated the two species. Given the high levels of intraspecifc diversity found in several species in this study, delimitations match- ing current taxonomy may not be the most successful, but rather an underestimate of the true species number. A full assessment of the taxonomic status of these species will require nuclear and morphological data, as the COI barcode is comparatively short and susceptible to the skewed inheritance patterns resulting from Wolbachia

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4

s L

PRAxM MrBayes RAxML

GMYCGMYC singlemPT multiplePmPT MrBayebPTPbPTPRESLABGDABGD X=1ABGD p=0.00774X=1ABGD p=0.0129X=1ABGD p=0.0215X=1.5 X=1.5 p=0.0077 p=0.0129 BOLD GBMIN22544-13 Acrapex syscia BOLD LSAFR1444-12 Acrapex albivena SOUTH AFRICA (+1) BOLD LSAFR1388-12 Acrapex minima SOUTH AFRICA BOLD LSAFR1402-12 Acrapex minima SOUTH AFRICA (+1) BOLD PHLCD1924-12 Acrapex albicostata AUSTRALIA BOLD ANICK694-10 Acrapex albicostata AUSTRALIA BOLD NSWHI106-09 Acrapex albicostata AUSTRALIA (+1) BOLD NSWHM726-11 Acrapex albicostata AUSTRALIA BOLD PHLCC341-11 Acrapex albicostata AUSTRALIA BOLD ANICK695-10 Acrapex albicostata AUSTRALIA BOLD IMLS482-12 Acrapex albicostata AUSTRALIA BOLD ANICK693-10 Acrapex albicostata AUSTRALIA BOLD ANICK696-10 Acrapex albicostata AUSTRALIA (+1) BOLD LSAFR1257-12 Acrapex aenigma SOUTH AFRICA BOLD LSAFR682-12 Acrapex aenigma SOUTH AFRICA BOLD LSAFR1389-12 Acrapex aenigma SOUTH AFRICA BOLD GWOTH728-12 Acrapex aenigma SOUTH AFRICA BOLD LSAFR1191-12 Acrapex aenigma SOUTH AFRICA (+2) am12538 Busseola fusca L KENYA (+1) am12539 Busseola segeta L KENYA am12533 Busseola segeta KENYA (+1) BOLD MILEP011-09 Acrapex relicta USA (+1) BOLD MILEP036-09 Acrapex relicta USA BOLD HKONS500-08 Acrapex relicta USA BOLD LNCB283-06 Acrapex relicta USA (+1) BOLD LNCC702-11 Acrapex relicta USA BOLD LEPMY1406-15 Sesamia submarginalis MALAYSIA K365807 Bathytricha sp. AUSTRALIA BOLD NSWHM1394-11 Bathytricha truncata AUSTRALIA BOLD ANICK709-10 Bathytricha truncata AUSTRALIA BOLD AMWW077-11 Bathytricha sp. AUSTRALIA am11075 Bathytricha truncata L AUSTRALIA BOLD PHLCC1027-11 Bathytricha truncata AUSTRALIA (+6) BOLD PHLCD1945-12 Bathytricha truncata AUSTRALIA BOLD ANICK716-10 Bathytricha truncata AUSTRALIA BOLD LNSWA023-05 Bathytricha truncata AUSTRALIA (+6) BOLD LNSWA028-05 Bathytricha truncata AUSTRALIA (+2) BOLD PHLCD2881-12 Bathytricha truncata AUSTRALIA BOLD LSM259-11 Bathytricha truncata AUSTRALIA BOLD ANICK712-10 Bathytricha truncata AUSTRALIA (+3) BOLD PHLCD2402-12 Bathytricha truncata AUSTRALIA BOLD ANICK713-10 Bathytricha truncata AUSTRALIA BOLD LSM714-11 Bathytricha sp. AUSTRALIA BOLD LSM717-11 Bathytricha sp. AUSTRALIA BOLD ANICK711-10 Bathytricha truncata AUSTRALIA BOLD PHSAU264-12 Bathytricha sp. AUSTRALIA BOLD ANICK710-10 Bathytricha truncata AUSTRALIA BOLD PHLCD664-12 Bathytricha truncata PS2 AUSTRALIA BOLD PHLCD545-12 Bathytricha truncata PS2 AUSTRALIA (+4) BOLD PHSAU1836-12 Bathytricha truncata PS2 AUSTRALIA BOLD PHLCD647-12 Bathytricha truncata PS2 AUSTRALIA (+12) BOLD ANICK720-10 Bathytricha phaeosticha AUSTRALIA BOLD ANIAG784-11 Bathytricha leonina AUSTRALIA (+5) 4 BOLD ANICK717-10 Bathytricha monticola AUSTRALIA BOLD ANICK719-10 Bathytricha monticola AUSTRALIA BOLD AMWW067-11 Bathytricha monticola AUSTRALIA (+10) BOLD NSWLP472-13 Bathytricha aethalion AUSTRALIA s s BOLD ANICK721-10 Bathytricha aethalion AUSTRALIA le p=0.0129 p=0.0215 .5 p=0.0129 BOLD NSWHM715-11 Bathytricha aethalion AUSTRALIA 1 1 1.5 p=0.007741 GlatzKI035 Bathytricha sp. AUSTRALIA Baye BOLD AMWW078-11 Bathytricha sp. AUSTRALIA Mr RAxML BOLD AMWW089-11 Bathytricha sp. AUSTRALIA BOLD AMWW087-11 Bathytricha sp.AUSTRALIA (+2) BOLD AMWW082-11 Bathytricha sp.AUSTRALIA GMYCGMYC singmPT multiplePmPT MrBayePbPTP RAxMLbPTPRESLABGDABGD X=1ABGD p=0.0077X= ABGD X= ABGD X= X= am12376 L SOUTH AFRICA am11074 Sesamia calamistis L REUNION am12537 Sesamia calamistis L KENYA BOLD LSAFR2106-12 Sesamia sp. SOUTH AFRICA am12531 KENYA am12536 Sesamia nonagrioides L KENYA BOLD GBMIN83539-17 Sesamia nonagrioides BOLD GBGL20667-18 Sesamia nonagrioides IRAN BOLD GBMIN83544-17 Sesamia nonagrioides IRAN (+2) am12556 Sesamia nonagrioides L IRAN BOLD GBGL1133-06 Sesamia nonagrioides BOLD GBMIN22547-13 Sesamia nonagrioides am12397 Sesamia aff. grisescens L PAPUA NEW GUINEA am12399 Sesamia aff. grisescens L PAPUA NEW GUINEA am12438 Sesamia grisescens P PAPUA NEW GUINEA am12402 Sesamia grisescens L PAPUA NEW GUINEA (+3) am12566 Sesamia grisescens PAPUA NEW GUINEA (+1) am12577 Sesamia grisescens PAPUA NEW GUINEA am12578 Sesamia grisescens PAPUA NEW GUINEA (+2) ww06192 Sesamia grisescens PAPUA NEW GUINEA (+1) am12575 Sesamia grisescens PAPUA NEW GUINEA am12384 Sesamia grisescens A PAPUA NEW GUINEA (+4) BOLD GBMIN83466-17 Sesamia cretica IRAN BOLD GBMIN83468-17 Sesamia cretica IRAN (+1) BOLD GBGL20668-18 Sesamia cretica IRAN Wang2017 Sesamia inferens Fuzhou1 CHINA (+1) Wang2017 Sesamia inferens Yacheng4 CHINA am12553 Sesamia inferens L CHINA Wang2017 Sesamiainferens Yacheng1CHINA (+2) am12554 Sesamia inferens P CHINA (+3) Wang2017 Sesamia inferens Fuzhou5 CHINA am12552 Sesamia inferens L CHINA BOLD MAMOT656-10 Sesamia sp. PAKISTAN BOLD ANICK697-10 Acrapex sp. ANIC2 AUSTRALIA BOLD LOQB228-05 Acrapex sp. ANIC2 AUSTRALIA BOLD LOQB122-05 Acrapex sp. ANIC2 AUSTRALIA BOLD LOLI199-08 Acrapex sp. AUSTRALIA (+3) BOLD ANICK707-10 Acrapex sp. ANIC2 AUSTRALIA BOLD GWOTH742-12 Sesamia sp. SOUTH AFRICA BOLD GBGL12188-13 Sesamia inferens INDIA (+2)* BOLD BIPR009-13 Sesamia inferens INDIA BOLD GBGL18425-15 Sesamia inferens CHINA* BOLD MAMOT957-10 Sesamia inferens PAKISTAN (+1) BOLD MAIMB276-09 Sesamia inferens PAKISTAN BOLD MAIMB278-09 Sesamia inferens PAKISTAN BOLD MAMOT546-10 Sesamia inferens PAKISTAN BOLD AGIMP010-13 Sesamia inferens INDIA (+1) BOLD GBGL20198-15 Sesamia inferens INDIA BOLD ANICK705-10 Acrapex sp. ANIC3 AUSTRALIA BOLD ANICK703-10 Acrapex sp. ANIC5 AUSTRALIA BOLD ANIAG776-11 Acrapex sp. ANIC1 AUSTRALIA BOLD ANICK702-10 Acrapex sp. ANIC1 AUSTRALIA (+1) BOLD ANICK691-10 Acrapex albicostata AUSTRALIA BOLD ANICK706-10 Acrapex sp. ANIC6 AUSTRALIA BOLD ANIAG773-11 Acrapex sp. ANIC6 AUSTRALIA BOLD IMLS388-12 Acrapex exsanguis AUSTRALIA BOLD ANICK704-10 Acrapex exsanguis AUSTRALIA BOLD NSWLP371-13 Acrapex exsanguis AUSTRALIA BOLD NSWHH932-09 Acrapex exsanguis AUSTRALIA BOLD NSWLP446-13 Acrapex exsanguis AUSTRALIA BOLD NSWHI104-09 Acrapex exsanguis AUSTRALIA (+1) BOLD NSWHI249-09 Acrapex exsanguis AUSTRALIA BOLD IMLR995-11 Acrapex exsanguis AUSTRALIA BOLD ANICK688-10 Acrapex exsanguis AUSTRALIA BOLD ANICK699-10 Acrapex exsanguis AUSTRALIA BOLD ANICK700-10 Acrapex exsanguis AUSTRALIA BOLD IMLS505-12 Acrapex exsanguis AUSTRALIA (+1) BOLD ANICK698-10 Acrapex exsanguis AUSTRALIA (+1) BOLD NSWHI101-09 Acrapex exsanguis AUSTRALIA BOLD NSWLP333-13 Acrapex exsanguis AUSTRALIA BOLD LOLI206-08 Acrapex exsanguis AUSTRALIA BOLD LOQB065-05 Acrapex exsanguis AUSTRALIA 0.2 BOLD IMLR833-08 Acrapex exsanguis AUSTRALIA BOLD ANICK689-10 Acrapex exsanguis AUSTRALIA (+5) BOLD NSWHH125-09 Acrapex exsanguis AUSTRALIA BOLD WALPB112-13 Acrapex exsanguis AUSTRALIA (+2) BOLD PHLCD356-12 Acrapex exsanguis AUSTRALIA

