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\begin{document}

\title{Population genomics reveals historical divergence and local adaptation
in polar bears}



\author[1]{René Malenfant}%
\author[2]{Catherine Cullingham}%
\author[2]{David Coltman}%
\author[3]{Evan Richardson}%
\author[4]{Markus Dyck}%
\author[5]{Nicholas Lunn}%
\author[6]{Martyn Obbard}%
\author[7]{Jodie Pongracz}%
\author[8]{Stephen Atkinson}%
\author[2]{Vicki Sahanatien}%
\author[9]{Kristin Laidre}%
\author[10]{Erik Born}%
\author[11]{Øystein Wiig}%
\author[2]{Corey Davis}%
\affil[1]{University of New Brunswick}%
\affil[2]{University of Alberta}%
\affil[3]{Environment and Climate Change Canada}%
\affil[4]{Government of Nunavut}%
\affil[5]{Environment and Climate Change Canada Prairie and Northern Region}%
\affil[6]{Ontario Ministry of Natural Resources and Forestry}%
\affil[7]{Government of the Northwest Territories}%
\affil[8]{Affiliation not available}%
\affil[9]{University of Washington Applied Physics Laboratory}%
\affil[10]{Gronlands Naturinstitut}%
\affil[11]{University of Oslo}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Single-nucleotide polymorphisms (SNPs) have numerous advantages over
microsatellites, including greater power to infer population structure
and history and to detect loci undergoing selection. Here, we conduct
the first continental-level SNP study of polar bears (Ursus maritimus)
using genotypes from an array of 5441 SNP loci genotyped in 16--30 polar
bears sampled in each of 16 geographic regions in Canada and West
Greenland. Our study aimed to assess population history and genetic
structure and to identify evidence of adaptive loci. Using these data,
we confirmed the existence of four broad-scale genetic clusters in North
America (FCT = 0.035) and identified nine fine-scale subclusters using
more powerful spatial methods. An assessment of historical patterns of
migration suggests that polar bears migrated into North America from the
Beaufort Sea after the last glacial maximum. Using a conservative
approach, we identified 17 loci that may represent adaptive variation,
including one SNP in the 3' untranslated region of PDLIM5 (PDZ And LIM
Domain 5), a gene involved in cardiovascular function, which has
undergone substantial selection in polar bears since their divergence
from brown bears. Outlier loci differentiated the Norwegian Bay genetic
cluster more strongly from remaining clusters than did our complete
dataset, suggesting possible adaptive differences in the High Arctic.
Through careful consideration of SNP loci, sample inclusion, and
analytical approaches, we provide a comprehensive picture of polar bear
population structure at a continental level. This study provides a model
for the analysis of wide-ranging species that can contribute to their
conservation and management.%
\end{abstract}%



\sloppy


\subsection*{Introduction}

{\label{introduction}}

With the development and increased accessibility to large-scale
genotyping approaches for non-model organisms (e.g. RAD-Seq, exome
capture, SNP arrays), new opportunities exist for the application of
genomic data for conservation (Allendorf, Hohenlohe, \& Luikart, 2010;
Ouborg, Pertoldi, Loeschcke, Bijlsma, \& Hedrick, 2010). While there is
discussion in the field as to whether there is effective uptake among
conservation practitioners (Shafer et al., 2015), there appears to be a
consensus that genomic approaches will provide additional benefits to
the field beyond traditional population genetics (Allendorf, Hohenlohe,
\& Luikart, 2010; Bradbury et al., 2015; Flanagan, Forester, Latch,
Aitken, \& Hoban, 2018; Funk, McKay, Hohenlohe, \& Allendorf, 2012;
Garner et al., 2016; McMahon, Teeling, \& H\selectlanguage{ngerman}öglund, 2014; Shafer et al.,
2015). This includes improving parameter estimates (Allendorf,
Hohenlohe, \& Luikart, 2010; Ouborg, Pertoldi, Loeschcke, Bijlsma, \&
Hedrick, 2010; Primmer, 2009), fine-scale resolution of population
clusters (Bradbury et al., 2015), and identifying locally adapted
populations to highlight important units for conservation (Flanagan,
Forester, Latch, Aitken, \& Hoban, 2018; Funk, McKay, Hohenlohe, \&
Allendorf, 2012; McMahon, Teeling, \& Höglund, 2014). Through the
accurate estimation of important parameters, and identifying locally
adapted units, we can use genomics to ensure resources are appropriately
allocated. This is important because practitioners are under pressure to
do more with less as threats to biodiversity increase (Naidoo et al.,
2006).

One of the major threats to biodiversity is climate change, the effects
of which are significantly impacting species in the Arctic (Turner \&
Overland, 2009). For example, polar bears (\emph{Ursus maritimus} ) have
been listed as Vulnerable by the IUCN Polar Bear Specialist Group as a
result of significant declines because of dwindling sea-ice habitat
(Armstrup, 2003, Wiig et al., 2015). Polar bears are Holarctic marine
mammals that are highly dependent on the sea ice as a platform for
movement, hunting, and mating. Currently, 23,000 individuals are thought
to exist in 19 subpopulations worldwide (Regehr et al., 2016);
approximately 50--65\% live in the 14 subpopulations lying partially or
entirely within Canadian jurisdiction (Durner, Laidre, \& York, 2018;
Wiig et al., 2015).

Genomic approaches have not been used for the analysis of contemporary
polar bear population structure at a continental level. To date,
large-scale studies of polar bear genetic structure have used only
14--21 microsatellite loci (Malenfant et al., 2016; Paetkau et al.,
1999; Peacock et al., 2015). These have shown four broad-scale genetic
clusters of polar bears worldwide, which are all represented in North
America by: the Beaufort Sea, the Canadian Arctic Archipelago, the
Hudson Bay Complex (incl. the Foxe Basin, Hudson Bay and the coasts of
Labrador and Newfoundland), and Norwegian Bay (Table 1). A number of
previous studies have shown additional differentiation within Hudson Bay
(Crompton, Obbard, Petersen, \& Wilson, 2008; Crompton, Obbard,
Petersen, \& Wilson, 2014; Peacock et al., 2015; Viengkone et al.,
2016), as well as east--west differentiation in the Canadian Arctic
Archipelago (Malenfant et al., 2016), including the differentiation of
the Gulf of Boothia subpopulation from the neighbouring M'Clintock
Channel subpopulation (Campagna et al., 2013; Malenfant et al., 2016). A
genomics approach to assessing neutral variation would provide a
consensus of population structure to base conservation decisions
(Bradbury et al., 2015; McCartney-Melstad, Vu, \& Shaffer, 2018).

While neutral population structure is important information for
directing conservation resources, the identification of locally adapted
units is also important to ensure their inclusion in conservation
efforts (Funk, McKay, Hohenlohe, \& Allendorf, 2012). The most divergent
cluster of polar bears occurs in Norwegian Bay: an isolated
subpopulation of \textasciitilde{}200 individuals in the High Arctic
(Taylor, Laake, McLoughlin, Cluff, \& Messier, 2009), which Inuit
harvesters have suggested are phenotypically distinct (Taylor et al.,
2001). However, genome scans for divergent loci among polar bear genetic
clusters have never been conducted, and the potential genetic basis for
any phenotypic differentiation is unknown despite the development of
methods to reliably test for local adaptation (e.g., Gautier, 2015; Luu,
Bazin, \& Blum, 2017; Whitlock \& Lotterhos, 2015). A genomics approach
will help determine whether any of the polar bear subpopulations harbour
local adaptations.

