Gene flow in the European coal tit, Periparus ater (Aves: Passeriformes): low among Mediterranean populations but high in a continental contact zone

Gene flow in the European coal tit, Periparus ater (Aves: Passeriformes): low among Mediterranean... Abstract Extant phylogeographical patterns of Palearctic terrestrial vertebrates are generally believed to have originated from glacial range fragmentation. Post-Pleistocene range expansions have led to the formation of secondary contact zones among genetically distinct taxa. For coal tits (Periparus ater), such a contact zone has been localized in Germany. In this study, we quantified gene flow between Fennoscandian and southern European coal tits using a set of 13 microsatellite loci. STRUCTURE analysis revealed four genetic clusters, two occurring on Mediterranean islands. German populations were genetically admixed but introgression of southern alleles was evident for Fennoscandian populations. In the south, we found negligible introgression of northern alleles (and haplotypes) but slight admixture of two southern genetic clusters in the Pyrenees and on the Balkan Peninsula and near complete sorting of these two allelic lineages on the islands of Corsica and Sardinia. Genetic distinctiveness of the Mediterranean island populations reflects general patterns of endemism in the Corso-Sardinian fauna and the Cypriot fauna. Wide-range gene flow in Central Europe suggests a broad zone of intergradation between subspecies of the coal tit rather than a narrow contact zone. This is in accordance with low morphological and bioacoustic differentiation of European coal tit populations. glacial refugia, island populations, microsatellites, phylogeography, subspecies INTRODUCTION Evolutionary biologists widely agree that the impact of glacial periods considerably shaped phylogeographical patterns and speciation of terrestrial vertebrates in the Palearctic (Avise & Walker, 1998; Hewitt, 2000, 2004; Lovette, 2005; Zink et al., 2008; Stewart et al., 2010). Pleistocene separation of Eastern and Western Palearctic populations led to divergence of gene pools among distant refugia, in a few extreme cases across a large extant distributional gap, such as seen in the marsh tit, Poecile palustris (Tritsch et al., 2017), or in the azure-winged magpie, Cyanopica cyanus (Zhang et al., 2012). Other east–west lineage splits dating back to Pleistocene events were reconstructed for example in corvids (Haring, Gamauf & Kryukov, 2007; Haring et al., 2012) and tits (Kvist et al., 2003; Päckert et al., 2005; Kvist & Rytkönen, 2006). One noticeable result from Holocene range expansion is the spatial overlap of genetically distinct populations that is manifested in secondary contact zones of highly variable extent (Woodruf, 1973; Haffer, 1989; Aliabadian et al., 2005). In Western Europe, the apparent spatial clustering of secondary contact zones among terrestrial vertebrate sister taxa was the result of post-glacial expansion from southern glacial refugia (Hewitt, 2000; Schmitt, 2007). Parapatry along sharp and narrow hybrid zones is typically found at geographical barriers, such as the European mountain systems that separate two larger glacial refugia from the continent: (1) the Iberian Peninsula in the Pyrenees (Fig. 1A, I; birds: Helbig et al., 2001; Pons et al., 2011; Backström, Sætre & Ellegren, 2013; Kuhn et al., 2013; reptiles: Milá et al., 2013; insects: Vazquez et al., 1994; Shuker et al., 2005; Bella et al., 2007; and (2) the Italian Peninsula in the Alps (Fig. 1A, II; birds: Hermansen et al., 2011; rodents: Sutter, Beysard & Heckel, 2013; Giménez et al., 2017; insects: Flanagan et al., 1999). Apart from parapatry across mountain ranges, narrow hybrid zones of a wide latitudinal extent exist in Central Europe (Fig. 1A), the best-studied examples being those of crows (Corvus c. corone, C. c. cornix: Haas et al., 2009; Haas, Knape & Brodin, 2010; Wolf et al., 2010; Poelstra et al., 2014; Poelstra, Ellegren & Wolf, 2014), other birds (Secondi et al., 2011), the house mouse (Mus m. musculus, M. m. domesticus: Macholán et al., 2008; Giménez et al., 2017) and hedgehogs (Erinaceus europaeus, E. roumanicus: Berggren et al., 2005; Bolfíková & Hulva, 2012; Waters, Fraser & Hewitt, 2013: fig. 2b; Pfäffle et al., 2014). Figure 1. View largeDownload slide Spatial patterns of secondary contact and hybrid zones among divergent genetic lineages (schematized haplotype networks) of terrestrial vertebrates in the Western Palearctic: (A) narrow contact zone along geographical barriers (Pyrenees, Alps) or of a wide latitudinal north–south extent; (B) broad intergradation zone often along phenotypic clines; (C) range-wide merger and local co-occurrence of distinct genetic lineages (reversal of past lineage divergence). Outline map of Europe inferred from www.freeworldmaps.net, accessed 18 April 2018. Figure 1. View largeDownload slide Spatial patterns of secondary contact and hybrid zones among divergent genetic lineages (schematized haplotype networks) of terrestrial vertebrates in the Western Palearctic: (A) narrow contact zone along geographical barriers (Pyrenees, Alps) or of a wide latitudinal north–south extent; (B) broad intergradation zone often along phenotypic clines; (C) range-wide merger and local co-occurrence of distinct genetic lineages (reversal of past lineage divergence). Outline map of Europe inferred from www.freeworldmaps.net, accessed 18 April 2018. In several bird species pairs, zones of secondary contact and hybridization are not restricted to a narrow band, but extend along a wide longitudinal range into Eastern Europe (Fig. 1B), such as found in flycatchers (Ficedula: Sætre et al., 2001; Hogner et al., 2012a), reed warblers (Acrocephalus: Reifová et al., 2016), nightingales (Luscinia: Vokurková et al., 2013), tits (Cyanistes: Woodruf, 1973; Stervander et al., 2015) and Old World buntings (Emberiza: Irwin, Rubtsov & Panov, 2009). In a few other examples, pre-mating barriers were either not established during a short separation time in refuge areas or they simply broke down in secondary contact, which led to merging of divergent genetic lineages. The signal from mitochondrial markers might then remain the only testimony of past (Pleistocene) lineage separation, presently contrasted by narrow or wide-range gene flow (Fig. 1C), as suggested for some passerine bird species (Zink et al., 2008; Päckert, Martens & Sun, 2010; Hogner et al., 2012b; Block et al., 2015). Interbreeding and merging of gene pools between cryptic genetic lineages of a phenotypically uniform species has been sometimes termed ‘speciation in reverse’ (e.g. in birds: Webb, Marzluff & Omland, 2011). However, in the strict sense, reverse speciation is more appropriately applied to those examples where gene pools become largely absorbed due to hybridization with one of the two parental species running the risk of going extinct (in fishes: Taylor et al., 2006; Seehausen et al., 2008; Hudson et al., 2013; Bhat et al., 2014; in Darwin’s finches: Kleindorfer et al., 2014). Our study focuses on the recent evidence of secondary range overlap in Western Europe among a north-eastern and a south-western mitochondrial lineage of the coal tit, Periparus ater (Pentzold et al., 2013). We aim to verify the extent and degree of nuclear gene flow among the two coal tit lineages using nuclear markers (microsatellites). We expect significant gene flow at least in the region of considerable mtDNA lineage overlap at a contact zone extending throughout Germany (Fig. 2). We also expect nuclear gene flow to extend across a wider range than mitochondrial introgression, as was shown for another parid hybrid zone (Parus major/P. minor: Kvist & Rytkönen, 2006). Figure 2. View largeDownload slide Distribution range and phylogeographical pattern of the coal tit, Periparus ater (modified from Pentzold et al., 2013; sampling sites of mtDNA data indicated by black dots; range boundaries in light brown according to BirdLife International, 2017); pie charts indicate percentages of haplotypes belonging to four different clusters (Scandinavia/Russia, W and SW Europe, North Africa and Cyprus; subspecies included in the mtDNA dataset are listed at the corresponding clusters of the haplotype network, upper right) indicated by different colours. Study populations (abbreviations): BF = Black Forest, Cors = Corsica, Cyp = Cyprus, Fin = Finland, Grec = Greece, Mor = Morocco, Nor = Norway, PF = Palatine Forest, Pyr = French Pyrenees, Sard = Sardinia, Sax = Saxony, SH = Schleswig-Holstein (strongly divergent North African subspecies atlas not included in our population genetic study); coal tit drawing: K. Rehbinder, University of Mainz. Figure 2. View largeDownload slide Distribution range and phylogeographical pattern of the coal tit, Periparus ater (modified from Pentzold et al., 2013; sampling sites of mtDNA data indicated by black dots; range boundaries in light brown according to BirdLife International, 2017); pie charts indicate percentages of haplotypes belonging to four different clusters (Scandinavia/Russia, W and SW Europe, North Africa and Cyprus; subspecies included in the mtDNA dataset are listed at the corresponding clusters of the haplotype network, upper right) indicated by different colours. Study populations (abbreviations): BF = Black Forest, Cors = Corsica, Cyp = Cyprus, Fin = Finland, Grec = Greece, Mor = Morocco, Nor = Norway, PF = Palatine Forest, Pyr = French Pyrenees, Sard = Sardinia, Sax = Saxony, SH = Schleswig-Holstein (strongly divergent North African subspecies atlas not included in our population genetic study); coal tit drawing: K. Rehbinder, University of Mainz. MATERIAL AND METHODS Study species Eight divergent mitochondrial lineages are presently known in the coal tit. These lineages are distributed across large parts of the Palearctic, the mountain forests of China, the Himalayas, Karakoram and Hindu Kush as well as on Taiwan (Tietze et al., 2011). Across the Western Palearctic, four distinct mtDNA lineages of the coal tit occur (Fig. 2): (1) the north-eastern Palearctic (ater subspecies group; distributed from Northern Europe across the Eurasian continent to the Pacific coast and Japan); (2) Central and Southern Europe (abietum subspecies group) including the British Isles and the islands of Corsica and Sardinia; (3) North Africa; and (4) Cyprus (Martens, Tietze & Sun, 2006; Tietze et al., 2011; Pentzold et al., 2013). To date, range overlap of the south-western abietum and the north-eastern ater lineages has been documented mainly for German populations (Fig. 2). Due to a lack of reliable morphological and bioacoustic distinctiveness of north-eastern versus south-western Palearctic coal tits, the spatial dimension of the contact zone cannot be delimited by geographical variation of phenotypes or song types (Tietze et al., 2011; Pentzold et al., 2013, 2016). Sampling and multilocus genotyping We sampled 166 birds from Russia, Kyrgyzstan, Kazakhstan, Finland, Norway, Germany, French Pyrenees, Cyprus, Corsica and Sardinia. DNA preparation was conducted either using the innuPREP DNA Mini Kit (muscle tissue: Analytik Jena AG, Germany) or the PEQLAB GOLD Blood DNA Mini Kit (blood samples: PEQLAB Biotechnologie GmbH, Germany), following the manufacturers’ recommendations. New microsatellite loci for P. ater were identified by Ecogenics GmbH (Switzerland) based on an enriched DNA library. Size-selected genomic DNA was ligated into SNXforward/SNX reverse-linker (Hamilton et al., 1999) and enriched by magnetic bead selection with biotin-labelled oligonucleotide repeats [(CT)13, (GT)13, (GTAT)7, (GATA)7; Gautschi, Widmer & Koella, 2000a; Gautschi et al., 2000b]. A total number of 528 recombinant colonies were screened and 415 gave a positive signal after dot-blot hybridization. Plasmids from 48 positive clones were Sanger sequenced and primers were designed for 16 microsatellite inserts, of which nine (listed in Table 1) were finally used for amplification of polymorphic microsatellite loci using the protocol described by Schuelke (2000). For this protocol an M13(-21) tail (18 bp) was adhered to the 5′ end of the forward primer. The reverse primer remained unmodified. In addition to the two regular PCR primers, a fluorescently labelled universal M13(-21) primer was added to the reaction mixture. The reaction contained 10–40 ng of template DNA, 0.04 µM of the M13-forward primer, 0.16 µM of the reverse primer and the labelled M13 primer, 0.2 mM of each dNTP, 1 µL of 10× PCR reaction buffer ‘complete’ and 0.5 units of DFS-Taq DNA polymerase (Bioron GmbH, Germany) in a total volume of 10 µL. The thermo-treatment consisted of two successive steps: (1) amplification of the microsatellite fragment with M13 fusion and (2) labelling of the fragment with the fluorescent dye. The PCR programme was 95 °C for 10 min followed by 30 cycles of 30 s at 95 °C, 45 s at 50 °C (Parate 8) or 56 °C (all other Parate loci) and 45 s at 72 °C (step 1), followed by eight cycles of 30 s at 95 °C, 45 s at 53 °C and 45 s at 72 °C (step 2) and a final elongation at 72 °C for 30 min. Table 1. Characteristics and variation of nine newly identified microsatellite loci for population genetic analysis of coal tits Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  The Parate loci were amplified using the protocol described by Schuelke (2000). For this protocol an 18-bp-long M13(-21) tail (5′-TGTAAAACGACGGCCAGT-3′) was adhered to the 5′ end of the forward primer (= Label). *Based on sequence clone. †Annealing temperature based on experimental optimization during microsatellite primer design. ‡Minimum and maximum sizes based on analyses of 14 populations View Large Table 1. Characteristics and variation of nine newly identified microsatellite loci for population genetic analysis of coal tits Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  The Parate loci were amplified using the protocol described by Schuelke (2000). For this protocol an 18-bp-long M13(-21) tail (5′-TGTAAAACGACGGCCAGT-3′) was adhered to the 5′ end of the forward primer (= Label). *Based on sequence clone. †Annealing temperature based on experimental optimization during microsatellite primer design. ‡Minimum and maximum sizes based on analyses of 14 populations View Large Four additional primer pairs targeting microsatellite loci were obtained from previous studies on Poecile atricapillus (Table 1). We tested for cross amplification with P. ater samples in a total volume of 10 µL containing 10–40 ng of template DNA, 0.3 µM of each primer, 0.2 mM of each dNTP, 1 µL of 10× PCR reaction buffer ‘complete’ and 0.5 units of DFS-Taq DNA polymerase (Bioron). The thermo-cycling protocol was as follows: 