‐ Dissertation ‐
INTEGRATIVE SPECIES DELIMITATION IN THE ALPINE
JUMPING‐BRISTLETAIL GENUS MACHILIS
LATREILLE, 1832
Thomas Dejaco May 2014
Molecular Ecology Group Institute of Ecology University of Innsbruck 6020 Innsbruck, Austria
1st supervisor: Univ.‐Prof. Dr. Birgit C. Schlick‐Steiner 2nd supervisor: PD Dr. Florian Steiner
i Leopold‐Franzens‐Universität Innsbruck
Eidesstattliche Erklärung
Ich erkläre hiermit an Eides statt durch meine eigenhändige Unterschrift, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Alle Stellen, die wörtlich oder inhaltlich den angegebenen Quellen entnommen wurden, sind als solche kenntlich gemacht.
Die vorliegende Arbeit wurde bisher in gleicher oder ähnlicher Form noch nicht als Magister- /Master-/Diplomarbeit/Dissertation eingereicht.
19. Mai 2014 Datum Unterschrift
Machilis sp. B from Trögener Klamm (southern Carinthia, Austria). Photograph courtesy of Gernot Kunz.
ii ABSTRACT
Jumping‐bristletails (Archaeognatha or Microcoryphia) are certainly one of the least studied insect groups. Information on their phylogenetic relationship (both within and among hexapod sister groups) and their general biology is very scarce. Within the genus Machilis, 94 species have been described, among which numerous Alpine small‐scale endemics are found. Endemic species are treasures of biodiversity that need to be conserved in the face of the ongoing biodiversity crisis. This is especially important for the European Alps, which are poor in endemic species compared with other biodiversity hotspots like the Mediterranean region. The very basic requirement for any biodiversity assessment is knowledge about the number and taxonomic affiliation of species inhabiting the area under observation. Unfortunately, species determination in Alpine representatives of the genus Machilis is obscured by vaguely defined species limits. Therefore, a comprehensive, integrative approach is urgently needed to clarify the taxonomically problematic situation in the genus Machilis. Within this PhD project, I first developed a methodological toolbox for enhanced species delimitation in the genus Machilis. Subsequently, I sampled type localities and additional localities of as many nominal species as possible across the Eastern Alps. By integrating data from traditional morphometrics and molecular genetics, I was able to determine actual species limits for most of the 18 nominal species included. Moreover, I found a high proportion of incongruence among disciplines, indicating complex evolutionary histories including several instances of hybridization and parthenogenesis. Additionally, by using chromosome preparations and genome size measurements, I could gain first insights into the evolution of parthenogenesis and polyploidy.
Overall, it became evident that the evolutionary history of Eastern‐Alpine Machilis species was strongly affected by hybridization, polyploidization, and parthenogenesis. These factors potentially have triggered speciation events that ultimately are responsible for the observed frequency of small‐scale endemics. Moreover, five new species have been discovered, mainly along the southern margin of the Eastern Alps. This indicates that Alpine species diversity in the genus Machilis is still underestimated. As a result of this comprehensive investigation, Eastern‐Alpine Machilis species are now widely accessible to researches interested in endemism and conservation, evolution of parthenogenesis, and genomic alterations like hybridization, polyploidization, and genome size variation. iii CONTENTS
EIDESSTATTLICHE ERKLÄRUNG...... i
PHOTOGRAPH OF MACHILIS SP. B...... ii
ABSTRACT...... iii
BACKGROUND ...... 1
AIMS AND OBJECTIVES ...... 4
1ST PUBLICATION: A toolbox for integrative species delimitation in Machilis
jumping‐bristletails (Microcoryphia: Machilidae) ...... 5
2ND MANUSCRIPT: Taxonomist’s nightmare … evolutionist’s delight: hybridization and parthenogenesis challenge species delimitation in Machilis jumping‐bristletails .15
3RD MANUSCRIPT: Karyotypic variability and genome‐size variation in sexual and parthenogenetic species of the jumping‐bristletail genus Machilis (Archaeognatha)...... 72
SYNTHESIS ...... 105
CONCLUSION...... 108
ACKNOWLEDGEMENTS...... 109
LITERATURE ...... 110
iv BACKGROUND Most subfields in natural sciences rely on the recognition of discrete categories, which usually provide a framework for hypotheses to be statistically tested. These can be artificial treatment variables or natural entities like molecules or cells. For researchers working on the level of organisms, categories will most likely be individuals, populations, or species. While the former two are easier to determine using physical and geographical properties, defining the species category has been a long‐lasting endeavour for generations of naturalists, taxonomists, and evolutionary biologists.
The species problem
Ever since Linnaeus' Systema Naturae (1735), organisms have been classified in an effort to systematically capture earth's biodiversity. The term 'species' was, at that time, no more than one among many categories based on morphological similarity, which could be further subdivided in varieties, races, etc. It was Charles Darwin who, by choosing the title for his monumental book On the origin of species (1859), assigned to the species category its fundamental role in his theory of natural selection. Following a century of intensive taxonomic discovery, the reconciliation of Darwinian Theory with the principles of genetics, the so‐called Modern Evolutionary Synthesis, became a landmark for the definition of species. Enlightened by the innovation of population genetic theory (Dobzhansky 1937), species were suddenly perceived as interbreeding populations, which are genetically isolated from other lineages. In this period, Ernst Mayr (1942) developed the framework for the Biological Species Concept (BSC), which was primarily based on the criterion of reproductive isolation. It is, until now, one of the most adopted species concepts among biologists. With the advent of phylogenetic systematics (Hennig 1966) and the availability of statistical methods for inferring phylogenies from morphological or molecular data, phylogenetic relationship (i.e., descent from a common ancestor) was claimed to be a superior operational criterion for defining species, leading to the Phylogenetic Species Concept (PSC; e.g., Eldrege & Cracraft 1980). Finally, in the last decades of the 20th century, several alternative species concepts have been proposed (see De Queiroz 2007; Mayden 1999). The majority of them are modifications of either the BSC or the PSC, and mostly differ in the criteria (e.g., monophyly, diagnosability, distinctiveness) used to recover clusters of individuals. Even though the primary goal of any species definition has been the search for
1 common ground among disciplines, it has become clear that, at the verge to the new millennium, taxonomists, systematists, and evolutionary biologists were more than ever divided in their view of how species should be defined. However, efforts have been made to reconcile species concepts (De Queiroz 2007; Mayden 1999), and lately, the Unified Species Concept (USC; De Queiroz 2007) is gaining popularity among systematists. Under the USC, species conceptualization and species delimitation are clearly separated. Regarding the former, the only defining property of the species category is the existence of a separately evolving metapopulation lineage, and this criterion is in fact inherent to any known species concept. Regarding the latter, different operational criteria should be taken as independent evidence for lineage separation along the speciation continuum. Consequently, the USC provides an open framework where no thresholds are defined. Instead, lineage divergence is corroborated by any additional, independent source of information. Regarding this, the USC is tightly linked to the concept of a multidisciplinary, integrative approach to taxonomy.
The integrative future of taxonomy
Integrative taxonomy is a multisource approach that takes advantage of complementarities among disciplines for improved species delimitation (Dayrat 2005; Padial et al. 2010; Schlick‐Steiner et al. 2010). Data can come from disciplines like molecular genetics (sequencing of mitochondrial or nuclear loci, fingerprinting, whole genome scans), morphology (qualitative or quantitative analyses), ecology (habitat, ecological niche, pathogen‐, symbiont‐, host‐, or foodplant‐association), chemistry (e.g., cuticular hydrocarbons, sex pheromones, venoms), behaviour (mating, social interactions) or life history (spatiotemporal, physiological, reproduction, or social traits). Integrative taxonomy does not replace traditional taxonomy, but rather compresses the traditional routine of visiting a taxonomic problem repeatedly into one procedure (Wheeler 2004). Data from all sources are first used separately and then integrated. A central imperative of integrative taxonomy is to use the same specimens under all disciplines applied. Otherwise, disagreements among disciplines may be wrongly inferred or existent disagreements overlooked. In accordance with the USC, the plausibility of a species‐delimitation hypothesis rises with the number and the inter‐independence of the disciplines. At least 3 disciplines should be combined to maximise performance (see Schlick‐Steiner et al. 2010 for a detailed
2 protocol). The use of several disciplines helps taxonomy go beyond a bare naming of species towards an understanding of the processes behind the formation of species. Any disagreement among disciplines demands an explanation in the light of evolutionary biology. Integrative taxonomy thus holds the potential to shed light on some of the “hot” topics in biogeography and evolution.
A short taxonomic history of the genus Machilis, Latreille 1832
Only few descriptions of apterygote hexapods were given prior to the 20th century. Most of them were superficial, thus applying to many organisms from Zygentoma to Archaeognatha (Sturm & Machida 2001). Latreille (1801) first described the genus "Machilis", including clear diagnostic characteristics of jumping bristletails. However, he also included Linnaeus' Lepisma polypoda in this genus, thus rendering it paraphyletic. Not until 31 years later, he established the genus Machilis in its still valid combination (Latreille 1832). After the framework for the higher‐level taxonomy of jumping bristletails was built (Verhoeff 1910), mainly three researchers discovered and described Alpine jumping bristletails: Heinz Janetschek, Hermann Riezler (both Univ. of Innsbruck) and Peter W. Wygodzinsky (Basel) all published several species descriptions and taxonomic reviews (Janetschek 1949a, b, 1950a, b, 1951a, b, 1953, 1954a, b, 1956, 1957, 1970; Riezler 1939, 1941; Wygodzinsky 1941a, b), primarily regarding the areas of North‐ and South‐Tyrol (Austria and Italy) and Switzerland. Consequently, the genus Machilis became, and still is the (comparatively!) best‐studied and most species‐rich genus among the Archaeognatha. Owing to the wealth of Alpine Machilis species, the Archaeognatha turned out to be richest in the relative amount of endemic species, compared with all other insects native to Austria (Rabitsch & Essl 2009). However, most of the Machilis species described by these authors have been delimited on the basis of variation in pigmentational patterns, and have never been taxonomically revised. One exception is Machilis distincta Janetschek (1949a), which Janetschek (1970) synonymized with M. engiadina Wygodzinsky (1941a) following examination of samples from a wider geographic range. As a consequence, it is unclear if the diversity of Alpine Machilis species, and therefore also the exceptional high amount of endemics, is the product of an outstanding adaptive radiation, or rather the product of taxonomic oversplitting due to the lack of suitable morphological characters.
3 AIMS AND OBJECTIVES
The main goal of this PhD thesis is to revise nominal species delimitation hypotheses using a comprehensive set of morphological, molecular, and cytogenetic methods. The outcome of this investigation will be relevant for biodiversity measures, given the potential high amount of endemic species in this animal group. The results might support both, congruence of nominal species with independently evolving lineages, or synonymy of several nominal species. In any case, this investigation will clarify the taxonomic uncertainty by which the genus Machilis is currently obscured. In more detail, this PhD thesis has four objectives:
1. Establish morphological and molecular marker systems suitable for species delimitation in Alpine Machilis species. 2. Apply these markers to a comprehensive sample of Eastern Alpine Machilis species. 3. Gain first insights into the evolution of parthenogenesis and polyploidy using cytogenetic and flow‐cytometric methods. 4. Identify promising study systems (e.g., species pairs or complexes) to further investigate the intertwined phenomena of parthenogenesis, hybridization, and polyploidization.
4 *Manuscript Click here to view linked References
A toolbox for integrative species delimitation in Machilis jumping 1 2 3 bristletails (Microcoryphia: Machilidae) 4 5 6 Thomas Dejaco1*§, Wolfgang Arthofer1*, H. David Sheets2, Karl Moder3, Barbara 7 8 1 4 5 1 9 Thaler-Knoflach , Erhard Christian , Luís F. Mendes , Birgit C. Schlick-Steiner and 10 11 Florian M. Steiner1 12 13 1 14 Molecular Ecology Group, Institute of Ecology, University of Innsbruck, 6020 Innsbruck, 15 16 Austria 17 18 2 19 Department of Physics, Canisius College, 14208 Buffalo (NY), USA 20 21 3 Institute of Mathematics and Applied Statistics, BOKU, University of Natural Resources 22 23 24 and Life Sciences, 1180 Vienna, Austria 25 26 4 Institute of Zoology, BOKU, University of Natural Resources and Life Sciences, 1180 27 28 29 Vienna, Austria 30 5 31 Zoologia, Jardim Botânico Tropical, Instituto de Investigação Científica Tropical, 1400-209 32 33 Lisboa, Portugal 34 35 36 *These authors contributed equally to this work as first authors 37 38 These authors contributed equally to this work as senior authors 39 40 § 41 Corresponding author 42 43 Email addresses: 44 45 46 TD: [email protected] 47 WA: [email protected] 48 49 HDS: [email protected] 50 51 KM: [email protected] 52 53 BK: [email protected] 54 55 EC: [email protected] 56 57 LFM: [email protected] 58 FMS: [email protected] 59 60 BCSS: [email protected] 61 62 63 64 1 65 Abstract 1 2 3 Accurate species delimitation is fundamental for many fields of biology. Although a wide 4 5 6 range of evolutionary-biological patterns are known to promote failure in species delimitation 7 8 when any single information source is considered, the majority of species characterisations 9 10 are still based on single disciplines. Using Alpine jumping bristletails we here showcase the 11 12 13 establishment of morphological and molecular tools for multi-source species delimitation in a 14 15 taxonomically problematic arthropod group and stress its advantages over single-discipline 16 17 18 approaches. Once sound species delimitations have been achieved, Alpine jumping bristletails 19 20 will be available as a prime model system for addressing questions related to the emergence 21 22 23 of endemism and parthenogenesis. 24 25 26 27 28 Keywords 29 30 31 Integrative taxonomy, routine identification, morphometrics, mtDNA, nuclear marker, 32 33 Microcoryphia, Machilis 34 35 36 37 38 1. Introduction 39 40 41 Jumping bristletails of the genus Machilis (Microcoryphia, formerly Archaeognatha) 42 43 44 comprise a considerable number of small-scale endemics in the European Alps (Christian and 45 46 Knoflach 2009; Rabitsch and Essl 2009). Consisting of both sexual and asexual species, they 47 48 49 are a promising study system for research into Alpine biogeography, speciation, and evolution 50 51 of parthenogenesis, provided that proper species delimitation can be achieved. Unfortunately, 52 53 species delimitation in many original descriptions of Alpine Machilis lacks rigour 54 55 56 (Wygodzinsky 1941; Janetschek 1949, 1950a, b) and taxonomic expertise is sparse currently. 57 58 The morphological descriptions include a number of characters that were loosely defined (e.g. 59 60 61 proportion of coxite and stylus length, article chains of antennae, shape of fossorial claws) or 62 63 64 2 65 are difficult to quantify (e.g. pigmentation on maxillary palps or legs) (Riezler 1941; 1 2 Wygodzinsky 1941; Palissa 1964). Due to deficient sampling, morphological variation within 3 4 5 species boundaries was often neglected, so that the congruence of nominal and ―natural‖ 6 7 species is questionable. Several Alpine species pairs [e.g. M. intermedia / M. tirolensis 8 9 10 (Janetschek 1954); M. alpina / M. lehnhoferi (Janetschek 1954; Christian and Knoflach 2009); 11 12 M. distincta / M. engiadina (Janetschek 1970); M. alpicola / M. vagans (Janetschek 1970)] 13 14 have been suspected to represent just single species. On the other hand, those weakly 15 16 17 circumscribed taxa might indicate high (and perhaps to some extent cryptic) diversity as well. 18 19 Thus, a sound revision of Alpine Machilis is required. 20 21 22 23 24 A challenging step in revisionary taxonomic work is the alignment of evolutionary lineages 25 26 27 revealed by molecular data with the nominal species established on morphological grounds. 28 29 The inclusion of morphological characters is therefore mandatory in delimitation approaches 30 31 notes‖ 32 (Schlick-Steiner et al. 2007; Steiner et al. 2009). ―Primer on species-specific molecular 33 34 markers have become en vogue, while the methodology concerning morphological characters 35 36 is, if at all, still provided separately in identification keys and monographs. If multi-source 37 38 39 approaches are to become a standard in taxonomy, however, we think that methodological 40 41 aspects of data acquisition for all information sources should be published together. 42 43 44 45 46 Integrative taxonomy (DeSalle et al. 2005; Padial et al. 2010; Schlick-Steiner et al. 2010) 47 48 49 has proven to significantly improve the rigour of species delimitation in various animal taxa 50 51 (Schlick-Steiner et al. 2006; Fontaneto et al. 2007; Bond and Stockman 2008; Macías- 52 53 Hernández et al. 2008; Leaché et al. 2009; Bailey et al. 2010; Damm et al. 2010; Glaw et al. 54 55 56 2010; Lumley and Sperling 2010; Ross et al. 2010; Steiner et al. 2010; Birky et al. 2011; 57 58 Gurgel-Gonçalves et al. 2011; Moyal et al. 2011; Seppä et al. 2011). Multi-source approaches 59 60 61 take advantage of complementarities among disciplines: It is assumed that the strength of any 62 63 64 3 65 species delimitation hypothesis rises with the independence of the information sources 1 2 (Schlick-Steiner et al. 2010), i.e. more different (agreeing) disciplines produce a more robust 3 4 5 delimitation hypothesis. One hallmark of integrative taxonomy is the examination of the same 6 7 specimens under all disciplines (Schlick-Steiner et al. 2010); otherwise, disagreements among 8 9 10 disciplines may be apparent rather than real, or discrepancies may not be discovered. 11 12 13 14 Our goal here is to showcase how an array of morphological and molecular tools for 15 16 17 integrative species delimitation can be established efficiently. To this end, we analysed three 18 19 species of Alpine machilids (Machilis glacialis, M. pallida and M. rubrofusca, each from 20 21 22 three localities) whose taxonomic integrity has not been questioned. These species are 23 24 presumably parthenogenetic since males have never been reported, just as in many other 25 26 27 Machilidae (Wygodzinsky 1941; Palissa 1964). We applied four disciplines to a total of 45 28 29 specimens: mitochondrial DNA (mtDNA; a fragment of the cytochrome c oxidase 1 gene), 30 31 st 32 nuclear DNA (nuDNA; five non-coding loci), traditional morphometrics (TM; articles of 1 33 34 leg and maxillary palp) and geometric morphometrics (GM; eye shape). mtDNA and nuDNA 35 36 are treated as separate disciplines because we do not assume strict clonality, under which the 37 38 39 whole genome would be inherited as one single locus without recombination. Even if 40 41 parthenogenesis should be confirmed for our study species, there are several alternative ways 42 43 44 for recombination to occur in the nuclear genome, e.g., automixis, mitotic recombination or 45 46 rare sex. Furthermore, while the mitochondrial genome accounts for recent divergence due to 47 48 49 its relatively fast evolutionary rate, nuDNA reflects mostly older genetic signals (with the 50 51 exception of rapidly evolving nuclear loci such as microsatellites which we did not analyse 52 53 here). Finally, our set of methods will need to be also useful in the sexual species of the 54 55 56 genus. The use of TM, i.e. the analysis of meristic [counts of discrete serially homologous 57 58 structures (Lawing et al. 2008)] or metric characters (i.e. linear distance measurements and 59 60 61 resulting proportions or angles) is crucial to preserve the connection with original 62 63 64 4 65 morphological descriptions when type material is not available. A reported advantage of GM, 1 2 i.e. the analysis of two- or three-dimensional shapes, lies in its sensitivity: Within a species, 3 4 5 shapes often turned out to be less variable than linear characters (e.g., Mutanen and Pretorius 6 7 2007; Crews 2009). 8 9 10 11 12 To evaluate the power of the various tools for the species-delimitation process in Alpine 13 14 Machilis, we follow the integrative-taxonomic protocol presented by Schlick-Steiner et al. 15 16 17 (2010): Information from independent sources is first analysed separately and 18 19 congruences/incongruences among results are subsequently interpreted. For incongruences 20 21 22 that emerged, we eventually discuss their evolutionary background. We follow the unified 23 24 species concept (De Queiroz 2007) under which the existence of a separately evolving 25 26 27 metapopulation lineage is the only necessary property of a species, with different lines of 28 29 evidence from whatever methodologies being applicable in assessing lineage separation. 30 31 32 33 34 2. Materials and Methods 35 36 37 Specimens of Machilis glacialis (Verhoeff, 1910), Machilis pallida (Janetschek, 1949) and 38 39 40 Machilis rubrofusca (Janetschek, 1949) were collected from three localities per species (see 41 42 Table 1 for detailed information), preserved in 99% EtOH p.a. and stored at -20°C at the 43 44 45 Molecular Ecology Group, University of Innsbruck. Palissa’s (1964) identification key and 46 47 the original species descriptions [M. glacialis: (Verhoeff 1910), M. pallida: (Janetschek 1949) 48 49 M. rubrofusca: (Janetschek 1950b)], were used to assign specimens to nominal species. 45 50 51 52 adult specimens (15 per species, five per locality) were used for analysis under four different 53 54 disciplines: TM, GM, and mtDNA and nuDNA sequence analyses. 55 56 57 58 59 2.1. Morphological data 60 61 62 63 64 5 65 2.1.1. Traditional morphometrics 1 2 Specimens were dissected under a Nikon SMZ-U stereomicroscope. Abdominal styli, 3 4 5 antennae, terminal appendages, legs, maxillae, labium, mandibulae, coxites 9 (including 6 7 gonapophyses 9) and the sheet of connected urosternites and coxites 1-8 (including 8 9 10 gonapophyses 8) were permanently slide-mounted using water-soluble Marc-André-1 11 12 mounting medium. After removal of muscle tissue (stored in 99% EtOH p.a. for DNA 13 14 preservation), the head and tergites were stored in 99% EtOH p.a. for further use under other 15 16 17 disciplines. After initial examination of five specimens per species, 30 character variables 18 19 were defined, of which seven had to be dropped because of frequently missing or broken body 20 21 22 parts in later examined individuals. Three additional characters (angles between distance 23 24 characters) were trigonometrically derived from the original dataset yielding a total of 26 25 26 27 characters for statistical analysis (see Table 2 and Figures 1 and 2). Measurements were 28 29 always performed at 60× magnification under a Nikon E600 bright field microscope equipped 30 31 32 with a measuring eyepiece. Frequently, reference points were identified and located at higher 33 34 magnification than that used for measuring. Multivariate statistical analyses were performed 35 36 with the Paleontological statistics software package PAST (Hammer et al. 2001) and SPSS 37 38 39 statistics v.17.0. PCA scores were imported to Sigmaplot v.11 for graphical editing. 40 41 42 43 44 2.1.2. Geometric morphometrics 45 46 Differences in eye shape among species had already been noted (length to width ratio) in 47 48 49 original species descriptions (Wygodzinsky 1941; Janetschek 1954; Palissa 1964) and 50 51 therefore eye shape was chosen for outline-based 2D morphometric analysis. In order to 52 53 produce standardised images, each head was placed on a layer of sand on the bottom of a 54 55 56 small glass dish filled with 99% EtOH p.a. Under a Leica Z6Apo macroscope, the head was 57 58 then orientated in such a way that four reference points along the outline of the eyes were 59 60 61 situated in the same focal plane at a magnification of 40×. Pictures of the frontal view of the 62 63 64 6 65 head were taken with a Leica DMC420 digital camera connected to the macroscope. In order 1 2 to extend the depth of field, multiple images per head were taken at focal plane steps of 20 3 4 5 µm using Leica Application Suite v.3.6 software which controlled the motorised z-stage on 6 7 the macroscope. The resulting stack of images (18/25 min/max, depending on the size of 8 9 10 head) was then merged into one single image using Helicon Focus v.5.1.8, with automatic 11 12 adjustment checked off, as this resulted in a noticeable deformation of the object. The 13 14 software MakeFan7 (Sheets 2001) was used to plot a fan of 22 equally angular spaced lines 15 16 17 onto each of the resulting images (Figure 3 D) for ease of consistent digitising. All images 18 19 were then merged into a single TPS file using the software TPSUtil v1.45 (Rohlf 2009). 20 21 22 23 24 A total of three landmarks (LM) and 20 semilandmarks (SLM) were digitised on each 25 26 27 image using TPSDig v2.16 (Rohlf 2009; see Figure 4D). SLMs were introduced to address 28 29 the problem of lacking homologous landmarks on outlines (Bookstein 1997; Zelditch et al. 30 31 32 2004). Consistent spacing of SLMs [by sliding to the perpendicular of the reference’s tangent 33 34 (Sampson et al. 1996)] was obtained after procrustes superimposition. Hereby, differences 35 36 associated with translation, rotation or scaling are removed from the original coordinates, thus 37 38 39 leaving only differences in shape. Both the procrustes superimposition and the sliding of 40 41 SLMs are implemented in the program CoordGen7 [IMP software series (Sheets 2001)]. In 42 43 44 order to reduce overweighting, ten of the 20 SLMs were subsequently omitted to yield a set of 45 46 13 coordinate pairs (three LMs and ten SLMs) for further statistical analyses (Zelditch et al. 47 48 49 2004). Thin-plate spline coefficients, or partial warp scores, PWS, were calculated using the 50 51 software CVAGen7 because – unlike coordinates directly obtained from procrustes 52 53 superimposition – PWS can be used in conventional statistical tests without the need to adjust 54 55 56 for the degrees of freedom that are lost during superimposition of SLMs (Zelditch et al. 2004). 57 58 PCA and MANOVA were carried out in PAST (Hammer et al. 2001), discriminant analyses 59 60 61 and leave-one-out cross validations (LOOCV) were computed using SPSS statistics v.17.0. 62 63 64 7 65 The CVA scores were calculated in CVAGen7 (Sheets 2001) and – like PCA scores – 1 2 imported to Sigmaplot v.11 for graphical editing. Deformation grids were directly exported as 3 4 5 Encapsulated PostScript files from CVAGen7. 6 7 8 9 10 2.2. Molecular data 11 12 2.2.1. Mitochondrial DNA 13 14 15 Genomic DNA was extracted from muscle tissue using SIGMA-ALDRICH GenEluteTM 16 17 Mammalian Genomic DNA Miniprep kit. Primer pairs for the amplification of the CO1 gene 18 19 20 were initially designed using complete mitochondrion sequences of four jumping bristletails 21 22 available in GenBank (Pedetontus silvestrii: EU621793.1; Petrobius brevistylis: 23 24 25 NC_007688.1; Nesomachilis australica: AY793551.1; Trigoniophthalmus alternatus: 26 27 NC_010532.1). After successful amplification in Machilis pallida, primers were adapted and 28 29 M. glacialis M. rubrofusca 30 used for additional amplification in and . On the basis of these 31 32 sequences, new primer pairs were designed for improved cross-species amplification 33 34 (MachF1: 5’-ACAAAYCATAAAGATATTGG-3’; MachR3: 5’- 35 36 37 TCTATTCCGTGAAGGGTTGC-3’). PCR products were purified using the PEQLAB Cycle 38 39 Pure kit and sequenced by a commercial provider (Eurofins MWG Operon, Munich, 40 41 42 Germany) using the forward primer MachF1. All sequences were deposited in GenBank 43 44 (Accession numbers JF826083 - JF826127), aligned using ClustalX2 (Larkin et al. 2007) and 45 46 47 checked for correct amino acid translation. The GTR+I model was determined as the best 48 49 fitting model of evolution by the algorithm implemented in MEGA 5 (Tamura et al. 2011) and 50 51 a maximum likelihood (ML) tree was subsequently constructed using 10,000 bootstrap 52 53 54 replications. 