Integrative taxonomy by molecular species delimitation: multi-locus data corroborate a new species of Balkan Drusinae micro-endemics
© The Author(s). 2017
Received: 24 February 2017
Accepted: 18 May 2017
Published: 6 June 2017
Taxonomy offers precise species identification and delimitation and thus provides basic information for biological research, e.g. through assessment of species richness. The importance of molecular taxonomy, i.e., the identification and delimitation of taxa based on molecular markers, has increased in the past decade. Recently developed exploratory tools now allow estimating species-level diversity in multi-locus molecular datasets.
Here we use molecular species delimitation tools that either quantify differences in intra- and interspecific variability of loci, or divergence times within and between species, or perform coalescent species tree inference to estimate species-level entities in molecular genetic datasets. We benchmark results from these methods against 14 morphologically readily differentiable species of a well-defined subgroup of the diverse Drusinae subfamily (Trichoptera, Limnephilidae). Using a 3798 bp (6 loci) molecular data set we aim to corroborate a geographically isolated new species by integrating comparative morphological studies and molecular taxonomy.
Our results indicate that only multi-locus species delimitation provides taxonomically relevant information. The data further corroborate the new species Drusus zivici sp. nov. We provide differential diagnostic characters and describe the male, female and larva of this new species and discuss diversity patterns of Drusinae in the Balkans. We further discuss potential and significance of molecular species delimitation. Finally we argue that enhancing collaborative integrative taxonomy will accelerate assessment of global diversity and completion of reference libraries for applied fields, e.g., conservation and biomonitoring.
Species represent a fundamental information unit in biological research [1–3]. Species-specific abundance fluctuations integrated with autecological attributes are used to assess stream health and ecological water quality, evaluate the potential of disease and parasite vectors, and were found to be highly informative in species distribution modelling approaches [4–15]. Aggregate taxa, i.e., taxonomic entities comprising more than one species, often do not provide sufficient resolution to reap the power of ecological analysis [16–18]. Thus, estimation of, e.g., ecological water quality in compliance with the EU Water Framework Directive crucially depends on precise taxonomy to delineate and define species .
Given the taxonomic impediment – the worldwide decline of taxonomic competence to identify species based on morphological characters – biological sciences and policymaking are severely hampered by difficulties in compiling relevant and up-to-date baseline diversity data [19–21]. Indeed, taxonomy and assessment of eukaryotes remains primarily reliant on identification and comparison of morphological characters to define and address species [22–25]. However, characterization of species ideally uses different sources of information (ecological, morphological, anatomical, physiological, genomic, geographical or others) in an integrative taxonomic approach for the identification, delineation and description of taxa [2, 3, 26, 27]. Advances in molecular genetic methods recently promoted molecular taxonomy: species recognition and delineation based on unique genomic characters [28–30].
In parallel, a variety of methods for recognizing new species or testing species hypotheses was developed, usually referred to as ‘species delimitation tools’ [31–33]. However, as the methods are computationally demanding, analyses of multi-locus data sets are usually employed to disentangle few morphologically defined species [34–40]. Studies addressing many (>10) morphotaxa often use few methods and single locus data to corroborate identification and delineation of new taxa [41–44]. Thus, speciose taxa are less likely to be addressed in molecular species delimitation analyses.
Also, despite the demonstrated informativeness of molecular taxon delimitation to test species hypotheses, not all studies that successfully employ molecular taxonomic taxa delimitation tools follow through to describe these new taxonomic entities [3, 45, 46]. Interestingly, this has been related to the complex taxonomic procedures associated with the formal description of new species . However it is more likely that molecular species hypotheses are ignored in integrative approaches due to insufficient morphological or ecological support (e.g., ).
In this contribution we benchmark results of molecular species delimitation against morphologically well-defined taxa in a highly diverse group of caddisflies. The subfamily Drusinae (Insecta, Trichoptera) constitutes an ideal model taxon to assess potential congruence of traditional and molecular taxonomic methods. This group of mostly cold-stenotopic species inhabiting Eurasian mountain ranges exhibits highly disjunct distribution patterns and high levels of micro-endemism, indicative of small-scale allopatric diversification and persistence of isolated lineages over geological time [48–52]. However, historic introgression – a process that complicates species delimitation – was demonstrated in some species of Drusinae . In the Western Balkans, taxonomic richness of Drusinae is particularly high [50, 51, 53–58] and was presumably shaped by multiple glacial cycles and karstification (cf. [48, 51, 59]). Additionally, Western Balkan Drusinae are morphologically distinct with multi-locus molecular data showing minimal differences which potentially result from recent speciation [51, 59]. Together, these conditions make Drusinae a good model for testing the suitability and precision of species delimitation methods.
Here, we assess taxonomic informativeness of four recently developed exploratory molecular species delimitation tools by inferring entity richness hypotheses on a 3798 bp, 6 loci (mtCOI5-P, mtCOI3-P, 16S mrDNA, CADH, WG, 28S nrDNA), 14 morphospecies dataset comprising 1 new and 1 recently described species. As a test case, we aim to clarify the systematic status of a morphologically distinct potential new species and corroborate its distinctiveness in an integrative taxonomic approach. We predict that only methods directly integrating information from several loci will provide taxonomically conclusive results.
Collection and taxonomic methods
Adult specimens were collected using sweep nets, larvae were collected by hand-picking. Collected specimens were stored in 96% EtOH. Specimens were cleared for genitalic dissections and examinations using either the Qiagen Blood and Tissue Kit for DNA-extraction according to the manufacturer’s recommendation and subsequent KOH-treatment , or KOH-treatment. Nomenclature of male terminalia follows  (for Limnephilus flavicornis Fabricius) using the simplifying terms “superior appendages” for the lateral processes of segment X (cerci sensu ), and “intermediate appendages” for the sclerite and the anterior process of segment X (paraproct sensu ). Illustrations were prepared according to  in which pencil drawings made with a camera lucida are digitized, edited and inked in Adobe Illustrator (v. 16.0.4, Adobe Systems Inc.).
Morphology-based delimitation of species-level taxa was achieved in a classical comparative taxonomic approach: we scrutinized as many specimens as possible from as many populations as possible from as many Drusinae species as possible to discriminate intraspecific from interspecific variation in more than 300 adult and larval features (e.g., structure of male and female terminalia, wing venation, larval feeding apparatus, and larval pronotum shape; ) and unequivocally assigned specimens to species-level taxa (cf. [50, 55–58, 64]).
Whole genomic DNA was extracted from the abdomen or the thorax of adult or larval specimens using the DNEasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s protocol. Standard PCR procedures and primers were used to amplify three mitochondrial gene regions (mtCOI5-P, mtCOI3-P, 16S rDNA [“mt16S”]) and three nuclear gene regions (nuCADH, nuWG [“nuWnt1”], 28S nrDNA [“nu28S”]) as previously described . This combination of loci was chosen based on their demonstrated informativeness for phylogenetic inference, phylogeography, and prior successful usage in integrative taxonomic approaches in Trichoptera (e.g., [48–51, 57, 64–68]). PCR reactions were set up in 10 μl reactions. Unpurified PCR products were sequenced on an ABI 3177XL capillary sequencer at BiK-F using the PCR primers and two additional internal primers for 28SrDNA (D2UP-4 and D2DN-B; ). Sequences were edited in Geneious R6 (https://www.geneious.com/; ) and aligned using MAFFT v7  as implemented in Geneious R6. The final dataset comprised 40 Drusinae specimens assigned to 14 morphological species, and three outgroup specimens (Anisogamus waringeri: fAns0101L; Melamphophylax austriacus: fMelaus0101M, fMelaus0102F) (Additional file 1).
Phylogenetic inference and molecular species delimitation
Substitution models used in phylogenetic analysis
Codon position 1
Codon position 2
Codon position 3
TN93 + I
GTR + G
TN93 + I
TN93 + G
HKY + I
T92 + G
Single gene phylogenies were estimated by Bayesian Inference through BEAST 2  (5 × 109 generations, sampling every 10,000th generation). Analyses were run 4× independently to assure topological convergence. BEAST log files were examined in Tracer v1.6  to assess when runs had reached a stationary phase. A maximum clade credibility tree was estimated via TreeAnnotator v1.8.1  based on the sampled trees after discarding the first 30% as burn-in. Also, congruence of phylogenetic signal among data partitions was assessed by examining ≥0.95 posterior probability topologies of single gene analyses.
A species trees was estimated using *BEAST  as implemented in BEAST 2 using unique specimen identifiers as the species trait, i.e. without a priori species definitions. We ran species tree analysis assuming a Yule speciation tree prior for 5 × 108 generations 4× independently, sampling every 10,000th generation. *BEAST log files were examined in Tracer v1.6 to assess if runs had reached a stationary phase and converged on model parameters; maximum clade credibility trees were estimated as described above.
We then performed molecular species delimitation using tools complying with the following set of criteria: (1) the method was designed as naive exploratory tool without a priori assignment of specimens to groups or assumptions about relationships between specimens (we consequently excluded BPP , as this method requires at least an a priori clustering of specimens into groups and a guide tree); (2) the method was originally designed for molecular species delimitation (we consequently excluded applications like Brownie [32, 77], or SpeDeSTEM [78–80]); (3) the method was designed to exploit ‘standard’ partial genetic sequence data.
