1

Population Genetic Structure of 

Common Eiders (Somateria mollissima) 

Nesting on Coastal Barrier Islands Adjacent to Oil 

Facilities in the Beaufort Sea, Alaska

Kevin G. McCracken

Principal Investigator 

Co-principal Investigators:

Sarah A. Sonsthagen

Sandy L. Talbot

Richard B. Lanctot

 Kim T. Scribner 

Final Report

OCS Study MMS 2006-040

Prepared By Sarah A. Sonsthagen 

August 2006
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 Contact information 

e-mail: carter@sfos.uaf.edu 

phone: 907.474.1811 

 fax: 907.474.1188 

 Coastal Marine Institute 

 P.O. Box 757220 

School of Fisheries and Ocean Sciences 

 University of Alaska Fairbanks 

 Fairbanks, AK 99775-7220 
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TABLE OF CONTENTS

LIST OF FIGURES .......................................................................................................................................5

LIST OF TABLES .........................................................................................................................................6

LIST OF APPENDICES.................................................................................................................................7

PART 1: Population Genetic Structure of Common Eiders (Somateria mollissima) Breeding in the 

Beaufort Sea, Alaska ......................................................................................................................................8

Abstract ..........................................................................................................................................................8

Introduction ....................................................................................................................................................8

Methods..........................................................................................................................................................9

Sample Collection .....................................................................................................................................................................................9

Microsatellite Genotyping .................................................................................................................................................................10

MtDNA and Nuclear Intron Sequencing ...................................................................................................................................10

Estimation of Genetic Diversity .................................................................................................................................................... 11

Estimation of Population Subdivision ........................................................................................................................................12

Estimation of Gene Flow Among Populations ......................................................................................................................12

Results ..........................................................................................................................................................12

Genetic Diversity ....................................................................................................................................................................................12

Bi-parentally inherited nuclear microsatellites ................................................................................12

Bi-parentally inherited nuclear introns .............................................................................................12

Maternally inherited mtDNA ............................................................................................................13

Population Structure .............................................................................................................................................................................13

Bi-parentally inherited nuclear microsatellites ................................................................................13

Bi-parentally inherited nuclear introns .............................................................................................13

Maternally inherited mtDNA ............................................................................................................14

Estimates of Gene Flow......................................................................................................................................................................14

Female Site Fidelity ..............................................................................................................................................................................14

Discussion ....................................................................................................................................................14

Population Genetic Structure ...........................................................................................................................................................14

Gene Flow ..................................................................................................................................................................................................16

Comparison to Other Waterfowl ....................................................................................................................................................17

Conclusions ..................................................................................................................................................18

Acknowledgments ........................................................................................................................................18

References ....................................................................................................................................................18

PART 2: Multilocus Phylogeography and Population Structure of Common Eiders Breeding in North 

America and Scandinavia ............................................................................................................................35

Abstract .......................................................................................................................................................35

Introduction ..................................................................................................................................................35

Methods........................................................................................................................................................36

Laboratory Techniques ........................................................................................................................................................................36

Statistical Analyses ................................................................................................................................................................................36

Genetic diversity ...............................................................................................................................36

Population genetic structure .............................................................................................................37

Historical demography and gene fl ow ..............................................................................................37

Results ..........................................................................................................................................................38

Genetic Diversity ....................................................................................................................................................................................38

Bi-parentally inherited microsatellite loci ........................................................................................38

Bi-parentally inherited nuclear introns .............................................................................................38

Maternally inherited mtDNA ............................................................................................................39
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Population Genetic Structure ...........................................................................................................................................................39

Bi-parentally inherited microsatellite loci ........................................................................................39

Bi-parentally inherited nuclear introns .............................................................................................39

Maternally inherited mtDNA ............................................................................................................40

Historical Demography and Gene Flow ....................................................................................................................................40

Population fl uctuations .....................................................................................................................40

Nested clade analysis ........................................................................................................................40

Dispersal ...........................................................................................................................................40

Discussion ....................................................................................................................................................41

Population Subdivision .......................................................................................................................................................................41

Phylogeography and Postglacial Colonization ......................................................................................................................43

Conclusions ..................................................................................................................................................45

Acknowledgments ........................................................................................................................................45

References ....................................................................................................................................................45

PART 3: Do Waterfowl Nest in Kin Groups? Evidence from the Common Eider (Somateria mollissima) 

Breeding in the Beaufort Sea, Alaska ..........................................................................................................81

Abstract .......................................................................................................................................................81

Introduction ..................................................................................................................................................81

Methods........................................................................................................................................................82

Sample Collection ..................................................................................................................................................................................82

Microsatellite Genotyping .................................................................................................................................................................82

Data Analysis ............................................................................................................................................................................................82

Results ..........................................................................................................................................................83

Discussion ....................................................................................................................................................83

Conclusions ..................................................................................................................................................84

Acknowledgments ........................................................................................................................................85

Study Products .............................................................................................................................................85

Guest Presentations ......................................................................................................................................86

References ....................................................................................................................................................86
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LIST OF FIGURES

Figure 1.1: Beaufort Sea barrier islands located in (A) Simpson Lagoon (western group) and (B) 

Mikkelsen Bay (eastern group). ...............................................................................................23

Figure 1.2: Unrooted parsimony tree illustrating relationships of (A) 25 lamin A alleles, (B) 22 gapdh

alleles, and (C) 11 mtDNA control region haplotypes. ..........................................................24

Figure 2.1: Subspecies distribution and localities of the 15 Common Eider populations sampled in this 

study. .......................................................................................................................................50

Figure 2.2: Unrooted parsimony trees illustrating relationships of (A) 70 lamin A alleles, (B) 48 gapdh

alleles, and (C) 64 mtDNA control region haplotypes, with the size of the circle node 

corresponding to the frequency of each allele. .......................................................................51

Figure 2.3: Canonical plot of the fi rst two principal components illustrating the partitioning of overall 

FST variance among Common Eider populations. Ellipses illustrate the relative positions of 

populations from each of the fi ve sampled subspecies. ...........................................................53

Figure 2.4: MtDNA control region haplotype network and nested design for the nested clade analysis. ..54

Figure 3.1: Beaufort Sea barrier islands located in (A) Simpson Lagoon (western group) and (B) 

Mikkelsen Bay (eastern group) with samples sizes for each island in a given year in 

parentheses. .............................................................................................................................89

Figure 3.2: Genetic correlation (r) of females breeding in (A) Simpson Lagoon and (B) Mikkelsen Bay at 

increasing distance class size intervals. ...................................................................................90

Figure 3.3: Bubble plots of two-dimensional local spatial autocorrelation analysis of Common Eider 

females nesting in Simpson Lagoon and Mikkelsen Bay in 2000–2003. Each plot shows the 

study area with squares indicating the nest location. .............................................................91
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LIST OF TABLES

Table 1.1: Estimates of genetic diversity. ...................................................................................................25

Table 1.2: Hierarchical analysis of molecular variance (AMOVA) of allelic and haplotypic frequencies 

for islands within Mikkelsen Bay and Simpson Lagoon. .......................................................27

Table 1.3: Estimates of pairwise inter-population variance in allelic frequency (ΦST) values calculated 

for lamin A (above diagonal) and gapdh (below diagonal) for Common Eiders from each pair 

of nine islands breeding in the Beaufort Sea. .............................................................................28

Table 1.4: Pairwise ΦST values calculated for 545 bp of mtDNA control region (above diagonal) and 

lamin A site 116 (below diagonal) for Common Eiders breeding from each pair of nine islands 

breeding in the Beaufort Sea. .....................................................................................................29

Table 1.5: Comparison of alternative models of Common Eider gene fl ow between Mikkelsen Bay and 

Simpson Lagoon. ......................................................................................................................30

Table 2.1: Observed (Ho) and expected heterozygosities (He), haplotype (h) and nucleotide (π) 

diversity ......................................................................................................................................55

Table 2.2: Pairwise FST, RST, and ΦST values for 12 microsatellite loci, lamin A, gapdh, and mtDNA 

control region for 15 Common Eider populations. .....................................................................58

Table 2.3: Hierarchical analysis of molecular variance (AMOVA) of allelic and haplotypic frequencies 

for populations classifi ed by (1) subspecies and (2) geographic proximity. ..............................61

Table 2.4: Proportion of individuals from sampled populations in each of the four clusters inferred from 

12 microsatellite loci in STRUCTURE. .....................................................................................63

Table 2.5: Hierarchical analysis of molecular variance of mtDNA haplotype frequencies for populations 

grouped to test putative source refugia for Common Eider populations breeding in North 

America and Scandinavia. ..........................................................................................................63

Table 2.6: Results of demographic analyses for 12 microsatellite loci under the infi nite allele model 

(IAM), stepwise mutation model (SMM), and two-phased model of mutation (TPM) and 

sequence data. .............................................................................................................................64

Table 2.7: Inferred demographic events of the nested clade analysis (only clades with signifi cant Dc/Dn

values are shown). ......................................................................................................................66

Table 2.8: Migration matrix calculated from 12 microsatellite loci, nuclear introns lamin A and gapdh, 

and mtDNA control region. .......................................................................................................67

Table 3.1: Number of alleles, fragment length, observed heterozygosity (Ho), and probability of identity 

among randomly mating individuals (PID), and siblings (PIDsib) for 14 microsatellite loci. ....93

Table 3.2: Average number of alleles, observed and expected heterozygosities (Ho/He), overall relatedness 

values (rxy) with variances, and sample sizes (n) for Common Eiders breeding on two island 

groups. ........................................................................................................................................93

Table 3.3: Pearson correlation values (r) for genetic distance (GD) and relatedness values (rxy) and 

geographic distance for female Common Eiders nesting on two island groups. ........................94

Table 3.4. Local autocorrelation (lr) values and percent of nesting females from Simpson Lagoon and 

Mikkelsen Bay in 2000–2003 with positive genetic correlation among a focal individual and 

her four nearest neighbors. .........................................................................................................94
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LIST OF APPENDICES 

Appendix 1.A: Latitude and longitude of Common Eider (Somateria mollissima) samples analyzed in 

this study. ...........................................................................................................................31

Appendix 1.B: Number of haplotypes per sampled island for lamin A, gapdh, and mtDNA control 

region. ................................................................................................................................33

Appendix 2.A: Localities of Common Eiders sampled* in this study. ........................................................72

Appendix 2.B: Variable positions and frequency (n) of nuclear intron lamin A alleles reconstructed in 

PHASE in Common Eiders. ..............................................................................................75

Appendix 2.C: Variable positions and frequency (n) of nuclear intron gapdh alleles reconstructed in 

PHASE in Common Eiders. ..............................................................................................77

Appendix 2.D: Variable positions and frequency (n) for each mtDNA control region haplotype from 

Common Eiders. ...............................................................................................................79
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PART 1: Population Genetic Structure of Common Eiders (Somateria mollissima) Breeding in the 

Beaufort Sea, Alaska

Abstract

We assessed the level of population subdivision 

within the Pacifi c Common Eider (Somateria mollissima v-nigrum) breeding on 12 barrier islands in the 

Beaufort Sea, Alaska, using molecular markers with 

differing modes of inheritance and rates of evolution, 

as well as recapture data. Common Eider populations 

exhibited fi ne-scale population structuring based on 

all marker types. Regional comparisons between two 

island groups, Mikkelsen Bay and Simpson Lagoon, 

revealed structuring at 14 microsatellite loci (FST = 

0.004, P = 0.016), mitochondrial DNA (mtDNA) control 

region (ΦST = 0.082, P = 0.047), and nuclear intron lamin A (ΦST = 0.022, P = 0.022). Given the geographic 

proximity of island groups (approximately 90 km apart), 

these values are noteworthy. Recapture data revealed 

substructuring between island groups, as we did not 

detect any female dispersal between groups (n = 34). In 

addition, inter-population variation in allelic frequencies was observed within mtDNA (ΦST = 0.135–0.271) 

and nuclear intron lamin A (ΦST = 0.089–0.173). Gene 

fl ow estimates based on microsatellite, mtDNA, and 

nuclear intron loci indicate asymmetrical western dispersal has occurred between island groups. Asymmetrical western gene fl ow may be driven by females from 

Mikkelsen Bay stopping early on spring migration at 

Simpson Lagoon to breed. Alternatively, young females 

arriving later may be forced to nest in Simpson Lagoon 

due to distribution of available nest sites. These data 

suggest that areas of genetic discordance can exist over 

very small spatial scales relative to the species dispersal 

capabilities and known dispersal distances. 

Introduction

Microgeographic population structure is greatly infl uenced by natal and breeding dispersal. Here we defi ne 

natal dispersal as the distance between an individual’s 

natal site and the site of its fi rst breeding attempt, and 

breeding dispersal as the distance an individual travels 

between each subsequent breeding attempt (Greenwood 

1980). For many species, detecting natal and breeding 

dispersal is diffi cult, especially for mobile organisms 

that may travel long distances prior to and between 

breeding attempts. Advances in molecular techniques 

have made it possible to assess genetic structure of natural populations and evaluate the roles of contemporary 

and past dispersal events among areas (Newton 2003). 

Birds are of particular interest because most species that 

breed in arctic or temperate regions migrate to other 

areas during the nonbreeding season, and thus, show less 

geographic structure than other vertebrate groups (Avise 

1996). Lack of population structure has been attributed 

to environmental variability of arctic and temperate regions, which increases dispersal and migratory behavior 

in birds, and can homogenize genetic diversity (Winker 

et al. 2000). Conversely, many birds exhibit high natal 

and breeding site fi delity (e.g., natal and breeding 

philopatry, respectively), which is expected to restrict 

gene fl ow among neighboring populations (Avise 1996), 

leading to population subdivision. 

Differences in the degree of philopatry also may exist 

between males and females. The most common pattern 

in birds is for females to disperse farther between natal 

and breeding sites than males (Greenwood 1980). However, female waterfowl typically show greater natal and 

breeding philopatry than males (Rohwer and Anderson 

1988). Males and females typically pair on the wintering grounds, and the male accompanies the female back 

to her natal area. Because ducks from different breeding areas frequently share a common wintering ground, 

males may disperse over long distances. Additionally, 

males may not mate with the same female each year, resulting in individual males breeding in distant locations 

from year-to-year (Anderson et al. 1992). Male behavior, thus, is expected to cause genetic mixing among 

individuals from multiple breeding areas, which may 

explain why many species of ducks are morphologically 

monotypic across the Holarctic (Newton 2003). 

Patterns of natal, breeding, and winter site fi delity 

may leave varying signatures in molecular markers. Accordingly, researchers using genetic tools to investigate 

levels of spatial structuring and gene fl ow should use 

markers that differ in their mode of inheritance. For 

example, if females exhibit high natal and breeding 

philopatry and males disperse over large distances; there 

should be genetic structuring at the maternally inherited 

marker and little or no structure at bi-parentally inherited nuclear markers. If data were collected from just one 

of these genomes, gene fl ow among populations might 

be grossly over- or under-estimated depending on which 

marker type was used (Avise 2004). However, combining markers with different modes of inheritance and 

rates of evolution, researchers may ask a wider range of 

questions involving species population genetic structure 

and behavior.

Common Eiders (Somateria mollissima) have a 

circumpolar distribution and inhabit coastal regions 

throughout the Holarctic. There are 6 or 7 recognized 

subspecies that have partially overlapping breeding 
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ranges (Goudie et al. 2000). The Pacifi c Common Eider 

(S. m. v-nigrum) breeds on the barrier islands of western 

Canada and Alaska (Johnson and Herter 1989, Johnson 

2000), and has experienced a marked population decline 

(approximately 53%), since the mid-1970s (1996 population estimate 111,635 ± 42,440; Suydam et al. 2000). 

More recent surveys, however, indicate the population 

may have stabilized (R. Suydam, pers. comm.). Reasons 

for the decline are unknown. However, these birds are 

long lived with a low reproductive rate, which may be 

limiting population growth (Goudie et al. 2000). Long 

generation times could potentially increase extinction or 

extirpation risk (Marzluff and Dial 1991). 

Rates of gene fl ow among breeding Pacifi c Common 

Eiders over scales relevant to conservation have not 

been measured. Satellite telemetry studies, however, indicate that adult female eiders that nest on islands in the 

Beaufort Sea may intermix with other eider populations 

during migration to wintering grounds in the Bering Sea 

south of the Chukotka Peninsula, Russia (Petersen and 

Flint 2002; L. Dickson, pers. comm.). Females that nest 

across large areas possibly pair with males from other 

breeding populations during winter, allowing gene fl ow 

through male-biased dispersal. Such gene fl ow would 

occur despite the observation that all transmittered 

females returned to their breeding areas in the western 

Beaufort Sea the following summer (Petersen and Flint 

2002; L. Dickson, pers. comm.).

Data on degree of spatial population genetic structure 

are currently available only for the European Common Eider (S. m. mollissima) that breeds in the Baltic 

Sea. Tiedemann et al. (1999) found high levels of 

population structure among colonies in the Baltic Sea 

(133–1010 km) for maternally inherited mtDNA (ΦST

= 0.262–0.343, P < 0.001), and signifi cant, but lower, 

levels for bi-parentally inherited microsatellite loci (FST

= 0.009–0.029, P < 0.05). The authors attributed high 

levels of spatial structure assayed using mtDNA to high 

rates of female natal philopatry. Proportionally lower 

spatial variance in allelic frequency at microsatellite loci 

was attributed to non-random mating by males on the 

wintering grounds (i.e., males mate with females from 

the same locality more often than expected). 

Information on degree of spatial population structure 

for Pacifi c Common Eiders in the western Beaufort 

Sea is of particular interest to management and industry agencies because of the close proximity of nesting 

areas to oil and gas development activities (Minerals 

Management Service 2003). The phylopatric nature of 

Common Eiders (Reed 1975, Swennen 1990) coupled 

with nesting proximity to major industry infrastructure 

(Flint et al. 2003) makes this population susceptible to 

human disturbance (i.e. aircraft fl ights, personnel, etc.). 

In addition, increases in numbers of avian and mammalian predators near oil development may adversely affect 

nest success and duckling survival (Johnson 2000). 

Effects of increased disturbances may be temporarily 

confounded as predators may avoid high use areas, potentially increasing nest success initially (Johnson 2000). 

Conversely, if increased disturbance causes females to 

fl ush from nests more readily, then nests would be more 

susceptible to predators. Subspecies risks can be exacerbated should genetically distinct populations occur in 

proximity to existing or proposed development. Thus, 

evaluation of population structure of Common Eiders in 

the western Beaufort Sea can provide means to assess 

potential risks of oil and gas exploration to the population or species. 

We estimated levels of spatial population structure of 

Pacifi c Common Eiders breeding on the barrier islands 

in the western Beaufort Sea using microsatellite genotypes and sequence information from mtDNA control 

region and two nuclear introns, coupled with banding 

and genetic recapture data. Microsatellite and mtDNA 

loci have been used extensively to examine genetic 

discordance at fi ne spatial scales among waterfowl 

populations (e.g., Lanctot et al. 1999, Tiedemann et al. 

1999, Scribner et al. 2001, Scribner et al. 2003, Pearce 

et al. 2004, Pearce et al. 2005). To our knowledge, 

however, this study is the fi rst to use nuclear introns to 

assess levels of microgeographic population subdivision. 

Variation in nuclear introns, due to their higher effective 

population size, relative to mtDNA, and slower mutation 

rate, enables us to ask questions about historic processes 

infl uencing population subdivision (Hare 2001) occurring within the Beaufort Sea population. We hypothesized that the nuclear markers (microsatellites and intron 

sequences) would show little population genetic structure, because Common Eiders breeding on these islands 

share a common wintering ground with eiders from 

several other breeding areas. Over time, male dispersal 

among populations could homogenize allelic frequencies 

within the nuclear genome. However, we predicted that 

population structure would be observed at the maternally 

inherited mtDNA because of the high degree of female 

natal and breeding philopatry reported in other subspecies of Common Eider.

Methods

Sample Collection

Blood or feather samples from breeding female 

eiders and egg samples from nests were collected during 

mark-recapture and monitoring efforts on the barrier 

islands in the Beaufort Sea, Alaska, between June and 

July of 2000–2003. Samples were collected from two 
10

island groups, consisting of 12 islands in total (Fig. 1.1). 

The western group, hereafter called Simpson Lagoon, 

consists of fi ve islands: Stump (70.419oN 148.601oW), 

Wannabe (70.437oN 148.725oW), Egg (70.440oN 

148.739oW), Long (70.480oN 148.937oW), and Spy 

(70.564oN 149.895oW) islands (Fig. 1.1A). The eastern 

group, hereafter called Mikkelsen Bay; consists of seven 

islands: Camp (70.172oN 146.226oW), Point Thomson (70.186oN 146.325oW), Mary Saches (70.200oN 

146.207oW), North Star (70.225oN 146.347oW), 

Duchess (70.233oN 146.405oW), Alaska (70.233oN 

146.559oW), and Challenge (70.237oN 146.640oW) 

islands (Fig. 1.1B). Distances between islands within 

each of the two groups ranged from 1.2–49.2 km, and 

distances between islands located in Simpson and Mikkelsen Bay ranged from 78.1–143.1 km. Two islands, 

Camp and Wannabe, are not offi cial names of islands on 

any recognized maps, but were given these names for 

the purpose of identifying areas in this study. 

Females (n = 198) were captured on nests using a 

dip net during initial nest searching efforts or with a 

bow net during late-incubation (Sayler 1962). Blood 

was collected from the tarsal, brachial, or jugular veins 

and placed in blood lysis buffer (Longmire et al. 1988). 

Feather samples (n = 114) were collected from nest 

bowls from unsampled females and stored in silica gel 

desiccant at room temperature. Egg samples (n = 15 

from 9 clutches) were collected opportunistically from 

abandoned or depredated nests or eggs that were cracked 

while trapping females. Egg membranes were placed 

in tissue preservation buffer (4.0 M Urea, 0.2 M NaCl, 

10 mM EDTA, 0.5% N-Lauroyl-sarcosine, and 100 mM 

Tris-HCl [pH 8.0]; S. Talbot unpubl. data). Pectoral 

muscle and heart also were collected from eggs with 

developed embryos and stored in tissue preservation 

buffer. 

After returning from the fi eld, samples were stored at 

-80°C at the U. S. Geological Survey Molecular Ecology Laboratory. Genomic DNAs were extracted using 

either a “salting out” protocol described in Medrano et 

al. (1990) with modifi cations described in Sonsthagen et 

al. (2004), or a QIAGEN DNeasy Tissue Kit (QIAGEN, 

Valencia, CA). Genomic DNA extractions were quantifi ed using fl uorometry and diluted to 50 ng/μL working 

solutions.

Microsatellite Genotyping

Primers used for microsatellite genotyping of Common Eiders (n = 327; Appendix 1.A) were obtained via 

cross-species screening of microsatellite primers developed for other waterfowl. We screened 12 Common 

Eiders at 50 microsatellite loci reported to be variable 

for other waterfowl species and selected 14 microsatellite loci found to be polymorphic: Aph02, Aph08, 

Aph20, Aph23 (Maak et al. 2003); Bcaμ1, Bcaμ11, 

Hhiμ3 (Buchholz et al. 1998); Cm09 (Maak et al. 2000); 

Sfiμ10 (Libants et al. unpubl. data); Smo4, Smo7, Smo08, 

Smo10, and Smo12 (Paulus and Tiedemann 2003). Microsatellites were amplifi ed using the polymerase chain 

reaction (PCR), and products were electrophoresed following protocols described in Sonsthagen et al. (2004) 

for tailed primers (Aph02, Aph08, Aph20, Aph23, Cm09, 

Smo4, Smo7, Smo08, Smo10, and Smo12) and Pearce et 

al. (2005) for direct-labeled primers (Bcaμ1, Bcaμ11, 

Hhiμ3, and Sfiμ10). For quality control purposes, 10% 

of the samples were randomly selected, re-amplifi ed, 

and genotyped in duplicate.

MtDNA and Nuclear Intron Sequencing

We amplifi ed a 545 bp portion of the control region 

domain I and II (Baker and Marshall 1997) using primer 

pairs L263 (5’-CCAAATYGCACRYCTGACAYTCCAAGC-3’) and H848 (5’-GCCCCATTATRTAGGAGCTGCGG-3’) approximately corresponding to 

positions 263 and 848 in the chicken mtDNA genome 

(Desjardins and Morais 1990). Only a subset of individuals, those for which we had blood samples, were sequenced (n = 98). PCR amplifi cations were carried out 

in a 50 μL volume reaction: 2–100 ng genomic DNA, 

0.5 μM each primer, 1.0 μM dNTPs, 1X PCR buffer 

(Fisher Scientifi c, Pittsburgh, PA), 2.5 μM MgCl2, and 

0.2 units Taq Polymerase. PCR reactions began with 

94oC for 7 minutes followed by 45 cycles each of 94°C 

for 20 s; 60°C for 20 s; 72°C for 1 min., concluded by a 

7 min. extension at 72°C. PCR products were gel purifi ed using a QIAGEN QIAquick Gel Extraction Kit and 

both strands were sequenced using Applied Biosystems 

BigDye v.3 Terminator Cycle Sequencing Kit diluted 

4-fold on an ABI 3100 DNA sequencer (ABI: Applied 

Biosystems, Foster City, CA). Sequences from opposite 

strands were assembled using Sequencher 4.1.2 (Gene 

Codes Corporation, Ann Arbor, MI). 

Due to the existence of nuclear pseudogenes in avian 

species (Sorenson and Fleischer 1996), we verifi ed that 

the amplifi ed sequences were mtDNA control region 

by comparing sequences from heart and blood samples 

from fi ve putative mother and offspring groups. Since 

bird heart tissue is relatively rich in mtDNA and blood 

is relatively rich in nuclear DNA, any differences in 

sequences from mother/offspring groups are predicted to 

refl ect the amplifi cation of mtDNA and nuclear pseudogenes. Other studies also have compared heart, blood, 

and muscle to determine if primers are amplifying true 

mtDNA and not nuclear pseudogenes (Pearce et al. 

2004). Common Eider sequences also were compared to 
11

those deposited in GenBank and individuals containing 

electropherogram double-peaks within mtDNA sequence 

data were re-sequenced. If co-amplifi ed peaks were 

still detected at one of the 13 variable sites, presumably due to nuclear pseudogenes present in this species 

(Tiedemann and Kistowski 1998, S. Sonsthagen unpubl. 

data) or heteroplasmy, those individuals were removed 

(~10%). Sequences will be deposited in GenBank 

(http//:www.ncbi.nlm.nih.gov) upon publication of the 

results of this report.

Six nuclear introns also were screened for polymorphism in Common Eiders: beta-fi brinogin (bf) intron 7 

(BF7F2 5’-GTTAGCATTATGAACTGCAAGTAATTG3’; BF7R2 5’-TTTCTTGAATCTGTAGTTAACCTGATG-3’; M. D. Sorenson unpubl. data), lamin A intron 

3 (McCracken and Sorenson 2005), chromosome Z 

chromo-ATPase/helicase/DNA binding protein (chd1-W; 

Fridolfsson and Ellegren 1999), glyceraldehyde-3-phosphate dehydrogenase (gapdh) intron 11 (McCracken and 

Sorenson 2005), and ornithine carboxylase (od) intron 

7 (OD7F 5’–TCGTTCAAGCCATTTCTGATGCC–3’; 

OD8R 5’-CCAGGRAAGCCACCAATRTC-3’; K. McCracken and M. Sorenson unpubl. data). Introns bf7, 

od7, and chd1-W showed very little variation within 

Common Eiders, with only 1–2 polymorphic sites in ten 

individuals. Two of the introns, gapdh (386–387 bp; 

McCracken and Sorenson 2005) and lamin A (280 bp; 

McCracken and Sorenson 2005) showed high levels of 

polymorphism (14 and 15 positions, respectively) and 

were sequenced using techniques described above with 

some modifi cations. PCR amplifi cations were carried 

out in a 50 μL volume; 2–100 ng genomic DNA, 0.5 

μM each primer, and 25 μL AmpliTaq Gold PCR master 

mix (Applied Biosystems, Foster City, California). PCR 

reactions began with 94°C for 7 minutes followed by 45 

cycles each of 94°C for 20 s; 64°C for 20 s; 72°C for 1 

min., and ended with a 7 min. fi nal extension at 72°C. 

