Introduction

Over the past two decades, the spectacular aggregation of species in biodiversity hotspots has attracted attention by scientists and stakeholders alike1,2,3,4. However, this exceptional concentration of often-endemic species at small spatial scales is threatened by the rise of anthropogenic disturbances. Of the 26 initially identified terrestrial biodiversity hotspots1, the ones located in Southeast Asia (SEA) (Indo-Burma, Philippines, Sundaland and Wallacea) currently rank among the most important both in terms of species richness and the extend of endemism but also rank as the most threatened by human activities3. Sundaland is currently the most diverse terrestrial biodiversity hotspot of SEA and is the most threatened5. Sundaland comprises Peninsular Malaysia and the islands of Java, Sumatra, Borneo, and Bali and its diversity originates from the complex geological history of the region, linked to major tectonic changes in the distribution of land and sea during the last 50 Million years (My)6, but also from eustatic fluctuations that have sporadically connected and disconnected Sundaland landmasses during glacial-interglacial cycles in the Pleistocene7,8,9. Therefore, Sundaland biogeography has received increased attention over the past decade resulting in the detection of contrasting spatial and temporal patterns in various groups9,10,11,12,13,14.

Species richness within vertebrate groups is high in Sundaland1 and freshwater fishes are no exceptions to that. With more than 900 species reported to date, and with nearly 45 percent of endemism, Sundaland’s ichthyofauna is the largest in SEA and accounts for nearly 75 percent of the entire ichthyodiversity of the Indonesian archipelago15. The inventory of Sundaland’s freshwater fishes started more than two centuries ago and despite the acceleration of species discovery over the last three decades, it is still a work in progress15. The complexity of this inventory was partly exacerbated by the abundance of minute species i.e. less than 5 cm length15, but also by the inconsistent use of species names through time for old descriptions due to the loss of type specimens or uncertainties in the location of type localities16,17. The family Cyprinidae sensu lato is a particularly good example for the complexity of Sundaland freshwater fishes taxonomy and systematics. The systematics of this large family of Cypriniformes, with over 3,000 species, has been controversial for more than a century18. Based on recent molecular phylogenetic studies19,20,21, Tan and Armbruster22 proposed a new classification dividing the Cyprinidae sensu lato into 12 families. The subfamily Rasborinae (Cypriniformes, Cyprinoidei, Danionidae) comprises roughly 140 species in 11 genera: Amblypharyngodon, Boraras, Brevibora, Horadandia, Kottelatia, Pectenocypris, Rasbora, Rasboroides, Rasbosoma, Trigonopoma, and Trigonostigma22. In Sundaland the subfamily Rasborinae is represented by 79 species in 7 genera. The genera Amblypharyngodon, Horadandia, Rasboroides, and Rasbosoma do not occur in Sundaland. By far the most species rich rasborine genus is Rasbora with over 100 species in total and 65 species in Sundaland. Long considered a catch-all group, several attempts have been made to provide a classification of the genus Rasbora that reflects phylogeny. In a comprehensive revision, Brittan23 recognized 3 subgenera (Rasbora, Rasboroides, and Megarasbora) and divided Rasbora into 8 species complexes, now regarded as species groups24 (Fig. 1). Subsequent authors have erected several new genera or suggested new species composition for the various Rasbora species groups19,24,25,26. Clearly, to better understand the evolutionary history of this unique group, the taxonomy and systematic of the Rasborinae needs to be better understood.

Figure 1
figure 1

Selected species of Rasborinae that illustrate the diversity of the subfamily in Sundaland. All pictures, except 1, 6.1, 7.1 and 7.2, originate from the Barcode of Life Datasystem (dx.doi.org/110.5883/DS-BIFRA, Creative Commons Attribution - Non Commercial - Share Alike), pictures 1, 6.1 and 7.2 originate from FFish.asia (https://ffish.asia, Creative Commons Attribution - Non Commercial - Share Alike).

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The use of standardized DNA-based approaches to the inventory of Sundaland’s ichthyofauna resulted in the detection of considerable knowledge gaps16,17,27. In addition, substantial levels of cryptic diversity (i.e. morphologically unrecognized diversity) were repeatedly reported for a wide range of Sundaland freshwater fish taxa10,27,28,29,30,31,32,33 including the Rasborinae16. The taxonomy of most Rasborinae species, particularly so for the genus Rasbora, remains challenging due to the diversity of the group and the morphological similarity of many closely related species. As a consequence, the actual distribution ranges of many species of Rasborinae are not well known.

