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. 2021 Jul 2;11(15):10327–10337. doi: 10.1002/ece3.7836

Excluding spatial sampling bias does not eliminate oversplitting in DNA‐based species delimitation analyses

Daniel Lukic 1, Jonas Eberle 2, Jana Thormann 1, Carolus Holzschuh 3, Dirk Ahrens 1,✉
PMCID: PMC8328443  PMID: 34367578

Abstract

DNA barcoding and DNA‐based species delimitation are major tools in DNA taxonomy. Sampling has been a central debate in this context, because the geographical composition of samples affects the accuracy and performance of DNA barcoding. Performance of complex DNA‐based species delimitation is to be tested under simpler conditions in absence of geographic sampling bias. Here, we present an empirical dataset sampled from a single locality in a Southeast‐Asian biodiversity hotspot (Laos: Phou Pan mountain). We investigate the performance of various species delimitation approaches on a megadiverse assemblage of herbivorous chafer beetles (Coleoptera: Scarabaeidae) to infer whether species delimitation suffers in the same way from exaggerate infraspecific variation despite the lack of geographic genetic variation that led to inconsistencies between entities from DNA‐based and morphology‐based species inference in previous studies. For this purpose, a 658 bp fragment of the mitochondrial cytochrome c oxidase subunit 1 (cox1) was analyzed for a total of 186 individuals of 56 morphospecies. Tree‐based and distance‐based species delimitation methods were used. All approaches showed a rather limited match ratio (max. 77%) with morphospecies. Poisson tree process (PTP) and statistical parsimony network analysis (TCS) prevailingly over‐splitted morphospecies, while 3% clustering and Automatic Barcode Gap Discovery (ABGD) also lumped several species into one entity. ABGD revealed the highest congruence between molecular operational taxonomic units (MOTUs) and morphospecies. Disagreements between morphospecies and MOTUs have to be explained by historically acquired geographic genetic differentiation, incomplete lineage sorting, and hybridization. The study once again highlights how important morphology still is in order to correctly interpret the results of molecular species delimitation.

Keywords: barcoding, cox1, geographic sampling bias, Laos, species delimitation

Performance of complex DNA‐based species delimitation is to be tested under simpler conditions in the absence of geographic sampling bias. Here, we present an empirical data set sampled from a single locality in a Southeast‐Asian biodiversity hotspot in Laos and investigate the performance of species delimitation approaches on a megadiverse assemblage of herbivore chafer beetles (Coleoptera: Scarabaeidae) to infer whether species delimitation suffers in the same way from exaggerate infraspecific variation despite the lack of geographic genetic variation that led to inconsistencies between entities from DNA‐based and morphology‐based species inference in previous studies.

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1. INTRODUCTION

Since DNA barcoding was formally proposed (Hebert et al., 2003, 2004; Hebert et al., 2003; Ratnasingham & Hebert, 2007), cox1 sequences have been rapidly accumulated from all around the world (Porter & Hajibabaei, 2018). Early studies mostly had a narrow systematic focus and targeted local or regional species assemblages. With emerging global comprehensiveness from the global iBOL project (International Barcode of Life), researchers became aware of the problems that arise with the use of cox1 (i.e., mitochondrial DNA) as taxonomic marker (Ballard & Whitlock, 2004; Dasmahapatra & Mallet, 2006; Dowton et al., 2014; Dupuis et al., 2012; Eberle et al., 2019; Funk & Omland, 2003; Nicholls et al., 2012; Ross, 2014; Smith et al., 2012), but also the effects of geographic scale on accuracy and performance of barcoding (Bergsten et al., 2012; Gaytán et al., 2020; Lohse, 2009). Therefore, geographic sampling has been a central debate (Ahrens et al., 2016; Lim et al., 2012; Reid & Carstens, 2012; Talavera et al., 2013), in particular with respect to DNA barcoding, one of the major tools of DNA taxonomy.

In order to infer in more detail the empirical behavior of species delimitation approaches that are currently in use in combination with particular genetic markers, it would be desirable to test commonly used methods on a dataset without geographic bias that still provides a sufficient number of closely related (i.e., congeneric) taxa that provide a sufficiently wide hypothesis space to test. Since low dispersal capacity might be a highly relevant factor for the outcome of such an investigation, we specifically investigate a soil‐dwelling group of beetles (Sericini chafers) which have short emergence periods, little‐specific host preferences, and a high degree of endemism (e.g., Ahrens, 2004, 2005; Dalstein et al., 2019; Eberle et al., 2017). We focus on cox1, since it continues and will continue to be a widely used marker for taxonomy in barcoding and metabarcoding studies.

So far, most comprehensive barcoding efforts have been made in “northern” and predominantly temperate countries (e.g., Bouchard et al., 2017; Gwiazdowski et al., 2015; Hebert et al., 2016; Hendrich et al., 2015; Pentinsaari et al., 2014; Pentinsaari et al., 2019; Rougerie et al., 2015; Rulik et al., 2017; Steinke et al., 2017; see also: http://www.ibol.org/phase1/resources/scientific‐publications/#; https://www.bolgermany.de/wp/startseite/news‐publikationen/publikationen/page/2/). The number of studies employing DNA‐based species delimitation in tropical or subtropical areas, that is, areas of high endemism and relative long‐term climatically stability, is comparatively low or limited to a narrow focal group (e.g., Ahrens et al., 2016; Astrin et al., 2012; Cancian de Araujo et al., 2019; Elias et al., 2007; Janzen & Hallwachs, 2011; Janzen et al., 2009), and only few authors assembled data on the global level (e.g., Zhou et al., 2016).

Interestingly, in regional (i.e., national)‐level libraries, molecular operational taxonomic units (MOTUs, i.e., BINs; Ratnasingham & Hebert, 2013) showed perfect matches to known morphospecies in nearly 90% of the studied species (e.g., Hendrich et al., 2015; Pentinsaari et al., 2014). Occasionally, mismatch to described species occurred due to splitting into clusters of different geographic origin (e.g., Morinière et al., 2017) or sharing of identical or closely related haplotypes among different morphospecies (e.g., Hawlitschek et al., 2017). However, matches generally decreased when geographic sampling of species was wider, for example, on a continental scale (Bergsten et al., 2012; Mutanen et al., 2016; Schmid‐Egger et al., 2019), with 12%–30% of the species resulting paraphyletic. Identification success may decrease with increasing spatial scale of sampling, up to a drop of 50% at continental scales (Bergsten et al., 2012). Sampling on a continental scale thus considerably increases the complexity of barcoding studies. Most of the “northern” latitude studies, however, are supposed to contain species with only low infraspecific haplotype diversity (due to extinctions and recolonization events during and after the Pleistocene; for example, Ahrens et al., 2013; Hewitt, 1996, 1999; Schmitt, 2007), and often assemblages only contain a small number of closely related species. Thus, these data do not represent suitable test cases of species delimitation performance when the component of actual geographic genetic variation is excluded. On the other hand, most of studies, which focused on tropical groups, collected specimens from more than one site (i.e., under geographical bias) (e.g., Elias et al., 2007; Janzen & Hallwachs, 2011; Janzen et al., 2009; Thormann et al., 2016). Often, mismatch of MOTUs with morphospecies was seen as evidence for cryptic diversity (e.g., Janzen & Hallwachs, 2011; Janzen et al., 2009).