Only sequences from the forward strand were collected 

on an ABI 3100 DNA sequencer because the PCR 

templates were short (280–387 bp) and sequences had a 

consistent electropherogram peak height throughout the 

length of the fragment. Sequences that contained double-peaks of approximately equal peak height, indicating 

the presence of two alleles, were coded with IUPAC degeneracy codes and treated as polymorphisms (Kulikova 

et al. 2004). Many sequences for gapdh contained a 

single recurring one base pair indel. To obtain data from 

the entire fragment for individuals that were heterozygous (~73%) for these alleles, we also sequenced the 

reverse strand. Sequences will be deposited in GenBank 

(http//:www.ncbi.nlm.nih.gov) upon publication of the 

results of this report.

Estimation of Genetic Diversity

To determine if the same individual was sampled 

across multiple years (between feather and blood or 

feather and feather samples), probabilities of identity for 

a randomly mating population (PID) and among siblings 

(PIDsib) were calculated in Gimlet 1.3.3 (Valière 2002) 

using genotypes from the 14 microsatellite loci. Samples with identical genotypes (n = 9) across the 14 loci 

and mother/offspring groups (n = 9) were removed from 

the analyses. For females that switched breeding islands 

among years (n = 13; 38%), we designated the island 

where the fi rst capture occurred as a female’s breeding 

population to maintain independence among samples.

We sequenced a subset of individuals for mtDNA 

and the two introns. Islands with low sample sizes were 

pooled based on geographic proximity (not greater than 

3 km) of nests to neighboring islands. Samples from 

Challenge Island were pooled with Alaska Island, samples from Mary Saches Island were pooled with North 

Star Island, and samples from Wannabe Island were 

pooled with Egg Island. Allelic phases for lamin A and 

gapdh introns were inferred from diploid sequence data 

using PHASE 2.0 (Stephens et al. 2001). This program 

uses a Bayesian approach to reconstruct haplotypes from 

population genotypic data, and allows for recombination and the decay of linkage disequilibrium (LD) with 

distance. The PHASE analysis (parameters: 1,000 iterations with a 1,000 burn-in period) was repeated three 

times to ensure consistency across runs, as suggested by 

Stephens et al. (2001). 

Using only adult breeding females, we calculated 

allelic frequencies, inbreeding coeffi cient (FIS), and 

expected and observed heterozygosities for each microsatellite locus, mtDNA, and nuclear introns in GENEPOP 3.1 (Raymond and Rousset 1995) and FSTAT 2.9.3 

(Goudet 1995, 2001). Hardy Weinberg Equilibrium 

(HWE) and linkage disequilibrium (LD) were tested in 

GENEPOP using the default parameters (Markov chain 

parameters: dememorization number 1,000, number of 

batches 100, and number of iterations per batch 10,000). 

MtDNA control region and nuclear introns lamin A 

and gapdh sequences were tested for selective neutrality and historical fl uctuations in population demography, using Fu’s Fs (Fu 1997) and Tajima’s D (Tajima 

1989) in ARLEQUIN. Critical signifi cance values 

of 5% required a P-value below 0.02 for Fu’s Fs (Fu 

1997). Unrooted phylogenetic trees for each gene were 

constructed in TCS 1.18 (Clement et al. 2000), which 

estimates genealogies using 95% statistical parsimony 

probabilities as defi ned by Templeton et al. (1992). Lamin A and gapdh intron sequences were also analyzed in 

NETWORK 4.1.0.8 (Fluxus Technology Ltd. 2004) using the Reduced Median network (Bandelt et al. 1995), 
12

to illustrate possible reticulations in the gene trees due to 

homoplasy or recombination. 

Estimation of Population Subdivision

The degree of population subdivision among islands 

and between each island group were assessed by calculating global and pairwise FST, RST, and ΦST for microsatellite genotype and sequence data in FSTAT 2.9.3 and 

ARLEQUIN 2.0 (Schneider et al. 2000), adjusting for 

multiple comparisons using Bonferroni corrections (α

= 0.05) or permutations (3000) in FSTAT and ARLEQUIN, respectively. Fixation indices (FST, RST, and 

ΦST) mentioned above differ in the underlying model 

used to calculate values; such that, FST uses the island 

model, RST uses the stepwise mutation model developed 

for microsatellites, and ΦST uses a nucleotide substitution model that best fi ts the sequence data. Inter-haplotypic and inter-allelic sequence divergences were used to 

calculate pairwise ΦST (Excoffi er et al. 1992). MODELTEST 3.06 (Posada and Crandall 1998) was used to determine the minimum parameter nucleotide substitution 

model that best fi t the mtDNA and intron sequence data 

under the Akaike Information Criterion (Akaike 1974). 

Pairwise genetic distances between unique haplotypes 

and alleles were calculated in PAUP* 4.0 (Swofford 

1998) for mtDNA and ARLEQUIN for nuclear introns. 

Additionally, a hierarchical analysis of molecular variance (AMOVA) was performed using ARLEQUIN to 

determine the magnitude of spatial variance in haplotypic and allelic frequencies among populations within and 

among island groups. An isolation by distance analysis 

was performed in IBD (Bohonak 2002), with microsatellite data and nuclear intron data using genotypic 

data inferred from the PHASE analysis, to determine if 

more geographically distant population pairs are also 

more genetically differentiated. IBD tests the statistical 

signifi cance of the relationship between genetic and geographic distance using a Mantel test and calculates slope 

and intercept from RMA regressions following Sokal 

and Rohlf (1981) with confi dence limits. 

Finally, microsatellite data were analyzed in STRUCTURE 2.1 (Pritchard et al. 2000) to detect the occurrence of population structure without a priori knowledge 

of putative populations. Data were analyzed using an 

admixture model assuming correlated frequencies to 

probabilistically assign individuals to putative populations with 10,000 burn-in period, 100,000 Markov chain 

Monte Carlo iterations, number of possible populations 

(K) ranging from 1–10; this analysis was repeated fi ve 

times to ensure consistency across runs. 

Estimation of Gene Flow Among Populations

We used MIGRATE 2.0.3 (Beerli 1998, 2002, 

Beerli and Felsenstein 1999) to calculate the number 

of migrants per generation (Nem) for microsatellite and 

nuclear intron data and number of female migrants per 

generation (Nfm) for mtDNA between the two island 

groups. Full models, θ (4Neμ, composite measure of 

effective population size and mutation rate), and all pairwise migration parameters were estimated individually 

from the data and compared to restricted island models 

for which θ and pairwise migration parameters are symmetrical among populations. 

MIGRATE was run using maximum likelihood 

search parameters: ten short chains (1,000 used trees 

out of 20,000 sampled), fi ve long chains (10,000 used 

trees out 200,000 sampled), and fi ve adaptively heated 

chains (start temperatures: 1, 1.5, 3, 6, and 12; swapping 

interval = 1). Full models were run three times to ensure 

the convergence of parameter estimates. Restricted 

models were run once. Competing models were evaluated for the goodness of fi t given the data using a loglikelihood ratio test. The resulting statistic from the log 

likelihood ratio test is equal to a χ2 distribution with the 

degrees of freedom equal to the difference in the number 

of parameters estimated in the two models (Beerli and 

Felsenstein 2001). 

Results

Genetic Diversity

Bi-parentally inherited nuclear microsatellites

The number of alleles per locus at the 14 polymorphic microsatellite loci ranged from 3–44, with an 

average of 11.3 alleles per locus. The average number 

of alleles per population ranged from 4.93–8.21. The 

observed heterozygosity for each population ranged 

from 11.9–91.8% with an overall value of 57.7%. The 

inbreeding coeffi cient (FIS) ranged from –0.071 to 0.060 

across all islands with an overall value of 0.027. None 

of the inbreeding coeffi cients were signifi cantly different 

from zero (P > 0.05). 

Bi-parentally inherited nuclear introns

Twenty-fi ve alleles for nuclear intron lamin A were 

reconstructed from 108 individuals in PHASE (Fig. 

1.2A; Appendix 1.B). Sixty (56%) individuals were 

homozygous at all variable sites, and 22 (20%) individuals were heterozygous at one site. Using PHASE, 

probabilities of reconstructed haplotypes for individuals 

that were heterozygous for more than one site ranged 

from 0.82–0.99 (n = 22), except for two individuals 

with haplotype probabilities of 0.62, and 0.68. PHASE 
13

calculated the background recombination rate (ρ) as 

0.50, with factors exceeding ρ ranging from 0.58–1.94 

between 14 variable sites. 

For nuclear intron gapdh, PHASE reconstructed 22 

alleles from 88 individuals (Fig. 1.2B; Appendix 1.B). 

Six (7%) individuals were homozygous at all variable 

sites, and one (1%) individual was heterozygous at one 

site. Probabilities of all other reconstructed haplotypes 

ranged from 0.92–1.00 (n = 57) and 0.43–0.87 (n = 24), 

which may be attributable to potentially high levels of 

recombination occurring within this marker (0.39–4.41 

factors exceeding ρ = 0.05, between 15 variable sites). 

There were seven variable sites that exceeded ρ by 

one or more factors: 2.12 factors between sites 16 and 

22, 1.42 factors between sites 22 and 26, 1.12 factors 

between sites 48 and 49, 1.02 factors between sites 136 

and 145, 1.11 factors between sites 165 and 170, 1.36 

factors between sites 186 and 192, and 4.41 factors 

between sites 232 and 252. 

Haplotype (h) and nucleotide (π) diversity ranged 

from 0.600–0.915 and 0.005–0.009, respectively, for 

lamin A, and from 0.874–0.954 and 0.006–0.009, 

respectively, for gapdh (Table 1.1). Observed and 

expected heterozygosity for lamin A was 41.6% and 

87.9%, respectively, which signifi cantly deviated from 

HWE (P = 0.004). Observed and expected heterozygosity for gapdh was 92.4% and 89.9%, respectively, which 

also signifi cantly deviated from HWE (P < 0.001). 

Observed and expected heterozygosity for lamin A and 

gapdh combined was 67.8% and 88.6%, respectively, 

which signifi cantly deviated from HWE (P = 0.004). 

Signifi cantly negative values for Fu’s Fs (P < 0.02) 

were observed for North Star and Mary Saches (lamin 

A -2.690; Table 1.1), Duchess (lamin A –4.704; gapdh

-3.602; Table 1.1), and Long (lamin A –4.943; Table 1.1) 

islands, suggestive of population expansion.

Maternally inherited mtDNA

Eleven unique mtDNA control region haplotypes 

were resolved from 83 individuals (Fig. 1.2C; Appendix 1.B) defi ned by 13 variable sites. Haplotype and 

nucleotide diversity was high for most populations with 

values for haplotype (h) and nucleotide (π) diversity 

ranging from 0.000–0.891 and 0.000–0.009, respectively 

(Table 1.1). Spy Island was monotypic for mtDNA 

control region variation. Other islands were represented 

by 2–6 unique haplotypes, with Duchess Island having 

the highest number of unique haplotypes (Table 1.1). 

Neutrality tests found no evidence for selection (Fu’s Fs

= 0.090–2.139, P > 0.02; Tajima’s D = –1.295–0.591, P 

> 0.05; Table 1.1).

Population Structure

Bi-parentally inherited nuclear microsatellites

After removing mother/offspring groups and identical 

genotypes (n = 18) at 14 microsatellite loci, the overall 

FST (global 0.004, P = 0.007) was signifi cant. However, 

we did not observe a signifi cant level of differentiation 

using a RST based approach (global -0.004, P > 0.05). 

Our overall estimate of population subdivision was 

low, and was not detected using the Bayesian clustering 

method implemented by the program STRUCTURE. 

The most likely model generated from the microsatellite data was maximized when the total number of 

populations was one. In addition, we did not detect any 

signifi cant pairwise FST and RST comparisons among 

islands. However, the comparison between Mikkelsen 

Bay and Simpson Lagoon was signifi cant (FST = 0.004, 

P = 0.016; Table 1.2). Moreover, a hierarchical analysis 

of molecular variance uncovered low but signifi cant 

variance within populations and populations within a 

group using the FST based approach (Table 1.2). Finally, 

we found no evidence of isolation by distance correlations between genetic and geographic distances (r = 

0.012, P = 0.46).

Bi-parentally inherited nuclear introns

Levels of spatial genetic structure for nuclear intron 

sequences were calculated using a nucleotide substitution model. MODELTEST indicated that the nucleotide 

substitution model that best fi t the intron sequence data 

was the Tamura-Nei (1993) model with an invariant site 

parameter for both lamin A and gapdh. We detected signifi cant differences in the spatial distribution of allelic 

frequencies for lamin A (global ΦST = 0.023, P = 0.02) 

among islands. AMOVA detected signifi cant variance 

among populations within groups and within populations (Table 1.2). A pairwise comparison between Mikkelsen Bay and Simpson Lagoon island groups also was 

signifi cant for lamin A (ΦST = 0.022, P = 0.02; Table 

1.2). Inter-island comparisons indicated large signifi cant 

pairwise differences within lamin A (ΦST = 0.089-0.173; 

Table 1.3), but not for gapdh (global ΦST = –0.071, P = 

0.94; Tables 2, 3). As with the microsatellite data, we 

detected no signifi cant correlations between genetic and 

geographic distances for lamin A and gapdh combined (r

= 0.096, P = 0.28) or analyzed separately (lamin A r = 

-0.012, P = 0.050; gapdh r = 0.142, P = 0.20).

Since we observed signifi cant pairwise comparisons 

within lamin A, we also calculated FST values for each 

polymorphic site in FSTAT. Signifi cant FST values occurred at one of the 14 polymorphic positions: site 116 

(FST = 0.153 ± 0.084). Signifi cant pairwise comparisons 

among islands were calculated for position 116, with 
14

signifi cant FST values ranging from 0.053–0.352 (Table 

1.4). However, site 116 is monomorphic for all islands 

in Mikkelsen Bay, therefore, the combination of several 

sites and the presence of rare alleles may be driving the 

observed population differentiation within Mikkelsen 

Bay. 

Maternally inherited mtDNA

Population subdivision estimates also were calculated 

using a nucleotide substitution model. MODELTEST 

indicated that the nucleotide substitution model that best 

fi t the data was the Tamura-Nei (1993) model with an invariant site parameter (substitute rate matrix: R[A–C] = 

1.0000, R[A–G] = 34.6051, R[A–T] = 1.0000, R[C–G] 

= 1.0000, R[C–T] = 23.3368, R[G–T] = 1.0000, p–inv. 

= 0.8325, A = 0.2179, C = 0.3064, G = 0.1940, T = 

0.2817). Mean inter-population variance in haplotypic 

frequency was low (global ΦST = 0.070, P = 0.05), along 

with a pairwise comparison between Mikkelsen Bay and 

Simpson Lagoon (ΦST = 0.082, P = 0.05; Table 1.2). 

Given the geographic proximity of these island groups, 

signifi cant inter-population variances in haplotypic frequency (ΦST) of 0.05 are noteworthy (Wright 1951). In 

addition, we observed high levels of genetic discordance 

between Duchess (Mikkelsen Bay) and all four islands 

located in the Simpson Lagoon (ΦST = 0.135–0.271; 

Table 1.4). Finally, an AMOVA detected signifi cant 

variance within populations and among populations 

within each group (Table 1.2), consistent with female 

philopatry over relatively short geographic distances 

(Scribner et al. 2001).

Estimates of Gene Flow

Analyses using the software STRUCTURE detected 

no population subdivision among samples, however, we 

tested a two-population model in MIGRATE based on 

geographic proximity of islands. Individuals breeding on islands located in Mikkelsen Bay and Simpson 

Lagoon were treated as separate populations (Fig. 1.1, 

Table 1.5). There appears to be asymmetrical dispersal between island groups in the Beaufort Sea across 

all marker types, though some comparisons (mtDNA 

and nuclear introns) are not signifi cant based on overlapping 95% confi dence intervals. The biases in the 

variances and the means indicate that, on average over 

generations, gene fl ow is greater from Mikkelsen Bay 

to Simpson Lagoon than vice versa (Table 1.5). Nem

and θ values calculated in MIGRATE from microsatellite genotypes, mtDNA, and nuclear intron sequence 

data ranged from 5.1–24.2 migrants per generation from 

Simpson Lagoon to Mikkelsen Bay with θ ranging from 

0.001–0.683, and 24.4–34.2 migrants per generation 

from Mikkelsen Bay to Simpson Lagoon with θ ranging 

from 0.006–0.635 (Table 1.5). 

The full model (all parameters allowed to vary 

independently) was found to have signifi cantly higher 

likelihoods than the restricted island model (equal interpopulation migration rate and equal θ across populations) for gene fl ow estimates based on microsatellite 

allelic, mtDNA haplotypic, and nuclear intron allelic 

frequencies (P < 0.001; Table 1.5), indicating gene fl ow 

is asymmetric between Mikkelsen Bay and Simpson 

Lagoon. 

Female Site Fidelity

Analyses using the software Gimlet calculated an 

overall PID of 3.2 x 10-12 for a population composed of 

randomly mating individuals and 5.3 x 10-5 for siblings 

using genotypes collected from 14 microsatellite loci. 

These denominator values are much larger than the population breeding on islands in the western Beaufort Sea 

(approximately 500 nests found on the islands each year; 

Johnson 2000), which gave us confi dence that identical 

genotypes for samples taken from different years were 

the same individual. Nine females were found to have 

identical genotypes; 56% had matching genotypes with 

feather samples taken on the same island in later years. 

Additionally, 25 females were captured in multiple 

years. The majority of these (n = 16/25; 64%) were 

recaptured on the same island. The remaining females 

switched breeding islands in subsequent years. 

Throughout the course of the 4-year study, 34 females 

were detected breeding in two different years (based on 

observations of banded individuals and genetic techniques). Most (n = 21/34; 62%) nested on the same 

islands, whereas 13 (38%) females switched breeding 

islands. Inter-nest distances between breeding attempts 

ranged from 1.1–12.1 km using band recapture data (J. 

Reed unpubl. data), and 1.1–12.5 km using genetic recapture data. We found no evidence for female dispersal 

between Mikkelsen Bay and Simpson Lagoon. Females 

that did disperse to a different nest site between years 

generally moved to an adjacent island within the same 

island group to breed (9 of 13; 69%). However, three 

females breeding in Mikkelsen Bay moved from islands 

in the bay to islands closer to the coast (Alaska to Pt. 

Thomson Island, 12.1 and 12.5 km; Duchess to Camp 

Island, 10.2 km). One female breeding in Simpson Lagoon dispersed three islands east of her original nest site 

(Long to Stump Island, 10.0 km). 

Discussion

Population Genetic Structure
15

Population subdivision was uncovered at all marker 

types. Comparably higher levels of structure were observed at maternally inherited mtDNA than bi-parentally 

inherited nuclear introns and microsatellite loci for interisland comparisons, which is consistent with our prediction and known patterns of dispersal. The magnitude of 

differentiation decreased for mtDNA and nuclear intron 

lamin A when islands were combined into island groups, 

but it increased for microsatellites. Patterns of genetic 

structure were similar to those observed in Tiedemann 

et al. (1999); however, higher differentiation among 

colonies of similar geographic distance was observed. 

Tiedemann et al. (1999) proposed that the main mechanism promoting genetic subdivision among populations 

in the Baltic Sea was differences in migration phenology among geographic regions coupled with a selective 

advantage of early pair formation. However, differences 

in migratory phenology do not appear to occur among 

island groups in the Beaufort Sea, as satellite telemetry 

data indicate that there is no difference in the start of 

autumn migration among eiders breeding in the Beaufort 

Sea and Yukon-Kuskokwim Delta, Alaska (approximately 1250 km southwest of Beaufort Sea population; Petersen and Flint 2002). Lack of differences in migration 

phenology between island groups may explain, in part, 

the lower levels of differentiation observed as island 

groups likely admix on the wintering grounds. 

Low levels of population structure resolved based 

on microsatellite markers were expected, mainly due to 

aspects of Common Eider breeding and wintering biology. Although female Common Eiders are reported to 

be highly philopatric to natal and breeding sites (S. m. 

dresseri, Reed 1975; S. m. mollisima, Swennen 1990), 

male eiders have large natal and breeding dispersal 

distances (0–1270 km; Swennen 1990). Tiedemann 

et al. (1999) reported evidence of non-random mating 

within Common Eiders breeding in the Baltic Sea, based 

on signifi cant FST values, such that males tended to pair 

with females from the same breeding area among populations. We did not observe signifi cant inter-population 

comparisons; however, our overall FST was signifi cant. 

Additionally, we did detect signifi cant genetic discordance in allelic frequencies between Simpson Lagoon 

and Mikkelsen Bay. Signifi cant structuring, albeit low, 

at this marker could be a result of high female philopatry 

to island groups (Reed 1975, Swennen 1990). However, 

random mating on the wintering ground should homogenize gene frequencies in the nuclear genome through 

male-mediated gene fl ow (Scribner et al. 2001, Pearce et 

al. 2004). 

Based on the number of nests found each year, 

Simpson Lagoon appears to support a larger number of 

breeding birds than Mikkelsen Bay (Johnson 2000, S. 

Sonsthagen per. obs.). Scribner et al. (2001) indicated 

that unequal population sizes among studied sites could 

bias estimates of population subdivision. Individuals 

from populations that are larger would appear to mate 

assortatively, given the higher probability of mating with 

an individual from the same colony. Assortative mating 

among sites would have an upward bias on estimators 

of subdivision. Therefore, there could be greater gene 

fl ow than FST refl ects. Though signifi cant, our estimate 

of population structure is low enough to allow for high 

levels of gene fl ow among sampled sites. Satellite 

telemetry data from breeding female eiders, likewise, 

show that eiders from the Beaufort Sea share wintering areas with eiders breeding on the Kent Peninsula, 

Canada, western Alaska, and eastern Russia (Petersen 

and Flint 2002; L. Dickson, pers. comm., M. Petersen 

pers. comm.). Because individuals winter in admixed 

groups, consisting of many breeding populations, there 

is a potential for pairing of females from the Beaufort 

Sea with males from other areas. 

The high level of population structure we observed 

within nuclear intron lamin A is surprising because we 

observed low to no population structure in the microsatellite markers and nuclear intron gapdh. As mentioned 

previously, our estimates of population structuring 

could be biased due to the assumption of equal breeding 

population sizes among islands (Scribner et al. 2001). 

Alternatively, we may not have observed high levels 

of population subdivision in the microsatellite data due 

to fragment size homoplasy. However, for the mutation process, and therefore homoplasy, to have an effect 

on estimators of population subdivision, subpopulations need to have different ratios of coalescent times 

of genes long enough to have two or more mutational 

events to occur (Estoup et al. 2002). Since our estimate 

of subdivision for FST (assumes migration is driving 

subdivision) was greater than RST (assumes mutation 

is the driving subdivision; O’Reilly et al. 2004) and 

because of the relatively close geographic proximity of 

the islands, mutation, and therefore homoplasy, is likely 

not playing a major role in differentiating populations of 

eiders breeding in the western Beaufort Sea. Moreover, 

PHASE estimated relatively low rates of recombination 

between variable sites within lamin A and higher recombination rates between variable sites within gapdh. The 

higher recombination rate observed within gapdh may 

have masked population structure among islands. Lamin 

A may be an “outlier” locus, as it may be in LD with a 

target of natural selection, which may have infl ated ΦST

(Storz et al. 2004). Charlesworth et al. (1997) stated that 

local adaptation tends to increase population differentiation at loci under selection, and very high FST values 

may be observed at closely linked neutral loci. Lamin 

A, thus, may be under balancing selection and coupled 
16

with genetic drift could create more alleles than what 

would be expected by chance. Low observed heterozygosity (Ho = 0.416, He = 0.879) and the larger number of 

alleles reconstructed by PHASE relative to gapdh (lamin

A 70 alleles and gapdh 48 alleles; S. Sonsthagen unpubl. 

data) are consistent with this hypothesis. However, we 

did not fi nd any evidence to indicate that lamin A is not 

selectively neutral (Table 1.1). 

Comparatively higher levels of population subdivision assayed using mtDNA than nuclear DNA also 

could be attributed to lineage sorting. MtDNA has a 

lower effective population size relative to nuclear DNA. 

Therefore, when mutation rate and selection are held 

constant, genetic drift has a larger effect on mtDNA 

than nuclear DNA (Avise 2004), translating in higher 

estimates of population subdivision (FST). The effects 

of lineage sorting and sex-biased differences in philopatry on spatial genetic subdivision are not mutually 

exclusive and both may be playing a role in the degree 

of population structure observed. However, microsatellite loci have a high rate of mutation relative to mtDNA 

control region (Avise 2004) resulting in new mutations 

arising more frequently within populations. By chance 

alone, one would expect new mutations to increase in 

frequency among isolated populations and dampen the 

effects of incomplete lineage sorting within microsatellite loci. Given differences in the degree of philopatry 

in Common Eiders between the sexes and congruence 

in results between microsatellite and nuclear intron loci, 

differences in estimates of population subdivision may 

be more attributable to male dispersal and high natal and 

breeding philopatry in females rather than incomplete 

lineage sorting for the Beaufort Sea population. 

We observed high levels of population structure within the maternally inherited mtDNA control region. Signifi cant population subdivision was observed between 

Duchess Island, located in Mikkelsen Bay, and all islands located in Simpson Lagoon. While we did not detect signifi cant pairwise comparisons among all islands 

in Mikkelsen Bay and Simpson Lagoon, we believe that 

the signifi cant structuring observed is noteworthy given 

that Duchess Island is the only island that contains a 

colony of breeding Common Eiders in Mikkelsen Bay. 

Nests on the remaining islands were scattered with relatively few nests per island. The presence of a colony on 

Duchess Island is likely driving the signifi cant pairwise 

comparisons observed at this marker. Common Eiders 

are typically colonial nesters (Goudie et al. 2000) and 

the low-density nesters could be “overfl ow” from Duchess Island, though demographic data are needed to confi rm this hypothesis. Although there are also colonies on 

three islands in Simpson Lagoon (Egg, Long, and Stump 

islands), these colonies occur on islands that are adjacent 

to each other and thus unlikely to be genetically isolated, 

as birds may disperse among islands. While we do not 

have natal dispersal data for this population, we do have 

breeding dispersal distances from recaptured individuals. Given that no breeding females dispersed between 

Mikkelsen Bay and Simpson Lagoon, we hypothesize 

that females breeding in the western Beaufort Sea are 

strongly philopatric to island groups rather than to a 

particular island. This differs from observations of S. m.

dresseri breeding in Maine (Wakely and Mendall 1976). 

Distances among islands in Maine are similar to those 

observed in our study (1.7–24.3 km apart), however, 

71% of females returned to their previous breeding 

island, and only 2% dispersed to neighboring islands 

(estimated 27% mortality rate; Wakely and Mendall 

1976). Over many generations, females dispersing 

among neighboring islands would have a homogenizing 

effect within island groups while maintaining population 

subdivision between island groups. 

Behavioral responses to a more stochastic arctic environment may play a role in the differences in the degree 

of breeding philopatry observed between Maine and 

Beaufort Sea eiders. Common Eider nests in the western Beaufort Sea are associated with driftwood (Goudie 

et al. 2000, Johnson 2000), and changes in driftwood 

locations will affect where eiders nest. Storms dramatically modify the shape and topography of these barrier 

islands, thus changing where available habitat is located 

annually (Noel et al. 2005, S. Sonsthagen pers. obs.). 

Finally, eiders breeding on the Beaufort Sea postpone 

nesting attempts until the island is surrounded by open 

water, reducing predation risk (Schamel 1977). Islands 

located in the same vicinity may not be surrounded 

by water at the same time (S. Sonsthagen pers. obs.). 

Therefore, in years when ice break-up is late, eiders may 

initiate nesting on the fi rst suitable island regardless of 

where they nested in previous years or hatched from 

because of the presumed selective advantage to nesting 

early (Milne 1974). 

Gene Flow

We do not completely understand the factors that 

infl uence the degree of migratory and homing behavior 

in eiders. Eiders appear to move the minimum distance 

to wintering areas (Petersen and Flint 2002), and the 

degree of movement is likely environmentally induced 

(Swennen 1990), which may explain, in part, the directionality of gene fl ow observed at microsatellite and 

mtDNA markers. 

Microsatellite and nuclear intron loci indicate signifi cant asymmetrical gene fl ow, such that, on average, 

more individuals were dispersing from Mikkelsen Bay 
17

to Simpson Lagoon (i.e., east to west) over evolutionary 

time. Since eiders breeding in the Beaufort Sea share a 

wintering area with eiders from other populations, there 

may be clinal variation of allele frequencies occurring 

across populations that share wintering areas. Clinal 

variation at nuclear-based characteristics that may be 

under selection (e.g., plumage) has been observed in 

waterfowl and other avian species (Cooke et al. 1988, 

Smallwood et al. 1999). However, we did not observe a 

signifi cant correlation between genetic and geographic 

distances. Therefore, samples from a larger geographic 

area are needed to confi rm this hypothesis. 

