Molecular Ecology (2004) 13, 2197–2209
doi: 10.1111/j.1365-294X.2004.02239.x
Genetic diversity and population structure of Tasmanian
devils, the largest marsupial carnivore
Blackwell Publishing, Ltd.
M E N N A E . J O N E S ,*‡ D A V I D P A E T K A U ,* E L I G E F F E N † and C R A I G M O R I T Z *
*Department of Zoology and Entomology, University of Queensland, Queensland 4072, Australia, †Institute for Nature Conservation
Research, Tel Aviv University, Rama Aviv 69978, Israel
Abstract
Genetic diversity and population structure were investigated across the core range of Tasmanian devils (Sarcophilus laniarius; Dasyuridae), a wide-ranging marsupial carnivore
restricted to the island of Tasmania. Heterozygosity (0.386–0.467) and allelic diversity (2.7–
3.3) were low in all subpopulations and allelic size ranges were small and almost continuous, consistent with a founder effect. Island effects and repeated periods of low population
density may also have contributed to the low variation. Within continuous habitat, gene
flow appears extensive up to 50 km (high assignment rates to source or close neighbour
populations; nonsignificant values of pairwise FST), in agreement with movement data. At
larger scales (150–250 km), gene flow is reduced (significant pairwise FST) but there is no
evidence for isolation by distance. The most substantial genetic structuring was observed
for comparisons spanning unsuitable habitat, implying limited dispersal of devils between
the well-connected, eastern populations and a smaller northwestern population. The
genetic distinctiveness of the northwestern population was reflected in all analyses: unique
alleles; multivariate analyses of gene frequency (multidimensional scaling, minimum
spanning tree, nearest neighbour); high self-assignment (95%); two distinct populations for
Tasmania were detected in isolation by distance and in Bayesian model-based clustering
analyses. Marsupial carnivores appear to have stronger population subdivisions than their
placental counterparts.
Keywords: bottleneck, carnivore, dispersal, genetic diversity, marsupial, population structure
Received 25 August 2003; revision received 3 March 2004; accepted 21 April 2004
Introduction
Integral to the conservation management of species is a
knowledge of genetic diversity and the spatial extent of
populations, and an understanding of the underlying causal
influences. Loss of genetic variation can reduce individual
fitness (fecundity and survival), including the ability to
resist disease, and the potential for populations to adapt to
environmental change (Lacy 1997; Altizer et al. 2003). Low
genetic variability may result from founder effects, island
effects and genetic bottlenecks. Many island populations
have lower levels of genetic variability than mainland populations of the same species; theory predicts that variability
Correspondence: Menna E. Jones. ‡Present address: School of
Zoology, University of Tasmania, Private Bag–5, Hobart Tasmania
7004, Australia. Fax: (613) 6226 2745;
E-mail: [email protected]
© 2004 Blackwell Publishing Ltd
is lost with time at a rate dependent on effective population
size (Ne) and the number of generations of isolation
(Frankham 1997). In a bottleneck, when populations are
reduced to low numbers by, for example, habitat loss,
persecution, or a disease epidemic, there is an immediate
loss of allelic diversity and a more gradual erosion of
heterozygosity (Cornuet & Luikart 1996).
Understanding the spatial structure of populations is
important for establishing the appropriate scale and subunits (Moritz 1999) for conservation management. Gene
flow is a composite of multiple individual dispersal movements and interbreeding within the local population by
immigrant individuals. Population structure derives from
sheer distance in relation to the scale of individual movements and from barriers or filtering effects on gene flow
caused by discontinuities in landscape features and heterogeneity in suitability of habitat. Wide-ranging species
with high dispersal capabilities, such as many mammalian
2198 M . E . J O N E S E T A L .
predators, should have relatively low genetic structuring.
This has been shown in taxonomically diverse, highly
mobile carnivore species such as wolverine (Gulo gulo;
Kyle & Strobeck 2001), pine marten (Martes americana; Kyle
et al. 2000), coyote (Canis latrans; Lehman & Wayne 1991),
grey wolf (C. lupus; Roy et al. 1994), lynx (Lynx lynx; Rueness
et al. 2003), cougar (Puma concolor; Sinclair et al. 2001), black
bear (Ursus americanus; Paetkau & Strobeck 1994) and brown
bear (U. arctos; Paetkau et al. 1998).
Tasmanian devils (Sarcophilus laniarius; Dasyuridae:
Marsupialia) are a medium-sized predator and specialized
scavenger, and are the largest remaining marsupial
carnivore (Jones 2001). They are restricted to the island of
Tasmania (165 000 km2; Fig. 1). Tasmanian populations
were separated from those on mainland Australia approximately 12 000 years ago with the flooding of the Bassian
plain at the end of the last glaciation. Mainland populations subsequently became extinct (between 430 ± 160 and
5000 years ago; Gill 1971; Archer & Baynes 1972), correlating
roughly with the anthropogenic introduction of dingoes
(Canis lupus dingo, earliest fossil record 3450 ± 95 before
present, Corbett 1995). Tasmania has the most intact marsupial predator fauna in Australia, with, until recently,
robust populations of three species (Sarcophilus, two Dasyurus spp.). All species of Australian marsupial carnivores
have declined since European settlement (Jones et al. 2003a).
