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Death by sex in an Australian icon: A continent-wide survey reveals extensive hybridization between dingoes and domestic dogs

Authors:
  • CSIRO - The Commonwealth Scientific and Industrial Research Organisation

Abstract

Hybridisation between domesticated animals and their wild counterparts can disrupt adaptive gene combinations, reduce genetic diversity, extinguish wild populations, and change ecosystem function. The dingo is a free-ranging dog that is an iconic apex predator and distributed throughout most of mainland Australia. Dingoes readily hybridise with domestic dogs, and in many Australian jurisdictions distinct management strategies are dictated by hybrid status. Yet, the magnitude and spatial extent of domestic dog-dingo hybridisation is poorly characterised. To address this, we performed a continent-wide analysis of hybridisation throughout Australia based on 24 locus microsatellite DNA genotypes from 3,637 free-ranging dogs. Although 46% of all free-ranging dogs were classified as pure dingoes, all regions exhibited some hybridisation, and the magnitude varied substantially. The south-east of Australia was highly admixed, with 99% of animals being hybrids or feral domestic dogs, whereas only 13% of the animals from remote central Australia were hybrids. Almost all free-ranging dogs had some dingo ancestry, indicating that domestic dogs could have poor survivorship in non-urban Australian environments. Overall, wild pure dingoes remain the dominant predator over most of Australia, but the speed and extent to which hybridisation has occurred in the approximately 220 years since the first introduction of domestic dogs indicates that the process may soon threaten the persistence of pure dingoes. This article is protected by copyright. All rights reserved.
Death by sex in an Australian icon: a continent-wide survey reveals
extensive hybridisation between dingoes and domestic dogs
Danielle Stephens1, Alan N. Wilton2, Peter J.S. Fleming3,4, Oliver Berry1,5
1. School of Animal Biology and Invasive Animals Cooperative Research Centre, M092,
The University of Western Australia, Crawley, Western Australia, 6009, Australia.
2. School of Biotechnology and Biomolecular Sciences and Clive and Vera Ramaciotti
Centre for Gene Function Analysis, University of New South Wales, New South Wales
2052, Australia.
3. Vertebrate Pest Research Unit, Biosecurity NSW, NSW Department of Primary
Industries, Orange Agricultural Institute, New South Wales, 2800, Australia.
4. School of Environmental and Rural Sciences, University of New England, Armidale, New
South Wales, 2351, Australia.
5. Current address: CSIRO Oceans and Atmosphere, PMB 5, Floreat, Western Australia,
Australia, 6014.
Keywords: Dingo, Canis familiaris dingo, Canis familiaris, hybridisation, admixture,
conservation, wild dog
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Abstract
Hybridisation between domesticated animals and their wild counterparts can disrupt adaptive
gene combinations, reduce genetic diversity, extinguish wild populations, and change
ecosystem function. The dingo is a free-ranging dog that is an iconic apex predator and
distributed throughout most of mainland Australia. Dingoes readily hybridise with domestic
dogs, and in many Australian jurisdictions distinct management strategies are dictated by
hybrid status. Yet, the magnitude and spatial extent of domestic dog-dingo hybridisation is
poorly characterised. To address this, we performed a continent-wide analysis of
hybridisation throughout Australia based on 24 locus microsatellite DNA genotypes from
3,637 free-ranging dogs. Although 46% of all free-ranging dogs were classified as pure
dingoes, all regions exhibited some hybridisation, and the magnitude varied substantially. The
south-east of Australia was highly admixed, with 99% of animals being hybrids or feral
domestic dogs, whereas only 13% of the animals from remote central Australia were hybrids.
Almost all free-ranging dogs had some dingo ancestry, indicating that domestic dogs could
have poor survivorship in non-urban Australian environments. Overall, wild pure dingoes
remain the dominant predator over most of Australia, but the speed and extent to which
hybridisation has occurred in the approximately 220 years since the first introduction of
domestic dogs indicates that the process may soon threaten the persistence of pure dingoes.
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Introduction
A common consequence of the expansion of human settlements is the interaction between
domestic and wild animals (Stronen et al. 2012 ). Gene flow from domestic animals to their
wild counterparts, through hybridisation, can threaten the integrity of wild lineages in several
ways, including; disrupting adaptive gene complexes, reducing reproductive opportunities,
and genomic swamping of small wild populations (Rhymer & Simberloff 1996). Globally,
rates of hybridisation are increasing (Allendorf et al. 2001), yet, although threats from
hybridisation are well-recognised, the sometimes morphologically cryptic nature of
hybridisation can make it a difficult process to identify and manage effectively (Allendorf et
al. 2001).
In Australia, there are large and widely distributed populations of free-ranging dogs,
including the dingo (Canis lupus dingo), domestic dogs, and their hybrids. Dingoes are
medium-sized, generalist predators (~15 kg adult weight) that have been, or are currently,
present in most regions of mainland Australia (Fleming et al. 2014 ). Dingoes are an ancient
breed of dog that form a discrete clade within the most basal domestic dog lineage (vonHoldt
et al. 2010), and were likely transported to Australia from East Asia approximately 5,000
years ago by seafarers via the south-east Asian archipelago (Gollan 1984; Savolainen et al.
2004). Dingoes have replaced the extinct Tasmanian tiger (Thylacinus cynocephalus) as
Australia’s mainland apex predator, and are thought to fulfil important ecosystem roles by
moderating densities of native herbivores (Letnic and Crowther 2013; Pople et al. 2000 ) and
possibly introduced mesocarnivores (Johnson et al. 2007; Brook et al. 2012 ; Ritchie et al.
2012). Dingoes are designated ‘native’ animals and subject to conservation efforts in some
jurisdictions (Fleming et al. 2001, 2014).
Domestic dogs of multiple breeds have been present in Australia since the arrival of
European settlers in 1788 (Fleming et al. 2001), and interbreeding between domestic dogs
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and dingoes has been observed both directly and through the identification of hybrid
offspring by skull morphometrics and DNA testing (Elledge et al. 2008; Newsome & Corbett
1982, 1985). These studies in several geographic regions suggest hybridisation between
domestic dogs and dingoes has been rapid and that the regional hybrid proportions
correspond with duration of European habitation. The high number of domestic dogs in
Australia and the regular contact between these and dingoes facilitate the potential swamping
of the dingo’s gene pool so that pure dingoes may soon no longer exist in the wild (Daniels &
Corbett 2003). This could lead to the loss of a relict taxon that has a unique position in
Australian ecosystems and could lead to new ecological roles (Claridge et al. 2014).
In addition to the potential loss of an iconic taxon, hybridisation can lead to the
disruption of adaptive gene combinations and a decrease in fitness (Tufto 2010). The
domestication of dogs has proportionally reduced their brain size and introduced other
differences in skull morphology between dingoes and dogs (Newsome et al. 1980). These
differences, which are associated with hearing and mandibular strength, might reduce
survival of hybrids. Alternatively, given the likely few progenitors of the dingoes extant at the
arrival of Europeans (Savolainen et al. 2004), hybridisation may enhance survival by
increasing genetic diversity (Seehausen 2004). The effect that hybridisation might have on
the behavioural ecology of dingoes and hybrids remains unexplored but is of great interest
because of the dingo’s putative role as a trophic regulator (Claridge & Hunt 2008; Claridge et
al. 2010, Glen 2010). A reliable test for the introgression of domestic dog genes into dingo
populations is required before investigation of these effects.
