Biol Invasions DOI 10.1007/s10530-017-1546-6

ORIGINAL PAPER

Barcode index numbers expedite quarantine inspections and aid the interception of nonindigenous (Pseudococcidae)

Jing-Mei Ren . Muhammad Ashfaq . Xu-Nan Hu . Jun Ma . Fan Liang . Paul D. N. Hebert . Li Lin . Jean François Germain . Muhammad Z. Ahmed

Received: 2 February 2017 / Accepted: 18 August 2017 © Springer International Publishing AG 2017

Abstract Quarantine interception of invasive and (836) of these specimens were assigned to a named nonindigenous pests at ports of entry is often species, but others were only identified to a or impeded by the lack of robust identification methods. family. Their sequence analysis revealed substantial Because of their inconspicuous morphology and wax- COI diversity with maximum divergences reaching covered bodies, mealybugs present a particular chal- 27%. While the identified specimens included repre- lenge. The present study employs DNA barcoding sentatives of 62 species, the Barcode Index Number (658 base pairs near the 5′-terminus of the cyto- (BIN) system assigned the 914 sequences to 120 chrome c oxidase I gene) as a tool for their BINs, nearly doubling the putative species count and discrimination because of its proven utility in revealing cases of potential cryptic species and discriminating closely related species. The current misidentifications. With a single exception, study considers DNA barcodes from 914 mealybugs intraspecific divergence values for named species (Pseudococcidae) collected in 31 countries. Most were less than their nearest-neighbor distances, but 13 species showed BIN splits and two species were merged in a BIN. High genetic diversity and presence Electronic supplementary material The online version of this article (doi:10.1007/s10530-017-1546-6) of cryptic species in the known mealybugs, revealed contains supplementary material, which is available to in this study, underscore the limitations of morphol- authorized users. ogy and potential utility of BINs for the rapid recognition of nonindigenous insect pests. Jing-Mei Ren and Muhammad Ashfaq have contributed equally to this work. M. Ashfaq · P. D. N. Hebert J.-M. Ren · X.-N. Hu (&) · J. Ma (&) · F. Liang · L. Lin Centre for Biodiversity Genomics, Biodiversity Institute Plant Quarantine Laboratory, Guangdong Inspection and of Ontario, University of Guelph, Guelph, ON, Canada Quarantine Technology Center, Guangzhou 510623, People’s Republic of China J. F. Germain e-mail: [email protected] ANSES, Laboratoire de la Sante´ des Ve´ge´taux, Unite´ J. Ma d’entomologie et Plantes Invasives, Campus International e-mail: [email protected] de Baillarguet, CS 30016, 34988 Montferrier-sur-Lez Cedex, France J.-M. Ren Department of Entomology, South China Agricultural M. Z. Ahmed University, Guangzhou 510642, People’s Republic of Florida Department of Agriculture and Consumer China Services, Division of Plant Industry, 1911 SW 34th Street, Gainesville, FL 32608, USA 123 J.-M. Ren et al.