Figure 5. RAxML tree based on the haplotypes dataset (Part 2 of 5). Bars to the right of the tree indicate species delimitation groupings according to the 12 diferent species delimitation methods. Te group of bars on the lef are delimitations based on the whole haplotypes sequence dataset, bars on the right are based on the genus- specifc analyses. Numbers in brackets indicate how many additional copies of this haplotype were present in the 1297 sequence dataset. Asterisks indicate sequences that we found to be misidentifed, and which have now been corrected on BOLD (new species names appear in this fgure). Names (or numbers in brackets) in bold indicate specimens where we performed genitalia dissections to confrm identity.

infection64,65. Nevertheless, we can make some assessments of the relative merits of these methods as applied to this dataset. Te ABGD method produced a broad range of delimitations depending on the prior maximal intraspecifc distance (PMID) selected. Given that range, and our inability to independently assess which PMID

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Genus Species epithet Match Single Merge Split Complex High congruence Acrapex aenigma 10 0 0 2 0 Acrapex albicostata 10 0 0 2 0 Acrapex albivena 0 5 7 0 0 Acrapex exsanguis 10 0 0 2 0 Acrapex minima 5 0 7 0 0 Acrapex relicta 12 0 0 0 0 MATCH Acrapex syscia 0 10 2 0 0 Bathytricha aethalion 9 0 3 0 0 Bathytricha leonina 0 0 12 0 0 MERGE Bathytricha monticola 0 0 12 0 0 MERGE Bathytricha phaeosticha 0 3 9 0 0 Bathytricha truncata 0 0 2 9 1 Busseola fusca 0 10 2 0 0 Busseola segeta 10 0 2 0 0 Chilo auricilius 3 0 0 9 0 Chilo crossostichus 7 0 5 0 0 Chilo crypsimetalla 1 0 0 11 0 (Split) Chilo demotellus 0 12 0 0 0 SINGLE Chilo difusilineus 0 12 0 0 0 SINGLE Chilo infuscatellus 5 0 0 7 0 12 0 0 0 0 MATCH Chilo orichalcociliellus 6 0 6 0 0 Chilo partellus 10 0 0 2 0 Chilo phragmitella 3 0 0 9 0 12 0 0 0 0 MATCH 0 12 0 0 0 SINGLE Chilo quirimbellus 3 0 6 3 0 Chilo sacchariphagus 3 0 0 9 0 Chilo suppressalis 11 0 0 1 0 (Match) Chilo terrenellus 12 0 0 0 0 MATCH Chilo thyrsis 6 0 6 0 0 12 0 0 0 0 MATCH Cnaphalocrocis medinalis 0 10 2 0 0 Cnaphalocrocis patnalis 0 10 2 0 0 7 0 0 5 0 0 11 1 0 0 (Single) Diatraea evanescens 12 0 0 0 0 MATCH Diatraea grandiosella 8 0 2 2 0 0 10 2 0 0 Diatraea lisetta 12 0 0 0 0 MATCH Diatraea mitteri 11 0 1 0 0 (Match) Diatraea saccharalis 5 0 0 7 0 Eldana saccharina 6 0 0 6 0 Emmalocera callirrhoda 0 0 0 12 0 SPLIT Emmalocera latilimbella 1 0 0 11 0 (Split) Eoreuma densella 10 0 0 2 0 Eoreuma lofini 5 0 0 7 0 Polyocha depressellus 0 12 0 0 0 SINGLE Rivula atimeta 0 12 0 0 0 SINGLE Scirpophaga excerptalis 0 0 0 12 0 SPLIT Scirpophaga imparellus 12 0 0 0 0 MATCH Scirpophaga incertulas 8 0 0 4 0 Scirpophaga innotata 0 0 3 5 4 Scirpophaga nivella 2 0 7 3 0 Scirpophaga percna 10 0 0 2 0 12 0 0 0 0 MATCH Continued

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Genus Species epithet Match Single Merge Split Complex High congruence Sesamia calamistis 12 0 0 0 0 MATCH Sesamia cretica 6 0 0 6 0 Sesamia grisescens 10 0 1 1 0 Sesamia inferens 0 0 0 11 1 (Split) Sesamia nonagrioides 11 0 0 1 0 (Match) Sesamia submarginalis 0 12 0 0 0 SINGLE Tetramoera gracilistra 0 0 0 12 0 SPLIT Tetramoera schistaceana 12 0 0 0 0 MATCH

Table 3. Summary of agreement among the twelve species delimitation methods applied to the haplotypes dataset. MATCH = delimitation agrees with current taxonomy, MERGE = taxon groups with one or more other species, SPLIT = taxon split into multiple species, COMPLEX = taxon split and at least one partition merged with another species. High congruence lists the delimitation of taxa where all twelve delimitation methods agreed, or where all but one agreed (these appear in brackets). Te seven species posing the highest level of threat to Australia according to Sallam13 are in bold and underlined; medium threat taxa are underlined but not in bold.