Asymmetrical gene fl ow from east to west observed 

for mtDNA appears to be consistent with estimates 

based on nuclear loci. Young female birds or failed 

breeders from the previous year from Mikkelsen Bay 

returning from the wintering grounds to breed, may 

stop earlier on their migration and attempt to breed at 

Simpson Lagoon. Islands become ice-free about two 

weeks earlier in Simpson Lagoon than in Mikkelsen 

Bay, likely due to the large volume of water fl owing 

out of the Kuparuk River. This appears to expedite ice 

break-up on the nearby barrier islands (Schamel 1977, 

S. Sonsthagen pers. obs), enabling eiders to initiate 

nests and hatch broods sooner. Islands that fi rst become 

free of ice produce the earliest broods in other populations (Ahlén and Andersson 1970). Should females 

from Mikkelsen Bay stop early on spring migration at 

Simpson Lagoon and successfully hatch young, they 

may be more likely to nest in Simpson Lagoon in succeeding years (Milne 1974). Thus, earlier nest initiation 

and previous nest success may be factors infl uencing 

females that hatched in Mikkelsen Bay to breed in 

Simpson Lagoon for the fi rst time and then return there 

in successive years to breed. It is important to note that 

this pattern of westward dispersal would have to occur 

over many generations to be observed genetically. Thus, 

increasingly early ice break-up in Simpson Lagoon 

may have driven the westward bias in dispersal over 

evolutionary time. However, evidence from Common 

Eiders and other arctic nesting waterfowl suggests that 

young females initiate nesting later than older females 

(Johnson et al. 1992). Thus, open water may be present in both areas when young females are ready to nest, 

depending on the timing of ice break-up in a given year. 

Alternatively, Common Eiders may be dispersing west 

due to the distribution of available nest sites. Within the 

two study areas, Simpson Lagoon has more available 

nesting habitat relative to Mikkelsen Bay, based on the 

number of nests found in each island group in a given 

year (Johnson 2000, S. Sonsthagen pers. obs.). Female 

eiders hatched from Mikkelsen Bay arriving later to the 

breeding ground, such as fi rst time breeders, may simply 

choose or be forced to nest in Simpson Lagoon due to 

unavailability of nest sites in Mikkelsen Bay. This may 

be particularly true for young female eiders that tend to 

arrive later from the winter grounds as females arriving 

earlier on the breeding grounds may have already secured many suitable nest sites. Over evolutionary time, 

the limited availability of nest sites could also be infl uencing the dispersal pattern observed. It is important to 

note that one bout of random dispersal per generation 

among individuals breeding in the western Beaufort 

Sea could homogenize gene frequencies among islands. 

Therefore, western biased dispersal must have occurred 

over many generations. 

Comparison to Other Waterfowl

The fi ne-scaled spatial genetic structuring that we 

observed in Common Eiders breeding on island groups 

90 km apart in the western Beaufort Sea is exceptional, 

especially when compared to other arctic nesting waterfowl. Pearce et al. (2004) examined levels of population 

subdivision within the Holarctic nesting King Eider (S. 

spectabilis) using mtDNA cytochrome b sequence data 

and genotypes from six nuclear microsatellite loci. Estimates of inter-population allelic and haplotypic frequencies were not signifi cantly different indicating panmixia 

across sampled sites in Russia, Alaska, and Canada. 

Stable isotope data from King Eiders suggest high levels 

of dispersal among western and eastern arctic populations (Mehl et al. 2004), which the authors contended is 

likely homogenizing gene frequencies among sampled 

sites. Levels of population structure also were assessed 

for Harlequin Ducks (Histrionicus histrionicus) breeding 

in Alaska (Lanctot et al. 1999). The authors did not detect any signifi cant genetic discordance among sampled 

sites at four autosomal microsatellite loci, two Z-specifi c 

microsatellite loci, and mtDNA control region. Lack of 

structure was attributed to recent range expansion and 

thus insuffi cient time for genetic differences to evolve, 

stochastic events causing episodic dispersal, and low 

levels of dispersal among regions. 

Among other waterfowl, population subdivision 

has been documented to varying degrees. Pearce et al. 

(2005) assessed population genetic structuring at seven 

microsatellite loci and cytochrome b mtDNA sequence 

among Steller’s Eiders (Polysticta stelleri) breeding in 

Alaska and Russia. Low inter-population estimates of 

subdivision were observed at microsatellite loci (FST = 

0.002–0.007). However, estimates based on mtDNA 

were not signifi cant. In contrast, Scribner et al. (2001) 

documented high levels of differentiation in mtDNA 

among sampled sites in Spectacled Eiders (S. fi sheri,

ΦST = 0.242). However, the authors did not detect any 

differences in allelic frequencies within the nuclear 
18

genome at fi ve autosomal microsatellite loci and one 

Z-linked microsatellite locus. Canada Geese (Branta 

canadensis) also exhibit high levels of genetic differentiation among sampled sites at fi ve autosomal microsatellite loci (FST = 0.077), one Z-linked microsatellite 

locus (FST = 0.116), and mtDNA control region (ΦST

= 0.177; Scribner et al. 2003). While these studies all 

documented signifi cant differences in gene frequencies 

among sampled sites, studies were conducted at much 

larger spatial scales than our study.

Differences in the degree of population subdivision could be attributed to behavioral characteristics of 

individual species. Several aspects of the biology of 

many of these species are similar to that of Common 

Eiders, including: (1) exhibition of some degree of 

breeding site fi delity, (2) seasonal migratory behavior, 

(3) population admixture in large winter aggregations, 

(4) formation of pair-bonds in winter months with males 

following females back to winter sites, and (5) seasonal 

monogamy. In contrast to Common Eiders, many waterfowl species are monotypic across their range and show 

little to no population structuring (Newton 2003). Species that exhibit fi ne scale spatial structure, likely have 

high natal, breeding, and winter site philopatry, as has 

been indicated for Common Eiders (Goudie et al. 2000). 

Conclusions

It appears that Common Eiders breeding in Simpson 

Lagoon are genetically differentiated relative to those 

breeding in Mikkelsen Bay, as we observed signifi cant 

levels of population subdivision across all marker types. 

Therefore, Common Eiders breeding in each area may 

host populations that are demographically independent, 

though more demographic data are needed to confi rm 

this hypothesis. Common Eiders appear to have high 

natal and breeding philopatry, as shown by the high 

inter-population variance estimates (ΦST) calculated 

for mtDNA and restricted female dispersal between 

island groups as shown by recapture data. In the event 

that a breeding area was extirpated it may be unlikely 

that the area would be easily re-colonized naturally by 

females hatched elsewhere, despite high levels of gene 

fl ow mediated by male dispersal (Wakely and Mendall 

1976, Avise 2004). Additionally, these data illustrate an 

important point. Genetic discordance can exist on very 

small spatial scales relative to the species dispersal capabilities and known male dispersal distance. High natal 

philopatry observed in waterfowl, as seen in Common 

Eiders, can have demonstrable effects on the degree of 

genetic partitioning among populations. 

This study was the fi rst, to our knowledge, to use 

nuclear introns in assessing inter-population variation in 

allelic frequencies at a microgeographic scale. Introns 

pose new challenges to phylogenetic and population 

genetic analysis, such as recombination impeding gene 

tree reconstruction (Hare 2001) and selective sweeps 

potentially confounding gene fl ow estimates (Storz et al. 

2004). However, high levels of variation found in loci 

potentially under balancing selection can provide valuable insight on historic processes infl uencing population 

demography. Advancements in analytical tools have 

enabled researchers to address issues of recombination 

(e.g., PHASE) and selective sweeps (DetSel, Vitalis et 

al. 2003; Fu’s Fs, Tajima’s D in ARLEQUIN) and use 

these types of markers for population genetic analyses. 

Finally, these data provide further evidence for the need 

to use multiple marker types with varying modes of 

inheritance. If researchers were to restrict their investigation to either nuclear or mtDNA markers when genetically characterizing populations, studies could under or 

over estimate levels of population structure. As seen 

in Common Eiders, nuclear and mtDNA markers show 

varying levels of genetic spatial partitioning. Not utilizing molecular markers with different modes of inheritance and evolution could mislead researchers characterizing the genetic variation within this population.

Acknowledgments

Funding was provided by: Minerals Management 

Service (1435-01-98-CA-30909); Coastal Marine Institute, University of Alaska Fairbanks; U. S. Geological 

Survey; Alaska EPSCoR Graduate Fellowship (NSF 

EPS-0092040); University of Alaska Foundation Angus 

Gavin Migratory Bird Research Fund; and BP Exploration (Alaska) Inc. We thank all of the U. S. Geological 

Survey researchers and biologists that worked on the 

Beaufort Sea Common Eider project, especially P. Flint, 

J. C. Franson, D. LaCroix, and J. Reed, as well as, J. 

Gust and G. K. Sage, who provided laboratory assistance. J. Gleason, C. Monnett, J. Pearce, M. Petersen, 

and J. Gust, U. S. Geological Survey, and four anonymous reviewers, provided valuable comments on earlier 

drafts of this manuscript. 

The USFWS banding number for the USGS is 20022 

and the master permit holder is Dirk Derksen.The 

IACUC number #02-01 was assigned for this work to 

Kevin McCrackin.

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23

Figure 1.1: Beaufort Sea barrier islands located in (A) Simpson Lagoon (western group) and (B) Mikkelsen 

Bay (eastern group) with sample sizes for each island in parentheses, the fi rst value is the number of samples 

(blood and feather) genotyped at 14 microsatellite loci and the second value is the number of samples (blood) 

sequenced for mtDNA and two nuclear introns. Wannabe and Camp islands are designations used by the 

authors and are not offi cial names of islands.
24

Figure 1.2: Unrooted parsimony tree illustrating relationships of (A) 25 lamin A alleles, (B) 22 gapdh alleles, 

and (C) 11 mtDNA control region haplotypes. The 95% probability set of parsimony trees are illustrated 

with bold branches, with the size of the circle node corresponding to the frequency of each allele. Gray lines 

indicate alternative branching patterns and possible reticulations. Small black squares indicate intermediate 

ancestral alleles that were not sampled. Mikkelsen Bay (eastern group) alleles are illustrated in white and 

Simpson Lagoon (western group) alleles are illustrated in gray. Sample sizes are shown in parentheses. 
25

Mikkelsen Bay 

Camp Pt. Thomson 

Mary

Saches & 

North Star Duchess

Challenge & 

Alaska

Lamin A 

h 0.901 0.864 0.844 0.884 0.600

SD 0.047 0.072 0.103 0.034 0.215

π 0.009 0.006 0.005 0.007 0.005

SD 0.006 0.004 0.004 0.005 0.004

Fu’s Fs * –1.503 –2.004 –2.690 –4.704 0.381

Tajima’s D –0.147 –0.212 –0.682 0.226 0.338

No. of 

alleles 7 6 6 11 3

n 7 6 5 14 3

Gapdh

h 0.924 0.911 0.927 0.909 0.933

SD 0.058 0.077 0.084 0.037 0.122

π 0.006 0.009 0.007 0.006 0.008

SD 0.004 0.006 0.005 0.004 0.006

Fu’s Fs * –1.584 –0.907 –0.930 –3.602 –1.466

Tajima’s D –0.323 –0.810 –0.856 –0.989 –0.631

No. of 

alleles 8 7 6 11 5

n 6 5 4 11 3

MtDNA

h 0.333 0.800 0.400 0.891 0.667

SD 0.215 0.172 0.237 0.063 0.314

π 0.002 0.007 0.001 0.009 0.001

SD 0.002 0.004 0.001 0.005 0.001

Fu’s Fs * 2.139 0.567 0.090 0.543 0.201

Tajima’s D –1.295 –0.516 –0.817 0.591 0.000

No. of 

haplotypes 23262

n 6 6 5 11 3

* Significant P–values are in bold text; Fu’s Fs (P < 0.02) and Tajima’s D (P < 0.05). 

Table 1.1: Estimates of genetic diversity, including; nucleotide (π) and haplotype (h) diversity (including 

the standard deviation SD), number of unique haplotypes per population, and sample size (n), for 280 bp of 

nuclear intron lamin A, 387 bp of nuclear intron gapdh, and 545 bp of mtDNA control region.
26

* Significant P–values are in bold text; Fu’s Fs (P < 0.02) and Tajima’s D (P < 0.05). 

Simpson Lagoon 

Stump 

Wannabe & 

Egg Long Spy

Lamin A 

h 0.849 0.818 0.915 0.876

SD 0.036 0.052 0.027 0.045

π 0.008 0.007 0.009 0.008

SD 0.005 0.004 0.006 0.005

Fu’s Fs * –3.499 –3.033 –4.943 –1.896

Tajima’s D –0.169 –0.126 –0.119 0.070

No. of alleles 12 10 13 8

n 24 13 15 9

Gapdh

h 0.900 0.874 0.886 0.954

SD 0.024 0.032 0.036 0.047

π 0.006 0.006 0.007 0.007

SD 0.004 0.004 0.004 0.005

Fu’s Fs * –2.850 –0.773 –0.996 –2.162

Tajima’s D –0.853 –0.836 –0.306 –0.964

No. of alleles 13 8 10 9

n 18 13 13 6

MtDNA

h 0.579 0.526 0.654 0.000

SD 0.114 0.152 0.106 0.000

π 0.003 0.003 0.003 0.000

SD 0.002 0.002 0.002 0.000

Fu’s Fs * 0.183 0.868 0.399 –

Tajima’s D –0.869 –0.167 –1.249 0.000

No. of 

haplotypes 5441

n 19 13 13 7

Table 1.1 cont.
27

Table 1.2: Hierarchical analysis of molecular variance (AMOVA) of allelic and haplotypic frequencies for 

islands within Mikkelsen Bay and Simpson Lagoon. Signifi cant comparisons are in bold text. 

Source of Variation d.f. Variance

components 

% of total 

variation Φ P–value

Microsatellite – FST

Variance among group 1 0.003 0.08 0.001 0.247

Variance among pop. within group 10 0.011 0.34 0.003 0.042

Variance within populations 61

4 3.251 99.58 0.004 0.016

Total 62

5 3.264 – – –

Microsatellite – RST

Variance among group 1 0.575 0.39 0.004 0.140

Variance among pop. within group 10 –0.106 –0.07 –0.001 0.652

Variance within populations 61

4 145.949 99.68 0.003 0.449

Total 62

5 146.418 – – –

Lamin A

Variance among group 1 –0.004 –0.34 –0.003 0.478

Variance among pop. within group 7 0.027 2.50 0.025 0.012

Variance within populations 19

3 1.066 97.84 0.022 0.022

Total 20

1 1.089 – – –

Gapdh

Variance among group 1 –0.000 –1.00 –0.000 0.685

Variance among pop. within group 7 –0.020 –1.89 –0.019 0.933

Variance within populations 14

9 1.061 101.90 –0.019 0.929

Total 15

7 1.042 – – –

Mitochondrial DNA 

Variance among group 1 0.000 2.78 0.028 0.202

Variance among pop. within group 7 0.000 5.38 0.055 0.030

Variance within populations 74 0.002 91.84 0.082 0.047

Total 82 0.002 – – –
28

Mikkelsen Bay Simpson Lagoon 

Population a Camp 

Pt.

Thomson 

Mary

Saches & 

North Star Duchess

Alaska & 

Challenge Stump 

Wannabe

& Egg Long Spy

Camp – –0.034 –0.007 0.001 0.088 0.074 0.027 0.026 0.052Pt. Thomson –0.027 – 0.034 0.009 0.173 0.037 0.059 0.024 0.089 

Mary Saches & North Star –0.025 –0.013 – –0.029 0.119 –0.025 0.003 0.003 0.033Duchess 0.002 0.026 –0.053 – 0.024 –0.007 –0.002 0.007 0.053Alaska & Challenge –0.110 –0.126 –0.056 –0.020 – 0.003 –0.042 0.024 0.148 Stump –0.029 0.024 –0.031 0.023 –0.039 – –0.012 0.022 0.092 Wannabe & Egg –0.036 –0.032 –0.021 0.000 –0.071 0.002 – 0.025 0.106 Long –0.037 –0.042 –0.040 –0.009 –0.082 0.003 –0.028 – 0.022Spy –0.060 –0.059 –0.035 0.002 –0.093 –0.023 –0.054 –0.049 –

a Islands are listed East to West. 

Table 1.3: Estimates of pairwise inter-population variance in allelic frequency (ΦST) values calculated for lamin A (above diagonal) and gapdh 

(below diagonal) for Common Eiders from each pair of nine islands breeding in the Beaufort Sea. Signifi cant pairwise comparisons (α = 0.05) are 

in bold text. 
29

Table 1.4: Pairwise ΦST values calculated for 545 bp of mtDNA control region (above diagonal) and lamin A site 116 (below diagonal) for Common 

Eiders breeding from each pair of nine islands breeding in the Beaufort Sea. Signifi cant pairwise comparisons (α = 0.05) are in bold text.

Mikkelsen Bay Simpson Lagoon 

Population a Camp 

Pt.

Thomson 

Mary

Saches & 

North Star Duchess

Alaska & 

Challenge Stump 

Wannabe

& Egg Long Spy

Camp – –0.085 –0.018 0.097 –0.058 –0.083 –0.082 0.000 0.028Pt. Thomson – – 0.032 –0.024 –0.174 –0.024 –0.082 0.026 0.127Mary Saches & North Star – – – 0.201 –0.299 –0.066 0.040 –0.095 0.073Duchess – – – – 0.116 0.183 0.135 0.230 0.271 Alaska & Challenge – – – – – –0.141 –0.002 –0.199 0.300Stump –0.035 –0.042 –0.051 –0.012 –0.092 – –0.010 –0.023 0.008Wannabe & Egg –0.032 –0.040 –0.050 –0.007 –0.091 –0.024 – 0.037 0.068Long 0.121 0.102 0.078 0.205 –0.006 0.217 0.169 – 0.029

Spy 0.208 0.182 0.151 0.330 0.053 0.352 0.284 –0.086 –

a Islands are listed East to West. 
30

Table 1.5: Comparison of alternative models of Common Eider gene fl ow between Mikkelsen Bay and Simpson Lagoon. Full model migration matrix (allowing all parameters to vary independently) and restricted model (symmetrical gene fl ow) migration rates calculated from 14 microsatellite 

loci, lamin A and gapdh, and mtDNA control region, were evaluated for signifi cance using a log likelihood ratio test. Ninety-fi ve percent confi dence 

intervals are in parentheses.

Simpson Lagoon to 

Mikkelsen Bay 

Mikkelsen Bay to

Simpson Lagoon 

Marker Hypothesis Ln(L) P–value N mf or N me θ N mf or N me θMicrosatellites Full –8782.1 <0.001 18.8

(17.8–20.3)

0.683

(0.650–0.717)

27.1

(25.4–29.6)

0.635

(0.612–0.659)

Restricted –8888.0 78.3 2.247 78.3 2.247

Nuclear introns Full –401.8 <0.001 24.2

(18.6–31.5)

0.003

(0.003–0.004)

34.2

(26.7–43.7)

0.010

(0.009–0.011)

Restricted –468.9 22.3 0.006 22.3 0.006

MtDNA Full 1.9 <0.001 5.1

(0.9–28.1)

0.001

(0.000–0.002)

24.4

(2.4–95.9)

0.006

(0.005–0.015)

Restricted –12.0 12.3 0.003 12.3 0.003
31

Appendix 1.A: Latitude and longitude of Common Eider (Somateria mollissima) samples** analyzed in this 

study.

USA: Alaska, North Slope, Bodfish, Spy Island 70.564oN, 149.895oW 

NS27325, NS27441, NS27442, NS27443, NS76480, NS76481, NS76482, NS82136, 

NS82164, NS82232, NS82234, NS82235, SP001, SP002, SP003, SP017–1, SP035, 

SP085, SP087, SP088, SP089, SP092, SP093, SP144–2 

USA: Alaska, North Slope, Bodfish, Long Island 70.480oN, 148.937oW 

LO001, LO002, LO003, LO004, LO008, LO009, LO010, LO011, LO012, LO014, 

LO017, LO018, LO019, LO020, LO021, LO023, LO141, LO033, LO035, NS82101, 

NS82109, NS82117, NS82118, NS82119, NS82120, NS82121, NS82122, NS82123, 

NS82129, NS82130, NS82137, NS82138, NS82153, NS82160, NS82161, NS82162, 

NS82163

USA: Alaska, North Slope, Bodfish, Egg Island 70.440oN, 148.739oW 

EG1, EG10–2, EG2, EG2–2, EG3, EG3–2, EG4, EG5, EG7, EG9, EG9–2, NS76478, 

NS82102, NS82104, NS82106, NS82107, NS82112, NS82127, NS82141, NS82146, 

NS82147, NS82151, NS82152, NS82156, NS82157, NS82158, NS82221 

USA: Alaska, North Slope, Bodfish, Wannabe Island* 70.437oN, 148.725oW 

NS27405, NS27406, NS76487, NS82150, NS82154, WA031, WA127, WA128, WA129, 

WA130, WA131 

USA: Alaska, North Slope, Bodfish, Stump Island 70.419oN, 148.601oW 

JAR144, JAR136, NS27321, NS27322, NS27323, NS27324, NS27351, NS27401, 

NS27402, NS27404, NS27407, NS76483, NS76485, NS76490, NS76491, NS76492, 

NS76493, NS76494, NS76495, NS76496, NS76497, NS76498, NS76499, NS76500, 

NS76551, NS76552, NS76553, NS76554, NS76555, NS76556, NS76557, NS76558, 

NS76559, NS76560, NS82133, NS82134, NS82135, NS82142, NS82143, NS82144, 

NS82145, NS82165, NS82166, NS82167, NS82168, NS82204, NS82205, NS82207, 

NS82209, NS82211, NS82224, NS82225, NS82237, ST024–2, ST024 

USA: Alaska, North Slope, Flaxman, Challenge Island 70.237oN, 146.640oW 

CH116, CH118, CH119, CH121, CH124, CH131, CH201, CH261, NS27280, NS27281, 

NS27282, NS27286, NS52252, NS52281, NS52282, NS52283, NS52284, NS52285, 

NS52286

USA: Alaska, North Slope, Flaxman, Alaska Island 70.233oN, 146.559oW 

AK132, AK134, AK135, AK136, AK137, AK138, AK139, AK140, AK142, AK143, 

AK150, AK151, AK240, NS27252, NS27253, NS27260, NS27272, NS27273, NS27279, 

NS27291, NS27292, NS27293, NS272xx, NS52253, NS52287, NS52288, NS52289, 

NS76453, NS76471, NS76472 
32

USA: Alaska, North Slope, Flaxman, Duchess Island 70.233oN 146.405oW 

DU136, DU210, DU227, JAR136, JAR144, NS24351, NS27251, NS27256, NS27264, 

NS27274, NS27275, NS27276, NS27277, NS27284, NS27337, NS27338, NS27339, 

NS27340, NS27351, NS27354, NS27422, NS27423, NS27424, NS27425, NS52256, 

NS52257, NS52258, NS52259, NS52260, NS52261, NS52262, NS52263, NS52264, 

NS52265, NS52266, NS52267, NS52268, NS52269, NS52270, NS52271, NS52291 

USA: Alaska, North Slope, Flaxman, North Star Island 70.225oN, 146.347oW 

JAR204, NS100, NS202, NS203–1, NS204, NS218, NS219, NS220, NS222, NS223, 

NS27268, NS27269, NS27270, NS27271, NS27278, NS27304, NS27305, NS27336, 

NS27420, NS52272 

USA: Alaska, North Slope, Flaxman, Point Thomson 70.186oN, 146.325oW 

NS27341, NS27342, NS27343, NS27344, NS27345, NS27346, NS27417, NS52251, 

NS52273, NS52274, NS52275, PT102, PT103, PT105, PT109, PT110, PT111, PT114, 

PT222, PT223, PT225, PT226 

USA: Alaska, North Slope, Flaxman, Camp Island* 70.172oN, 146.226oW 

CA1–1, CA149, CA150, CA152, CA153, CA156, CA159, CA162, NSCAMP1–1, 

NS24348, NS27347, NS27348, NS27349, NS27350, NS27418, NS27419, NS52254, 

NS52255, NS52276, NS52277, NS52278, NS52279, NS52280 

USA: Alaska, North Slope, Flaxman, Mary Saches Island 70.200oN, 146.207oW 

MS224–2, MS226–2, MS227, MS230–5, MS231, MS233, MS235, MS262, MS303, 

NS27352, NS52290 

* Camp Island and Wannabe Island are not official names of locations on any recognized 

maps, but were given these names for the purpose of identifying areas in this study. 

**Samples are located in non–museum research collections. 

Appendix 1.A cont.
33

Appendix 1.B: Number of haplotypes per sampled island for lamin A, gapdh, and mtDNA control region.

Number of haplotypes per island 

Camp 

Pt.

Thomson 

Mary

Saches

&

Northstar Duchess Alaska Stump

Wannabe

& Egg Long Spy

Lamin A

01 – – – – – – – 3 4

02 – – – – – 1 – – –

03 2 1 1 2 – 2 2 1 1

10 – – – 1 – – – – –

11 – – – 1 – – – – 1

18 – – – – – – – 1 –

19 – – – – – – 1 4 –

20 – 1 1 4 – 4 1 1 –

24 2 4 4 4 4 6 4 2 4

26 2 – 2 7 – 16 14 5 4

28 – – – 1 – 1 – – 1

29 – – 1 1 – – – – –

32 – – 1 – 1 5 3 – –

34 – 2 – 1 – 2 2 2 –

36 – – – – 1 – 1 – –

37 3 2 – 5 – 6 5 6 1

43 1 – – – – 2 – – –

45 – – – – – – – 2 –

47 – – – – – – – 1 –

56 1 – – – – – – – –

57 – – – – – – – – 2

58 3 2 – 1 – 2 3 – –

60 – – – – – 1 – – –

63 – – – – – – – 1 –

64 – – – – – – – 1 –

n 14 12 10 28 6 48 36 30 18

Gapdh

01 1 – – – – – – – –

03 2 – 1 1 1 6 5 2 1

04 1 – – – – – – 3 1

05 1 3 – 5 1 6 6 6 1

06 – – – 1 – 1 – 0 1

11 – – – – – 1 – 1 –

12 – 1 – 1 – 2 2 1 2

14 – – – – – 1 – – –
34

Number of haplotypes per island 

Camp 

Pt.

Thomson 

Mary

Saches

&

Northstar Duchess Alaska Stump

Wannabe

& Egg Long Spy

15 – – – 1 – – – – –

16 1 – – – – – – – –

17 – – – 1 – – – – –

18 1 1 1 3 1 2 3 2 1

23 3 1 2 4 2 2 5 1 2

24 2 1 1 2 – 6 2 6 2

31 – 1 1 1 – – 2 2 1

37 – – – – – – 1 – –

38 – 2 – – 1 1 – – –

40 – – – – – 1 – – –

43 – – – – – 1 – – –

45 – – 2 2 – – – – –

46 – – – – – – – 2 –

47 – – – – – 6 – – –

n 12 10 8 22 6 36 26 26 12

MtDNA

01 5 3 4 2 2 12 9 7 7

02 – – – – – – 1 1 –

03 – 1 1 3 1 4 – 4 –

04 – – 1 – – – 1 –

05 – 1 – – – – – – –

06 – – – – – 1 2 – –

07 1 – – 2 – 1 – – –

08 – – – 2 – – – – –

09 – – – 1 – – – – –

10 – – – – – 1 – – –

11 – – – – – – 1 – –

n 6 6 5 11 3 19 13 13 7

Appendix 1.B cont.
35

Abstract 

We investigated the population genetic structure, 

subspecies classifi cation, and postglacial colonization 

of Common Eiders (Somateria mollissima) breeding in 

North America and Scandinavia and evaluated localities 

of proposed glacial refugia using microsatellite genotypes, mtDNA control region, and intron sequences from 

two autosomal nuclear genes. Common Eiders exhibited high levels of structuring at all marker types. Variance in molecular data was better accounted for when 

populations were grouped by subspecies for nuclear 

markers, supporting subspecies classifi cations. Furthermore, populations grouped by subspecies for both 

principal components analysis and a Bayesian clustering program using microsatellite genotype data. In 

contrast to nuclear data, mitochondrial DNA (mtDNA) 

variance was better accounted for when populations 

were grouped based on geographic proximity indicating 

a stepwise post-glacial colonization of North America 

and Scandinavia. Historical population demographic 

data suggest that Common Eiders were restricted to four 

glacial refugia during the last glacial maxima; Belcher 

Islands, Newfoundland, Alaskan North Slope, and Svalbard. Newfoundland, North Slope, and Svalbard localities coincide with previously identifi ed glacial refugia; 

Beringia (northern Alaskan shelf), Newfoundland Bank, 

and Spitsbergen Bank, respectively (Ploeger 1968). The 

Belcher Islands population may have retreated with the 

Laurentide ice sheet to its present day location. Southern refugia appear to have served as the main source 

populations for postglacial colonization of Canada, 

southern Alaska, and Scandinavia by Common Eiders. 

