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An isolated population reveals greater genetic structuring of the Australian dingo

The Australian dingo is a recent anthropogenic addition to the Australian fauna, which spread rapidly across the continent and has since widely interbred with modern dogs. Genetic studies of dingoes have given rise to speculation about their entry to the continent and subsequent biogeographic effects, but few studies of their contemporary population structure have been conducted. Here we investigated the dingo ancestry and population structure of free-living dogs in western Victoria and contrasted it with a wider southern Australian sample. We wished to determine whether their geographic isolation was mirrored in genetic isolation. To address this question, we analysed 34 microsatellite markers using Bayesian clustering and discriminant analysis of principal components, and summarised genetic diversity at the population and individual level. The broader southern Australia sample (n = 1138) comprised mostly hybrid animals, with 30% considered pure dingoes. All western Victorian individuals (n = 59) appeared to be hybrids with high dingo ancestry. The population showed no evidence of admixture with other populations and low genetic diversity on all measures tested. Based upon our characterisation of this unusual mainland population, we advise against assuming homogeneity of dingoes across the continent.


Results
Dingo ancestry. Dingo ancestry testing was performed using the learning samples approach in Structure (detailed in the Structure v2 documentation), which assigns test individuals to one of two populations (dingoes or domestic dogs) by updating the allele frequencies from assigned reference individuals from each group. Twenty-three microsatellite loci established for allele frequency differences between dingoes and domestic dogs 18 were used for testing. Details of the reference populations are provided in Stephens et al. 3 . Dingo ancestry testing identified seven individuals (0.6%) with ≤ 10% dingo DNA present (considered modern domestic dogs), 340 (29.9%) individuals with ≥ 90% dingo DNA (considered likely to be pure dingoes) and the remaining 791 (69.5%) individuals were likely hybrids with 11-89% dingo DNA (Table 1). In general, there were more pure dingoes and high-dingo hybrids in the north west of New South Wales and in northern South Australia than in south eastern and eastern samples (Fig. 1). Within the WV population, all samples provided usable DNA, and all exhibited between 62 and 86% dingo ancestry (Table 1). No pure modern dogs (i.e. ≤ 10% dingo ancestry) were detected in the WV sample.
Eighty individuals with less than 60% dingo DNA were removed from the wider sample for subsequent analyses to reduce the noise from modern domestic dog DNA (whose genetic history is uncoupled from the local geographic movement patterns) but still retain good coverage of the study area, including all the WV individuals.
Population structure. From the Structure results, inspection of ΔK and lnPr(k) indicated K = 4 as the most likely number of clusters, dividing the sample into clusters from eastern Victoria, eastern New South Wales across to South Australia, western New South Wales and Western Victoria (Fig. 2a, b). Thermodynamic integration in rMaverick showed a maximum at six clusters ( Supplementary Fig. S1). The greatest decrease in BIC output for assessing the number of clusters for DAPC were for K = 4-6, with a sharp decrease until K = 4, then slower decrease until K = 6 with 150 principal components retained ( Supplementary Fig. S2). Ward's clustering indicated optimal K of 6. At values above K = 4 the structure replicates were inconsistent and DAPC clusters were overlapping on the Y-axis. The Evanno method of determining K (∆K) captures the highest level of population structuring in the data, and inspection of the Structure bar plots for K = 6 indicates that there is sub-structuring within the northern New South Wales/South Australia (NNS) population (dividing at the approximate location of the western NSW population, WN) and within the WN population (splitting into a north and south population) (Supplementary  S3). K = 4 is presented here as the focus is on the WV population rather than the surrounding areas. Both values of K showed the same trends in the summary statistics and absence of admixture in the WV population. Inspection of the replicates for both K = 4 and K = 6 from the Structure iterations showed nine replicates with very similar results and one replicate with some differences in assignment (the major and minor modes 23 ). As the nine similar replicates contained the runs with the highest log likelihood, these nine were aligned and averaged and the outlier replicate removed.
