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Article

DNA Barcoding of Lepidoptera Species from the Maltese Islands: New and Additional Records, with an Insight into Endemic Diversity

by
Adriana Vella
1,2,*,
Clare Marie Mifsud
1,2,
Denis Magro
2 and
Noel Vella
1,2
1
Conservation Biology Research Group, Biology Department, University of Malta, MSD 2080 Msida, Malta
2
BICREF, Biological Conservation Research Foundation, P.O. BOX 30, HMR 1000 Hamrun, Malta
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(12), 1090; https://0-doi-org.brum.beds.ac.uk/10.3390/d14121090
Submission received: 31 October 2022 / Revised: 25 November 2022 / Accepted: 6 December 2022 / Published: 9 December 2022
(This article belongs to the Special Issue Global Diversity of Lepidopteras)
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Abstract

:
This work presents the first outcomes resulting from a DNA barcode reference library of lepidopteran species from Malta. The library presented here was constructed from the specimens collected between 2015 and 2019 and covers the genetic barcodes of 146 species (ca. 25% of lepidopterous Maltese fauna), including four newly recorded Lepidoptera species from the Maltese islands: Apatema baixerasi, Bostra dipectinialis, Oiketicoides lutea, and Phereoeca praecox. The DNA reference barcode library constructed during this study was analyzed in conjunction with publicly available DNA barcodes and used to assess the ability of the local DNA barcodes to discriminate species. Results showed that each species occupies a different BOLD BIN; therefore, DNA barcoding was able to discriminate between the studied species. Our data led to the formation of 12 new BOLD BINs—that is, OTUs that were identified during this work—while nearly 46% of the barcodes generated during this study were never recorded on conspecifics, further indicating the uniqueness of genetic diversity on these central Mediterranean islands. The outcomes of this study highlight the integrative taxonomic approach, where molecular taxonomy plays an important role for biodiversity investigation in its entirety.

1. Introduction

Biodiversity conservation heavily relies on the use of appropriate tools for characterizing and monitoring various components of biological diversity. Frequently, these conservation efforts are limited by a lack of basic ecological information and efficient large-scale monitoring tools. One of the drawbacks of such assessments is that the morphological identification of species, especially for arthropods, often requires taxonomists with experience and specialization in specific taxa [1]. Additionally, morphological keys are frequently gender specific or life-stage specific, such as the one specifically for adult males, which lacks the taxonomic keys for immature or female specimens [2,3,4,5]. Moreover, several descriptions are based on few individuals and may include characters that exhibit phenotypic plasticity [4], while they may miss cryptic complexes [5,6]. To overcome these limitations, one of the most effective tools for species identification is DNA barcoding [2,7,8,9], which has the potential to accelerate taxonomic workflows while enabling the sorting of specimens into operational taxonomic units (OTUs) [10,11]. This standardized technique allows for rapid species identification and has valuable applications in improving biodiversity monitoring, both in terms of efficiency and accuracy in a wide range of taxa and ecosystems [12,13,14,15,16,17,18,19]. It also allows for the accurate genetic identification of alien species for timely mitigation of biological invasions [20,21,22].
Molecular taxonomy and its applications are dependent on comprehensive molecular data, which at times require the use of multiple genes, including both mitochondrial and nuclear sequences, to better comprehend the underlying genetic diversity and divergence between species [15,23,24,25,26]. Therefore, while there is no single universal tool to delimitate all species, the use of COI for several animal taxa, including arthropods [15], is a widely accepted tool for the construction of DNA barcode libraries of taxonomically verified specimens, augmenting the data available for this gene [27,28,29]. Even though there has been an increase in initiatives to barcode the diversity of life [30], the goal of sequencing all species still requires considerable efforts to be achieved, especially for taxonomically diverse groups, such as insects that contain an estimated 5.5 million species [31], of which half a million are estimated to be Lepidoptera [32]. Currently, close to one third of the estimated number of Lepidoptera species have been described, with just over 1000 new species added and around 200 species names synonymized annually, with the majority of the additions covering micro-moths [32,33]. Additionally, on BOLD, there are nearly 1,670,000 Lepidoptera specimens that have been barcoded, with the publicly available data covering 76,298 species, which form 123,775 barcode index numbers (BINs) [34]. These BINs are part of an automated system that clusters DNA sequences algorithmically using refined single linkage (RESL) analysis, generating a unique identifier, known as a BIN, for each out [35]. Therefore, as these BINs cluster OTUs according to their DNA barcode, they assist in finding species boundaries and allow for improvements in taxonomic revisions and biodiversity assessments [35,36].
As with other taxa, in recent years, taxonomic additions and changes have been characterized by molecular phylogenetic analyses updating classification with the discovery of new species [9,14,15,16,37,38,39,40]. Taxonomic rearrangements involving certain taxonomic levels have been elevated to higher ranks, while others have been reconsidered to be synonyms [16]. These changes may be more frequently encountered in Lepidoptera due to the high degree of non-monophyly noted at species level associated with morphological misidentifications or subjectivity in species delimitation [41].
The identification of species is essential for defining the ecological functions of organisms within ecosystems; therefore, additions to the existing DNA barcode libraries are of the utmost urgency. Lepidoptera species are important pollinators, with moths being the major nocturnal pollinators of flowers [42,43], and therefore being of economic importance. However, various other species within this group feature among the increasing alien and invasive species, negatively affecting host plants and economic growth [44,45,46]. Data on the species richness, abundance, and spatiotemporal distributions of lepidopterans are frequently used in evaluating the quality of ecosystems [47,48]. Their utility as bioindicators comes from the fact that these species are sensitive to environmental changes, with the general trends showing that most native populations are adversely affected. Species and population numbers decline with increases in anthropogenic activities and the impacts of climate change, urbanization, insecticides and other chemical pollutants, light pollution, and the presence of alien plant species [43,47,48,49]. At the same time, other species such as Zeuzera pyrina, a woodborer species that is considered to be a pest to several trees [50] is found to be expanding in its abundance with increasing episodes of drought [51]. Within this scenario, lepidopteran studies from Malta are important, as this 316 km2 central Mediterranean archipelago is the most densely populated country in Europe [52], and consequently, its natural habitats are constantly exposed to human activities. Local knowledge on the diversity of Lepidoptera, both at species and genetic levels, provide the required tools for accurate and efficient monitoring. Malta is estimated to host around 600 Lepidoptera species [53,54,55].
This study uses molecular taxonomy to produce a local DNA reference library for the Lepidoptera species found in Malta, and to increase the data on the barcodes found in international reference DNA databases. This is intended to allow for better taxonomic resolution that considers genetic diversity from a central Mediterranean archipelago that is distant from mainland Europe (80 km south of Sicily) and northern Africa (285 km from the Tunisian coast).