Keywords Invasive species · Biosecurity · morphology and genetic data of well recognized Plant pests · Cryptic species · DNA barcoding species (Malausa et al. 2011). DNA sequences provide a reliable option to resolve complex taxa but the technique is limited by non-availability of Introduction reference data. For example, China has documented 154 species of mealybugs, some representing alien With increasing global trade, the cross-border move- invasive species (Wang et al. 2016), but most of them ment of nonindigenous and invasive insect pests is on lack genetic data. The use of a morphology-based the rise (Westphal et al. 2008; Koch et al. 2014; identification approach for the quarantine inspection of http://www.eea.europa.eu/). Dwindling expertise in mealybugs, particularly their immature stages, is conventional identification techniques and the lack of difficult and time-consuming because it often requires robust methods for species recognition have created slide mounting and rearing (Gullan 2000; Johnson and new challenges for the quarantine interception of Giliomee 2013). Moreover, the perishable nature of invading (Bacon et al. 2012; Wan and Yang fruits and vegetables demands the capacity to rapidly 2016). With their inconspicuous morphology, mealy- and reliably recognize nonindigenous pests at quaran- bugs (: Pseudococcidae) are a particular tine inspection stations. challenge for identification (Qin et al. 2010). Many of Because DNA-based methods overcome the imita- the nearly 2000 described species of Pseudococcidae tions associated with morphology-based are important plant pests (Abd-Rabou et al. 2012; identifications, they are gaining increasing adoption Garcı´a Morales et al. 2016) and several species have in resolving mealybug species (Saccaggi et al. 2008; shown their capacity to adapt to new hosts (Chellap- Ashfaq et al. 2010; Daane et al. 2011). A range of DNA pan et al. 2013). Over the past decade, there have markers, such as elongation factor 1α (EF-1α), 18S been damaging invasions by mealybugs (Ahmed rRNA, 28S rRNA, internal transcribed spacer (ITS), et al. 2015a; Mendel et al. 2016) which have resulted and cytochrome c oxidase I (COI), have been used to in substantial economic losses for agriculture and discriminate mealybug species (Gullan et al. 2010; horticulture (Zeddies et al. 2001; Hodgson et al. Pieterse et al. 2010; Ashfaq et al. 2011; Abd-Rabou 2008). For example, the solenopsis mealybug et al. 2012; Beltra` et al. 2012; Beuning et al. 2014), but (Phenacoccus solenopsis) has invaded South Asia the lack of marker standardization has limited the (Hodgson et al. 2008; Ashfaq et al. 2010), China utility of sequence data. The implementation of a (Ahmed et al. 2015b) and Egypt (Ibrahim et al. 2015) DNA-based approach for species identification at a where it has become a major pest on cotton. global scale requires the generation of a sequence Likewise, invasions of the pine mealybug (Oracella library based upon a standard gene region to enable acuta) in China, the papaya mealybug (Paracoccus identifications. The adoption of COI-5′ as the standard marginatus) in West Africa, and the hibiscus mealy- barcode region (Hebert et al. 2003) for species bug (Maconellicoccus hirsutus) in the Caribbean have identification in the kingdom has led to the been documented (Sagarra and Peterkin 1999; Goer- assembly of more than four million barcode records, gen et al. 2011; You et al. 2013). including 1500 for the Pseudococcidae (accessed 16 The reduction of introductions of exotic species July 2017), in the Barcode of Life Data System linked to international trade is one of the main (BOLD) (www.boldsystems.org) (Ratnasingham and functions of plant quarantine programs and mealybugs Hebert 2007). Those sequences meeting designated represent an important group of concern. The taxo- criteria ([507 bp, no stop codons, \1% ambiguous nomic situation in Pseudococcidae is complex as some bases) have each been assigned to a Barcode Index genera, such as Antonina, Chorizococcus, Dysmicoc- Number (BIN) (Ratnasingham and Hebert 2013) that is cus, , and Spilococcus are currently an effective species proxy (Telfer et al. 2015; Ashfaq recognized as artificial assemblages of species created et al. 2017). BINs have been frequently used to resolve merely for ease in identification than for phylogenetic cryptic species complexes and to analyze their distri- relationships (McKenzie 1967; Gavrilov-Zimin 2016). butions (Ashfaq et al. 2014; Mutanen et al. 2015; This is also evidenced by incongruence between Ashfaq and Hebert 2016).