Movement patterns for polar bears may be changing as a result of sea-ice
loss, and understanding these patterns will be important for
understanding future demographic changes. Two recent microsatellite
studies drew differing conclusions regarding the potential movement
response of polar bears to climate-change-induced sea-ice loss. Peacock
et al. (2015) suggested that polar bears from the eastern Polar Basin
(i.e., the Eastern Greenland, Barents Sea, Kara Sea, and Laptev Sea
subpopulations) are migrating into the Canadian Arctic Archipelago to
take refuge from climate change-induced habitat loss. A reanalysis of
these data attributed this high apparent immigration rate to
non-convergence of the program BayesAss (Malenfant et al., 2016).
However, the reanalysis had limited resolving ability because only 14
microsatellite loci were used, making it difficult to completely
discount the possibility of high gene flow from the Polar Basin into the
Archipelago or to identify other large-scale migrations. The use of
high-resolution markers that provide more precise admixture estimates
would help to further test for migration events (including into North
America), which is important for determining how polar bear population
dynamics are being affected by current climate change.

Of course, current patterns of substructure and migration are more
informative in light of the population history (He, Edwards, \& Knowles,
2013; Epps \& Keyghobadi, 2015); however, the history of polar bear
migration into North America is unknown. Polar bear--brown bear
(\emph{Ursus arctos} ) divergence time is disputed (e.g., Hailer et al.,
2012; Liu et al., 2014; Miller et al., 2012), but we know that the
contemporary distribution of polar bears in North America was
established only after the last glacial maximum (LGM)
\textasciitilde{}20,000 years ago, which left most of the continent and
the Arctic Basin uninhabitable (Bradley \& England, 2008). The range of
polar bears during the LGM has not been determined; however, the
presence of polar bears' primary prey---ringed seals (\emph{Pusa
hispida} )---in caves of the Alaska Panhandle (i.e., Southeast Alaska)
(Heaton \& Grady, 2003), as well as the presence of polar bear
mitochondrial haplotypes and X-linked variants among some Panhandle
brown bears (Cahill et al., 2013), suggest that polar bears used the
Northeast Pacific as a refugium during this period. However,
mitochondrial haplotypes from modern polar bears are closer to Irish
brown bears than to Panhandle brown bears (Edwards et al., 2011), and an
Atlantic refugium is supported by polar bear subfossils from Scandinavia
immediately predating and postdating the LGM (Ingólfsson \& Wiig, 2009).
Likewise, winter sea ice as far south as Spain (de Vernal et al., 2005)
may have allowed for colonization across the Atlantic into North
America. Recent methodological advances using SNP data allow for the
determination of historical population divergences and subsequent
migration events (Pickrell \& Pritchard, 2012), though these have not
yet been applied to polar bears.

In this study, we use a 9K Illumina BeadChip (Malenfant, Coltman, \&
Davis, 2015) to determine the current population structure and
historical divergence of polar bears, and to identify evidence of local
adaptation. Our SNP assay interrogates restriction-site associated DNA
(RAD) SNPs---most of which are in non-coding DNA and are putatively
neutral---and potentially adaptive transcriptomic SNPs developed from
fat and blood allowing for the assessment of neutral and adaptive
genetic variation. This study provides an estimation of polar bear
history, and helps to clarify population structure and identify locally
adapted subpopulations. This contribution gives wildlife managers
important information for developing a conservation plan for this iconic
Arctic species.

\subsection*{Materials and methods}

{\label{materials-and-methods}}

\subsubsection*{SNP chip development and
genotyping}

{\label{snp-chip-development-and-genotyping}}

The SNP chip development, sampling, and genotyping process has been
described by René M. Malenfant et al. (2015). In brief, we used
high-throughput transcriptome and RAD sequencing to develop a 9K
Infinium iSelect HD BeadChip. Transcriptomic SNPs were detected in blood
and fat samples of 10 bears from Western Hudson Bay (Genomic Resources
Development Consortium et al., 2014), whereas RAD SNPs were developed
from 38 bears from across their circumpolar range (Malenfant et al.,
2015). To reduce effects of the SNP ascertainment process, RAD SNPs were
selected without consideration of the frequency or subpopulation in
which the SNP was detected in the ascertainment sample. In total, 3411
RAD SNPs and 2030 transcriptomic SNPs were found to be well-clustered
and polymorphic. We used 107 SNPs that displayed X-linked inheritance to
confirm the sex of all individuals. Genotype scores were highly
reliable: no errors were detected among 14 duplicate samples. The full
polar bear dataset comprised 1427 individuals genotyped at 5328 unique
autosomal SNPs; 860 of these samples came from the Western Hudson Bay
subpopulation, primarily for a quantitative genetics study (Malenfant,
2016).

\subsubsection*{Quality control}

{\label{quality-control}}

We used quality-control procedures similar to those of Jakobsson et al.
(2008). First, to select a subset of individuals for this population
genomics study, we excluded ten individuals whose genetic sex did not
match their reported sex (Malenfant et al., 2015) and four additional
individuals who had identical SNP (or microsatellite) genotypes to
different bears, suggesting unknown duplicate sampling, sample labelling
or laboratory errors. We excluded four samples collected from the Arctic
Basin subpopulation from this analysis because sample size was
inadequate to make inferences. We also excluded three Foxe Basin bears
lacking sampling coordinates because it was unclear if they were sampled
in northern or southern Foxe Basin. We excluded 101 individuals sampled
prior to 1990, which we selected as a cutoff date for inclusion in our
study. Because the presence of relatives can confound genetic clustering
analyses (Rodriguez-Ramilo \& Wang, 2012), we used KING 1.4 (Manichaikul
et al., 2010) to generate a subset of individuals containing no first-
or second-degree relatives (cf. Rosenberg, 2006). Finally, we used
the\emph{sample()} function in R 3.3.1 (R Core Team, 2016) to reduce the
sample from each geographic sampling location to a maximum of 30
individuals, resulting in a total of 391 samples from 16 locations for
the analyses (Table 1). These locations included subpopulations with
partial or full Canadian jurisdiction (\emph{N} = 13). For three of
these subpopulations, two geographic regions were included. To ensure we
obtained quality genotypes, samples that we chose were run on agarose
gel, and were subject to spectrophotometry and floutometry to ensure
they were of high quality molecular weight prior to genotyping.

Previous studies have shown north--south genetic differentiation in the
Foxe Basin, Davis Strait and Southern Hudson Bay subpopulations
(Crompton et al., 2008; Crompton et al., 2014; Malenfant et al., 2016;
Peacock et al., 2015; Viengkone et al., 2016), so we treated Northern
Foxe Basin, Southern Foxe Basin, Northern Davis Strait, Southern Davis
Strait, Southern Hudson Bay and James Bay (in Southern Hudson Bay) as
being distinct. The median collection date for the remaining 391 samples
was 2008 (range: 1991--2012; Table 1).

We removed all SNPs that had call rate \textless{}0.9 or that had a
minor allele frequency of \textless{}0.01 (cf. Huckins et al., 2014)
among the unrelated individuals. Preliminary analyses suggested
non-significant overall \emph{F} \textsubscript{IS} in the Baffin Bay,
Southern Beaufort Sea, and southern Foxe Basin sampling locations. We
used hweStrata 1.0 to perform exact stratified tests (Schaid, Batzler,
Jenkins, \& Hildebrandt, 2006) across Baffin Bay, Southern Beaufort Sea,
and southern Foxe Basin to identify loci that deviated from
Hardy--Weinberg equilibrium; however, no locus deviated significantly
(\selectlanguage{greek}\emph{α =}\selectlanguage{english} 0.05). Because Bayesian clustering methods assume that
linkage disequilibrium (LD) is generated by population structure rather
than background LD caused by physical linkage between loci, we generated
an ``LD-pruned dataset'' from the filtered RAD SNPs, using pairwise LD
pruning (--indep-pairwise 10 1 0.1) in a customized version of PLINK
1.07 (Ren\selectlanguage{ngerman}é M. Malenfant et al., 2015; Purcell et al., 2007). Unless
otherwise indicated, all subsequent analyses used this LD-pruned RAD
dataset of 2770 SNPs genotyped in 391 unrelated individuals. We used
GenoDive 2.0b27 (Meirmans \& Van Tienderen, 2004) to calculate genetic
diversity statistics (using 1000 permutations to test for significance)
and ADZE 1.0 (Szpiech, Jakobsson, \& Rosenberg, 2008) to calculate
allelic richness with a per-location missing-data tolerance of 1\%,
which resulted in the maximum possible sample size.

