NORTH PACIFIC RESEARCH BOARD PROJECT FINAL REPORT
Genetic diversity of ringed seals sampled at breeding sites; implications for population structure and sensitivity to sea ice loss
NPRB PROJECT 631 FINAL REPORT
Brendan P. Kelly 1, 3 , Micaela Ponce 1 , David A. Tallmon 1 , Bradley J Swanson 2 , Stephanie K. Sell 1, 2
1 Biology Program, University of Alaska Southeast, 11120 Glacier Hwy., Juneau, AK 99801.
2 Department of Biology Central Michigan University, Mount Pleasant, MI 48858
3 Office of Polar Programs, National Science Foundation, 4201 Wilson Blvd., Arlington, VA 22230 email: brendan.kelly@uas.alaska.edu
(907) 209-6531
April 2009
1

Abstract
Ringed seals depend on snow and sea ice as critical breeding habitat. The timing and extent of snow and ice cover are changing rapidly as the climate warms. The ringed seal’s ability to adapt to those changes will depend, in part, on their population structure. Movements of adults seals tracked telemetrically indicated fidelity to breeding sites by adult ringed seals but left open the question of natal philopatry.
Mitochondrial and nuclear DNA were extracted from tissues (N = 358) collected at ringed seal breeding sites and used to examine population structure. Analyses of 9 microsatellite loci and a 359 bp sequence of the Cytochrome Oxidase I mtDNA locus region were consistent with ongoing gene flow between breeding sites. A history of large effective population sizes among ringed seals, however, prevented us from ruling out genetically isolated populations in which genetic drift has been weak.
Keywords
: Arctic, gene flow, genetic divergence, microsatellites, mtDNA, philopatry, population structure, ringed seal, Phoca hispida , Baltic Sea.
Citation
Kelly, B. P., M. E. Ponce, D. A. Tallmon, B. J. Swanson, and S. K. Sell. 2009. Genetic diversity of ringed seals sampled at breeding sites; implications for population structure and sensitivity to sea ice loss. North
Pacific Research Board Final Report 631, pp.
28.
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Table of Contents
Title Page ……………………………………………………………………………………………… 1
Abstract ………………………………………………………………………………………………… 2
Keywords……………………………………………………………………………………………… . 2
Citation ………………………………………………………………………………………………… .2
Table of Contents…………………...……………………………………………………………………3
Study Chronology…………………...……………………………………………………………..…… 4
Introduction…………………...……………………………………………………………..……………5
Objectives…………………...……………………………………………………………..………..……7
Methods…………………...……………………………………………………………..………………..7
Results……………..……...……………………………………………………………..…………….…11
Discussion…………………...…………………….……………………………………..………………18
Conclusions…………………...……………………………………………………………..……..…….19
Publications…………………...……………………………………………………………..………..….20
Outreach…………………...……………………………………………………………..……………....20
Acknowledgements…………………...……………………………………………………………….....24
Literature Cited…………………...……………….………………………………………………..……25
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Study Chronology
This study extended an investigation begun as part of NPRB Project 515: Ice Seal Movements and Stock
Structure in a Changing Cryosphere.
April through June 2005
In the first months of the project, we extracted DNA from skin samples collected from live seals and from resting sites on the sea ice. We also captured and attached satellite-linked and conventional VHF radio tags to the hind flippers of 11 ringed seals and 2 bearded seals near Point Barrow, Alaska.
July through December 2005
We analyzed DNA at 6 microsatellite loci for 54 ringed seals. We continued to track seals via satellitelinked transmitters. We began preliminary analysis of time spent out of the water by the tagged seals. We also presented on the project in seven local, national, and international venues.
January through June 2006
In May 2006, we collected an additional 28 samples of shed ringed seal skin between Peard Bay and
Point Barrow. We also identified 1,503 ringed seal specimens collected during the breeding season
(March-June) and archived at the University of Alaska Museum of the North. We continued to track by satellite seals tagged 2005 and 12 additional seals tagged in 2006. The study progress was presented at 16 meetings between January and June 2006.
July through December 2006
We focused our genetic analyses on samples collected during the breeding season. In August 2006, we extracted cheek teeth from 162 museum specimens, and we recovered DNA from those samples. We began analysis of microsatellite DNA at the ATCG laboratory (Central Michigan University) and of mtDNA in Tallmon’s laboratory (University of Alaska Southeast). Results were presented at 13 meetings.
During the second half of 2006, we continued to track ringed seals tagged with satellite-linked transmitters in 2005 and in 2006.
January through June 2007
In 2007, we added 264 additional DNA samples to the 151 samples collected in 2005 and 2006. The additional skin samples were collected on the sea ice in Kotzebue Sound and near Oliktok Point (Beaufort
Sea) as well as archived specimens from three other sites. In May 2007, we attached satellite-linked transmitters to two ringed seals captured in their breeding sites in Kotzebue Sound. The 2007 tagging was
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done in collaboration with the Native Village of Kotzebue. Results of this study were presented in 11 venues between January and July 2007.
July 2007 through April 2008
During the breeding season of 2008, we collected 325 additional DNA samples from seals in Kotzebue
Sound (Chukchi Sea), Point Barrow, Oliktok Point, and Kaktovik (Beaufort Sea). Those samples were added to our mitochondrial and nuclear DNA analysis. We attached satellite transmitters to two seals captured near Point Barrow during the breeding season of 2008. The tagging was done in collaboration with the Barrow Whaling Captains’ Association. Results of this study were presented at 16 venues between July 2007 and April 2008 and two videos were produced and made available online.
May 2008 through April 2009
We continued to process our DNA samples and run the phylogenetic analyses presented in this report.
We also continued to track seals tagged with satellite-linked transmitters. Results will have been presented at 19 venues between May 2009 and July 2009.