Here we present a dataset that was sampled from one local assemblage in a Southeast‐Asian biodiversity hotspot (Laos: Phou Pan mountain). We investigate the performance of various species delimitation approaches on a megadiverse assemblage of herbivore Sericini chafer beetles (Coleoptera: Scarabaeidae). Our objective is to infer whether species delimitation suffers from exaggerate infraspecific variation in the same way that led to inconsistencies between entities from DNA‐based and morphology‐based species inference in previous studies, despite the lack of geographic genetic variation. We are interested in the degree of deep coalescence (Maddison, 1997) in this local species assemblage and in how species delimitation approaches handle these data. Excluding geographic genetic variation, we would expect less problems due to deep coalescences and thus higher rates of taxonomic congruence between morphospecies and MOTUs. Furthermore, we employ clustering algorithms similar to those used in metabarcoding approaches, to explore the reliability of this critical step in current metabarcoding analysis pipelines (e.g., Coissac et al., 2012; Deiner et al., 2017; Macher et al., 2018; Ruppert et al., 2019).

2. MATERIAL AND METHODS

2.1. Study group, sampling, and identification

The study group is the megadiverse tribe Sericini that contains worldwide nearly 4,000 described species in about 200 genera (Ahrens, 2006). They are one of the oldest lineages of phytophagous Scarabaeidae and diversified with the rise of the angiosperms 108 Ma (Ahrens et al., 2014; Eberle et al., 2017). Sericini are nearly worldwide distributed, except in Australia, most oceanic islands, archipelagos, and circumpolar regions (Ahrens, 2006). The polyphagous herbivore adults are fully winged while larvae feed on roots and underground stems of living plants (Ritcher, 1966). Some species are considered as crop pests (Nair, 1986). Their highly similar external morphology makes the species difficult to distinguish, but highly complex male genitalia are well‐differentiated between species and show only little intraspecific variation (Ahrens & Lago, 2008).

Sampling was conducted during 4 weeks in April 2014 by Carolus Holzschuh and local collectors in the Phou Pan mountain area (Laos, Hua Phan province) (Figure 1) (ca. 20°12′N, 104°01′E), at an elevation between 1,300 and 2,000 m. Specimens were collected using light traps, by hand, or netting during daytime. The Phou Pan mountain is situated in the Indo‐Burmese biodiversity hotspot area (Myers et al., 2000) which is characterized by extremely high endemism. The habitat with its dense rainforests (Müller et al., 2013) offers a large variety of plant species for herbivore insects to feed on. For this study, we used only males (1,086 specimens), since they were assignable to distinct morphospecies, while females are often not distinguishable among closely related syntopically occurring species. Samples were pinned after DNA extraction, dry mounted, labeled, and preserved at the ZFMK (Zoologisches Forschungsmuseum Alexander König, Bonn, Germany) (see Table S1).

FIGURE 1.

FIGURE 1

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Collecting area in Laos (20°12′N, 104°01′E) (marked with a black dot)

Specimens were presorted to morphospecies using the complex shape of their copulation organ, that is, aedeagus, which has been proven to be the best suited trait system to robustly infer species entities for this group (Dalstein et al., 2019; Eberle et al., 2016). For this purpose, male genitalia of all specimens were dissected. Habitus and genitalia of each species were photographed with a stereomicroscope (ZEISS Stereo Discovery.V20) connected to a ZEISS Axiocam. Presumably undescribed species that were not yet referable to an available species name were numbered consecutively (sp1, sp2, etc.).

2.2. DNA sequencing

We sequenced the cox1 gene (5′‐end) of multiple specimens (3–5) per morphospecies (in total 190). Laboratory work followed the standard protocols of the German Barcode of Life project (Geiger et al., 2016). DNA was extracted from a mesothoracic leg and attached muscles using the Qiagen DNeasy Blood and Tissue Kit or the Qiagen BioSprint96 magnetic bead extractor.

The PCR reaction was carried out in total reaction mixes of 20 μl, including 2 μl of undiluted DNA template, 0.8 μl of each primer (10 pmol/μl), and standard amounts of the reagents provided with the “Multiplex PCR” kit from Qiagen using primers LCO1490‐JJ [5′‐CHACWAAYCATAAAGATATYGG‐3′] and HCO2198‐JJ [5′‐AWACTTCVGGRTGVCC AAARAATCA‐3′] (Astrin & Stüben, 2008). Thermal cycling was performed on Applied Biosystems 2,720 thermal cyclers (Life Technologies), using a PCR program with two cycle sets, combining a “touchdown” and a “step‐up” routine as follows: hot start Taq activation: 15 min at 95℃; first cycle set (15 repeats): 35 s denaturation at 94℃, 90 s annealing at 55℃ (−1℃ per cycle) and 90 s elongation at 72℃; second cycle set (25 repeats): 35 s denaturation at 94℃, 90 s annealing at 40℃, and 90 s elongation at 72℃; final elongation 10 min at 72℃. Unpurified PCR products were subsequently sent for bidirectional Sanger sequencing to BGI Tech Solutions.

Raw DNA sequences were assembled (forward and reverse sequence) and edited in Geneious R7 (version 7.1.3, Biomatters Ltd.) to correct base‐calling errors and to assign ambiguities (when forward and reverse sequence were not congruent for certain nucleotides). Sequences were aligned with Muscle (Edgar, 2004) as implemented into Geneious using the default settings. Primers were trimmed subsequently. All data are deposited in BOLD (project: SCOIB; https://doi.org/10.5883/DS‐DS‐SCOIBL) and GenBank (accession numbers MW128167–MW128351) respectively (see Table S1).

2.3. Phylogenetic analysis and species delimitation

Putative morphospecies were compared with results obtained from the DNA‐based species delimitation methods. We applied Poisson tree process (PTP) (Zhang et al., 2013), statistical parsimony network analysis (TCS) (Templeton et al., 1992), Automatic Barcode Gap Discovery (ABGD) (Puillandre et al., 2012), distance‐based clustering, and Barcode of Life database (BOLD)—Barcode Index Numbers (BINs). These methods were applied on all sequenced beetles to result in clusters that are considered molecular taxonomic units (MOTUs) (Floyd et al., 2002), that is, DNA‐based species‐assignments by the respective method.

A phylogenetic tree was calculated with maximum likelihood from the final multiple alignment of all DNA sequences using the IQ‐TREE web server (IQ‐TREE version 1.6.12; http://iqtree.cibiv.univie.ac.at/) (Nguyen et al., 2015; Trifinopoulos et al., 2016); the best substitution model (GTR+F+I+G4) was chosen with ModelFinder (Kalyaanamoorthy et al., 2017) according to Bayesian information criterion (BIC). Branch support was calculated by generating 1,000 samples for ultrafast bootstrapping (Hoang et al., 2018). The resulting tree was midpoint rooted in FigTree v1.4.3 (available from http://tree.bio.ed.ac.uk/software/figtree/). This tree was the basis for the PTP analysis. Additionally, split networks were generated using SplitsTree4 v. 4.16.1 (Huson & Bryant, 2006) to visualize incompatible and ambiguous signals in the cox1 dataset. In these networks, the parallel edges, rather than the single branches, illustrate splits concluded from the data.

We used both versions of the Poisson tree process model (PTP) on the PTP web server (https://species.h‐its.org/; accessed on 5 August 2020): bPTP, which adds Bayesian support (pp) values to branches that delimit species in the input tree, and the refined multirate mPTP. PTP uses the shift in the number of substitutions at internal nodes to identify branching rate transition points (Zhang et al., 2013) which indicate speciation events. We used default settings for the bPTP analysis (100,000 MCMC generations, thinning: 100, burn‐in: 0.1, seed 123).

Statistical network analysis as performed with TCS v. 1.21 separates the sequence data into clusters of closely related haplotypes connected by changes that are nonhomoplastic with a 95% probability (Templeton et al., 1992); if applied to mtDNA the extent of the networks has been found to be largely congruent with morphospecies (Ahrens et al., 2007; Meier et al., 2006).

