Leaf beetles (Coleoptera: Chrysomelidae), with more than 37,000 species worldwide and about 2,300 in the Euro-Mediterranean region, are an ecological and economical relevant family, making their molecular identification of interest also in agriculture. This study, part of the Mediterranean Chrysomelidae Barcoding project (www.c-bar.org), aims to: (i) develop a reference Cytochrome c oxidase I (COI) library for the molecular identification of the Euro-Mediterranean Chrysomelidae; (ii) test the efficiency of DNA barcoding for leaf beetles identification; (iii) develop and compare optimal thresholds for distance-based identifications estimated at family and subfamily level, minimizing false positives and false negatives. Within this study, 889 COI nucleotide sequences of 261 species were provided; after the inclusion of information from other sources, a dataset of 7,237 sequences (542 species) was analysed. The average intra-interspecific distances were in the range of those recorded for Coleoptera: 1.6–24%. The estimated barcoding efficiency (~94%) confirmed the usefulness of this tool for Chrysomelidae identification. The few cases of failure were recorded for closely related species (e.g., Cryptocephalus marginellus superspecies, Cryptocephalus violaceus - Cryptocephalus duplicatus and some Altica species), even with morphologically different species sharing the same COI haplotype. Different optimal thresholds were achieved for the tested taxonomic levels, confirming that group-specific thresholds significantly improve molecular identifications.
Chrysomelidae, or leaf beetles, is one of the most species-rich families of Coleoptera. Leaf beetles are distributed worldwide (except Antarctica) and inhabit almost all habitats presenting vegetation. Leaf beetles include more than 37,000 species at global level belonging to more than 2,000 genera1. In the Palearctic region approximately 3,500 species have been described so far2, and about 2,300 of them occur in the Euro-Mediterranean region3,4,5. With few exceptions, leaf beetles are phytophagous insects adapted to feed on plant species, including some of agricultural interest (e.g., Diabrotica virgifera LeConte, 1868 and Leptinotarsa decemlineata Say, 1824). The species-specific strict association with the host plants makes leaf beetles an interesting group for evolutionary studies (e.g.6,7); however, they have received attention also due to their impact on agriculture (e.g.8) and to their use as biological control agents of invasive plants9,10. The correct identification of organisms is regarded to be essential in both applicative field and evolutionary studies; at present, their taxonomy, based on morphological features, requires a high level of expertise that could be reached only after years of study. In some cases, as for several species groups, the accurate species identification of adults can only be achieved extracting genitalia11,12,13. Therefore, preimaginal developmental stages can be only rarely identified to species level. Thus, approaches based on morphology may not be efficient for beetles identification and become strongly time consuming especially in large scale studies, for example in biomonitoring surveys for agricultural biocontrol.
DNA based approaches have emerged as useful tools for the identification of organisms14,15, and the efficacy of the cytochrome c oxidase subunit I (COI) marker in molecular identification of Coleoptera (including leaf beatles) was demonstrated16,17,18. At present, on-line databases harbour about 8,000 COI sequences assigned to approximately 1,200 leaf beetles species worldwide, roughly about 4% of the overall described species. We are still far from having a reliable reference database, and most of the detailed barcoding studies for European leaf beetles have been developed within limited geographic contexts19,20. In order to increase the number of barcoded species, including also the rare ones, large scale biodiversity studies focused on leaf beetles inhabiting different biogeographic regions are needed. The Mediterranean Chrysomelidae Barcoding project (C-bar; www.c-bar.org; 3), started in 2009 and, involving many taxonomists and specialists of different subfamilies, aims to develop a reference database of sequences for the molecular identification of leaf beetles inhabiting the Euro-Mediterranean region. In the present study, we analysed the dataset of COI gene sequences obtained within the C-bar project with the purpose of: (i) evaluate the efficiency of the DNA barcoding dataset; (ii) estimate the optimal intraspecific and interspecific thresholds for the identification of leaf beetles species at different taxonomic level (i.e., family vs subfamily level).
Materials and Methods
No species of Coleoptera Chrysomelidae are listed in national laws as protected or endangered. All the specimens were collected between 2009–2013 in state-owned properties. The collection of these invertebrates is not subjected to restriction by national or international laws and does not require special permission. All the organisms were collected before the approval of Nagoya protocol 283/2014/UE.
Sample collection and identification
Leaf beetles were collected in sampling campaigns occurred from 2009 to 2013 in different ecoregions of central and southern Europe and North Africa. The animals were collected using different methods: from the vegetation by sweep net or by beating sheet, and directly by hand in specific habitats. All the specimens were stored in absolute ethanol in order to preserve the genomic DNA and preserved at −20 °C. Specimen manipulation and dissection (when necessary) were completed with the auxiliary use of a stereomicroscope Leica MS5, a compound microscope Zeiss Axio Zoom V16, and images were acquired with the digital camera Zeiss Axiocam 506. The specimens were morphologically identified by the authors and other expert taxonomists. The nomenclature adopted in this study follows that of the European Fauna (https://fauna-eu.org/).