94 °C for 5 min followed by 35 cycles of 94 °C for 30 s, 60 °C (Pat2-43) or 57 °C (PmaC25, PmaTGAn33, Pma69) for 30 s, 72 °C for 45 s and a final elongation at 72 °C for 5 min. Specimens were genotyped at 13 loci (Table 1) using the reaction conditions outlined above and labelled PCR fragments were run on a 16-column ABI 3130xl capillary sequencer (Applied Biosystems). The alleles were scored using the STRand Analysis Software v.2.4 (UC Davis, veterinary genetics lab, http://www.vgl.ucdavis.edu/STRand;Toonen & Hughes, 2001). The software package MICROCHECKER 2.2.3 (van Oosterhout et al., 2004) was used to test the probability that experimental errors occurred during microsatellite genotyping, i.e. large allelic dropout, scoring errors due to misinterpretation of stutter bands and null alleles. Diversity and divergence Summaries of allele sizes and the existence and frequencies of population-specific alleles (private alleles) were calculated using the program CONVERT v. 1.31 (Glaubitz, 2004), which was also used to generate input files for various software packages. Linkage between loci was determined using ARLEQUIN v.3.5.1.3 (Excoffier, Laval & Schneider, 2005). The same software package was used to calculate locus-specific observed and expected heterozygosities (HO, HE) for each sample population and to test for locus-specific deviations from Hardy–Weinberg equilibrium (HWE). Population-specific deviations from HWE (excess or deficiency of heterozygosity) across all loci were explored using inbreeding coefficients (FIS), calculated with the software FStat (v.2.9.3.2; Goudet, 1995) using a randomization test (3600 randomizations) to test for significance. The same software was used to estimate the mean number of alleles per locus and populations as well as the mean allelic richness (AR) per population across all loci. For these, we analysed a reduced dataset consisting of 145 specimens that belong to 14 distinct populations with a minimum of five specimens per population (Table 2 and Supporting Information, Table S1). Table 2. Diversity parameters and inbreeding coefficients (FIS) for the surveyed population samples of in total 145 individuals Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Loci Parate 06 and Parate 08 were excluded due to the presence of null alleles. N = number of sampled indifiduals, HO = observed heterozygosity, HE = expected heterozygosity. *Far East = pooled samples from Far East Russia (N = 11) and Japan (N = 2). †Schleswig-Holstein = pooled samples from Itzehoe (N = 15), Amrum (N = 3) and Brillit (N = 1). View Large Table 2. Diversity parameters and inbreeding coefficients (FIS) for the surveyed population samples of in total 145 individuals Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Loci Parate 06 and Parate 08 were excluded due to the presence of null alleles. N = number of sampled indifiduals, HO = observed heterozygosity, HE = expected heterozygosity. *Far East = pooled samples from Far East Russia (N = 11) and Japan (N = 2). †Schleswig-Holstein = pooled samples from Itzehoe (N = 15), Amrum (N = 3) and Brillit (N = 1). View Large Divergence between populations was estimated using F-statistics (inferred from microsatellite allele frequencies) and Φ-statistics (inferred from mitochondrial nucleotide sequences) by pairwise FST and ΦST values as well as by non-hierarchical and hierarchical locus-by-locus analysis of molecular variance (AMOVA with FCT and ΦCT values as a measure of divergence among groups) using ARLEQUIN. In total, 20000 permutations were performed to test for significance of these values. All P-values obtained from tests implementing multiple comparisons (i.e. test for deviation from HWE, test for linkage between loci) were Bonferroni corrected to adjust the significance threshold (Rice, 1989). To depict divergence between populations, pairwise FST values were used in a distance matrix to construct a UPGMA phenogram with MEGA v.6 (Tamura et al., 2013). Bayesian inference of the population structure Non-spatial Bayesian inference of population structure was performed using the software package STRUCTURE v.2.3.3 (Pritchard, Stephens & Donnelly, 2000; Falush, Stephens & Pritchard, 2003, 2007). STRUCTURE runs were performed (1) under the a priori assumption of genetic admixture and correlated allele frequencies and (2) under a LOCPRIOR model that allows for classification of the individuals into groups, which are given to the algorithm as an a priori parameter (Hubisz et al., 2009). The model was run under two different LOCPRIOR settings: (1) by classifying the individuals of the complete data set (N = 166) according to their assignment to mitochondrial lineages (inferred from the data set of Pentzold et al., 2013); and (2) by assigning the individuals to 14 local populations of N ≥ 5 (total sampling N = 145, Table 2). All STRUCTURE runs were conducted for 1–10 putative genetic clusters (K) with ten replicates for each value of K. The number of Markov chain Monte Carlo (MCMC) runs was 105 with a burn-in period of 25000 throughout all model runs. For further processing of the STRUCTURE output, STRUCTURE HARVESTER (Earl & von Holdt, 2012) was used. To select the most likely number of genetic clusters (K), we used the approach of Evanno, Regnaut & Goudet (2005). STRUCTURE analysis was also used to estimate the extent of genetic admixture in different populations according to the method described by Randi (2008). Accordingly, we used a threshold q > 0.80 for the assignment of individuals to a cluster or we classified individuals as admixed individuals, if the proportion of membership was q < 0.80 (see Randi, 2008). Spatial Bayesian clustering was performed using the software packages TESS v.2.3.1 (Chen et al., 2007) and GENELAND v.4.03 (Guillot, Mortier & Estoup, 2005b). Unlike the non-spatial models of STRUCTURE, the spatially explicit models implemented in TESS and GENELAND consider the geographical coordinates of the samples, but do not consider affiliation as a model parameter. Data exploration under different models is recommended, because the explanatory power of spatial versus non-spatial models depends on demographic scenarios to be tested and clustering output from one model-based method might reveal a finer scaled spatial structure that other models fail to detect (François & Durand, 2010). Both admixture models in TESS (BYM and CAR models; Durand et al., 2009a; Durand, Chen & François, 2009b) were run with the complete dataset of N = 166 individuals (MCMC iterations: 105, burn-in period: 20000, Kmax: 2–10, five replicates for each Kmax for both models). Unlike STRUCTURE or TESS the standard models of GENELAND do not account for admixture, but assign posterior probabilities of cluster membership to single individuals. A benefit of GENELAND is that it can correct for the occurrence of null alleles. Although from a biological perspective it seems obvious to assume the allele frequencies to be correlated between populations, the respective model was assessed to systematically overestimate the number of clusters (Guillot et al., 2005a). Hence according to the authors’ recommendations the analysis was conducted in two steps: first, resolving the number of populations (K) using the D model (frequency model = uncorrelated; K: 1–10); second, deriving the correct population assignment by applying the F model (frequency model = correlated) with a fixed number of K (which was determined in the first step, Guillot et al., 2005a; GENELAND Development Group, 2012). The model parameters were: 106 MCMC iterations, Thinning = 1000, Null allele model = TRUE and ten replicates per analysis step. The outputs of both STRUCTURE and TESS were further processed with CLUMPP (Jakobsson & Rosenberg, 2007). For visualization, DISTRUCT (Rosenberg, 2004) was used. Admixture rate in the hybrid zone For estimating admixture rate in the hybrid zone we applied demographic modelling based on Approximate Bayesian Computation (ABC) using the program DIYABC v.2.0.4 (Cornuet, Ravigné & Estoup, 2010). We first used both microsatellite and mitochondrial control-region sequences and included individuals for which both data were available. We chose samples from Norway and Finland (N = 18) with q-values from STRUCTURE above 0.8 to their cluster ‘ater’ to represent the ‘northern’ parental population and samples from the French Pyrenees with q-values from STRUCTURE above 0.8 to their cluster ‘abietum’ to represent the ‘southern’ (N = 8) parental population. This was based on further evidence that these populations represented only one mitochondrial lineage each and were clearly separated from, but still related to, the admixture populations in the STRUCTURE analysis from the microsatellite data. In the central European admixture population, we included samples from Schleswig-Holstein, Harz, Saxony, Palatine Forest and Black Forest (N = 47, all Germany). We started by constructing four historical models (Fig. 3): (1) the parental ‘northern’ population and ‘southern’ population were split from each other at time t2 and come into contact at time t1 to form the admixture population; (2) the parental populations split first from each other and the central European population was split later from the northern population; (3) the northern and central populations split first from each other and the southern population was split later from the northern population; and (4) the parental populations split first from each other and the central European population was split later from the southern population. The mutation rate for the microsatellite data was set to 10–4–10–3 as was done with another tit species, the blue tit, Cyanistes caeruleus (Hansson et al., 2014). For the mitochondrial sequences, we applied the substitution rate 1.156 × 10–8 calibrated for the coal tit control region by Pentzold et al. (2013), and HKY+gamma model with gamma = 0.09 (as suggested by the test for the substitution model implemented in MEGA v.6.06; Tamura et al., 2013). As the fit of the observed data with the simulated data was poor, we next performed the same analysis separately for the microsatellite data and the mitochondrial data. For the microsatellite data, uniform prior distributions for effective population sizes were set to 10–10000, and for coalescence times t1 was set to 10–1000 and t2 to 10–4000. The uniform prior distribution for the admixture rate was 0.001–0.999. The priors for effective population sizes (Ne) and coalescence times were changed for mitochondrial analyses to Ne = 1000–1000000 for ‘northern’, ‘southern’ and ancient populations, and Ne = 1000–2000000 for the population at the contact zone, and t1 = 10–20000 and t2 = 1000–2000000, as the fit of the observed and simulated data was poor when using the same priors as for the microsatellite data. Altogether, 4000000 data sets were simulated for both microsatellite and mitochondrial data. Figure 3. View largeDownload slide Historical models used for DIYABC analyses: North = north-eastern populations from Finland and Norway; Central = populations from the German zone of overlap (Pentzold et al., 2013); South = south-western population from the French Pyrenees. Figure 3. View largeDownload slide Historical models used for DIYABC analyses: North = north-eastern populations from Finland and Norway; Central = populations from the German zone of overlap (Pentzold et al., 2013); South = south-western population from the French Pyrenees. For calculation of time of divergence and time since admixture we assumed a mean generation time of approximately 2 years in tits [as applied by Hansson et al., 2014; cf. 1.5 years for the great tit, Parus major, in Qu et al. (2015) and 2.26 years for the willow tit, Poecile montanus, in Kvist et al. (2001)]. We expect time estimates inferred from mtDNA to correspond with the onset of lineage splitting and admixture caused by palaeoclimatic events more accurately, because nuclear loci will generally reach coalescence more slowly and at a later stage of evolution (Palumbi, Cipriano & Hare, 2001). Therefore, time estimates inferred from microsatellite data are generally considered to post-date palaeoclimatic events that triggered lineage divergence. Furthermore, the ratio between male to female gene flow (mm/mf) was calculated according to the equations in Hedrick, Allendorf & Baker (2013). The main assumptions implemented in this approach are that populations can be described according to the island model and that populations are in migration-drift equilibrium. The approach uses divergence levels caused by female gene flow as FST(f) values derived from mtDNA, and estimates divergence levels caused by male gene flow FST(m) from microsatellites (eqn. 7a in Hedrick et al., 2013). Both divergence levels were used to calculate mm/mf (eqn. 7b in Hedrick et al., 2013). Given the island model and migration drift equilibrium as basic assumptions, the estimates were alternately performed (1) for the total set of populations and (2) under exclusion of Mediterranean island populations for all continental Eurasian populations. RESULTS Microsatellite genotyping Deviations from HWE were predominately found at loci Parate 6 (six populations) and Parate 8 (seven populations), but also in single populations at loci PmaC25, Parate 15, Parate 16, Parate 3 and Parate 2. The occurrence of deviations from HWE at these loci was associated with the presence of null alleles (Table S1). Most deviations from HWE were found in the population from Schleswig-Holstein, which also had the highest positive FIS value (Table 2). Furthermore, in this population we found evidence of linkage disequilibrium among alleles at six loci (Table S1). None of the other study populations showed a signal of linkage disequilibrium except two island populations from Cyprus and Corsica (at two loci each; Table S1). Our analyses did not provide evidence of genotyping artefacts due to large allele dropout, and misscoring of genotypes due to stuttering was almost absent except at locus Parate 8 in a single population (Cyprus). These analyses suggested that loci Parate 6 and Parate 8 should be treated with caution in subsequent analyses (see below). Diversity and divergence Given the null allele bias at loci Parate 6 and Parate 8, we excluded these data from estimating diversity and divergence for 14 populations (N = 145). The mean allele number over loci varied between 9.9 (Schleswig-Holstein) and 4.5 (Central Asia; Table 2) due to variation in sample sizes. However, allelic richness corrected for differences in sample sizes was similar across populations (4.1–5.2), except for Sardinia, where it was considerably lower than in other populations (3.5; Table 2). Based on