The dingo is also an excellent case study in the ability to detect complicated
hybridisation patterns in a widespread terrestrial mammal. Although hybridisation has been
reported in other wolf-like canids, such as between wolves (Canis lupus and ssp.) and dogs
(C. familiaris: Randi & Lucchini 2002; Muñoz-Fuentes et al. 2010), and between wolves and
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coyotes (Canis latrans; Koblmüller et al. 2009; Wilson et al. 2009), it has usually been at low
levels or geographically restricted (Stronen et al. 2012 ). The regular contact between dingoes
and dogs, the presence of multiple contact sites and often dense populations of free-roaming
domestic dogs, the difficulty in confidently identifying pure dingoes for a genetic ‘baseline’
(e.g., Newsome et al. 1980), the relatively low level of genetic difference between dingoes
and other dogs (compared to interspecific hybrids), the acknowledged use of dingo genes in
at least one established domestic breed (the Australian Cattle Dog or Blue Heeler; Arnstein et
al. 1964) and the likelihood of multiple generations of backcrossing and hybrid interbreeding
are all challenges to predicting accurately the proportion of dingo ancestry in wild dogs.
The extent of domestic dog-dingo hybridisation is poorly known throughout most of
their range, although it is an important consideration for both dingo conservation and
management of free-ranging dogs for livestock protection (Fleming et al. 2006 ; Fleming et
al. In press ). All Australian States and Territories have legislation and policies for the
management and conservation of dingoes (Fleming et al. 2001), yet implementation is
hampered by lack of knowledge about the distribution and abundance of pure dingoes. In
some regions, the identification of a free-ranging dog as a hybrid, domestic dog or a pure
dingo can mean the difference between mandated destruction and active conservation.
Historically in Australia, dingoes and dingo hybrids were identified through
comparison of the skull morphometrics of remotely sourced dingoes with those of known
domestic dogs and captive bred hybrids (Newsome et al. 1980; Newsome & Corbett 1982,
1985). However, this method cannot be used on either live or juvenile animals, and fails to
accurately identify backcrossed animals (Wilton et al. 1999; Daniels & Corbett 2003; Elledge
et al. 2006). Morphometric measurements are also affected by non-heritable factors, such as
levels of nutrition during growth (Newsome & Corbett 1982). Additionally, both the dingo
and domestic dog samples used in the initial comparisons were assumed not to be hybrids
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because the dingoes were acquired from remote central Australian sources and the domestic
dogs were obtained from an urban dog pound 200 km away (Newsome et al. 1980). Although
it would appear reasonable that these populations were isolated and that pure dingoes were
more likely to be extant in remote areas, there is no surety that either group excluded hybrids.
Genetic analysis offers a more direct means to identify hybridisation. Suitable microsatellite
genetic markers have been developed for the purpose of dingo hybrid testing (Wilton et al.
1999; Wilton 2001), and have been successfully applied on relatively small spatial scales
(Elledge et al. 2008; Robley et al. 2010; Newsome et al. 2013).
This study is the first continent-wide survey to estimate the extent of hybridisation in
an animal, and the first extensive survey of Australian free-ranging dogs to identify pure
dingoes. We focus in particular on the western third of the continent, which had been little
studied compared with south-eastern and central Australia. We expected (after Newsome et
al. 1980 and Corbett 2001) the majority of south-eastern Australia to have high levels of
hybridisation and central Australia to have lower levels, in line with previous regional studies
of skull morphometrics (e.g. Newsome & Corbett 1985; Woodall et al. 1996). We employ
both Bayesian clustering and log-of-odds methods for identification of hybrids. We also
evaluate the performance of these methods against a comprehensive set of simulated hybrid
crosses.
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Methods
Sample collection and laboratory analysis
Tissue samples, mostly ear tips or biopsies, of approximately 5 mm2 were collected between
2007 and 2009 from free-ranging dogs culled for livestock protection by land managers or
trapped for behavioural research (e.g. movement behaviour, Claridge et al. 2009; Newsome
et al. 2013). Samples were stored at room temperature in 3 ml of lysis buffer (Longmire et al.
1997). Map coordinates or descriptions of the collection locality were recorded for each
specimen. Specimens of free-roaming dogs (n= 3,941; Figure 1) were obtained from across
every mainland Australian state, representing all of the bioregions in which they occur (West
2008) and comprising the largest and broadest sampling of dingoes and other free-roaming
dogs to date. In addition, samples were collected from 92 owned domestic dogs of multiple
breeds and their crosses, the majority (n = 86) from Yuendumu (22.253° S, 131.801° E) in
central Australia (see Newsome et al. 2013). The owned dogs were used for creation of a
posteriori reference populations (see details below), but were not included in spatial
interpolation of the extent of hybridisation because they were not part of wild-living dog
populations.
DNA was extracted using a manual glass-fibre method (Ivanova et al. 2006) on 96-well
plates. A 1/3 dilution of extracts was used for amplification to mitigate oversized fluorescence
peaks. Twenty-four microsatellite loci were amplified in 5 multiplex PCR reactions. Primer
combinations are listed in Table 1. PCRs were carried out in 10 μl, consisting of: 5 μl Qiagen
Multiplex PCR solution (Qiagen Inc. Valencia, CA, USA), 1 μl Qiagen Q-Solution, 1 μl
DNA, 0.2 μM of each primer and DNAase/RNAase-free water. PCRs were run with 15
minutes at 95 oC for polymerase activation, followed by 35 cycles of 30 seconds at 94 °C, 90
s at 60 °C and 60 s at 72 °C, with 30 minutes final extension at 60 °C. Fragments were run on
an ABI 3730 capillary sequencer, and analysed using GeneMarker® software (SoftGenetics,
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LLC.). To determine the level of error in genotyping, 48 randomly selected specimens were
tested twice to create consensus genotypes, and the error rate calculated using the software
Microchecker (Van Oosterhout et al. 2004).
‘Average 3Q’ tests of dingo purity
All specimens were analysed for dingo purity with the ‘average 3Q’ method of
Wilton (2001) and Wilton et al. 1999. Categorisation of dingo ancestry relied on a
reference sample set of allele frequencies and private alleles from 90 domestic dogs
acquired from urban populations and held by one of the authors (ANW) and 60 dingoes
collected from central Australia (South Australia and Northern Territory) determined pure
from their pedigrees, which were derived from skull morphometrics and appearance of
their progenitors using the methods of Newsome et al. (1980), and Newsome and Corbett
(1982, 1985) and previously tested pure by Wilton et al. (1999) using 14 markers. New
genotypes were compared to the respective dingo and domestic dog reference allele
frequencies to establish the relative probability that a dog is from pure dingo ancestry and
not that of a ¾ dingo. This comparison provides an ‘average 3Q score’, the log of the
probability ratio (log(Pdingo/P3/4 dingo)) (Elledge et al. 2008; Wilton et al. 1999). The
probability was then averaged across loci for comparison across samples with different
numbers of successfully scored loci. Other hybrid crosses were further inferred from the
reference alleles, e.g., a 50% dingo DNA profile was created by averaging the frequency
from domestic dogs and dingoes for each allele (described below). Individuals were then
assigned to a category most likely to represent their percentage of dingo ancestry.
The presence of alleles diagnostic for domestic dog ancestry (‘doglike’ alleles) was
used in conjunction with the 3Q scores to assign individuals to one of 7 categories (as per
Elledge et al. 2008). The categories used were: (1) Pure dingo (3Q score > 0.1, no doglike
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alleles); (2) probable pure dingo (0.05 < 3Q < 0.1, no doglike alleles); (3) probable hybrid,
>75% dingo (0 < 3Q < 0.05), 1 doglike alleles); (4) 65-75% dingo (-0.1 < 3Q < 0) , 1
doglike alleles; (5) 50-65% dingo (-0.25 < 3Q < -0.1) , 1 doglike alleles; (6) less than 50%
dingo (-0.5 < 3Q < -0.25), , 1 doglike alleles, and; (7) domestic dog (3Q < -0.5) , 1 doglike
alleles. As the 90 domestic dogs and 60 dingoes used for reference in this method were
required to develop the test, we refer to them as ‘a priori reference populations.
The fragment analysis equipment used in this study was different from that used to
analyse the fragment data from the original reference groups. Because different equipment
and choice of fluorophores for each locus can cause size differences in microsatellite data
(Pasqualotto et al. 2007), we adjusted microsatellite sizes to match the reference groups of
Wilton (2001) using a combination of running 20 samples on both fragment analysers to
compare size differences, and matching the pattern of allele frequencies between the data
sets. If a locus could not be matched to the a priori reference group (e.g., inconsistent sizes
when both samples were run on each fragment analyser) it was discarded.