This study aims to re-examine Rasborinae diversity in Sundaland. We generated a DNA barcode reference library to (1) explore biological species boundaries with DNA-based species delimitation methods, (2) validate species identity, taxonomy and precise range distribution by producing DNA barcodes from type localities or neighboring watersheds, (3) validate or revise of the previously published DNA barcodes records for the subfamily Rasborinae available on GenBank.

Results

Sequencing of the DNA barcode marker Cytochrome Oxidase 1 (COI) yielded a total of 991 new sequences (Table S2) from 189 sampling sites distributed across Sundaland (Fig. 2). Together with 106 DNA barcodes mined from GenBank and BOLD (Table S3), we assembled a DNA barcode reference library of 1,097 sequences from 65 taxa of Rasborinae and 1 taxon of Sundadanionidae (Sundadanio retiarius). The number of specimens analyzed per species ranged from 1 to 143, with an average of 14.6 sequences per species and only six species represented by a single sequence. The sequences ranged from 459 bp to 651 bp long, with 99 percent of the sequences being above 500 bp length, and no stop codons were detected, suggesting that all the sequences correspond to functional mitochondrial COI sequences. DNA barcodes for 61 of the 79 nominal species of Rasborinae reported from Sundaland were recovered (approximately 78%) corresponding to the 7 Rasborinae genera currently recognized (Table S1). The present study achieved a complete coverage at the species level for the genera Boraras (2 species), Brevibora (3 species), Kottelatia (1 species), Trigonopoma (2 species) and Trigonostigma (3 species). In turn, two out of the three Pectenocypris species (66%) and 48 out of the 65 Rasbora species (74%) currently recognized in Sundaland were collected (Table S1). Geographically, our dataset includes 86% of the Rasborinae of Borneo (38 out of 44), all the Rasbora species of Java (4 species) and 68% of the Rasborinae species of Sumatra (26 out of 38) were collected (Table S1). Finally, four undescribed taxa are highlighted, two taxa of Rasbora in Java, one taxon of Trigonostigma in Borneo (Table S2) and an additional Rasbora taxon, previously assigned to R. paucisqualis in the literature (Table S3).

Figure 2
figure 2

Collection sites for the newly generated 991 samples analyzed here. Each dot may represent several collection sites. Map data: Google, DigitalGlobe. Modified using Adobe Illustrator CS5 v 15.0.2. http://www.adobe.com/products/illustrator.html.

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Species delimitation analyses provided varying numbers of Operational Taxonomic Units (OTUs) among methods (Fig. 3): 129 for PTP, 95 for mPTP, 178 for GMYC, 191 for mGMYC, 175 for ABGD and 146 for RESL (Table S3). Our consensus delimitation scheme yielded 166 OTUs, including 165 OTUs for the 65 Rasborinae taxa, 2.5-fold more than by using morphological characters. The number of OTUs observed within species ranged from two for 22 species to 11 for Trigonostigma pauciperforata (Table 1). Based on the results of the species delimitation analyses, a re-examination of the original species identity associated with 105 DNA barcodes mined from BOLD and GenBank revealed 13 cases of conflicts that likely originated from mis-identifications (Table S4). These concerned the genera Boraras (four records, two species), Brevibora (two records, two species) and Rasbora (seven records, six species). Along the same line, 12 uncertain identifications were revised for the genera Rasbora (10 records, five taxa) and Trigonostigma (two records, one taxa).

Figure 3
figure 3

Bayesian maximum credibility tree of the Rasborinae DNA barcodes (identical haplotypes removed) and species delimitation according to GMYC, mGMYC, PTP, mPTP, ABGD, BIN and the 50% consensus delimitation.

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Table 1 List of the morphological species displaying more than one OTU including the maximum intraspecific and minimum nearest neighbor K2P distances for species and OTUs.
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The examination of the maximum K2P genetic distances for species with multiple OTUs and within OTUs revealed large differences with maximum K2P distances ranging between 0.26 and 13.64 within species and between 0.00 and 2.37 within OTUs (Table 1). This trend was largely confirmed by the distribution of the genetic distances at both species and OTUs levels (Fig. 4). At the species level, the distribution of the maximum intraspecific K2P genetic distance broadly overlap with the distribution of the K2P distances to the nearest neighbor (Fig. 4A,B, Table S5) and no barcoding gap is observed. On average, the nearest neighbor K2P genetic distances are only 3.5-fold higher than the maximum intraspecific K2P distances. Plotting genetic distance for each species provides little improvement as a substantial number of species display maximum intraspecific K2P genetic distances higher than the minimum distance to the nearest neighbor (Fig. 4C). At the OTU level, the overlap is drastically reduced peaking between 0 and 0.99 for maximum intraspecific K2P distances and ranging between 1.0 and 1.99 for the K2P distance to the nearest neighbor (Fig. 4D,E). The distribution width of the maximum intraspecific K2P distance is much more restricted for OTUs than species and fewer cases of maximum intraspecific distance higher than the minimum distance to the nearest neighbor are observed (Fig. 4F). At the OTU level, the nearest neighbor K2P genetic distances were 7.2-fold higher on average than the maximum intraspecific K2P genetic distances.