DNA extraction, PCR and sequencing
DNA extractions were performed in two laboratories (the Biodiversity Institute of Ontario - University of Guelph; the Laboratory of Molecular Entomology at Dipartimento di Scienze Agrarie e Ambientali - Università degli Studi di Milano) adopting the following different protocols: (i) DNA extraction from one hind leg of the specimen, and (ii) DNA extraction from the whole body, in both cases using Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) as reported in Magoga et al.3.
After DNA extraction, the voucher specimens were dry mounted on pins in the case of whole body DNA extraction, or preserved in absolute ethanol at −30 °C in the case of DNA extraction from a single leg. An aliquot of the extracted DNA was preserved in both laboratories at −80 °C as reference. The standard barcode region of the mitochondrial COI was amplified by PCR using standard barcode primers LCO1490/HCO219821. In case of unsuccessful amplifications, the alternative COI primers LepF1/LepR1 were adopted to amplify the selected region22. PCRs were performed in a volume of 25 μL reaction mix containing: 1X GoTaq reaction Buffer (10 mM Tris-HCl at pH 8.3, 50 mM KCl and 1.5 mM MgCl2), 0.2 mM of each deoxynucleoside triphosphate, 0.5 pmol of each primer, 0.3 U of GoTaq DNA Polymerase and 10/20 ng of template DNA. The adopted thermal protocol is reported in Montagna et al.11. Positive amplicons were directly sequenced on both strands using the marker-specific primers from ABI technology (Applied Biosystems, Foster City, USA). Consensus sequences were obtained editing electropherograms using Geneious R8 (Biomatters Ltd., Auckland, New Zealand. License owned by Matteo Montagna). Spurious amplifications of COI sequences were checked using Standard Nucleotide BLAST23. The presence of open reading frame was verified for the obtained sequences by using the on-line tool EMBOSS Transeq (http://www.ebi.ac.uk/Tools/st/emboss_transeq/), then sequences were aligned at codon level using MUSCLE24 in MEGA 6.0625. Consensus sequences were deposited in the Bold Systems26 and in the European Nucleotide Archive to make them available for future studies (accession numbers reported in Table S1).
Sequence mining and dataset development
Accession numbers of orthologous sequences belonging to European and Mediterranean Leaf Beatles species were recovered from previously published DNA barcoding studies19,20,27 and used to download the corresponding nucleotide sequences from public repositories (i.e., BOLD and GeneBank); this operation was completed using the R 3.3.3 (R Core Team, 2017) library ape v4.128 and rentrez v1.1.029.
Overall a total of 6,348 COI gene sequences were retrieved from public repositories. These nucleotide sequences and those obtained in the present study were organised in two datasets: (i) dataset DS1, composed only by the nucleotide sequences developed in this study; (ii) dataset DS2, composed by the sequences mined from online databases plus dataset DS1. We keep separated the two datasets in order to evaluate the efficiency of the here developed dataset, and to estimate the barcoding efficiency for the whole family using available COI sequences (DS2).
Taxonomy was standardised checking for the presence of synonymous names and assigning only one name (the accepted one following European Fauna https://fauna-eu.org/ nomenclature) to each species. DS1 and DS2 were also split in sub-datasets in order to obtain datasets including only one leaf beetles subfamily each. Only subfamily level datasets consisting of at least 2 species were retained. The procedure led to obtain datasets for the following ten subfamilies: Alticinae, Cassidinae, Chrysomelinae, Criocerinae, Cryptocephalinae, Donaciinae, Eumolpinae, Galerucinae, Hispinae and Orsodacninae.
For all the morphologically identified species of DS1 and DS2, intraspecific and interspecific nucleotide divergences were calculated starting from a pairwise distance matrix developed using R library spider v1.1-530 adopting Kimura-two parameters (K2P) as substitution model31. With the same R package a Threshold optimisation analysis was performed on DS1, DS2 and on each subfamily-level dataset in order to calculate the value of nucleotide distance (optimal threshold; OT) that minimises the error related to molecular identification. This error is caused by the discordance between morphological and molecular identification and is called cumulative error (CE), calculated as the sum of the number of false positives (FP, conspecifics with a value of nucleotide divergence higher than the threshold value) plus the number of false negatives (FN, heterospecifics with a value of nucleotide divergence lower than the threshold value)32. Differences in CE values estimated at family and subfamily level were assessed using Student t test. The efficiency of molecular identification was estimated performing Best Close Match analyses, defined by Meier et al.33, on DS1 and DS2 (family level). The method compares each sequence of dataset with the others included in it and checks if the best matches (i.e., pairs of sequences with the lowest values of nucleotide distance) are between sequences of organisms morphologically identified as the same species. Each best match results in one of the following four states: “correct”, when the two closest sequences under the defined threshold belong to the same species; “incorrect”, the opposite situation; “ambiguous”, when the closest match is represented by more than one species; and, “no id” when no match is recorded under the chosen threshold.
