The lack of an updated checklist for European Gelechiidae (see Karsholt 2004-2019) was such a major impediment to the present study that it necessitated the assembly of a new systematic list (Huemer and Karsholt 2020). This list, which includes 865 species of Gelechiidae in 109 genera, provided the basis for selecting the specimens that were analysed in this study.

One major challenge was the difficulty in accessing specimens suitable for molecular analysis, reflecting the rarity of many species. In addition, DNA quality of the specimens was another very important limitation as sequence recovery from older specimens of rare taxa was either partial or failed completely even with protocols that employed high-throughput sequencers to analyze short amplicons. In some cases, efforts were made to recollect taxa that lacked a sequence record.

Voucher material was obtained from Europe (Fig. 2) except for eleven taxa whose sequences could not be recovered from specimens from this continent or where it seemed important to analyze specimens to clarify taxonomy (e.g., extra-European type-material) (Suppl. material 2, 3). Approximately two-thirds of specimens originated from four nations - Germany (1319), Austria (1157), Italy (906), and Finland (707). The remaining specimens derived from 33 other countries (Fig. 2).

Many institutions and private collectors contributed to the dataset (see below), supplemented by DNA barcodes from earlier studies.

BIOUG Centre for Biodiversity Genomics, Guelph, Canada

INDO Inatura, Dornbirn, Austria

LMK Landesmuseum Kärnten, Klagenfurt, Austria

MFSN Museo Friulano di Storia Natural, Udine, Italy

MZH Finnish Museum of Natural History, Helsinki, Finland

NHM Natural History Museum, London, United Kingdom

NHMO Natural History Museum, University of Oslo, Oslo, Norway

NHMW Naturhistorisches Museum, Vienna, Austria

NMPC National Museum Prague, Czech Republic

NMS Naturmuseum Südtirol, Bozen, Italy

RCAH Research Collection Alfred Haslberger, Teisendorf, Germany

RCER Research Collection Emily Requena Miret, Gurb, Spain

RCGB Research Collection Giorgio Baldizzone, Asti, Italy

RCGT Research Collection Giovanni Timossi, Oderzo, Italy

RCHW Research Collection Hartmut Wegner, Adendorf, Germany

RCIB Research Collection Ian Barton, Cambs, United Kingdom

RCIR Research Collection Ignác Richter, Malá Čausa, Slovakia

RCJD Research Collection Jordi Dantart, Barcelona, Spain

RCJJ Research Collection Jari Junnilainen, Vantaa, Finland

RCJK Research Collection Jari-Pekka Kaitila, Vantaa, Finland

RCJL Research Collection Gérard Labonne, Montpellier, France

RCJN Research Collection Jacques Nel, La Ciotat, France

RCJS Research Collection Jan Skyva, Prague, Czech Republic

RCJSC Research Collection Jürg Schmid, Illanz, Switzerland

RCKB Research Collection Kai Berggren, Kristiansand, Norway

RCKN Research Collection Kari and Timo Nupponen, Espoo, Finland

RCMC Research Collection Martin Corley, Faringdon, U.K.

RCOB Research Collection Oleksiy Bidzilya, Kiev, Ukraine

RCOR Research Collection Oliver Rist, Vienna, Austria

RCPB Research Collection Peter Buchner, Schwarzau am Steinfeld, Austria

RCPL Research Collection Peter Lichtmannecker, Adlkofen, Germany

RCRH Research Collection Robert Heckford, Plympton, Plymouth, U.K.

RCRHE Research Collection Richard Heindel, Günzburg, Germany

RCSP Research Collection Serge Peslier, Perpignan, France

RCTG Research Collection Thomas Guggemoos, Ohlstadt, Germany

RCTM Research Collection Toni Mayr, Feldkirch, Austria

RCTV Research Collection Thierry Varenne, Nice, France

RCWS Research Collection Wolfgang Stark, Trübensee, Austria

RCZT Research Collection Zdenko Tokár, Šal’a, Slovakia

TLMF Tiroler Landesmuseum Ferdinandeum, Innsbruck, Austria

USNM Smithsonian Institution, National Museum of Natural History, Washington DC, U.S.A.