Figure 4
figure 4

Summary of the distribution of the K2P distances. (A,D) Maximum intraspecific K2P distances; (B,E) Minimum nearest-neighbor K2P distances; (C,F) Individual plotting of maximum intraspecific K2P distances and minimum nearest neighbor K2P distances. (A–C) Distributions of K2P distances for species delimitated using morphological characters. (D–F) Distributions of K2P distances for OTUs delimitated by the 50% consensus among species delimitation methods.

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Range distributions inferred from the new records generated for this study indicate that most type localities are embedded in the observed species range (Fig. 5). The degree of overlap between species range, however, largely varies among genera with little or no overlap observed for Boraras, Pectenocypris, Trigonostigma and most Rasbora species while a substantial amount of overlap is observed for Brevibora and Trigonopoma species (Fig. 5).

Figure 5
figure 5

Maps depicting species distribution ranges as established based on the present sampling sites (black margin) and type localities (white margin) following the checklist generated for this study (Table S1). 1 Sampling sites and type localities of Boraras maculatus and B. merah. 2 Sampling sites, type localities and distribution ranges of Brevibora cheeya, B. dorsiocellata and B. exilis. 3 Type locality of Kottelatia brittani, sampling sites unknown, sequences originating from GenBank. 4 Sampling sites, type localities and distribution ranges of Pectenocypris korthausea and P. balaena. 5 Sampling sites, type localities and distribution ranges of Trigonopoma gracile, T. pauciperforatum and T. sp16. 6 Sampling sites, type localities and distribution ranges of Trigonostigma heteromorpha, T. hengeli (sampling sites outside the map); T. espei not displayed, type locality outside the map and sampling sites unknown, sequences originating from GenBank. 7 Sampling sites, type localities and distribution ranges of Rasbora argyrotaenia, R. sumatrana and R. borapetensis, multiple type localities for Rasbora argyrotaenia as detailed in16, Type locality of R. borapetensis outside the map. 8 Sampling sites, type localities and distribution ranges of Rasbora caudimaculata, R. trilineata and R. meinkeni, sampling sites of R. meinkeni unknown, sequence originating from GenBank. 9 Sampling sites, type localities and distribution ranges of Rasbora vulcanus, R. maninjau, R. subtilis and R. jacobsoni. 10 Sampling sites, type localities and distribution ranges of Rasbora tawarensis, R. paviana, R. sp. 116, R. volzii, R. tubbi, R. laticlavia and R. vaillanti, sampling sites corresponding to the type locality for Rasbora tawarensis. 11 Sampling sites, type localities and distribution ranges of Rasbora vulgaris, R. arundinata and R. chrysotaenia, type locality of Rasbora chrysotaenia located in Sumatra with no further details (Table S1). 12 Sampling sites, type localities and distribution ranges of Rasbora aprotaenia, R. elegans and R. spilotaenia. 13 Sampling sites, type localities and distribution ranges of Rasbora sarawakensis, R. rutteni, R. lacrimula, R. hubbsi, R. semilineata and R. kalbarensis. 14 Sampling sites, type localities and distribution ranges of Rasbora sp216, R. lateristriata and R. baliensis. 15 Sampling sites, type localities and distribution ranges of Rasbora beauforti, R. johannae, R. trifasciata and R. paucisqualis. 16 Sampling sites, type localities and distribution ranges of Rasbora tuberculata, R. ennealepis and R. bankanensis. 17 Sampling sites, type localities and distribution ranges of Rasbora tornieri and R. dusonensis. 18 Sampling sites, type localities and distribution ranges of Rasbora cephalotaenia, R. kottelati and R. kalochroma. 19 Sampling sites, type localities and distribution ranges of Rasbora patrickyapi and R. einthovenii. 20 Sampling sites, type localities and distribution ranges of Rasbora borneensis, R. myersi, R. rheophila. Sampling sites and type locality of R. daniconius not displayed, outside the map. Each locality may represent several sampling sites. Map data: https://maps-for-free.com/. Modified using Adobe Illustrator CS5 v 15.0.2. http://www.adobe.com/products/illustrator.html.

