POPULATION GENETIC STRUCTURE OF MOOSE (ALCES ALCES) OF
SOUTH-CENTRAL ALASKA

Robert E. Wilson1,2, Thomas J. McDonough3, Perry S. Barboza1, Sandra L. Talbot4, and
Sean D. Farley5

1University of Alaska Fairbanks, Institute of Arctic Biology, Fairbanks, Alaska 99775; 3Alaska
Department of Fish and Game, 3298 Douglas St., Homer, Alaska 99603; 4U. S. Geological Survey,
Alaska Science Center, 4210 University Drive, Anchorage, Alaska 99508; 5Alaska Department of Fish
and Game, 333 Raspberry Road, Anchorage, Alaska 99518

ABSTRACT: The location of a population can influence its genetic structure and diversity by impact-
ing the degree of isolation and connectivity to other populations. Populations at range margins are
often thought to have less genetic variation and increased genetic structure, and a reduction in genetic
diversity can have negative impacts on the health of a population. We explored the genetic diversity
and connectivity between 3 peripheral populations of moose (Alces alces) with differing potential
for connectivity to other areas within interior Alaska. Populations on the Kenai Peninsula and from
the Anchorage region were found to be significantly differentiated (FST = 0.071, P < 0.0001) with
lower levels of genetic diversity observed within the Kenai population. Bayesian analyses employing
assignment methodologies uncovered little evidence of contemporary gene flow between Anchorage
and Kenai, suggesting regional isolation. Although gene flow outside the peninsula is restricted,
high levels of gene flow were detected within the Kenai that is explained by male-biased dispersal.
Furthermore, gene flow estimates differed across time scales on the Kenai Peninsula which may
have been influenced by demographic fluctuations correlated, at least in part, with habitat change.

ALCES VOL. 51: 71–86 (2015)

Key words: Alaska, genetic diversity, gene flow, moose, population genetic structure

The pattern of geographical variation in
genetic diversity and divergence is dictated
by the interaction of genetic drift, gene
flow, and natural selection (Eckert et al.
2008), and these evolutionary processes can
be influenced by the location of a population
within the species’ geographic range (Briggs
1996, Wisely et al. 2004, Howes and Lough-
eed 2008). At the local and regional scales,
the relative location of a population can
strongly impact patterns of dispersal and
degree of isolation influenced by both histor-
ical and contemporary events (Vucetich and
Waite 2003, Eckert et al. 2008), ultimately
determining the level of genetic structure
and diversity. Genetic diversity is lowest at

the range margins and highest at the center
of a species distribution (Yamashita and
Polis 1995, Schwartz et al. 2003, Eckert et al.
2008, Howes and Lougheed 2008). Marginal
populations are more likely to be isolated,
occur in patchy habitats, and may reflect
recent colonization. Peripheral populations
are less likely to receive immigrants whereas
the core populations typically occupy prime
habitat and experience greater levels of
gene flow (Hoffmann and Blows 1994,
Brown et al. 1995, Wisely et al. 2004, Miller
et al. 2010, Schrey et al. 2011).

Evolutionary theory suggests that the
reduction of genetic diversity within periph-
eral populations can impede adaptation to

2Present address: U. S. Geological Survey, Alaska Science Center, 4210 University Drive, Anchorage,
Alaska 99508

71



changing environmental conditions (Brad-
shaw 1991, Hoffmann and Parsons 1991,
Hoffmann and Blows 1994, Blows and Hoff-
mann 2005). Such adaptation is largely
determined by the availability of additive
genetic variation in heritable traits with fit-
ness consequences. Several studies have
shown that even small changes in genetic
variation can have large effects on popula-
tion fitness (Frankham 1995, Reed and
Frankham 2003) including juvenile survival
(Coulson et al. 1999, Mainguy et al. 2009,
Silva et al. 2009), antler growth (Von Hard-
enberg et al. 2007), and parasite resistance
(Coltman et al. 1999) within ungulates.
However, adaptability is also affected by
effective dispersal which can have either
positive or negative effects on the population
depending on the rate of gene flow and the
strength of selection acting on the local
population (García-Romos and Kirkpatrick
1997, Akerman and Bürger 2014, Bourne
et al. 2014, Frankham 2015). Thus, examin-
ing conditions (habitat, genetic diversity,
gene flow rates, and life history) under
which peripheral populations exist can aid
in understanding the processes that maintain
geographical ranges, predicting the conse-
quences of climate change (Parmesan and
Yohe 2003, Root et al. 2003, Hampe and
Petit 2005), and conserving populations at
range margins (Howes and Lougheed 2008).