55 56 57 58 59 2.2.2. Nuclear DNA loci 60 61 62 63 64 8 65 A partial genomic library consisting of fragments of 200 to 1500 bp was constructed from 1 2 one individual of Machilis pallida following the FIASCO-protocol presented by Zane et al. 3 4 5 (2002). A total of 132 clones were sequenced and subsequently selected for suitability 6 7 following criteria proposed in Carstens and Knowles (2006), which resulted in 14 non-coding 8 9 10 candidate loci. Further cross-species amplification tests reduced the number of presumably 11 12 polymorphic, single-copy loci to five (primers were: 83F: 5’- 13 14 GCAAAACGAGCCTCGAGAT-3’; 83R: 5’-CCATTTGGCACACAAAACAC-3’; 89F: 5’- 15 16 17 CCCGAAAGGGAAACACAGTA-3’; 89R: 5’- CTCGAAAACCAAGGATGGAG-3’; 108F: 18 19 5’- ACATCCCTCGCGAACAATAC-3’; 108R 5’- TGGGGTACAACTAAGCAACG-3’; 20 21 22 111F: 5’-ACATTCTTGGGGAAGTGCAG-3’; 111R: 5’- ATGCAACCCAAAACCAGAGG- 23 24 3’; 114F: 5’-TGGATGCACAGAAGTTGGAGC-3’; 114R: 5’- 25 26 27 AGACGCGCAAGCTTATGAAC-3’). After PCR amplification in six specimens (two per 28 29 species), insert lengths were checked by gel-electrophoresis. All PCR products were cloned 30 31 TM 32 using the FERMENTAS InsTAclone PCR Cloning kit, and insert size was determined by 33 34 PCR with M13 vector primers. Clones containing inserts of expected length were sequenced. 35 36 We generally aimed at eight clone sequences per locus and specimen. Sequences were aligned 37 38 39 using ClustalX2 (Larkin et al. 2007) and indels were automatically coded using the program 40 41 GapCoder (Young and Healy 2003) which applies simple indel coding (SIC) and appends a 42 43 44 0/1 matrix to the 3’-end of the alignment. The characters 0 and 1 were subsequently replaced 45 46 by A and C, respectively, because 0 and 1 are not used as informative characters during tree 47 48 49 reconstruction in MEGA 5 (Tamura et al. 2011). NJ trees were then constructed based on p- 50 51 distances assuming uniform mutation rates. We applied this simplest distance metric because 52 53 sequences varied mostly with respect to indel position and length, thus making any complex 54 55 56 assumptions on base substitutions dispensable. 57 58 59 60 61 3. Results and Discussion 62 63 64 9 65 3.1. Traditional morphometrics 1 2 3 Seven meristic characters were taken from the literature (Wygodzinsky 1941; Palissa 1964) 4 5 (Figure 1, Table 2) and one meristic as well as 18 metric characters (Figure 2, Table 2) were 6 7 8 newly defined. A plot of the two first principal components that were extracted from all these 9 10 characters showed three non-overlapping, but closely spaced clusters that corresponded to the 11 12 three species (data not shown). However, without a-priori information on grouping, the three 13 14 15 species would not be recognisable from this PCA plot. In order to investigate the 16 17 discriminative power of individual characters, we performed a combination analysis (Moder 18 19 20 et al. 2007), which is basically a concatenation of discriminant analyses (DA) using random 21 22 characters from the TM dataset (for details, see Materials and Methods). Starting with a 23 24 25 number of characters of n=1, DAs of every possible character combination up to n=10 were 26 27 computed. The analysis revealed no successful discrimination with only one character, but 28 29 30 five combinations of two characters that yielded no classification error (including G9FCN, 31 32 G8ArN, G9ArN, Cx9SN and MPL7; see Table 3). When three or more characters were 33 34 allowed (n≥3), the number of combinations resulting in no classification error exceeded 100 35 36 37 (data not shown), but all of them included at least two of the above mentioned characters. 38 39 Thus we considered these five character combinations to be the best discriminators and 40 41 42 restricted further analyses to this ―reduced character set‖. 43 44 45 46 47 A multivariate analysis of variance (MANOVA) supported significant difference between 48 49 groups for any of the five character combinations but only in two cases, all individuals were 50 51 correctly classified in a cross-validated discriminant analysis (including G8ArN, G9ArN and 52 53 54 Cx9SN; see Table 3). We therefore considered these three characters to have the greatest 55 56 discriminatory power and a PCA including only these three characters produced three clearly 57 58 59 separated non-overlapping clusters that fully corresponded to the three nominal species 60 61 (Figure 3A). The first two principal components explained 98.47% of the variation. 62 63 64 10 65
1 2 3 3.2. Geometric morphometrics 4 5 Because of the general lack of reliable landmarks on the body, eye shape was chosen for 6 7 8 GM analysis using 3 landmarks (LM) and 10 sliding semi-landmarks (SLM) that were placed 9 10 along the outline of the left eye (Figure 4D; for detailed description, see Materials and 11 12 Methods section). 13 14 15 Centroid size did not differ significantly between groups (analysis of variance, ANOVA, 16 17 F=2.796, p=0.072) and was therefore not included in further analyses. The first two 18 19 20 components extracted from a PCA of partial warp scores (PWS) explained 90.2% of the total 21 22 variation in eye shape. Machilis pallida and M. rubrofusca showed a clear clustering (Figure 23 24 25 3B) while there was a partial overlap of both with M. glacialis, which showed an intermediate 26 27 position. A MANOVA on PWS confirmed significant differences between the three groups 28 29 30 (Wilk’s λ=0.0037, F=14.69, p≤0.01). 31 32 33 34 In a cross-validated DA based on PWS, all of the specimens were assigned to the correct 35 36 37 nominal species (Table 4). The combination analysis was not performed on GM data as the 38 39 interpretation of single PWS, i.e. the decomposition of shape into multiple characters, is 40 41 42 generally not accepted (Rohlf 2002; Zelditch et al. 2004). Alternatively, a canonical variates 43 44 analysis (CVA) plot in combination with grid plots that visualise deformations along each CV 45 46 47 axis are a helpful tool for identifying possible regions of localised shape change between 48 49 species. However, as one can see from Figure 4B-C, the two CV axes describe large scale 50 51 changes in the overall shape rather than localised changes in specific features of the eye. 52 53 54 Nevertheless, the CV axes can clearly separate the three species. 55 56 57 58 59 3.3. Molecular data 60 61 62 3.3.1. Mitochondrial DNA 63 64 11 65 Amplification success was highest with the MachF1 / MachR3 primer combination. A 1 2 maximum likelihood tree based on the alignment of 760 bp (Figure 5) provided strong support 3 4 5 for three monophyletic clades matching the results of both morphometric analyses with 6 7 maximum node support values. Three haplotypes were found in M. pallida, two in M. 8 9 10 rubrofusca and one in M. glacialis. The minimum between-group distance was 12.7% (M. 11 12 pallida / M. rubrofusca), the maximum within-group distance was 0.2% (M. rubrofusca). 13 14 15 16 17 3.3.2. Nuclear DNA 18 19 Initial amplification of five candidate loci (see Table 5) in six specimens (two of each 20 21 22 species) was successful and mostly showed single bands in gel electrophoresis. Alignment of 23 24 cloned sequences (two to ten per specimen and locus, 285 in total) revealed up to seven 25 26 27 different alleles within species (Table 5) with most of the variation represented by indels. 28 29 Despite previous testing (for details, see Materials and Methods), two of the five cloned loci 30 31 32 (111 and 114) showed signs of multicopy status in the genome and excessive levels of 33 34 sequence variation and were thus excluded from further interpretation. In the other three loci 35 36 we found between one and four different alleles in each specimen (see column '#A' in Table 37 38 39 5) and substantial allele sharing among species (Figure 6A-C). Only in one locus (108), 40 41 alleles found in M. pallida formed a monophyletic clade which would allow delimiting this 42 43 44 species from the other ones. 45 46 47 48 49 4. Discussion 50 51 52 Three out of four disciplines, namely traditional morphometrics, geometric morphometrics 53 54 and mitochondrial DNA, agreed in unambiguously assigning 45 specimens, which key out to 55 56 57 three generally accepted species, to three distinct groups. Therefore, these disciplines are the 58 59 first choice for future attempts to delimit Alpine Machilis species. Additional information 60 61 62 inferred from the fourth discipline, nuclear DNA, namely potential polyploidy and shared 63 64 12 65 alleles among species, tentatively indicates the involvement of hybridisation and can be 1 2 explained under the assumption of an asexual history (Suomaleinen et al. 1987; Birky 1996; 3 4 5 Judson and Normark 1996; Birky and Barraclough 2009). As noted by Birky (1996), in such 6 7 cases it may be impossible to infer correct species trees from nuclear sequence data without 8 9 10 knowing the specific mode of reproduction (e.g. ploidy level, apomixis or automixis) and 11 12 evolutionary history (e.g. presence or absence of rare sex and of mitotic recombinations, 13 14 ploidy cycles). In the context of marker development for integrative taxonomy, 15 16 17 parthenogenesis serves as a reasonable evolutionary explanation for the signature of the 18 19 nuDNA markers, and the nuDNA results do not undermine the usefulness of mtDNA and 20 21 22 morphometrics for species delimitation approaches. 23 24 25 26 27 Cloning and sequencing the nuDNA of just six individuals seemingly conflicts with the 28 29 demand that, in integrative taxonomy, every individual should be examined under all 30 31 32 disciplines (Schlick-Steiner et al. 2010). However, once the ineffectiveness of nuclear 33 34 markers for species delimitation became clear, we decided against the laborious inclusion of 35 36 additional specimens, because an examination of the genetic signature in view of 37 38 39 parthenogenesis is beyond the scope of this study. 40 41 42 43 44 The geometric morphometric analysis of eye shape did not separate the three species in an 45 46 unsupervised (PCA) approach, but succeeded in unambiguously classifying all individuals 47 48 49 when information on group membership was given (i.e., supervised approach; CVA). For 50 51 future delimitation efforts in the genus Machilis, eye shape may thus not serve as an 52 53 exploratory tool, but will be useful for a quantification of morphological divergence between 54 55 56 hypothesised evolutionary lineages. 57 58 59 60 61 62 63 64 13 65 Traditional morphometric characters from the ovipositor, first thoracic leg and maxillary 1 2 palp (TM) showed only weak clustering in the PCA morphospace when all characters were 3 4 5 included, but all individuals could be unambiguously classified using (supervised) 6 7 discriminant analysis. However, after identification of those characters possessing the highest 8 9 10 discriminatory power (using a combination analysis), three easily distinguishable clusters 11 12 formed in a PCA morphospace based on these characters. This illustrates the important 13 14 potential of TM data for defining species-delimitation hypotheses in the integrative taxonomy 15 16 17 of the genus Machilis. 18 19 20 21 22 The combination analysis on TM data proved a sophisticated tool for effectively reducing the 23 24 number of characters and finding the most effective discriminators. An advantage over 25 26 27 traditionally used dimension reduction through PCA is that, because original characters are 28 29 retained instead of principal components, the interpretation of multivariate statistics is much 30 31 32 easier. Furthermore, the combination analysis empirically analyses a subset of characters 33 34 (exhaustive search) unless a very high amount of characters is needed to achieve low 35 36 classification error. Thus the rule of thumb of having at least three times more samples per 37 38 39 group than characters (Lachenbruch and Goldstein 1979) is hardly violated. In contrast, with 40 41 PCA, the same assumption may only be met with an unreasonably high sample size – 42 43 44 especially when a high number of characters are to be included. Whichever statistical method 45 46 is used, the full character set should always be considered when additional taxa are added to 47 48 49 the analysis and a larger dataset should be examined before single characters are selected for 50 51 routine identification. Finally, the combination analysis is not applicable to GM data as all 52 53 measurement variables are coupled and have to be interpreted as one single character which is 54 55 56 shape [see Zelditch et al. (2004), chapter 14, for a review on this topic]. 57 58 59 60 61 62 63 64 14 65 Accurate species delimitation inferred from TM, GM, and mtDNA will constitute the 1 2 backbone for the interpretation of nuDNA data. The strength lies in the congruence of the 3 4 5 three different disciplines. Besides the methodological aspect, the integrative taxonomic 6 7 approach applied in this study revealed incongruence that would not have been recognised 8 9 10 when using mtDNA as the only molecular marker, which is crucial for understanding 11 12 evolutionary patterns in these insects. It exemplifies the importance of evolutionary biological 13 14 thinking to multi-source species delimitation and demonstrates how modern taxonomy can go 15 16 17 beyond the naming of species. We thus suggest that, when taxonomically problematic groups 18 19 are treated, multiple information sources should be used right from the establishment of 20 21 22 methods. Otherwise, we run the risk of being misled by erroneous information. All of the five 23 24 nuDNA markers presented herein are not suited for delimitation of the three asexual Machilis 25 26 27 species analysed here. As cross-amplification success was high, these markers may 28 29 nevertheless prove useful in sexually reproducing Machilis species or asexuals where 30 31 32 convergence events (e.g. rare sex, mitotic recombination, ploidy cycles) occur frequently 33 34 enough to maintain some degree of recombination (Birky 1996; Birky and Barraclough 2009). 35 36 The five nuclear non-coding markers will also be very useful for further investigating the 37 38 39 evolutionary history of Alpine Machilis species, including that of Machilis glacialis, M. 40 41 pallida and M. rubrofusca. 42 43 44 45 46 47 5. Conclusions 48 49 In this study, we tested the applicability of four different disciplines for integrative species 50 51 52 delimitation in Machilis jumping bristletails. Three disciplines, namely traditional 53 54 morphometrics, geometric morphometrics and mitochondrial DNA proved to be powerful 55 56 57 tools for discriminating among valid species and thus, the procedures presented here are 58 59 currently the best available toolkit for future delimitation approaches. In contrast, the nuclear 60 61 62 DNA markers were not able to unambiguously discriminate among species and should 63 64 15 65 therefore not be used for delimitation studies in Machilis. However, the incongruence of the 1 2 nuDNA data can be explained by the asexual history of the examined species and the nuDNA 3 4 5 markers will thus be useful for inferring the evolutionary history of the genus Machilis. 6 7 8 9 10 The protocols presented herein enable robust, multi-source species delimitation in this 11 12 genus for the first time, which is mandatory for establishing jumping bristletails as a new 13 14 model system for research into the evolution of parthenogenesis and endemism. 15 16 17 18 19 20 Acknowledgements 21 22 23 We thank Gregor Wachter, Lukas Rinnhofer and Michael Url for collecting some of the 24 25 specimens used in this study. Clemens Folterbauer gave technical lab support for which we 26 27 are very grateful. Two anonymous reviewers helped to improve the paper offering 28 29 30 constructive criticism. 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For each species, the name of the locality 1 (I=Italy, A=Austria, CH=Switzerland), the geographical coordinates and the 2 minimal/maximal distance among the three sampled populations are given. 3 4 5 Species Locality name Coordinates (WGS84) Min/max dist. 6 M. glacialis Piz Lad, Reschen, (I) 46° 50.963'N / 10° 28.354'E 7 Mäuerlscharte, Brenner (A) 46° 59.428'N / 11° 31.990'E 27/105 km 8 Il Fuorn, Engadin (CH) 46° 40.538'N / 10° 13.879'E 9 M. pallida Mäuerlscharte, Brenner (A) 46° 59.428'N / 11° 31.990'E 10 Padasterjochhaus, Gschnitz (A) 47° 4.862'N / 11° 21.643'E 17/69 km 11 Schlern, Dolomites (I) 46° 30.719'N / 11° 42.095'E 12 M. rubrofusca Larstigalm, Ötztal (A) 47° 8.639'N / 11° 0.064'E 13 Umhausen, Ötztal (A) 47° 7.878'N / 10° 57.210'E 4/23 km 14 Obergurgl, Ötztal (A) 46° 56.381'N / 11° 1.754'E 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 22 65 1 2 3 4 5 6 Table 2: Definition of characters used in traditional morphometrics analysis. 7 8 Character ID Type of data Description 9 10 1 Cx9SN meristic Number of spines on coxite 9; to include also broken or removed spines, the insertion points of spines were counted 11 2 Fe1AL metric Length of the line connecting dorsoproximal and ventrodistal edge of femur 1 in lateral view 12 3 Fe1BL metric Length of the line connecting dorsoproximal and dorsodistal edge of femur 1 in lateral view 13 4 Fe1CL metric Length of the line connecting ventrodistal and dorsodistal edge of femur 1 in lateral view 14 15 5 G8ArN meristic Number of articles (pseudosegments) of gonapophyse 8 16 6 G8FCN meristic Number of fossorial claws of gonapophyse 8 17 7 G8SGN meristic Number of groups of sensory spines on gonapophyse 8 18 8 G9ArN meristic Number of articles (pseudosegments) of gonapophyse 9 19 9 G9FCN meristic Number of fossorial claws of gonapophyse 9 20 21 10 MP3L metric Length of 3rd article of maxillary palp, from dorsoproximal to dorsodistal edge in lateral view 22 11 MP5L metric Length of 5th article of maxillary palp, with measuring points in analogy to those of MP3L 23 12 MP6L metric Length of 6th article of maxillary palp, with measuring points in analogy to those of MP3L 24 13 MP7L metric Length of 7th article of maxillary palp, from dorsoproximal edge to dorsalmost point of base of distal spine in lateral view 25 14 Ta1AL metric Length of line connecting dorsoproximal and dorsodistal edge of 2nd article of tarsus 1 in lateral view 26 15 Ta1BL metric Length of line connecting dorsoproximal and ventrodistal edge of 2nd article of tarsus 1 in lateral view 27 28 16 Ta1CL metric Length of line connecting dorsodistal and ventrodistal edge of 2nd article of tarsus 1 in lateral view 29 17 Ta1DL metric Length of line connecting dorsoproximal and dorsodistal edge of 3rd article of tarsus 1 in lateral view 30 18 Ta1EL metric Length of line connecting dorsodistal and ventroproximal edge of 3rd article of tarsus 1 in lateral view 31 19 Ta2An metric Angle between lines TaAL and TaBL 32 20 Ta3An metric Angle between lines TaDL and TaEL 33 34 21 Ti1SN meristic Number of spines on ventral side of tibia 1; to include also broken or removed spines, the insertion points of spines were counted 35 22 Ti2SN meristic Number of spines on ventral side of tibia 2 36 23 Tr1AL metric Length of line connecting ventroproximal and ventrodistal edge of trochanter 1 in lateral view 37 24 Tr1BL metric Length of line connecting ventroproximal and dorsodistal edge of trochanter 1 in lateral view 38 25 Tr1CL metric Length of line connecting ventrodistal and dorsodistal edge of trochanter 1 in lateral view 39 40 26 Tr1An metric Angle between lines TrAL and TrBL 41 Numbers in the first column correspond to those in Figures 1 and 2. Column three refers to the meristic (count data) or metric (distance measurements) type of data. Metric characters 42 were defined on the basis of several slide-mounted specimens and have not been used in the taxonomy of jumping bristletails so far. Meristic characters (except C9SN) have been 43 adopted from previous works (Wygodzinsky 1941; Palissa 1964). 44 45 46 47 48 23 49 Table 3: Results of the combination analysis (n=2 only) and of cross-validated discriminant 1 analyses of TM data 2 3 Character Classification P value Cross-validated (LOOCV) classification error (%) 4 combinations error (%) (MANOVA) 5 M. glacialis M. pallida M. rubrofusca 6 7 G8ArN, Cx9SN 0 <0.01 0 0 0 8 G9ArN, Cx9SN 0 <0.01 0 0 0 9 G9ArN, MP7L 0 <0.01 0 6.7 0 10 G9FCN, Cx9SN 0 <0.01 0 6.7 0 11 G9ArN, G9FCN 0 <0.01 0 6.7 0 12 13 Five out of 325 possible combinations with n=2 (column 1) produced no classification error in the combination 14 analysis. Out of 2,600 possible combinations with n=3, 195 produced no classification error (data not shown). 15 Columns 4-6 give the species-specific classification errors after a leave-one-out cross validation (LOOCV). Only 16 two combinations of two meristic characters retained the original classification error of 0%. In the other three 17 combinations, M. pallida was the only species to be wrongly classified. The three characters representing the 18 ―reduced TM dataset‖ are highlighted in boldface. 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 24 65 Table 4: Results of the discriminant analyses on partial warp scores. Columns 2-4 give the 1 numbers of individuals that have been unambiguously assigned to one of the three groups. All 2 45 assignments based on the discriminant function were correct. 3 4 5 Group assignment 6 A-priori groups M. glacialis M. pallida M. rubrofusca % correct 7 M. glacialis 15 0 0 100 8 M. pallida 0 15 0 100 9 M. rubrofusca 0 0 15 100 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 25 65 Table 5: Allele frequencies in three nuclear loci. 1 2 Alleles #A #C 3 Locus 83 ID 83A 83B 83C 83D 83E 83F 83G 83H 83I 4 M. glacialis 90117 1 - 1 ------2 2 5 90072 5 ------1 - 2 6 6 90292 - - - - - 10 2 - - 2 12 M. pallida 7 90244 - - - - 2 8 1 - - 3 11 8 90660 ------8 2 2 10 M. rubrofusca 9 90095 3 1 - 1 - - - 7 - 4 12 10
11 Locus 89 ID 89A 89B 89C 89D 89E 89F 12 90117 9 ------1 9 13 M. glacialis 14 90072 5 1 - - - 2 - - - 3 8 90292 1 6 - 5 1 - - - - 4 13 15 M. pallida 16 90244 4 4 1 2 - - - - - 4 11 90660 3 - - - - 9 - - - 2 12 17 M. rubrofusca 18 90095 7 - - - - 5 - - - 2 12 19 20 Locus 108 ID 108A 108B 108C 108D 108E 108F 108G 90117 - - - - 6 2 - - - 2 8 21 M. glacialis 22 90072 - - - 2 7 - - - - 2 9 90292 1 1 1 9 - - - - - 4 12 23 M. pallida 24 90244 1 - - 10 - - - - - 2 11 90660 ------8 - - 1 8 25 M. rubrofusca 26 90095 3 - - - - - 1 - - 2 4 27 28 Columns 3 to 11 specify alleles that have been recovered from cloning PCR products of three loci (83A-I, 29 89A-F and 108A-G). Numbers indicate how frequently they were found in the six specimens examined. 30 Columns 12 and 13 summarise the number of different alleles (#A) and the number of clones (#C) analysed in 31 each specimen. 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 26 65 Figure 1 1 2 Meristic characters used in traditional morphometrics: The right coxite of the 9th abdominal 3 4 th 5 segment (A), one of the two gonapophyses of the 8 abdominal segment (B), the two 6 7 gonapophyses of the 9th segment (C) and the tibiae of the 1st (D) and the second (E) right leg 8 9 10 of one specimen of Machilis rubrofusca are shown. Numbers in circles denote the meristic 11 12 characters and correspond to those in Table 2. 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 27 65 Figure 2 1 2 Metric characters used in traditional morphometrics: Outlines of the right first leg (A) and the 3 4 5 left maxillary palp (B) of the same specimen as in Figure 1 are shown. Numbers in circles 6 7 correspond to those in Table 2. Distance characters are denoted as black lines. Calculated 8 9 10 angles are indicated with curved lines enclosing the angle. (St=stipes; La=lacinia; Ga=galea; 11 12 Mp1-7=maxillary palp; Cx=coxa; Tr=trochanter; Fe=femur; Ti=tibia; Ta2-3=tarsus) 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 28 65 Figure 3 1 2 PCA plots of traditional and geometric morphometric analyses. Principal component analyses 3 4 5 were performed on the basis of (A) three meristic TM variables that were identified as best 6 7 discriminators in the combination and LOOCV analyses (see Table 3) and (B) 22 GM 8 9 10 variables, representing X- and Y-values of partial warp scores plus the uniform component. 11 12 Values in brackets give the percentage of variance explained by each PC axis. 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 29 65 Figure 4 1 2 Results of GM data analysis: CVA plot (A) showing strong clustering of the three species; 3 4 5 Deformation grids indicate the relative displacement at each landmark relative to those of the 6 7 mean specimen, as CV axes scores increase along the first axis (B) and the second axis (C). 8 9 10 The deformation along CV1 (C) is a dorsoventral compression (especially on the median), 11 12 while the deformation on CV2 (B) is a lateral compression, together with a dorsal elongation. 13 14 Thus, M. glacialis is separated from the other two species due to its median, dorsoventral 15 16 17 compression (because of the high scores on CV1 (A) compared to the other species). Machilis 18 19 pallida (negative values on CV2) and M. rubrofusca (positive values on CV2) differ in the 20 21 22 deformation depicted by CV2, while M. glacialis is close to the overall mean shape (with 23 24 values around 0). An exemplary image of Machilis glacialis used for digitisation (D), 25 26 27 showing the position of landmarks (1, 22, 23) and semilandmarks (2-20; SLM with odd 28 29 numbers were only used as helper points and removed after sliding). 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 30 65 Figure 5 1 2 The maximum likelihood (ML) tree based on 762 bp of the mitochondrial gene CO1 shows 3 4 5 three monophyletic groups which correspond to the three species. Five-digit numbers are 6 7 collection IDs of sampled individuals. Node support (only values >90 are shown) is given as 8 9 10 bootstrap value inferred from 10,000 replications. In M. pallida, each sampled population is 11 12 represented by one haplotype. In M. rubrofusca, only the population from Obergurgl shows a 13 14 private haplotype, which differs clearly from the uniform haplotype of the other two 15 16 17 populations. In M. glacialis, all specimens sampled from three populations share the same 18 19 haplotype. 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 31 65 Figure 6 1 2 Unrooted Neighbour Joining trees for loci 83 (A), 89 (B) and 108 (C) were inferred from p- 3 4 5 distances among cloned sequences including gap information obtained from simple indel 6 7 coding (SIC). Node support (only values >75 are shown) is given as bootstrap value inferred 8 9 10 from 10,000 replications. For each recovered allele, one sequence per species was included to 11 12 show the degree of allele sharing between species. For more detail, see Table 5. 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 32 65 Author Agreement
To the editor,
We the undersigned declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.
We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.
We understand that the Corresponding Author is the sole contact for the Editorial process. He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.
on behalf of all co-authors:
Thomas Dejaco
Figure1
A 0.5 mm B C D E 6 9 7
21 1 5 22
8 Figure2
A 19 Ti 4 14 Fe 3
17 15 Ta2 2 Cx 20 Ta3 16 25 11 24 18 Tr Mp4 Mp5 23 26 B 12 Mp6 10 Mp3 dorsal Mp7 distal proximal 13 ventral Mp2
Mp1
2 mm St Ga La Figure3
A B 2 4
3 1 2
0 1
0 -1
PC 2 (25.5%) -1 PC2 (33.34%) -2 M. glacialis M. pallida -2 M. rubrofusca -3 -3 -2 -1 0 1 2 -2 -1 0 1 2 PC 1 (65.13%) PC 1 (64.7%) Figure4
A BB 4 0.4
3
2 0.2
1
0 0
-1 -0.2 CV 2 (10.7%) -2 M. glacialis -3 M. pallida M. rubrofusca -0.4 -4 -3 -2 -1 0 11 2 33 44 5 -0.4 -0.2 0 0.2 0.4 CV 1 (89.3%) C D 0.4
0.2
0
-0.2
-0.4
-0.4 -0.2 0 0.2 0.4 Figure5 90656 90660 90096 90095 90099 90097 99 90094 90643 90087 90642 90678 M. rubrofusca 90681 95 90677 90684 90679 90383 90327 90382 90369 90326 90292 90299 90291 100 90283 90293 M. pallida 90243 99 90244 90252 90174 90151 91369 90071 91370 90078 90278 90276 90245 91368 100 90277 91366 M. glacialis 90070 90261 90072 90117 91367 Trigoniophthalmus sp. Lepismachilis sp. Petrobius sp.
0.05 Figure6
A M. rubrofusca 83 M. glacialis 77 M. rubrofusca 79 M. glacialis 87 M. rubrofusca M. pallida 81 M. pallida M. pallida M. rubrofusca M. glacialis 100 M. rubrofusca
B M. pallida M. rubrofusca M. glacialis M. pallida 90 M. glacialis 99 M. pallida M. pallida M. pallida M. glacialis 100 M. rubrofusca
C M. rubrofusca M. pallida M. pallida M. pallida 88 M. pallida 96 M. glacialis M. glacialis 85 M. glacialis M. rubrofusca
0.005 Taxonomist’s nightmare … evolutionist’s delight: hybridization and parthenogenesis challenge species delimitation in Machilis jumping bristletails
Thomas Dejaco1*, Melitta Gassner1, Wolfgang Arthofer1, Birgit C. Schlick‐Steiner1, Florian M.