2. Materials and Methods

2.1. Specimen Collection and Morphological Identification

The lepidopteran tissue samples collected from 374 specimens undertaken by the Conservation Biology Research Group at the University of Malta between 2015 and 2019 were used in this study. The specimen tissues were collected using insect nets or captured during the night using UV light traps set in the field between May and October of each sampling year. The specimens were collected (Figure 1) from various habitat types, including urban and rural areas, across the islands of Malta and Gozo from the central Mediterranean (geographical coordinates of the islands: Malta 35.917973 N 14.409943 E; and Gozo 36.044399 N 14.251222 E). The specimen tissues were individually stored in labelled sample bottles and placed at −20 °C on the same day as collection until further processing. Each specimen was photographed and morphologically identified to the lowest taxonomic level following [53,54,56,57,58,59,60,61], before sampling the tissues for genetic analyses. Morphologically identified species were checked for their occurrence in Malta using the Fauna Europaea database portal [55] and the published literature [59,61,62,63,64,65,66]. The collection of protected Lepidoptera specimens was conducted under permits NP0095/16 and NP0271/17, issued by the Environment and Resource Authority (Malta).

2.2. DNA Extraction, Amplification, and Sequencing

Genomic DNA was extracted from a leg of the collected specimens using the GF-1 Tissue DNA Extraction Kit (Vivantis, Shah Alam, Malaysia), following the manufacturer’s manual. PCR amplification of the standard DNA barcode region, mitochondrial cytochrome c oxidase subunit I gene (COI), was carried out using LCO1490/HCO2198 [67] and LepF1/LepR1 [38], appended with the universal M13 oligonucleotide tails. Amplification reactions were carried out following Mifsud et al. [68]. The PCR products were visualized on a 1.5% agarose gel stained with ethidium bromide to confirm amplification and estimate concentration. PCR products were then purified and sequenced using both the forward and reverse primers via an ABI3730XL sequencer.

2.3. Molecular Identification

The quality checks, editing, and alignments of the resulting DNA sequences were conducted using Geneious v. 11.1.2 [69]. DNA barcode sequences were aligned using MUSCLE [70], primer nucleotide sequences were removed, and chromatograms were checked for the presence of double peaks, stop codons, and frameshifts, which could indicate the amplification of nuclear mitochondrial (NUMT) pseudogenes. None of the DNA sequences showed evidence of pseudogenes.
All new DNA barcodes were searched against the NCBI GenBank® database (GenBank, https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/genbank, accessed on 25th November 2022 [71]) nucleotide collection (nr/nt) using BLASTn v 2.9.0 [72,73], and against the species-level barcode records available at the Barcode of Life Data System (BOLD, http://www.boldsystems.org, accessed on 25th November 2022 [34]) using the Species Level Barcode Records within the identification portal system. Sequences were assigned to the BINs by the RESL algorithm, as implemented in BOLD [34,35]. Data related to each BIN, including the average and maximum intra-BIN p-distance and the minimum p-distance to the nearest neighboring BIN, as estimated through BOLD [34], were recorded.
Some cases were further investigated, using genetic sequences from GenBank and BOLD, with regard to the phylogenetic pattern of the specific taxa. In these cases, the sequences were aligned using MUSCLE [70]. The model of best fit, as identified by jModel [74], was used while constructing phylogenetic trees using Bayesian inference. This was estimated via MrBayes v3.2 [75,76] and used 8 × 106 generations with a sampling frequency of every 2000 generations and a burn-in of 25% to allow for the log-likelihood scores to stabilize. These phylogenetic trees allowed for better visualization of clusters to evaluate species delimitation using DNA barcodes. In some instances, as indicated in the results section, the BOLD TaxonID Tree within BOLD was used to visualize divergence, which includes data that are not publicly available.

3. Results

3.1. Taxonomic Coverage and General Overview

This study represents the first DNA barcode reference library of lepidopteran species from Malta, with a COI barcode dataset obtained from 374 specimens representing a total of 146 species belonging to 23 families: Autostichidae (1 species); Blastobasidae (1 species); Cosmopterididae (2 species); Cossidae (1 species); Crambidae (16 species); Erebidae (19 species); Gelechiidae (8 species); Geometridae (18 species); Lycaenidae (1 species); Lasiocampidae (2 species); Momphidae (1 species); Noctuidae (30 species); Nymphalidae (4 species); Papilionidae (1 species); Pieridae (3 species); Plutellidae (1 species); Psychidae (2 species); Pterophoridae (5 species); Pyralidae (16 species); Sesiidae (1 species); Sphingidae (3 species); Tineidae (3 species); and Tortricidae (7 species) (Table 1). This dataset represents around 25% of the currently known Maltese Lepidoptera species [55] and contributes to the knowledge of 147 species. The family represented by the largest sample number is the Noctuidae, which accounts for 26.5% (n = 100) of the total collected specimens, while 13 families are represented by one or two species. The newly amplified data did not include any insertions, deletions, or stop codons, thus indicating that these sequences represent functional mitochondrial COI sequences.
All the species identified in this study were distinguishable from each other through their DNA barcodes, with each species being assigned to a different BOLD BIN (Table 1). Within our data set, the maximum intraspecific p-distance (MxD) noted was 1.83% for both Charissa variegata and Lamoria anella (Supplementary Material Table S1). When considering all the data present in each of the analyzed BOLD BIN, the average p-distance (AvD) within the BINs ranged from 0% to 1.78% (overall mean 0.47% ± 0.35%), with the MxD within BINs reaching 4.41% (overall mean 1.65% ± 1.11%). The distance from the nearest neighboring BIN (DNN) varied between 1.00% and 8.67% (mean 3.24% ± 1.78%). In all instances, the AvD was smaller than the DNN, while in 18% of the taxa, the MxD was larger than the DNN.