The Kenai Peninsula is a peripheral
region situated in south-central Alaska that
was separated from the mainland by a nar-
row (16 km wide) isthmus at the end of the
last ice age. Due to its diverse landscape,
biodiversity in this region is unusually high
at this latitude (Morton et al. 2009), and the
moose (Alces alces) is one of the most recog-
nizable and socio-economically important
species. Moose populations on the Kenai
Peninsula are characterized by fluctuations
in population size, peaking after the occur-
rence of forest fires that promote optimal for-
age habitat (Oldemeyer et al. 1977). While

moose populations on the Kenai have fluctu-
ated between 5,000–8,000 animals over the
past several decades (T. J. McDonough,
unpublished data), these fluctuations have
not been uniform across moose management
units on the peninsula. While population size
in Game Management Unit (GMU) 15C in
southwest Kenai has increased, numbers in
GMU 15A (northwest) have declined drasti-
cally, ∼40% in the last 20 years as quality
forage has diminished since the last major
fire in 1969. Relative isolation from neigh-
boring regions with a strong history of fluc-
tuations in population size might lead to
reduced genetic variability on the Kenai
Peninsula which could ultimately be detri-
mental to the long-term health of moose in
this region.

Using microsatellite loci, we compared
levels of genetic variation and gene flow in
2 areas within the Kenai Peninsula and the
Anchorage area. These 3 areas are situated
on the periphery of overall moose distribu-
tion in Alaska but differ in levels of potential
connectivity to the core area of interior
Alaska. First, we investigated the connectiv-
ity between GMUs on the Kenai Peninsula
that have been affected by a long history of
land alteration and demographic changes.
Second, we predicted that 2 sites within the
disjunct Kenai Peninsula region, where
opportunities for genetic exchange may be
more limiting than Anchorage, would exhi-
bit relatively lower genetic diversity.

METHODS
Sample collection

A total of 163 moose were sampled from
3 populations in south-central Alaska (Fig. 1).
Ear-plugs and blood were taken from 33 col-
lared female moose in 2008–2010 and 2012
from the city of Anchorage and adjacent
Eagle River (called Anchorage hereafter).
In addition, muscle tissue was taken from
32 hunter-killed moose (16 female, 15
male, and 1 unknown) during the winter of

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POPULATION GENETIC STRUCTURE – WILSON ET AL. ALCES VOL. 51, 2015



2011–2012. In spring 2012, blood was taken
from radio-collared female moose from
GMU 15A (n = 49; 3,367 km2) and GMU
15C (n = 49; 3,030 km2) on the Kenai Penin-
sula, the borders of which are approximately
20 km apart. Anchorage samples are archived
at the Molecular Ecology Laboratory, U.S.
Geological Survey, Anchorage, Alaska, and
Kenai Peninsula samples at the Alaska
Department of Fish and Game, Homer,
Alaska. All animal capturing and genetic
sampling were conducted under Division of
Wildlife Conservation ACUC approval (#
2012–2007, 2013–2021, and 90–05) and
under the University of Alaska Fairbanks
IACUC approval (# 14885 and 182744).

Molecular techniques
Genomic DNAwas extracted from blood

and tissue samples using a “salting out” pro-
cedure described by Medrano et al. (1990),
with modifications described in Sonsthagen
et al. (2004). Genomic DNA concentrations
were quantified using fluorometry and
diluted to 50 ng mL–1 working solutions.
Individuals were initially screened at 17
microsatellite loci. Thirteen autosomal loci
were found to be polymorphic of which 9

with dinucleotide repeat motifs were selected
for further analysis that were polymorphic in
all populations: BL42, BM888, BM203,
BM2830 (Bishop et al. 1994), NVHRT21,
NVHRT22 (Røed and Midthjell 1998),
RT1, RT5, and RT30 (Wilson et al. 1997).
Polymerase chain reaction (PCR) amplifica-
tion and electrophoresis followed protocols
described in Roffler et al. (2012). Ten per-
cent of the samples were amplified and gen-
otyped in duplicate for the 9 microsatellite
loci for quality control.