Steiner1
1 Institute of Ecology, University of Innsbruck, Technikerstraße 25, 6020 Innsbruck, Austria
* Corresponding author
Corresponding author's contact information:
Tel: (+43) 0512 507 51756
Running title: Species delimitation in Machilis jumping bristletails
Keywords: Integrative taxonomy, gene tree discordance, AFLP, Archaeognatha
1 ABSTRACT
Accurate species delimitation is an important prerequisite for many fields in biology. Besides conceptual reasons (i.e., which species concept is used), taxonomic failure may be caused by application of unsuitable marker systems or by complex evolutionary histories of the species under investigation. Integrative taxonomy, a multisource approach that incorporates results from different methodological disciplines, guards against common sources of taxonomic failure. Therefore, its use is especially promising in poorly studied non‐model organisms.
Jumpig‐bristletails of the genus Machilis are one such group, since the taxonomic status of many species is problematic due to the lack of informative morphological characters.
Eastern‐Alpine Machilis species include a comparatively high number of small‐scale endemics, and are thus especially valuable for biodiversity measures. Here, we apply traditional morphometrics, each one mitochondrial and nuclear sequence marker, and AFLP fingerprinting to geographically representative samples of 18 Machilis species in order to clarify actual species limits. Our results exemplify the advantage of multidisciplinary approaches over single disciplines. Only four out of 18 nominal species showed congruence among all disciplines. Instances of incongruence revealed one case of hybrid speciation, at least one cryptic species, and four synonyms. Other cases of incongruence can be explained by evolutionary scenarios, including hybridization, introgression, parthenogenesis, and ongoing speciation. Finally, the presence of five potentially new species highlights the current underestimation of Alpine biodiversity in the genus Machilis, especially in the
Southern Alps.
2 INTRODUCTION
In the last decade, the topic of species delimitation has experienced a renaissance among taxonomists and evolutionary biologists. With the unified species concept (de Queiroz 2005,
2007), a promising synthesis has been achieved, which relaxes stereotyped thinking owed to rivalling species concepts, and fosters multidisciplinary reasoning. Methodological advances regarding DNA based species delimitation (Knowles & Carstens 2007; Pons et al. 2006; Yang
& Rannala 2010), and increasing popularity of an integrative approach to taxonomic problems (Dayrat 2005; Padial et al. 2010; Schlick‐Steiner et al. 2010; Will et al. 2005), have triggered a growing number of excellent species delimitation studies across different animal taxa (Gebiola et al. 2012; Glaw et al. 2010; Leaché et al. 2009; Pinzón & LaJeunesse 2011;
Puillandre et al. 2012; Raxworthy et al. 2007; Ross et al. 2010; Satler et al. 2013; Seppä et al.
2011; Sistrom et al. 2013).
However, species delimitation remains a challenging task in poorly studied or morphologically homogenous (e.g., Satler et al. 2013) animal taxa. In such groups, usually little a‐priori information is available on geographic distribution of nominal species, their general biology, and, most aggravating, appropriate marker systems (morphological and molecular) to quantify variation among proposed (nominal) species hypotheses are lacking.
In most instances, mitochondrial genes will be the first markers of choice, due to ease of amplification and sequencing using universal primers. But relying on mitochondrial genes alone has proven to yield misleading results due to, e.g., the potential presence of nuclear pseudogenes (NUMTs) (Song et al. 2008), or discordant gene histories among mitochondrial and nuclear genomes (Funk & Omland 2003). Modern species delimitation approaches thus require sequencing of mitochondrial and, preferably multiple, nuclear genes.
3 One issue concerning sequencing of nuclear loci is heterozygosity. Even though bioinformatic tools have made molecular cloning dispensable for distinguishing allelic sequences in diploids, elevated ploidy levels pose a serious challenge to sequence‐based marker amplification and phylogenetic analysis. As a consequence, fragment‐length based molecular marker systems (e.g., AFLPs) have frequently been used to characterize nuclear genomic variation among species when polyploidy is involved, especially in plants (Guo et al.
2005; Hedrén et al. 2001; Koopman et al. 2008). These and many other studies have shown that AFLPs are highly suitable to reconstruct complex evolutionary histories involving hybridization and polyploidization.
In this study, we apply an integrative taxonomic approach to verify nominal species hypotheses in the morphologically homogenous and poorly studied jumping bristletail genus
Machilis. Jumping bristletails (Archaeognatha, or Microcoryphia) are the most basal wingless insects (Trautwein et al. 2012), and they have successfully colonized habitats as different as tropical rainforests and high‐alpine mountaintops. On a worldwide scale, about 500 species have been described so far (Sturm & Machida 2001), 94 of which belong to the genus
Machilis – making it the most diverse group of jumping bristletails. The centre of its distribution lies within the European Alps, and Machilis species are often closely associated to rocks. Due to their rock‐dwelling lifestyle and the absence of wings, they are supposed to be slow dispersers, and in accordance with that, the genus Machilis shows a very high degree of endemism compared with other arthropod groups within the Eastern Alps
(Rabitsch & Essl 2009). However, the number of biological species, and thus also the number of true endemics, may heavily differ from the number of described, nominal species. In fact, many of the original species delimitation hypotheses (i.e., nominal species) were based on few individuals from single geographical samples (see Tab. 1). Moreover, due to the absence
4 of prominent morphological characters, many species were delimited on the basis of patterns in hypodermal pigmentation on the head, legs, or maxillary palps. At the same time, these pigmentational patterns have been reported to vary considerably within other
Machilis species (Janetschek 1954). A comprehensive, integrative species delimitation approach is therefore urgently needed to make this group accessible to biodiversity measures and evolutionary research.
Recently, morphological and mitochondrial marker systems have been established to facilitate species delimitation in Machilis species (Dejaco et al. 2012). Moreover, Gassner et al. (in prep.) found compelling evidence for polyploidy in two Eastern‐Alpine Machilis species, thus complicating the use of nuclear sequence markers. Based on preliminary information from these two studies, we here apply traditional morphometrics, one mitochondrial marker (CO1), one nuclear marker (ITS‐2), and AFLP genotyping to geographically representative samples of nominal Machilis species from throughout the
Eastern Alps for enhanced species delimitation.
MATERIALS AND METHODS
Sampling design
We defined our study area (i.e., the Eastern Alps) based on the line connecting Lake
Constance in the north and Lake Como in the south, which represents a geological and biogeographical barrier between Western and Eastern Alps (Grimm & Mattmüller 2004;
Schönswetter et al. 2005). Our primary goal was to sample type localities of all 27 nominal species that have been described from within the Eastern Alps (Tab. 1). By systematically sampling across this area (Fig. 1), we aimed at a final number of at least three populations,
5 or 15 individuals per nominal species. All specimens were sampled 2010 ‐ 2013, placed in
96% Ethanol and stored at ‐20°C.
Traditional Morphometrics
Ethanol‐preserved specimens were dissected and mounted on glass slides using water‐ soluble Marc‐André 2 medium (Christiansen 1990). Eight meristic characters, as defined in
Dejaco et al. (2012), were determined using a Nikon Eclipse E600 bright field microscope.
Multivariate statistical analyses were performed in PAST v17.0 (Hammer et al. 2001) and
SPSS v21 (IBM, New York, USA). Graphs were edited in Sigmaplot v12.5 (Systat Software Inc,
San Jose, USA).
Sequence marker amplification and sequencing
Genomic DNA was extracted from ethanol‐preserved muscle tissues using the GenEluteTM
Mammalian Genomic DNA Miniprep kit (Sigma‐Aldrich, USA). Amplification of the partial
CO1 gene included multiple primer pairs: MachF1/ MachR3 (Dejaco et al. 2012),
MachF5/MachR7 (Gassner et al., in prep.), and MachF4 (5'‐ATTCGAGCTGAACTAGGNC‐3')/
UEA10. Ten µl reaction volumes contained 0.8 ‐ 1.2µl template DNA, 2µl 5xPCR‐Buffer
(including MgCl2 and dNTPs), 0.2µM of each primer, and 0.5 U MyTaq polymerase (Bioline
USA Inc., USA). PCR conditions were as follows: 95 °C for 2 min, 35 cycles (94 °C for 30 s, 50
°C for 45 s, 72 °C for 90 s), 72 °C for 10 min.
For the nuclear marker (ITS‐2), primers Bel28S and revBel28s (Belshaw & Quicke 1997) were used to amplify stretches of DNA spanning the 5.8S, ITS‐2, and 28S domains of the ribosomal DNA cistron. Consequently, a new forward primer (ITS‐MachF2, 5'‐
GGGTCGATGAAGAACGCAGCTA‐3') was designed to improve cross amplification among
6 species. The primer combination ITSMachF2/revBel28s was then used throughout this study.
Fifteen µl reaction volumes contained 1.2µl template DNA, 2µl 5xPCR‐Buffer (including
MgCl2 and dNTPs), 0.2µM of each primer, and 0.5 U MyTaq polymerase (Bioline USA Inc.,
USA). PCR conditions were as follows: 95 °C for 2 min, 35 cycles (94 °C for 30 s, 62 °C for 45 s,
72 °C for 105 s), and 72 °C for 10 min.
For both markers, amplicons were checked for presence of target bands via gel electrophoresis and subsequently purified enzymatically: Ten µl reaction volumes containing
8µl PCR product, 1U Exo1, and 0.05 U FastAP (both Thermo Fisher Scientific Inc., Waltham,
USA) were incubated at 37 °C for 15 min followed by 80 °C for 15 min. Sanger sequencing was conducted by a commercial sequencing facility (Eurofins MWG Operon, Germany) using amplification primers. Electropherograms were visually inspected for quality and absence of double peaks, and aligned using the ClustalW algorithm implemented in MEGA 5 (Tamura et al. 2011). CO1 sequences were checked for correct amino acid translation. All sequences were deposited in Genbank (Appendix Tab. A1). In the ITS‐2 alignment, gaps were coded using the simple indel coding algorithm (SIC; Simmons & Ochoterena 2000) implemented in
GapCoder (Young & Healy 2003). The resulting 0/1 matrix (0=absence, 1=presence, ‐=not applicable because one or more gaps fall within a larger gap found in another taxon) was appended to the ITS‐2 alignment.
AFLP fingerprinting
A detailed description of the AFLP protocol applied is given in Wachter et al. (2012). In short, DNA samples of 574 individuals were digested with enzymes Mse1 and EcoR1, followed by a preselective amplification step using the primers Eco‐* and Mse‐C.
Subsequently, a selective amplification step was applied, using four primer combinations
7 (tEco‐ACA/Mse‐CAA; tEco‐ACA/Mse‐CTT; tEco‐ACC/Mse‐CAA; tEco‐(TA)/Mse‐CTC). Each of the forward primers was labelled with FAM, HEX, NED, and PET dyes, respectively. Four individuals were replicated on each of the six 96‐well plates and continuously replicated during preselective and selective amplification, giving a total of 60 replicates. Fragment analysis was performed on an ABI 3130 sequencer (Applied Biosystems, USA). Data were analysed with Peakscanner v1.0 (Applied Biosystems) and imported into tinyFLP (Arthofer
2010) for automated peak selection. Scoring parameters were further refined in optiFLP
(Arthofer et al. 2011) using the unsupervised mode. Pairwise distances between replicates based on the Jaccard similarity index were calculated in Splitstree 4 (Huson & Bryant 2006) to estimate error rates.
Phylogenetic Analyses
Phylogenetic trees were inferred using both Bayesian (MrBayes 3.2; Ronquist et al. 2012) and Maximum Likelihood (Garli 2.0; Zwickl 2006) approaches. The CO1 alignment was partitioned into codon positions and evolutionary models were estimated by sampling over the entire GTR+G model space (nst=mixed) using the reversible‐jump MCMC algorithm implemented in MrBayes v.3.2. The advantage of directly estimating substitution models over specifying one single model is that uncertainties in model selection can be integrated out according to the posterior probability of each model (Huelsenbeck et al. 2004). Model parameters were unlinked between partitions and two parallel runs, each consisting of four chains, were run for 3×106 generations. The ITS‐2 alignment was partitioned in sequence and gap information. Gap characters were treated as standard data, while correcting for coding bias (coding=variable) to account for absence of invariable characters. Trees were sampled every 1,000th generation and the first 5,000 trees were discarded as burn‐in. AFLPs were
8 treated as standard data and were corrected for coding bias using the 'noabsencesites' option. The 'temp' parameter was set to 0.01 and two parallel runs, each consisting of six chains, were run for 15×106 generations. Trees were sampled every 5,000 generations and the 1,750 first trees were discarded as burn‐in.
For the ML analysis, 25 independent runs were computed and the tree showing the lowest likelihood value was defined as best topology. To estimate node support, 100 bootstrap replicates were performed in a separate run and bootstrap values were then summarized and mapped onto the best topology using the program SumTrees, included in the DendroPy software package v.3.12.0 (Sukumaran & Holder 2010).
The Bayesian Poisson tree processes (bPTP) algorithm (Zhang et al. 2013) was used to test for species limits on both gene trees. Compared with the Generalized Mixed Yule Coalescent
(GMYC) approach, bPTP models speciation (or branching events) in terms of number of substitutions, not in terms of time units. Therefore, bPTP can be used when time‐calibrated
(ultrametric) trees are not available. GMYC and bPTP have been shown to produce comparable results on real data and performed even better on simulated data (Zhang et al.
2013).
Admixture analyses
Bayesian clustering as implemented in the program BAPS v6.0 (Corander & Marttinen 2006) was used to infer the number of species in our final AFLP dataset. BAPS was chosen because the comparable software alternative STRUCTURE (Pritchard et al. 2000) has been reported to produce misleading results on datasets with variable sample sizes among groups
(Kalinowski 2011). In another study based on a large dataset (Wilkinson et al. 2011),
STRUCTURE performed weak from K=16 upwards (i.e., appearance of 'ghost' populations),
9 while BAPS consistently found biologically meaningful clusters of sub‐populations. BAPS was also used to test for admixture in cases of incongruence between gene trees. In doing so, specimens showing discordance among the two sequence markers were treated as unknown in an admixture analysis based on predefined groups.
RESULTS
Morphology
In total, 574 specimens were collected, of which 503 were assigned to 18 nominal species and 73 could not be unambiguously assigned. Concerning the closely related nominal species
M. inermis, M. ladensis, and M. robusta, determination was complicated by a gradient in the characters relevant to species diagnosis (i.e., patterns of pigmentation). Consequently, we assigned individuals from the three type localities to nominal species (M. inermis: n=16, M. ladensis: n=10, M. robusta: n=11) and assigned 16 specimens sampled from additional sites to a fourth category (M. cf. inermis). Moreover, 18 specimens that were mainly sampled from the South‐Eastern Alps could not be determined and were hence assigned to four unknown morphospecies (M. sp. B to M. sp. E). Details on morphological determination, locality, sex, and GenBank accessions are given in the Appendix Table A1.
Traditional morphometrics
Meristic characters were determined in 323 individuals from 18 nominal and one unknown species (M. sp. B). Three major groups were recognized in the principal component analysis
(PCA) scatterplot (Fig. 2): M. inermis, M. ladensis, M. robusta, and 13 undetermined individuals (M. cf. inermis) clearly formed a separate cluster. Species living in alpine and high‐alpine habitats clustered on the right side, while species inhabiting lowland and
10 montane ecosystems clustered on the left side. Even though species considerably overlapped within these groups, most of their mean values varied significantly in pairwise comparisons (MANOVA, Tab. 2). Nominal species pairs which did not differ significantly in their means included M. inermis, M. ladensis, and M. robusta (in all pairwise comparisons),
M. alpicola and M. distincta, M. alpicola and M. rubrofusca, M. hrabei and M. sp. B, M. lehnhoferi and M. sp. B, and M. helleri and M. pulchra.
Whenever hypotheses splitting nominal species were raised by other disciplines, these were tested for significant different mean values on the TM dataset using MANOVA. Significant differences were found in M. glacialis, where M. glacialis s. str. differed significantly from M. sp. A (F=24.65, p=4.48x10‐12), and in M. mesolcinensis, where central Alpine populations differed significantly from the southern Alpine population (F=30.36, p=2.51x10‐4). No significant differences were found between the western M. helleri clade (including M. pulchra) and the eastern clade (F=1.53, p=0.20), between sexual and parthenogenetic populations of M. ticinensis (F=0.80, p=0.62), and between two subpopulations of M. hrabei
(F=0.95, p=0.57).
Mitochondrial DNA
Approximately 800 bp from the 5' region of the CO1 gene were successfully amplified and sequenced from 563 individuals representing 18 nominal and five unknown species. The final alignment included 703 bp and was reduced to 111 unique haplotypes for phylogenetic inference. In the Bayesian majority rule consensus tree (Fig. 3), most nominal species formed monophyletic groups with nodes supported by posterior probabilities higher than 0.95. In contrast to that, most basal nodes were only weakly supported. Paraphyly of nominal species either occurred because of splitting, e.g., in M. glacialis, where two distantly related
11 clades were recovered (hereafter named M. glacialis s str. and M. sp. A), or due to shared haplotypes among species, e.g., among M. alpicola, M. engiadina, and M. rubrofusca, among
M. helleri and M. pulchra, among M. helleri and M. lehnhoferi, among M. helleri and M. hrabei, and among M. inermis, M. ladensis, and M. robusta. In M. alpicola, M. engiadina, and
M. rubrofusca, one prevalent haplotype (indicated by an arrow in Fig. 3) was found across
72.1% of all individuals (M. alpicola: 45%; M. engiadina: 63.9%; M. rubrofusca: 68.6%).
The ML approach retrieved the same topology; Bootstrap support values are displayed in
Fig. 3. Applying the bPTP algorithm to the Bayesian tree, 20 species were recovered, which are highlighted as red terminal branches in Fig. 3. Three nominal species/morphospecies were each split in two (M. hrabei, M. sp. B) or three (M. glacialis) species by the bPTP algorithm. In three other cases, two (M. helleri and M. pulchra, M. glacialis and M. montana) or three (M. alpicola, M. engiadina, and M. rubrofusca) nominal species were lumped together by the bPTP algorithm. The four unassigned morphospecies (M. sp. B to M. sp. E) were recovered as separate species. However, M. sp. D was nested within the 'M. sp. A' ‐ clade of M. glacialis.
Nuclear ribosomal DNA
Between 600 and 800 bp of the ITS‐2 rDNA were successfully amplified and sequenced from
562 individuals. The sequence alignment consisted of 761 characters and contained numerous gaps, spanning between one and 98 bp. Consequently, 219 gaps were coded and the resulting 0/1 matrix appended to 73 unique sequences for the final alignment.
Compared with the CO1 gene tree, basal nodes were more often supported by posterior probabilities higher than 0.95 (Fig. 3). Here as well, splits occurred in two nominal species
(M. helleri, M. lehnhoferi), and in three instances (M. helleri and M. pulchra, M. engiadina
12 and M. ticinensis, M. alpicola and M. rubrofusca), two nominal species shared the same sequence. Among M. alpicola and M. rubrofusca, 96.4% of the samples shared exactly the same sequence and only two M. alpicola individuals had a different ITS‐2 sequence.
Like in the CO1 tree, 20 species were recovered by the bPTP algorithm. They are highlighted as red terminal branches in Fig. 3. However, in nine instances, species designations were not congruent with the bPTP result on the CO1 tree. In four instances, nominal species were split in two (M. lehnhoferi, M. helleri, M. rubrofusca) or three (M. glacialis) independent lineages. On the other hand, nominal species pairs lumped together by the bPTP algorithm were M. alpicola and M. rubrofusca (except two individuals), M. aleamaculata and M. montana, M. engiadina and the sexual population of M. ticinensis, one lineage of M. helleri and M. pulchra, and M. hrabei and 12 individuals of M. lehnhoferi. The four unassigned morphospecies were recovered as separate species, but unlike the CO1 phylogeny, M. sp. D was not nested within the 'M. sp. A' ‐clade of M. glacialis.
AFLP genotyping
Of 576 generated AFLP profiles, 61 were excluded from further analyses due to the absence of peaks in at least one primer combination. The remaining 515 profiles were used to calculate NJ‐trees for each primer combination separately (data not shown). To avoid errors caused by cross‐contamination of DNA samples, we additionally excluded 27 profiles which clustered to three or four different species across the four NJ‐trees, leaving a final alignment of 488 profiles including 49 replicates. A mean error rate of 26% (SD=4.7%) was calculated from pairwise Jaccard distances of replicates, which is similar to that of other studies that applied the same calculation (Holland et al. 2008).
13 The Bayesian phylogeny was star‐like (Fig. 4), indicating that our dataset lacks information about the basal topology of the phylogeny. Resolution at the interspecific level was good, though, since most monophyletic clusters corresponded to nominal species. However, additional patterns of significant clustering were found in the following instances: Like in the gene trees, M. glacialis was paraphyletic, showing two distantly related clades. Reciprocal monophyletic subgroups occurred within M. helleri, and the two subclades corresponded to western and eastern populations. Interestingly, another nominal species, M. pulchra, was nested within the western subclade. Individuals of M. ladensis, M. robusta, and M. inermis fell within one clade, whose monophyly was only weakly supported. M. ticinensis was paraphyletic, with two monophyletic clades corresponding to northern and southern Alpine populations. All but two individuals of M. engiadina clustered together on the branch midway between the southern population of M. ticinensis and M. rubrofusca. All individuals of M. alpicola were monophyletic, but nested within M. rubrofusca.
The Bayesian clustering (BAPS) retrieved 22 discrete clusters and the admixture analysis revealed almost no gene flow among clusters (Fig. 5). In three instances, nominal species were split in two lineages, corresponding to northern and southern Alpine populations in M. ticinensis, to eastern and western populations in M. hrabei, and to eastern and western populations in M. helleri. Moreover, M. pulchra was part of the western M. helleri cluster. M. glacialis was split in three subpopulations. The first cluster (dark green) corresponded to M. glacialis s. str., and the latter two (dark grey and purple) corresponded to central and southern Alpine populations of a distantly related (see Fig. 3), previously unrecognized species (hereafter named M. sp. A).
We used BAPS to separately test for admixture between M. alpicola, M. rubrofusca, M. engiadina, and M. ticinensis, because on one side, all individuals of M. engiadina shared
14 identical or similar CO1 haplotypes with the former two nominal species, but on the other side shared exactly the same ITS‐2 sequence with the latter species. In doing so, we assumed that M. alpicola and M. rubrofusca together represent one parental species, while only the southern (sexual) population of M. ticinensis represents the other parental species. As shown in Fig. 6, all individuals of M. engiadina possess about 50% of the M. ticinensis genome and 50% of the M. alpicola/M. rubrofusca genome. A Principal Coordinate Analysis on AFLP profiles of theses four nominal species corroborated the intermediate position of M. engiadina (Fig. 7).
We also tested for admixture between M. helleri, M. hrabei, and M. lehnhoferi in a pairwise design to see whether gene tree discordance in several individuals (see Fig. 3) was due to hybridization or incomplete lineage sorting. Among M. hrabei and M. lehnhoferi (Fig.
8a), 7 individuals with discordant gene trees (only 9 were included in the final AFLP matrix) showed admixed genotypes (~75% M. lehnhoferi, ~25% M. hrabei), while two individuals showed pure M. lehnhoferi genotypes. Surprisingly, one additional individual showing no gene tree discordance had an admixed genotype as well (see arrow in Fig. 3A). Among M. helleri and M. hrabei, none of the two individuals showing gene tree discordance had admixed genotypes (Fig. 8B). Among M. helleri and M. lehnhoferi, three individuals showing gene tree discordance showed no admixture, but the same additional individual as in Fig. 8A was almost 50% admixed (see arrow in Fig. 8C).
DISCUSSION
Our results show a high degree of incongruence between different disciplines (Fig. 9), reflecting the urgent need for a taxonomic revision in the genus Machilis, based on a
15 comprehensive, integrative species delimitation approach. Only four out of 18 nominal species showed congruence among all disciplines, i.e., M. tirolensis, M. pallida, M. fuscistylis, and M. mesolcinensis. In the following, we discuss instances of incongruence and suggest evolutionary explanations for the observed patterns.
Morphological oversplitting
From a morphological perspective, M. inermis, M. ladensis, and M. robusta clearly differ in pigmentational patterns on legs, head, and maxillary palps. However, the original descriptions were based on few samples from single or very few populations (Wygodzinsky
1941). By sampling multiple populations in between type localities, we found that pigmentational patterns did not fit discrete categories (13 specimens could not unambiguously be assigned), and thus hypothesized synonymy of the three nominal species.
This hypothesis is corroborated by the results of traditional morphometrics, both mitochondrial and nuclear sequence markers, and AFLPs. All three original descriptions are included in Wygodzinsky (1941), but since the description of M. inermis is given first in terms of page numbers, it is given priority following the rules of the International Commission on
Zoological Nomenclature (ICZN). Therefore, we synonymize the names M. ladensis and M. robusta with M. inermis.
We also point out that in Wygodzinsky (1941), three additional nominal species descriptions (i.e., M. anderlani, M. nigrifrons, and M. vallicola) potentially fall within the morphological variation now present in M. inermis. Unfortunately, we did not succeed in sampling type localities of these nominal species (see Tab. 1). However, future studies should be aware of the potential presence of additional synonyms in the M. inermis species complex.
16 Gene tree discordance in the M. helleri species complex
Several cases of gene tree discordance were observed among M. lehnhoferi, M. helleri, M. hrabei, and M. pulchra (see Fig. 3). Genomic admixture based on AFLPs was found in some of the discordant individuals, indicating that hybridization might be involved. Concerning two populations of M. lehnhoferi harbouring the 'M. hrabei'‐like ITS‐2 sequences, our results suggest that most of these individuals share about 25% of the M. hrabei genome, while only two showed a pure M. lehnhoferi genotype (see Fig.8A). This pattern would be expected in
F2‐hybrids, and hence indicate recent hybridization. However, known distribution ranges of
M. lehnhoferi and M. hrabei do not overlap, and both species are usually found at different altitudes (600‐2,000m, and below 500m, respectively). Also, under a recent hybridization scenario, we would expect to find the "native" ITS‐2 sequence of M. hrabei (i.e., the sequences found across pure populations) in hybrid individuals. Instead, putative hybrid populations had a private set of ITS‐2 sequences, which were monophyletic and basal to all other populations of M. hrabei. This pattern indicates a private evolutionary history, and therefore, mito‐nuclear discordance seen here might rather be caused by incomplete lineage sorting of ancestral polymorphism in the ITS‐2 locus, or by nuclear introgression due to past hybridization events (Petit & Excoffier 2009). Unfortunately, patterns of incomplete lineage sorting are difficult to distinguish from those of nuclear introgression (Funk & Omland 2003;
Toews & Brelsford 2012). However, under the former scenario, not only incomplete sorting of ITS‐2, but also of about 25% of the M. hrabei genome would have to be assumed to explain the pattern found in AFLPs. Under a scenario of nuclear introgression, the pattern of
AFLPs could be explained by minor adaptive advantages of slightly admixed genotypes, which could have persisted within these two hybrid populations over time. We therefore
17 hypothesize that in this case, gene tree discordance is more likely a result of nuclear introgression rather than incomplete lineage sorting of the ITS‐2 locus.
Concerning one specimen of M. lehnhoferi and three M. hrabei individuals harbouring an mtDNA haplotype of M. helleri, no signs of admixture were found in AFLPs. In both instances, gene tree discordance was restricted to one single (M. lehnhoferi) or two adjoining (M. hrabei) populations. Because both discordant mtDNA haplotypes differed only in single mutations from widespread M. helleri haplotypes, and because sequence divergence compared with M. lehnhoferi and M. hrabei was high (~7 %), we consider incomplete sorting of ancestral polymorphic CO1 haplotypes to be very unlikely. Moreover, all three populations are geographically situated at the margins of their species' distribution, and within the distribution range of M. helleri. Because no genomic admixture was found, and mitochondrial introgression is known to occur frequently at contact zones (Toews &
Brelsford 2012), we suggest that these instances of mito‐nuclear discordance are best explained by mitochondrial introgression across contact zones.