Introduction
Ringed seals,
Phoca hispida
1 , inhabit all seasonally ice-covered seas of the northern hemisphere as well as some freshwater lakes in Finland and Russia. They are the primary prey of polar bears ( Ursus maritmus
) and have been an important resource to Arctic people for thousands of years. Assessment of the impacts of harvests, industrial development in the Arctic Ocean, and rapid changes to the sea ice habitat (Kelly 2001), is impaired by insufficient knowledge of ringed seal population structure (Dizon et al. 1992, O'Corry-Crowe et al. 2003). For example, the impact of on-ice industrial activities on ringed seals has been examined using a variety of metrics (Kelly et al. 1988, Moulton et al. 2002, 2005), but in each case the assumption was that seals in the study areas were part of a much larger interbreeding population. If that assumption was incorrect, however, the impacts on local demes may well have been greatly underestimated. Similarly, impacts of locally severe weather or ice conditions (Lydersen and
Gjertz 1986, Smith and Harwood 2001, Kelly 2001, Stirling and Smith 2004) can only be properly assessed if it is known whether the affected seals are a portion of a single deme or of multiple demes.
Knowledge of the genetic structure of populations generally has been lacking for marine mammals, and it has been assumed that there are minimal barriers to gene flow. For example, Árnason and Widegren
(1986) suggested that the “reproductive biology of these animals and their high mobility in an environment without distinct physical barriers are primary factors counteracting the establishment of
1
We follow Burns and Fay (1970) in referring ringed seals to the genus, Phoca (subgenus, Pusa ).
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reproductive isolates.” More recent analyses, however, have revealed a surprising amount of fine scale population structure even in the absence of physical barriers among a variety of marine mammals
(Barrett-Lennard 2000, Chivers et al. 2002, O'Corry-Crowe et al. 2003). Thus, as suggested by Hoelzel
(1988), genetic diversity among marine mammals “cannot easily be determined from an intuitive assessment of geography.”
Several authors have recognized six subspecies of ringed seals based primarily on morphological variation and geographic distribution: Pusa hispida hispida (Arctic Ocean); P. h. krascheninikovi (Bering
Sea);
P. h. ochotensis
(Okhotsk Sea);
P. h. botnica
(Baltic Sea);
P. h. saimensis
(Lake Saimaa, Finland); and P. h. ladogensis (Lake Ladoga, Russia) (Scheffer 1958, Müller-Wille 1969, Fedoseev and Nazarenko
1970, Youngman 1975, King 1983, Hyvärinen and Nieminen 1990, Rice 1998, Amano et al. 2002).
Others have suggested that genetic data support only three sub-species, one in the Arctic Ocean and two isolated in Lake Saimaa and Lake Ladoga (Palo et al. 2001, 2003, Sasaki et al. 2003). Subsistence hunters and some biologists have suggested that there are two morphs of Arctic ringed seals comprising two distinct genetic units, one inhabiting the shorefast ice and the other inhabiting the moving ice (C.
Noongwook pers. comm., Fedoseev 1975, Finley et al. 1983). Telemetry studies indicated that ringed seals typically have small home ranges of 500 – 1,500 m 2 during the breeding season, range widely (up to
1800 km) outside of the breeding season, but return to the same small sites in successive breeding seasons
(Kelly et al. 2008). Interannual fidelity to breeding sites and limited movements during the breeding season suggest that genetic structure may exist on a much smaller scale than within the shorefast ice versus the pack ice. Determining the scale of genetic structuring in ringed seals is fundamental to understanding the impacts of harvests and habitat changes.
Annual subsistence harvests of ringed seals have been reported at approximately 10,000 in Alaska
(Angliss and Outlaw 2006), 50,000-65,000 in Canada (Reeves et al.
1998), 70,000 in Greenland
(Teilmann and Kapel 1998), and 13,000 in Russia (Popov 1982). Those harvests are thought to be sustainable based on a crude population estimate of over 6,000,000 Arctic ringed seals and the assumption of a single panmictic population. Ringed seals also face rapid alteration of their habitat as
Arctic sea ice responds to climate warming. Early snow melts have been associated with increased mortality from premature exposure to severe weather and predators (Kelly 2001, Stirling and Smith 2004,
Ferguson et al. 2005). Population level effects of harvests, habitat change, and other threats will depend on population structure. The potential for local depletions or extinctions is increased if ringed seals, like their sister species, harbor seals (
Phoca vitulina
), are distributed in many demographically independent subpopulations (Goodman 1998; O'Corry-Crowe et al. 2003). The harbor seal example and our recent
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observations of inter-annual fidelity to breeding sites by ringed seals (Kelly et al. 2008) indicate that the assumption of a single panmictic ringed seal population needs to be tested.
The combination of extensive at-sea movements during a foraging phase, intermixing of individuals from multiple populations at sea, and strong fidelity to specific breeding sites among ringed seals is reminiscent of Pacific salmon Oncorhynchus spp. biology (Hendry and Stearns 2004). That is, despite movements over vast distances, ringed seals may be philopatric, returning to their natal sites to breed.
Such philopatry has been described for Steller sea lions, Eumetopias jubatus , and is the basis of stock distinctions in that species (Angliss and Lodge 2002). Rather than being panmictic, the circumpolar ringed seal may, in fact, be made up of a series of demographically isolated subpopulations.
Davis et al. (2008) examined heterozygosity among 303 ringed seals collected at 8 locations and concluded that there was no evidence of population structuring. Many of their samples, however, were collected outside of the breeding season, and seals from multiple populations may have been intermixed.
Thus, one could easily infer that there is no population structure as an artifact of the sampling design, even if populations are strongly differentiated.