Devils have reached very low densities at least three times
in the last 150 years (1850s, 1900s and 1940s), with population recovery taking two to three decades (Guiler 1992). A
causal agent has never been determined. Devils and their
major prey species became increasingly common between
1975 and 1998 (Hocking, personal communication; Driessen
& Hocking 1992). Since 1997, a fatal cancer condition, dubbed
Devil Facial Tumour Disease, which appears to cause dramatic
local population decline, has been reported in devils
across a large part of Tasmania (Nick Mooney, Tasmanian
Nature Conservation Branch, personal communication to
M. Jones; M. Jones, unpublished data).
Tasmanian devils are a wide-ranging carnivore with
extensively overlapping home ranges in the order of
4 –27 km2 (Pemberton 1990), and with larger male than
female ranges (M. Jones, unpublished data). Devils are
more abundant in habitats (open eucalypt forests and
woodlands, coastal scrub) that support dense populations
of their prey (macropods, wombats, possums; Jones &
Barmuta 1998) and that facilitate hunting (open understorey
with dense patches), which is probably a mixture of
ambush and persistent pursuit ( Jones & Barmuta 2000;
Jones 2003). Population densities of devils are low in dense
wet forests, low heathlands, alpine areas, open grasslands
and extensively cleared farmland ( Jones, unpublished
data). The potential core distribution of devils (population
densities probably ≥ 1 per 2 km2), derived from environmental domain modelling, comprises the low to moderate
rainfall zones of eastern and northern Tasmania, although
devils are found state-wide (Fig. 1, Jones & Rose 1996).
There are both natural and anthropogenic habitat discontinuities, resulting from landscape features such as estuaries
and alpine plateau and from habitat fragmentation, that
may influence population connectivity.
This study had three purposes. First, we wanted to find
out whether genetic diversity in Tasmanian devils is low,
which we might expect from its restriction to an island
(Frankham 1997) and the repeated periods of low population density. Second, we wished to define the spatial scale
of dispersal and population genetic structure. Third, we
wanted to elucidate the effects of natural and anthropogenic landscape features on gene flow and population
structure in devils across their core range.
We used highly variable nuclear microsatellite markers,
which have been proven to be a powerful tool for revealing
the microgeographical population structure that results
from recent gene flow (dispersal and interbreeding on a
time scale up to hundreds or thousands of years) (O’Connell & Slatkin 1993; Estoup & Cornuet 1999), including in
marsupials (e.g. Moritz et al. 1997; Pope et al. 2000; KraaijeveldSmit et al. 2002). The combination of molecular genetic
markers and population genetic theory provide the means
for analysing the effects of gene flow over greater and longer
geographical and time scales than is practical for field studies
(movement data from trapping and radio-tracking) (Slatkin
1987).
Materials and methods
Population sampling and microsatellite analysis
The sampling regime, which was based on environmental
domain modelling for this species (Jones & Rose 1996), was
designed to encompass the core distributional range of
devils, which reflects areas of primary habitat and higher
density of devils and their major prey. We selected six
study sites (Fig. 1), to cover a range of interpopulation
distances from 10 to 340 km apart and to span potential
habitat discontinuities. Between Marrawah and the eastern
Tasmanian sites, the core distributional range of devils
comprises a long (140 km), narrow coastal strip that historically was unsuitable dense, wet-forested habitat. While
this region is within the top third of the state’s 1-km2 grid
cells in terms of the probability of finding a devil (based on
algorithms of known locations and environmental Geographical Information System (GIS) layers), it represents
less suitable habitat than other parts of the core distribution (Fig. 1). Since European settlement, the suitability of
this region has been further reduced through development
of agriculture, production forestry and urban land use. For
200 km south to the southern ocean, the landscape comprises dense wet eucalypt forest and rainforest, alpine
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
M A R S U P I A L C A R N I V O R E D I V E R S I T Y A N D S T R U C T U R E 2199
Barmuta 1998), and after the removal of a biopsy of ear
tissue, were released at the point of capture. Tissue samples
were stored in 70% alcohol, at −20 °C once back in the
laboratory.
Genomic DNA was extracted from a tiny piece of the
ear tissue using standard phenol–chloroform extraction
(Sambrook et al. 1989) or with Chelex (Bio-Rad™; SingerSam et al. 1989). Samples were screened at 11 polymorphic
microsatellite loci which conformed with expectations of no
linkage disequilibrium and Hardy–Weinberg equilibrium,
as described in Jones et al. (2003b).
Statistical analyses
Fig. 1 Current distribution of Tasmanian devils and sampling
localities. The dash-shaded area indicates the core distribution of
devils, defined as a high probability of finding a devil (the top
third of all of the 1-km2 grid cells in the state), derived from
environmental domain modelling ( Jones & Rose 1996). Within
this shaded area, habitat suitability and devil density will vary.
Also, devils are found throughout mainland Tasmania. The arrow
indicates the location of the Freycinet site. The small maps in boxes
show the location of Tasmania in relation to mainland Australia
(top centre), and King Island in Bass Strait, which is further
offshore than indicated (top left). In recent times (between > 400
and > 5000 years before present), devils were distributed over large
parts of southern mainland Australia.
areas, and dense wet heaths and moorlands, on either highaltitude plateau (1000 m) or heavily dissected river systems,
all of which support only low population densities of devils.
Gene flow between eastern and western Tasmania via a
more southerly route would also be severely impeded.
Each study site comprised from 50 to 100 km2 of continuous habitat, an area sufficient to trap a sample of 35 – 40
individuals in a few days, but presumably not so large for
there to be internal genetic structure. Trapping was conducted between June and September 1999, after the main
dispersal period (December to June) and before the next
year’s cohort of juveniles entered the trappable population
(from November) (Pemberton 1990). Devils were trapped
overnight in specially designed meat-baited, wire cage
carnivore traps. They were weighed, sexed and aged (on
a combination of sex, age, weight and tooth wear, Jones &
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
Genetic diversity among the six subpopulations was compared with a one-way analysis of variance (anova) and
Scheffe’s post hoc test performed in systat (SPSS 1998) on
measures of the mean number of alleles per locus (A) and
the unbiased expected heterozygosity (HE) for each locus
obtained using fstat (Goudet 1995, 2001). The observed
number of alleles in a population is highly dependent on
sample size. To test whether the field sampling regime
adequately represented allelic richness in Tasmanian devils,
we calculated the expected number of alleles in an infinite
population by Monte Carlo simulations (Roy et al. 1994).
A resampling without replacement procedure using the
pooled data set was repeated 1000 times and a quasiNewton best fit curve was applied to the mean number of
alleles by sample size using the equation y = αx/(x + β),
where y = mean number of alleles, x = number of individuals
selected, the constant α represents the number of alleles
expected in an infinite population, and β is the slope or rate
at which the curve is asymptotic.
