SEARCH

SEARCH BY CITATION

Keywords:

  • cryptic species;
  • Ecuador;
  • integrative taxonomy;
  • Larentiinae;
  • larvae

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Abstract  The genus Eois comprises an important part of megadiverse assemblages of geometrid moths in mountain rainforests of southern Ecuador. In this study we report: (i) on the construction of a DNA barcode library of Eois for identification purposes; and (ii) the exploration of species diversity through species delimitation by pair-wise distance thresholds. COI barcode sequences were generated from 408 individuals (at least 105 species) collected on a narrow geographic scale (∼40 km2) in the Reserva Biológica San Francisco. Analyses of barcode sequence divergence showed that species delimitations based solely on external morphology result in broad overlap of intra- and interspecific distances. Species delimitation at a 2% pair-wise distance threshold reveals a clear barcoding gap. Fifty-two previously unrecognized species were identified, 31 of which could only be distinguished by an integrative taxonomy approach. Twelve additional putative species could only be recognized by threshold-based delimitation. Most splits resulted in two or three newly perceived cryptic taxa. The present study increased the number of Eois species recorded from that small area of Andean mountain forest from 102 to 154 (morphology- plus integrative taxonomy-based) or even 166 (sequence-based), leaving the species accumulation curve still far from reaching an asymptote. Notably, in no case did two or more previously distinguished morphospecies have to be lumped. This barcode inventory can be used to match larvae to known adult samples without rearing, and will therefore be of vital help to extend the currently limited knowledge about food plant relationships and host specialization.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

In recent years DNA barcoding has developed into a quick and increasingly inexpensive tool for species-level identification of all Metazoa. A 658 bp fragment from the 5′ part of the mitochondrial cytochrome c oxidase subunit I (COI) gene, as amplified by the primers LCO1490/HCO2198 described by Folmer et al. (1994), has been proposed as a universal marker for animals (Hebert et al., 2003a, 2003b). Two major applications of DNA barcoding are: (i) the exploration of species boundaries in insufficiently known taxa; and (ii) the identification of already known species, especially in cases where morphological differentiation is minimal or remains unexplored, as is the case in the early life cycle stages of many arthropod species.

The conceptual foundation for DNA barcode-aided species discrimination is the assumption that interspecific genetic distances exceed intraspecific distances by such a margin that a distinct gap exists. The presence of this so-called “barcoding gap” allows one to set a threshold for species discrimination. Sequence pairs with distances below the threshold are thought to be conspecific, whereas pairs with distances above the threshold are accepted as belonging to separate species. Proponents of DNA barcoding claim that this assumption is virtually always true and deviations are either caused by a negligible number of cases of incomplete lineage sorting or can be attributed to shortcomings in traditional taxonomy (i.e. failure to recognize cryptic species) of the taxa in question (Barrett & Hebert, 2005; Hajibabaei et al., 2006; Hebert et al., 2003a, 2003b, 2004a). The universal presence of a barcoding gap would then allow for instant species delimitation even in previously unknown taxa. However, a number of case studies where the power of DNA barcoding, to distinguish morphologically or otherwise well-defined species, has been subject to scrutiny show that this does not always apply. Recent examples that demonstrate problems with the assumption of a barcoding gap come from diverse groups such as lycaenid butterflies (Wiemers & Fiedler, 2007), ithomiine butterflies (Elias et al., 2007), orthopterans (Trewick, 2008), harvestmen (Boyer et al., 2007), or land snails (Davidson et al., 2009). However, the limited performance of DNA barcoding in these case studies could be attributed to shortcomings in taxonomy of the respective target group (i.e. classical taxonomy failed to recognize cryptic species) or to occurrences of very young species splits.

One major objective of DNA barcoding is identification of species without the need for taxonomic expertise. It is quite obvious that the success of identification through barcoding is crucially dependent on comprehensive taxon sampling (Elias et al., 2007; Wiemers & Fiedler, 2007). The short barcode sequences contain only very limited phylogenetic information and correct assignments of focal samples do require quite closely related sequences from a template library with which to compare. DNA barcodes are usually not suitable for taxonomic assignments above the species level. Furthermore, barcoding is bound to fail when applied to non-monophyletic, recently diverged species and in cases of hybridization. Funk and Omland (2003) found that in a sample of 2 319 animal species, 23% were not monophyletic. Many publications tried to highlight the conceptual shortcomings of DNA barcoding (e.g., DeSalle et al., 2005; Meyer & Paulay, 2005).

The first objective of this study is to test the utility of DNA barcoding to explore species richness of one diverse, taxonomically understudied, tropical moth genus and the applicability of standard pair-wise distance thresholds within this context.

Eois Hübner (Lepidoptera: Geometridae, Larentiinae) is a speciose genus of rather small-sized moths and comprises an important part of a megadiverse assemblage of geometrid moths in the mountain rainforests of southern Ecuador (Brehm et al., 2005). Over the past 10 years, the geometrid fauna of one particular locality, the Estación Científica San Francisco (ECSF), situated in the Reserva Biológica San Francisco (RBSF), has served as a paradigm to investigate patterns in species diversity and community structure of tropical moths (e.g. Brehm et al., 2003a, 2003b, 2005; Brehm & Fiedler, 2003, 2005; Hilt et al., 2006; Fiedler et al., 2008). The moth fauna of that area is arguably the best known of all Andean mountain forests. In these ecosystems, representatives of Eois account for 8.1% of the morphospecies and 10.2% of all individuals of geometrid ensembles (Brehm et al., 2005). The genus Eois occurs in the Americas, ranging from Mexico to Argentina, as well as in South-east Asia, Australia and Africa (Scoble, 1999). Scoble (1999) recognized 250 species, the majority of which (207) occur in the Neotropical region. Brehm et al. (2005) found 102 Eois species, exclusively delimited by wing patterns, to occur in the RBSF and adjacent areas between 1 000–2 700 m elevation.

