https://orcid.org/0000-0003-4513-8540
Figures
Abstract
Bactrocera jarvisi is an endemic Australian fruit fly species (Diptera: Tephritidae). It occurs commonly across tropical and subtropical coastal Australia, from far-northern Western Australia, across the ‘Top End’ of the Northern Territory, and then down the Queensland east coast. Across this range, its distribution crosses several well documented biogeographic barriers. In order to better understand factors leading to the divergence of Australian fruit fly lineages, we carried out a population genetic study of B. jarvisi from across its range using genome-wide SNP analysis, utilising adult specimens gained from trapping and fruit rearing. Populations from the Northern Territory (NT) and Western Australia were genetically similar to each other, but divergent from the genetically uniform east-coast (= Queensland, QLD) population. Phylogenetic analysis demonstrated that the NT population derived from the QLD population. We infer a role for the Carpentaria Basin as a biogeographic barrier restricting east-west gene flow. The QLD populations were largely panmictic and recognised east-coast biogeographic barriers play no part in north-south population structuring. While the NT and QLD populations were genetically distinct, there was evidence for the historically recent translocation of flies from each region to the other. Flies reared from different host fruits collected in the same location showed no genetic divergence. While a role for the Carpentaria Basin as a barrier to gene flow for Australian fruit flies agrees with existing work on the related B. tryoni, the reason(s) for population panmixia for B. jarvisi (and B. tryoni) over the entire Queensland east coast, a linear north-south distance of >2000km, remains unknown.
Citation: Manawaduge CG, Clarke AR, Hurwood DA (2023) Divergent east-west lineages in an Australian fruit fly, (Bactrocera jarvisi), associated with the Carpentaria Basin divide. PLoS ONE 18(6): e0276247. https://doi.org/10.1371/journal.pone.0276247
Editor: Michael Knapp, University of Otago, NEW ZEALAND
Received: October 1, 2022; Accepted: May 14, 2023; Published: June 2, 2023
Copyright: © 2023 Manawaduge et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data is deposited in DRYAD and can be accessed via the link DOI https://doi.org/10.5061/dryad.6wwpzgn40.
Funding: This research was funded as a component of the project Phenology, demography and distribution of Australia’s fruit flies delivered under the Australian Commonwealth Government’s Strengthening Australia’s Fruit Fly System Research Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
In the western Pacific, the Dacini fruit flies (Diptera: Tephritidae) are a super-diverse clade of more than 400 species, predominantly occurring within the genus Bactrocera Macquart [1,2]. Larval breeding occurs in fleshy fruits and most species are restricted to tropical rainforests, the presumed ancestral habitat of the genus [1]. A few species, such as Bactrocera tryoni (Froggatt) and B. frauenfeldi (Schiner) are damaging pests of horticulture [3,4], but most species are non-pestiferous.
The evolutionary drivers for the extensive speciation of Bactrocera are not obvious. As fruit specialists, diversification could follow ecological specialisation onto new hosts [5]. However, Bactrocera is unusual in the high levels of polyphagy exhibited by many species [6] and regional radiation has not been associated with increasing levels of host-use specialism [7]. The case for allopatric speciation is also difficult to develop, as Bactrocera communities are typified by high alpha-diversity and low beta-diversity, that is a single location has many co-occurring species but there is little species turn-over between locations [8,9]. Starkie et al [7] found evidence of species radiations associated with the movement of flies from New Guinea into South Pacific islands and Australia which is supportive of allopatrically driven divergence; but within Australia there was no evidence for the east-coast biogeographic barriers [10] being associated with clade divergence, which is not supportive of allopatric speciation. More research is thus clearly needed to better understand the drivers of divergence in the Dacini.
Population genetics seeks to understand population structuring and divergence within a species and is thus another tool for untangling the drivers of initial divergence within Bactrocera species. Population structuring in tephritid species is generally weak [11,12], but nevertheless population-level divergence within tephritid species has been linked with differential host use [13,14], increasing geographic distance [15,16], and gene flow restriction associated with biogeographic barriers [17,18]. Within Australia, population genetic studies have only been applied to the pest B. tryoni [19–22] and its closely related sister taxa, B. aquilonis (May) [19,22]. Bactrocera aquilonis is morphologically very similar to B. tryoni [19]. It is currently unclear however, whether B. aquilonis actually is a separate species or possibly just a still-diverging lineage of B. tryoni [22]. These studies have used various genetic markers; allozymes [20], microsatellites [19,21] as well as genome-wide single nucleotide polymorphisms (SNPs, [22]) to investigate structure across the entire natural range of the species. The general pattern found in B. tryoni was that there was little or no genetic structuring across the vast east coast distribution (>2000km) of B. tryoni, but with divergence from east to west across Australia’s tropical north [19–22] between B. tryoni and B. aquilonis; possibly the result of the isolating effect of the drier Carpentaria Basin region acting as a historical and contemporary barrier to geneflow [48]. Whether B. aquilonis represents a sister species to B. tryoni, or a diverging lineage of B. tryoni is unclear from molecular data [22], but either scenario demonstrates the potential for allopatric divergence to drive the diversification of Australian fruit flies. However, with only one species studied, how general a pattern of east-west divergence might be for Australian fruit flies is unknown.