Evidence for population bottlenecks was assessed using
two different statistical approaches: heterozygosity excess
and deficiency (Cornuet & Luikart 1996), and the ‘M’ ratio
test (Garza & Williamson 2001). Each subpopulation and
the pooled sample, from across the distributional range of
Tasmanian devils, was tested for evidence of recent reductions or expansions in effective population size (Ne) using
the statistical approach of Cornuet & Luikart (1996) in the
computer program bottleneck (Piry et al. 1999). Microsatellite loci typically conform to neither the stepwise
mutation model (SMM) nor the infinite allele model (IAM),
but better fit a two-phased mutation model (TPM) that
consists mostly of one-step changes with a small percentage (5–10%) of multistep changes (Di Rienzo et al. 1994;
Piry et al. 1999). Accordingly, the program was run under
a TPM with a mix of 90 : 10% SMM : IAM and with 10%
variance. Heterozygosity excess resulting from population
bottlenecks can most easily be detected using markers that
conform to the TPM model, but heterozygosity deficiencies are best detected using markers that fit the IAM model,
which provides the lowest estimate of expected equilibrium
2200 M . E . J O N E S E T A L .
heterozygosity for the number of alleles in the sample
(Cornuet & Luikart 1996). A Wilcoxon sign-rank test was
used to detect heterozygosity excess or deficiencies across
loci. With the relatively large sample sizes (36 –61) and
respectable number of loci (11), this test should have reasonable power to detect recent genetic bottlenecks. The total
data set was also analysed using the ‘M’ ratio test (Garza &
Williamson 2001; http://www.pfeg.noaa.gov/tib/staff/
carlos_ garza/carlossoftware.html). A significant reduction
in population size is deemed to have occurred if less than
5% of simulated replicates are below the observed value of
M, which is the ratio of the total number of alleles (k) to the
overall range in allele size (r). During a population reduction,
k should reduce faster than r (Garza & Williamson 2001).
Population structure was assessed by several different
methods. All analyses were run on adults, and separately
on adult males and females. First, allele frequencies were
used to describe genetic differentiation among populations.
Several complementary multivariate techniques were
used to represent relationships in the data that are free of
assumptions of genetic structure: multidimensional scaling (MDS) using a minimum stress difference of 0.005 as
the stopping rule, a minimum spanning tree (MST), and
nearest neighbour analysis with Bond strengths calculated
to weight the importance of each pairwise link independently of all others. All were implemented using the patn,
pattern analysis package; (Belbin 1993). MDS was examined in two and three dimensions for all analyses; in all
cases, the two-dimensional plot contained most of the
information in the three-dimensional plot. The MST and
bond analyses complement the MDS ordination because
they accurately represent local structure in the data,
whereas ordination tends to weight global structure. In the
present case the MST and bond analyses (where first and
second nearest neighbours were plotted) showed that the
two-dimensional MDS did not distort local relationships.
In addition, the bond analyses use the actual association
values for all pairs of sites rather than amalgamation of
distances via averaging inherent in upgma (unweighted
pair-groups method using arithmetic averages).
Second, population subdivision was examined using
both F and ρ statistics as suggested by Slatkin (1995). FST
and RST were calculated for all population pairs using
fstat Goudet (1995, 2001) and R ST calc Goodman (1997),
respectively. To test for correlation between the values of
FST and RST, a Mantel test (Pearson’s correlation, with 1000
iterations) was conducted in PATN (Belbin 1993). Population pairwise FST and corresponding probabilities that
deviations from FST = 0 are due to chance were calculated
for adults combined. To evaluate qualitative population
structure we partitioned variance in allele frequencies
[using an analysis of molecular variance (Excoffier et al.
1992) approach with R ST in the R ST calc package] using (i)
all six populations, corresponding to an assumption of
equal probability of gene flow, and (ii) based on predicted
habitat discontinuity, the five eastern populations as one
group and the western Marrawah as a second. Finally, isolation by distance (the relationship between genetic and
geographical distance) was analysed using Mantel tests
and Pearson’s correlation [with 5000 iterations, conducted
in PATN Belbin (1993)] on the two matrices of FST/(1 −
FST) and ln(km). This analysis was run for all six sites and
then for the eastern sites excluding Marrawah.
Third, population differentiation was assessed using
individual assignment tests developed by Paetkau et al.
(1995). Two different analyses were conducted, the first
using the assignment calculator program (J. Brzustowski,
http://www.biology.ualberta.ca/jbrzusto/Doh.html), and
the second using the software structure (version 2.1,
Pritchard et al. 2000). In the assignment calculator, each individual is assigned to the population in which its expected
genotype frequency is the highest (greatest probability of
occurrence). A matrix of pairwise population genotype
likelihood ratio distances (DLR), which performs well for
discriminating fine-scale population structure (Paetkau
et al. 1997), was generated. DLR represents the order of
magnitude likelihood of individual genotypes being born
in the population of capture rather than the other population of the pair (Paetkau et al. 1997). structure (Pritchard
et al. 2000) is a Bayesian model-based clustering method,
derived from assignment tests, which is free of assumptions of any particular mutation process. An important difference between structure and assignment tests is that in
structure the number of populations and their membership is defined a posteriori, whereas assignment tests
require that samples are grouped into ‘populations’ a priori
(omitting the reference sample). In structure, posterior
probabilities of the data fitting all possibilities of number of
populations (K) were calculated using the admixture
model, a burn-in of 10 000 iterations (checking that parameters α and likelihood had converged), and a run of 100 000
iterations. Three simulations were done for each K under
both the independent and correlated models. The estimated number of subpopulations, indicating the structure
in the data, is the value of K at which Pr(X/K) plateaus.
Pr(K) is likely to be very small for K less than the appropriate value (Pritchard et al. 2000).