123 Barcode index numbers expedite quarantine inspections

China imports fresh produce from more than 34 from 24 nations were identified and sequenced at countries, and international travelers often carry IQTC (Table S1; Fig. 1). These specimens were undeclared plant material, creating an ongoing chal- preserved in 95–100% ethanol and stored at −20 °C lenge for quarantine inspections. Each year more than until DNA extraction. Morphological identifications 30 mealybug species, some regulated and nonindige- were based upon taxonomic keys in Tang (1992) and nous, are intercepted and identified at the Plant Williams (2004). Voucher specimens are held in the Quarantine Laboratory at the Guangdong Inspection Insect Collection of Plant Quarantine Laboratory at and Quarantine Technology Center (IQTC). The IQTC. current study aimed to establish a platform to support DNA extraction, polymerase chain reaction (PCR), barcode-based quarantine inspections for all inter- and sequencing: cepted mealybug species by testing the efficacy of DNA was extracted from individual specimens BINs as a tool for the prompt detection of newly using DNeasy Blood & Tissue Kit (Qiagen, Shang- encountered species. This is the first study on hai, China) following the manufacturer’s protocol. mealybugs which examines the congruence between DNA elution was performed with 50–80 μlofAE morphological species and BINs, and then maps their buffer. PCR amplification of COI-5′ (DNA barcode) distribution by integrating barcode data for Chinese (Hebert et al. 2003) was performed with primers mealybugs with those from other countries. PcoF1 (5′-CCTTCAACTAATCATAAAAATATYA G-3′)/LepR1 (5′-TAAACTTCTGGATGTCCAAA AAATCA-3′) (Park et al. 2011) or C1-1554F (5′- Materials and methods CAGGAATAATAGGAACATCAATAAG-3′)/C1-23 42R (5′-ATCAATGTCTAATCCGATAGTAAATA- Mealybugs were obtained from collections within 3′) (Deng et al. 2012) using 2 μl of DNA template. China, quarantine interceptions of specimens from The 30 μl PCR reaction contained 3.0 μlof19 PCR imported plant material at the port-of-entry in buffer, 2.4 μl of 2.5 mM dNTP mix with (MgCl2), Guangdong, and contributions from collaborators in 0.5 μl of 20 mM each primer, 2 μl of 50–125 ng Thailand, France, South Korea and the USA DNA template, 0.5 μl of LA Taq DNA polymerase (Table S1). A total of 2169 batches of mealybugs containing 5 U/μl (Takara, Guangzhou, China) and were recovered from intercepted plant material 21.4 μl of nuclease free water (Thermo Fisher during 2013–2015. From this total, 506 specimens Scientific). The PCR thermocycling regime was

Fig. 1 Map showing collection localities for specimens examined in this study 123 J.-M. Ren et al.

Table 1 Intraspecific divergence (K2P), nearest-neighbor (NN) identified to a genus or family only were treated as species distances, and BIN assignment of mealybug species (Pseudo- proxies to calculate intraspecific and NN distances coccidae) identified for quarantine. BINs for the specimens Common name Identification Count Max. Dist. to NN BIN divergence