\subsubsection*{Population structure}

{\label{population-structure}}

To explore population structure using a non-model-based method, we used
a principal component analysis (PCA) in adegenet 2.0.1 with default
settings (Jombart, 2008; Jombart \& Ahmed, 2011). The number of
significant principal components (PCs) was determined using Velicer's
minimum average partial test (Shriner, 2011; Velicer, 1976). We then
performed Bayesian genetic clustering using BAPS 6.0, which also
assesses the significance of admixture and is much faster than other
Bayesian methods such as structure (Corander \& Marttinen, 2006).
Population structure was estimated in BAPS in three phases: first, the
optimal number of genetic clusters (\emph{K} \textsubscript{max} = 20)
was determined in a mixture analysis using stochastic partitioning
(Corander, Marttinen, \& Mäntyniemi, 2006); then, admixture coefficients
for each individual were estimated by averaging their maximum \emph{a
posteriori} estimates obtained from each of 100 realizations of allele
frequencies simulated from the posterior distribution from mixture
analysis (Corander \& Marttinen, 2006); finally, a \emph{p} -value for
the significance of admixture for each individual was obtained by
comparison to an empirical distribution of admixture coefficients for
200 simulated reference individuals from each cluster (generated by
averaging over 20 realizations of simulated allele frequencies).
Admixture plots were created using distruct 1.1 (Rosenberg, 2004).
Mixture analysis determined four clusters as being optimal, which
roughly corresponded to the Canadian Arctic Archipelago, the Beaufort
Sea, the Hudson Bay Complex, and Norwegian Bay. The 323 individuals that
were not significantly admixed (\emph{p} \selectlanguage{english}[?] 0.05) were assigned to four
cluster-specific datasets (\emph{n} \textsubscript{Archipelago} =
121,\emph{n} \textsubscript{Beaufort} = 53, \emph{n}
\textsubscript{Hudson} = 137, \emph{n} \textsubscript{Norwegian Bay} =
12). We then estimated allelic richness and the private allelic richness
for each of these cluster-specific datasets using the rarefaction method
implemented in ADZE, using a per-cluster missing-data tolerance of
0.1\%, which guaranteed the maximum possible sample size. Our
cluster-specific datasets were used as the pre-defined populations for
outlier analyses. (See below.)

To test for more subtle population structure, we used tess3r 1.10, which
is more powerful than non-spatial methods because it incorporates
geographical sampling information to produce accurate least-squares
estimates of admixture coefficients (Caye, Jay, Michel, \& Francois,
2016). We ran tess3r from \emph{K} =1--20 for ten repetitions for each
\emph{K} -value and a tolerance threshold of 1x10\textsuperscript{--7}.
The optimal \emph{K} -value was determined with the cross-entropy
criterion using cross-validation with 5\% of genotypes masked.

We characterized the genetic diversity of polar bears across their range
using an analysis of molecular variance (AMOVA; Excoffier, Smouse, \&
Quattro, 1992) in GenoDive, which, as a first step, uses\emph{K} -means
clustering to generate hierarchical structure (Meirmans, 2012). To force
all 16 sampling locations into the four major clusters identified by
clustering of individuals in BAPS, we used \emph{K} = 4 for \emph{K}
-means clustering. To determine pairwise genetic differentiation between
locations for comparison with previous studies (R. M. Malenfant et al.,
2016; Paetkau et al., 1999; Peacock et al., 2015), we then used a custom
R script to calculate Reynolds' distance (Reynolds, Weir, \& Cockerham,
1983), a coancestry estimator that assumes that populations diverge by
drift without mutation, and Hudson's\emph{F} \textsubscript{ST} (Hudson,
Slatkin, \& Maddison, 1992), an unbiased measure of differentiation for
SNPs that is robust to unequal sample sizes (Bhatia, Patterson,
Sankararaman, \& Price, 2013). Significance was assessed using 10,000
permutations. Finally we performed an assignment test in GenoDive
(default settings, 1000 permutations) to identify recent migrants and
the extent of admixture among the subpopulations.

\subsubsection*{Outlier detection}

{\label{outlier-detection}}

To identify RAD or transcriptomic loci that may have undergone
selection, we used three recent approaches: BayPass 2.1 (Gautier, 2015),
pcadapt 3.0.3 (Luu et al., 2017), and OutFLANK 0.1 (Whitlock \&
Lotterhos, 2015). Each of these methods takes a very different approach
to outlier identification, and by looking for concordance between
results, we hoped to reduce the number of false positives. In total,
4912 non-LD pruned loci were tested.

BayPass calculates the \emph{X\textsuperscript{T}X} statistic (Gunther
\& Coop, 2013), which is analogous to\emph{F} \textsubscript{ST}, but
which accounts for genetic variance--covariance of the populations and
differences in sample size because it is based on standardized allele
frequencies. After calculating \emph{X\textsuperscript{T}X} values for
the original dataset, a pseudo-observed dataset (POD) of 1000 loci is
created using the variance--covariance matrix estimated from the real
data, and\emph{X\textsuperscript{T}X} values are calculated for the POD.
Empirical \emph{X\textsuperscript{T}X} values from the POD are used as a
null distribution; its 99\textsuperscript{th} percentile is a suggested
cutoff for identifying loci in the real dataset that may be undergoing
selection. We used default settings for all steps.

pcadapt generates a vector for each SNP which contains a\emph{z} -score
for each of the \emph{K} PCs retained. The Mahalanobis distance for each
SNP is calculated, and \emph{p} -values are obtained assuming that
distances are \selectlanguage{english}?\textsuperscript{2}-distributed with\emph{K} degrees of
freedom. We used default settings and retained five PCs (based on the
results of Velicer's minimum average partial test described above).

OutFLANK assumes that \emph{F} \textsubscript{ST} is
\selectlanguage{english}?\textsuperscript{2}-distributed with unknown degrees of freedom. Loci
in the 5\% tails of the distribution of \emph{F} \textsubscript{ST},
which is calculated without a sample-size correction, are removed, then
the degrees of freedom are estimated using maximum likelihood, and
a\emph{p} -value is obtained for each locus. We used the method of
Francois, Martins, Caye, and Schoville (2016), which takes into
consideration genomic inflation, to combine \emph{p} -values from
pcadapt and OutFLANK. Significance was assessed using a false-discovery
rate (FDR) cut-off of \emph{q} \textless{} 0.1.

We identified 19 loci as significant outliers (see Results). To test
whether outlier loci were over-represented in any biological pathways,
we assessed gene ontology (GO) enrichment using SNP2GO 1.0.5 (FDR = 0.1;
Szkiba, Kapun, von Haeseler, \& Gallach, 2014). GO information was
obtained from BioMart's (Kasprzyk, 2011) ailMel1 gene set for panda
(last accessed Sept. 4, 2014), and mapped onto NCBI \emph{Ursus
maritimus} Annotation Release 100.

\subsubsection*{}

{\label{section}}

\subsubsection*{Historical divergence}

{\label{historical-divergence}}

To examine historical divergence of polar bears from each sampling area
and subsequent migration among them, we used TreeMix 1.12 (Pickrell \&
Pritchard, 2012), exploring 0--5 migration events. We rooted the tree
using SNP chip genotypes obtained from a single grizzly bear
(\emph{Ursus arctos horribilis} ) sampled from Kootenay National Park
(British Columbia, Canada). Of the 2770 LD-pruned RAD SNPs, 2740 loci
were genotyped in the grizzly bear sample, of which 94 loci were
heterozygous. Because the TreeMix algorithm is sensitive to recent
admixture and migration, we excluded admixed individuals based on the
assignment test using GenoDive. After excluding all significantly
admixed individuals and significantly cross-assigned individuals, 317
individuals remained for TreeMix analysis. We also attempted to
determine current migration rates using BayesAss; however, the program
did not converge (cf. Meirmans, 2014).