The dataset developed in this study (i.e., DS1) consists of 889 COI sequences (average 654 bp [range: 494–658]), with a base composition of A = 29.4%; C = 19.6%; G = 16.1%; T = 34.9%. The dataset includes sequences of 261 leaf beetles species, the 11.4% of the Euro-Mediterranean species (74 singletons), belonging to 64 genera collected from ten countries within the Euro-Mediterranean region (Fig. 1 and Table S1). Out of the 261 barcoded species, COI sequences of 52 species were not already present in any online repository (Table S4).
Dataset DS2, consisting of the previously available sequences with the addition of DS1, is composed by 7,237 COI sequences (average 652 bp [range: 460–658]); with a base composition of A = 29.6%; C = 18.7%; G = 16.1%; T = 35.5%. In DS2 the COI sequences of 542 species (~24% of the Euro-Mediterranean fauna) sampled in 19 different countries of Europe and North Africa are included (Fig. 1 and Table S5).
Morphospecies intra-interspecific nucleotide distance
The distributions of intraspecific and interspecific pairwise nucleotide distances overlap, thus resulting in the absence of a clear barcode gap in both family-level datasets (Fig. S1). The mean intraspecific nucleotide distance, estimated with the K2P nucleotide substitution model, resulted in 2% [0–20.6] for dataset DS1 and 1.6% [0–27.6] for DS2 (Figs 2 and S1). The exceptionally high maximum value of the intraspecific nucleotide distance in DS1 of 20.6% is the result of the comparisons between sequences of two Lachnaia tristigma (Lacordaire, 1848) populations, both collected in France in the Alpes-de-Haute-Provence department. The interspecific nucleotide distance resulted in 25.1% [0–37.1%] in the case of DS1 and of 24% [0–43.2%] in the case of DS2 (Fig. S1). Noteworthy, 0 or close to 0 values of nucleotide distance were recovered between specimens belonging to different species (Fig. S1); among others, exemplar cases are represented by: Cryptocephalus violaceus Laicharting, 1781 - Cryptocephalus duplicatus Suffrian, 1845; Lachnaia italica Weise, 1881 - Lachnaia tristigma; members of Cryptocephalus marginellus Olivier, 1791 species complex, and of the Cryptocephalus hypochaeridis (Linnaeus 1758) species complex. In detail, specimens of C. duplicatus collected in Turkey and of C. violaceus collected in Greece possessed the same COI haplotype; within the Cryptocephalus marginellus complex, notable is the case of Cryptocephalus renatae Sassi, 2001 collected in Savona province having only ~0.6% of nucleotide distance from C. marginellus collected in a geographically close locality (Nice, FR) and of the Cryptocephalus eridani Sassi, 2001 having ~0.4% from Cryptocephalus hennigi Sassi, 2011, both collected in Cuneo province. The COI haplotype network of C. marginellus complex (Fig. 3a) confirms the previous results and shows that the currently known species in the group are not well distinguished as species clusters; the only exceptions are represented by Cryptocephalus zoiai Sassi, 2001 and Cryptocephalus aquitanus Sassi, 2001, unambiguously separated from the other species (Fig. 3a). In addition, within the subfamily of Alticinae some specimens belonging to the following eight out of 14 Altica morphospecies present in the DS2 showed the same or highly similar COI haplotype (range of nucleotide intraspecific distances: 0–12.6% and of nucleotide interspecific distances: 0–13.8%): Altica aenescens (Weise, 1888), Altica ampelophaga Guérin-Meneville, 1858, Altica ericeti (Allard, 1859), Altica brevicollis Foudras, 1860, Altica engstroemi (Sahlberg, 1894), Altica lythri Aubé, 1843, Altica longicollis (Allard, 1860) and Altica oleracea (Linnaéus, 1758) (Fig. 3b).
Optimal threshold and barcode efficiency
The optimal threshold that minimises the number of false positive and false negative identifications resulted in 2.6% of distance for DS1, with an associated cumulative error of 97 sequences out of 889 (10.9%, FP = 38, FN = 59); for DS2 it resulted in a value of 1% of nucleotide distance, with a cumulative error of 816 sequences out of 7237, 11.3%, FP = 209, FN = 607). The sum of the cumulative errors obtained from optimal threshold analyses performed on the subfamily level datasets obtained from DS1 resulted in 85 sequences (9.6%), and in 748 sequences (10.3%) in the case of datasets from DS2. (Table 1). These error values are significantly different from the cumulative errors obtained for the total datasets, i.e., DS1: t = −13.7, p-value < 0.001; DS2: t = −17.6, p-value < 0.001 (Table 1). The highest error values, related with subfamily datasets, were observed for Cryptocephalinae obtained from DS1 (66 sequences out of 416, 15.8%, threshold 1%, Fig. 2) and for Alticinae from DS2 (413 out of 2690 sequences, 15.3%, threshold 0.9%, Fig. 2). By contrast, the lowest error of only one sequence was obtained for both datasets of Cassidinae, with 53 sequences in DS1 and 168 sequences in DS2; the associated OTs were higher than those observed for the other subfamilies, 4.6% for DS1 and 5.9% for DS2 (Fig. 2).