Rhodesgrass mealybug Antonina graminis 1 N/A 9.7 ACG8144 N/A Coccidohystrix lubersaci 1 N/A 12.9 ADB4693 Marsh mealybug Atrococcus paludinus 3 0 4.0 AAF8194 The New Zealand flax mealybug Balanococcus diminutus 1 N/A 6.6 ACH4996 Takahashi lawn mealybug Balanococcus takahashii 2 0 6.6 AAI7656 Kunow´s mealybug Coccura comari 5 0 10.2 AAF4760 N/A Crisicoccus matsumotoia 28 4.0 8.8 ACF3021, AAB2764, AAB2765 Kuwana pine mealybug Crisicoccus pini 3 0.3 7.7 AAF1561 N/A Delottococcus aberiae 1 N/A 5.8 N/A Gray sugarcane mealybug Dysmicoccus boninsis 5 0.4 10.5 ADB3244 Pineapple mealybug Dysmicoccus brevipes 34 2.5 7.8 AAC7192 N/A Dysmicoccus lepelleyi 17 2.2 6.5 AAD3214 Gray pineapple mealybug Dysmicoccus neobrevipes 62 0.3 1.8 AAB6207 N/A Dysmicoccus sylvarum 2 0.3 10.8 ADD2460 N/A Dysmicoccus texensis 1 N/A 1.8 AAB6207 Cocoa mealybug Exallomochlus hispidus 3 0.5 5.8 ADA5757 Malvastrum mealybug Ferrisia malvastra 4 0 6.58 AAE6641 N/A Ferrisia terani 2 0.2 7.0 ADD3271 Striped mealybug Ferrisia virgata 33 0.3 6.8 AAJ0375 N/A Heliococcus bohemicus 2 0.2 12.0 N/A N/A Heliococcus kurilensis 8 1.4 11.3 AAD3012 N/A Heliococcus puerariae 1 N/A 10.6 AAX1500 Cactus mealybug Hypogeococcus pungens 1 N/A 8.4 N/A Pink hibiscus mealybug Maconellicoccus hirsutus 57 6.6 7.8 ADD2152, ADD2613, AAE8615, ADD3831 N/A Maconellicoccus multipori 1 N/A 8.0 ADA9465 Spiked mealybug Nipaecoccus nipae 1 N/A 7.2 AAG8794 Spherical mealybug Nipaecoccus viridis 20 1.5 12.3 ADB5317, ADB5318 Bamboo Mealybug Palmicultor lumpurensis 3 2.1 8.6 AAG8792 Palm mealybug Palmicultor palmarum 2 0 8.6 ADA6625 Agave mealybug Paracoccus gillianae 5 1.2 4.0 ADD1830 Papaya mealybug 36 0.2 5.6 AAD5426 Apple mealybug Phenacoccus aceris 12 7.3 10.2 AAC6448, AAC6449, ADA6558, ADA8976, ACH7312 Iris mealybug Phenacoccus avenae 1 N/A 12.2 AAY2351 N/A Phenacoccus baccharidis 2 7.3 10.2 ADD3470, ADD3471 Cassava mealybug Phenacoccus madeirensis 14 8.5 10.9 ADA8937, ADA9138, AAU1482 N/A Phenacoccus parvus 6 4.2 10.3 AAM5052, ACI0365, ADD9575 N/A Phenacoccus peruvianus 3 0.2 9.8 ADA5624

123 Barcode index numbers expedite quarantine inspections

Table 1 continued Common name Identification Count Max. Dist. to NN BIN divergence