\subsection*{Results}

{\label{results}}

\subsubsection*{SNP genotyping}

{\label{snp-genotyping}}

After MAF-filtering and LD-pruning, 2770 RAD SNPs remained on 247
scaffolds representing 92.4\% of the draft polar bear genome (Liu et
al., 2014). Sample size per location ranged from 16 to 30 (Table 1). The
inbreeding coefficient (\emph{G} \textsubscript{IS}) was significant in
samples from 10 out of 16 locations (9 out of 16 after Holm correction
(Holm, 1979)); however, in all cases but one, \emph{G}
\textsubscript{IS}values were low (\selectlanguage{english}[?]0.02). Norwegian Bay displayed
moderate\emph{G} \textsubscript{IS} (=0.05) consistent with the presence
of immigrants near its southern border. (See below.) Allelic richness
was slightly lower in among sampling sites in Norwegian Bay and the
Hudson Bay Complex than in Canadian Arctic Archipelago or the Beaufort
Sea (Table 1).

\subsubsection*{Population structure}

{\label{population-structure-1}}

We retained the first five PCs when examining population structure based
on Velicer's minimum average partial test. Collectively, these PCs
accounted for 8.2\% of the observed genetic variation. Plots of PC 1 vs.
PCs 2--5 are shown in Figure 1. PCs 1--3 distinguish the four primary
clusters of polar bears identified by Paetkau et al. (1999); PCs 4 and 5
highlight east--west differentiation in the Archipelago (e.g., Malenfant
et al., 2016) and north--south differentiation in the Hudson Bay
Complex, including the divergence of James Bay from Southern Hudson Bay
(Crompton et al., 2008).

BAPS identified four genetic clusters as being optimal, which correspond
roughly to the Hudson Bay Complex, the Canadian Archipelago, the
Beaufort Sea, and Norwegian Bay (Table 1 lists the sampling sites
belonging to each cluster). Substantial admixture was observed in Davis
Strait (particularly in northern Davis Strait), southern and western
Norwegian Bay and in Viscount Melville Sound, with lower rates of
admixture also observed in Gulf of Boothia (Figure 2). \emph{K} -means
clustering in GenoDive (using \emph{K} = 4) generated clusters of
geographic sampling areas concordant with those identified by BAPS. In
an AMOVA, genetic clusters accounted for 3.5\% of total genetic
variation (\emph{F} \textsubscript{CT} = 0.035) and
subpopulations/sampling areas within clusters accounted for 1.0\% of
total genetic variation (\emph{F\textsubscript{SC}} = 0.011).

tess3r's cross-validation procedure suggested 5--8 genetic clusters were
optimal (Figure 3; top-left panel); however, results for\emph{K} = 5--9
were plotted, because nine clusters captured all previously reported
genetic differentiation amongst polar bears. The nine clusters
correspond roughly to: 1) the Beaufort Sea (northern and southern); 2)
the western Archipelago (Viscount Mellville Sound, M'Clintock Channel);
3) Gulf of Boothia; 4) the Eastern Archipelago (Lancaster Sound, Kane
Basin, Baffin Bay); 5) Norwegian Bay; 6) southern Davis Strait (incl.
Hudson Strait); 7) northern Foxe Basin; 8) Hudson Bay (southern Foxe
Basin, Southern Hudson Bay, Western Hudson Bay); 9) James Bay. At
\emph{K} = 10, a discontiguous cluster containing parts of Lancaster
Sound and part of Viscount Melville Sound appeared, and therefore
\emph{K} -values greater than 9 were not considered.

\subsubsection*{Diversity and
differentiation}

{\label{diversity-and-differentiation}}

Patterns of allelic richness for SNPs revealed that overall richness was
greatest in the Archipelago; however, few alleles were private to this
cluster (Figure S1). Private allelic diversity was highest in the
Beaufort Sea. Norwegian Bay had significantly lower levels of both
allelic richness and private allelic richness than any other cluster.
These observed patterns across clusters were not caused by our SNP
ascertainment scheme, as rarefaction analyses using microsatellites
revealed a similar pattern.

Pairwise Hudson's \emph{F} \textsubscript{ST} values and Reynolds'
distances (\selectlanguage{greek}\emph{θ} \selectlanguage{english}\textsubscript{W}) suggest low differentiation
between sampling locations within a cluster (\emph{F}
\textsubscript{ST}[?] 0.03, \selectlanguage{greek}\emph{θ} \selectlanguage{english}\textsubscript{W} [?] 0.05) and
low--moderate differentiation between clusters (\emph{F}
\textsubscript{ST} [?] 0.10,\selectlanguage{greek}\emph{θ} \selectlanguage{english}\textsubscript{W} [?] 0.12). The most
divergent pair of locations was Norwegian Bay in the High Arctic vs.
James Bay at the southernmost limit of the polar bear range. \emph{F}
\textsubscript{ST} values did not seem to be affected by ascertainment
scheme: corresponding values calculated using microsatellites were
highly correlated and did not differ significantly in a paired \emph{t}
-test (Malenfant, 2016). Except for the Northern and Southern Beaufort
Sea (\emph{p} = 0.26), all other pairs were significantly differentiated
(with or without a Holm correction); however, sampling across the border
of these two subpopulations was also much more continuous than was the
sampling across any of the other borders (Figure 2).

\subsubsection*{Migration rates}

{\label{migration-rates}}

In an assignment test, 221/391 (=56.5\%) of individuals were assigned to
their home sampling location, and 359/391 (=91.8\%) were assigned to a
location within their broad genetic cluster. Rather than suggesting
large-scale migration of bears amongst locations within a cluster, the
low home-location assignment results likely reflect the inability of our
genetic data to discriminate individuals from neighbouring locations.
However, only 7/391 individuals (=1.8\%) were significantly
cross-assigned (i.e., significantly identified as migrants). These
results were consistent with migrants that are apparent in the BAPS
results (Figure 2). We found no substantial Polar Basin ancestry
(represented by the Beaufort cluster in our sample) in the eastern
Canadian Arctic Archipelago.

\subsubsection*{Outlier analysis}

{\label{outlier-analysis}}

We identified 17 SNPs (three transcriptomic and 14 RAD) as significant
outliers (\emph{q} \textless{} 0.1) using the combined OutFLANK and
pcadapt \emph{p} -values (Figure 5; Table 4). Eight of the 17 SNPs were
in a gene: The two top-ranked SNPs were both located in a 3'
untranslated region (UTR); five SNPs were in an intron, and one SNP was
in a long non-coding RNA. Another four SNPs were located within 10 kb of
a gene, corresponding to LD of \emph{r} \textsuperscript{2} = 0.25--0.35
in polar bears (Malenfant et al., 2015).\emph{X\textsuperscript{T}X}
values for 16 of the 17 significant outlier loci also fell into the
upper 1\% tail of simulated neutral values calculated using BayPass; the
remaining locus (88377\_240) had the third highest \emph{p} -value of
all the putative outliers and was also the SNP most distant from any
gene (147,780 bp downstream of the closest gene). Outlier SNPs better
distinguished Norwegian Bay from the remaining genetic clusters than did
the pruned RAD SNP dataset (Figure 6; cf. Figure 1). One transcriptomic
SNP, scaffold62\_6020088, located in the 3' UTR of the gene PDLIM5 (PDZ
and LIM domain 5) had a substantially lower \emph{p} -value than any
other locus (\emph{p} = 1.5\selectlanguage{ngerman}×10\textsuperscript{--9}). The globally minor
allele of this SNP reached a frequency of 0.91 in the Norwegian Bay
cluster but did not exceed 0.10 elsewhere. Likewise, for the second-most
significant SNP (RAD SNP 85370\_147; \emph{p} =
3.9×10\textsuperscript{--7}) in the 3' UTR of the gene SPECC1 (sperm
antigen with calponin homology and coiled-coil domains 1), the globally
minor allele had a frequency of \textgreater{}0.70 in Norwegian Bay, but
\textless{}0.1 elsewhere (incl. a frequency of 0.0 in Hudson Bay). In
both cases, the Kootenay grizzly was homozygous for the allele that was
most common in Norwegian Bay. SNP2GO did not detect any significant GO
enrichment using all 17 outlier loci as candidates or using the 16 loci
recognized consistently across methods (i.e., excluding 88377\_240).