To assess potential congruence of molecular species delimitation methods, we performed a series of analyses employing several analytical resources selected as described above and report results against the benchmark of 14 morphologically identifiable species:
(I) Automatic Barcode Gap Detection [ABGD] was performed for each locus and a concatenated sequence data set via the ABGD webmask . ABGD is a tool designed to infer species hypotheses based on automatized identification of barcode gaps between inter- and intraspecific pairwise distances in partial sequence data sets. The method does not make assumptions about data structure or evolutionary history, and only requires input data (a single locus alignment) to be sufficiently variable. Pairwise distances are computed either as simple p-distances, or as substitution-corrected distances (via either JC69 , or K2P  models). Barcode gaps are discovered as slope maxima of a function describing the relation of pairwise distance ranks and pairwise distances. The method partitions specimens into groups in a recursive manner in which every group is split again, until no further splits are possible, while integrating user-provided priors on maximum and minimum intraspecific differentiation and barcode gap width. The prior on intraspecific divergence (denoted P in the original publication and the software ) defines the threshold between intra- and interspecific pairwise distances, and is iterated from minimum to maximum through a user-defined number of steps [by default 10 steps from P min = 0.001while P max = 0.1]; the prior on barcode gap width (denoted X in the original publication and the software ) defines sensitivity of the algorithm as scaling factor of empiric maximum intraspecific divergence to estimate minimum barcode gap width between intra- and interspecific distances [by default X = 1.5]. From the thus provided set of possible partitions (up to 10 using default settings) Puillandre et al.  suggest to select the most plausible one(s) and assess their potential informativeness in an integrative approach. To avoid oversplitting of single species, we used the default distance metric (JC69) as this was previously found to produce more conservative species hypotheses [84, 85]. Likewise, we used the default minimum barcode gap width prior to derive relatively conservative species hypotheses — modifications (i.e., increasing or decreasing the default value) thereof were previously reported to either increase or decrease numbers of species hypotheses proposed, with smaller X values generally leading to more delimited entities and vice versa [84, 85]. Assuming that the potential barcode gap space would be satisfyingly described, we used default intraspecific divergence minima and maxima, and report delimitations at partition maximum as identified through recursive partitioning. Following the suggestions by Puillandre et al. , we only present species hypotheses at partition maxima as these correspond most closely to expected numbers of taxa.
(II) We used the GMYC [Generalized Mixed Yule Coalescent] model [86, 87], implementing single and multiple thresholds via the ‘splits’ package in R 3.2.1 [88, 89] on single gene trees and a *BEAST species tree to infer GMYC species. The GMYC model aims to discern stochastic birth-death processes (effectively a pure-birth Yule model) between species from neutral coalescent processes within species by analysis of time intervals between branching events (which, in turn, can be summarized as combination of independent Poisson processes) in time-calibrated single gene trees. Input prerequisites require a well-sampled, well-estimated, ultrametric single neutral locus tree that ideally represents the true species genealogy in absence of population structure and population size fluctuation. The method defines sets of species hypotheses based on single or multiple threshold times that potentially distinguish coalescent events from speciation events, and searches for a single maximum likelihood model of mixed speciation and diversification processes across the search space, i.e., sets of species hypotheses. Species hypotheses thus delineated correspond to the phylogenetic species concept.
(III) We analyzed single gene trees and a *BEAST species tree using the PTP [Poisson Tree Processes] model via the PTP webmask  using both heuristic ML and Bayesian implementations of the PTP algorithm. Somewhat similar to the GMYC model, the PTP model aims to discern speciation processes among species from diversification processes within species, but analyses numbers of substitutions between branching events instead of time intervals. Input prerequisites enforce the same assumptions as the GMYC model, but this method does not require an ultrametric input tree to delineate entities corresponding to the phylogenetic species concept. This delineation is instead achieved by heuristically inferring species delimitations and searching for a delimitation pattern that maximizes likelihood of a mixed model describing speciation and diversification processes as two independent Poisson process classes across the search space, i.e., sets of species hypotheses. Removing the outgroup in initial runs did not affect delimitation results; we consequently did not use this option.
(IV) We performed combined species tree estimation and species delimitation analysis as available via Species Tree And Classification Estimation, Yarely [STACEY] in BEAST 2 . Simplified, this method (as its predecessor, DISSECT ) is an extension of the multispecies coalescence model used in *BEAST , in which a birth-death-collapse model is used to estimate the species tree [90, 91]. Further, specialized operators are included that model population sizes along branches, prune and regraft subtrees, modify node heights, and merge tips to minimal clusters. The method aims to maximize tree likelihood over a Bayesian tree space by using a MCMC model in which single tips can be merged to minimal clusters to estimate a species or minimal cluster (SMC) tree, while specific priors ensure compatibility between species and gene trees . User-supplied priors define behaviour of MCMC moves, and provide a probability space for the expected number of species primarily through the Collapse Weight prior in combination with a Collapse Height parameter [90, 91]. While not extensively tested, a wide range of values [1e-4–1e-7] for Collapse Height was found to provide similar species delimitation results . Contrastingly, the Collapse Weight prior was found to confound delimitation results if fixed, which can be circumvented if estimated during the MCMC process [90, 91]. Here, we assess importance of prior space settings for growth rate and population size scaling parameters in STACEY analysis, and test influence of ploidy settings on species delimitation results.
Initially, we assumed a birth-death speciation tree prior while using a Collapse Height of 0.0003, and estimated Collapse Weight with an initial value of 0.5 using a beta prior (1,1) around [0,1]; following suggestions for prior choice in species tree analysis using *BEAST , Jeffreys prior was used for growth rate and population scaling factor; the relative death was estimated using a beta prior (1,1) around [0,1]. We used equal ploidy settings, following results and arguments presented in [93–95]. We chose this approach to avoid disproportionate influence from mitochondrial partial sequence data and, consequently, treat each gene tree as likely as any other to diverge from the species tree. We did not modify the Collapse Height as preliminary experimental runs confirmed the patterns described in Jones et al. : we found that values larger than or equal to 1e-3 lead to merging of all included specimens to very few (1–2) groups. Further, the NodeReheight operator was set to 3× its value as suggested by . Genealogical relationships were estimated via STACEY 4× independently (1 × 107 generations, sampling every 5,000th generation) after incorporating suggestions obtained from an initial run. STACEY log files were examined as stated above. Support for tree topologies estimated by STACEY were examined by constructing a maximum clade credibility tree running TreeAnnotator v1.8 after discarding the first third of all estimated trees. Species delimitations based on trees estimated by STACEY were assessed using speciesDA (), using the same burn-in, a collapse height of a tenth of the average branch length (corresponding to a value of 0.0005), and default similarity cut-off. We conducted additional analyses to explore the sensitivity of the method to ploidy settings, and prior space for growth rate and population scaling factor. We used different combinations of ploidy settings for mitochondrial loci (using either the same value as for nuclear loci, or ¼ of the value used for nuclear loci which is commonly used in species tree analysis), Jeffreys priors, and logarithmic normal priors for growth rate and population scaling factor. Logarithmic normal priors were used to estimate both parameters, where one set mimicked empirical posterior distributions of growth rate and population scaling factor (where growth rate M = 2.5, S = 1.2, and population size M = −8.5, S = 2, respectively) while the others covered a prior space around M∈ [3, 5, 7], S∈[1.5, 2.5, 3.5] (resulting in nine possible prior combinations). Further, we edited ploidy settings in the original setup file and re-ran the original STACEY analysis to check for congruence with the new version as we noticed version-dependent differences between setup file templates. These analyses were run for 10 × 109 generations, sampling every 10,000th generation, and analysed as described above. In total we thus tested the same data set with 24 prior setting combinations (cf. Table 3.)
Both GMYC and PTP methods were run on single gene trees estimated via BEAST exclusively, as taxa delimitations using both methods on BEAST trees were found to be consistent . Further, both methods were used to estimate taxa delimitations on a *BEAST species tree. The practice of inferring species hypotheses on a species tree estimated through *BEAST represents a violation of many of the assumptions that lay the base for both the GMYC and the PTP model and we strictly advise against this approach. Here, however, we took this measure to allow for a more comprehensive comparison of species delimitation methods.
Properties of the molecular dataset
Final alignments of mtCOI5-P (658 bp), mtCOI3-P (541 bp), mt16S (365 bp), nuCADH (848 bp), nuWnt1 (346 bp), and nu28S (1040 bp) comprised 27.65%, 29.76%, 15.34%, 12.97%, 23.98%, and 1.73% variable sites, respectively. Further, these alignments comprised 23.38%, 25.32%, 12.60%, 10.85%, 14.16%, and 1.25% parsimony-informative sites, respectively.
Performance of molecular species delimitation methods
Automated barcode gap analysis
ABGD based on single loci does not consistently propose species hypotheses. Based on the barcode region (mtCOI-5P), ABGD suggests 13 groups [7 morphological species] (aggregating Drusus bureschi + D. discophoroides + D. osogovicus + D. dardanicus + D. muranyorum + D. balcanicus; overpartitioning Drusus zivici sp. nov.), and suggests D. popovi and several D. zivici sp. nov. clades as different entities. Barcode gap analysis of partial mtCOI-3P sequence data suggests 11 groups [7 morphological species] (aggregating D. bureschi + D. discophoroides + D. osogovicus + D. muranyorum + D. balcanicus) overpartitioning D. zivici sp. nov.), and suggests D. popovi and and two D. zivici sp. nov. clades as different entities. ABGD analysis of partial mt16S sequences returns 15 groups [7 morphological species] (aggregating D. bureschi [partly] + D. dardanicus + D. zivici sp. nov. [partly] and D. osogovicus + D. muranyorum; overpartitioning D. bureschi and Ecclisopteryx dalecarlica), and suggests D. popovi differentiated from several D. zivici sp. nov. entities. Automated barcode gap analysis based on partial nuCADH sequence data suggests 15 groups [13 morphological species] (overpartitioning Drusus zivici sp. nov.), and suggests D. popovi and two D. zivici sp. nov. clades as different entities. Barcode gap analysis of the partial nuWnt1 sequence dataset in which data are missing for D. popovi, Melampophylax austriacus, Anisogamus waringeri suggests 12 groups [7 morphological species] (overpartitioning D. osogovicus, D. muranyorum), but is not informative regarding the status of D. popovi and D. zivici sp. nov. ABGD based on partial nu28S sequence data using default settings does not delimit any entities and is therefore not meaningful.