the expected heterozygosity (HE), the genetic variation appeared relatively high and fairly constant between the samples (0.75–0.86), excluding again Sardinia which had the lowest HE (0.63; Table 2). Most population samples showed moderate heterozygote deficit, as indicated by positive inbreeding coefficients (Table 2). This was most pronounced in the three German populations from Schleswig-Holstein, Harz and Saxony, but also in Norway. Non-hierarchical AMOVA indicated divergence between populations (FST = 0.065, P < 0.0001). Pairwise FST values were calculated (Table S2) and depicted as an UPGMA phenogram reflecting the existence of four clusters of populations (Fig. 4). There was a significantly high level of genetic divergence between these four groups as indicated by a hierarchical AMOVA (microsatellites: FCT = 0.076, P < 0.001; mtDNA: ΦCT = 0.662, P < 0.001). Two of the Mediterranean island populations exhibited the strongest divergence (Cyprus vs. all: FST = 0.13–0.28, Sardinia vs. all: FST = 0.10–0.26; Table S2). For Cyprus, this was apparent not only in terms of high FST values, but also by a considerably high accumulation of private alleles despite the small sample size (Table 2). In comparison, ΦST values inferred from the mtDNA data set (control-region sequences) were much higher than FST values from the microsatellite data, but likewise indicated the strongest divergence between island and continental populations (Cyprus vs. all: ΦST = 0.58–0.99, Sardinia vs. all: ΦST = 0.41–1.00). Figure 4. View largeDownload slide UPGMA phenogram inferred from pairwise FST values (scale bars: refers to FST distances, computed with MEGA v.6, and listed in Table S2). Figure 4. View largeDownload slide UPGMA phenogram inferred from pairwise FST values (scale bars: refers to FST distances, computed with MEGA v.6, and listed in Table S2). The continental populations can be considered as two population clusters (Fig. 4). North-eastern Eurasian populations (Russia and Fennoscandia) are divergent from the central and south-western European populations (Fig. 4). In general, pairwise FST values within these two groups were lower than between populations of the two groups (Table S2). Maximum divergence between the two continental groups was observed between southern European populations (Pyrenees, Greece) and north-eastern Palearctic populations (Russian Far East, Central Asia; FST = 0.075–0.106; Table S2). Lowest divergence was observed between German populations from the zone of overlap and all other continental Eurasian populations (FST = 0.00–0.088; Table S2). For the entire set of populations, the ratio between male to female gene flow (mm/mf) was estimated based on AMOVA performed for microsatellite data (FST = 0.065) and mtDNA (FST(f) = 0.667). These estimates suggest that male gene flow contributes less to the total divergence (FST(m) = 0.13) than female gene flow and mm/mf was 13.41. When we limited the analysis to Eurasian continental populations (under exclusion of island populations) the ratio increased slightly to 19.96 with FST = 0.029 (microsatellites), divergence caused by female gene flow FST(f) = 0.556 (mtDNA) and divergence caused by male gene flow FST(m) = 0.059 (microsatellites). Bayesian inference of population structure For the complete data set (N = 166) under the admixture – frequency-correlated model, Evanno’s ∆K separated two large clusters (K = 2) as the most plausible population structure (Fig. S1). There was a high level of admixture between these two groups all across Europe and only a few populations appeared to be pure representatives of either of the two clusters: (1) Central Asian and Far East Russian populations of the north-eastern cluster and (2) the island population from Sardinia of the south-western cluster. All continental European study populations included a high number of genetically admixed individuals. Using LOCPRIOR (classification according to mitochondrial lineages), a population structure with four genetic groups (K = 4) resulted as the most plausible situation (Fig. S1). Likewise, for the reduced population dataset (N = 145 for 14 local populations) four genetic clusters (K = 4) were identified by the ∆K method to be the most plausible scenario, independent of the model applied (with and without LOCPRIOR; Figs 5 and S1). The spatial differentiation pattern under K = 4 was as follows. In the West Palearctic, four groups were distinguished (Fig. 5): (1) the north-eastern Palearctic cluster including Central Asian, Far East Russian and Fennoscandian populations; (2) the south-western Palearctic cluster including all central and southern continental European populations; (3) the Mediterranean cluster comprising populations from Corsica and Sardinia; and (4) the Cypriot population. No signs of genetic admixture were found in Far East Russia, on Sardinia and on Cyprus (Fig. 5). Local genetic admixture was found all across Europe: (1) introgression of southern European alleles into Fennoscandian populations; (2) introgression of north-eastern Palearctic alleles into German populations, but (near) absence of north-eastern alleles in the Pyrenean and Greek populations (Fig. 5); and (3) wide admixture of two southern clusters (continental: orange; Mediterranean islands: yellow) and introgression of continental European alleles into the Corsican island population (but not into the Sardinian population; Fig. 5). Figure 5. View largeDownload slide Genetic variation of 14 Western European and Mediterranean coal tit populations (N = 145) based on 13 microsatellite loci; STRUCTURE analysis under the admixture – frequency-correlated model without locpriors a priori defined, STRUCTURE plots for K = 2 to K = 5 (left); threshold q > 0.8 for assignment of individuals to genetic clusters (according to Randi, 2008) indicated for the most plausible scenario of K = 4; coloured bars above the plots indicate individual assignment to three mitochondrial lineages (control region; data from Pentzold et al., 2013); grey bars above indicate regional origin of samples; right: (a) estimate of most plausible K = 4 according to Evanno et al. (2005; ΔK plotted against the number of modelled genetic clusters) and (b) according to L(K) (Pritchard et al., 2000). Abbreviations of populations: BF = Black Forest, Cor = Corsica, Cyp = Cyprus, PF = Palatine Forest, Sard = Sardinia, Sax = Saxony, Schl. Holstein = Schleswig-Holstein. Figure 5. View largeDownload slide Genetic variation of 14 Western European and Mediterranean coal tit populations (N = 145) based on 13 microsatellite loci; STRUCTURE analysis under the admixture – frequency-correlated model without locpriors a priori defined, STRUCTURE plots for K = 2 to K = 5 (left); threshold q > 0.8 for assignment of individuals to genetic clusters (according to Randi, 2008) indicated for the most plausible scenario of K = 4; coloured bars above the plots indicate individual assignment to three mitochondrial lineages (control region; data from Pentzold et al., 2013); grey bars above indicate regional origin of samples; right: (a) estimate of most plausible K = 4 according to Evanno et al. (2005; ΔK plotted against the number of modelled genetic clusters) and (b) according to L(K) (Pritchard et al., 2000). Abbreviations of populations: BF = Black Forest, Cor = Corsica, Cyp = Cyprus, PF = Palatine Forest, Sard = Sardinia, Sax = Saxony, Schl. Holstein = Schleswig-Holstein. Spatial clustering studied with GENELAND identified three clusters which exactly matched the phylogeographical pattern of three mitochondrial lineages: a north-eastern Palearctic cluster (Russian Far East, Central Asia, Fennoscandia), a south-western Palearctic cluster (central and southern Europe, including Corsica and Sardinia) and Cyprus (Fig. 6). This pattern was identical in nine out of ten model runs. In a single run, Corsica and Sardinia together represented one separate group whereas the north-eastern and south-western coal tits were united in a second cluster (the number of possible clusters in the analysis was fixed at K = 3). Both of the two spatially explicit admixture models that were run in TESS reflected the same population subdivision inferred by GENELAND (Figs 6 and S2). Neither TESS nor GENELAND separated populations from Corsica and Sardinia from the south-western mainland group. Figure 6. View largeDownload slide Spatial clustering of coal tit populations as inferred from GENELAND analysis; assignment probabilities of the individuals to spatial clusters identified by GENELAND displayed in a contour map for (A) the north-western, (B) the south-western and (C) the Cypriot cluster. The spatial membership probability is visualized by colour: bright yellow indicates a high-, dark red a low assignment probability; black dots: sampling localities. Figure 6. View largeDownload slide Spatial clustering of coal tit populations as inferred from GENELAND analysis; assignment probabilities of the individuals to spatial clusters identified by GENELAND displayed in a contour map for (A) the north-western, (B) the south-western and (C) the Cypriot cluster. The spatial membership probability is visualized by colour: bright yellow indicates a high-, dark red a low assignment probability; black dots: sampling localities. Admixture rate in the hybrid zone For both the mitochondrial and the microsatellite data set the best fit was for the admixture model (scenario 1, Fig. 3). However, parameter estimates inferred from microsatellite data do not seem reasonable given the extremely recent and unreliable estimates for times of divergence and time since admixture (over the last 600 years). For the mitochondrial data, scenario 1 was the best model with high support (posterior probabilities 0.9740 and 0.9983 for the direct and logistic regression approaches, respectively), also supported by all 40 summary statistics used. Type I and II errors were small; type I errors were 0.006 and 0.192 and type II errors were 0.011 and 0.049 for the direct and logistic approaches, respectively. Modes of the effective population sizes for Northern Europe (Norway and Finland) were 280000 [95% highest posterior density (HPD) = 105000–940000], for the hybrid zone 9820000 (95% HPD = 3120000–9910000) and for Southern Europe 595000 (95% HPD = 222000–981000). The admixture was estimated to have occurred 57600 generations ago (95% HPD = 12400–95900) with an admixture rate of 0.216 (95% HPD = 0.079–0.422) relative to the northern population and divergence of southern and northern lineages 1260000 generations ago (95% HPD = 521000–5710000). Applying a mean generation time of 2 years, our estimates correspond to a mean time of divergence of 2.52 mya (95% HPD 1.04–11.42 mya) and a mean time since admixture of 0.114 Mya (95% HPD 0.024–0.192 Mya). DISCUSSION Patterns of gene flow in Europe Despite considerable genetic differences, the European range of secondary overlap among south-western and north-eastern coal tit lineages does not match the general pattern of a narrow and geographically restricted secondary contact zone (Fig. 1A; cf. Haffer, 1989; Aliabadian, 2005). All German populations appeared to be genetically strongly admixed and introgression of south-western alleles extended northward into Fennoscandian populations, where corresponding (south-western) mtDNA haplotypes were absent in our study (but see Johnsen et al., 2010). A moderate deficit of heterozygotes and linkage disequilibrium in three German populations (strongest in Schleswig-Holstein) indicate local admixture of two diverged genetic lineages and are typically found in populations from the centre of a hybrid zone (Jiggins & Mallet, 2000; Alexandrino et al., 2005; Brelsford & Irwin, 2009). The actual eastward and southward extent of the contact zone is far from being fully described, because to a lesser degree the north-western alleles and haplotypes were present in southern European populations (Greece). Despite all the limitations of our sampling, northward allelic introgression and strong differences between pairwise ΦST and FST values hint to a wider spatial extent of nuclear gene flow as compared to mtDNA introgression. Such mito-nuclear discordance can arise from selection against hybrids and/or sterility of the F1 heterogametic sex (Haldane’s rule: Davies & Pomiankowski, 1995; Wu, Johnson & Palopoli, 1996) as suggested to be the case in other passerine hybrid zones (European Ficedula flycatchers: Tegelström & Gelter, 1990; Sætre et al., 2001; Qvarnström, Rice & Ellegren, 2010; Far East Russian great tits: Kvist & Rytkönen, 2006). However, in coal tits there was no evidence of hybrid sterility or selection against hybrids from cross-fostering experiments with individuals from European and Afghan populations (Löhrl, 1994). Therefore, we can rule out selection against hybrids in admixed European coal tit populations, too. Secondly, sex-biased dispersal is considered as another possible cause of mito-nuclear discordance (reviewed by Prugnolle & de Meeus, 2002; in birds: Kvist & Rytkönen, 2006; Illera et al., 2011; Lin, Jiang & Ding, 2011). Although the common paradigm of female-biased dispersal in birds (Clarke, Sæther & Roskaft, 1997; Petit & Excoffier, 2009) has recently been challenged (Li & Merilä, 2010; Both, Robinson & van der Jeugd, 2012; Dobson, 2013), there is only very scarce information on sex-specific dispersal distances for many bird species, including the coal tit (except Dietrich et al., 2003). Because of such data deficiency further evidence from field studies is required to substantiate assumptions on any putative correlation between sex-biased dispersal and mito-nuclear discordance in coal tits. Thirdly and lastly, extreme ratios between male to female gene flow might arise from stochastic effects when comparing different levels of genetic diversity, such as high allelic variation of microsatellite loci and sequence variation between deeply divergent lineages (Karl et al., 2012; Putman & Carbone, 2014). Due to relatively long coalescence times, incomplete lineage sorting of nuclear markers might blur spatial patterns of genetic variation. In the coal tit, this is reflected by strong admixture of two southern European allelic clusters (yellow and orange for K = 4; Fig. 5) in continental populations on the one hand and near-complete allelic lineage sorting in island populations of Corsica and Sardinia on the other. This is in accordance with low parameters of genetic variation on these islands and with the general assumption that density-dependent processes, such as founder effects and genetic drift, are most effective in island populations (Waters et al., 2013; birds: Padilla et al., 2015). Even over short evolutionary time spans, fast