\subsubsection*{Historical migration}

{\label{historical-migration}}

After removing significantly cross-assigned and admixed individuals,
TreeMix generated a tree showing dispersal from the Beaufort Sea through
the Archipelago into the Hudson Bay Complex (Figure 4). The tree also
suggests that Norwegian Bay has undergone substantial drift since its
divergence from its neighbouring Archipelago sampling locations. The
single migration event in the tree was from the Southern Beaufort Sea
into Viscount Melville Sound (weight = 0.40, \emph{p}\textless{}
1×10\textsuperscript{--10}), which is consistent with observed admixture
in Viscount Melville Sound, and which may have resulted from residual
admixture in individuals that were retained because their admixtures
coefficients failed to reach statistical significance. We chose to
display only one migration event, because allowing for additional
migration suggested gene flow from James Bay into grizzly bears (Reich,
Thangaraj, Patterson, Price, \& Singh, 2009), and which may represent an
artefact caused by cross-species application of the grizzly bear on the
SNP array. TreeMix results including a single-migration event explained
98.8\% of the variance in the data.

\subsection*{Discussion}

{\label{discussion}}

Our genomic analysis of polar bears has increased our understanding of
this iconic Arctic species. We have been able to provide an
understanding of population history, assess both large-scale and
fine-scale population structure, estimate current levels of migration,
and identify locally adapated populations. This information will be
important for the future management and conservation of the polar bear,
a species that is particularly vulnerable to loss of critical sea ice
habitat as a result of rapid climate change (Hunter et al., 2010; Wiig
et al., 2015).

\subsubsection*{}

{\label{section-1}}

\subsubsection*{Neutral population
structure}

{\label{neutral-population-structure}}

Our study supports the primary findings of previous population genetics
studies of polar bears that have used low-resolution microsatellite
marker sets (Malenfant et al., 2016; Paetkau et al., 1999), as well as
our own preliminary population genetics study using these SNPs on a
subset of six individuals per subpopulation (Malenfant et al., 2015).
All of the clustering methods (BAPS, PCA, and tess3r) detected the four
major genetic clusters of polar bears in North America, corresponding to
the Hudson Bay Complex, the Canadian Arctic Archipelago, the Beaufort
Sea, and Norwegian Bay. Additonal fine-scale clusters were identified
using the more powerful spatial method of tess3r, which simultaneously
detected all genetic clusters among North American polar bears that have
previously been reported (Campagna et al., 2013; Crompton et al., 2008;
Crompton et al., 2014; Malenfant et al., 2016; Viengkone et al., 2016).
All subpopulations (including those subdivided into two sampling
locations constructed for this study: northern and southern Davis
Strait, northern and southern Foxe Basin, and James Bay and southern
Hudson Bay) were significantly differentiated from each other, except
for the Northern and Southern Beaufort Sea. The lack of distinction
between these subpopulations may be caused by our continuous sampling
distribution across their management boundary.

Overall, the four major regions were slightly--moderately differentiated
(\emph{F} \textsubscript{CT} = 0.035), and demarcations between clusters
align with geographical features---such as the presence of land or thick
multiyear ice. Norwegian Bay is surrounded by islands to the north,
south, and east, and by polynyas in the south and thick ice to the west,
which limit exchange with neighbouring subpopulations (Taylor et al.,
2001). The Beaufort Sea and the Archipelago are separated by dense
multiyear ice, which forms a barrier to movement (Bethke, Taylor,
Amstrup, \& Messier, 1996). However, there is some overlap of bears from
the Northern Beaufort Sea and Viscount Melville Sound when individuals
mix on the pack ice together (Taylor et al., 2001), thus resulting in
the admixture in Viscount Melville Sound observed in our study. The
Hudson Bay Complex is separated from the Archipelago by land to the
north and east. Substantial Hudson Bay--Archipelago admixture was
observed in northern Davis Strait, corresponding to observed movements
between southern and northern Davis Strait and between northern Davis
Strait and Baffin Bay (Peacock, Taylor, Laake, \& Stirling, 2013).

\subsubsection*{Adaptive population
structure}

{\label{adaptive-population-structure}}

Using a conservative approach, we detected 17 putative outlier loci,
indicating that divergent selection may have occurred between polar bear
genetic clusters across the North American Arctic, although it is
difficult to rule out past demographic events such as bottlenecks and
range expansions (e.g., Hofer, Ray, Wegmann, \& Excoffier, 2009;
Lotterhos \& Whitlock, 2014). Outlier loci distinguished Norwegian Bay
more strongly from remaining clusters than did our RAD SNP dataset, and
lower allelic richness and private allelic richness (as seen in
rarefaction analyses) suggest the loss of genetic diversity owing to
inbreeding and drift. Lack of significant GO enrichment for outlier SNPs
suggests that if selection is occurring, it is not targeting genes in a
subset of pathways, or that the gene coverage of our SNP panel is
inadequate to detect this. However, the top two outlier loci are of
particular interest: both are in 3' UTRs, which contain regions that
regulate gene expression (Matoulkova, Michalova, Vejtesek, \& Hrstka,
2012).

The top-ranked SNP (scaffold62\_6020088) is in the gene PDLIM5, which is
expressed in striated muscle, is believed to play a role in heart
development by modulating cardiomyocyte expansion (Bang et al., 2014),
and which has been associated with dilated cardiomyopathy in mice (Cheng
et al., 2010). Since their divergence with brown bears,
cardiomyopathy-related genes have experienced the strongest selection in
polar bears, possibly causing a reorganization of the circulatory system
related to selective pressure for long-distance swimming (Liu et al.,
2014). The brown bear allele is found at high frequency in Norwegian
Bay, we suggest it is because of the lack of long-distance swimming or
migration in this subpopulation: as an isolated region characterized by
thick multiyear ice, long-range swimming is generally unnecessary as
most individuals restrict themselves to small bays, fiords, and coastal
tide cracks in the east (Taylor et al., 2001; Taylor et al., 2009).
Therefore---as opposed to elsewhere in their range---there may have been
little selection in Norwegian Bay for genes related to the
cardiovascular system. The second-highest-ranked SNP (85370\_147) is in
the gene SPECC1, which is highly expressed in the testes and likely
produces a sperm antigen (Sang, Fath, \& Giordano, 2004). The grizzly
bear allele is frequent among Norwegian Bay bears, and it is possible
that the isolation and persistence of the Norwegian Bay cluster (in the
face of some observed migration and admixture) is partially attributable
to immunological subfertility related to anti-sperm antibodies, which
has been posited as a cause of postmating, prezygotic isolation (Howard,
Palumbi, Birge, \& Manier, 2009).