Generalized mixed Yule coalescent approach
GMYC results based on a single gene tree of partial mtCOI-5P sequence data suggests 16 groups [12 morphological species] (overpartitioning D. osogovicus and D. zivici sp. nov.) using the single threshold method, suggesting D. popovi and two clades of D. zivici sp. nov. as different entities — and 14 groups [11 morphological species] (aggregating (D. bureschi + D. discophoroides) overpartitioning D. osogovicus) using the multiple threshold method, suggesting D. popovi and D. zivici sp. nov. as different entities. Results of GMYC analysis based on a single gene phylogeny of partial mtCOI-3P sequence data suggests 4 groups [1 morphological species] (aggregating (D. bureschi + D. discophoroides + D. osogovicus + D. dardanicus + D. muranyorum + D. balcanicus + D. zivici sp. nov. + D. popovi), (E. keroveci + E. dalecarlica + D. discolor), and (M. austriacus + A. waringeri)) using the single threshold method, rejecting D. popovi and of D. zivici sp. nov. as different entities — and 13 groups [12 morphological species] (aggregating (D. zivici sp. nov. + D. popovi)) using the multiple threshold method, rejecting D. popovi and of D. zivici sp. nov. as different entities. GMYC analysis of a phylogenetic tree estimated using partial mt16S sequence data suggests 12 groups [6 morphological species] (aggregating overpartitioned (D. bureschi [partly] + D. zivici sp. nov. [partly]) twice, (D. bureschi [partly] + D. discophoroides [partly] + D. dardanicus [partly]), aggregating (D. osogovicus + D. muranyorum) and (D. balcanicus + D. popovi)) using the single threshold method, suggesting D. popovi differentiated from two clades comprising D. zivici sp. nov. partially — and 24 groups [6 morphological species] (aggregating overpartitioned (D. bureschi [partly] + D. zivici sp. nov. [partly]) twice, (D. bureschi [partly] + D. dardanicus [partly]) twice, (D. osogovicus [partly] + D. muranyorum [partly]) twice, aggregating (M. austriacus + A. waringeri); overpartitioning D. osogovicus, D. muranyorum, D. zivici sp. nov., D. discophorus, E. dalecarlica) using the multiple threshold method, suggesting D. popovi differentiated from a multitude of D. zivici sp. nov. entities. Results of GMYC analysis of a single gene phylogeny of partial nuCADH sequences suggest 3 groups [1 morphological species] (aggregating (D. bureschi + D. discophoroides + D. osogovicus + D. dardanicus + D. muranyorum + D. balcanicus + D. zivici sp. nov. + D. popovi + D. discophorus + E. keroveci + E. dalcarlica) and (M. austriacus + A. waringeri)) using the single threshold method, rejecting D. popovi and of D. zivici sp. nov. as different entities — and 9 groups [2 morphological species] (aggregating (D. discophoroides + D. balcanicus), (D. osogovicus + D. dardanicus), (D. zivici sp. nov. + D. popovi), (E. keroveci + E. dalecarlica), and (D. discolor + M. melampophylax + A. waringeri); overpartitioning D. muranyorum) using the multiple threshold method, rejecting D. popovi and of D. zivici sp. nov. as different entities. Results of GMYC analysis based on a single gene phylogeny of nuWnt1 suggest 11 groups [8 morphological species] (overpartitioning D. muranyorum) using both the single threshold method — and 10 groups [8 morphological species] (aggregating overpartitioned D. muranyorum + D. discophorus) using the mulitple threshold method, but is generally not informative regarding the status of D. popovi and D. zivici sp. nov. as data are missing; however, all specimens of D. zivici sp. nov. are recovered as a distinct group. GMYC analysis of single gene trees of partial nu28S sequence data suggest 3 groups (aggregating (D. bureschi + D. discophoroides + D. osogovicus + D. dardanicus + D. balcanicus), (D. zivici sp. nov. + D. popovi + D. discophorus) and (D. muranyorum + E. keroveci + E. dalecarlica + D. discolor + M. melampophylax + A. waringeri)) using the single threshold method, rejecting D. popovi and of D. zivici sp. nov. as different entities — and 18 groups [4 morphological species] (aggregating (D. bureschi [partly] + D. osogovicus [partly]), (D. osogovicus [partly] + D. balcanicus), (D. popovi [partly] + D. zivici sp. nov. [partly]), (E. keroveci [partly] + E. dalecarlica), and (M. austriacus + A. waringeri); overpartitioning D. osogovicus, D. muranyorum, D. zivici sp. nov., D. popovi, E. keroveci) using the multiple threshold method, rejecting D. popovi and of D. zivici sp. nov. as different entities.
Poisson tree process
Results from PTP based on a single gene phylogeny for mtCOI-5P suggests 15 groups [10 morphological species] (aggregating (Drusus bureschi + D. discophoroides + D. osogovicus); overpartitioning D. zivici sp. nov.), and suggests D. popovi differentiated from several D. zivici sp. nov. entities — results from bPTP suggest 20 groups [10 morphlogical species] (overpartitioning D. bureschi, D. osogovicus, D. zivici sp. nov.), and suggests D. popovi differentiated from several D. zivici sp. nov. entities. Results of PTP analysis based on a single gene genealogy of mtCOI-3P suggest 12 groups [9 morphological species] (aggregating (Drusus bureschi + D. discophoroides + D. osogovicus + D. balcanicus); overpartitioning D. zivici sp. nov.), and suggests D. popovi and D. zivici sp. nov. as different entities — bPTP suggests 17 groups [11 morphological species] (overpartitioning D. bureschi, D. osogovicus, D. zivici sp. nov.), and suggests D. popovi differentiated from several D. zivici sp. nov. entities. Based on a single gene phylogeny of partial mt16S genetic sequences, PTP suggests 15 groups [8 morphological species] (aggregating (D. bureschi [partly] + D. zivici sp. nov. [partly]) twice, (D. bureschi [partly] + D. discophoroides [partly] + D. dardanicus [partly]), and (D. osogovicus + D. muranyorum); overpartitioning D. bureschi, and D. dardanicus), and suggests D. popovi as differentiated from several clades containing D. zivici sp. nov. partially — using the same data, bPTP suggests 24 groups [7 morphological species] (overpartitioning D. bureschi, D. osogovicus, D. muranyorum, D. zivici sp. nov., D. discophorus, E. dalecarlica), and recovers D. popovi differentiated from several D. zivici sp. nov. entities. Poisson tree process analysis of a single gene tree of nuCADH suggests 16 groups [12 morphological species] (overpartitioning D. muranyorum, D. discolor) — bPTP recovers the same set of groups; both suggest D. popovi and D. zivici sp. nov. as different entities. PTP results based on a single gene tree of partial nuWnt1 sequence data suggest 16 groups [4 morphological species] (aggregating (D. dardanicus + D. osogovicus), overpartitioning D. bureschi, D. muranyorum, D. zivici sp. nov., and E. keroveci) — results of bPTP suggest 17 groups [3 morphological species] (aggregating (D. dardanicus + overpartitioned D. osogovicus), overpartitioning D. bureschi, D. osogovicus, D. muranyorum, D. zivici sp. nov., and E. keroveci), but is generally not informative regarding the status of D. popovi and D. zivici sp. nov. as data are missing; further, specimens of D. zivici sp. nov. are recovered in 2 distinct groups. Based on a single gene phylogeny of partial nu28S sequences, PTP identified 19 groups [6 morphological species] (aggregating (D. bureschi [partly] + D. osogovicus [partly]), (D. zivici sp. nov. + D. popovi), and (E. keroveci [partly] + E. dalecarlica); overpartitioning D. bureschi, D. osogovicus, D. muranyorum, D. zivici sp. nov., and E. keroveci), and rejects D. popovi and D. zivici sp. nov. as different entities — results of bPTP suggest 20 groups [6 morphological species] (aggregating (D. bureschi [partly] + D. osogovicus [partly]), (D. zivici sp. nov. + D. popovi), and (E. keroveci [partly] + E. dalecarlica); overpartitioning D. bureschi, D. osogovicus, D. muranyorum, D. zivici sp. nov., D. popovi, and E. keroveci), and rejects D. popovi and D. zivici sp. nov. as different entities.
Concatenated partial sequence data
Results from ABGD based on concatenated sequence data suggests 7 groups [6 morphological species] (aggregating D. bureschi + D. discophoroides + D. osogovicus + D. dardanicus + D. muranyorum + D. balcanicus + D. zivici sp. nov. + D. popovi), and rejects D. popovi and D. zivici sp. nov. as different entities.
Species tree analyses
Results from PTP based on a species tree estimated via *BEAST suggest 9 groups [8 morphological species] (aggregating (D. bureschi + D. discophoroides + D. osogovicus + D. muranyorum + D. balcanicus)), supporting D. popovi and D. zivici sp. nov. as different entities — bPTP recovers identical results. Species hypothesis estimation through single threshold GMYC on a species tree recovers 3 groups [2 morphological species] (aggregating (D. bureschi + D. discophoroides + D. osogovicus + D. dardanicus + D. muranyorum + D. balcanicus + D. zivici sp. nov. + D. popovi + D. discophorus + E. keroveci + E. dalecarlica + D. discolor)) — multiple threshold GMYC suggests 9 groups [8 morphological species] (aggregating (D. bureschi + D. discophoroides + D. osogovicus + D. muranyorum + D. balcanicus)), supporting D. popovi and D. zivici sp. nov. as different entities.