Cross-validation of the DAPC indicated an optimal number of principal components of 20 with missing data replaced by zeroes, and 30 with replacement with mean allele frequencies. The number of PCs retained was 25, and all three eigenvalues (Fig. 2c).
To establish whether the population structure identified was being influenced by introgression between dingoes and domestic dogs as well as location, we repeated the Structure analysis for K = 1-10 and the DAPC with 34 loci and the dingo and domestic dog reference individuals included. The Structure analysis showed an optimal ∆K = 2 (Fig. S4a), separating the dingoes and domestic dogs at the highest level of population structure (Fig. S4b). The DAPC analysis showed the same pattern as the DAPC in Fig. 2c for the populations included in both analyses, with the dingoes clustering with the EV/NNS/WN populations, the modern domestic dogs separated on X-axis, and the WV individuals separated on the Y-axis (Fig. S4c). These results indicate that the population structure is occurring on a level of hierarchical structure below the separation of dingoes and dogs.
Genetic diversity statistics. Two individuals from the north of the WV area clustered with the 57 WV specimens, bringing the size of the WV population analysed for population summary statistics to 59. The WV population showed lower genetic diversity than the other populations on all population summary statistics tested, including allelic richness, which accounts for the difference in sample size, but did not have a higher number of private alleles (Table 2). Mean relatedness values were significantly higher than zero in all populations (zero representing random mating across populations), but the WV population showed mean relatedness over three times higher than the next highest population (WN). Due to the low genetic diversity and potential inbreeding in this population, this measure may not represent kinship in the strict sense, but is a further reflection that a high number of alleles are identical, presumably by descent from founding (if there was a small founder population) or surviving individuals (if there was a later bottleneck event). Effective population size was lower in the WV www.nature.com/scientificreports/ population than the NNS and EV populations, although similar to the WN population ( Table 2). Both the WV and WN populations cover a smaller geographic area than the other two populations. Pairwise differentiation, as measured by F ST , also confirmed strong genetic differentiation between the WV and all other populations, with the lowest value from the 95% confidence intervals of the WV comparisons (0.23 between WV and NNS) greater than the highest value from the comparisons between the other populations (0.15 between WN and EV) ( Table 3). Jost's D EST showed the same pattern of differentiation as the F ST (0.24-0.28 between WV and other clusters, 0.08-0.15 between the WN/NNS/EV clusters with no overlap of confidence intervals between the WV and others).
The results of the sGD analyses support the results of the population-level, non-spatial analyses with respect to the WV population (Fig. 3). Observed heterozygosity (Fig. 3a) and allelic richness (Fig. 3b) measures were notably lower in the WV population area. The mean number of individuals included in each calculation was 180.2 (range 10-364, SD = 84.1). Effective population size (Fig. 3c) showed a general pattern of being higher at the coast and lower inland, excepting samples near to the New South Wales/South Australian border. Most individuals had values < 100, indicating overall low effective population size in dingoes.