Mean no. of samples Standard Delimitation method df F p value r2 per species Error GMYC single threshold 1,46 54.04 <0.05 0.54 9.60 1.58 bPTP RAxML 1,46 22.98 <0.05 0.33 9.60 1.58 mPTP MrBayes 1,41 47.88 <0.05 0.54 10.23 1.73 RESL 1,44 128.86 <0.05 0.75 9.35 1.59 ABGD 0.00774 TN X = 1 1,39 44.47 <0.05 0.53 9.83 1.76

Table 4. Regression analysis calculated on the relationship between number of taxa delimited and number of samples per species

is the most realistic, we exclude the ABGD method from the following comparisons. On this basis of matching current taxonomy, GMYC single threshold was the best method, with the highest combined number of matching and single taxa. If instead the criterion for successful delimitation is agreement with the consensus among the dif- ferent methods we applied, the highest scoring method was again GMYC single threshold, with one disagreement with the consensus out of 64 taxa in the haplotypes dataset. Te next best method was mPTP MrBayes, with two disagreements, then RESL with six. In the genus-level subtree analyses, the highest scoring methods are mPTP MrBayes and bPTP MrBayes, with two disagreements each out of 30 taxa, and GMYC single threshold and mPTP RAxML, with 4 disagreements each. Ultimately, congruence in delimitations across multiple methods remains the best method for assessing delimitation accuracy40, and we found this across all delimitations in several species (Table 3). It is difficult to assess whether sampling was sufficient to delimit species accurately. When dealing with mitochondrial-only data, introgression and selective sweeps may make any amount of COI-only data insufcient to make an accurate assessment of species-level diversity. In the case of GMYC, Talavera et al.66 found that the most signifcant factor in sampling was capturing the extremities of each species’ diversity. Given the number of taxa included in our study with extreme within-species diversity above 5% (Table 2), we can be confdent that at least for some species we have captured sufcient diversity. Future sampling eforts should be directed towards those species for which our sampling is poor, particularly Sesamia grisescens and Chilo terrenellus. Our regression analyses indicate that there is a correlation between the number of individuals sampled with the number of taxa delimited in each species, although the r2 values in three out of four cases were under 0.6, and in the last case lowers to less than 0.6 when one outlier is removed. Such a correlation is not unexpected, given that no taxon can ever have more species delimited than it has samples. Te low r2 values indicate that variables not in the model are having an efect on the relationship between number of sequences and number of taxa delim- ited; these other variables almost certainly include the actual absence or presence of cryptic diversity in these taxa. Of the pairs of species with less than 3% minimum inter-specifc diversity (Table 1), one species appears on the Sallam14 list: Chilo orichalcociliellus (Low threat, similar to C. thyrsis). Including only two C. thyrsis sequences in the dataset also does not allow us to properly explore the level of diversity in that group, and whether it is gener- ally poorly diferentiated from Chilo orichalcociliellus. In addition, our dataset lacks sequences from some species identifed by Sallam14 as posing a low or medium threat to Australian sugarcane, which should be a high priority for future sequencing eforts. Apart from these caveats, the identity of all other stemborers of economic risk to Australia included in this study can be safely established through the use of the COI barcode. When dealing with sequences downloaded from online public databases, one cannot verify the accuracy of specimen identifcations (photographs, unless of genitalia dissections, are of little use in identifying stemborers). We found several instances of clear misidentifcation in our trees, where individuals identifed as one species clustered closely with species in a diferent genus, or even a diferent family. Tese errors, which have now been corrected on BOLD (see Supplementary Table 2), are a cautionary tale for the uncritical use of public databases

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4 4

s s

MrBaye RAxML MrBayes RAxML P MrBayes RAxML P

GMYCGMYC singlemPT multiplePmPT MrBayebPTPbPTPRESLABGDABGD X=1ABGD p=0.00774X=1ABGD p=0.0129X=1ABGD p=0.0215X=1.5 X=1.5 p=0.0077GMYC p=0.0129GMYC singlemPT multiplemPTPbPTP RAxMLbPTPRESLABGDABGD X=1ABGD p=0.0077X=1 p=0.0129X=1 p=0.0215 BOLD IMLQ194-07 Scirpophaga praelata AUSTRALIA (+2) BOLD IMLQ215-07 Scirpophaga praelata AUSTRALIA BOLD IMLS586-12 Scirpophaga praelata AUSTRALIA S2 Scirpophaga sp. AUSTRALIA S3 Scirpophaga nivella AUSTRALIA S1 Scirpophaga sp. AUSTRALIA S8 Scirpophaga nivella AUSTRALIA S9 Scirpophaga nivella AUSTRALIA BOLD NSWLP478-13 Scirpophaga nivella AUSTRALIA (+17) BOLD NSWLP827-13 Scirpophaga nivella AUSTRALIA BOLD LOQTI438-11 Scirpophaga nivella AUSTRALIA S7 Scirpophaga nivella AUSTRALIA BOLD NSWLP660-13 Scirpophaga nivella AUSTRALIA (+7) BOLD GBGL6605-09 Scirpophaga innotata INDONESIA BOLD BIPR012-13 Scirpophaga innotata INDIA BOLD BIPR013-13 Scirpophaga innotata INDIA BOLD WALPA4489-13 Scirpophaga nivella AUSTRALIA BOLD ANIAB734-11 Scirpophaga nivella AUSTRALIA BOLD IMLS069-12 Scirpophaga nivella AUSTRALIA (+1) BOLD ANICS882-11 Scirpophaga nivella AUSTRALIA SJA041605b Crambidae AUSTRALIA (+2) BOLD GBGL6595-09 Scirpophaga incertulas INDONESIA BOLD GBGL6598-09 Scirpophaga incertulas INDONESIA BOLD GBGL6596-09 Scirpophaga incertulas INDONESIA (+4) BOLD GBGL6600-09 Scirpophaga incertulas INDONESIA BOLD GBGL6599-09 Scirpophaga incertulas INDONESIA BOLD GBGL6603-09 Scirpophaga incertulas INDONESIA P11 Scirpophaga sp. -LESTE ukzn0532 Scirpophaga incertulas PHILIPPINES P12 Scirpophaga sp. TIMOR-LESTE BOLD GBGL12693-13 Scirpophaga incertulas BOLD MAMOT497-10 Scirpophaga incertulas PAKISTAN (+2) BOLD BIPR014-13 Scirpophaga incertulas INDIA BOLD BIPR015-13 Scirpophaga incertulas INDIA BOLD GBGL12897-14 Scirpophaga incertulas INDIA BOLD IARI066-08 Scirpophaga sp. 6 AUSTRALIA BOLD LOQT1056-07 Scirpophaga sp. AUSTRALIA (+1) BOLD LOQTD200-08 Scirpophaga sp. AUSTRALIA BOLD MAMOT596-10 Scirpophaga sp. PAKISTAN BOLD MAMOT3030-12 Scirpophaga sp. PAKISTAN BOLD ANICS883-11 Scirpophaga percna AUSTRALIA BOLD WALPC497-14 Scirpophaga percna AUSTRALIA (+1) BOLD WALPB396-13 Scirpophaga percna AUSTRALIA (+1) BOLD WALPC494-14 Scirpophaga percna AUSTRALIA BOLD NSWHM1527-11 Scirpophaga imparellus AUSTRALIA BOLD NSWLP786-13 Scirpophaga imparellus AUSTRALIA BOLD NSWHH098-09 Scirpophaga imparellus AUSTRALIA BOLD ANICS879-11 Scirpophaga imparellus AUSTRALIA (+1) BOLD IMLS041-12 Scirpophaga imparellus AUSTRALIA BOLD MAMOT1164-11 Scirpophaga sp. PAKISTAN BOLD MAMOT1615-12 Scirpophaga sp. PAKISTAN BOLD MAMOT1614-12 Scirpophaga sp. PAKISTAN BOLD MAMOT1611-12 Scirpophaga sp. PAKISTAN (+1) BOLD MAMOT1616-12 Scirpophaga sp. PAKISTAN BOLD MAMOT1609-12 Scirpophaga sp. PAKISTAN BOLD MAMOT1613-12 Scirpophaga sp. PAKISTAN BOLD MAMOT1617-12 Scirpophaga sp. PAKISTAN ww06169 Scirpophaga excerptalis INDONESIA am12511b Scirpophaga excerptalis L INDONESIA SRB021a Scirpophaga excerptalis INDONESIA (+1) am12496 Scirpophaga excerptalis L INDONESIA SRB020a Scirpophaga excerptalis INDONESIA am12515b Scirpophaga excerptalis L INDONESIA am12498b Scirpophaga excerptalis L INDONESIA am12508b Scirpophaga excerptalis L INDONESIA am12501b Scirpophaga excerptalis L INDONESIA am12500b Scirpophaga excerptalis L INDONESIA am12463 Scirpophaga excerptalis INDONESIA (+3) am12514b Scirpophaga excerptalis L INDONESIA am12507b Scirpophaga excerptalis L INDONESIA am12503b Scirpophaga excerptalis L INDONESIA am12495 Scirpophaga excerptalis L INDONESIA am12509b Scirpophaga excerptalis L INDONESIA am12505b Scirpophaga excerptalis L INDONESIA am12502b Scirpophaga excerptalis L INDONESIA ww06171 Scirpophaga excerptalis INDONESIA am12504b Scirpophaga excerptalis L INDONESIA am12516b Scirpophaga excerptalis L INDONESIA SRB023b Scirpophaga excerptalis INDONESIA (+1) SRB023a Scirpophaga excerptalis INDONESIA am12518b Scirpophaga excerptalis INDONESIA am12513b Scirpophaga excerptalis L INDONESIA (+1) am12524 Scirpophaga excerptalis L INDONESIA am12512b Scirpophaga excerptalis L INDONESIA (+1) ww06197 Scirpophaga excerptalis INDONESIA ww06195 Scirpophaga excerptalis INDONESIA ww06198 Scirpophaga excerptalis INDONESIA Wang2017 Guangzhou1 Scirpophaga excerptalis CHINA Wang2017 Guangzhou2 Scirpophaga excerptalis CHINA Wang2017 Guangzhou3 Scirpophaga excerptalis CHINA Wang2017 Guangzhou5 Scirpophaga excerptalis CHINA BOLD MAMOT485-10 Scirpophaga excerptalis PAKISTAN BOLD MAMOT531-10 Scirpophaga excerptalis PAKISTAN BOLD MAMOT595-10 Scirpophaga excerptalis PAKISTAN am11065 Scirpophaga excerptalis L PAKISTAN BOLD MAMOT096-10 Scirpophaga excerptalis PAKISTAN (+3) BOLD MAMOT534-10 Scirpophaga excerptalis PAKISTAN BOLD MAMOT533-10 Scirpophaga excerptalis PAKISTAN (+1) BOLD MAIMB240-09 Scirpophaga excerptalis PAKISTAN BOLD MAIMB242-09 Scirpophaga excerptalis PAKISTAN BOLD MAMOT710-10 Scirpophaga excerptalis PAKISTAN BOLD MAMOT532-10 Scirpophaga excerptalis PAKISTAN (+1) BOLD MAMOT095-10 Scirpophaga excerptalis PAKISTAN BOLD MAMOT535-10 Scirpophaga excerptalis Pakistan am12441 Scirpophaga excerptalis L PAPUA NEW GUINEA (+1) am12562 Scirpophaga excerptalis P PAPUA NEW GUINEA am12421 Scirpophaga excerptalis L PAPUA NEW GUINEA am12419 Scirpophaga excerptalis L PAPUA NEW GUINEA am12440 Scirpophaga excerptalis L PAPUA NEW GUINEA am12424 Scirpophaga excerptalis L PAPUA NEW GUINEA am12447 Scirpophaga excerptalis L PAPUA NEW GUINEA ww06210 Scirpophaga excerptalis PAPUA NEW GUINEA (+1) SRB013a Scirpophaga excerptalis PAPUA NEW GUINEA ww06215 Scirpophaga excerptalis PAPUA NEW GUINEA am12569 Scirpophaga sp. PAPUA NEW GUINEA (+1) 0.2 ww06204 Scirpophaga excerptalis PAPUA NEW GUINEA (+1) ww06200 Scirpophaga excerptalis PAPUA NEW GUINEA (+1) ww06214 Scirpophaga excerptalis PAPUA NEW GUINEA SRB013b Scirpophaga excerptalis PAPUA NEW GUINEA (+1) am12570 Scirpophaga sp. L PAPUA NEW GUINEA ww06202 Scirpophaga excerptalis PAPUA NEW GUINEA (+1)