Because we found evidence from telemetry studies of breeding site fidelity, despite extensive wandering outside of the breeding season (Kelly et al. 2008), we investigated the population structure of the ringed seal based on an analysis of molecular markers (nuclear microsatellites and mtDNA) in tissue samples collected during the breeding season at 11 locations in the Chukchi, Beaufort, and Baltic seas and in Lake
Saimaa. The two molecular markers allowed us to investigate multiple facets of population structure. The maternally inherited mtDNA allowed us to investigate any sex-bias that might occur regarding dispersal and philopatry. Additionally, the different mutational rates of the two markers allowed us to examine dispersal and philopatry on different time scales.
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Objectives
Our finding of fidelity to breeding sites among adult ringed seals (NPRB Project 515) logically prompted the question, does that fidelity reflect natal philopatry and fine scale population structure. We tested the hypothesis that there are many genetically distinct populations of ringed seals located around breeding sites throughout the Arctic versus one or a few large pan-Arctic populations.
Methods
Tissue collection at breeding sites.
Tissue samples were collected from ringed seals during the breeding season (mid-April to late-May 2004-
2008) along the northern shore of Alaska in Kotzebue Sound, Peard Bay, Point Barrow, Oliktok Point,
Prudhoe Bay, and Kaktovik (Figure 1a). Additionally, the Canadian Department of Fisheries and Oceans provided samples from seals captured or harvested during the breeding season in the western Canadian
Arctic near Tuktoyaktuk, an offshore oil well (Paktoa), and Holman Island (Figure 1a). Likewise, the
Finnish Game and Fisheries Research Institute provided samples from the Baltic Sea and Lake Saimaa,
Finland (Figure 1b).
Figure 1. Tissue samples were collected along the northern coast of Alaska and in western Canada at (a)
Kotzebue Sound (1), Peard Bay (2), Point Barrow (3), Oliktok Point (4), Prudhoe Bay (5), Kaktovik (6),
Tuktoyaktuk (7), Paktoa (8), and Holman Island (9). Ringed seal samples were also collected in (b) the
Bay of Bothnia, Baltic Sea (10) and Lake Saimaa, Finland (11).
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Skin samples were collected in Alaska from ringed seals live-captured in breathing holes (Kelly 1996) or from the ice around the breathing holes where seals were molting (Swanson et al. 2006). Breathing holes were located by trained dogs (Smith and Stirling 1975, Kelly and Quakenbush 1987) or by visually scanning the ice. Additional skin samples from Canada and the Baltic Sea were taken from ringed seals live-captured for telemetry studies or from seals harvested by subsistence hunters. Samples were collected from 415 individual seals.
Nuclear microsatellites . DNA was extracted from tissue using the Qiagen DNeasy kit (Qiagen, Valencia,
CA) protocol (Qiagen 2006). Each sample was amplified at 9 microsatellite loci: SGPV9, SGPV10,
SGPV11, SGPV16, Hg 4.2, Hg 6.1, Hg 6.3, Hg 8.10, Hl-16 (Goodman 1997, Allen et al. 1995, Davis et al. 2002). Reverse primers were labeled on the 5’ end with a fluorescent dye (FAM, TET, or HEX).
Microsatellite amplification was conducted on an Eppendorf MasterGradient Thermocycler (Brinkman
Instruments Inc., Westbury, NY, USA) and consisted of an initial denaturation step for 2 min at 94°C followed by three cycles of 20 s at 94°C, 20 s at 53°C – 55°C, and 5 s at 72°C. This was followed by 33 cycles of 15 s at 94°C, 20 s at 53°C – 55°C, 10 s at 72°C, and a terminal extension step of 3 min at 72°C
(Swanson et al. 2006, Davis et al. 2002). The PCR products were run through an ABI Prism 310 Genetic
Analyzer using GENESCAN analysis 3.1.2 and GENOTYPER 2.5 software (Applied Biosystems, Foster
City, CA, USA) to determine genotypes.
Genotypes were examined for null alleles, consistent repeat motif, allelic dropout, and calling errors by
MicroChecker (Oosterhaut et al. 2004). The program GENECAP (Wilberg and Dreher 2004) was used to determine if shed skin samples were from recaptured individuals. We used a one mis-match model, which compared all genotypes in the data set to determine which samples differed by zero or one allele.
Individuals flagged by GENECAP were considered duplicate genotypes; we retained only one genotype from each individual for analysis. All genotypes were then analyzed using ARLEQUIN ver. 3.1
(Excoffier et al. 2005) and GENEPOP on the Web (Raymond and Rousset 1995) to calculate deviations from Hardy-Weinberg equilibrium (HWE), the amount of linkage disequilibrium if present, and Fstatistics (FST, FIS) and their 95% confidence interval (Weir and Cockerham 1984, Excoffier et al. 1992,
Weir 1996).
The Bayesian programs STRUCTURE version 2.1 (Pritchard et al. 2000) and Bayesian Analysis of
Population Structure (BAPS) version 5.2 (Corander et al. 2004; Corander and Marttinen 2006) were used to estimate population structure from the genetic data. Both programs group individuals, with similar genotypes, into populations without requiring a priori definition of populations. Both programs determine
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what the maximum likelihood is for a variable number of populations (K) using Markov chain Monte
Carlo (MCMC) simulations (Coulon et al. 2006). In STRUCTURE, we assumed correlated allele frequencies and admixture. We then averaged the likelihoods across ten iterations of each K from 1 to 12.
For all STRUCTURE analyses, a burn-in period of 100,000 iterations was run followed by 250,000 iterations of data collection. For the program BAPS, we clustered groups of individuals using both mixture and admixture models for ten iterations for upper bound values of K= 10, 15, 20. We used values suggested by Corander and Marttinen (2006) for the admixture model.
The assignment test, GeneClass2 (Piry et al. 2004), was run to determine if individuals were dispersing between sample locations based on the likelihood that an individual’s genotype came from it’s location of capture (Paetkau et al. 1995, Waser and Strobeck 1998). We used the likelihood computation L
=
L home
L max where L home
is the population where the individual was sampled and L max
is all population samples including the population where the individual was sampled (Paetkau et al. 2004), with a frequency of 0.01 for missing alleles (Paetkau et al. 1995). We used a re-sampling algorithm with 1000 as the minimum number of simulated individuals with a type 1 error value of α =0.01. To determine if there were any disproportionate impacts on the results, GeneClass2 was also run sequentially removing each locus in turn.