Bayesian clustering tests of dingo purity
Although the reference sample-based method of Wilton (2001) has been criticised because of
uncertainty whether dingo reference samples are genuinely free of domestic dog DNA
(Daniels & Corbett 2003), the level of such impurities is likely very low because large allele
frequency differences exist between the reference groups (Wilton et al. 1999). However, the
amount of domestic dog ancestry in hybrids might be underestimated, consequently
overemphasising the dingo ancestry and biasing towards a more optimistic understanding of
the status of dingo hybridisation. Although unlikely in our case given the history of
acquisition and distribution of animals used to obtain the reference collection, a further
potential problem is that the dingo reference samples might not adequately represent the
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extent of genetic diversity throughout the Australian continent (Elledge et al. 2006; Bray et
al. 2009).
To overcome this potential bias, an alternative method, less reliant on a priori
reference samples, using a model-based clustering algorithm that seeks to identify long-
interbreeding groups of individuals can be used. Such methods are widely used to detect
population structure, but have also been applied to characterise admixture, for example
between domestic cats (Felis catus) and wildcats (F. silvestris) in Europe (Beaumont et al.
2001), and red wolves (Canis rufus) and coyotes (Canis latrans; Bohling & Waits 2011;
Bohling et al. 2013). We used the program Structure (v2.3.3; Pritchard et al. 2000) to
estimate the contribution of dingo and domestic dog ancestry to each individual’s genome.
Structure clusters individuals in order to maximise conformance to Hardy-Weinberg and
linkage equilibria, thereby identifying clusters of interbreeding individuals, and attributes all
or part of each individual’s genome to a pre-defined, but adjustable, number of clusters
(Pritchard et al. 2000). Each analysis performed in this study was run for 200,000 iterations
with 20,000 burn-in runs, which was sufficient for the parameters to reach convergence,
using the admixture and correlated allele frequency models (Falush et al. 2003). In addition,
10 replicates were performed for the selected value of K and q values averaged with the
program CLUMPP (Jakobsson & Rosenberg 2007).
The Bayesian framework underlying Structure permits prior identity information,
such as the sampling location, to assist in modelling. Moderate population structure exists
within free-ranging dingo populations (Fst ~ 0.05; Stephens 2012), but is significantly weaker
than that between dingoes and domestic dogs (Fst ~ 0.35; see results). In order to focus
clustering on subdivision between dogs and dingoes without influence of geographic
subdivision we employed a ‘learning samples’ approach (Pritchard et al. 2010), where we
identified a posteriori domestic dog and dingo reference samples to establish two reference
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clusters, which provide allele frequency estimates to assign the unknown samples to either
cluster. These new reference individuals were sampled for this purpose. First, all specimens
identified by the average 3Q method as category 1 (pure dingo; n = 361) or 7 (domestic dog;
n = 113, including the 86 Yuendumu dogs and 27 domestic dogs caught during the study)
were selected from the rest of the sample, to reduce the test data to a manageable size and
eliminate samples with a low probability of being useful. Principal coordinates analysis
(PCoA) was then used to identify the domestic dogs and dingoes most genetically distinct
from each other and therefore least likely to be admixed. The PCoA was performed on a
genetic distance matrix (described in Smouse & Peakall 1999) between all individuals in
categories 1 and 7, as implemented in Genalex v6.4 (Peakall & Smouse 2006). Although the
a posteriori domestic dog reference sample selected using PCoA is not representative of all
dog breeds, it is likely to represent the types of breeds and crossbreeds that come into contact
with dingoes, which is more suitable for the purpose of this study. The samples were
subsequently screened with Structure for the level of Q-assignment to each of two source
populations (k=2), and only samples with 95% assignment to the appropriate population
were retained to create two a posteriori reference populations from the most dissimilar
individuals in each group.
The individuals in the a posteriori reference groups were then added to Structure with
the USEPOPINFO flag turned on and updating allele frequencies from the reference
populations. Structure was re-run with all specimens, the USEPOPINFO flag was turned off
for all non-reference specimens, and the cluster number of k = 2 .These settings ensure only
the allelic information from the a posteriori reference groups are used to assign the remainder
of the sample, and does not use data from the non-reference samples to inform the model.
The selected a posteriori reference group for dingoes contained a large proportion of
individuals from the Western third of the Australian continent (Figure 2). To evaluate whether
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population subdivision could influence estimates of admixture we trialled alternative
reference groups for dingoes varying the contribution of individuals from the western region.
Our results indicated that the geographic origins of the dingo reference group had trivial
influence on estimates of admixture (Supplementary Table 1).
Additional analyses
Heterozygosity (Ho and He) statistics and the number of alleles were also calculated from the
a posteriori reference groups to determine any differences in genetic diversity between the
domestic dogs and dingoes with Arlequin v3.5 (Excoffier & Lischer 2010). Related samples
Wilcoxon signed-rank tests were used to determine if the difference between groups was
significant, implemented using PASW statistics 18 for Windows (SPSS Inc.). The level of
genetic subdivision (Fst) between the a posteriori domestic dog and dingo references was
calculated with Genalex, and significance tested with 999 permutations of individuals among
the two groups.
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Spatial patterns of dingo purity
Results of purity tests were geographically mapped using ArcGIS 9.3 (ESRI Inc., Redlands,
CA, USA), including ordinary kriging analysis to interpolate to areas not sampled and to
display multiple specimens that were sampled at the same site. A prediction map was
generated, using the mean purity results when there were multiple points at the same location.
The spherical model in ArcGIS was used with between 2 and 50 neighbours included. Dogs
sampled in captivity at Yuendumu, Northern Territory, were not included in the interpolation.
Simulations of hybrid populations
Because the true ancestry of wild animals is unknown, and therefore the accuracy of the
reference assignments and clusters difficult to evaluate, we simulated hybrid crosses with the
program Hybridlab (Nielsen et al. 2006) to test the performance of both methods in detecting
various hybrid and backcrossed individuals. Fifty randomly selected individuals from each of
the a posteriori reference samples were used as the ‘parent’ populations, and all simulated
hybrid groups also contained 50 individuals. The crosses (detailed in Table 2) were then
analysed using the clustering and average 3Q methods. The settings used in Structure to
analyse the simulated data were the same as described above for the real data, with all the a
posteriori reference samples that were not used in the simulation included as learning
samples. The means and 95% confidence intervals of each cross were calculated, using the
estimate of the proportion of ancestry in the dingo cluster for the clustering method, and the
average 3Q score was used for the average 3Q method. Both were then compared to the
theoretically expected content of dingo ancestry (Table 2). In relation to the a posteriori
analysis, Structure estimates “probability intervals” around each mean q. However, these
estimates are acknowledged as being conservative (e.g. Bohling & Waits 2011, Trigo et al.
2008), whereas simulation and empirical studies demonstrate that mean q values accurately
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estimate admixture proportions for populations as divergent as dogs and dingoes (Vähä &
Primmer 2006). Therefore, we elected to use simulations as a more direct evaluation of
accuracy than considering the probability intervals reported by Structure.
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Results
Distribution of samples
Although sampling was undertaken predominantly in Western Australia, the distribution of
samples (Fig 1) was broadly representative of the density and dispersion of free-ranging
dingoes and other wild dogs across the continent (see Fig 2 in Fleming et al. 2001, Fleming
et al. 2014). After the removal of specimens without spatial coordinates, those with fewer
than 14 loci successfully amplified, and those with evidence of sporadic contamination (more
than 3 alleles at a locus for more than 4 loci), 3,637 wild dog specimens and 88 captive dogs
were available for analysis (Fig. 1). Loci with 3 or more alleles at < 4 loci were treated as
having missing data. Locus FH2175 was removed from the data set because it could not be
reliably scored due to an inconsistent repeat pattern. Locus FH2293 was not used in average
3Q method tests because it could not be consistently matched to the original a priori
reference population alleles. The number of loci used was therefore 22 for the average 3Q
method tests and 23 for the clustering analysis. The error rate determined from the replicated
genotyping was 0.000 (negligible).