Host plant associations of Neotropical Eois are incompletely known, although a number of host plant records for Eois have accumulated in recent years as part of massive campaigns to elucidate tropical food webs. The predominant host plant family is the Piperaceae with Piper being by far the most commonly used genus. A smaller number of host records exist from Chloranthaceae, Monimiaceae and Gesneriaceae (Bodner et al., 2010; Connahs et al., 2009; Dyer et al., 2009; Dyer & Gentry, 2009; Janzen & Hallwachs, 2009). In order to firmly establish host plant records in the absence of reliable identification literature for life cycle stages, most tropical Lepidopteran larvae must be collected and then reared to adulthood for identification. This approach is often troubled by high mortality of caterpillars through parasitoids and pathogens as well as by the massive need for manpower. In this situation identification of larvae through DNA barcodes, without the need of rearing individuals to adulthood, provides an elegant solution. The second objective of this study is therefore to generate a barcode library that allows larvae to be matched to sequences obtained from identified adult moth vouchers. The use of DNA barcode sequences for the matching of larvae to adult stages can be considered a well-established method and has been applied in a number of studies (e.g., Miller et al., 2005; Webb et al., 2006; Ahrens et al., 2007; Pfenninger et al., 2007).

Material and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Collection and DNA sequencing

Most moths (327 individuals, 80%) used in this study were collected at light traps in a small area of mountain ravine (quebrada) forests (approximately 1 km2 in extension) in the surroundings of the Estación Científica San Francisco (southern Ecuador, 3°58′S, 79°04′W, elevational range 1 850–2 000 m a.s.l.). Light-trapping in the ravine forest occurred at nine sites (Zimmermann, 2005; Günter et al., 2008, Table S1). All geometrid moths arriving at a weak light source (2 × 15 W tubes, placed in a gauze cylinder at ground level) were manually collected in the time interval between 19:00–22:00 h during 30 nights in February to May 2005. Moths were stored in a freezer and subsequently representatives of Eois were sorted out. In all, Eois specimens accounted for approximately 12% of the geometrids sampled. Most Eois specimens were sequenced except for a few abundant species (> 20 individuals) where 70%–80% of the specimens were sequenced. All other samples were taken in November 2008 from nearby lower and higher sites using the same methodology. Sixty-eight samples were obtained from lower elevations (Parque Nacional Podocarpus, Bombuscaro entrance, 4°06′S, 78°58′W, ca. 1 000–1 025 m a.s.l.), and 13 samples from higher elevations (12 from 3°59.7′S, 79°04.1′W, 2 670 m a.s.l., one from 4°06.7′S, 79°10.5′W, 2 985 m). Collection sites for all specimens are indicated in Table S1; for details about the area see Günter et al. (2008). Moths were killed with cyanide and stored at −20°C until DNA extraction. Heads and/or legs of moths were homogenized with ceramic beads using a Precellys 24 homogenizer (Bertin Technologies, Montigny le Bretonneux, France) set to 5 000/min for 2 × 20 s. When extracting DNA from larvae the homogenization step was skipped and larvae were instead cut into small pieces. The remaining protocol was identical for larvae and adults stages. DNA extraction was performed with the DNEasy Tissue Kit (Qiagen, Hilden, Germany) or the Peqgold Tissue DNA mini Kit (Peqlab, Erlangen, Germany) according to the protocol supplied with the respective kit. The target fragments were amplified using the Fermentas polymerase chain reaction (PCR) system (Fermentas, Burlington,ON, Canada). PCR reactions were set up with 2.5 μL of 10 × (NH4)2SO4 PCR buffer, 2 μL 25 mmol/L MgCl2, 0.1 μL 10 mmol/L dNTPs, 1 μL of each primer, 1 μL genomic DNA, 1 μL Taq polymerase and filled to 25 μL with PCR-grade H2O. A PCR cycler program modified from Hebert et al. (2003a) was used. PCR reactions were purified by digestion with shrimp alkaline phosphatase and exonuclease for 15 min at 37°C followed by 15 min at 80°C for enzyme deactivation. Sequencing reactions were set up with 1 μL ABI BigDye 3.1 (Applied Biosystems, Carlsbad, CA, USA), 1 μL primer, 1 μL template DNA and filled to 10 μL with PCR grade H2O and sequenced on an ABI capillary sequencer. PCR products were sequenced in both directions. PCR and sequencing was performed with primer pairs LCO1490 (5′-GGT CAA CAA ATC ATA AAG ATA TTG G-3′)/HCO2198 (5′-TAA ACT TCA GGG TGA CCA AAA AAT CA-3′), LCO1490/Nancy (5′-CCC GGT AAA ATT AAA ATA-3′) or LepF (5′-ATT CAA CCA ATC ATA AAG ATA TTG G-3′)/LepR (5′-TAA ACT TCT GGA TGT CCA AAA AAT CA-3′). The thermal cycler was set to 25 cycles of 20 s at 94°C, 20 s at 48°C and 4 min at 60°C.

Morphological examination of moths and species coding

Moths were either spread or the wings of the right body side were cut off and mounted on a piece of cardboard. Photographs in dorsal and ventral views were taken, resized and visually examined using Adobe Photoshop versions 8 and 9. Moths were provisionally identified by comparison of wing patterns with type material or photographs of type material (98% of all described Neotropical species available, Brehm et al. submitted). Examination of type specimens revealed that ∼87% of species from Ecuador recognized by morphological sorting are still formally undescribed (G. Brehm, F. Bodner, P. Strutzenberger, F. Hünefeld, F. K. Fiedler, submitted). All Eois morphotypes that could not be matched to type material or to a morphospecies already found by Brehm et al. (2005) were subsequently treated as separate novel entities.