Bactrocera jarvisi (Tryon), Jarvis’ fruit fly, is an Australian Dacini species endemic to northern and eastern coastal regions of Australia [23]. It is a polyphagous species, having been reared from 83 wild and commercial hosts [24]. While a recognised pest of the Northern Territory mango industry [25,26], its major native host over most of its range is considered to be the small woodland tree, Planchonia careya (F.Muell.) R.Kunuth (Lecythidaceae), commonly known as cocky apple [27,28].
Our primary aim here was to explore whether biogeographic barriers may be associated with population structure in B. jarvisi. In this study, we have used genome-wide SNP derived from DArTseq markers [29] to investigate the genetic variation found in B. jarvisi adult samples from across most of the species’ geographic distribution to identify where population differentiation may exist. Additionally, to determine if there is any evidence for host-associated divergence, we analysed SNP variation from individuals reared from commercial and native host fruit species.
Materials and methods
Sampling
Individuals of B. jarvisi were collected between December 2020 and February 2022, either using zingerone lure traps in the field or rearing from infested fruits in the laboratory (Tables 1 and S1). This research was conducted as a component of the project Phenology, Demography and Distribution of Australia’s Fruit Flies delivered under the Australian Commonwealth Government’s Strengthening Australia’s Fruit Fly System Research Program, and the samples were obtained from the traps deployed by each state government as a part of an ongoing surveillance network. The geographic range coverage included samples from the very west of the known B. jarvisi distribution in Western Australia (WA), then across the Northern Territory (NT) to the east coast of Queensland (QLD); while latitudinal sampling was undertaken from Cooktown in far-north Queensland (FNQ) to its southern most distribution in far northern New South Wales (NSW) (Fig 1). From zingerone trapping, 188 individuals were sampled from 18 locations across the species’ geographic distribution. Further, 81 individuals were reared from five hosts (native hosts: cocky apple, white bush apple (Syzygium forte); introduced hosts: mango (Mangifera indica), plum (Prunus domestica), guava (Psidium guajava)). All samples were preserved in 100% ethanol and stored at -20°C until processing.
Each colour represents a different sampling location. Dashed lines indicate the locations of recognised well-known biogeographic barriers, Carpentaria Basin Region (CBR), The Black Mountain Corridor (BMC), the Burdekin Gap (BG), and the St Lawrence Gap (StLG). The map was created in R platform using the packages sf and ggplot2. Source of the base map: Australian Bureau of Statistics (https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.001July%202016?OpenDocument).
DNA extraction and genotyping
For DNA extractions, either the head and legs or the full body without the abdomen was used depending on the samples’ freshness and quality. DNA was extracted using QIAGEN DNeasy® Blood & Tissue Kit following the manufacture’s protocol with only slight modifications (i.e., overnight incubation of the tissue with lysis buffer and proteinase K at 36°C and eluting the genomic DNA into 30–50 μL of elution buffer for a more concentrated DNA extraction. DNA samples were screened for quality and quantity, using both gel electrophoresis and Qubit assay, and then sent to Diversity Arrays Technology Pty Ltd, Canberra (DArT P/L), for DArTseq high-density genotyping. The restriction enzyme combination, PstI/SphI, was used by DArT P/L for the current B. jarvisi dataset, and the fragments (up to 90bp) were sequenced on an Illumina Hiseq2500 as single end reads.
SNP analysis
Sequences were processed in proprietary DArT analytical pipelines, and the results of the SNP calling were returned as a matrix. These SNPs results were viewed and assessed for quality and informativeness using the DArTR package [30] in the R platform [31]. Low quality loci (< 80% call rate and < 95% reproducibility) and individuals (< 80% call rate) were filtered out. Monomorphic loci were also removed before further analysis.