Relatedness version 5.0.8 (Queller and Goodnight 1989) was used to determine the amount of relatedness
(r) by distance. We calculated r for all pair-wise comparisons of individuals within each sample area and for all pair-wise comparisons between individuals for all population pairs. We assumed all individuals within a sampling site were separated by 0 km and used Google Earth ( http://earth.google.com
) to determine the distances between all pairs of sampling sites. Paths consisted of the most direct straight-line route through the marine environment. Routes from Alaska and western Canada to the Baltic Sea and
Lake Saimaa, however, bisected Finland and Sweden. Relatedness values were compared to distance values in 200 km increments. mtDNA. We sequenced the Cytochrome Oxidase I (COI) locus of the mitochondrial genome consistent with phylogenetic studies of related species (Árnason et al. 1993). DNA was extracted from 113 individuals representing 8 geographically distinct breeding areas: Kotzebue Sound, Peard Bay, Oliktok
Point, Paktoa, Tuktoyaktuk, Holman, the Baltic Sea, and Lake Saimaa. Mitochondrial DNA was not sequenced from Point Barrow, Prudhoe Bay, or Kaktovik due the low quantity of epidermis collected for each animal or sample degradation. DNA was extracted using proteinase K and ammonium acetate
10

according to a procedure developed by Tony Gharrett of the University of Alaska Fairbanks.
Mitochondrial DNA amplification consisted of an initial denaturation step for 6 min, at 94°C, followed by
48°C for 1 min, 72°C for 1 min 30 s, 34 cycles of 1 min at 94°C, 5 s at 72°C, and refrigeration at 4°C using the P. hispida COI left primer 5’-TTA ATC CGC GCA GAA CTA GG-3’ and right primer 5’-
GCA GGG TCG AAG AAT GTT GT-3’. The PCR products and primers were purified and sequenced by the High-Throughput Genomics Unit, Department of Genome Sciences, University of Washington. Cycle sequencing was done in both the forward and reverse direction. Thus, two independent, but complimentary, sequences were supplied for the COI locus of each individual. To check the precision of the High-Throughput Genomics Unit, some individuals were repeatedly sequenced independently.
The bioinformatics software Geneious (Drummond et al. 2009) was used for editing sequences, creating phylogentic trees, running ClustalW sequence alignments, NCBI BLAST, and PAUP. Genetic variation between and within breeding sites was partitioned using an Analysis of Molecular Variance (AMOVA) generated by ARLEQUIN ver. 3.1 (Excoffier et al. 2005). Complimentary sequences were used together for optimal editing. Sequence quality and length varied greatly between individuals. The greatest uncertainty in sequence accuracy occurred at the ends of each sequence; thus, we used bases 92-450, a
359 bp region, in the forward direction.
Results
Nuclear microsatellites . We analyzed microsatellite DNA from 354 seals sampled during the breeding seasons of 2004 – 2008 (Kotzebue Sound, n=44; Peard Bay, n=50; Point Barrow, n=32; Oliktok Point, n=27; Prudhoe Bay, n=30; Kaktovik, n=30, Paktoa, n=20; Tuktoyaktuk, n=40; Holman, n=20; Bothnia
Bay, n=23; lake Saimaa, n=38). There was some evidence of departure from Hardy-Weinberg proportions across the sites of capture. Following Bonferroni correction (adjusted α = 0.0005), 7 out of 99 loci were out of Hardy-Weinberg proportions due to excess homozygosity. We found 16 of 396 pairs of loci exhibiting linkage disequilibrium when examined in all pair-wise combinations within each site following
Bonferroni correction (adjusted α = 0.0001). There was no consistent pattern, however, as to which loci were out of Hardy-Weinberg proportions or expressed linkage disequilibrium.
At the marine sites, the mean within population heterozygosity (0.71 + 0.16 SD) and average allelic diversity (11.7 + 3.04 SD) were high but within the range of values typical of microsatellite loci (Table
1). The Lake Saimaa population had significantly less heterozygosity (0.25 + 0.18 SD; U n1=9, n2=9
= 81, p
<<0.0001) and allelic diversity (3 + 1.79 SD, p << 0.0001) than the marine populations. The Baltic population also showed some reduced microsatellite allelic richness relative to the other six marine populations. The average inbreeding coefficient (F
IS
) for marine sites was high (0.07) consistent with null
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alleles at these microsatellite loci. There was little divergence among Arctic sites (mean Fst = 0.006 +
0.002 SE) or all marine sites (mean Fst = 0.009 + 0.002 SE; Table 2). Much higher levels of genetic divergence were found between the Baltic and Arctic populations (mean Fst = 0.021 + 0.001 SE) and between the Lake Saimaa and all seven marine populations (mean Fst = 0.342 + 0.000 SE).
Table 1. Number of alleles,
A
, observed heterozygosity,
H
O the inferred breeding sites at a given locus.