Beringia (North Slope) contributed little to colonizing 

deglaciated regions and remain genetically differentiated from southern Alaskan, Canadian, and Scandinavian 

populations.

Introduction

Pleistocene glacial cycles have infl uenced genetic 

diversity and distribution of species breeding in northern latitudes (Hewitt 2004a). Throughout the Arctic, 

colder climates and ice sheets displaced species to lower 

latitudes and high latitude ice-free areas during the last 

glacial maximum (Hewitt 2004a). Fossil and molecular 

data, however, suggest that some areas of the Arctic, 

notably Beringia, were unglaciated. Species’ ranges 

contracted into refugia during glacial maxima, and 

during inter-glacial periods expanded and colonized icefree areas (Hewitt 2004a). Population expansion from 

glacial refugia has left predictable genetic patterns in 

recently colonized regions. Molecular data coupled with 

coalescent theory have enabled researchers to investigate historical species distribution and demography and 

identify areas that exhibit a signature of rapid population 

expansion (Lessa et al. 2004). Conversely, populations 

that do not exhibit genetic signatures of expansion have 

aided in the identifi cation and location of glacial refugia.

Despite the importance of glacial refugia in species 

persistence during glacial maxima and as sources of 

colonizers of the Arctic, number, locations, and signifi -

cance of refugia remain largely unknown (Byun et al. 

1997, Demboski et al. 1999). Ploeger (1968) provided a 

comprehensive review of proposed ice-free areas during 

the last Pleistocene glacial period and postulated the 

relative importance of ice-free areas as potential refugia 

for arctic Anatidae based on current species distributions. High arctic ice-free areas proposed by Ploeger 

(1968) included Beringia, Canadian Arctic Archipelago, 

northern Greenland, Spitsbergen Bank near Svalbard, 

and northwest Norway. Proposed temperate ice-free 

areas included Newfoundland, western Greenland, 

Iceland, and western Europe. Without fossil evidence, 

however, it is diffi cult to determine whether ice-free 

areas were inhabited by arctic species and contributed to 

species persistence. More recently, molecular data coupled with coalescent theory have substantiated Beringia, 

Canadian Arctic Archipelago, and western Greenland as 

ice-free refugia for arctic vertebrates (Holder et al. 1999; 

2000, Fedorov and Stenseth 2002, Fedorov et al. 2003, 

Flagstad and Røed 2003, Scribner et al. 2003, Waltari 

and Cook 2005). Convergence in genetic signatures 

of population expansion among arctic species could 

provide insights into the locations of proposed refugia 

and their relative importance as historical reservoirs of 

species genetic diversity.

In addition to climatic oscillations during the Pleistocene, patterns in the degree of natal, breeding, and 

winter philopatry also leave varying signatures in molecular markers (Avise 2004). Female natal and breeding philopatry can lead to high levels of spatial genetic 

subdivision at maternally inherited mitochondrial DNA 

(mtDNA). Conversely, males dispersing large distances 

may homogenize gene frequencies among populations at 

bi-parentally inherited markers present in the nuclear genome (Scribner et al. 2001). If data were collected from 

just one of these genomes, gene fl ow among populations 

might be grossly over or under-estimated depending on 

which marker type was used (Avise 2004). However, by 

combining markers with different modes of inheritance 

PART 2: Multilocus Phylogeography and Population Structure of Common Eiders Breeding in 

North America and Scandinavia
36

and rates of evolution, researchers may ask a wider 

range of questions involving species population genetic 

structure and behavior.

Here we investigate the postglacial colonization of 

North America and Scandinavia and population genetic 

structure of Common Eiders (Somateria mollissima) 

using microsatellite genotypes, mtDNA control region, 

and intron sequences from two autosomal nuclear genes. 

Common Eiders are an arctic-nesting seaduck, composed of 6–7 morphologically distinct subspecies that 

have a circumpolar distribution (Goudie et al. 2000). As 

observed in other waterfowl, female Common Eiders are 

highly philopatric to natal and breeding sites, whereas 

males disperse among populations that share common 

wintering grounds. Both sexes, however, display winter 

site fi delity (Spurr and Milne 1976). Common Eiders 

are unusual among seaducks, as they exhibit fi ne scale 

spatial genetic structuring for both mtDNA and nuclear 

markers (Tiedemann et al. 1999, Sonsthagen et al. 

submitted a). High levels of natal, breeding, and winter 

site philopatry coupled with microgeographic genetic 

partitioning observed for Common Eiders, enabled us to 

investigate patterns of population subdivision and gain 

insight into the locations of potential Pleistocene refugia 

for Common Eiders and the contribution of refugia 

to the postglacial colonization of North America and 

Scandinavia. We evaluated localities that have been proposed as ice-free areas or glacial refugia in other arctic 

vertebrates and Common Eider, including; the southern 

edge of the Bering Land Bridge, northern Beringia, High 

Arctic Canadian Archipelago, Newfoundland, Spitsbergen Bank, and northwest Norway. 

We present the fi rst analysis to assess genetic relationships among North American and Scandinavian eiders that uses microsatellite, nuclear intron, and mtDNA 

loci. The primary goals of this study were threefold. 

First, we aimed to use a multilocus approach to evaluate 

subspecies classifi cations. Second, we evaluated genetic 

diversity within populations to test for refugial populations and directions of post-glacial colonization. Third, 

we estimated gene fl ow among populations within and 

between subspecies

Methods

Laboratory Techniques

We collected data from 12 microsatellite loci (Aph08, 

Aph20, Aph23; Maak et al. 2003; Bcaμ1, Bcaμ11, 

Hhiμ3; Buchholz et al. 1998; Sfiμ10; Libants et al. 

unpubl. data; Smo4, Smo7, Smo08, Smo10, and Smo12; 

Paulus and Tiedemann 2003), mtDNA control region 

(545–563 bp; Sonsthagen et al. submitted a), 280 base 

pairs of intron 3 of lamin A, and 386–387 base pairs of 

intron 11 of gapdh (McCracken and Sorenson 2005) 

from 716 Common Eiders sampled from fi ve subspecies 

(Fig. 2.1, Appendix 2.A; Sonsthagen et al. submitted 

a, b): S. m. v-nigrum (Alaska and western Canada), S. 

m. borealis (northern Canada and Svalbard, Norway), 

S. m. sedentaria (southern Hudson Bay, Canada), S. m. 

dresseri (eastern Canada), and S. m. mollissima (Scandinavia). 

Genomic DNA was isolated from blood, feather, 

or frozen tissues. Methods for DNA extraction, polymerase chain reaction (PCR) amplifi cation, electrophoresis, and cycle sequencing are described in Sonsthagen 

et al. (submitted a). For quality control purposes, 10% 

of samples were randomly selected, re-amplifi ed, and 

genotyped at the 12 microsatellite loci in duplicate. 

Three primer pairs were used for amplifi cation and 

sequencing of the mtDNA control region: L263 and 

H848 (Sonsthagen et al. submitted a), L263rev (5’–

CCAAACTGCGCACCTGACATTCC–‘3) and H848, 

and L319 (5’–TGAATGCTCTAAGAYCCAAACTGC–

‘3) and H848. MtDNA PCR products were sequenced 

in both directions and assembled using Sequencher 

4.1.2 (Gene Codes Corporation, Ann Arbor, MI). Only 

sequences from the forward strand of the nuclear introns 

were collected because PCR templates were short 

(280–387 bp) and sequences had a consistent electropherogram peak high throughout the length of the fragment. 

Nuclear sequences that contained double-peaks of approximately equal height, indicating the presence of two 

alleles, were coded with IUPAC degeneracy codes and 

treated as polymorphisms. Individuals that were heterozygous (48%) for a single one base pair indel occurring 

in gapdh were also sequenced with the reverse strand to 

obtain data from the entire fragment. Sequences will be 

deposited in GenBank (http//:www.ncbi.nlm.nih.gov) 

upon publication of the results of this report.

Statistical Analyses

Genetic diversity

Allelic phases for lamin A and gapdh introns were 

inferred from diploid sequence data using PHASE 2.0 

(parameters: 1,000 burn-in period followed by 1,000 

iterations; Stephens et al. 2001), which uses a Bayesian 

approach to reconstruct haplotypes from genotypic data 

and allows for recombination and the decay of linkage 

disequilibrium with distance. The PHASE analysis 

was repeated three times to ensure consistency across 

runs. Unrooted phylogenetic trees for each gene were 

constructed in TCS 1.18 (Clement et al. 2000), which 

estimates genealogies using 95% statistical parsimony 

probabilities as defi ned by Templeton et al. (1992). 

Lamin A and gapdh sequences also were analyzed in 
37

NETWORK 4.1.0.8 (Fluxus Technology Ltd. 2004) using the Reduced Median network (Bandelt et al. 1995), 

to illustrate reticulations in the gene trees due to homoplasy or recombination.

Allelic frequencies, inbreeding coeffi cient (FIS), and 

expected and observed heterozygosities for microsatellite, mtDNA, and nuclear intron loci were calculated in 

GENEPOP 3.1 (Raymond and Rousset 1995). Nucleotide and haplotype diversity for each population was 

estimated in ARLEQUIN 2.0 (Schneider et al. 2000). 

Tests of selective neutrality and historical fl uctuations in 

population demography were performed in ARLEQUIN 

using Fu’s Fs (Fu 1997) and Tajima’s D (Tajima 1989). 

Critical signifi cance values of 5% required a P-value 

below 0.02 for Fu’s Fs (Fu 1997). 

Population genetic structure

Estimates of inter-population variance in allelic 

and haplotypic frequencies (FST, RST, and ΦST) were 

calculated in ARLEQUIN and FSTAT 2.9.3 (Goudet 

1995, 2001). Signifi cance levels were adjusted based on 

3,000 permutations or Bonferroni correction (α = 0.05), 

respectively. We used MODELTEST 3.06 (Posada and 

Crandall 1998) and the Akaike Information Criterion 

(Akaike 1974) to determine the minimum parameter 

nucleotide substitution model that best fi t the mtDNA 

and intron sequence data. Pairwise genetic distances 

between unique alleles and haplotypes were calculated 

in PAUP* (Swofford 1998) for mtDNA and ARLEQUIN 

for nuclear introns. Chi-square tests were conducted 

to determine if allele or haplotype groups were associated with a particular locality or region. Hierarchical analyses of molecular variance (AMOVA) were 

conducted in ARLEQUIN to assess genetic diversity 

among and within populations grouped based on (1) 

subspecies classifi cations (groups: Aleutians, Bodfi sh, 

Flaxman, Kent Peninsula, YK Delta; Baffi n, Belcher, 

Hudson Straits, Mansel, Southampton, Svalbard; New 

Brunswick, Nova Scotia; and Soderskar, Tromsø), and 

(2) geographic proximity (groups: Aleutians, Bodfi sh, Flaxman, YK Delta; Baffi n, Hudson Straits, Kent 

Peninsula, Mansel, Southampton; Belcher, New Brunswick, Nova Scotia; and Soderskar, Svalbard, Tromsø) 

using the nucleotide substitution model that best fi t the 

alleles and haplotypes. Principal components analysis 

(PCA) was performed on microsatellite genotype data to 

illustrate overall trends. In addition, Bayesian clustering 

method implemented by STRUCTURE 2.1 (Pritchard et 

al. 2000) was used to infer the occurrence of population 

structure without a priori knowledge of putative populations and probabilistically assign individuals to putative 

populations based on microsatellite allelic frequencies. 

Data were analyzed using an admixture model assuming correlated frequencies with 10,000 burn-in period, 

100,000 Markov chain Monte Carlo iterations, and number of possible populations (K) ranging from 1–13; the 

analysis was repeated three times to ensure consistency 

across runs. To determine if more geographically distant 

population pairs are also more genetically differentiated (isolation by distance), simple Mantel tests were 

performed in zt 1.0 (Bonnet and Van de Peer 2002). 

Signifi cance of Pearson correlation coeffi cients (r) was 

assessed using a randomization procedure, in which the 

original value of the statistic was compared to the distribution of a random reallocation of the distance values in 

one of the matrices (randomization = 10,000).

To assess the relative contributions of refugia for 

Common Eiders as possible source populations for sampled populations, we conducted hierarchical analyses 

of variance and grouped populations based on proximity to potential refugia. Given the high level of natal 

and breeding philopatry reported for female Common 

Eiders, AMOVAs were conducted on mtDNA haplotype 

data because gene fl ow among populations through male 

mediated dispersal may make it diffi cult to distinguish 

between contemporary and historical dispersal among 

populations. Population groups that maximized the 

variance among groups (ΦCT) were predicted to indicate 

source populations for colonized areas. 

Historical demography and gene fl ow

We assessed evidence for historical fl uctuations in 

population demography of Common Eider populations 

to determine if populations were located in potential 

refugia. Population growth rates were estimated in 

BOTTLENECK 1.2.02 (Cornuet and Luikart 1996) for 

microsatellite loci and FLUCTUATE 1.4 (Kuhner et 

al. 1995) for sequence data. Bottleneck compares the 

number of alleles and gene diversity at polymorphic loci 

under the infi nite allele model (IAM; Maruyama and 

Fuerst 1985), stepwise mutation model (SMM; Ohta 

and Kimura 1973), and two-phased model of mutation 

(TPM; Di Rienzo et al. 1994; parameters: 79% SMM, 

variance 9; Piry et al. 1999, Garza and Williamson 

2001). One thousand simulations were performed for 

each population. Signifi cance was assessed using a Wilcoxon sign-rank test, which determines if the average of 

standardized differences between observed and expected 

heterozygosities is signifi cantly different from zero 

(Cornuet and Luikart 1996). Signifi cant heterozygote 

defi ciency relative to the number of alleles indicates 

recent population growth, whereas heterozygote excess 

relative to the number of alleles indicates a recent population bottleneck (Cornuet and Luikart 1996, Luikart 

1997). It is important to note that heterozygote defi ciency and excess calculated in BOTTLENECK differs from 
38

values calculated in other population genetic programs. 

As mentioned previously, BOTTLENECK compares 

heterozygote defi ciency and excess relative to allelic diversity, not to Hardy-Weinberg equilibrium expectation 

(Cornuet and Luikart 1996). FLUCTUATE estimates a 

population growth parameter, g, incorporating coalescence theory (parameters: ten short chains with 200 

out of 4,000 sampled trees, and three long chains with 

20,000 out of 400,000 sampled trees). Positive values 

of g indicate population growth over time and negative 

values indicate population decline. Data were run three 

times to ensure convergence of parameters across runs. 

Finally, mismatch distributions of mtDNA haplotype 

data were calculated in ARLEQUIN to gain further 

insight into historical population demography. Distributions multimodal in shape indicate a population that is 

at demographic equilibrium, whereas unimodal distributions suggest that a population has undergone a recent 

demographic expansion (Rogers and Harpending 1992). 

We examined the infl uence of current and historical 

processes on population genetic structure by performing 

a nested clade analysis (NCA) of mtDNA sequence data 

(Templeton et al. 1995, Templeton 1998). The haplotype network inferred by TCS was used to defi ne nested 

series of clades according to Crandall and Templeton 

(1993). Clades were analyzed in GeoDis 2.0 (Posada 

et al. 2000), and demographic events were inferred 

based on an inference key (Templeton 1998, Posada and 

Templeton 2001). 

To further assess gene fl ow among populations, 

number of migrants per generation (Nem) and number 

of female migrants per generation (Nfm) were calculated 

for nuclear microsatellite and intron loci and mtDNA, 

respectively, in MIGRATE v2.0.6 (Beerli 1998, 2002, 

Beerli and Felsenstein 1999) among sampled localities. 

Full models, θ (4Neμ or Nfμ) and all pairwise migration 

parameters were allowed to vary and estimated individually from the data, and were compared to restricted 

island models for which θ and pairwise migration 

parameters were equal among populations (symmetrical gene fl ow). MIGRATE was run using maximum 

likelihood search parameters; ten short chains (2000 

out of 400,000 sampled trees), fi ve long chains (10,000 

out of 2,000,000 sampled trees), and fi ve adaptively 

heated chains (start temperatures 1, 1.5, 3, 6, and 12; 

swapping interval = 1). Full models were run three 

times to ensure the convergence of parameter estimates. 

Restricted models were run once. Alternative models 

were evaluated for goodness of fi t given the data using a 

log-likelihood ratio test. The resulting statistic from the 

log likelihood ratio test is equivalent to a χ2 distribution 

with the degrees of freedom equal to the difference in 

the number of parameters estimated in the two models 

(Beerli and Felsenstein 2001). 

Results

Genetic Diversity

Bi-parentally inherited microsatellite loci

The number of alleles at the 12 polymorphic microsatellite loci ranged from 3–49, with an average of 13.8 

alleles per locus. The average number of alleles per 

population ranged from 2.7–9.9. Observed heterozygosity ranged from 44.5–57.7% for each population with 

an overall heterozygosity of 54.3% (Table 2.1). The 

inbreeding coeffi cient (FIS) ranged from -0.005 to 0.445 

among sampled sites with an overall value of 0.030. 

None of the inbreeding coeffi cients were signifi cantly 

different from zero (P > 0.05). 

Bi-parentally inherited nuclear introns

Seventy alleles were reconstructed for lamin A from 

592 individuals in PHASE with 22 variable sites (Fig. 

2.2A, Appendix 2.B). 207 individuals (40%) were 

homozygous, and 147 (25%) were heterozygous at one 

site. Probabilities of reconstructed haplotypes for 77% 

(n = 184) of individuals that were heterozygous for more 

than one site exceeded 0.85, and the probabilities for the 

remaining individuals ranged from 0.71–0.84 (n = 30, 

13%), 0.50–0.68 (n = 23, 10%) and 0.34 (n = 1, 0.4%). 

The background recombination rate (ρ) was 0.50, with 

factors exceeding ρ ranging from 0.40–1.94 between 22 

variable sites. 

For nuclear intron gapdh, 48 alleles were reconstructed from 474 individuals with 22 variable sites 

(Fig. 2.2B, Appendix 2.C). 75 individuals (16%) were 

homozygous at all variable sites, and 48 (10%) were 

heterozygous at one site. Probabilities of 77% (n = 272) 

of reconstructed haplotypes that were heterozygous for 

more than one site exceeded 0.90, and the probabilities for remaining individuals ranged from 0.71–0.87 

(n = 26, 7%) and 0.43–0.68 (n = 53, 15%), which we 

attribute to potentially high levels of recombination occurring within this locus (0.39–4.41 factors exceeding 

ρ = 0.05, between 22 variable sites). There were two 

variable sites that exceeded ρ by two or more factors: 

2.12 factors between sites 16 and 22, and 4.41 factors 

between sites 232 and 252. 

Haplotype (h) and nucleotide (π) diversity ranged 

from 0.733–0.901 and 0.005–0.009, respectively for 

lamin A, and 0.506–0.897 and 0.004–0.007, respectively 

for gapdh (Table 2.1). Observed heterozygosity ranged 

from 32.1–89.2% and 35.3–96.4% for lamin A and 

gapdh, respectively (Table 2.1). Signifi cant Fu’s Fs (P 

< 0.02) were observed for Aleutian Islands, YK Delta, 

Bodfi sh, Flaxman, Kent Peninsula, Belcher Islands, New 

Brunswick, Nova Scotia, Svalbard, Tromsø, and Sod-
39

erskar (Table 2.1). We did not observe any signifi cant 

Tajima’s D values (Table 2.1). 

Maternally inherited mtDNA

Sixty-four unique haplotypes were identifi ed from 

456 individuals with 36 variable sites; 78% of variable 

sites were located within the fi rst 174 bp (e.g., domain I; 

Marshall and Baker 1997) of mtDNA control region, and 

22% of variable sites were located within the remaining 

239 bp (central domain and domain II; Fig. 2.2C, Appendix 2.D). Haplotype (h) and nucleotide (π) diversity 

ranged from 0.230–1.000 and 0.001–0.009, respectively 

(Table 2.1). Signifi cant Fu’s Fs were observed for Baffi n Island, Hudson Straits, and Soderskar (Table 2.1). 

Nova Scotia also had a signifi cant Tajima’s D (Table 

2.1). 

Population Genetic Structure

Bi-parentally inherited microsatellite loci

Overall estimates of population subdivision were 

signifi cant (FST = 0.060, P < 0.000; RST = 0.020, P = 

0.010). Signifi cant estimates of inter-population variance in microsatellite allelic frequency primarily were 

observed among but not within subspecies, except 

among the Aleutian Islands and the other S. m. v-nigrum populations (Table 2.2). Estimates of FST generally were higher than RST, with values ranging from 

0.021–0.166 and 0.021–0.203, respectively (Table 2.2). 

AMOVA revealed partitioning among groups, among 

populations, and within populations (Table 2.3). More 

variation was accounted for when populations were 

grouped by subspecies classifi cation rather than by geographic proximity for both FST and RST (Table 2.3). 

PCA grouped populations by subspecies classifi cation 

into four clusters with Belcher Islands (S. m. sedentaria) 

grouping with S. m. dresseri (Fig. 2.3). The Bayesian 

clustering method implemented in STRUCTURE indicated that the likelihood generated for the microsatellite 

data was maximized when the total number of populations was four (data not shown). Results were similar 

to the PCA; however, S. m. v-nigrum populations were 

subdivided into two clusters. S. m. mollissima and S. m. 

borealis populations clustered together along with S. m. 

dresseri and S. m. sedentaria populations (Table 2.4). 

Finally, there was a positive correlation between genetic 

(FST/[1-FST] and RST/[1-RST]) and geographic distances 

(FST; r = 0.822, P = 0.001, RST; r = 0.655, P = 0.001).

Bi-parentally inherited nuclear introns

The nucleotide substitution model that best fi t lamin

A and gapdh sequence data was the Tamura-Nei (1993) 

model with an invariant site parameter. Our overall 

estimate of spatial variance in allelic frequencies (ΦST) 

was signifi cant for lamin A and gapdh, 0.072 and 0.075, 

respectively. Moreover, inter-population comparisons 

(ΦST) showed moderate levels of genetic differentiation 

with values ranging from 0.014–0.290 and 0.017–0.220 

for lamin A and gapdh, respectively (Table 2.2). Interpopulation comparisons calculated from lamin A sequence data were lower within subspecies, whereas most 

signifi cant variances in gapdh allelic frequency occurred 

between S. m. v-nigrum and all other subspecies (Table 

2.2). Alleles in each of the two-allele groups observed 

for lamin A and gapdh are not equally distributed among 

populations (Fig. 2.2; lamin A χ2 = 86.9, d.f. = 13, P < 

0.001; gapdh χ2 = 159.6, d.f. = 13, P < 0.001). More 

v-nigrum individuals are present in one allele group with 

the remaining subspecies predominately in the other 

allele group.

We also calculated FST values for each of the 22 

polymorphic single nucleotide polymorphisms (SNPs). 

Signifi cant (P < 0.05) variance in lamin A allelic frequency occurred at fi ve SNPs; 55 (FST = 0.049), 116 

(FST = 0.040), 174 (FST = 0.124), 179 (FST = 0.152), and 

195 (FST = 0.057). Signifi cant overall allelic frequency 

variance was also observed at six SNPs for gapdh; 122 

(FST = 0.131), 129 (FST = 0.050), 165 (FST = 0.151), 170 

(FST = 0.058), 232 (FST = 0.122), and 258 (FST = 0.142). 

Within lamin A, it does not appear that a single nucleotide position is driving the signifi cant pairwise comparisons observed, and no single SNP accounted for any 

of the variance observed among populations (data not 

shown). However, site 258 of gapdh appears to account 

for discordance among S. m. v-nigrum and the other 

subspecies as all populations had signifi cant pairwise 

comparisons except New Brunswick and Mansel Island 

and accounted for 54.1% of the variance among populations (data not shown). Gapdh sites 122, 165, and 232 

are likely driving the differentiation observed between 

New Brunswick and S. m. v-nigrum populations and 

accounted for 0.148, 0.064, and 0.082 of the variance 

among populations, respectively (data not shown). The 

remaining positions (129 and 170) did not account for 

any of the variance observed among populations.

AMOVA indicated that variance among groups was 

better accounted for by lamin A and gapdh when populations were grouped based on subspecies classifi cations 

(Table 2.3). There was also a signifi cant positive correlation between genetic (ΦST/[1-ΦST]) and geographic 

distances assayed using nuclear intron sequence information (lamin A; r = 0.706, P = 0.001; gapdh; r = 0.791, 

P = 0.001).
40

Maternally inherited mtDNA

The nucleotide substitution model that best fi t the 

mtDNA data was the Tamura-Nei (1993) model with an 

invariant site parameter (substitute rate matrix: R[A–C] 

= 1.0000, R[A–G] = 31.1491, R[A–T] = 1.0000, 

R[C–G] = 1.0000, R[C–T] = 32.3007, R[G–T] = 1.0000, 

P–inv. = 0.9187, A = 0.2248, C = 0.3065, G = 0.1907, T 

= 0.2780). Our overall estimate of population subdivision was very high (ΦST = 0.497, P < 0.000), and interpopulation comparisons (ΦST) ranged from 0.051–0.927 

(Table 2.2). Few signifi cant comparisons were observed 

among the Hudson Bay eiders (Baffi n Island, Hudson 

Straits, Southampton Island, Mansel Island, and Belcher 

Islands; Table 2.2). Populations that predominately 

were represented by haplotypes located at the tips of the 

haplotype network (Aleutian Islands, Bodfi sh, Flaxman, 

and Soderskar; Fig. 2.2C), exhibited very high levels 

of structuring among populations (Table 2.2). Haplotypes in each of the two-haplotype groups observed for 

mtDNA are not equally distributed among populations 

(mtDNA χ2 = 263.6, d.f. = 13, P < 0.001; Fig. 2.2). 

More Bodfi sh and Flaxman individuals are present in 

one haplotype group with the remaining populations 

predominately in the other haplotype group. There also 

was a signifi cant positive correlation between genetic 

(ΦST/[1-ΦST]) and geographic distances for mtDNA 

haplotype (r = 0.705, P = 0.001).

In contrast to the nuclear loci, variance among 

groups in mtDNA haplotypic frequencies was better 

accounted for when populations were grouped based on 

geographic proximity rather than subspecies (Table 2.3). 

Among group variance (ΦCT) was higher when North 

Slope (Bodfi sh and Flaxman) populations were grouped 

together exclusively than groups based on geographic 

proximity (Table 2.3, 2.5), indicating that the Aleutian 

Islands and YK Delta populations may be more genetically similar to Canadian populations. Moreover, more 

of the variation among groups was accounted for when 

North Slope populations were in one group and all remaining populations were in another group (Table 2.5). 

Among-group variance was also higher when Tromsø 

was grouped with New Brunswick and Nova Scotia, 

indicating Tromsø may be genetically more similar to 

eastern Canadian than Scandinavian populations.

Historical Demography and Gene Flow

Population fl uctuations

Evidence for signifi cant fl uctuations in historical 

population demography was detected based on genotypic data from 12 microsatellite loci. Under the IAM, 

YK Delta, Bodfi sh, Flaxman, Nova Scotia, and New 

Brunswick showed excess heterozygosity suggestive 

of a population bottleneck (Table 2.6) and is consistent with band and resight data indicating population 

declines in Alaskan localities (Stehn et al. 1993, Suydam 

et al. 2000). Population growth, based on heterozygote 

defi ciency, was observed for all populations except 

Mansel Island, Nova Scotia, and New Brunswick under 

the SMM (Table 2.6). Heterozygote defi ciency was also 

observed under the TPM for three populations; Southampton Island, Tromsø, and Soderskar (Table 2.6). 

Signifi cant population growth based on nuclear intron 

sequences was detected using FLUCTUATE for most 

populations except; Mansel Island, Belcher Islands, 

and Soderskar with lamin A, and Baffi n Island, Hudson 

Straits, Svalbard, Tromsø, and Soderskar with gapdh

(Table 2.6). Theta (4Neμ) ranged from 0.009–0.138 for 

lamin A and 0.003–0.047 for gapdh (Table 2.6). 