Analysis of genetic diversity and
population genetic subdivision

We calculated allelic richness, inbreed-
ing coefficient (FIS), observed and expected
heterozygosities, and tested for deviations
from Hardy-Weinberg equilibrium (HWE)
and linkage disequilibrium (LD) for each
microsatellite locus and population in
FSTAT ver. 2.9.3 (Goudet 1995). The degree
of genetic subdivision among moose popula-
tions was assessed by calculating overall and
pairwise FST and RST, adjusting for multiple
comparisons using Bonferroni correction (α
= 0.05) in Arlequin v3.5.1.3 (Excoffier and
Lischer 2010). Because the upper possible
FST value for a set of microsatellite loci is
usually <1.0 (Hedrick 2005), we used
RECODEDATA, version 1.0 (Meirmans
2006) to calculate the uppermost limit of
FST for our data set.

We also used the Bayesian-clustering
program STUCTURE 2.2.3 (Pritchard et al.
2000) to determine the level of population
structure in the autosomal microsatellite
data set. We performed 2 sets of analyses to
look at structure within south central Alaska:
1) between Anchorage and Kenai Peninsula
and 2) within the Kenai Peninsula (GMU
15A and 15C). Structure assigns individuals
to populations maximizing Hardy-Weinberg
equilibrium and minimizing linkage disequi-
librium. The analysis was conducted for 1–
10 populations (K) using an admixture

Fig. 1. Sampling areas for three moose
populations in south-central Alaska: An-
chorage, Game Management Unit (GMU)
15A (northwest Kenai Peninsula), and
GMU15C (southwest Kenai Peninsula).

ALCES VOL. 51, 2015 WILSON ET AL. – POPULATION GENETIC STRUCTURE

73



model with 100,000 burn-in iterations and
1,000,000 Markov chain Monte Carlo
(MCMC) iterations without providing a
priori information on the geographic origin
of the individuals; the analyses were
repeated 10 times for each K to ensure con-
sistency across runs. We used the ▵K
method of Evanno et al. (2005) and evalua-
tion of the estimate of the posterior probabil-
ity of the data given K, Ln P(D), to
determine the most likely number of groups
at the uppermost level of population struc-
ture. For the Kenai Peninsula analysis we
used the LOCPRIOR which is able to detect
weak signals of population structure in data-
sets not detectable under standard models
(Hubisz et al. 2009). We determined if loca-
tion was informative by the value of r, which
parameterizes the amount of information
contained by the location of the samples.
Values of r > 1 indicates either there is no
population structure or that structure is inde-
pendent of locality.

Gene flow
We estimated gene flow between moose

populations using 2 methodologies:
MIGRATE v3.2.16 (Beerli and Felsenstein
1999, 2001) and BayesAss 3.0 (Wilson and
Rannala 2003). These programs differ in
the underlying model used to estimate gene
flow. MIGRATE uses a steady-state two-
island coalescent model of population differ-
entiation which incorporates parameters
scaled to the mutation rate (µ), the effective
population size parameter Θ (4Neµ), and
the migration rate M (m/µ) between popula-
tions. BayesAss uses an assignment metho-
dology which does not incorporate
genealogy or assume that populations are in
Hardy-Weinberg equilibrium (Wilson and
Rannala 2003). Thus, estimates of migration
rate can be interpreted differently and at dif-
ferent temporal scales. BayesAss reflects
gene flow over the last several generations
(referred to as contemporary gene flow

hereafter) whereas MIGRATE gene flow
estimates are averaged over the past n gen-
erations, where n equals the number of
generations the populations have been at
mutation-drift equilibrium (Beerli and
Felsenstein 1999, 2001). It is generally
agreed that microsatellite mutation rates
are several orders of magnitude higher than
mutation rates of DNA sequences (mito-
chondrial or nuclear; Schlötterer 2000,
Ellegren 2004). Thus, microsatellite markers
can reflect recent (within the last 10,000
years) and almost contemporaneous events,
but increases in homoplasy associated with
microsatellites reduce their ability to capture
older demographic events (Hartl and Clark
2007, Hughes 2010). Therefore, MIGRATE
analyses are referred to as estimating recent
gene flow.

MIGRATE was run with a full migration
model; θ (4Neµ, composite measure of effec-
tive population size and mutation rate) and
all pairwise migration parameters were esti-
mated individually from the data. Gene
flow was estimated using maximum likeli-
hood search parameters; 10 short chains
(5000 trees used out of 1,500,000 sampled),
10 long chains (15,000 trees used out
5,250,000 sampled), and 5 static heated
chains (1, 1.33, 2.0, 4.0, and 1,000,000;
swapping interval = 1). Full models were
run 10 times to ensure the convergence of
parameter estimates.