Finally, the nominal species M. pulchra was placed within M. helleri based on the results from all disciplines, except traditional morphometrics. We therefore synonymize M. pulchra with M. helleri. Interestingly, there was a clear split between central Alpine M. helleri (to which M. pulchra is closely related) and eastern M. helleri. This partition was also found in the ITS‐2 marker and in AFLPs, but not in traditional morphometrics. Moreover, this partition corresponds to different chromosome numbers found by Gassner et al. (in prep.). However, based on the presence of mitochondrial haplotypes from the central Alpine lineage within the eastern lineage, we suspect either the presence of mitochondrial introgression across lineages, or incomplete sorting of ancestral CO1 haplotypes. Either way, these results altogether indicate ongoing speciation within M. helleri.
18 Hybridization and parthenogenesis in the M. engiadina species complex
Even though not all being closely related (see Fig. 3), M. alpicola, M. engiadina, M. rubrofusca, and M. ticinensis are morphologically very similar (see Fig. 2). They can be identified on the basis of pigmentational patterns on maxillary palps, but the taxonomic utility of these characters is questionable due to their wide intraspecific variability in other
Machilis species (Janetschek 1954; see discussion on M. inermis above). The differences in these patterns are especially subtle between M. alpicola and M. rubrofusca, and, in fact, both sequence markers used in this study support synonymy of these two nominal species.
However, a different pattern is seen in the AFLP dataset: Bayesian clustering clearly indicates two separate lineages, while in the Bayesian phylogeny, only M. alpicola is monophyletic and nested within M. rubrofusca. Additionally, principal coordinate analysis indicates advanced partitioning. This nuclear divergence is surprising given the low divergence in mitochondrial and ribosomal DNA but is nevertheless biologically relevant when additional sources of information are included: First, both lineages reproduce exclusively via parthenogenesis and, therefore, gene flow is most likely absent since the origin of clonal reproduction. Second, the distribution ranges of the two lineages do not overlap: While M. rubrofusca is restricted to the central Eastern Alps and found on silicate rock, M. alpicola is widespread along the northern side of the Alps and predominantly found on calcareous rock. The fact that four M. alpicola individuals from the French Alps cluster within M. alpicola from Austria (while neighbouring M. rubrofusca individuals do not) argues against geographical distance being the main source of divergence seen in AFLPs.
One possible explanation for the two nuclear clusters is parallel origin of parthenogenesis, stemming from two individuals belonging to divergent populations of the last common ancestor, which already had accumulated substantial nuclear genetic divergence (e.g., due
19 to ongoing ecological or parapatric speciation). Under this scenario, significant mitochondrial divergence would be expected as well, since mitochondrial genes usually evolve faster than nuclear genes. However, mitochondrial introgression between diverging lineages could explain homogeneity of mtDNA haplotypes in the presence of nuclear differences.
Alternatively, nuclear and mitochondrial introgression from another species (e.g., through hybridization and subsequent backcrossing) could have led to pure and slightly admixed genotypes (see Fig. 6), which share the maternally inherited mitochondrial haplotype. In fact, the latter explanation is corroborated by further results including M. engiadina and M. ticinensis.
These results show that M. rubrofusca and M. alpicola share the same mitochondrial haplotypes with M. engiadina, while all M. engiadina individuals share the ITS‐2 sequence with one southern Alpine population of M. ticinensis. Both the admixture analysis (Fig. 6) and the principal coordinate analysis (Fig. 7) indicate admixed or intermediate genotypes in
M. engiadina based on AFLPs. We therefore hypothesize that M. engiadina individuals are hybrids of M. rubrofusca and sexual M. ticinensis, while M. alpicola represents a separate, parthenogenetic lineage of backcrossed individuals. The genetic uniformity within M. engiadina, the parthenogenetic mode of reproduction, and its wide geographical distribution support one hybridization event and subsequent hybrid speciation, rather than occasional hybridization at contact zones.
Gassner et al. (in prep.) investigated chromosome numbers and genome sizes of several
Eastern‐Alpine Machilis species, including M. engiadina and M. ticinensis. Since their results show that both, M. engiadina and M. ticinensis, are diploid, we hypothesize that M. engiadina originated via homoploid hybrid speciation. Homoploid (or recombinational) hybrid speciation is thought to evolve rarely compared with allopolyploid hybrid speciation
20 (Gompert et al. 2006; Mavárez et al. 2006; Nice et al. 2013; but see Schwarz et al. 2005), since a clear reproductive barrier against both parental species is missing in the absence of polyploidy (Mallet 2007). Consequently, homoploid hybrid speciation becomes more likely when hybrids find available ecological niches or become reproductively isolated by asexual reproduction (Abbott et al. 2013; Rieseberg et al. 2003). Intriguingly, both conditions are met in M. engiadina and M. alpicola, since their distribution ranges largely differ from that of both parental species, and no males have been found.
The second parental species of M. engiadina, M. ticinensis, is also partitioned in two clades. As indicated by the Bayesian phylogeny based on AFLPs, the Bayesian clustering, and by the principal coordinates analysis, nuclear genomes of the southern and northern populations of M. ticinensis are highly diverged. The amount of divergence equals divergence between other well defined species, e.g., M. helleri and M. lehnhoferi, and this is in stark contrast to the low level of divergence seen in both, CO1 and ITS‐2 sequences within nominal M. ticinensis. This pattern is similar, though, to that found in M. rubrofusca and M. alpicola, even if AFLPs show greater divergence here. Likewise, a scenario of hybridization with an unknown species, followed by a switch to parthenogenesis in hybrids (corresponding to the northern populations), thus freezing of the hybrid nuclear and the maternal mitochondrial genome, could explain this pattern. In the absence of alternative hypotheses explaining incongruence between traditional morphometrics and sequence markers on one side, and AFLPs on the other side, we acknowledge the presence of two separately evolving lineages within M. ticinensis. However, due to the lack of additional samples from the
Southern Alps, we refrain from splitting M. ticinensis in two species. More comprehensive
21 sampling and application of additional methodological disciplines may further clarify the evolutionary history of independently evolving lineages within M. ticinensis.
Cryptic species uncovered
Both sequence markers and AFLPs support the presence of at least one cryptic species within the nominal species M. glacialis. The potentially new species included two mitochondrial subclades that were characterized by 9.6% mean net‐between‐group distance. These subclades correspond to individuals from five central and one southern
Alpine population. While in the former, only females were found (n=26), four males were sampled in the latter (n=8), indicating that the central Alpine populations might be parthenogenetic. Clonal reproduction is further supported by the uniformity of CO1 sequences among central Alpine populations (as expected under clonal reproduction;
Halkett et al. 2005), compared with the observed sequence diversity in the southern (sexual) population. Even though southern and northern populations had private ITS‐2 sequences, reciprocal monophyly was not significantly supported. However, one male individual from the southern population was recognized as separate cluster by bPTP. Similarly, the Bayesian phylogeny of AFLPs did not support reciprocal monophyly, while Bayesian clustering (BAPS) recovered central and. southern Alpine lineages as discrete clusters. In the absence of additional samples from the area in between central and southern populations, and due to the potentially different reproductive system, we decline from erecting two separate species. Instead, we suggest one new species that we temporarily name M. sp. A, and highlight the possibility of ongoing speciation among central and southern Alpine populations. Following the re‐evaluation of traditional morphometrics, M. sp.A differs morphologically from M. glacialis and is therefore uncovered from morphological crypsis.
22 CONCLUSION
This study exemplifies the advantage of a multidisciplinary approach to species delimitation in taxonomically challenging arthropod groups. Out of the 18 nominal species investigated, only four were supported by full congruence among disciplines. We were able to explain instances of incongruence by suggesting evolutionary explanations for the observed pattern.
These explanations provide a solid framework for more specific investigations. Overall, we showed that both hybridization and parthenogenesis affected lineage diversification in
Eastern Alpine Machilis species.
Moreover, the presence of at least five, up to now unrecognized, morphospecies in our dataset suggests that the diversity of Alpine Machilis species is currently underestimated, especially within the Southern Alpine region. The repeatedly found partitioning of nominal species in Central and Southern, or Eastern lineages indicates that demographic shifts may have followed glacial expansions and retreats during Pleistocene glaciation cycles.
Consequently, climatic oscillations, together with hybridization and parthenogenesis, may provide a framework for explaining the remarkable diversification in the genus Machilis, compared with other genera of jumping bristletails.
ACKNOWLEDGEMENTS
The authors thank Hannes Rauch, Michael Url, Lukas Rinnhofer, and Gregor Wachter for help with field sampling, and Clemens Folterbauer for valuable support in the wet lab. The
University of Innsbruck funded this project via stipends to TD and MG. For additional funding, we thank the Autonomous Province of South Tyrol (project‐ID: 1/40.3; 27 January
2014).
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27 Tab. 1: Nominal species described from within the Eastern Alps. Information about reproductive modes refers to explicit statements in the literature or mention of males in original descriptions. Information on ploidy level is taken from Gassner et al. (submitted). AT=Austria, CH=Switzerland, CZ=Czech Republic, DE=Germany, FL=Liechtenstein, FR=France, IT=Italy, PL=Poland, SK=Slovac Republic. Except for M. helleri and M. hrabei, the distribution ranges given are restricted to Alpine regions of mentioned countries. species taxon authority reproductive ploidy level # of individuals geographic distribution mode sampled aciliata Janetschek, 1955 sexual ? no one location in Styria (AT) aleamaculata Wygodzinsky, 1941 parthenogenetic ? yes one location in CH alpicola Janetschek, 1953 parthenogenetic ? yes AT, FR, ES, CH engiadina Wygodzinsky, 1941 parthenogenetic 2n yes AT, IT, FR, CH fuscistylis Riezler, 1941 parthenogenetic 2n (?) yes AT, CH glacialis Verhoeff, 1910 sexual ? yes AT, CH helleri Verhoeff, 1910 sexual 2n yes AT, CZ, DE, PL, SK hrabei Kratochvil, 1945 sexual 2n yes AT, CZ inermis Wygodzinsky, 1941 sexual ? yes CH ladensis Janetschek, 1950 sexual ? yes one location at AT/CH/IT tripoint lehnhoferi Riezler, 1941 sexual 2n yes AT/DE border region longiseta Janetschek, 1949 parthenogenetic ? no one location in Tyrol (AT) melanarthra Wygodzinsky, 1941 sexual ? no one location in CH mesolcinensis Wygodzinsky, 1941 sexual ? yes one location in CH montana Wygodzinsky, 1941 sexual ? yes CH nigrifrons Wygodzinsky, 1941 sexual ? no one location in CH oblitterata Janetschek, 1970 sexual ? no one location in IT pallida Janetschek, 1949 parthenogenetic 3n yes Tyrol (AT), IT pasubiensis Bach de Roca, 1983 sexual ? no one location in IT pulchra Janetschek, 1950 parthenogenetic 2n yes two locations in Tyrol (AT) rubrofusca Janetschek, 1949 parthenogenetic 2n yes AT, CH, IT robusta Wygodzinsky, 1941 sexual ? yes CH, AT ticinensis Wygodzinsky, 1941 geogr. 2n yes CH, AT tirolensis Verhoeff, 1910 geogr. 2n, 3n yes AT, IT, FR, DE, CH vagans Wygodzinsky, 1941 parthenogenetic ? no one location in FL vallicola Wygodzinsky, 1941 sexual ? no one location in CH vicina Wygodzinsky, 1941 sexual ? no one location in FL
28 Tab.2: P‐values of all pairwise comparison (MANOVA) based on eight TM characters between nominal species. Non‐significant comparisons are highlighted in red. Numbers above and below the diagonal correspond to uncorrected and Bonferroni‐corrected P‐ values, respectively. The additional group named M. cf. inermis does not correspond to a nominal species (see text).
29 Fig. 1
30 Fig. 2
31 Fig. 3
32 Fig. 4
33 Fig. 5
34 Fig. 6
35 Fig. 7
36 Fig. 8
37 Fig. 9
38 Figure captions Fig. 1: Sampling sites of specimens used in this study. Colour codes represent species and correspond to all other figures. Different shapes distinguish species with similar colour code in this figure and Fig. 2. One population of M. alpicola from France and one population of M. helleri from northern Czech Republic are not included in this figure (see Appendix Table A1 for geographical coordinates).
Fig. 2: PCA scatterplot based on eight traditional morphometric characters. M. aleamaculata and M. montana, as well as M. sp. C, M. sp. D, and M. sp. E are not included due to low number of specimens.
Fig. 3: CO1 and ITS‐2 gene trees based on 111 and 73 unique sequences, respectively. Coloured bands visualize congruence/incongruence between gene trees, and correspond to colour codes used in all other figures.
Fig. 4: Bayesian majority‐rule consensus tree based on 439AFLP profiles. Nodes that are supported by posterior probabilities above 0.95 are indicated by stars. Nominal species are colour‐coded corresponding to all other figures.
Fig. 5: BAPS clustering including 18 nominal species and M. sp. B, based on 439 AFLP profiles. Colours represent clusters recovered by BAPS. Black bars and names above the plot indicate nominal species. Additional black bars and names below the plot indicate clusters that are not congruent with nominal species.
Fig. 6: Admixture based on predefined groups among M. ticinensis (South), M. engiadina, M. alpicola, and M. rubrofusca. Based on incongruences among gene trees and the results of AFLP (MrBayes and PCoA), M. ticinensis (South) and M. rubrofusca we defined as parental species. All individuals of M. engiadina and M. alpicola were treated as unknown.
Fig. 7: Principal coordinate analysis of AFLP profiles of M. alpicola, M. engiadina, M. ticinensis, and M. rubrofusca.
39 Fig. 8: Admixture based on predefined groups. a) M. hrabei vs. M. lehnhoferi, b) M. helleri vs. M. hrabei, c) M. lehnhoferi vs. M. helleri. Individuals showing incongruence between CO1 and ITS‐2 were treated as unknown and are framed in red.
Fig. 9: Summarized results from all disciplines. In every line except the last one (Final species), coloured rectangles indicate congruence with the morphological species delimitation hypothesis. Empty rectangles indicate incongruence. No rectangles are displayed when no data were generated fort the respective species/discipline. In the 'Final species' line, thick frames indicate species where ongoing speciation was diagnosed. These species may be split following in‐depth investigations.
40 Appendix Table A1
Traditional ITS‐2 locality CO1 Genbank AFLP ID species locality name Lat [°] Long [°] altitude [m asl] morphometrics Genbank sex code accession applied applied accession 91269 aleamaculata Sta. Maria di Calanca, CH SMC 46.28442 9.15005 1400 KJ691108 KJ691460 yes m 91270 aleamaculata Sta. Maria di Calanca, CH SMC 46.28442 9.15005 1400 KJ691109 KJ691461 f 91271 aleamaculata Sta. Maria di Calanca, CH SMC 46.28442 9.15005 1400 KJ691110 KJ691462 yes f 91299 aleamaculata Sta. Maria di Calanca, CH SMC 46.28442 9.15005 1400 KJ691111 KJ691463 yes m 91300 aleamaculata Sta. Maria di Calanca, CH SMC 46.28442 9.15005 1400 KJ691112 KJ691464 yes m 91030 alpicola Brandnertal, AT BRA 47.12195 9.76128 1050 yes KJ691071 KJ691420 yes f 90570 alpicola Dornbirn, AT BRK 47.39000 9.78000 540 yes KJ691028 KJ691367 yes f 91386 alpicola Forchach, AT FCH 47.42505 10.58000 920 yes KJ691120 KJ691477 yes f 91389 alpicola Forchach, AT FCH 47.42505 10.58000 920 yes KJ691121 KJ691478 yes f 91835 alpicola La Bérarde, FR LBR 44.91658 6.30642 1850 KJ691568 f 91841 alpicola La Bérarde, FR LBR 44.91658 6.30642 1850 yes KJ691207 KJ691569 yes f 91844 alpicola La Bérarde, FR LBR 44.91658 6.30642 1850 yes KJ691210 KJ691572 yes f 91842 alpicola Les Ètages, FR LES 44.93740 6.25830 1640 yes KJ691208 KJ691570 yes f 91843 alpicola Les Ètages, FR LES 44.93740 6.25830 1640 yes KJ691209 KJ691571 yes f 90895 alpicola Leutasch, AT LEU 47.37760 11.15058 1270 yes KJ691061 KJ691408 f 91259 alpicola Mapragg, CH MAP 46.94667 9.48167 870 yes KJ691103 KJ691455 yes f 92085 alpicola NenzingerHimmel NZH 47.16266 9.69109 850 KJ691241 KJ691707 yes f 92086 alpicola NenzingerHimmel NZH 47.16266 9.69109 850 KJ691242 KJ691708 f 92087 alpicola NenzingerHimmel NZH 47.16266 9.69109 850 KJ691243 KJ691709 f 92110 alpicola Prandegg, AT PRG 48.41458 14.66675 727 KJ691253 KJ691729 f 90556 alpicola Stuben, AT STU 47.14000 10.16000 1510 yes KJ691025 KJ691364 yes f 90563 alpicola Stuben, AT STU 47.14000 10.16000 1510 yes KJ691026 KJ691365 yes f 90566 alpicola Stuben, AT STU 47.14000 10.16000 1510 yes KJ691027 KJ691366 yes f 91526 alpicola Wasserauen, CH WAS 47.27782 9.41437 940 yes KJ691138 KJ691495 yes f 91479 alpicola Wiesele, AT WIE 47.19399 10.14284 1580 yes KJ691130 KJ691487 yes f 91480 alpicola Wiesele, AT WIE 47.19399 10.14284 1580 yes KJ691131 KJ691488 yes f 91105 alpicola Oberschaan, LIE PAL 47.09600 9.42462 1770 KJ691076 KJ691425 yes f 90132 distincta Brandberg, AT BRA 47.16736 11.89689 1130 yes KJ690989 KJ691311 yes f 90134 distincta Brandberg, AT BRA 47.16736 11.89689 1130 yes KJ690990 KJ691312 yes f 90136 distincta Brandberg, AT BRA 47.16736 11.89689 1130 yes KJ690991 KJ691313 f
41 92018 distincta Leopoldsteiner See, AT EIS 47.57746 14.09827 775 yes KJ501716 KJ691661 yes f 92019 distincta Leopoldsteiner See, AT EIS 47.57746 14.09827 775 yes KJ501717 KJ691662 yes f 92020 distincta Leopoldsteiner See, AT EIS 47.57746 14.09827 775 KJ501718 KJ691663 f 92021 distincta Leopoldsteiner See, AT EIS 47.57746 14.09827 775 KJ501719 KJ691664 yes f 92022 distincta Leopoldsteiner See, AT EIS 47.57746 14.09827 775 KJ501720 KJ691665 yes f 92023 distincta Leopoldsteiner See, AT EIS 47.57746 14.09827 775 KJ501721 KJ691666 yes f 92167 distincta Leopoldsteiner See, AT EIS 47.57746 14.09827 775 KJ501738 KJ691772 yes f 92092 distincta Engelswand, AT ENG 47.16356 10.91552 939 KJ691244 KJ691714 yes f 92094 distincta Engelswand, AT ENG 47.16356 10.91552 939 KJ691245 KJ691715 f 92095 distincta Engelswand, AT ENG 47.16356 10.91552 939 KJ691246 KJ691716 yes f 92096 distincta Engelswand, AT ENG 47.16356 10.91552 939 KJ691247 KJ691717 f 92097 distincta Engelswand, AT ENG 47.16356 10.91552 939 KJ691718 f 91823 distincta Kranebitter Klamm, AT KRK 47.27405 11.32771 830 yes KJ691201 KJ691562 yes f 91869 distincta Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ691214 KJ691576 yes f 91874 distincta Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ691218 KJ691580 yes f 92089 distincta Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ501734 KJ691711 f 92124 distincta Lorenzago di Cadore, IT LDC 46.46364 12.48886 1081 KJ501737 KJ691743 yes f 91986 distincta Murau, AT MUR 47.11155 14.20253 811 KJ501708 KJ691639 yes f 92005 distincta Murau, AT MUR 47.11155 14.20253 811 KJ501712 KJ691648 yes f 92006 distincta Murau, AT MUR 47.11155 14.20253 811 KJ501713 KJ691649 yes f 92007 distincta Murau, AT MUR 47.11155 14.20253 811 KJ501714 KJ691650 yes f 92008 distincta Murau, AT MUR 47.11155 14.20253 811 KJ501715 KJ691651 yes f 91950 distincta Nikolsdorf, AT NIK 46.77864 12.89217 670 KJ501697 KJ691621 yes f 91951 distincta Nikolsdorf, AT NIK 46.77864 12.89217 670 KJ501698 KJ691622 yes f 91952 distincta Nikolsdorf, AT NIK 46.77864 12.89217 670 KJ501699 KJ691623 yes f 91954 distincta Nikolsdorf, AT NIK 46.77864 12.89217 670 KJ501700 KJ691625 f 91963 distincta Nikolsdorf, AT NIK 46.77864 12.89217 670 KJ501701 KJ691629 f 91964 distincta Nikolsdorf, AT NIK 46.77864 12.89217 670 KJ501702 KJ691630 yes f 91502 distincta Inntal/Ötztal, AT OET 47.22647 10.86462 880 yes KJ691134 KJ691491 yes f 90648 distincta Pfelders, IT PFE 46.78552 11.05950 1878 yes KJ691038 KJ691379 yes f 90651 distincta Pfelders, IT PFE 46.78552 11.05950 1878 yes KJ691039 KJ691380 yes f 91568 distincta Virgental, AT PRG 47.01632 12.33626 1360 yes KJ691157 KJ691516 yes f 91573 distincta Virgental, AT PRG 47.01632 12.33626 1360 yes KJ691159 KJ691518 yes f 92039 distincta Saalfelden, AT SAA 47.48759 12.83152 823 KJ501726 KJ691676 yes f 92052 distincta Saalfelden, AT SAA 47.48759 12.83152 823 KJ501730 KJ691687 yes f
42 92004 distincta Schladming, AT SLD 47.40479 13.57371 825 KJ501711 KJ691647 yes f 92041 distincta Schladming, AT SLD 47.40479 13.57371 825 KJ501727 KJ691678 f 92044 distincta Schladming, AT SLD 47.40479 13.57371 825 KJ501728 KJ691679 yes f 92045 distincta Schladming, AT SLD 47.40479 13.57371 825 KJ501729 KJ691680 f 91976 distincta Sankt Martin in Thurn, IT SMT 46.63812 11.85378 1502 yes KJ501705 KJ691634 yes f 92054 distincta Sankt Martin in Thurn, IT SMT 46.63812 11.85378 1502 KJ501731 KJ691689 yes f 92055 distincta Sankt Martin in Thurn, IT SMT 46.63812 11.85378 1502 KJ501732 KJ691690 yes f 92056 distincta Sankt Martin in Thurn, IT SMT 46.63812 11.85378 1502 KJ501733 KJ691691 yes f 92091 distincta Stabiziane, IT STA 46.54764 12.32539 1060 KJ501735 KJ691713 yes f 92101 distincta Stabiziane, IT STA 46.54764 12.32539 1060 KJ501736 KJ691721 yes f 92076 distincta Scharnitz, AT STZ 47.39049 11.27916 1105 KJ691239 KJ691700 yes f 91991 distincta Toblach, IT TOB 46.71572 12.22392 1243 KJ501710 KJ691641 yes f 90106 distincta Umhausen, AT UMH 47.14403 10.93153 1111 yes KJ690985 KJ691306 yes f 91457 distincta Varusch, CH VAR 46.60773 10.03217 1768 yes KJ691129 KJ691486 yes f 91997 distincta Varusch, CH VAR 46.60773 10.03217 1768 KJ691230 KJ691645 yes f 92059 distincta Varusch, CH VAR 46.60773 10.03217 1768 KJ691235 KJ691692 yes f 92060 distincta Varusch, CH VAR 46.60773 10.03217 1768 KJ691236 KJ691693 yes f 92061 distincta Varusch, CH VAR 46.60773 10.03217 1768 KJ691237 KJ691694 yes f 91827 distincta Vahrner See, IT VSE 46.76752 11.63451 730 yes KJ691204 KJ691565 yes f 91828 distincta Vahrner See, IT VSE 46.76752 11.63451 730 yes KJ691205 KJ691566 f 92103 distincta Zwieselstein, AT ZWI 46.94825 11.02219 1487 KJ691250 KJ691723 yes f 92104 distincta Zwieselstein, AT ZWI 46.94825 11.02219 1487 KJ691251 KJ691724 yes f 91217 fuscistylis Feldalm, AT FAM 47.10643 11.77708 2080 yes KJ691101 KJ691453 yes f 91980 fuscistylis Fotschertal, AT FOT 47.14521 11.22524 2265 yes KJ501743 KJ691636 yes f 91981 fuscistylis Fotschertal, AT FOT 47.14521 11.22524 2265 yes KJ501744 KJ691637 yes f 91982 fuscistylis Fotschertal, AT FOT 47.14521 11.22524 2265 yes KJ501745 KJ691638 yes f 90880 fuscistylis Hoher Dieb, IT HDB 46.57033 10.93370 2650 KJ691058 KJ691405 f 92239 fuscistylis Hintertux, AT HIN 47.09979 11.65683 2160 KJ501747 KJ691801 yes f 92240 fuscistylis Hintertux, AT HIN 47.09979 11.65683 2160 KJ501748 KJ691802 yes f 92242 fuscistylis Hintertux, AT HIN 47.09979 11.65683 2160 KJ501749 KJ691804 yes f 92244 fuscistylis Hintertux, AT HIN 47.09979 11.65683 2160 KJ501750 KJ691806 yes f 92247 fuscistylis Hintertux, AT HIN 47.09979 11.65683 2160 KJ501751 KJ691808 yes f 92250 fuscistylis Hintertux, AT HIN 47.09979 11.65683 2160 KJ501752 KJ691811 yes f 91260 fuscistylis Niederelbhütte, AT NEH 47.06270 10.31343 2300 yes KJ691104 KJ691456 yes f 91261 fuscistylis Niederelbhütte, AT NEH 47.06270 10.31343 2300 yes KJ691105 KJ691457 yes f