Construction of a posteriori reference groups
PCoA revealed that the specimens identified with the average 3Q method as being pure dingo
formed a single, well-defined cluster composed of animals from throughout the sampled
regions of Australia except the far south-east (Fig 2). The horizontal outlier from the dingo
group in Fig. 2 (x = -0.19) is probably separated from the other dingo specimens because
only 15 loci were successfully typed for this individual. Specimens with a coordinate 1 score
of > 0.15 were used as a preliminary group of reference ‘pure’ dingoes. This value captured
the densest cluster of dingoes and provided an adequate sample size but still a conservative
cut-off. Individuals with PCoA scores < -0.5 were used as the a posteriori reference
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population for domestic dogs in construction of simulated hybrid crosses as they were most
dissimilar to pure dingoes. This population consisted of 86 captive animals and 16 dogs
caught in the wild, from Western Australia (10 specimens), Victoria (5) and Queensland (1),
which clustered with the other domestic dogs in the PCoA. The dingo reference group
contained 242 samples from WA, 23 from QLD, 14 from SA, 1 from NSW and 44 from NT
(Fig. 2 inset). After specimens with < 0.95 population assignment in Structure were removed,
the final sample numbers were n = 322 for the dingo reference group (2 from Western
Australia removed), and n = 102 for the dog group.
The expected and observed heterozygosities and the mean number of alleles per locus
from the a posteriori reference groups were all significantly lower in the dingo than the dog
group (Wilcoxon sign rank test, P < 0.001 for all tests). He, Ho and mean number of alleles
for dingoes were 0.47 (± 0.34 S.D.), 0.38 (± 0.28) and 9.23 (± 7.89) respectively, and for
domestic dogs were 0.76 (± 0.19), 0.69 (± 0.17) and 11.78 (± 7.93). Significant genetic
subdivision between these reference groups was recorded (Fst = 0.353, P < 0.001).
Hybridisation levels and spatial distribution of dingoes
Using the average 3Q method, 1,695 (47%) of the wild dogs were ‘pure’ dingoes (category 1
or 2). Analysis using the clustering method showed 1,664 specimens with cluster assignment
0.90 for dingo ancestry (46% of the sample) (Fig. 3). Assignment of 0.90 was chosen as
an arbitrary cut-off for the classification of ‘pure’ dingoes using the Bayesian method. The
clustering method showed equal or fewer pure dingoes in all states than the average 3Q
method (Fig 3(b) and (d)), but hybrids from the former results generally were assigned a
higher percentage of dingo ancestry. The highest proportion of pure dingoes was found in the
Northern Territory (87% of individuals by clustering), with intermediate proportions in WA,
SA and QLD, but very few dingoes were detected in NSW and VIC (1% in each of these
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states according to the clustering method). Less than one percent of all free-ranging dog
specimens exhibited no dingo ancestry.
Performance of hybridisation estimation methods using simulated hybrids
Both methods showed strong agreement between the expected purity scores and the average
purity of individuals that was identified from analysis (Fig. 4). The maximum deviation from
the expected mean was 2.5% for the Bayesian clustering method, on cross 6 (mean deviation
over all crosses was 0.95%). All of the expected values were within the 95% CI of observed
results for both methods. Between 2% and 58% of the individuals within each simulated
cross were incorrectly classified using the a priori method (outside of the ranges shown in
Fig. 4a;
x
= 26%, SD=21.35). Using the a posteriori methods 2-26% were outside of the
expected value ± 10% (
x
= 13.67%, SD=9.05). The percentages of misclassifications were
roughly equal above and below the range (6.7% under and 8% over).
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Discussion
Distribution of dingoes and hybrids
This study is the first range-wide assessment of hybridisation between dingoes and domestic
dogs, and the largest investigation of hybridisation in any animal. The ubiquity of domestic
dogs in human settlements and extensive livestock husbandry across Australian rangelands
has provided many contact points where hybridisation with dingoes has occurred. Hybrids are
most prevalent in coastal areas (Fig. 3), which have the longest duration of European
settlement and extensive agriculture, and the highest densities of human settlement, and most
opportunities for contact between dogs and dingoes. These more densely populated areas also
have the most intensive and continued culling programs, which could further facilitate
hybridisation if culling encouraged dispersal through social disruption, as suggested by
Wallach et al. (2009). Although dingoes remain in relatively high proportions in the central
and western parts of the Australian continent, no area is free from hybrids. This finding
places the pattern of hybridisation in the second most extreme category under the scheme of
Allendorf et al. (2001) (type 5: widespread introgression), and on course towards the most
extreme category (type 6: anthropogenically mediated complete admixture).
The pattern of hybridisation found in this study accords with previous morphometrics
studies (e.g., Newsome & Corbett 1985; Woodall et al. 1996; Corbett 2001), but provides
much more detail. This is particularly the case in Western Australia, where only one
morphologically-based study has been reported, which found 74% pure dingoes in a region of
the north west (Corbett 2001), and in South Australia, where no studies have been published.
The trend to hybridisation in coastal areas and near human settlement is also shown on the
west coast, but to a lesser extent than found in the southeast of Australia. Although the central
areas in the Northern Territory contained mostly pure dingoes, the specimens were mostly
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sourced near small, remote human settlements, and the purity level may be higher in more
remote areas.
Southeastern Australia exhibited the highest proportion of hybrids, which reflects the
earlier and more intensive European settlement and livestock production (Fleming et al.
2001). A high incidence of hybrids in this area has been reported previously (22/24 free-
ranging dog samples from southeastern New South Wales and northeastern Victoria, which
were analysed using Elledge et al. (2008) classifications; Claridge et al. (2009): and ranging
from 23% pure dingoes on the south coast of New South Wales to 65% dingoes captured in
the Victorian highlands during 1970-79 using the morphometric method; Newsome &
Corbett 1985). The discrepancy between the DNA and morphology-based assessments likely
reflects inaccuracy of morphological assessments as well as an increased proportion of
hybrids since skulls were collected from this area in the 1960s and 1970s. The question of
dingo purity is particularly relevant to the state of Victoria, as the dingo has recently been
declared a threatened species, with requirements for its protection (Anonymous 2007; Glen
2010), whilst landholders are responsible for controlling wild dogs for livestock protection.
Efforts for dingo conservation in Victoria clearly require a comprehensive program to address
the process of hybridisation. Because of the ubiquity of hybrids and difficulty in
distinguishing them on appearance from dingoes (Elledge et al. 2008), such a program would
likely require physical isolation of extant dingo populations.
The higher prevalence of introgression between dingoes and domestic dogs than
between wolves and domestic dogs may be due to the dingo’s history of domestication
followed by feralisation (Fleming et al. 2012), resulting in decreased wariness of human
settlements and food and water resources (Newsome et al. 2013 ), their concentration around
livestock or artificial watering points on grazing land containing working dogs, or due to
higher population densities of free-ranging dogs, including dingoes, than occurs in wolves
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(Koblmüller et al. 2009; Muñoz-Fuentes et al. 2010). Culling of wild dogs is also widespread
in Australia and Wallach et al. (2009), proposed that subsequent reduced population densities
disrupt social groups and might increase the encounter rate between domestic dogs and
dingoes (Corbett 2001; also discussed with respect to wolves in Vilà et al. 2003).
Alternatively, targeted culling of free-ranging dogs closer to human settlements and livestock
production areas could provide a separation between domestic dogs and pure dingoes
inhabiting more remote regions, which could slow introgression.