BayesAss was initially run with the
default delta values for allelic frequency
(P), migration rate (m), and inbreeding (F).
Subsequent runs incorporated different delta
values to ensure that acceptance rate for pro-
posed changes was between 20–40% for
each parameter to maximize log likelihood
values and ensure the most accurate esti-
mates (Wilson and Rannala 2003). Final
delta values used were ▵P = 0.5 (27% accep-
tance rate), ▵m = 0.2 (27%), and ▵F = 0.85
(31%). We performed 10 independent runs
(10 million iterations, 1 million burn-in,

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POPULATION GENETIC STRUCTURE – WILSON ET AL. ALCES VOL. 51, 2015



and sampling frequency of 1000) and 2 addi-
tional longer runs (50 million iterations, 5
million burn-in) with different random seeds
to ensure convergence and consistency
across runs. Convergence was also assessed
by examining the trace file in program Tracer
v1.5 to ensure proper mixing of parameters
(Rambaut and Drummond 2007).

Population demography
To estimate the effective population size

(Ne) for each GMU on the Kenai Peninsula
and Anchorage area, we used the approxi-
mate Bayesian computation method (Beau-
mont et al. 2002) implemented in the
program ONeSAMP 1.2 (Tallmon et al.
2008). We used a lower prior of 100 for all
populations and a maximum prior that
reflected the current census size (1,000 for
Anchorage, 2,000 for GMU 15A, and 3,000
for GMU 15C). Similar values were obtained
for larger maximum possible effective popu-
lation sizes.

Lastly, we used BOTTLENECK which
compares the number of alleles and gene
diversity at polymorphic loci under the infi-
nite allele model (IAM; Maruyama and
Fuerst 1985), stepwise mutation model
(SMM; Ohta and Kimura 1973), and two-
phase model of mutation (TPM; Di Rienzo
et al. 1994; parameters: 79% SSM, variance
9; Piry et al. 1999, Garza and Williamson
2001). One thousand simulations were per-
formed for each population and parameters
were changed among 5 runs to evaluate the
robustness of results. Significance was
assessed using a Wilcoxon sign-rank test
which determines if the average of standar-
dized differences between observed and
expected heterozygosities is significantly
different from zero (Cornuet and Luikart
1996). Significant heterozygote deficiency
relative to the number of alleles indicates
recent population growth, whereas heterozy-
gote excess relative to the number of alleles
indicates a recent population bottleneck

(Cornuet and Luikart 1996). BOTTLENECK
compares heterozygote deficiency and excess
relative to genetic diversity, not to Hardy-
Weinberg equilibrium expectation (Cornuet
and Luikart 1996).

RESULTS
Genetic diversity and population
subdivision

Multilocus genotypes were collected
from 163 individuals and each individual
had a unique genotype. The number of
alleles per locus observed ranged from 3.4–
4.7 per population with an overall estimate
of 5.1 (Tables 1, 2). The observed heterozyg-
osity ranged from 43–55% with an overall
mean heterozygosity of 49%. The Kenai
Peninsula exhibited a 19% lower allelic rich-
ness (20% in GMU 15A and 25% in GMU
15C) compared to the Anchorage area, and
3x more private alleles were observed in
the Anchorage region (Table 1). In addition,
the observed (Ho) and expected (He) hetero-
zygosity was significantly lower (all
P-values < 0.0001) in the Kenai Peninsula
(Ho by 18%, He by 16% expected), in
GMU 15C (15%, 14%), and in GMU 15A
(22%, 20%). On average, individuals on the
Kenai showed a greater level of homozygos-
ity (Kenai: 4.94 loci [SD = 1.46] vs. Ancho-
rage: 3.98 loci [SD = 1.51]; t = 4.02, P <
0.0001). The inbreeding coefficient (FIS)
did not differ significantly from zero in any
population (Table 1). All loci and popula-
tions were in HWE and linkage equilibrium.

Significant genetic structure was
observed at the 9 microsatellite loci between
Anchorage and the two GMUs on the Kenai
Peninsula (Table 3). No significant differ-
ence was found within the Kenai Peninsula.
The upper limit of the FST for our microsa-
tellite data set was 0.499. Therefore, the
overall FST of 0.071 accounted for 14.2%
of the maximum possible level of genetic
structure and 19% for the pairwise

ALCES VOL. 51, 2015 WILSON ET AL. – POPULATION GENETIC STRUCTURE

75



comparisons between Anchorage and Kenai
Peninsula GMUs.