43 91262 fuscistylis Niederelbhütte, AT NEH 47.06270 10.31343 2300 yes KJ691106 KJ691458 yes f 91264 fuscistylis Niederelbhütte, AT NEH 47.06270 10.31343 2300 yes KJ691107 KJ691459 yes f 90914 fuscistylis Nürnberger Hütte, AT NUH 46.99398 11.21730 2295 KJ691066 KJ691414 f 91904 fuscistylis Obergurgl, AT OBG 46.86153 11.06287 KJ501739 KJ691594 yes f 91905 fuscistylis Obergurgl, AT OBG 46.86153 11.06287 KJ501740 KJ691595 yes f 91906 fuscistylis Obergurgl, AT OBG 46.86153 11.06287 KJ501741 KJ691596 yes f 91907 fuscistylis Obergurgl, AT OBG 46.86153 11.06287 yes KJ501742 KJ691597 yes f 90074 fuscistylis Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes KJ690982 KJ691296 yes f 92083 fuscistylis Spronser Seen, IT SPS 46.73075 11.08233 2390 yes KJ691240 KJ691706 yes f 90901 fuscistylis Schwarzenstein, IT SWS 47.02376 11.83256 2100 yes KJ691063 KJ691411 yes f 90909 fuscistylis Schwarzenstein, IT SWS 47.02376 11.83256 2100 yes KJ691064 KJ691412 yes f 92040 fuscistylis Vordere Rotspitze, IT VRS 46.47809 10.70601 2930 yes KJ501746 KJ691677 yes f 90688 fuscistylis Zirmkogel, AT ZKG 46.89118 10.99683 3010 yes KJ691043 KJ691390 yes f 90691 fuscistylis Zirmkogel, AT ZKG 46.89118 10.99683 3010 yes KJ691044 KJ691391 yes f 90692 fuscistylis Zirmkogel, AT ZKG 46.89118 10.99683 3010 yes KJ691045 KJ691392 yes f 90694 fuscistylis Zirmkogel, AT ZKG 46.89118 10.99683 3010 KJ691046 KJ691393 f 91629 glacialis Bernina, CH BER 46.41271 10.02663 2350 yes KJ691170 KJ691530 yes f 91630 glacialis Bernina, CH BER 46.41271 10.02663 2350 yes KJ691171 KJ691531 yes f 91631 glacialis Bernina, CH BER 46.41271 10.02663 2350 yes KJ691172 KJ691532 yes f 90872 glacialis Hoher Dieb, IT HDB 46.57043 10.94613 2200 yes KJ501754 KJ691402 yes f 90873 glacialis Hoher Dieb, IT HDB 46.57043 10.94613 2200 yes KJ691056 KJ691403 yes f 90874 glacialis Hoher Dieb, IT HDB 46.57043 10.94613 2200 yes KJ691057 KJ691404 yes f 90885 glacialis Hoher Dieb, IT HDB 46.57043 10.94613 2200 KJ691059 KJ691406 yes m 91597 glacialis Morteratsch, CH MOR 46.44782 9.94026 1956 yes KJ501756 KJ691524 yes f 91598 glacialis Morteratsch, CH MOR 46.44782 9.94026 1956 KJ691165 KJ691525 yes m 91601 glacialis Morteratsch, CH MOR 46.44782 9.94026 1956 yes KJ691166 KJ691526 yes f 91602 glacialis Morteratsch, CH MOR 46.44782 9.94026 1956 yes KJ691167 KJ691527 yes f 91604 glacialis Morteratsch, CH MOR 46.44782 9.94026 1956 yes KJ691168 KJ691528 yes f 91627 glacialis Morteratsch, CH MOR 46.44782 9.94026 1956 yes KJ691169 KJ691529 yes f 90925 glacialis Nürnberger Hütte, AT NUH 46.99200 11.21621 2370 KJ501755 KJ691415 yes m 90927 glacialis Nürnberger Hütte, AT NUH 46.99200 11.21621 2370 yes KJ691067 KJ691416 yes f 90928 glacialis Nürnberger Hütte, AT NUH 46.99200 11.21621 2370 yes KJ691068 KJ691417 f 90929 glacialis Nürnberger Hütte, AT NUH 46.99200 11.21621 2370 yes KJ691069 KJ691418 yes f 90647 glacialis Pfelders, IT PFE 46.78645 11.06180 1855 yes KJ691037 KJ691378 yes f
44 92151 helleri Admont, AT ADM 47.54547 14.47487 1000 KJ501774 KJ691760 yes f 92152 helleri Admont, AT ADM 47.54547 14.47487 1000 KJ501775 KJ691761 f 92153 helleri Admont, AT ADM 47.54547 14.47487 1000 KJ501776 KJ691762 m 92154 helleri Admont, AT ADM 47.54547 14.47487 1000 KJ501777 KJ691763 yes m 91968 helleri Leopoldsteiner See, AT EIS 47.57746 14.09827 775 yes KJ501762 KJ691631 yes f 91998 helleri Leopoldsteiner See, AT EIS 47.57746 14.09827 775 KJ501763 KJ691646 yes f 92168 helleri Leopoldsteiner See, AT EIS 47.57746 14.09827 775 yes KJ501778 KJ691773 yes f 92169 helleri Leopoldsteiner See, AT EIS 47.57746 14.09827 775 yes KJ501779 KJ691774 yes f 92170 helleri Leopoldsteiner See, AT EIS 47.57746 14.09827 775 KJ501780 KJ691775 yes m 92171 helleri Leopoldsteiner See, AT EIS 47.57746 14.09827 775 yes KJ501781 KJ691776 yes f 92077 helleri Gießhübl, AT GIE 48.10151 16.21928 480 yes KJ501764 KJ691701 yes f 92078 helleri Gießhübl, AT GIE 48.10151 16.21928 480 yes KJ501765 KJ691702 yes f 92079 helleri Gießhübl, AT GIE 48.10151 16.21928 480 yes KJ501766 KJ691703 yes f 92081 helleri Gießhübl, AT GIE 48.10151 16.21928 480 KJ501767 KJ691705 yes m 92111 helleri Gießhübl, AT GIE 48.10151 16.21928 480 KJ501768 KJ691730 yes m 92112 helleri Gießhübl, AT GIE 48.10151 16.21928 480 KJ501769 KJ691731 yes f 92113 helleri Gießhübl, AT GIE 48.10151 16.21928 480 KJ501770 KJ691732 yes m 92114 helleri Gießhübl, AT GIE 48.10151 16.21928 480 KJ501771 KJ691733 yes f 92115 helleri Gießhübl, AT GIE 48.10151 16.21928 480 KJ501772 KJ691734 yes f 90477 helleri Höllenstein, AT HOE 48.08499 16.18801 600 KJ691005 f 92279 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501907 KJ691829 yes f 92280 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501908 KJ691830 yes f 92281 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501909 KJ691831 yes f 92282 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501910 KJ691832 f 92283 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501911 KJ691833 f 92286 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501912 KJ691834 yes f 92287 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501913 KJ691835 yes f 92288 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 yes KJ501914 KJ691836 yes f 92289 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 KJ501915 KJ691837 yes m 92290 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 KJ501916 KJ691838 yes m 92291 helleri Hohe Wand, AT HWA 47.80979 16.01896 870 KJ501917 KJ691839 yes m 90479 helleri Hohe Wand, AT MYL 47.80979 16.01896 870 KJ691006 f 91949 helleri Rax, AT RAX 47.74515 15.76283 690 KJ501760 KJ691620 yes m 92048 helleri Rax, AT RAX 47.74515 15.76283 690 KJ501782 KJ691683 f 92148 helleri Rax, AT RAX 47.74515 15.76283 690 KJ501773 KJ691758 yes f
45 91805 helleri Sankt Martin in Thurn, IT SMT 46.63552 11.85549 1470 KJ691199 KJ691560 m 91895 helleri Ustí, CZ UST 50.63962 14.05107 190 yes KJ501757 KJ691589 yes f 91896 helleri Ustí, CZ UST 50.63962 14.05107 190 yes KJ501758 KJ691590 yes f 91897 helleri Ustí, CZ UST 50.63962 14.05107 190 yes KJ501759 KJ691591 yes f 91953 helleri Ustí, CZ UST 50.63962 14.05107 190 KJ501761 KJ691624 yes f 91170 hrabei Aggstein, AT AGG 48.32472 15.41694 300 KJ691097 KJ691448 m 91914 hrabei Hády, CZ BRN 49.22050 16.66933 370 yes KJ501783 KJ691598 yes f 91915 hrabei Hády, CZ BRN 49.22050 16.66933 370 yes KJ501784 KJ691599 yes f 91916 hrabei Hády, CZ BRN 49.22050 16.66933 370 KJ501785 KJ691600 m 91917 hrabei Hády, CZ BRN 49.22050 16.66933 370 KJ501786 KJ691601 yes m 91918 hrabei Hády, CZ BRN 49.22050 16.66933 370 KJ501787 KJ691602 yes m 91919 hrabei Hády, CZ BRN 49.22050 16.66933 370 yes KJ501788 KJ691603 f 91920 hrabei Hády, CZ BRN 49.22050 16.66933 370 yes KJ501789 KJ691604 yes f 91929 hrabei Hády, CZ BRN 49.22050 16.66933 370 KJ501790 KJ691609 yes m 91930 hrabei Hády, CZ BRN 49.22050 16.66933 370 KJ501791 KJ691610 yes m 91932 hrabei Hády, CZ BRN 49.22050 16.66933 370 KJ501792 KJ691611 yes m 92117 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 yes KJ501799 KJ691736 yes f 92125 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 KJ501800 KJ691744 yes m 92126 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 yes KJ501801 KJ691745 yes f 92127 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 yes KJ501802 KJ691746 yes f 92128 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 KJ501803 KJ691747 yes m 92129 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 KJ501805 KJ691748 yes m 92141 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 KJ501804 KJ691753 yes m 92142 hrabei Krummnußbaum, AT KNB 48.21771 15.18507 235 KJ501806 KJ691754 yes m 90572 hrabei Rottenhof, AT ROT 48.19972 15.09444 310 yes KJ691029 KJ691368 yes f 91394 hrabei Rottenhof, AT ROT 48.19972 15.09444 310 yes KJ691122 KJ691479 yes f 92015 hrabei Leopoldsberg, AT VIE 48.27659 16.35276 240 yes KJ501793 KJ691658 yes f 92031 hrabei Leopoldsberg, AT VIE 48.27659 16.35276 240 yes KJ501794 KJ691669 yes f 92032 hrabei Leopoldsberg, AT VIE 48.27659 16.35276 240 yes KJ501795 KJ691670 f 92034 hrabei Leopoldsberg, AT VIE 48.27659 16.35276 240 KJ501796 KJ691671 yes m 92035 hrabei Leopoldsberg, AT VIE 48.27659 16.35276 240 KJ501797 KJ691672 yes m 92036 hrabei Leopoldsberg, AT VIE 48.27659 16.35276 240 yes KJ501798 KJ691673 yes f 92198 hrabei Stift Zwettl, AT ZWT 48.61711 15.20471 500 yes KJ691259 KJ691782 yes f 91551 inermis Alpstein, CH MSH 47.25582 9.37393 1870 yes KJ691148 KJ691507 yes f 91552 inermis Alpstein, CH MSH 47.25582 9.37393 1870 yes KJ691149 KJ691508 yes f
46 91555 inermis Messmer Haus, CH MSH 47.25582 9.37393 1870 yes KJ691150 KJ691509 yes f 91557 inermis Messmer Haus, CH MSH 47.25582 9.37393 1870 yes KJ691151 KJ691510 yes f 91558 inermis Messmer Haus, CH MSH 47.25582 9.37393 1870 yes KJ691152 KJ691511 yes f 91536 inermis Seealpe, CH SLP 47.27117 9.40182 1190 yes KJ501807 KJ691499 yes f 91537 inermis Seealpe, CH SLP 47.27117 9.40182 1190 yes KJ691142 KJ691500 yes m 91539 inermis Seealpe, CH SLP 47.27117 9.40182 1190 KJ691501 m 91544 inermis Seealpe, CH SLP 47.27117 9.40182 1190 yes KJ691144 KJ691503 yes f 91546 inermis Seealpe, CH SLP 47.27117 9.40182 1190 yes KJ691145 KJ691504 f 91547 inermis Seealpe, CH SLP 47.27117 9.40182 1190 yes KJ691146 KJ691505 yes f 91548 inermis Seealpe, CH SLP 47.27117 9.40182 1190 yes KJ691147 KJ691506 yes f 91530 inermis Wasserauen, CH WAS 47.27320 9.40568 940 yes KJ691139 KJ691496 yes f 91531 inermis Alpstein, CH WAS 47.27320 9.40568 940 yes KJ691140 KJ691497 yes f 91534 inermis Wasserauen, CH WAS 47.27320 9.40568 940 yes KJ691141 KJ691498 yes f 91541 inermis Wasserauen, CH WAS 47.27320 9.40568 940 yes KJ691143 KJ691502 yes f 90808 inermis group Brandnertal, AT BRA 47.08802 9.73298 1200 yes KJ691047 KJ691394 yes f 90809 inermis group Brandnertal, AT BRA 47.08802 9.73298 1200 KJ691048 m 90811 inermis group Brandnertal, AT BRA 47.08802 9.73298 1200 yes KJ691049 KJ691395 yes f 90812 inermis group Brandnertal, AT BRA 47.08802 9.73298 1200 yes KJ691050 KJ691396 yes f 90813 inermis group Brandnertal, AT BRA 47.08802 9.73298 1200 yes KJ691051 KJ691397 yes f 91125 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 KJ691087 KJ691437 m 91126 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691088 KJ691438 yes f 91129 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691089 KJ691439 yes f 91130 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691090 KJ691440 yes f 91131 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691091 KJ691441 yes f 91132 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691092 KJ691442 yes f 91133 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691093 KJ691443 yes f 91134 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691094 KJ691444 yes f 91136 inermis group Gaflei, LIE GAF 47.14942 9.54963 1650 yes KJ691095 KJ691445 yes f 90817 inermis group Schesaplana, AT LSE 47.05403 9.74368 2050 yes KJ691052 KJ691398 yes f 90818 inermis group Schesaplana, AT LSE 47.05403 9.74368 2050 yes KJ691053 KJ691399 yes f 91921 inermis group NenzingerHimmel NZH 47.16210 9.69109 850 KJ691605 yes m 92105 inermis group NenzingerHimmel NZH 47.16210 9.69109 850 KJ691252 KJ691725 yes m 92159 inermis group NenzingerHimmel NZH 47.16210 9.69109 850 KJ691257 KJ691767 yes f 92160 inermis group NenzingerHimmel NZH 47.16210 9.69109 850 KJ691258 KJ691768 yes f 91099 inermis group Palfris, LIE PAL 47.09600 9.42462 1770 yes KJ691073 KJ691422 f
47 91101 inermis group Palfris, LIE PAL 47.09600 9.42462 1770 yes KJ691074 KJ691423 yes f 91103 inermis group Palfris, LIE PAL 47.09600 9.42462 1770 yes KJ691075 KJ691424 f 90050 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 KJ690975 KJ691285 f 90057 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 KJ690976 KJ691286 f 90059 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 KJ690977 KJ691287 yes m 90060 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 KJ690978 KJ691288 yes m 90121 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 KJ690987 KJ691309 m 90480 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 yes KJ691007 KJ691345 yes f 90481 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 yes KJ691008 KJ691346 f 90482 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 ‐ KJ691347 f 90483 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 KJ691009 KJ691348 yes f 90484 ladensis Piz Lad, CH/IT/AT PLD 46.84175 10.46417 2740 KJ691010 KJ691349 f 91409 lehnhoferi Ehnbachklamm, AT EBK 47.27915 11.25828 825 yes KJ691125 KJ691482 yes f 91413 lehnhoferi Ehnbachklamm, AT EBK 47.27915 11.25828 825 yes KJ691126 KJ691483 yes f 92118 lehnhoferi Gschöllkopf, AT GLK 47.44573 11.76482 1910 KJ501825 KJ691737 yes f 92119 lehnhoferi Gschöllkopf, AT GLK 47.44573 11.76482 1910 KJ501826 KJ691738 yes f 92120 lehnhoferi Gschöllkopf, AT GLK 47.44573 11.76482 1910 KJ501827 KJ691739 yes f 92121 lehnhoferi Gschöllkopf, AT GLK 47.44573 11.76482 1910 KJ501828 KJ691740 yes f 92122 lehnhoferi Gschöllkopf, AT GLK 47.44573 11.76482 1910 KJ501829 KJ691741 f 92263 lehnhoferi Haindlkar, AT HAI 47.62940 14.61328 689 KJ691277 KJ691816 yes f 92264 lehnhoferi Haindlkar, AT HAI 47.62940 14.61328 689 KJ501832 KJ691817 f 92265 lehnhoferi Haindlkar, AT HAI 47.62940 14.61328 689 KJ691818 yes f 92266 lehnhoferi Haindlkar, AT HAI 47.62940 14.61328 689 KJ501833 KJ691819 f 92146 lehnhoferi Hundskopf, AT HUN 47.33949 11.56764 1860 KJ501830 KJ691756 yes m 92147 lehnhoferi Hundskopf, AT HUN 47.33949 11.56764 1860 KJ501831 KJ691757 yes m 90573 lehnhoferi Leutasch, AT LEU 47.37687 11.14793 1270 yes KJ691030 KJ691369 yes f 90894 lehnhoferi Leutasch, AT LEU 47.37687 11.14793 1270 yes KJ691060 KJ691407 yes f 90896 lehnhoferi Leutasch, AT LEU 47.37687 11.14793 1270 yes KJ691062 KJ691409 yes f 90948 lehnhoferi Leutasch, AT LEU 47.37687 11.14793 1270 KJ691070 KJ691419 f 91960 lehnhoferi Oberammergau, DE OAG 47.58606 11.10432 1650 yes KJ501814 KJ691626 yes f 91961 lehnhoferi Oberammergau, DE OAG 47.58606 11.10432 1650 KJ501815 KJ691627 m 91962 lehnhoferi Oberammergau, DE OAG 47.58606 11.10432 1650 yes KJ501816 KJ691628 yes f 91972 lehnhoferi Oberammergau, DE OAG 47.58606 11.10432 1650 KJ501817 KJ691633 yes f 91992 lehnhoferi Oberammergau, DE OAG 47.58606 11.10432 1650 KJ501818 KJ691642 yes m 92267 lehnhoferi Oberlaussa, AT OBL 47.70873 14.52755 605 KJ501834 KJ691820 f
48 92268 lehnhoferi Oberlaussa, AT OBL 47.70873 14.52755 605 KJ501835 KJ691821 f 92269 lehnhoferi Oberlaussa, AT OBL 47.70873 14.52755 605 KJ501836 KJ691822 f 92270 lehnhoferi Oberlaussa, AT OBL 47.70873 14.52755 605 KJ501837 KJ691823 f 92271 lehnhoferi Oberlaussa, AT OBL 47.70873 14.52755 605 KJ501838 KJ691824 f 92038 lehnhoferi Saalfelden, AT SAA 47.48759 12.83152 823 KJ501819 KJ691675 yes m 92051 lehnhoferi Saalfelden, AT SAA 47.48759 12.83152 823 KJ501820 KJ691686 yes m 92053 lehnhoferi Saalfelden, AT SAA 47.48759 12.83152 823 KJ501821 KJ691688 yes m 91867 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 KJ691213 KJ691575 yes f 91882 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 yes KJ691220 KJ691582 yes f 91883 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 yes KJ691221 KJ691583 f 91884 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 yes KJ691222 KJ691584 yes f 91885 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 yes KJ691223 KJ691585 yes f 91886 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 KJ501810 KJ691586 yes m 91887 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 KJ501811 KJ691587 yes m 91898 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 KJ501812 KJ691592 yes m 91899 lehnhoferi Salzachklamm, AT SAL 47.55595 13.16782 560 KJ501813 KJ691593 yes m 91927 lehnhoferi Nordkette, AT SGR 47.30820 11.37652 1990 yes KJ691225 KJ691607 f 91928 lehnhoferi Seegrube, AT SGR 47.30820 11.37652 1990 yes KJ691226 KJ691608 yes f 92073 lehnhoferi Scharnitz, AT STZ 47.39049 11.27916 1105 yes KJ501822 KJ691697 yes f 92074 lehnhoferi Scharnitz, AT STZ 47.39049 11.27916 1105 KJ501823 KJ691698 yes f 92075 lehnhoferi Scharnitz, AT STZ 47.39049 11.27916 1105 KJ501824 KJ691699 yes f 91516 lehnhoferi Wilder Kaiser, AT WKR 47.54992 12.32213 1246 yes KJ691135 KJ691492 f 91517 lehnhoferi Wilder Kaiser, AT WKR 47.54992 12.32213 1246 yes KJ691136 KJ691493 yes f 91518 lehnhoferi Wilder Kaiser, AT WKR 47.54992 12.32213 1246 yes KJ691137 KJ691494 yes f 92194 Lysignata Admont, AT ADM 47.54547 14.47487 1000 KJ691844 yes f 90487 Lysignata Heiligkreuz, CH HGK 47.06160 9.42083 700 KJ501918 f 91202 mesolcinensis Feldalm, AT FAM 47.10643 11.77708 2080 yes KJ501841 KJ691449 yes f 91209 mesolcinensis Feldalm, AT FAM 47.10643 11.77708 2080 yes KJ691098 KJ691450 f 91211 mesolcinensis Feldalm, AT FAM 47.10643 11.77708 2080 yes KJ691099 KJ691451 f 91213 mesolcinensis Feldalm, AT FAM 47.10643 11.77708 2080 yes KJ691100 KJ691452 yes f 91230 mesolcinensis Feldalm, AT FAM 47.10643 11.77708 2080 KJ691102 KJ691454 f 92243 mesolcinensis Hintertux, AT HTX 47.09979 11.65683 2160 yes KJ691269 KJ691805 yes f 92246 mesolcinensis Hintertux, AT HTX 47.09979 11.65683 2160 yes KJ691270 KJ691807 yes f 92248 mesolcinensis Hintertux, AT HTX 47.09979 11.65683 2160 yes KJ691271 KJ691809 f 92249 mesolcinensis Hintertux, AT HTX 47.09979 11.65683 2160 yes KJ691272 KJ691810 yes f
49 91684 mesolcinensis Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ501840 KJ691544 yes f 91701 mesolcinensis Monte Frerone, IT MFR 45.94489 10.40904 2550 KJ691192 KJ691553 yes m 91702 mesolcinensis Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691193 KJ691554 yes f 91703 mesolcinensis Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691194 KJ691555 yes f 91704 mesolcinensis Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691195 KJ691556 yes f 91705 mesolcinensis Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691196 KJ691557 yes f 91996 mesolcinensis Radlsee, IT RLS 46.70769 11.57577 2300 KJ691230 KJ691644 f 92016 mesolcinensis Radlsee, IT RLS 46.70253 11.57256 2300 yes KJ691231 KJ691659 yes f 92017 mesolcinensis Radlsee, IT RLS 46.69737 11.56934 2300 yes KJ691232 KJ691660 yes f 90899 mesolcinensis Schwarzenstein, IT SWS 47.02376 11.83256 2100 yes KJ501839 KJ691410 yes f 90911 mesolcinensis Schwarzenstein, IT SWS 47.02376 11.83256 2100 yes KJ691065 KJ691413 yes f 91376 montana Fregeira, CH FRG 46.44442 9.21422 1360 KJ691116 KJ691473 yes m 91377 montana Fregeira, CH FRG 46.44442 9.21422 1360 KJ691117 KJ691474 yes f 91301 montana San Bernardino, CH SBD 46.44442 9.21422 1360 KJ691113 KJ691465 yes f 91302 montana San Bernardino, CH SBD 46.44442 9.21422 1360 KJ691114 KJ691466 yes f 91303 montana San Bernardino, CH SBD 46.44442 9.21422 1360 KJ691115 KJ691467 yes m 91940 pallida Grosté, IT MAD 46.21508 10.90108 2450 yes KJ501842 KJ691614 yes f 91941 pallida Grosté, IT MAD 46.21508 10.90108 2450 yes KJ501843 KJ691615 yes f 91942 pallida Grosté, IT MAD 46.21508 10.90108 2450 yes KJ501844 KJ691616 yes f 91943 pallida Grosté, IT MAD 46.21508 10.90108 2450 yes KJ501845 KJ691617 yes f 91944 pallida Grosté, IT MAD 46.21508 10.90108 2450 yes KJ501846 KJ691618 f 91979 pallida Grosté, IT MAD 46.21508 10.90108 2450 KJ501847 KJ691635 f 92150 pallida Grosté, IT MAD 46.21508 10.90108 2450 KJ501858 KJ691759 f 90145 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 KJ690993 yes f 90150 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 KJ690994 KJ691315 f 90151 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 yes JF826098 KJ691316 f 90162 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 KJ690995 KJ691317 yes f 90174 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 yes JF826099 KJ691318 f 90231 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 KJ690996 KJ691319 yes f 90232 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 KJ690997 KJ691320 f 90236 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 KJ690998 KJ691321 yes f 90243 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 yes JF826100 KJ691322 f 90244 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 yes JF826101 KJ691323 yes f 90252 pallida Mäuerlscharte, AT/IT MUS 46.99057 11.53298 2330 yes JF826102 KJ691325 yes f 90326 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 yes JF826108 KJ691337 yes f
50 90327 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 yes JF826109 KJ691338 yes f 90330 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 KJ691002 KJ691339 yes f 90369 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 yes JF826110 KJ691340 yes f 90382 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 yes JF826111 KJ691341 f 90383 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 yes JF826112 KJ691342 f 90410 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 yes KJ691003 KJ691343 yes f 90463 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 yes KJ691004 KJ691344 yes f 92009 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 KJ501849 KJ691652 yes f 92010 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 KJ501850 KJ691653 f 92011 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 KJ501851 KJ691654 yes f 92012 pallida Plattkofel, IT PTK 46.51191 11.70027 2181 KJ501852 KJ691655 yes f 90283 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 yes JF826103 KJ691330 yes f 90289 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ690999 KJ691331 yes f 90291 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 yes JF826104 KJ691332 f 90292 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 yes JF826105 KJ691333 yes f 90293 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 yes JF826106 f 90294 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ691000 KJ691334 f 90299 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 yes JF826107 KJ691335 yes f 90310 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ691001 KJ691336 yes f 92046 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ501853 KJ691681 f 92047 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ501854 KJ691682 yes f 92049 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ501855 KJ691684 yes f 92050 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ501856 KJ691685 yes f 92143 pallida Padasterjochhaus, AT TRI 47.07755 11.36072 2264 KJ501857 KJ691755 f 90613 pulchra Sellrain, AT SEL 47.18797 11.14862 1300 yes KJ691031 KJ691370 yes f 90616 pulchra Sellrain, AT SEL 47.18797 11.14862 1300 yes KJ691032 KJ691371 yes f 90618 pulchra Sellrain, AT SEL 47.18797 11.14862 1300 yes KJ691033 KJ691372 yes f 90622 pulchra Sellrain, AT SEL 47.18797 11.14862 1300 yes KJ691034 KJ691373 yes f 90623 pulchra Sellrain, AT SEL 47.18797 11.14862 1300 yes KJ691035 KJ691374 yes f 90625 pulchra Sellrain, AT SEL 47.18797 11.14862 1300 yes KJ691036 KJ691375 yes f 92238 pulchra Umhausen, AT UMH 47.12706 10.94637 1270 yes KJ691267 KJ691800 yes f 92241 pulchra Umhausen, AT UMH 47.12706 10.94637 1270 yes KJ691268 KJ691803 yes f 92273 pulchra Umhausen, AT UMH 47.12706 10.94637 1270 yes KJ691278 KJ691825 yes f 92274 pulchra Umhausen, AT UMH 47.12706 10.94637 1270 yes KJ691279 KJ691826 yes f 92278 pulchra Umhausen, AT UMH 47.12706 10.94637 1270 yes KJ691281 KJ691828 yes f
51 91108 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691077 KJ691426 yes f 91109 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691078 KJ691427 yes f 91110 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691079 KJ691428 yes f 91111 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691080 KJ691429 yes f 91112 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691081 KJ691430 yes f 91114 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 KJ691082 KJ691431 yes m 91116 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ501809 KJ691432 yes f 91120 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691083 KJ691433 yes f 91121 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691084 KJ691434 yes f 91122 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691085 KJ691435 yes f 91123 robusta Heiligkreuz, CH HKZ 47.06468 9.41747 750 yes KJ691086 KJ691436 yes f 92251 rubrofusca Franz‐Senn Hütte, AT FSH 47.08508 11.16602 2170 yes KJ691273 KJ691812 yes f 92252 rubrofusca Franz‐Senn Hütte, AT FSH 47.08508 11.16602 2170 yes KJ691274 KJ691813 yes f 92255 rubrofusca Franz‐Senn Hütte, AT FSH 47.08508 11.16602 2170 yes KJ691275 KJ691814 yes f 92256 rubrofusca Franz‐Senn Hütte, AT FSH 47.08508 11.16602 2170 yes KJ691276 KJ691815 yes f 91483 rubrofusca Grawandhaus, Zillertal, AT GRW 47.01986 11.78617 1800 yes KJ691132 KJ691489 yes f 91486 rubrofusca Grawandhaus, Zillertal, AT GRW 47.01986 11.78617 1800 yes KJ691133 KJ691490 yes f 90087 rubrofusca Larstigalm, AT LGA 47.13875 10.98303 1759 yes JF826113 KJ691300 yes f 90094 rubrofusca Larstigalm, AT LGA 47.13875 10.98303 1759 yes JF826114 KJ691301 yes f 90095 rubrofusca Larstigalm, AT LGA 47.13875 10.98303 1759 yes JF826115 KJ691302 yes f 90096 rubrofusca Larstigalm, AT LGA 47.13875 10.98303 1759 yes JF826116 KJ691303 f 90097 rubrofusca Larstigalm, AT LGA 47.13875 10.98303 1759 yes JF826117 KJ691304 yes f 90099 rubrofusca Larstigalm, AT LGA 47.13875 10.98303 1759 yes JF826118 KJ691305 yes f 90642 rubrofusca Niederthai, AT LGA 47.12953 10.96489 1631 yes JF826119 KJ691376 f 90643 rubrofusca Niederthai, AT LGA 47.12953 10.96489 1631 yes JF826120 KJ691377 yes f 90655 rubrofusca Niederthai, AT LGA 47.12953 10.96489 1631 KJ691042 f 90656 rubrofusca Niederthai, AT LGA 47.12953 10.96489 1631 yes JF826121 KJ691383 yes f 90660 rubrofusca Niederthai, AT LGA 47.12953 10.96489 1631 yes JF826122 KJ691384 yes f 90140 rubrofusca Obergurgl, AT OBG 46.87282 11.02450 1900 yes KJ690992 KJ691314 f 90677 rubrofusca Obergurgl, AT OBG 46.87282 11.02450 1900 yes JF826123 KJ691385 yes f 90678 rubrofusca Obergurgl, AT OBG 46.87282 11.02450 1900 yes JF826124 KJ691386 yes f 90679 rubrofusca Obergurgl, AT OBG 46.87282 11.02450 1900 yes JF826125 KJ691387 yes f 90681 rubrofusca Obergurgl, AT OBG 46.87282 11.02450 1900 yes JF826126 KJ691388 yes f 90684 rubrofusca Obergurgl, AT OBG 46.87282 11.02450 1900 yes JF826127 KJ691389 yes f 90652 rubrofusca Pfelders, IT PFE 46.78552 11.05950 1878 yes KJ691040 KJ691381 yes f