Distribution of feral domestic dogs
The low incidence of purely domestic dogs in wild populations (<1% of all specimens)
suggests that they have poor survivorship in the wild. The most likely mating between
dingoes and dogs is generally considered to be between a female dingo and a male domestic
dog, due to the difficulty of a domestic female raising a litter outside of captivity (Corbett
2001). The hybrid offspring of owned domestic dogs and dingoes raised in the wild may be
better able to survive and integrate into dingo societies than domestic dogs raised in captivity,
which would have little experience hunting or socialising with dingoes (Vilà & Wayne 1999;
Daniels & Corbett 2003).
As few populations of feral domestic dogs have been reported in Australia (Newsome
and Corbett 1985, Jones 2009), the extent of hybridisation is likely caused either by historical
introgression and subsequent interbreeding of the hybrids, or an ongoing influx of genes from
roaming domestic pets or wild females visiting domestic dogs and subsequent interbreeding
of hybrids. The presence of mostly hybrids with few representatives from either of the parent
populations suggests that free-ranging dogs in some areas may be approaching the status of a
stable hybrid swarm, where the majority of individuals are the product of multiple
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generations of hybrid crosses and backcrosses (Rhymer & Simberloff 1996; Allendorf et al.
2001).
Performance of the methods against simulated data
Both of the purity testing methods identified all simulated hybrid crosses with high accuracy
(Fig. 4). The minimal bias exhibited by the clustering method indicates that it can measure
dingo purity within a few per cent of the average expected value (not accounting for random
inheritance past the F1 generation). For both methods the simulation provide a conservative
measure of accuracy, as in the clustering method 100 of the specimens were removed from
the a posteriori reference groups to create the simulated hybrids, and in the average 3Q
method only the 3Q score was considered, not the inclusion of doglike alleles. This also
affects the calculation of misclassification rates, which are an underestimate of the true
likelihood of misclassification, but provide a useful comparison between the methods.
Although the clustering method still relies on reference samples, as criticised by Daniels and
Corbett (2003), the samples used were selected due to their distinctness by 3 sequentially
applied tests, rather than prior assumptions of purity.
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The a posteriori dingo reference group used in this study is the most geographically
diverse for testing purity to date (Fig 2); whereas the references used to develop canonical
skull scores were selected from central Australia (Newsome et al.1980; Newsome & Corbett
1982), as were the a priori dingo reference group. It should be noted, however, that despite a
western continent bias in the origins of the a posteriori reference groups, and a central
continent bias in the origins of the a priori groups, the patterns of hybridisation and total
number of pure dingoes found are similar, possibly due to the low genetic diversity and
genetic homogeneity within Australian dingoes (Savolainen et al. 2004). Despite this, the
impact of founder effects and genetic drift on isolated areas such as Fraser Island, where
entry of domestic dogs is prohibited to maintain the purity of its dingoes, should be
considered when using any reference method, as the effects of regional allelic variation and
hybridisation may otherwise be confounded.
The DNA-based methods presented here provide a considerable advance in precision
and versatility on morphometric methods, due to their ability to test juveniles and live
animals, and to detect backcrossed hybrids, and for the clustering method that gives a precise
percentage estimate of dingo ancestry. This study also demonstrates the ability to apply the
DNA testing of hybrid individuals on a large scale; it would have been unfeasible to measure
the number of skull samples equal to the number of tissue specimens used in this study.
Implications for the management of wild dogs
Like wolves in Europe and North America, the dingo retains a unique status in Australia as
both an icon of the natural world and an agricultural pest. There are cultural considerations
surrounding the loss of pure dingoes, including their value to indigenous Australian cultures,
tourism, and the perception by many Australians that they are part of the native fauna
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(Fleming et al. 2001). Conflict between cultural and community attitudes, and management
targets (e.g., culling for livestock protection), can be more productively addressed when the
true status of free-ranging dog populations is known.
The impact of dingo hybridisation on the dynamics of ecosystems could be informed
by this study and the application of purity testing. An important aspect to predicting the
success of dingo reintroductions for mesopredator suppression is the effect that hybridisation
has on the ecological roles of free-ranging dogs (Claridge and Hunt 2008).
A decision on whether some degree of mixing between dogs and dingoes will affect
conservation goals, and whether it is possible or necessary to attempt to retain historical
patterns of genetic diversity, should be made prior to the development of management
strategies (Daniels & Corbett 2003; Claridge & Hunt 2008; Rutledge et al. 2010). This
question has also been raised for the preservation of wolves in the Great Lakes Region of
North America, as pre-European admixture between grey wolves and Great Lakes wolves has
been found (Schwartz & Vucetich 2009; Wheeldon & White 2009). The answer to this
question for dingoes is somewhat dependent on whether admixture has affected their fitness
or adaptive potential. Savolainen et al. (2004) detected evidence that a severe bottleneck
occurred in dingoes upon their arrival in Australia, to the extent that perhaps only one family
had arrived. Significantly lower genetic diversity in dingoes than dogs was also found in this
study, on all measures tested. Breeding with dogs, and the associated increase in genetic
diversity, may be beneficial to dingoes' ability to adapt, but whether this is the case requires
careful testing (Barton 2001; Yuri et al. 2009). However, the high number of hybrids,
including backcrosses, found in this study supports the capacity of hybrids to survive and
breed successfully in the wild. Maladaptation or wasted reproductive opportunities therefore
do not appear to be major issues for dingo-dog hybridisation, but whether traits beneficial to
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free-ranging dogs, but undesirable for humans, livestock production and conservation values,
will emerge remains unknown.
The rapid hybridisation between dingoes and domestic dogs at a continental scale
illustrates the importance of establishing baseline genetic data for taxa in the early stages of
admixture, if genetic swamping is likely to become a problem in the future. Once
hybridisation has become widespread it becomes more difficult to reconstruct what
constitutes a 'pure' wild animal, although this study shows that methods can be developed if a
sufficient sample size is available. Additionally, some genetic diversity within the 'pure'
population may be lost by the almost complete admixture in certain regions of the animal's
range, such as appears to be the case in south-eastern Australia. Because the extent of
hybridisation in dingoes is far greater than that of wolves and dogs in Europe (Randi &
Lucchini 2002) or North America (Muñoz-Fuentes et al. 2010) the study of dingoes provides
a unique opportunity to test the impact of large-scale hybridisation on an ecologically
significant top-order predator.
Conclusions and applications
Both of our purity testing methods revealed very similar patterns (Fig. 3), showing
hybridisation mainly concentrated around the more densely inhabited coastal areas and
settlements. This supports previous assertions that duration and the density of settlement
since 1788 are strong predictors of dingo hybridisation levels (Newsome & Corbett 1985;
Woodall et al. 1996). The low genetic diversity found in dingoes may indicate that
conservation efforts would be better targeted at the more remote regions or islands where
successful isolation of dingoes and dogs is more probable, as minimal genetic diversity would
be lost overall. A study of whether dingoes exhibit population structure between regions
would assist in establishing if this is an appropriate strategy.
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The extent and pace of hybridisation found in this study means that preventing gene
flow between dogs and dingoes requires concerted effort (Fleming et al. 2001; Allendorf et
al. 2001). There are three main areas where a better understanding of the pattern of
hybridisation has advantages in wildlife management: establishing the roles of dingoes and
hybrids within ecosystems; understanding the effect that hybridisation may have on livestock
predation; and providing evidence for the formulation of dingo conservation policy
encompassing social attitudes and values. Changes in body size, prey preference and pack
structure through the hybridisation process could all have great ramifications for the status
quo of ecosystems across the Australian continent and require investigation before the dingo
is lost or changed irreversibly.
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Acknowledgements
Over 280 people contributed tissue samples for this study. A. Woolnough, B. Davies, T.
Thompson and Y. Hitchen coordinated and encouraged sampling in Western Australia, as well
as providing valuable support and feedback throughout the project. Y. Hitchen also provided
valuable laboratory assistance. P. Thomson and K. Rose provided feedback on project design
and shared their extensive knowledge of dingo ecology. T. Newsome provided the Yuendumu
dog samples used in the dog reference population and G. Ballard provided samples and
assisted with discussions on project design. F. Allendorf, B. Sacks and A. Glen provided
helpful comments on a draft manuscript, as did Lisette Waits and three anonymous reviewers.