In order to unequivocally label the entities encountered, three ‘types’ of species names were assigned in the present study (Table 1, second column). (1) When a specimen in our sample could be conclusively matched to a described species, it was designated as being identical to that species (e.g. Eois borrata 396). Being matched to a described species implies that the species was already known from morphological sorting prior to this study. Numeric identifiers after the species epithet are unique species code numbers used in all ecological studies of the RBSF Lepidopteran fauna thus far. (2) Specimens that could not be matched to a described species with sufficient certainty but have a wing pattern that is highly similar to a described species were assigned as ‘near’ the described species (e.g. Eois spnr azafranata). If species epithets in such cases are followed by a numeric identifier, these species had again already been known from previous investigations of the local fauna (e.g. Eois spnr azafranata 397). In contrast, species assigned as ‘spnr’ but lacking a numeric identifier in Table 1 were newly recognized in the course of this study (e.g. Eois spnr borrata). (3) Specimens where no match could be made to any named species in reference collections were designated as unknown species without a species epithet (i.e. Eois sp.). Again, species with a numeric identifier were already known prior to this barcoding study (e.g. Eois sp. 1070), whereas species without such a numeric identifier are newly recognized ones (e.g. Eois sp.). In view of this complexity the identity of species is hereafter exclusively defined by a novel 3-digit numeric code (e.g. Sp067) (see Table 1, first column) and not by the assigned name (i.e. identical species epithets alone do not necessarily imply conspecificity).

Table 1.  List of all 118 species covered in this study. The number of individuals per species is indicated for all three means of species delimitation. Species newly recognized for the RBSF area are printed in bold; species recognized by integrative taxonomy are marked with grey shading; species that were only recognized by threshold delimitation are underlined.
SpeciesNameNo. individuals morphoNo. individuals 3% thresholdNo. individuals 2% threshold
  1. Monophyly of the species is not supported in maximum likelihood trees.

  2. Strong evidence against monophyly of the species is present in maximum likelihood trees; species: newly adopted numerical code of species as recognized in this study.

  3. Name: species names and numerical identifiers according to Brehm et al. (2005).

Sp001E. spnr azafranata 397994
Sp002E. spnr azafranata 397Included in Sp001Included in Sp0014
Sp003E. sp. 696766
Sp004E. spnr cobardata222
Sp005E. escamata 390222
Sp006E. sp. 977222
Sp007E. spnr adimaria 399333
Sp008E. spnr adimaria222
Sp009E. sp. 385777
Sp010E. spnr heza222
Sp011E. sp. 400444
Sp012E. spnr golosata 3741277
Sp013E.spnr golosata 374Included in Sp01244
Sp014E. spnr margarita 3982155
Sp015E.spnr margarita 398Included in Sp0141616
Sp016E. spnr borrata333
Sp017E. spnr borrata666
Sp018E. sp.444
Sp019E. planetaria 383777
Sp020E. spnr restrictata 837333
Sp021E. chrysocraspedata 1029242424
Sp022E. sp. 425121212
Sp023E. spnr paraviolascens 423221111
Sp024E. sp.222
Sp025E.spnr paraviolascens 423Included in Sp0231111
Sp026E. sp. 411888
Sp027E. spnr trillista 414333
Sp028E. sp.222
Sp029E. sp. 427333
Sp030E. spnr odatis 419777
Sp031E. sp. 836444
Sp032E. angulata 376222
Sp033E. spnr nigrosticta 388333
Sp034E. sp. 405222
Sp035E. biradiata 410664
Sp036E. biradiata410Included in Sp035Included in Sp0352
Sp037E. spnr lunifera222
Sp038E. spnr lunifera 395222
Sp039E. sp. 394777
Sp040E. spnr nigrinotata332
Sp041E. sp.555
Sp042E. sp.222
Sp043E. spnr guapa444
Sp044E. spnr violada777
Sp045E. spnr violada 403191919
Sp046E. chasca 392161616
Sp047E. spnr lilacea telegraphica444
Sp048E. spnr inflammata 515333
Sp049E. spnr encina 412222
Sp050E. spnr pallidicosta222
Sp051E. spnr pallidicosta 1023555
Sp052E. spnr goodmanii888
Sp053E. spnr goodmanii444
Sp054E. spnr goodmanii444
Sp055E. spnr goodmanii544
Sp056E. spnr muscosa 803666
Sp057E. spnr olivacea222
Sp058E. spnr olivacea222
Sp059E. spnr olivacea 4162399
Sp060E.spnr olivacea 416Included in Sp0591414
Sp061E. spnr serrilineata322
Sp062E. spnr catana 426101010
Sp063E. sp. 409333
Sp065E. spnr camptographata 739333
Sp066E. sp.222
Sp067E. sp. 820666
Sp068E. spnr lucivittata 384333
Sp069E. sp. 386111
Sp070E.spnr serrilineataIncluded in Sp06111
Sp071E. antiopata 799111
Sp072E. spnr paraviolascens111
Sp073E. spnr inflammata 402111
Sp074E. sp.111
Sp075E. spnr. olivacea 408111
Sp076E. sp. 961111
Sp077E. sp. 1070111
Sp078E. spnr chasca111
Sp079E. sp. 961111
Sp080E. spnr ignefumata 389111
Sp081E. spnr goodmanii111
Sp082E. spnr sagittaria 377111
Sp083E.spnr goodmaniiIncluded in Sp05511
Sp084E. spnr borrata111
Sp085E. sp. 420111
Sp086E. spnr concatenata111
Sp087E.spnr nigrinotataIncluded in Sp040Included in Sp0401
Sp088E.spnr golosata 374Included in Sp01211
Sp089E. spnr concatenata111
Sp090E. spnr pallidicosta111
Sp091E. spnr nigrinotata111
Sp092E. sp. 382111
Sp093E. sp. 2128111
Sp094E. spnr 2129111
Sp095E. sp.111
Sp096E.sp. 696Included in Sp00311
Sp097E. spnr violada111
Sp100E. spnr pararussearia111
Sp101E. spnr paraviolascens111
Sp102E. sp. 1041111
Sp103E. spnr olivacea111
Sp104E. sp.111
Sp105E. spnr olivacea111
Sp106E. spnr nigrinotata111
Sp107E. spnr goodmanii111
Sp108E. spnr deleta111
Sp109E. spnr delicatula111
Sp110E. spnr ignefumata 30111
Sp111E. spnr fucosa111
Sp112E. borrata 396111
Sp113E. spnr camptographata111
Sp114E.spnr azafranata 397Included in Sp001Included in Sp0011
Sp115E. spnr goodmanii111
Sp116E. sp.111
Sp117E. spnr azafranata111
Sp118E. spnr azafranata111
Sp119E. sp.111
Sp120E. spnr trillista111
Sp121E. spnr lavendula 851111