Results
Genetic diversity among populations and bottlenecks
The curve of allelic richness estimated by Monte Carlo simulations (Fig. 2) has its asymptote at about 30 individuals, at
which point 37 of the 41 alleles expected in an infinite population are expected to have been detected. With subpopulation
sample sizes ranging from 36 to 61, we consider that field
sampling was adequate to detect most alleles present,
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
M A R S U P I A L C A R N I V O R E D I V E R S I T Y A N D S T R U C T U R E 2201
Table 1 Genetic diversity determined from 11 microsatellite loci
for Tasmanian devils
Fig. 2 Estimated cumulative number of alleles expected in an
infinite population derived using Monte Carlo simulations. In the
equation used to fit the curve, y = number of alleles, x = number of
individuals, and 41.116 (constant α) represents the number of
alleles expected in an infinite population. r2 = 0.966.
so that comparisons of uncorrected estimates of allelic
diversity among subpopulations are valid.
Measures of genetic diversity were uniformly low at all
sites across the species range (Table 1). They did not differ
significantly among subpopulations, although the Marrawah
subpopulation consistently had slightly lower values. Uncorrected, mean allelic diversity per locus (A) varied from 2.7
for the Marrawah subpopulation to 3.3 for Narawntapu
(anova F = 0.522; d.f. = 5; P = 0.759). HE ranged from 0.386
for Marrawah to 0.467 for Narawntapu (anova F = 0.247;
d.f. = 5; P = 0.940). No genetic signature of recent reductions
(genetic bottleneck) or expansions in effective population
size were found in any of the subpopulations (significant
heterozygosity excess or deficiency under TPM mutation
model) or the total population (‘M’ ratio test).
Population structure
Sixty per cent of the 41 alleles detected in the sample of 262
adult devils were present in all six populations (Appendix).
Relative to other populations, Marrawah had from two
to four unique alleles and was missing seven or eight
alleles, the latter mostly being at low frequency across other
populations and corresponding to the slightly lower allelic
diversity of this population (Table 2). Freycinet and
Narawntapu both had four unique alleles in comparison
with Bicheno. All other population pairs were in the range
of one to three unique or missing alleles.
Close agreement between the results of MDS, MST, and
nearest neighbour analyses show that Marrawah is quite
different from all of the eastern Tasmanian populations,
and that Freycinet is quite different from Little Swanport,
Pawleena and Narawntapu (Fig. 3). Qualitative patterns
for males and females were similar, with some minor differences among the arrangement of the three central sites,
Narawntapu, Little Swanport and Pawleena. Among the
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
Sampling location Pop N A
HO
HE
Freycinet
Bicheno
Little Swanport
Pawleena
Narawntapu
Marrawah
0.427 (0.062)
0.417 (0.055)
0.418 (0.054)
0.402 (0.058)
0.475 (0.042)
0.399 (0.074)
0.419 (0.060)
0.410 (0.055)
0.408 (0.054)
0.410 (0.055)
0.467 (0.040)
0.386 (0.062)
F
B
L
P
N
M
61
49
36
40
37
39
3.2 (0.3)
3.1 (0.4)
3.2 (0.3)
3.2 (0.4)
3.3 (0.3)
2.7 (0.3)
Pop = abbreviation used for sampling site/population;
N = number of individuals sampled for each sampling site;
A = mean number of alleles per locus; HO = observed
heterozygosity; HE = unbiased expected heterozygosity; standard
error is given in parentheses for all three.
Table 2 Number of unique alleles that are present in each
population of a pair of sampling localities for adult Tasmanian
devils
Total
no. alleles
Freycinet
Bicheno
Little Swanport
Pawleena
Narawntapu
Marrawah
Total
35
34
35
35
36
30
41
F
B
L
P
N
M
—
3
2
2
3
3
4
—
3
2
4
4
2
2
—
2
2
2
2
1
2
—
2
3
2
2
1
1
—
2
8
8
7
8
8
—
The entry for each population pair represents the number of alleles
that are unique to the row population and missing from the
column population.
eastern sites, the two pairs of populations that are geographically closest were also genetically most similar,
although the bond strengths suggest slightly closer similarities between Pawleena and Little Swanport, which are
40 km apart, than between Freycinet and Bicheno, which
are only 10 km apart. As expected from the geography
Bicheno is genetically intermediate between the peninsula
Freycinet sample and other east mainland sites. Figure 3
also indicates that Narawntapu and Marrawah are genetically closer to Pawleena and Little Swanport (200–240 km
and 330–340 km distant, respectively), than to the geographically closer Bicheno (150 km distant) and Narawntapu
(160 km distant) populations, respectively (Fig. 1).
Population structure: F and ρ statistics and isolation by
distance
Pairwise values of FST and R ST were closely correlated for
all populations (Pearson’s r = 0.979, P = 0.0001). Pairwise
2202 M . E . J O N E S E T A L .
Fig. 4 Geographic distance (km) vs FST for Tasmanian devils. Site
pairs including Marrawah are indicated with a triangle and an
arrow shows the Marrawah to Narawntapu pair.
Fig. 3 Genetic differentiation of sampling localities for Tasmanian
devils assessed using allele frequencies and represented by
multidimensional scaling and the values of a minimum spanning
tree with bond strengths indicated. Solid (bond strength 3) and
dashed (bond strength 2) lines emphasize strong and loose
relationships in the data.
of reduced gene flow across less suitable habitats. Population pairs involving the Marrawah site have substantially higher FST values than comparisons among eastern
populations separated by similar geographical distances
(see particularly the Marrawah/Narawntapu pair; Fig. 4).
When the Marrawah site was excluded from the analysis,
there was no significant association between genetic
divergence and geographical distance over 250 km (r = 0.582,
P = 0.961).
testing of FST values for adults indicated genetic differentiation significantly greater than zero (FST ranging from
0.0162 to 0.1895) among all populations except for the two
pairs of sites (Freycinet and Bicheno; Little Swanport and
Pawleena) that are geographically closest (Table 3). Over
all six sites, FST was 0.076 and RST was 0.072. A larger
proportion of variance was distributed among populations
when the eastern sites were grouped to the exclusion of
Marrawah (RST = 0.133).