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Discussion

This study represents the most comprehensive molecular survey conducted for the subfamily Rasborinae19,34. Our DNA barcode reference library consists of 65 Rasborinae species distributed across 7 genera and covering 78% of the Rasborinae diversity reported from Sundaland. DNA barcoding delivers reliable species-level identifications when taxa possess unique COI sequence clusters characterized by multiple private mutations. This condition was met for all the Rasborinae species examined here and no cases of retention of ancestral polymorphism were detected35. However, this clearly contrasts with multiples discrepancies observed within the set of previously published COI sequences obtained on GenBank and BOLD. About 25 percent of these records were either misidentified or associated with uncertain identifications. Such mis-identifications were expected considering the morphological uniformity within some Rasborinae genera, particularly in the genus Rasbora where multiple cases of taxonomic conflicts have been highlighted already16,36,37,38,39. Unexpectedly, most of the conflicts we detected were within the larger species of Rasbora, particularly those of the Rasbora argyrotaenia group and the R. sumatra group, and not within closely related smaller species such as members of the R. trifasciata group (Fig. 1). In facts, conflicts in species level population assignments have been previously reported for the R. argyrotaenia group in Java and Bali where R. lateristriata and R. baliensis have been confounded for decades as recently revealed by re-examination of species boundaries and distribution through DNA barcodes16. Other morphologically similar species of the Rasbora argyrotaenia group have been previously confused with R. lateristriata, such as R. elegans, R. spilotaenia and R. chrysotaenia. These species are difficult to separate due to overlapping meristic counts and coloration patterns40. Our study, however, highlights that these species have disjunct range distributions (Fig. 5) and cluster into well-differentiated mitochondrial lineages (Fig. S1, Table S3). Several of the detected misidentifications also involve species from different Rasbora species groups24 such as Rasbora dusonensis, from the R. argyrotaenia group, that has been previously mistaken for R. sumatrana from the sumatrana group and R. myersi, from the R. sumatrana group, that has been confounded with R. dusonensis from the argyrotaenia group. Despite being distantly related (Fig. S1), these species show overlapping meristic counts and similar coloration patterns with no dark spots on the body40. This result further calls for a broader assessment of the monophyly of the different Rasbora groups, previously identified by Liao24, as they are poorly supported by our study (Fig. S1).

The observed average ratio of 3.5 between intraspecific and interspecific distances is very low compared to earlier values found for the Javanese ichtyofauna, where minimum nearest neighbor genetic distances are on average 28-fold higher than the maximum intraspecific genetic distances27. This value is also very low in comparison to previous large-scale fish DNA barcode surveys41,42,43,44,45,46. This deviation can be attributed to a substantial amount of cryptic diversity revealed by our species delimitation analyses. For 61 species, delimitated on the basis of morphological characters, and validated by a match between species range distributions and type localities, we recovered a total of 166 OTUs. When accounting for this cryptic diversity the ratio between the minimum nearest neighbor and maximum intraspecific distances rose to 7.5. Earlier large scale surveys in Sundaland already pointed to substantial levels of cryptic diversity28,29,30,31,33 and it has also been demonstrated that small-size species are more sensitive to fragmentation, experience faster genetic drift and as such accumulate cryptic diversity at a faster rate than large-size species45,47. Along the same line, small-size species are more frequently confounded and lumped together, a bias that tend to inflate the proportion of hidden diversity48.