Sequence data processing

Proofreading of sequences and contig assembly was done with ChromasLite Version 2.01, ChromasPro Ver 1.34 (Technelysium Pty Ltd, Tewantin, Queensland, Australia, http://www.technelysium.com.au/) and DNAStar Lasergene SeqMan Pro Ver. 7.1 or Ver. 8 ('DNASTAR Inc., Madison, WI, USA, http://www.dnastar.com/). Sequences were either 658 or 676 bp in length, longer sequences were cropped to 676 bp in length. All sequences were aligned manually using Bioedit Ver 7.0.4.1 (Hall, 1999). Sequence data was prepared for analysis using the programs FORCON version 1.0 and MEGA version 4 (Tamura et al., 2007). Sequences were screened for unusual nucleotide composition and the presence of stop codons to control for possible nuclear mitochondrial pseudogene (NUMT) amplification; see Song et al. (2008) for a review on potential problems associated with NUMTs in DNA barcoding.

Sequence analyses

Pair-wise Kimura-2-parameter distances (Kimura, 1980) were calculated with PAUP* (Swofford, 1999). This particular measure of genetic distance has been chosen to facilitate comparability with other DNA barcoding studies where it has been used extensively. Distances were analyzed with Microsoft Excel for Mac version 12.1.0. Maximum likelihood trees were calculated with RAxML (Stamatakis, 2006) performing a search for the best known likelihood tree with 500 replicates and bootstrapping using the rapid hill climbing algorithm with 1 000 replicates. Maximum likelihood trees and neighbor-joining diagrams were midpoint rooted. Maximum likelihood trees were used to assess the monophyly of threshold-defined species in a phylogenetic context and therefore their status under the phylogenetic species concept. In the analysis of barcode sequences a comparison has been made between species delimited by a sequence divergence threshold of 3% and 2%, respectively, and species delimited on morphospecies level. Morphospecies were initially defined after morphological examination (see above), and through the application of an integrative taxonomy approach (Schlick-Steiner et al., 2010) in cases where the amount of sequence divergence between seemingly conspecific individuals made the recognition of morphological differences possible in hindsight. Threshold-based species delimitation was assisted by neighbor-joining diagrams. Every monophyletic clade with at least one internal sequence pair with a distance of less than the threshold value was considered one species. Species represented by only a single individual (45 species) were excluded from analyses of intra- versus interspecific genetic distances.

Species accumulation curves

To visualize the progress in Eois species coverage at the RBSF area we produced species accumulation curves (with 50 randomizations) using the software EstimateS 8.20 (Colwell, 2009). They were calculated separately: (i) for the data set collected between 1999 and 2003 (species only sorted by wing patterns; this corresponds to the species list published by Brehm et al., 2005); (ii) for the data on newly recognized species gathered from 2005 to 2008 (as presented in this study, using DNA barcodes); and (iii) for the entire sample.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

We obtained COI barcode sequences from 408 individuals of Eois, ranging in length from 635 to 676 bp; 94.7% of sequences were at least 658 bp in length and the average length was 671 bp. All sequenced specimens are listed in Table S1 along with their assignment to species according to all three modes of species delimitations. Sequences were deposited in Genbank and accession numbers are indicated in Table S1 for each specimen. Specimen vouchers are stored in the research collections of P. Strutzenberger (333 specimens), G. Brehm (72 specimens) and F. Bodner (3 specimens). Upon completion of research all specimens will be transferred to the Phyletisches Museum Jena (Germany). Sequence alignment was straightforward and without gaps; the alignment had a length of 676 bp. No cases of NUMT amplification could be detected. Examination of the moths by wing pattern morphology revealed that from a total of 106 Eois morphospecies that could be recognized in the sample, 52 (49%) were previously unknown, that is they could not be matched to any of the 102 morphotypes from the RBSF area distinguished thus far. Only eight species in our sample (∼8%) could be assigned to formally described species with certainty. From the 52 ‘new’ morphospecies 31 are closely related, and therefore morphologically similar, to previously known morphospecies and would probably have gone unnoticed in a purely morphological sorting as performed prior to this study. The recognition of those 31 morphospecies was only possible after examination of the maximum likelihood tree (Fig. S1) and neighbor-joining diagrams (not shown) followed by a thorough reexamination of wing patterns. Due to the application of barcodes, 13 morphospecies as delimited by Brehm et al. (2005) could be split in an integrative taxonomy approach. Most frequently, such splits resulted in the segregation of earlier defined morphotypes into two or three species (Fig. 1). In only three cases (i.e. E. spnr borrata, E. spnr olivacea and E. spnr goodmanii) did the integrative taxonomy approach demand that morphospecies be split into four, five or even seven species, respectively. We screened our sample for additional, cryptic species by application of a sequence divergence threshold. Delimitation with a 3% divergence threshold produced eight putative additional species. When applying a 2% threshold value, four more ‘new’ species could be distinguished. All of these cases are missed by the 3% threshold only by a small margin. Note that in case of E. spnr azafranata Sp042 and E. spnr biradiata Sp008 the maximum interspecific distance to the respective sister clade is above the 3% threshold. However, the 3% threshold did not split these species, as at least one pair-wise distance was less than 3% (see also Table 1). All species accepted from the sample analyzed in this study are listed in Table 1 with their new numeric codes and highlighting newly recognized species. Splits based on pair-wise distance thresholds always resulted in the species being split into two or three putative species (Fig. 1).

image

Figure 1. Frequency distribution of splits of Eois morphotypes into ‘cryptic’ species. White bars: species recognized by integrative taxonomy; black bars: species recognized exclusively by delimitation with a 2% pair-wise distance threshold.