The barcode efficiency of DS1, evaluated through the best close match analysis gave an OT of 2.6%, resulting in 93% of correct identification (828 out of 889); 58 species, consisting of a single COI sequence, were considered as correctly identified since no match with other heterospecific sequences occurred. Of the 61 sequences that revealed identification errors, 14 were classified as incorrect identifications. These sequences belong to taxa between which very low interspecific nucleotide distances were observed (e.g., C. hennigi - C. eridani; A. brevicollis - A. lythri; L. italica - L. tristigma); in addition to these, they include also sequences from Longitarsus apicalis (Beck, 1817), showing the best match with Longitarsus aeneicollis (Faldermann, 1837) (pairwise nucleotide distance of 0.2%), and one specimen of Oulema melanopus (Linnaeus, 1758) that matched with Oulema duftschmidi (Redtenbacher, 1874) (1.2% of pairwise nucleotide distance). A total of 39 sequences (34 morphospecies) resulted in no match with conspecifics because of a pairwise nucleotide distance higher than the adopted OT. Among these cases, a sequence of Cassida denticollis Suffrian, 1844 showed about 15% of nucleotide divergence from other sequences assigned to the same species. The eight ambiguous identifications involve the sister species C. violaceus - C. duplicatus and L. tristigma.
The same analysis performed on DS2 highlighted the presence of 94.1% correct identifications (6,811 sequences out of 7,237), 52 incorrect, 164 ambiguous and 210 missing identifications (Table S2), with an OT of 1%. Among incorrect and ambiguous identifications, beyond the DS1 cases mentioned above, only one match involved at least one sequence from DS1 (i.e., Psylliodes brisouti Bedel, 1898 specimen code MS0000647 with Psylliodes instabilis Foudras, 1860 accession number KM445439). Incorrect and ambiguous identifications were observed also among the retrieved sequences: e.g. one sequence of L. tristigma and one of Lachnaia gallaeca Baselga & Ruiz-García, 2007 (nucleotide distance 0.2%); Plateumaris sericea (Linnaeus, 1761) and Plateumaris discolor (Panzer, 1795) sequences and, Galerucella pusilla (Duftschmid, 1825) and Galerucella calmariensis (Linnaeus, 1767). As regard the missing identifications, sequences assigned to 11 species of Cassida, 27 of Cryptocephalus and 6 of Smaragdina genera did not match those of conspecifics because of intraspecific genetic distances higher than the OT.
The results achieved by the performed analyses confirmed the usefulness of the DNA barcoding approach as a tool for the molecular identification of Chrysomelidae. The obtained identification efficiencies are comparable for both datasets; our dataset (NDS1 = 889 sequences) showed 93% of correct identifications, while 94% of correct identifications was obtained for DS2 (i.e., the available COI sequences +DS1; NDS2 = 7,237 sequences), which cover the ~24% of the Euro-Mediterranean species. The barcoding efficiency recovered in the present study is similar to those achieved in other studies dealing with beetles, as example 89% in the case of Bembidion species36, approximately 92% in the case of the Central European Coleoptera (39% of the fauna)19 and 100% in the case of Crioceris species37. In any case, in these studies different approaches were adopted to estimate the barcoding efficiency, thus a direct comparison could not be performed.
Incorrect, ambiguous and missing identifications observed in our study are possibly related with the inability of DNA barcoding in identifying taxa in the presence of: (i) superspecies (two or more close related species with allopatric distribution that can occasionally hybridise38) and cryptic species complexes39,40; (ii) cases of hybridisation or introgression; (iii) incomplete lineage sorting; and (iv) bacterial endosymbionts changing pathways of mtDNA inheritance41,42. In these cases, the lack of a clear barcode gap between intraspecific and interspecific nucleotide distances vanish the possibility to identify species32,43. The phenomenon is evident also in the analysed datasets, where a clear barcode gap cannot be found (Fig. S1).
Interestingly, the estimated optimal threshold of DS2 was lower than that of DS1, 1% and 2.6% respectively. These results could be related to the different haplotype diversity and to the different taxonomic composition of the two datasets. The mean number of haplotypes per species of the two datasets is 6.7 (on average 13.4 sequences per species) and 2.5 (on average 3.4 sequences per species) in the case of DS2 and DS1, respectively; thus, DS1 possesses fewer sequences per species but a higher number of haplotype per species (approximately one haplotype per sequence) than DS2. The differences between the two datasets might be related to the sampling strategies adopted in C-Bar project, where attempts have been made to maximise the number of conspecifics from different localities, rather than to process numerous specimens of the same species from the same locality.