52 90653 rubrofusca Pfelders, IT PFE 46.78552 11.05950 1878 yes KJ691041 KJ691382 yes f 90869 rubrofusca Patscherkofel, AT PKF 47.21086 11.45218 1950 yes KJ691055 KJ691401 yes f 91571 rubrofusca Virgental, AT PRG 47.01632 12.33626 1360 yes KJ691158 KJ691517 yes f 90002 rubrofusca Umhausen, Ötztal, AT UMH 47.14403 10.93153 1111 yes KJ690972 KJ691282 yes f 90003 rubrofusca Umhausen, Ötztal, AT UMH 47.14403 10.93153 1111 yes KJ690973 KJ691283 yes f 90005 rubrofusca Umhausen, Ötztal, AT UMH 47.14403 10.93153 1111 yes KJ690974 KJ691284 yes f 92275 rubrofusca Umhausen, AT UMH 47.14403 10.93153 1111 KJ691280 KJ691827 f 91453 rubrofusca Varusch, CH VAR 46.60773 10.03217 1768 KJ691128 KJ691485 f 91938 rubrofusca Varusch, CH VAR 46.60773 10.03217 1768 KJ691227 KJ691612 f 91939 rubrofusca Varusch, CH VAR 46.60773 10.03217 1768 KJ691228 KJ691613 f 91948 rubrofusca Varusch, CH VAR 46.60773 10.03217 1768 KJ691229 KJ691619 f 92062 rubrofusca Varusch, CH VAR 46.60773 10.03217 1768 KJ691238 KJ691695 yes f 91366 sp. A Il Fuorn, CH ILF 40.67530 10.23063 2150 yes JF826093 KJ691468 f 91367 sp. A Il Fuorn, CH ILF 40.67530 10.23063 2150 yes JF826094 KJ691469 f 91368 sp. A Il Fuorn, CH ILF 40.67530 10.23063 2150 yes JF826095 KJ691470 yes f 91369 sp. A Il Fuorn, CH ILF 40.67530 10.23063 2150 yes JF826096 KJ691471 yes f 91370 sp. A Il Fuorn, CH ILF 40.67530 10.23063 2150 yes JF826097 KJ691472 yes f 91685 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 KJ691184 KJ691545 yes m 91686 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 KJ691185 KJ691546 yes m 91687 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691186 KJ691547 yes f 91688 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691187 KJ691548 f 91689 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 KJ691188 KJ691549 yes m 91694 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691189 KJ691550 yes f 91696 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 KJ691190 KJ691551 yes m 91697 sp. A Monte Frerone, IT MFR 45.94489 10.40904 2550 yes KJ691191 KJ691552 yes f 90245 sp. A Mäuerlscharte, AT/IT MUS 46.99161 11.53166 2181 yes JF826088 KJ691324 yes f 90261 sp. A Mäuerlscharte, AT/IT MUS 46.99161 11.53166 2181 yes JF826089 KJ691326 yes f 90276 sp. A Mäuerlscharte, AT/IT MUS 46.99161 11.53166 2181 yes JF826090 KJ691327 yes f 90277 sp. A Mäuerlscharte, AT/IT MUS 46.99161 11.53166 2181 yes JF826091 KJ691328 yes f 90278 sp. A Mäuerlscharte, AT/IT MUS 46.99161 11.53166 2181 yes JF826092 KJ691329 yes f 91164 sp. A Niederelbhütte, AT NEH 47.06270 10.31343 2317 yes KJ501861 KJ691446 f 91165 sp. A Niederelbhütte, AT NEH 47.06270 10.31343 2317 yes KJ691096 KJ691447 yes f 90067 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 KJ690979 KJ691289 f 90068 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes KJ690980 KJ691290 yes f 90069 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes KJ501859 KJ691291 yes f
53 90070 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes JF826083 KJ691292 f 90071 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes JF826084 KJ691293 yes f 90072 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes JF826085 KJ691294 yes f 90073 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes KJ690981 KJ691295 yes f 90075 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes KJ690983 KJ691297 yes f 90076 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes KJ690984 KJ691298 yes f 90078 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes JF826086 KJ691299 yes f 90117 sp. A Piz Lad, CH/IT/AT PLD 46.84092 10.46417 2733 yes JF826087 KJ691308 yes f 92029 sp. A Radlsee, IT RLS 46.70769 11.57577 2300 yes KJ691233 KJ691667 yes f 92030 sp. A Radlsee, IT RLS 46.70769 11.57577 2300 yes KJ691234 KJ691668 yes f 92098 sp. A Radlsee, IT RLS 46.70769 11.57577 2300 KJ691248 yes f 91419 sp. B Ehnbachklamm, AT EBK 47.27915 11.25828 825 KJ691127 KJ691484 f 91831 sp. B Nikolsdorf, AT NIK 46.77381 12.89946 760 KJ691206 KJ691567 f 91853 sp. B Sarche, IT SAR 46.04768 10.94014 405 KJ691211 KJ691573 m 92200 sp. B Sarche, IT SAR 46.04768 10.94014 405 KJ691260 KJ691784 yes f 92202 sp. B Sarche, IT SAR 46.04768 10.94014 405 KJ691261 KJ691786 yes m 92203 sp. B Sarche, IT SAR 46.04768 10.94014 405 KJ691262 KJ691787 yes f 92205 sp. B Sarche, IT SAR 46.04768 10.94014 405 KJ691263 KJ691789 yes m 92209 sp. B Sarche, IT SAR 46.04768 10.94014 405 KJ691264 KJ691793 yes f 92099 sp. B Trögener Klamm, AT TRK 46.45987 14.50251 756 KJ691249 KJ691719 yes f 92123 sp. B Trögener Klamm, AT TRK 46.45987 14.50251 756 KJ691254 KJ691742 f 92134 sp. B Trögener Klamm, AT TRK 46.45987 14.50251 756 yes yes f 92213 sp. B Val d'Ampola, IT VDA 45.86189 10.64134 745 KJ691266 KJ691797 yes f 91708 sp. C Monte Frerone, IT MFR 45.94489 10.40904 2550 KJ691197 KJ691558 f 91709 sp. C Monte Frerone, IT MFR 45.94489 10.40904 2550 KJ691198 KJ691559 f 90862 sp. D Cima d'Asta, IT CDA 46.16807 11.59926 2000 KJ691054 KJ691400 f 92156 sp. E Sarche, IT SAR 46.04768 10.94014 405 KJ691255 KJ691764 yes f 92157 sp. E Sarche, IT SAR 46.04768 10.94014 405 KJ691256 KJ691765 yes f 92210 sp. B Val d'Ampola, IT VDA 45.86189 10.64134 745 KJ691265 KJ691794 yes f 91032 ticinensis Brandnertal, AT BRA 47.12195 9.76128 1020 KJ691072 KJ691421 f 92013 ticinensis Brandnertal, AT BRA 47.12195 9.76128 1020 KJ501864 KJ691656 f 92014 ticinensis Brandnertal, AT BRA 47.12195 9.76128 1020 KJ501865 KJ691657 yes f 92304 ticinensis Ehnbachklamm, AT EBK 47.27915 11.25828 825 KJ691281 KJ691843 f 90500 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691011 KJ691350 f 90501 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691012 KJ691351 f
54 90502 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691013 KJ691352 yes f 90503 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691014 KJ691353 yes f 90504 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691015 KJ691354 yes f 90528 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691016 KJ691355 f 90529 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691017 KJ691356 yes f 90531 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes KJ691018 KJ691357 f 90541 ticinensis Heiligkreuz, CH HKZ 47.06365 9.42295 930 yes f 91864 ticinensis Kranebitter Klamm, AT KRK 47.27405 11.32771 800 KJ691212 KJ691574 yes f 91878 ticinensis Kranebitter Klamm, AT KRK 47.27405 11.32771 800 KJ691219 KJ691581 yes f 92090 ticinensis Kranebitter Klamm, AT KRK 47.27405 11.32771 800 KJ501870 KJ691712 yes f 91639 ticinensis Miralago, CH MIR 46.27277 10.10249 968 yes KJ691173 KJ691533 yes f 91640 ticinensis Miralago, CH MIR 46.27277 10.10249 968 yes KJ691174 KJ691534 yes f 91644 ticinensis Miralago, CH MIR 46.27277 10.10249 968 yes KJ691175 KJ691535 yes f 91645 ticinensis Miralago, CH MIR 46.27277 10.10249 968 yes KJ691176 KJ691536 yes f 91648 ticinensis Miralago, CH MIR 46.27277 10.10249 968 yes KJ691177 KJ691537 yes f 91649 ticinensis Miralago, CH MIR 46.27277 10.10249 968 yes KJ691178 KJ691538 yes f 91650 ticinensis Miralago, CH MIR 46.27277 10.10249 968 yes KJ691179 KJ691539 yes f 91679 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ691180 KJ691540 m 91680 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ691181 KJ691541 m 91681 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ691182 KJ691542 m 91682 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ691183 KJ691543 m 92199 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501875 KJ691783 f 92201 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501876 KJ691785 f 92204 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501877 KJ691788 f 92206 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501878 KJ691790 yes m 92207 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501879 KJ691791 yes f 92208 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501880 KJ691792 f 92211 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501881 KJ691795 yes m 92212 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501882 KJ691796 yes f 92214 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501883 KJ691798 yes f 92215 ticinensis Miralago, CH MIR 46.27277 10.10249 968 KJ501884 KJ691799 f 91924 ticinensis NenzingerHimmel NZH 47.16210 9.69109 850 KJ691224 KJ691606 f 91995 ticinensis NenzingerHimmel NZH 47.16210 9.69109 850 KJ501863 KJ691643 f 92063 ticinensis NenzingerHimmel NZH 47.16210 9.69109 850 KJ501868 KJ691696 yes f 92088 ticinensis NenzingerHimmel NZH 47.16210 9.69109 850 KJ501869 KJ691710 yes f
55 92106 ticinensis NenzingerHimmel NZH 47.16210 9.69109 850 KJ501871 KJ691726 f 92133 ticinensis NenzingerHimmel NZH 47.16210 9.69109 850 KJ501874 KJ691752 f 90542 ticinensis Pfänder (Vorarlberg), AT PFD 47.48927 9.76795 680 yes KJ691023 KJ691362 yes f 90544 ticinensis Pfänder (Vorarlberg), AT PFD 47.48927 9.76795 680 yes KJ691024 KJ691363 yes f 90538 ticinensis Rauschbrunnen, AT RAU 47.27778 11.34733 1088 yes KJ691020 KJ691359 yes f 90539 ticinensis Rauschbrunnen, AT RAU 47.27778 11.34733 1088 yes KJ691021 KJ691360 yes f 92116 ticinensis Höttinger Graben, AT RAU 47.28499 11.37221 900 KJ501872 KJ691735 yes f 92131 ticinensis Höttinger Graben, AT RAU 47.28499 11.37221 900 KJ501873 KJ691750 yes f 91559 ticinensis Rankweil, AT RKW 47.27537 9.65978 115 yes KJ691153 KJ691512 f 91560 ticinensis Rankweil, AT RKW 47.27537 9.65978 115 yes KJ691154 KJ691513 yes f 91561 ticinensis Rankweil, AT RKW 47.27537 9.65978 115 yes KJ691155 KJ691514 yes f 91562 ticinensis Rankweil, AT RKW 47.27537 9.65978 115 yes KJ691156 KJ691515 yes f 92037 ticinensis Rankweil, AT RKW 47.27537 9.65978 115 KJ501866 KJ691674 yes f 91582 tirolensis Ardez, CH ADZ 46.77094 10.21201 1300 yes KJ691163 KJ691522 yes f 91583 tirolensis Ardez, CH ADZ 46.77094 10.21201 1300 yes KJ691164 KJ691523 yes f 91395 tirolensis Ehnbachklamm, AT EBK 47.27915 11.25828 825 yes KJ691123 KJ691480 yes f 91406 tirolensis Ehnbachklamm, AT EBK 47.27915 11.25828 825 yes KJ691124 KJ691481 yes f 92300 tirolensis Ehnbachklamm, AT EBK 47.27915 11.25828 825 KJ501903 KJ691840 f 92301 tirolensis Ehnbachklamm, AT EBK 47.27915 11.25828 825 KJ501904 KJ691841 f 92302 tirolensis Ehnbachklamm, AT EBK 47.27915 11.25828 825 KJ501905 KJ691842 f 92303 tirolensis Ehnbachklamm, AT EBK 47.27915 11.25828 825 KJ501906 f 91385 tirolensis Forchach (Lechtal), AT FCH 47.42505 10.58000 920 yes KJ691119 KJ691476 yes f 91384 tirolensis Hornbachtal, AT HBT 47.36668 10.52033 1015 yes KJ691118 KJ691475 yes f 91822 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 yes KJ691200 KJ691561 yes f 91824 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 yes KJ691202 KJ691563 yes f 91825 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ691203 KJ691564 f 91871 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ691215 KJ691577 f 91872 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ691216 KJ691578 f 91873 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ691217 KJ691579 f 92100 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ501888 KJ691720 yes f 92130 tirolensis Kranebitter Klamm, AT KRK 47.27405 11.32771 830 KJ501892 KJ691749 yes f 92080 tirolensis Lorenzago di Cadore, IT LDC 46.46364 12.48886 1081 yes KJ501887 KJ691704 yes f 92172 tirolensis Lorenzago di Cadore, IT LDC 46.46364 12.48886 1081 yes KJ501898 KJ691777 yes f 92173 tirolensis Lorenzago di Cadore, IT LDC 46.46364 12.48886 1081 yes KJ501899 KJ691778 yes f 92174 tirolensis Lorenzago di Cadore, IT LDC 46.46364 12.48886 1081 yes KJ501900 KJ691779 yes f
56 92175 tirolensis Lorenzago di Cadore, IT LDC 46.46364 12.48886 1081 yes KJ501901 KJ691780 f 92176 tirolensis Lorenzago di Cadore, IT LDC 46.46364 12.48886 1081 yes KJ501902 KJ691781 yes f 92163 tirolensis Dürnsee, IT LDL 46.63089 12.22806 1460 yes KJ501895 KJ691769 yes f 92164 tirolensis Dürnsee, IT LDL 46.63089 12.22806 1460 yes KJ501896 KJ691770 yes f 92165 tirolensis Dürnsee, IT LDL 46.63089 12.22806 1460 yes KJ501897 KJ691771 yes f 90124 tirolensis Magreid, IT MGD 46.27838 11.19553 943 yes KJ690988 KJ691310 yes f 91574 tirolensis Pfunds, AT PFD 46.97636 10.54271 1169 yes KJ691160 KJ691519 f 91575 tirolensis Pfunds, AT PFD 46.97636 10.54271 1169 yes KJ691161 KJ691520 yes f 91576 tirolensis Pfunds, AT PFD 46.97636 10.54271 1169 yes KJ691162 KJ691521 yes f 90115 tirolensis Höttinger Graben, AT RAU 47.28499 11.37221 900 yes KJ690986 KJ691307 f 90537 tirolensis Rauschbrunnen, AT RAU 47.27778 11.34733 1088 yes KJ691019 KJ691358 yes f 90540 tirolensis Rauschbrunnen, AT RAU 47.27778 11.34733 1088 yes KJ691022 KJ691361 yes f 92132 tirolensis Höttinger Graben, AT RAU 47.28499 11.37221 900 KJ501893 KJ691751 yes f 91889 tirolensis Salzachklamm, AT SAL 47.55595 13.16782 560 KJ691588 f 91970 tirolensis Sarche, IT SAR 46.04768 10.94014 405 KJ501885 KJ691632 yes f 91989 tirolensis Sarche, IT SAR 46.04768 10.94014 405 KJ501886 KJ691640 yes f 92102 tirolensis Sarche, IT SAR 46.04768 10.94014 405 yes KJ501889 KJ691722 yes f 92158 tirolensis Sarche, IT SAR 46.04768 10.94014 405 KJ501894 KJ691766 yes f 92107 tirolensis Stabiziane, IT STA 46.54764 12.32539 1060 yes KJ501890 KJ691727 yes f 92108 tirolensis Stabiziane, IT STA 46.54764 12.32539 1060 yes KJ501891 KJ691728 yes f
57 Karyotypic variability and genome‐size variation in sexual and parthenogenetic species of the jumping‐bristletail genus Machilis (Archaeognatha)
Melitta Gassnera§, Thomas Dejacoa*§, Peter Schönswetterb, František Marecc, Wolfgang Arthofera, Birgit C. Schlick‐Steinera, Florian M. Steinera a Institute of Ecology, University of Innsbruck, Technikerstraße 25, 6020 Innsbruck, Austria b Institute of Botany, University of Innsbruck, Sternwartestraße 15, 6020 Innsbruck, Austria c Institute of Entomology, Biology Centre ASCR, Branisovska 31, 37005 Ceské Budejovice, Czech Republic § These authors contributed equally to this work as first authors. These authors contributed equally to this work as senior authors. * Corresponding author
Corresponding author's contact information: [email protected] Tel: (+43) 0512 507 51756
1 Abstract
The origin and persistence of parthenogenesis in animals is a challenging topic in evolutionary biology. In recent years, benefits from frequent correlates of parthenogenesis, i.e., polyploidy and hybridization, have been proposed to compensate deleterious effects caused by the lack of recombination. Such benefits possibly explain the advantage of parthenogens over sexual congeners in colonizing extreme habitats. However, evolutionary routes to parthenogenesis may vary across study systems with different life histories or ecological background. Therefore, case studies from a wide range of taxa are needed to grasp the fundamental mechanisms driving the evolution of parthenogenesis. The jumping‐ bristletail genus Machilis (Insecta: Archaeognatha) includes sexual as well as parthenogenetic species, and recently, the occurrence of polyploidy has been postulated. Here, we applied flow cytometry, karyotyping, and mitochondrial DNA sequencing to three sexual and five parthenogenetic Eastern‐Alpine Machilis species to evaluate whether 1) parthenogenesis originated once or multiply, 2) parthenogenesis is strictly associated with polyploidy, and 3) if it is possible to infer evolutionary pathways to parthenogenesis. The mitochondrial phylogeny revealed that parthenogenesis evolved at least five times independently among Eastern‐Alpine representatives of this genus. We found one parthenogenetic species to be exclusively triploid, whereas a second parthenogenetic species consisted of both diploid and triploid populations. The sexual and three other parthenogenetic species were diploid. Our results indicate that polyploidy can co‐evolve with parthenogenesis, but that it was not mandatory for the emergence of parthenogenesis in Machilis. In the light of Pleistocene glaciation cycles, we discuss two possible routes to parthenogenesis in Alpine Machilis species, i.e., survival on nunataks and survival in peripheral refugia with subsequent recolonization of inner‐Alpine areas. Finally, we discuss the evolutionary consequences of intraspecific chromosomal rearrangements in one sexual and one parthenogenetic species, and the presence of B‐chromosomes in one population of another species. In doing so, we highlight the potential of Machilis for research on chromosome‐ and genome size alterations during speciation.
2 Keywords
Asexuality, polyploidy, nunatak, recolonization, chromosomal speciation, Archaeognatha
Abbreviations
My, million years; Mya, million years ago
Highlights
We compare genome sizes and chromosome numbers in sexual and parthenogenetic Machilis species
Parthenogenesis is not strictly associated with polyploidy in the species examined
Parthenogenesis originated at least five times independently in Eastern‐Alpine Machilis species
Pleistocene glaciation cycles potentially triggered the evolution of parthenogenesis
Machilis genomes vary considerably in chromosome number and genome‐size
3
Graphical abstract
4 1. Introduction Sexual reproduction prevails in animals, and asexual reproduction is only marginally found in most taxonomic groups (Suomaleinen et al., 1987). It is assumed that asexual organisms have originated from sexual ancestors (Bell, 1982), and this transition can occur multiple times independently within a taxonomic group (Bode et al., 2010; Elzinga et al., 2013; Janko et al., 2012; Schön et al., 2000; Schwander and Crespi, 2009; Stenberg et al., 2003). Apart from some entirely asexual clades (e.g., bdelloid rotifers or darwinulid ostracods, the so‐called ancient asexuals (Judson and Normark, 1996)), strictly asexual animal species are rare (Bengtsson, 2009; Vrijenhoek and Parker Jr., 2009). Instead, asexuality often occurs as alternative reproductive strategy within a sexual taxon. Vandel (1928) first used the term geographic parthenogenesis to explain why these asexuals commonly live in disturbed or extreme habitats (e.g., recently deglaciated areas), compared with their sexual relatives. Following this, the advantage of asexuals over sexual congeners for colonizing new habitats has been attributed to their ability to establish new populations from single females and to highly specialized or general purpose genotypes (Vrijenhoek and Parker Jr., 2009). It has been unclear, though, to what extent asexuality itself (Cuellar, 1977; Glesener and Tilman, 1978; Law and Crespi, 2002; Maniatsi et al., 2011) or frequent correlates like polyploidy (Adolfsson et al., 2010; Comai, 2005; Stenberg et al., 2003; Zhang and Lefcort, 1991), or hybridization (Ghiselli et al., 2007; Kearney, 2005; Kearney, 2003) promote the spread of organisms into these habitats. The proposed advantages of polyploidy and hybridization include protection against deleterious mutations (Comai, 2005) and increased heterozygosity (Kearney, 2005). However, in the latest review of empirical data (Lundmark and Saura, 2006), polyploidy turned out to be the factor best explaining geographical patterns in asexual species. Parthenogenesis is mostly associated with polyploidy in plants and, to a lesser extant, this correlation holds for animals as well (Choleva and Janko, 2013; Otto and Whitton, 2000; Suomalainen, 1940). Compared with plants, though, the successful establishment of polyploid lineages in animals is hampered by dioecy (i.e., male and female gametes come from different individuals) and chromosomal sex determination (for a detailed discussion see Otto and Whitton, 2000, and references therein). Because asexual reproduction erodes both factors, polyploidy is thought to emerge more likely in parthenogenetic diploid populations, while in plants, polyploidy usually predates asexuality (Otto and Whitton, 2000). Bell (1982)
5 suggested that in animals, polyploidy may be fundamental for the persistence of parthenogenetic lineages but not essential for their emergence. However, the interactions between asexuality and polyploidy and their ultimate effects on the evolution and spatial distribution of species still remain unclear (Choleva and Janko, 2013; Gregory and Mable, 2005; Stenberg and Saura, 2013). To enhance our understanding of the interrelation between asexuality and polyploidy, it is mandatory to gather empirical data from previously neglected animal groups with high incidence of parthenogenesis. Jumping bristletails (Insecta: Archaeognatha) of the genus Machilis meet these criteria. Within the Eastern Alps, 25 nominal species are known and at least nine of them putatively reproduce via parthenogenesis, since only females have been reported (Janetschek, 1954; Wygodzinsky, 1941). Moreover, Dejaco et al. (2012) hypothesized the occurrence of polyploidy in a study including three Machilis species. In this study, we apply genome‐size measurements, karyotyping, and mitochondrial DNA sequencing to sexual and parthenogenetic Machilis species to address the following questions: 1) Is parthenogenesis always coupled with polyploidy? 2) Did parthenogenesis arise once or multiple times independently across species? 3) Is it possible to infer evolutionary scenarios underlying the origin of parthenogenesis and polyploidy?
2. Materials and methods
2.1. Specimen collection
We focused on eight species of which three reproduce sexually (M. helleri, M. hrabei, and M. lehnhoferi) and five parthenogenetically (M. fuscistylis, M. pallida, M. engiadina, M. ticinensis, and M. tirolensis). In the parthenogenetic species, males are either completely absent (M. fuscistylis, M. pallida, and M. engiadina) or restricted to specific geographic areas (M. ticinensis and M. tirolensis) (Palissa, 1964; Wygodzinsky, 1941). Between spring 2012 and summer 2013, 209 specimens were sampled at 41 geographically representative sites throughout the known distribution areas of the species (Fig. 1; Appendix Table A1). Moreover, 12 specimens of four additional Machilis species were sampled: M. glacialis, M. inermis, M. mesolcinensis, and one unknown species, which morphologically keyed out as M. glacialis, but was named M. spA due to its distant position in the mitochondrial phylogeny. These additional species were included in our phylogeny but excluded from other analyses due to poor geographic coverage. One specimen of Lepismachilis y‐signata was used as 6 outgroup, resulting in a total of 222 specimens. Species were determined using the identification key in Palissa (1964) and original species descriptions (Janetschek, 1949; Kratochvil, 1945; Riezler, 1941; Wygodzinsky, 1941). In some cases, specimens were kept alive in a climate cabinet at 19 °C until further use.
2.2. Karyotyping With minor adaptations, we followed the protocol of Sahara et al. (1999) for chromosome preparations. Specimens were anesthetized with carbon dioxide prior to dissection, and three legs were snap‐frozen in liquid nitrogen and stored at ‐70 °C for use in flow cytometry. Specimens were dissected in physiological solution (Glaser, 1917) containing
154 mM NaCl, 5 mM KCl, 1.8 mM CaCl2, and 2.9 mM NaHCO3. Gonads were removed and placed into hypotonic solution (0.075 M KCl) for swelling (15‐20 min), followed by fixation in Carnoy's solution (ethanol:chloroform:acetic acid=6:3:1) for 20 to 30 minutes. Immediately after the removal of gonads, the specimens were preserved in 99% ethanol and stored at ‐20 °C for further use. Glass slides were incubated in acid ethanol (1% HCl:96% ethanol=1:100) for at least half an hour before use. Gonads were dispersed on dry‐cleaned slides in a drop of acetic acid (60%) with tungsten needles and spread using a heating plate (40 °C). Chromosome preparations were first checked for quality spreads under a Nikon Eclipse E600 phase contrast microscope and then stained with 0.02 µg/mL DAPI in antifade solution based on DABCO (1,4‐diazabicyclo[2.2.2]octane; Sigma‐Aldrich, St. Louis, MO, USA). Pictures of chromosome spreads were taken with a Leica DFC 495 digital camera mounted on a Leica DM 5000B fluorescence microscope. Single chromosomes were cut and ordered according to their size using Adobe Creative Suite 4 software (Adobe, USA). Chromosome lengths were measured using the software MicroMeasure version 3.3 (Reeves, 2000). Whenever necessary, the fundamental number of arms (FN; i.e., the total number of chromosome arms in a 2n metaphase complement) was determined from aligned karyotypes. Statistical tests including chromosome number (ChN), chromosome length (ChL), and genome size (GS) were calculated in SigmaPlot v.12.5 (Systat Software Inc., San Jose, CA, USA), applying a significance level of α=0.05.