Automatic Barcode Gap Discovery (ABGD) was conducted using the ABGD web server (https://bioinfo.mnhn.fr/abi/public/abgd/abgdweb.html; accessed on 17 August 2020) with default parameters (i.e., using Jukes‐Cantor model (JC69) distances, a relative gap width of 1 and 50 steps, Pmin = 0.001, Pmax = 0.1, Nb bins for distance distribution = 20). ABGD partitions individuals for a range of prior intraspecific distances, instead of using one predefined distance threshold (Fontaneto et al., 2015; Puillandre et al., 2012). A robust result across a range of prior intraspecific distances was chosen as the best partition scheme. This outcome was also closest to the number of morphospecies and simultaneously matched the presumptive barcode gap in the histogram of distances.

Distance‐based clustering was done with the tclust‐function in the R‐package spider (v. 1.5.0; Brown et al., 2012). A threshold of 3% was applied to the pairwise distance matrix of all specimens that was corrected with the Kimura model (K80). The logic of this approach underlies most metabarcoding protocols (Liu et al., 2020; Macher et al., 2018; Piper et al., 2019), relying on the presence of a barcoding gap (Elbrecht et al., 2017), which was chosen as a gap at 3% pairwise distance by the majority of studies (however, see Beentjes et al., 2019 for an 2% example). Finally, we compared outcome from species delimitations to Barcode Index Number (BIN) assignments (Ratnasingham & Hebert, 2013) in the BOLD data base (Project—SCOIB: Sericini COI Barcoding).

To check the performance and accuracy of the DNA‐based delimitation methods compared to the a priori morphospecies hypotheses based on the genital morphology, the match ratio (Ahrens et al., 2016) was calculated: Match ratio = 2 * N match/(N MOTU + N morph). N match is the number of species with exact matches, when the morphospecies and DNA‐based species delimitation results to include the same specimens. NMOTU is the number of classified groups by the different delimitation methods, and finally, N morph is the number of morphospecies. All resulting MOTUs were mapped onto the phylogenetic tree beside terminal's labels (Figure 2).

FIGURE 2.

FIGURE 2

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Rooted maximum likelihood tree with information about morphospecies assignments, results of species delimitations (bPTP, mPTP, TCS, ABGD, 3%‐clustering, and BOLD‐BINs) and photographs of a selection of aedeagi (lateral view). Green boxes indicate agreement between molecular species delimitation method and morphospecies assignment, while red boxes indicate disagreement. Ultrafast bootstrap supports >0.5 are shown above nodes. Genus name abbreviations: Chr.—Chrysoserica, G.—Gastroserica, Ma.—Maladera, Mi.—Microserica, N.—Neoserica, L.—Lasioserica, S.—Serica

3. RESULTS

3.1. Morphospecies and sequencing success

Fifty‐six morphospecies could be determined from the Phou Phan mountain area. Twenty‐seven of the morphospecies (48%) were supposedly undescribed species or could not yet be assigned to an existing species (the fauna of mainland Asia has been fully revised in terms of type specimen revision (D. Ahrens, unpublished data; D. Ahrens, personal communication, May 2020); however, several species from Indonesia are known to widely occur in the Oriental region. Some species might thus still be assigned to already described species, when taxonomic revisions are finished for all parts of Asia.

A total of 186 specimens were sequenced successfully. The length of the aligned cox1 sequences was 658 base pairs (bp). For Maladera sp 16 and Neoserica phuphanensis, more than five individuals per morphospecies have been sequenced, since a few specimens were initially mistakenly assigned to other morphospecies. Of the 56 morphospecies 14 were singletons, that is, only represented by one specimen per species.

Due to shared haplotypes in different morphospecies, lowest inter‐ and infraspecific distances were both zero (Table 1), while maximum infraspecific distances were around 7%. The infraspecific mean distance was 1.5%, and the median even lower (0.8%). Nine morphospecies (i.e., 16% of the taxa) had infraspecific distances larger than 3%.

TABLE 1.

Infra‐ and interspecific genetic distances of the cox1 dataset based on morphospecies assignments, as well as number of cases beyond an arbitral 3% threshold distance being often used for MOTU clustering in Metabarcoding studies

Interspecific Infraspecific
k2p ml k2p ml
Min 0 0 0 0
Max 0.24 0.24 0.074 0.073
Mean 0.17 0.18 0.015 0.015
Median 0.17 0.18 0.0083 0.0083
N dist>3% — — 9 9
N dist<3% 9 9 — —
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3.2. Species delimitation

All morphospecies included in the analysis were monophyletic with three exceptions (Figure 2): (a) Microserica sp 11 and Microserica sp 13 were nested within the clade of Microserica varians; (b) one of the five specimens of Neoserica sp 29 was within the clade of Neoserica martinui; and (c) Maladera sp 27 was placed within the clade of the morphologically very similar Maladera sp 9.

Three morphospecies shared identical haplotypes (Maladera sp 3a, sp 3b; sp 4; Figures 2, 3). Branch support values (ultrafast bootstrapping) of morphospecies clades are high with values of 0.8 to 1. DNA‐based species delimitation applying PTP, TCS, and ABGD resulted in different clusters. Thirty‐one morphospecies showed congruent results for all DNA‐based delimitation (Figure 2). For 46 morphospecies, the results of at least one method matched with the morphospecies assignment. All methods showed splitting and also lumping of morphospecies.

FIGURE 3.

FIGURE 3

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Split network of all examined specimens. Singletons are highlighted in blue squares, and others in orange colors. Morphospecies nested within others are highlighted with red squares or circles around them

bPTP and mPTP subdivided the specimens into 70 and 65 MOTUs (Table 2), with 37 (bPTP) and 38 (mPTP) matches between the morphospecies and MOTUs. Deviations are caused by erroneously inferred splitting events (i.e., individuals of one morphospecies were separated into two or more different MOTUs). Match ratios of both PTP variants were relatively low: 0.59 and 0.63, for bPTP and mPTP, respectively. TCS resulted in 69 MOTUs and had same number of matches (37) as bPTP. The match ratio (0.60) was higher than bPTP, but lower than mPTP.

TABLE 2.

Match ratio (after Ahrens et al., 2016) of DNA‐based species delimitation methods on Sericini chafer data based on number of MOTUs and number of matches between MOTUs and morphospecies (N morph = 56)

bPTP mPTP TCS ABGD 3%Clust BINs
N matches 37 38 37 41 40 40
N MOTU 70 65 68 51 62 65
Match ratio 0.59 0.63 0.60 0.77 0.68 0.66
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ABGD yielded 51 MOTUs and showed the highest match ratio of all delimitation methods (0.77). It was the species delimitation method that showed most lumping of different morphospecies (Figure 2). Examples for lumping are one MOTU for Maladera exima plus Maladera parexima; Maladera sp 9 plus Ma. sp 27; Neoserica sp 37, Neoserica martinui plus N. sp 29; as well as Microserica varians, Mi. sp 11 plus Mi. sp 13.

Distance‐based clustering at the 3% level yielded similar results to the previous methods. It found 62 MOTUs and matched with 40 morphospecies; the match ratio (0.68) was the second highest, after ABGD. Barcode Index Number (BIN) assignments revealed 65 MOTUs and matched as well with 40 morphospecies; however, its match ratio was lower (0.66) than that of 3% clustering.