Phil Goulding and Mike Nunweek provided advice on GIS analysis. M.S. Johnson provided
invaluable guidance thoughout this project. This study was undertaken while DS was
supported by an Invasive Animals Cooperative Research Centre Balanced Scientist
Scholarship. This manuscript is dedicated to the memory of Alan Wilton, a gentleman and
pioneer of dingo genetics research.
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Data Accessibility
Data used in this research are lodged online in the Dryad data repository under
doi:10.5061/dryad.2rd32. This includes raw genotype and sample location data, 3Q test
results and an input file for the PCoA, and Structure input file and proportion of dingo
ancestry results.
Figure Legends.
Fig. 1. Locations of free-ranging dog specimens collected for this project, including
only those with a successfully determined genotype. The states and territory included
are Western Australia (WA, n = 2,284), Northern Territory (NT, n = 128), South
Australia (SA, n = 148), Queensland (QLD, n = 356), New South Wales (NSW, n =
95) and Victoria (VIC, n = 626). Specimens collected in the Australian Capital
Territory were classified as ‘NSW’.
Fig. 2. Principal coordinates analysis of specimens identified as being either ‘pure
dingo’ (category 1) or ‘domestic dog’ (7) from the average 3Q method results.
Clusters enclosed by rectangles were used for the a posteriori reference dingo
(coordinate 1 > 0.15) and domestic dog (coordinate 1 < -0.5) groups. The horizontal
dimension accounts for 66.2% of the variation, and an additional 8.1% is explained
by the vertical dimension. Inset map shows the geographic distribution of dingo
reference specimens (• = one specimen).
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Fig. 3. Ordinary kriging of purity results and proportions of dingoes and hybrids by
state. State abbreviations are described in Fig 1. For kriging maps red-yellow patches
represent areas with a high level of dingo purity, blue regions contain individuals
with lower purity categories. Unsampled areas are shown in grey. (a) Average 3Q
method results with categories 1-6 interpolated. Major towns (>25,000 residents,
excluding capital cities) and their population sizes are shown for association with
hybridisation. Two smaller towns (+; Broome and Kununurra) are also shown
because they occur near higher levels of hybridisation than surrounding regions. (b)
Percentage of dingoes and hybrids in each Australian state, based on results from the
average 3Q method purity tests: dingoes are categories 1 and 2, hybrids categories 3-
6. (c) Ordinary kriging on the percentage of dingo ancestry from the clustering
method results. Results below 60% dingo ancestry are grouped as few specimens
were less than approximately half dingo. Towns referenced in the discussion are
labelled. (d) Percentage of dingoes and hybrids in each state as determined by the
clustering method. Dingoes are defined as specimens with 0.90 Q-assignment to the
dingo cluster.
Fig. 4. Comparison of expected component of dingo ancestry from simulated data
and the results from both the average 3Q and clustering methods. The x-axis values
are the simulated hybrid types 1-10 as classified in Table 2. Error bars show 95%
confidence intervals. (a) Results from the average 3Q method categories. Each box
for the ‘expected’ values shows the range of the average 3Q score for the appropriate
reference category. The expected values for crosses 2 and 7 cover two categories as
the expected value is in between them. (b) Clustering results showing the percentage
of dingo ancestry for each cross.
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... Following European settlement in Australia, previously untouched land was encroached upon by human populations, increasing the probability of interactions between dingoes and humans. Consequently, interactions between dingoes and domestic dogs also increased resulting in widespread interbreeding, which ultimately led to a reduction in the number of genetically pure dingoes in the wild (Stephens et al., 2015). As a result, most contemporary Australian wild dogs are dingo-domestic dog hybrids (Claridge et al., 2014), although this may depend on the area inhabited (Cairns et al., 2020;Stephens et al., 2015). ...
... Consequently, interactions between dingoes and domestic dogs also increased resulting in widespread interbreeding, which ultimately led to a reduction in the number of genetically pure dingoes in the wild (Stephens et al., 2015). As a result, most contemporary Australian wild dogs are dingo-domestic dog hybrids (Claridge et al., 2014), although this may depend on the area inhabited (Cairns et al., 2020;Stephens et al., 2015). With increasing urbanisation and land use, the proximity and interactions with such populations of dingoes and their hybrid counterparts (henceforth collectively referred to as wild dogs) becomes more frequent, so too does their potential role in the transmission of pathogens of agricultural significance, and possibly of public health relevance (Smout et al., 2018). ...
... Quantitative PCR was performed with primers specific for red fox (Vulpes vulpes), cat (Felis catus), and dog (Canis familiaris) DNA (Supplementary Table S1), and melt curve analysis was carried out as described by Berry and Sarre (2007). We conservatively used a cycle threshold (CT) of less than 25 (Supplementary Table S2), to select samples confirmed to be dogs as candidate for attempting genotyping using 23 microsatellite markers as described in Stephens et al. (2015). This was done because it was demonstrated that success rate in scat samples is dramatically reduced when the CT > 20 (Stephens, 2011;von Thaden et al., 2017). ...
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Infection with Neospora caninum parasites is a leading cause of reproduction losses in cattle worldwide. In Australia, this loss is estimated to total AU$110 million every year. However, despite this considerable economic impact, the transmission cycle and the host(s) responsible for the sylvatic transmission of the parasite remain to be defined. Dingoes (Canis familiaris) have been suggested to be a wildlife host of N. caninum in Australia, but this is yet to be proven in a non-experimental setting. This study aimed to determine the prevalence of natural N. caninum shedding in Australian wild dogs (defined as dingoes, dingo-domestic dog hybrids and feral dogs) by performing molecular analysis of faecal samples collected in wild dog populations in south-east Australia. Molecular analysis allowed host species identification and dingo purity testing, while genetic analysis of Coccidia and Neospora conserved genes allowed for parasite identification. Among the 115 samples collected and determined to belong to dingoes, dingo-domestic dog hybrids and foxes, Coccidian parasites were detected in 41 samples and N. caninum was identified in one sample of canine origin from South East Australia (Mansfield). Across all samples collected in Mansfield only 15 individuals were successfully identified by genotype. Thereby our study determined that 6.7% (1/15, 95% confidence intervals 1.2 – 29.9) of wild dogs were actively shedding N. caninum oocysts at this site. Further, only four individuals were identified at a second site (Swift Creek), and none were positive. This study conclusively confirms the role of wild dogs in the horizontal transmission of N. caninum parasites in Australia. This article is protected by copyright. All rights reserved
... The occurrence of genetic admixture may be modern or historical, and in some cases is the result of anthropogenic actions. In canids, the phenomenon of interspecific introgression has been observed between species such as grey wolves and dogs (Vilà and Wayne 1999;Anderson et al. 2009;vonHoldt et al. 2011vonHoldt et al. , 2016Schweizer et al. 2018), coyotes and wolves (Bohling et al. 2016;vonHoldt et al. 2016), red wolves and coyotes (Miller et al. 2003;Adams et al. 2007;Schmutz et al. 2007;Bohling and Waits 2015), jackals and dogs (Galov et al. 2015), dingoes and dogs (Newsome and Corbett 1985;Wilton 2001;Claridge et al. 2014;Stephens et al. 2015;Cairns et al. 2019). ...
... Indeed, most wildcats in Scotland carry significant domestic cat ancestry, and the occurrence of hybridisation is believed to have accelerated in the last 50-100 years (Mattucci et al. 2019;Senn et al. 2019). Similar concerns have been raised in Australia with many dingoes, particularly in southeastern Australia, exhibiting genetic, morphological or phenotypic evidence of domestic dog ancestry (Newsome and Corbett 1985;Daniels and Corbett 2003;Jones 2009;Stephens et al. 2015). There is also widespread concern that feral domestic dogs have established in the wild across Australia (Fleming et al. 2001;NSW Threatened Species Scientific Committee 2009). ...