Figure 6. RAxML tree based on the haplotypes dataset (Part 3 of 5). Bars to the right of the tree indicate species delimitation groupings according to the 12 diferent species delimitation methods. Te group of bars on the lef are delimitations based on the whole haplotypes sequence dataset, bars on the right are based on the genus- specifc analyses. Numbers in brackets indicate how many additional copies of this haplotype were present in the 1297 sequence dataset. Asterisks indicate sequences that we found to be misidentifed, and which have now been corrected on BOLD (new species names appear in this fgure). Names (or numbers in brackets) in bold indicate specimens where we performed genitalia dissections to confrm identity.

for quarantine identifcations. Ideally, reference DNA barcode datasets should be established for quarantine pests and be validated through independent review processes to ensure the veracity of each specimen’s species identity. Tis is difcult in taxa such as Chilo which lack modern integrative revisionary taxonomic studies and associ- ated identifcation resources. As for our own specimens, although we aimed to perform a genitalia dissection to confrm the identity of at least one individual from each cluster, we were unable to perform this in all clusters. We were also unable to verify the identity of juvenile specimens in most cases, although this study will help future researchers develop larval morphology keys through providing an improved barcode identifcation tool

Scientific Reports | (2019)9:7039 | https://doi.org/10.1038/s41598-019-42995-0 11 www.nature.com/scientificreports/ www.nature.com/scientificreports