Threshold optimisation analyses showed also a significant decrease of the cumulative error when OTs were estimated at the subfamily level in comparison to when they were estimated at the family level (Table 1). Phylogenetically closely related species are supposed to have similar rates of nucleotide substitution due to shared morphological, biological and ecological traits (e.g., number of generation per year, tendency to isolation of the populations due to the habitat structure or to the dispersal ability of the species44, and for this reason should be easier to define a reliable threshold between intraspecific and interspecific divergence. We can hypothesise that not all Chrysomelidae share the same rate in nucleotide substitutions, since different subfamilies are characterised by different morphological, ecological and physiological adaptation, as the Maulik’s organ that confers jumping capabilities to Alticinae45,46, the limited dispersal capabilities of Chrysomelinae and Cryptocephalinae47 or the presence of bacterial endosymbiont that, in the case of Donacinae, allows the larvae to survive in anoxic conditions under water48. Moreover, the different OTs achieved for Chrysomelidae subfamilies underline that the use of a unique threshold for the entire family decreases the identification efficiency of DNA barcoding (Table 1). Beyond classical barcoding studies, the implementation of group specific thresholds, leading to a more accurate taxonomic identification, should be also evaluated for OTUs clustering in metabarcoding analyses instead of the employment of fixed thresholds (as in the case of49,50).
Concerning the cumulative error, the highest value was obtained for Cryptocephalinae subfamily (DS1). This dataset, accounting for 46.8% of DS1 sequences, includes different species complexes (e.g., Cryptocephalus marginellus superspecies and Cryptocephalus hypochoeridis complex). The presence of species complexes increases the overlap between intra and interspecific distances and consequently the cumulative error at the optimal threshold. In the case of DS2, Alticinae resulted the subfamily with the highest error associated to the OT of 0.9%. This finding could be associated to a high proportion of sequences belonging to the genus Altica in this dataset (229 out of 2,690), a taxon for which inconsistences between molecular and morphological signals were already found51.
Molecular identification of closely related species
Barcode sequences of closely related species within the groups Cryptocephalus hypochaeridis52,53 and Oulema melanopus54,55 were here analysed; as expected, low values of nucleotide interspecific distances within groups were observed. Moreover, our study highlighted other interesting cases of sequences belonging to morphologically similar species groups not properly identified by best close match analyses. Cryptocephalus marginellus superspecies, including six species that differ in their distributions and in the shape of the median lobe of aedeagus56,57 represents one of these cases. These species are present in Spain (C. aquitanus), France (C. aquitanus, C. marginellus, C. eridani and C. zoiai), Italy (C. eridani, C. marginellus, C. renatae, C. hennigi and C. zoiai) and Switzerland (C. eridani), and their distributions partially overlap in some areas. The close relationships among these species highlighted by morphological features were here confirmed by the COI variability and by the structure of the haplotype network (Fig. 3a); however, no shared haplotypes between species were observed. Well-separated clusters were recovered for C. aquitanus and C. zoiai that, in addition to C. marginellus, resulted the only monophyletic taxon within this group (Fig. 3a). The analysis of pairwise nucleotide distances showed low values between different species, the lowest one between specimens collected in the area where the range of the species overlap (e.g., C. eridani - C. hennigi). Incomplete lineage sorting could be considered an explanation for these results, even if introgression between species with overlapping distribution has to be taken into account. A further interesting result concerns C. violaceus and C. duplicatus, two morphologically very similar species distinguishable only on the basis of the shape of the median lobe of the aedeagus. C. violaceus is present in central and southern Europe while C. duplicatus in the southern east of Europe and the Middle east. No nucleotide differences were observed between the COI sequences of C. violaceus collected in Greece and C. duplicatus collected in Turkey. Since the distribution of the species overlaps in Greece, we can hypothesise recent events of introgression. This phenomenon is known to occur when, after an allopatric speciation, two sister species come in contact and establish an area of secondary sympatry; due to the lack of reproductive isolation they have the possibility to hybridise with the result of a stable integration of genetic material from one species into the other one58,59.
Shared haplotypes were observed among the following Altica species: A. ericeti - A. ampelophaga; A. ampelophaga - A. oleracea - A. brevicollis; A. brevicollis - A. aenescens; A. ericeti - A. ampelophaga - A. brevicollis; A. ampelophaga - A. brevicollis; A. lythri - A. engstroemi. Identification of many species belonging to Altica, included those above mentioned, is not easy adopting morphological criterion; it is mainly based on the observation of adult male genitalia, which in some cases is not totally informative because of the presence of intraspecific morphological variation60. In addition, adult females are often indeterminable61. This difficulty in species identification is also mirrored at the molecular basis, where the species of this group are unidentifiable using COI (Fig. 3b) as well as by using other mtDNA markers51. Morphological and COI nucleotide similarity suggests the possible need of a taxonomic revision of the Altica species mentioned above. The obtained results, viz the low interspecific nucleotide divergence and the presence of shared haplotypes, is congruent with a scenario of incomplete lineage sorting due to the recent origin of the group of species and hybridization. A further possibility, supported by the presence of different strains of the maternally inherited endosymbiont Wolbachia within and between Altica species, consists of a rapid spread within populations of ancestral or introgressed haplotypes, caused by the cytoplasmic incompatibility induced by Wolbachia51. In this last scenario, Wolbachia might have played a crucial role in mating isolation and thus in the speciation process, as suggested for other groups of close related taxa (e.g.62,63,64). Further studies, using genomic approaches, are required to disentangle among the reported possibilities.
