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Genetic structure and phylogeography of a European flagship species, the white-tailed sea eagle Haliaeetus albicilla

Tobias Langguth, Ann-Christin Honnen, Frank Hailer, Tadeusz Mizera, Stefan Skoric, Ülo Väli and Frank E. Zachos

T. Langguth, Dept of Biology, Queen’s Univ., Kingston, ON, K7L 3N6, Canada. – F. E. Zachos (frank.zachos@nhm-wien.ac.at) and TL, Zoological Inst., Christian-Albrechts-Univ. zu Kiel, DE-24118 Kiel, Germany. FEZ also at: Natural History Museum Vienna, AT-1010 Vienna, Austria. – A.-C. Honnen, Inst. for Freshwater Ecology and Inland Fisheries Berlin, DE-12587 Berlin, Germany. – F. Hailer, LOEWE Biodiversity and Climate Research Center, BiK-F, Senckenberg Gesellschaft für Naturforschung and Goethe Univ. Frankfurt, DE-62325 Frankfurt, Germany. FH also at: Center for Conservation and Evolutionary Genetics, Smithsonian Conservation Biology Inst., National Zoological Park, PO Box 37012, MRC 5513, Washington, WA 20013-7012, USA. – T. Mizera, Zoology Dept, Poznan Univ. of Life Sciences, PL-60-625 Poznan, Poland. – S. Skoric, Inst. for Multidisciplinary Research, Univ. of Belgrade, RS-11030 Belgrade, Serbia. – Ü. Väli, Inst. of Agricultural and Environmental Sciences, Estonian Univ. of Life Sciences, EE-51014-Tartu, Estonia.

We analysed 120 white-tailed sea eagles Haliaeetus albicilla from eastern (Poland and Estonia) and southeastern (Serbian Danube population) Europe for genetic variability and structuring at the mitochondrial control region and seven nuclear microsatellite loci. We combined this new dataset with sequence and genotype data from previous analyses covering Greenland and Eurasia (total sample sizes of 420 and 186 individuals for mtDNA and microsatellites, respec-tively) to address the following questions: 1) does the large eastern population in Europe add significantly to the species’ overall genetic diversity? 2) Do the new sequence data match the clinal distribution pattern (west to east) of the two major mtDNA lineages? 3) Does the preliminary hypothesis of two nuclear genetic clusters recently found in this species hold for the whole of Europe, and do these clusters show a geographic pattern? Our results confirmed Europe as a strong-hold of genetic diversity in white-tailed sea eagles, and the east of the continent contributed disproportionately to this, the reason being the admixture of eagles with different genetic background. As hypothesised, both mitochondrial lineages were recovered also in eastern Europe, but the globally more eastern lineage was dominant. The presence of two micro-satellite clusters was also confirmed, and these groups, too, show a non-random geographic distribution, with, except for Poland, a high proportion of ‘eastern-type’ eagles in the populations of east–central and eastern Europe.

During the 19th and early to mid-20th centuries many iconic European vertebrate species became reduced in num-bers and often regionally extinct due to direct persecution, habitat deterioration and environmental pollution. Apart from large mammalian carnivores this particularly holds for large birds of prey, where, for example, Spanish imperial eagles Aquila adalberti and bearded vultures Gypaetus barbatus were threatened with extinction in their European distribution range and only recently have begun to recover through conservation efforts and/or reintroduction pro-grammes (IUCN Red List of Threatened Species,  www.iucnredlist.org/ ). The white-tailed sea eagle Haliaeetus albicilla is another such case in point. It is disjunctly distri-buted across the Palaearctic and Greenland and suffered severely from persecution and insecticides until strict pro-tection measures and the prohibition of DDT in the 1970s stopped the decline (Helander and Mizera 1997, Hauff 1998). By that time, many local populations had

become extinct, but sea eagles have recovered since and recolonised much of their former distribution range. As a consequence, they have been downgraded in the IUCN Red List from Vulnerable to Least Concern (BirdLife International 2008).

Previously, the white-tailed sea eagle has gained attention among conservation geneticists, and a couple of studies with regard to genetic diversity, its partitioning and impli-cations for phylogeography have been published (Hailer et al. 2006, 2007, Literák et al. 2007, Honnen et al. 2010). On a global scale, two main mitochondrial lineages were found that have been interpreted as evidence of two glacial refugia in western (lineage A) and central (lineage B) Eurasia (Hailer et al. 2007, Honnen et al. 2010). Both lineages meet in Europe, where, as a consequence, genetic diversity was found to be comparatively high, also because the long generation time of sea eagles is believed to have buffered the detrimental effects of the human-induced

Journal of Avian Biology 000: 001–009, 2012 doi: 10.1111/j.1600-048X.2012.00075.x

© 2012 The Authors. Journal of Avian Biology © 2012 Nordic Society Oikos Subject Editor: Martin Paeckert. Accepted 7 November 2012

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bottleneck (Hailer et al. 2006). Interestingly, genetic vari-ability was not correlated with population size as the largest European population in Norway was genetically very homogeneous (Hailer et al. 2006, 2007). Apart from being a stronghold with respect to genetic diversity, Europe is also important in terms of numbers, harbouring about 60% (5000–6600 breeding pairs) of the global sea eagle population (8500–11 700 breeding pairs, Mizera 1999). As far as genetic diversity is concerned, it is mainland Europe rather than Fennoscandia that holds the largest share: while Hailer et al. (2007), concentrating on northern Europe and Asia, found a mere 13 mtDNA haplotypes in 237 individuals, the additional analysis of ca 100 sea eagles from central Europe yielded 12 new haplotypes (Honnen et al. 2010). Based on microsatellites, the differences in variability were less clear, but two genetic clusters were found with different frequencies from west (Germany) to east (Austria, Czech Republic and Slovakia) (Honnen et al. 2010).