2.3. Flow cytometry measurements
7 We applied flow cytometry to obtain relative GS estimates from leg muscle tissue (see 2.2) using Bellis perennis (Asteraceae; 2C= 3.38 pg, Schönswetter et al., 2007) as an internal standard. Samples were treated following Suda et al. (2007). In detail, single legs and approx. 0.5 cm² of a B. perennis leaf were chopped with 500 µl of ice cold Otto 1 Buffer (0.1 M citric acid, 0.5% Tween®20 (Merck KGaA, Darmstadt, Germany)) in a small Petri dish. The suspension was then filtered through a 42 µm nylon mesh and incubated for a few minutes. Finally, 1 ml of staining solution (DAPI (4 µl/ml) and 2‐mercaptoethanol (2 µl/ml) in Otto 2 buffer (0.4 M Na2HPO4∙12 H2O)) was added. Fluorescence intensity of 3000 cell nuclei per sample was measured using a Partec CyFlow® space flow cytometer (Partec GmbH, Münster, Germany). Gating and peak analysis were performed automatically using the Partec Flo Max software. The few samples producing skewed peaks due to accidentally fast defrosting were gated manually.
2.4. DNA extraction, PCR conditions, and phylogenetic reconstruction Genomic DNA was extracted from muscle tissue using the GenEluteT Mammalian Genomic DNA Miniprep kit (Sigma‐Aldrich, St.Louis, MO, USA). Two newly designed primers, MachF5 (5'‐TAGTTATACCYATYATAATYGGHGG‐3') and MachR7 (5'‐ CCTATRATAGCAAATACTGCYCC‐3'), were used to amplify approx. 750 bp of the mitochondrial cytochrome c oxidase 1 gene (CO1). PCR conditions were: 95 °C for 2 min, 35 cycles (94 °C for 30 s, 50 °C for 45 s, 72 °C for 90 s), 72 °C for 10 min. Amplicons were checked via gel electrophoresis and purified using a mastermix containing the enzymes Exo1 (1 U/µl) and FastAP (0.05 U/µl) (Thermo Fisher Scientific Inc., Waltham, MA, USA) applying two incubation steps (37 °C for 15 min, followed by 80 °C for 15 min). Sanger sequencing was conducted by a commercial sequencing facility (Eurofins MWG Operon, Munich, Germany) using the forward primer MachF5. All sequences were deposited in GenBank (accession numbers KJ501697 ‐ KJ501918). Sequences were checked for correct amino acid translation and aligned using the ClustalW algorithm implemented in MEGA 5 (Tamura et al., 2011), yielding a final alignment of 703 bp. Net between‐ and within‐group genetic distances were calculated in MEGA 5. The HKY+G model of nucleotide substitution was determined as best fitting our data based on the Akaike information criterion using jModeltest 2 (Darriba et al., 2012; Guindon and Gascuel, 2003).
8 Bayesian inference trees were constructed with MrBayes 3.2 (Ronquist et al., 2012). Three partitions were specified corresponding to codon positions. Two parallel runs, each consisting of three heated and one cold chain, were run for 106 generations, sampling trees every 1,000th generation. Convergence was checked using the average standard deviation of splits frequencies, which fell below 0.01 after approx. 8.5×105 generations. The first 500 trees were discarded as burn‐in.
3. Results 3.1. Mitochondrial phylogeny Our phylogeny included the eight target species, as well as the four additional species mentioned in section 2.1. All species formed monophyletic clusters supported by posterior probabilities above 0.95 (Fig. 2). In contrast, some deeper nodes were only weakly supported. The three sexual species formed a monophyletic group, while the parthenogenetic species did not. Mean net distances between species as measured by p‐ distances ranged between 3.04% and 16.22% (mean=13.30%, standard error= 1.40%). Mean within‐group distances were significantly higher in sexual than in parthenogenetic species (Student's t‐test: t=‐5.672; df=5; P=3.73×10‐3). Intraspecific splits occurred in M. helleri, M. hrabei, M. ticinensis, and M. tirolensis. In M. ticinensis and M. tirolensis, these splits were congruent with reproductive mode and ploidy level, respectively. In M. helleri, the three resulting clades were partly congruent with differing ChN (see Fig. 3 and section 3.2). In M. hrabei, the two clades corresponded to populations from Austria (KNB, VIE) and the Czech Republic (BRN; see Table 1).
3.2. Chromosome numbers We found predominantly mitotic chromosomes in female gonads and meiotic chromosomes in male gonads, except in two males (each one M. helleri and M. lehnhoferi), where both meiotic and mitotic chromosomes were found. In most populations of the three sexual species (M. helleri, M. hrabei, and M. lehnhoferi) we consistently counted 2n=52 chromosomes in females, and n=26 bivalents in males. The assembled karyotypes of these species revealed that they differed in the numbers of metacentric, submetacentric, and acrocentric chromosomes (see Appendix Fig. A1). In three populations of M. helleri, however, we found only 2n=50 in females and n=25 bivalents in males (HWD and RAX), and
9 2n=51 chromosomes and n=25 bivalents in one male from GIE (Fig. 2; Table 1). Unfortunately, no unambiguously countable chromosome spread could be produced in female M. helleri specimens from GIE and RAX. All specimens with 52 chromosomes fell into one well‐supported mitochondrial sub‐clade, together with specimens having 50 chromosomes from populations RAX (all) and HWD (six individuals). The remaining individuals from HWD (five individuals) and GIE corresponded to the two basal sub‐clades within M. helleri (Fig. 3). In the asexual species, ChN ranged from 2n=46 to 2n=3x=78 (Fig. 2, Table 1). In M. fuscistylis, we detected intraspecific variation with 2n=54 chromosomes across four populations (OBG, FOT, MAR and SRK) but consistently 2n=56 chromosomes in the HIN population (Fig. 2 and Appendix Fig. A1). In all specimens of M. pallida, we counted 2n=3x=78 chromosomes. In M. tirolensis, we found diploid populations with 2n=50 chromosomes, as well as triploid populations with 2n=3x=75 chromosomes (Appendix Fig. A1). We found 2n=50 chromosomes in all individuals of M. engiadina. Evidence for geographic parthenogenesis was found in M. ticinensis, where one population (MIR) consisted of both males and females, while exclusively females were found in the other five populations (see Table 1). In the sexual population, individuals had 2n=46 chromosomes plus a varying number of supernumerary elements, i.e., B‐chromosomes, representing 2n=46 (N=3), 2n=46+1B (N=1), 2n=46+4B (N=3), and 2n=46+5B (N=1) in females, and n=23 (N=2) bivalents in male meiotic spreads (Appendix Fig. A1 and Table A1). In contrast, the parthenogenetic populations consistently had 2n=46 chromosomes and no B‐chromosomes. Individuals from the sexual population had, on average, larger chromosomes than individuals in parthenogenetic populations (Nsex=5, xsex̄ =4.26±0.38 µm,
Nparth=9, xparth̄ =3.33±0.12 µm). Within the sexual population, individuals with B‐ chromosomes had larger chromosomes than individuals without B‐chromosomes (NwithBs=3, x̄withBs=4.52±0.21 µm, NnoBs=2, x̄noBs=3.88±0.02 µm).
3.3. Genome size measurements The 2C‐values ranged from 4.84 pg (M. helleri) to 7.18 pg (M. ticinensis) in diploids, and from 7.64 pg (M. pallida) to 8.7 pg (M. tirolensis) in triploids (Fig. 4). Among sexual species, GS varied significantly in overall (Kruskal‐Wallis‐ANOVA: H=78.158, dF=2, P<0.001) and multiple pairwise comparisons (Dunn's test: M. hrabei vs. helleri: P<0.05, M. hrabei vs.
10 lehnhoferi: P<0.05, M. lehnhoferi vs. helleri: P<0.05). Moreover, intraspecific variation (as measured by standard deviations of GS for each species) was significantly higher in sexual than in parthenogenetic species (Student's t‐test: t=‐6.174, dF=8, P=2.67×10‐4; Fig. 4). In M. hrabei, GS varied significantly among the two mitochondrial subclades (Student's t‐test: t=‐ 5.33, dF=22, P=2.4×10‐5), with the population from Czech Republic (BRN) having, on average, larger genomes than populations from Austria. In the parthenogenetic species M. fuscistylis, GS differed significantly between individuals with 54 vs. 56 chromosomes (Student's t‐test: t=‐3.916, dF=11, P=2.41×10‐3). The GS in triploid individuals of M. tirolensis exceeded the GS in conspecific diploids by a factor of 1.58, corroborating their triploid status. In the geographically parthenogenetic species M. ticinensis, GS differed significantly between sexual and parthenogenetic individuals (Mann‐
Whitney U test: U=0, Nasex=13, Nsex=10, P<0.001). The GS of sexual individuals (6.74 pg, SD=0.20) was larger than that of parthenogenetic individuals (5.68 pg, SD=0.07; Table 1). The GS significantly increased with the number of B‐chromosomes (Pearson product moment correlation: r=0.939; N=10, P=5.46×10‐5) within the sexual population, but nevertheless the GS of individuals without B‐chromosomes (2n=46) was significantly larger than that of parthenogenetic individuals (Student's t‐test: t=‐25.211, dF=16, P=1.31×10‐14; Fig. 4).
4. Discussion In this study, we investigated the link between parthenogenesis and polyploidy in the jumping bristletail genus Machilis by generating relative genome‐size estimates and karyotypes from geographically representative samples of three sexual and five parthenogenetic species. By mapping genome size and chromosome numbers onto a mitochondrial phylogeny, we tackled the questions raised in section 1 and identified promising trajectories for future research.
4.1. Multiple origin of parthenogenesis As this is the first phylogenetic framework in the genus Machilis, no a priori hypotheses concerning species relationships and evolution of parthenogenesis were available. Under the assumption that a reversal from parthenogenetic to sexual reproduction is unlikely, the non‐ monophyly of parthenogenetic species indicated that asexuality originated at least five times independently (Fig. 2). Multiple evolution of parthenogenesis within a genus has been
11 demonstrated in other animal groups such as stick insects (Schwander and Crespi, 2009) and lizards (Manriquez‐Moran et al., 2014), and such groups offer exciting opportunities for studying evolutionary scenarios leading to parthenogenesis. In Alpine Machilis species, multiple origins of parthenogenesis may have been facilitated by Pleistocene glaciation cycles, which are thought to have increased the incidence of asexual reproduction in plants and animals (Hörandl, 2009). In line with the concept of geographic parthenogenesis (Hörandl, 2009; Vandel, 1928), the parthenogenetic species included in our study mainly occurred in central parts of the Alps whereas sexual species were mostly scattered close to the margin of the Alps and in extra‐Alpine lowland (Fig. 1).
4.2. Incidence of polyploidy Polyploidy was found in two parthenogenetic species: In M. tirolensis, both populations with 50 and 75 chromosomes were identified. Because GS in the latter was on average 1.58 times higher than in the former, their triploid status is strongly supported. The second case of triploidy is hypothesized in M. pallida (2n=3x=78) based on the 1.5 ratio in ChN compared with sexual species. However, the direct sexual ancestor of M. pallida is unknown and possibly extinct, and thus, accurate inference of a phylogenetically unbiased GS ratio is not possible. Still, a ratio of 1.34 was calculated using the average GS across sexual species included in this study. Deviation from the expected 1.5 ratio might be explained by phylogenetic distance between M. pallida and these sexual taxa. Since the other three parthenogenetic species were diploid, polyploidy does not seem to be an essential correlate of parthenogenesis in Machilis. Parthenogenetic diploids have been found in numerous other arthropod taxa, e.g., brine shrimps (Maccari et al., 2013), weevils (Stenberg et al., 2003), and walking sticks (Schwander and Crespi, 2009). Our results thus corroborate the hypothesis, that in animals, polyploidy emerges secondarily, after the establishment of diploid parthenogenetic populations (Suomaleinen et al., 1987).
4.3. Evolutionary pathways to parthenogenesis in Machilis By taking into account ecological aspects, we discuss two plausible scenarios that may have promoted the origin and persistence of parthenogenesis in Machilis.
12 4.3.1. Survival on inner‐Alpine nunataks Survival on so‐called nunataks (i.e., peaks protruding from the ice‐shield during glacial maxima) has been considered unlikely in terrestrial arthropods (Holdhaus, 1954). In fact, extremely hostile environmental conditions and small population sizes are hampering persistence in these habitats. However, for two species included in this study (M. pallida and M. fuscistylis) survival on nunataks has been postulated (Janetschek, 1956; Wachter et al., 2012). Since polyploidy was found only in the former, potential benefits from additional gene copies may be ruled out as key factor required for the survival of populations in extreme habitats in these species. Therefore, we hypothesize that benefits from parthenogenesis itself (i.e., no need to produce males, low minimum viable population size) may have been more important than benefits from polyploidy for persistence in high‐Alpine environments during Pleistocene glaciation cycles. However, Wachter et al. (2012) also hypothesized a hybrid genome in M. pallida, thus making it difficult to pin down a single factor that enabled survival on nunataks. Nevertheless, M. pallida and M. fuscistylis represent valuable study systems for testing the nunatak theory more comprehensively. In doing so, future approaches should 1) verify if M. pallida (and possibly also M. fuscistylis) are of hybrid origin, 2) identify the presence or absence of genetic structuring among populations, and 3) compare the population genetic signal with patterns from other parthenogenetic species that have recolonized the inner‐Alpine area after deglaciation.
4.3.2. Recolonization from peripheral refugia The other three parthenogenetic species (M. engiadina, M. ticinensis, M. tirolensis) were found in montane areas except alongside the Alpine main ridge, thus implying a recent recolonization from peripheral refugia, rather than in‐situ survival in inner‐Alpine areas. Peripheral refugia, situated close to the margin of the Alps, were not covered by ice during glacial maxima and are thought to have acted as retreats and as starting points for recolonization of central parts of the Alps (Chodat and Pampanini, 1902). The recolonization of previously glaciated areas is commonly associated with an increased incidence of asexual reproduction and polyploidy in plants and animals (Bell, 1982; Hörandl, 2009; Kearney, 2005). Our results give some evidence for recurrent migrations associated with Pleistocene glaciation cycles, thus indirectly supporting the connection of climate oscillations and parthenogenesis in some Machilis species. In M. ticinensis, parthenogenetic populations to
13 the north and the sexual population to the south of the Alpine main ridge were both characterized by private CO1 haplotypes. Conservatively applying slow (1% per My) and fast (3% per My) mitochondrial (CO1) mutation rates as reported for several insect groups (see Table A1 in Papadopoulou et al., 2010), we estimated the age of this split between 0.33 and 1.00 Mya, well within the Pleistocene. We suggest that M. ticinensis survived glaciations in southern refugia, where sexual reproduction was retained. Selection for parthenogenesis during northward recolonization in interglacial periods could explain the extant distribution of parthenogenetic populations in this species. In M. tirolensis, triploid populations represented a monophyletic group in the mitochondrial phylogeny, thus indicating that triploidy originated only once in this species. Because triploids were found north and south of the Alpine main ridge, migration via the connective, inner‐Alpine area must have occurred during interglacials. Applying the same divergence rates as above, we estimated the timeframe for the origin of triploidy between 0.23 and 0.70 Mya, likewise well within the Pleistocene. Interestingly, diploid populations of M. tirolensis were only found close to peripheral refugia of plants (Schönswetter et al., 2005), possibly indicating that diploids were not able to evade refugial areas. A similar pattern is seen in the Alpine plant Ranunculus kuepferi and in the moth Dahlica triquetrella. In both cases, tetraploid lineages recolonized the Alpine arc while diploids persisted within the refugial area of the Maritime Alps (Burnier et al., 2009; Cosendai et al., 2013; Suomaleinen et al., 1987). We therefore hypothesize that diploid, parthenogenetic M. tirolensis persisted Pleistocene glaciations in a refugium at the south‐eastern margin of the Alps, and triploids successively recolonized the Eastern Alps. Here, polyploidy seems to have played a crucial role for the recolonization of post‐glacial environments.
4.4. Opportunities for studying genome evolution in the genus Machilis In this study, we found substantial variation in ChN and GS among and within Machilis species. In the following, we highlight two peculiarities in our data that emphasize the potential of the genus Machilis as a study system for investigating the role of chromosomal rearrangements and genome‐size alterations in evolutionary diversifications.
14 4.4.1. Intraspecific chromosomal rearrangements We found intraspecific variation in ChN within two species. In the parthenogen M. fuscistylis, one population (HIN) harboured two additional chromosomes compared with the other populations. As the fundamental number of arms differed between these subgroups, we excluded the possibility of chromosome fusion or fission, since both types of rearrangement would not alter FN. This assumption is further supported by GS measurements, which confirmed a significantly higher 2C‐value in the HIN population. We propose that the duplication of one chromosome pair in population HIN (Appendix Fig. A1) is more parsimonious than the parallel loss of one chromosome pair in the other populations. Also, since both karyotypic groups shared the same CO1 haplotype, the novel karyotype must have originated recently. The fixation of the novel haplotype within the HIN population may have been driven by increased adaptation to the ecological niche or a strong bottleneck during nunatak survival, or a combination of both. We stress the importance of chromosomal rearrangements and karyotypic variation as a potential source of divergence among parthenogenetic lineages, which has been postulated previously by others (Blackman et al., 2000; Sunnucks et al., 1998). Differing ChNs in the sexual species M. helleri (52 vs. 50 chromosomes, one male with 51) showed that chromosomal rearrangements are not restricted to parthenogenetic species. As the fundamental number of arms was congruent across different karyotypes, the change in ChN is best explained by fission of one or fusion of two chromosomes, while the single male with 51 chromosomes may represent a hybrid karyotype or a chromosomal aberration. The geographical spacing of populations with 50 and 52 chromosomes (Fig. 3) is contrasted by an increase of 10% in GS along a west‐east gradient, resulting in larger GS in populations having fewer chromosomes. Assuming that the ancestral ChN was 52, an increase in GS might be explained as a by‐product of the fusion of two chromosomes. Alternatively, the GS gradient may represent an independent phenomenon, possibly linked to an ecological gradient or varying retrotransposon activity. We hypothesize that the change in ChN in M. helleri could have built up a reproductive barrier, thus initiating genetic divergence. The evolutionary implications are different from those in parthenogenetic species (M. fuscistylis), where asexuality itself is a barrier to gene flow. This becomes evident in our mitochondrial phylogeny, where karyotypically differing individuals did not form reciprocally monophyletic groups but showed some incongruence in
15 the populations RAX and HWD (Fig. 3), possibly a consequence of limited, ongoing gene flow between the karyotypic groups. Recently, it has been shown that chromosomal rearrangements are key factors promoting speciation during secondary contact in several species pairs of rodents (Castiglia, 2014). Similarly, the pattern seen in M. helleri could be interpreted as an early stage of speciation triggered by a chromosomal rearrangement.
4.4.2. Occurrence of B‐chromosomes Varying numbers of B‐chromosomes were found in the sexual population of M. ticinensis. Interestingly, GS significantly increased with the number of B‐chromosomes within this population, but the significant difference in GS between sexual and parthenogenetic populations could not be explained by their presence alone. This is consistent with the results of Trivers et al. (2004), who found approx. 35% larger GS in plants with reported B‐ chromosomes compared with plants without B‐chromosomes. Because B‐chromosomes are thought to include a high amount of transposable elements (Camacho, 2005), repeated transposition of mobile elements to the A‐chromosomes and subsequent proliferation could explain increased GS in the sexual population of M. ticinensis. Studying B‐chromosomes is an excellent opportunity to learn more about the dynamics of evolving genomes (Houben et al., 2014), and, thus, M. ticinensis represents a prime study system for future research targeting chromosomal evolution.
5. Acknowledgements We thank Marianne Magauer, Daniela Pirkebner, and Barbara Pernfuß for technical assistance with flow cytometry, Richard Hastik for help with specimen collection, and Francesco Cicconardi for helpful comments on figures. TD was funded by the association for the support of South‐Tyrolean students at University of Innsbruck and by the South‐Tyrolean science fund (project‐ID: 1/40.3; 27.01.2014). MG received funding from the University of Innsbruck. FM acknowledges the support of the Grant Agency of the Czech Republic (project‐ ID: 14‐22765S).
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20 Table 1. Summary of average chromosome number and genome size (standard deviation) values for all Machilis populations sampled in this study, including additional information on reproduvctive mode, number of individuals per population, and sex. AUT=Austria, CZE=Czech Republic, GER=Germany, ITA=Italy, SUI=Switzerland. repr. number of number of Average relative genome size Ploidy species name population mode specimens chromosomes [pg] level Total male female (2n) male (n) female male sex. M. helleri ADM (AUT) 2 2 52 26 5.03 (0.02) 4.95 (0.08) 2n sex. EIS (AUT) 4 1 52 26 5.14 (0.21) 4.90 (n/a) 2n sex. GIE (AUT) 5 4 - n=25, 2n=51 5.39 (0.04) 5.40 (0.05) 2n sex. HWD (AUT) 8 3 50 25 5.41 (0.07) 5.31 (0.04) 2n sex. RAX (AUT) 3 1 - 25 5.24 (0.12) 5.03 (n/a) 2n sex. UST (CZE) 4 0 52 - 4.90 (0.07) - 2n sex. M. hrabei BRN (CZE) 4 6 52 26 6.37 (0.08) 6.40 (0.08) 2n sex. KNB (AUT) 3 5 52 26 6.19 (0.04) 6.00 (0.08) 2n sex. VIE (AUT) 4 2 52 26 6.28 (0.08) 6.24 (0.09) 2n sex. M. lehnhoferi GLK (AUT) 4 1 52 n=26, 2n= 52 5.85 (0.11) 5.99 (n/a) 2n sex. HAI (AUT) 3 0 52 - 5.82 (0.13) - 2n sex. HUN (AUT) 1 1 52 n=26, 2n= 52 5.76 (n/a) 5.97 (n/a) 2n sex. OBE (GER) 3 2 52 26 5.96 (0.06) 5.95 (0.08) 2n sex. OBL (AUT) 4 0 52 - 5.90 (0.06) - 2n sex. SAA (AUT) 0 3 - 26 - 6.05 (0.10) 2n sex. SAL (AUT) 0 4 - 26 - 5.96 (0.07) 2n sex. STZ (AUT) 3 0 52 - 5.83 (0.06) - 2n parth. M. engiadina EIS (AUT) 10 50 5.84 (0.09) 2n parth. KRK (AUT) 1 50 5.84 (n/a) 2n parth. LDC (ITA) 1 50 5.88 (n/a) 2n parth. MUR (AUT) 9 50 5.85 (0.04) 2n parth. NIK (AUT) 8 50 5.82 (0.07) 2n parth. SAA (AUT) 2 50 5.89 (0.01) 2n parth. SLD (AUT) 4 50 5.88 (0.07) 2n parth. SMT (ITA) 4 50 5.81 (0.06) 2n parth. STA (ITA) 2 50 5.58 (0.23) 2n parth. TOB (ITA) 1 50 5.85 (n/a) 2n parth. M. fuscistylis FOT (AUT) 3 54 4.93 (0.07) 2n parth. HIN (AUT) 6 56 5.08 (0.02) 2n parth. MAR (ITA) 1 54 4.95 (n/a) 2n parth. OBG (AUT) 4 54 4.98 (0.10) 2n parth. SRK (AUT) 1 54 4.93 (n/a) 2n parth. M. pallida MAD (ITA) 8 78 7.77 (0.09) 3x parth. SEI (ITA) 4 78 7.80 (0.12) 3x parth. TRI (AUT) 5 78 7.74 (0.06) 3x parth. M. tirolensis EBK (AUT) 4 75 8.59 (0.12) 3x parth. KRK (AUT) 2 75 8.60 (0.13) 3x parth. LDC (ITA) 6 50 5.44 (0.07) 2n parth. LDL (ITA) 3 50 5.49 (0.07) 2n parth. RAU (AUT) 1 75 8.60 (n/a) 3x parth. SAR (ITA) 3 75 8.60 (0.07) 3x parth. STA (ITA) 3 50 5.42 (0.02) 2n parth. M. ticinensis BRA (AUT) 2 46 5.65 (0.19) 2n parth. KRK (AUT) 1 46 5.59 (n/a) 2n parth. NEN (AUT) 7 46 5.71 (0.05) 2n parth. RAN (AUT) 1 46 5.65 (n/a) 2n parth. RAU (AUT) 2 46 5.68 (0.06) 2n sex. MIR (SUI) 8 2 46, 46 +Bs 23 6.78 (0.21) 6.58 (0.03) 2n Total 172 37
21 Fig.1
22 Fig. 2
23 Fig. 3
24 Fig. 4
25 Figure captions
Fig. 1. Geographic locations of all populations sampled in this study. White and black symbols correspond to sexual and parthenogenetic species, respectively.
Fig. 2. Bayesian phylogeny based on 222 mitochondrial CO1 sequences from 12 Eastern‐ Alpine Machilis species and Lepismachilis y‐signata as outgroup. All nodes supported by posterior probabilities higher than 0.95 are indicated by stars. Red lines highlight parthenogenetic species. Average genome size (GS) and chromosome number (ChN) are given for all species and major intraspecific clades. Diploid and triploid chromosome numbers are indicated by circled and squared symbols, respectively. The number of individuals used to calculate average genome size values is given to the right side of bars. Species included in the phylogeny but excluded from other analyses are given in gray.
Fig. 3. Geographic locations of sampled Machilis helleri populations, with corresponding number of chromosomes and average genome size per population. Below, a simplified drawing of the corresponding branch of the mitochondrial phylogeny is displayed. All nodes supported by posterior probabilities higher than 0.95 are indicated by stars.
Fig. 4. Genome size (GS) of 191 Machilis individuals for which chromosome number (ChN) has been determined as well. Individual measurements are grouped according to species affiliation, and, in M. ticinensis and M. tirolensis, splitted according to reproductive mode (sexual/parthenogenetic) and ploidy level (diploid/triploid), respectively. ChN is coded using the same colours as in Fig. 2. This graph was built using the R package ggplot2 that applies random horizontal scattering within groups. Please note that the y‐axis starts at 4pg for better visualization of intraspecific variation.