This study provides COI sequences of 261 Chrysomelidae species (~12% of the Euro-Mediterranean Fauna; 889 barcodes) collected in the Euro-Mediterranean area (52 species new to on-line repositories) and confirms the usefulness and efficiency of DNA barcoding for the identification of these beetles. Cases of barcoding failure in identifying members of the family were observed especially for closely related species, and some of them are reported for the first time in this study. The comparisons among optimal thresholds estimated at different taxonomic levels, viz family and subfamily, have underlined the importance of using taxon-specific thresholds to increase the efficacy of molecular identification.
Jolivet, P., Santiago-Blay, J. A. & Schmitt, M. Research on Chrysomelidae 3 (Pensoft Publishers, 2011).
Konstantinov, A. S., Korotyaev, B. A. & Volkovitsh, M. G. Insect Biodiversity in the Palearctic Region in Insect Biodiversity: Science and Society (ed. Foottit, R. G. & Adler, P. H.) (Wiley-Blackwell, 2009).
Magoga, G. et al. Barcoding Chrysomelidae: a resource for taxonomy and biodiversity conservation in the Mediterranean Region. In: Jolivet, P., Santiago-Blay, J. & Schmitt, M. (Eds) Research on Chrysomelidae 6. ZooKeys 597, 27–38 (2016).
Blondel, J., Aronson, J., Bodiou, J. Y. & Boeuf, G. The Mediterranean Region – Biological diversity in space and time. (Oxford Univ. Press, 2010).
Warchalowski, A. Chrysomelidae. The Leaf-beetles of Europe and the Mediterranean area. (Natura Optima Dux Fundation, 2003).
Futuyma, D. J. & McCafferty, S. S. Phylogeny and the evolution of host plant association in the leaf beetle genus Ophraella (Coleoptera, Chrysomelidae). Evolution 44, 1885–1913 (1990).
Chung, S. H. et al. Host plant species determines symbiotic bacterial community mediating suppression of plant defenses. Sci. Rep. 7, 39690, https://doi.org/10.1038/srep39690 (2017).
Sawadogo, A., Nagalo, E., Nacro, S., Rouamba, M. & Kenis, M. Population dynamics of Aphthona whitfieldi (Coleoptera: Chrysomelidae), pest of Jatropha curcas, and environmental factors favoring its abundance in Burkina Faso. J. Insect Sci. 15, 108, https://doi.org/10.1093/jisesa/iev084 (2015).
Grevstad, F. S. Ten-year impacts of the biological control agents Galerucella pusilla and G. calmariensis (Coleoptera: Chrysomelidae) on purple loosestrife (Lythrum salicaria) in Central New York State. J. Biol. Control 39, 1–8 (2006).
Szűcs, M., Schaffner, U., Price, W. J. & Schwarzländer, M. Post-introduction evolution in the biological control agent Longitarsus jacobaeae (Coleoptera: Chrysomelidae). Evol. Appl. 5, 858–868 (2012).
Montagna, M., Sassi, D. & Giorgi, A. Pachybrachis holerorum (Coleoptera: Chrysomelidae: Cryptocephalinae), a new species from the Apennines, Italy, identified by integration of morphological and molecular data. Zootaxa 3741, 243–253 (2013).
Sassi, D. Taxonomic remarks, phylogeny and evolutionary notes on the leaf beetle species belonging to the Cryptocephalus sericeus complex (Coleoptera: Chrysomelidae: Cryptocephalinae). Zootaxa 3, 333–378 (2014).
Montagna, M. et al. Exploring species-level taxonomy in the Cryptocephalus flavipes species complex (Coleoptera: Chrysomelidae). Zool. J. Linn. Soc. 179, 92–109 (2016).
Hebert, P. D. N., Cywinska, A., Ball, S. L. & De Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).
Hebert, P. D. N. & Gregory, T. R. The Promise of DNA Barcoding for Taxonomy. Syst. Biol 54, 852–859 (2005).
García-Robledo, C., Kuprewicz, E. K., Staines, C. L., Kress, W. J. & Erwin, T. L. Using a comprehensive DNA barcode library to detect novel egg and larval host plant associations in a Cephaloleia rolled-leaf beetle (Coleoptera: Chrysomelidae). Biol. J. Linn. Soc. 110, 189–198 (2013).
Lopes, S. T. et al. Molecular Identification of Western-Palearctic Leaf Beetles (Coleoptera, Chrysomelidae). J. Entomol. Res. Soc. 17, 93–101 (2015).
Thormann, B. et al. Exploring the Leaf Beetle Fauna (Coleoptera: Chrysomelidae) of an Ecuadorian Mountain Forest Using DNA Barcoding. Plos One 11, e0148268, https://doi.org/10.1371/journal.pone.0148268 (2016).
Hendrich, L. et al. A comprehensive DNA barcode database for Central European beetles with a focus on Germany: adding more than 3500 identified species to BOLD. Mol. Ecol. Resour. 15, 795–818 (2015).