Still, the previous studies did not yield a complete picture of white-tailed sea eagle population genetics, because two main regions of the species’ occurrence in Europe have been neglected so far: the Danube population in the Balkans and large parts of eastern Europe, particularly Poland which harbours more than 1000 breeding pairs (Mizera unpubl.). In our present study, we therefore analysed mtDNA sequences and microsatellite genotypes from Poland, Estonia and Serbia. Similar to other populations, sea eagle numbers in these eastern countries have been recovering recently after serious declines due to persecution and DDT in the 20th century (Randla and Tammur 1996, Mizera 2002). The molecular markers chosen are well-suited for intraspecific analyses and have been used in various raptor species before (Hendrickson et al. 2003, Martínez-Cruz et al. 2004, Johnson et al. 2008). Specifically, we addressed the following questions: 1) do eastern European/Danubian sea eagles further add to the high genetic diversity in Europe, and do they yield evidence of a further genetic lineage/glacial refugium? 2) Do the two main mitochondrial haplogroups still occur in a west– east gradient? 3) Do the new data confirm or refute the hypothesis of two nuclear genetic clusters with a geogra-phically non-random distribution, in line with mtDNA data (Honnen et al. 2010)?

The question of a further refugium 1) arose because apart from the A and B lineages Hailer et al. (2007) found an intermediate, yet clearly separate haplotype (C1) which was exclusive to the Swedish population. Since eastern Europe was underrepresented in previous studies, it seemed possible that a third, more widely spread mtDNA lineage had been missed due to a sampling bias. We therefore pres-ent data from populations which were not (Poland, Serbia) or not extensively (Estonia) sampled in previous surveys of mtDNA variation in H. albicilla (Hailer et al. 2007, Honnen et al. 2010). Phylogeographically, i.e. with respect to lineage C, particularly Estonia and Serbia are of interest, being close to Sweden (Estonia) or being part of the Balkans (Serbia) which is known to have been a glacial refugium for a wide array of taxa (Hewitt 1999).

Material and methods

Sampling and DNA extraction

Altogether 120 white-tailed sea eagles from Estonia (n 24), Poland (n 66) and Serbia ( 30) were analysed (mtDNA: n 102, microsatellites: n 81, 75 samples yielded data for both markers; Table 1). Except for some of the Estonian and four samples from Poland and Serbia which were tissue or blood samples, DNA was extracted from moulted feathers collected in the wild or from specimens of known origin in zoological gardens. For extraction, we used the Qiagen DNeasy Tissue Kit. The manufacturer’s pro-tocol was followed and optimised to accommodate the use of feathers. Following Segelbacher (2002) and Honnen et al. (2010), the volume of ATL buffer was adjusted to 270 ml, proteinase K increased to 30 ml, and lysis time was extended to 72 h. By including data from two previous studies (Hailer et al. 2007, Honnen et al. 2010) we increased our data set to 186 genotyped individuals and 420 mtDNA sequences.

Mitochondrial DNA

Control region amplification was performed as described in Hailer et al. (2006). Both forward and reverse strands were sequenced using the 3730 XL DNA Analyzer. Alignment and haplotype collapsing were done with BioEdit ver. 7.0.5.3 (Hall 1999). The occurrence of Numts (nuclear copies of the mitochondrial sequences) is unlikely for the following reasons: most samples were feathers which have been shown to be a reliable source for mtDNA (Sorenson and Quinn 1998); ambiguous sequences were reanalysed and discarded if no clear consensus sequence could be obtained from both strands; and there were no outlying haplotypes in our data set, all sequences fit in with the ones already published.

Haplotype and nucleotide diversities were calculated using DnaSP 5.10.01 (Librado and Rozas 2009). Arlequin 3.5.1.2 (Excoffier and Lischer 2010) was used for AMOVA calculations (analyses of molecular variances), using a distance matrix among haplotypes (F-statistics) based on the Tamura and Nei (1993) model with a gamma correction of 0.04 (as found appropriate by the Findmodel online software,  www.hiv.lanl.gov/content/sequence/find model/findmodel.html ).

Haplotype relationships for all 420 eagles from through-out the distribution range were visualized as a median- joining haplotype network constructed with the Network software (Bandelt et al. 1999). We chose a network approach

Table 1. Sample sizes of the present study and information on breeding pairs (taken from Elts et al. 2009, Ham et al. 2009 and Mizera unpubl.) in the sampled countries.

Sample n (mtDNA)n

(microsatellites) n (total)Breeding

pairs

Poland 55 38 66  1000Estonia 23 23 24 150–170Serbia 24 20 30 86Total 102 81 120  1200

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instead of a tree as networks are considered to be better suited at the intraspecific level (Posada and Crandall 2001).

Inferences of demographic history of the complete mtDNA data set (i.e. the two haplogroups), particularly expansion events, were drawn from mismatch distribution analysis and Tajima’s D (Tajima 1989) and Fu’s Fs (Fu 1997) statistics with Arlequin and DnaSP. Mismatch distributions, the frequency distributions of pairwise differ-ences between sequences in a sample, are typically multi-modal for populations at demographic equilibrium, while unimodal distributions are indicative of an expansion event (Rogers and Harpending 1992). Tajima’ D and Fu’s Fs are neutrality tests with demographic information content in that statistically significant negative values are typical of recent expansion events.