, and inbreeding coefficient,
F
IS
, for each of
Site N H
O
A H
O
A H
O
A H
O
A H
O
A H
O
A H
O
A H
O
A H
O
A F
IS
Holman 20 0.60 8 0.90 14 0.85 11 0.90 7 0.90 15 1.00 14 0.85 10 0.80 8 1.00 12 -0.04
Tuk 40 0.63 9 0.90 14 0.83 14 0.80 12 0.88 16 0.93 18 0.93 15 0.68 12 0.93 17 0.02
Paktoa 20 0.80 7 0.90 13 0.85 10 0.75 7 0.90 17 0.80 11 0.85 13 0.65 11 0.85 12 0.01
Kaktovik 30 0.60 11 0.70 14 0.23 9 0.37 9 0.70 13 0.57 14 0.60 9 0.63 9 0.57 12 0.20
Prudhoe 30 0.57 7 0.87 13 0.73 12 0.57 9 0.67 13 0.87 15 0.73 12 0.47 8 0.93 11 0.05
Oliktok 27 0.59 9 0.59 12 0.59 12 0.56 8 0.81 17 0.33 10 0.78 12 0.56 7 0.78 11 0.11
Barrow 32 0.59 8 0.81 13 0.47 11 0.63 8 0.59 13 0.50 12 0.53 10 0.56 9 0.56 12 0.12
Kotz 44 0.55 10 0.82 14 0.64 14 0.50 10 0.82 17 0.45 16 0.77 14 0.57 11 0.91 18 0.13
Baltic 23 0.52 6 0.78 11 0.78 9 0.78 6 0.91 14 0.91 12 0.78 12 0.70 5 0.91 10 0.03
Marine 316 0.61 9 0.82 14 0.65 11 0.66 9 0.80 15 0.69 14 0.77 12 0.61 9 0.82 13 0.07
L.Saimaa 38 0.13 2 0.42 2 0.37 7 0.16 4 0.55 2 0.05 2 0.00 1 0.34 2 0.21 3 0.07
Table 2. Fst estimates of genetic divergence (below the diagonal) with statistical significance (above the diagonal; + p < 0.05).
Holman - - - - - - - - - +
Prudhoe 0.005 0.001 -0.004 0.014 - - - + + +
+
Baltic 0.012 0.011 0.015 0.028 0.023 0.037 0.028 0.021 0.014
+
+
In agreement with the F-statistics, clustering algorithms suggested there was little genetic divergence among marine ringed seal populations. STRUCTURE clustered all individual genotypes into two populations (Figure 2): Lake Saimaa and all the Arctic sites including the Baltic Sea. Similarly, using
BAPS, both the mixture and admixture models (when clustered by groups of individuals) confirmed the results seen by STRUCTURE. All individual genotypes from the Arctic breeding sites and the Baltic Sea
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had a 100% probability of being clustered into one population (marine), while all genotypes from Lake
Saimaa were clustered into a second population.
Figure 2. Likelihood (LnP(D)) of the number of populations (K) and likelihood of belonging to assigned population (mean Q max) averaged across individuals based on the analysis from STRUCTURE.
Larger LnP(D) values indicate a greater likelihood that the number of populations is correctly estimated.
Likelihood ratios matched the breeding site to the capture site for 96.3% (341 of 354) of the individuals sampled (Table 3). Two individuals captured in the Baltic Sea were assigned to the Beaufort Sea of
Alaska, while one individual captured in Kotzebue Sound was assigned to the Baltic Sea. The remaining
10 individuals were assigned to within 1,400 km of their capture site. Sequential removal of loci resulted in the number of immigrants varying between 9-13 individuals (results not shown) indicating no one locus was having a disproportionate effect.
Table 3. Results for the assignment test (GeneClass2). Rows indicate breeding sites where seals were captured, while columns indicate the site where seals were most likely to be born (* = p ≤ 0.01).
Holman Tuk Paktoa Kaktovik Prudhoe Oliktok Barrow Peard Kotzebue Baltic L.
Holman 19 1*
Tuk 1* 39
Kaktovik 1* 29
Paktoa 19
Prudhoe 30
Barrow 32
Peard
1*
Baltic
L.Saimaa
13
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The average relatedness between individuals within a sampling site (r = 0.032 + 0.012 SD) was significantly greater (U n1=52, n2=11
= 623, p <<0.0001) than the degree of relatedness between dyads at any other distance (average r = -0.024 + 0.010 SD). We found no relationship (Mantel test g=0.11; p = 0.81), however, between relatedness and distance once the zero distance class was removed (Figure 3).
Although we found significantly higher relatedness values between individuals within sample locations, the low average relatedness value between individuals within sites, suggested no biologically significant pattern.
Figure 3. Relatedness by distance for all pair-wise comparisons within and between breeding sites.
mtDNA
. Ringed seals from eight sites were sequenced at 359 or more bases of the COI locus (Kotzebue
Sound, n=6; Peard Bay, n=17; Oliktok Point, n=1; Paktoa, n=14; Tuktoyaktuk, n=27; Holman, n=15;
Bothnia Bay, n=11; Lake Saimaa, n=22). The mtDNA samples included 109 of the same individuals sampled for microsatellite DNA and 4 additional individuals. Thirty-one unique haplotypes were distributed throughout the Arctic. Phylogenetic analysis of the mtDNA haplotypes yielded similar results to those from the nuclear microsatellite loci. When all 8 sites are included, 87% of the observed genetic variation was found within sites, and there was little evidence of geographic structuring of genotypes
(Table 4). The exception is the Lake Saimaa population, which has only two haplotypes, one of which is shared with the marine populations and one that is unique to an individual within the Lake (Table 4). The
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low haplotype diversity in Lake Saimaa accounted for more than 10% of the overall between-site variation.
Maximum likelihood, UPGMA, and neighbor-joining trees provide no evidence of any clear phylogeographic signal, and these trees have little bootstrap support (Figure 4). Haplotypes present in many of the populations are found in other populations, providing no evidence of maternal philopatry. In fact, the phylogenetic data suggest that marine ringed seals are paraphyletic. The lack of haplotype divergence within
P. hispida
or between
P. hispida and Baikal seals (
P. sibirica
) suggests historically large population sizes and minimal genetic drift.
Table 4.
AMOVAs partitioning genetic variation between and within breeding sites. The first AMOVA includes 8 breeding sites: Kotzebue Sound, Peard Bay, Oliktok Point, Paktoa, Tuktoyaktuk, Holman
Island, the Baltic Sea, and Lake Saimaa. The second AMOVA excludes Lake Saimaa.