Solanum mealybug Phenacoccus solani 21 2.3 5.0 AAC6432, ADB3907, ADB3908 Cotton mealybug Phenacoccus solenopsis 74 0.8 5.0 AAC6433 Citrus mealybug 55 2.1 1.6 AAC5730 Vine mealybug Planococcus ficus 12 3.8 5.3 AAE4117, AAE4118, ACI1744 Japanese mealybug Planococcus kraunhiae 16 0.3 7.6 AAC5734 Coffee mealybug Planococcus lilacinus 28 3.8 5.6 AAD3991, AAI2742, ADA5576, ADA7924 Passionvine mealybug Planococcus minor 70 0.7 1.6 ACE8487 Cypress tree mealybug Planococcus vovae 1 N/A 7.7 ACH9022 Aerial root mealybug Pseudococcus baliteus 29 2.9 6.5 ADA6588 Scarlet mealybug Pseudococcus calceolariae 2 0 8.8 AAD3396 Comstock mealybug 29 4.1 5.0 AAB7649, ABZ7509 Citriculus mealybug Pseudococcus cryptus 14 4.6 5.0 AAE0469, ADA8482 Jack Beardsley mealybug Pseudococcus jackbeardsleyi 8 0.6 7.3 AAD3410 Long-tailed mealybug 18 4.6 7.2 AAC2547, AAC2548, ADB5085, ADB5086 Grape mealybug 1 N/A 7.7 ADB4685 N/A Pseudococcus nr. maritimus 1 N/A 5.8 N/A N/A Pseudococcus nr. sociabilis 1 N/A 8.4 ADD0824 N/A Pseudococcus nr. viburni 1 N/A 6.6 ADD1840 Osbcure mealybug 33 2.8 6.6 AAF6923 Mango mealybug Rastrococcus invadens 1 N/A 10.1 ADB4205 Philippine mango mealybug Rastrococcus spinosus 2 1.7 10.1 ADA6433 Grey sugarcane mealybug Saccharicoccus sacchari 8 0.2 10.1 ACH5658 N/A Trionymus bambusae 2 0 10.3 ACI1423 N/A Tylococcus westwoodi 1 N/A 5.6 ADA6014 N/A Vryburgia rimariae 1 N/A 7.6 N/A N/A Exallomochlus 2 0.6 1.5 ADB5208 N/A Exallomochlus 1 N/A 1.2 ACV6882 N/A Planococcus 1 N/A 6.2 ADB3703 N/A Pseudococcus 1 N/A 6.2 ADD4017 N/A Pseudococcus 6 2.4 4.6 ADB4258 N/A Pseudococcus 4 1.0 1.5 ADB4938 N/A Pseudococcus 2 0.0 1.8 ADB5015 N/A Pseudococcus 3 0.0 1.8 ADB5016 N/A Pseudococcus 1 N/A 3.1 AAH8265 N/A Pseudococcus 1 N/A 6.2 AAF6922 N/A Spilococcus 9 1.04 2.8 AAG3584 N/A Pseudococcidae 3 0.0 6.8 AAF9672 N/A Pseudococcidae 2 0.0 16.9 ADA8397 N/A Pseudococcidae 7 0.8 2.6 ADB4261 N/A Pseudococcidae 2 1.5 6.9 ADA8920 N/A Pseudococcidae 3 0.3 7.3 ADA7814 123 J.-M. Ren et al.

Table 1 continued Common name Identification Count Max. Dist. to NN BIN divergence

N/A Pseudococcidae 2 0.0 11.9 ADB5306 N/A Pseudococcidae 3 0.2 11.5 ACI2777 N/A Pseudococcidae 3 1.0 11.7 ADA5485 N/A Pseudococcidae 1 N/A 8.4 ACL9653 N/A Pseudococcidae 1 N/A 4.1 AAF9671 N/A Pseudococcidae 1 N/A 12.4 ACN9515 N/A Pseudococcidae 1 N/A 8.8 ADD1916 N/A Pseudococcidae 1 N/A 2.6 ADD1915 N/A Pseudococcidae 1 N/A 6.9 ACI7276 N/A Pseudococcidae 1 N/A 5.9 ADA7813 N/A Pseudococcidae 1 N/A 13.9 ACP9096 N/A Pseudococcidae 1 N/A 6.0 ADB4594 N/A Pseudococcidae 1 N/A 5.6 ADB4595 N/A Pseudococcidae 1 N/A 7.9 ADB5308 N/A Pseudococcidae 1 N/A 6.4 ADB5307 N/A Pseudococcidae 1 N/A 7.3 ADA6465 N/A Pseudococcidae 1 N/A 8.6 ADA6655 N/A Pseudococcidae 1 N/A 6.0 ADB4592 N/A Pseudococcidae 1 N/A 10.5 ADA8710 N/A Pseudococcidae 1 N/A 9.6 ADB4593 N/A Pseudococcidae 1 N/A 12.2 ACY0210 a Species assigned to multiple BINs or BINs shared between species are bold faced