3.2. Endemic Diversity

Even though most species have been barcoded in other studies, and our specimens were grouped with conspecifics in their respective BOLD BINs, it was nonetheless noted that 46% of the barcodes generated here, accounting for 54% of the haplotypes, have never been recorded in conspecifics, revealing uniqueness of genetic diversity in these central Mediterranean islands (Table 1 and Supplementary Material Table S1). Additionally, our data cover 12 new BOLD BINs, therefore contributing newly barcoded OTUs to the existing literature. Apart from the newly barcoded species, such as Agdistis frankeniae and Selania capparidana (BOLD:AED1693 and BOLD:AET4374), these new OTUs include the two endemic noctuid moth species Leucania putrescens vallettai and Nyctobrya segunai (BOLD:AER7912 and BOLD:AET0743), and other species that formed new OTUs different from their conspecifics found elsewhere, such as Agonopterix subpropinquella, Penestoglossa dardoinella, and Zeuzera pyrina (BOLD:AER7434, BOLD:AEU4296, and BOLD:AET9156).

3.2.1. Noctuidae: Leucania putrescens vallettai Boursin, 1952

The first endemic species covered in this study was Leucania putrescens vallettai, which is represented by two specimens that differ from each other by 2 bp (99.7% pairwise identity). Although this taxon is considered to be a subspecies, the level of genetic distance from Leucania putrescens is 2.2%, surpassing the threshold that is usually quoted for delimiting species [7,8,16,77,78,79], while the Barcode Index Number System places L. putrescens vallettai in the unique BIN of BOLD:AER7912. The BI analysis using publicly available data showed that these two taxa formed distinct, non-overlapping clusters (Figure 2), with a similar outcome noted when using the BOLD TaxonID Tree (data not shown), which includes more private data on L. putrescens. This level of genetic divergence is also corroborated by clear morphological differences between the two [80], indicating that the endemic subspecies represent a taxon that may be promoted to species level.

3.2.2. Noctuidae: Nyctobrya segunai Fibiger, Steiner, & Ronkay, 2009

Four specimens of N. segunai were identified during this study, with each having a unique haplotype and 99.0% identical nucleotide positions, with a mean pairwise identity of 99.5%. These data represent the first barcodes for N. segunai, which diverges from its closest related species, Nyctobrya muralis, by at least 1.8% using BOLD data and 3.3% using GenBank data. Phylogenetic analysis shows that, genetically, N. segunai forms a monophyletic clade within the paraphyletic N. muralis. The BI tree using both publicly available data (Figure 3) and BOLD TaxonID Tree (data not shown) indicates the presence of multiple clades for N. muralis, highlighting the need for taxonomic revisions of possibly undescribed cryptic species within the N. muralis complex.

3.2.3. Lasiocampa sp.

In this study, we collected five specimens of Lasiocampa sp., represented by three haplotypes which differ from each other by a maximum of 2 bp. Genetic data indicates that all of these specimens belong to the same species, and when checked on BOLD, they were all clustered into the same cluster: BOLD:AES9600. This BIN is solely composed of our specimens, with its closest neighboring BIN being that of Lasiocampa tripolitania (BOLD:AAW9949) (Figure 4), a recently described species found in northern Africa, specifically in Tunisia, Libya, and Egypt [57]. A similar outcome was observed using the BOLD TaxonID Tree (data not shown), which presents outcomes using private data on Lasiocampa species, including L. tripolitania and Lasiocampa terreni. Lewandowski and Fischer [57,81] have indicated that the Lasiocampa species found in Malta is the subspecies Lasiocampa trifolii mauritanica (Staudinger, 1891), a subspecies also found in North-West Africa and on the island of Lampedusa in Italy. Although we cannot exclude the presence of the latter subspecies in Malta, especially given that there are records of L. trifolii documented by other local authors [53,63], yet the specimens we collected are genetically more closely related to L. tripolitania than to L. trifolii. Given that our specimens formed a separate cluster to L. tripolitania, we have kept our records here as Lasiocampa sp., highlighting that this genus requires more studies to better understand the genetic diversity of the various subspecies present in the central Mediterranean.

3.3. New Additions to the Entomofauna of Malta

Four of the species identified in this study are new records for the Maltese islands. These include Apatema baixerasi, Bostra dipectinialis, Oiketicoides lutea, and Phereoeca praecox.

3.3.1. Autostichidae: Apatema baixerasi Vives, 2001

In this study, we encountered one specimen of A. baixerasi. This specimen was collected on 16 May 2017 in Mtaħleb, Rabat at night, using a UV light trap. The area where it was found consists of a cliff site garigue area, in a rupestrial habitat.
In Malta, there are records of Apatema mediopallidum [55], which is genetically paraphyletic compared to A. baixerasi. The two species genetically differ from each other by more than 7% (Figure 5). The former species is represented by sequences that cluster in multiple BOLD BINs (BOLD:AAJ1446, BOLD:AAU3743, and BOLD:ADF1474), while the sequences of the latter only cluster in BOLD:AAV4815.

3.3.2. Psychidae: Oiketicoides lutea (Staudinger, 1870)

In this study, we encountered four specimens of O. lutea. These specimens were collected in Fawwara, Siggiewi on 28 August 2018 from a quarry with a cliff-like habitat.
The four specimens analyzed here formed three distinct haplotypes, with 99.4% identical nucleotide positions and a mean pairwise identity of 99.6%, and they were all clustered into BOLD:AAM0038 (Figure 6) which is the only BIN that represents this species. The p-distance to the nearest neighboring BIN was more than 7.5%, with the closest neighbors being Oiketicoides sp. and Oiketicoides tedaldii, with the BINs BOLD:ABU9696 and BOLD:ABA9698, respectively (Table 1; Figure 6). Previous studies have indicated the occurrence of O. tedaldii in Malta [53,82], with this study presenting data on the occurrence of another species for this genus.