Download figure to PowerPoint

Species accumulation curves are presented in Figure 2. Earlier sampling at 39 sites (> 3 600 specimens) between 1999 and 2003 revealed 102 morphospecies, with the total estimated being 122.5 ± 4.4 (Jacknife1 estimator ± 1 SD, as recommended by Brose and Martinez (2004)). This corresponds to a coverage of 83.3% and would suggest a good sampling for this part of a highly diverse tropical insect fauna. However, for all samples combined, that is including species recognized by integrative taxonomy and species recognized by delimitation at a 2% pair-wise distance threshold, the recorded species number steeply increased to 166. At this lower level of coverage the Jacknife2 estimator should be used (Brose & Martinez, 2004), yielding a species total of 269.8 ± 23.4 (coverage 62.6%). Hence, by additional sampling in ravine forest, as well as at low and high elevation sites, in combination with barcode-based species delimitation, the species accumulation pattern changed distinctly from a fairly good coverage to a still very incomplete one. Incorporating recent samples indicates that about 100 additional Eois species might occur in the small area around RBSF in southern Ecuador.

image

Figure 2. Randomized species accumulation curves (Mao's Tau as a function of sampling sites: Colwell, 2009) of Eois moths in the Reserva Biológica San Francisco (southern Ecuador). Dashed line – morphotype delimitations of samples taken in the years 1999–2003. Solid line – additional species recorded from new samples (2005–2008) which were subjected to DNA barcoding. Dotted line – accumulation across combined data set.

Download figure to PowerPoint

Average intra- and interspecific distances are summarized in Table 2 for morphological species delimitation as well as for delimitation with a 3% and 2% threshold. A plot of intraspecific and interspecific distances (Fig. 3a) reveals that in the case of purely morphology-based delimitation there is substantial overlap of intraspecific and interspecific divergences. Analysis of cumulative error rates with morphology-based delimitation (Fig. 4a) shows that error is minimized with 15% at a 3% barcode sequence divergence (all false positives). False positives are completely eliminated at a threshold of 7.4%, producing 84% false negatives. When a 3% distance threshold for species delimitation is applied, overlap between intra- and interspecific distances is much reduced, but not completely eliminated (Fig. 3b). Error is minimized at a threshold of 3% pair-wise distance, all being false positives (Fig. 4b). Two of the three instances causing overlap between intra- and interspecific divergence are cases where some interspecific comparisons between the two clades in question give distances below the threshold, while others are above the threshold. False positives are completely eliminated at a threshold of 4.8%, producing 34.8% false negatives. Intraspecific distances show a pronounced bimodal distribution when morphological or 3% threshold delimitation is applied. Delimitation at 2% results in a distinct gap between 1.6% and 2.8% sequence divergence (Figs. 3c and 4c).

Table 2.  General characteristics of the dataset used for analysis of intra- versus interspecific distances, relative to the three modes of species delimitation. Note that the number of taxa and species varies because singleton species were excluded from analyses of intra- versus interspecific distances.
 2% threshold3% thresholdMorphology
Mean interspecific distance9.31%9.31%9.34%
Standard error0.01%0.01%0.01%
Range of interspecific distances2.88%–15.6%3.2%–15.6%3.2%–15.6%
No. of interspecific sequence pairs63 18863 87564 590
Mean intraspecific distance0.33%0.40%1.38%
Standard error0.01%0.02%0.05%
Range of intraspecific distances0–1.55%0–4.6%0–7.3%
No. of intraspecific sequence pairs1 4321 4661 840
No. of taxa360362365
No. of species686662
Mean number of individuals per species5.35.55.6
Range of individuals per species2–242–242–24
image

Figure 3. Relative frequencies of intra- and interspecific COI barcode sequence distances within Eois moths from southern Ecuador for morphological species delimitation (a), for 3% threshold delimitation (b) and 2% threshold delimitation (c).

Download figure to PowerPoint

image

Figure 4. Plots of cumulative error rates for given threshold values for morphological species delimitation (a), for 3% threshold delimitation (b) and 2% threshold delimitation (c).

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

With the discovery of 52 novel morphospecies, the species count in one small area of tropical forest increased from 102 to 154 in the genus Eois alone. The number rises further to 162 or even 166 when accepting the species recovered only by threshold delimitation at 3% and 2%, respectively. This overall growth of the species list is quite remarkable, since earlier inventories were based on > 3 600 individuals of the genus Eois sampled during massive light-trapping campaigns at 39 sites spread over several years (Brehm et al., 2005; Hilt et al., 2006). However, much of the material available for sequence analysis came from ravine forests that support a distinct flora, including a high number of Piper species (Günter et al., 2008; Homeier et al., 2010). It is likely that at least a part of those species serve as host plants for Eois and ravine forests are therefore able to support a high number of Eois species. Preliminary results suggest that ravine forests do indeed harbor a higher number of Eois species than non-ravine forests. In comparison, in the moth family Arctiidae, rather few species were added to the local species list by sampling in ravine forests (Zimmermann, 2005). Twenty-six of the 52 newly discovered Eois morphospecies were found in the most densely sampled elevational zone at RBSF (1 800–2 000 m). Seventy-eight Eois morphospecies were known from this elevational zone. Hence our findings increase that number by 33% to 104 morphospecies even in this core area of ecological investigations (Beck et al., 2008).