Discussion
Our southern Australian sample showed a similar pattern of dingo hybridisation to other studies, with higher numbers of hybrids near the more densely human-occupied coastal areas and higher dingo ancestry in the sparsely populated inland 3,17 . Similarly, pure modern domestic dogs were very rare. The WV population displayed a high level of dingo ancestry and isolation. It is possible that both are interrelated, with the large area of remnant mallee woodland in Big Desert and Wyperfeld National Parks providing a refuge for an already isolated dingo population, and the low human population density reducing the rate of ingress of modern dog genes into the population. Current patterns of modern domestic dog introgression in dingoes may not be possible to reverse without intervention by humans, where high dingo content hybrids and pure animals are captured, tested, and retained and backcrossed with pure animals, and other hybrids are culled. One process for reversal of hybridisation is captive breeding from "pure" stock, as recommended for the endangered wildcat (Felis silvestris ssp. 24 ). It is unlikely that such direct dingo conservation processes will be introduced into the region because of extant livestock protection imperatives and the legal and animal welfare obligations of livestock producers. However, the current Victorian policy of limiting control activities on public lands to within 3 km of the edge (Agriculture Victoria website, https:// agric ulture. vic. gov. au/ biose curity/ pest-anima ls/ manag ing-wild-dogs-invicto ria) could slow the rate of hybridisation by reducing the probability of interbreeding between extant highdingo hybrids and lower dingo hybrids from elsewhere. This suggestion is contrary to the findings of Bohling and Waits 25 that lethal control increased hybridisation between red wolves (Canis rufus) and coyotes (C. latrans) and the suggestions of Cairns et al. 17 that lethal control increases hybridisation of dingoes and other dogs. Data on the impact of lethal control on dingo hybridisation is lacking, so there is still much to be learned about these processes. Hybridisation proportions might also correlate with expansion of livestock production and associated water infrastructure 26 . Conversion of ephemeral water supplies to permanency is only recent in the Mallee and Wimmera regions of Victoria. There is no permanent surface water drainage in the Mallee and most water availability was ephemeral prior to the introduction of a channel system over the 50 years from 1856 27 . Indigenous land use practices could also have affected dingo population density and distribution before British colonisation, but permanent water availability would have had influence on their distribution and that of their prey (dogs need between 26.2 and 122.6 mL kg -1 day -1 of water) 28,29 . It is likely that pre-British dingo population densities were low in WV because it is semi-arid (250-400 mm mean annual rainfall) and has low primary productivity due to poor soils. Any expansion of dingoes' range due to a sudden increase in resources, coupled with the rapid spread of modern domestic dogs and associated mating opportunities is likely to have had conflicting effects on the genetic composition of dingoes: a decrease in genetic diversity in areas where rapid expansion has occurred due to small groups colonising newly hospitable regions, and an increase in genetic diversity where domestic dog genes were entering the population.
Although the population-wide proportion of dingo ancestry was high in the WV population, we are cautious about the finding that there were no 'pure' dingoes in the sample. The method of testing relies on updating of the model priors from reference populations, and sufficient deviation in allele frequencies from those represented in the reference populations is likely to decrease the accuracy of dingo ancestry estimation. The genetic diversity of dingoes across Australia is low overall 3,11 , which makes it easier for the reference population to capture the allelic variation in most dingoes, but unusually isolated populations such as WV and K'gari may show decreased accuracy with any reference sample approach. The DAPC analysis with the dingo and domestic dog references included (Fig. S4c) confirms that the WV population allele frequencies are distinct from the dingo and modern domestic dog reference populations, and that WV individuals are unlikely to be accurately represented in any method of ancestry analysis which requires such references. Although the number of private alleles was not high in the WV population, which would mean there is no analogue in the reference animals, the low genetic diversity and skewness from the references mean the comparisons are more susceptible to stochastic allele changes from genetic drift and the impact of data scoring errors.