3.3.3. Pyralidae: Bostra dipectinialis Hampson, 1906

Seven specimens of B. dipectinialis were encountered in this study. These specimens were collected in Msida on 14 August 2017 (urban habitat), and in Mtarfa on 8 August 2018 (small pine tree woodland).
The seven specimens analyzed here all had the same haplotype and clustered into BOLD:AAU4121, which is the only BIN that represents this species (Figure 7). The BOLD TaxonID Tree (data not shown) indicates that this BIN contains private data on the same species based on specimens collected from Sicily, which completely match the Maltese specimens, and specimens from Ethiopia, which differ by less than 1% from the ones presented here. Previous studies based on morphology indicate the presence of Bostra obsoletalis in Malta [63]; thus, this study presents data on the occurrence of the second species of this genus on these islands.

3.3.4. Tineidae: Phereoeca praecox (Gozmany & Vari, 1973)

During this study, we found three specimens of P. praecox. These specimens were collected in Msida on 24 September 2015 and 14 May 2018, and in Attard on 5 May 2018. In all cases, the samples were collected within urban dwellings.
Each of the analyzed specimens had a distinct haplotype, with that were 99.6% identical. These specimens were clustered within BOLD:AAU1282 (Figure 8), with the nearest neighboring BIN being BOLD:AAH8518,which is composed of Phereoeca uterella. This species of case-bearing moth is becoming increasingly common in several countries, where they are usually associated with households, warehouses, and storage rooms [83,84]. The current three records represent the first records of this species in Malta.

4. Discussion

The current study provides a better understanding of the inter- and intraspecific variation of Lepidoptera species from the Maltese islands. Using DNA barcoding, we took the first steps towards a comprehensive Maltese collection of DNA reference sequences for this order, recoding genetic data for around 25% of the locally known Lepidoptera species and capturing new first records, while highlighting species and OTUs that may require taxonomic revisions and identifying genetic diversity.
It is well known that the identification of Lepidoptera species is at times biased by the morphological characters chosen for delimitating a species, and consequently, a significant number of formal descriptions are considered to be synonyms, while the lack of standardized methods frequently leads to misidentifications [32,33]. In this scenario, DNA barcoding is an added tool that is used to support, refine, or challenge taxonomic descriptions.
In this study, we confirmed the presence of four newly recorded species for the Maltese islands. These include A. baixerasi, B. dipectinialis, O. lutea, and P. praecox (Figure 5, Figure 6, Figure 7 and Figure 8). The close morphological resemblance of these species with conspecifics could have led to misidentifications, such as the presence of A. mediopallidum and A. baixerasi. One such recent genetic study from the Canary Islands confirmed that the presence of A. mediopallidum on this archipelago was based on misidentifications [77]. Additionally, in the case of A. baixerasi, the recent description may have led to this taxon being overlooked in other studies relying solely on morphological characters. In our study, the presence of these species was observed through morphology and confirmed genetically through comparisons with conspecifics. Such additions to the local entomofauna show the importance of molecular taxonomy in biodiversity research and monitoring [68,85].
Molecular taxonomy has led to the recording of the first barcodes for two endemic species, L. putrescens vallettai and N. segunai, and the development of the first phylogenetic analyses for them. The latter was found to be a species within the paraphyletic N. muralis complex (Figure 3), with a 1.8% distance from the closest N. muralis clade. L. putrescens vallettai exhibits clear morphological differences [80] and a high level of genetic variation from L. putrescens, leading to the formation of a unique BOLD BIN, indicating that this endemic subspecies reveals enough differences to be considered for promotion to species level. Apart from the described endemics, the results also show that several other species were clustered in a different BOLD BIN from their conspecifics (Table 1). One such example is Lasiocampa sp., which differs from L. trifolii by more than 5%, and from L. tripolitania by around 2% (Figure 4). Moreover, we have found a high proportion of unique barcodes that differ by a few base pairs from the conspecifics collected from other countries (Table 1), showing that some local populations have diverged from other populations found on mainland southern Europe and northern Africa.
Mediterranean islands have been characterized by various biogeographical changes, including sea-level changes and the intermittent connectivity of between islands and with the mainland during glacial periods [86]. These past events and the region’s geography have shaped this area into a biodiversity hotspot, where different islands have unique faunal and floral assemblages [87,88,89], with isolation being the driving force towards the diversification of species and subspecies [89]. Discoveries of new species and the large number of unique DNA barcodes clearly demonstrates that the entomofauna of Malta deserves further attention for complete biodiversity inventories. Lack of knowledge on island fauna and the presence of endemic species, which are geographically very restricted, are considered crucial issues for island biodiversity conservation [90]. The number of unique haplotypes noted indicates that the Lepidoptera species in Malta may have low immigration probabilities, forming isolated populations from the mainland, in which case, as reported in other Mediterranean islands, local extinctions may not only mean the loss of this allopatric diversification. However, this also highlights the possible unlikeliness of recolonization [87].
In this respect, genetic and genomic data are essential additional tools for identifying species and indicating the degree of intraspecific divergence across a geographical range, allowing for a better understanding of the management and conservation needs of insular biodiversity [91]. Studies on other Mediterranean islands show that old mature forests, with the highest levels of environmental stability, have the highest abundance and number of lepidopteran species, including endemic species [92]. Consequently, the protection of endemic biodiversity is tightly linked to the preservation of native natural habitats, which are highly threatened by climate change, fragmentation, urbanization, and other landscape modifications, including plantations of non-native flora and the introduction of alien fauna. The latter is facilitated by globalization and the multifaceted transportation of diverse goods and merchandise onto these islands [49,93,94,95].