Combined species tree and species delimitation estimation
STACEY analysis suggests 14 groups equivalent to the 14 morphological species, and supports D. popovi and D. zivici sp. nov. as different entities using a collapse height of 0.0005 in species delimitation analysis (Fig. 1, Table 2).
Effect of prior choice on STACEY delimitation results
However, using one quarter of the nuclear ploidy value in STACEY analyses results in a higher number of species hypotheses (Table 3). These analyses concordantly suggest 17 groups [12 morphological species] (overpartitioning D. osogovicus and D. zivici sp. nov.) and support D. popovi and three distinct D. zivici sp. nov. groups as different entities using a collapse height corresponding to one tenth of the average branch length (oscillating between 0.0006 and 0.0007 between different analyses) in species delimitation analysis.
Drusus zivici sp. nov. Kučinić, Previšić, Stojanović and Vitecek. http://zoobank.org/urn:lsid:zoobank.org:act:25366DC3-3829-41B0-8373-394D2A9DDFBA.
Holotype. 1 male: Serbia, Stara Planina Mountains, spring of the river Tovarnička Reka; N43.3362367 E22.583983; 1493 m a.s.l.; 19.vi.2013–21.vi.2013; leg. M. Kučinić, K. Stojanović, M. Živić; specimen identifier fDpo0202M. Paratypes: 1 male, 1 female, 5 larvae: same data; specimen identifiers: 1 female: fDpo0201F, 1 male: fDpo0203M, 5 larvae: fDpo0204L–fDpo0208L. 2 males: Serbia, Stara Planina Mountains, spring of the river Rekička Reka; N43.372150 E22.625333; 1540 m a.s.l.; 19.vi.2013; leg. M. Kučinić, K. Stojanović, M. Živić; specimen identifiers: fDpo0301M, fDpo0302M. 1 female, 3 larvae: Serbia, Stara Planina Mountains, spring of Kaluđerske vode; N43.388690 E22.677934; 1930 m a.s.l.; 20.vi.2013; leg. M. Kučinić, K. Stojanović, M. Živić; specimen identifiers: 1 female: fDpo0401F, 3 larvae: fDpo0402L–fDpo0404L. 1 female: Serbia, Stara Planina Mountains, spring of the river Javorska Reka; N43.386150 E22.689817; 1890 m a.s.l.; 20.vi.2013; leg. M. Kučinić, K. Stojanović, M. Živić; specimen identifier: fDpo0501F.
Holotype and paratypes currently in coll. W. Graf, will be deposited in the Biologiezentrum des Oberösterreichischen Landesmuseums, Linz, Austria, and the Senckenberg Museum Frankfurt, Frankfurt am Main, Germany.
Serbia, Stara Planina Mountains, Midžor Massiv.
Males of the new species are most similar to Drusus popovi but exhibit (1) subcircular, elongate superior appendages in lateral view; (2) high tips of the intermediate appendages in lateral view, high and wide tips of intermediate appendages in caudal view; (3) suboval, elongate, approximately straight inferior appendages in lateral view; and (4) a high segment IX with a distinct, rounded, caudad medial protrusion in lateral view. Drusus popovi males have subcircular, short superior appendages; short and narrow tips of the intermediate appendages in lateral and caudal view; suboval, dorsadly curved inferior appendages; a wide segment IX lacking a distinct medial indentation in lateral view.
Females of the new species are most similar to females of D. popovi but exhibit (1) in dorsal view distinct, rounded lateral shoulders of segment X, (2) in dorsal view a ragged outline of the lateral lobes of segment X. Drusus popovi females have an evenly rounded lateral outline of segment X in dorsal view and evenly rounded lateral lobes of segment X.
Larvae of the new species are most similar to D. serbicus Marinković-Gospodnetić as larvae of both species have an intermittent lateral line ([98, 99]), but exhibit a pronotum with a distinct, rounded pronotal ridge (type B sensu ). Larvae of D. serbicus have an annular pronotal ridge (type E sensu ).
Habitus fawn to yellow; head and thorax yellow, head with a dark mark around ocelli, metathorax with a dark mark, abdominal sclerites and tergites brown; cephalic and thoracic setal areas pale; cephalic, thoracic and abdominal setation blond; legs light yellow, distally darker; haustellum and intersegmental integument pale, whitish. Wings yellow to fawn, with blond setae. Male maxillary palp 3-segmented. Forewing length 7.9–10 mm, spur formula 1–3–3 in males; forewing length 8–10 mm, spur formula 1–3–3 in females.
Male genitalia (Fig. 2a-e)
Female genitalia (Fig. 3a-d)
Fifth instar larva (Fig. 4a–j)
Named for Miroslav Živić, biophysicist, for his continuous support of faunistic surveys.
Drusinae diversity in the Balkans
Integrated assessment of morphology and results from multi-locus molecular species delimitation corroborate the new species D. zivici and indicate a sister species relationship with D. popovi. The description of this species increases total Drusinae diversity in the Balkans to 43 species [50–58, 64], demonstrating the significance of this region comprising a highly endemic fauna and flora for European biodiversity . High diversity of aquatic taxa in the Balkans can be attributed to species communities of isolated habitats [101–104], including numerous Drusinae species. Intriguingly, Drusinae distribution patterns in the Balkans indicate continuance of segregated populations potentially dissociated by climatic or orogenetic processes that in combination with limited dispersal potential [52, 105] resulted in small-scale allopatric speciation and extant disjunct distribution of species. As several other taxonomic groups exhibit combinations of ecological traits similar to those of Drusinae (e.g., some Leuctridae, Consorophylax spp.; [15, 106–108]), Drusinae may serve as model taxon to estimate unexplored biodiversity. Indeed, recent taxonomic surveys recovered several new Drusinae species in the Western Balkans, demonstrating the necessity and urgency of organismic studies in Europe . Additionally, the Balkan aquatic biodiversity is imminently threatened by anthropogenic habitat modification and climate change [110, 111]. Endemic taxa such as the majority of Drusinae species are particularly vulnerable to both climate change and habitat degradation [112–115]. Socio-economic change and the resulting acceleration of natural resource exploitation [110, 116–121] threaten natural habitats world-wide with deleterious effects on biodiversity.
Conservation measures to counter such development crucially depend on comprehensive faunistic data to identify the most critically imperiled habitats and implement adequate protection measures. While such comprehensive data is currently not available, the highly diversified and ecologically specialized Drusinae could serve as umbrella taxon for highland stream and spring habitats in the Western Balkans.
Model taxon, limitations of the molecular dataset and molecular species delimitation
The Drusinae are morphologically well-defined, yet high diversity and intricate morphological characters make identification of several species challenging. Recognition and delineation of new species thus requires high taxonomic skill, and is best supported by additional, independent information such as molecular sequence data. We therefore assume that the Drusinae are representative of taxa that are likely to be investigated using molecular species delimitation tools. The molecular markers used in this study were successfully used to infer species-level relationships in Drusinae and were also found to corroborate taxonomic status of various new species within Drusinae [55, 56, 64, 68]. We thus assume they are sufficiently informative to support molecular taxon delimitation. Indeed, combined species tree estimation and taxa delimitation inferred on a multi-locus data set via STACEY delineated 14 entities corresponding to the 14 morphologically distinguishable species included.
However, the majority of molecular species delimitation tools exploiting single-locus data only did not produce conclusive taxon hypotheses when benchmarked against morphologically distinct entities. Likewise, automated barcode gap analyses on a concatenated molecular data set and GMYC and PTP analyses of a species tree estimated through *BEAST did not recover species hypotheses corresponding to morphologically identifiable species.
Taxonomic estimates based on different loci differ distinctly, indicating a certain necessity to select loci based on their informativeness . We found delimitation results inferred on single locus data to reflect locus variability/proportion of parsimony-informative sites, as more variable loci led to a higher number of proposed species hypotheses. However, the majority of these delimitations are taxonomically not informative. We consequently suggest that, if only single locus data be used, a locus should be selected (ideally through benchmarking locus informativeness based on a known set of species-level taxa) that is conservative for single species in the focal group (e.g., ). Similar to our results, other studies report overpartitioning or overaggregation of morphological species by single-locus molecular taxa delimitation tools. Differential variability of molecular markers and differences in phylogenetic inference method used as well as effects of population sizes and speciation rates, but also low traditional taxonomic resolution were previously found to affect significance of molecular species delimitation [97, 122–125]. Altogether, single locus molecular species delimitation and identification tools currently seem limited in their general applicability. Results obtained here corroborate overaggregation and overpartitioning of morphological species by several single-locus molecular species delimitation tools, indicating a moderate potential as additional information source in integrative taxonomy [97, 122–125]. Ultimately, this problem is also related to deviation of single gene trees from the true species tree [126, 127]. STACEY likely outperforms single-locus approaches by directly integrating available information in a multispecies coalescent model to estimate a SMC tree. Further, we found the STACEY algorithm to be rather impervious to deviations in prior space controlling model speciation rate and population sizes. However, substantial variation of delimitation results can be induced when modifying ploidy settings. In line with [93–95] we argue that using the same ploidy settings for all loci represents a more robust approach to estimate species or minimal cluster trees by equally weighting variability in each locus. Our results strongly support promoting STACEY as the method of choice for integrative taxonomy, at least in caddisflies. Yet, further studies are necessary to gauge how well this method performs in other groups.