Isolation-by-distance analysis indicated no significant
linear correlation between genetic divergence and distance
when all six sites were included (r = 0.698, P = 0.9998; the
high P-value means that most of the randomizations performed in the Mantel Test returned stronger correlations
than the observed value of r). Figure 4 shows that there are
two separate groups in the data, consistent with expectations
Population structure and gene flow: assignment tests
The majority of individuals were assigned to either the
population in which they were trapped if this population
was isolated, or, if the closest population was within 50 km,
to a combination of the population in which they were
trapped and the nearest population (Table 4). ‘Correct’
assignment rates (i.e. to the population in which the
Table 3 Pairwise comparisons of FST (lower matrix) and DLR (upper matrix) between all sampling sites for adult Tasmanian devils
Population
F
B
L
P
N
M
Freycinet
Bicheno
Little Swanport
Pawleena
Narawntapu
Marrawah
—
−0.0010NS
0.0475*
0.0201*
0.0297*
0.1807*
0.0544
—
0.0454*
0.0162*
0.0393*
0.1895*
0.8586
1.3484
—
0.0137NS
0.0583*
0.1555*
0.4775
0.7570
0.0489
—
0.0301*
0.1674*
0.8592
1.0732
1.2212
0.7261
—
0.1355*
4.6992
4.8879
3.1818
2.9570
3.1840
—
NS = not significant, * denotes significance at the 5% nominal level (P = 0.003) after Bonferroni adjustment for multiple comparisons.
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
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Table 4 Assignment of Tasmanian devils to populations (sampling localities) across Tasmania
Number assigned to population
Trapped at:
N
F
B
L
P
N
M
Closest population
(distance)
Freycinet (F)
Bicheno (B)
Little Swanport (L)
Pawleena (P)
Narawntapu (N)
Marrawah (M)
61
49
37
34
36
40
20
19
2
4
2
2
25
21
6
11
3
0
4
1
19
12
3
0
4
2
9
8
4
0
8
5
1
3
21
0
0
1
0
0
3
38
B (10)
F (10)
P (40)
LS (40)
B (150), F & M (160)
N (160)
The two pairs of populations enclosed by boxes are geographically close together.
individual was trapped) were very high (95%) for the most
genetically distinct site, Marrawah, and next highest for
Narawntapu (58%), which was closer to, but still 150 km
from, the other eastern sites. Considering the two geographically proximal pairs of sites, Freycinet and
Bicheno (10 km apart) and Little Swanport and Pawleena
(40 km apart) had totals of 82% and 75% of individuals,
respectively, ‘correctly’ assigned to either one of the site
pair.
Population pairwise values for DLR (Table 3) indicate that
in all pairwise comparisons with Marrawah, genotypes
have a 3.0–4.9 orders of magnitude higher probability of
occurrence in the population in which they were trapped
than in the other population, consistent with the genetic
distinctiveness of this population. Values for pairwise
assignments with Narawntapu range from slightly more
than one order of magnitude more likely to be assigned to
the population in which the individual was trapped than
to the other of the pair (for Bicheno and Little Swanport),
to slightly less than one (for Freycinet and Pawleena). Except
for a value of 1.3 between Little Swanport and Bicheno, all
other values of DLR, for population pairs in the eastern part
of Tasmania, were less than 1.0. Consistent with high rates
of reciprocal assignment of individuals, the geographically
close sites have very low DLR values (> 0.06).
Two population clusters within the sampled range of
Tasmanian devils were estimated in structure under
both the independent and correlated models. The real
value of K, that is the number of populations in the data set,
is taken as the smallest value of Pr(K) once the posterior
probabilities plateau, rather than at the highest likelihood
(Pritchard et al. 2000). Mean values (over three runs) of
posterior probabilities, under the independent model, for
possible population numbers 1– 6, and changes in value between population sizes, were: −4480, −4322, −4230, −4273,
−4300 and −4422, representing changes of 158, 92, −43, −27
and −122. Corresponding values under the correlated
model were: −4480, −4319, −4303, −4426, −4711, and −4 850;
changes of 161, 16, −123, −285, and 139. Posterior probabilities plateau at population sizes of two or three. That the
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
proportion of individuals assigned to these two populations was strongly asymmetric, with nearly all individuals
from Marrawah being strongly assigned to one population
and most individuals from the five east coast sites being
strongly assigned to the other, strongly suggests the existence of two separate populations.
Discussion
Genetic diversity within populations
Heterozygosity and allelic diversity of Tasmanian devils at
microsatellite loci are quite low (HE was 0.39–0.47, A was
2.7–3.3) compared with other Australian marsupials with
similar moderate or widespread distributions, such as the
northern (HE ≈ 0.51–0.66, A 3.6–9.0) and western quoll (HE
≈ 0.79–0.88, A 8.8–9.2) (Firestone et al. 2000), agile antechinus (HE 0.84–0.94) (Kraaijeveld-Smit et al. 2002), allied
rock wallaby (HE 0.86, A 11.6) (Spencer et al. 1997), Western
Australian and Northern Territory bilby populations (HE
0.81) (Moritz et al. 1997), koala populations that are not sizereduced (HE 0.54–0.78, A 5.6–8.0) (Houlden et al. 1996) and
southern hairy-nosed wombat (HE 0.83) (Alpers et al. 1998).