3 min at 95 °C; five cycles of 30 s at 94 °C; 40 s at family Pseudococcidae from GenBank were also 45 °C, 45 s at 72 °C; 40 cycles of 40 s at 94 °C; 40 s added to this dataset creating a total of 914 at 51 °C; 45 s at 72 °C; 5 min at 72 °C; held at 4 °C. sequences. All sequences meeting the quality require- PCR products were visualized on 1.5% agarose gels ments ([507 bp, \1% Ns, no stop codon or and bidirectional sequencing was performed using contamination flag) were assigned BINs by the BigDye v3.1 on an ABI 3730xl DNA Analyzer Refined Single Linkage (RESL) algorithm imple- (Applied Biosystems, Foster City, CA). mented on BOLD (Ratnasingham and Hebert 2013). With these criteria all 506 sequences generated at Data analysis IQTC and 375 of the 408 from GenBank received a BIN assignment. ClustalW nucleotide sequence Collection sites for the 914 mealybugs were dis- alignments (Thompson et al. 1994), genetic diver- played with Simple-Mapper (www.simplemappr.net) gence, and neighbor-joining (NJ) clustering analysis (Fig. 1). The sequences generated in this study were were performed in MEGA5 (Tamura et al. 2011). edited in CodonCode Aligner (CodonCode Corp. Analysis employed the Kimura-2-Parameter (K2P) USA) and inspected to verify that they were free of (Kimura 1980) distance model with pairwise deletion stop codons and that they were not contaminants. The of missing sites and 500 bootstrap replicates for nodal 506 new sequences were submitted to BOLD ( support. Barcode gap analysis (BGA) for identified www.boldsystems.org) where they are available in species was performed using analytical tools on the dataset DS-MAMBUG (Mealybugs of the BOLD. Employing the barcode gap criterion, a World). Another 408 barcode sequences for the species is distinct from its nearest neighbor (NN) if

123 Barcode index numbers expedite quarantine inspections

bFig. 2 NJ analysis of mealybug species based on the analysis of 914 COI sequences from Pseudococcidae. Bootstrap values ([50%) (500 replicates) are shown above the branches. The scale bar shows K2P distances. The node for each species with multiple specimens is collapsed to a vertical line or triangle, with the horizontal depth indicating the level of intraspecific divergence. Species assigned to multiple BINs are indicated by brackets. Analyses were conducted in MEGA5

its maximum intraspecific distance is less than the distance to its NN sequence (Meyer and Paulay 2005). The phylogenetic trees were inferred using Baye- sian analysis performed by MCMC using MrBayes v3.1.2 (Ronquist et al. 2012) with GTR + G model. The best model and partitioning scheme were chosen using the Bayesian Information Criterion (BIC) in PartitionFinder v1.0.1 (Lanfear et al. 2012). Each analysis was run for 10,000,000 generations and the tree was sampled every 1000 generations (burn-in = 98%). To test the convergence of chains, the log file of the MrBayes analyses was examined by calculat- ing the effective sample sizes of all parameters using Tracer v1.5 (Drummond and Rambaut 2007) and finally the complied consensus tree was displayed in FigTree v1.4.2.

Results

Morphological study at IQTC of 506 mealybugs from 24 countries resolved 453 of these specimens to 44 named species while 22 more were placed in four genera and the remaining 31 could only be assigned to the family Pseudococcidae (Table S1). DNA barcodes from these 506 specimens were assigned to 84 BINs. Members of the 44 identified species were allocated to 56 BINs with eight species (Nipaecoccus viridis, Phenacoccus aceris, Phenacoc- cus madeirensis, Phenacoccus solani, Planococcus ficus, Planococcus lilacinus, Pseudococcus cryptus, Pseudococcus longispinus) showing BIN splits (Tables 1, S1). Integration of these sequences with the 408 from GenBank raised the BIN count to 120, derived from 31 countries (Tables 1, S1). The 836 sequences derived from 62 named species were assigned to 83 BINs while the 32 specimens which were only placed to genus included representatives of

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b Fig. 3 Bayesian phylogenetic analysis of Pseudococcidae based on COI-5′ sequences. Posterior probabilities are indi- cated at nodes. Taxa are followed by the BINs. Drosicha mangiferae (JF792870) was employed as outgroup