Integrating molecular species delimitation and traditional taxonomy
Molecular species delimitation and identification tools can be used to infer operational taxon units for evolutionary or ecological analysis, or to obtain alternative taxa hypotheses or discover distinct evolutionary lineages in morphologically uniform taxa [128–134]. Using these tools is particularly beneficial for studying evolutionary or ecological patterns in taxonomically understudied areas [128, 134]. When integrated into larger databases these data on evolutionary lineages and species can be further used for large-scale evolutionary or phylogenetic studies . Also, taxonomic resolution in ecological assessment of water quality could be greatly increased through molecular species identification [131, 135]. Such an approach would impart increased and standardized resolution of existing multi-metric indices and thus harbours the potential to enhance existing water resource management schemes.
However, while integrative taxonomy based on (initial) molecular genetic species identification has been proposed as remedy to the taxonomic impediment [136–138], the majority of taxonomic studies do not exploit this opportunity. This is likely due to financial or temporal limitations, and because species delimitation studies – including some of our own – do not follow through with formal species description or implementation of other taxonomic consequences.
Nevertheless, the potential to develop and test species hypotheses using molecular data is likely to act as an incentive for accelerated taxonomic and ecological research. Collaborative integrative taxonomic projects comprising molecular taxonomists and classical taxonomists are particularly likely to expedite discovery of global biodiversity. Increased collaborative taxonomic efforts will further provide a wealth of information for other disciplines of biological sciences, like biomonitoring of aquatic ecosystems [132, 133, 139–141], or conservation ecology [142–146]. Currently, constraints on integrative taxonomy, community meta-barcoding, or eDNA approaches in environmental monitoring assays are imposed by inadequate completeness and precision of molecular databases [19, 147–150]. We anticipate collaborative integrative taxonomic approaches will accelerate alpha-taxonomy and thus provide reference data in addition to on-going international efforts to develop reference libraries. Further, development of conservation management plans or novel tools for aquatic ecosystem assessments will benefit from data thus compiled.
Only multi-locus molecular species delimitation via STACEY reliably delineated molecular species corresponding to morphologically identifiable taxa, confirming a priori expectations on taxonomic significance of different molecular species delimitation tools. We assume that taxonomically relevant molecular species delimitation tools hold potential to accelerate identification of new species (cf. [137, 138]), local and global biodiversity estimation and thus enforcement of conservation policies  by providing a meaningful assessment of biodiversity richness. However, the capacities of purely molecular species identification based on single-locus data seem to have been overestimated. This clearly demonstrates that while molecular genetic procedures will likely be of relevance in routine monitoring applications, they are currently not fit to serve as surrogate for ‘classical’ explorative taxonomy [152–154].
Nevertheless, we expect multispecies-coalescent-based molecular species delimitation to mitigate the taxonomic impediment and accelerate taxonomic and ecological studies. Under the prevailing biodiversity crisis we direly need to uncover what we are losing fastest – life’s uncharted diversity [150, 154]. Thus, we advocate a truly holistic integrative taxonomy in order to comprehensively scrutinize our planets declining biodiversity, and so provide essential information for applied biologists, ecologists, conservationists and policy-makers.
The authors thank various colleagues who kindly donated material for study and/or helped us in the field collections. Karl Kjer diligently provided highly appreciated comments on an earlier version of the manuscript. We appreciate the efforts of Niklas Wahlberg and two anonymous reviewers, whose prudent comments substantially improved the quality of the manuscript.
This study was supported by the Austrian Science Funds FWF (project number P23687-B17, PI Johann Waringer); the Croatian Ministry of Science, Education and Sports (Project Nos. 119–1,193,080-1206, 119–1,193,080-3076); the University of Zagreb (Project No. 202310); the Romanian Ministry of National Education (CNCS–UEFISCDI–nr. PN–II–ID–PCE–2012–4–0595); and the research funding programme ‘LOEWE – Landes-Offensive zur Entwicklung wissenschaftlich-ökonomischer Exzellenz’ of Hesse’s Ministry of Higher Education, Research, and the Arts.
Availability of data and materials
The datasets generated and analysed during the current study are available in the BOLD (http://www.boldsystems.org/) repository, and can be retrieved using the BOLD Process IDs provided in Additional file 1. Further, the datasets are available from the corresponding author on reasonable request.
Specimens were collected and identified by MK, AP, IZ, KS, LK, MB and WG. Comparative morphological analyses were performed by SV, MK, AP, JW and WG; diagnostic characters were illustrated by SV, the species description and differential diagnosis were drafted by SV. Molecular data were generated by SV and FH; final molecular data sets were edited and compiled by SV and SUP; analyses of molecular data and compilation figures were done by SV with assistance from SUP. The manuscript was drafted by SV and SUP; MK, AP, IZ, LS, LK, MB, FH, JW and WG edited draft versions of the manuscript. All authors read, edited, and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Sites JW Jr, Marshall JC. Operational criteria for delimiting species. Annu Rev Ecol. 2004;35:199–227.View ArticleGoogle Scholar
- Dayrat B. Towards integrative taxonomy. Biol J Linn Soc. 2005;85:407–15.View ArticleGoogle Scholar
- Schlick-Steiner BC, Steiner FM, Seifert B, Stauffer C, Christian E, Crozier RH. Integrative taxonomy: a multisource approach to exploring biodiversity. Annu Rev Entomol. 2010;55:421–38.PubMedView ArticleGoogle Scholar
- Barbour MT, Gerritsen J, Snyder BD, Stribling JB: Rapid bioassessment protocols for use in wadeable streams and rivers: periphyton, benthic macroinvertebrates and fish. 2nd edition. Washington: USEPA; 1999.Google Scholar
- Barbour MT, Yoder CO: The multimetric approach to bioassessment, as used in the United States of America. In Wright JF, Sutcliffe DW, Furse MT. Ambleside, editors. Assessing the biological quality of fresh waters. Ambleside: Freshwater Biological Association; 2000:281–92.Google Scholar
- Yasuoka J, Levins R. Ecology of vector mosquitoes in Sri Lanka--suggestions for future mosquito control in rice ecosystems. Southeast Asian J Trop Med Public Health. 2007;38:646–57.PubMedGoogle Scholar
- Perrin P, Herbreteau V, Hugot JP, Morand S: Biogeography, humans, and their parasites. In Morand S, Krasnov BR, editors. The biogeography of host-parasite interactions. New York: Oxford University Press; p. 41–58.Google Scholar
- Godfray HCJ. Mosquito ecology and control of malaria. J Anim Ecol. 2013;82:15–25.PubMedView ArticleGoogle Scholar
- Osório HC, Zé-Zé L, Amaro F, Nunes A, Alves MJ. Sympatric occurrence of Culex pipiens (Diptera, Culicidae) biotypes pipiens, molestus and their hybrids in Portugal, Western Europe: feeding patterns and habitat determinants. Med Vet Entomol. 2013;28:103–9.PubMedView ArticleGoogle Scholar
- Emery NJ. Enhanced species distribution models: a case study using essential population data from Actinotus helianthi (flannel flower), PhD-Thesis, University of Sydney; 2014. p. 1–239.Google Scholar
- Harris DB, Gregory SD, Brook BW, Ritchie EG, Croft DB, Coulson G, et al. The influence of non-climate predictors at local and landscape resolutions depends on the autecology of the species. Austral Ecol. 2014;39:710–21.View ArticleGoogle Scholar
- Emery NJ, Henwood MJ, Offord CA, Wardle GM. Actinotus helianthi populations across a wide geographic range exhibit different climatic envelopes and complex relationships with plant traits. Int J Plant Sci. 2015;176:739–50.View ArticleGoogle Scholar
- Börstler J, Jöst H, Garms R, Krüger A, Tannich E, Becker N, et al. Host-feeding patterns of mosquito species in Germany. Parasit Vectors. 2016;9:1–14.Google Scholar
- Schmidt-Kloiber A, Hering D. www.freshwaterecology.info – an online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol Indic. 2015;53:271–82.View ArticleGoogle Scholar
- Graf W, Murphy J, Zamora-Muñoz C, Jesus Lopez-Rodriguez M. Distribution and ecological preferences of European freshwater organisms. Volume 1 - Trichoptera. Sofia–Moscow: Pensoft Publishers; 2008.Google Scholar
- Lenat DR, Resh VH. Taxonomy and stream ecology - the benefits of genus- and species-level identifications. J N Am Benthol Soc. 2001;20:287–98.View ArticleGoogle Scholar
- Bonada N, Prat N, Resh VH, Statzner B. Developments in aquatic insect biomonitoring: a comparative analysis of recent approaches. Annu Rev Entomol. 2006;51:495–523.PubMedView ArticleGoogle Scholar
- Waringer J, Graf W, Malicky H. Problems associated with extrapolating ecological traits to higher-than-species level exemplified in the description of the larvae of Potamophylax haidukorum Malicky, 1999, Potamophylax winneguthi (Klapálek, 1902) and Melampophylax austriacus Malicky, 1990. Limnologica. 2013;43:441–50.View ArticleGoogle Scholar
- Wheeler QD, Raven PH, Wilson EO. Taxonomy: impediment or expedient? Science. 2004;303:285.PubMedView ArticleGoogle Scholar
- Boero F. The study of species in the era of biodiversity: a tale of stupidity. Diversity. 2010;2:115–26.View ArticleGoogle Scholar
- Ebach MC, Valdecasas AG, Wheeler QD. Impediments to taxonomy and users of taxonomy: accessibility and impact evaluation. Cladistics. 2011;27:550–7.View ArticleGoogle Scholar