In the short term, an assessment of the effect of reference population selection and quantification of the genetic variation of dingoes across their range is required to fully understand these impacts. In the longer term, expanding genome coverage should better characterise isolated and distinctive populations such as WV (e.g. 30,31 ). A major advantage of microsatellite genotyping currently is the ability to add new samples to a dataset containing many thousands of already typed individuals 3 , and it will be some time before the availability of genomic data sets can provide similar baseline data. Expanding the genomic coverage of markers can also provide opportunities to clarify the history of the WV population. The increased resolution afforded by resampling a sub-set of the data with single nucleotide polymorphisms (SNPs) should allow much greater insight into the timings of population www.nature.com/scientificreports/ separations 32 . Our study also did not provide a clear indication of which of the surrounding areas was most closely related to the WV population, and higher-resolution markers may help to address this question. The geographic limits of the WV population have not been determined by this study; further sampling in the surrounding areas would enable a greater understanding of the extent of this population, and the local environment that has enabled the isolation of these dingoes despite the dispersal capacity of free-roaming dogs 33,34 . Two individuals from the 2015 data collected north of the WV site clustered with the WV specimens in our study. However, the two individuals were not submitted with geographic coordinates, so their location www.nature.com/scientificreports/ is approximate. Although the presence of free-roaming dogs may be sparse in the surrounding areas, our study contains dingoes and hybrids caught in areas of South Australia marked as "not known to be occupied" by free-roaming dogs in a major 2013 survey 14 . Consequently, there is potential to improve our understanding of this region by sampling to the north and west of the WV population. There is also the potential for landscape analysis of co-correlates with population structure, such as land use, vegetation type, faunal assemblages and resource availability, to quantify potential barriers to gene flow in dogs that may help to identify areas where other isolated populations could occur. The individuals across most of the study area displayed a pattern consistent with weak or clinal population structure: low F ST values combined with evidence of admixture between all clusters in the north and east of the study area. The WV population displayed a distinctly different pattern, with high genetic differentiation, as demonstrated by both the F ST and DAPC results, low genetic diversity and no evidence of admixture of individuals within the WV area; this implies sustained isolation of WV from surrounding areas. The differentiation, as measured by F ST , was considerably greater between the WV and other populations than would be expected for its distance from the other samples. The F ST between the other populations tested were mostly around 0.1 (range 0.06-0.139), but all values between the other populations and the WV population were 0.240-0.321, indicating strong genetic discontinuity between WV and the surrounding populations, even over larger geographic distance than between the other populations (e.g. there is approximately 600 km between the centre of the WN and WV clusters, and approximately 850 km between WN and EV). The sGD results also show lower individual values for observed heterozygosity and allelic richness in the WV than any other area, which supports the results of the partitioned summary statistics and provides further evidence for the isolation of the WV population.
The effective population size, by contrast, is not uniquely low within the WV, with individuals in western New South Wales and in South Australia showing similar values. Broader comparisons of effective population size in high dingo-ancestry populations, particularly in inland, north and western Australia where high numbers of 'pure' dingoes are available, could indicate the typical effective population size for Australian dingoes, and their capacity to persist with low genetic diversity when dog introgression is minimal.
The presence of low genetic diversity, high mean relatedness and, in particular, the low incidence of private alleles is consistent with a population that is a reduced version of a larger regional gene pool and subject to drift and inbreeding-remnant after ~ 150 years of intermittent control in the region-rather than a population translocated from a different ancestral source 35 .
The WV population of free-ranging dogs shows measures of genetic diversity close to those found in the dingo population on K'gari 36 . Allelic richness, observed heterozygosity and effective population size values were similar (Allelic richness = 3.61 in K'gari and 2.61 in WV, observed heterozygosity = 0.27 in K'gari and 0.26 in WV, effective population size 25.7 in K'gari and 22.0 in WV), with the K'gari study using a reduced version of the same microsatellite marker set employed in this study. Although low genetic diversity due to founder effects, inbreeding and genetic drift are common in island populations 37,38 , finding comparable levels of isolation in a mainland dingo population was unexpected.
The reasons for genetic isolation in a large mobile apex predator in the face of an increasingly hybridised swarm require investigation. Prior to British colonisation and settlement, indigenous population density and their trading and cultural movements likely influenced the spread and continental distribution of dingoes. Biogeographic barriers to human and animal movements possibly affected colonisation by dingoes and subsequently impeded gene flow through biogeographic semi-isolation. Previous analyses of genetic subdivision in dingoes focussed on either a broad geographic range with few samples 4,11 or a more limited area with more intensive sampling 16,36 . The Australia-wide studies identified a split between the lineages of dingoes in the south east and north west. The population structure analysis in this study, which is the first to characterise the west of NSW and Victoria, did not identify a contemporary boundary of the south east-north west split 4,11 . At the highest level of population structure (K = 4) the north of the study area showed a continuous population from eastern NSW to South Australia, with the exception of the WN population, which appears to exist within this continuum and may be the result of local environmental factors or a migration event. A discontinuity may still be present to the west or north of the area studied here, but this requires further investigation. Although the northern population is not the focus of this study, it further highlights the need for adequate sampling of dingo populations to disentangle their complex history of migration, colonisation, hybridisation, persecution and translocation which has occurred in the very short time, in evolutionary timescales, since their arrival in Australia.