Populations source of var.
d.f.
sum of squares var. components % of var among
All 8 sites populations within populations among
7
105
40.95
202.5
0.29 Va
1.93 Vb
13.08
86.92
7 sites, Lake Saimaa excluded populations within populations
6
84
17.01
176.96
0.06 Va
2.11 Vb
2.75
97.25
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a,b,c,d,e,f,g,h
Figure 4.
UPGMA tree of the 31 unique haplotypes for 113 individuals representative of 8 breeding sites based on the Tamura-Nei genetic distance model.
Each haplotype is labeled using the number of individuals possessing that haplotype at each site: (a) Kotzebue Sound, (b) Peard Bay, (c) Oliktok Point,
(d) Paktoa, (e) Tuktoyaktuk, (f) Holman, (g) Baltic Sea, (h) Lake Saimaa] ( i.e.
the haplotype at the top of the tree is only found in one individual from Tuktoyaktuk).
Discussion
Ringed seals adapted to breeding on sea ice at least 12 million years ago (Árnason et al. 2006). During glacial maxima, expansive sea ice and lowered sea levels fragmented many marine habitats and populations including those of ringed seals (Mann and Peteet 1994, Mann and Hamilton 1995, Cronin et al. 1996, Clark and Mix 2002, Harlin-Cognato et al. 2005, Harington 2008). In the Pleistocene, ringed seals were periodically restricted to refugia south of their current distribution (Davies 1958, Hoberg 1995,
Heaton and Grady 2003). During interglacials, their range expanded to be circumpolar including seasonally ice-covered marginal seas. That more or less continuous and expansive sea ice habitat has supported large populations of ringed seals, thought to number several million. How those populations are structured is important to understanding the likely impacts of the rapidly changing sea ice cover as well as other threats.
16

Despite telemetric evidence of breeding site fidelity among ringed seals, our nuclear and mtDNA data are consistent in suggesting little divergence among sample sites in the Arctic Ocean. Nor did we find evidence of genetic differentiation or isolation between the Arctic Ocean and Baltic Sea seals despite their classifications as distinct subspecies. Palo et al. (2001) reached a similar conclusion based on genetic diversity of ringed seals from Svalbard and from the Baltic Sea. We found microsatellite loci to be highly variable, and our average number of alleles per locus (11.7) was similar to the average number (12.4) observed by Palo et al. (2001). The maintenance of high diversity among marine populations of ringed seals might be attributed to on going gene flow. Alternatively, large effective population sizes might minimize the effects of genetic drift even in currently isolated populations.
We did observe divergence between the land-locked ringed seals of Lake Saimaa and the marine populations, suggesting that gene flow, in fact, limits divergence within the marine ringed seals. Our consistent records of breeding site fidelity (Kelly et al. 2008) suggest that such gene flow must be mediated by natal dispersal.
The combination of divergence between marine and land-locked populations, a lack of divergence within marine populations, and fidelity to breeding sites suggest two plausible scenarios for the low divergence in the marine populations: (1) divergence is minimized by gene flow and (2) large effective population sizes minimize the effect of drift in presently isolated populations.
The continuous distribution of ringed seals across the Arctic may facilitate “stepping-stone” gene flow of adults moving between neighboring breeding sites. Likewise, juveniles may disperse over larger distances before they select breeding sites to which they will be faithful as adults. Although few putative immigrants were detected in our samples using a genetic assignment method, if these samples are representative of 6,000,000 or more ringed seals in the Arctic Ocean, there could well be adequate numbers of migrants to homogenize allele frequencies. The number of migrants per generation is a product of the effective population size and migration rate; with a very large population of ringed seals, low migration rates would be sufficient to generate a large number of migrants per generation and offset the effects genetic drift in local populations (Mills and Allendorf 1996). The large number of seals found in the Baltic and Arctic, in conjunction with even small dispersal rates, would be sufficient to offset the effects of genetic drift. Palo et al. (2001) estimated that 8.7 Arctic seals migrating to the Baltic Sea per generation would be sufficient to prevent divergence. Inferring gene flow among marine species, however, is problematic (Waples 1998).
17

We do not know if juvenile ringed seals return to their own birth sites to breed as is the case for many pinnipeds (Härkönen and Harding 2001, Harlin-Cognato et al. 2005, Hoffman et al. 2006). Such natal philopatry, however, should lead eventually to divergence among populations. Saimaa seals have been isolated from marine ringed seals for over 10,000 years and yet have diverged only moderately. That divergence may have been accelerated by the much reduced population size (less than 300 individuals).
The marine populations of ringed seals, however, have remained large, possibly too large for genetic drift to have promoted divergence.
Using a coalescence simulation, Palo et al. (2001) found substantial support (Bayes factor = 4.6) for a model showing recurrent gene flow from the Arctic to the Baltic Sea over a model of genetic drift in isolation. Their Arctic sample, however, consisted of 39 individuals from a single location (Svalbard), and we remain wary of distinguishing between divergence hypotheses given the limited sampling. Our Arctic samples included more sites, but nonetheless were limited to the near shore areas of Alaska and the western Canadian Arctic. The spatial and temporal distribution of our samples – some of which were collected from live-captured animals, some from molting sites, and some from subsistence harvests – were variable. Schwartz and McKelvey (2009) recently demonstrated that local autocorrelation in population genetic data can confound results when spatial and temporal scales of sampling vary. We also note that Pritchard et al. (2007) recently cautioned that STRUCTURE has difficulty discerning the correct number of populations when dispersal is local among continuously distributed individuals. Simulation analyses indicated that the patchy sampling could greatly influence results where there is isolation by distance (Schwartz and McKelvey 2009).