4 4 9

s

MrBayes RAxML P MrBayesP RAxML MrBayes RAxML P MrBayeP RAxML

GMYCGMYC singlemPT multiplemPT bPTPbPTPRESLABGDABGD X=1ABGD p=0.00774X=1ABGD p=0.0129X=1ABGD p=0.0215X=1.5 X=1.5 p=0.00774GMYC p=0.0129GMYC singlemPT multiplemPT bPTPbPTPABGDABGD X=1ABGD p=0.0077X=1ABGD p=0.0129X=1ABGD p=0.0215X=1.5 X=1.5 p=0.0077 p=0.012 BOLD GBGL18424-15 Chilo sp. CHINA* BOLD ANICS081-11 Chilo sp.ANIC1 AUSTRALIA BOLD ANICS080-11 Chilo sp. ANIC1 AUSTRALIA BOLD LOQB529-05 Chilo sp. AUSTRALIA* BOLD LOQB548-05 Chilo sp. AUSTRALIA* BOLD LOQB541-05 Chilo crossostichus AUSTRALIA BOLD LOQB531-05 Chilo crossostichus AUSTRALIA (+1) BOLD LOQB537-05 Chilo crossostichus AUSTRALIA BOLD ANICS067-11 Chilo crossostichus AUSTRALIA BOLD LOQB530-05 Chilo crossostichus AUSTRALIA (+1) BOLD LOQB528-05 Chilo crossostichus AUSTRALIA ww06131 TANZANIA ww06136 MALAWI am12545 Unknown L NIGERIA BOLD KSLEP108-15 Eoreuma densella CANADA (+1) BOLD LOFLB456-06 Eoreuma densella PS1 USA BOLD BBLOD1467-11 Eoreuma densella USA BOLD LOFLA657-06 Eoreuma densella USA BOLD GBGL15128-14 Eoreuma densella BOLD GBGL17265-15 Eoreuma loftini BOLD GBGL17264-15 Eoreuma loftini MEXICO BOLD GBGL17263-15 Eoreuma loftini MEXICO ukzn0363 Eoreuma loftini USA (+3) BOLD BBLOD664-11 Eoreuma loftini USA (+3) ukzn0362 Eoreuma loftini USA BOLD BBLOC286-11 Eoreuma loftini USA BOLD NSWLP608-13 Chilo sp. AUSTRALIA ww06137 Unknown MALAWI ww06133 Unknown KENYA (+1) am12572 Unknown PAPUA NEW GUINEA ukzn0529 Cnaphalocrocis medinalis PHILIPPINES ukzn0528 Cnaphalocrocis patnalis PHILIPPINES BOLD GBGL18411-15 Chilo sp. CHINA* BOLD GBGL18410-15 Chilo sp. CHINA* ww06132 Unknown TANZANIA ww06128 Unknown KENYA ww06139 Unknown KENYA ww06129 Unknown UGANDA SRB025a Chilo sacchariphagus sacchariphagus (+1) BOLD GBGL12189-13 Chilo sacchariphagus INDIA (+2)* BOLD GMIN79350-17 Chilo sacchariphagus INDONESIA am00780 Chilo sacchariphagus PS1 ww06208 Chilo sacchariphagus PS1 INDONESIA Wang2017 Fuzhou7 Chilo sacchariphagus CHINA Wang2017 Fuzhou1 Chilo sacchariphagus CHINA Wang2017 Zhangzhou1 Chilo sacchariphagus CHINA (+1) Wang2017 Nanning1 Chilo sacchariphagus CHINA (+1) Wang2017 Yacheng6 Chilo sacchariphagus CHINA am12468 Chilo sacchariphagus L INDONESIA am11083 Chilo sacchariphagus L CHINA ww06216 Chilo sacchariphagus PS1 INDONESIA am00781 Chilo sacchariphagus PS1THAILAND (+26) Wang2017 Chilo sacchariphagus Zhanjiang2 CHINA Wang2017 Chilo sacchariphagus Yacheng1 CHINA ukzn0167 Chilo sacchariphagus MOZAMBIQUE (+1) ukzn0105 Chilo sacchariphagus MOZAMBIQUE (+1) SRB028a Chilo sacchariphagus sacchariphagus INDONESIA ukzn0110 Chilo sacchariphagus REUNION ukzn0109 Chilo sacchariphagus REUNION ukzn0107 Chilo sacchariphagus REUNION (+1) am12387 Chilo sacchariphagus INDONESIA (+5) am12478 Chilo sacchariphagus L INDONESIA am12473 Chilo sacchariphagus L INDONESIA am12484 Chilo sacchariphagus L INDONESIA (+1) am12481 Chilo sacchariphagus L INDONESIA (+3) am12477 Chilo sacchariphagus L INDONESIA am12388 Chilo sacchariphagus INDONESIA (+3) Cs12 Chilo sacchariphagus INDONESIA am12491 Chilo sacchariphagus L INDONESIA am12472 Chilo sacchariphagus L INDONESIA am12482 Chilo sacchariphagus L INDONESIA am12469 Chilo sacchariphagus L INDONESIA am12374 Crambidae L ECUADOR BOLD LNAUU2073-15 Diatraea lineolata MEXICO (+1) BOLD GBGL18594-15 Diatraea grandiosella MEXICO BOLD GBGL12829-13 Diatraea grandiosella MEXICO (+8) BOLD DMAS005-14 Diatraea crambidoides USA (+2) BOLD DMAS003-14 Diatraea mitteri USA (+1) BOLD DMAS002-14 Diatraea mitteri USA BOLD GBGL14921-14 Diatraea lisetta BOLD BBLOE1922-12 Diatraea lisetta USA (+2) BOLD LPOKA067-08 Diatraea evanescens USA BOLD BBLOC1454-11 Diatraea evanescens USA BOLD BBLOE212-11 Diatraea evanescens USA BOLD LOFLA067-06 Diatraea evanescens USA (+12) BOLD LOFLD540-07 Diatraea evanescens USA BOLD GBGL12807-13 Diatraea aff. considerata magnifactella MEXICO BOLD GBGL12806-13 Diatraea aff. considerata magnifactella MEXICO BOLD GBGL12817-13 Diatraea considerata MEXICO (+3) BOLD GBGL12819-13 Diatraea considerata MEXICO (+2) BOLD GBGL12797-13 Diatraea aff. considerata magnifactella MEXICO (+16) BOLD GBGL12805-13 Diatraea aff. considerata magnifactella MEXICO BOLD GBGL17309-15 Diatraea saccharalis USA BOLD GBGL17310-15 Diatraea saccharalis USA BOLD GBGL17315-15 Diatraea saccharalis USA (+3) BOLD GBGL17316-15 Diatraea saccharalis USA (+1) BOLD GBGL17312-15 Diatraea saccharalis USA BOLD BBLOD166-11 Diatraea saccharalis USA (+1) BOLD GBGL12827-13 Diatraea saccharalis MEXICO (+8) BOLD GBGL15014-14 Diatraea saccharalis (+2) BOLD GBGL14997-14 Diatraea saccharalis BOLD GBMIN30589-13 Diatraea saccharalis (+101) BOLD GBGL15001-14 Diatraea saccharalis BOLD GBGL14970-14 Diatraea saccharalis BOLD GBGL14936-14 Diatraea saccharalis BOLD GBGL14964-14 Diatraea saccharalis BOLD GBGL14959-14 Diatraea saccharalis BOLD GBGL14960-14 Diatraea saccharalis (+2) BOLD GBGL15020-14 Diatraea saccharalis BOLD GBGL15015-14 Diatraea saccharalis 0.2 BOLD GBGL15019-14 Diatraea saccharalis (+11) BOLD GBGL14927-14 Diatraea saccharalis BOLD GBMIN30587-13 Diatraea saccharalis

Figure 7. RAxML tree based on the haplotypes dataset (Part 4 of 5). Bars to the right of the tree indicate species delimitation groupings according to the 12 diferent species delimitation methods. Te group of bars on the lef are delimitations based on the whole haplotypes sequence dataset, bars on the right are based on the genus- specifc analyses. Numbers in brackets indicate how many additional copies of this haplotype were present in the 1297 sequence dataset. Asterisks indicate sequences that we found to be misidentifed, and which have now been corrected on BOLD (new species names appear in this fgure). Names (or numbers in brackets) in bold indicate specimens where we performed genitalia dissections to confrm identity.

for juvenile specimens. Tis study has resolved instances of misidentifcation and indicated the possible need for taxonomic revision, both operational factors that must be resolved in robust barcoding systems67. Global analy- ses coupled with morphological taxonomic study are necessary, and incremental refnements to reference DNA barcode datasets should be performed as more data accumulates68. Te high levels of diversity that we fnd in this study, and the tendency in several cases for that diversity to be correlated with geography, indicate that barcoding could be used in this group to determine the source popula- tion of a specimen. Tis might be particularly important in situations where diferent populations require difer- ent biosecurity approaches. For example, Eldana saccharina populations in Africa are host to diferent species, and are diferentiated geographically and by their COI barcodes69, and whitefy (Bemisia tabaci) biotypes are known to have diferent pesticide resistance profles70. Further work is required to determine whether diver- gent COI clusters in diverse species require diferent biosecurity responses.

Scientific Reports | (2019)9:7039 | https://doi.org/10.1038/s41598-019-42995-0 12 www.nature.com/scientificreports/ www.nature.com/scientificreports