In 21% of the morphospecies (n = 12), we found relatively deep coalescence (i.e., distinct infraspecific phylogenetic structure) (e.g., Ma. sp 4, sp 6, sp 16, Ma. fuscipes, Ma. futschauana, Ma. obscurata, Ma. peregoi, N. sp 26, Mi. panzona, Mi. varians, G. marginalis, G. carolusi). In all others, infraspecific branches were rather shallow. Taxa sampled with more than three specimens and that were represented by a single haplotype did not occur. For all those cases with deep coalescence, at least one of the DNA‐based species delimitations split the morphospecies, which in turn decreased the match ratio.

4. DISCUSSION

In the present paper, we investigated DNA‐based species delimitation using the mitochondrial cox1 gene in a megadiverse assemblage of chafer beetles (Sericini) with particular focus on the performance of commonly used species delineation methods. The setup of examining barcodes of beetles from a single locality was chosen to investigate molecular species delimitation performance using data without geographic bias. While we know that match ratios strongly vary in tropical taxa (e.g., from 0.14 to 1.00; Ahrens et al., 2016), we theoretically expected that match ratios would go against one due to the exclusion of geography‐induced variance. Instead, for different standard species delimitation methods, we could also not report match ratios higher than 0.77. Interestingly, the 3% threshold clustering that is commonly used in metabarcoding approaches did not perform worse than more sophisticated approaches (like PTP or TCS); however, an accuracy of only less than 80% is not really what one could call a reliable taxonomy assessment.

DNA‐based species delimitation approaches may oversplit morphological entities (Ahrens et al., 2016), while at the same time the opposite may be also the case (Dalstein et al., 2019), even in the same taxon (as demonstrated here for the tribe Sericini). This particularly proved to be true in presence of incomplete lineage sorting and hybridization and if geographic bias is not excluded (match ratio <0.5; Dalstein et al., 2019). Extreme oversplitting has been reported for both mtDNA and nDNA, when sex‐biased dispersal occurs, which also limits general dispersal (Eberle et al., 2019).

Oversplitting in our data is caused by the relatively deep coalescence in 21% of the species, which widely corresponds with the missing match to the morphospecies (Figure 4). Also, the lack of significant interspecific divergence (i.e., a barcoding gap) seems to coincide with failure of accurate species delimitation. The impact is high with only 31 out of the 56 morphospecies matching perfectly the boundaries of inferred MOTUs (Figure 2). The nature of maternal inheritance of mtDNA and its very low recombination rate is probably the major reason for these patterns of deep coalescence. Historically acquired genetic differentiation, for example, in previously isolated populations is maintained in secondarily mixing populations (e.g., Ahrens et al., 2013). The more often such isolated populations occur in time and space, for example, due to climatic fluctuation during the Pleistocene in geographically highly structured areas such as Southeast Asia, the more often we encounter such “paleogeographically induced” infraspecific variation which leads to the same result as current geographic variation. This effect consequently impedes species delimitation methods in the same way, particularly in a single marker system (e.g., cox1).

FIGURE 4.

FIGURE 4

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Frequency of intra‐ and interspecific distances of the Sericini data from Mt. Phou Pan (Laos)

In addition to such historical effects, our data also appear to have cases of hybridization and/or incomplete lineage sorting. In three cases, morphospecies were not monophyletic (Microserica sp 11/Microserica sp 13 vs. Microserica varians; Neoserica sp 29 vs. Neoserica martinui; Maladera sp 27 vs. Maladera sp 9.), while another three morphospecies shared identical haplotypes (Maladera sp 3a, sp 3b; sp 4). In all cases, we may exclude cross‐contamination based on the position of the single samples on the DNA extraction microtiter‐plates. These cases do occur in only rather closely related species, which might show similar life traits (e.g., daytime activity in Microserica), chemical communication, or mating behavior (which is however, unknown for all species). In those instances, lumping of morphospecies in DNA‐based species delimitation seems to be more likely; however, also oversplitting was observed (e.g., Microserica). Despite strong divergence in male genital morphology, hybridization between closely related species of Sericini has been reported (e.g., Dalstein et al., 2019). The rather divergent structure of the aedeagus between species might indicate a case of mechanical isolation (lock‐and‐key hypothesis) that prevents mating between different species (Eberhard, 1985). However, although there have been some recent work on the morphology of female genitalia (Özgül‐Siemund & Ahrens, 2015), our knowledge on copulation functionality and mechanics is still not sufficient to tell if morphological structures of males and female genitalia actually function as a barrier, if only through tactile recognition by cryptic female choice (Barnard et al., 2017).

Again, the present study demonstrates the necessity of an integrative taxonomy in the sense of Yeates et al. (2011) (see also Padial et al., 2010; Schlick‐Steiner et al., 2010; Tautz et al., 2002). We showed that the use of different clustering‐ and tree‐based delimitation methods (Carstens et al., 2013) with the same single maker reproduces the same erroneous signal in slightly different variations. It is thus critical to corroborate results from DNA‐based species delimitation with data from other sources (e.g., genital or larval morphology, feeding traits, behavior, etc.; e.g., Janzen et al., 2009) to allow for independent testing of species boundaries.

Sericini chafers proved to be a valuable model system, because of robust morphospecies assignments that were facilitated by highly dissimilar and morphologically complex male genitalia that serve as accurate species diagnostic trait (Dalstein et al., 2019; Eberle et al., 2016).

Overall, the initial hypothesis of impeccable DNA‐based species boundaries in syntopical species assemblages clearly had to be rejected. This was rather unexpected, especially since there was no additional evidence from other sources that these oversplittings could relate to cryptic diversity (Janzen & Hallwachs, 2011; Janzen et al., 2009, 2017).

Given the highly simplified parameters of DNA‐based species delimitation in this one‐site species assemblage, it becomes clear how complex species delimitation with DNA‐based methods is. Performance with mean error rates of more than 30% is under the expectations for proper use for applied sciences and conservation management. Methods that are more sophisticated did not perform better than over‐simplified threshold clustering methods as used, for example, in metabarcoding. Once more, we highlight the necessity of morphology for the verification of de novo species delimitation results and the constant need of integrative taxonomic approaches.

CONFLICT OF INTEREST

We have no conflicts of interest to declare.

AUTHOR CONTRIBUTIONS

Daniel Lukic: Data curation (lead); Formal analysis (lead); Writing‐original draft (lead); Writing‐review & editing (supporting). Jonas Eberle: Methodology (supporting); Writing‐review & editing (supporting). Jana Thormann: Data curation (lead); Methodology (supporting); Writing‐review & editing (supporting). Carolus Holzschuh: Investigation (supporting); Resources (lead); Writing‐review & editing (supporting). Dirk Ahrens: Conceptualization (lead); Data curation (supporting); Formal analysis (supporting); Funding acquisition (lead); Investigation (lead); Methodology (supporting); Project administration (lead); Supervision (lead); Validation (lead); Writing‐original draft (lead); Writing‐review & editing (lead).

Supporting information

Table S1

Click here for additional data file. (37.3KB, xlsx)

ACKNOWLEDGMENTS

We are grateful to Benedict Wipfler for his technical assistance. Furthermore, we are thankful to Silvia Fabrizi, Sasanka Ranasinghe, Julian Enß, Ralph Tollrian, Thomas Eltz, and Hans‐Joachim Krammer for helpful advice and discussions. This research was supported by institutional funding of the ZFMK. Finally, we thank B. Sibbett and the three anonymous referees for their helpful comments on the manuscript.

Lukic, D. , Eberle, J. , Thormann, J. , Holzschuh, C. , & Ahrens, D. (2021). Excluding spatial sampling bias does not eliminate oversplitting in DNA‐based species delimitation analyses. Ecology and Evolution, 11, 10327–10337. 10.1002/ece3.7836

DATA AVAILABILITY STATEMENT

DNA sequences: Genbank accessions (MW128167–MW128351); DNA sequences (incl. abi files, locality data): BARCODE OF LIFE DATA SYSTEM (https://doi.org/10.5883/DS‐DS‐SCOIBL.%0d%0d%20).