Populations showing positive growth rates using 

mtDNA were Aleutian Islands, YK Delta, Kent Peninsula, Baffi n Island, Hudson Straits, Southampton Island, 

Mansel Island, Tromsø, and Soderskar (Table 2.6). Theta (2Nfμ) ranged from 0.004–0.050. Mismatch distributions did not reject the sudden expansion model based 

on sum of squared deviation statistic, with the exceptions of YK Delta, Kent Peninsula, and New Brunswick. 

Mismatch distributions did not reject the sudden expansion model based on Harpending’s raggedness index for 

any population (Harpending 1994). Parameter estimates 

for time of expansion (τ) ranged from 0.497–7.877, with 

the smallest values observed for Aleutian Islands and 

Soderskar and larger estimates calculated for Kent Peninsula and the other Alaskan populations (Table 2.6). 

Nested clade analysis

Thirteen clades had signifi cant correlations among 

haplotypes and geography (Fig. 2.4), and NCA inferences for these clades are shown in Table 2.7. Continuous range expansion was supported by two clades; (1) 

Aleutian Islands/Kent Peninsula to Flaxman, and (2) 

Tromsø to Baffi n Island and Nova Scotia. Three clades 

(I-4, I-12, II-5; Table 2.7) were indicative of past fragmentation and/or long distance colonization involving 

all analyzed regions. Restricted gene fl ow with isolation 

by distance was supported by four clades (I-10, I-15, 

II-1, II-2; Table 2.7) and with long distance colonization 

supported by three clades (I-21, II-3, II-4; Table 2.7) 

including all populations. 

Dispersal

Gene fl ow among populations was estimated by 

grouping populations based on geographic proximity 

and subspecies designation, and among populations that 

exhibited a genetic signature of population stability, to 
41

examine historic and contemporary dispersal among 

proposed refugial populations. Full models (all parameters allowed to vary independently) had signifi cantly 

higher ln likelihoods than the restricted island model 

(symmetric gene fl ow) across all population groupings 

and all marker types, indicating asymmetric dispersal 

among analyzed populations (data not shown).

Gene fl ow estimates based on microsatellite loci are, 

in general, higher than estimates based on nuclear intron 

and mtDNA loci (Table 2.8). Among refugial populations, number of migrants per generation (Nem or Nfm) 

ranged from 4.53–15.22, 0.27–34.95, and 0.00–3.95 

Nem for estimates based on microsatellites, introns, and 

mtDNA loci, respectively (Table 2.8). Asymmetrical 

gene fl ow, as indicated by non-overlapping 95% confi dence intervals, was observed among all populations 

analyzed. Calculations of Nem from nuclear DNA indicated that, on average across generations, more individuals dispersed from Belcher Islands to New Brunswick, 

North Slope to New Brunswick, and Svalbard to Belcher 

Islands, New Brunswick, and North Slope (Table 2.8). 

Most gene fl ow estimates based on mtDNA control 

region were low and suggested symmetrical gene fl ow 

among populations, with several exceptions. More 

females dispersed from New Brunswick to North Slope 

and Svalbard (Table 2.8).

S. m. v-nigrum populations’ gene fl ow estimates 

ranged from 10.79–25.50, 0.01–85.90, and 0.00–3.17 

Nem for estimates based on microsatellites, introns, and 

mtDNA loci, respectively (Table 2.8). Asymmetrical 

gene fl ow was observed among all S. m. v-nigrum populations for nuclear loci, with more individuals dispersing 

from North Slope to Aleutian Islands and Kent Peninsula, and YK Delta to all populations. Directionality 

of dispersal differed between microsatellite and intron 

estimates for the Aleutian Islands and Kent Peninsula. Microsatellite-based calculations indicated more 

individuals dispersed from Kent Peninsula to Aleutian 

Islands, whereas estimates based on introns suggested 

the reciprocal (Table 2.8). Gene fl ow estimates based on 

mtDNA control region were low and indicated symmetrical gene fl ow among populations, with two exceptions. 

More females dispersed from Aleutian Islands to Kent 

Peninsula, and North Slope to Kent Peninsula (Table 

2.8). 

Among Central Canadian and Svalbard populations, 

Nem ranged from 5.47–31.81, 0.00–55.88, and 0.00–

41.37 for estimates based on microsatellites, introns, and 

mtDNA loci, respectively (Table 2.8). Asymmetrical 

gene fl ow was observed among all Central Canadian and 

Svalbard populations for nuclear loci, with more individuals dispersing from Belcher Islands to Baffi n, Kent 

Peninsula to Baffi n, Hudson Straits to Baffi n, and Kent 

Peninsula, Southampton to Belcher Islands, and Svalbard to Baffi n, Belcher Islands, Hudson Straits, and Kent 

Peninsula. Directionality of dispersal differed between 

microsatellite and intron estimates for Baffi n, Hudson 

Straits, and Kent Peninsula, and Southampton. Microsatellite-based calculations indicated more individuals 

dispersed from Southampton to Baffi n, Hudson Straits, 

and Kent Peninsula. Conversely, estimates based on 

introns indicated more individuals dispersed from Baffi n, Hudson Straits, and Kent Peninsula to Southampton Island (Table 2.8). Gene fl ow estimates based on 

mtDNA control region indicated more females dispersed 

from Hudson Straits to Southampton, and Southampton 

to Belcher Islands; however, populations have overlapping 95% confi dence intervals (Table 2.8).

Gene fl ow estimates among southern Canadian populations ranged from 7.66–19.26, 3.27–21.35, and 0.21–

8.93 Nem for estimates based on microsatellites, introns, 

and mtDNA loci, respectively (Table 2.8). Asymmetrical gene fl ow was observed between two populations, 

more individuals dispersed from Belcher Islands to New 

Brunswick based on nuclear introns (Table 2.8). Gene 

fl ow estimates based on mtDNA control region indicated 

asymmetrical gene fl ow among most populations. More 

females dispersed from New Brunswick to Belcher 

Islands and Nova Scotia (Table 2.8). 

Among Scandinavian populations, Nem ranged from 

9.20–24.08, 3.31–15.31, and 0.00–12.96 for estimates 

based on microsatellites, introns, and mtDNA loci, 

respectively (Table 2.8). Nem estimates among populations based on nuclear DNA indicated more individuals 

dispersed from Soderskar to Svalbard and Tromsø, and 

Svalbard to Tromsø (Table 2.8). Gene fl ow estimates 

based on mtDNA are congruent with nuclear DNA 

estimates, except Nfm estimates between Svalbard and 

Tromsø have overlapping 95% confi dence intervals, but 

the variances suggest that more individuals are dispersing from Svalbard to Tromsø (Table 2.8).

 

Discussion

Population Subdivision

Low to moderate levels of spatial genetic structuring observed for bi-parentally inherited nuclear markers 

were not surprising, due to aspects of Common Eider 

breeding and wintering biology. Pair formation occurs 

in coastal waters during non-breeding months, where 

admixture of several breeding populations of Common 

Eiders likely occurs. Male eiders follow females back 

to breeding sites, and males have been reported to have 

high natal and breeding dispersal distances (Wakely 

and Mendall 1976, Swennen 1990). Male dispersal, 

therefore, is expected to homogenize allelic frequen-
42

cies in the nuclear genome (Scribner et al. 2001). The 

overall lack of population subdivision observed within 

subspecies and more signifi cant comparisons between 

subspecies assayed using microsatellite and nuclear 

intron loci, supports the hypothesis that male gene fl ow 

among populations homogenizes gene frequencies in the 

nuclear genome, as populations within the same subspecies share common wintering grounds (Ploeger 1968, 

Tiedemann and Noer 1998, Petersen and Flint 2002). 

Aleutian Islands were the only locality with signifi cant 

inter-population comparisons among populations within 

the same subspecies. We attribute signifi cant comparisons observed to S. m. v-nigrum populations wintering 

in disparate locations in the Bering Sea. Eiders breeding on the Aleutian Islands are believed to be residents, 

because eiders are observed year-round in near shore 

waters of the Aleutian chain (M. Petersen pers. comm.). 

In contrast, eiders from Bodfi sh, Flaxman, Kent Peninsula, and YK Delta are migratory to varying degrees and 

winter in the near shore waters of Chukotka Peninsula, 

Bristol Bay, and Yukon-Kuskokwim Delta (Petersen and 

Flint 2002, L. Dickson pers. comm.). Eiders from S. m. 

v-nigrum populations likely wintered in different locations over evolutionary time, allowing the accumulation 

of genetic differences among populations.

Genetic discordance observed among Common Eider 

populations appears to be driven more by migration rather than mutation, as our overall estimate of subdivision 

FST was higher than RST assayed from 12 microsatellite 

loci. Furthermore, inter-population variances in allelic 

frequency were higher, along with more signifi cant comparisons observed for FST based estimates. Populations 

could not have been subdivided long enough to accumulate two or more mutational events (Estoup et al. 2002). 

However, concordance between the rapidly evolving 

microsatellite loci and the more slowly evolving nuclear 

intron loci in the lack of population structure within 

subspecies, suggests that populations have been subdivided long enough for mutations to accumulate. Low 

levels of contemporary gene fl ow probably are occurring 

among populations, despite high winter site philopatry 

reported for waterfowl (Robertson and Cooke 1999). A 

S. m. borealis male was collected from Point Barrow, 

Alaska, during fall migration (07 August 1994, University Alaska Museum specimen UAM6631), which is 

part of the S. m. v-nigrum migratory route (Petersen and 

Flint 2002, L. Dickson pers. comm.). Occasional male 

dispersal among populations that do not normally share 

common wintering grounds may provide enough gene 

fl ow among wintering areas to limit the accumulation 

of genetic differences among populations resulting in 

dispersal playing a larger role in population differentiation, rather than mutation.

Subspecies classifi cations for the fi ve subspecies represented in this study are strongly supported by nuclear 

data. Few signifi cant inter-population comparisons were 

observed within subspecies for both microsatellite and 

nuclear intron loci based on intra-subspecies comparisons, variance in allelic frequencies among groups, and 

PCA. Moreover, S. m. v-nigrum appears to be well supported by the Bayesian clustering method implemented 

in STRUCTURE, which assigned a majority of S. m. 

v-nigrum individuals to two of the four clusters almost 

exclusively. However, S. m. borealis and S. m. mollissima individuals were grouped in one cluster and S. m. 

dresseri and S. m. sedentaria individuals were assigned 

to another cluster. Groupings could be a result of colonization of populations from the same glacial refugia or 

contemporary gene fl ow among populations. Common 

Eiders breeding in central and eastern Canada and Scandinavia may exhibit low levels of winter site fi delity. In 

areas where subspecies distributions overlap, individuals 

may winter in areas that are geographically closer and 

intermix with another subspecies, rather than migrating 

farther to winter with populations of the same subspecies. For example, S. m. borealis is reported to winter 

in the coastal waters of northern Norway and Labrador 

(Ploeger 1968), adjacent to S. m. mollissima and S. m. 

dresseri breeding sites. Because there is little overlap 

among S. m. v-nigrum distribution and other subspecies, 

the split between S. m. v-nigrum and the other subspecies would be expected. Therefore, individuals breeding 

in Canada and Scandinavia may be more likely to intermix with populations of different subspecies, resulting 

in lower inter-subspecies comparisons. 

High spatial genetic structure, assayed for mtDNA 

control region, support banding data which clearly indicate that female Common Eiders exhibit high natal and 

breeding philopatry (Goudie et al. 2000). In contrast 

to microgeographic population subdivision assayed between Alaskan North Slope populations (~90 km apart) 

and between S. m. dresseri populations (~200 km apart), 

few signifi cant inter-population comparisons were 

observed between S. m. borealis and S. m. sedentaria

populations. Female dispersal among Hudson Bay populations would be expected to homogenize mtDNA haplotype frequencies. Researchers have hypothesized that 

if suitable habitat is available, fi rst-time female breeders 

may nest near their wintering grounds rather than returning to natal sites (Tiedemann et al. 2004). Alternatively, 

S. m. borealis and S. m. sedentaria populations could 

have been recently colonized populations expanding 

from the same glacial refugium. Given signifi cant positive growth rates observed at nuclear and mtDNA markers, except for Belcher Islands and Svalbard populations, 

Hudson Bay populations probably were colonized more 

recently and have not had suffi cient time for genetic dif-
43

ferences to evolve among populations. 

Comparatively higher levels of population subdivision observed in mtDNA relative to nuclear DNA, alternatively could be attributed to lineage sorting. MtDNA 

has a lower effective population size relative to nuclear 

DNA; therefore, when mutation rate and selection are 

held constant, genetic drift has a larger effect on mtDNA 

than nuclear DNA (Avise 2004), translating to higher estimates of population subdivision (FST). The effects of 

lineage sorting and sex-biased differences in philopatry 

on spatial genetic subdivision; however, are not mutually exclusive and both factors may be playing a role in 

the degree of population structure observed. However, 

microsatellite loci have a high rate of mutation relative 

to mtDNA (Avise 2004); as a result, new mutations are 

arising more frequently within populations. By chance 

alone, one would expect new mutations to increase in 

frequency among isolated populations and, over time, 

dampen the effects of lineage sorting within microsatellite loci. Given differences in the degree of philopatry 

in Common Eiders between the sexes and congruence 

in results between microsatellite and nuclear intron loci, 

we suggest that differences in estimates of population 

subdivision probably are more attributable to male biased dispersal and high natal and breeding philopatry in 

females, rather than lineage sorting for sampled populations. 

Differences in the degree of population subdivision 

observed between mtDNA and nuclear DNA also may 

be attributable to homoplasy. Not identifying unique 

alleles because fragments of the same length may have 

different sequence information, or have mutated back 

to the ancestral state are issues with fragment analyses 

(Estoup et al. 2002). Both types of homoplasy could 

pose problems when assessing population structure 

based on detecting allelic frequency differences among 

populations, where not identifying unique alleles among 

populations may lower population subdivision estimates 

(Estoup and Cornuet 1999). However, Rousset (1996) 

showed that there are no simple effects of homoplasy 

on population differentiation estimators (FST and RST) 

for loci evolving under the stepwise mutation model or 

an island model of migration. For the mutation process, 

and therefore homoplasy, to have an effect on estimators 

of population subdivision, subpopulations need to have 

different ratios of coalescent times of genes long enough 

to have two or more mutational events to occur (Estoup et al. 2002). Because our estimate of subdivision 

for FST was greater than RST, mutation, and therefore 

homoplasy, does not appear to be playing a major role in 

differentiating populations of Common Eiders. 

Phylogeography and Postglacial Colonization

Concordance in allele and haplotype groups among 

nuclear microsatellite loci, introns, and mtDNA control 

region sequences suggest that Common Eiders were 

subdivided into populations occupying at least two 

long-term glacial refugia during the Pleistocene: S. 

m. v-nigrum (exclusively North Slope populations for 

mtDNA) and the other four subspecies. The presence 

of a distinctive northern group suggests a historical 

split into an arctic refugium northwest of the continental ice sheets and subarctic refugia south of the ice 

sheets, a pattern identifi ed in mammals by Nadler and 

Hoffmann (1977). The vicariant event that resulted in 

the divergence among North Slope populations and the 

other Alaskan, Canadian, and Scandinavian populations 

appears to have been maintained through evolutionary 

time, as few populations share similar haplotypes with 

the North Slope populations. 

Estimates of genetic diversity appear incongruent 

with demographic analyses, as populations in formerly 

glaciated regions do not have signifi cantly lower genetic 

diversity (based overlapping 95% CI) than proposed 

refugial populations. Populations residing in previously 

glaciated regions are expected to have lower haplotype 

diversity due to successive founder events (Hewitt 

1996). However, admixture of mtDNA haplotypes from 

different Pleistocene refugia may have increased genetic 

diversity of Common Eiders in formerly glaciated areas 

(Fedorov et al. 1999). Regions where distinct haplotype 

groups co-occur, such as Kent Peninsula, support admixture of individuals from different arctic and subarctic 

refugia. Presence of a contact zone between Beringian 

and eastern Canadian haplotypes in Kent Peninsula provides further evidence for the Mackenzie River suture 

zone (Hewitt 2004b). Mackenzie River coincides with 

the eastern and western extent of the North American 

Cordilleran and Laurentide ice sheets and likely formed 

an eastern boundary for the Beringian refugium (Hewitt 

2004b). Concordance for contact zones in other arctic 

vertebrates in the Mackenzie River region indicates 

that this region was a strong geographic barrier limiting 

dispersal from Beringian populations (e.g. Collared lemming, Dichrostonyx ssp., Fedorov and Stenseth 2002; 

True lemming, Lemmus spp., Fedorov et al. 2003; Rock 

Ptarmigan, Lagopus mutus, Holder et al. 1999; 2000). 

Our data suggest that this region also may have contributed to divergence between eiders from the Alaskan 

North Slope and eiders inhabiting all other regions. 

Historical population demographic data suggest that 

Common Eiders were restricted to four glacial refugia 

during the last glacial maxium; Belcher Islands, Newfoundland, North Slope, and Svalbard. Three regions 

exhibit a signal of a demographically stable population 
44

based on mtDNA estimates, North Slope, S. m. dresseri, 

and Svalbard populations, and coincide with previously 

identifi ed glacial refugia; Beringia (northern Alaskan 

shelf), Newfoundland Bank, and Spitsbergen Bank, 

respectively (Ploeger 1968). The proposed location of 

the Beringian refugium for Common Eiders; however, 

differs from Ploeger (1968), who hypothesized that 

Common Eiders where restricted to the southern edge of 

the Bering Land Bridge. Our data suggest that eiders in 

this region may have occupied an arctic refugium, north 

of the land bridge. Moreover, populations sampled 

from the Aleutian Islands and YK Delta have a genetic 

signature of relatively recent population expansion, and 

therefore, it is unlikely that these areas served as refugia 

for Common Eider populations. Belcher Islands did not 

exhibit a signal of population growth, despite Hudson 

Bay being glaciated during the Wisconsin glacial. Given 

the central position of haplotypes within the mtDNA 

network representing S. m. sedentaria individuals, it is 

likely that Belcher Island haplotypes are more historic 

relative to the other sampled haplotypes (Alsos et al. 

2005). S. m. sedentaria might have been restricted 

south of the Laurentide ice sheet, as proposed for other 

arctic vertebrates (Flagstad and Røed 2003, Scribner et 

al. 2003), and slowly colonized behind the retreating 

ice sheet to its present day location. Shorter movements from a location south of the ice sheet to present 

day locations would allow populations to retain genetic 

diversity because effective population sizes would not 

be reduced (Hewitt 1996), especially if colonization 

occurred over a long period. Maintenance of genetic diversity while colonizing recently glaciated areas would, 

therefore, not be expected to produce a genetic signature 

of population expansion because this signature assumes 

low-diversity founder populations (Galbreath and Cook 

2004). 

Beringian populations (Bodfi sh and Flaxman) apparently contributed little to the postglacial colonization of North America, as few populations share North 

Slope haplotypes. Limited post-glacial colonization 

of unglaciated regions by Beringian populations has 

been observed in other arctic vertebrates (Lemmus ssp., 

Fedorov et al. 2003). Analyses suggest that Common 

Eiders dispersed west to Kent Peninsula and northern 

Hudson Bay, and some long distance dispersal to Scandinavian populations may have occurred. The contact 

zone between arctic and subarctic refugia in the vicinity of Kent Peninsula may explain why data for eiders 

in this region do not fi t the sudden expansion model, 

as population subdivision violates the assumption of 

the sudden expansion model (Marjoram and Donnelley 

1994). Surprisingly, few North Slope individuals appear 

to have dispersed to the Aleutian Island and YK Delta. 

Minimal dispersal from the Beringian refugium to southern Alaska could be attributed to the longer persistence 

of the Laurentide ice sheet relative to the Cordilleran 

ice sheet (Westgate et al. 1987). The presence of the 

Cordilleran ice sheet may have inhibited colonization 

of southwest Alaska by Beringian Common Eiders due 

to the unavailability of habitat and is supported by the 

relatively late estimated time of population expansion (τ) 

for the Aleutian Islands. 

The central position of southern refugia haplotypes (Belcher Islands and Newfoundland) indicate 

that populations expanding out of the southern refugia 

likely colonized formally glaciated areas of Canada, 

southern Alaska, and Scandinavia. Though the time of 

divergence (τ) calculated for New Brunswick is recent 

relative to other populations, mismatch distributions 

may not be a good estimator of population expansion, 

as New Brunswick did not fi t the sudden expansion 

model. The Newfoundland populations, in particular, 

share haplotypes with most sampled sites suggesting this 

region was a main source for colonizers during glacial 

retreats through both range expansion and long distance 

colonization. Low genetic diversity estimates calculated 

for Nova Scotia may have been caused by a reduction 

in effective population size through founding events, as 

peripheral expanding populations are expected to have 

lower genetic diversity relative to central populations 

(Nei et al. 1975). Belcher Island haplotypes appear 

more restricted in their geographic range and this region 

may have been the main source of colonizers for Hudson 

Bay and southern Alaska. Slower colonization through 

short dispersal may have acted as a barrier to colonization (Runck and Cook 2005) of Hudson Bay and 

southern Alaska by other refugia. Belcher Island and 

Newfoundland haplotypes are shared with a majority 

of populations and are located centrally in the mtDNA 

haplotype network; therefore, these regions were likely 

important refugia for Common Eider postglacial colonization.

The Spitsbergen Bank (Svalbard) refugium also appears to have played a role in the colonization of glaciated areas in Canada and Scandinavia. The peripheral 

location of Svalbard haplotypes in the mtDNA haplotype 

network suggests that this region was not a main source 

of colonizers for Canadian populations. Svalbard was 

likely the main colonizer of Soderskar because Soderskar shares few haplotypes with other regions. Shared 

haplotypes from Canadian and Scandinavian refugia 

suggest that Tromsø may be a contact zone for these 

regions. Evidence of ice-free areas in northern Norway 

during the last glacial is controversial (Ploeger 1968). 

However, eider relics dating to approximately 115,000 

years ago have been identifi ed from northern Norway 

(Lauritzen et al. 1996). If ice-free areas did occur, this 
45

may explain, in part, the distribution of haplotypes 

observed for Tromsø and the relatively moderate time of 

divergence estimate. Tromsø could have been colonized initially by the Newfoundland refugium and later 

came in contact with Svalbard colonizers through range 

expansion during glacial retreat. Northern Norway has 

been identifi ed as a contact zone for other vertebrates 

(Microtus agrestis, Jaarola and Searle 2002; M. oeconomus, Brunhoff et al. 2003). In addition, Tiedemann et al. 

(2004) examined the post-glacial colonization of Europe 

by Common Eiders and hypothesized that Europe was 

colonized through range expansion by populations 

expanding from a single refugium located in southern 

Norway. This scenario is consistent with our fi ndings. 

Eiders could have colonized southern Norway from 

the Newfoundland refugium, which was a main source 

population for the subsequent colonization of Europe 

during glacial retreat. 

Conclusions

High levels of natal and breeding philopatry and winter site fi delity observed in waterfowl have predictable 

effects on population genetic structure, and researchers 

characterizing populations using genetic techniques 

could under- or over-estimate the degree of population 

genetic differentiation if estimates are based on a single 

marker type. As seen in Common Eiders, nuclear and 

mtDNA markers show varying levels of genetic partitioning among breeding sites. Therefore, not utilizing 

molecular markers with varying modes of heritance 

could mislead researchers characterizing genetic variation within populations. 

Concordance of proposed glacial refugia utilized by 

Common Eiders with other arctic species indicates that 

arctic and subarctic refugia northwest and southeast of 

the ice-sheets, respectively, were important reservoirs of 

genetic diversity during the Pleistocene. Southern refugia appear to have served as the main source populations 

for postglacial colonization of Canada, southern Alaska, 

and Scandinavia as proposed for other vertebrates (Flagstad and Røed 2003, Scribner et al. 2003). Data suggest 

a stepwise postglacial colonization of North America 

and Scandinavia by Common Eiders with some bouts of 

long distance dispersal. Restricted gene fl ow expanding 

out from proposed refugia is supported by the increase 

in genetic differentiation with distance (Kimura and 

Weiss 1964). In contrast to Common Eiders restricted to 

southern refugia, eiders residing in Beringia (and those 

on the North Slope of Alaska in particular) contributed 

little to colonizing deglaciated regions and remain genetically differentiated from Canadian and Scandinavian 

populations. Minimal colonization by the Beringian 

refugium is particularly evident in that geographically 

close populations (Aleutian Islands, Kent Peninsula, and 

YK Delta) share few haplotypes with North Slope Common Eiders and appear to be more genetically similar 

to central and eastern Canadian populations. Genetic 

discordance among populations residing in Beringia and 

other refugia has been maintained through evolutionary 

time despite contemporary gene fl ow among populations 

through male dispersal. 

Acknowledgments

Funding was provided by: Minerals Management 

Service (1435-01-98-CA-309); Coastal Marine Institute, University of Alaska Fairbanks; U. S. Geological 

Survey; Alaska EPSCoR Graduate Fellowship (NSF 

EPS-0092040); University of Alaska Foundation Angus 

Gavin Migratory Bird Research Fund; and BP Exploration (Alaska) Inc. We thank all of the researchers for 

generously providing samples; B. Barrow, F. Broerman, J.O. Bustness, K. Dickson, L. Dickson, P. Flint, G. 

Gilchrist, M. Hario, D. Kellet, M. Kilpi, K. Mawhinney, 

M. Petersen, R. Suydam, P. Tuomi, and University of 

Alaska Museum, as well as; J. Gust and G.K. Sage, U.S. 

Geological Survey, who provided laboratory assistance, 

and C. Monnett and J. Gleason, Minerals Management 

Service. 

The USFWS banding number for the USGS is 20022 

and the master permit holder is Dirk Derksen.The 

IACUC number #02-01 was assigned for this work to 

Kevin McCrackin.

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50

Figure 2.1: Subspecies distribution and localities of the 15 Common Eider populations sampled in this 

study: S. m. borealis; Baffi n Island (BFN), Hudson Straits (HDS), Mansel Island (MSL), Southampton Island 

(SHP), and Svalbard (SVD), S. m. dresseri; New Brunswick (NBW), and Nova Scotia (NVS), S. m. mollissima; 

Soderskar (SDK), and Tromsø (TRM), S. m. sedentaria; Belcher Islands (BCH), and S. m. v-nigrum; Aleutian 

Islands (ALN), Bodfi sh (BOD), Flaxman (FLX), Kent Peninsula (KTP), and Yukon-Kuskokwim Delta (YKD). 

Arrows indicate populations with a positive growth signature. Extent of the most recent last glacial ice sheets 

are illustrated in white, and unglaciated regions are illustrated in gray (Hewitt 2004b). 
51

A. lamin A

Figure 2.2: Unrooted parsimony trees illustrating relationships of (A) 70 lamin A alleles, (B) 48 gapdh alleles, 

and (C) 64 mtDNA control region haplotypes, with the size of the circle node corresponding to the frequency 

of each allele. Small black squares indicate intermediate ancestral alleles that were not sampled. Each 

sampled subspecies has a unique color.
52

B. gapdh

C. mtDNA control region 

Figure 2.2 cont.
53

Figure 2.3: Canonical plot of the fi rst two principal components illustrating the partitioning of overall FST

variance among Common Eider populations. Ellipses illustrate the relative positions of populations from 

each of the fi ve sampled subspecies.
54

Figure 2.4: MtDNA control region haplotype network and nested design for the nested clade analysis.
55

Table 2.1: Observed (Ho) and expected heterozygosities (He), haplotype (h) and nucleotide (π) diversity, with 

standard deviation (SD), mean number of alleles or number of unique haplotypes per population, and sample 

size (n), for 12 microsatellite loci, lamin A, gapdh, and mtDNA control region.

ALN YKD BOD FLX KTP BFN HDS SHP

Msats

Ho 0.516 0.577 0.578 0.563 0.549 0.478 0.548 0.535

He 0.537 0.595 0.602 0.587 0.602 0.487 0.540 0.537

Mean no. 

alleles 6.75 9.50 8.86 8.93 7.43 5.50 6.50 8.33

n 50 124 100 99 41 15 28 52

Lamin A 

Ho 0.804 0.563 0.375 0.487 0.484 0.643 0.321 0.462

He 0.879 0.899 0.871 0.871 0.901 0.870 0.825 0.835

h 0.879 0.899 0.871 0.871 0.901 0.870 0.825 0.835

SD 0.019 0.010 0.019 0.019 0.019 0.039 0.023 0.020

π 0.007 0.008 0.008 0.007 0.009 0.007 0.007 0.007

SD 0.004 0.005 0.005 0.004 0.005 0.005 0.004 0.004

Fu’s Fs –7.07* –18.03* –9.91* –6.16* –6.97* –3.22 –0.26 –3.92

Tajima’s 

D –0.43 –0.29 –0.29 –0.26 0.37 0.01 1.32 0.60

No.