lineage sorting derived from ancestral polymorphisms in founder populations can occur in organisms with high dispersal ability, as inferred from a comparison of historical and extant Mediterranean populations of hawkmoths (Hyles; Mende & Hundsdoerfer, 2013). Genetic admixture on the European continent Extant phylogeographical patterns and lineage diversification in the coal tit are likely to have emerged from glacial range fragmentation (Martens et al., 2006; Pentzold et al., 2013) as suggested for other tit species (Kvist et al., 2003; Päckert et al., 2013; Stervander et al., 2015; Tritsch et al., 2017). Our time estimates inferred from the mitochondrial data set support a scenario of lineage divergence close to the Pliocene–Pleistocene boundary at a mean time of divergence of 2.5 Mya (in accordance with Päckert et al., 2012). Our mean coalescence-based estimate for time since admixture of 0.114 Mya pre-dates a Holocene post-glacial expansion and thus suggests that admixture of north-eastern and south-western gene pools could have already started in southern refuges during the late Pleistocene. This assumption is supported by sound evidence that northward dispersal of forest birds from Mediterranean refuges already started before the onset of the Holocene, because fossil remains of forest birds from interglacial periods have been found north of the Alps across Central Europe up to a latitude of 50°N (Holm & Svenning, 2014). Furthermore, mean coalescence time estimates are rather rough because there is no reliable empirical value for the generation time of coal tits and several authors have applied shorter generation times for tits (Garant et al., 2005; Qu et al., 2015) that would shift our time since admixture estimates closer to a Holocene expansion scenario. The wide range of mitochondrial introgression and nuclear gene flow in central European coal tits indicates a partial reversal of Pleistocene divergence patterns (for a similar case in North American chickadees see Manthey, Klicka & Spellman, 2012). The phylogeographical pattern in continental European coal tits matches a broad trans-European zone of intergradation at the subspecies level, similarly to that in Eurasian nuthatches, Sitta europaea (Red’kin & Konovalova, 2006). Unlike in the latter species, phenotypic variation of continental European coal tits is very subtle and body size parameters and plumage coloration vary along a pan-European cline with phenotypic extreme forms vieirae and abietum in the South and ater in the North (Niethammer, 1943; Wolters, 1968; Glutz von Blotzheim & Bauer, 1993; Martens, 2012). Furthermore, the vocal repertoire of coal tits is remarkably uniform throughout continental Eurasia and seems to provide a less effective premating barrier compared to songs of other tit species (Thielcke, 1973; Tietze et al., 2011; Pentzold et al., 2016). In contrast, in many cases of asymmetric gene flow across narrow hybrid zones among Holarctic bird taxa (regardless of their taxonomic rank), assortative mating seems to be associated with strong divergence of vocal repertoires (Helbig et al., 2001; Haavie et al., 2004; Päckert et al., 2005; Kvist & Rytkönen, 2006; Sattler, Sawaya & Braun, 2007; Vorkurková et al., 2013; Shipilina et al., 2017). Generally, separation of gene pools is strongly enhanced by the variation of morphological and behavioural traits that play a key role for species recognition, such as in Ficedula flycatchers (Sætre et al., 2001; Ellegren et al., 2012; Backström et al., 2013). In contrast, it seems that phenotypic and behavioural differentiation between northern and southern European coal tits is too subtle to provide an effective premating barrier. The same holds true for potential segregation of ecological or climatic niches in secondary contact (in tits and chickadees: Päckert et al., 2005; Zhao et al., 2012; Taylor et al., 2014). Webb et al. (2011) pointed out that merging of genetic lineages might be more likely to occur in generalist species having a lower probability of evolving unique adaptations. This argument may apply to the coal tits as well, because despite a strong adaptation to coniferous forests, they exploit a great variety of food resources. In those regions where the species has adapted to deciduous forests, coal tits use a broader range of the tree’s canopy and trunk compared with many other parid species (Glutz von Blotzheim & Bauer, 1993; Gosler & Clement, 2007). Habitat structure might also have a considerable effect on local population structure, because, in mixed conifer–broadleaved forests of Ussuriland (Far Eastern Russia), population densities of coal tits were estimated 2.5–3 times higher than in pure spruce–fir taiga forests of the upper mountain–forest belt (Nazarenko, 1984). Finally, there is in fact recent evidence of spatial variation in an adaptive trait of European coal tits. Schmoll & Kleven (2011) found differences in sperm size between coal tits from Norway and Germany, as was reported among European and Afro-Canarian blue tits (Cyanistes caeruleus and C. teneriffae; Gohli et al., 2015). Whether in blue tits these differences would constitute an effective post-mating barrier cannot be judged due to a lack of range overlap in the field and missing evidence from experimental studies. In European coal tits, intraspecific differences in sperm morphology do not seem to effectively prevent gene flow across the European contact zone. Allopatric differentiation on Mediterranean islands Typically, in the central areas of a species’ range, the degree of gene flow is often high, whereas it is low at the range margins (Kvist et al., 2007; Lehtonen et al., 2009; Küpper et al., 2012; Päckert et al., 2013). In widespread Palearctic bird species, greatest phylogeographical structure is often observed at the south-western range margins, for example in the Mediterranean and on the southern European peninsulae (revision in Stewart et al., 2010; birds: Tietze et al., 2011; Brambilla et al., 2008). Because genetic drift and lineage sorting are most effective in small isolated populations, genetic distinctiveness of island populations is a common phylogeographical pattern. At the global scale levels of vertebrate endemism are significantly higher on islands when compared to the same ecoregions on the adjacent mainland (Fa & Funk, 2007; Kier et al., 2009). In the coal tit, the population from Cyprus (cypriotes) stands out as a genetically and phenotypically distinctive form that dates back to a more ancient (but still Pleistocene) colonization (Pentzold et al., 2013). Phylogenetic studies have revealed complex circum-Mediterranean phylogeographical patterns including distinct island lineages on Cyprus (Voelker & Light, 2011) and highly distinctive populations and even endemic species or subspecies. Apart from the famous examples of the extinct megafauna from Cyprus (Hadjisterkotis & Masala, 1995), weak insular endemism has also been postulated for the extant Cypriot herpetofauna (Böhme & Wiedel, 1994) and the Cypriot avifauna (Förschler & Randler, 2009; Randler et al., 2012). Genetic distinctiveness of Corsican and Sardinian coal tit populations was less manifest than that of P. a. cypriotes. The shallow genetic divergence of P. a. sardus from its continental relatives (see also Tietze et al., 2011; Pentzold et al., 2013) contrasts with the long evolutionary histories of some Corso-Sardinian faunal elements (reviewed in Ketmaier & Caccone, 2013). Accordingly, rather ancient (pre-Pleistocene) Corso-Sardinian species-level lineages have been found in amphibians and reptiles (Salvi et al., 2010; Salvi, Pinho & Harris, 2017; Fritz, Corti & Päckert, 2012; Rodríguez et al., 2017). In a Corsican endemic frog species, Discoglossus montalentii, phylogeographical structure in microallopatry was found even within the island (Bisconti, Canestrelli & Nascetti, 2013). However, in highly mobile vertebrates, such as birds, several endemic species occur on these islands, such as the Corsican nuthatch, Sitta whiteheadi (Pasquet et al., 2014), and the Corsican finch, Carduelis corsicana (Förschler et al., 2009), which also breeds on the Balearic Islands and on a few smaller neighbouring islands. In addition, there are distinct genetic lineages at the subspecies level in other bird species (Pons et al., 2016). Subtle genetic admixture of the Corsican population might imply that this island does or did receive more influx from continental populations than the Sardinian population, for example due to its closer proximity to the mainland and along a north–south migratory pathway of migrants and/or dispersers. However, a greater number of local samplings from both islands would be needed to reliably confirm this hypothesis. Moreover, the dispersal behaviour of coal tits is quite variable (Löhrl, 1974; Glutz von Blotzheim & Bauer, 1993; Gosler & Clement, 2007) and seems to depend on the availability of food resources (Löhrl, 1974; Harrap & Quinn, 1996). On Corsica, the breeding phenology of coal tits has strongly adapted to local food peaks (Blondel, Chessel & Frochot, 1988) and such adaptive processes might effectively have contributed to the fixation of genetic lineages on islands, for example in Corsican blue tits (Cyanistes caeruleus ogliastrae; Porlier et al., 2012). Generally, the genetic composition of the Corsican coal tit population might be the result of both incomplete lineage sorting during a short separation time (Pentzold et al., 2013) and recent gene flow from irregular influx of continental vagrant individuals and/or dispersers. These examples demonstrate that the circum-Mediterranean phylogeographical pattern in the coal tit is partly or often paralleled in other island endemics of the Corso-Sardinian fauna. Traditionally, phenotypic distinctiveness has been a crucial factor for species delimitation and, in fact, distinct genetic lineages of the coal tit in North Africa (atlas subspecies group) and on Cyprus (subsp. cypriotes) are corroborated by differences in plumage coloration (Harrap & Quinn, 1996; Gosler & Clement, 2007) and partially by subtle differences in song (Tietze et al., 2011; Pentzold et al., 2016). A deeper understanding of the range-wide intraspecific differentiation in the coal tit will therefore benefit (1) from an integrative taxonomic approach and (2) from broad population sampling across gradients of genetic introgression (e.g. in narrow hybrid zones that exist for example in the Himalayas). SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher’s web-site. Figure S1. Genetic variation in the complete data set of the Western European and Mediterranean coal tits (N = 166) based on 13 microsatellite loci; STRUCTURE analysis under the admixture – frequency-correlated model with locpriors a priori defined (assignment according to mtDNA lineages), STRUCTURE plots for K = 2 to K = 4 (left); right: estimate of most plausible K according to Evanno et al. (2005; ΔK plotted against the number of modelled genetic clusters) (a) under the admixture – frequency-correlated model without locpriors a priori defined (K = 2) and (b) with locpriors a priori defined (K= 4); assignment according to mtDNA lineages, control region: coloured bars above the plots. Figure S2. Spatial clusters as inferred by the spatial explicit CAR admixture model of TESS (Kmax = 3). The TESS admixture models did not distinguish more than three units even if the number of possible clusters in a model is higher (Kmax > 3). The DIC criterion (arithmetic mean of ten replicates) confirmed three spatial clusters. Table S1. Observed and expected heterozygosity, departure from Hardy–Weinberg equilibrium, evidence of null alleles and linkage disequilibrium for each locus and population. Bonferroni-corrected P-value for HWE = 0.05/13 = 0.0038; Bonferroni-corrected P-value for linkage disequilibrium = 0.05/78 = 0.00064. Table S2. Matrix of pairwise FST values between all local populations (computed with Arlequin 3.1). Bold: significant FST values after Bonferroni correction for the number of comparisons at P = 0.05 (Bonferroni correction alpha (α) = 0.05/for 91 comparisons α = 0.0005). SHARED DATA Data deposited in the Dryad digital repository (Trisch et al., 2018). ACKNOWLEDGEMENTS We are particularly grateful to all colleagues who provided samples for molecular analyses: T. Dolich, M. Fischer (Naturkundemuseum Erfurt, Germany), A. Johnsen (Natural History Museum Oslo, Norway), T. Lubjuhn, P. Lymberakis (Natural History Museum of Crete), S. Martens, A. Ostastshenko, F. Schramm and H. Zang. We are particularly grateful to F. Spina for providing collection permits in Italy. B. Martens helped on various field trips. Travel and research funds for field trips by J.M. were granted by the Deutsche Ornithologen-Gesellschaft and the Gesellschaft für Tropenornithologie, by Feldbausch-Stiftung and Wagner-Stiftung (both University of Mainz, Germany) (various grants from all four). This project was substantially funded by the State Ministry of Science and Arts of Saxony (Staatsministerium für Wissenschaft und Kunst Sachsen, AZ 4-7531.50-02-621-09/1). 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Journal of Ornithology  149: 399– 413. Google Scholar CrossRef Search ADS   © 2018 The Linnean Society of London, Biological Journal of the Linnean Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biological Journal of the Linnean Society Oxford University Press

Gene flow in the European coal tit, Periparus ater (Aves: Passeriformes): low among Mediterranean populations but high in a continental contact zone

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Oxford University Press
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© 2018 The Linnean Society of London, Biological Journal of the Linnean Society
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0024-4066
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Abstract