STRUCTURE uncovered genetic parti-
tioning within south central moose popula-
tions, supporting a two-population model
(ΔK = 188.3, average Ln P(D) = −2758.7).
Most individuals from Anchorage were
assigned to one genetic cluster (87.7%),
whereas individuals from Kenai GMU 15A
and 15C were assigned to a second cluster
with high probability, 93.6 and 92.6%,
respectively (Fig. 2). Seven Anchorage indi-
viduals were assigned to the Anchorage clus-
ter with <60% certainty, conversely, only a
single Kenai individual was assigned to the
Kenai cluster with <60% certainty. Genetic
partitioning was not observed within Kenai
Peninsula, as including capture location
(LOCIPRIOR) was not informative (r > 9).

Gene flow
Restricted gene flow over the past sev-

eral generations was observed under the
BayesAss model between Anchorage and
Kenai Peninsula, with 96.8% (93.3–100%)
of the Anchorage population comprised of
a non-migrant origin (Fig. 3). Within the
Kenai Peninsula, there was a signal of a

northern direction of contemporary gene
flow from GMU 15C into 15A (proportion
of individuals with migrant origin: 27.8%
in 15A vs. 6.9% in 15C); although the 95%
confidence intervals do overlap (Fig. 3).

Asymmetrical recent gene flow as esti-
mated by MIGRATE was observed among
sampled populations. The directionality of
gene flow was from Kenai Peninsula into
Anchorage (Fig. 3). The number of migrants
per generation (Nem) ranged from 2.56
(GMU 15A; 1.97–3.29) and 2.78 (GMU
15C; 2.20–3.48) into Anchorage and 0.99
(0.74–1.32) and 1.08 (0.78–1.47) into the
Kenai GMU 15A and 15C, respectively.
Within Kenai there was a signal of asymme-
trical gene flow from GMU 15A into 15C
(3.3 migrants/generation; Fig. 3).

Population demography
The estimated effective size using the

Bayesian computation method for the
Anchorage region was 74.3 (95% CI: 67.6–
83.0). GMUs 15A and 15C on the Kenai
Peninsula had lower estimated effective sizes
with non-overlapping confidence intervals with
Anchorage (Table 1). The BOTTLENECK
analysis showed no evidence of significant

Table 1. Estimates of genetic diversity of the moose sampled from three locales in south-central Alaska,
including: average number of alleles, allelic richness (AR), observed and expected heterozygosities
(Ho/He), inbreeding coefficient (FIS), effective population size (Ne) estimated in ONeSAMP and sample
size (n) calculated from nine microsatellite loci. Allelic richness is based on smallest sample size of 65 for
Anchorage and overall Kenai. Within Kenai Peninsula (GMU 15A and GMU 15C) based on sample size
of 49.

Kenai Peninsula

Anchorage GMU 15A GMU 15C Overall Kenai

No. Alleles 4.67 3.67 3.44 4.00

No. Private Alleles 10 1 2 3

AR 4.59 3.67 3.44 3.78

Ho (SD) / 0.55 (0.02)/ 0.43 (0.02)/ 0.47 (0.02)/ 0.45 (0.02)/
He (SD) 0.56 (0.06) 0.45 (0.07) 0.48 (0.05) 0.47 (0.06)

FIS 0.007 0.056 0.031 0.043

Ne 74.3 (67.6–83.0) 47.9 (43.2–56.8) 36.8 (33.3–43.9) 145.5 (123.3–255.9)
n 65 49 49 98

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POPULATION GENETIC STRUCTURE – WILSON ET AL. ALCES VOL. 51, 2015



heterozygosity excess or deficit under the
SMM or TPM. However, there was evidence
of a recent population decline (heterozygote

excess) based on the infinite allele model
(IAM) for Kenai GMU 15C.

DISCUSSION
Climatic and glaciation history has

played a major role in shaping the evolution-
ary history of many taxa in south-central
Alaska. It was not until approximately
7,000 years before present that the Kenai
Peninsula became distinct and relatively iso-
lated from the mainland by a 16 km wide
mountainous isthmus (Pielou 1991, Muhs
et al. 2001). This isolation has fostered
genetically or morphologically distinct popu-
lations for a variety of taxa (e.g., wolverine

Table 2. Estimates of observed and expected heterozygosity, number of alleles per locus for nine autosomal
nuclear microsatellite loci assayed in three moose populations in south-central Alaska. All loci were in
Hardy-Weinberg equilibrium. Sample size is in parentheses; Ho = heterozygosity observed, He =
heterozygosity expected, and Na = number of alleles.