Alleles 17 31 22 15 17 10 8 14

n 92 220 128 74 62 28 56 104

Gapdh

Ho 0.918 0.918 0.920 0.931 0.964 0.857 0.900 0.778

He 0.821 0.889 0.897 0.897 0.840 0.773 0.669 0.781

h 0.821 0.889 0.897 0.897 0.840 0.773 0.669 0.781

SD 0.022 0.010 0.013 0.019 0.026 0.052 0.050 0.031

π 0.005 0.007 0.007 0.007 0.006 0.005 0.005 0.006

SD 0.003 0.004 0.004 0.004 0.004 0.003 0.003 0.003

Fu’s Fs –5.76* –2.55 –2.74 –2.29 –0.68 0.33 2.51 –0.68

Tajima’s 

D –0.94 –0.44 –0.48 –0.58 –0.52 1.60 1.37 0.88

No.

Alleles 15 21 17 15 12 7 7 13

n 98 170 100 58 56 28 40 72
56

MSL SVD BCH NBW NVS TRM SDK

Msats

Ho 0.472 0.500 0.496 0.550 0.519 0.445 0.456

He 0.517 0.481 0.534 0.543 0.538 0.444 0.454

Mean no. 

alleles 2.67 6.92 6.50 6.50 7.33 5.83 5.75

n 3 37 22 40 40 38 27

Lamin A 

Ho 0.333 0.892 0.400 0.821 0.692 0.730 0.846

He 0.733 0.866 0.782 0.903 0.853 0.861 0.856

h 0.733 0.866 0.782 0.903 0.853 0.861 0.856

SD 0.155 0.020 0.063 0.017 0.030 0.023 0.029

π 0.005 0.007 0.008 0.008 0.007 0.009 0.007

SD 0.004 0.005 0.005 0.005 0.005 0.005 0.005

Fu’s Fs 0.46 –8.89* –4.96* –11.35* –15.87* –6.16* –5.51*

Tajima’s 

D 0.60 –0.24 –0.20 –0.14 –0.40 –0.05 –0.45

No.

Alleles 3 18 13 21 23 17 14

n 6 74 40 78 78 74 52

Gapdh

Ho 0.667 0.714 0.650 0.879 0.840 0.704 0.353

He 0.733 0.684 0.754 0.886 0.873 0.587 0.506

h 0.733 0.684 0.754 0.886 0.873 0.587 0.506

SD 0.155 0.056 0.065 0.016 0.028 0.048 0.069

π 0.006 0.005 0.005 0.007 0.006 0.005 0.004

SD 0.004 0.003 0.003 0.004 0.004 0.003 0.003

Fu’s Fs 1.31 –0.48 –2.57 –1.41 –3.39 1.57 3.13

Tajima’s 

D 0.37 –0.12 0.28 0.87 –0.40 0.71 0.72

No.

Alleles 3 9 10 14 14 6 3

n 6 56 40 66 50 56 34

Table 2.1 cont.
57

MSL SVD BCH NBW NVS TRM SDK

MtDNA

h 1.000 0.900 0.726 0.714 0.230 0.877 0.380

SD 0.272 0.032 0.092 0.058 0.110 0.043 0.134

π 0.004 0.006 0.004 0.003 0.001 0.006 0.001

SD 0.003 0.003 0.003 0.002 0.001 0.003 0.001

Fu’s Fs –0.69 –2.54 –0.81 –0.22 –1.46 –3.76 –2.07*

Tajima’s 

D 0.00 –0.31 0.04 –0.89 –2.09* –0.38 –1.42

No.

haplotypes 3 11 7 6 4 12 4

n 3 27 20 31 25 26 19

Table 2.1 cont.
58

Msat–FST Msat–RST Lamin A Gapdh MtDNA

Aleutians vs. 

—YK Delta 0.021 0.018 0.027 0.019 0.580

—Bodfish 0.024 0.019 0.049 0.012 0.820

—Flaxman 0.025 0.007 0.028 0.017 0.677

—Kent Pen. 0.029 0.021 0.034 0.032 0.610

—Baffin 0.085 0.017 0.207 0.119 0.819

—Hudson Straits 0.067 0.050 0.165 0.199 0.749

—Southampton 0.069 0.006 0.158 0.128 0.718

—Mansel Is. 0.092 0.195 0.290 0.097 0.823

—Svalbard 0.088 0.014 0.108 0.095 0.758

—Belcher 0.088 0.085 0.285 0.087 0.797

—New Brunswick 0.062 0.015 0.054 0.154 0.848

—Nova Scotia 0.050 0.022 0.049 0.107 0.892

—Tromsø 0.122 0.050 0.127 0.138 0.760

—Soderskar 0.148 0.123 0.208 0.135 0.927

YK Delta vs. 

—Bodfish 0.002 0.007 0.006 0.000 0.579

—Flaxman 0.001 –0.008 0.001 0.002 0.424

—Kent Pen. 0.003 –0.007 0.004 –0.005 0.185

—Baffin 0.090 0.043 0.158 0.093 0.146

—Hudson Straits 0.068 0.028 0.095 0.170 0.142

—Southampton 0.072 0.000 0.095 0.110 0.121

—Mansel Is. 0.096 0.046 0.206 0.066 0.308

—Svalbard 0.098 0.034 0.081 0.103 0.204

—Belcher 0.083 0.046 0.224 0.105 0.173

—New Brunswick 0.061 0.003 0.015 0.129 0.246

—Nova Scotia 0.056 0.020 0.020 0.081 0.256

—Tromsø 0.132 0.086 0.088 0.139 0.155

—Soderskar 0.145 0.135 0.154 0.143 0.573

Bodfish vs. 

—Flaxman 0.001 0.000 0.000 –0.005 0.051

—Kent Pen. 0.001 0.003 0.005 0.001 0.300

—Baffin 0.091 0.032 0.141 0.085 0.714

—Hudson Straits 0.067 0.009 0.071 0.173 0.545

—Southampton 0.073 0.006 0.070 0.109 0.532

—Mansel Is. 0.102 –0.055 0.187 0.058 0.637

—Svalbard 0.100 0.024 0.071 0.092 0.623

—Belcher 0.084 0.018 0.209 0.090 0.626

—New Brunswick 0.060 0.009 0.014 0.122 0.771

Table 2.2: Pairwise FST, RST, and ΦST values for 12 microsatellite loci, lamin A, gapdh, and mtDNA control 

region for 15 Common Eider populations. Signifi cant pairwise comparisons (α = 0.05) are in bold text, and 

populations representing the same subspecies are shaded in gray.
59

Msat–FST Msat–RST Lamin A Gapdh MtDNA

—Nova Scotia 0.058 0.017 0.027 0.072 0.827

—Tromsø 0.134 0.045 0.079 0.134 0.654

—Soderskar 0.145 0.057 0.141 0.134 0.851

Flaxman vs. 

—Kent Pen. 0.002 –0.008 –0.004 0.002 0.155

—Baffin 0.102 0.021 0.170 0.055 0.514

—Hudson Straits 0.077 0.001 0.088 0.158 0.331

—Southampton 0.082 –0.005 0.082 0.086 0.361

—Mansel Is. 0.110 –0.052 0.245 0.031 0.304

—Svalbard 0.107 0.013 0.073 0.074 0.453

—Belcher 0.091 0.010 0.246 0.076 0.406

—New Brunswick 0.069 –0.003 0.004 0.094 0.633

—Nova Scotia 0.061 0.004 0.012 0.043 0.690

—Tromsø 0.146 0.040 0.086 0.123 0.486

—Soderskar 0.159 0.058 0.160 0.124 0.725

Kent Pen. vs. 

—Baffin 0.096 0.051 0.143 0.114 0.180

—Hudson Straits 0.076 0.034 0.070 0.220 0.071

—Southampton 0.082 0.000 0.068 0.139 0.091

—Mansel Is. 0.106 0.034 0.180 0.109 0.086

—Svalbard 0.111 0.040 0.059 0.134 0.153

—Belcher 0.081 0.047 0.210 0.137 0.150

—New Brunswick 0.073 0.006 0.009 0.141 0.330

—Nova Scotia 0.067 0.025 0.016 0.084 0.377

—Tromsø 0.157 0.095 0.062 0.186 0.185

—Soderskar 0.166 0.142 0.135 0.195 0.506

Baffin Is. vs.

—Hudson Straits 0.000 0.006 0.038 0.037 0.079

—Southampton 0.011 0.015 0.051 –0.012 0.036

—Mansel Is. 0.024 0.148 –0.035 –0.096 0.410

—Svalbard 0.017 –0.010 0.053 –0.004 0.019

—Belcher 0.014 0.044 –0.008 0.013 0.131

—New Brunswick 0.041 0.026 0.124 –0.014 0.153

—Nova Scotia 0.037 0.013 0.127 –0.012 0.242

—Tromsø 0.061 0.001 0.030 0.023 0.006

—Soderskar 0.061 0.035 0.029 0.030 0.597

Hudson Straits vs. 

—Southampton 0.001 0.010 –0.010 0.005 –0.013

—Mansel Is. 0.006 0.027 0.014 –0.076 0.122

—Svalbard 0.004 0.002 0.034 0.041 0.044

—Belcher 0.015 –0.015 0.078 0.092 0.017

—New Brunswick 0.026 0.008 0.060 0.031 0.290

Table 2.2 cont
60

Msat–FST Msat–RST Lamin A Gapdh MtDNA

—Nova Scotia 0.023 0.003 0.065 0.100 0.403

—Tromsø 0.036 0.030 0.016 0.003 0.099

—Soderskar 0.039 0.040 0.036 0.024 0.547

Southampton Is. vs.

—Mansel Is. 0.012 0.089 0.044 –0.092 0.165

—Svalbard 0.004 0.008 0.039 0.010 0.043

—Belcher 0.029 0.039 0.104 0.039 0.022

—New Brunswick 0.030 –0.004 0.059 0.002 0.192

—Nova Scotia 0.026 0.011 0.062 0.029 0.253

—Tromsø 0.024 0.052 0.030 0.009 0.060

—Soderskar 0.033 0.099 0.049 0.022 0.476

Mansel Is. vs. 

—Svalbard 0.004 0.129 0.064 –0.083 0.272

—Belcher 0.015 –0.039 –0.067 –0.047 0.152

—New Brunswick 0.027 0.047 0.176 –0.078 0.626

—Nova Scotia 0.022 0.084 0.183 –0.041 0.851

—Tromsø 0.031 0.199 0.007 –0.084 0.359

—Soderskar 0.029 0.203 –0.016 –0.074 0.888

Svalbard vs. 

—Belcher 0.029 0.032 0.108 –0.010 0.132

—New Brunswick 0.041 0.016 0.051 0.027 0.239

—Nova Scotia 0.028 0.003 0.044 0.035 0.300

—Tromsø 0.014 0.005 0.003 0.000 0.053

—Soderskar 0.021 0.040 0.025 –0.008 0.333

Belcher Is. vs. 

—New Brunswick 0.035 0.024 0.194 0.046 0.337

—Nova Scotia 0.025 0.014 0.197 0.037 0.485

—Tromsø 0.075 0.066 0.064 0.025 0.140

—Soderskar 0.071 0.063 0.054 0.009 0.655

New Brunswick vs. 

—Nova Scotia 0.002 –0.002 –0.004 0.009 0.077

—Tromsø 0.079 0.064 0.063 0.038 0.131

—Soderskar 0.086 0.112 0.109 0.050 0.690

Nova Scotia vs. 

—Tromsø 0.065 0.039 0.057 0.082 0.143

—Soderskar 0.071 0.076 0.109 0.088 0.860

Tromsø vs. 

—Soderskar 0.006 0.007 0.030 –0.019 0.495

Table 2.2 cont
61

Table 2.3: Hierarchical analysis of molecular variance (AMOVA) of allelic and haplotypic frequencies for 

populations classifi ed by (1) subspecies and (2) geographic proximity. Signifi cant comparisons are in bold 

text.

Source of Variation d.f. Variance

components 

% total 

variation Φ P-value

Grouped by Subspecies 

Microsatellite – FST

Variance among ssp. 3 0.275 7.66 0.077 ≤ 0.001 

Variance among pop. within ssp. 11 0.027 0.75 0.008 ≤ 0.001

Variance within populations 1415 3.295 91.60 0.084 ≤ 0.001

Total 1429 3.597 – – –

Microsatellite – RST

Variance among ssp. 3 6.476 2.62 0.026 ≤ 0.001

Variance among pop. within ssp. 11 0.702 0.28 0.003 ≤ 0.001

Variance within populations 1415 240.340 97.10 0.029 ≤ 0.001

Total 1429 247.572 – – –

Lamin A 

Variance among ssp. 3 0.629 5.21 0.052 ≤ 0.001

Variance among pop. within ssp. 11 0.040 3.27 0.035 ≤ 0.001

Variance within populations 1151 1.105 91.52 0.085 ≤ 0.001

Total 1165 1.208 – – –

Gapdh

Variance among ssp. 3 0.099 9.20 0.092 ≤ 0.001

Variance among pop. within ssp. 11 0.009 0.79 0.009 ≤ 0.001

Variance within populations 913 0.970 90.01 0.100 ≤ 0.001

Total 927 1.078 – – –

Mitochondrial DNA 

Variance among ssp. 3 0.001 16.60 0.166 ≤ 0.001

Variance among pop. within ssp. 11 0.002 35.42 0.425 ≤ 0.001

Variance within populations 435 0.002 47.99 0.520 ≤ 0.001

Total 449 0.005 – – –
62

Source of Variation d.f. Variance

components 

% total 

variation Φ P-value

Grouped by Geographic Proximity 

Microsatellite – FST

Variance among region 3 0.206 5.78 0.058 ≤ 0.001

Variance among pop. within region 11 0.063 1.78 0.019 ≤ 0.001

Variance within populations 1415 3.295 92.45 0.076 ≤ 0.001

Total 1429 3.564 – – –

Microsatellite – RST

Variance among region 3 4.719 1.91 0.019 ≤ 0.001

Variance among pop. within region 11 1.648 0.67 0.007 ≤ 0.001

Variance within populations 1415 240.340 97.42 0.026 ≤ 0.001

Total 1429 246.706 – – –

Lamin A 

Variance among region 3 0.066 5.44 0.054 ≤ 0.001

Variance among pop. within region 11 0.035 2.93 0.031 ≤ 0.001

Variance within populations 1151 1.105 91.63 0.084 ≤ 0.001

Total 1165 1.206 – – –

Gapdh

Variance among region 3 0.068 6.37 0.064 ≤ 0.001

Variance among pop. within region 11 0.028 2.63 0.028 ≤ 0.001

Variance within populations 913 0.971 91.00 0.090 ≤ 0.001

Total 927 1.066 – – –

Mitochondrial DNA 

Variance among region 3 0.001 17.86 0.179 ≤ 0.001

Variance among pop. within region 11 0.002 34.03 0.414 ≤ 0.001

Variance within populations 435 0.002 48.10 0.519 ≤ 0.001

Total 449 0.005 – – –

Table 2.3 cont.
63

Table 2.4: Proportion of individuals from sampled populations in each of the four clusters inferred from 12 

microsatellite loci in STRUCTURE (Pritchard et al. 2000).

Population Inferred Cluster n

1234

ALN 0.069 0.591 0.211 0.129 48

YKD 0.039 0.424 0.442 0.095 124

BOD 0.033 0.385 0.483 0.099 100

FLX 0.039 0.411 0.470 0.080 99

KTP 0.031 0.379 0.476 0.114 41

BFN 0.528 0.094 0.111 0.267 15

HDS 0.536 0.062 0.056 0.346 28

SHP 0.617 0.068 0.047 0.269 52

MSL 0.757 0.030 0.026 0.188 3

SVD 0.677 0.036 0.058 0.228 37

BCH 0.344 0.051 0.047 0.557 22

NBW 0.159 0.072 0.049 0.721 40

NVS 0.172 0.062 0.058 0.708 40

TRM 0.867 0.022 0.021 0.090 38

SDK 0.916 0.016 0.016 0.052 27

Table 2.5: Hierarchical analysis of molecular variance of mtDNA haplotype frequencies for populations 

grouped to test putative source refugia for Common Eider populations breeding in North America and Scandinavia. Signifi cant comparisons are in bold text (P < 0.01).

Regionsa No. Groups ΦCT ΦSC ΦST

AC, BD, E, FG 4 0.259 0.367 0.531

A, BCD, E, FG 4 0.273 0.364 0.537

A, BCD, EG, F 4 0.284 0.356 0.540

AB, CDE, FG 3 0.179 0.422 0.519

A, BCDE, FG 3 0.264 0.396 0.555

A, BCD, EFG 3 0.279 0.369 0.544

A, BCDEG, F 3 0.296 0.393 0.573

A, BCDEFG 2 0.325 0.417 0.606

ABC, DEFG 2 0.232 0.413 0.549 a

Regions; BOD and FLX (A), ALN and YKD (B), KTP (C), SHP, HDS, BCH, MSL, and 

BFN (D), NVS and NBW (E), SVD and SDK (F), and TRM (G) 
64

Table 2.6: Results of demographic analyses for 12 microsatellite loci under the infi nite allele model (IAM), 

stepwise mutation model (SMM), and two-phased model of mutation (TPM) and sequence data. Parameter 

estimates θ (4Neμ for nuclear DNA, 2Nfμ for mtDNA), exponential growth rate (g) with standard deviation 

(SD), and time of expansion (τ) calculated from mismatch distributions for each population. Signifi cant comparisons are in bold text.

ALN YKD BOD FLX KTP BFN HDS SHP

Msats a

IAM Eq HExc HExc HExc Eq Eq Eq Eq

SSM HDef HDef HDef HDef HDef HDef HDef HDef

TPM Eq Eq Eq Eq Eq Eq Eq HDef

Lamin A

θ 0.033 0.122 0.138 0.039 0.044 0.045b 0.008 0.019

SD 0.003 0.007 0.012 0.005 0.010 0.022 0.001 0.002

g 327.0 550.8 800.5 589.9 442.6 2339.5 672.9 400.5

SD 61.9 31.3 46.2 88.5 102.2 355.3 198.7 64.4

Gapdh

θ 0.035 0.047 0.042 0.007 0.047 0.004 0.003b 0.011

SD 0.003 0.003 0.004 0.001 0.009 0.002 0.001 0.001

g 1212.9 400.0 875.8 307.2b 1778.2 53.8 -140.3 283.5

SD 91.3 50.8 66.6 144.0 182.9 260.5 155.1 101.0

MtDNA

θ 0.012 0.023 0.007 0.007 0.021 0.017 0.050 0.034

SD 0.001 0.002 0.001 0.001 0.003 0.005 0.014 0.006

g 3673.2 534.1 133.9 16.8 303.4 1203.8 850.7 788.0

SD 272.7 71.4 123.8 86.0 65.4 230.6 132.2 161.9

τ 0.992 5.709c 6.496 7.598 7.877c 2.758 3.555 3.863

95% CI 0.074,

1.363

1.559,

11.959

2.832,

10.496

2.950,

11.543

3.921,

15.290

0.443,

4.088

1.099,

7.133

1.082,

5.777 a Significant heterozygote deficiency (HDef) indicates population growth and 

heterozygote excess (Hexc) indicates a population decline, non-significant population 

estimates at equilibrium (Eq). 

b Significant to P < 0.05, all others P < 0.003 c

Population differs significantly from the sudden expansion model based on SSD statistic 

but not under Harpending’s (1994) raggedness index
65

Table 2.6 cont. 

MSL SVD BCH NBW NVS TRM SDK

Msats a

IAM Eq Eq Eq HExc HExc Eq Eq

SSM Eq HDef HDef Eq Eq HDef HDef

TPM Eq Eq Eq Eq Eq HDef HDef

Lamin A 

θ 0.010 0.031 0.018b 0.530 0.100 0.025 0.009

SD 0.008 0.005 0.008 0.052 0.010 0.003 0.002

g 373.3 428.8 352.3 1130.7 562.2 464.0 437.9

SD 287.9 84.4 272.0 34.8 38.8 63.8 208.5

Gapdh

θ 100.0 0.007 0.009 0.009 0.015 0.003b 0.003b

SD 157.0 0.002 0.002 0.001 0.003 0.001 0.001

g 4269.1 242.9 770.3 749.9 842.3 -69.4 -127.1

SD 0.1 178.0 208.7 161.1 178.6 171.4 156.8

MtDNA

θ 8.3 0.010 0.006b 0.006 0.004 0.016 0.008

SD 3.6 0.002 0.002 0.001 0.001 0.005 0.005

g 6507.1 260.9 254.9 132.9 102.1 518.0b 10000

SD 298.4 137.3 257.2 120.0 247.6 174.2 2514.1

τ 2.346 2.556 4.115 1.103c 3.000 2.689 0.497

95% CI 0.000,

4.888

1.181,

4.111

0.897,

11.396

0.429,

1.697

0.566,

3.984

0.682,

8.814

0.000,

1.091
66

Table 2.7: Inferred demographic events of the nested clade analysis (only clades with signifi cant Dc/Dn values 

are shown; Templeton 1998).

Inferred demographic 

event

Geographic units involved Clade Chain of inference 

Continuous range 

expansion

ALN/KTP to FLX I-2 1-2-11-12-NO

TRM to BFN and NVS I-12 1-19-20-2-11-12-NO

Past fragmentation 

and/or long distance 

colonization 

BFN/BCH/HDS/MSL/SHP to 

BOD/FLX/YKD and NBW 

I-4 1-2-3-5-15-NO

BOD/FLX/HDS/KTP/SHP/ 

TRM/YKD to SVD 

II-5 1-19-20-2-3-5-15-NO 

All regions Total 1-2-3-5-15-NO

Restricted gene flow 

with isolation by 

distance 

Among BFN, BOD, FLX, 

HDS, KTP, NBW, NVS, SHP, 

SVD, TRM, and YKD 

I-10 1-2-3-4-NO

Among BCH, BFN, HDS, 

SDK, SHP, SVD, and TRM 

I-15 1-2-3-4-NO

Among ALN, BOD FLX, KTP, 

and YKD 

II-1 1-2-3-4-NO 

Among ALN, BCH, BFN, 

BOD, FLX, KTP, HDS, MSL, 

NBW, SHP, SVD, TRM, and 

YKD

II-2 1-2-11-17-4-NO 

Restricted gene flow 

with some long 

distance colonization 

BOD/FLX to KTP/HDS/SHP 

and TRM and YKD 

I-21 1-2-3-5-6-7-8-YES

BFN/BOD/FLX/HDS/KTP/ 

NBW/NVS/SHP/SVD/TRM/ 

YKD to BCH 

II-3 1-2-3-5-6-7-YES 

BCH/BFN/HDS/SDK/SHP/

SVD/TRM to KTP/NVS/NBW 

II-4 1-2-3-5-6-7-YES 
67

Table 2.8: Migration matrix calculated from 12 microsatellite loci, nuclear introns lamin A and gapdh, and 

mtDNA control region. Receiving populations and θ (Neμ or Nfμ) are in bold text and population pairs with 

overlapping 95% confi dence intervals are shaded in gray.

Number of Migrants per Generation (Nem or Nfm)

Population Comparisons a Microsatellites Introns MtDNA

Refugia

North Slope 0.8615

(0.8165–0.9088)

0.0036

(0.0032–0.0041)

0.0109

(0.0080–0.0154)

—Belcher 13.22

(11.87–14.70)

2.18

(1.29–3.54)

0.97

(0.62–3.65)

—New Brunswick 6.91

(6.07–7.86)

0.46

(0.16–1.07)

1.94

(0.55–5.68)

—Svalbard 8.29

(7.34–9.35)

2.41

(1.46–3.86)

0.00

(0.00–0.93)

Belcher Is. 0.8495

(0.7887–0.9168)

0.0070

(0.0061–0.0082)

0.0004

(0.0003–0.0006)

—North Slope 15.22

(13.35–17.35)

1.23

(0.58–2.35)

0.14

(0.08–0.77)

—New Brunswick 13.44

(11.76–15.38)

1.62

(0.84–2.93)

0.55

(0.15–1.74)

—Svalbard 14.96

(13.11–17.07)

0.53

(0.17–1.29)

0.28

(0.04–1.12)

New Brunswick 0.8556

(0.8093–0.9053)

0.0107

(0.0087–0.0134)

0.0007

(0.0006–0.0009)

—North Slope 8.26

(7.28–9.37)

6.10

(3.14–11.34)

0.00

(0.00–0.15)

—Belcher 12.52

(11.19–14.01)

25.25

(16.63–38.63)

0.09

(0.01–0.40)

—Svalbard 11.21

(9.98–12.59)

34.95

(23.65–51.91)

0.00

(0.00–0.15)

Svalbard 0.2165

(0.2059–0.2278)

0.0004

(0.0003–0.0004)

0.0040

(0.0028–0.0060)

—North Slope 4.53

(3.99–5.15)

2.03

(1.27–3.19)

0.00

(0.00–0.81)

—Belcher 6.18

(5.52–6.92)

0.27

(0.09–0.66)

0.40

(0.03–2.18)

—New Brunswick 7.05

(6.30–7.87)

5.54

(3.93–7.80)

3.95

(1.55–9.65)
68

Table 2.8 cont.

b e . co .

Number of Migrants per Generation (Nem or Nfm)

Population Comparisons a Microsatellites Introns MtDNA

S. m. v–nigrum

Aleutians 0.9373

(0.8759–1.0033)

0.0008

(0.0007–0.0020)

0.0003

(0.0002–0.0004)

—YK Delta 17.82

(15.74–20.13)

0.44

(0.26–1.60)

0.00

(0.00–0.14)

—North Slope 16.21

(14.30–18.35)

2.39

(1.81–7.14)

0.15

(0.10–0.56)

—Kent Pen. 17.92

(15.86–20.23)

0.41

(0.24–1.52)

0.00

(0.00–0.14)

YK Delta 0.5445

(0.5199–0.5708)

0.0000

(0.0000–0.0000)

0.0021

(0.0016–0.0029)

—Aleutians 11.14

(10.09–12.29)

0.01

(0.00–0.05)

0.00

(0.00–0.31)

—North Slope 13.83

(12.59–15.20)

0.10

(0.05–0.21)

1.23

(0.46–2.99)

—Kent Pen. 10.91

(9.87–12.05)

1.38

(1.00–1.94)

0.52

(0.13–1.61)

North Slope 0.5984

(0.5676–0.6314)

0.0050

(0.0045–0.0056)

0.0052

(0.0040–0.0072)

—Aleutians 10.79

(9.63–12.09)

5.97

(4.46–7.93)

0.62

(0.15–1.95)

— YK Delta 18.28

(16.55–20.19)

1.03

(0.58–1.73)

1.25

(0.44–3.16)

—Kent Pen. 25.50

(23.26–27.96)

1.03

(0.58–1.74)

0.21

(0.14–1.04)

Kent Pen. 0.6823

(0.6487–0.7171)

0.0061

(0.0045–0.0096)

0.0034

(0.0021–0.0057)

—Aleutians 11.66

(10.48–12.95)

85.90

(53.19–159.22)

0.53

(0.29–3.26)

—YK Delta 15.43

(13.97–17.01)

43.39

(24.97–85.69)

3.17

(0.95–9.84)

—North Slope 21.34

(19.47–23.36)

75.32

(46.07–141.19)

2.74

(0.75–9.04)
69

Table 2.8 cont. 