The discovery of 8–12 additional ‘cryptic’ new species by means of barcode analyses was not unexpected when compared to other recent studies that employ barcodes in tropical insect faunas (e.g. Condon et al., 2008; Smith et al., 2008). The amount of interspecific COI sequence divergence of the eight additional species recognized by the 3% threshold is well within the range of divergence found between other, morphologically well defined, species within Eois. Future studies on male and female genitalia morphology will reveal how many of those putative species, currently defined only by pair-wise distances, can be distinguished by morphological characters. However, even if neither in wing patterns nor in genitalia anatomy morphological differences were to be found, the observed high levels of sequence divergence in sympatric populations would remain a strong indicator for reproductive isolation. Yet, to conclusively support the species status of the newly recognized sequence types, it will be necessary to supplement the evidence for these putative species with data on life histories, resource use or microdistribution. Most splits of morphotype delimitations that became necessary lead to the recognition of two or three cryptic species, with only one case of splitting into seven species by integrative taxonomy. Thus, occasions where 10 species are hidden in one (Hebert et al., 2004b; but see Brower, 2006) are likely to be exceptional, even in under-explored tropical insect faunas.

The use of DNA barcodes may substantially increase estimates of local insect species richness (Hajibabaei et al., 2006; Condon et al., 2008), especially in tropical regions where taxonomic coverage and biodiversity inventories are still very incomplete (Foottit & Adler, 2009). Our case study on Eois clearly illustrates this. Species accumulation based on morphospecies sorting as done in the years 1999–2003 suggested that species numbers were already approaching saturation. However, combining morphological examinations with the application of DNA barcoding not only increased the number of species but also showed that recorded species numbers are far from approaching local saturation. We now must consider that, in addition to the approximately 154–166 Eois species (depending on delimitation) recorded from just a small area in southern Ecuador, about 100 additional species may await discovery. Hence, local diversity of this moth genus in and around the RBSF area in southern Ecuador may well exceed the number of recognized described Eois species worldwide.

DNA barcoding performed badly when tested within a framework of purely morphological species delimitation. This is not surprising in a group still lacking proper taxonomic treatment and suggests that the resolution of earlier morphospecies sorting yielded too conservative richness estimations. When using morphology-based species delimitations, error is minimized at 3% sequence divergence, and species delimitation at 2.8% would already result in a clear barcoding gap. This is in good agreement with early claims by proponents of DNA barcoding. A threshold value of 3% for the minimum sequence divergence between congeneric species enabled Hebert et al. (2003a) to correctly distinguish 98% of morphologically defined lepidopteran test species. This value has also been confirmed by Barrett and Hebert (2005) for arachnids, and Hebert et al. (2004a) found that a 2.7% threshold value for birds identifies 90% of the examined species. Setting the threshold at ten times the mean intraspecific divergence as proposed by Hebert et al. (2004a) for identification of potentially new species with minimal false positives would in the case of morphology-based species delimitations within Eois result in the threshold being set to 13.8%. This limit would fail to correctly identify any of the included species. When the species delimitation at a threshold of 3% sequence divergence is used as reference, the threshold would be set to 4% corresponding to a total error rate of 25.8%, including one false positive. When using species delimitation at a 2% divergence the threshold would be 3.3% which produces an error rate of 13%, all being false negatives. Hence, a threshold of ten times the mean interspecific divergence does well in minimizing false positives but generates up to 100% false negatives, as in the case of morphology-based species. This is the most obvious scenario when screening for potential new species. Thus, in agreement with Meyer and Paulay (2005) and Davidson et al. (2009) we were unable to confirm the applicability of a general standard threshold defined in this way. In the present study a threshold of 2% proved to be the most useful to screen for novel taxa. Yet, the applicability of barcoding in Eois from Ecuador was likely to be greatly facilitated by the very limited geographic range of sampling. No intraspecific geographic variation can be expected to occur when all samples come from the same few square-kilometers. Inclusion of samples from more distant populations of the same species might be more challenging for the barcoding approach.

Non-monophyly of species was not important in our data set. With morphological delimitation this occurred in only four cases, and strong support for non-monophyly was only present in the case of Sp003. In the other three cases it could not be determined if the species is really poly- or paraphyletic or if the true relationships could just not be recovered due to insufficient phylogenetic signal. Species defined by a 2% pair-wise distance threshold were all monophyletic with strong bootstrap support. The same was true for 3% threshold delimitation with one exception (Sp001 Eois spnr azafranata). This provides additional support for the validity of the threshold-based approach. Accuracy of tree-based species identification is expected to be high in all cases, as in morphological species delimitation there are only four cases of non-monophyletic species and only one case with the 3% threshold-based delimitation.

Having established the DNA barcode library for 106 Eois species (including 45 species with only one sequence available) from the RBSF area in southern Ecuador we were able to use this information to identify larval samples. Thus far, 87 caterpillars that could not be successfully reared could be matched to adult moths. Thereby we were able to add host plant records for a further 17 species of Eois, information that would have been lost without the application of DNA barcoding. Therefore, our case study on the performance of DNA barcoding in a highly species-rich tropical insect genus with unresolved taxonomy highlighted the usefulness of this approach in detecting cryptic species, even in a region where massive sampling campaigns had been performed. Results of barcode analyses fostered the successful search for additional, albeit subtle morphological characters. This exemplifies how synergistic or reciprocal use of ‘classical’ and molecular techniques can improve our understanding of biodiversity in the sense of integrative taxonomy (Smith et al., 2008; Schlick-Steiner et al., 2010). The new insights gained into the local species richness of Eois now await their application in answering ecological questions pertaining to co-evolution, host plant specificity and niche partitioning.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

We thank Manuela Zimmermann for providing the majority of moths used in this study and Florian Bodner for providing additional specimens, assistance in species determination and helpful comments on earlier drafts of the manuscript. Furthermore we thank Brigitte Gottsberger, Christine Truxa, Christian Schulze and Martin Wiemers for their various contributions. This study was financially supported by grants from the Deutsche Forschungsgemeinschaft (FOR 402, Fi 547/6-3; FOR 816, Fi 547/10-1). The foundation Nature and Culture International (Loja/Ecuador, Del Mar/USA) provided access to their property for field work. The Ministerio del Ambiente (Ecuador) kindly issued the necessary research permit (002-PNP-DBAP-RLZCH/MA).