Microsatellites

Genotyping was done on an automated capillary sequencer and analysed with the Genetic Profiler 2.2 software using the primers Hal01, Hal03, Hal09, Hal10, Hal13, Hal14 and IEAAAG05 (Busch et al. 2005, Hailer et al. 2005) so that our data set could be merged with that of Honnen et al. (2010). All individuals were separately screened at least twice. In the case of homozygotes at least three runs were performed as proposed by Segelbacher (2002) for feather samples. Only genotypes with unambiguous results were included in the final data set. The microsatellite data from Honnen et al. (2010) were produced in the same lab as the present genotypes with the same sequencer and size marker, and we re-analysed samples of the previous study to ensure direct comparability of allele lengths. We checked our data set for stutter bands, large-allele dropout and null alleles using the software Micro-Checker 2.2.3 (van Oosterhout et al. 2004). No evidence of statisti-cally significant gametic disequilibrium was found between any two loci after Bonferroni-correction for mul-tiple tests (Genepop software, Rousset 2008), so all loci were included in subsequent multi-locus analyses. Observed and expected heterozygosities, potential deviations from Hardy–Weinberg equilibrium (HWE) and, for the two structure groups, an AMOVA based on conventional F- statistics and microsatellite-specific R-Statistics were calcu-lated with Arlequin. Allelic richness, as a measure of allelic diversity corrected for differences in sample size (by means of a rarefaction approach based on the population with the smallest number of samples), was computed with Fstat 2.9.3.2 (Goudet 1995).

The Bayesian software Structure 2.3.3 (Pritchard et al. 2000) was used to infer the most probable number of genetic clusters in our overall microsatellite data set (n 186). We calculated posterior likelihood values for K 1 to K 8 groups. Each K-value was run 10 times with 500 000 Markov chain Monte Carlo (MCMC) replications and a burn-in period of 100 000. Results were additionally evaluated with Structure Harvester 0.6.7 (Earl 2011) which implements the ad hoc statistic ΔK introduced by Evanno et al. (2005). Apart from the Bayesian structure analysis we applied another individual-based approach to inferring the number of genetic groups in our European data set, a factorial correspondence analysis (FCA) with Genetix 4.05.2 (Belkhir et al. 2004).

The Bottleneck 1.2.02 software (Cornuet and Luikart 1996) was used to test for a genetic signature of the known demographic bottleneck in sea eagles. The software makes use of the fact that during a bottleneck allele diversity decreases faster than heterozygosity, resulting in an excess of heterozygosity relative to expectation under a mutation-drift equilibrium, a signature that is usually detectable for 2–4 Ne generations (Ne being the effective population size, Luikart et al. 1998). The excess was tested for by means of a Wilcoxon test with 70% of all mutations following the stepwise model and 30% being multi-step changes. Further, the Garza–Williamson index (Garza and Williamson 2001) was calculated as M k/r, where k is the allele number and r is the difference in length between the longest and the shortest allele at a given locus. During a bottleneck allele number decreases faster than allele range (which will only be affected if the shortest and/or the longest alleles get lost). As a critical value to be interpreted as evidence of a bottleneck with seven loci, Garza and Williamson (2001) suggest M 0.68, while values of M 0.82 have usually been found in non-bottlenecked popula-tions. These bottleneck tests were performed for the whole data set (n 186) and for each Structure group separately.

Results

Mitochondrial DNA

Sequencing of 499 bp of the hypervariable region I of the mitochondrial control region was successful in 102 samples. We found 20 different haplotypes in Poland, Estonia and Serbia, eight of which had previously also been found by Hailer et al. (2007) and Honnen et al. (2010), leaving 12 hitherto undescribed sea eagle haplotypes (A12-18, B15- 19, GenBank accession numbers JQ435485–JQ435496). Seven of these haplotypes belonged to lineage A and five to lineage B (Fig. 1, Supplementary material Appendix 1, Table A1). The complete data set of 420 sea eagles then com-prised 38 haplotypes (Supplementary material Appendix 1, Table A1) with 31 polymorphic sites (19 transitions, 13 transversions and two indels). As found in previous studies, there were three dominant haplotypes: A01, A02 and B01 which were found in 73 or more than 70% of our 102 mtDNA samples. While A02 was the most common haplotype in Poland, it was absent from Estonia and Serbia where, in turn, B01 (which was very rare in Poland) was the most common.

The overall median-joining network (Fig. 1) yielded the two known haplogroups A and B with the intermediate haplotype C01 found in Sweden (Hailer et al. 2007, Honnen et al. 2010). Except for Iceland all sampled European countries harbour both A and B haplotypes, with B frequencies increasing from west to east (Fig. 1, 2a). This clinal variation also holds on a global scale: the eastern-most populations of the Amur region and Japan only show B haplotypes, while the westernmost in Greenland and Iceland only exhibit haplogroup A (Fig. 1, Supplementary material Appendix 1, Fig. A1).

Data on genetic diversity as quantified by haplotype (h) and nucleotide diversities (p) is summarised in Table 2.