Number of Migrants per Generation (Nem or Nfm)

Population Comparisons a Microsatellites Introns MtDNA

Central Canada and Svalbard

Kent Pen. 0.9449

(0.8953–0.9990)

0.0089

(0.0079–0.0102)

0.0024

(0.0016–0.0037)

—Baffin 15.88

(14.24–17.73)

0.15

(0.01–0.63)

0.90

(0.20–3.18)

—Hudson Straits 13.03

(11.62–14.62)

2.99

(1.78–4.84)

0.60

(0.10–2.47)

—Southampton 11.03

(9.79–12.44)

0.79

(0.30–1.74)

0.00

(0.00–0.63)

—Svalbard 14.79

(13.24–16.53)

0.61

(0.21–1.43)

0.60

(0.10–2.47)

—Belcher 13.53

(12.08–15.16)

0.15

(0.01–0.63)

0.60

(0.10–2.47)

Baffin Is. 0.9198

(0.8425–1.0053)

0.0020

(0.0016–0.0026)

0.0007

(0.0004–0.0017)

—Kent Pen. 21.50

(18.59–24.92)

0.00

(0.00–0.32)

0.00

(0.00–2.43)

—Hudson Straits 30.15

(26.28–34.68)

5.14

(2.93–8.89)

0.78

(0.04–6.56)

—Southampton 17.29

(14.85–20.18)

4.93

(2.79–8.56)

1.56

(0.19–9.53)

—Svalbard 31.81

(27.76–36.56)

5.14

(2.93–8.87)

0.78

(0.04–6.56)

—Belcher 23.20

(20.09–26.85)

2.38

(1.15–4.68)

11.66

(3.83–40.08)

Hudson Straits 0.5752

(0.5364–0.6181)

0.0008

(0.0007–0.0009)

0.0076

(0.0062–0.0096)

—Kent Pen. 11.31

(9.88–12.95)

0.80

(0.36–1.60)

0.34

(0.07–1.13)

—Baffin 20.52

(18.24–23.12)

0.10

(0.01–0.43)

0.68

(0.21–1.76)

—Southampton 9.23

(7.97–10.72)

4.25

(2.80–6.36)

0.51

(0.36–1.46)

—Svalbard 23.00

(20.50–25.84)

6.48

(4.50–9.27)

0.68

(0.21–1.76)

—Belcher 21.25

(18.90–23.92)

1.37

(0.71–2.44)

0.68

(0.21–1.76)
70

Table 2.8 cont. 

Number of Migrants per Generation (Nem or Nfm)

Population Comparisons a Microsatellites Introns MtDNA

Southampton 0.2855

(0.2688–0.3035)

0.0050

(0.0038–0.0068)

0.0100

(0.0071–0.0147)

—Kent Pen. 5.47

(4.79–6.25)

49.15

(30.51–80.51)

1.63

(0.38–5.45)

—Baffin 8.50

(7.54–9.57)

22.04

(12.33–39.44)

0.00

(0.00–1.08)

—Hudson Straits 5.92

(5.17–6.78)

55.88

(35.13–90.50)

25.05

(13.84–46.43)

—Svalbard 10.99

(9.92–12.31)

0.00

(0.00–1.22)

4.36

(1.63–10.86)

—Belcher 6.79

(5.99–7.70)

2.00

(0.49–6.17)

0.54

(0.04–2.91)

Svalbard 0.3228

(0.3062–0.3402)

0.0105

(0.0092–0.0120)

0.0125

(0.0090–0.0180)

—Kent Pen. 8.75

(7.84–9.76)

1.37

(0.41–3.73)

0.00

(0.00–1.08)

—Baffin 13.21

(11.97–14.55)

14.77

(8.81–24.84)

0.55

(0.04–2.91)

—Hudson Straits 10.13

(9.11–11.25)

2.34

(0.88–5.50)

2.22

(0.62–6.58)

—Southampton 10.65

(9.60–11.80)

2.74

(1.11–6.15)

2.22

(0.62–6.58)

—Belcher 8.55

(7.66–9.52)

4.12

(1.91–8.46)

6.09

(2.56–13.87)

Belcher Is. 0.6792

(0.6350–0.7275)

0.0035

(0.0027–0.0046)

0.0056

(0.0032–0.0112)

—Kent Pen. 14.10

(12.43–15.99)

1.18

(0.52–2.37)

0.00

(0.00–2.80)

—Baffin 17.53

(15.56–19.76)

2.35

(1.28–4.06)

2.07

(0.28–10.97)

—Hudson Straits 21.39

(19.08–24.00)

3.85

(2.35–6.10)

1.03

(0.06–7.55)

—Southampton 10.89

(9.53–12.45)

1.88

(0.96–3.41)

41.37

(17.95–106.19)

—Svalbard 13.03

(11.46–14.81)

1.04

(0.43–2.18)

1.03

(0.06–7.55)
71

Table 2.8 cont. 

Number of Migrants per Generation (Nem or Nfm)

Population Comparisons a Microsatellites Introns MtDNA

Southern Canadian Populations

Belcher Is. 0.9651

(0.8899–1.0385)

0.0017

(0.0015–0.0020)

0.0194

(0.0012–0.0346)

—New Brunswick 8.78

(7.53–10.14)

3.27

(2.06–5.09)

8.93

(3.07–25.25)

—Nova Scotia 13.13

(11.41–14.97)

4.65

(3.09–6.94)

4.09

(1.05–14.07)

New Brunswick 0.4708

(0.4449–0.4989)

0.0013

(0.0011–0.0016)

0.0015

(0.0012–0.0020)

—Belcher 7.66

(6.74–8.72)

13.56

(9.06–20.26)

0.41

(0.29–1.11)

—Nova Scotia 19.26

(17.40–21.33)

21.35

(14.93–30.63)

0.21

(0.04–0.71)

Nova Scotia 0.4161

(0.3948–0.4390)

0.0102

(0.0088–0.0119)

0.0003

(0.0002–0.0004)

—Belcher 10.33

(9.26–11.54)

8.21

(5.47–12.14)

0.40

(0.07–9.73)

—New Brunswick 15.95

(14.45–17.62)

13.80

(9.78–19.37)

1.79

(1.12–13.24)

Scandinavia

Svalbard 0.3761

(0.3542–0.4002)

0.0041

(0.0034–0.0050)

0.0067

(0.0041–0.0474)

—Tromsø 16.76

(15.03–18.68)

9.62

(6.11–14.98)

12.96

(5.23–133.55)

—Soderskar 11.47

(10.13–12.95)

9.38

(5.94–14.66)

11.44

(4.48–120.53)

Tromsø 1.0265

(0.9599–1.0990)

0.0022

(0.0019–0.0026)

0.0079

(0.0059–0.0309)

—Svalbard 24.08

(21.49–27.00)

3.88

(2.39–6.14)

3.68

(1.67–21.92)

—Soderskar 16.14

(14.22–18.28)

10.26

(7.22–14.53)

0.56

(0.37–24.49)

Soderskar 0.4809

(0.4511–0.5116)

0.0103

(0.0086–0.0125)

0.0004

(0.0000–0.0006)

—Svalbard 11.12

(9.81–12.75)

3.31

(1.77–5.91)

0.07

(0.01–0.26)

—Tromsø 9.20

(8.09–10.42)

15.31

(10.44–22.47)

0.00

(0.00–0.00) a Ninety-five percent confidence intervals are in parentheses. 
72

Somateria mollissima v-nigrum 

USA: Alaska, Aleutian Islands, Attu Island 52.938°N, 173.238°E 

DAR157, MRP294, MRP295, UAMX1708 

USA: Alaska, Aleutian Islands, Agattu Island 52.435°N, 173.576°E 

MRP296, MRP297, MRP298, MRP299, MRP306, MRP307, MRP308, MRP309, 

MRP310, MRP311, MRP312 

USA: Alaska, Aleutian Islands, Alaid Island 52.763°N, 173.898°E 

DBI2, DBI3, DBI4, DBI5, MRP285, MRP286, MRP287, MRP288, MRP289, MRP290, 

MRP291, MRP292, MRP293, MRP300, MRP301, MRP302, MRP303, MRP304, 

MRP305, MRP313, MRP314, MRP315, MRP316, MRP317, MRP318 

USA: Alaska, Aleutian Islands, Amchitka Island 51.567°N, 178.878°E 

MRP283, MRP284, MRP320, MRP321, MRP322, MRP323, MRP324, MRP325, 

MRP326

USA: Alaska, Aleutian Islands, Adak Island 51.880°N, 176.658°W 

PBAI935

Canada: Nunavut, Kent Peninsula 68.5oN, 107.0oW 

COEI-M, DEAD-02, DEAD-03, DEAD-F, EUTH-M, KP1502, KP1505, KP1508, 

KP1510, KP1512, KP1516, KP1517, KP1518, KP1519, KP1828, KP1829, KP1833, 

KP1835, KP1836, KP1840, KP4553, KP4596, KP4597, KP4617, KP9816, KP9818, 

KP9820, KP9823, KP9824, KP9825, KP9827, KP9828, KP9829, KP9830, KP9831, 

KP9832, KP9833, KP9835, KP9836, KP9837, KP9838 

Somateria mollissima borealis 

Canada: Nunavut, Southampton Island 64.33oN, 84.667oW 

H317, H321, H322, H325, H326, H327, H328, H329, H330, H333, H334, H335, H338, 

H352, H25052, H25064, H25067, H25069, H25071, H25094, H25095, H25097, 

H25269, H25276, H91488, H91574, H91575, H91579, H91697, H91698, H91701, 

H91718, H91723, H91725, H91726, H91727, H91729, H91730, H91736, H91754, 

H91756, H91981, H95196, H95967, H95969, H95970, H95972, H95974, H95975, 

H95976, H95987, H95995 

Canada: Nunavut, Baffin Island 

M801, M803, M804, M805, M806, M808, M809, M810, M811, M812, M813, M814, 

M815, M816, M817 

Appendix 2.A: Localities of Common Eiders sampled* in this study.
73

Canada: Nunavut, Mansel Island 63.417oN, 77.917oW 

F45, F47, F49 

Canada: Nunavut, Baffin Island, Hudson Straits, Foxe Peninsula 64.1oN, 73.5oW 

G33, G34, G37, G38, G39, G41, G42, G43, G49, G50, G51, G56, G57, G59, G60, G61, 

G66, G68, G72, G73, G74, G75, G76, G77, G79, G80, G82, G85 

Norway: Svalbard 78.2oN, 15.5oE 

D1, D2, D3, D4, D5, D6, D7, D8, D9, D10, D11, D12, D13, D14, D15, D16, D17, D18, 

D19, D20, D21, D22, D23, D24, D25, D26, D27, D28, D29, D30, D31, D32, D33, D34, 

D35, D36, D37 

Somateria mollissima sedentaria 

Canada: Nunavut, Belcher Islands 56.183oN, 79.250oW 

T11, T12, T13, T14, T15, T16, T17, T18, T19, T20, TB01, TB02, TB03, TB04, TF01, 

TF02, TF03, TF04, TM01, TM02, TM03, TM04 

Somateria mollissima dresseri 

Canada: New Brunswick 45.5oN, 67.0oW 

BD#, BNB, B6, B33, B43, B44, B45, B47, B48, B49, B51, B52, B53, B99, B209, B244, 

B515, B609, B5170, B21627, B25423, B26170, B40194, B40622, B45320, B45509, 

B48222, B49008, B49028, B49055, B49071, B49073, B49583, B59510, B59513, 

B59516, B59519, B59521, B59524, B85408 

Canada: Nova Scotia 44.716oN, 65.200oW 

C1, C10, C14, C15, C17, C20, C21, C24, C30, C32, C34, C37, C39, C42, C45, C46, 

C50, C52, C53, C54, C55, C56, C57, C60, C61, C62, C63, C64, C66, C67, C68, C69, 

C70, C71, C73, C77, C80, C81, C83, C116 

Somateria mollissima mollissima 

Norway: Troms, Tromsø 69.7oN, 18.9oE 

E9, E10, E13, E15, E18, E20, E25, E28, E33, E35, E75, E86, E95, E98, E104, E107, 

E107-2, E117, E118, E123, E129, E137, E138, E139, E142, E148, E150, E153, E154, 

E162, E167, E175, E188, E191, E192, E195, E200, E202 

Finland: Southern Finland, Soderskar 60.25oN, 25.5oE 

A510178, A510588, A510658, A510678, A580197, A580297, A580397, A580497, 

A584297, A584393, A586797, A586897, A588097, A588597, DT12063, DT35166, 

DX1028, DX2714, DX3241, DX4333, DX4739, DX5559, DX7428, DX35013, 

DX120319, DX12218, DX324119 

Appendix 2.A cont.
74

**Sample IDs starting with UAM are located at the University of Alaska Museum, 

Fairbanks, Alaska. Remaining samples are located in non-museum research collections. 
75

Appendix 2.B: Variable positions and frequency (n) of nuclear intron lamin A alleles reconstructed in PHASE 

in Common Eiders.

7112599111111111111222

 899508011566677779277 

 467507814595023 

n

Allele01 GGACCCCCTGTACGCCGCCTGA 23

Allele02 GGACCCCCTGTACGTCGCCTGA 1

Allele03 GGACCCCCCGTACGCCGCCTGA 76

Allele04 GGACCCCCCGTACGCCGCCTAG 2

Allele05 GGACCCCCCGTACGCCGCCCGA 1

Allele06 GGACCCCCCGTACGCCGCTTGA 4

Allele07 GGACCCCCCGTACGCCACCTGA 6

Allele08 GGACCCCCCGTACGTCGTCTGA 5

Allele09 GGACCCCCCGTCCGCCGCCTGA 5

Allele10 GGACCCCCCGTCCGCCGCCCGA 2

Allele11 GGACCCCCCGTCCGCCGCTTGA 6

Allele12 GGACCCCCCGTCCGCCGCTTAG 1

Allele13 GGACCCCCCGTCCGCTGCCTGA 1

Allele14 GGACCCCCCGTCCGTCGTCTGA 1

Allele15 GGACCCCCCGTCCGTCGTCTAG 2

Allele16 GGACCCCCCGGACGCCGCCTGA 1

Allele17 GGACCCGCTGTACGCCGCCTGA 5

Allele18 GGACCCGCTGTACGCCGCCCGA 2

Allele19 GGACCCGCTGTCCGCCGCCTGA 9

Allele20 GGACCCGCCGTACGCCGCCTGA 30

Allele21 GGACCCGCCGTACGCCGCCCGA 1

Allele22 GGACCCGCCGTACGCCACCTGA 2

Allele23 GGACCCGCCGTACGTCGCCTGA 3

Allele24 GGACCCGCCGTCCGCCGCCTGA 207

Allele25 GGACCCGCCGTCCGCCGCCTAG 4

Allele26 GGACCCGCCGTCCGCCGCCCGA 177

Allele27 GGACCCGCCGTCCGCCGCCCAG 1

Allele28 GGACCCGCCGTCCGCCGCTTGA 29

Allele29 GGACCCGCCGTCCGCCGTCTGA 24

Allele30 GGACCCGCCGTCCGCCACCTGA 2

Allele31 GGACCCGCCGTCCGCTGCCTGA 4

Allele32 GGACCCGCCGTCCGCTGTCTGA 128

Allele33 GGACCCGCCGTCCGCTGTCTAG 5

Allele34 GGACCCGCCGTCCGTCGCCTGA 37

Allele35 GGACCCGCCGTCCGTCGCCTAG 1

Allele36 GGACCCGCCGTCCGTCGCCCGA 3

Allele37 GGACCCGCCGTCCGTCGTCTGA 195

Allele38 GGACCCGCCGTCCGTCGTCTAG 6

Allele39 GGACCCGCCGTCCGTCGTCCGA 1

Allele40 GGACCCGCCGTCCGTCGTCCAG 1
76

pp

7112599111111111111222

 899508011566677779277 

 467507814595023 

n

Allele41 GGACCCGCCGTCCGTTGTCTGA 3

Allele42 GGACCCGCCGTCCACTGCCTGA 1

Allele43 GGACCCGCCGTCCACTGTCTGA 10

Allele44 GGACCCGCCGTCTGCCGCCCGA 1

Allele45 GGACCCGCCATCCGCCGCCTGA 11

Allele46 GGACCCGTCGTCCGCCGCCTGA 1

Allele47 GGACCCGTCGTCCGCCGCCCGA 4

Allele48 GGACCCGTCGTCCGCCGCTTGA 1

Allele49 GGACCGGCCGTACGCCGCCTGA 3

Allele50 GGACCGGCCGTCCGCCGCCTGA 11

Allele51 GGACCGGCCGTCCGCCGCCCGA 5

Allele52 GGACCGGCCGTCCGCTGTCTGA 3

Allele53 GGACCGGCCGTCCGTCGCCTGA 10

Allele54 GGACCGGCCGTCCGTCGTCTGA 3

Allele55 GGACCGGCCATCCGCCGCCTGA 1

Allele56 GGACTCCCCGTCCGCCGCCTGA 2

Allele57 GGACTCGCTGTCCGCCGCCTGA 3

Allele58 GGACTCGCCGTCCGCCGCCTGA 41

Allele59 GGACTCGCCGTCCGCCGCCCGA 1

Allele60 GGACTCGCCGTCCGCCGCTTGA 19

Allele61 GGATCCCCCGTACGCCGCCTGA 1

Allele62 GGATCCGCCGTCCGCCGCCTGA 1

Allele63 GAGCCCGCCGTCCGCCGCCCGA 1

Allele64 GAGCCCGCCGTCCGCTGTCTGA 1

Allele65 GAGCCCGCCATCCGCCGCCTGA 2

Allele66 CGACCCCCCGTACGCCGCCTGA 1

Allele67 CGACCCGCCGTCCGCCGCCTGA 13

Allele68 CGACCCGCCGTCCGCCGCCTAG 1

Allele69 CGACCCGCCGTCCGCCGCCCGA 6

Allele70 CGACCCGCCGTCCGTCGTCTGA 3

Appendix 2.B cont.
77

Appendix 2.C: Variable positions and frequency (n) of nuclear intron gapdh alleles reconstructed in PHASE 

in Common Eiders.

1244481111111111222233

6238931223446789355756

 4296565062228768 

n

Allele01 CCCCGGCCAGCGAAGGCGAGAG 5

Allele02 CCCCGGCCAGCGAAGGCGAGGG 1

Allele03 CCCCGGCCAGCGAAGGCGGGAG 86

Allele04 CCCCGGCCAGCGAACACGGGAG 9

Allele05 CCCCGGCCAGCGGAGGCGAGAG 134

Allele06 CCCCGGCCAGCGGAGGCGGGAG 11

Allele07 CCCCGGCCAGCGGAGGTGAGAG 1

Allele08 CCCCGGCCAGCGGAGGTGGGAG 1

Allele09 CCCCGGCCAGCGGGGGCGAGAG 10

Allele10 CCCCGGCCAGCGGGGGTGAGAG 4

Allele11 CCCCGGCCAGTGAAGGCGAGAG 5

Allele12 CCCCGGCCAGTGAAGGCGGGAG 18

Allele13 CCCCGGCCAGTGAAGGCGGAAG 1

Allele14 CCCCGGCCAACGAAGGCGGGAG 1

Allele15 CCCCGGCCAACGGAGGCGAGAG 2

Allele16 CCCCGGCCAACGGAGGCGGGAG 5

Allele17 CCCCGGCCAACGGAGGCGGGAA 1

Allele18 CCCCGGCCAACGGGGGCGAGAG 82

Allele19 CCCCGGCCAACGGGGGCGGGAG 10

Allele20 CCCCGGCCAACGGGGGCGGAAG 1

Allele21 CCCCGGCCAACGGGGGCAAGAG 1

Allele22 CCCCGGCCGGCGAAGGCGAGAG 1

Allele23 CCCCGGCCGGCGGAGGCGAGAG 258

Allele24 CCCCGGCCGGCGGAGGCGGGAG 73

Allele25 CCCCGGCCGGCGGAGGCGGAAG 5

Allele26 CCCCGGCCGGCGGAGGTGAGAG 3

Allele27 CCCCGGCCGGCGGGGGCGGGAG 15

Allele28 CCCCGGCCGGCGGGGGCGGAAG 1

Allele29 CCCCGGCCGGCAGAGGCGAGAG 2

Allele30 CCCCGGCTAGCGGAGGCGAGAG 3

Allele31 CCCCGGCTAGCGGAGGTGAGAG 132

Allele32 CCCCGGCTAGCGGAGGTGAGGG 7

Allele33 CCCCGGCTAGCGGAGGTGGGAG 3

Allele34 CCCCGGCTAGCGGGGGTGAGAG 1

Allele35 CCCCGGGCAGCGGAGGCGAGAG 1

Allele36 CCCCGACTAGCGGAGGTGAGAG 1

Allele37 CCCCAGCCAGCGAACACGAGAG 5

Allele38 CCCCAGCCAGCGAACACGGGAG 18

Allele39 CCCTGGCCAGCGGAGGTGAGAG 1

Allele40 CCCTGGCCGGCGGAGGCGAGAG 3
78

1244481111111111222233

6238931223446789355756

 4296565062228768 

n

Allele41 CCTCGGCCAGCGGGGGTGAGAG 1

Allele42 CCTCGGCTAGCGGAGGTGAGAG 2

Allele43 CTCCGGCCAGCGGAGGCGAGAG 3

Allele44 ACCCGGCCAGCGAAGGCGGGAG 1

Allele45 ACCCGGCCAGCGGAGGCGAGAG 8

Allele46 ACCCGGCCAACGGGGGCGAGAG 2

Allele47 ACCCGGCCGGCGGAGGCGAGAG 6

Allele48 ACCCGGCCGGCGGAGGCGGGAG 1

Appendix 2.C cont.
79

Appendix 2.D: Variable positions and frequency (n) for each mtDNA control region haplotype from Common 

Eiders (dots indicate missing data; – indicates deletion). 

341122333445666677778811111133344555

 1212156248578914590601116724768555 

 31562445385589 

n

Hap01 ....AACGCGGTCCCACCTAGTGCATCTTTCCGTCT 57

Hap02 ....AACGCGGTCCCACCTAGTGCATCTTTCCGTTT 2

Hap03 ....AACGCGGTCCCATCTAGTGCATCTTTCCGTCT 17

Hap04 ....GACGCGATCCCACCCAGAGCATCTTTTCGTTT 109

Hap05 ....GACGCGATCCCACCCAGAGCGTCTTTCCGTTT 14

Hap06 ....GACGCGGTCCCACCCAGAGCATCTTTCCGTTT 7

Hap07 ....GACGCGGTCCCGCCCAGAGCATCTTTCCGTCT 12

Hap08 ....GACGCGGTCCCGCCCGGAACATTTTTCCGTCT 3

Hap09 ....GACGCGGTCCCGCCCGGAACATTTTTCTGTCT 1

Hap10 ....GACGCGGTCCCGCCCGGAGCATCCTTCCGTCT 3

Hap11 GTTTGACGCGGTCCCGCCCGGAGCATCTTTCCGTCT 27

Hap12 ....AACACGGTCCCACCTAGTGCATCTTTCCGTCT 2

Hap13 ....GACGCGGTCCCACCCAGAGCATCCTTCCGTCT 11

Hap14 ....GACGCGGTCCCACCTAGAGCAGCTTTCCGTCT 1

Hap15 ....GACGCGGTCCCGCCCGAAGCATCTTTCCGTCT 1

Hap16 ....GGCGCGATCCCACCCAGAGCATCTTTTCGTTT 5

Hap17 GTTTGACGCGGTCCCGCCCGGAGCATTTTTCCGTCT 12

Hap18 GTTTGACGCGGTCCTACCCGGAGCATTTTTCCGTCT 2

Hap19 GTTTGACGCGGTCCTGCCCGGAGCATTTTTCCGTCT 25

Hap20 ....AACGCAGTCCCACCTAGTGCATCTTTCCGTCT 10

Hap21 ....GACGCGATCCCACCCAGAGCATCCTTTCGTTT 5

Hap22 ....GACGCGATTCCACCCAGAGCGTCTTTCCGTTT 5

Hap23 ....AACGCGATTCCACCCAGAGCGTCTTTCCGTTT 1

Hap24 ....GGCGCGATCCCACCCAGAGCATCCTTTCGTTT 1

Hap25 ACTTGACGTGATCCCACCCAGAGCGTCTTTCCGTTT 21

Hap26 ATTTGACGTGATCCCACCCAGAGCGTCTTTCCGTTT 2

Hap27 ACTTGACGTGATCCCACCCAGAGTGTCTTTCCGTTT 1

Hap28 ACTTGACGTGATCCCACCCAGAGCATCTTTCCGTTT 1

Hap29 ACTTGACGCGATCCCACCCAGAGTATCTTTTCGTTT 10

Hap30 ....GACGCGATCCCACCCAGAGCATCTTTTCATTT 5

Hap31 ....GACGCAGTCCCACCCAGAGCATCTTTCCGTCT 3

Hap32 ....GACGTGATCCCACCCAGAGTATCTTTTCGTTT 1

Hap33 ATTTGACACGATCCCACCCAGAGCATCTTTTCGTTC 1

Hap34 ACTTAACGCGATCCCACCCAGAGCGTCTTTCCGTTT 1

Hap35 ....GACGCGATCCCACCCAGAGCATCTTTCCGTTT 5

Hap36 ACTTGATGCGATCCCACCCAGAGCATCTTTCCGTTT 1

Hap37 ....AACACGGTCCCACCTAGTGCATCCTTCCATCT 2

Hap38 ....GACGCGGTCCCACCCAGAGCATCTTTTCGTTT 3

Hap39 ....AACGCGGTCCCACCTAGTGCATTTTTCCGTCT 1

Hap40 ....GACACGATCCCACCCAGAGCATCTTTCCGTTT 2
80

341122333445666677778811111133344555

 1212156248578914590601116724768555 

 31562445385589 

n

Hap41 ....GACGCGATCCCACCCAGAGCATCTTTTCGTCT 1

Hap42 ....GACACGATCCCACCCAGAGCATCTTTTCGTTT 6

Hap43 ACTTGACGCGATCCCACCCAGAGCGTCTTCCCGGTT 1

Hap44 ....GACGCGATCCCACCCAGAGCGTCTTCCCGTTT 4

Hap45 ....AACGCGGTCCCACCCAGTGCATCTTTCCGTCT 1

Hap46 ....GACGCGATCCCACCCAGAGCGTCTTTCCGTCT 1

Hap47 ....GACGCGATTCCACCCAGAGCATCTTTCCGTTT 2

Hap48 ACTTGACGCGGTCCCACCCAGAGCATTCTTCCGTCT 1

Hap49 ACTTGACGCGGTCCCACCCAGAGCATCTTTCCGTCT 26

Hap50 ACTTGACGCGGTTCCACCCAGAGCATCTTTCCGTCT 2

Hap51 GCCTGACGCGGTCCCACCCAGAGCATCTTTCCGTCT 1

Hap52 ACTTGACGCGATCCCACCTAGAGCATCTTTCCGTTT 1

Hap53 ACCTGACGCGGTCCCACCCAGAGCATCTTTCCGTCT 1

Hap54 ACTTGACGCGGTCCCACCCAGAGTATCTTTCCGTCT 1

Hap55 ACTTGACGCGACCCCACCCAGAGCGTCTTTCCGTTT 3

Hap56 ACTTGACGCGGTCACACTCAGAGCATCTTTCCGTCT 1

Hap57 ACTTGACGCGATCCCACCCAGAGCATCTTTCCATTC 2

Hap58 ACTTGACGCGATCCCACCCAGAGCATCTCTTCGTTT 1

Hap59 ACTTGACGCGATCCCACCCGGAGCATCTTTTCGTTT 1

Hap60 ACTCGACGCG-TCCCACCCAGAGCATCTTTCCGTTT 1

Hap61 ACTTGACACGATCCCACCCAGAGCATCCTTTCGTTT 1

Hap62 ACTTGACGCGATCCCACCCAGAGCATCTTCCCGTTT 1

Hap63 ACTTGACGCGGTCCCACCTAGAGCATCTTTCCGTCT 1

Hap64 ACTTGGCGCGATCCCACCCAGAGCATCTTCTCGTTT 2

Appendix 2.D cont.
81

PART 3: Do Waterfowl Nest in Kin Groups? Evidence from the Common Eider (Somateria 

mollissima) Breeding in the Beaufort Sea, Alaska

Abstract 

We investigated local genetic associations among 

female Common Eiders nesting on two island groups in 

the Beaufort Sea, Alaska, in 2000–2003 using multivariate autocorrelation analyses and highly variable 

microsatellite markers. Global analyses revealed strong 

correlations between genetic and geographic distances 

among years and island groups (Pearson’s r = 0.534 

- 0.813, P < 0.001), and between genetic relatedness 

(rxy) and geographic distance (Pearson’s r = -0.012 to 

-0.181, P < 0.001), indicating that females are nesting in 

closer proximity to more genetically related individuals. 