Pentinsaari, M., Hebert, P. D. N. & Mutanen, M. Barcoding beetles: a regional survey of 1872 species reveals high identification success and deep interspecific divergences. Plos One 9, e108651, https://doi.org/10.1371/journal.pone.0108651 (2014).
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).
Hebert, P. D. N., Stoeckle, M. Y., Zemlak, T. S. & Francis, C. M. Identification of birds through DNA barcodes. Plos Biol. 2, e312, https://doi.org/10.1371/journal.pbio.0020312 (2004).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113, https://doi.org/10.1186/1471-2105-5-113 (2004).
Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol 30, 2725–2729 (2013).
Ratnasingham, S. & Hebert P. D. N. Bold: The Barcode of Life Data System, http://www.barcodinglife.org. Mol. Ecol. Notes 7, 355–364 (2007).
Gómez-Rodríguez, C., Crampton-Platt, A., Timmermans, M. J. T. N., Baselga, A. & Vogler, A. P. Validating the power of mitochondrial metagenomics for community ecology and phylogenetics of complex assemblages. Methods Ecol. Evol. 6, 883–894 (2015).
Popescu, A. A., Huber, K. T. & Paradis, E. Ape 3.0: new tools for distance based phylogenetics and evolutionary analysis in R. Bioinformatics 28, 1536–1537 (2012).
Winter, D. Rentrez: Entrez in R. R package version 1.1.0, https://CRAN.R-project.org/package=rentrez (2017).
Brown, S. D. J. et al. SPIDER: An R package for the analysis of species identity and evolution, with particular reference to DNA barcoding. Mol. Ecol. Resour 12, 562–565 (2012).
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal Mol. Evol 16, 111–120 (1980).
Meyer, C. P. & Paulay, G. DNA barcoding: error rates based on comprehensive sampling. PLoS Biol. 3, 2229–2238 (2005).
Meier, R., Shiyang, K., Vaidya, G. & Ng, P. DNA barcoding and taxonomy in Diptera: a tale of high intraspecific variability and low identification success. Syst. Biol 55, 715–728 (2006).
Bandelt, H. J., Forster, P. & Röhl, A. Median-Joining Networks for Inferring Intraspecific Phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).
Leigh, J. W. & Bryant, D. Popart: full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Raupach, M. J., Hannig, K., Morinière, J. & Hendrich, L. A. DNA barcode library for ground beetles (Insecta, Coleoptera, Carabidae) of Germany: The genus Bembidion Latreille, 1802 and allied taxa. ZooKeys 592, 121–141 (2016).
Kubisz, D., Kajtoch, Ł., Mazur, M. A. & Rizun, V. Molecular barcoding for central-eastern European Crioceris leaf-beetles (Coleoptera: Chrysomelidae). Cent. Eur. J. Biol. 7, 69 (2012).
Mayr, E. Birds collected during the Whitney South Sea Expedition. XII. Notes on Halcyon chloris and some of its subspecies. Amer. Mus. Novit. 469 (1931).
Van Velzen, R., Weitschek, E., Felici, G. & Bakker, F. T. DNA Barcoding of Recently Diverged Species: Relative Performance of Matching Methods. Plos One 7, e30490, https://doi.org/10.1371/journal.pone.0030490 (2012).
Jiang, F., Jin, Q., Liang, L., Zhang, A. B. & Li, Z. H. Existence of species complex largely reduced barcoding success for invasive species of Tephritidae: a case study in Bactrocera spp. Mol. Ecol. Resour. 14, 1114–1128 (2014).
Smith, M. A. et al. Wolbachia and DNA Barcoding Insects: Patterns, Potential, and Problems. Plos One 7, e36514, https://doi.org/10.1371/journal.pone.0036514 (2012).
Klopfstein, S., Kropf, C. & Baur, H. Wolbachia endosymbionts distort DNA barcoding in the parasitoid wasp genus Diplazon (Hymenoptera: Ichneumonidae). Zool. J. Linn. Soc. 177, 541–557 (2016).
Wiemers, M. & Fiedler, K. Does the DNA barcoding gap exist? - a case study in blue butterflies (Lepidoptera: Lycaenidae). Front. Zool. 4, 8, https://doi.org/10.1186/1742-9994-4-8 (2007).
Fujisawa, T., Vogler, A. P. & Barraclough, T. G. Ecology has contrasting effects on genetic variation within species versus rates of molecular evolution across species in water beetles. Proc. R. Soc. Lond. B Biol. Sci. 282, 20142476, https://doi.org/10.1098/rspb.2014.2476 (2015).
Scherer, O. Das Genus Livolia Jacoby und seine umstrittene Stellung um System. Ent. Arb. Mus. Frey 22, 1–37 (1971).
Furth, D. G. The jumping apparatus of flea beetles (Alticinae) — The metafemoral spring in Biology of Chrysomelidae (eds Jolivet, P., Petitpierre, E. & Hsiao T. H.) (Springer, 1988).