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information
  • Ahrens, D., Monaghan, M.T. and Vogler, A.P. (2007) DNA-based taxonomy for associating adults and larvae in multi-species assemblages of chafers (Coleoptera: Scarabaeidae). Molecular Phylogenetics and Evolution, 44, 436449.
  • Barrett, R.D.H. and Hebert, P.D. (2005) Identifying spiders through DNA barcodes. Canadian Journal of Zoology, 83, 481491.
  • Beck, E., Bendix, J., Kottke, I., Makeschin, F. and Mosandl, R. (2008) Gradients in a Tropical Mountain Ecosystem of Ecuador. Ecological Studies, Vol. 198. Springer, Berlin Heidelberg . 526 pp.
  • Bodner, F., Brehm, G., Homeier, J., Strutzenberger, P. and Fiedler, K. (2010) Caterpillars and host plant records for 59 species of Geometridae (Lepidoptera) from a montane rainforest in southern Ecuador. Journal of Insect Science, 10, 67, available online: http://insectscience.org/10.67.
  • Boyer, S.L., Baker, J.M. and Giribet, G. (2007) Deep genetic divergences in Aoraki denticulata (Arachnida, Opiliones, Cyphophthalmi): a widespread ‘mite harvestman’ defies DNA taxonomy. Molecular Ecology, 16, 49995016.
  • Brehm, G. and Fiedler, K. (2003) Faunal composition of geometrid moths changes with altitude in an Andean montane rain forest. Journal of Biogeography, 30, 431440.
  • Brehm, G., Homeier, J. and Fiedler, K. (2003a) Beta diversity of geometrid moths (Lepidoptera: Geometridae) in an Andean montane rainforest. Diversity and Distributions, 9, 351366.
  • Brehm, G., Süßenbach, D. and Fiedler, K. (2003b) Unique elevational diversity patterns of geometrid moths in an Andean montane rainforest. Ecography, 26, 456466.
  • Brehm, G. and Fiedler, K. (2005) Diversity and community structure of geometrid moths of disturbed habitat in a montane area in the Ecuadorian Andes. Journal of Research on the Lepidoptera, 38, 114.
  • Brehm, G., Pitkin, L.M., Hilt, N. and Fiedler, K. (2005) Montane Andean rain forests are a global diversity hotspot of geometrid moths. Journal of Biogeography, 32, 16211627.
  • Brose, U. and Martinez, N.D. (2004) Estimating the richness of species with variable mobility. Oikos, 105, 292300.
  • Brower, A.V.Z. (2006) Problems with DNA barcodes for species delimitation: ‘ten species’ of Astraptes fulgerator reassessed (Lepidoptera: Hesperiidae). Systematics and Biodiversity, 4, 127132.
  • Colwell, R.K. (2009) EstimateS: Statistical estimation of species richness and shared species from samples. Version 8.2. Persistent URL: http://purl.oclc.org/estimates. Accessed on 10 January 2010.
  • Connahs, H., Rodriguez-Castaneda, G., Walters, T., Walla, T. and Dyer, L. (2009) Geographic variation in host-specificity and parasitoid pressure of an herbivore (Geometridae) associated with the tropical genus Piper (Piperaceae). Journal of Insect Science, 9, 28.
  • Condon, M., Adams, D.C., Bann, D., Flaherty, K., Gammons, J., Johnson, J., Lewis, M.L., Marsteller, S., Scheffer, S.J., Serna, F. and Swensen, S. (2008) Uncovering tropical diversity: six sympatric cryptic species of Blepharoneura (Diptera: Tephritidae) in flowers of Gurania spinulosa (Cucurbitaceae) in eastern Ecuador. Biological Journal of the Linnean Society, 93, 779797.
  • DeSalle, R., Egan, M.G. and Siddall, M. (2005) The unholy trinity: Taxonomy, species delimitation and DNA barcoding. Philosophical Transactions of the Royal Society B, 360, 19051916.
  • Davidson, A., Blackie, R.L.E. and Scothern, G.P. (2009) DNA barcoding of stylommatophoran land snails: a test of existing sequences. Molecular Ecology Resources, 9, 10921101.
  • Dyer, L.A., Gentry, G.L., Greeney, H. and Walla, T. (2009) Caterpillars and parasitoids of an Ecuadorian cloud forest. http://www.caterpillars.org. Accessed on 20 November 2009.
  • Dyer, L.A. and Gentry, G.L. (2009) Caterpillars and parasitoids of a tropical lowland wet forest. http://www.caterpillars.org. Accessed on 20 November 2009.
  • Elias, M., Hill, R.I., Willmott, K.R., Dasmahapatra, K.K., Brower, A.V.Z., Mallet, J. and Jiggins, C.D. (2007) Limited performance of DNA barcoding in a diverse community of tropical butterflies. Proceedings of the Royal Society B, 274, 28812889.
  • Fiedler, K., Brehm, G., Hilt, N., Süßenbach, D. and Häuser, C.L. (2008) Variation of diversity patterns across moth families along a tropical altitudinal gradient. Gradients in a Tropical Mountain Ecosystem of Ecuador, Ecological Studies, Vol. 198 (eds. E.Beck, J.Bendix, I.Kottke, F.Makeschin & R.Mosandl), pp. 347359. Springer, Berlin .
  • Folmer, O., Black, M., Hoeh, W., Lutz, R. and Vrijenhoek, R. (1994) DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology, 3, 294299.
  • Foottit, R.G. and Adler, P.H. (2009) Insect Biodiversity: Science and Society. John Wiley & Sons, West Sussex . 656 pp.
  • Funk, D.J. and Omland, K.E. (2003) Species-level paraphyly and polyphyly: Frequency, causes and consequences, with insights from animal mitochrondrial DNA. Annual Review of Ecology, Evolution, and Systematics, 34, 397423.
  • Günter, S., Cabrera, O., Weber, M., Stimm, B., Zimmermann, M., Fiedler, K., Knuth, J., Boy, J., Wilcke, W., Iost, S., Makeschin, F., Werner, F., Gradstein, R. and Mosandl, R. (2008) Natural forest management in neotropical mountain rain forests – An ecological experiment. Gradients in a Tropical Mountain Ecosystem of Ecuador, Ecological Studies Vol. 198 (eds. E.Beck, J.Bendix, I.Kottke, F.Makeschin & R.Mosandl), pp. 347359. Springer, Berlin .