... In New South Wales (NSW), the listing of 'predation and hybridisation by feral dogs (Canis familiaris)' as a key threatening process implies that dingoes are 'under serious decline as a consequence of hybridisation' (NSW Threatened Species Scientific Committee 2009). Indeed, there has been concern in NSW that feral dogs and dingo  dog hybrids with low levels of dingo ancestry have essentially replaced dingoes in the wild (Claridge et al. 2014;Stephens et al. 2015). For example, the NSW key threatening process determination states that 'due to the constant influx of Domestic Dogs into natural ecosystems, lasting eradication of even local populations of Feral Dogs is difficult' (NSW Threatened Species Scientific Committee 2009). ...
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Hybridisation between wild and domestic canids is a global conservation and management issue. In Australia, dingoes are a distinct lineage of wild-living canid with a controversial domestication status. They are mainland Australia’s apex terrestrial predator. There is ongoing concern that the identity of dingoes has been threatened from breeding with domestic dogs, and that feral dogs have established populations in rural Australia. We collate the results of microsatellite DNA testing from 5039 wild canids to explore patterns of domestic dog ancestry in dingoes and observations of feral domestic dogs across the continent. Only 31 feral dogs were detected, challenging the perception that feral dogs are widespread in Australia. First generation dingo × dog hybrids were similarly rare, with only 27 individuals identified. Spatial patterns of genetic ancestry across Australia identified that dingo populations in northern, western and central Australia were largely free from domestic dog introgression. Our findings challenge the perception that dingoes are virtually extinct in the wild and that feral dogs are common. A shift in terminology from wild dog to dingo would better reflect the identity of these wild canids and allow more nuanced debate about the balance between conservation and management of dingoes in Australia.
... Since their introduction, dingo populations have been naturally selected to thrive on a diet of marsupials and reptiles (4,5). The first domestic dogs were brought to Australia in 1788, and with the subsequent expansion of settlers, domestic dog DNA has introgressed into the dingo gene pool (6). ...
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Dogs are uniquely associated with human dispersal and bring transformational insight into the domestication process. Dingoes represent an intriguing case within canine evolution being geographically isolated for thousands of years. Here, we present a high-quality de novo assembly of a pure dingo (CanFam_DDS). We identified large chromosomal differences relative to the current dog reference (CanFam3.1) and confirmed no expanded pancreatic amylase gene as found in breed dogs. Phylogenetic analyses using variant pairwise matrices show that the dingo is distinct from five breed dogs with 100% bootstrap support when using Greenland wolf as the outgroup. Functionally, we observe differences in methylation patterns between the dingo and German shepherd dog genomes and differences in serum biochemistry and microbiome makeup. Our results suggest that distinct demographic and environmental conditions have shaped the dingo genome. In contrast, artificial human selection has likely shaped the genomes of domestic breed dogs after divergence from the dingo.
... For example, land managers within the Little Karoo region of South Africa contribute to tripling the number of conservation targets achieved and significantly increasing the types of habitats and species conserved within the region (Gallo et al., 2009). Similarly, tissue samples collected by land managers in Australia simultaneously contribute to both the conservation of dingoes (Canis familiaris) and their management as a pest species (Stephens et al., 2015). This involvement by land managers in conservation or pest management programs can be essential to their success and deliver high economic benefits (Gallo et al., 2009;Naidoo & Iwamura, 2007). ...
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To mitigate the negative impacts of invasive rabbits in Australia, land managers are permitted to release the biocontrol virus, rabbit hemorrhagic disease virus (RHDV), to reduce rabbit numbers. However, it is strongly recommended that RHDV is not released when young rabbits are present in the population as infection in this cohort is sublethal and induces life‐long virus immunity. The recruitment of these rabbits into the breeding population may make the population harder to control in future, potentially leading to increasing rather than decreased population size. To investigate whether the recommended release guidelines are followed, we obtained data on the supply and release of RHDV by land managers. We then used generalized additive models to investigate Australia‐wide and state‐specific annual and long‐term temporal trends in the supply and release of RHDV. Half of all RHDV supply (47%) and three quarters of reported releases (74%) Australia‐wide occurred during the anticipated major rabbit breeding seasons and when the risk of immunizing young rabbits is greatest. We found evidence of both RHDV supply and release during the anticipated major rabbit breeding seasons in almost all states for which data existed. RHDV supply increased with below average annual rainfall. This may indicate a tendency for land managers to notice, and want to control, rabbits and their impacts more following drier years when both rabbits and their impacts are potentially more damaging. Our study raises concerns regarding the inappropriate release of RHDV by land managers and whether its supply should be restricted to ensure ongoing and effective management of invasive rabbits. More broadly, our study serves as a warning to other conservation and pest management activities reliant on land managers or citizens following implementation guidelines. In some cases, good intentions may have adverse outcomes. Rabbit hemorrhagic disease virus release timing.
... Using ancient DNA could further elucidate the origins of such populations, and indeed it is probable that no wild-living populations of any domesticated species are fully representative of the original undomesticated populations. In many examples, the ancient ferals are among the earliest diverging extant groups in their species' phylogenies (Stephens et al., 2015). ...
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... The single most influential move towards restoring the dingo to its rightful level of immense ecological and productive worth would be for all government departments to discontinue the use of the term 'wild dogs' to describe dingoes. This move could easily be justified by the recent, current and ongoing genetic research, which overwhelmingly shows that public funds are not currently being used to kill wild dogs, because they are so few in number that it is arguable they don't even exist in Australia's wild places (Cairns et al. 2021a,b;Stephens et al. 2015). The reason governments are able to continue in this fallacy is that the definition of the term wild dog, as it is described by government agencies, includes pure-bred dingoes. ...
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The predominant grazing management system used in the arid rangelands regions of Australia, set stocking, is not conducive to sustainable land management. More appropriate grazing management systems based upon periodic rest periods for important pasture species have not been adopted by pastoralists because the unmanaged grazing pressure from animals such as goats and kangaroos has been too high. Dingoes are the only cost-effective and long-term management solution to the effect of unmanaged grazing by goats and kangaroos. Yet government funding targets dingo eradication in pastoral areas, and it does so by adopting misleading and scientifically inaccurate terms for describing dingoes.
... More than 4 million domestic dogs live as pets in Australia [2], with a significant population of native wild dogs (dingoes) also living freely in peri-urban, rural and remote areas [3,4]. The wider impacts of dog populations on human health in in relative abundance in and around human settlement such that hybridisation between domestic dogs and dingoes is far more common in rural and peri-urban areas [29]. Media analyses of the public health impacts of human-dog interactions in Canada and the United States point to geographic location as being a key determinate of how culpability for dogrelated harms and responsibility for interventions are represented in this discourse [30,31]. ...
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That dogs can live and breed as free-living animals contributes to public health risks including zoonotic transmission, dog bites, and compromising people’s sense of safety in public spaces. In Australia, free-living dog populations are comprised of domestic dogs, dingoes, and dog–dingo hybrids, and are described using various terms (for example, stray or community), depending on social or geographic context. Urban expansion and regional migration mean that risks associated with contact between humans and free-living dogs are increasing. Public health authorities, local governments, and community organisations have called for transdisciplinary partnerships to address dog-related health risks with a sustainable long-term approach. Values pluralism and a lack of sustained community engagement in affected areas have meant that the outcome of such efforts to date has been mixed. To identify ideas in public circulation about the impact of unrestrained and free-living dogs on human health and well-being, and understand the framework through which these animals are problematised and solutions are proposed in public discourse, we systematically examined coverage of these issues in print media. Our analyses indicate that reporting in Australian newspapers tends to frame the public health impacts of free-living dogs as problems of public order requiring direct government action to re-establish control. The public health impacts of free-living dog populations in Australia have complex causes that intersect at the nexus between human and canine behaviour, agricultural and land management practices, local bylaws, and efforts to conserve ecological systems. Placing responsibility on governments limits opportunities for greater community involvement in developing integrated One Health approaches. Better-quality evidence of the impacts of dog populations on community health and well-being, and broad community support are needed to reshape public debates on animal control, which, ultimately, will promote more effective approaches to mitigate dog-related public health risks at the human–animal–environment interface.