Kenai Peninsula

Locus
Anchorage

(65)
GMU 15A

(49)
GMU 15C

(49)
Overall Kenai

(98)
All populations

(165)

NVHRT22 Ho/He 0.69/0.76 0.49/0.54 0.57/0.53 0.53/0.53 0.60/0.68

Na 6 5 4 6 6

NVHRT21 Ho/He 0.49/0.50 0.55/0.46 0.39/0.45 0.47/0.45 0.48/0.48

Na 5 3 2 3 5

RT1 Ho/He 0.49/0.46 0.27/0.29 0.39/0.38 0.31/0.34 0.38/0.40

Na 2 2 2 2 2

RT5 Ho/He 0.54/0.52 0.18/0.21 0.25/0.32 0.21/0.26 0.34/0.40

Na 4 3 3 3 4

RT30 Ho/He 0.55/0.58 0.69/0.67 0.74/0.72 0.71/0.70 0.65/0.66

Na 5 4 4 4 5

BM203 Ho/He 0.20/0.20 0.37/0.41 0.51/0.50 0.44/0.46 0.34/0.38

Na 5 3 4 4 6

BM2830 Ho/He 0.46/0.49 0.37/0.43 0.41/0.41 0.39/0.42 0.42/0.45

Na 2 2 2 2 2

BM888 Ho/He 0.63/0.65 0.22/0.26 0.20/0.27 0.21/0.27 0.38/0.46

Na 4 4 3 4 4

BL42 Ho/He 0.91/0.84 0.71/0.81 0.80/0.75 0.76/0.79 0.82/0.83

Na 9 6 7 8 12

Overall Loci Ho/He 0.55/0.56 0.43/0.45 0.47/0.48 0.45/0.47 0.49/0.53

Na 4.67 3.67 3.44 4.00 5.11

Table 3. Pairwise and overall values of FST and
RST calculated from nine microsatellite loci.
Significant values after Bonferroni correction
(P < 0.0001) are marked with an asterisk.

FST RST

Anchorage

– Kenai GMU 15A 0.094* 0.014
– Kenai GMU 15C 0.092* 0.028
Kenai GMU 15A

– Kenai GMU 15C 0.001 0.000
Overall 0.071* 0.016

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77



[Gulo gulo], Tomasik and Cook 2005; Amer-
ican marten [Ursus americanus], Robinson
et al. 2007; song sparrow [Melospiza melo-
dia], Patten and Pruett 2009). The moose

populations residing on the Kenai are no
exception. Using a multi-locus approach,
we observed that moose on the Kenai were
genetically distinct from those in the

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Pr
o

b
ab

ili
ty

 o
f A

ss
ig

n
m

en
t

Anchorage Kenai GMU 15A Kenai GMU 15C

Fig. 2. Structure analysis showing posterior probability of assignment of individuals to each (K =
2) genetic cluster. White bar represents the estimated probability of assignment to cluster one and
grey bar is the estimated probability of assignment to cluster two.

0.99  
(0.74-1.32) 

2.56  
(1.97-3.29) 

3.29  
(2.63-4.07) 

1.18  
(0.89-1.54) 

a b

2.78
(2.20-3.48)

1.08
(0.78-1.47) 0.02

(0.00-0.05)
0.01

(0.00-0.03)

0.28
(0.13-0.43)

0.07
(0.00-0.25)

0.01
(0.00-0.02)

0.01
(0.00-0.04)

Fig. 3. Estimates of (a) recent (number of migrants per generation, Nem) estimated in MIGRATE
and (b) contemporary (proportion of individuals with migrant origin, m) calculated in BayesAss
for moose populations in south-central Alaska as calculated from nine microsatellite loci with
relative magnitude indicated by width of arrow; 95% confidence intervals are in parentheses.

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POPULATION GENETIC STRUCTURE – WILSON ET AL. ALCES VOL. 51, 2015



mainland Anchorage population and exhib-
ited significantly lower levels of genetic
diversity at microsatellite loci.