What possible explanations are there for low genetic diversity in devils? Carnivores live at lower population densities
than other mammals, and account for most of the lowest
heterozygosities recorded (Gillespie 1994). Expected heterozygosity and allelic diversity, which is likely to be the
most immediately effected in a bottleneck, were lower and
slightly lower, respectively, in Tasmanian devils, however,
than in the confamilial sympatric spotted-tailed quoll
(Dasyurus maculatus, HE ≈ 0.59–0.62, A 2.5–4.7) (Firestone
et al. 1999), despite quolls in Tasmania living at lower densities (estimated 1/20 km2 for quolls, 1/2 km2 for devils;
Jones, unpublished data) with a much smaller total population size (estimated 3500 compared with 130 000 for
devils, Jones & Rose 1996). Devils also have lower genetic
diversity than a number of placental carnivores, for example: wolverine HE 0.42–0.69, A 2.8–5.8 (Kyle & Strobeck
2001); fisher HE 0.58–0.68, A 3.9–5.9 (Kyle et al. 2001); pine
2204 M . E . J O N E S E T A L .
marten HE 0.62–0.68, A 3.4 – 6.4 (Kyle et al. 2000); grey wolf
HE 0.57–0.74, A 3.4 – 6.4; red wolf HE 0.55, A 5.3; golden
jackal HE 0.52, A 4.8 (Roy et al. 1994), lion HE 0.66, A 2–7;
puma HE 0.61, A 2– 8 (Menotti-Raymond & O’Brien 1995),
and polar bear HE 0.84 – 0.94, A 6.0 – 6.9 (Paetkau et al. 1999).
Could a founder effect in Tasmanian populations of the
devil or the restriction of devils to an island population for
the last 10 000–12 000 years be responsible for the low
genetic diversity? The size range of alleles is small and
almost completely continuous at every locus in devils,
suggesting limited evolution, that is, a founder effect.
Allowing for restriction of eucalypt forests in Tasmania
during and immediately after the last glaciation (Kirkpatrick
& Fowler 1998), the founding population of Tasmanian
devils could have been quite small. Comparison of Tasmanian
and mainland populations of other species of dasyurid
carnivores suggest that some erosion of genetic diversity as a
result of island effects may have occurred in devils. Eastern
quolls (D. viverrinus), which have a similar total population
size to devils (estimated at 130 000 prior to the Devil Facial
Tumour Disease epidemic, Jones & Rose 1996), have
slightly lower diversity than the now extinct mainland
populations (Tasmanian populations: HE ≈ 0.51–0.59;
mainland populations: HE ≈ 0.7–0.78). Tasmanian spottedtailed quolls, however, are intermediate in genetic variability compared with several mainland populations (Firestone
et al. 1999). Devils were possibly much less common prior
to European settlement than in recent decades, although
the estimated population size is not that small. It can be
estimated roughly at 45 000, given known population
densities of devils in suitable unmodified habitats (0.3–
0.7 km2; Jones unpublished data), the area of suitable habitat (half of Tasmania as predicted by bioclimatic models;
Jones & Rose 1996), and the land area of Tasmania
(165 000 km2).
An alternative, but nonexclusive, explanation is that
genetic diversity has been compromised by the repeated,
extended periods of low population density in Tasmanian
devils. The effect of low density on genetic variability
would be exacerbated by reproductive skew, in which a
few male devils dominate paternity (Jones, unpublished
data). Levels of variability in devils (HE 0.39– 0.47; A 2.7–
3.3) are similar to those reported for some moderately sizereduced mainland populations of marsupials, for example,
translocated (HE 0.44) and southeastern Australian (HE
0.33– 0.44; A 1.7–4.2) koala populations (Houlden et al. 1996),
and to that reported for the cheetah (HE 0.39) (MenottiRaymond & O’Brien 1995). However, genetic diversity in
devils is not quite as low as that reported for severely sizerestricted populations such as the northern hairy nosed
wombats (N ≤ 100, HE 0.27) (Taylor et al. 1994) or the longisolated Barrow Island population of the black-footed rock
wallaby (N = 150, HE 0.05) (Eldridge et al. 1999). That diversity has been reduced during periods of moderate, but not
severe, reduction in population size is consistent with the
absence of any signature of recent population bottlenecks.
Bottlenecks are likely to produce an allele size pattern of
distributions with gaps. However, if allele size distributions were limited initially because of founder effects, this
pattern may not be so evident. Monitoring of genetic diversity through the current population reduction will indicate
if moderate bottlenecks during disease epidemics contribute to the low diversity in devils.
It is possible that the repeated reductions and expansions of devil populations are a phenomenon only of the
last 200 years since the European settlement of Tasmania.
Conversion of a large proportion of the dry forests within
the devils’ core range to a patchy mosaic of grazing land
and forest remnants (excluding extensively cleared areas
which have a detrimental effect on devil populations) is
likely to have resulted in increased numbers of grazing
prey species (wombats, macropods and brushtail possums)
with a concomitant long-term increase in devil populations. Since pastoral development in the early 1800s, devil
populations may have reached unnaturally high densities
at times, conditions conducive to epidemic spread of
diseases, such as the current apparently infections cancer
epidemic (Devil Facial Tumour Disease) that have appeared
since 1995 (Nick Mooney; Phillip Ladds, Tasmanian
Regional Veterinary Pathology Laboratory, personal communication to M. Jones).
Spatial scale of dispersal and population structure
We examined gene flow and its effect on genetic population
structure at scales from 10 km to 340 km, across the entire
core distributional range of the Tasmanian devil. Our
sampling allows for detection of both distance effects
across continuous habitat and disruptions to gene flow
caused by variation in habitat — the former among eastern
populations and the latter between the western Marrawah
population and others (Fig. 1).
Given the limited sampling of populations relative to the
species’ range, the low divergence among most populations
examined here and the low genetic diversity, it is unlikely
that individual assignments can be made with high confidence (Manel et al. 2002). Nonetheless, the overall pattern
of assignments relative to distance between samples is
informative. Most individual assignments are to the source
population, or where sampling permits, to geographically
close (< 50 km) populations. The high self-assignment
rates for the Marrawah population (95%) reflect the substantial genetic divergence. The implied scale of dispersal
distances is realistic with respect to recorded movements
of devils. Radio-tracking studies have reported average
nightly within-home range movements of 13 km (Pemberton
1990), and occasional overnight out-of-home-range distances of 50 km (M. Jones, unpublished data). Dispersal
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
M A R S U P I A L C A R N I V O R E D I V E R S I T Y A N D S T R U C T U R E 2205
Fig. 5 Relationship between FST and body size for wild-caught,
ecologically equivalent marsupial and placental carnivores. The
approximate geographical scale (km) of sample populations
included in each estimate of FST is indicated in brackets next
to each point. Species represented by letters: Tasmanian devil
(d), spotted-tailed quoll (s, S), eastern quoll (e), northern quoll
(N), wolverine (w), fisher (f ), and pine marten (m, 800 km).