Norwegian Bay is expected to be one of the last Arctic sea-ice areas and
may become a refugium for polar bears if climate change continues
(Derocher, 2012); however, it has not been surveyed since 1997 (when our
samples were collected), and the current status of this unique genetic
cluster is unknown. Because of rapid LD decay in polar bears (Malenfant
et al., 2015), our limited scan for differentiated loci is also unlikely
to have captured the full array of potentially adaptive differences
among subpopulations. Because local adaptation is considered when
defining evolutionarily significant units (e.g., Crandall,
Bininda-Emonds, Mace, \& Wayne, 2000; Waples, 1991) and designatable
units for conservation under Canadian law (COSEWIC, 2015), the potential
adaptive divergence between Norwegian Bay and the other subpopulations
needs to be considered. Further tests using more recent samples should
be conducted to determine the degree to which Norwegian Bay may be
locally adapted and reproductively isolated or has merely been subject
to loss of adaptive diversity.



\section*{\texorpdfstring{\emph{Polar bear
migration}}{Polar bear migration}}

{\label{polar-bear-migration}}

Although polar bears are capable of dispersing long distances (e.g.,
Durner \& Amstrup, 1995), evidence of long-distance migration between
the four major genetic clusters was limited in our data. We detected a
single (male) migrant from the Beaufort Sea (or possible from elsewhere
in the Polar Basin genetic cluster) in the Gulf of Boothia, and a small
number of individuals of Norwegian Bay ancestry who had apparently
migrated to the neighbouring Lancaster Sound and Kane Basin
subpopulations. Amongst our samples from northern Davis Strait, Baffin
Bay, and Kane Basin, which are the most likely subpopulations to receive
immigrants from the eastern Polar Basin, and which were very recently
sampled (median years of collection: 2010--2012), we did not detect any
Polar Basin immigrants or any significant ancestry from the global Polar
Basin cluster (which is reflected in our sample by the Beaufort Sea
subpopulations). Although our conclusion would be bolstered by further
sampling from the Eastern Polar Basin (e.g., Barents Sea, Eastern
Greenland), there is no evidence in this study to suggest that the
Canadian Arctic Archipelago is genetically or demographically linked
with the Eastern Polar Basin. Limited migration among the four clusters
suggests they should currently be managed as independent units.

\subsubsection*{}

{\label{section-3}}

\subsubsection*{Historical migration of polar bears into North
America}

{\label{historical-migration-of-polar-bears-into-north-america}}

Our TreeMix results suggest that polar bears populated North America
from the northwest, migrating from the Beaufort Sea through the Canadian
Archipelago into the High Arctic and Baffin Bay, then south through
Davis Strait and into the Hudson Bay Complex. Based on the oldest known
Canadian subfossil---undated Pleistocene remains from Baillie Island,
Northwest Territories (Vincent, 1989)---polar bears were present in the
Beaufort Sea by the late Pleistocene (i.e., \selectlanguage{english}[?]11,700 years ago), and
their subsequent migration into North America would be consistent with
the breakup sequences of the Laurentide and Innuitian ice sheets (Dyke,
2004). Ice sheets had receded in most of the Archipelago and Norwegian
Bay 9500 years ago and in most of Hudson Bay by 8400 years ago (Dyke,
2004), making the regions habitable for polar bears and their prey.
However, our sampling did not include bears from northern Europe or
Asia, and this question should be re-examined using global sampling.

\textbf{Conclusion}

Genomic data are increasingly being used to study wildlife populations,
but the uptake to conservation research has been slow (or unrecognized
Garner \emph{et al.} 2016). Here we have developed a SNP dataset that is
capable of examining a range of important questions for the conservation
and management of polar bears. While our current study has been limited
to the Canadian population, this encompasses well over half of the
global population. Through careful consideration of SNP ascertainment,
sample inclusion, and analytical approaches, we have provided a
comprehensive overview of the status of polar bear subpopulations at a
continental scale. The resources we have developed can be applied to a
global analysis of polar bear subpopulations to develop a comprehensive
management strategy that transcends geo-political boundaries.

\subsection*{}

{\label{section-4}}

\subsection*{Acknowledgements}

{\label{acknowledgements}}

The authors thank Julian Dupuis, Josh Miller, Michelle Viengkone, Dennis
Andriashek, Marsha Branigan, Mike Harte, John Woods, Ian Hatter, Sarah
Boyle, Parks Canada, the West Slopes Bear Research Project, and
Inuvialuit harvesters for their contributions to this project. The
authors would also like to thank the anonymous reviewers that resulted
in an improved manuscript. This project was funded by grants to CSD from
Environment and Climate Change Canada (Grant ID 1009997) and the World
Wildlife Fund (Contract number G-1213-015-00-D) and by an NSERC
Discovery Grant to DWC (Grant ID 312207-2011). RMM was funded by
scholarships from Alberta Innovates-Technology Futures, the University
of Alberta, and the Province of Alberta.

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\textbf{Data Accessibility}

The data that support the findings of this study (e.g., SNP genotypes
for all 391 polar bears plus the Kootenay grizzly and lat/lon
coordinates of individuals where known) are openly available in Dryad at
https://doi.org/10.5061/dryad.rbnzs7h7m

\textbf{Author Contributions}

RMM conducted all analyses. CSD conceived the initial study design and
prepared samples for genotyping. RMM, CSD, and DWC collaborated in
determining the nature of the analyses. ESR, MGD, NJL, MO, JP, SNA, VS,
KLL, ØW, and EWB provided samples and support, and they assisted in
interpretation of results. RMM and CIC drafted the manuscript. All
authors read and approved the final manuscript.

Table 1. Summary statistics for LD-pruned SNPs in each of 16 sampling
locations; the officially recognized Davis Strait, Foxe Basin, and
Southern Hudson Bay subpopulations have been further subdivided into
northern and southern areas based on previous genetic studies. The
broad-scale genetic clusters (``Clusters'') have been included for
reference. \emph{n} = sample size; \emph{YOC} = median year of
collection; \emph{R} (32) = allelic richness calculated in ADZE using a
standardized sample size of 32 gene copies; \emph{H\textsubscript{O}} =
observed heterozygosity; \emph{H\textsubscript{E}} = expected
heterozygosity; \emph{G} \textsubscript{IS} = inbreeding coefficient.
Significant \emph{G} \textsubscript{IS} values (\emph{p} \textless{}
0.05, determined using 1000 permutations) are bolded.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Sampling location} & \textbf{Clusters} & n & YOC &
\emph{R}\textbf{(32)} & \emph{H}\textsubscript{\textbf{O}} &
\emph{H}\textsubscript{\textbf{E}} &
\emph{G}\textsubscript{\textbf{IS}}\tabularnewline
\midrule
\endhead
Baffin Bay (BB) & Archipelago & 25 & 2010 & 1.82 & 0.231 & 0.232 &
0.002\tabularnewline
Davis Strait -- northern (nDS) & Archipelago & 27 & 2010 & 1.82 & 0.230
& 0.232 & \textbf{0.008}\tabularnewline
Davis Strait -- southern (sDS) & Hudson Bay & 21 & 1993 & 1.79 & 0.223 &
0.227 & \textbf{0.017}\tabularnewline
Foxe Basin -- northern (nFB) & Hudson Bay & 30 & 2008 & 1.78 & 0.219 &
0.224 & \textbf{0.020}\tabularnewline
Foxe Basin -- southern (sFB) & Hudson Bay & 30 & 2010 & 1.76 & 0.222 &
0.222 & 0.001\tabularnewline
Gulf of Boothia (GB) & Archipelago & 24 & 2010 & 1.83 & 0.232 & 0.233 &
\textbf{0.006}\tabularnewline
Kane Basin (KB) & Archipelago & 22 & 2012 & 1.82 & 0.233 & 0.235 &
0.006\tabularnewline
Lancaster Sound (LS) & Archipelago & 28 & 2010 & 1.82 & 0.229 & 0.232 &
\textbf{0.013}\tabularnewline
M'Clintock Channel (MC) & Archipelago & 22 & 2000 & 1.79 & 0.226 & 0.230
& \textbf{0.018}\tabularnewline
Northern Beaufort Sea (NB) & Beaufort Sea & 25 & 2005 & 1.81 & 0.231 &
0.232 & 0.002\tabularnewline
Norwegian Bay (NW) & Norwegian Bay & 17 & 1997 & 1.74 & 0.201 & 0.212 &
\textbf{0.052}\tabularnewline
Southern Beaufort Sea (SB) & Beaufort Sea & 27 & 2005 & 1.80 & 0.229 &
0.230 & 0.004\tabularnewline
Southern Hudson Bay -- proper (SH) & Hudson Bay & 30 & 2007 & 1.73 &
0.218 & 0.220 & 0.007\tabularnewline
Southern Hudson Bay -- James Bay (JB) & Hudson Bay & 16 & 2008 & 1.72 &
0.213 & 0.218 & \textbf{0.024}\tabularnewline
Viscount Melville Sound (VM) & Archipelago & 17 & 2010 & 1.82 & 0.230 &
0.233 & \textbf{0.013}\tabularnewline
Western Hudson Bay (WH) & Hudson Bay & 30 & 2000 & 1.73 & 0.215 & 0.220
& \textbf{0.020}\tabularnewline
\bottomrule
\end{longtable}