5. Conclusions

Molecular taxonomy of Maltese Lepidoptera has led to the identification of new records, also unravelling aspects related to the taxonomic rank of several species. This comprehensive DNA barcode library of Maltese Lepidoptera can be applied to monitor and regulate potential introductions of pest species, biodiversity inventories, ecosystem biomonitoring, and conservation.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/d14121090/s1, Table S1: A list of the species analysed (F first records for Malta; E endemic species), including the number of specimens per species (n), haplotypic variants per species (H), the mean percentage distance within species (AvD), the maximum percentage distance within species (MxD), the accession numbers, the percentage similarity to the closest BOLD match (matches < 100% indicate that the haplotypic variant was recorded for the first time).

Author Contributions

Conceptualization: A.V., C.M.M. and N.V. Sample collection: C.M.M., A.V., D.M. and N.V. Formal analysis & Methodology: C.M.M., N.V. and A.V. Funding acquisition: C.M.M. and A.V. Resources: A.V. Supervision: A.V. Project Administration: A.V. Data Curation: C.M.M., A.V. Writing—original draft: N.V., C.M.M., A.V. Writing—review and editing: A.V., N.V. and C.M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the ENDEAVOUR Scholarships Scheme, Malta [grant No. 168], which was awarded to C.M.M.; and the BioCon_Innovate Research Excellence Grant from the University of Malta, [grant No. I18LU06-01] which was awarded to A.V.

Institutional Review Board Statement

This research followed the University of Malta’s Research Ethics Review Procedures.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data generated from this work are available in GenBank as indicated in Table 1.

Acknowledgments

The collection of protected Lepidoptera specimens, was conducted under permits NP0095/16 and NP0271/17, issued by the Environment and Resource Authority (Malta). The authors would like to thank other members of the Conservation Biology Research Group, who assisted in the laboratory work related to some of the specimens presented here.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Images showing examples of the diversity of species that form part of the current data set. (A): Lasiommata megera (MW305918); (B): Papilio machaon (MW305956); (C): Acherontia atropos (MW305743); (D): Pechipogo plumigeralis (MW305957); (E): Utetheisa pulchella (MW306031). (A–C) are locally protected species. Photos by Denis Magro.
Figure 1. Images showing examples of the diversity of species that form part of the current data set. (A): Lasiommata megera (MW305918); (B): Papilio machaon (MW305956); (C): Acherontia atropos (MW305743); (D): Pechipogo plumigeralis (MW305957); (E): Utetheisa pulchella (MW306031). (A–C) are locally protected species. Photos by Denis Magro.
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Figure 2. Bayesian inference phylogram showing the genetic relationship between the Leucania putrescens vallettai from the current study (in bold) and other species of Leucania, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
Figure 2. Bayesian inference phylogram showing the genetic relationship between the Leucania putrescens vallettai from the current study (in bold) and other species of Leucania, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
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Figure 3. Bayesian inference phylogram showing the genetic relationship between the Nyctobrya segunai from the current study (in bold) and other Nyctobrya species, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
Figure 3. Bayesian inference phylogram showing the genetic relationship between the Nyctobrya segunai from the current study (in bold) and other Nyctobrya species, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
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Figure 4. Bayesian inference phylogram showing the genetic relationship between the Lasiocampa sp. analyzed in the current study (in bold) and other Lasiocampa species, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
Figure 4. Bayesian inference phylogram showing the genetic relationship between the Lasiocampa sp. analyzed in the current study (in bold) and other Lasiocampa species, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
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Figure 5. Bayesian inference phylogram showing the genetic relationship between the newly recorded Apatema baixerasi specimen from Malta (in bold) and other Apatema species, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
Figure 5. Bayesian inference phylogram showing the genetic relationship between the newly recorded Apatema baixerasi specimen from Malta (in bold) and other Apatema species, using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
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Figure 6. Bayesian inference phylogram showing the genetic relationship between the newly recorded Oiketicoides lutea specimens from Malta (in bold) and other publicly available Oiketicoides species data. Numbers at nodes indicate Bayesian posterior probabilities.
Figure 6. Bayesian inference phylogram showing the genetic relationship between the newly recorded Oiketicoides lutea specimens from Malta (in bold) and other publicly available Oiketicoides species data. Numbers at nodes indicate Bayesian posterior probabilities.
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Figure 7. Bayesian inference phylogram showing the genetic relationship between the newly recorded Bostra dipectinialis specimens from Malta (in bold) and other publicly available Bostra species data. Numbers at nodes indicate Bayesian posterior probabilities.
Figure 7. Bayesian inference phylogram showing the genetic relationship between the newly recorded Bostra dipectinialis specimens from Malta (in bold) and other publicly available Bostra species data. Numbers at nodes indicate Bayesian posterior probabilities.
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Figure 8. Bayesian inference phylogram showing the genetic relationship between the newly recorded Phereoeca praecox specimens from Malta (in bold) and other Phereoeca praecox specimens using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
Figure 8. Bayesian inference phylogram showing the genetic relationship between the newly recorded Phereoeca praecox specimens from Malta (in bold) and other Phereoeca praecox specimens using publicly available data. Numbers at nodes indicate Bayesian posterior probabilities.
Diversity 14 01090 g008
Table
Table 1. A list of the species analyzed (F first records for Malta; E endemic species), including the number of specimens per species (n), number of haplotypic variants per species (H) (with the superscript indicating the number of newly identified haplotypes), the barcode index number (N indicates a new BIN that contains only current sequences), and associated data obtained from BOLD. [nB = number of sequences in BIN; AvD = average p-distance within BIN; MxD = maximum p-distance within BIN; DNN = distance to nearest neighbor; NN BIN = nearest neighbor BIN; NN taxonomy = species assigned to nearest neighbor BIN; nBN = number of sequences in nearest neighbor BIN; NN AvD = average p-distance within nearest neighbor’s BIN; NN MxD = maximum p-distance within the nearest neighbor’s BIN]. BOLD data presented here was last accessed on 25th November 2022.
Table 1. A list of the species analyzed (F first records for Malta; E endemic species), including the number of specimens per species (n), number of haplotypic variants per species (H) (with the superscript indicating the number of newly identified haplotypes), the barcode index number (N indicates a new BIN that contains only current sequences), and associated data obtained from BOLD. [nB = number of sequences in BIN; AvD = average p-distance within BIN; MxD = maximum p-distance within BIN; DNN = distance to nearest neighbor; NN BIN = nearest neighbor BIN; NN taxonomy = species assigned to nearest neighbor BIN; nBN = number of sequences in nearest neighbor BIN; NN AvD = average p-distance within nearest neighbor’s BIN; NN MxD = maximum p-distance within the nearest neighbor’s BIN]. BOLD data presented here was last accessed on 25th November 2022.
FAMILY
Species		nHBIN