Abstract Extant phylogeographical patterns of Palearctic terrestrial vertebrates are generally believed to have originated from glacial range fragmentation. Post-Pleistocene range expansions have led to the formation of secondary contact zones among genetically distinct taxa. For coal tits (Periparus ater), such a contact zone has been localized in Germany. In this study, we quantified gene flow between Fennoscandian and southern European coal tits using a set of 13 microsatellite loci. STRUCTURE analysis revealed four genetic clusters, two occurring on Mediterranean islands. German populations were genetically admixed but introgression of southern alleles was evident for Fennoscandian populations. In the south, we found negligible introgression of northern alleles (and haplotypes) but slight admixture of two southern genetic clusters in the Pyrenees and on the Balkan Peninsula and near complete sorting of these two allelic lineages on the islands of Corsica and Sardinia. Genetic distinctiveness of the Mediterranean island populations reflects general patterns of endemism in the Corso-Sardinian fauna and the Cypriot fauna. Wide-range gene flow in Central Europe suggests a broad zone of intergradation between subspecies of the coal tit rather than a narrow contact zone. This is in accordance with low morphological and bioacoustic differentiation of European coal tit populations. glacial refugia, island populations, microsatellites, phylogeography, subspecies INTRODUCTION Evolutionary biologists widely agree that the impact of glacial periods considerably shaped phylogeographical patterns and speciation of terrestrial vertebrates in the Palearctic (Avise & Walker, 1998; Hewitt, 2000, 2004; Lovette, 2005; Zink et al., 2008; Stewart et al., 2010). Pleistocene separation of Eastern and Western Palearctic populations led to divergence of gene pools among distant refugia, in a few extreme cases across a large extant distributional gap, such as seen in the marsh tit, Poecile palustris (Tritsch et al., 2017), or in the azure-winged magpie, Cyanopica cyanus (Zhang et al., 2012). Other east–west lineage splits dating back to Pleistocene events were reconstructed for example in corvids (Haring, Gamauf & Kryukov, 2007; Haring et al., 2012) and tits (Kvist et al., 2003; Päckert et al., 2005; Kvist & Rytkönen, 2006). One noticeable result from Holocene range expansion is the spatial overlap of genetically distinct populations that is manifested in secondary contact zones of highly variable extent (Woodruf, 1973; Haffer, 1989; Aliabadian et al., 2005). In Western Europe, the apparent spatial clustering of secondary contact zones among terrestrial vertebrate sister taxa was the result of post-glacial expansion from southern glacial refugia (Hewitt, 2000; Schmitt, 2007). Parapatry along sharp and narrow hybrid zones is typically found at geographical barriers, such as the European mountain systems that separate two larger glacial refugia from the continent: (1) the Iberian Peninsula in the Pyrenees (Fig. 1A, I; birds: Helbig et al., 2001; Pons et al., 2011; Backström, Sætre & Ellegren, 2013; Kuhn et al., 2013; reptiles: Milá et al., 2013; insects: Vazquez et al., 1994; Shuker et al., 2005; Bella et al., 2007; and (2) the Italian Peninsula in the Alps (Fig. 1A, II; birds: Hermansen et al., 2011; rodents: Sutter, Beysard & Heckel, 2013; Giménez et al., 2017; insects: Flanagan et al., 1999). Apart from parapatry across mountain ranges, narrow hybrid zones of a wide latitudinal extent exist in Central Europe (Fig. 1A), the best-studied examples being those of crows (Corvus c. corone, C. c. cornix: Haas et al., 2009; Haas, Knape & Brodin, 2010; Wolf et al., 2010; Poelstra et al., 2014; Poelstra, Ellegren & Wolf, 2014), other birds (Secondi et al., 2011), the house mouse (Mus m. musculus, M. m. domesticus: Macholán et al., 2008; Giménez et al., 2017) and hedgehogs (Erinaceus europaeus, E. roumanicus: Berggren et al., 2005; Bolfíková & Hulva, 2012; Waters, Fraser & Hewitt, 2013: fig. 2b; Pfäffle et al., 2014). Figure 1. View largeDownload slide Spatial patterns of secondary contact and hybrid zones among divergent genetic lineages (schematized haplotype networks) of terrestrial vertebrates in the Western Palearctic: (A) narrow contact zone along geographical barriers (Pyrenees, Alps) or of a wide latitudinal north–south extent; (B) broad intergradation zone often along phenotypic clines; (C) range-wide merger and local co-occurrence of distinct genetic lineages (reversal of past lineage divergence). Outline map of Europe inferred from www.freeworldmaps.net, accessed 18 April 2018. Figure 1. View largeDownload slide Spatial patterns of secondary contact and hybrid zones among divergent genetic lineages (schematized haplotype networks) of terrestrial vertebrates in the Western Palearctic: (A) narrow contact zone along geographical barriers (Pyrenees, Alps) or of a wide latitudinal north–south extent; (B) broad intergradation zone often along phenotypic clines; (C) range-wide merger and local co-occurrence of distinct genetic lineages (reversal of past lineage divergence). Outline map of Europe inferred from www.freeworldmaps.net, accessed 18 April 2018. In several bird species pairs, zones of secondary contact and hybridization are not restricted to a narrow band, but extend along a wide longitudinal range into Eastern Europe (Fig. 1B), such as found in flycatchers (Ficedula: Sætre et al., 2001; Hogner et al., 2012a), reed warblers (Acrocephalus: Reifová et al., 2016), nightingales (Luscinia: Vokurková et al., 2013), tits (Cyanistes: Woodruf, 1973; Stervander et al., 2015) and Old World buntings (Emberiza: Irwin, Rubtsov & Panov, 2009). In a few other examples, pre-mating barriers were either not established during a short separation time in refuge areas or they simply broke down in secondary contact, which led to merging of divergent genetic lineages. The signal from mitochondrial markers might then remain the only testimony of past (Pleistocene) lineage separation, presently contrasted by narrow or wide-range gene flow (Fig. 1C), as suggested for some passerine bird species (Zink et al., 2008; Päckert, Martens & Sun, 2010; Hogner et al., 2012b; Block et al., 2015). Interbreeding and merging of gene pools between cryptic genetic lineages of a phenotypically uniform species has been sometimes termed ‘speciation in reverse’ (e.g. in birds: Webb, Marzluff & Omland, 2011). However, in the strict sense, reverse speciation is more appropriately applied to those examples where gene pools become largely absorbed due to hybridization with one of the two parental species running the risk of going extinct (in fishes: Taylor et al., 2006; Seehausen et al., 2008; Hudson et al., 2013; Bhat et al., 2014; in Darwin’s finches: Kleindorfer et al., 2014). Our study focuses on the recent evidence of secondary range overlap in Western Europe among a north-eastern and a south-western mitochondrial lineage of the coal tit, Periparus ater (Pentzold et al., 2013). We aim to verify the extent and degree of nuclear gene flow among the two coal tit lineages using nuclear markers (microsatellites). We expect significant gene flow at least in the region of considerable mtDNA lineage overlap at a contact zone extending throughout Germany (Fig. 2). We also expect nuclear gene flow to extend across a wider range than mitochondrial introgression, as was shown for another parid hybrid zone (Parus major/P. minor: Kvist & Rytkönen, 2006). Figure 2. View largeDownload slide Distribution range and phylogeographical pattern of the coal tit, Periparus ater (modified from Pentzold et al., 2013; sampling sites of mtDNA data indicated by black dots; range boundaries in light brown according to BirdLife International, 2017); pie charts indicate percentages of haplotypes belonging to four different clusters (Scandinavia/Russia, W and SW Europe, North Africa and Cyprus; subspecies included in the mtDNA dataset are listed at the corresponding clusters of the haplotype network, upper right) indicated by different colours. Study populations (abbreviations): BF = Black Forest, Cors = Corsica, Cyp = Cyprus, Fin = Finland, Grec = Greece, Mor = Morocco, Nor = Norway, PF = Palatine Forest, Pyr = French Pyrenees, Sard = Sardinia, Sax = Saxony, SH = Schleswig-Holstein (strongly divergent North African subspecies atlas not included in our population genetic study); coal tit drawing: K. Rehbinder, University of Mainz. Figure 2. View largeDownload slide Distribution range and phylogeographical pattern of the coal tit, Periparus ater (modified from Pentzold et al., 2013; sampling sites of mtDNA data indicated by black dots; range boundaries in light brown according to BirdLife International, 2017); pie charts indicate percentages of haplotypes belonging to four different clusters (Scandinavia/Russia, W and SW Europe, North Africa and Cyprus; subspecies included in the mtDNA dataset are listed at the corresponding clusters of the haplotype network, upper right) indicated by different colours. Study populations (abbreviations): BF = Black Forest, Cors = Corsica, Cyp = Cyprus, Fin = Finland, Grec = Greece, Mor = Morocco, Nor = Norway, PF = Palatine Forest, Pyr = French Pyrenees, Sard = Sardinia, Sax = Saxony, SH = Schleswig-Holstein (strongly divergent North African subspecies atlas not included in our population genetic study); coal tit drawing: K. Rehbinder, University of Mainz. MATERIAL AND METHODS Study species Eight divergent mitochondrial lineages are presently known in the coal tit. These lineages are distributed across large parts of the Palearctic, the mountain forests of China, the Himalayas, Karakoram and Hindu Kush as well as on Taiwan (Tietze et al., 2011). Across the Western Palearctic, four distinct mtDNA lineages of the coal tit occur (Fig. 2): (1) the north-eastern Palearctic (ater subspecies group; distributed from Northern Europe across the Eurasian continent to the Pacific coast and Japan); (2) Central and Southern Europe (abietum subspecies group) including the British Isles and the islands of Corsica and Sardinia; (3) North Africa; and (4) Cyprus (Martens, Tietze & Sun, 2006; Tietze et al., 2011; Pentzold et al., 2013). To date, range overlap of the south-western abietum and the north-eastern ater lineages has been documented mainly for German populations (Fig. 2). Due to a lack of reliable morphological and bioacoustic distinctiveness of north-eastern versus south-western Palearctic coal tits, the spatial dimension of the contact zone cannot be delimited by geographical variation of phenotypes or song types (Tietze et al., 2011; Pentzold et al., 2013, 2016). Sampling and multilocus genotyping We sampled 166 birds from Russia, Kyrgyzstan, Kazakhstan, Finland, Norway, Germany, French Pyrenees, Cyprus, Corsica and Sardinia. DNA preparation was conducted either using the innuPREP DNA Mini Kit (muscle tissue: Analytik Jena AG, Germany) or the PEQLAB GOLD Blood DNA Mini Kit (blood samples: PEQLAB Biotechnologie GmbH, Germany), following the manufacturers’ recommendations. New microsatellite loci for P. ater were identified by Ecogenics GmbH (Switzerland) based on an enriched DNA library. Size-selected genomic DNA was ligated into SNXforward/SNX reverse-linker (Hamilton et al., 1999) and enriched by magnetic bead selection with biotin-labelled oligonucleotide repeats [(CT)13, (GT)13, (GTAT)7, (GATA)7; Gautschi, Widmer & Koella, 2000a; Gautschi et al., 2000b]. A total number of 528 recombinant colonies were screened and 415 gave a positive signal after dot-blot hybridization. Plasmids from 48 positive clones were Sanger sequenced and primers were designed for 16 microsatellite inserts, of which nine (listed in Table 1) were finally used for amplification of polymorphic microsatellite loci using the protocol described by Schuelke (2000). For this protocol an M13(-21) tail (18 bp) was adhered to the 5′ end of the forward primer. The reverse primer remained unmodified. In addition to the two regular PCR primers, a fluorescently labelled universal M13(-21) primer was added to the reaction mixture. The reaction contained 10–40 ng of template DNA, 0.04 µM of the M13-forward primer, 0.16 µM of the reverse primer and the labelled M13 primer, 0.2 mM of each dNTP, 1 µL of 10× PCR reaction buffer ‘complete’ and 0.5 units of DFS-Taq DNA polymerase (Bioron GmbH, Germany) in a total volume of 10 µL. The thermo-treatment consisted of two successive steps: (1) amplification of the microsatellite fragment with M13 fusion and (2) labelling of the fragment with the fluorescent dye. The PCR programme was 95 °C for 10 min followed by 30 cycles of 30 s at 95 °C, 45 s at 50 °C (Parate 8) or 56 °C (all other Parate loci) and 45 s at 72 °C (step 1), followed by eight cycles of 30 s at 95 °C, 45 s at 53 °C and 45 s at 72 °C (step 2) and a final elongation at 72 °C for 30 min. Table 1. Characteristics and variation of nine newly identified microsatellite loci for population genetic analysis of coal tits Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  The Parate loci were amplified using the protocol described by Schuelke (2000). For this protocol an 18-bp-long M13(-21) tail (5′-TGTAAAACGACGGCCAGT-3′) was adhered to the 5′ end of the forward primer (= Label). *Based on sequence clone. †Annealing temperature based on experimental optimization during microsatellite primer design. ‡Minimum and maximum sizes based on analyses of 14 populations View Large Table 1. Characteristics and variation of nine newly identified microsatellite loci for population genetic analysis of coal tits Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  Locus  Primer sequence (5′3′)  GenBank accession number  Repeat motif*  Ta (°C)†  Allele size (bp)‡  Newly designed primers  Parate 01  F: (Label) TCCTGGAGCACATTATGTCTATG  MG970333  (TAGA)14 (CAGA)4  56  209–271  R: AATCTGCTGCTCCATACTTGG  Parate 02  F: (Label)-AGGGACAGAATTGTGCAAGG  MG970334  (ATCT)14  56  189–373  R: TGCATTCATGCATACATAGACAC  Parate 03  F: Label-TGTTGTCTGCAAAAGGCAAG  MG970335  (ATAG)14  56  117–177  R: CAAAGCCTTCATCTGCTTGG  Parate 06  F: (Label)-TTCAGTGCAGGTGCATAATTG  MG970336  (CTAT)15  56  219–355  R: GGCCAAGAGAAGTAGGGTGTAG  Parate 07  F: (Label)-CTCCCAAGAGAGTCTGTGTCG  MG970337  (CTAT)12  56  166–199  R: AAGGCTTTTGAAACAGGAGAAG  Parate 08  F: (Label)-TTGTAACGACCTTGCACCTC  MG970338  (CA)20  50  90–153  R: AGGCAGTAAAACCCTCATGG  Parate 09  F: (Label)-GGCACAGATGCATATTTTGTTTAC  MG970339  (GT)13  56  122–136  R: TGCACAATCATGCTTAATCCTC  Parate 15  F: (Label)-TCACAAAAAGGCATTTGCAG  MG970340  (TC)12(C)4(TC)7CC(TC)4  56  129–204  R: GGAGACAGGAGAGCAGCAAC  Parate 16  F. (Label)-CTTTCTTGAATGCTCAGATTGC  MG970342  (CT)27  56  166–263  R: CAAGCCCATGTTCAAGGTTC  Primers from previous studies  Pat2-43  F: ACAGGTAGTCAGAAATGGAAAG  – Otter et al. (1998)  (CT)n  60  126–213  R: (Label)-GTATCCAGAGTCTTTGCTGATG  PmaTGAn33  F: (Label)-TTCCCCAAGTATCCTGCATC  AY260539 Saladin et al. (2003)  (GATA)14GAT(GATA)8  57  258–398  R: AAACCATATCACCCAGTGCC  Pma69u  F: (Label)-CCCAGACAAAGCATCACTGG  AB094107 Kawano (2003)  (TG)6  57  214–222  R: GACAGTTCACATAGCCCTGG  PmaC25  F: CGTCCTGCTGTTTGTATTTCTG  AY260526 (Saladin et al., 2003)  (GAT)11  57  313–349  R: (Label)-CCATGAACCATTTTTAGGGTG  The Parate loci were amplified using the protocol described by Schuelke (2000). For this protocol an 18-bp-long M13(-21) tail (5′-TGTAAAACGACGGCCAGT-3′) was adhered to the 5′ end of the forward primer (= Label). *Based on sequence clone. †Annealing temperature based on experimental optimization during