Sampling in inner-eastern Australia is necessary to understand the contemporary and finer-scale processes at work in this separation, but the current study highlights an important caveat; sampling of individuals in the WV as representative of this area would provide an inaccurate genetic profile of the broader area. The high mean relatedness and lack of admixture with surrounding individuals could appear as a distinct lineage under some analyses, and care should be taken to examine demographic parameters for inbreeding and outbreeding that may be caused by isolation or introgression, respectively.
Although the south east/north west split and the K'gari subdivision have previously been combined under 'ESUs' 4 , subsequent analyses suggest the processes that caused them differed. The K'gari (and WV) populations appear to have arisen from isolation after the colonisation of the continent by dingoes 36 , while the mitochondrial south east/north west lineages are more likely to have occurred during their initial or early migration into Australia, with unknown contemporary ecological significance. We should therefore consider more subtle effects across the landscape given complex but temporally shallow demographic history of dingoes, to then relay this information to local managers to enable more precise recommendations in the context of regional conditions and pressures.
The fate of the Australian dingo is a concern for many stakeholders across its broad and varied range 39,40 . Many questions remain about their introduction, ecology and behaviour, and the use of molecular data will continue to elucidate aspects of dingo distribution, subpopulation structure, and lineages nationally. This information www.nature.com/scientificreports/ will assist in the management of dingoes so that their genetic diversity can be maintained in the context of necessary control of livestock predation 15 and conservation of predation-susceptible fauna (e.g. Olive Ridley turtles, Lepidochelys olivacea 41 and northern hairy-nosed wombats, Lasiorhinus krefftii 42 ). Here our objective was to add to that general body of work by detailing the genetics of one isolated population of dingoes. We hope it will fit into a broader context of studies into free-ranging dog genetic diversity across the continent and significant island populations of dingoes such as that on K'gari, utilising different genetic markers, analysis techniques and spatial scales of interest 11,43 .

Methods
Study region. The Mallee region of north-western Victoria is sparsely populated by people and contains a large area of unoccupied mallee (Eucalyptus spp.) woodland as national park (gazetted in 1909) and forestry lands. The region is part of the Murray-Darling Depression bioregion 44 , is semi-arid with mean annual rainfall of 300-320 mm 45 and characterised by sandy soils of low fertility and productivity 46 . Agricultural enterprises, primarily cropping with some livestock production (mostly sheep at low carrying capacity), surround the mallee woodland. This geographic arrangement contributes to conflict between sheep graziers and free-ranging dogs, and the region is subject to ongoing free-ranging dog management by lethal control through trapping and baiting. The Wyperfeld/ Big Desert Wild Dog Management Zone employed five registered private trappers during the study, and contractors in contiguous nearby South Australia were responsible for controlling errant dogs. Together, these areas comprised the WV sampling region.
Sampling. Over (Fig. 4). Excepting some areas Population analyses. Dingo purity testing was carried out on all samples using the method outlined in Stephens et al. 3 23 microsatellite loci 18 were analysed in STRU CTU RE v2.3.4 49 along with 322 reference dingoes and 109 domestic dogs 3 . Reference dingoes were collected from across Australia, and although higher numbers from the central and west were used, repeating the tests with different references showed mean differences between results of < 1% 3 . Reference animals were coded with USEPOPINFO = 1 and test animals with USEPOPINFO = 0 (the 'learning samples' approach 49 ). The sample was run for 300,000 iterations and 30,000 burn-in replicates, with allele frequencies initialised and updated from the reference individuals. Ten replicates were performed and the results combined using the R package pophelper v2.3.1 50 . The assignment of each individual's genotype to the dingo reference population was then reported as a percentage. Individuals with less than 60% dingo ancestry were excluded from further analyses, to maximise the geographically relevant information in the genotype.