BOLD:		nBAvD
(%)		MxD
(%)		DNN
(%)		NN BIN

BOLD:		NN TaxonomynBNNN AvD
(%)		NN MxD
(%)
AUTOSTICHIDAE
Apatema baixerasiF11 1AAV4815100.560.984.04ADR6916Apatema sp.40.140.33

BLASTOBASIDAE
Blastobasis phycidella33 3AAF0414690.572.432.97AAZ8649Blastobasis sp.20.460.46

COSMOPTERIGIDAE
Bifascioides leucomelanella11 1ABA4555180.270.842.41ADU3943Lepidoptera sp.1--
Pyroderces argyrogrammos33 3AAQ02421140.783.056.46AEJ1178Lepidoptera sp.1--

COSSIDAE
Zeuzera pyrina21 2AET9156 N20.000.001.93ADC8403Zeuzera sp.1--

CRAMBIDAE
Agriphila trabeatellus22 2ACA941090.701.778.26ABA4409Catoptria confusellus30.270.33
Ancylolomia pectinatellus11 1ACA933550.490.677.87ACI1349Crambidae sp.20.150.15
Antigastra catalaunalis33 2AAE6976380.611.533.22AAP5696Antigastra catalaunalis1--
Aporodes floralis31 0AAN7323400.130.592.62AAV4122Aporodes floralis60.651.12
Dolicharthria bruguieralis11 1AED110220.160.161.28AAO3560Dolicharthria bruguieralis100.120.48
Duponchelia fovealis53 2AAD97271060.161.992.25ACR2019Duponchelia fovealis90.360.80
Euchromius cambridgei42 2ABY3890121.242.187.21ADZ9070Phycitinae sp.1--
Euchromius ocellea84 2AAA56712310.371.773.19ABA8488Euchromius sp.1--
Evergestis sp.22 2AEU3700 N20.160.162.73ADL3576Evergestis isatidalis30.540.80
Hellula undalis54 1AAC8519780.481.613.45AAE6944Hellula rogatalis540.060.55
Herpetogramma licarsisalis11 1AAA39652920.141.773.98AAA3967Herpetogramma licarsisalis380.060.32
Nomophila noctuella31 0AAA78803190.463.753.86AAB5466Nomophila corticalis1230.351.12
Palpita vitrealis11 0AAC10431000.722.572.51AAB0733Palpita margaritacea510.180.64
Spoladea recurvalis11 0AAA36663640.723.196.18ABA0182Scoparia paracycla1--
Udea ferrugalis32 0AAC37291040.643.043.47ABA1630Udea stellata1--
Uresiphita gilvata11 0ACF5204430.501.771.10AAA3568Uresiphita ornithopteralis1700.151.02

EREBIDAE
Clytie illunaris11 1AAK5589180.250.651.12AEH3335Clytie sp.30.110.16
Cymbalophora pudica32 2AAG6227120.741.504.81ABZ5736Turuptiana obliqua1--
Dysauxes famula22 1AAM0427240.240.801.12ACF0669Dysauxes famula80.530.96
Eilema caniola21 0AAF6264710.782.734.19AAA4503Manulea bicolor1980.302.68
Eublemma ostrina11 0AAG1829340.562.252.39ABW0690Eublemma staudingeri220.060.37
Eublemma parva53 1AAM5884430.250.643.52ACL9149Eublemma saldaitis1--
Eublemma scitula11 1ACD071760.621.774.33ACN9797Noctuidae sp.1--
Eublemma sp.11 1AAL4752130.811.771.12ACL7422Eublemma parva40.480.80
Hypena lividalis32 0AAE1121690.191.155.93AAA2868Chytolita morbidalis1870.701.92
Hypena obsitalis105 3AAK3686230.120.393.19ACF0234Hypena sordidula170.160.65
Metachrostis velocior32 1AAH693180.511.251.44ACK1973Metachrostis dardouini60.050.16
Metachrostis velox22 1AAH6930140.450.981.77ACK1973Metachrostis dardouini60.050.16
Nodaria nodosalis11 0AAK3749500.763.003.10AAD1694Simplicia cornicalis500.252.17
Ophiusa tirhaca11 0ABZ7648210.401.181.77ABZ4334Ophiusa sp.220.050.36
Orgyia trigotephras11 1AAM080441.031.444.17ACB6683Orgyia sp.40.000.00
Pechipogo plumigeralis11 0AAI4196220.050.323.37AAA2868Chytolita morbidalis1870.701.92
Phragmatobia fuliginosa41 0AAA6178940.592.252.25AAN2564Phragmatobia fuliginosa20.360.36
Utetheisa pulchella11 0AAF0098610.170.641.42ACT3042Utetheisa elata30.821.22
Zebeeba falsalis61 0AAJ9181260.982.578.01AAN6974Elaphria sp.1--

GELECHIIDAE
Agonopterix olusatri11 1ABW7168150.521.611.93ADF2495Agonopterix sp.50.100.16
Agonopterix subpropinquella11 1AER7434 N1--1.93AAZ9000Agonopterix subpropinquella130.050.38
Aproaerema sp.11 1AET5627 N1--1.77AEA1472Aproaerema sp.50.000.00
Ornativalva plutelliformis11 1ABW9166110.651.772.49ABX8241Ornativalva plutelliformis40.330.66
Phthorimaea operculella41 0AEL8356950.184.315.54AED9067Phthorimaea sp.1--
Platyedra subcinerea65 4AAD8749490.541.505.36AAU3620Pexicopia sp.20.000.00
Ptocheuusa paupella11 0AAV2188180.170.823.21ACW2460Ptocheuusa paupella70.571.29
Tuta absoluta11 0AAJ80339730.041.80------