- Steyskal GC. Another view of the future of taxonomy. Syst Biol. 1967;16:265–8.Google Scholar
- Wheeler QD, Meier R. Species concepts and phylogenetic theory: a debate. New York: Columbia University Press; 2000.Google Scholar
- Will KW, Rubinoff D. Myth of the molecule: DNA barcodes for species cannot replace morphology for identification and classification. Cladistics. 2004;20:47–55.View ArticleGoogle Scholar
- Dubois A. Species and “strange species” in zoology: do we need a ‘unified concept of species’? Comptes Rendus Palevol. 2011;10:77–94.View ArticleGoogle Scholar
- Will KW, Mishler BD, Wheeler QD. The perils of DNA barcoding and the need for integrative taxonomy. Syst Biol. 2005;54:844–51.PubMedView ArticleGoogle Scholar
- Valdecasas AG, Williams D, Wheeler QD. “Integrative taxonomy” then and now: a response to Dayrat (2005). Biol J Linn Soc. 2008;93:211–6.View ArticleGoogle Scholar
- Tautz D, Arctander P, Minelli A, Thomas RH, Vogler AP. DNA points the way ahead of taxonomy. Nature. 2002;418:479.PubMedView ArticleGoogle Scholar
- Tautz D, Arctander P, Minelli A, Thomas RH, Vogler AP. A plea for DNA taxonomy. Trends Ecol Evol. 2003;18:70–4.View ArticleGoogle Scholar
- Hebert PDN, Ratnasingham S, de Waard JR. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc Biol Sci. 2003;270:S96–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Wiens JJ. Species delimitation: new approaches for discovering diversity. Syst Biol. 2007;56:875–8.PubMedView ArticleGoogle Scholar
- O'Meara BC. New heuristic methods for joint species delimitation and species tree inference. Syst Biol. 2010;59:59–73.PubMedView ArticleGoogle Scholar
- Rannala B. The art and science of species delimitation. Curr Zool. 2015;61:846–53.View ArticleGoogle Scholar
- Leaché AD, Fujita MK. Bayesian species delimitation in West African forest geckos (Hemidactylus fasciatus). Proc R Soc Lond B. 2010;277:3071–7.View ArticleGoogle Scholar
- Kubatko LS, Gibbs HL, Bloomquist EW. Inferring species-level phylogenies and taxonomic distinctiveness using multilocus data in Sistrurus rattlesnakes. Syst Biol. 2011;60:393–409.PubMedView ArticleGoogle Scholar
- Astrin JJ, Stüben PE, Misof B, Wägele JW, Gimnich F, Raupach MJ, et al. Exploring diversity in cryptorhynchine weevils (Coleoptera) using distance-, character- and tree-based species delineation. Mol Phylog Evol. 2012;63:1–14.View ArticleGoogle Scholar
- Bannikova AA, Zemlemerova ED, Colangelo P, Sözen M, Sevindik M, Kidov AA, et al. An underground burst of diversity - a new look at the phylogeny and taxonomy of the genus Talpa Linnaeus, 1758 (Mammalia: Talpidae) as revealed by nuclear and mitochondrial genes. Zool J Linnean Soc. 2015;175:930–48.View ArticleGoogle Scholar
- Lang AS, Bocksberger G, Stech M. Phylogeny and species delimitations in European Dicranum (Dicranaceae, Bryophyta) inferred from nuclear and plastid DNA. Mol Phylog Evol. 2015;92(C):217–25.View ArticleGoogle Scholar
- Mrinalini, Thorpe RS, Creer S, Lallias D, Dawnay L, Stuart BL, et al. Convergence of multiple markers and analysis methods defines the genetic distinctiveness of cryptic pitvipers. Mol Phylog Evol. 2015;92(C):266–79.Google Scholar
- Wu Y, Murphy RW. Concordant species delimitation from multiple independent evidence: a case study with the Pachytriton brevipes complex (Caudata: Salamandridae). Mol Phylog Evol. 2015;92(C):108–17.View ArticleGoogle Scholar
- Butcher BA, Smith MA, Sharkey MJ, Quicke DLJ. A turbo-taxonomic study of Thai Aleiodes (Aleiodes) and Aleiodes (Arcaleiodes) (Hymenoptera: Braconidae: Rogadinae) based largely on COI barcoded specimens, with rapid descriptions of 179 new species. Zootaxa. 2012;3457:1–232.Google Scholar
- Ceccarelli FS, Sharkey MJ, Zaldívar-Riverón A. Species identification in the taxonomically neglected, highly diverse, neotropical parasitoid wasp genus Notiospathius (Braconidae: Doryctinae) based on an integrative molecular and morphological approach. Mol Phylog Evol. 2012;62:485–95.View ArticleGoogle Scholar
- Riedel A, Sagata K, Surbakti S, Tänzler R, Balke M. One hundred and one new species of Trigonopterus weevils from New Guinea. ZooKeys. 2013;280:1–150.View ArticleGoogle Scholar
- Riedel A, Tänzler R, Balke M, Rahmadi C, Suhardjono YR. Ninety-eight new species of Trigonopterus weevils from Sundaland and the Lesser Sunda Islands. ZooKeys. 2014;467:1–162.Google Scholar
- Pante E, Schoelinck C, Puillandre N. From integrative taxonomy to species description: one step beyond. Syst Biol. 2014;64:152–60.PubMedView ArticleGoogle Scholar
- Steiner FM, Pautasso M, Zettel H, Moder K, Arthofer W, Schlick-Steiner BC. A falsification of the citation impediment in the taxonomic literature. Syst Biol. 2015;64:860–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Pauls SU, Blahnik RJ, Zhou X, Wardwell CT, Holzenthal RW. DNA barcode data confirm new species and reveal cryptic diversity in Chilean Smicridea (Smicridea) (Trichoptera: Hydropsychidae). J N Am Benthol Soc. 2010;29:1058–74.View ArticleGoogle Scholar
- Pauls SU, Lumbsch HT, Haase P. Phylogeography of the montane caddisfly Drusus discolor: evidence for multiple refugia and periglacial survival. Mol Ecol. 2006;15:2153–69.PubMedView ArticleGoogle Scholar
- Pauls SU, Theissinger K, Ujvarosi L, Bálint M, Haase P. Patterns of population structure in two closely related, partially sympatric caddisflies in Eastern Europe: historic introgression, limited dispersal, and cryptic diversity. J N Am Benthol Soc. 2009;28:517–36.View ArticleGoogle Scholar
- Previšić A, Graf W, Vitecek S, Kučinić M, Bálint M, Keresztes L, et al. Cryptic diversity of caddisflies in the Balkans: the curious case of Ecclisopteryx species (Trichoptera: Limnephilidae). Arthropod Sys Phylogeny. 2014;72:309–29.Google Scholar
- Previšić A, Schnitzler J, Kučinić M, Graf W, Ibrahimi H, Kerovec M, et al. Microscale vicariance and diversification of Western Balkan caddisflies linked to karstification. Freshw Sci. 2014;33:250–62.PubMedView ArticleGoogle Scholar
- Geismar J, Haase P, Nowak C, Sauer J, Pauls SU. Local population genetic structure of the montane caddisfly Drusus discolor is driven by overland dispersal and spatial scaling. Freshw Biol. 2015;60:209–21.View ArticleGoogle Scholar
- Marinković-Gospodnetić M. The differentiation of Drusus species of the group bosnicus. In: Malicky H, editor. Proceedings of the First International Symposium on Trichoptera. The Hague: Proceedings of the Ist International Symposium on Trichoptera; 1974. p. 77–85.Google Scholar
- Malicky H. Atlas of European Trichoptera. 2nd ed. Dordrecht: Springer; 2004.Google Scholar
- Vitecek S, Kučinić M, Oláh J, Previšić A, Bálint M, Keresztes L, et al. Description of two new filtering carnivore Drusus species (Limnephilidae, Drusinae) from the Western Balkans. ZooKeys. 2015;513:79–104.View ArticleGoogle Scholar
- Vitecek S, Previšić A, Kučinić M, Bálint M, Keresztes L, Waringer J, et al. Description of a new species of Wormaldia from Sardinia and a new Drusus species from the Western Balkans (Trichoptera, Philopotamidae, Limnephilidae). ZooKeys. 2015;496:85–103.View ArticleGoogle Scholar
- Ibrahimi H, Vitecek S, Previšić A, Kučinić M, Waringer J, Graf W, et al. Drusus sharrensis sp. n. (Trichoptera, Limnephilidae), a new species from Sharr National Park in Kosovo, with molecular and ecological notes. ZooKeys. 2016;559:107–24.View ArticleGoogle Scholar
- Ibrahimi H, Kučinić M, Vitecek S, Waringer J, Graf W, Previšić A, et al. New records for the Kosovo caddisfly fauna with the description of a new species, Drusus dardanicus sp. nov. (Trichoptera: Limnephilidae). Zootaxa. 2015;4032:551–68.PubMedPubMed CentralView ArticleGoogle Scholar
- Previšić A, Walton C, Kučinić M, Mitrikeski PT, Kerovec M. Pleistocene divergence of Dinaric Drusus endemics (Trichoptera, Limnephilidae) in multiple microrefugia within the Balkan peninsula. Mol Ecol. 2009;18:634–47.PubMedView ArticleGoogle Scholar
- Böhm A, Bartel D, Szucsich NU, Pass G. Confocal imaging of the exo- and endoskeleton of Protura after non-destructive DNA extraction. Soil Organisms. 2011;83:335–45.Google Scholar
- Nielsen A. A comparative study of the genital segments and their appendages in male trichoptera. Biol Skr Kong Dansk Vid Sel. 1957;8:1–159.Google Scholar
- Snodgrass RE. Principles of insect morphology. New York–London: McGraw-Hill Book Company; 1935.Google Scholar
- Thomson RE, Holzenthal RW. New Neotropical species of the genus Austrotinodes Schmid (Trichoptera: Ecnomidae). Zootaxa. 2010;2437:38–50.Google Scholar
- Vitecek S, Graf W, Previšić A, Kučinić M, Oláh J, Bálint M, et al. A hairy case: the evolution of filtering carnivorous Drusinae (Limnephilidae, Trichoptera). Mol Phylog Evol. 2015;93:249–60.View ArticleGoogle Scholar
- Malm T, Johanson KA, Wahlberg N. The evolutionary history of Trichoptera (Insecta): a case of successful adaptation to life in freshwater. Syst Ent. 2013;38:459–73.View ArticleGoogle Scholar
- Zhou X, Frandsen PB, Holzenthal RW, Beet CR, Bennett KR, Blahnik RJ, et al. The Trichoptera barcode initiative: a strategy for generating a species-level tree of life. Philos Trans R Soc B Biol Sci. 2016;371:20160025–11.View ArticleGoogle Scholar
- Zhou X, Kjer KM, Morse JC. Associating larvae and adults of Chinese Hydropsychidae caddisflies (Insecta:Trichoptera) using DNA sequences. J N Am Benthol Soc. 2007;26:719–42.View ArticleGoogle Scholar
- Pauls SU, Graf W, Haase P, Lumbsch HT, Waringer J. Grazers, shredders and filtering carnivores—the evolution of feeding ecology in Drusinae (Trichoptera: Limnephilidae): insights from a molecular phylogeny. Mol Phylog Evol. 2008;46:776–91.View ArticleGoogle Scholar
- Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28:1647–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.PubMedPubMed CentralView ArticleGoogle Scholar
- Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H, Xie D, et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol. 2014;10:e1003537–6.PubMedPubMed CentralView ArticleGoogle Scholar
- Rambaut A, Suchard MA, Xie D, Drummond AJ. Tracer v1.6. http://beast.bio.ed.ac.uk/Tracer. Accessed 12 Feb 2014.