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different loci varying between seven (Hal01, Hal09, Hal10) and 21 alleles (Hal14). Micro-Checker did not produce evidence of large-allele dropout or genotyping errors due to stutter bands, but a null allele signal for locus Hal14. As in our previous study (Honnen et al. 2010) we therefore carried out calculations, particularly the Structure analysis, with and without Hal14. Since conclusions did not differ, we kept Hal14 in our multi-locus data set (see also Honnen et al. 2010).

As mentioned earlier, the Structure analysis yielded the highest probability for two groups, which was confirmed by the ΔK statistic (Supplementary material Appendix 1, Fig. A3, Table A2). Values for K 3 were similar, merely resulting in a subdivision of the eastern cluster rather than introducing an otherwise undetected geographic structur-ing. The geographic distribution of the eagles belonging to each group along with country-specific frequency pie charts is shown in Fig. 2b. There is a difference in overall cluster membership between Germany and Poland, which predominantly harbour eagles from the (therefore called) western group (see also Honnen et al. 2010), and the east and south (including southeastern Sweden, Estonia, Serbia, the Czech Republic/Slovakia and Austria) where the eastern group dominates. Supplementary material Appendix 1, Fig. A3 shows that there are quite a number of individuals with similar values for both clusters (presumably admixed specimens), particularly eagles from Germany and Poland, but it also shows that the majority of eagles from the other populations have high membership values for one of the clusters. Apart from a strict 50% cut-off criterion (under which an individual is assigned to the cluster for which its membership value is higher) we also applied two more conservative cut-off criteria (70% as in Honnen et al. 2010, also shown in Fig. 2b and 80%, cf. Randi 2008; not shown in a figure). The FST and RST values between the

The global overall values were 0.718% (p) and 0.797 (h). Europe, especially mainland Europe, showed considerably higher diversity values than Greenland or central and eastern Asia (for extremes, compare the Czech Republic and Slovakia with Japan). The two haplogroups A and B showed similar values (the low nucleotide diversity for both of them compared to most single countries is due to the fact that, here, there is no among-haplogroup distance included). Interestingly, mtDNA diversity in the eastern microsatellite-based Structure group was higher than in the western Structure cluster, in line with results for nuclear genetic variability. FST between the two haplogroups was 0.886 and highly significant (p 0.00001), indicating that 88.6% of the total mitochondrial diversity was due to differences between A and B.

Tajima’s D and Fu’s Fs were significantly negative for both haplogroups (A: D 21.47, p 0.038, Fs 215.55, p 0.00001; B: D 21.87, p 0.005, Fs 218.21, p 0.00001), indicating expansion events, which is in line with the star-like structure of the network that, for each haplogroup, shows common central haplotypes and many satellite haplotypes of low frequency. For the whole mitochondrial data set combined, only Fu’s Fs was signifi-cantly different from zero (Fs 225.48, p 0.00001; D 20.49, p 0.373). The mismatch analysis yielded a significant deviation from the expansion hypothesis for haplogroup A (sum of squared deviations (SSD) 0.017, p 0.005) but not for B (SSD 0.00024, p 0.845) (Supplementary material Appendix 1, Fig. A2).

Microsatellites

The total data set of 81 successfully genotyped individuals from Poland, Estonia and Serbia yielded 71 different alleles, i.e. an average number of 10.1 alleles per locus, with the

Figure 1. Median-joining network of all 38 mitochondrial control region haplotypes globally found in white-tailed sea eagles (see Supplementary material Appendix 1, Table A1 for details). Circle size is proportional to haplotype frequency. Connections between haplotypes depict a single mutational step except for the connection between A01 and C01 (two mutations as indicated by the dashes).

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Figure 2. (a) Distribution of the two major mtDNA haplogroups in European white-tailed sea eagles. Haplogroup A: black, haplogroup B: white. The single Swedish haplotype C01 is shown in grey. Size of circles is proportional to sample size. Total sample size for Europe is 357 (420 minus the samples from Greenland and Asia) For Holarctic haplogroup distribution see Supplementary material Appendix 1, Fig. A1. (b) Results of the Bayesian structure analysis based on microsatellite genotypes. Left: 50% cut-off criterion, right: 70% cut-off criterion. Individuals assigned to the two clusters are shown in black and white; grey triangles denote individuals with membership values 70% in the right figure. Pie charts show the relative frequencies of both groups in the different countries (anticlockwise: Sweden, Germany, Austria, Serbia, Czech Republic/Slovakia, Poland, Estonia). Total sample size is n 186.

two Structure groups were both 0.035 (50% cut-off), 0.072 and 0.064 (70% cut-off) and 0.118 and 0.107 (80% cut-off) (all p 0.00001), indicating low but signifi-cant differentiation, regardless of whether allele frequencies (FST) or allele length differences (RST) were considered. The factorial correspondence analysis (FCA) confirmed the two groups and their low differentiation (Supplementary material Appendix 1, Fig. A4). The number of private alleles (alleles exclusively present in one group) differed substan-tially between the two Structure groups: while 30 alleles were exclusive to the eastern group, there was only a single

private allele in the western group. The overall allele num-bers were 41 and 70 for west and east, respectively (50% cut-off criterion; 70%: 35 and 64 for west and east; 80%: 32 and 60). The number of private alleles did not change with a 70% cut-off criterion and only marginally (one in the west and 29 in the east) with 80%. Samples sizes for the eastern vs western group were 96 versus 90 (50%), 58 versus 64 (70%) and 36 versus 47 (80%). A statistical artefact due to lower sample size in the west can thus be excluded.