Genetic relationship and admixture analyses
A principal component analysis (PCA) was performed on the filtered data set using DArTR package, to visualise the overall genetic relationships of the individuals among locations. Because the data set consisted of some individuals that were reared from infested fruits, pairwise kinship coefficients were estimated using the maximum likelihood estimation method in the R package SNPRelate [32]. Preliminary analysis of these data has suggested that there may be some analytical artefacts driven by the inclusion of individuals that may be very closely related to each other because they have been reared together. Therefore, only the trapped samples were used to investigate the genetic structure and relationship across the distribution. Fruit-reared samples were analysed separately.
Admixture analysis.
To estimate the individual admixture coefficients, the R package, LEA [33], that implements a sparse, non-negative matrix factorisation algorithm (sNMF) to estimate ancestry coefficients from large genotypic matrices and to evaluate the number of ancestral populations (K), was used. The entire data set was run for K = 1–20, with 100 repetitions per each K value. The value of K that corresponded with the lowest cross-entropy criterion was selected as the value that best explained the results [33].
Phylogenetic analysis.
For the phylogenetics analysis, the filtered SNP dataset was concatenated and stored in a fasta format file, in which SNPs were converted into base pairs following IUPAC ambiguity codes for heterogeneous SNPs using the DArTR package. Phylogenetic trees were then constructed using a maximum likelihood method in IQ-TREE 2.12 [34]. The model finder option coupled with the ascertainment bias correction was used to select the best fit model, and the phylogenetic trees were reconstructed with 10,000 ultrafast-bootstraps and 1,000 bootstrap replicates for the SH-like approximate likelihood ratio test. An additional step to further optimise UFBoot trees by nearest neighbour interchange based directly on bootstrap alignments was also incorporated to reduce the risk of overestimating branch supports due to severe model violations. Finally, the support of alternative topologies was tested using the RELL approximation method [35] in IQ-TREE. All the tree files were viewed and edited in Figtree v.1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/).
Results
A total of 29,286 binary SNPs was obtained for 269 individuals with 36% of missing data. Loci call rate ranged from 20–100% with an average of 64%, and scoring reproducibility ranged from 93–100% with an average of 99.7%. After filtering out low quality data, the final dataset retained 9,769 SNPs for 260 individuals with only 5% missing data. The average loci call rate and reproducibility were 94% and 99% respectively.
The initial principal component analysis of the combined data set (i.e., adult trapping + fruit rearing) revealed some level of genetic structure within B. jarvisi (Fig 2). Along the first two PCA axes, that account for 8.2% of the sample variance, the majority of individuals from the NT form a tight cluster and were largely divergent from individuals from the east that formed a more diffuse group. However, a few individuals sampled from NT clustered with individuals from the east, and vice versa. For the individuals obtained from fruit rearing, no clear genetic pattern was found with respect to host, but there was a degree of family structuring (Fig 3) with close genetic relatedness among the flies reared from a single host. This was supported by pairwise kinship coefficient analysis (S1 Fig). Close family relatedness in host-reared material is not unexpected, as individuals may be derived from an individual egg clutch (i.e., they have the same mother).
Each colour represents a different sampling location corresponding with Fig 1.
Each colour represents a different sampling location corresponding with Fig 1 and each shape represent a different host species.
After removing the host-reared individuals and filtering out low-quality data, the data set containing only trapped samples consisted of 182 individuals with 8,102 SNP loci with a 94% average loci call rate and 5% missing data. The admixture analysis of these data supported K = 3 (i.e., 3 major groups, S2 Fig) as the values corresponding with the lowest cross-entropy from the sNMF algorithm in LEA (Fig 4) and supports the existence of two major groups along with one additional small cluster. Almost all the samples from the NT and WA, along with a few FNQ samples belong to a separate genetic group, hereafter referred as ‘NT cluster’, whereas the majority of the east coast samples (QLD and NSW), together with a few individuals from Darwin, represent a second genetic group, here after referred as ‘QLD cluster’. A third group contained only six individuals that came from Darwin. However, when viewing the phylogenetic tree (Fig 5) these individuals do not cluster together as may be expected; in the PCA, they were clustered with the NT samples. Therefore, we assumed that this third cluster is an artefact of the algorithm used to search for the optimum number of genetically differentiated groups. As such we consider them to be members of the NT cluster.
Each bar is an individual and displays the relative assignment probability to each of three identified genetic groups.
[SNP maximum likelihood (IQ-TREE) consensus tree from 10,000 ultrafast-bootstrap replicates. Each colour of the tip labels (sample IDs) corresponds with the location colours given in Fig 1. The numbers at each node represent the ultrafast-bootstrap support].