microsatellite primer design. ‡Minimum and maximum sizes based on analyses of 14 populations View Large Four additional primer pairs targeting microsatellite loci were obtained from previous studies on Poecile atricapillus (Table 1). We tested for cross amplification with P. ater samples in a total volume of 10 µL containing 10–40 ng of template DNA, 0.3 µM of each primer, 0.2 mM of each dNTP, 1 µL of 10× PCR reaction buffer ‘complete’ and 0.5 units of DFS-Taq DNA polymerase (Bioron). The thermo-cycling protocol was as follows: 94 °C for 5 min followed by 35 cycles of 94 °C for 30 s, 60 °C (Pat2-43) or 57 °C (PmaC25, PmaTGAn33, Pma69) for 30 s, 72 °C for 45 s and a final elongation at 72 °C for 5 min. Specimens were genotyped at 13 loci (Table 1) using the reaction conditions outlined above and labelled PCR fragments were run on a 16-column ABI 3130xl capillary sequencer (Applied Biosystems). The alleles were scored using the STRand Analysis Software v.2.4 (UC Davis, veterinary genetics lab, http://www.vgl.ucdavis.edu/STRand;Toonen & Hughes, 2001). The software package MICROCHECKER 2.2.3 (van Oosterhout et al., 2004) was used to test the probability that experimental errors occurred during microsatellite genotyping, i.e. large allelic dropout, scoring errors due to misinterpretation of stutter bands and null alleles. Diversity and divergence Summaries of allele sizes and the existence and frequencies of population-specific alleles (private alleles) were calculated using the program CONVERT v. 1.31 (Glaubitz, 2004), which was also used to generate input files for various software packages. Linkage between loci was determined using ARLEQUIN v.3.5.1.3 (Excoffier, Laval & Schneider, 2005). The same software package was used to calculate locus-specific observed and expected heterozygosities (HO, HE) for each sample population and to test for locus-specific deviations from Hardy–Weinberg equilibrium (HWE). Population-specific deviations from HWE (excess or deficiency of heterozygosity) across all loci were explored using inbreeding coefficients (FIS), calculated with the software FStat (v.2.9.3.2; Goudet, 1995) using a randomization test (3600 randomizations) to test for significance. The same software was used to estimate the mean number of alleles per locus and populations as well as the mean allelic richness (AR) per population across all loci. For these, we analysed a reduced dataset consisting of 145 specimens that belong to 14 distinct populations with a minimum of five specimens per population (Table 2 and Supporting Information, Table S1). Table 2. Diversity parameters and inbreeding coefficients (FIS) for the surveyed population samples of in total 145 individuals Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Loci Parate 06 and Parate 08 were excluded due to the presence of null alleles. N = number of sampled indifiduals, HO = observed heterozygosity, HE = expected heterozygosity. *Far East = pooled samples from Far East Russia (N = 11) and Japan (N = 2). †Schleswig-Holstein = pooled samples from Itzehoe (N = 15), Amrum (N = 3) and Brillit (N = 1). View Large Table 2. Diversity parameters and inbreeding coefficients (FIS) for the surveyed population samples of in total 145 individuals Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Population number  Population  N  Mean number of alleles per locus  Mean allelic richness  Private alleles  Mean HO  Mean HE  FIS  P-value (FIS)  1  Central Asia  5  4.455  4.089  1  0.777  0.753  −0.036  0.692  2  Far East*  13  7.818  4.717  5  0.825  0.814  −0.015  0.688  3  Finland  11  8.455  4.993  10  0.807  0.832  0.031  0.204  4  Norway  13  8.545  4.992  7  0.769  0.839  0.087  0.016  5  Schleswig-Holstein†  19  9.909  5.026  7  0.740  0.833  0.115  0.0003  6  Harz  12  8.273  4.886  2  0.758  0.822  0.082  0.018  7  Saxony  5  5.364  4.770  4  0.755  0.824  0.094  0.080  8  Palatine Forest  7  7.364  5.237  3  0.831  0.848  0.022  0.373  9  Black Forest  8  7.364  5.175  3  0.849  0.860  0.014  0.336  10  Pyrenees  13  8.636  4.803  4  0.816  0.819  0.003  0.473  11  Greece  12  7.909  4.718  14  0.760  0.807  0.062  0.084  12  Corsica  10  6.273  4.249  6  0.755  0.765  0.015  0.414  13  Sardinia  10  4.818  3.462  1  0.605  0.630  0.042  0.240  14  Cyprus  7  5.182  4.059  13  0.786  0.771  −0.020  0.683  Loci Parate 06 and Parate 08 were excluded due to the presence of null alleles. N = number of sampled indifiduals, HO = observed heterozygosity, HE = expected heterozygosity. *Far East = pooled samples from Far East Russia (N = 11) and Japan (N = 2). †Schleswig-Holstein = pooled samples from Itzehoe (N = 15), Amrum (N = 3) and Brillit (N = 1). View Large Divergence between populations was estimated using F-statistics (inferred from microsatellite allele frequencies) and Φ-statistics (inferred from mitochondrial nucleotide sequences) by pairwise FST and ΦST values as well as by non-hierarchical and hierarchical locus-by-locus analysis of molecular variance (AMOVA with FCT and ΦCT values as a measure of divergence among groups) using ARLEQUIN. In total, 20000 permutations were performed to test for significance of these values. All P-values obtained from tests implementing multiple comparisons (i.e. test for deviation from HWE, test for linkage between loci) were Bonferroni corrected to adjust the significance threshold (Rice, 1989). To depict divergence between populations, pairwise FST values were used in a distance matrix to construct a UPGMA phenogram with MEGA v.6 (Tamura et al., 2013). Bayesian inference of the population structure Non-spatial Bayesian inference of population structure was performed using the software package STRUCTURE v.2.3.3 (Pritchard, Stephens & Donnelly, 2000; Falush, Stephens & Pritchard, 2003, 2007). STRUCTURE runs were performed (1) under the a priori assumption of genetic admixture and correlated allele frequencies and (2) under a LOCPRIOR model that allows for classification of the individuals into groups, which are given to the algorithm as an a priori parameter (Hubisz et al., 2009). The model was run under two different LOCPRIOR settings: (1) by classifying the individuals of the complete data set (N = 166) according to their assignment to mitochondrial lineages (inferred from the data set of Pentzold et al., 2013); and (2) by assigning the individuals to 14 local populations of N ≥ 5 (total sampling N = 145, Table 2). All STRUCTURE runs were conducted for 1–10 putative genetic clusters (K) with ten replicates for each value of K. The number of Markov chain Monte Carlo (MCMC) runs was 105 with a burn-in period of 25000 throughout all model runs. For further processing of the STRUCTURE output, STRUCTURE HARVESTER (Earl & von Holdt, 2012) was used. To select the most likely number of genetic clusters (K), we used the approach of Evanno, Regnaut & Goudet (2005). STRUCTURE analysis was also used to estimate the extent of genetic admixture in different populations according to the method described by Randi (2008). Accordingly, we used a threshold q > 0.80 for the assignment of individuals to a cluster or we classified individuals as admixed individuals, if the proportion of membership was q < 0.80 (see Randi, 2008). Spatial Bayesian clustering was performed using the software packages TESS v.2.3.1 (Chen et al., 2007) and GENELAND v.4.03 (Guillot, Mortier & Estoup, 2005b). Unlike the non-spatial models of STRUCTURE, the spatially explicit models implemented in TESS and GENELAND consider the geographical coordinates of the samples, but do not consider affiliation as a model parameter. Data exploration under different models is recommended, because the explanatory power of spatial versus non-spatial models depends on demographic scenarios to be tested and clustering output from one model-based method might reveal a finer scaled spatial structure that other models fail to detect (François & Durand, 2010). Both admixture models in TESS (BYM and CAR models; Durand et al., 2009a; Durand, Chen & François, 2009b) were run with the complete dataset of N = 166 individuals (MCMC iterations: 105, burn-in period: 20000, Kmax: 2–10, five replicates for each Kmax for both models). Unlike STRUCTURE or TESS the standard models of GENELAND do not account for admixture, but assign posterior probabilities of cluster membership to single individuals. A benefit of GENELAND is that it can correct for the occurrence of null alleles. Although from a biological perspective it seems obvious to assume the allele frequencies to be correlated between populations, the respective model was assessed to systematically overestimate the number of clusters (Guillot et al., 2005a). Hence according to the authors’ recommendations the analysis was conducted in two steps: first, resolving the number of populations (K) using the D model (frequency model = uncorrelated; K: 1–10); second, deriving the correct population assignment by applying the F model (frequency model = correlated) with a fixed number of K (which was determined in the first step, Guillot et al., 2005a; GENELAND Development Group, 2012). The model parameters were: 106 MCMC iterations, Thinning = 1000, Null allele model = TRUE and ten replicates per analysis step. The outputs of both STRUCTURE and TESS were further processed with CLUMPP (Jakobsson & Rosenberg, 2007). For visualization, DISTRUCT (Rosenberg, 2004) was used. Admixture rate in the hybrid zone For estimating admixture rate in the hybrid zone we applied demographic modelling based on Approximate Bayesian Computation (ABC) using the program DIYABC v.2.0.4 (Cornuet, Ravigné & Estoup, 2010). We first used both microsatellite and mitochondrial control-region sequences and included individuals for which both data were available. We chose samples from Norway and Finland (N = 18) with q-values from STRUCTURE above 0.8 to their cluster ‘ater’ to represent the ‘northern’ parental population and samples from the French Pyrenees with q-values from STRUCTURE above 0.8 to their cluster ‘abietum’ to represent the ‘southern’ (N = 8) parental population. This was based on further evidence that these populations represented only one mitochondrial lineage each and were clearly separated from, but still related to, the admixture populations in the STRUCTURE analysis from the microsatellite data. In the central European admixture population, we included samples from Schleswig-Holstein, Harz, Saxony, Palatine Forest and Black Forest (N = 47, all Germany). We started by constructing four historical models (Fig. 3): (1) the parental ‘northern’ population and ‘southern’ population were split from each other at time t2 and come into contact at time t1 to form the admixture population; (2) the parental populations split first from each other and the central European population was split later from the northern population; (3) the northern and central populations split first from each other and the southern population was split later from the northern population; and (4) the parental populations split first from each other and the central European population was split later from the southern population. The mutation rate for the microsatellite data was set to 10–4–10–3 as was done with another tit species, the blue tit, Cyanistes caeruleus (Hansson et al., 2014). For the mitochondrial sequences, we applied the substitution rate 1.156 × 10–8 calibrated for the coal tit control region by Pentzold et al. (2013), and HKY+gamma model with gamma = 0.09 (as suggested by the test for the substitution model implemented in MEGA v.6.06; Tamura et al., 2013). As the fit of the observed data with the simulated data was poor, we next performed the same analysis separately for the microsatellite data and the mitochondrial data. For the microsatellite data, uniform prior distributions for effective population sizes were set to 10–10000, and for coalescence times t1 was set to 10–1000 and t2 to 10–4000. The uniform prior distribution for the admixture rate was 0.001–0.999. The priors for effective population sizes (Ne) and coalescence times were changed for mitochondrial analyses to Ne = 1000–1000000 for ‘northern’, ‘southern’ and ancient populations, and Ne = 1000–2000000 for the population at the contact zone, and t1 = 10–20000 and t2 = 1000–2000000, as the fit of the observed and simulated data was poor when using the same priors as for the microsatellite data. Altogether, 4000000 data sets were simulated for both microsatellite and mitochondrial data. Figure 3. View largeDownload slide Historical models used for DIYABC analyses: North = north-eastern populations from Finland and Norway; Central = populations from the German zone of overlap (Pentzold et al., 2013); South = south-western population from the French Pyrenees. Figure 3. View largeDownload slide Historical models used for DIYABC analyses: North = north-eastern populations from Finland and Norway; Central = populations from the German zone of overlap (Pentzold et al., 2013); South = south-western population from the French Pyrenees. For calculation of time of divergence and time since admixture we assumed a mean generation time of approximately 2 years in tits [as applied by Hansson et al., 2014; cf. 1.5 years for the great tit, Parus major, in Qu et al. (2015) and 2.26 years for the willow tit, Poecile montanus, in Kvist et al. (2001)]. We expect time estimates inferred from mtDNA to correspond with the onset of lineage splitting and admixture caused by palaeoclimatic events more accurately, because nuclear loci will generally reach coalescence more slowly and at a later stage of evolution (Palumbi, Cipriano & Hare, 2001). Therefore, time estimates inferred from microsatellite data are generally considered to post-date palaeoclimatic events that triggered lineage divergence. Furthermore, the ratio between male to female gene flow (mm/mf) was calculated according to the equations in Hedrick, Allendorf & Baker (2013). The main assumptions implemented in this approach are that populations can be described according to the island model and that populations are in migration-drift equilibrium. The approach uses divergence levels caused by female gene flow as FST(f) values derived from mtDNA, and estimates divergence levels caused by male gene flow FST(m) from microsatellites (eqn. 7a in Hedrick et al., 2013). Both divergence levels were used to calculate mm/mf (eqn. 7b in Hedrick et al., 2013). Given the island model and migration drift equilibrium as basic assumptions, the estimates were alternately performed (1) for the total set of populations and (2) under exclusion of Mediterranean island populations for all continental Eurasian populations. RESULTS Microsatellite genotyping Deviations from HWE were predominately