GEOMETRIDAE
Charissa variegata54 4AAC1039351.623.492.75AAC4341Charissa subtaurica180.351.50
Cyclophora puppillaria11 0AAB2523600.030.653.17ACF3607Cyclophora albipunctata360.431.44
Epirrhoe alternata21 0ACE41421340.632.811.02AAA3371Epirrhoe alternata850.852.09
Eucrostes indigenata22 2AAC6469160.371.163.60ADF4899Eucrostes sp.1--
Eupithecia centaureata22 2ACE9420671.764.336.06ACJ9495Eupithecia sp.1--
Gymnoscelis rufifasciata11 1AAA7404970.742.202.74ADL3671Gymnoscelis rufifasciata20.000.00
Idaea elongaria11 1AAA898570.260.552.57ACK1747Idaea elongaria1--
Idaea fractilineata43 2AAK425280.340.972.85ACM9078Idaea purpurariata50.000.00
Idaea obsoletaria11 1AAB493960.110.332.30ACE4926Idaea obsoletaria40.400.71
Idaea seriata31 1AAA9645560.130.711.92ABZ4137Idaea seriata60.100.31
Isturgia pulinda11 0AAA6139950.401.343.50AAU7783Isturgia exerraria20.000.00
Menophra japygiaria51 0AAB6706420.412.642.66AAC8802Menophra berenicidaria130.180.75
Phaiogramma etruscaria11 0ABY4065420.200.671.12ACW6537Phaiogramma etruscaria90.170.32
Phaiogramma faustinata11 0AAB4914820.682.671.04ACW6536Phaiogramma stibolepida140.641.44
Rhodometra sacraria63 1AAA89831381.115.415.92AAQ1498Rhodometra sacraria31.472.25
Scopula imitaria21 0AAB6665560.231.441.80ABZ6950Scopula imitaria syriacaria50.000.00
Scopula minorata11 1AAA93571250.943.862.12AEO1263Scopula sp.1--
Xanthorhoe disjunctaria11 0ABY6341270.451.151.77AET6043Xanthorhoe sardisjuncta110.230.64

LASIOCAMPIDAE
Gastropacha quercifolia11 1AAF4844940.481.548.67AAI7018Gastropacha sikkima501.082.09
Lasiocampa sp.53 3AES9600 N30.000.001.93AAW9949Lasiocampa tripolitania30.200.31

LYCAENIDAE
Polyommatus celina43 1AAA33042750.922.413.71AAA3303Polyommatus erotides9761.283.88

MOMPHIDAE
Mompha subbistrigella11 1AAD0702710.301.443.61ADB9986Mompha glaucella30.100.15

NOCTUIDAE
Acontia lucida32 1AAD6258380.251.015.22ABV2194Lepidoptera sp.40.150.31
Agrotis biconica11 0AAE4276360.671.512.09ABZ5220Agrotis munda280.281.02
Agrotis ipsilon32 0AAA33643360.311.941.00ACE7272Agrotis infusa1200.020.37
Agrotis lata44 2ACE7288110.290.801.28AEH3853Agrotis lata1--
Agrotis puta51 0AAB9164790.090.642.19AAB9165Lepidoptera sp.100.310.64
Agrotis segetum32 1AAC38841720.171.692.32AAB9113Agrotis exclamationis900.070.80
Agrotis trux146 6AET6510140.190.501.12AAM0539Agrotis trux130.982.09
Anarta trifolii11 0ABZ14282010.583.331.71AAA9985Anarta columbica710.131.07
Autographa gamma32 1AAB43456260.032.022.17AAB2628Autographa californica1100.120.67
Callopistria latreillei11 1AAP2182470.281.283.24AAN8804Callopistria sp.20.000.00
Caradrina clavipalpis31 0AAB6999830.362.172.41ABZ7109Caradrina selini480.130.64
Caradrina flava21 0AAK490880.120.483.21AET1610Lepidoptera sp.1170.030.64
Caradrina flavirena61 0AAB7000950.672.731.18ADB8712Caradrina flavirena90.040.16
Chrysodeixis chalcites31 0AAB33843540.563.163.05AAG0704Chrysodeixis kebea60.330.61
Condica viscosa11 0AAN1812130.340.722.09ADU4648Noctuidae sp.20.000.00
Cryphia algae52 2AAD6780100.401.281.28AAD6780Lepidoptera sp.650.300.96
Hadena sancta21 1AAY8457100.481.042.09ABY4816Hadena ruetimeyeri21.071.07
Heliothis peltigera53 0AAC6990600.140.643.53AAV6844Heliothis saskai20.000.00
Leucania putrescens vallettaiE22 2AER7912 N20.340.342.18AAK9298Leucania putrescens60.610.96
Mythimna sicula11 1AAF8181530.491.132.75ABX0055Mythimna opaca20.320.32
Mythimna unipuncta11 0AAA24825550.514.412.08ACG2559Mythimna unipuncta30.000.00
Noctua pronuba22 0AAA26323210.221.613.69AAD0229Noctua interjecta560.411.46
Nyctobrya segunai E44 4AET0743 N40.530.681.68AAN0805Cryphia muralis80.210.50
Pseudozarba bipartita66 6AAE4331481.153.283.17AAE8111Pseudozarba orthopetes240.541.93
Spodoptera cilium11 0AAC8279790.150.992.11ACE3456Spodoptera depravata710.130.80
Spodoptera exigua83 0AAA66446320.383.372.60ADB9075Spodoptera exigua1--
Synthymia fixa31 0AAN013730.000.001.12AES6312Synthymia fixa60.270.80
Trichoplusia ni11 0AAC3410500.020.354.01AAC3409Trichoplusia ni860.192.57
Tyta luctuosa52 0AAD5088390.270.805.35AEI5594Epharmottomena tenera1--
Xylena exsoleta11 1AAE4735200.541.124.00ACD6521Xylena formosa90.791.61