ZMAK Zoologisches Forschungsmuseum Alexander Koenig, Bonn, Germany

ZMKU Taras Shevchenko National University of Kiev, Kiev, Ukraine

ZMUC Zoological Museum, Natural History Museum of Denmark, Copenhagen, Denmark

ZMUO Zoological Museum, University of Oulu, Finland

ZSM Zoologische Staatssammlung, Munich, Germany

A single leg was removed from each specimen and placed in a 96-well lysis plate that was submitted for analysis at the CCDB (Canadian Center for DNA Barcoding, University of Guelph, Canada) where DNA extraction, PCR amplification, and sequencing were performed following standard high-throughput protocols (deWaard et al. 2008). In total, 5986 specimens of European Gelechiidae, initially pre-identified from external and partially genitalia morphology by several colleagues and cross-checked by PH and OK in dubious cases, were successfully sequenced. Details of specimens, including complete voucher data, images, and GenBank accession numbers are available on BOLD (Ratnasingham 2018, Ratnasingham and Hebert 2007) in the public dataset “Lepidoptera (Gelechiidae) of Europe” under the DOI: dx.doi.org/10.5883/DS-GELECHEU.

Levels of intra- and interspecific variation in the DNA barcode fragment were calculated under the Kimura 2-parameter (K2P) model of nucleotide substitution using analytical tools in BOLD systems v4.0 (http://www.boldsystems.org). Fifty-three Neighbor-Joining trees (Maximum Composite Likelihood method, default settings), most including representatives of a single genus, were constructed using MEGA X (Kumar et. al 2018) (Suppl. material 2 and 3). Node confidences were estimated using 500 bootstrap replicates. For genera with few species, several morphologically closely related genera were included in a single tree. For calculating these trees only sequences ≥ 500 bp were used, except for ten species where only shorter sequences were available (Suppl. material 1). In those cases where the specimens of a single species were assigned to two or more different BINs, they were discriminated by a letter code. Because of the high number of BINs for Megacraspedus dolosellus and M. lanceolellus, these taxa were figured in two separate NJ trees with BINs separated as single clusters. Species sharing a BIN, but still with a diagnostic barcode were grouped in separate clusters. A three-letter code (ISO 3166-1 alpha-3, https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3) was used to abbreviate country names.

Identification success was assessed by the Barcode Index Number (BIN) system as implemented on BOLD (Ratnasingham and Hebert 2013). This system employs a two-stage algorithm that groups all sequences > 500 bp that meet defined quality criteria into Operational Taxonomic Units (OTUs) and automatically assigns new sequences, irrespective of their previous taxonomy and origin. Concordance or discordance between BINs and morphological species identification was assessed.

DNA barcode sequences were recovered from 5986 specimens representing 751 of the 865 species of Gelechiidae described from Europe (Suppl. material 1). In addition, the analysis revealed 65 putative species whose members were each assigned to a different unique BIN. Most sequences (5476) were compliant with the barcode standard as described in BOLD (http://www.boldsystems.org). Most subsequent analyses only considered the 741 species with sequences ≥ 500bp, but ten additional species with sequences ≥ 300 bp were included in the NJ trees. Sequences from 723 species qualified for BIN analysis.

Intraspecific DNA barcode variation in the 741 named species with sequences ≥500 bp averaged 0.54%, but this may be an underestimate as sample sizes for 224 taxa were low and only represented by singletons. In respect to the distribution of mean intraspecific DNA barcode variation: 73.1% of sequenced species had variation ranging from 0–1%, 15.8% between 1–2%, 6.3% between 2–3%, and 4.8% > 3%.