We also recognize that our samples were largely limited to shorefast ice habitats. In the northern Bering
Sea, St. Lawrence Island Yupiks distinguish as separate species ringed seals that are large as adults and ringed seals that are small as adults (C. Noongwook, pers. comm.). That distinction is reminiscent of
Fedoseev’s (1972) ringed seal “ecotypes.” Conceivably, ringed seals occupying shorefast ice may have diverged from those occupying the pack ice just as offshore killer whales ( Orcinus orca ) appear to be divergent from inshore killer whales (Ford et al. 2000).
Whatever the historical and current patterns of genetic divergence among ringed seals, the rapidly changing snow and ice cover of the Arctic (Comiso 2002, Johannessen et al. 2004, Lindsay and Zhang
2005, Overpeck 2005) will inevitably force changes in habitat use and in patterns of gene flow. The degree to which genetic diversity will be lost will depend on current population structure and the rate of environmental changes.
18

Conclusion
We found no evidence of unique genetic populations of ringed seals in the marine environment including the Baltic Sea. Whether these results reflect on going gene flow or minimal genetic drift in historically large populations is unresolved. The fact that we did not find any significant genetic differentiation between Arctic ringed seals and Baltic Sea ringed seals (morphologically distinguished as distinct subspecies) argues for large effective populations with minimal drift hypothesis. Future investigations should focus on determining the degree of natal dispersal among ringed seals and on fine-grained genetic sampling.
Publications
Sell, S. K. 2008. Investigating population structure and philopatry in ringed seals ( Phoca hispida ). M.S, thesis, Central Michigan University, pp.30.
Outreach
Web pages developed;
Eyewitness To Change; Checking Out Seals http://forces.si.edu/arctic/03_00_01.html
Low-profile ringed seals are warming victims http://www.msnbc.msn.com/id/17345168/
Climate change and ocean acidification
http://amcctour0809.wordpress.com/dr-brendan-p-kelly-bio/
Peard Bay 2005 – Alaska Ringed Seal Project http://www.nanuuq.info/peardbay.html
Arctic Seals Vulnerable to Fast Pace of Change http://www.earthsky.org/radioshows/52291/fast-pace-of-arctic-change-affecting-seals
Labrador retrievers assist ringed seal researchers http://www.fakr.noaa.gov/newsreleases/2006/ringedseal041106.htm
19

Conference presentations;
Strengthening Cities: Mayors Respond to Global Climate Change, Girdwood, AK – 16-18 Sep. 2006
Wildlife Society, 13 th Annual Conference, Anchorage, AK – 23 – 27 September 2006
NOAA Educational Partnership Program Annual Meeting. Tallahassee, FL - 2 November 2006
Carnivores 2006: Habitats, Challenges and Opportunities, St. Petersburg, Fl. – 12-15 Nov 2006
Marine Mammals of the Holoarctic, St. Petersburg, Russia - September 2006
Alaska Marine Science Symposium, Anchorage, AK - January 2007
Midwest Ecology and Evolutionary Conference, Kent State University, OH – 9-11 March 2007
Alaska Chapter of the Wildlife Society Annual Conference, Juneau, AK - April 2007
Society of Conservation Biology, Port Elizabeth, South Africa – 1-7 July 2007
AAAS – Arctic Division meeting, Anchorage, AK – 23–26 September 2007
CMU Chapter of The Wildlife Society, Central Michigan University, MI - 10 October 2007
Animal-borne Imaging Symposium, Washington, DC – 11 October 2007
Seals & Society; international symposium, Vaasa, Finland – 16–18 October 2007
National Science Teachers Association, Birmingham, AL - 7-8 December 2007
American Geophysical Union meeting, San Francisco, CA - 10-14 December 2007
Scientific Review Groups for Marine Mammal Stocks, Monterey, CA – 9 January 2008
Alaska Marine Science Symposium, Anchorage, AK – 23 January 2008
Marine Mammal Commission, Co-management Meeting, Anchorage, AK – 8 February 2008
West Coast Biol. Sci. Ann. Undergraduate Research Conf., San Diego, CA - 12 April 2008
Presidential Awards for Excellence in Math. & Sci. Teaching, Arlington, VA – 29 April 2008
Am. Inst. Biol. Sci. Ann. Conf.: Climate, Environ., & Infectious Dis. Wash. DC - 12 May 2008
Arctic Forum, Washington, DC – 14 May 2008
88 th Ann. Meeting of American Society Mammologist, South Dakota State Univ., SD – 21-25 June 2008
Ecological Society of America Annual Conference. Milwaukee, WI. – 5 Aug 2008
Edith Munson Marine Conservation Lecture, Yale University – 30 September 2008
Marine Mammals of the Holarctic, Odessa, Ukraine – 16 October 2008
Alaska Forum on the Environment (seal research), Anchorage, AK – 4 Feb 2009
Alaska Forum on the Environment (co-management), Anchorage, AK – 5 Feb 2009
89th Annual Meeting of the American Society of Mammalogists, Fairbanks, AK. - June 2009
Community Meetings;
Ice Seal Committee meeting, Anchorage, Alaska – 24 October 2006
North Slope Borough Fish and Game Committee, Barrow – 20 December 2006
20

Presentation to the IRA, Unalakleet, Alaska – 24 April 2007
North Slope Borough Fish and Game Committee, Barrow - 4 December 2007
Presentations at Festivals/Events;
Climate Camp: Alaska - 30 October – 1 November 2006
Municipal League of Alaska (keynote address), Juneau, Alaska – 15 November 2006
Testimony before U.S. Senate Subcommittee on
Private Sector and Consumer Solutions to Global
Warming and Wildlife Protection , Washington, DC – February 2007 http://epw.senate.gov/public/index.cfm?FuseAction=Hearings.Testimony&Hearing_ID=7efcd166
-802a-23ad-4634-25057d9d08bf&Witness_ID=9ee2e8ca-5581-47c3-a857-66e172ee22e4
Panel Discussion on Global Warming and Wildlife, (Gerald Kooyman, Brendan Kelly, Greg Marshall),
National Geographic Society, Washington, DC – 13 October 2007
Testimony before U. S. Senate Committee on Environment and Public Works, Hearing
on , Wash., DC - 30 Jan.