REFERENCES

  1. Ahrens, D. (2004). Monographie der Sericini des Himalaya (Coleoptera, Scarabaeidae) (pp. 534). Dissertation.de, Verlag im Internet GmbH, Berlin. [Google Scholar]
  2. Ahrens, D. (2005). The diversification of the endemic Himalayan monsoon‐season beetles genus Calloserica inferred from a cladistic analysis (Coleoptera: Scarabaeidae: Sericini). Invertebrate Systematics, 19, 217–230. [Google Scholar]
  3. Ahrens, D. (2006). The phylogeny of Sericini and their position within the Scarabaeidae based on morphological characters (Coleoptera. Scarabaeidae). Systematic Entomology, 31(1), 113–144. 10.1111/j.1365-3113.2005.00307.x [DOI] [Google Scholar]
  4. Ahrens, D. , Fabrizi, S. , Sipek, P. , & Lago, P. (2013). Integrative analysis of DNA phylogeography and morphology of the European rose chafer (Cetonia aurata) to infer species taxonomy and patterns of postglacial colonisation in Europe. Molecular Phylogenetics and Evolution, 69, 83–94. [DOI] [PubMed] [Google Scholar]
  5. Ahrens, D. , Fujisawa, T. , Krammer, H.‐J. , Eberle, J. , Fabrizi, S. , & Vogler, A. P. (2016). Rarity and incomplete sampling in DNA‐based species delimitation. Systematic Biology, 65(3), 478–494. 10.1093/sysbio/syw002 [DOI] [PubMed] [Google Scholar]
  6. Ahrens, D. , & Lago, P. K. (2008). Directional asymmetry reversal of male copulatory organs in chafer beetles (Coleoptera: Scarabaeidae): Implications on left–right polarity determination in insect terminalia. Journal of Zoological Systematics and Evolutionary Research, 46(2), 110–117. 10.1111/j.1439-0469.2007.00449.x [DOI] [Google Scholar]
  7. Ahrens, D. , Monaghan, M. T. , & Vogler, A. P. (2007). DNA‐based taxonomy for associating adults and larvae in multi‐species assemblages of chafers (Coleoptera. Scarabaeidae). Molecular Phylogenetics and Evolution, 44(1), 436–449. 10.1016/j.ympev.2007.02.024 [DOI] [PubMed] [Google Scholar]
  8. Ahrens, D. , Schwarzer, J. , & Vogler, A. P. (2014). The evolution of scarab beetles tracks the sequential rise of angiosperms and mammals. Proceedings of the Royal Society B: Biological Sciences, 281(1791), 20141470. 10.1098/rspb.2014.1470 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Astrin, J. J. , & Stüben, P. E. (2008). Phylogeny in cryptic weevils: Molecules, morphology and new genera of western Palaearctic Cryptorhynchinae (Coleoptera: Curculionidae). Invertebrate Systematics, 22(5), 503–522. 10.1071/IS07057 [DOI] [Google Scholar]
  10. Astrin, J. J. , Stüben, P. E. , Misof, B. , Wägele, J. W. , Gimnich, F. , Raupach, M. J. , & Ahrens, D. (2012). Exploring diversity in cryptorhynchine weevils (Coleoptera) using distance‐, character‐ and tree‐based species delineation. Molecular Phylogenentics and Evolution, 63, 1–14. [DOI] [PubMed] [Google Scholar]
  11. Ballard, J. W. O. , & Whitlock, M. C. (2004). The incomplete natural history of mitochondria. Molecular Ecology, 13, 729–744. 10.1046/j.1365-294X.2003.02063.x [DOI] [PubMed] [Google Scholar]
  12. Barnard, A. A. , Fincke, O. M. , McPeek, M. A. , & Masly, J. P. (2017). Mechanical and tactile incompatibilities cause reproductive isolation between two young damselfly species. Evolution, 71(10), 2410–2427. 10.1111/evo.13315 [DOI] [PubMed] [Google Scholar]
  13. Beentjes, K. K. , Speksnijder, A. G. C. L. , Schilthuizen, M. , Hoogeveen, M. , Pastoor, R. , & van der Hoorn, B. B. (2019). Increased performance of DNA metabarcoding of macroinvertebrates by taxonomic sorting. PLoS One, 14(12), e0226527. 10.1371/journal.pone.0226527 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Bergsten, J. , Bilton, D. T. , Fujisawa, T. , Elliott, M. , Monaghan, M. T. , Balke, M. , Hendrich, L. , Geijer, J. , Herrmann, J. , Foster, G. N. , Ribera, I. , Nilsson, A. N. , Barraclough, T. G. , & Vogler, A. P. (2012). The effect of geographical scale of sampling on DNA barcoding. Systematic Biology, 61, 851–869. 10.1093/sysbio/sys037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Bouchard, P. , Smith, A. B. T. , Douglas, H. , Gimmel, M. L. , Brunke, A. J. , & Kanda, K. (2017). Biodiversity of Coleoptera. In Foottit R. G., & Adler P. H. (Eds.), Insect biodiversity: Science and society (pp. 337–417) John Wiley & Sons Ltd. 10.1002/9781444308211.ch11 [DOI] [Google Scholar]
  16. Brown, S. D. J. , Collins, R. A. , Boyer, S. , Lefort, M.‐C. , Malumbres‐Olarte, J. , Vink, C. J. , & Cruickshank, R. H. (2012). SPIDER: An R package for the analysis of species identity and evolution, with particular reference to DNA barcoding. Molecular Ecology Resources, 12, 562–565. [DOI] [PubMed] [Google Scholar]
  17. Cancian de Araujo, B. , Schmidt, S. , Schmidt, O. , von Rintelen, T. , von Rintelen, K. , Floren, A. , Ubaidillah, R. , Peggie, D. , & Balke, M. (2019). DNA barcoding data release for Coleoptera from the Gunung Halimun canopy fogging workpackage of the Indonesian Biodiversity Information System (IndoBioSys) project. Biodiversity Data Journal, 7, e31432. 10.3897/BDJ.7.e31432 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Carstens, B. C. , Pelletier, T. A. , Reid, N. M. , & Satler, J. D. (2013). How to fail at species delimitation. Molecular Ecology, 22(17), 4369–4383. 10.1111/mec.12413 [DOI] [PubMed] [Google Scholar]
  19. Coissac, E. , Riaz, T. , & Puillandre, N. (2012). Bioinformatic challenges for DNA metabarcoding of plants and animals. Molecular Ecology, 21, 1834–1847. 10.1111/j.1365-294X.2012.05550.x [DOI] [PubMed] [Google Scholar]
  20. Dalstein, V. , Eberle, J. , Fabrizi, S. , Etzbauer, C. , & Ahrens, D. (2019). COI‐based species delimitation in Indochinese Tetraserica chafers reveal hybridisation despite strong divergence in male copulation organs. Organisms Diversity and Evolution, 19(2), 277–286. 10.1007/s13127-019-00398-z [DOI] [Google Scholar]
  21. Dasmahapatra, K. K. , & Mallet, J. (2006). Taxonomy: DNA barcodes: Recent successes and future prospects. Heredity, 97, 254–255. 10.1038/sj.hdy.6800858 [DOI] [PubMed] [Google Scholar]
  22. Deiner, K. , Bik, H. M. , Mächler, E. , Seymour, M. , Lacoursière‐Roussel, A. , Altermatt, F. , Creer, S. , Bista, I. , Lodge, D. M. , de Vere, N. , Pfrender, M. E. , & Bernatchez, L. (2017). Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Molecular Ecology, 26, 5872–5895. [DOI] [PubMed] [Google Scholar]
  23. Dowton, M. , Meiklejohn, K. , Cameron, S. L. , & Wallman, J. (2014). A preliminary framework for DNA barcoding, incorporating the multispecies coalescent. Systematic Biology, 63, 639–644. 10.1093/sysbio/syu028 [DOI] [PubMed] [Google Scholar]