We found very high numbers of OTUs with deep genetic divergences (up to 13.64% in Trigonopoma gracile) in a number of species (ranging from 7 to 11) such as in Rasbora bankanensis, Rasbora einthovenii, Rasbora trilineata, Trigonopoma gracile and Trigonopoma pauciperforatum. These five species also display some of the widest range distributions in Sundaland with OTUs occurring in Borneo, Sumatra, Peninsular Malaysia and several small islands across the Java sea (R. bankanensis, Fig. 5(16); R. einthovenii, Fig. 5(19); R. trilineata, Fig. 5(8); T. gracile, Fig. 5(5); T. pauciperforatum, Fig. 5(4)). However, the scarcity of OTU range overlap for those species suggests ongoing population fragmentation across the species range distribution (Tables S2 and S3). This pattern is likely connected to the complex geological history of Sundaland which over the last 10 Million years was influenced by the subduction activity of the Asian and Australian plates and the resulting intense volcanic activity which produced multiple volcanic arches5. Furthermore, climatic fluctuations during the Pleistocene induced major sea levels changes leading to merging of Sundaland landmasses during glacial maxima and multiple fragmentations during glacial sea level low-stands7,8. In such dynamic landscapes, complex patterns of distribution and high lineage diversity are to be expected10. The influence of eustatic fluctuations in Sundaland is exemplified by Rasbora bankanensis, Rasbora einthovenii, Rasbora trilineata, Trigonopoma gracile and Trigonopoma pauciperforatum all of which display wide range distributions among watersheds neighboring the Java sea. Those have been repeatedly connected during glacial maxima (Fig. 5(5), 5(8), 5(16) and 5(19)). This pattern strongly contrasts with the lower genetic diversity and restricted range distribution of the species occurring in the Eastern part of Borneo such as Rasbora vaillantii (Fig. 5(10)), R. laticlavia (Fig. 5(10)), R. trifasciata (Fig. 5(15)) and R. reophila (Fig. 5(20)) or species occurring in the Western part of Sumatra such as Rasbora vulcanus (Fig. 5(9)), R. maninjau (Fig. 5(9)), R. jacobsoni (Fig. 5(9)), R. tawarensis (Fig. 5(10)); R. chrysotaenia (Fig. 5(11)) and R. arundinata (Fig. 5(11)) and species in Java and Bali such as Rasbora sp1 (Fig. 5(10)), R. sp2 (Fig. 5(14)), R. lateristriata (Fig. 5(14)) and R. baliensis (Fig. 5(14)). These parts of Borneo, Sumatra and partially Java were disconnected from the central region of Sundaland around the Java sea during the Pleistocene. This trend highlights the sensitive status of the endemic Rasborinae species in the peripheral areas of Sundaland due to their highly restricted distribution ranges. The present study also argues against translocation programs for the most widespread species, considering the high proportion of cryptic diversity, if species and OTUs identity are not determined through DNA barcodes16,31.

Conclusions

The subfamily Rasborinae is the most diverse freshwater fish group of Sundaland and therefore represents an excellent model to explore the evolutionary response of local freshwater biotas to a dynamic geological history and repeated eustatic fluctuations. Affected by taxonomic confusions for decades, the genus Rasbora has been left aside of recent large-scale molecular studies aimed at exploring the diversification of aquatic biotas in Sundaland. Our comprehensive DNA barcode reference library for the subfamily enables further evolutionary studies on the diversification of the group, in particular within the genus Rasbora, which allowed us to trace evolutionary dynamics at the local scale in Sundaland16. The contrasting patterns of molecular diversity and species range distributions between Rasborinae species inhabiting the watersheds neighboring the Java sea and the species located on the Eastern part of Borneo call for a larger assessment of their dynamics of species proliferation based on broader genomic analyses. Clearly, future studies will also have to address the systematics of the Rasborinae as no evidence supporting the monophyly of Rasbora nor the different Rasbora species groups are detected here.

Material and Methods

Sampling and collection management

Material used in the present study is the result of a collective effort to assemble a global Rasborinae DNA barcode reference library through various field sampling efforts conducted by several of the coauthors in Sundaland over the past decade. Specimens were captured using gears such as electrofishing, seine nets, cast nets and gill nets across sites that encompass the diversity of freshwater lentic and lotic habitats in Sundaland (Fig. 2). Specimens were identified following original descriptions where available, as well as monographs40,49. Species names were further validated using several online catalogs50,51. Specimens were photographed, individually labeled and voucher specimens were preserved in a 5% formalin solution. Prior to fixation a fin clip or a muscle biopsy was taken and fixed separately in a 96% ethanol solution for further genetic analyses. Both tissues and voucher specimens were deposited in the national collections at the Museum Zoologicum Bogoriense (MZB), Research Center for Biology (RCB), Indonesian Institute of Sciences (LIPI).

Assembling a checklist of the Sundaland Rasborinae

A checklist of the Rasborinae species occurring in Sundaland was assembled from available online catalogs including Fishbase51 and Eschmeyer’s Catalog of Fishes50 as detailed in Hubert et al.15. This checklist was used to estimate the taxonomic coverage of the present DNA barcoding campaign and to identify type localities for each species. The following information was included: (1) authors of the original description, (2) type locality, (3) latitude and longitude of the type locality, (4) holotype and paratypes catalog numbers, (5) distribution in Sundaland. This information is available as online Supplementary Material (Table S1).