Phylogenetic analysis in IQ-TREE required exclusion of constant genetic sites, that resulted in a final dataset with 6,519 SNPs, of which 3,767 were parsimony-informative. For the maximum likelihood analysis, the SYM+ASC+R10 model was selected as the best fit model according to the Bayesian information criterion of 30 candidate DNA models, analysed by the model-finder option in IQ-TREE. Bactrocera cacuminata was considered as the out group, and the resulting phylogenetic tree highly supported B. jarvisi (100% ultrafast-bootstrap support) as a monophyletic group (Figs 5 and S3). However, the individuals from the NT cluster are recently evolved from a single linage. Individuals of this clade form a relatively tight cluster opposed to the large and defuse QLD cluster, indicating relatively low genetic variation compared to the individuals from the QLD cluster that correspond with the older lineages.
Discussion
The results here represent the first comprehensive study on the genetic structure of B. jarvisi across its distribution. After the B. tryoni/B. aquilonis species complex [22,36], this is only the second Australian fruit fly species for which population genetics has been completed. The major findings were that B. jarvisi exhibits: i) significant genetic structure between QLD and NT populations, ii) a lack of differentiation across the east coast (QLD+NSW), as well as across northern Australia (NT+WA), iii) some evidence for translocation in both directions and iv) no evidence of host-associated divergence. Most population genetic studies undertaken on Dacini fruit flies have found only weak genetic structuring over very large geographic distances [37–40]; however, in some cases, structure has been detected that can be associated with known biogeographic barriers [17,18]. In the current study we have found both; some structuring which can be associated with a biogeographic barrier, but also a lack of structure despite biogeographic barriers and very large distances between sampling sites. The following discussion addresses these issues further.
Role of biogeographic barriers in B. jarvisi population structuring
A number of biogeographic barriers have been identified along the northern and eastern coastlines of Australia (Fig 1) [10,41] and these barriers have been shown to influence the dispersal of many taxa including amphibians [42], reptiles [43], mammals [44,45], birds [46], insects [47] and plants [48]. To date however, there is no evidence to suggest that the east-coast biogeographic barriers have had any significant influence on the historical movement of Dacini [7,10], and this is again the case for B. jarvisi. However, we found a significant differentiation of B. jarvisi populations between QLD and NT. Given the homogeneous nature of east coast B. jarvisi populations over >2000kms, the observed level of differentiation between QLD and NT populations cannot be accounted for by distance alone. If this is the case, we need to look for a barrier to dispersal, whether physical or climatic, between NT and QLD.
The Jurassic-Cretaceous (~201 to 66 million years ago) intracratonic Carpentaria Basin is a well-documented phylogeographic barrier in northern Australia that extends from QLD to the NT (Fig 1) [49]. This barrier has played a major role in differentiating many taxa including the avian fauna of northern Australia [50]. In their study of B. tryoni, Popa-Baez et al [22], also found significant genetic structure between B. tryoni and B. aquilonis along the east-west transect of northern Australia. Whether B. aquilonis represents a sister species to B. tryoni or a still-diverging lineage of B. tryoni is debatable, but nevertheless these lineages clearly exhibit restricted gene flow [19,22]. The Carpentaria Basin is a semi-arid region extremely poor in vegetation [50], and currently, there are no records of either of these two species, or indeed any Dacini fruit fly species in this region [24,51]. Together, with the prior work on the B. tryoni/B. aquilonis system, our study strongly suggests that the Carpentaria Basin acts as a biogeographic barrier to Dacini and plays a role in the allopatric diversification of fruit fly species in Australia.
Role of human mediated dispersal in B. jarvisi population structuring
While the influence of the Carpentaria barrier in constraining dispersal of B. jarvisi populations is evident, we still detected instances of high gene flow between NT and QLD in the data. Specifically, the Mackay sample is made up of ‘NT’ genotypes and a few individuals from Darwin and Mataranka in the NT have a QLD genotype. The most parsimonious explanation for this anomaly is human-mediated translocation. It is interesting to note that the whole Mackay sample was made up of introduced NT types with little evidence for introgression with local QLD types. While our study did not have the scope to address such issues, questions arising regarding non-random mating among lineages and competitive exclusion between a local and introduced lineage should be investigated.