Contrastingly, barcode gap analysis resulted in mean distances of 5.58% (maximum 12.75%) to the Nearest Neighbor (NN) with only 5.68% of all species showing a NN distance of 0–1% (Table 2). In this latter group, only four species pairs/triplets (Dirhinosia cervinella / D. interposita, Iwaruna biguttella / I. klimeschi, Teleiodes brevivalva / T. italica / T. vulgella, Xenolechia aethiops / X. lindae / X. pseudovulgella) shared barcodes so they could not be discriminated on that basis. In eight other cases, shared DNA barcodes meant that assignments were sometimes unreliable, but these species also possessed unique haplotypes (Acompsia antirrhinella / A. tripunctella, Anacampsis blattariella / A. populella, Bryotropha affinis / B. umbrosella, Sattleria pyrenaica / S. melaleucella, Scrobipalpa arenbergeri / S. mercantourica, Stomopteryx lineolella / S. nougatricella, Thiotricha subocellea / T. coleella, and partially also Teleiopsis bagriotella /T. diffinis / T. paulheberti). Finally, low distances between Scrobipalpa alterna / S. lutea and S. halymella / S. stabilis were only based on a single sequence for each of these species so they may represent additional cases of barcode overlap. On the other hand, five other species pairs with low interspecific divergence could be reliably separated by barcodes (Monochroa arundinetella / M. suffusella, Scrobipalpa stangei / S. artemisiella, Scrobipalpa salinella / S. salicorniae, Scrobipalpula spp., Teleiopsis rosalbella / T. albifemorella). Considering all these cases, DNA barcodes showed either incomplete or no resolution for 31 species (4.2%), while species identification was effective for 710 species (95.8%).

In total, 5877 sequences were assigned to a BIN. These records were assigned to 992 BINs that belong to 788 putative taxa (Suppl. material 2 and 3). Among these, 723 corresponded with named species, while another 65 belong to a unique BIN that is currently unidentified, but many likely represent additional, unrecognised species. Specimens from another 114 named species were assigned to more than one BIN; members of 68 species were placed in two BINs, while BIN counts for the other 46 species ranged from three to 22 (Table 2).

Altogether 668 (92.4%) of 723 named species have one or more unique BINs, while 55 species (7.6%) share a BIN with up to four species (Table 3). BIN sharing was particularly frequent in six genera (Acompsia, Dirhinosia, Iwaruna, Scrobipalpula, Teleiopsis, Xenolechia) where species often cannot be discriminated by DNA barcodes. However, most specimens in these taxa have diagnostic barcodes and all possess diagnostic morphological characters.

High levels of ‘intraspecific’ barcode variation often reflect overlooked species, but there is no fixed level of divergence that indicates species status. Furthermore, deep barcode splits can also arise as a result of the inadvertent recovery of pseudogenes, as a consequence of hybridisation, or Wolbachia infection (Mally et al. 2018, Werren et al. 2008). In Lepidoptera, 2–3% divergence is occasionally viewed as signalling the need for further integrative analysis (Hausmann et al. 2013), but there is clear evidence that no such threshold values exist (see e.g., Kekkonen et al. 2015). In the present dataset 146 of 741 nominal species possessed a maximum intraspecific divergence of > 2%, 88 species > 3%, while 33 species showed greater than > 5% (Table 4).

In some recently revised taxa with high, geographically structured intraspecific barcode divergence such as Megacraspedus (Huemer and Karsholt 2018) or the Oxypteryx libertinella species-group (Huemer et al. 2013), no evidence for cryptic diversity was found. However, even lower ‘intraspecific’ barcode divergence may reflect cases of either allopatric or sympatric speciation, as proven e.g., for the genus Sattleria (Huemer and Hebert 2011, Huemer and Timossi 2014). In consequence, several species with unusual genetic pattern need to be carefully re-assessed as they may include additional species. Cryptic diversity was, for example, already suspected for some Caryocolum (Huemer et al. 2015) or Stomopteryx remissella, but may also be detected in recently revised genera such as Acompsia or Chionodes (Huemer and Karsholt 2002, Huemer and Sattler 1995).

A further group of unrevised species in our dataset includes 65 unidentified DNA barcode clusters which were assigned to separate BINs (Table 5). Many of these cases are likely to represent undescribed species or alternatively, they may represent described species that currently lack barcode coverage. Altogether 26 genera representing approximately one-quarter of European genera are candidates for additional taxa. In fact, four genera (Aproaerema, Aristotelia, Monochroa, Scrobipalpa) are each represented by more than five unidentified clusters. For detailed comments on these cases, see Huemer and Karsholt (2020).