- Drummond AJ, Suchard MA, Xie D, Rambaut A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol Biol Evol. 2012;29:1969–73.PubMedPubMed CentralView ArticleGoogle Scholar
- Heled J, Drummond AJ. Bayesian inference of species trees from multilocus data. Mol Biol Evol. 2010;27:570–80.PubMedView ArticleGoogle Scholar
- Zhang J, Kapli P, Pavlidis P, Stamatakis A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics. 2013;29:2869–76.PubMedPubMed CentralView ArticleGoogle Scholar
- O'Meara BC, Ané C, Sanderson MJ, Wainwright PC. Testing for different rates of continuous trait evolution using likelihood. Evolution. 2006;60:922–33.PubMedView ArticleGoogle Scholar
- Kubatko LS, Carstens BC, Knowles LL. STEM: species tree estimation using maximum likelihood for gene trees under coalescence. Bioinformatics. 2009;25:971–3.PubMedView ArticleGoogle Scholar
- Kubatko LS. Identifying hybridization events in the presence of coalescence via model selection. Syst Biol. 2009;58:478–88.PubMedView ArticleGoogle Scholar
- Ence DD, Carstens BC. SpedeSTEM: a rapid and accurate method for species delimitation. Mol Ecol Resour. 2011;11:473–80.PubMedView ArticleGoogle Scholar
- Puillandre N, Lambert A, Brouillet S, Achaz G. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Mol Ecol. 2012;21:1864–77.PubMedView ArticleGoogle Scholar
- Jukes TH, Cantor CR. Evolution of protein molecules. In: Munro HN, editor. Mammalian protein metabolism III. New York: Academic Press Elsevier; 1969. p. 21–132.View ArticleGoogle Scholar
- Kimura M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol. 1980;16:111–20.PubMedView ArticleGoogle Scholar
- Kekkonen M, Hebert PDN. DNA barcode-based delineation of putative species: efficient start for taxonomic workflows. Mol Ecol Resour. 2014;14:706–15.PubMedPubMed CentralView ArticleGoogle Scholar
- Kekkonen M, Mutanen M, Kaila L, Nieminen M, Hebert PDN. Delineating species with DNA barcodes: a case of taxon dependent method performance in moths. PLoS One. 2015;10:e0122481–32.PubMedPubMed CentralView ArticleGoogle Scholar
- Fujisawa T, Barraclough TG. Delimiting species using single-locus data and the generalized mixed yule coalescent approach: a revised method and evaluation on simulated data sets. Syst Biol. 2013;62:707–24.PubMedPubMed CentralView ArticleGoogle Scholar
- Pons J, Barraclough T, Gomez-Zurita J, Cardoso A, Duran D, Hazell S, et al. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Syst Biol. 2006;55:595–609.PubMedView ArticleGoogle Scholar
- Ezard T, Fujisawa T, Barraclough TG: Splits: SPecies’ LImits by Threshold Statistics. 2014. R package version 1.0-19/r51. http://r-forge.r-project.org/projects/splits/.Google Scholar
- R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015. http://www.r-project.org/ Google Scholar
- Jones G. Algorithmic improvements to species delimitation and phylogeny estimation under the multispecies coalescent. J Math Biol. 2017;74:447–67.PubMedView ArticleGoogle Scholar
- Jones G, Aydin Z, Oxelman B. DISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent. Bioinformatics. 2015;31:991–8.PubMedView ArticleGoogle Scholar
- Drummond AJ, Bouckaert RR. Bayesian evolutionary analysis with BEAST. Cambridge: Cambridge University Press; 2015.View ArticleGoogle Scholar
- Jacobsen F, Omland KE. Species tree inference in a recent radiation of orioles (Genus Icterus): multiple markers and methods reveal cytonuclear discordance in the northern oriole group. Mol Phylog Evol. 2011;61:460–9.View ArticleGoogle Scholar
- DuBay SG, Witt CC. An improved phylogeny of the Andean tit-tyrants (Aves, Tyrannidae): more characters trump sophisticated analyses. Mol Phylog Evol. 2012;64:285–96.View ArticleGoogle Scholar
- Jockusch EL, Martinez-Solano I, Timpe EK. The effects of inference method, population sampling, and gene sampling on species tree inferences: an empirical study in slender salamanders (Plethodontidae: Batrachoseps). Syst Biol. 2014;64:66–83.PubMedView ArticleGoogle Scholar
- Jones G. speciesDA.jar. http://www.indriid.com/2014/speciesDA.jar. Accessed 18 June 2015.
- Tang CQ, Humphreys AM, Fontaneto D, Barraclough TG, Paradis E. Effects of phylogenetic reconstruction method on the robustness of species delimitation using single-locus data. Methods Ecol Evol. 2014;5:1086–94.PubMedPubMed CentralView ArticleGoogle Scholar
- Waringer J, Previšić A, Kučinić M, Graf W, Vitecek S, Keresztes L, et al. Larval morphology of the Western Balkans endemic caddisflies Drusus krusniki Malicky 1981, D. vernonensis Malicky 1989, and D. vespertinus Marinković 1976 (Trichoptera, Limnephilidae, Drusinae). Zootaxa. 2016;4083:483–500.Google Scholar
- Waringer J, Graf W, Bálint M, Kučinić M, Pauls SU, Previšić A, et al. Larval morphology and phylogenetic position of Drusus balcanicus, D. botosaneanui, D. serbicus and D. tenellus (Trichoptera: Limnephilidae: Drusinae). Eur J Entomol. 2015;112:344–61.Google Scholar
- Griffiths HI, Kryštufek B, Reed JM. Balkan biodiversity. Dordrecht: Kluwer Academic Publishers; 2004.View ArticleGoogle Scholar
- Petkovski T, Scharf BW, Keyser D. Freshwater Ostracoda (Crustacea) collected from caves and the interstitial habitat in Herzegovina, NW Balkan, with the description of two new species. Bull Soc Nat Luxemb. 2009;110:173–82.Google Scholar
- Wilke T, Schultheiß R, Albrecht C, Bornmann N, Trajanovski S, Kevrekidis T. Native Dreissena freshwater mussels in the Balkans: in and out of ancient lakes. Biogeosciences. 2010;7:3051–65.Google Scholar
- Pešić V, Glöer P. A new freshwater snail genus (Hydrobiidae, Gastropoda) from Montenegro, with a discussion on gastropod diversity and endemism in Skadar Lake. ZK. 2013;281:69–90.View ArticleGoogle Scholar
- Ivković M, Plant A. Aquatic insects in the Dinarides: identifying hotspots of endemism and species richness shaped by geological and hydrological history using Empididae (Diptera). Insect Conserv Divers. 2015;8:302–12.View ArticleGoogle Scholar
- Müller-Peddinghaus E, Hering D. The wing morphology of limnephilid caddisflies in relation to their habitat preferences. Freshw Biol. 2013;58:1138–48.View ArticleGoogle Scholar
- Graf W, Vitecek S. A new species of Limnephilidae (Insecta: Trichoptera) from the Western Alps (Insecta: Trichoptera). Zootaxa. 2016;4085:431–7.PubMedPubMed CentralView ArticleGoogle Scholar
- Graf W, Vitecek S, Previšić A, Malicky H. New species of Limnephilidae (Insecta: Trichoptera) from Europe: Alps and Pyrenees as harbours of unknown biodiversity. Zootaxa. 2015;3911:381–95.PubMedPubMed CentralView ArticleGoogle Scholar
- Graf W, Lorenz AW, Tierno de Figueroa JM, Lücke S, Jesus Lopez-Rodriguez M, Davies C. Distribution and ecological preferences of European freshwater organisms. Volume 2 - Plecoptera. Sofia–Moscow: Pensoft Publishers; 2009.Google Scholar
- Fontaine B, van Achterberg K, Alonso-Zarazaga MA, Araujo R, Asche M, Aspöck H, et al. New species in the old world: Europe as a frontier in biodiversity exploration, a test bed for 21st century taxonomy. PLoS One. 2012;7:e36881–7.Google Scholar
- Schwarz U. Hydropower projects on the Balkan Rivers – update. Vienna: RiverWatch & EuroNatur; 2015.Google Scholar
- Schwarz U. Balkan Rivers – the blue heart of Europe. Vienna: RiverWatch & EuroNatur; 2012.Google Scholar
- Hering D, Schmidt-Kloiber A, Murphy J, Lücke S, Zamora-Muñoz C, Jesus Lopez-Rodriguez M, et al. Potential impact of climate change on aquatic insects: a sensitivity analysis for European caddisflies (Trichoptera) based on distribution patterns and ecological preferences. Aquat Sci. 2009;71:3–14.View ArticleGoogle Scholar