4 4 4 9 5

s s s p=0.0129 le ayes

MrBaye RAxML MrB MrBaye RAxML

GMYCGMYC singlemPT multiplePmPT MrBayePbPTP RAxMLbPTPRESLABGDABGD X=1ABGD p=0.0077X=1ABGD X=1ABGD p=0.0215X=1.5 X=1.5 p=0.0077GMYC p=0.0129GMYC singmPT multiplePmPTPbPTP RAxMLbPTPRESLABGDABGD X=1ABGD p=0.00774X=1ABGD p=0.012X=1ABGD p=0.021X=1.5 X=1.5 p=0.0077 p=0.0129 ww06205 Chilo infuscatellus INDONESIA (+1) am12476 Chilo infuscatellus L INDONESIA am11068 Chilo infuscatellus L PAKISTAN am11069 Chilo infuscatellus L PAKISTAN ukzn00535 Chilo infuscatellus INDIA am00786 Chilo infuscatellus INDIA Wang2017 Laibing4 Chilo infuscatellus CHINA Wang2017 Laibing2 Chilo infuscatellus CHINA (+2) Wang2017 Zhanjiang1 Chilo infuscatellus CHINA (+1) Wang2017 Zhanjiang4 Chilo infuscatellus CHINA Wang2017 Zhanjiang2 Chilo infuscatellus CHINA am00785 Chilo infuscatellus THAILAND (+1) am11017 Chilo infuscatellus THAILAND am00783 Chilo infuscatellus THAILAND (+11) Ci13 Chilo infuscatellus THAILAND Ci9 Chilo infuscatellus THAILAND ukzn0371 Chilo infuscatellus THAILAND Wang2017 Dehong4 Chilo infuscatellus CHINA Wang2017 Dehong1 Chilo infuscatellus CHINA Wang2017 Dehong7 Chilo infuscatellus CHINA Wang2017 Dehong5 Chilo infuscatellus CHINA Wang2017 Dehong3 Chilo infuscatellus CHINA Wang2017 Kaiyuan3 Chilo infuscatellus CHINA (+1) Wang2017 Dehong2 Chiloinfuscatellus CHINA Wang2017 Kaiyuan2 Chilo infuscatellus CHINA Wang2017 Dehong6 Chilo infuscatellus CHINA Wang2017 Kaiyuan1 Chilo infuscatellus CHINA ukzn0370 Chilo tumidicostalis THAILAND am00779 Chilo tumidicostalis THAILAND (+2) am12378 Chilo tumidicostalis THAILAND ukzn0369 Chilo tumidicostalis THAILAND BOLD PHLAD536-11 Chilo luteellus BOLD PHLAD537-11 Chilo luteellus ITALY BOLD LMDH170-11 Chilo plejadellus USA BOLD LNCB347-06 Chilo plejadellus USA (+3) BOLD LNAUU2071-15 Chilo demotellus USA (+1) BOLD GBGL18417-15 Chilo suppressalis CHINA BOLD GBGL18416-15 Chilo suppressalis CHINA BOLD GBGL18419-15 Chilo suppressalis CHINA BOLD CYTC730-12 Chilo suppressalis BOLD GBGL18413-15 Chilo suppressalis CHINA BOLD GBGL18414-15 Chilo suppressalis CHINA BOLD GBGL18415-15 Chilo suppressalis CHINA BOLD GBGL18412-15 Chilo suppressalis CHINA am12370 Chilo suppressalis BOLD GBGL7462-12 Chilo suppressalis JAPAN BOLD GBGL18418-15 Chilo suppressalis CHINA am00766 Chilo suppressalis IRAN (+1) BOLD GBMIN29754-13 Chilo suppressalis JAPAN BOLD GBGL7463-12 Chilo suppressalis JAPAN BOLD GBMIN29755-13 Chilo suppressalis JAPAN (+17) am00778 Chilo suppressalis IRAN am12558 Chilo supressalis L IRAN am00773 Chilo suppressalis IRAN BOLD GBGL18423-15 Chilo suppressalis CHINA BOLD LTOLB122-08 Chilo suppressalis PHILIPPINES BOLD GBGL18420-15 Chilo suppressalis CHINA BOLD GBGL18421-15 Chilo suppressalis CHINA BOLD GBGL18422-15 Chilo suppressalis CHINA am12382 Chilo partellus SOUTH AFRICA (+3) am12540 Chilo partellus L KENYA (+2) ww06141 Chilo partellus KENYA Cp2 Chilo partellus SOUTH AFRICA BOLD AGIRI006-17 Chilo partellus INDIA (+1) am00787 Chilo partellus INDIA (+3) BOLD MAMOT512-10 Chilo partellus PAKISTAN BOLD LEPIN053-14 Chilo partellus INDIA BOLD MAMOT575-10 Chilo partellus PAKISTAN (+15) BOLD LEPIN013-12 Chilo partellus INDIA* BOLD MAMOT201-10 Chilo partellus PAKISTAN BOLD MAMOT325-10 Chilo partellus PAKISTAN ww06151 Chilo diffusilineus AM09 TANZANIA (+3) ww06164 Chilo sp. AM08 CAMEROON ww06165 Chilo sp. AM08 CAMEROON BOLD ANICS065-11 Chilo crypsimetalla AUSTRALIA BOLD ANICS075-11 Chilo crypsimetalla AUSTRALIA BOLD ANICS076-11 Chilo crypsimetalla AUSTRALIA BOLD ANICS074-11 Chilo crypsimetalla AUSTRALIA (+1) ADR036AA Chilo crypsimetalla AUSTRALIA (+3) DMM079 Chilo crypsimetalla AUSTRALIA ADR036A Chilo crypsimetalla AUSTRALIA SAC342 Chilo crypsimetalla AUSTRALIA CR300T Chilo crypsimetalla AUSTRALIA (+3) CR300M Chilo crypsimetalla AUSTRALIA ADR0542 Chilo crypsimetalla AUSTRALIA am12412 Chilo aff. terrenellus louisiadalis L INDONESIA (+1) am12409 Chilo aff. terrenellus louisiadalis L INDONESIA am12395 Chilo aff. terrenellus louisiadalis L PAPUA NEW GUINEA am12398 Chilo aff. terrenellus louisiadalis L PAPUA NEW GUINEA am12433 Chilo aff. terrenellus louisiadalis L PAPUA NEW GUINEA am12571 Chilo aff. terrenellus louisiadalis PAPUA NEW GUINEA am12437 Chilo aff. terrenellus louisiadalis L PAPUA NEW GUINEA (+8) am11086 Chilo aff. terrenellus louisiadalis L INDONESIA (+1) am12434 Chilo aff. terrenellus louisiadalis L PAPUA NEW GUINEA am12413 Chilo aff. terrenellus louisiadalis L PAPUA NEW GUINEA am12430 Chilo aff. terrenellus louisiadalis L PAPUA NEW GUINEA (+2) Ci5 Chilo terrenellus PAPUA NEW GUINEA ww06194 Chilo terrenellus PAPUA NEW GUINEA Ci2 Chilo terrenellus PAPUA NEW GUINEA (+1) ww06193 Chilo terrenellus PAPUA NEW GUINEA BOLD GBGL12175-13 Chilo polychrysus INDIA CR300i Chilo sp. AUSTRALIA (+1) 00338ak Chilo sp. AUSTRALIA Wang2017 Guangzhou1 Chilo auricilius CHINA (+2) U2 Chilo auricilius Wang2017 Guangzhou2 Chilo auricilius CHINA Wang2017 Guangzhou5 Chilo auricilius CHINA BOLD LEPIN045-13 Chilo auricilius INDIA* Ci4 Chilo auricilius INDONESIA BOLD AGIRI005-17 Chilo auricilius INDIA (+8)* am12480 Chilo auricilius INDONESIA am11098 Chilo auricilius L INDONESIA (+3) ww06170 Chilo sp. INDONESIA Ca2 Chilo auricilius INDONESIA BOLD BIPR004-13 Chilo auricilius INDIA* BOLD BIPR003-13 Chilo auricilius INDIA* BOLD LEFIB667-10 Chilo phragmitella FINLAND BOLD LEFIF770-10 Chilo phragmitella FINLAND BOLD LEFIG259-10 Chilo phragmitella FINLAND BOLD NLLEA1059-12 Chilo phragmitella NETHERLANDS (+3) BOLD CGUKB395-09 Chilo phragmitella UK (+4) BOLD LON589-08 Chilo phragmitella NORWAY BOLD GWORL633-09 Chilo phragmitella BOLD PHLAA247-09 Chilo phragmitella AUSTRIA (+2) BOLD PHLAE246-11 Chilo phragmitella AUSTRIA (+8) ww06145 Chilo sp. AM11 TANZANIA (+1) ww06143 Chilo sp. AM15 TANZANIA ww06154 Chilo sp. AM15 TANZANIA ww05573 Chilo sp. AM15 TANZANIA ww06155 Chilo sp. AM15 TANZANIA ww06157 Chilo sp. AM15 TANZANIA ww05569 Chilo sp. AM15 TANZANIA ww06148 Chilo sp. AM15 TANZANIA (+4) ww03991 Chilo quirimbellus ww05571 Chilo quirimbellus ww05572 Chilo quirimbellus ww05563 Chilo quirimbellus ww06125 Chilo sp. AM12 UGANDA ww05581 Chilo sp. AM13 KENYA ww05580 Chilo sp. AM13 KENYA ukzn0722 Chilo sp. AM16 ETHIOPIA ww04054 Chilo sp. AM16 CAMEROON ww04087 Chilo sp. AM16 CAMEROON (+3) ww04083 Chilo sp. AM16 CAMEROON (+3) ww06178 Chilo sp. AM16 CAMEROON (+2) ww06162 Chilo sp. AM16 CAMEROON ww06161 Chilo sp. AM16 CAMEROON (+2) ww05596 Chilo sp. AM16 CAMEROON ww05568 Chilo thyrsis TANZANIA ww05575 Chilo thyrsis TANZANIA ww05570 Chilo orichalcociliellus TANZANIA ww05561 Chilo orichalcociliellus TANZANIA ww05565 Chilo orichalcociliellus TANZANIA (+1) ww05601 Chilo orichalcociliellus KENYA ww05605 Chilo orichalcociliellus KENYA (+2) ww05584 Chilo orichalcociliellus TANZANIA ww05602 Chilo orichalcociliellus KENYA (+2) ww04065 Chilo orichalcociliellus KENYA ww04039 Chilo orichalcociliellus KENYA ww04003 Chilo orichalcociliellus KENYA ww04114 Chilo orichalcociliellus KENYA ww06174 Chilo orichalcociliellus TANZANIA ww06180 Chilo orichalcociliellus TANZANIA ww04011 Chilo orichalcociliellus KENYA ww03993 Chilo orichalcociliellus KENYA (+4) ww05566 Chilo orichalcociliellus TANZANIA ww03986 Chilo orichalcociliellus KENYA (+8) ww03982 Chilo orichalcociliellus KENYA (+85) ww04116 Chilo orichalcociliellus KENYA (+3) ww04056 Chilo orichalcociliellus KENYA (+7) ww05562 Chilo orichalcociliellus TANZANIA ww04086 Chilo oricalcociliellus ww05579 Chilo orichalcociliellus KENYA ww06160 Chilo oricalcociliellus ww06147 Chilo orichalcociliellus TANZANIA ww05593 Chilo orichalcociliellus SOUTH AFRICA 0.2 ww05594 Chilo orichalcociliellus SOUTH AFRICA ww05592 Chilo orichalcociliellus SOUTH AFRICA ww05588 Chilo orichalcociliellus SOUTH AFRICA ww05595 Chilo orichalcociliellus SOUTH AFRICA