  24. Dupuis, J. R. , Roe, A. D. , & Sperling, F. A. H. (2012). Multi‐locus species delimitation in closely related animals and fungi: One marker is not enough. Molecular Ecology, 21, 4422–4436. 10.1111/j.1365-294X.2012.05642.x [DOI] [PubMed] [Google Scholar]
  25. Eberhard, W. G. (1985). Sexual selection and animal genitalia. Harvard University Press. [Google Scholar]
  26. Eberle, J. , Bazzato, E. , Fabrizi, S. , Rossini, M. , Colomba, M. , Cillo, D. , Uliana, M. , Sparacio, I. , Sabatinelli, G. , Warnock, R. C. M. , Carpaneto, G. , & Ahrens, D. (2019). Sex‐Biased dispersal obscures species boundaries in integrative species delimitation approaches. Systematic Biology, 68, 441–459. 10.1093/sysbio/syy072 [DOI] [PubMed] [Google Scholar]
  27. Eberle, J. , Fabrizi, S. , Lago, P. , & Ahrens, D. (2017). A historical biogeography of megadiverse Sericini‐another story “out of Africa”? Cladistics, 33(2), 183–197. 10.1111/cla.12162 [DOI] [PubMed] [Google Scholar]
  28. Eberle, J. , Rödder, D. , Beckett, M. , & Ahrens, D. (2017). Landscape genetics indicate recently increased habitat fragmentation in African forest‐associated chafers. Global Change Biology, 23, 1988–2004. 10.1111/gcb.13616 [DOI] [PubMed] [Google Scholar]
  29. Eberle, J. , Warnock, R. C. M. , & Ahrens, D. (2016). Bayesian species delimitation in Pleophylla chafers (Coleoptera) – The importance of prior choice and morphology. BMC Evolutionary Biology, 16, 94. 10.1186/s12862-016-0659-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Edgar, R. C. (2004). MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 32(5), 1792–1797. 10.1093/nar/gkh340 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Elbrecht, V. , Vamos, E. E. , Meissner, K. , Aroviita, J. , & Leese, F. (2017). Assessing strengths and weaknesses of DNA metabarcoding‐based macroinvertebrate identification for routine stream monitoring. Methods in Ecology and Evolution, 8, 1265–1275. 10.1111/2041-210x.12789 [DOI] [Google Scholar]
  32. Elias, M. , Hill, R. I. , Willmott, K. R. , Dasmahapatra, K. K. , Brower, A. V. Z. , Mallet, J. , & Jiggins, C. D. (2007). Limited performance of DNA barcoding in a diverse community of tropical butterflies. Proceedings of the Royal Society B: Biological Sciences, 274(1627), 2881–2889. 10.1098/rspb.2007.1035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Floyd, R. , Abebe, E. , Papert, A. , & Blaxter, M. (2002). Molecular barcodes for soil nematode identification. Molecular Ecology, 11(4), 839–850. 10.1046/j.1365-294X.2002.01485.x [DOI] [PubMed] [Google Scholar]
  34. Fontaneto, D. , Flot, J.‐F. , & Tang, C. Q. (2015). Guidelines for DNA taxonomy, with a focus on the meiofauna. Marine Biodiversity, 45(3), 433–451. 10.1007/s12526-015-0319-7 [DOI] [Google Scholar]
  35. Funk, D. J. , & Omland, K. E. (2003). Species‐level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA. Annual Review of Ecology and Evolutionary Systematics, 34, 397–423. 10.1146/annurev.ecolsys.34.011802.132421 [DOI] [Google Scholar]
  36. Gaytán, Á. , Bergsten, J. , Canelo, T. , Pérez‐Izquierdo, C. , Santoro, M. , & Bonal, R. (2020). DNA Barcoding and geographical scale effect: The problems of undersampling genetic diversity hotspots. Ecology and Evolution, 10, 10754–10772. 10.1002/ece3.6733 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Geiger, M. F. , Moriniere, J. , Hausmann, A. , Haszprunar, G. , Wägele, W. , Hebert, P. D. N. , & Rulik, B. (2016). Testing the Global Malaise Trap Program – How well does the current barcode reference library identify flying insects in Germany? Biodiversity Data Journal, 4, e10671. 10.3897/BDJ.4.e10671 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Gwiazdowski, R. A. , Foottit, R. G. , Maw, H. E. L. , Hebert, P. D. N. , Imondi, R. , & La Rocca, G. (2015). The Hemiptera (Insecta) of Canada: Constructing a reference library of DNA barcodes. PLoS One, 10, e0125635. 10.1371/journal.pone.0125635 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Hawlitschek, O. , Morinière, J. , Lehmann, G. U. C. , Lehmann, A. W. , Kropf, M. , Dunz, A. , Glaw, F. , Detcharoen, M. , Schmidt, S. , Hausmann, A. , Szucsich, N. U. , Caetano‐Wyler, S. A. , & Haszprunar, G. (2017). DNA barcoding of crickets, katydids and grasshoppers (Orthoptera) from Central Europe with focus on Austria, Germany and Switzerland. Molecular Ecology Resources, 17, 1037–1053. 10.1111/1755-0998.12638 [DOI] [PubMed] [Google Scholar]
  40. Hebert, P. D. N. , Cywinska, A. , Ball, S. L. , & deWaard, J. R. (2003). Biological identifications through DNA barcodes. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1512), 313–321. 10.1098/rspb.2002.2218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Hebert, P. D. N. , Ratnasingham, S. , & deWaard, J. R. (2003). Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proceedings of the Royal Society B: Biological Sciences, 270, S96–S99. 10.1098/rsbl.2003.0025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Hebert, P. D. N. , Ratnasingham, S. , Zakharov, E. V. , Telfer, A. C. , Levesque‐Beaudin, V. , Milton, M. A. , Pedersen, S. , Jannetta, P. , & DeWaard, J. R. (2016). Counting animal species with DNA barcodes: Canadian insects. Philosophical Transactions of the Royal Society of London B, 371, e1001127. 10.1098/rstb.2015.0333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hebert, P. D. N. , Stoeckle, M. Y. , Zemlak, T. S. , & Francis, C. M. (2004). Identification of Birds through DNA Barcodes. PLoS Biology, 2(10), e312. 10.1371/journal.pbio.0020312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Hendrich, L. , Morinière, J. , Haszprunar, G. , Hebert, P. D. N. , Hausmann, A. , Köhler, F. , & Balke, M. (2015). A comprehensive DNA barcode database for Central European beetles with a focus on Germany: Adding more than 3,500 identified species to BOLD. Molecular Ecology Resources, 15, 795–818. [DOI] [PubMed] [Google Scholar]
  45. Hewitt, G. M. (1996). Some genetic consequences of ice ages, and their role in divergence and speciation. Biological Journal of the Linnean Society, 58, 247–276. 10.1006/bijl.1996.0035 [DOI] [Google Scholar]
  46. Hewitt, G. M. (1999). Post‐glacial re‐colonization of European biota. Biological Journal of the Linnean Society, 68, 87–112. 10.1111/j.1095-8312.1999.tb01160.x [DOI] [Google Scholar]
  47. Hoang, D. T. , Chernomor, O. , von Haeseler, A. , Minh, B. Q. , & Vinh, L. S. (2018). UFBoot2: Improving the ultrafast bootstrap approximation. Molecular Biology and Evolution, 35, 518–522. 10.1093/molbev/msx281 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Huson, D. H. , & Bryant, D. (2006). Application of phylogenetic networks in evolutionary studies. Molecular Biology and Evolution, 23(2), 254–267. 10.1093/molbev/msj030 [DOI] [PubMed] [Google Scholar]