Loss of genetic diversity between
peninsula and mainland

Populations residing in areas with bar-
riers that limit dispersal (e.g., peninsulas and
islands) across the landscape are expected to
have lower genetic variation (Gaines et al.
1997). Our results were consistent with
Gaines et al. (1997) prediction: moose occu-
pying the Kenai Peninsula had significantly
reduced genetic diversity (∼18%) compared
to the nearest mainland population in Ancho-
rage. A reduction of genetic variability has
also been reported for other Alaskan moose
populations (Hundertmark 2009, Schmidt
et al. 2009) as well as other mammals on the
Kenai Peninsula (e.g., Canada lynx [Lynx
canadensis], Schwartz et al. 2003). The loss
of genetic variation in peripheral populations
may be due to numerous factors such as lim-
ited number of connections to other popula-
tions or smaller population size (Schwartz
et al. 2003).

Cook Inlet waters, mountains, and a
highway and railways may represent formid-
able dispersal barriers for moose between
these regions. Although Kenai Peninsula
and Anchorage are in close geographic
proximity (straight line distance over land
is ∼105 km), the costs of dispersal over the
rugged terrain and highways or swimming
across the inlet are likely high. In agreement
with limited effective dispersal, we found
restricted contemporary gene flow between
Kenai Peninsula and mainland Anchorage
populations with confidence intervals sug-
gesting there has been limited genetic
exchange over the past several generations.
Telemetry studies of the sampled females in
this study showed that individuals remained
in the same general area throughout the
year (Farley et al. 2012, T. J. McDonough,
unpublished data), further suggesting a low

likelihood of long-distance dispersal between
these two regions.

However, connectivity could be
mediated through a contact zone north of
the isthmus located at Portage Valley that is
used by black bears (Ursus americanus)
(Robinson et al. 2007). The isthmus is not
an absolute/strong barrier as movements of
radio-collared moose occur across the isth-
mus; this movement was restricted within
intermountain valleys that spanned both
sides of the isthmus (T. Lohuis, Alaska
Department of Fish and Game, unpublished
data). Furthermore, STRUCTURE analysis
estimated a low probability assignment to a
genetic cluster for ∼12% of the individuals
in Anchorage, suggestive of genetic
exchange that has occurred during or after
population divergence, with higher rate
going into the Anchorage area based on the
MIGRATE analysis. This northward direc-
tion of gene flow is also found in other
peninsular populations (Schmidt et al.
2009) and may reflect post-colonization
gene flow rates. Further study of moose in
areas between Anchorage and Kenai Penin-
sula might identify if a contact zone exists
for moose at the isthmus as seen in other
mammals, or if these regions are truly iso-
lated as indicated by the contemporary gene
flow analysis.

Relationships within the peninsula
Unlike the potential strong barriers to

dispersal between the peninsula and main-
land populations, there are relatively few
natural barriers to movement in the western
part of the peninsula, and gene flow esti-
mates suggest that there is ongoing genetic
exchange. The directionality of gene flow
across the western Kenai Peninsula has not
remained constant over time. Differences in
directionality across time scales may be
attributed to the fluctuating nature of moose
population dynamics that is correlated at
least in part with habitat change, in particular

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79



in GMU 15A where population size fluctu-
ates with major fire events (Oldemeyer et al.
1977, Schwartz and Franzmann 1989, Lor-
anger et al. 1991). The habitat in GMU
15A has changed drastically over the last
century after major fires in 1947 and 1969
transformed previously low quality habitat
to ideal foraging habitat, which subsequently
declined to the current condition (Oldemeyer
et al. 1977, Schwartz and Franzmann 1989).
If periodic population increase has been suf-
ficiently frequent throughout the history of
moose in this area, and dispersal is influ-
enced by population density and habitat
quality, we might expect the directionality
of gene flow to change over time with more
moose dispersing from areas of high produc-
tivity into areas of lower density or less pre-
ferred habitat as competition for resources
increases. Indeed, contemporary gene flow
estimated in BayesAss indicates gene flow
from a higher density area (GMU 15C)
with better quality habitat into an area char-
acterized by poor habitat conditions and
lower density area (15A).