There was a noted lack of genetic differentiation among B. jarvisi samples along the east coast (QLD/NSW), a pattern also seen in B. tryoni [22], with the exception of the Mackay population. This may be evidence for repeated, human-assisted movement of fruit flies to that region. Human carriage of fruit fly infested fruit with resultant panmixia has been used as an explanation for the lack of genetic structuring in numerous pest fruit fly species [38,52–54]. While often proposed, this explanation must assume for B. jarvisi and B. tryoni, that human movement of infested fruit is sufficiently extensive to account for the lack of genetic differentiation across >2000km and to hide the historical genetic signatures of multiple, well documented biogeographic barriers. While we consider this unlikely in the well-managed Australian horticultural system, particularly for B. jarvisi which is a lesser pest, the hypothesis is unfortunately very difficult to test. Fortunately, this is not the case for the corollary of the hypothesis. If human-mediated movement does explain the lack of genetic structure for pest species such as B. tryoni and B. jarvisi, then it is logical that a non-pest species would not be exposed to human-mediated dispersal and so should show a genetic structure more consistent with historical biogeography, current landscapes, and host-species’ distributions. To better understand the drivers of Dacini population genetic structuring, we consider population genetic studies on wide-spread, non-pest species to be a priority.
Role of host in B. jarvisi population structuring
Population level divergence of some tephritid fruit flies through adaptation to novel hosts has been documented [13,14,55]. For example, genetic differentiation of Rhagoletis pomonella between co-occurring hosts, hawthorn (the native host) and domestic apples, has been reported with a reduction in gene flow and sympatric divergence [13,14,55]. However, the genetic differentiation observed across the different host species in B. jarvisi, while consistent with family structuring, provided no evidence for sympatric, host-associated divergence in the species.
The close association between B. jarvisi and its native host, cocky apple, has long been known [27,56,57] and it has been speculated that cocky apple abundance plays an important role in shaping and maintaining B. jarvisi populations, even in the presence of alternate commercial hosts such as mango [26,27,58]. If breeding populations of B. jarvisi were dependent on cocky apple for the primary maintenance of populations, it could be argued that samples collected from cocky apple should be of the same genetic type as those samples collected from nearby but different host(s). However, such similarity or relatedness was not found among the flies from Walkamin Research Centre (FNQ) that were reared from four different host fruits: cocky apple, mango, guava and plum. Kinship analysis revealed no close relatedness between the flies reared from cocky apple and other hosts in sympatry.
Conclusion
This is the first study of the genetic structure of B. jarvisi. We determined that: B. jarvisi exhibits significant genetic structure between QLD and NT populations, although there is evidence for recent translocation in both directions; a lack of genetic differentiation among the east-coast populations; and no evidence of host association divergence. Our study suggests that while the biogeographic barrier of the Carpentaria basin may be playing a role in the diversification of B. jarvisi in northern Australia, there is no evidence for the biogeographic barriers along the east coast of QLD influencing the genetic structure of B. jarvisi. Whether human-mediated dispersal could account for the lack of genetic differentiation across >2000k and several well-established biogeographic barriers is questionable. To test this a corollary of the human-mediated dispersal hypothesis, we should target a population genetic study on a non-pest species That is less likely to be moved by humans. Within Australia, the wild tobacco fly, B. cacuminata (Hering), is an abundant, widely distributed non-pest species suitable for such a study.
Supporting information
S1 Fig. Heat map showing the pairwise kinship coefficients between the reared individuals.
(Kinship coefficient: 0.5 = monozygotic twins, 0.25 = full siblings, 0.125 = half siblings, 0.0 = unrelated).
https://doi.org/10.1371/journal.pone.0276247.s001
(DOCX)
S2 Fig. Plot of the cross-entropy values obtained from the sNMF algorithm in package LEA for K = 1–20, with 100 repetitions per each K value.
https://doi.org/10.1371/journal.pone.0276247.s002
(DOCX)
S3 Fig. SNP maximum likelihood (IQ-TREE) consensus tree from 10,000 ultrafast-bootstrap replicates showing the true branch lengths.
Each colour of the tip labels (sample IDs) corresponds with the location colours given in Fig 1. The numbers at each node represent the ultrafast-bootstrap support.
https://doi.org/10.1371/journal.pone.0276247.s003
(DOCX)
S1 Table. Details of the samples used in the study.
(Sampling method = T: Trapping, R: Rearing).
https://doi.org/10.1371/journal.pone.0276247.s004
(DOCX)
Acknowledgments
We acknowledge the overall project leadership of Mr Peter Leach (Department of Agriculture and Fisheries, Queensland). Dr Thilini Ekanayake (Department of Primary Industry and Resources, Northern Territory), Dr Solomon Balagawi (New South Wales Department of Primary Industries), Dr Melissa Starkie and Mr Stefano De Faveri (Department of Agriculture and Fisheries, Queensland), Dr Touhidur Rahman (Department of Primary Industries and Regional Development, Western Australia), all supplied material without which the project would have been impossible to complete. We thank them and their institutions for their support and engagement.
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