  49. Janzen, D. H. , Burns, J. M. , Cong, Q. , Hallwachs, W. , Dapkey, T. , Manjunath, R. , Hajibabaei, M. , Hebert, P. D. N. , & Grishin, N. V. (2017). Nuclear genomes distinguish cryptic species. Proceedings of the National Academy of Sciences of the United States of America, 114(31), 8313–8318. 10.1073/pnas.1621504114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Janzen, D. H. , & Hallwachs, W. (2011). Joining inventory by parataxonomists with DNA barcoding of a large complex tropical conserved wildland in northwestern Costa Rica. PLoS One, 6, e18123. 10.1371/journal.pone.0018123 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Janzen, D. H. , Hallwachs, W. , Blandin, P. , Burns, J. M. , Cadiou, J.‐M. , Chacon, I. , Dapkey, T. , Deans, A. R. , Epstein, M. E. , Espinoza, B. , Franclemont, J. G. , Haber, W. A. , Hajibabaei, M. , Hall, J. P. W. , Hebert, P. D. N. , Gauld, I. D. , Harvey, D. J. , Hausmann, A. , Kitching, I. J. , … Wilson, J. J. (2009). Integration of DNA barcoding into an ongoing inventory of complex tropical biodiversity. Molecular Ecology Resources, 9, 1–26. 10.1111/j.1755-0998.2009.02628.x [DOI] [PubMed] [Google Scholar]
  52. Kalyaanamoorthy, S. , Minh, B. Q. , Wong, T. K. F. , von Haeseler, A. , & Jermiin, L. S. (2017). ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature Methods, 14, 587–589. 10.1038/nmeth.4285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Lim, G. , Balke, M. , & Meier, R. (2012). Determining species boundaries in a world full of rarity: Singletons, species delimitation methods. Systematic Biology, 61, 165–169. 10.1093/sysbio/syr030 [DOI] [PubMed] [Google Scholar]
  54. Liu, M. , Clarke, L. J. , Baker, S. C. , Jordan, G. J. , & Burridge, C. P. (2020). A practical guide to DNA metabarcoding for entomological ecologists. Ecological Entomology, 45, 373–385. 10.1111/een.12831 [DOI] [Google Scholar]
  55. Lohse, K. (2009). Can mtDNA barcodes be used to delimit species? A response to Pons et al Systematic Biology, 58, 439–442. [DOI] [PubMed] [Google Scholar]
  56. Macher, J.‐N. , Vivancos, A. , Piggott, J. J. , Centeno, F. C. , Matthaei, C. D. , & Leese, F. (2018). Comparison of environmental DNA and bulk‐sample metabarcoding using highly degenerate cytochrome c oxidase I primers. Molecular Ecology Resources, 18(6), 1456–1468. 10.1111/1755-0998.12940 [DOI] [PubMed] [Google Scholar]
  57. Maddison, W. P. (1997). Gene trees in species trees. Systematic Biology, 46(3), 523–536. 10.1093/sysbio/46.3.523 [DOI] [Google Scholar]
  58. Meier, R. , Shiyang, K. , Vaidya, G. , & Ng, P. K. L. (2006). DNA barcoding and taxonomy in Diptera: A tale of high intraspecific variability and low identification success. Systematic Biology, 55(5), 715–728. 10.1080/10635150600969864 [DOI] [PubMed] [Google Scholar]
  59. Morinière, J. , Hendrich, L. , Balke, M. , Beermann, A. J. , König, T. , Hess, M. , Koch, S. , Müller, R. , Leese, F. , Hebert, P. D. N. , Hausmann, A. , Schubart, C. D. , & Haszprunar, G. (2017). A DNA barcode library for Germany's mayflies, stoneflies and caddisflies (Ephemeroptera, Plecoptera and Trichoptera). Molecular Ecology Resources, 17, 1293–1307. 10.1111/1755-0998.12683 [DOI] [PubMed] [Google Scholar]
  60. Müller, D. , Suess, S. , Hoffmann, A. A. , & Buchholz, G. (2013). The value of satellite‐based active fire data for monitoring, reporting and verification of REDD+ in the Lao PDR. Human Ecology, 41(1), 7–20. 10.1007/s10745-013-9565-0 [DOI] [Google Scholar]
  61. Mutanen, M. , Kivelä, S. M. , Vos, R. A. , Doorenweerd, C. , Ratnasingham, S. , Hausmann, A. , Huemer, P. , Dincă, V. , van Nieukerken, E. J. , Lopez‐Vaamonde, C. , Vila, R. , Aarvik, L. , Decaëns, T. , Efetov, K. A. , Hebert, P. D. N. , Johnsen, A. , Karsholt, O. , Pentinsaari, M. , Rougerie, R. , … Godfray, H. C. J. (2016). Species‐level para‐ and polyphyly in DNA barcode gene trees: Strong operational bias in European Lepidoptera. Systematic Biology, 65(6), 1024–1040. 10.1093/sysbio/syw044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Myers, N. , Mittermeier, R. , Mittermeier, C. , da Fonseca, G. A. B. , & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853–858. 10.1038/35002501 [DOI] [PubMed] [Google Scholar]
  63. Nair, M. R. G. K. (1986). Insects and mites of crops in India (pp. 404). (Ed. 2). Indian Council of Agricultural Research, New Delhi, 1975. [Google Scholar]
  64. Nguyen, L.‐T. , Schmidt, H. A. , von Haeseler, A. , & Minh, B. Q. (2015). IQ‐TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Molecular Biology and Evolution, 32, 268–274. 10.1093/molbev/msu300 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Nicholls, J. A. , Challis, R. J. , Mutun, S. , & Stone, G. N. (2012). Mitochondrial barcodes are diagnostic of shared refugia but not species in hybridizing oak gall wasps. Molecular Ecology, 21, 4051–4062. 10.1111/j.1365-294X.2012.05683.x [DOI] [PubMed] [Google Scholar]
  66. Özgül‐Siemund, A. , & Ahrens, D. (2015). Taxonomic utility of female copulation organs in Sericini chafers (Coleoptera, Scarabaeidae), with special reference to asymmetry. Contributions to Zoology, 84(2), 167–178. 10.1163/18759866-08402005 [DOI] [Google Scholar]
  67. Padial, J. M. , Miralles, A. , de La Riva, I. , & Vences, M. (2010). The integrative future of taxonomy. Frontiers in Zoology, 7(1), 16. 10.1186/1742-9994-7-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Pentinsaari, M. , Anderson, R. , Borowiec, L. , Bouchard, P. , Brunke, A. , Douglas, H. , Smith, A. B. T. , & Hebert, P. D. N. (2019). DNA barcodes reveal 63 overlooked species of Canadian beetles (Insecta, Coleoptera). ZooKeys, 894, 53–150. 10.3897/zookeys.894.37862 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Pentinsaari, M. , Hebert, P. D. N. , & Mutanen, M. (2014). Barcoding Beetles: A regional survey of 1872 species reveals high identification success and unusually deep interspecific divergences. PLoS One, 9, e108651. 10.1371/journal.pone.0108651 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Piper, A. M. , Batovska, J. , Cogan, N. O. I. , Weiss, J. , Cunningham, J. P. , Rodoni, B. C. , & Blacket, M. J. (2019). Prospects and challenges of implementing DNA metabarcoding for high‐throughput insect surveillance. Giga Science, 8, 1–22. 10.1093/gigascience/giz092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Porter, T. M. , & Hajibabaei, M. (2018). Over 2.5 million COI sequences in GenBank and growing. PLoS One, 13(9), e0200177. 10.1371/journal.pone.0200177 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Puillandre, N. , Lambert, A. , Brouillet, S. , & Achaz, G. (2012). ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Molecular Ecology, 21(8), 1864–1877. 10.1111/j.1365-294X.2011.05239.x [DOI] [PubMed] [Google Scholar]