Piper, R. W. & Compton, S. G. Subpopulations of Cryptocephalus beetles (Coleoptera: Chrysomelidae): geographically close but genetically far. Divers. Distrib 9, 29–42 (2003).
Kleinschmidt, B. & Kölsch, G. Adopting Bacteria in Order to Adapt to Water-How Reed Beetles Colonized the Wetlands (Coleoptera, Chrysomelidae, Donaciinae). Insects 9, 540–554 (2011).
Fonseca, V. G. et al. Revealing higher than expected meiofaunal diversity in Antarctic sediments: a metabarcoding approach. Sci. Rep. 7, 6094, https://doi.org/10.1038/s41598-017-06687-x (2017).
Potter, C. et al. De novo species delimitation in metabarcoding datasets using ecology and phylogeny. Peer J. Preprints 5, e3121v1, https://doi.org/10.7287/peerj.preprints.3121v1 (2017).
Jäckel, R., Mora, D. & Dobler, S. Evidence for selective sweeps by Wolbachia infections: phylogeny of Altica leaf beetles and their reproductive parasites. Mol. Ecol. 22, 4241–4255 (2013).
Leonardi, C. & Sassi, D. Studio critico sulle specie di Cryptocephalus del gruppo hypochaeridis (Linné, 1758) e sulle forme ad esse attribuite (Coleoptera, Chrysomelidae). Atti Soc. ital. sci. nat. Mus. civ. stor. nat. Milano 142, 3–96 (2001).
Gómez-Zurita, J., Sassi, D., Cardoso, A. & Balke, M. Evolution of Cryptocephalus leaf beetles related to C. sericeus (Coleoptera: Chrysomelidae) and the role of hybridisation in generating species mtDNA paraphyly. Zool. Scr. 41, 47–67 (2011).
Berti, N. Contribution à la Faune de France. L’identité d’Oulema (O.) melanopus (L.) (Col. Chrysomelidae Criocerinae). Bull. Soc. Entomol. Fr. 94, 47–57 (1989).
Bezdek, J. & Baselga, A. Revision of western Palaearctic species of the Oulema melanopus group, with description of two new species from Europe (Coleoptera: Chrysomelidae: Criocerinae). Acta Entomol. Mus. Nat. Pragae 55, 273–304 (2015).
Sassi, D. Nuove specie del genere Cryptocephalus vicine a Cryptocephalus marginellus (Coleoptera Chrysomelidae). Mem. Soc. entomol. Ital. 80, 107–138 (2001).
Sassi, D. A new species of the Cryptocephalus marginellus complex from Italian Western Alps (Coleoptera: Chrysomelidae: Cryptocephalinae). Genus 22, 123–132 (2011).
Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).
Baack, E. J. & Rieseberg, L. H. A genomic view of introgression and hybrid speciation. Curr. Opin. Genet. Dev. 17, 513–518 (2007).
Aslan, I., Calmasur, O. & Bilgin, O. C. A morphometric study of Altica oleracea (Linnaeus, 1758) and A. deserticola (Weise, 1889) (Coleoptera: Chrysomelidae: Alticinae). Entomol. Fenn. 15, 1–5 (2004).
Warchalowski, A. The Palearctic Chrysomelidae: identification keys (Natura optima dux Foundation, 2010).
Jaenike, J., Dyer, K. A., Cornish, C. & Minhas, M. S. Asymmetrical Reinforcement and Wolbachia Infection in Drosophila. Plos Biol. 4, e325, https://doi.org/10.1371/journal.pbio.0040325 (2006).
Kajtoch, Ł., Montagna, M. & Wanat, M. Species delimitation within the Bothryorrhynchapion weevils: multiple evidence from genetics, morphology and ecological associations. Mol. Phylogenet. Evol. 120, 354–363 (2018).
Plewa, R. et al. Morphology, genetics and Wolbachia endosymbionts support distinctiveness of Monochamus sartor sartor and M. s. urussovii (Coleoptera: Cerambycidae). Arthropod Syst. Phylo 72, 123–135 (2018).
Kahle, D. & Wickham, H. ggmap: Spatial Visualization with ggplot2. R Journal 5, 144–161 (2013).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York (2009).
Baquero, O. S. North Symbols and Scale Bars for Maps Created with ‘ggplot2’ or ‘ggmap’, https://rdrr.io/cran/ggsn/ (2017).
The Authors would like to thank Davide Sassi, Mauro Daccordi, Carlo Leonardi, Renato Regalin and Stefano Zoia for their contribution to morphological identifications and for their precious suggestions. In addition, a special thank to all those involved in the initiative who helped during sample collection across the Mediterranean region (http://www.cbar.org/about/people-actively-involved/).
The authors declare no competing interests.
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Magoga, G., Sahin, D.C., Fontaneto, D. et al. Barcoding of Chrysomelidae of Euro-Mediterranean area: efficiency and problematic species. Sci Rep 8, 13398 (2018). https://doi.org/10.1038/s41598-018-31545-9
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