2008
Climate Change and Ocean Acidification Speaking Tour, Concord, NH – 17 July 2008
Congressional briefing on Arctic climate change U. S. House of Representatives,
Washington, DC –– 14 October 2008
Public lecture on climate change, Juneau, AK – 27 Jan 2009
Discussion with Governor Palin’s staff on climate change, Juneau, AK – 28 Jan 2009
Discussion with Alaska State legislators on climate change, Juneau, AK – 29 Jan 2009
Research presentation to Alaska Dept. Fish & Game, Juneau, AK – 30 Jan 2009
Polar weekend, Maryland Science Museum, Baltimore, MD – 5 Apr 2009
Workshop Participations;
NOAA EPP Workshop, Florida A&M University, Tallahassee, Florida – 30 Oct - 1 Nov 2006
Collaborations on Climate Change Research, Univ. Alaska Workshop – 10 November 2006
Workshop on Monitoring Arctic Marine Mammals, Valencia, Spain – 4 - 6 March 2007 http://sitios.cac.es/microsites/belugas_workshop/docs/Arctic.pdf
21

SAP4.3 Authors’ meeting (contributed Sea ice ecosystem ), Boulder - 14-15 November 2007
Workshop: mapping cultural & resource sites in coastal Alaska, Anchorage - 10 Jan 2008
Aspen Inst.’s Dialogue & Commission: Arctic Climate Change, Fairbanks – 12 Aug 2008
Presentations in Schools (K-12, undergraduate, graduate);
Mentoring high school student, Emily Johnson in Science Fair project – 21 September 2006
Classroom presentation at Long Branch Elementary, Arlington, Virginia – 12 April 2007
Adolescent Montessori Program, Juneau, Alaska – 28 September 2007
Marine Science and Limnology 695, graduate course in
Methods of Sea Ice Research
,
Barrow, Alaska (taught field course module on studying seals in sea ice
Environment) – 18-29 May 2008
Lecture in Biology of Marine Mammals course, Univ. Alaska Southeast – 15 Jan. 2009
Climate change in Alaska discussion, U. S. Forestry Sci. Lab., Juneau, AK - 27 Jan 2009
Lecture in Biology of Marine Mammals course, Univ. Alaska Southeast – 12 Mar 2009
Press Articles (Newspaper/Journal/Newsletter);
Interview with Public Broadcasting Service for documentary on climate change – 29 June 2007
Associated Press interview - 3 Jan 2007 http://www.adn.com/news/alaska/story/8671415p8565319c.html
Interview (Ocean Conservancy) for Snowed in; ringed seals scratch out a life in the harshest of worlds, –
Spring 2007
“Rapid climate change and the sea ice ecosystem” in World Wildlife Fund’s Arctic Bulletin ) – April
2007. http://assets.panda.org/downloads/ab0107.pdf
Science News interview - 5 Oct 2007 (http://www.sciencenews.org/articles/20071201/bob9.asp)
“Undergraduate Scientists Learn Marine Biology With Man's Best Friends!”
Current (National Science Foundation) – September 2008 http://www.nsf.gov/news/newsletter/sep_08/index.jsp
Interview with Houghton Mifflin author, Kieran Mulvaney, Arlington, VA - polar bear biology and conservation – 19 February 2008
Interview with Susan Milius, Science magazine, Washington, DC – 14 May 2008
Interview for Research Channel on climate change, Arlington, VA – 20 Oct 2008
Interview about ringed seal biology with author Kieran Mulvaney, Washington, DC
- 13 November 2008
Video interview on climate change with Alaska Marine Conservation Council, Anchorage
- 22 Jan 2009
22

Public radio interview on Arctic climate change, Juneau, AK – 26 Jan 2009
Factsheets Produced;
Rapid climate change and the sea ice ecosystem - World Wildlife Fund’s Arctic
Bulletin – April 2007 http://assets.panda.org/downloads/ab0107.pdf (p. 14 -16).
Consultation to Minnesota Zoo, (consult on seals and polar bears for exhibit) – 8 November 2007
Video Produced;
Ice Masters , a documentary featuring this project,
National Geographic Society, Washington, DC – 9 October 2007
Cooper Sniffs Out Seals ,
Exploratorium, San Francisco - June 2008 http://icestories.exploratorium.edu/dispatches/cooper-sniffs-out-seals/
Tagging seals ,
Exploratorium, San Francisco - June 2008 http://icestories.exploratorium.edu/dispatches/tagging-seals/
Radio/Television Interviews.
Video interview: Ron Meyer (documentary film on climate change) – 11 September 2006
Video interview: Flying Fast Productions (climate change documentary) – 11 October 2006
Interview: Public Broadcasting Service for documentary on climate change – 29 June 2007
Video interview: AdvanBridge Inc. (Japan) on sea ice and polar bears, Juneau – 17 Nov 2007
Radio interview:
Earth and Sky
, National Public Radio, Washington, DC – 20 December 2007 http://www.earthsky.org/radioshows/52291/fast-pace-of-arctic-change-affecting-seals
Acknowledgements
North Pacific Marine Research Board
BP Exploration, Alaska
Department of Wildlife Management, North Slope Borough, Alaska
Canadian Department of Fisheries and Oceans
Finnish Game and Fisheries Research Institute
Alaska Nanuuq Commission
National Geographic Society
B. Akootchook, J. Bengtson, M. Duchin, A. Eavitt, R. Flinn, L. Harwood, J. Jones, M. Kunnasranta, J.
Moran, I. & N. Olemaun, C. Patkotak, E. Rexford, R. Schaeffer, R. Snyder, and A. Whiting.
23

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