  • Hajibabaei, M., Janzen, D.H., Burns, J.M., Hallwachs, W. and Hebert, P.D.N. (2006) DNA barcodes distinguish species of tropical Lepidoptera. Proceedings of the National Academy of Sciences of the United States of America, 103, 968971.
  • Hall, T.A. (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series, 41, 9598.
  • Hebert, P.D.N., Cywinska, A., Ball, S.L. and deWaard, J.R. (2003a) Biological identifications through DNA barcodes. Proceedings of the Royal Society London B, 270, 313321.
  • Hebert, P.D.N., Ratnasingham, S. and deWaard, J.R. (2003b) Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proceedings of the Royal Society London B (Supplement), 270, S9699.
  • Hebert, P.D., Stoeckle, M.Y., Zemlak, T.S. and Francis, C.M. (2004a) Identification of birds through DNA barcodes. Public Library of Science Biology, 2(10), e312.
  • Hebert, P.D.N., Penton, E.H., Burns, J.M., Janzen, D.H. and Hallwachs, W. (2004b) Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proceedings of the National Academy of Sciences of the United States of America, 101, 1481214817.
  • Hilt, N., Brehm, G. and Fielder, K. (2006) Diversity and ensemble composition of geometrid moths along a successional gradient in the Ecuadorian Andes. Journal of Tropical Ecology, 22, 155166.
  • Homeier, J., Breckle, S.W., Günter, S., Rollenbeck, R.T. and Leuschner, C. (2010) Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest. Biotropica, 42, 140148.
  • Janzen, D.H. and Hallwachs, W. (2009) Dynamic database for an inventory of the macrocaterpillar fauna, and its food plants and parasitoids, of Area de Conservacion Guanacaste (ACG), northwestern Costa Rica. http://janzen.sas.upenn.edu. Accessed on 21 November 2009.
  • Kimura, M. (1980) A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution, 16, 111120.
  • Miller, K.B., Alarie, Y., Wolfe, G.W. and Whiting, M.F. (2005) Association of insect life stages using DNA sequences: The larvae of Philodytes umbrinus (Motschulsky) (Coleoptera: Dytiscidae). Systematic Entomology, 30, 499509.
  • Meyer, C.P. and Paulay, G. (2005) DNA barcoding: Error rates based on comprehensive sampling. Public Library of Science Biology, 3(12), e422.
  • Pfenninger, M., Nowak, C., Kley, C., Steinke, D. and Streit, B. (2007) Utility of DNA taxonomy and barcoding for the inference of larval community structure in morphologically cryptic Chironomus (Diptera) species. Molecular Ecology, 16, 19571968.
  • Schlick-Steiner, B.C., Steiner, F.M., Seifert, B., Stauffer, C., Christian, E. and Crozier, R.H. (2010) Integrative taxonomy: A multisource approach to exploring biodiversity. Annual Review of Entomology, 55, 421438.
  • Scoble, M.J. (1999) Geometrid Moths of the World International Edition: A Catalogue. CSIRO Publishing, Melbourne . 1304 pp.
  • Smith, A.M., Rodriguez, J.J., Whitfield, J.B., Deans, A.R., Janzen, D.H., Hallwachs, W. and Hebert, P.D.N. (2008) Extreme diversity of tropical parasitoid wasps exposed by iterative integration of natural history, DNA barcoding, morphology, and collections. Proceedings of the National Academy of Sciences of the United States of America, 105, 1235912364.
  • Song, H., Buhay, J.E., Whiting, M.F. and Crandall, K.A. (2008) Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proceedings of the National Academy of Sciences the United States of America, 105, 1348613491.
  • Stamatakis, A. (2006) RAxML-VI-HPC: Maximum Likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics, 22, 26882690.
  • Swofford, D.L. (1999) PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods), version 4.0. Sinauer Associates, Sunderland , Massachusetts .
  • Tamura, K., Dudley, J., Nei, M. and Kumar, S. (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Molecular Biology and Evolution, 24, 15961599.
  • Trewick, S.A. (2008) DNA barcoding is not enough: mismatch of taxonomy and genealogy in New Zealand grasshoppers (Orthoptera: Acrididae). Cladistics, 24, 240254.
  • Webb, K.E., Barnes, D.K.A., Clark, M.S. and Bowden, D.A. (2006) DNA barcoding: A molecular tool to identify Antarctic marine larvae. Deep-Sea Research Part II: Topical Studies in Oceanography, 53, 10531060.
  • Wiemers, M. and Fiedler, K. (2007) Does the DNA barcoding gap exist?– a case study in blue butterflies (Lepidoptera: Lycaenidae). Frontiers in Zoology, 4, 8.
  • Zimmermann, M. (2005) Reaktion von Nachtfalter-Gemeinschaften im ecuadorianischen Bergregenwald auf einen experimentellen forstlichen Eingriff. Diploma thesis, University of Bayreuth .

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Fig. S1 Best known maximum likelihood tree calculated with RAxML. Values next to nodes are bootstrap support values.

Table S1 All included taxa are listed along with their assignment to species for all three methods of species delimitation as well as length of sequence (bp), collection site (code and elevation), Genbank accession number and the assigned species name. SpeciesM: Species assignment under morphological delimitation; Species2%: Species assignment under 2% threshold delimitation; Species3%: Species assignment under 3% threshold delimitation. Table is sorted after species assignment under morphological delimitation. Codes: Q and 4a, 4b: ravine forest sites; 1a, 1b and BC: sites at Bombuscaro; 11a and KP1: high elevation sites.

FilenameFormatSizeDescription
INS_1366_sm_tableS1.xls86KSupporting info item
INS_1366_sm_figS1.pdf32KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.