Values of heterozygosity and allelic richness are given in Table 2, ranging from 0.56–0.72 (HO), 0.65–0.79 (HE)

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Discussion

The present study is the most comprehensive on white-tailed sea eagles so far, and the sampling scheme covers all major areas of the species’ distribution in Europe, one of the global strongholds for sea eagles. Interestingly, the inclu-sion of east-European populations yielded even higher amounts of genetic diversity than found by Honnen et al. (2010) who, in turn, found central European sea eagles to be more diverse than Scandinavian ones, particularly the largest European breeding population in Norway. A simi-larly counterintuitive distribution of genetic diversity was also found in black vultures Aegypius monachus: the large Iberian population exhibited lower diversity at both mtDNA and microsatellite loci than the small relict popu lation in Greece (Poulakakis et al. 2008). Thus, for European sea eagles genetic diversity continually increases from the north via central Europe to the east and southeast of the continent. This is also reflected by a comparison of microsatellite allele numbers: Honnen et al. (2010), focusing on central Europe, found 65 microsatellite alleles in 105 individuals, while our more eastern sample yielded 71 alleles in 81 eagles. One possible explanation for the high allelic diversity and the high number of private alleles in eastern Europe could be gene flow from Asia. In the absence of Asian genotype data, however, we unfortunately cannot test this hypothesis. The large populations in Germany and Poland (the largest in mainland Europe) are somewhat less diverse than sea eagles in Austria, the Czech Republic/Slovakia and Estonia, confirming that present population size is a poor proxy for genetic diversity in this species. A potential reason may be the bottleneck in the 20th century, although on the whole it seems to have been largely buffered by the species’ long generation time (Hailer et al. 2006). There are not many comparable data sets from other Haliaeetus species. Mitochondrial DNA diversity of white-bellied sea eagles H. leucogaster from Australasia was much lower than in white-tailed sea eagles (p 0.081%, h 0.350, Shephard et al. 2005), and expect-edly the same held true for microsatellite diversity of a criti-cally endangered island endemic, the Madagascar fish eagle H. vociferoides (HO and HE both 0.2, Johnson et al. 2009). No detailed large-scale studies of the white-tailed sea eagle’s closest relative, the North American bald eagle H. leucocephalus, are available for comparison with our data.

Austria, although only recently recolonised (Probst 2002), harbours high levels of sea eagle diversity, and our data show that Austria is predominantly part of the eastern groups. Recolonisation, therefore, probably occurred from the east (in line with ringing data according to which two of the analysed Austrian eagles were overwintering birds from Finland and Estonia) rather than via Germany, which is in accordance with the fact that by far most German sea eagles occur in the very North of the country. Apart from the two rare long-distance migrants (sea eagles are usually philo-patric, Helander et al. 2003, Struwe-Juhl and Grünkorn 2007) the source population for Austria may be the Balkans (via the Danube) or the Czech Republic.

The fact that there is more diversity in the east may be due to higher long-term effective population sizes and immigration from Asia and/or more pronounced gene flow

Table 2. Mitochondrial and nuclear genetic diversity in white-tailed sea eagles. h: haplotype diversity, p: nucleotide diversity, HO: observed heterozygosity, HE: expected heterozygosity, AR: allelic richness. CzR/Slov.: Czech Republic and Slovakia. A and B refer to the two haplogroups, Strwest and Streast to the more western and east-ern Structure clusters. The three values for the Structure clusters are for the different cut-off criteria (top: 50%, middle: 70%, bottom: 80%). Allelic richness values are based on five individuals for single samples (countries), 36 individuals for the two haplogroups and 67/45/28 individuals for the two Structure clusters for the 50%/70%/80% cut-off criterion. Values are therefore only directly comparable within these three groups. Heterozygosity and allelic richness values for haplogroups were calculated by considering the genotypes of all eagles with A and B haplotypes, respectively. Haplotype and nucleotide diversities for the Structure clusters refer to the mtDNA variability of the eagles assigned to west and east based on their microsatellite genotypes.

Sample h p [%] HO HE AR

Poland 0.732 0.549 0.66 0.65 3.53Estonia 0.779 0.477 0.72 0.73 4.35Serbia 0.667 0.653 0.61 0.65 3.55Germany 0.493 0.280 0.56 0.66 3.75Austria 0.808 0.632 0.71 0.79 4.89CzR/Slov. 0.895 0.886 0.63 0.66 3.80Sweden 0.642 0.666 0.66 0.71 3.98Greenland 0.250 0.050 – – –Iceland 0.409 0.082 – – –Norway 0.061 0.073 – – –Lapland 0.667 0.686 – – –Kola 0.933 0.507 – – –Kazakhstan 0.657 0.371 – – –Amur 0.325 0.068 – – –Japan 0.250 0.050 – – –A 0.612 0.153 0.61 0.68 7.1B 0.571 0.163 0.67 0.74 8.1Strwest (50%) 0.621 0.409 0.59 0.64 5.7

(70%) 0.564 0.348 0.57 0.61 4.8(80%) 0.663 0.435 0.58 0.59 4.3

Streast (50%) 0.803 0.766 0.66 0.73 9.7(70%) 0.749 0.701 0.65 0.74 8.9 (80%) 0.767 0.698 0.67 0.75 8.3

Total 0.797 0.718 – – –

and 3.53–4.89 (allelic richness). Diversity for Poland, Estonia and Serbia was comparable to that found in other European countries, with Estonia showing rather high values. Significant deviations (Bonferroni-corrected) from HWE were only found for locus Hal01 in Serbia and for Hal14 in Poland and for four out of seven loci (Hal01, Hal04, Hal09, Hal14) if all samples were combined into a single artificial population, thus indicating substantial substructuring (Wahlund effect). All diversity indices were higher for individuals of the eastern group compared to that of the western group, irrespective of whether these groups were defined by mtDNA (haplogroups A and B) or micro-satellites (Structure clusters Strwest and Streast) and also irre-spective of the cut-off criterion used to define the western and eastern Structure clusters (50%, 70% or 80%).