Table 2. Hierarchical AMOVA for 391 polar bears from 16 sampling
locations grouped into four regions identified by \emph{K} -means
clustering of locations in GenoDive.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Source of variation} & \textbf{\% variance} &
\emph{F}\textbf{-statistic} & \emph{F}\textbf{-value (95\%
C.I.)}\tabularnewline
\midrule
\endhead
Within individuals & 94.4\% & \emph{F}\textsubscript{IT} & 0.056
(0.054--0.059)\tabularnewline
Among individuals & 1.1\% & \emph{F}\textsubscript{IS} & 0.012
(0.009--0.014)\tabularnewline
Among locations & 1.0\% & \emph{F}\textsubscript{SC} & 0.011
(0.011--0.011)\tabularnewline
Among clusters & 3.5\% & \emph{F}\textsubscript{CT} & 0.035
(0.033--0.037)\tabularnewline
\bottomrule
\end{longtable}

Table 3. Pairwise Hudson's \emph{F} \textsubscript{ST} values (below
diagonal) and Reynolds' distances (above diagonal). Location
abbreviations are as in Table 1. Significance was assessed using 10,000
permutations, and \emph{non-significant} values are indicated with
asterisks. Colouring indicates each of the four clusters identified
by\emph{K} -means clustering in GenoDive; Beaufort sea -- red, Norwegian
Bay -- orange, Archipelago -- green, Hudson Bay -- blue.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllllllll@{}}
\toprule
& \textbf{SB} & \textbf{NB} & \textbf{NW} & \textbf{VM} & \textbf{MC} &
\textbf{GB} & \textbf{LS} & \textbf{KB} & \textbf{BB} & \textbf{nDS} &
\textbf{sDS} & \textbf{nFB} & \textbf{sFB} & \textbf{WH} & \textbf{SH} &
\textbf{JB}\tabularnewline
\midrule
\endhead
\textbf{SB} & -- & 0.02\textsuperscript{*} & 0.09 & 0.05 & 0.06 & 0.06 &
0.05 & 0.05 & 0.06 & 0.06 & 0.07 & 0.07 & 0.08 & 0.09 & 0.09 &
0.10\tabularnewline
\textbf{NB} & 0.00\textsuperscript{*} & -- & 0.09 & 0.04 & 0.06 & 0.05 &
0.05 & 0.05 & 0.05 & 0.05 & 0.07 & 0.07 & 0.08 & 0.08 & 0.09 &
0.10\tabularnewline
\textbf{NW} & 0.07 & 0.07 & -- & 0.08 & 0.08 & 0.07 & 0.06 & 0.06 & 0.08
& 0.08 & 0.09 & 0.10 & 0.11 & 0.11 & 0.11 & 0.12\tabularnewline
\textbf{VM} & 0.02 & 0.02 & 0.05 & -- & 0.04 & 0.04 & 0.04 & 0.04 & 0.05
& 0.05 & 0.07 & 0.07 & 0.08 & 0.08 & 0.09 & 0.10\tabularnewline
\textbf{MC} & 0.04 & 0.04 & 0.05 & 0.01 & -- & 0.04 & 0.03 & 0.04 & 0.04
& 0.05 & 0.06 & 0.06 & 0.07 & 0.08 & 0.08 & 0.09\tabularnewline
\textbf{GB} & 0.04 & 0.03 & 0.05 & 0.02 & 0.01 & -- & 0.03 & 0.03 & 0.03
& 0.03 & 0.04 & 0.04 & 0.05 & 0.05 & 0.06 & 0.07\tabularnewline
\textbf{LS} & 0.03 & 0.03 & 0.04 & 0.01 & 0.01 & 0.01 & -- & 0.02 & 0.02
& 0.03 & 0.05 & 0.05 & 0.06 & 0.06 & 0.07 & 0.08\tabularnewline
\textbf{KB} & 0.03 & 0.03 & 0.04 & 0.02 & 0.02 & 0.01 & 0.00 & -- & 0.02
& 0.03 & 0.05 & 0.05 & 0.06 & 0.07 & 0.07 & 0.08\tabularnewline
\textbf{BB} & 0.04 & 0.03 & 0.05 & 0.02 & 0.02 & 0.01 & 0.01 & 0.00 & --
& 0.03 & 0.04 & 0.05 & 0.05 & 0.06 & 0.06 & 0.07\tabularnewline
\textbf{nDS} & 0.04 & 0.04 & 0.06 & 0.03 & 0.03 & 0.01 & 0.01 & 0.01 &
0.01 & -- & 0.03 & 0.03 & 0.04 & 0.04 & 0.04 & 0.06\tabularnewline
\textbf{sDS} & 0.05 & 0.05 & 0.07 & 0.04 & 0.04 & 0.02 & 0.03 & 0.03 &
0.02 & 0.01 & -- & 0.03 & 0.03 & 0.04 & 0.04 & 0.05\tabularnewline
\textbf{nFB} & 0.06 & 0.05 & 0.08 & 0.05 & 0.04 & 0.02 & 0.03 & 0.03 &
0.03 & 0.01 & 0.01 & -- & 0.03 & 0.03 & 0.03 & 0.05\tabularnewline
\textbf{sFB} & 0.06 & 0.06 & 0.09 & 0.05 & 0.05 & 0.03 & 0.04 & 0.04 &
0.04 & 0.02 & 0.01 & 0.01 & -- & 0.02 & 0.02 & 0.04\tabularnewline
\textbf{WH} & 0.07 & 0.07 & 0.09 & 0.06 & 0.06 & 0.03 & 0.05 & 0.05 &
0.04 & 0.03 & 0.02 & 0.01 & 0.00 & -- & 0.02 & 0.04\tabularnewline
\textbf{SH} & 0.07 & 0.07 & 0.09 & 0.06 & 0.06 & 0.04 & 0.05 & 0.05 &
0.04 & 0.03 & 0.02 & 0.02 & 0.00 & 0.00 & -- & 0.03\tabularnewline
\textbf{JB} & 0.08 & 0.07 & 0.10 & 0.07 & 0.07 & 0.04 & 0.06 & 0.05 &
0.05 & 0.03 & 0.03 & 0.03 & 0.02 & 0.02 & 0.01 & --\tabularnewline
\bottomrule
\end{longtable}