Figure 8. RAxML tree based on the haplotypes dataset (Part 5 of 5). Bars to the right of the tree indicate species delimitation groupings according to the 12 diferent species delimitation methods. Te group of bars on the lef are delimitations based on the whole haplotypes sequence dataset, bars on the right are based on the genus- specifc analyses. Numbers in brackets indicate how many additional copies of this haplotype were present in the 1297 sequence dataset. Asterisks indicate sequences that we found to be misidentifed, and which have now been corrected on BOLD (new species names appear in this fgure). Names (or numbers in brackets) in bold indicate specimens where we performed genitalia dissections to confrm identity.

Similar wide-ranging studies of North American and European Lepidoptera have tended to fnd considerably lower intraspecifc diversity than we fnd here. In a study by Yang et al.71 on the North American Pyraustinae (Crambidae) including 1589 COI sequences from 103 species, maximum intraspecific distances (K2P) all

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were below 6%, and only three instances were found above 4%. Huemer et al.72 examined 1004 species (4925 sequences) of butterfy in Austria and Finland, fnding the highest maximum intraspecifc distance was 9.6% K2P, with only 3 instances above 8%; 12.3% of included species included more than 2% maximum intraspecifc divergence. Hausmann et al.73 included 1395 sequences across 331 species of the Geometridae fauna of Bavaria, and found 9.2% maximum intraspecifc divergence with seven species greater than 4%. In contrast, in our study of 1297 sequences across 64 species we fnd 27 species with maximum intraspecifc K2P distances above 2%, 13 above 4%, and up to 11% in S. inferens. Te unusually large intraspecifc diversity in COI sequences observed for many species in this study needs to be resolved through the analysis of appropriate nuclear gene sequences and morphological work, to rigorously reassess species boundaries. Overall, we fnd that COI DNA barcoding initiatives aimed at identifying stemborers of economic interest are likely to be successful. Four out of the seven species of greatest economic signifcance to Australia were found to have intraspecifc distances >6%: Chilo infuscatellus, C. sacchariphagus, Scirpophaga excerptalis and Sesamia inferens. Species delimitation eforts in this large, unevenly sampled single-locus dataset were mixed, although several species exhibited congruent delimitations across methods. Non-monophyly within species was rare, encountered only three times, indicating that tree-based clustering may be a useful way to assign species identity to unknown individuals. Errors in identifcation found in online databases underline the importance of expertly identifed voucher specimens and curation of sequence collections in establishing robust reference databases for accurate DNA barcode based identifcations. Tis study is the frst step in that direction for the lepidopteran stemborers of sugarcane and cereals. Methods Specimens. Specimens were collected by the authors or donated by colleagues from many countries. We attempted to sample as broadly as possible from cereal and sugarcane growing regions around the world, pri- oritising the “high risk” species of Sallam14. Two thirds of the specimens sequenced for this study were adults and one third were larvae. Adults are usually the only life stage reliably identifed using current morphology techniques, but sampling adults usually precludes the collection of host plant information. In this case 68% of the adults sequenced for this study, including most of those from Africa, were collected from host plants as larvae and laboratory reared.

Species identifcation. Where possible, adults and larvae were identifed to species level in the feld based on experience, morphological appearance and/or ecological and geographic distribution, and were gifed to us with this existing identifcation. Afer DNA sequencing and preliminary phylogenetic analysis (see below), adult specimens for which the morphological (feld) identifcations disagreed with the DNA barcode identifcations were reassessed based on external morphological appearance. Our next step was to examine at least one specimen from each putative species or each DNA barcode cluster (whichever was the less inclusive group) and reassess its mor- phological identifcation (e.g. for Scirpophaga excerptalis, 11 specimens were examined). Genitalia dissections were conducted on adult specimens and compared with available images of type specimens (for certain Chilo species), original species descriptions, taxonomic revisions, and published resources for stemborer identifcation. Te main literature referred to: Barrion74, Bleszynski75, Bojer22, Butani76, Common77, Chen et al.78, Dudgeon23, Holloway79, Kapur57, Lewvanich80, Maes81, Meijerman and Ulenberg82, Munroe and Solis83, Pagenstecher20, Polaszek6, Rao and Nagaraja84, Siddalingappa et al.85, Solis and Metz11, Snellen21, Swinhoe86, Tams and Bowden87, Walker24. For sequences downloaded from BOLD, which includes data mined from GenBank, we used the species iden- tifcation provided. However, samples which we had good reason to believe had been incorrectly identifed were excluded from some analyses, as described below. We contacted BOLD about such samples and their identifca- tions on the database were changed.

DNA extraction. DNA was extracted from adult moths either from a single leg or from a whole abdomen if a genitalia dissection was required. For larvae, depending on the specimen size, either a proleg or a piece of abdom- inal integument and associated muscle, or in some cases the entire rear half of the specimen (up to 10–20 mg) was sampled. To avoid any cross contamination, dissection instruments and forceps were wiped with laboratory tissue, dipped in ethanol and fame-sterilized between samples. DNA was extracted using commercial silica-gel membrane-based kits, either Qiagen DNeasy (Qiagen, Chadstone, Australia) or Bioline Isolate II Genomic DNA Isolation Kit (Bioline, Eveleigh, Australia) following the manufacturers’ instructions, except for whole abdomen dissections we used two to three times the recommended volumes of tissue digestion bufer and proteinase-K, and stored the excess volume at −80 °C for potential later use.

PCR amplifcation. PCR amplifcation used the protocols and primers described in Mitchell88, with some PCRs using the Folmer primers89. Samples were amplifed using the primer pair AMbc0f1m and AMbc0r1m, and PCR products were visualised on a 1.5% agarose gel, stained with 1 drop of Biotium GelRed (Gene Target Solutions, Dural, Australia) per 50 mL of gel mix. Samples which did not show a band were re-amplifed, using the primers M13F and AMbc0r2m, using 1 μL of the PCR product from the frst amplifcation as a template. If this re-amplifcation failed, then no further amplifcation attempts were made. PCR protocols for initial amplifcations and re-amplifcations were the same and used the following reaction mixture per well: 2.29 μL MilliQ water, 7.5 μL of 10% Trehalose solution, 1.5 μL 10x reaction bufer (no MgCl2), 0.75 μL MgCl2, 0.3 μL of dNTP mix, 0.3 μL forward and reverse primer at 5 μM each, 0.06 μL Platinum Taq and 2 μL template, (1 μL for reamplifcations).

Sequencing. Sequencing was carried out by Macrogen Inc. (Korea) and the Australian Genome Research Facility (Brisbane). Chromatograms were edited and consensus sequences generated using Geneious 10.2.2 (http://www.geneious.com)90.