found at loci Parate 6 (six populations) and Parate 8 (seven populations), but also in single populations at loci PmaC25, Parate 15, Parate 16, Parate 3 and Parate 2. The occurrence of deviations from HWE at these loci was associated with the presence of null alleles (Table S1). Most deviations from HWE were found in the population from Schleswig-Holstein, which also had the highest positive FIS value (Table 2). Furthermore, in this population we found evidence of linkage disequilibrium among alleles at six loci (Table S1). None of the other study populations showed a signal of linkage disequilibrium except two island populations from Cyprus and Corsica (at two loci each; Table S1). Our analyses did not provide evidence of genotyping artefacts due to large allele dropout, and misscoring of genotypes due to stuttering was almost absent except at locus Parate 8 in a single population (Cyprus). These analyses suggested that loci Parate 6 and Parate 8 should be treated with caution in subsequent analyses (see below). Diversity and divergence Given the null allele bias at loci Parate 6 and Parate 8, we excluded these data from estimating diversity and divergence for 14 populations (N = 145). The mean allele number over loci varied between 9.9 (Schleswig-Holstein) and 4.5 (Central Asia; Table 2) due to variation in sample sizes. However, allelic richness corrected for differences in sample sizes was similar across populations (4.1–5.2), except for Sardinia, where it was considerably lower than in other populations (3.5; Table 2). Based on the expected heterozygosity (HE), the genetic variation appeared relatively high and fairly constant between the samples (0.75–0.86), excluding again Sardinia which had the lowest HE (0.63; Table 2). Most population samples showed moderate heterozygote deficit, as indicated by positive inbreeding coefficients (Table 2). This was most pronounced in the three German populations from Schleswig-Holstein, Harz and Saxony, but also in Norway. Non-hierarchical AMOVA indicated divergence between populations (FST = 0.065, P < 0.0001). Pairwise FST values were calculated (Table S2) and depicted as an UPGMA phenogram reflecting the existence of four clusters of populations (Fig. 4). There was a significantly high level of genetic divergence between these four groups as indicated by a hierarchical AMOVA (microsatellites: FCT = 0.076, P < 0.001; mtDNA: ΦCT = 0.662, P < 0.001). Two of the Mediterranean island populations exhibited the strongest divergence (Cyprus vs. all: FST = 0.13–0.28, Sardinia vs. all: FST = 0.10–0.26; Table S2). For Cyprus, this was apparent not only in terms of high FST values, but also by a considerably high accumulation of private alleles despite the small sample size (Table 2). In comparison, ΦST values inferred from the mtDNA data set (control-region sequences) were much higher than FST values from the microsatellite data, but likewise indicated the strongest divergence between island and continental populations (Cyprus vs. all: ΦST = 0.58–0.99, Sardinia vs. all: ΦST = 0.41–1.00). Figure 4. View largeDownload slide UPGMA phenogram inferred from pairwise FST values (scale bars: refers to FST distances, computed with MEGA v.6, and listed in Table S2). Figure 4. View largeDownload slide UPGMA phenogram inferred from pairwise FST values (scale bars: refers to FST distances, computed with MEGA v.6, and listed in Table S2). The continental populations can be considered as two population clusters (Fig. 4). North-eastern Eurasian populations (Russia and Fennoscandia) are divergent from the central and south-western European populations (Fig. 4). In general, pairwise FST values within these two groups were lower than between populations of the two groups (Table S2). Maximum divergence between the two continental groups was observed between southern European populations (Pyrenees, Greece) and north-eastern Palearctic populations (Russian Far East, Central Asia; FST = 0.075–0.106; Table S2). Lowest divergence was observed between German populations from the zone of overlap and all other continental Eurasian populations (FST = 0.00–0.088; Table S2). For the entire set of populations, the ratio between male to female gene flow (mm/mf) was estimated based on AMOVA performed for microsatellite data (FST = 0.065) and mtDNA (FST(f) = 0.667). These estimates suggest that male gene flow contributes less to the total divergence (FST(m) = 0.13) than female gene flow and mm/mf was 13.41. When we limited the analysis to Eurasian continental populations (under exclusion of island populations) the ratio increased slightly to 19.96 with FST = 0.029 (microsatellites), divergence caused by female gene flow FST(f) = 0.556 (mtDNA) and divergence caused by male gene flow FST(m) = 0.059 (microsatellites). Bayesian inference of population structure For the complete data set (N = 166) under the admixture – frequency-correlated model, Evanno’s ∆K separated two large clusters (K = 2) as the most plausible population structure (Fig. S1). There was a high level of admixture between these two groups all across Europe and only a few populations appeared to be pure representatives of either of the two clusters: (1) Central Asian and Far East Russian populations of the north-eastern cluster and (2) the island population from Sardinia of the south-western cluster. All continental European study populations included a high number of genetically admixed individuals. Using LOCPRIOR (classification according to mitochondrial lineages), a population structure with four genetic groups (K = 4) resulted as the most plausible situation (Fig. S1). Likewise, for the reduced population dataset (N = 145 for 14 local populations) four genetic clusters (K = 4) were identified by the ∆K method to be the most plausible scenario, independent of the model applied (with and without LOCPRIOR; Figs 5 and S1). The spatial differentiation pattern under K = 4 was as follows. In the West Palearctic, four groups were distinguished (Fig. 5): (1) the north-eastern Palearctic cluster including Central Asian, Far East Russian and Fennoscandian populations; (2) the south-western Palearctic cluster including all central and southern continental European populations; (3) the Mediterranean cluster comprising populations from Corsica and Sardinia; and (4) the Cypriot population. No signs of genetic admixture were found in Far East Russia, on Sardinia and on Cyprus (Fig. 5). Local genetic admixture was found all across Europe: (1) introgression of southern European alleles into Fennoscandian populations; (2) introgression of north-eastern Palearctic alleles into German populations, but (near) absence of north-eastern alleles in the Pyrenean and Greek populations (Fig. 5); and (3) wide admixture of two southern clusters (continental: orange; Mediterranean islands: yellow) and introgression of continental European alleles into the Corsican island population (but not into the Sardinian population; Fig. 5). Figure 5. View largeDownload slide Genetic variation of 14 Western European and Mediterranean coal tit populations (N = 145) based on 13 microsatellite loci; STRUCTURE analysis under the admixture – frequency-correlated model without locpriors a priori defined, STRUCTURE plots for K = 2 to K = 5 (left); threshold q > 0.8 for assignment of individuals to genetic clusters (according to Randi, 2008) indicated for the most plausible scenario of K = 4; coloured bars above the plots indicate individual assignment to three mitochondrial lineages (control region; data from Pentzold et al., 2013); grey bars above indicate regional origin of samples; right: (a) estimate of most plausible K = 4 according to Evanno et al. (2005; ΔK plotted against the number of modelled genetic clusters) and (b) according to L(K) (Pritchard et al., 2000). Abbreviations of populations: BF = Black Forest, Cor = Corsica, Cyp = Cyprus, PF = Palatine Forest, Sard = Sardinia, Sax = Saxony, Schl. Holstein = Schleswig-Holstein. Figure 5. View largeDownload slide Genetic variation of 14 Western European and Mediterranean coal tit populations (N = 145) based on 13 microsatellite loci; STRUCTURE analysis under the admixture – frequency-correlated model without locpriors a priori defined, STRUCTURE plots for K = 2 to K = 5 (left); threshold q > 0.8 for assignment of individuals to genetic clusters (according to Randi, 2008) indicated for the most plausible scenario of K = 4; coloured bars above the plots indicate individual assignment to three mitochondrial lineages (control region; data from Pentzold et al., 2013); grey bars above indicate regional origin of samples; right: (a) estimate of most plausible K = 4 according to Evanno et al. (2005; ΔK plotted against the number of modelled genetic clusters) and (b) according to L(K) (Pritchard et al., 2000). Abbreviations of populations: BF = Black Forest, Cor = Corsica, Cyp = Cyprus, PF = Palatine Forest, Sard = Sardinia, Sax = Saxony, Schl. Holstein = Schleswig-Holstein. Spatial clustering studied with GENELAND identified three clusters which exactly matched the phylogeographical pattern of three mitochondrial lineages: a north-eastern Palearctic cluster (Russian Far East, Central Asia, Fennoscandia), a south-western Palearctic cluster (central and southern Europe, including Corsica and Sardinia) and Cyprus (Fig. 6). This pattern was identical in nine out of ten model runs. In a single run, Corsica and Sardinia together represented one separate group whereas the north-eastern and south-western coal tits were united in a second cluster (the number of possible clusters in the analysis was fixed at K = 3). Both of the two spatially explicit admixture models that were run in TESS reflected the same population subdivision inferred by GENELAND (Figs 6 and S2). Neither TESS nor GENELAND separated populations from Corsica and Sardinia from the south-western mainland group. Figure 6. View largeDownload slide Spatial clustering of coal tit populations as inferred from GENELAND analysis; assignment probabilities of the individuals to spatial clusters identified by GENELAND displayed in a contour map for (A) the north-western, (B) the south-western and (C) the Cypriot cluster. The spatial membership probability is visualized by colour: bright yellow indicates a high-, dark red a low assignment probability; black dots: sampling localities. Figure 6. View largeDownload slide Spatial clustering of coal tit populations as inferred from GENELAND analysis; assignment probabilities of the individuals to spatial clusters identified by GENELAND displayed in a contour map for (A) the north-western, (B) the south-western and (C) the Cypriot cluster. The spatial membership probability is visualized by colour: bright yellow indicates a high-, dark red a low assignment probability; black dots: sampling localities. Admixture rate in the hybrid zone For both the mitochondrial and the microsatellite data set the best fit was for the admixture model (scenario 1, Fig. 3). However, parameter estimates inferred from microsatellite data do not seem reasonable given the extremely recent and unreliable estimates for times of divergence and time since admixture (over the last 600 years). For the mitochondrial data, scenario 1 was the best model with high support (posterior probabilities 0.9740 and 0.9983 for the direct and logistic regression approaches, respectively), also supported by all 40 summary statistics used. Type I and II errors were small; type I errors were 0.006 and 0.192 and type II errors were 0.011 and 0.049 for the direct and logistic approaches, respectively. Modes of the effective population sizes for Northern Europe (Norway and Finland) were 280000 [95% highest posterior density (HPD) = 105000–940000], for the hybrid zone 9820000 (95% HPD = 3120000–9910000) and for Southern Europe 595000 (95% HPD = 222000–981000). The admixture was estimated to have occurred 57600 generations ago (95% HPD = 12400–95900) with an admixture rate of 0.216 (95% HPD = 0.079–0.422) relative to the northern population and divergence of southern and northern lineages 1260000 generations ago (95% HPD = 521000–5710000). Applying a mean generation time of 2 years, our estimates correspond to a mean time of divergence of 2.52 mya (95% HPD 1.04–11.42 mya) and a mean time since admixture of 0.114 Mya (95% HPD 0.024–0.192 Mya). DISCUSSION Patterns of gene flow in Europe Despite considerable genetic differences, the European range of secondary overlap among south-western and north-eastern coal tit lineages does not match the general pattern of a narrow and geographically restricted secondary contact zone (Fig. 1A; cf. Haffer, 1989; Aliabadian, 2005). All German populations appeared to be genetically strongly admixed and introgression of south-western alleles extended northward into Fennoscandian populations, where corresponding (south-western) mtDNA haplotypes were absent in our study (but see Johnsen et al., 2010). A moderate deficit of heterozygotes and linkage disequilibrium in three German populations (strongest in Schleswig-Holstein) indicate local admixture of two diverged genetic lineages and are typically found in populations from the centre of a hybrid zone (Jiggins & Mallet, 2000; Alexandrino et al., 2005; Brelsford & Irwin, 2009). The actual eastward and southward extent of the contact zone is far from being fully described, because to a lesser degree the north-western alleles and haplotypes were present in southern European populations (Greece). Despite all the limitations of our sampling, northward allelic introgression and strong differences between pairwise ΦST and FST values hint to a wider spatial extent of nuclear gene flow as compared to mtDNA introgression. Such mito-nuclear discordance can arise from selection against hybrids and/or sterility of the F1 heterogametic sex (Haldane’s rule: Davies & Pomiankowski, 1995; Wu, Johnson & Palopoli, 1996) as suggested to be the case in other passerine hybrid zones (European Ficedula flycatchers: Tegelström & Gelter, 1990; Sætre et al., 2001; Qvarnström, Rice & Ellegren, 2010; Far East Russian great tits: Kvist & Rytkönen, 2006). However, in coal tits there was no evidence of hybrid sterility or selection against hybrids from cross-fostering experiments with individuals from European and Afghan populations (Löhrl, 1994). Therefore, we can rule out selection against hybrids in admixed European coal tit populations, too. Secondly, sex-biased dispersal is considered as another possible cause of mito-nuclear discordance (reviewed by Prugnolle & de Meeus, 2002; in birds: Kvist & Rytkönen, 2006; Illera et al., 2011; Lin, Jiang & Ding, 2011). Although the common paradigm of female-biased dispersal in birds (Clarke, Sæther & Roskaft, 1997; Petit & Excoffier, 2009) has recently been challenged (Li & Merilä, 2010; Both, Robinson & van der Jeugd, 2012; Dobson, 2013), there is only very scarce information on sex-specific dispersal distances for many bird species, including the coal tit (except Dietrich et al., 2003). Because of such data deficiency further evidence from field studies is required to substantiate assumptions on any putative correlation between sex-biased dispersal and mito-nuclear discordance in coal tits. Thirdly and lastly, extreme ratios between male to female gene flow might arise from stochastic effects when comparing different levels of genetic diversity, such as high allelic variation of microsatellite loci and sequence variation between deeply divergent lineages (Karl et al., 2012; Putman & Carbone, 2014). Due to relatively long coalescence times, incomplete lineage sorting of nuclear markers might blur spatial patterns of genetic variation. In the coal tit, this is reflected by strong admixture of two southern European allelic clusters (yellow and orange for K = 4; Fig. 5) in continental populations on the one hand and near-complete allelic lineage sorting in island populations of Corsica and Sardinia on the other. This is in accordance with low parameters of genetic variation on these islands and with the general assumption that density-dependent processes, such as founder effects and genetic drift, are most effective in island populations (Waters et al., 2013; birds: Padilla et al., 2015). Even over short evolutionary time spans, fast lineage sorting derived from ancestral polymorphisms in founder populations can occur in organisms with high dispersal ability, as inferred from a comparison of historical and extant Mediterranean populations of hawkmoths (Hyles; Mende & Hundsdoerfer, 2013). Genetic admixture on the European continent Extant phylogeographical patterns and lineage diversification in the coal tit are likely to have emerged from glacial range fragmentation (Martens et al., 2006; Pentzold et al., 2013) as suggested for other tit species (Kvist et al., 2003; Päckert et al., 2013; Stervander et al., 2015; Tritsch et al., 2017). Our time estimates inferred from the mitochondrial data set support a scenario of lineage divergence close to the Pliocene–Pleistocene boundary at a mean time of divergence of 2.5 Mya (in accordance with Päckert et al., 2012). Our mean coalescence-based estimate for time since admixture of 0.114 Mya pre-dates a Holocene post-glacial expansion and thus suggests that admixture of north-eastern and south-western gene pools could have already started in southern refuges during the late Pleistocene. This assumption is supported by sound evidence that northward dispersal of forest birds from Mediterranean refuges already started before the onset of the Holocene, because fossil remains of forest birds from interglacial periods have been found north of the Alps across Central Europe up to a latitude of 50°N (Holm & Svenning, 2014). Furthermore, mean coalescence time estimates are rather rough because there is no reliable empirical value for the generation time of coal tits and several authors have applied shorter generation times for tits (Garant et al., 2005; Qu et al., 2015) that would shift our time since admixture estimates closer to a Holocene expansion scenario. The wide range of mitochondrial introgression and nuclear gene flow in central European coal tits indicates a partial reversal of Pleistocene divergence patterns (for a similar case in North American chickadees see Manthey, Klicka & Spellman, 2012). The phylogeographical pattern in continental European coal tits matches a broad trans-European zone of intergradation at the subspecies level, similarly to that in Eurasian nuthatches, Sitta europaea (Red’kin & Konovalova, 2006). Unlike in the latter species, phenotypic variation of continental European coal tits is very subtle and body size parameters and plumage coloration vary along a pan-European cline with phenotypic extreme forms vieirae and abietum in the South and ater in the North (Niethammer, 1943; Wolters, 1968; Glutz von Blotzheim & Bauer, 1993; Martens, 2012). Furthermore, the vocal repertoire of coal tits is remarkably uniform throughout continental Eurasia and seems to provide a less effective premating barrier compared to songs of other tit species (Thielcke, 1973; Tietze et al., 2011; Pentzold et al., 2016). In contrast, in many cases of asymmetric gene flow across narrow hybrid zones among Holarctic bird taxa (regardless of their taxonomic rank), assortative mating seems to be associated with strong divergence of vocal repertoires (Helbig et al., 2001; Haavie et al., 2004; Päckert et al., 2005; Kvist & Rytkönen, 2006; Sattler, Sawaya & Braun, 2007; Vorkurková et al., 2013; Shipilina et al., 2017). Generally, separation of gene pools is strongly enhanced by the variation of morphological and behavioural traits that play a key role for species recognition, such as in Ficedula flycatchers (Sætre et al., 2001; Ellegren et al., 2012; Backström et al., 2013). In contrast, it seems that phenotypic and behavioural differentiation between northern and southern European coal tits is too subtle to provide an effective premating barrier. The same holds true for potential segregation of ecological or climatic niches in secondary contact (in tits and chickadees: Päckert et al., 2005; Zhao et al., 2012; Taylor et al., 2014). Webb et al. (2011) pointed out that merging of genetic lineages might be more likely to occur in generalist species having a lower probability of evolving unique adaptations. This argument may apply to the coal tits as well, because despite a strong adaptation to coniferous forests, they exploit a great variety of food resources. In those regions where the species has adapted to deciduous forests, coal tits use a broader range of the tree’s canopy and trunk compared with many other parid species (Glutz von Blotzheim & Bauer, 1993; Gosler & Clement, 2007). Habitat structure might also have a considerable effect on local population structure, because, in mixed conifer–broadleaved forests of Ussuriland (Far Eastern Russia), population densities of coal tits were estimated 2.5–3 times higher than in pure spruce–fir taiga forests of the upper mountain–forest belt (Nazarenko, 1984). Finally, there is in fact recent evidence of spatial variation in an adaptive trait of European coal tits. Schmoll & Kleven (2011) found differences in sperm size between coal tits from Norway and Germany, as was reported among European and Afro-Canarian blue tits (Cyanistes caeruleus and C. teneriffae; Gohli et al., 2015). Whether in blue tits these differences would constitute an effective post-mating barrier cannot be judged due to a lack of range overlap in the field and missing evidence from experimental studies. In European coal tits, intraspecific differences in sperm morphology do not seem to effectively prevent gene flow across the European contact zone. Allopatric differentiation on Mediterranean islands Typically, in the central areas of a species’ range, the degree of gene flow is often high, whereas it is low at the range margins (Kvist et al., 2007; Lehtonen et al., 2009; Küpper et al., 2012; Päckert et al., 2013). In widespread Palearctic bird species, greatest phylogeographical structure is often observed at the south-western range margins, for example in the Mediterranean and on the southern European peninsulae (revision in Stewart et al., 2010; birds: Tietze et al., 2011; Brambilla et al., 2008). Because genetic drift and lineage sorting are most effective in small isolated populations, genetic distinctiveness of island populations is a common phylogeographical pattern. At the global scale levels of vertebrate endemism are significantly higher on islands when compared to the same ecoregions on the adjacent mainland (Fa & Funk, 2007; Kier et al., 2009). In the coal tit, the population from Cyprus (cypriotes) stands out as a genetically and phenotypically distinctive form that dates back to a more ancient (but still Pleistocene) colonization (Pentzold et al., 2013). Phylogenetic studies have revealed complex circum-Mediterranean phylogeographical patterns including distinct island lineages on Cyprus (Voelker & Light, 2011) and highly distinctive populations and even endemic species or subspecies. Apart from the famous examples of the extinct megafauna from Cyprus (Hadjisterkotis & Masala, 1995), weak insular endemism has also been postulated for the extant Cypriot herpetofauna (Böhme & Wiedel, 1994) and the Cypriot avifauna (Förschler & Randler, 2009; Randler et al., 2012). Genetic distinctiveness of Corsican and Sardinian coal tit populations was less manifest than that of P. a. cypriotes. The shallow genetic divergence of P. a. sardus from its continental relatives (see also Tietze et al., 2011; Pentzold et al., 2013) contrasts with the long evolutionary histories of some Corso-Sardinian faunal elements (reviewed in Ketmaier & Caccone, 2013). Accordingly, rather ancient (pre-Pleistocene) Corso-Sardinian species-level lineages have been found in amphibians and reptiles (Salvi et al., 2010; Salvi, Pinho & Harris, 2017; Fritz, Corti & Päckert, 2012; Rodríguez et al., 2017). In a Corsican endemic frog species, Discoglossus montalentii, phylogeographical structure in microallopatry was found even within the island (Bisconti, Canestrelli & Nascetti, 2013). However, in highly mobile vertebrates, such as birds, several endemic species occur on these islands, such as the Corsican nuthatch, Sitta whiteheadi (Pasquet et al., 2014), and the Corsican finch, Carduelis corsicana (Förschler et al., 2009), which also breeds on the Balearic Islands and on a few smaller neighbouring islands. In addition, there are distinct genetic lineages at the subspecies level in other bird species (Pons et al., 2016). Subtle genetic admixture of the Corsican population might imply that this island does or did receive more influx from continental populations than the Sardinian population, for example due to its closer proximity to the mainland and along a north–south migratory pathway of migrants and/or dispersers. However, a greater number of local samplings from both islands would be needed to reliably confirm this hypothesis. Moreover, the dispersal behaviour of coal tits is quite variable (Löhrl, 1974; Glutz von Blotzheim & Bauer, 1993; Gosler & Clement, 2007) and seems to depend on the availability of food resources (Löhrl, 1974; Harrap & Quinn, 1996). On Corsica, the breeding phenology of coal tits has strongly adapted to local food peaks (Blondel, Chessel & Frochot, 1988) and such adaptive processes might effectively have contributed to the fixation of genetic lineages on islands, for example in Corsican blue tits (Cyanistes caeruleus ogliastrae; Porlier et al., 2012). Generally, the genetic composition of the Corsican coal tit population might be the result of both incomplete lineage sorting during a short separation time (Pentzold et al., 2013) and recent gene flow from irregular influx of continental vagrant individuals and/or dispersers. These examples demonstrate that the circum-Mediterranean phylogeographical pattern in the coal tit is partly or often paralleled in other island endemics of the Corso-Sardinian fauna. Traditionally, phenotypic distinctiveness has been a crucial factor for species delimitation and, in fact, distinct genetic lineages of the coal tit in North Africa (atlas subspecies group) and on Cyprus (subsp. cypriotes) are corroborated by differences in plumage coloration (Harrap & Quinn, 1996; Gosler & Clement, 2007) and partially by subtle differences in song (Tietze et al., 2011; Pentzold et al., 2016). A deeper understanding of the range-wide intraspecific differentiation in the coal tit will therefore benefit (1) from an integrative taxonomic approach and (2) from broad population sampling across gradients of genetic introgression (e.g. in narrow hybrid zones that exist for example in the Himalayas). SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher’s web-site. Figure S1. Genetic variation in the complete data set of the Western European and Mediterranean coal tits (N = 166) based on 13 microsatellite loci; STRUCTURE analysis under the admixture – frequency-correlated model with locpriors a priori defined (assignment according to mtDNA lineages), STRUCTURE plots for K = 2 to K = 4 (left); right: estimate of most plausible K according to Evanno et al. (2005; ΔK plotted against the number of modelled genetic clusters) (a) under the admixture – frequency-correlated model without locpriors a priori defined (K = 2) and (b) with locpriors a priori defined (K= 4); assignment according to mtDNA lineages, control region: coloured bars above the plots. Figure S2. Spatial clusters as inferred by the spatial explicit CAR admixture model of TESS (Kmax = 3). The TESS admixture models did not distinguish more than three units even if the number of possible clusters in a model is higher (Kmax > 3). The DIC criterion (arithmetic mean of ten replicates) confirmed three spatial clusters. Table S1. Observed and expected heterozygosity, departure from Hardy–Weinberg equilibrium, evidence of null alleles and linkage disequilibrium for each locus and population. Bonferroni-corrected P-value for HWE = 0.05/13 = 0.0038; Bonferroni-corrected P-value for linkage disequilibrium = 0.05/78 = 0.00064. Table S2. Matrix of pairwise FST values between all local populations (computed with Arlequin 3.1). Bold: significant FST values after Bonferroni correction for the number of comparisons at P = 0.05 (Bonferroni correction alpha (α) = 0.05/for 91 comparisons α = 0.0005). SHARED DATA Data deposited in the Dryad digital repository (Trisch et al., 2018). ACKNOWLEDGEMENTS We are particularly grateful to all colleagues who provided samples for molecular analyses: T. Dolich, M. Fischer (Naturkundemuseum Erfurt, Germany), A. Johnsen (Natural History Museum Oslo, Norway), T. Lubjuhn, P. Lymberakis (Natural History Museum of Crete), S. Martens, A. Ostastshenko, F. Schramm and H. Zang. We are particularly grateful to F. Spina for providing collection permits in Italy. B. Martens helped on various field trips. Travel and research funds for field trips by J.M. were granted by the Deutsche Ornithologen-Gesellschaft and the Gesellschaft für Tropenornithologie, by Feldbausch-Stiftung and Wagner-Stiftung (both University of Mainz, Germany) (various grants from all four). This project was substantially funded by the State Ministry of Science and Arts of Saxony (Staatsministerium für Wissenschaft und Kunst Sachsen, AZ 4-7531.50-02-621-09/1). 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