Estimates of FST for northern quolls and spotted-tailed quolls (S)
include populations defined as different evolutionarily significant
units. Data taken from Firestone et al. (2000) and Kyle et al. (2000,
2001).
movements from trapping records include 25 km in 10 days,
followed by 8 km in the next 2 days for a sub-adult female
born elsewhere (M. Jones, unpublished data). This individual
appeared to explore to the tip of the Freycinet peninsula
(site of long-term study, M. Jones) and back. Most dispersal
movements are probably of this circuitous, exploratory
nature rather than being straight-line distances, which
would reduce the total geographical distance moved
between the natal and adult breeding home ranges.
The geographical scale of dispersal inferred from the
assignment tests is corroborated by analysis of F statistics.
The pattern of significant differences between sites (pairwise FST; also reflected in DLR) suggests high levels of gene
flow at distances of less than 50 km, and low but significant
differentiation between sites at greater distances within
continuous habitat. However, the absence of isolation-bydistance suggests that the Tasmanian devil population in
the eastern half of Tasmania (at distances of < 250 km) is
well connected.
Tasmanian devils show similar levels of genetic structuring (overall and pairwise FST) to other marsupial carnivores at moderate geographical scales (Fig. 5) (Firestone
et al. 2000). This indicates high dispersal ability in all species, irrespective of differences in body size, which may
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
affect home-range size and dispersal ability. Dispersal is
probably male-biased in all species, so bi-parentally inherited microsatellite markers are unlikely to detect variation
in structure resulting from differences in social organization (female spotted-tailed and northern quolls are intrasexually territorial; female devils and eastern quolls and all
males are not) (Jones, unpublished data; Godsell 1983;
Pemberton 1990; Belcher 2000; Oakwood 2002). At larger
scales, spotted-tailed and northern quoll populations show
greater structure.
Although results are not directly comparable, given the
different loci used for different species, values of pairwise
FST suggest that genetic structuring is greater in marsupial
carnivores than in ecologically equivalent (Jones 2003)
placental carnivores (wolverines Gulo gulo, fishers Martes
pennanti, and pine martens M. americana, Kyle et al. 2000, 2001;
Kyle & Strobeck 2001) after differences in geographical
scale of the studies are taken into account, and irrespective of
body size (Fig. 5). Marsupials have a greater FST for the three
species comparisons matched for body size and geographical
extent of sampling: devil : wolverine, spotted-tailed quoll
over 3000 km : fisher, northern quoll : marten (Fig. 5). Lower
structuring in wolverines than in devils is consistent
with the known capability of wolverines to disperse
long distances (hundreds of kilometres) across physical
barriers and the potential for this in marten (Kyle et al.
2000; Kyle & Strobeck 2001). The greater structuring in
fishers, compared with wolverines and martens, is unusual, and is thought to reflect the stronger anthropogenic
influences to which fishers in that study were exposed
(Kyle et al. 2001). An explanation for the greater genetic
structuring in marsupial carnivores may lie in the much
broader scale of habitat heterogeneity across North America
compared with Australia. The mesic-adapted Tasmanian
devil and quoll species are restricted to bands on the southeastern, eastern and northern coastlines that are naturally
fragmented by mountain ranges and arid sections on a
smaller geographical scale. Data on genetic structuring
in western quolls (D. geoffroii) across the vast arid part of
their former range (see Jones et al. 2003a), were it available,
may elucidate this issue.
Effect of natural and anthropogenic landscape features on
gene flow
The analyses of devil populations across Tasmania are consistent with the hypothesis that habitat-related impedance
to dispersal affects gene flow. As expected from modelling
of habitat suitability (Fig. 1) and knowledge of the patchiness of habitat within the devils’ core distributional range,
the strongest genetic differentiation appears in comparisons
between the eastern half of the island, which is more or less
panmictic, and the northwestern corner (Marrawah site).
Marrawah was genetically distinct in all analyses with
2206 M . E . J O N E S E T A L .
slightly (but statistically nonsignificant) lower levels of
genetic and allelic diversity indicative of a relatively isolated
and perhaps smaller gene pool of devils on the northwest
and west coasts. While some of the increase in FST levels
between Marrawah and other sites can be attributed to
reduced diversity within the former (Hedrick 1999), the
pattern is also evident in multivariate analyses of gene
frequencies, in relative rates of assignment, and in the
Bayesian model-based clustering performed in structure.
The region between Marrawah and the eastern sites does
not have a lot of habitat that is suitable for devils. The
narrow coastal strip comprises much dense, wet forest
that is now highly fragmented by intensive agriculture
and urban development. South of this coastal strip, an
extensive alpine plateau with deeply incised drainages
and escarpment offers exposed alpine moorland and
structurally complex wet forest, respectively, habitat types
which are not used by devils ( Jones & Barmuta 2000).
Further field studies (trapping and radio-tracking) and
genetic sampling are needed, across mosaics of suitable
(grassy woodlands and open forest; Jones & Rose 1996;
Jones & Barmuta 2000) and unsuitable (farmland, dense
wet forest and alpine) habitats, to evaluate the effect of
landscape heterogeneity and anthropogenic influences on
dispersal potential and gene flow in devils. We suggest,
however, that if devils do not use wet forest, alpine and
open farmland habitats very much, then dispersal movements and gene flow across extensive areas of unsuitable
habitat, such as between Marrawah and eastern Tasmania,
might be impeded.
the Devil Facial Tumour Disease and should be included
as a factor in management decisions. Artificial movements
of animals (e.g. translocations of animals and release of
hand-reared orphans) between these two regions should
also be restricted. The high connectivity among eastern
populations reduces the number of options for managing
the geographical spread of the disease but indicates
greater flexibility for translocations of individuals
(Moritz 1999).