11 BINs. The final 46 sequences that were only identified to a family level were assigned to 26 BINs. The combined data revealed five additional species (Crisicoccus matsumotoi, Maconellicoccus hirsutus, Phenacoccus baccharidis, Phenacoccus parvus, Pseu- dococcus comstocki) with BIN splits, raising the count of species with multiple BINs to 13 (Table 1). NJ analysis indicated that all 120 BINs formed a monophyletic cluster (Fig. 2) which were also supported by Bayesian phylogenetic analysis (Fig. 3). K2P distances at COI-5′ among the Pseudococci- dae reached a maximum distance value of 27.0% (Fig. 4). Intraspecific distances in the named species ranged from 0 to 8.5% with 17 species showing a maximum distance of more than 2% (Table 1). Excepting one species pair (Planococcus citri– Planococcus minor) where maximum intraspecific distances overlapped, mean and maximum intraspeci- fic distances in all identified species were less than the distance to their NN species (Fig. 5). However, P. citri and P. minor were still assigned to two different BINs and were also separated into separate sequence clusters with bootstrap support of 99% in the NJ analysis (Fig. 2) and posterior probability of 0.99 in the Bayesian analysis (Fig. 3).

Discussion

This study had the primary goal of testing the utility of the Barcode Index Number System as a basis for discriminating closely-related mealybug species with a view towards employing BINs as a rapid screening tool for mealybugs of quarantine and economic importance. Although most specimens could be assigned to a species by morphological analysis, 53 could be placed to a genus or family. Inclusion of 408 sequences of Pseudococcidae from GenBank raised the number of records without a species name to 78. Cases such as these where species identification cannot be accomplished have often acted to limit the effectiveness of quarantine screening programs for

123 Barcode index numbers expedite quarantine inspections

60000

50000

40000

30000 Frequency Frequency 20000

10000

0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233 Distance (K2P) value (%)

Fig. 4 Distributions of sequence divergences (K2P) for 914 COI-5′ sequences from members of the family Pseudococcidae pest insects (Armstrong et al. 1997). DNA barcoding Hendrich et al. 2015) has promoted their use for can overcome this limitation, enabling monitoring counting species (Hebert et al. 2016), revealing programs which track the cross-border movement of cryptic species (Mutanen et al. 2015; Iftikhar et al. insects (Armstrong and Ball 2005; Darling and Blum 2016) and assessing insect biodiversity (Telfer et al. 2007). For example, Nagoshi et al. (2011) used DNA 2015), opening avenues for their utility in quarantine barcodes to monitor the invasions of two armyworm and biosurveillance (Ashfaq and Hebert 2016). species, Spodoptera litura and S. littoralis, in Florida, Among the 836 specimens identified to one of 62 while Mastrangelo et al. (2014) employed the same named species, BIN splits were noted in 13 taxa, technique to examine the spread of Helicoverpa raising the BIN count to 83. Importantly, six of the armigera in Brazil. Likewise, Wu et al. (2015) used species with a BIN split are important plant pests. barcode sequences to reveal the introduction of novel They included M. hirsutus (K2P = 6.6%; 4 BINs), P. genetic lineages of P. solenopsis into China. The madeirensis (K2P = 8.5%; 3 BINs), P. ficus current data will provide additional tool to relieve the (K2P = 3.8%; 3 BINs), P. cryptus (K2P = 4.6%; 2 bottleneck of morphology-based quarantine screening BINs), P. solani (K2P = 2.3%; 3 BINs), and P. and will have implications for the cross-border lilacinus (K2P = 3.8%; 4 BINs). The assignment of movement of mealybugs. specimens in these species to multiple BINs was Excepting 33 sequences from GenBank that did further supported by NJ and Bayesian analysis which not meet BIN criteria, all barcode sequences consid- revealed monophyletic clusters that may well corre- ered in this study were assigned to a BIN making it spond to cryptic species. The partitioning of named possible to analyze unidentified specimens by treating species into multiple BINs is not uncommon in BINs as a “species proxy”. In addition, all of the 33 insects (Ashfaq and Hebert 2016), and further sequences without a BIN clustered closely with analysis has often revealed the presence of cryptic sequences of specimens with a BIN in the NJ tree. species (Leys et al. 2016), including scale insects and The 78 unidentified mealybugs were assigned to 37 mealybugs (Malausa et al. 2011). For example, BINs, suggesting the presence of a substantial nuclear and mitochondrial analysis of one scale number of undescribed taxa. The strong congruence insect complex (Chionaspis heterophyllae, C. pinifo- revealed between morphologically discriminated liae) in North America revealed eight overlooked species and BINs (Kekkonen and Hebert 2014; species (Gwiazdowski et al. 2011). DNA barcode