Sequencing and international repositories

Genomic DNA was extracted using a Qiagen DNeasy 96 tissue extraction kit following manufacturer’s specifications. A 651-bp segment from the 5′ region of the cytochrome oxidase I gene (COI) was amplified using primers cocktails C_FishF1t1/C_FishR1t1 including M13 tails52. PCR amplifications were done on a Veriti 96-well Fast (ABI-AppliedBiosystems) thermocycler with a final volume of 10.0 μl containing 5.0 μl Buffer 2× 3.3 μl ultrapure water, 1.0 μl each primer (10 μM), 0.2 μl enzyme Phire Hot Start II DNA polymerase (5U) and 0.5 μl of DNA template (~50 ng). Amplifications were conducted as followed: initial denaturation at 98 °C for 5 min followed by 30 cycles denaturation at 98 °C for 5 s, annealing at 56 °C for 20 s and extension at 72 °C for 30 s, followed by a final extension step at 72 °C for 5 min. The PCR products were purified with ExoSap-IT (USB Corporation, Cleveland, OH, USA) and sequenced in both directions. Sequencing reactions were performed using the “BigDye Terminator v3.1 Cycle Sequencing Ready Reaction” and sequencing was performed on the automatic sequencer ABI 3130 DNA Analyzer (Applied Biosystems). DNA barcodes obtained at the Naturhistorisches Museum Bern were generated as previously described in Conte-Grand et al.33.

The sequences and associated information were deposited on BOLD53 and are available in the data set DS-BIFRA (Table S2, dx.doi.org/10.5883/DS-BIFRA). DNA sequences were submitted to GenBank (accession numbers are accessible directly at the individual records in BOLD). An additional set of 106 Rasborinae COI sequences were downloaded from GenBank (Table S3).

Genetic distances and species delimitation

Kimura 2-parameter (K2P)54 pairwise genetic distances were calculated using the R package Ape 4.155. Maximum intraspecific and nearest neighbor genetic distances were calculated from the matrice of pairwise K2P genetic distances using the R package Spider 1.556. We checked for the presence of a barcoding gap, i.e. the lack of overlap between the distributions of the maximum intraspecific and the nearest neighbor genetic distances57, by plotting both distances and examining their relationships on an individual basis instead of comparing both distributions independently58. A neighbor-joining (NJ) tree was built based on K2P distances using PAUP 4.0a59 in order to visually inspect genetic distances and DNA barcode clusters (Fig. S1). This NJ tree was rooted using Sundadanio retiarius.

Several alternative methods have been proposed for delimitating molecular lineages60,61,62,63. Each of these methods have pitfalls, particularly when it comes to singletons (i.e. delimitated lineages represented by a single sequence) and a combination of different approaches is increasingly used to overcome potential pitfalls arising from uneven sampling16,43,64,65,66. We used four different sequence-based methods of species delimitation. For the sake of clarity, we refer to species identified based on morphological characters as species while species delimited using DNA sequences are referred to as Operational Taxonomic Unit (OTU)67,68,69. OTUs were delimitated using the following algorithms: (1) Refined Single Linkage (RESL) as implemented in BOLD and used to generate Barcode Index Numbers (BIN)62, (2) Automatic Barcode Gap Discovery (ABGD)61, (3) Poisson Tree Process (PTP) in its multiple rates version (mPTP) as implemented in the stand-alone software mptp_0.2.363,70, (4) General Mixed Yule-Coalescent (GMYC) in its multiple rate version (mGMYC) as implemented in the R package Splits 1.0–1971. RESL and ABGD used DNA alignments as input files while a ML tree was used for mPTP and a Bayesian Chronogram based on a strict-clock model using a 1.2% of genetic distance per million year72 for mGMYC. The mPTP algorithm uses a phylogenetic tree as an input file, thus, a maximum likelihood (ML) tree was first reconstructed using RAxML73 based on a GTR + Γ substitution model. Then, an ultrametric and fully resolved tree was reconstructed using the Bayesian approach implemented in BEAST 2.4.874. Two Markov chains of 50 millions each were ran independently using the Yule pure birth model tree prior, a strict-clock model and a GTR + I + Γ substitution model. Trees were sampled every 10,000 states after an initial burnin period of 10 millions. Both runs were combined using LogCombiner 2.4.8 and the maximum credibility tree was constructed using TreeAnnotator 2.4.774. Identical haplotypes were pruned for further species delimitation analyses.