Acknowledgements
We would like to thank Reuven Hefner for his help with field
work, and the numerous people who assisted with access and
logistic support for the sampling programme: staff at Freycinet,
Narawntapu and Arthur River National Parks offices, Buckland
Military Training Centre, and Forestry Tasmania, Peter Stokes at
Kellevie and Mike and Sue Lyne of Greenlawn for access to their
properties, and Geoff King at Marrawah for enthusiastic interest
and generous friendship. Many thanks to colleagues in the Molecular Zoology Laboratory at the University of Queensland for
an enjoyable year and for answering lots of questions, especially
Nancy Fitzsimmons, Dani Tikel, Andrew Hugall and Arnaud
Estoup. Claire Sullivan is especially acknowledged for her excellent and cheerful work in screening large numbers of samples.
Nick Mooney and three anonymous reviewers provided valuable
comments on the manuscript. This project was funded by grants
from the National Geographic Society, the Estate of W.V. Scott and
the Australian Research Council. The work was conducted with
permission from the University of Queensland Animal Ethics
Committee and the Tasmanian Nature Conservation Branch.
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High levels of genetic variability in an isolated colony of rockwallabies (Petrogale assimilis): evidence from three classes of
molecular markers Australian Journal of Zoology, 45, 199–210.
SPSS (1998) SYSTAT 9.01: New Statistics. SPSS, Chicago IL.
Taylor AC, Sherwin WB, Wayne RK (1994) Genetic variation of
microsatellite loci in a bottlenecked species: the northern hairy
nosed wombat Lasiorhinus krefftii. Molecular Ecology, 3, 277–290.
This project, conducted by Menna Jones as a postdoctoral researcher
at the University of Queensland Molecular Zoology Laboratory, is
part of a research programme investigating social and spatial
structure, sexual selection and life history of the larger marsupial
carnivores. David Paetkau runs a private laboratory that sits at the
interface between academic research and applied wildlife management. Eli Geffen participated in the trapping and processing
of devils across Tasmania, and contributed to the analysis and
interpretation of the genetic information. Craig Moritz has moved
to the Museum of Vertebrate Zoology at UC Berkeley and sometimes even has time for research!
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209
M A R S U P I A L C A R N I V O R E D I V E R S I T Y A N D S T R U C T U R E 2209
Appendix
Allele frequency distributions for 11 microsatellite loci and six populations of devils (males and females combined). (N) = sample size
Population
Locus:
Allele
Freycinet
Bicheno
Little
Swanport
Pawleena
Narawntapu
Marrawah
(N)
334:
334:
334:
334:
232
234
236
238
61
0.033
0.795
0.172
0.000
49
0.000
0.755
0.224
0.020
37
0.500
0.446
0.041
0.014
39
0.333
0.603
0.064
0.000
36
0.069
0.764
0.167
0.000
40
0.350
0.613
0.038
0.000
(N)
336:
336:
336:
336:
122
124
126
128
61
0.582
0.033
0.385
0.000
49
0.541
0.000
0.449
0.010
37
0.662
0.014
0.284
0.041
39
0.526
0.013
0.423
0.038
36
0.569
0.139
0.278
0.014
40
0.388
0.000
0.275
0.338
(N)
346:
346:
346:
120
126
128
61
0.041
0.893
0.066
49
0.082
0.837
0.082
37
0.054
0.838
0.108
39
0.051
0.885
0.064
36
0.069
0.847
0.083
40
0.000
0.950
0.050
(N)
366:
366:
366:
178
180
182
60
0.058
0.058
0.883
49
0.051
0.071
0.878
37
0.068
0.041
0.892
39
0.064
0.038
0.897
36
0.181
0.014
0.806
40
0.000
0.000
1.000
(N)
372:
372:
372:
190
192
194
61
0.566
0.434
0.000
49
0.510
0.490
0.000
34
0.515
0.485
0.000
39
0.462
0.538
0.000
36
0.431
0.556
0.014
38
0.776
0.224
0.000
(N)
332:
332:
120
124
61
0.721
0.279
49
0.847
0.153
37
0.878
0.122
39
0.833
0.167
36
0.611
0.389
40
0.800
0.200
(N)
338:
338:
338:
338:
338:
338:
338:
194
196
198
200
204
206
210
61
0.033
0.311
0.443
0.107
0.000
0.025
0.082
49
0.031
0.306
0.520
0.071
0.010
0.020
0.041
37
0.027
0.392
0.473
0.041
0.000
0.000
0.068
38
0.079
0.355
0.461
0.053
0.013
0.026
0.013
36
0.056
0.417
0.306
0.097
0.097
0.000
0.028
40
0.475
0.425
0.050
0.025
0.000
0.000
0.025
(N)
342:
342:
342:
342:
228
230
232
234
61
0.049
0.467
0.189
0.295
49
0.061
0.531
0.133
0.276
37
0.054
0.500
0.135
0.311
39
0.038
0.577
0.205
0.179
36
0.083
0.583
0.264
0.069
40
0.113
0.688
0.200
0.000
(N)
356:
356:
356:
356:
203
209
211
217
61
0.000
0.352
0.607
0.041
49
0.000
0.347
0.612
0.041
37
0.000
0.176
0.811
0.014
39
0.000
0.256
0.731
0.013
36
0.000
0.125
0.778
0.097
40
0.038
0.175
0.788
0.000
(N)
360:
360:
360:
146
148
150
61
0.016
0.541
0.443
49
0.000
0.541
0.459
37
0.000
0.446
0.554
38
0.000
0.658
0.342
36
0.000
0.569
0.431
40
0.000
0.188
0.813
(N)
370:
370:
370:
370:
148
150
152
156
61
0.016
0.967
0.016
0.000
49
0.031
0.969
0.000
0.000
37
0.068
0.919
0.014
0.000
38
0.066
0.934
0.000
0.000
36
0.222
0.750
0.028
0.000
40
0.588
0.300
0.088
0.025
© 2004 Blackwell Publishing Ltd, Molecular Ecology, 13, 2197–2209