None of the Wilcoxon tests for a bottleneck were signifi-cant, neither for the whole microsatellite data set combined (n 186) nor for the two Structure groups tested separately (all p 0.23). Further, none of the Garza–Williamson M values was below the threshold of 0.68 indicative of a recent bottleneck (whole data set: M 0.72, western and eastern Structure groups M 0.75 to 0.86 and M 0.71 to 0.76, respectively, depending on the cut-off criterion).

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White-tailed sea eagles, like other raptor and mammalian carnivore species, have recovered well from anthropogenic population declines particularly in the late 19th and 20th centuries. Fortunately, numbers are still increasing, and many areas of the former distribution range have been and are being recolonised. Levels of genetic diversity have been shown to be surprisingly high, and the large-scale phylo-geography of the species has been uncovered. Mainland Europe is a stronghold not only in terms of population numbers but also, as a consequence of the sympatric occur-rence of different lineages, with respect to genetic diversity, especially so in the eastern and southeastern part of the continent, which is one of the main findings of the present analysis (although Asia is comparatively poorly studied and may harbour more diversity than previously found). Again, this is reminiscent of the genetic structure in bearded vultures, where the highest overlap of two different haplo-groups was found in Greece and in the Alps, whereas diver-sity was lower further to the west and east (Godoy et al. 2004). The genetic signature of two distinct groups of both the mitochondrial and the nuclear genome is still visible in the extant sea eagle population. The mitochondrial dichot-omy can be safely assumed to be a consequence of two disjunct Pleistocene refugia, the nuclear split might also be due to more recent demographic dynamics. While Europe is well-studied for this species, the large distribution range in Asia needs further attention. Also, our microsatellite data set is confined to Europe, and an extension to Greenland, Iceland and Asia is indispensable for a full understanding of the genetic structure of these iconic birds.

Acknowledgements – FH was partly supported by the ‘LOEWE – Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz’ of Hesse, Germany, and by the Smithsonian Conserva-tion Biology Center. We thank Martin Paeckert for helpful criti-cisms and comments on an earlier version of this manuscript.

References

Bandelt, H.-J., Forster, P. and Röhl, A. 1999. Median-joining networks for inferring intraspecific phylogenies. – Mol. Biol. Evol. 16: 37–48.

Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N. and Bonhomme, F. 2004. Genetix 4.05.2, logiciel sous Windows pour la genetique des populations. – Laboratoire Génome, Populations, Interactions, CNRS UMR 5000, Univ. de Montpellier II.

BirdLife International 2008. Haliaeetus albicilla. – In: IUCN 2009. IUCN Red List of Threatened Species, ver. 2009.1, www.iucnredlist.org .

Busch, J. D., Katzner, T. E., Bragin, E. and Keim, P. 2005. Tetranucleotide microsatellites for Aquila and Haliaeetus eagles. – Mol. Ecol. Not. 5: 39–41.

Cornuet, J.-M. and Luikart, G. 1996. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. – Genetics 144: 2001–2014.

Earl, D. A. 2011. Structure Harvester ver. 0.6.7. – http://users.soe.ucsc.edu/~ dearl/software/struct_harvest/ .

Elts, J., Kuresoo, A., Leibak, E., Leito, A., Leivits, A., Lilleleht, V., Luigujoe, L., Mägi, E., Nellis, R., Nellis, R. and Ots, M. 2009. Status and numbers of Estonian birds, 2003–2008. – Hirundo 22: 3–31.

Evanno, G., Regnault, S. and Goudet, J. 2005. Detecting the number of clusters of individuals using the software

from west to east than vice versa. This would explain the near-absence of private alleles in the western Structure cluster, whereas almost half of all alleles in the eastern clus-ter were not shared with the western group. It has to be added, though, that the differentiation between the two Structure groups is rather shallow, as evidenced by a num-ber of individuals with shared genetic ancestry and the low FST and RST values (3.5% for the 50% cut-off criterion) in spite of the high number of private alleles in one of the groups. Therefore, the two groups are both admixed as will be expected for a recombinant marker system like micro-satellites if the groups (at least partly) occur in sympatry and interbreed. Occasional long-distance migrants may further contribute to the homogenisation of the nuclear genome. We do not have microsatellite data from the west-ern and central Asian distribution range, but if Asian sea eagles fall within one of the two Structure clusters (rather than presenting one or more additional groups), i.e. if only two nuclear groups are found across the whole dis-tribution, then we may hypothesize that Asia harbours predominantly (or even completely) eagles from the eastern cluster. Such a partitioning into two groups across the whole of Eurasia was not only found in sea eagles (see our mtDNA data) but also in two other large accipitrid spe-cies, the black vulture (Poulakakis et al. 2008, mtDNA and microsatellites) and the bearded vulture Gypaetus barbatus (Godoy et al. 2004, mtDNA), as well as the saker falcon Falco cherrug (Nittinger et al. 2007). This is indica-tive of a similar biogeographic history of these four birds of prey and may reflect a more general pattern in Palaearctic birds as a clear west–east differentiation into two distinct mtDNA clades has also been found for a number of corvid species (both at the intraspecific level and for sibling species, e.g. carrion and hooded crows, rooks, jackdaws and mag-pies, Haring et al. 2007).