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Barcode analysis. For this study we sequenced 508 specimens. Our target taxa included all genera contain- ing species listed as sugarcane pests by Sallam14, i.e., Tetramoera Diakonof, 1968 (Tortricidae), Eldana Walker, 1865 and Emmalocera Raganot, 1888 (Pyralidae), Chilo Zincken, 1817, Diatraea Guilding, 1828, Eoreuma (Ely, 1910) and Scirpophaga Treitschke, 1832 (Crambidae), and Sesamia Guenée, 1852 (Noctuidae). No samples or sequences could be obtained for Acigona Hübner (1825) (Erebidae) or Maliarpha Raganot (1888) (Pyralidae). Te Australian native stemborer genus Bathytricha Turner, 1920 (Noctuidae) was included because B. truncata, despite being a minor pest, is the only native stemborer species recorded to infest cane in Australia91, and there is a need to distinguish it from exotic species. Similarly, Acrapex Hampson, 1894 (Noctuidae) was included as the Australian species currently placed in this genus appear closely related to Asian Sesamia species, while Busseola Turau, 1904 (Noctuidae), Rivula Guenée, 1845 (Erebidae) and Cnaphalocrocis Lederer, 1863 (Crambidae) were added as they contain signifcant cereal pest species for which we had obtained specimens. This dataset, including specimen collection data, sequences and sequence trace files, is available on the Barcode of Life Data System website (BOLD)33 as public project LSTEM (Lepidopteran Stemborers) (https://doi. org/10.5883/DS-LSTEM) (Supplementary Material 2). We compiled a Supplementary Dataset on BOLD, consist- ing of all BOLD sequences for taxa identifed as being congeneric with our sample of species. Te BOLD public database sequences were downloaded on 11 October 2017. We used a set of sequences from the study on Chinese sugarcane borers by Wang et al.47 supplied to us by the senior author. When the specimens we sequenced were added to those downloaded from BOLD and the sequences from Wang et al., this produced a fnal dataset of 1297 individuals, including representatives from all seven of the ‘high threat’ species and 11 of the 15 ‘medium threat’ species and two of the 14 ‘low threat’ species identifed by Sallam14. Sequences were aligned in Geneious using Multiple Alignment using Fast Fourier Transform (MAFFT)92. Te resulting alignments were cropped to a length of 667 bp. Only sequences longer than 486 bp, the minimum bar- code standard length93, were used, however exceptions were made to this rule for two Chilo sacchariphagus spec- imens: ww06216 and ukzn0269 (at 476 bp and 413 bp respectively). Te frst sequence was included because our preliminary analysis showed it to occupy a long branch and be of possible taxonomic interest, and the latter was included because it was the only C. sacchariphagus sequence in our dataset from the type locality, Mauritius. Of the 1297 individuals in this fnal dataset, 1089 were initially identifed to species level (Supplementary Material 3). GMYC37,66,94 and mPTP39, are known to encounter difculties with datasets including identical sequences. As identical sequences are also ofen removed when performing delimitations to speed up the analysis (e.g., in bPTP95) we removed such duplicates using USEARCH 9.2.6496, removing the shortest of sequences with “max- imum diferences = 0”, “maximum substitutions = 0”, or “minimum match percentage identity = 100”. We then checked the resulting 623 sequence dataset in a Geneious distance matrix to determine whether any 100% iden- tity sequences remained, and a further three sequences were removed afer this. We verifed that no species had been eliminated from the dataset through this procedure. Te resulting 620 sequence dataset, henceforth the ‘haplotypes dataset’, was the main dataset used for species delimitation. Preliminary analysis of the alignment was carried out using FastTree 2.1.597,98 in Geneious, using default set- tings, to generate approximately maximum-likelihood trees. Tis analysis was the one used to identify likely misidentifed sequences. Nucleotide substitution models were tested using PartitionFinder299, using the greedy algorithm100 and the PhyML phylogeny estimator101, implemented on the CIPRES science gateway computing platform102. Te best model was chosen based on the Bayesian Information Criterion. Tis was SYM + I + G for codon position 1, TRN + I + G for codon position 2 and GTR + G for codon position 3 in the full dataset, with a separate partition for each codon position. In order to better investigate the efect of sample size and diversity on species delimitation, three further data- sets were used: Chilo only, Scirpophaga only and Sesamia only subsets of the haplotypes tree. Tese datasets were formed by taking the smallest possible clades including all identifed samples of those genera from the FastTree tree. Tis means that these datasets contain a mix of samples identifed as being of that genus, and those that were not identifed as being of that genus but grouped with them (i.e., putatively misidentifed or unidentifed sequences). A single BOLD sequence labelled as Sesamia submarginalis was excluded from the Sesamia analysis due to its deep divergence from other Sesamia samples. Model selection was also run on these datasets: for Chilo, this was TRN + G for codon position 1, F81 + G for codon position 2 and GTR + G for codon position 3; for Scirpophaga this was TRN + G for position 1, F81 for position 2 and TIM + I + G for position 3; for Sesamia this was TRN + I for position 1, F81 + I for position 2 and TIM for position 3. Maximum-likelihood analyses were carried out using RAxML 8.2.10103 on the CIPRES science gateway. In each case, the tree was estimated using 100 random stating points, and levels of bootstrap support at nodes were calculated using a bootstrapping analysis with 1000 pseudoreplicates. Bayesian analyses were carried out using MrBayes 3.2.6104 on the CIPRES science gateway. Te analyses were terminated automatically when the standard deviation of split frequencies dropped below 0.01, so the num- ber of generations was diferent in each analysis, (haplotypes dataset: 18,580,000, Chilo: 4,765,000, Scirpophaga: 2,495,000, Sesamia: 480,000). Samples were taken every 1000 steps, and the frst 10% of samples were discarded as burnin. In each case two independent analyses were conducted, each consisting of one cold chain and seven heated chains. BEAST analyses were carried out on the CIPRES portal, using the estimate for the rate of evolution in the insect COI gene from Papadopoulou et al.105 Analyses used an MCMC chain of 10,000,000 steps, with a burnin of 1000 steps. Trees from the maximum-likelihood analyses and Bayesian analyses were visualized and Figures generated using FigTree 1.4.3 (http://tree.bio.ed.ac.uk/sofware/fgtree/). Te haplotypes dataset alignment was exported to TaxonDNA/Species Identifer 1.8106, and all individuals with an aberrant position on the tree with species level identifcation, i.e., likely misidentifcations, were removed

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prior to calculating the Kimura 2 parameter distance107 within and between species; 18 such sequences were found in the haplotypes dataset. Specimens not identifed to species were also excluded from this analysis. Mean intraspecifc and mean interspecifc distances were calculated in the same dataset using MEGA7108, using K2P distances, uniform rates among sites, and the default 500 bootstrap replicates to calculate standard error.

Species delimitation. Five diferent species delimitation methods were applied to each dataset to further investigate instances where barcode diversity was inconsistent with current taxonomy, and to help determine how many species there are among the unidentifed specimens in the tree. GMYC analysis, (single and multiple threshold), was carried out using the ‘splits’ package v 1.0-1937,109 in R v3.3.3110, using the BEAST trees as input. ABGD was carried out using the online version of ABGD sofware34 (http://wwwabi.snv.jussieu.fr/public/ abgd/abgdweb.html). Default settings were used, following the approach of Kekkonen and Hebert111, however distance matrices based on TrN distance calculated in MEGA7 were used as input, as the TrN model of evolu- tion was more applicable to our dataset than JC or K2P, based on our Partitionfnder2 results. All analyses were run twice, using two diferent relative gap width (X) settings, X = 1.5 (the default) and X = 1. Only the recursive results were used as they allow for diferent gap thresholds among taxa34. bPTP delimitation was carried out using the bPTP.py module v0.5138 in Python v2.7.14112, using both the MrBayes and RAxML trees in all cases. mPTP delimitation was conducted using the mPTP webserver was used for this analysis (http://mptp.h-its. org/#/tree), using the MrBayes and RAxML trees as input. Trees that had any multifurcations frst randomly resolved into 0-length bifurcating branches in Mesquite v3.5113. RESL delimitation35 was carried out online at the BOLD website, using the default settings of the “cluster sequences” function. Data Availability DNA Sequences, raw sequence trace fles and specimen collection data is available on BOLD as public project LSTEM. Te 508 COI sequences produced in this study have been submitted to GenBank, accession numbers MK566231 – MK566738. Te 508 sequence dataset is also available for direct download from BOLD using the https://doi.org/10.5883/DS-LSTEM. Full sequence alignment: Supplementary Material 3. References 1. Vallée, G. C., Muñoz, D. S. & Sankof, D. Economic importance, taxonomic representation and scientifc priority as drivers of genome sequencing projects. BMC Genomics 17, 125–133, https://doi.org/10.1186/s12864-016-3100-9 (2016). 2. Awika, J. M. In Advances in cereal science: implications to food processing and health promotion ACS Symposium Series. (eds Awika, J. M., Piironen, V. & Bean, S.) Ch. 1, 1–13 (American Chemical Society, 2011). 3. FAO FAOSTAT. Crop Statistics, http://www.fao.org/faostat/en/#data/QC (2018). 4. FAO. Te future of food and agriculture - trends and challenges. (Rome, 2017). 5. Boykin, L. M., Armstrong, K. F., Kubatko, L. & De Barro, P. Species delimitation and global biosecurity. Evolutionary bioinformatics online 8, 1–37, https://doi.org/10.4137/ebo.s8532 (2012). 6. Polaszek, A. 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