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Dogs are ubiquitous and strongly associated with human communities, but many roam freely, away from the owners' property and control. Free-roaming owned dogs can pose risks through disease transmission to and from other dogs, attacking domestic animals, fauna or humans, and involvement in road accidents. However, little research has focused on understanding their movement ecology, thereby hindering the development of effective management plans. We modified store-bought GPS collars and used them to track a sample of 43 free-roaming owned dogs from peri-urban sites in north-east New South Wales and south-east Queensland, Australia. Our aim was to quantify the activity ranges of owned dogs and the distances they travelled, whether free-roaming or accompanying people, and to identify some associated factors. The total activity ranges of our sample of dogs were variable (0.80-1776.20 ha), and the mean daily activity range of collared dogs was relatively large (7.23 ± 11.99 ha), with mean daily accumulated distances travelled ranging from 0.25 to 4.81 km (mean = 1.95 ± 1.10 km). The dogs exhibited two temporal activity peaks, one between 0700 and 1000 and a second between 1600 and 1900 hrs. Most human-mediated dog movements were short in duration, ranging from 45 min to 6 h, with dogs moving an average of 48.60 ± 64.00 km, but up to 329.00 km from their home. The large activity ranges and relatively long movements in this sample of free-roaming owned dogs suggests they have potential to contribute to the spread of exotic and endemic zoonotic and canid diseases in the peri-urban coastal regions of eastern Australia. The baseline information collected here is crucial to our understanding of disease transmission among peri-urban dogs, and modelling spread within and between communities. Additionally, it provides valuable information for authorities seeking to improve management of free-roaming owned dogs.
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In northern Australia, wild dog populations potentially interact with domestic dogs from remote communities, which would create opportunities for disease transmission at the wild–domestic interface. An example is rabies, in the event of an incursion into northern Australia. However, the likelihood of such wild–domestic interactions is ambiguous. Hybridisation analyses based on 23 microsatellite DNA markers were performed on canine‐origin scats collected in bushland areas around remote Indigenous communities in the Northern Peninsula Area, Queensland. Sufficient DNA was extracted from 6 of 41 scats to assess the percentage of dingo purity. These scats most likely originated from two 'pure' domestic dogs (0% dingo purity), one hybrid (20% dingo purity) and three 'pure' dingoes (92%–98% dingo purity). The two domestic dog samples were collected in the vicinity of communities. The location of two of the dingo‐origin samples provides genetic evidence that dingoes are present in areas close to the communities. The availability of anthropogenic food resources likely creates opportunities for interactions with domestic dogs in the region. The hybrid sample demonstrates the occurrence of antecedent contacts between both populations by means of mating and supports the likelihood of a spatio‐temporal overlap at the wild–domestic interface. This represents the first genetic survey involving a wild dog population of equatorial northern Queensland, with evidence of dingo purity. Our results have implications for potential disease transmission within a priority area for biosecurity in northern Australia.
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ContextLethal control through the application of 1080 baits is widely used in Australia to manage the negative impacts of wild dogs (dingoes, wild domestic dogs and their hybrids) on cattle production, but its effectiveness in this regard is not well understood. AimsTo evaluate the efficacy of once yearly 1080 baiting on dingoes and its effects in mitigating predation and sublethal impacts on beef cattle. MethodsA replicated experiment with two paired treatments (1080 poisoned and non-poisoned) was conducted on each of four cattle stations of 3782–10850km2, over 2.5 years (2000–02) in the southern Northern Territory. The study was undertaken in relatively good rainfall years. Key resultsTrack-based surveys indicated that dingo abundance declined on poisoned relative to non-poisoned areas immediately following a single baiting episode. However, there was no detectable difference about 8 months after baiting. No difference was detected in observed levels of calf damage or calf loss between poisoned and non-poisoned areas. Conclusions The results add to the growing body of consistent evidence that contemporary dingo control practices yield little benefit to rangeland beef producers most of the time. ImplicationsRoutine dingo baiting (as currently undertaken) may be largely unnecessary for beef cattle producers in arid and semiarid areas. Alternative strategies and practices to reduce dingo mauling and predation impacts should be investigated using replicated and controlled field studies.
Chapter
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Wild canids are widespread across most of mainland Australia. They can have major impacts on livestock production and biodiversity values and often necessitate active management. The impacts of free-ranging dogs and foxes should be managed concurrently, as there is often substantial overlap in their impacts and because most available control methods do not discriminate between the two species. Effective management of the impacts of wild canids requires a strategic approach that is driven by participants and based on specific local issues and available knowledge. Strategic management programs, as undertaken, are a form of adaptive management, in which participants gain knowledge about the problems they are addressing by conducting quasi-experiments. These examine the effects of management actions on clearly defined objectives. Importantly, it is the specific local impacts of wild canids that define the management objectives in these approaches, not simply the numbers of animals. Adaptive management can be used to suppress or enhance populations of wild canids depending on the management objectives; that is, mitigation of damage to livestock and biodiversity, or conservation of dingoes. This chapter discusses a strategic approach to managing the impacts of wild canids. The nature of those impacts, including new density:damage functions, and the specific tools and methods that are available to counter them are also discussed.
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* Apex predators can benefit ecosystems through toptextendashdown control of mesopredators and herbivores. However, apex predators are often subject to lethal control aimed at minimizing attacks on livestock. Lethal control can affect both the abundance and behaviour of apex predators. These changes could in turn influence the abundance and behaviour of mesopredators. * We used remote camera surveys at nine pairs of large Australian rangeland properties, comparing properties that controlled dingoes Canis lupus dingo with properties that did not, to test the effects of predator control on dingo activity and to evaluate the responses of a mesopredator, the feral cat Felis catus. * Indices of dingo abundance were generally reduced on properties that practiced dingo control, in comparison with paired properties that did not, although the effect size of control was variable. Dingoes in uncontrolled populations were crepuscular, similar to major prey. In populations subject to control, dingoes became less active around dusk, and activity was concentrated in the period shortly before dawn. * Shifts in feral cat abundance indices between properties with and without dingo control were inversely related to corresponding shifts in indices of dingo abundance. There was also a negative relationship between predator visitation rates at individual camera stations, suggesting cats avoided areas where dingoes were locally common. Reduced activity by dingoes at dusk was associated with higher activity of cats at dusk. * Our results suggest that effective dingo control not only leads to higher abundance of feral cats, but allows them to optimize hunting behaviour when dingoes are less active. This double effect could amplify the impacts of dingo control on prey species selected by cats. In areas managed for conservation, stable dingo populations may thus contribute to management objectives by restricting feral cat access to prey populations. * ~Synthesis and applications. Predator control not only reduces indices of apex predator abundance but can also modify their behaviour. Hence, indicators other than abundance, such as behavioural patterns, should be considered when estimating a predator's capacity to effectively interact with lower trophic guilds. Changes to apex predator behaviour may relax limitations on the behaviour of mesopredators, providing enhanced access to resources and prey.
Article
We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
Chapter
The effective conservation of the dingo in Australia requires that a purebred animal can be reliably distinguished from a hybrid domestic dog. Even though the dingo and the dog are closely related differences in DNA between them can be used as the basis of a test to detect hybrids. Genetic markers that show high variability between individuals may show differences between related groups. Microsatellite loci show very high variability and many are available for the dog. From 72 microsatellites tested, 12 show distinct differences in the alleles found in dingoes and dogs. Microsatellites that are diagnostic, that is show no overlap between alleles in the dingo and the dog, are the most useful for testing. However, conditions are not ideal for detecting these since some dog breeds have dingo ancestry and the purity of dingoes currently in captivity is not guaranteed. Estimates of purity can still be made by comparing the relative likelihoods of an animal belonging to one group or another based on the frequency of the alleles found in that animal in the groups being compared.