26 Appendix Fig. A1. Karyotypes from selected individuals are given for each species included in this study.
27
28 Appendix Table A1. Identification number (ID), number of chromosomes (ChN), genome size (GS) and geographical coordinates of respective population (Pop) for all Machilis specimens included in this study. Whenever chromosome number could not exactly be determined, minimum number of chromosomes and potential additional chromosomes are given in brackets.
repr. ID species sex ChN GS [pg] pop. country lat. (N°) long. (E°) mode
91969 M. engiadina female parth. 2n=50 5.956 EIS AUT 47.57692 14.84944 91971 M. engiadina female parth. 2n=50 5.754 EIS AUT 47.57692 14.84944 91990 M. engiadina female parth. 2n=50 5.786 EIS AUT 47.57692 14.84944 92018 M. engiadina female parth. 2n=50 5.682 EIS AUT 47.57692 14.84944 92019 M. engiadina female parth. 2n=50 5.862 EIS AUT 47.57692 14.84944 92020 M. engiadina female parth. 2n=48 (+2) 5.843 EIS AUT 47.57692 14.84944 92021 M. engiadina female parth. 2n=50 5.898 EIS AUT 47.57692 14.84944 92022 M. engiadina female parth. 2n=50 5.855 EIS AUT 47.57692 14.84944 92023 M. engiadina female parth. 2n=50 5.963 EIS AUT 47.57692 14.84944 92167 M. engiadina female parth. 2n=50 (+2) n.d. EIS AUT 47.57692 14.84944
92089 M. engiadina female parth. 2n=50 5.842 KRK AUT 47.27405 11.32771 92124 M. engiadina female parth. 2n=50 5.877 LDC ITA 46.46364 12.48886 91986 M. engiadina female parth. 2n=50 5.857 MUR AUT 47.11155 14.20253 92005 M. engiadina female parth. 2n=50 5.840 MUR AUT 47.11155 14.20253 92006 M. engiadina female parth. n.d. 5.840 MUR AUT 47.11155 14.20253 92007 M. engiadina female parth. 2n=50 5.761 MUR AUT 47.11155 14.20253 92008 M. engiadina female parth. 2n=50 5.886 MUR AUT 47.11155 14.20253 92024 M. engiadina female parth. 2n=50 5.901 MUR AUT 47.11155 14.20253 92025 M. engiadina female parth. 2n=48 (+2) 5.889 MUR AUT 47.11155 14.20253 92026 M. engiadina female parth. 2n=50 5.878 MUR AUT 47.11155 14.20253 92028 M. engiadina female parth. 2n=50 5.823 MUR AUT 47.11155 14.20253 91950 M. engiadina female parth. 2n=50 5.892 NIK AUT 46.77864 12.89217 91951 M. engiadina female parth. 2n=50 5.723 NIK AUT 46.77864 12.89217 91952 M. engiadina female parth. 2n=50 5.882 NIK AUT 46.77864 12.89217 91954 M. engiadina female parth. 2n=50 5.874 NIK AUT 46.77864 12.89217 91963 M. engiadina female parth. 2n=50 5.855 NIK AUT 46.77864 12.89217 91964 M. engiadina female parth. 2n=46 (+4) 5.852 NIK AUT 46.77864 12.89217 91977 M. engiadina female parth. 2n=50 5.736 NIK AUT 46.77864 12.89217 91983 M. engiadina female parth. 2n=50 5.783 NIK AUT 46.77864 12.89217 92039 M. engiadina female parth. 2n=50 5.885 SAA AUT 47.48759 12.83152 92052 M. engiadina female parth. n.d. 5.904 SAA AUT 47.48759 12.83152 92004 M. engiadina female parth. 2n=50 5.915 SLD AUT 47.40464 13.57408 92041 M. engiadina female parth. 2n=50 5.939 SLD AUT 47.40464 13.57408 92044 M. engiadina female parth. 2n=50 5.872 SLD AUT 47.40464 13.57408 92045 M. engiadina female parth. 2n=50 5.787 SLD AUT 47.40464 13.57408 91976 M. engiadina female parth. 2n=50 5.865 SMT ITA 46.63812 11.85378 92054 M. engiadina female parth. 2n=50 5.825 SMT ITA 46.63812 11.85378 92055 M. engiadina female parth. 2n=48 (+2) 5.730 SMT ITA 46.63812 11.85378 92056 M. engiadina female parth. 2n=50 5.816 SMT ITA 46.63812 11.85378 92091 M. engiadina female parth. 2n=50 5.906 STA ITA 46.54764 12.32539 92101 M. engiadina female parth. 2n=48 5.756 STA ITA 46.54764 12.32539 91991 M. engiadina female parth. 2n=50 5.846 TOB ITA 46.71572 12.22392
91982 M. fuscistylis female parth. 2n=54 4.868 FOT AUT 47.14544 11.20284 91981 M. fuscistylis female parth. 2n=54 4.917 FOT AUT 47.14544 11.20284 29 91980 M. fuscistylis female parth. 2n=54 5.011 FOT AUT 47.14544 11.20284 92239 M. fuscistylis female parth. 2n=56 5.051 HIN AUT 47.09967 11.65637 92244 M. fuscistylis female parth. 2n=56 5.066 HIN AUT 47.09967 11.65637 92247 M. fuscistylis female parth. 2n=56 5.084 HIN AUT 47.09967 11.65637 92250 M. fuscistylis female parth. 2n=56 5.090 HIN AUT 47.09967 11.65637 92240 M. fuscistylis female parth. 2n=56 5.112 HIN AUT 47.09967 11.65637 92242 M. fuscistylis female parth. 2n=56 n.d. HIN AUT 47.09967 11.65637 92040 M. fuscistylis female parth. 2n=54 4.954 MAR ITA 46.49048 10.68144 91904 M. fuscistylis female parth. 2n=54 4.892 OBG AUT 46.86153 11.06287 91906 M. fuscistylis female parth. 2n=54 4.954 OBG AUT 46.86153 11.06287 91905 M. fuscistylis female parth. 2n=54 5.088 OBG AUT 46.86153 11.06287 91907 M. fuscistylis female parth. 2n=54 n.d. OBG AUT 46.86153 11.06287 92272 M. fuscistylis female parth. 2n=54 4.934 SRK AUT 47.04041 11.10862
92153 M. helleri male sexual 1n=26 4.891 ADM AUT 47.54547 14.47487 92154 M. helleri male sexual 1n=26 5.005 ADM AUT 47.54547 14.47487 92152 M. helleri female sexual 2n=52 5.011 ADM AUT 47.54547 14.47487 92151 M. helleri female sexual 2n=52 5.042 ADM AUT 47.54547 14.47487 92113 M. helleri male sexual 1n=25; 2n=51 5.335 GIE AUT 48.10085 16.21872 92111 M. helleri male sexual 1n=25 5.384 GIE AUT 48.10085 16.21872 92115 M. helleri male sexual 1n = 22 5.426 GIE AUT 48.10085 16.21872 92112 M. helleri female sexual n.d. 5.431 GIE AUT 48.10085 16.21872 92081 M. helleri male sexual 1n=25 5.436 GIE AUT 48.10085 16.21872 92114 M. helleri female sexual n.d. 5.445 GIE AUT 48.10085 16.21872 92078 M. helleri female sexual n.d. 5.358 GIE AUT 48.10085 16.21872 92077 M. helleri female sexual n.d. 5.418 GIE AUT 48.10085 16.21872 92079 M. helleri female sexual n.d. 5.421 GIE AUT 48.10085 16.21872 92289 M. helleri male sexual 1n=25 5.267 HWD AUT 47.81623 15.19730 92283 M. helleri female sexual 2n=50 5.292 HWD AUT 47.81623 15.19730 92291 M. helleri male sexual 1n=25 5.313 HWD AUT 47.81623 15.19730 92281 M. helleri female sexual 2n=50 5.341 HWD AUT 47.81623 15.19730 92290 M. helleri male sexual 1n=25 5.352 HWD AUT 47.81623 15.19730 92286 M. helleri female sexual 2n=50 5.353 HWD AUT 47.81623 15.19730 92279 M. helleri female sexual 2n=50 5.426 HWD AUT 47.81623 15.19730 92288 M. helleri female sexual 2n=50 5.433 HWD AUT 47.81623 15.19730 92282 M. helleri female sexual 2n=50 5.457 HWD AUT 47.81623 15.19730 92287 M. helleri female sexual 2n=50 5.474 HWD AUT 47.81623 15.19730 92280 M. helleri female sexual 2n=50 5.507 HWD AUT 47.81623 15.19730 91998 M. helleri female sexual 2n=52 5.277 EIS AUT 47.57692 14.84944 91968 M. helleri female sexual 2n=52 5.364 EIS AUT 47.57692 14.84944 92168 M. helleri female sexual 2n=52 5.331 EIS AUT 47.57692 14.84944 92170 M. helleri male sexual 1n=26 4.902 EIS AUT 47.57692 14.84944 92171 M. helleri female sexual 2n=52 5.108 EIS AUT 47.57692 14.84944 91949 M. helleri male sexual 1n=25 5.031 RAX AUT 47.7452 15.7628 92048 M. helleri female sexual n.d. 5.370 RAX AUT 47.7452 15.7628 92169 M. helleri female sexual n.d. 5.217 RAX AUT 47.7452 15.7628 92148 M. helleri female sexual n.d. 5.126 RAX AUT 47.7452 15.7628 91895 M. helleri female sexual 2n=52 4.844 UST CZE 50.63962 14.05107 91896 M. helleri female sexual 2n=52 4.851 UST CZE 50.63962 14.05107 91953 M. helleri female sexual 2n=52 4.920 UST CZE 50.63962 14.05107 91897 M. helleri female sexual 2n=52 4.996 UST CZE 50.63962 14.05107 30 91932 M. hrabei male sexual 2n=52 6.251 BRN CZE 49.22050 16.66933 91920 M. hrabei female sexual 2n=52 6.332 BRN CZE 49.22050 16.66933 91929 M. hrabei male sexual 2n=50 (+2) 6.396 BRN CZE 49.22050 16.66933 91917 M. hrabei male sexual 1n=26 6.398 BRN CZE 49.22050 16.66933 91914 M. hrabei female sexual 2n=52 6.399 BRN CZE 49.22050 16.66933 91930 M. hrabei male sexual 1n=26 6.432 BRN CZE 49.22050 16.66933 91916 M. hrabei male sexual 2n=52 6.449 BRN CZE 49.22050 16.66933 91915 M. hrabei female sexual 2n=52 6.473 BRN CZE 49.22050 16.66933 91919 M. hrabei female sexual 2n=52 6.479 BRN CZE 49.22050 16.66933 91918 M. hrabei male sexual 1n=26 6.496 BRN CZE 49.22050 16.66933 92035 M. hrabei male sexual 1n=26 6.178 VIE AUT 48.27581 16.34922 92036 M. hrabei female sexual 2n=52 6.222 VIE AUT 48.27581 16.34922 92031 M. hrabei female sexual 2n=52 6.238 VIE AUT 48.27581 16.34922 92015 M. hrabei female sexual 2n=52 6.252 VIE AUT 48.27581 16.34922 92034 M. hrabei male sexual 1n=26 6.302 VIE AUT 48.27581 16.34922 92032 M. hrabei female sexual 2n=52 6.388 VIE AUT 48.27581 16.34922 92129 M. hrabei male sexual 1n=26 5.918 KNB AUT 48.21771 15.18507 92125 M. hrabei male sexual 1n=26 5.922 KNB AUT 48.21771 15.18507 92128 M. hrabei male sexual 1n=26 6.005 KNB AUT 48.21771 15.18507 92142 M. hrabei male sexual 1n=26 6.041 KNB AUT 48.21771 15.18507 92141 M. hrabei male sexual 1n=26 6.109 KNB AUT 48.21771 15.18507 92117 M. hrabei female sexual 2n=52 6.147 KNB AUT 48.21771 15.18507 92126 M. hrabei female sexual 2n=52 6.191 KNB AUT 48.21771 15.18507 92127 M. hrabei female sexual 2n=52 6.222 KNB AUT 48.21771 15.18507 92119 M. lehnhoferi female sexual 2n=52 5.695 GLK AUT 47.44879 11.76482 92122 M. lehnhoferi female sexual 2n=52 5.868 GLK AUT 47.44879 11.76482 92120 M. lehnhoferi female sexual 2n=52 5.905 GLK AUT 47.44879 11.76482 92121 M. lehnhoferi female sexual 2n=52 5.928 GLK AUT 47.44879 11.76482 92118 M. lehnhoferi male sexual 1n=26; 2n=52 5.993 GLK AUT 47.44879 11.76482 92264 M. lehnhoferi female sexual 2n=52 5.681 HAI AUT 47.62940 14.61328 92266 M. lehnhoferi female sexual 2n=52 5.843 HAI AUT 47.62940 14.61328 92271 M. lehnhoferi female sexual 2n=52 5.940 HAI AUT 47.62940 14.61328 92147 M. lehnhoferi female sexual 2n=52 5.760 HUN AUT 47.33987 11.56484 92146 M. lehnhoferi male sexual 1n=26; 2n=52 5.967 HUN AUT 47.33987 11.56484 91992 M. lehnhoferi male sexual 1n=26 5.897 OAG GER 47.58606 11.10432 91962 M. lehnhoferi female sexual 2n=52 5.905 OAG GER 47.58606 11.10432 91972 M. lehnhoferi female sexual 2n=52 5.951 OAG GER 47.58606 11.10432 91961 M. lehnhoferi male sexual 1n=26 6.006 OAG GER 47.58606 11.10432 91960 M. lehnhoferi female sexual 2n=52 6.018 OAG GER 47.58606 11.10432 92268 M. lehnhoferi female sexual 2n=52 5.521 OBL AUT 47.70873 14.52755 92270 M. lehnhoferi female sexual 2n=52 5.833 OBL AUT 47.70873 14.52755 92267 M. lehnhoferi female sexual 2n=52 5.836 OBL AUT 47.70873 14.52755 92269 M. lehnhoferi female sexual 2n=52 5.938 OBL AUT 47.70873 14.52755 92038 M. lehnhoferi male sexual 2n=52 5.971 SAA AUT 47.48759 12.83152 92053 M. lehnhoferi male sexual 1n=26 6.016 SAA AUT 47.48759 12.83152 92051 M. lehnhoferi male sexual 1n=26 6.155 SAA AUT 47.48759 12.83152 91898 M. lehnhoferi male sexual n.d. 5.876 SAL AUT 47.55595 13.16782 91899 M. lehnhoferi male sexual 1n=26 5.942 SAL AUT 47.55595 13.16782 91887 M. lehnhoferi male sexual n.d. 5.992 SAL AUT 47.55595 13.16782 91886 M. lehnhoferi male sexual n.d. 6.029 SAL AUT 47.55595 13.16782 31 92075 M. lehnhoferi female sexual 2n=52 5.794 STZ AUT 47.39337 11.28920 92073 M. lehnhoferi female sexual 2n=52 5.798 STZ AUT 47.39337 11.28920 92074 M. lehnhoferi female sexual 2n=52 5.892 STZ AUT 47.39337 11.28920
91942 M. pallida female parth. 3x=78 7.642 MAD ITA 46.21508 10.90108 91943 M. pallida female parth. 3x=78 7.683 MAD ITA 46.21508 10.90108 91940 M. pallida female parth. 3x=78 7.760 MAD ITA 46.21508 10.90108 91941 M. pallida female parth. 3x=75 (+3) 7.773 MAD ITA 46.21508 10.90108 91979 M. pallida female parth. 3x=78 7.780 MAD ITA 46.21508 10.90108 91984 M. pallida female parth. 3x=78 7.781 MAD ITA 46.21508 10.90108 92150 M. pallida female parth. 3x=75 (+3) 7.819 MAD ITA 46.21508 10.90108 91944 M. pallida female parth. 3x=78 7.928 MAD ITA 46.21508 10.90108 92047 M. pallida female parth. 3x=78 7.677 TRI AUT 47.08272 11.35906 92050 M. pallida female parth. 3x=78 7.691 TRI AUT 47.08272 11.35906 92143 M. pallida female parth. 3x=78 7.760 TRI AUT 47.08272 11.35906 92046 M. pallida female parth. 3x=78 7.763 TRI AUT 47.08272 11.35906 92049 M. pallida female parth. 3x=75 (+3) 7.817 TRI AUT 47.08272 11.35906 92009 M. pallida female parth. 3x=78 7.679 SEI ITA 46.51198 11.70047 92011 M. pallida female parth. 3x=78 7.756 SEI ITA 46.51198 11.70047 92010 M. pallida female parth. 3x=78 7.802 SEI ITA 46.51198 11.70047 92012 M. pallida female parth. 3x=78 7.953 SEI ITA 46.51198 11.70047 92014 M. ticinensis female parth. 2n=46 5.516 BRA AUT 47.08802 9.73298 92013 M. ticinensis female parth. 2n=46 5.785 BRA AUT 47.08802 9.73298 92090 M. ticinensis female parth. 2n=46 5.587 KRK AUT 47.27405 11.32771 92204 M. ticinensis female sexual 2n=46 6.556 MIR CH 46.27277 10.10249 92211 M. ticinensis male sexual 1n=23 6.563 MIR CH 46.27277 10.10249 92214 M. ticinensis female sexual 2n=46 6.577 MIR CH 46.27277 10.10249 92206 M. ticinensis male sexual 1n=23 6.604 MIR CH 46.27277 10.10249 92208 M. ticinensis female sexual 2n=46 6.609 MIR CH 46.27277 10.10249 92207 M. ticinensis female sexual 2n=46+1B 6.723 MIR CH 46.27277 10.10249 92199 M. ticinensis female sexual 2n=46+4B 6.851 MIR CH 46.27277 10.10249 92212 M. ticinensis female sexual 2n=46+4B 6.865 MIR CH 46.27277 10.10249 92215 M. ticinensis female sexual 2n=46+4B 6.890 MIR CH 46.27277 10.10249 92201 M. ticinensis female sexual 2n=46+5B 7.181 MIR CH 46.27277 10.10249
92088 M. ticinensis female parth. 2n=46 5.655 NEN AUT 47.16210 9.69109 92063 M. ticinensis female parth. 2n=46 5.676 NEN AUT 47.16210 9.69109 92106 M. ticinensis female parth. 2n=46 5.693 NEN AUT 47.16210 9.69109 91924 M. ticinensis female parth. 2n=46 5.695 NEN AUT 47.16210 9.69109 91995 M. ticinensis female parth. 2n=46 5.724 NEN AUT 47.16210 9.69109 92057 M. ticinensis female parth. 2n=46 5.743 NEN AUT 47.16210 9.69109 92133 M. ticinensis female parth. 2n=46 5.794 NEN AUT 47.16210 9.69109 92037 M. ticinensis female parth. 2n=46 5.652 RAN AUT 47.27537 9.65978 92131 M. ticinensis female parth. 2n=46 5.638 RAN AUT 47.27537 9.65978 92116 M. ticinensis female parth. 2n=46 5.727 RAN AUT 47.27537 9.65978 92165 M. tirolensis female parth. 2n=50 5.412 LDL ITA 46.63089 12.22806 92163 M. tirolensis female parth. 2n=50 5.513 LDL ITA 46.63089 12.22806 92164 M. tirolensis female parth. 2n=50 5.548 LDL ITA 46.63089 12.22806 92302 M. tirolensis female parth. 3x=75 8.451 EBK AUT 47.27916 11.25869 92301 M. tirolensis female parth. 3x=75 8.546 EBK AUT 47.27916 11.25869 92303 M. tirolensis female parth. 3x=75 8.674 EBK AUT 47.27916 11.25869 92300 M. tirolensis female parth. 3x=75 8.702 EBK AUT 47.27916 11.25869 32 92130 M. tirolensis female parth. 3x=72 (+3) 8.509 KRK AUT 47.27405 11.32771 92100 M. tirolensis female parth. 3x=73 (+2) 8.690 KRK AUT 47.27405 11.32771 92176 M. tirolensis female parth. 2n=50 5.313 LDC ITA 46.46364 12.48886 92174 M. tirolensis female parth. 2n=50 5.433 LDC ITA 46.46364 12.48886 92172 M. tirolensis female parth. 2n=50 5.438 LDC ITA 46.46364 12.48886 92175 M. tirolensis female parth. 2n=50 5.487 LDC ITA 46.46364 12.48886 92080 M. tirolensis female parth. 2n=50 5.489 LDC ITA 46.46364 12.48886 92173 M. tirolensis female parth. 2n=50 5.499 LDC ITA 46.46364 12.48886 92132 M. tirolensis female parth. 3x=72 (+3) 8.596 RAU AUT 47.27481 11.35114 91989 M. tirolensis female parth. 3x=73 (+2) 8.507 SAR ITA 46.06095 10.92383 91970 M. tirolensis female parth. 3x=75 8.590 SAR ITA 46.06095 10.92383 92158 M. tirolensis female parth. 3x=75 8.641 SAR ITA 46.06095 10.92383 92108 M. tirolensis female parth. 2n=48 (+2) 5.395 STA ITA 46.54764 12.32539 92107 M. tirolensis female parth. 2n=50 5.417 STA ITA 46.54764 12.32539 92102 M. tirolensis female parth. 2n=50 5.433 STA ITA 46.54764 12.32539
33 SYNTHESIS
The benefit of integrative species delimitation
The multidisciplinary species delimitation approach applied in this study proved to be highly efficient in detecting evolutionary independent lineages that would otherwise have been overlooked. Out of the 18 nominal species included, 14 showed incongruence with morphological species hypotheses in at least one of the applied disciplines. Some of these incongruences could be explained by evolutionary patterns of hybridization or ongoing speciation. However, in one instance, a nominal species (M. glacialis) has been split, and a new species erected (M. sp. A). In another instance, three nominal species were synonymised (M. inermis, M. ladensis, and M. robusta). At first sight, using only mitochondrial DNA would have yielded similar results in both instances. However, it would also have produced misleading information: First, M. sp. A would have been further split in two separate species based on high mtDNA divergence, even though both lineages are closely related based on nuclear markers. This pattern might be explained by hybridization or mitochondrial introgression, and requires in‐depth investigation before a completed speciation event can be postulated. Second, like in the case of M. inermis, M. ladensis, and M. robusta, synonymy would have been suggested for M. alpicola, M. engiadina, and M. rubrofusca. However, based on the results of ITS‐2 and AFLPs, a complex evolutionary history involving hybridization, backcrossing, and parallel origins of parthenogenesis has been uncovered, supporting three independently evolving lineages. These patterns would not have uncovered using only one single discipline. Only four nominal species showed complete congruence among morphology, traditional morphometrics, and molecular markers (M. fuscistylis, M. mesolcinensis, M. pallida, and M. tirolensis; see 2nd manuscript). Of these four species, two showed additional partitioning based on chromosome numbers and genome sizes: M. fuscistylis and M. tirolensis (see 3rd manuscript). In M. fuscistylis, one population was characterized by a different chromosome number (2n=56) compared to all other populations (2n=54). In M. tirolensis, diploid populations were found in the South‐eastern Alps, while populations in the Northern Alps and in one southern population were triploid (see 2nd and 3rd manuscript). In the light of species delimitation, such chromosomal alterations would provide evidence for lineage separation under the assumption that changes in chromosome number increase
105 reproductive isolation. However, M. fuscistylis and M. tirolensis are presumably parthenogenetic, since no males have been sampled. Under parthenogenesis, reproductive isolation is an inherent property of every single individual. This is why the Biological Species Concept is not applicable to parthenogenetic species and the reason why we apply the Unified Species Concept. The chromosomal alteration/triploidization seen in M. fuscistylis and M. tirolensis, seem to be rather young events, though, since no, or only weak evidence for lineage separation was detected using other disciplines. Nevertheless, I want to highlight the importance of chromosomal alterations and polyploidizations for inducing genomic diversity – a process that might contribute substantially to lineage diversification in asexually reproducing taxa.
The role of parthenogenesis, hybridization, and polyploidization during speciation
Evidence for parthenogenesis (both general and geographical) was found in 9 out of 20 species delimited in this study. Instances of geographic parthenogenesis have been reported in several other insect species (Jensen et al. 2002; Law & Crespi 2002; Morgan‐Richards et al. 2010; Polihronakis et al. 2010; Stenberg et al. 2003). Even though in some cases substantial genetic divergence was found between sexual and asexual lineages (Jensen et al. 2002; Polihronakis et al. 2010), these authors avoided taxonomic implications (i.e., erecting new species on the basis of genetic divergence and reproductive mode variation) due to inapplicability of the Biological Species Concept to parthenogenetic populations. In this study, parthenogenesis was associated with triploidy in two species (M. pallida and M. tirolensis), but data on ploidy levels are missing for some species. In four other parthenogens (M. alpicola, M. engiadina, M. ticinensis, and M. rubrofusca), hybridization and backcrossing, in combination with parallel origins of parthenogenesis, seems to have triggered several independent evolutionary lineages. They differ in the amount of shared nuclear DNA, but the maternal lineage, the hybrid, and the backcross mostly share the same mitochondrial haplotype. This example corroborates the hypothesis that hybridization and polyploidy are often linked to parthenogenesis in animals (Suomaleinen et al. 1987). Hybridization has been reported to be a key factor promoting the spread of asexual populations towards recently deglaciated or extreme habitats (Ghiselli et al. 2007; Kearney 2005). Alternatively, other authors have ascribed the same benefit to polyploidy in combination with parthenogenesis (Adolfsson et al. 2010; Comai 2005; Stenberg et al. 2003).
106 Since I found no evidence of polyploidy in M. engiadina (preliminary data indicate that M. alpicola and M. rubrofusca are as well diploid), its successful spread across the Eastern Alps may indeed be attributed to heterotic effects in combination with parthenogenesis. On the other side, triploid populations of M. tirolensis successfully spread across the Alpine main ridge, while diploid populations were only found in a south‐eastern marginal area. Thus, beneficial effects due to both, hybridization and polyploidy in combination with parthenogenesis can be found among Machilis species, showing that their genomes are highly dynamic regarding structural alterations. The fact that M. engiadina has not only been uncovered as homoploid hybrid, but has also been confirmed in his species status, shows that hybridization has been a driving force of speciation in combination with parthenogenesis. Polyploidy, on the other side, has led to little genetic divergence in M. tirolensis. However, this may be explained by a younger origin of polyploidization in M. tirolensis compared with hybridization in M. engiadina. It is difficult to say to what extent triploidy in M. pallida has contributed to the origin of this species, since no closely related sister lineage has been sampled in this study. Assuming that all sister lineages have gone extinct, triploidy in M. pallida still highlights the potential importance of polyploidy for survival in extreme, high Alpine habitats.
107 CONCLUSION
Overall, the main results of this PhD project can be summarized as follows:
1) Actual species limits in Eastern‐Alpine Machilis species are only partly congruent with those proposed by original species descriptions based on morphology. Both, over‐ and underestimations of nominal morphospecies have been uncovered. Consequently, taking into account the discovery of additional species new to science, the overall species diversity has slightly risen. 2) Since I was not able to sample several of the putative small‐scale endemic species (i.e., species known from only one geographical site; see Tab. 1 in 2nd manuscript), I can make only putative inferences on the degree of endemism. Since three of the species described by Wygodzinsky (1941) turned out to be synonyms, and three additional species also described by him are suspected to be synonyms as well, I hypothesize that the species diversity in Switzerland is currently overestimated. Moreover, distribution ranges of some species previously treated as small‐scale endemics have been substantially widened during this investigation. Therefore, the portion of small‐scale endemics may be lower than previously thought. However, most species are still endemic to geographical subdivisions of the Eastern Alps. 3) The frequency of parthenogenesis, hybridization, and chromosomal variation found in this study is astonishing. Even in the absence of a time‐calibrated phylogeny, it seems very likely that these traits evolved in the face of a constantly changing environment during the Pleistocene. Given the low presence of Machilis species in surrounding areas, the "Alpine radiation" in this animal group might thus be an indirect consequence of Pleistocene glaciation cycles.
108 ACKNOWLEDGEMENTS
I thank Univ.‐Prof. Birgit Schlick‐Steiner and Dr. PD Florian Steiner, who gave me the opportunity to start this research project in the absence of funding. Thank you both for assistance in planning, conducting, and presenting this research project. Special thanks for teaching and assistance in the wet lab, as well as fundraising goes to Dr. Wolfgang Arthofer.
For assistance with specimen collection in the field and memorable days at out institute, I thank Melitta Gassner, Clemens Folterbauer, Gregor Wachter, Herbert "C." Wagner, Barbara Thaler‐Knoflach, Lukas Rinnhofer, Johannes Schied, Steffi Fischnaller, Hannes Rauch, and Michael Url. A second round of cheers goes to Melitta Gassner, who did a great diploma thesis within the framework of this research project. I also thank Francesco Cicconardi for valuable assistance in bioinformatics and graphics.
For excellent teaching on chromosome preparation techniques and a cheerful week in České Budějovice, I thank Prof. František Marec.
I am especially indebted to my parents, who supported me throughout these years: thank you very much! Very special thanks to Sandra for being there for me!
This PhD was financially supported by the University of Innsbruck, the association for the support of South‐Tyrolean students at the University of Innsbruck, the Theodor Körner Fund, the Sonnblick Observatory, the Austrian Research Association, and the Autonomous Province of South Tyrol (project‐ID: 1/40.3; 27 January 2014).
109 LITERATURE
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