  73. Ratnasingham, S. , & Hebert, P. D. N. (2007). BOLD: The Barcode of Life Data System (http://www.barcodinglife.org). Molecular Ecology Notes, 7(3), 355–364. 10.1111/j.1471-8286.2007.01678.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Ratnasingham, S. , & Hebert, P. D. N. (2013). A DNA‐based registry for all animal species: The Barcode Index Number (BIN) system. PLoS One, 8, e66213. 10.1371/journal.pone.0066213 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Reid, N. M. , & Carstens, B. C. (2012). Phylogenetic estimation error can decrease the accuracy of species delimitation: A Bayesian implementation of the general mixed Yule‐coalescent model. BMC Evolutionary Biology, 12, 196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Ritcher, P. O. (1966). White grubs and their allies. A Study of North American Scarabaeoid Larvae. In Lattin J. D. (Ed.), Oregon state monographs. Studies in entomology (Vol. 4, pp. 219). Oregon State University Press. [Google Scholar]
  77. Ross, H. A. (2014). The incidence of species‐level paraphyly in animals: A re‐assessment. Molecular Phylogenetics and Evolution, 76, 10–17. 10.1016/j.ympev.2014.02.021 [DOI] [PubMed] [Google Scholar]
  78. Rougerie, R. , Lopez‐Vaamonde, C. , Barnouin, T. , Delnatte, J. , Moulin, N. , Noblecourt, T. , Nusillard, B. , Parmain, G. , Soldati, F. , & Bouget, C. (2015). PASSIFOR: A reference library of DNA barcodes for French saproxylic beetles (Insecta, Coleoptera). Biodiversity Data Journal, 3, e4078. 10.3897/BDJ.3.e4078 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Rulik, B. , Eberle, J. , Jung, M. , Köhler, F. , Apfel, W. , Weigel, A. , Kopetz, A. , Köhler, J. , Hadulla, K. , Hartmann, M. , Fritzlar, F. , Schmidt, J. , Astrin, J. , Wägele, W. , Geiger, M. , & Ahrens, D. (2017). Using taxonomic consistency with semi‐automatized data preprocessing for high quality Barcode data. Methods in Ecology and Evolution, 8, 1878–1887. [Google Scholar]
  80. Ruppert, K. M. , Kline, R. J. , & Rahman, M. S. (2019). Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Global Ecology and Conservation, 17, e00547. 10.1016/j.gecco.2019.e00547 [DOI] [Google Scholar]
  81. Schlick‐Steiner, B. C. , Steiner, F. M. , Seifert, B. , Stauffer, C. , Christian, E. , & Crozier, R. H. (2010). Integrative taxonomy: A multisource approach to exploring biodiversity. Annual Review of Entomology, 55, 421–438. [DOI] [PubMed] [Google Scholar]
  82. Schmid‐Egger, C. , Straka, J. , Ljubomirov, T. , Blagoev, G. A. , Morinière, J. , & Schmidt, S. (2019). DNA barcodes identify 99 percent of apoid wasp species (Hymenoptera: Ampulicidae, Crabronidae, Sphecidae) from the Western Palearctic. Molecular Ecology Resources, 19, 476–484. 10.1111/1755-0998.12963 [DOI] [PubMed] [Google Scholar]
  83. Schmitt, T. (2007). Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Frontiers in Zoology, 4, 11. 10.1186/1742-9994-4-11 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Smith, M. A. , Bertrand, C. , Crosby, K. , Eveleigh, E. S. , Fernandez‐Triana, J. , Fisher, B. L. , Gibbs, J. , Hajibabaei, M. , Hallwachs, W. , Hind, K. , Hrcek, J. , Huang, D.‐W. , Janda, M. , Janzen, D. H. , Li, Y. , Miller, S. E. , Packer, L. , Quicke, D. , Ratnasingham, S. , … Zhou, X. (2012). Wolbachia and DNA barcoding insects: Patterns, potential, and problems. PLoS One, 7, e36514. 10.1371/journal.pone.0036514 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Steinke, D. , Breton, V. , Berzitis, E. , & Hebert, P. D. N. (2017). The School Malaise Trap Program: Coupling educational outreach with scientific discovery. PLoS Biology, 15, e2001829. 10.1371/journal.pbio.2001829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Talavera, G. , Dinca, V. , & Vila, R. (2013). Factors affecting species delimitations with the GMYC model: Insights from a butterfly survey. Methods in Ecology and Evolution, 4, 1101–1110. 10.1111/2041-210X.12107 [DOI] [Google Scholar]
  87. Tautz, D. , Arctander, P. , Minelli, A. , Thomas, R. H. , & Vogler, A. P. (2002). DNA points the way ahead in taxonomy. Nature, 418(6897), 479. 10.1038/418479a [DOI] [PubMed] [Google Scholar]
  88. Templeton, A. R. , Crandall, K. A. , & Sing, C. F. (1992). A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics, 132(2), 619–633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Thormann, B. , Ahrens, D. , Marín Armijos, D. , Peters, M. K. , Wagner, T. , & Wägele, J. W. (2016). Exploring the leaf beetle fauna (Coleoptera: Chrysomelidae) of an Ecuadorian mountain forest using DNA barcoding. PLoS One, 11(2), e0148268. 10.1371/journal.pone.0148268 [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Trifinopoulos, J. , Nguyen, L.‐T. , von Haeseler, A. , & Minh, B. Q. (2016). W‐IQ‐TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Research, 44(W1), W232–W235. 10.1093/nar/gkw256 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Yeates, D. K. , Seago, A. , Nelson, L. , Cameron, S. L. , Joseph, L. E. O. , & Trueman, J. W. H. (2011). Integrative taxonomy, or iterative taxonomy? Systematic Entomology, 36(2), 209–217. 10.1111/j.1365-3113.2010.00558.x [DOI] [Google Scholar]
  92. Zhang, J. , Kapli, P. , Pavlidis, P. , & Stamatakis, A. (2013). A general species delimitation method with applications to phylogenetic placements. Bioinformatics, 29(22), 2869–2876. 10.1093/bioinformatics/btt499 [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Zhou, X. , Frandsen, P. B. , Holzenthal, R. W. , Beet, C. R. , Bennett, K. R. , Blahnik, R. J. , Bonada, N. , Cartwright, D. , Chuluunbat, S. , Cocks, G. V. , Collins, G. E. , deWaard, J. , Dean, J. , Flint, O. S. , Hausmann, A. , Hendrich, L. , Hess, M. , Hogg, I. D. , Kondratieff, B. C. , … Kjer, K. M. (2016). The Trichoptera barcode initiative: A strategy for generating a species‐level Tree of Life. Philosophical Transactions of the Royal Society B, 371, 20160025. 10.1098/rstb.2016.0025 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1

Click here for additional data file. (37.3KB, xlsx)

Data Availability Statement

DNA sequences: Genbank accessions (MW128167–MW128351); DNA sequences (incl. abi files, locality data): BARCODE OF LIFE DATA SYSTEM (https://doi.org/10.5883/DS‐DS‐SCOIBL.%0d%0d%20).

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