The inference of the demographic history of sea eagles yielded ambiguous results. Bottleneck tests and M-ratio values for the microsatellite data did not show evidence of recent population declines (which are known to have occurred), in line with a previous study and the long gen-eration times of large raptors that buffer the loss of genetic diversity (Hailer et al. 2006). Our increased mitochondrial data set for the first time yielded significant signals of an expansion for both haplogroups based on Tajima’s D and Fu’s Fs, while previous analyses only found clear evidence for haplogroup B (Hailer et al. 2007, Honnen et al. 2010). However, the mismatch analysis still showed significant deviations from the expansion scenario for haplogroup A, although the network shows rare satellite haplotypes around the two central and most common sequences A01 and A02 (i.e. a star-like shape), which is a typical outcome of an increase in population numbers after a bottleneck. Perhaps the fact that there are two central haplotypes in group A (as opposed to a single one in group B) is veiling an expan-sion signal (Supplementary material Appendix 1, Fig. A2). Past geographic substructuring within this group may explain these results, consistent with the differing frequen-cies of the A01 and A02 haplotypes in western populations (Supplementary material Appendix 1, Table A1). Still, our data show that a more comprehensive sampling across Europe yielded better resolution of past dynamics.

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Johnson, J. A., Tingay, R. E., Culver, M., Hailer, F., Clarke, M. L. and Mindell, D. P. 2009. Long-term survival despite low genetic diversity in the critically endangered Madagascar fish-eagle. – Mol. Ecol. 18: 54–63.

Librado, P. and Rozas, J. 2009. DnaSP v5: a software for compre-hensive analysis of DNA polymorphism data. – Bioinformatics 25: 1451–1452.

Literák, I., Mrlík, V., Hovorková, A., Mikulíček, P., Lengyel, J., Št’astaný, K., Cepák, J. and Dubská, L. 2007. Origin and genetic structure of white-tailed sea eagles Haliaeetus albicilla in the Czech Republic: an analysis of breeding distribution, ringing data and DNA microsatellites. – Eur. J. Wildl. Res. 53: 195–203.

Luikart, G., Allendorf, F. W., Cornuet, J.-M. and Sherwin, W. B. 1998. Distortion of allele frequency distributions provides a test for recent population bottlenecks. – J. Hered. 89: 238–247.

Martínez-Cruz, J., Godoy, J. A. and Negro, J. J. 2004. Population genetics after fragmentation: the case of the endangered Spanish imperial eagle Aquila adalberti. – Mol. Ecol. 13: 2243–2255.

Mizera, T. 1999. Bielik. Monografie przyrodnicze No. 4. Wydawnictwo Lubuskiego Klubu Przyrodników. – Świebodzin, in Polish.

Mizera, T. 2002. Bestandsentwicklung und Schutz des Seeadlers (Haliaeetus albicilla) in Polen im Verlauf des 20. Jahrhunderts. – Corax 19, Sonderheft 1: 85–91.

Nittinger, F., Gamauf, A., Pinsker, W., Wink, M., Haring, E. 2007. Phylogeography and population structure of the saker falcon (Falco cherrug) and the influence of hybridiza-tion: mitochondrial and microsatellite data. – Mol. Ecol. 16: 1497–1517.

Posada, D. and Crandall, K. A. 2001. Intraspecific gene genea-logies: trees grafting into networks. – Trends Ecol. Evol. 16: 37–45.

Poulakakis, N., Antoniou, A., Mantziou, G., Parmakelis, A., Skartsi, T., Vasilakis, D., Elorriaga, J., de la Puente, J., Gavashelishvili, A., Ghasabyan, M., Katzner, T., McGrady, M., Batbayar, N., Fuller, M. and Natsagdorj, T. 2008. Population structure, diversity, and phylogeography in the near-threatened Eurasian black vultures Aegypius monachus (Falconiformes; Accipitridae) in Europe: insights from micro-satellite and mitochondrial DNA. – Biol. J. Linn. Soc. 95: 859–872.

Pritchard, J. K., Stephens, M. and Donnelly, P. 2000. Inference of population structure from multilocus genotype data. – Genetics 155: 945–959.

Probst, R. 2002. Bestandsentwicklung und Schutz des Seeadlers (Haliaeetus albicilla) in Österreich. – Corax 19, Sonderheft 1: 92–95.

Randi, E. 2008. Detecting hybridization between wild species and their domesticated relatives. – Mol. Ecol. 17: 285–293.

Randla, T. and Tammur, E. 1996. The white-tailed sea eagle Haliaeetus albicilla population and breeding productivity in Estonia and some regions of NW Europe. – In: Meyburg, B. U. and Chanceller, R. D. (eds), Eagle studies. World Working Group on Birds of Prey, pp. 51–56.

Rogers, A. R. and Harpending, H. 1992. Population growth makes waves in the distribution of pairwise genetic differences. – Mol. Biol. Evol. 9: 552–569.

Rousset, F. 2008. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. – Mol. Ecol. Resour. 8: 103–106.