NYMPHALIDAE
Coenonympha pamphilus11 0AAA73513050.151.381.25ADJ7308Coenonympha pamphilus420.391.28
Danaus chrysippus11 0ABX51222150.592.751.58AAB3216Danaus chrysippus220.020.20
Lasiommata megera11 0AAB01233420.461.261.12ACE4512Lasiommata paramegaera550.050.50
Vanessa atalanta22 0AAA86382710.222.713.85AAE5211Antanartia abyssinica220.130.61

PAPILIONIDAE
Papilio machaon32 0AAA58104401.013.581.77ABZ2147Papilio machaon20.120.12

PIERIDAE
Colias croceus22 0ABZ30394400.061.381.72ACF0844Colias pelidne1151.162.57
Pieris brassicae11 1AAB05524050.644.252.13ACN0735Pieris brassicae1--
Pieris rapae11 1AAA22249040.463.373.10AAB3783Pieris mannii1280.240.80

PLUTELLIDAE
Plutella xylostella33 1AAA151337920.774.346.57AAC6876Plutella australiana1210.060.62

PSYCHIDAE
Oiketicoides lutea F43 3AAM003862.093.357.50ABU9696Oiketicoides sp.1--
Penestoglossa dardoinella31 1AEU4296 N30.000.001.28AAL3705Penestoglossa dardoinella120.030.17

PTEROPHORIDAE
Agdistis frankeniae11 1AED1693 N1--3.37ABV2042Lepidoptera sp.20.160.16
Emmelina monodactyla21 0ACE48621100.080.701.34AAA3882Emmelina monodactyla1110.451.77
Merrifieldia malacodactylus11 0ACS6787170.280.644.49ADZ0299Pterophoridae sp.1--
Pterophoridae sp.11 1ADZ038720.180.186.31AAV5270Procapperia linariae110.751.31
Stenoptilia sp.11 1ABW685970.120.324.65ACS3431Stenoptilia sp.60.802.14

PYRALIDAE
Aglossa caprealis11 1ACY869131.071.916.26ADR4870Aglossa sp.1--
Apomyelois ceratoniae22 0AAU48121140.431.443.00ACR0358Cadra sp.50.320.80
Bostra dipectinialis F71 0AAU4121110.300.812.09AEO2408Bostra sp.150.761.61
Cadra abstersella71 0AAW5130220.201.305.54AAB9605Cadra cautella1671.244.21
Cadra cautella22 1AAB96051671.244.213.04ADV8858Cadra sp.1--
Cadra figulilella62 0AAZ9283810.201.621.61ADS7823Cadra sp.70.410.80
Ceutholopha isidis22 1ABA4962280.190.682.45ABA4962Ceutholopha petalocosma420.240.75
Ephestia elutella11 0AAC6157570.761.614.15AAD1430Ephestia parasitella790.722.57
Lamoria anella113 3ACY8237260.831.934.94AAY8816Lamoria anella20.480.48
Oxybia transversella11 1ACA9658131.122.575.78AAB9775Salebriaria roseopunctella1151.162.59
Phycita diaphana11 1ACA965280.320.805.78ACB7132Phycitinae sp.20.000.00
Phycitodes saxicola21 1AAD9531420.632.026.04ABX8977Phycitodes sp.30.310.46
Plodia interpunctella21 0AAB24621010.524.056.96ADG1988Pyralidae sp.1--
Psorosa dahliella11 0ACA975330.550.842.09AEF6784Psorosa ferrugatella30.000.00
Pyralis farinalis22 2AAB3316570.402.702.41AAY8728Pyralis farinalis40.190.32
Stemmatophora brunnealis32 1AAV6933130.280.655.16AEO2608Stemmatophora brunnealis1--

SESIIDAE
Bembecia albanensis11 1AAM245381.222.094.09ABX3895Bembecia albanensis1--

SPHINGIDAE
Acherontia atropos11 0AAB7886410.060.934.97AAD2845Acherontia styx240.281.39
Agrius convolvuli11 0AAA23931620.602.093.32AAA2392Agrius convolvuli1580.232.82
Hippotion celerio22 0ABZ5722280.190.641.10ACE8834Sphingidae sp.180.100.33

TINEIDAE
Niditinea fuscella11 1AAF3430590.902.876.37AAG3681Niditinea truncicolella50.120.32
Phereoeca praecox F33 2AAU1282330.060.663.75AAH8518Phereoeca uterella110.632.43
Tinea murariella11 0AAE747080.190.932.90AEI9096Tinea translucens1--

TORTRICIDAE
Aethes sp.32 2AEU4088 N20.390.393.53AAP7561Aethes sp.260.501.44
Cacoecimorpha pronubana21 1AAD3477330.260.961.96ACS9337Cacoecimorpha pronubana20.000.00
Clepsis sp.22 2AED2423 N20.500.503.34ACT3810Clepsis consimilana40.080.16
Eucosma sp.41 0ACT004250.711.342.10AAB4296Eucosma sp.830.261.66
Lobesia botrana22 1ACH2178770.571.704.24AAC9385Lobesia reliquana370.110.64
Pseudococcyx tessulatana11 0ACT060680.140.347.70AAH4831Retinia sabiniana30.100.15
Selania capparidana11 1AET4374 N1--2.50ABA4981Selania sp.1--
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Vella, A.; Mifsud, C.M.; Magro, D.; Vella, N. DNA Barcoding of Lepidoptera Species from the Maltese Islands: New and Additional Records, with an Insight into Endemic Diversity. Diversity 2022, 14, 1090. https://0-doi-org.brum.beds.ac.uk/10.3390/d14121090

AMA Style

Vella A, Mifsud CM, Magro D, Vella N. DNA Barcoding of Lepidoptera Species from the Maltese Islands: New and Additional Records, with an Insight into Endemic Diversity. Diversity. 2022; 14(12):1090. https://0-doi-org.brum.beds.ac.uk/10.3390/d14121090

Chicago/Turabian Style

Vella, Adriana, Clare Marie Mifsud, Denis Magro, and Noel Vella. 2022. "DNA Barcoding of Lepidoptera Species from the Maltese Islands: New and Additional Records, with an Insight into Endemic Diversity" Diversity 14, no. 12: 1090. https://0-doi-org.brum.beds.ac.uk/10.3390/d14121090

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