The remaining samples were then assessed for population differentiation using all 34 loci. The additional 11 loci were added to the 23 loci used for dingo ancestry testing to mitigate potential bias introduced by the selection of the 23 loci for allele frequency differences between dogs and dingoes. Each sample was analysed using STRU CTU RE using the admixture model and correlated allele frequencies for 500,000 iterations and 50,000 burnins. The sample was run with initial Alpha = 0.25 and allowed to vary among populations 51 . Runs were repeated from K = 1-15 for 10 replicates. STRU CTU RE output was assessed for optimal number of clusters (K) by the ΔK method 52 , by inspection of lnPr(K) using Structure Harvester v0.6.94 53 and using the thermodynamic integration approach with the admixture model implemented in rMaverick v1.0.5 54 . The latter method provides an alternative estimate of K from multiple MCMC chains without assuming normal distribution of the likelihood. Replicate runs of the optimal K were then aligned, averaged and visualised using pophelper. Individuals were assigned to the cluster to which they had the highest ancestry coefficient (Q-value).
Populations were also assessed using Discriminant Analysis of Principal Components (DAPC) in R using the package adegenet 2.1.3 55,56 which provides an assumption-free method for comparison with the Bayesian clustering approach. The number of clusters was inferred using the find.clusters function 56 for K = 1-20 and inspection of the BIC graph for the minimum values. Optimal K value was also informed by the Ward's clustering algorithm implemented in adegenet (criterion "diffNgroup"). The number of principal components to retain was chosen using the cross-validation method in adegenet, with missing data replaced with either zeroes or the mean of allele frequencies and the results compared.
Population summary statistics. Population summary statistics were calculated for each of the STRU CTU RE populations to characterise the relative genetic diversity within and between each population. The per-population allelic richness was calculated using diveRsity v1.9.90 57 . The number of private alleles, unbiased expected heterozygosity, observed homozygosity and bootstrapped mean relatedness were calculated using GenAlEx v6.51b2 58,59 . Mean relatedness values were calculated from the matrix of Lynch & Ritland estimators 60 between individuals then averaged per population. These data were bootstrapped and permuted for 999 replicates to obtain 95% confidence intervals (CI) and to test for significant deviation from the null hypothesis of average relatedness, respectively. Pairwise Weir & Cockerham's F ST 61 and D EST 62 were also calculated using diveRsity and bootstrapped for 1,000 replicates to obtain 95% CI values. Effective population size was calculated using NeEstimator V2.1 63 and the results are reported for the single-sample linkage disequilibrium method with random mating and alleles with frequency below 0.02 excluded, to reduce bias introduced by the inclusion of rare alleles.
Summary statistics were additionally analysed across the study area using the R package sGD v2.11 64 . This method does not assume pre-defined clusters to assess population statistics, but instead uses a user-defined radius around each individual to calculate properties such as observed heterozygosity and allelic richness. This method may reveal subtle genetic variations within populations, as well as being less reliant on assumptions of population boundaries which may be unclear in continuously distributed taxa such as free-ranging dogs. Based on previous extents of spatial autocorrelation in dingoes 16 we selected a distance of 200 km across the study area of approximately 1500 km × 1200 km. Geographic coordinates were converted to UTM using PBSmapping v2.73.0 in R 65 . Although the study area covered three UTM zones, the majority of the individuals were collected in Zone 55, so this zone was selected by PBSmapping and the results inspected to ensure the individual locations were reasonably accurate. Observed heterozygosity, allelic richness and effective population size using NeEstimator www.nature.com/scientificreports/ V2.1 were calculated against a Euclidean distance matrix for individuals with a minimum of 10 other individuals within the radius. Individuals which did not have a minimum of 10 other individuals within 200 km were recorded as missing data.

Data availability
The datasets generated and analysed during the current study are available in the Dryad repository, https:// doi. org/ 10. 5061/ dryad. k98sf 7m83.