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A 14 among samples from different localities, but there were exceptions. For example, maximum barcode 12 divergence among 74 specimens of P. solenopsis

10 from five countries was only 0.8%, but 14 specimens of P. madeirensis also from five countries showed up 8 to 8.5% divergence. These differing patterns of molecular variation may indicate either phylogeo- 6 graphic variants of a single species or the presence of 4 a species complex (Correa et al. 2012) as suggested

Distance to NN (%) (%) NN to Distance for the introduction of Dysmicoccus brevipes into 2 China (He et al. 2012). 0 Because of the large volumes of plant material 012345678910 crossing borders, accurate detection of pest insect Mean intraspecific dist. (%) species is a challenge that can only be addressed by B 14 quarantine screening systems that are based on molecular analysis. This study has established the 12 utility of BINs for detecting invasive species at 10 border points, making it possible to ascertain their source locality. The fact that a considerable number 8 of important pest insects are likely to represent a 6 species complex further highlights the need for the adoption of standardized detection approaches at 4 borders, such as that enabled by the BIN system. Distance to NN (%) (%) NN to Distance 2 Acknowledgements This research was supported by Chinese 0 National Key Research and Development Program for Bio- 012345678910 safety (2016YFC1201201). Many thanks to Dr. Suh Soo-jung (National Plant Quarantine Service, Busan 600-016, Korea), Max. intraspecific dist. (%) Dr. Cai Bo (Hainan Inspection and Quarantine Technology Center), Mr. Deng Yu-liang (Xishuangbanna Inspection and Fig. 5 Barcode gap analysis for species of mealybugs with Quarantine Technology Center) and Mr. Bai Yong-hua (Mohan three or more specimens as revealed by plotting mean (a) and Exit and Entry Inspection and Quarantine Bureau) for their maximum intraspecific (b) distances against the nearest- assistance in sample collection. David Plotkin (University of neighbor (NN) distances Florida) provided useful suggestions during phylogenetic analysis. This is a contribution from the “Food from analysis of 75 species of and Pseudo- Thought” research program enabled by an award from the coccidae showed high sequence divergence ([2%) Canada First Research Excellence Fund to the University of Guelph. in nine species (Park et al. 2011). Cryptic morphol- ogy may also lead to incorrect species assignments that result in morphology-BINs incongruence (Barco et al. 2016). References Although intraspecific barcode variation in several pest mealybugs was high, the maximum divergence Abd-Rabou S, Shalaby H, Germain JF, Ris N, Kreiter P, was less than the distances to the NN in all but one Malausa T (2012) Identification of mealybug pest species species pair (P. citri–P. minor), creating a clear (Hemiptera: Pseudococcidae) in Egypt and France, using barcode gap which enabled unambiguous species a DNA barcoding approach. Bull Entomol Res 102:515– 523 discrimination. Although intermediate forms between Ahmed MZ, He RR, Wu MT et al (2015a) First report of the P. citri and P. minor have been reported (Rung et al. papaya mealybug, Paracoccus marginatus (Hemiptera: 2008), the species were placed in different BINs and Pseudococcidae), in China and genetic record for its formed monophyletic clusters in the NJ and Bayesian recent invasion in Asia and Africa. Fla Entomol 98:1157– 1162 trees. Most species showed little COI divergence 123 Barcode index numbers expedite quarantine inspections

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