Nonrandom genetic associations also were observed 

using a global spatial autocorrelation analyses for 

distance classes up to 1000 m in Simpson Lagoon but 

not Mikkelsen Bay. Nearest-neighbor analyses identifi ed 

clusters of genetically related females in both Simpson 

Lagoon and Mikkelsen Bay. Differences in the degree 

of genetic structuring between island groups may be 

attributable to the availability or distribution of nesting habitat, as Simpson Lagoon has three islands with 

colonies, whereas Mikkelsen Bay has only one. Signifi -

cant structuring observed at microgeographic scales 

indicates eiders may nest in kin groups. Though the underlying mechanism enabling female eiders discriminate 

kin is unknown, waterfowl may achieve kin recognition 

indirectly through association during brood rearing. 

Introduction

Genetic consequences of philopatric behavior have 

been demonstrated in several taxa (e.g., Tiedemann 

et al. 1999, Scribner et al. 2001, Avise 2004). Dispersal homogenizes allelic frequencies, whereas natal 

and breeding philopatry can lead to patterns of spatial 

genetic subdivision among populations. Unfortunately, 

dispersal is diffi cult to study because it often requires 

long-term demographic studies (Koenig et al. 1996), and 

in species that are highly mobile, estimates may be more 

diffi cult to obtain since individuals may disperse out 

of the study site. Genetic studies frequently have been 

used to assess intergenerational dispersal (gene fl ow) 

among populations. However, most studies that have 

characterized population genetic structure have assessed 

allelic frequency differences within and among populations and are not designed to detect local groupings of 

genetically related individuals within populations, which 

would be expected in species that exhibit restricted 

dispersal (Double et al. 2005). 

Researchers have hypothesized several mechanisms 

promoting philopatric behavior within species, including 

selective advantages of increased assistance from relatives during the breeding season (Lessells et al. 1994); 

decreased competition and aggression between related 

or familiar neighbors (Greenwood et al. 1979, Waldman 1988, Eason and Hannon 1994); or site familiarity 

(Anderson et al. 1992). Kin association and philopatry 

may have different effects on spatial genetic structure 

at the inter-individual scale. Individuals preferentially 

breeding near more genetically-related individuals 

might create clusters of non-random genetic associations 

among individuals at fi ne-spatial scales (Fowler et al. 

2004, Double et al. 2005). Conversely, if individuals are 

philopatric to an area alone, fi ne-scale spatial associations may not be observed depending upon the size of 

the study area and density of the population. 

Here we investigate microgeographic genetic 

structuring in Common Eiders (Somateria mollissima). 

Pacifi c Common Eiders (S. m. v-nigrum) breeding on 

coastal barrier islands in the Beaufort Sea, Alaska, nest 

in association with driftwood. Female Common Eiders 

either nest in dense colonies or scattered locations on 

islands because of the availability of nesting habitat 

(Goudie et al. 2000). As observed for other waterfowl, 

female Common Eiders exhibit high natal and breeding philopatry (Goudie et al. 2000), which promotes 

high levels of genetic partitioning among populations at 

mitochondrial DNA (mtDNA; Tiedemann et al. 1999, 

Tiedemann et al. 2004, Sonsthagen et al. submitted a, 

b). Furthermore, researchers investigating the colonial 

nesting of eiders breeding in Hudson Bay (S. m. sedentaria) hypothesized that eiders breeding in groups were 

composed of extended family, as some groups of Common Eiders exhibited greater nesting synchrony than 

expected by chance (Schmutz et al. 1983). Variance in 

egg shape among females within these groups suggested 

genetic relatedness. In addition, Common Eiders (S. m. 

borealis) breeding on Southampton Island in Hudson 

Bay arrive to the colony, nest, and brood rear in female 

kin-based social groups, which were determined using 

molecular techniques (McKinnon 2005).

We used a multivariate autocorrelation analyses 

developed by Double et al. (2005) to investigate local 

genetic associations among female Common Eiders 

breeding on 12 islands in the Beaufort Sea. Given 

evidence from previous studies in other subspecies of 

Common Eiders and high natal and breeding philopatry 

observed for female Common Eiders, we predicted that 

the Beaufort Sea eiders nest in close association with 

more genetically related individuals than expected by 

chance. We also hypothesized that, due to differences 
82

in nesting habitat, spatial genetic associations may not 

be as pronounced as those observed within Hudson 

Bay colonies. Seasonal arctic storms in the Beaufort 

Sea dramatically modify island topology, changing 

where nesting habitat is located annually (Noel et al. 

2005). In contrast, Hudson Bay Common Eiders nest on 

coastal wetland tundra habitat (Goudie et al. 2000) that 

has remained relatively unchanged across consecutive 

breeding seasons. 

Methods

Sample Collection

Blood or feather samples were collected from breeding female Common Eiders opportunistically during 

mark-recapture and monitoring efforts on barrier islands 

in the Beaufort Sea, Alaska, from 2000–2003. Samples 

were collected from two island groups, consisting 

of 12 islands in total. The western group, hereafter called Simpson Lagoon, consists of fi ve islands: 

Stump (70.419oN 148.601oW), Wannabe (70.437oN 

148.725oW), Egg (70.440oN 148.739oW), Long 

(70.480oN 148.937oW), and Spy (70.564oN 149.895oW) 

islands (Fig. 2.1A). The eastern group, hereafter 

called Mikkelsen Bay; consists of seven islands: Camp 

(70.172oN 146.226oW), Point Thomson (70.186oN 

146.325oW), Mary Saches (70.200oN 146.207oW), 

North Star (70.225oN 146.347oW), Duchess (70.233oN 

146.405oW), Alaska (70.233oN 146.559oW), and 

Challenge (70.237oN 146.640oW) islands (Fig. 2.1B). 

Distances between islands within each of the two island 

groups ranged from 1.2–49.2 km, and distances between 

islands located in Simpson and Mikkelsen Bay ranged 

from 78.1–143.1 km. Two islands, Camp and Wannabe, are not offi cial names of islands on any recognized 

maps, but were given these names for the purpose of 

identifying areas in this study.

Females were captured on nests using a dip net during initial nest searching efforts, or with a bow net during late-incubation (Sayler 1962). Blood was collected 

from the tarsal, brachial, or jugular veins and placed in 

lysis buffer (Longmire et al. 1988). Feather samples 

were collected from nest bowls from unsampled females 

and stored in silica gel desiccant at room temperature. 

After returning from the fi eld, samples were archived at 

–80°C at the U. S. Geological Survey Molecular Ecology Laboratory, Anchorage, Alaska. Genomic DNAs 

were extracted using either a “salting out” protocol 

described in Medrano et al. (1990) with modifi cations 

described in Sonsthagen et al. (2004), or a QIAGEN 

DNeasy Tissue Kit (QIAGEN, Valencia, CA). Concentrations of genomic DNA extracts were quantifi ed using 

fl uorometry and diluted to 50 ng/μL working solutions.

Microsatellite Genotyping

Primers used for microsatellite genotyping of Common Eiders (n = 317) were obtained via cross-species 

screening of microsatellite primers developed for other 

waterfowl. We screened 12 Common Eiders at 50 microsatellite loci reported to be variable for other waterfowl species and selected 14 microsatellite loci found to 

be polymorphic: Aph02, Aph08, Aph20, Aph23 (Maak 

et al. 2003); Bcaμ1, Bcaμ11, Hhiμ3 (Buchholz et al. 

1998); Cm09 (Maak et al. 2000); Sfiμ10 (Libants et al. 

unpubl. data); Smo4, Smo7, Smo08, Smo10, and Smo12 

(Paulus and Tiedemann 2003). Microsatellites were 

amplifi ed using the polymerase chain reaction (PCR), 

and products were electrophoresed following protocols 

described in Sonsthagen et al. (2004) for tailed primers 

(Aph02, Aph08, Aph20, Aph23, Cm09, Smo4, Smo7, 

Smo08, Smo10, and Smo12) and Pearce et al. (2005) 

for direct-labeled primers (Bcaμ1, Bcaμ11, Hhiμ3, 

and Sfiμ10). For quality control purposes, 10% of the 

samples were randomly selected, re-amplifi ed, and 

genotyped in duplicate.

Data Analysis

Allelic frequencies, and expected and observed heterozygosities for each microsatellite locus were calculated in GENEPOP 3.1 (Raymond and Rousset 1995) 

and FSTAT 2.9.3 (Goudet 1995, 2001). Hardy Weinberg 

Equilibrium and linkage disequilibrium were tested in 

GENEPOP using the default parameters (Markov chain 

parameters: dememorization number 1000, number of 

batches 100, and number of iterations per batch 10,000), 

adjusting for multiple comparisons using Bonferroni 

corrections (α = 0.05). To determine if we could accurately identify individuals, and therefore assess levels of 

relatedness among individuals, probabilities of identity 

for a randomly mating population (PID) and among siblings (PIDsib) were calculated in Gimlet 1.3.3 (Valière 

2002) using genotypes from the 14 microsatellite loci. 

Queller and Goodnight’s (1989) index of relatedness 

(rxy) was calculated overall and among pairs of individuals breeding on each island group within a given year 

using IDENTIX 1.1 (Belkhir et al. 2002). Relatedness 

values range from –1 to 1, where rxy equals 0.5 for fullsibling relationships, 0.25 for half-sibling relationships, 

and 0 for unrelated individuals. Genetic discordance 

among sampled areas may cause incorrect relatedness 

values, as values measure genetic differences in overall 

allelic frequency (Queller and Goodnight 1989). Therefore, spatial analyses of individuals were partitioned 

by island groups because Sonsthagen et al. (submitted 

a) observed signifi cant genetic differentiation between 

Mikkelsen Bay and Simpson Lagoon. Squared genetic 
83

distance was calculated between pairs of individuals within each island group following the method of 

Smouse and Peakall (1999) in GenAlEx 6 (Peakall and 

Smouse 2006). Geographic distances among sampled 

nests were calculated using Universal Transverse Mercator (UTM) coordinates. 

 Overall correlation between genetic similarity and geographic distance at the population level was 

assessed using Mantel tests implemented in the software 

zt 1.0 (Bonnet and Van de Peer 2002). Signifi cance of 

Pearson correlation coeffi cients were assessed using a 

randomization procedure, where the original value of the 

statistic was compared to the distribution of a random 

reallocation of the distance values in one of the matrices 

(randomization = 10,000).

Global spatial autocorrelation analyses were conducted in GenAlEx to further investigate spatial partitioning 

of individuals within an island group in a given year, as 

weak or scattered patterns may not be detected using a 

simple Mantel analysis. Genetic and geographic matrices calculated in GenAlEx were used to determine 

 

Results

Multi-locus genotypes were obtained for 317 individuals. The number of alleles per locus for the 14 

polymorphic microsatellite loci ranged from 3–44 (Table 

3.1), with an average 11.3 alleles per locus. The average 

number of alleles per island group in a given year ranged 

from 6.21–8.79 (Table 3.2). The observed heterozygosity for each area in a given year ranged from 56.1–60.6% 

with an overall value of 57.7% (Table 3.2). All loci did 

not signifi cantly deviate from Hardy-Weinberg equilibrium and were in linkage equilibrium (Padj. > 0.05).

We calculated an overall PID of 3.2 x 10-12 for a 

population composed of randomly mating individuals 

and 5.3 x 10-5 for siblings using genotypes collected 

from 14 microsatellite loci (Table 3.1). These denominator values are much larger than the population 

breeding on the western Beaufort Sea (approximately 

500 nests found on the islands each year; Johnson 2000), 

which gave us confi dence in identifying individuals correctly among years. Overall rxy values from Mikkelsen 

Bay and Simpson Lagoon ranged from -0.037 to -0.008, 

and -0.063 to -0.014, respectively (Table 3.2). Mean rxy

values close to zero indicate that, on average, Mikkelsen 

Bay and Simpson Lagoon are composed of unrelated 

females. Variances were large (Table 3.2), indicating populations are comprised of some highly related 

individuals, and some individuals that are not closely 

related.

Signifi cant correlations between genetic distance 

and rxy values with geographic distance were observed 

among years and island groups (Table 3.3), indicating 

that more genetically related individuals are nesting 

geographically closer to each other than expected by 

chance. Fine-scale spatial structure was observed in 

Simpson Lagoon at the 0–50 m distance class in 2001; 

0–100, 0–250, 0–500, and 0–1000 m distance classes 

in 2002; and 0–10 and 0–25 m distance classes in 2003 

(Fig. 3.2A). Nesting female Common Eiders in Mikkelsen Bay did not depart from a nonrandom distribution 

of genotypes at any distance class (Fig. 3.2B). 

For Common Eiders nesting in Simpson Lagoon, 

0–29% of the lr values calculated for the four nearest neighbors were positive (one-tailed P-values = 

0.001–0.046; Table 3.4). Positive values clustered 

around Stump, Long, and Egg Islands (Fig. 3.3). Within 

Mikkelsen Bay, 0–14% of the lr values were positive 

(one-tailed P-values = 0.004–0.046; Table 3.4) for the 

four nearest neighbors. Positive lr values clustered 

around Camp, Duchess, Alaska, and Challenge Islands 

(Fig. 3.3). A similar number and distribution of positive 

values were observed among years and island groups 

for estimates based on four, six, eight, and ten nearest 

neighbors (data not shown).

Discussion

Global correlation analyses revealed fi ne-scale 

genetic structure among nesting females in the Beaufort 

Sea, indicating that genetically related individuals nested 

closer to each other than expected by chance. The 

pattern of spatial genetic structure revealed by global 

autocorrelation analyses using distance class sampling 

was not strong. Low r values were observed for Simpson Lagoon in 2002 and 2003, and females nesting in 

Mikkelsen Bay did not deviate from a random distribution. Differences in the degree of genetic structuring 

between Simpson Lagoon and Mikkelsen Bay may be 

attributable to the availability or distribution of nesting 

habitat. Three islands (Egg, Long, and Stump) contain Common Eider colonies within Simpson Lagoon; 

approximately 50, 35, and 155 nests were found in 

2003 on each, respectively (J. Reed unpubl. data). In 

Mikkelsen Bay, only one island, Duchess, contains a 

colony, with approximately 90 nests found in 2003 (J. 

Reed unpubl. data). On islands without colonies, approximately 10–20 nests were found scattered across 

each. McKinnon (2005) found that females nesting in 

high densities had higher levels of relatedness among 

focal females and the two nearest females than those 

nesting in low-density areas. Females in high density 

nesting areas may prefer to nest in closer proximity to 

more genetically-related individuals because of reduced 

aggression among kin (Greenwood et al. 1979, Waldman 

1988, Eason and Hannon 1994). In contrast, on low 
84

nesting density islands there may not be an advantage 

to nesting in close association with kin due to presumably fewer interactions among neighbors. Alternatively, 

females in Simpson Lagoon may simply be able to nest 

in closer proximity to kin because of the availability of 

suitable habitat.

Microgeographic genetic structure was uncovered by 

the two-dimensional local spatial autocorrelation analysis, indicating that females are nesting in association 

with more genetically related females. Clusters of nonrandom genetic associations were observed in Simpson 

Lagoon and in Mikkelsen Bay. Double et al. (2005) 

hypothesized that clusters of local positive genetic autocorrelation observed may exist because of an individual 

being more successful reproductively. In highly philopatric species, progeny from successful lineages might 

cluster around natal sites. Female Common Eiders have 

been reported to be philopatric to natal sites (Swennen 

1990), areas within colonies (Cooch 1965), and to exhibit fi delity to specifi c nest bowls (Bustnes and Erikstad 

1993). Therefore, clusters of related females may be 

due to extreme natal and breeding philopatry coupled 

with high reproductive output.

Kin recognition among female Common Eiders also 

may contribute to the local clusters of positive genetic 

autocorrelation observed. Kin-based clusters have 

been postulated to occur among nesting females on the 

Belcher Islands, Hudson Bay (Schmutz et al. 1983). 

Furthermore, female eiders breeding on Southampton Island, Hudson Bay, form kin-based social groups 

during colony arrival, nesting, and colony departure, 

which suggests some form of kin recognition (McKinnon 2005). A variety of mechanisms enabling individuals to discriminate kin have been identifi ed (Komdeur 

and Hatchwell 1999) and could be achieved indirectly 

though association (Hatchwell et al. 2001, Komdeur 

et al. 2004). In the highly philopatric Barnacle Goose 

(Branta leucopsis), females preferentially nested in kin 

groups that were based on kin recognition rather than 

extreme natal philopatry, as females that bred away 

from natal sites nested in close geographic proximity to 

sisters that they were familiar with as brood mates (van 

der Jeugd et al. 2002). If recognition among female 

Common Eiders infl uences nest site selection, this may 

explain, in part, why only some females nest in kin 

groups. In Common Eiders, females may rear broods 

alone or randomly form brood amalgamations (i.e., not 

kin-based; Öst et al. 2005). Therefore, Common Eiders 

may nest in close proximity to brood mates, independent of their genetic relatedness, because of decreased 

competition and aggression between related or familiar 

neighbors (Greenwood et al. 1979, Waldman 1988, 

Eason and Hannon 1994).

Asymmetrical gene fl ow between islands groups may 

explain, in part, differences between island groups in 

the degree of genetic structuring. Gene fl ow estimates, 

calculated from mitochondrial DNA control region, 14 

microsatellite loci, and two nuclear introns, indicate that 

more individuals are dispersing from Mikkelsen Bay 

to Simpson Lagoon (Sonsthagen et al. submitted a). 

Asymmetrical gene fl ow between island groups could 

generate a pattern of lower genetic structuring in the 

“source” population and clusters of more genetically 

related individuals in the “receiving” population coupled 

with unrelated individuals, as observed in our study. In 

the source population, females may be less able to nest 

in close proximity to kin because genetically-related individuals may have dispersed to the other island group. 

In the receiving population, females may nest in close 

proximity to kin, creating clusters of positive genetic 

autocorrelations; however, fewer clusters of positive 

autocorrelation may be observed because of random associations created with source population females. 

Differences in genetic structure observed for Mikkelsen Bay between global distance class sampling and 

local autocorrelation analyses may be attributable to 

the spatial scale at which analyses were conducted. We 

may have not selected distance classes at an interval 

to detect structure among females. Distance intervals 

larger than actual spatial genetic structure would lead 

to failure to detect structure, whereas distance classes 

smaller than actual genetic structure would result in 

increased inter-individual variance and decrease the 

probability of detecting structure. Local autocorrelation 

analyses, however, are conducted among a focal female 

and her four nearest neighbors, irrespective of distance, 

and therefore, may be more biologically signifi cant as 

analyses refl ect genetic associations among females that 

are interacting with each other during nesting.

Conclusions

Common Eiders nesting on the coastal barrier islands 

in the Beaufort Sea nested in closer proximity to more 

genetically related individuals, creating clusters of nonrandom associations among individuals. Although we 

were able to detect signifi cant microgeographic genetic 

structuring among nesting Common Eiders, this study 

likely underestimates the degree of relatedness, as not 

all females nesting on the study islands were sampled. 

Therefore, a female’s nearest-neighbors for this study 

may not be the nearest individuals that a female interacted with during nest site selection. Finally, we cannot 

exclude the possibility that Common Eiders are nesting in close proximity to kin because of extreme natal 

philopatry rather than preferentially nesting close to kin. 

Long-term demographic data coupled with molecular 
85

techniques are needed to determine if the pattern of 

fi ne-scale genetic structuring observed in Beaufort Sea 

Common Eiders is because of extreme natal philopatry 

or female kin association. 

Acknowledgments

Funding was provided by: Minerals Management 

Service (1435-01-98-CA-309); Coastal Marine Institute, University of Alaska Fairbanks; U.S. Geological 

Survey; Alaska EPSCoR Graduate Fellowship (NSF 

EPS-0092040); University of Alaska Foundation Angus 

Gavin Migratory Bird Research Fund; and BP Exploration (Alaska) Inc. We thank P. Flint, J. C. Franson, 

D. LaCroix, and J. Reed, U.S. Geological Survey, for 

providing samples, as well as; J. Gust and G. K. Sage, 

who provided laboratory assistance, and C. Monnett and 

J. Gleason, Minerals Management Service. 

The USFWS banding number for the USGS is 20022 

and the master permit holder is Dirk Derksen.The 

IACUC number #02-01 was assigned for this work to 

Kevin McCrackin.

Study Products

Manuscripts

Sonsthagen, S.A. 2006. Population genetic structure 

and phylogeography of Common Eiders (Somateria mollissima). Ph.D. dissertation. University of 

Alaska Fairbanks.

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, K. Scribner, 

and K.G. McCracken. 2006. Population genetic 

structure of Common Eiders (Somateria mollissima) breeding in the Beaufort Sea, Alaska. Conservation Genetics. Submitted for peer-review.

Sonsthagen, S.A., S.L. Talbot, and K.G. McCracken. 

Genetic characterization of Common Eiders 

(Somateria mollissima) breeding in the YukonKuskokwim Delta, Alaska. Condor. Submitted for 

peer-review.

Sonsthagen, S.A., J.R. Gust, G.K. Sage, S.L. Talbot, R. 

Tiedemann, and K.G. McCracken. 2006. Detection of sex-linkage in waterfowl “autosomal” microsatellite locus and its implication on an estimator 

of population differentiation. Molecular Ecology 

Notes. Submitted for peer-review.

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, and K.G. 

McCracken. Do waterfowl nest in kin groups? 

Evidence from the Common Eider (Somateria 

mollissima) breeding in the Beaufort Sea, Alaska. 

Conservation Genetics. In prep.

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, K. T. 

Scribner, and K.G. McCracken. Stepwise postglacial colonization of Common Eiders (Somateria mollissima) breeding in North America and 

Scandinavia. Molecular Ecology. In prep.

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, K. T. Scribner, and K.G. McCracken. Multilocus population 

genetic structure of Common Eiders (Somateria 

mollissima) breeding in North America and Scandinavia. Molecular Ecology. In prep.

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, K. T. Scribner, and K.G. McCracken. Diagnosibility of subspecies using a multilocus approach: An example 

from the Common Eider (Somateria mollissima). 

Molecular Ecology. In prep.

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, and K.G. 

McCracken. Population genetic structure of Common Eiders (Somateria mollissima) breeding on 

the Aleutian Islands, Alaska. Condor. In prep.

Annual Reports

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2005. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Report.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2004. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Report.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2003. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Report.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2002. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Report.

Oral Presentations

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, K. Scribner, 

and K.G. McCracken. 2005. Population structure 

and phylogeography of Common Eiders. 2nd 

Annual North American Sea Duck Conference, 

Annapolis, Maryland.

Sonsthagen, S.A., S.L. Talbot, R.B. Lanctot, K. Scribner, 

and K.G. McCracken. 2005. Population struc-
86

ture of Common Eiders. Evolution Conference, 

Fairbanks, Alaska.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2004. Population structure 

of Common Eiders breeding in North America and 

Scandinavia. Alaska Bird Conference, Anchorage, 

Alaska.

Poster Presentations

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2004. Population structure 

of Common Eiders breeding in North America 

and Scandinavia. 55th Arctic Science Conference, 

American Association for the Advancement of 

Science, Anchorage, Alaska.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, and K.G. McCracken. 2003. Population 

structure of Common Eiders breeding in Alaska 

and Canada. North American Duck Symposium, 

Sacramento, California.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2002. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. North American Seaduck Conference and 

Workshop, Victoria, Canada.

Guest Presentations

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2006. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Meeting, 

Fairbanks, Alaska.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2005. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Mineral Management Service 10th Information Transfer Meeting, Anchorage, Alaska.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2005. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Meeting, 

Fairbanks, Alaska.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2004. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Meeting, 

Fairbanks, Alaska.

Sonsthagen, S.A., R.B. Lanctot, S.L. Talbot, K. Scribner, 

and K.G. McCracken. 2003. Population structure 

of Common Eiders nesting on the coastal barrier 

islands adjacent to oil facilities in the Beaufort 

Sea. Coastal Marine Institute Annual Meeting, 

Fairbanks, Alaska.

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89

Figure 3.1: Beaufort Sea barrier islands located in (A) Simpson Lagoon (western group) and (B) Mikkelsen 

Bay (eastern group) with samples sizes for each island in a given year in parentheses; 2000–2003, respectively; 

no samples were collected in 2001 for Mikkelsen Bay. Camp designation is used by the authors and is not the 

offi cial name of the island.
90

A. Simpson Lagoon 

B. Mikkelsen Bay 

Genetic correlation (

r) Genetic (

r) correlation

Distance Class size (m) 

Distance Class size (m) 

Figure 3.2: Genetic correlation (r) of females breeding in (A) Simpson Lagoon and (B) Mikkelsen Bay 

at increasing distance class size intervals. Symbols represent females breeding in 2000 (diamonds), 2001 

(circles), 2002 (squares), and 2003 (triangles). Number of pairwise comparisons for each distance class is 

shown above the plotted values. The 95% confi dence error bars about r were estimated by bootstrapping. 
91

Figure 3.3: Bubble plots of two-dimensional local spatial autocorrelation analysis of Common Eider females 

nesting in Simpson Lagoon and Mikkelsen Bay in 2000–2003. Each plot shows the study area with squares 

indicating the nest location. Bubbles surround the nests with positive lr values, based on the four nearest 

neighbors, and within 5% tail of the permutated distribution. The size of the circle is proportional to the 

magnitude of lr. 

Simpson Lagoon Mikkelsen Bay 

2000 2000

2001

Y coordinates 

2002 2002

X coordinates
92

Figure 3.3 cont.
93

Locus Number of 

alleles 

Fragment 

length Ho PID PIDsib

Aph02 4 110–116 0.516 1/3.52 1/1.82

Aph08 3 138–142 0.459 1/2.53 1/1.61

Aph20 9 162–184 0.645 1/5.90 1/2.20

Aph23 7 206–218 0.599 1/5.10 1/2.60

Cm09 9 102–124 0.599 1/4.89 1/1.99

Bcaμ1 4 108–114 0.451 1/2.97 1/1.59

Bcaμ11 7 135–147 0.395 1/2.54 1/1.54

Hhiμ3 3 110–114 0.119 1/1.61 1/1.26

Sfiμ10 19 129–181 0.875 1/38.87 1/3.13

Smo4 44 155–257 0.918 1/251.38 1/3.63

Smo7 6 197–213 0.362 1/2.57 1/1.55

Smo8 7 115–127 0.625 1/4.90 1/2.00

Smo10 21 115–163 0.782 1/14.87 1/2.62

Smo12 15 100–117 0.729 1/11.84 1/2.50

Total loci – – 0.577 3.21 x 10-12 5.34 x10-5

Table 3.1: Number of alleles, fragment length, observed heterozygosity (Ho), and probability of identity 

among randomly mating individuals (PID), and siblings (PIDsib) for 14 microsatellite loci.

No. alleles Ho/He (%) rxy variance n

Simpson Lagoon 

2000 7.36 59.5/59.5 –0.026 0.033 40

2001 7.29 60.1/60.3 –0.033 0.029 31

2002 6.21 60.6/58.3 –0.063 0.039 17

2003 8.64 56.1/59.3 –0.014 0.037 69

Mikkelsen Bay 

2000 6.64 58.2/58.5 –0.037 0.036 28

2002 8.00 57.3/59.8 –0.021 0.042 43

2003 8.79 56.1/58.6 –0.008 0.037 89

Table 3.2: Average number of alleles, observed and expected heterozygosities (Ho/He), overall relatedness values (rxy; Queller and Goodnight 1989) with variances, and sample sizes (n) for Common Eiders 

breeding on two island groups (Simpson Lagoon and Mikkelsen Bay) in the Beaufort Sea, Alaska, in 

2000–2003.
94

Table 3.3: Pearson correlation values (r) for genetic distance (GD) and relatedness values (rxy) and geographic distance for female Common Eiders nesting on two island groups (Simpson Lagoon and Mikkelsen Bay) in 

the Beaufort Sea, Alaska, in 2000–2003. Signifi cant correlations (P < 0.001) are in bold text.

2000 2001 2002 2003

Simpson Lagoon 

r–GD 0.534 0.608 0.579 0.616

r–rxy –0.088 –0.047 –0.168 –0.033

Mikkelsen Bay 

r–GD 0.786 – 0.800 0.813

r–rxy –0.181 – –0.032 –0.012

Table 3.4. Local autocorrelation (lr) values and percent of nesting females from Simpson Lagoon and Mikkelsen Bay in 2000–2003 with positive genetic correlation among a focal individual and her four nearest 

neighbors.

2000 2001 2002 2003

Simpson Lagoon 

Positive lr 0.141–0.176

8% (n = 3/40) – 0.098–0.15929% (n = 5/17) 

0.137–0.250

13% (n = 9/69) 

Mikkelsen Bay 

Positive lr – – 0.139–0.232

14% (n = 6/43) 

0.124–0.180

8% (n = 7/89)