Table 4. Seventeen SNPs identified as significant outliers
(\emph{q}\textless{} 0.1) using combined \emph{p} -values from OutFLANK
and pcadapt. A checkmark in the rightmost column indicates whether a SNP
also fell into the upper 1\% tail of
simulated\emph{X\textsuperscript{T}X} values in BayPass. Protein roles
and mutation effects were taken from GeneCards (www.genecards.org; last
accessed: Nov. 22, 2016).\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{SNP}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Type}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Position} \textbf{(scaffold: bp)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Nearest gene}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Protein role} \textbf{(\textsuperscript{*}or mutation
effect)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Gene feature located in} \textbf{(or distance from gene)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\emph{p}\textbf{-value}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{In 1\% tail of \emph{X\textsuperscript{T}X}}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
scaffold62\_6020088\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Transcriptomic\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
62: 6020088\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
PDLIM5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Heart development\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3' UTR\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.5\selectlanguage{ngerman}×10\textsuperscript{--9}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
85370\_147\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
174: 2025836\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
SPECC1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Sperm antigen\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3' UTR\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3.9\selectlanguage{ngerman}×10\textsuperscript{--7}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
87557\_138\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
160: 836356\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
MCPH1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textsuperscript{\textbf{*}}Microcephaly\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
86,891 bp upstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2.7\selectlanguage{ngerman}×10\textsuperscript{--6}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
16421\_111\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
80: 3930568\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
CD82\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textsuperscript{\textbf{*}}Cancer (esp. prostate)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Intron\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7.1\selectlanguage{ngerman}×10\textsuperscript{--6}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
scaffold29\_8739023\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Transcriptomic\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
29: 8739023\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
TMED4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
TNF signaling\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
272 bp downstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.1\selectlanguage{ngerman}×10\textsuperscript{--5}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
75533\_92\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
85: 4212523\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
SLC15A5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Symporter\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
34,296 bp downstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2.6\selectlanguage{ngerman}×10\textsuperscript{--5}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
97766\_277\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
130: 1438494\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
LOC103661688\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
N/A (lncRNA)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
31,658 bp upstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2.9\selectlanguage{ngerman}×10\textsuperscript{--5}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25548\_115\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
17: 5826726\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
ATP8A1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Phospholipid translocation\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Intron\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4.4\selectlanguage{ngerman}×10\textsuperscript{--5}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
scaffold94\_1529937\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Transcriptomic\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
94: 1529937\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
ACER2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Sphingolipid metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1525 bp downstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2.2\selectlanguage{ngerman}×10\textsuperscript{--4}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
129937\_226\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
98: 6537390\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
LOC103675794\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
N/A (lncRNA)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3.5\selectlanguage{ngerman}×10\textsuperscript{--4}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
124135\_183\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
35: 562475\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
NABP2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
DNA repair\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
194 bp downstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4.6\selectlanguage{ngerman}×10\textsuperscript{--4}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
124534\_129\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
67: 3540470\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
ISX\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Vitamin A metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
23,238 bp upstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
5.6\selectlanguage{ngerman}×10\textsuperscript{--4}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
88689\_94\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
65: 10384665\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
TMEM150C\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Transmembrane protein\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4933 bp downstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6.4\selectlanguage{ngerman}×10\textsuperscript{--4}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
64159\_205\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
168: 168381\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
MRVI1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Platelet activation\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Intron\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7.5\selectlanguage{ngerman}×10\textsuperscript{--4}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
88377\_240\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
30: 11311226\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
ZNF804B\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Gene expression\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
147,780 bp downstream\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7.8\selectlanguage{ngerman}×10\textsuperscript{--4}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
133814\_91\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
29: 8805815\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
NUDCD3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Dynein chain stabilization\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Intron\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.0\selectlanguage{ngerman}×10\textsuperscript{--3}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12566\_120\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25: 8999569\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
PCDH7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Platelet degranulation\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Intron\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.3\selectlanguage{ngerman}×10\textsuperscript{--3}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Principal component analysis of 391 polar bears; principal
components 1--5. Sampling location abbreviations are as in Table 1, and
locations are colour-coded according to the four major genetic clusters
identified by \emph{K} -means clustering in GenoDive (Beaufort Sea --
red, Norwegian Bay -- orange, Archipelago -- green, Hudson Bay -- blue).
Inertia ellipses are drawn using default settings, such that
approximately 67\% of all individuals sampled in each location are
contained in the ellipse.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Sampling locations and cluster membership for 388 of the 391
polar bears used in this analysis (top); for the remaining three
individuals, sampling coordinates were unavailable. Individuals are
colour-coded by genetic cluster according to the BAPS clustering results
for \emph{K} = 4 (bottom; Beaufort Sea -- red, Norwegian Bay -- orange,
Archipelago -- green, Hudson Bay \textbf{--} blue). Samples coloured
black in the map were significantly admixed according to BAPS
(\emph{p}\textless{} 0.05). Sea-ice extent during the breeding season is
shown using measurements for April 15, 2008 (Fetterer, Savoie, Helfrich,
\& Clemente-Col\selectlanguage{ngerman}ón, 2010, updated daily). Sampling location name
abbreviations are as in Table 1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. tess3r results. The top-left panel shows the cross-entropy for
ten repetitions of each \emph{K} -value for \emph{K} = 1--20. The
remaining panels show the genetic clusters generated by tess3r assuming
\emph{K} = 5--9. Each of the 388 black points represents a polar bear;
the remaining three individuals that lacked sampling coordinates were
omitted from this analysis.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4. Maximum-likelihood tree of historical splits (solid lines),
allowing for one subsequent migration event (dashed line) in TreeMix.
Names of sampling locations are color-coded to match each of the four
clusters identified by \emph{K} -means clustering in GenoDive: Beaufort
Sea -- red, Norwegian Bay -- orange, Archipelago -- green, Hudson Bay --
blue\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5. Results of a genome scan (\emph{n} \textsubscript{loci} =
4912) for outlier loci using combined \emph{p} -values from pcadapt and
OutFLANK. The dashed line in the left panel indicates genome-wide
significance based on a strict Bonferroni correction (i.e.,\emph{p}
\textless{} 0.05 \selectlanguage{ngerman}÷ no. of loci); the dotted line indicates genome-wide
suggestiveness (i.e., \emph{p} \textless{} 1 ÷ no. of loci). However,
these cutoffs are overly conservative because of non-independence among
loci. SNPs deemed significant using a\emph{q} -value \textless{} 0.1 are
annotated with the name of the nearest gene. (See Table 4.) Colored
points represent SNPs with\emph{X\textsuperscript{T}X} values that fall
into the upper 1\% tail of values calculated on simulated neutral loci
in BayPass. The right panel shows a quantile--quantile plot for \emph{p}
-values. The dotted line indicates the 1:1 line of expectation; the
shaded area represents its 95\% confidence interval. The genomic
inflation factor (\selectlanguage{greek}\emph{λ} \selectlanguage{english}) was calculated using the regression method.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 6. Left panel: Principal component analysis of 16 polar bear
sampling locations using 17 outlier SNPs. Right panel: Allele
frequencies for the transcriptomic SNP scaffold62\_6020088, located in
the 3' UTR of the gene PDLIM5 (PDZ and LIM domain 5). Frequencies were
calculated using non-admixed individuals from each of the four major
genetic clusters of polar bears, which correspond roughly to the four
regions delimited on the map. A single grizzly sampled from Kootenay
National Park (British Columbia, Canada; indicated as ``*'') was
homozygous for A.

\textbf{Hosted file}

\verb`Table1.docx`  available at \url{https://authorea.com/users/296778/articles/425802-population-genomics-reveals-historical-divergence-and-local-adaptation-in-polar-bears}

\textbf{Hosted file}

\verb`Table2.docx`  available at \url{https://authorea.com/users/296778/articles/425802-population-genomics-reveals-historical-divergence-and-local-adaptation-in-polar-bears}

\textbf{Hosted file}

\verb`Table3.docx`  available at \url{https://authorea.com/users/296778/articles/425802-population-genomics-reveals-historical-divergence-and-local-adaptation-in-polar-bears}

\textbf{Hosted file}

\verb`Table4.docx`  available at \url{https://authorea.com/users/296778/articles/425802-population-genomics-reveals-historical-divergence-and-local-adaptation-in-polar-bears}

\selectlanguage{english}
\FloatBarrier
\end{document}