Segelbacher, G. 2002. Noninvasive genetic analysis in birds: testing reliability of feather samples. – Mol. Ecol. Not. 2: 367–369.

Shephard, J. M., Hughes, J. M., Catterall, C. P. and Olson, P. D. 2005. Conservation status of the white-bellied sea-eagle

STRUCTURE: a simulation study. – Mol. Ecol. 14: 2611–2620.

Excoffier, L. and Lischer, H. 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. – Mol. Ecol. Resour. 10: 564–567.

Fu, Y.-X. 1997. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. – Genetics 147: 915–925.

Garza, J. C. and Williamson, E. G. 2001. Detection of reduction in populations size using data from microsatellite loci. – Mol. Ecol. 10: 305–318.

Godoy, J. A., Negro, J. J., Hiraldo, F. and Donázar, J. A. 2004. Phylogeography, genetic structure and diversity in the endangered bearded vulture (Gypaetus barbatus, L.) as revealed by mitochondrial DNA. – Mol. Ecol. 13: 371–390.

Goudet, J. 1995. FSTAT: a computer program to calculate F- statistics. – J. Hered. 86: 485–486.

Hailer, F., Gautschi, B. and Helander, B. 2005. Development and multiplex PCR amplification of novel microsatellite markers in the white-tailed sea eagle, Haliaeetus albicilla (Aves: Falconiformes, Accipitridae). – Mol. Ecol. Not. 5: 938–940.

Hailer, F., Helander, B., Folkestad, A. O., Ganusevich, S. A., Garstad, S., Hauff, P., Koren, C., Nygård, T., Volke, V., Vilà, C. and Ellegren, H. 2006. Bottlenecked but long-lived: high genetic diversity retained in white-tailed eagles upon recovery from population decline. – Biol. Lett. 2: 316–319.

Hailer, F., Helander, B., Folkestad, A. O., Ganusevich, S. A., Garstad, S., Hauff, P., Koren, C., Masterov, V. B., Nygård, T., Rudnick, J. A., Shiraki, S., Skarphedinsson, K., Volke, V., Wille, F. and Vilà, C. 2007. Phylogeography of the white-tailed eagle, a generalist with large dispersal capacity. – J. Biogeogr. 34: 1193–1206.

Hall, T. A. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. – Nucl. Acids Symp. Ser. 41: 95–98.

Ham, I., Skoric, S. and Vucanovic, M. 2009. Distribution, breeding success and population size of white-tailed eagle Haliaeetus albicilla in Serbia in 2009. – Ciconia 18: 15–28.

Haring, E., Gamauf, A., Kryukov, A. 2007. Phylogeographic patterns in widespread corvid birds. – Mol. Phyl. Evol. 45: 840–862.

Hauff, P. 1998. Bestandsentwicklung des Seeadlers Haliaeetus albicilla in Deutschland seit 1980 mit einem Rückblick auf die vergangenen 100 Jahre. – Vogelwelt 119: 47–63.

Helander, B. and Mizera, T. 1997. White-tailed eagle Haliaeetus albicilla. – In: Hagemeijer, W. J. M. and Blair, M. J. (eds), The EBCC atlas of European breeding birds: their distribu-tion and abundance. Poyser, pp. 136–137.

Helander, B., Marquiss, M. and Bowerman, W. (eds) 2003. Sea eagle 2000. – Swe. Soc. Nature Conserv./SNF & Åtta.45 Tryckeri AB, Stockholm.

Hendrickson, S. L., Bleiweiss, R., Matheus, J. C., de Matheus, L. S., Jácome, N. L. and Pavez, E. 2003. Low genetic variability in geographically widespread Andean condor. – Condor 105: 1–12.

Hewitt, G. M. 1999. Post-glacial re-colonization of European biota. – Biol. J. Linn. Soc. 68: 87–112.

Honnen, A. C., Hailer, F., Kenntner, N., Literák, I., Dubská, L. and Zachos, F. E. 2010. Mitochondrial DNA and nuclear microsatellites reveal high diversity and genetic structure in an avian top predator, the white-tailed sea eagle, in central Europe. – Biol. J. Linn. Soc. 99: 727–737.

Johnson, J. A., Gilbert, M., Virani, M. Z., Asim, M. and Mindell, D. P. 2008. Temporal genetic analysis of the critically endangered oriental white-backed vulture in Pakistan. – Biol. Conserv. 141: 2403–2409.

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Supplementary material (Appendix JAV-00075 at  www.oikosoffice.lu.se/appendix ). Appendix 1.

Haliaeetus leucogaster in Australia determined using mtDNA contol region sequence data. – Conserv. Genet. 6: 413–429.

Sorenson, M. D. and Quinn, T. W. 1998. Numts: a challenge for avian systematics and population biology. – Auk 115: 214–221.

Struwe-Juhl, B. and Grünkorn, T. 2007. Ergebnisse der Farbberingung von Seeadlern Haliaeetus albicilla in Schleswig-Holstein mit Angaben zu Ortstreue, Umsiedlung, Dispersion, Geschlechtsreife, Altersstruktur und Geschwisterverpaarung. – Vogelwelt 128: 117–129.

Tajima, F. 1989. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. – Genetics 123: 585–595.

Tamura, K. and Nei, M. 1993. Estimation of the number of nucle-otide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. – Mol. Biol. Evol. 10: 512–526.

van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. and Shipley, P. 2004. Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. – Mol. Ecol. Not. 4: 535–538.