- de Figueroa JMT, López-Rodríguez MJ, Lorenz A, Graf W, Schmidt-Kloiber A, Hering D. Vulnerable taxa of European Plecoptera (Insecta) in the context of climate change. Biodivers Conserv. 2010;19:1269–77.View ArticleGoogle Scholar
- Bálint M, Domisch S, Engelhardt CHM, Haase P, Lehrian S, Sauer J, et al. Cryptic biodiversity loss linked to global climate change. Nat Clim Chang. 2011;1:313–8.Google Scholar
- Conti L, Schmidt-Kloiber A, Grenouillet G, Graf W. A trait-based approach to assess the vulnerability of European aquatic insects to climate change. Hydrobiologia. 2014;721:297–315.View ArticleGoogle Scholar
- Foster GN: Conserving insects of aquatic and wetland habitats, with special reference to beetles. In Collins NM, Thomas JA, editors. The conservation of insects and their habitats. London; 1991:237–262.Google Scholar
- Polhemus DA. Conservation of aquatic insects: worldwide crisis or localized threats? Am Zool. 1993;33:588–98.View ArticleGoogle Scholar
- Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ, Lévêque C, et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev. 2006;81:163–82.PubMedView ArticleGoogle Scholar
- Barquin J, Death RG. Patterns of invertebrate diversity in streams and freshwater springs in Northern Spain. Arch Hydrobiol. 2004;161:329–49.View ArticleGoogle Scholar
- Vidic RD, Brantley SL, Vandenbossche JM, Yoxtheimer D, Abad JD. Impact of shale gas development on regional water quality. Science. 2013;340:1235009.PubMedView ArticleGoogle Scholar
- Zarfl C, Lumsdon AE, Berlekamp J, Tydecks L, Tockner K. A global boom in hydropower dam construction. Aquat Sci. 2014;77:161–70.View ArticleGoogle Scholar
- Olave M, Sola E, Knowles LL. Upstream analyses create problems with DNA-based species delimitation. Syst Biol. 2014;63:263–71.PubMedView ArticleGoogle Scholar
- Talavera G, Dincă V, Vila R. Factors affecting species delimitations with the GMYC model: insights from a butterfly survey. Methods Ecol Evol. 2013;4:1101–10.View ArticleGoogle Scholar
- Dellicour S, Flot J-F. Delimiting species-poor data sets using single molecular markers: a study of barcode gaps, Haplowebs and GMYC. Syst Biol. 2015;64:900–8.PubMedView ArticleGoogle Scholar
- Schwarzfeld MD, Sperling FAH. Comparison of five methods for delimitating species in Ophion Fabricius, a diverse genus of parasitoid wasps (Hymenoptera, Ichneumonidae). Mol Phylog Evol. 2015;93:234–48.View ArticleGoogle Scholar
- Pamilo P, Nei M. Relationships between gene trees and species trees. Mol Biol Evol. 1988;5:568–83.PubMedGoogle Scholar
- Maddison WP. Gene trees in species trees. Syst Biol. 1997;46:523–36.View ArticleGoogle Scholar
- Craft KJ, Pauls SU, Darrow K, Miller SE, Hebert PDN, Helgen LE, et al. Population genetics of ecological communities with DNA barcodes: an example from New Guinea Lepidoptera. Proc Natl Acad Sci. 2010;107:5041–6.Google Scholar
- Keith R, Hedin M. Extreme mitochondrial population subdivision in southern Appalachian paleoendemic spiders (Araneae: Hypochilidae: Hypochilus), with implications for species delimitation. J Arachnol. 2012;40:167–81.View ArticleGoogle Scholar
- Parmakelis A, Kotsakiozi P, Stathi I, Poulikarakou S, Fet V. Hidden diversity of Euscorpius (Scorpiones: Euscorpiidae) in Greece revealed by multilocus species-delimitation approaches. Biol J Linn Soc. 2013;110:728–48.Google Scholar
- Pauls SU, Alp M, Bálint M, Bernabò P, Ciampor FJ, Čiamporová-Zaťovičová Z, et al. Integrating molecular tools into freshwater ecology: developments and opportunities. Freshw Biol. 2014;59:1559–76.View ArticleGoogle Scholar
- Stein ED, Martinez MC, Stiles S, Miller PE, Zakharov EV. Is DNA barcoding actually cheaper and faster than traditional morphological methods: results from a survey of freshwater bioassessment efforts in the United States? PLoS One. 2014;9:e95525.PubMedPubMed CentralView ArticleGoogle Scholar
- Zimmermann J, Glöckner G, Jahn R, Enke N, Gemeinholzer B. Metabarcoding vs. morphological identification to assess diatom diversity in environmental studies. Mol Ecol Resour. 2014;15:526–42.PubMedView ArticleGoogle Scholar
- Hoppeler F, Tachamo Shah RD, Shah DN, Jähnig SC, Tonkin JD, Sharma S, et al. Environmental and spatial characterisation of an unknown fauna using DNA sequencing - an example with Himalayan Hydropsychidae (Insecta: Trichoptera). Freshw Biol. 2016;61:1905–20.View ArticleGoogle Scholar
- Leese F, Altermatt F, Bouchez A, Ekrem T, Hering D, Meissner K, et al. DNAqua-net: developing new genetic tools for bioassessment and monitoring of aquatic ecosystems in Europe. RIO. 2016;2:e11321–4.View ArticleGoogle Scholar
- Godfray HCJ. Linnaeus in the information age. Nature. 2007;446:259–60.PubMedView ArticleGoogle Scholar
- Tänzler R, Sagata K, Surbakti S, Balke M, Riedel A. DNA barcoding for community ecology-how to tackle a hyperdiverse, mostly undescribed Melanesian fauna. PLoS One. 2012;7:e28832.PubMedPubMed CentralView ArticleGoogle Scholar
- Hubert N, Hanner R. DNA barcoding, species delineation and taxonomy: a historical perspective. DNA Barcodes. 2015;3:44–58.Google Scholar
- Stein ED, White BP, Mazor RD, Jackson JK, Battle JM, Miller PE, et al. Does DNA barcoding improve performance of traditional stream bioassessment metrics? Freshw Sci. 2014;33:302–11.View ArticleGoogle Scholar
- Elbrecht V, Leese F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS One. 2015;10:e0130324.PubMedPubMed CentralView ArticleGoogle Scholar
- Valentini A, Taberlet P, Miaud C, Civade R, Herder J, Thomsen PF, et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol Ecol. 2016;25:929–42.PubMedView ArticleGoogle Scholar
- Moritz C. Applications of mitochondrial DNA analysis in conservation: a critical review. Mol Ecol. 1994;3:401–11.View ArticleGoogle Scholar
- Haig SM. Molecular contributions to conservation. Ecology. 1998;79:413–25.View ArticleGoogle Scholar
- Bickford D, Lohman DJ, Sodhi NS, Ng PKL, Meier R, Winker K, et al. Cryptic species as a window on diversity and conservation. Trends Ecol Evol. 2007;22:148–55.PubMedView ArticleGoogle Scholar
- Haig SM, Bronaugh WM, Crowhurst RS, D'Elia J, Eagles-Smith CA, Epps CW, et al. Genetic applications in avian conservation. Auk. 2011;128:205–29.Google Scholar
- Bock F, Fennessy J, Bidon T, Tutchings A, Marais A, Deacon F, et al. Mitochondrial sequences reveal a clear separation between Angolan and South African giraffe along a cryptic rift valley. BMC Evol Biol. 2014;14:1–12.View ArticleGoogle Scholar
- Harris DJ. Can you bank on GenBank? Trends Ecol Evol. 2003;18:317–9.View ArticleGoogle Scholar
- Nilsson RH, Ryberg M, Kristiansson E, Abarenkov K, Larsson K-H, Kõljalg U. Taxonomic reliability of DNA sequences in public sequence databases: a fungal perspective. PLoS One. 2006;1:e59–4.PubMedPubMed CentralView ArticleGoogle Scholar
- Boykin LM, Armstrong K, Kubatko L, De Barro P. DNA barcoding invasive insects: database roadblocks. Invertebr Syst. 2012;26:506–14.View ArticleGoogle Scholar
- Collins RA, Cruickshank RH. The seven deadly sins of DNA barcoding. Mol Ecol Resour. 2012;13:969–75.PubMedGoogle Scholar
- Cardoso P, Erwin TL, Borges PAV, New TR. The seven impediments in invertebrate conservation and how to overcome them. Biol Conserv. 2011;144:2647–55.View ArticleGoogle Scholar
- Sluys R. The unappreciated, fundamentally analytical nature of taxonomy and the implications for the inventory of biodiversity. Biodivers Conserv. 2013;22:1095–105.View ArticleGoogle Scholar
- Solís-Lemus C, Knowles LL, Ané C. Bayesian species delimitation combining multiple genes and traits in a unified framework. Evolution. 2015;69:492–507.PubMedView ArticleGoogle Scholar
- Dijkstra K-DB. Restore our sense of species. Nature. 2016;533:172–4.PubMedView ArticleGoogle Scholar