Moose populations on the Kenai Penin-
sula have also fluctuated in size partially
due to human activities (land development
and forest fires), with changes in habitat
potentially affecting fertility and survival of
young (Klein 1970, Franzmann and Arneson
1973, Schwartz and Franzmann 1989, Testa
and Adams 1989). While moose populations
initially respond positively to wildfires
through the emergence of optimal habitat,
populations eventually decline as the habitat
changes to late succession (non-optimal for-
age) vegetation. During the 20 years follow-
ing the last major fire in GMU 15A (1969),
the population has declined by approxi-
mately 40%. Current and previous assess-
ment of calf survival from this area has
identified low calf survival (Franzmann et al.
1980, T. J. McDonough, unpublished data).
Such a drastic decline in population size
coupled with low productivity can

negatively impact genetic diversity of a
population; this may partially explain the
significantly low genetic diversity on the
Kenai Peninsula. A reduction in genetic
diversity can lower viability and fecundity
(Falconer 1981, Ralls et al. 1983, Frankham
1995, Crnokrak and Roff 1999), and at the
extreme can lead to inbreeding depression;
decreased viability and fecundity occur cur-
rently on the Kenai Peninsula (Franzmann
and Schwartz 1985, ADF&G 2013, unpub-
lished data). Whether lower reproductive
rates (twinning rates and calf survival) on
the Kenai Peninsula are correlated solely
with genetic variability or are influenced in
addition, or solely by environmental factors,
is an area for future investigation.

Conservation implications
Although the effects of inbreeding

depression can diminish over time (Lynch
1977), a general loss of genetic diversity
can be detrimental over evolutionary time
as it may lower the ability of populations to
respond to environmental stressors such as
novel predators, parasites, or climatic condi-
tions (Lacy 1987, Quattro and Vrijenhoek
1989, Leberg 1993). Following the recom-
mendations of Frankham et al. (2014), all 3
populations fall below the minimum effec-
tive population size of 1,000 required to
maintain long-term viability. In addition,
the GMUs on the Kenai Peninsula, when
considered separately, have an effective
population size lower than both recent
(> 100; Frankham et al. 2014) and earlier
(> 50; Franklin 1980, Soulé 1980) recom-
mendations to avoid inbreeding depression.
Indeed, the Kenai Peninsula does have a
higher inbreeding coefficient (although not
significantly different from zero) and higher
levels of homozygosity. However, when
considering the Kenai Peninsula as a single
population, the effective population exceeds
100 but remains below the threshold for
long-term viability.

80

POPULATION GENETIC STRUCTURE – WILSON ET AL. ALCES VOL. 51, 2015



Neutral loci are commonly used to infer
evolutionary history of populations and
make inferences about overall variation (see
Howes and Lougheed 2008), but it is still
unclear whether the trends in putatively neu-
tral loci are reflective of quantitative-trait
variation found in genes for physiological,
morphological, or life history traits that are
likely important for the adaptive potential
of populations (Merilä and Crnokrak 2001,
Reed and Frankham 2001, Eckert et al.
2008). Therefore, a conclusion that reduced
genetic diversity observed at neutral micro-
satellite markers reflects reduction of diver-
sity in the genome overall is premature.
Our results showing significant population
structure and limited connectivity to outside
populations for the Kenai Peninsula provide
a working hypothesis for the potential effects
on genetic diversity, which can be tested by
assaying both selectively neutral and func-
tional diversity. Such studies can provide
greater resolution on the processes responsi-
ble for the distribution of genetic diversity
among moose populations within south-
central Alaska.

ACKNOWLEDGEMENTS
Funding was provided by Joint Base

Elmendorf-Richardson, under the guidance
of H. Griese, C. Garner, D. Battle, and R.
Graham, and U. S. Geological Survey.
Extensive field assistance was provided by
the military conservation agent program on
Elmendorf Air base. J. Crouse, J. Selinger,
and pilots J. DeCreft, T. Levanger, and J.
Fieldman, and the Kenai National Wildlife
Refuge provided field assistance on the
Kenai Peninsula. Z. Grauvogel, K. Sage,
and S. Sonsthagen provided laboratory and
manuscript advice, while K. Hundertmark
provided advice on marker selection and G.
Roffler and E. Solomon provided help with
maps. Any use of trade, firm, or product
names is for descriptive purposes only and

does not imply endorsement by the U.S.
Government.

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	POPULATION GENETIC STRUCTURE OF MOOSE (ALCES ALCES) OF SOUTH-ENTRAL ALASKA
	METHODS
	Sample collection
	Molecular techniques
	Analysis of genetic diversity and population genetic subdivision
	Gene flow
	Population demography

	RESULTS
	Genetic diversity and population subdivision
	Gene flow
	Population demography

	DISCUSSION
	Loss of genetic diversity between peninsula and mainland
	Relationships within the peninsula
	Conservation implications

	ACKNOWLEDGEMENTS
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